From 301005d1f07bc404e6f6aa3d8528400593c951a6 Mon Sep 17 00:00:00 2001 From: Vicky Zauner Date: Wed, 27 May 2020 18:04:06 +0200 Subject: [PATCH 1/9] commit on Wednesday --- .../code/Code 1 - Exploring the Dataset.ipynb | 230 + your-project/code/Code 10 - Build Tool.ipynb | 260 + ...ating Bags of Words | NLP Analysis 1.ipynb | 934 ++ ...- Splitting Targets | NLP Analysis 2.ipynb | 1595 +++ .../code/Code 4 - Extending Data.ipynb | 892 ++ .../code/Code 5 - Neural Network.ipynb | 1011 ++ ...ode 6 - Piecing together by Accuracy.ipynb | 453 + ...Code 8 - Trying Models on Quant Data.ipynb | 1071 ++ your-project/code/testing nlp on Darya.ipynb | 972 ++ .../code/testing nlp on Laurents.ipynb | 972 ++ .../code/testing nlp on Theresa.ipynb | 972 ++ .../code/testing quant on Darya.ipynb | 972 ++ .../code/testing quant on Laurents.ipynb | 972 ++ your-project/data/quant_personalities.csv | 8676 +++++++++++++++++ 14 files changed, 19982 insertions(+) create mode 100644 your-project/code/Code 1 - Exploring the Dataset.ipynb create mode 100644 your-project/code/Code 10 - Build Tool.ipynb create mode 100644 your-project/code/Code 2 - Creating Bags of Words | NLP Analysis 1.ipynb create mode 100644 your-project/code/Code 3 - Splitting Targets | NLP Analysis 2.ipynb create mode 100644 your-project/code/Code 4 - Extending Data.ipynb create mode 100644 your-project/code/Code 5 - Neural Network.ipynb create mode 100644 your-project/code/Code 6 - Piecing together by Accuracy.ipynb create mode 100644 your-project/code/Code 8 - Trying Models on Quant Data.ipynb create mode 100644 your-project/code/testing nlp on Darya.ipynb create mode 100644 your-project/code/testing nlp on Laurents.ipynb create mode 100644 your-project/code/testing nlp on Theresa.ipynb create mode 100644 your-project/code/testing quant on Darya.ipynb create mode 100644 your-project/code/testing quant on Laurents.ipynb create mode 100644 your-project/data/quant_personalities.csv diff --git a/your-project/code/Code 1 - Exploring the Dataset.ipynb b/your-project/code/Code 1 - Exploring the Dataset.ipynb new file mode 100644 index 00000000..47417afb --- /dev/null +++ b/your-project/code/Code 1 - Exploring the Dataset.ipynb @@ -0,0 +1,230 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exploring the Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "data = pd.read_csv(r'../data/mbti_1.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Build Tool.ipynb b/your-project/code/Code 10 - Build Tool.ipynb new file mode 100644 index 00000000..ad599c18 --- /dev/null +++ b/your-project/code/Code 10 - Build Tool.ipynb @@ -0,0 +1,260 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Build Ultimate Tool!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Scrape Instagram" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "import click\n", + "\n", + "import requests\n", + "from bs4 import BeautifulSoup\n", + "\n", + "\n", + "def save_captions(prefix, user, caption_all):\n", + " \"\"\"\n", + " Write a list of captions to files (one file per caption).\n", + " :param prefix: The directory in which the files will be saved\n", + " :param prefix: str\n", + " :param user: Username of the account the posts' captions come from\n", + " (will be used as a prefix in the filenames)\n", + " :param user: str\n", + " :param caption_all: A list of captions\n", + " :param caption_all: list\n", + " :return: None\n", + " \"\"\"\n", + " for idx, caption in enumerate(caption_all):\n", + " filename = '{0}_{1}.txt'.format(user, idx)\n", + " filename = os.path.join(prefix, filename)\n", + " print('saving caption {0}'.format(filename))\n", + " text_file = open(filename, 'w')\n", + " text_file.write(caption)\n", + " text_file.close()\n", + "\n", + "\n", + "def download_and_save_images(prefix, user, img_url_all):\n", + " \"\"\"\n", + " Download and save a list of images from their urls.\n", + " :param prefix: The directory in which the images will be saved\n", + " :param prefix: str\n", + " :param user: Username of the account the posts' images come from\n", + " (will be used as a prefix in the filenames)\n", + " :param user: str\n", + " :param img_url_all: A list of image urls\n", + " :param img_url_all: list\n", + " :return: None\n", + " \"\"\"\n", + " for idx, img_url in enumerate(img_url_all):\n", + " img_data = requests.get(img_url, verify=False).content\n", + " filename = '{0}_{1}.jpg'.format(user, idx)\n", + " filename = os.path.join(prefix, filename)\n", + " print('saving image {0}'.format(filename))\n", + " with open(filename, 'wb') as handler:\n", + " handler.write(img_data)\n", + "\n", + "\n", + "@click.command()\n", + "@click.option('--images/--no-images', default=True,\n", + " help='Scrap also images.')\n", + "@click.option('--captions/--no-captions', default=True,\n", + " help='Scrap also captions.')\n", + "@click.option('--user', '-u', required=True,\n", + " help='The account to scrap.')\n", + "@click.option('--number', '-n', default=10,\n", + " help='Number of posts to scrap. (newer posts are scraped first).')\n", + "def scrap(images, captions, user, number):\n", + " \"\"\"\n", + " Scrap photos and captions from posts of a single user.\n", + " :param images: Include images in the data scraped\n", + " :param images: boolean\n", + " :param captions: Include captions in the data scraped\n", + " :param captions: boolean\n", + " :param user: The account to scrap\n", + " :param user: str\n", + " :return: None\n", + " \"\"\"\n", + " BASE_URL = 'https://deskgram.org'\n", + " SIG = \"?_nc_ht=scontent-dfw5-1.cdninstagram.com\"\n", + " USER = user\n", + " PATTERN_FOR_IMAGES = {'name': \"div\", 'attrs': {\"class\": \"post-img\"}}\n", + " PATTERN_FOR_CAPTIONS = {'name': \"div\", 'attrs': {\"class\": \"post-caption\"}}\n", + " DEST_FOLDER = './{0}'.format(USER)\n", + "\n", + " url = BASE_URL + '/' + USER\n", + " img_url_all = list() if images else None\n", + " caption_all = list() if captions else None\n", + "\n", + " if not os.path.exists(DEST_FOLDER):\n", + " os.makedirs(DEST_FOLDER)\n", + "\n", + " while True:\n", + " print('fetching {0}'.format(url))\n", + " r = requests.get(url, verify=False)\n", + " soup = BeautifulSoup(r.text, 'html.parser')\n", + "\n", + " if images:\n", + " images = soup.findAll(**PATTERN_FOR_IMAGES)\n", + " for image in images:\n", + " img_url = image.img['src'].split('?')[0]\n", + " img_url += SIG\n", + " img_url_all.append(img_url)\n", + " if len(img_url_all) == number and not captions:\n", + " break\n", + " print('Found {0} images.'.format(len(images)))\n", + " if len(img_url_all) == number and not captions:\n", + " break\n", + "\n", + " if captions:\n", + " captions = soup.findAll(**PATTERN_FOR_CAPTIONS)\n", + " for caption in captions:\n", + " caption_all.append(caption.text)\n", + " print(len(caption_all))\n", + " if len(caption_all) == number:\n", + " break\n", + " print('Found {0} captions.'.format(len(captions)))\n", + " if len(caption_all) == number:\n", + " break\n", + "\n", + " links = soup.findAll('a')\n", + " next_link = list(filter(lambda x: 'next_id' in x['href'], links))\n", + " if len(next_link) == 0:\n", + " break\n", + " else:\n", + " dest = next_link[0]['href']\n", + " url = BASE_URL + dest\n", + "\n", + " if images:\n", + " download_and_save_images(DEST_FOLDER, USER, img_url_all)\n", + "\n", + " if captions:\n", + " save_captions(DEST_FOLDER, USER, caption_all)\n", + "\n", + "\n", + "if __name__ == '__main__':\n", + " scrap()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Read Captions & Clean" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Prepare Data for Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Run Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Show Results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/your-project/code/Code 2 - Creating Bags of Words | NLP Analysis 1.ipynb b/your-project/code/Code 2 - Creating Bags of Words | NLP Analysis 1.ipynb new file mode 100644 index 00000000..7b185e5c --- /dev/null +++ b/your-project/code/Code 2 - Creating Bags of Words | NLP Analysis 1.ipynb @@ -0,0 +1,934 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## General NLP" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "import re\n", + "\n", + "import nltk\n", + "from nltk.stem import PorterStemmer\n", + "from nltk.stem import SnowballStemmer\n", + "from nltk import wordnet\n", + "from nltk.corpus import stopwords\n", + "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n", + "from sklearn.model_selection import train_test_split, cross_val_score\n", + "from sklearn.metrics import classification_report\n", + "from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score, roc_curve" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " type posts \\\n", + "0 INFJ 'http://www.youtube.com/watch?v=qsXHcwe3krw|||... \n", + "1 ENTP 'I'm finding the lack of me in these posts ver... \n", + "2 INTP 'Good one _____ https://www.youtube.com/wat... \n", + "3 INTJ 'Dear INTP, I enjoyed our conversation the o... \n", + "4 ENTJ 'You're fired.|||That's another silly misconce... \n", + "\n", + " text_processed \\\n", + "0 [intj, moment, sportscent, top, ten, play, pra... \n", + "1 [find, lack, post, veri, alarm, sex, bore, pos... \n", + "2 [good, one, cours, say, know, bless, cur, doe,... \n", + "3 [dear, intp, enjoy, convers, day, esoter, gab,... \n", + "4 [fire, anoth, silli, misconcept, approach, log... \n", + "\n", + " text_ready \n", + "0 intj moment sportscent top ten play prank ha l... \n", + "1 find lack post veri alarm sex bore posit often... \n", + "2 good one cours say know bless cur doe absolut ... \n", + "3 dear intp enjoy convers day esoter gab natur u... \n", + "4 fire anoth silli misconcept approach logic go ... " + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "data.to_csv(r'../data/personalities_cleaned.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "x = data['text_ready']\n", + "y = data['type']" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.feature_extraction.text import CountVectorizer\n", + "\n", + "vector = CountVectorizer(ngram_range=(2, 2)).fit(x) \n", + "X = vector.transform(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape of the sparse matrix: (8675, 1986297)\n", + "Non-Zero occurences: 5339470\n", + "Density of the matrix = 0.03098735307727465\n" + ] + } + ], + "source": [ + "print(\"Shape of the sparse matrix: \", X.shape)\n", + "print(\"Non-Zero occurences: \", X.nnz)\n", + "\n", + "density = (X.nnz/(X.shape[0]*X.shape[1]))*100\n", + "print(\"Density of the matrix = \", density)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=10)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(6940, 1986297)" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_train.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[ 0 0 0 0 0 0 0 0 8 22 0 1 0 0 0 0]\n", + " [ 0 1 0 0 0 0 0 0 14 116 1 1 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 12 41 1 1 0 0 0 0]\n", + " [ 0 1 0 0 0 0 0 0 29 88 0 6 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 2 7 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 3 6 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 4 13 0 0 0 0 0 0]\n", + " [ 0 2 0 0 0 0 0 0 118 173 0 2 0 0 0 0]\n", + " [ 0 1 0 1 0 0 0 0 16 361 2 1 0 0 0 0]\n", + " [ 0 1 0 1 0 0 0 0 52 144 18 17 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 26 164 2 40 0 1 0 0]\n", + " [ 0 0 0 0 0 0 0 0 4 23 0 0 0 0 0 0]\n", + " [ 0 1 0 0 0 0 0 0 6 53 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 9 30 0 1 0 0 0 0]\n", + " [ 0 0 0 1 0 0 0 0 8 62 1 6 0 0 0 0]]\n", + "Score: 31.01\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " ENFJ 0.00 0.00 0.00 31\n", + " ENFP 0.14 0.01 0.01 133\n", + " ENTJ 0.00 0.00 0.00 55\n", + " ENTP 0.00 0.00 0.00 124\n", + " ESFJ 0.00 0.00 0.00 9\n", + " ESFP 0.00 0.00 0.00 9\n", + " ESTJ 0.00 0.00 0.00 9\n", + " ESTP 0.00 0.00 0.00 17\n", + " INFJ 0.38 0.40 0.39 295\n", + " INFP 0.28 0.95 0.43 382\n", + " INTJ 0.72 0.08 0.14 233\n", + " INTP 0.53 0.17 0.26 233\n", + " ISFJ 0.00 0.00 0.00 27\n", + " ISFP 0.00 0.00 0.00 60\n", + " ISTJ 0.00 0.00 0.00 40\n", + " ISTP 0.00 0.00 0.00 78\n", + "\n", + " accuracy 0.31 1735\n", + " macro avg 0.13 0.10 0.08 1735\n", + "weighted avg 0.30 0.31 0.21 1735\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n" + ] + } + ], + "source": [ + "from sklearn.naive_bayes import MultinomialNB\n", + "\n", + "mnb = MultinomialNB()\n", + "mnb.fit(x_train, y_train)\n", + "\n", + "predmnb = mnb.predict(x_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predmnb))\n", + "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predmnb))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[ 0 0 0 0 0 0 0 0 0 31 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 133 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 55 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 124 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 17 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 1 294 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 382 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 1 231 0 1 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 233 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 27 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 60 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 40 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 78 0 0 0 0 0 0]]\n", + "Score: 22.07\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " ENFJ 0.00 0.00 0.00 31\n", + " ENFP 0.00 0.00 0.00 133\n", + " ENTJ 0.00 0.00 0.00 55\n", + " ENTP 0.00 0.00 0.00 124\n", + " ESFJ 0.00 0.00 0.00 9\n", + " ESFP 0.00 0.00 0.00 9\n", + " ESTJ 0.00 0.00 0.00 9\n", + " ESTP 0.00 0.00 0.00 17\n", + " INFJ 0.50 0.00 0.01 295\n", + " INFP 0.22 1.00 0.36 382\n", + " INTJ 0.00 0.00 0.00 233\n", + " INTP 0.00 0.00 0.00 233\n", + " ISFJ 0.00 0.00 0.00 27\n", + " ISFP 0.00 0.00 0.00 60\n", + " ISTJ 0.00 0.00 0.00 40\n", + " ISTP 0.00 0.00 0.00 78\n", + "\n", + " accuracy 0.22 1735\n", + " macro avg 0.05 0.06 0.02 1735\n", + "weighted avg 0.13 0.22 0.08 1735\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n" + ] + } + ], + "source": [ + "from sklearn.feature_extraction.text import TfidfVectorizer\n", + "\n", + "vectorizer = TfidfVectorizer()\n", + "X = vectorizer.fit_transform(x)\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=10)\n", + "mnb = MultinomialNB()\n", + "mnb.fit(x_train, y_train)\n", + "\n", + "predmnb = mnb.predict(x_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predmnb))\n", + "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predmnb))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Confusion Matrix for Decision Tree:\n", + "[[ 6 2 0 1 0 1 0 1 4 7 3 2 0 3 0 1]\n", + " [ 4 58 3 9 0 2 0 0 16 12 12 11 1 2 3 0]\n", + " [ 0 4 10 9 0 0 0 0 5 5 4 8 1 2 3 4]\n", + " [ 0 5 2 56 1 0 0 0 15 13 8 12 3 5 4 0]\n", + " [ 0 1 0 1 0 0 0 0 1 0 0 5 1 0 0 0]\n", + " [ 0 1 1 0 0 0 0 0 3 0 1 3 0 0 0 0]\n", + " [ 0 0 1 0 0 0 0 0 2 3 0 0 0 2 1 0]\n", + " [ 0 0 1 1 0 0 0 4 3 1 4 1 0 2 0 0]\n", + " [ 3 9 2 13 0 1 0 1 162 38 26 22 8 5 2 3]\n", + " [ 9 14 7 15 2 2 1 3 31 228 17 21 7 15 4 6]\n", + " [ 3 14 9 11 0 5 1 0 24 14 110 21 0 10 7 4]\n", + " [ 1 4 1 13 2 0 0 1 21 15 26 140 2 1 1 5]\n", + " [ 1 0 0 1 1 0 0 0 4 2 2 1 10 2 3 0]\n", + " [ 1 4 0 1 1 2 0 2 5 11 4 2 2 18 5 2]\n", + " [ 0 1 0 3 0 1 0 0 4 7 3 6 3 2 9 1]\n", + " [ 1 4 0 4 0 2 0 0 2 9 7 6 0 4 2 37]]\n", + "Score: 48.88\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " ENFJ 0.21 0.19 0.20 31\n", + " ENFP 0.48 0.44 0.46 133\n", + " ENTJ 0.27 0.18 0.22 55\n", + " ENTP 0.41 0.45 0.43 124\n", + " ESFJ 0.00 0.00 0.00 9\n", + " ESFP 0.00 0.00 0.00 9\n", + " ESTJ 0.00 0.00 0.00 9\n", + " ESTP 0.33 0.24 0.28 17\n", + " INFJ 0.54 0.55 0.54 295\n", + " INFP 0.62 0.60 0.61 382\n", + " INTJ 0.48 0.47 0.48 233\n", + " INTP 0.54 0.60 0.57 233\n", + " ISFJ 0.26 0.37 0.31 27\n", + " ISFP 0.25 0.30 0.27 60\n", + " ISTJ 0.20 0.23 0.21 40\n", + " ISTP 0.59 0.47 0.52 78\n", + "\n", + " accuracy 0.49 1735\n", + " macro avg 0.32 0.32 0.32 1735\n", + "weighted avg 0.49 0.49 0.49 1735\n", + "\n" + ] + } + ], + "source": [ + "from sklearn.tree import DecisionTreeClassifier\n", + "\n", + "dt = DecisionTreeClassifier()\n", + "dt.fit(x_train,y_train)\n", + "preddt = dt.predict(x_test)\n", + "print(\"Confusion Matrix for Decision Tree:\")\n", + "print(confusion_matrix(y_test,preddt))\n", + "print(\"Score:\",round(accuracy_score(y_test,preddt)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,preddt))" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Confusion Matrix for Random Forest Classifier:\n", + "[[ 8 6 0 1 0 0 0 0 7 9 0 0 0 0 0 0]\n", + " [ 1 69 1 5 0 0 0 0 10 45 1 0 0 1 0 0]\n", + " [ 2 7 11 2 0 0 0 0 8 22 2 0 0 0 0 1]\n", + " [ 0 11 1 40 1 0 0 0 24 39 5 3 0 0 0 0]\n", + " [ 0 0 0 0 8 0 0 0 0 1 0 0 0 0 0 0]\n", + " [ 0 1 1 0 1 1 0 0 3 2 0 0 0 0 0 0]\n", + " [ 0 1 1 0 0 0 2 0 1 4 0 0 0 0 0 0]\n", + " [ 0 2 0 0 0 0 0 7 3 3 0 1 0 1 0 0]\n", + " [ 3 18 0 3 2 0 0 1 174 86 4 3 0 1 0 0]\n", + " [ 2 26 0 2 1 0 0 1 58 284 5 1 0 1 1 0]\n", + " [ 1 31 0 8 2 0 0 0 37 81 57 10 5 0 1 0]\n", + " [ 1 14 2 9 2 0 0 0 45 95 10 55 0 0 0 0]\n", + " [ 0 1 0 0 0 0 0 0 6 3 1 0 16 0 0 0]\n", + " [ 0 7 0 1 0 0 0 1 10 32 0 0 0 9 0 0]\n", + " [ 0 3 0 1 0 0 0 0 11 14 2 0 1 0 8 0]\n", + " [ 0 8 0 3 0 0 0 0 13 39 5 2 1 0 0 7]]\n", + "Score: 23.05\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " ENFJ 0.00 0.00 0.00 31\n", + " ENFP 0.00 0.00 0.00 133\n", + " ENTJ 0.00 0.00 0.00 55\n", + " ENTP 0.00 0.00 0.00 124\n", + " ESFJ 0.00 0.00 0.00 9\n", + " ESFP 0.00 0.00 0.00 9\n", + " ESTJ 0.00 0.00 0.00 9\n", + " ESTP 0.00 0.00 0.00 17\n", + " INFJ 0.62 0.03 0.06 295\n", + " INFP 0.22 1.00 0.37 382\n", + " INTJ 0.00 0.00 0.00 233\n", + " INTP 0.73 0.03 0.07 233\n", + " ISFJ 0.00 0.00 0.00 27\n", + " ISFP 0.00 0.00 0.00 60\n", + " ISTJ 0.00 0.00 0.00 40\n", + " ISTP 0.00 0.00 0.00 78\n", + "\n", + " accuracy 0.23 1735\n", + " macro avg 0.10 0.07 0.03 1735\n", + "weighted avg 0.25 0.23 0.10 1735\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n" + ] + } + ], + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "\n", + "rfn = RandomForestClassifier(max_depth=5, random_state=0)\n", + "rfn.fit(x_train,y_train)\n", + "predrf = rfn.predict(x_test)\n", + "print(\"Confusion Matrix for Random Forest Classifier:\")\n", + "print(confusion_matrix(y_test,predknn))\n", + "print(\"Score: \",round(accuracy_score(y_test,predrf)*100,2))\n", + "print(\"Classification Report:\")\n", + "print(classification_report(y_test,predrf))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Confusion Matrix for K Neighbors Classifier:\n", + "[[ 8 6 0 1 0 0 0 0 7 9 0 0 0 0 0 0]\n", + " [ 1 69 1 5 0 0 0 0 10 45 1 0 0 1 0 0]\n", + " [ 2 7 11 2 0 0 0 0 8 22 2 0 0 0 0 1]\n", + " [ 0 11 1 40 1 0 0 0 24 39 5 3 0 0 0 0]\n", + " [ 0 0 0 0 8 0 0 0 0 1 0 0 0 0 0 0]\n", + " [ 0 1 1 0 1 1 0 0 3 2 0 0 0 0 0 0]\n", + " [ 0 1 1 0 0 0 2 0 1 4 0 0 0 0 0 0]\n", + " [ 0 2 0 0 0 0 0 7 3 3 0 1 0 1 0 0]\n", + " [ 3 18 0 3 2 0 0 1 174 86 4 3 0 1 0 0]\n", + " [ 2 26 0 2 1 0 0 1 58 284 5 1 0 1 1 0]\n", + " [ 1 31 0 8 2 0 0 0 37 81 57 10 5 0 1 0]\n", + " [ 1 14 2 9 2 0 0 0 45 95 10 55 0 0 0 0]\n", + " [ 0 1 0 0 0 0 0 0 6 3 1 0 16 0 0 0]\n", + " [ 0 7 0 1 0 0 0 1 10 32 0 0 0 9 0 0]\n", + " [ 0 3 0 1 0 0 0 0 11 14 2 0 1 0 8 0]\n", + " [ 0 8 0 3 0 0 0 0 13 39 5 2 1 0 0 7]]\n", + "Score: 43.57\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " ENFJ 0.44 0.26 0.33 31\n", + " ENFP 0.34 0.52 0.41 133\n", + " ENTJ 0.65 0.20 0.31 55\n", + " ENTP 0.53 0.32 0.40 124\n", + " ESFJ 0.47 0.89 0.62 9\n", + " ESFP 1.00 0.11 0.20 9\n", + " ESTJ 1.00 0.22 0.36 9\n", + " ESTP 0.70 0.41 0.52 17\n", + " INFJ 0.42 0.59 0.49 295\n", + " INFP 0.37 0.74 0.50 382\n", + " INTJ 0.62 0.24 0.35 233\n", + " INTP 0.73 0.24 0.36 233\n", + " ISFJ 0.70 0.59 0.64 27\n", + " ISFP 0.69 0.15 0.25 60\n", + " ISTJ 0.80 0.20 0.32 40\n", + " ISTP 0.88 0.09 0.16 78\n", + "\n", + " accuracy 0.44 1735\n", + " macro avg 0.65 0.36 0.39 1735\n", + "weighted avg 0.54 0.44 0.41 1735\n", + "\n" + ] + } + ], + "source": [ + "from sklearn.neighbors import KNeighborsClassifier\n", + "\n", + "knn = KNeighborsClassifier(n_neighbors=10)\n", + "knn.fit(x_train,y_train)\n", + "predknn = knn.predict(x_test)\n", + "print(\"Confusion Matrix for K Neighbors Classifier:\")\n", + "print(confusion_matrix(y_test,predknn))\n", + "print(\"Score: \",round(accuracy_score(y_test,predknn)*100,2))\n", + "print(\"Classification Report:\")\n", + "print(classification_report(y_test,predknn))" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already up-to-date: xgboost in /usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages (1.1.0)\n", + "Requirement already satisfied, skipping upgrade: scipy in /usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages (from xgboost) (1.4.1)\n", + "Requirement already satisfied, skipping upgrade: numpy in /usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages (from xgboost) (1.18.2)\n", + "\u001b[33mWARNING: You are using pip version 19.3.1; however, version 20.1.1 is available.\n", + "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n" + ] + } + ], + "source": [ + "import sys\n", + "!{sys.executable} -m pip install --upgrade xgboost" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "ename": "XGBoostError", + "evalue": "XGBoost Library (libxgboost.dylib) could not be loaded.\nLikely causes:\n * OpenMP runtime is not installed (vcomp140.dll or libgomp-1.dll for Windows, libomp.dylib for Mac OSX, libgomp.so for Linux and other UNIX-like OSes). Mac OSX users: Run `brew install libomp` to install OpenMP runtime.\n * You are running 32-bit Python on a 64-bit OS\nError message(s): ['dlopen(/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/xgboost/lib/libxgboost.dylib, 6): Library not loaded: /usr/local/opt/libomp/lib/libomp.dylib\\n Referenced from: /usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/xgboost/lib/libxgboost.dylib\\n Reason: image not found']\n", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mXGBoostError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mxgboost\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/xgboost/__init__.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDMatrix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDeviceQuantileDMatrix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mBooster\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 12\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mtraining\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mrabit\u001b[0m \u001b[0;31m# noqa\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/xgboost/core.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 173\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 174\u001b[0m \u001b[0;31m# load the XGBoost library globally\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 175\u001b[0;31m \u001b[0m_LIB\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_load_lib\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 176\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 177\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/xgboost/core.py\u001b[0m in \u001b[0;36m_load_lib\u001b[0;34m()\u001b[0m\n\u001b[1;32m 164\u001b[0m \u001b[0;34m'`brew install libomp` to install OpenMP runtime.\\n'\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 165\u001b[0m \u001b[0;34m' * You are running 32-bit Python on a 64-bit OS\\n'\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 166\u001b[0;31m 'Error message(s): {}\\n'.format(os_error_list))\n\u001b[0m\u001b[1;32m 167\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mXGBGetLastError\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrestype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mctypes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mc_char_p\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 168\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallback\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_log_callback_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mXGBoostError\u001b[0m: XGBoost Library (libxgboost.dylib) could not be loaded.\nLikely causes:\n * OpenMP runtime is not installed (vcomp140.dll or libgomp-1.dll for Windows, libomp.dylib for Mac OSX, libgomp.so for Linux and other UNIX-like OSes). Mac OSX users: Run `brew install libomp` to install OpenMP runtime.\n * You are running 32-bit Python on a 64-bit OS\nError message(s): ['dlopen(/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/xgboost/lib/libxgboost.dylib, 6): Library not loaded: /usr/local/opt/libomp/lib/libomp.dylib\\n Referenced from: /usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/xgboost/lib/libxgboost.dylib\\n Reason: image not found']\n" + ] + } + ], + "source": [ + "import xgboost\n" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'XGBClassifier' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mxgb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mXGBClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mxgb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mpredxgb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mxgb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Confusion Matrix for XGBoost Classifier:\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfusion_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mpredxgb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'XGBClassifier' is not defined" + ] + } + ], + "source": [ + "xgb = XGBClassifier()\n", + "xgb.fit(x_train,y_train)\n", + "predxgb = xgb.predict(x_test)\n", + "print(\"Confusion Matrix for XGBoost Classifier:\")\n", + "print(confusion_matrix(y_test,predxgb))\n", + "print(\"Score: \",round(accuracy_score(y_test,predxgb)*100,2))\n", + "print(\"Classification Report:\")\n", + "print(classification_report(y_test,predxgb))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/your-project/code/Code 3 - Splitting Targets | NLP Analysis 2.ipynb b/your-project/code/Code 3 - Splitting Targets | NLP Analysis 2.ipynb new file mode 100644 index 00000000..79721b08 --- /dev/null +++ b/your-project/code/Code 3 - Splitting Targets | NLP Analysis 2.ipynb @@ -0,0 +1,1595 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Splitting the target" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from sklearn.model_selection import train_test_split, cross_val_score\n", + "from sklearn.model_selection import cross_validate\n", + "from sklearn.model_selection import StratifiedKFold\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.model_selection import learning_curve\n", + "from sklearn.ensemble import ExtraTreesClassifier\n", + "from sklearn.decomposition import TruncatedSVD\n", + "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.naive_bayes import MultinomialNB\n", + "from sklearn.metrics import classification_report\n", + "from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score, roc_curve" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "data = pd.read_csv(r'../data/personalities_cleaned.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "data2 = data.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "data2['I-E'] = data2['type'].astype(str).str[0]\n", + "data2['N-S'] = data2['type'].astype(str).str[1]\n", + "data2['T-F'] = data2['type'].astype(str).str[2]\n", + "data2['J-P'] = data2['type'].astype(str).str[3]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "data2 = data2.drop(columns=['Unnamed: 0', 'posts', 'text_processed'])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "type 0\n", + "text_ready 1\n", + "I-E 0\n", + "N-S 0\n", + "T-F 0\n", + "J-P 0\n", + "dtype: int64" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data2.isnull().sum()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " type text_ready I-E N-S T-F J-P\n", + "3559 INFP NaN I N F P" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df1 = data2[data2.isna().any(axis=1)]\n", + "df1" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "data2.drop(data2.index[3559], inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "type 0\n", + "text_ready 0\n", + "I-E 0\n", + "N-S 0\n", + "T-F 0\n", + "J-P 0\n", + "dtype: int64" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data2.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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typetext_readyI-EN-ST-FJ-P
0INFJintj moment sportscent top ten play prank ha l...INFJ
1ENTPfind lack post veri alarm sex bore posit often...ENTP
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3INTJdear intp enjoy convers day esoter gab natur u...INTJ
4ENTJfire anoth silli misconcept approach logic go ...ENTJ
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" + ], + "text/plain": [ + " type text_ready I-E N-S T-F J-P\n", + "0 INFJ intj moment sportscent top ten play prank ha l... I N F J\n", + "1 ENTP find lack post veri alarm sex bore posit often... E N T P\n", + "2 INTP good one cours say know bless cur doe absolut ... I N T P\n", + "3 INTJ dear intp enjoy convers day esoter gab natur u... I N T J\n", + "4 ENTJ fire anoth silli misconcept approach logic go ... E N T J" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data2.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# learning with the model" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# defining learnings for each \n", + "x = data2['text_ready']\n", + "\n", + "y_IE = data2['I-E']\n", + "y_NS = data2['N-S']\n", + "y_TF = data2['T-F']\n", + "y_JP = data2['J-P']" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# for vector functions\n", + "from sklearn.feature_extraction.text import CountVectorizer\n", + "\n", + "vector = CountVectorizer(ngram_range=(2, 2)).fit(x) \n", + "X = vector.transform(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.decomposition import TruncatedSVD\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "\n", + "svd = TruncatedSVD(n_components = 100)\n", + "reduced = svd.fit_transform(X)\n", + "\n", + "scaler = MinMaxScaler()\n", + "scaled = scaler.fit_transform(reduced)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Optimizing Parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "I 6675\n", + "E 1999\n", + "Name: I-E, dtype: int64" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_IE.value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I-E Accuracy for 1 neigbours.\n", + "Label I-E test score is : 0.7636887608069164\n", + "Confusion Matrix for KNeighbors:\n", + "[[ 1 410]\n", + " [ 0 1324]]\n", + "I-E Accuracy for 3 neigbours.\n", + "Label I-E test score is : 0.768299711815562\n", + "Confusion Matrix for KNeighbors:\n", + "[[ 1 400]\n", + " [ 2 1332]]\n", + "I-E Accuracy for 5 neigbours.\n", + "Label I-E test score is : 0.7648414985590778\n", + "Confusion Matrix for KNeighbors:\n", + "[[ 0 408]\n", + " [ 0 1327]]\n", + "I-E Accuracy for 7 neigbours.\n", + "Label I-E test score is : 0.7769452449567723\n", + "Confusion Matrix for KNeighbors:\n", + "[[ 0 385]\n", + " [ 2 1348]]\n", + "I-E Accuracy for 9 neigbours.\n", + "Label I-E test score is : 0.7711815561959654\n", + "Confusion Matrix for KNeighbors:\n", + "[[ 0 397]\n", + " [ 0 1338]]\n", + "I-E Accuracy for 11 neigbours.\n", + "Label I-E test score is : 0.7757925072046109\n", + "Confusion Matrix for KNeighbors:\n", + "[[ 0 389]\n", + " [ 0 1346]]\n", + "I-E Accuracy for 13 neigbours.\n", + "Label I-E test score is : 0.7694524495677233\n", + "Confusion Matrix for KNeighbors:\n", + "[[ 0 400]\n", + " [ 0 1335]]\n", + "I-E Accuracy for 15 neigbours.\n", + "Label I-E test score is : 0.7769452449567723\n", + "Confusion Matrix for KNeighbors:\n", + "[[ 0 387]\n", + " [ 0 1348]]\n" + ] + } + ], + "source": [ + "from sklearn.neighbors import KNeighborsClassifier\n", + "\n", + "\n", + "for x in range(1, 16, 2):\n", + " x_train, x_test, y_train, y_test = train_test_split(reduced, y_IE, test_size=0.2)\n", + " knn = KNeighborsClassifier(n_neighbors = x)\n", + " clf = knn.fit(x_train, y_train)\n", + " predknn = clf.predict(x_test)\n", + " print(\"I-E Accuracy for \", x, \" neigbours.\")\n", + " print('Label I-E test score is :',accuracy_score(y_test, predknn))\n", + " print(\"Confusion Matrix for KNeighbors:\")\n", + " print(confusion_matrix(y_test, predknn))\n", + " #print(\"Score:\",round(accuracy_score(predknn, y_test)*100,2))\n", + "\n", + "#print(\"*\" * 100)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I-E Accuracy for 1 max_depth.\n", + "[[1497 0]\n", + " [ 238 0]]\n", + "Score: 86.28\n", + "I-E Accuracy for 2 max_depth.\n", + "[[1497 0]\n", + " [ 238 0]]\n", + "Score: 86.28\n", + "I-E Accuracy for 3 max_depth.\n", + "[[1497 0]\n", + " [ 238 0]]\n", + "Score: 86.28\n", + "I-E Accuracy for 4 max_depth.\n", + "[[1497 0]\n", + " [ 238 0]]\n", + "Score: 86.28\n", + "I-E Accuracy for 5 max_depth.\n", + "[[1497 0]\n", + " [ 238 0]]\n", + "Score: 86.28\n", + "I-E Accuracy for 6 max_depth.\n", + "[[1497 0]\n", + " [ 238 0]]\n", + "Score: 86.28\n", + "I-E Accuracy for 7 max_depth.\n", + "[[1497 0]\n", + " [ 238 0]]\n", + "Score: 86.28\n", + "I-E Accuracy for 8 max_depth.\n", + "[[1497 0]\n", + " [ 238 0]]\n", + "Score: 86.28\n", + "I-E Accuracy for 9 max_depth.\n", + "[[1497 0]\n", + " [ 238 0]]\n", + "Score: 86.28\n", + "I-E Accuracy for 10 max_depth.\n", + "[[1497 0]\n", + " [ 238 0]]\n", + "Score: 86.28\n", + "I-E Accuracy for 11 max_depth.\n", + "[[1497 0]\n", + " [ 238 0]]\n", + "Score: 86.28\n", + "I-E Accuracy for 12 max_depth.\n", + "[[1497 0]\n", + " [ 238 0]]\n", + "Score: 86.28\n", + "I-E Accuracy for 13 max_depth.\n", + "[[1497 0]\n", + " [ 238 0]]\n", + "Score: 86.28\n", + "I-E Accuracy for 14 max_depth.\n", + "[[1497 0]\n", + " [ 238 0]]\n", + "Score: 86.28\n", + "I-E Accuracy for 15 max_depth.\n", + "[[1497 0]\n", + " [ 238 0]]\n", + "Score: 86.28\n" + ] + } + ], + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "\n", + "\n", + "for x in range(1, 16):\n", + " rfn = RandomForestClassifier(max_depth= x, random_state=0)\n", + "\n", + " # NS\n", + " x_train, x_test, y_train, y_test = train_test_split(reduced, y_NS, test_size=0.2, random_state=10)\n", + " rfn.fit(x_train, y_train)\n", + "# ieb_train = knn.score(x_train,y_train)\n", + "# ieb_test = knn.score (x_train,y_train)\n", + " predrfn = rfn.predict(x_test)\n", + " print(\"I-E Accuracy for \", x, \" max_depth.\")\n", + "# print('Label I-E train score is :',ieb_train)\n", + " print(confusion_matrix(y_test,predrfn))\n", + " print(\"Score:\",round(accuracy_score(y_test,predrfn)*100,2))\n", + " #print(\"Score:\",round(accuracy_score(predknn, y_test)*100,2))\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### MultinomialNB" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "Negative values in data passed to MultinomialNB (input X)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;31m# IE\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreduced\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_IE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mmnb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 10\u001b[0m \u001b[0mieb_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmnb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0mieb_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmnb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/naive_bayes.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m 633\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 634\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_init_counters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_effective_classes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_features\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 635\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 636\u001b[0m \u001b[0malpha\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_alpha\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 637\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_feature_log_prob\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/naive_bayes.py\u001b[0m in \u001b[0;36m_count\u001b[0;34m(self, X, Y)\u001b[0m\n\u001b[1;32m 754\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 755\u001b[0m \u001b[0;34m\"\"\"Count and smooth feature occurrences.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 756\u001b[0;31m \u001b[0mcheck_non_negative\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"MultinomialNB (input X)\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 757\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeature_count_\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0msafe_sparse_dot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mY\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 758\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclass_count_\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_non_negative\u001b[0;34m(X, whom)\u001b[0m\n\u001b[1;32m 992\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 993\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mX_min\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 994\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Negative values in data passed to %s\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mwhom\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 995\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 996\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: Negative values in data passed to MultinomialNB (input X)" + ] + } + ], + "source": [ + "from sklearn.naive_bayes import MultinomialNB\n", + "\n", + "mnb = MultinomialNB()\n", + "mnb.fit(x_train, y_train)\n", + "\n", + "\n", + "# IE\n", + "x_train, x_test, y_train, y_test = train_test_split(reduced, y_IE, test_size=0.2, random_state=10)\n", + "mnb.fit(x_train, y_train)\n", + "ieb_train = mnb.score (x_train,y_train)\n", + "ieb_test = mnb.score (x_train,y_train)\n", + "predmnb = mnb.predict(x_test)\n", + "print(\"I-E RESULTS\")\n", + "print('Label I-E train score is :',ieb_train)\n", + "print('Label I-E test score is :',ieb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predmnb))\n", + "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", + "print(\"*\" * 100)\n", + "\n", + "# NS\n", + "x_train, x_test, y_train, y_test = train_test_split(reduced, y_NS, test_size=0.2, random_state=10)\n", + "mnb.fit(x_train, y_train)\n", + "nsb_train = mnb.score (x_train,y_train)\n", + "nsb_test = mnb.score (x_train,y_train)\n", + "predmnb = mnb.predict(x_test)\n", + "print(\"N-S RESULTS\")\n", + "print('Label N-S train score is :',nsb_train)\n", + "print('Label N-S test score is :',nsb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predmnb))\n", + "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", + "print(\"*\" * 100)\n", + "\n", + "# TF\n", + "x_train, x_test, y_train, y_test = train_test_split(reduced, y_TF, test_size=0.2, random_state=10)\n", + "mnb.fit(x_train, y_train)\n", + "tfb_train = mnb.score (x_train,y_train)\n", + "tfb_test = mnb.score (x_train,y_train)\n", + "predmnb = mnb.predict(x_test)\n", + "print(\"T-F Results\")\n", + "print('Label T-F train score is :',tfb_train)\n", + "print('Label T-F test score is :',tfb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predmnb))\n", + "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", + "print(\"*\" * 100)\n", + "\n", + "# JP\n", + "x_train, x_test, y_train, y_test = train_test_split(reduced, y_JP, test_size=0.2, random_state=10)\n", + "mnb.fit(x_train, y_train)\n", + "jpb_train = mnb.score (x_train,y_train)\n", + "jpb_test = mnb.score (x_train,y_train)\n", + "predmnb = mnb.predict(x_test)\n", + "print(\"J-P Results\")\n", + "print('Label J-P train score is :',jpb_train)\n", + "print('Label J-P test score is :',jpb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predmnb))\n", + "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", + "print(\"*\" * 100)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Decision Tree" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I-E RESULTS\n", + "Label I-E train score is : 1.0\n", + "Label I-E test score is : 1.0\n", + "Confusion Matrix for Decision Tree:\n", + "[[ 91 302]\n", + " [353 989]]\n", + "Score: 62.25\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " E 0.20 0.23 0.22 393\n", + " I 0.77 0.74 0.75 1342\n", + "\n", + " accuracy 0.62 1735\n", + " macro avg 0.49 0.48 0.48 1735\n", + "weighted avg 0.64 0.62 0.63 1735\n", + "\n", + "****************************************************************************************************\n", + "N-S RESULTS\n", + "Label N-S train score is : 1.0\n", + "Label N-S test score is : 1.0\n", + "Confusion Matrix for Decision Tree:\n", + "[[1260 237]\n", + " [ 186 52]]\n", + "Score: 75.62\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " N 0.87 0.84 0.86 1497\n", + " S 0.18 0.22 0.20 238\n", + "\n", + " accuracy 0.76 1735\n", + " macro avg 0.53 0.53 0.53 1735\n", + "weighted avg 0.78 0.76 0.77 1735\n", + "\n", + "****************************************************************************************************\n", + "T-F Results\n", + "Label T-F train score is : 1.0\n", + "Label T-F test score is : 1.0\n", + "Confusion Matrix for Decision Tree:\n", + "[[583 377]\n", + " [359 416]]\n", + "Score: 57.58\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " F 0.62 0.61 0.61 960\n", + " T 0.52 0.54 0.53 775\n", + "\n", + " accuracy 0.58 1735\n", + " macro avg 0.57 0.57 0.57 1735\n", + "weighted avg 0.58 0.58 0.58 1735\n", + "\n", + "****************************************************************************************************\n", + "J-P Results\n", + "Label J-P train score is : 1.0\n", + "Label J-P test score is : 1.0\n", + "Confusion Matrix for Decision Tree:\n", + "[[304 376]\n", + " [418 637]]\n", + "Score: 54.24\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " J 0.42 0.45 0.43 680\n", + " P 0.63 0.60 0.62 1055\n", + "\n", + " accuracy 0.54 1735\n", + " macro avg 0.52 0.53 0.52 1735\n", + "weighted avg 0.55 0.54 0.54 1735\n", + "\n", + "****************************************************************************************************\n" + ] + } + ], + "source": [ + "from sklearn.tree import DecisionTreeClassifier\n", + "\n", + "dt = DecisionTreeClassifier()\n", + "dt.fit(x_train, y_train)\n", + "\n", + "\n", + "# IE\n", + "x_train, x_test, y_train, y_test = train_test_split(reduced, y_IE, test_size=0.2, random_state=10)\n", + "dt.fit(x_train, y_train)\n", + "ieb_train = dt.score (x_train,y_train)\n", + "ieb_test = dt.score (x_train,y_train)\n", + "preddt = dt.predict(x_test)\n", + "print(\"I-E RESULTS\")\n", + "print('Label I-E train score is :',ieb_train)\n", + "print('Label I-E test score is :',ieb_test)\n", + "print(\"Confusion Matrix for Decision Tree:\")\n", + "print(confusion_matrix(y_test,preddt))\n", + "print(\"Score:\",round(accuracy_score(y_test,preddt)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,preddt))\n", + "print(\"*\" * 100)\n", + "\n", + "# NS\n", + "x_train, x_test, y_train, y_test = train_test_split(reduced, y_NS, test_size=0.2, random_state=10)\n", + "dt.fit(x_train, y_train)\n", + "nsb_train = dt.score (x_train,y_train)\n", + "nsb_test = dt.score (x_train,y_train)\n", + "preddt = dt.predict(x_test)\n", + "print(\"N-S RESULTS\")\n", + "print('Label N-S train score is :',nsb_train)\n", + "print('Label N-S test score is :',nsb_test)\n", + "print(\"Confusion Matrix for Decision Tree:\")\n", + "print(confusion_matrix(y_test,preddt))\n", + "print(\"Score:\",round(accuracy_score(y_test,preddt)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,preddt))\n", + "print(\"*\" * 100)\n", + "\n", + "# TF\n", + "x_train, x_test, y_train, y_test = train_test_split(reduced, y_TF, test_size=0.2, random_state=10)\n", + "dt.fit(x_train, y_train)\n", + "tfb_train = dt.score (x_train,y_train)\n", + "tfb_test = dt.score (x_train,y_train)\n", + "preddt = dt.predict(x_test)\n", + "print(\"T-F Results\")\n", + "print('Label T-F train score is :',tfb_train)\n", + "print('Label T-F test score is :',tfb_test)\n", + "print(\"Confusion Matrix for Decision Tree:\")\n", + "print(confusion_matrix(y_test,preddt))\n", + "print(\"Score:\",round(accuracy_score(y_test,preddt)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,preddt))\n", + "print(\"*\" * 100)\n", + "\n", + "# JP\n", + "x_train, x_test, y_train, y_test = train_test_split(reduced, y_JP, test_size=0.2, random_state=10)\n", + "dt.fit(x_train, y_train)\n", + "jpb_train = dt.score (x_train,y_train)\n", + "jpb_test = dt.score (x_train,y_train)\n", + "preddt = dt.predict(x_test)\n", + "print(\"J-P Results\")\n", + "print('Label J-P train score is :',jpb_train)\n", + "print('Label J-P test score is :',jpb_test)\n", + "print(\"Confusion Matrix for Decision Tree:\")\n", + "print(confusion_matrix(y_test,preddt))\n", + "print(\"Score:\",round(accuracy_score(y_test,preddt)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,preddt))\n", + "print(\"*\" * 100)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### K Nearest Neighbour" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I-E RESULTS\n", + "Label I-E train score is : 0.7867127828217323\n", + "Label I-E test score is : 0.7867127828217323\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[ 39 354]\n", + " [ 58 1284]]\n", + "Score: 76.25\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " E 0.40 0.10 0.16 393\n", + " I 0.78 0.96 0.86 1342\n", + "\n", + " accuracy 0.76 1735\n", + " macro avg 0.59 0.53 0.51 1735\n", + "weighted avg 0.70 0.76 0.70 1735\n", + "\n", + "****************************************************************************************************\n", + "N-S RESULTS\n", + "Label N-S train score is : 0.8648220204640438\n", + "Label N-S test score is : 0.8648220204640438\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[1475 22]\n", + " [ 233 5]]\n", + "Score: 85.3\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " N 0.86 0.99 0.92 1497\n", + " S 0.19 0.02 0.04 238\n", + "\n", + " accuracy 0.85 1735\n", + " macro avg 0.52 0.50 0.48 1735\n", + "weighted avg 0.77 0.85 0.80 1735\n", + "\n", + "****************************************************************************************************\n", + "T-F Results\n", + "Label T-F train score is : 0.7122063697939184\n", + "Label T-F test score is : 0.7122063697939184\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[509 451]\n", + " [282 493]]\n", + "Score: 57.75\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " F 0.64 0.53 0.58 960\n", + " T 0.52 0.64 0.57 775\n", + "\n", + " accuracy 0.58 1735\n", + " macro avg 0.58 0.58 0.58 1735\n", + "weighted avg 0.59 0.58 0.58 1735\n", + "\n", + "****************************************************************************************************\n", + "J-P Results\n", + "Label J-P train score is : 0.6921746649373108\n", + "Label J-P test score is : 0.6921746649373108\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[196 484]\n", + " [282 773]]\n", + "Score: 55.85\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " J 0.41 0.29 0.34 680\n", + " P 0.61 0.73 0.67 1055\n", + "\n", + " accuracy 0.56 1735\n", + " macro avg 0.51 0.51 0.50 1735\n", + "weighted avg 0.53 0.56 0.54 1735\n", + "\n", + "****************************************************************************************************\n" + ] + } + ], + "source": [ + "from sklearn.neighbors import KNeighborsClassifier\n", + "\n", + "knn = KNeighborsClassifier(n_neighbors=7)\n", + "\n", + "\n", + "\n", + "# IE\n", + "x_train, x_test, y_train, y_test = train_test_split(reduced, y_IE, test_size=0.2, random_state=10)\n", + "knn.fit(x_train, y_train)\n", + "ieb_train = knn.score (x_train,y_train)\n", + "ieb_test = knn.score (x_train,y_train)\n", + "predknn = knn.predict(x_test)\n", + "print(\"I-E RESULTS\")\n", + "print('Label I-E train score is :',ieb_train)\n", + "print('Label I-E test score is :',ieb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predknn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predknn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predknn))\n", + "print(\"*\" * 100)\n", + "\n", + "# NS\n", + "x_train, x_test, y_train, y_test = train_test_split(reduced, y_NS, test_size=0.2, random_state=10)\n", + "knn.fit(x_train, y_train)\n", + "nsb_train = knn.score (x_train,y_train)\n", + "nsb_test = knn.score (x_train,y_train)\n", + "predknn = knn.predict(x_test)\n", + "print(\"N-S RESULTS\")\n", + "print('Label N-S train score is :',nsb_train)\n", + "print('Label N-S test score is :',nsb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predknn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predknn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predknn))\n", + "print(\"*\" * 100)\n", + "\n", + "# TF\n", + "x_train, x_test, y_train, y_test = train_test_split(reduced, y_TF, test_size=0.2, random_state=10)\n", + "knn.fit(x_train, y_train)\n", + "tfb_train = knn.score (x_train,y_train)\n", + "tfb_test = knn.score (x_train,y_train)\n", + "predknn = knn.predict(x_test)\n", + "print(\"T-F Results\")\n", + "print('Label T-F train score is :',tfb_train)\n", + "print('Label T-F test score is :',tfb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predknn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predknn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predknn))\n", + "print(\"*\" * 100)\n", + "\n", + "# JP\n", + "x_train, x_test, y_train, y_test = train_test_split(reduced, y_JP, test_size=0.2, random_state=10)\n", + "knn.fit(x_train, y_train)\n", + "jpb_train = knn.score (x_train,y_train)\n", + "jpb_test = knn.score (x_train,y_train)\n", + "predknn = knn.predict(x_test)\n", + "print(\"J-P Results\")\n", + "print('Label J-P train score is :',jpb_train)\n", + "print('Label J-P test score is :',jpb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predknn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predknn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predknn))\n", + "print(\"*\" * 100)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Random Forest" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I-E RESULTS\n", + "Label I-E train score is : 0.769275111687563\n", + "Label I-E test score is : 0.769275111687563\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[ 0 393]\n", + " [ 0 1342]]\n", + "Score: 77.35\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " E 0.00 0.00 0.00 393\n", + " I 0.77 1.00 0.87 1342\n", + "\n", + " accuracy 0.77 1735\n", + " macro avg 0.39 0.50 0.44 1735\n", + "weighted avg 0.60 0.77 0.67 1735\n", + "\n", + "****************************************************************************************************\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "N-S RESULTS\n", + "Label N-S train score is : 0.8622279867416054\n", + "Label N-S test score is : 0.8622279867416054\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[1497 0]\n", + " [ 238 0]]\n", + "Score: 86.28\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " N 0.86 1.00 0.93 1497\n", + " S 0.00 0.00 0.00 238\n", + "\n", + " accuracy 0.86 1735\n", + " macro avg 0.43 0.50 0.46 1735\n", + "weighted avg 0.74 0.86 0.80 1735\n", + "\n", + "****************************************************************************************************\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "T-F Results\n", + "Label T-F train score is : 0.7238795215448912\n", + "Label T-F test score is : 0.7238795215448912\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[726 234]\n", + " [361 414]]\n", + "Score: 65.71\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " F 0.67 0.76 0.71 960\n", + " T 0.64 0.53 0.58 775\n", + "\n", + " accuracy 0.66 1735\n", + " macro avg 0.65 0.65 0.65 1735\n", + "weighted avg 0.65 0.66 0.65 1735\n", + "\n", + "****************************************************************************************************\n", + "J-P Results\n", + "Label J-P train score is : 0.6235768842772734\n", + "Label J-P test score is : 0.6235768842772734\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[ 9 671]\n", + " [ 3 1052]]\n", + "Score: 61.15\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " J 0.75 0.01 0.03 680\n", + " P 0.61 1.00 0.76 1055\n", + "\n", + " accuracy 0.61 1735\n", + " macro avg 0.68 0.51 0.39 1735\n", + "weighted avg 0.67 0.61 0.47 1735\n", + "\n", + "****************************************************************************************************\n" + ] + } + ], + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "\n", + "rfn = RandomForestClassifier(max_depth=5, random_state=0)\n", + "rfn.fit(x_train, y_train)\n", + "\n", + "\n", + "# IE\n", + "x_train, x_test, y_train, y_test = train_test_split(reduced, y_IE, test_size=0.2, random_state=10)\n", + "rfn.fit(x_train, y_train)\n", + "ieb_train = rfn.score (x_train,y_train)\n", + "ieb_test = rfn.score (x_train,y_train)\n", + "predrfn = rfn.predict(x_test)\n", + "print(\"I-E RESULTS\")\n", + "print('Label I-E train score is :',ieb_train)\n", + "print('Label I-E test score is :',ieb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predrfn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predrfn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predrfn))\n", + "print(\"*\" * 100)\n", + "\n", + "# NS\n", + "x_train, x_test, y_train, y_test = train_test_split(reduced, y_NS, test_size=0.2, random_state=10)\n", + "rfn.fit(x_train, y_train)\n", + "nsb_train = rfn.score (x_train,y_train)\n", + "nsb_test = rfn.score (x_train,y_train)\n", + "predrfn = rfn.predict(x_test)\n", + "print(\"N-S RESULTS\")\n", + "print('Label N-S train score is :',nsb_train)\n", + "print('Label N-S test score is :',nsb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predrfn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predrfn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predrfn))\n", + "print(\"*\" * 100)\n", + "\n", + "# TF\n", + "x_train, x_test, y_train, y_test = train_test_split(reduced, y_TF, test_size=0.2, random_state=10)\n", + "rfn.fit(x_train, y_train)\n", + "tfb_train = rfn.score (x_train,y_train)\n", + "tfb_test = rfn.score (x_train,y_train)\n", + "predrfn = rfn.predict(x_test)\n", + "print(\"T-F Results\")\n", + "print('Label T-F train score is :',tfb_train)\n", + "print('Label T-F test score is :',tfb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predrfn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predrfn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predrfn))\n", + "print(\"*\" * 100)\n", + "\n", + "# JP\n", + "x_train, x_test, y_train, y_test = train_test_split(reduced, y_JP, test_size=0.2, random_state=10)\n", + "rfn.fit(x_train, y_train)\n", + "jpb_train = rfn.score (x_train,y_train)\n", + "jpb_test = rfn.score (x_train,y_train)\n", + "predrfn = rfn.predict(x_test)\n", + "print(\"J-P Results\")\n", + "print('Label J-P train score is :',jpb_train)\n", + "print('Label J-P test score is :',jpb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predrfn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predrfn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predrfn))\n", + "print(\"*\" * 100)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Neural Network" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import random\n", + "import re\n", + "\n", + "import keras\n", + "from keras import models\n", + "from keras.models import Sequential\n", + "from keras.layers import Conv2D\n", + "from keras.layers import MaxPooling2D\n", + "from keras.layers import Flatten\n", + "from keras.layers import Dense\n", + "from sklearn import preprocessing\n", + "from tensorflow.keras.models import load_model\n", + "import tensorflow as tf\n", + "from sklearn.model_selection import train_test_split, cross_val_score" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0typepoststext_processedtext_readyI-EN-ST-FJ-P
00INFJ'http://www.youtube.com/watch?v=qsXHcwe3krw|||...['intj', 'moment', 'sportscent', 'top', 'ten',...intj moment sportscent top ten play prank ha l...0010
11ENTP'I'm finding the lack of me in these posts ver...['find', 'lack', 'post', 'veri', 'alarm', 'sex...find lack post veri alarm sex bore posit often...1001
22INTP'Good one _____ https://www.youtube.com/wat...['good', 'one', 'cours', 'say', 'know', 'bless...good one cours say know bless cur doe absolut ...0001
33INTJ'Dear INTP, I enjoyed our conversation the o...['dear', 'intp', 'enjoy', 'convers', 'day', 'e...dear intp enjoy convers day esoter gab natur u...0000
44ENTJ'You're fired.|||That's another silly misconce...['fire', 'anoth', 'silli', 'misconcept', 'appr...fire anoth silli misconcept approach logic go ...1000
\n", + "
" + ], + "text/plain": [ + " Unnamed: 0 type posts \\\n", + "0 0 INFJ 'http://www.youtube.com/watch?v=qsXHcwe3krw|||... \n", + "1 1 ENTP 'I'm finding the lack of me in these posts ver... \n", + "2 2 INTP 'Good one _____ https://www.youtube.com/wat... \n", + "3 3 INTJ 'Dear INTP, I enjoyed our conversation the o... \n", + "4 4 ENTJ 'You're fired.|||That's another silly misconce... \n", + "\n", + " text_processed \\\n", + "0 ['intj', 'moment', 'sportscent', 'top', 'ten',... \n", + "1 ['find', 'lack', 'post', 'veri', 'alarm', 'sex... \n", + "2 ['good', 'one', 'cours', 'say', 'know', 'bless... \n", + "3 ['dear', 'intp', 'enjoy', 'convers', 'day', 'e... \n", + "4 ['fire', 'anoth', 'silli', 'misconcept', 'appr... \n", + "\n", + " text_ready I-E N-S T-F J-P \n", + "0 intj moment sportscent top ten play prank ha l... 0 0 1 0 \n", + "1 find lack post veri alarm sex bore posit often... 1 0 0 1 \n", + "2 good one cours say know bless cur doe absolut ... 0 0 0 1 \n", + "3 dear intp enjoy convers day esoter gab natur u... 0 0 0 0 \n", + "4 fire anoth silli misconcept approach logic go ... 1 0 0 0 " + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_neural = data.copy()\n", + "\n", + "data_neural['I-E'] = data_neural['I-E'].astype('category')\n", + "data_neural['N-S'] = data_neural['N-S'].astype('category')\n", + "data_neural['T-F'] = data_neural['T-F'].astype('category')\n", + "data_neural['J-P'] = data_neural['J-P'].astype('category')\n", + "\n", + "cat_columns = data_neural.select_dtypes(['category']).columns\n", + "\n", + "data_neural[cat_columns] = data_neural[cat_columns].apply(lambda x: x.cat.codes)\n", + "\n", + "data_neural" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "x = data_neural['text_ready']\n", + "\n", + "y_IE = data_neural['I-E']\n", + "y_NS = data_neural['N-S']\n", + "y_TF = data_neural['T-F']\n", + "y_JP = data_neural['J-P']\n", + "\n", + "from sklearn.feature_extraction.text import CountVectorizer\n", + "\n", + "vector = CountVectorizer(ngram_range=(2, 2)).fit(x) \n", + "X = vector.transform(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "model = tf.keras.models.Sequential()\n", + "model.add(tf.keras.layers.Dense(units=256, kernel_initializer='uniform', activation='relu', input_dim=1986297))\n", + "model.add(tf.keras.layers.Dense(units=128, kernel_initializer='uniform', activation='relu'))\n", + "model.add(tf.keras.layers.Dense(units=64, kernel_initializer='uniform', activation='relu'))\n", + "model.add(tf.keras.layers.Dense(activation = 'sigmoid', units = 1, kernel_initializer = 'uniform')) # output layer has number of outputs not number of neurons\n", + "\n", + "model.compile(optimizer = 'adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])\n", + "# calculating loss, model is always trying to minimize loss, adam is the default optimize\n", + "\n", + "\n", + "# model.fit(X_train, y_train, epochs = 10, batch_size= 25)\n", + "\n", + "# val_loss, val_acc = model.evaluate(X_test, y_test, verbose=0)\n", + "# print(f'Test loss: {val_loss}')\n", + "# print(f'Test accuracy: {val_acc}')" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Keras is training/fitting/evaluating on array-like data. Keras may not be optimized for this format, so if your input data format is supported by TensorFlow I/O (https://github.com/tensorflow/io) we recommend using that to load a Dataset instead.\n", + "Epoch 1/10\n" + ] + }, + { + "ename": "InvalidArgumentError", + "evalue": " TypeError: 'SparseTensor' object is not subscriptable\nTraceback (most recent call last):\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/ops/script_ops.py\", line 241, in __call__\n return func(device, token, args)\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/ops/script_ops.py\", line 130, in __call__\n ret = self._func(*args)\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/autograph/impl/api.py\", line 309, in wrapper\n return func(*args, **kwargs)\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/data_adapter.py\", line 513, in py_method\n return [slice_array(inp) for inp in flat_inputs]\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/data_adapter.py\", line 513, in \n return [slice_array(inp) for inp in flat_inputs]\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/data_adapter.py\", line 512, in slice_array\n contiguous=contiguous)\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/training_utils.py\", line 391, in slice_arrays\n entries = [[x[i:i + 1] for i in indices] for x in arrays]\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/training_utils.py\", line 391, in \n entries = [[x[i:i + 1] for i in indices] for x in arrays]\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/training_utils.py\", line 391, in \n entries = [[x[i:i + 1] for i in indices] for x in arrays]\n\nTypeError: 'SparseTensor' object is not subscriptable\n\n\n\t [[{{node EagerPyFunc}}]]\n\t [[IteratorGetNext]] [Op:__inference_train_function_1799]\n\nFunction call stack:\ntrain_function\n", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mInvalidArgumentError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# IE\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m 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"\u001b[0;32m/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m 846\u001b[0m batch_size=batch_size):\n\u001b[1;32m 847\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 848\u001b[0;31m \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 849\u001b[0m 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the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 643\u001b[0m \u001b[0;31m# stateless function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 644\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 645\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 646\u001b[0m \u001b[0mcanon_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcanon_kwds\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 2418\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2419\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2420\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_filtered_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2421\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2422\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_filtered_call\u001b[0;34m(self, args, kwargs)\u001b[0m\n\u001b[1;32m 1663\u001b[0m if isinstance(t, (ops.Tensor,\n\u001b[1;32m 1664\u001b[0m resource_variable_ops.BaseResourceVariable))),\n\u001b[0;32m-> 1665\u001b[0;31m self.captured_inputs)\n\u001b[0m\u001b[1;32m 1666\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1667\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_call_flat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcaptured_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcancellation_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m 1744\u001b[0m \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1745\u001b[0m return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1746\u001b[0;31m ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m 1747\u001b[0m forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m 1748\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m 596\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 597\u001b[0m \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 598\u001b[0;31m ctx=ctx)\n\u001b[0m\u001b[1;32m 599\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 600\u001b[0m outputs = execute.execute_with_cancellation(\n", + "\u001b[0;32m/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m 58\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 59\u001b[0m tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 60\u001b[0;31m inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m 61\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mInvalidArgumentError\u001b[0m: TypeError: 'SparseTensor' object is not subscriptable\nTraceback (most recent call last):\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/ops/script_ops.py\", line 241, in __call__\n return func(device, token, args)\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/ops/script_ops.py\", line 130, in __call__\n ret = self._func(*args)\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/autograph/impl/api.py\", line 309, in wrapper\n return func(*args, **kwargs)\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/data_adapter.py\", line 513, in py_method\n return [slice_array(inp) for inp in flat_inputs]\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/data_adapter.py\", line 513, in \n return [slice_array(inp) for inp in flat_inputs]\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/data_adapter.py\", line 512, in slice_array\n contiguous=contiguous)\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/training_utils.py\", line 391, in slice_arrays\n entries = [[x[i:i + 1] for i in indices] for x in arrays]\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/training_utils.py\", line 391, in \n entries = [[x[i:i + 1] for i in indices] for x in arrays]\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/training_utils.py\", line 391, in \n entries = [[x[i:i + 1] for i in indices] for x in arrays]\n\nTypeError: 'SparseTensor' object is not subscriptable\n\n\n\t [[{{node EagerPyFunc}}]]\n\t [[IteratorGetNext]] [Op:__inference_train_function_1799]\n\nFunction call stack:\ntrain_function\n" + ] + } + ], + "source": [ + "\n", + "\n", + "# IE\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y_IE, test_size=0.2, random_state=10)\n", + "model.fit(X_train, y_train, epochs = 10, batch_size= 25)\n", + "predmodel = model.predict(x_test)\n", + "print(\"I-E RESULTS\")\n", + "# print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "# print(confusion_matrix(y_test,predmodel))\n", + "# print(\"Score:\",round(accuracy_score(y_test,predmodel)*100,2))\n", + "# print(\"Classification Report:\",classification_report(y_test,predmodel))\n", + "val_loss, val_acc = model.evaluate(X_test, y_test, verbose=0)\n", + "print(f'Test loss: {val_loss}')\n", + "print(f'Test accuracy: {val_acc}')\n", + "print(\"*\" * 100)\n", + "\n", + "# NS\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y_NS, test_size=0.2, random_state=10)\n", + "model.fit(X_train, y_train, epochs = 10, batch_size= 25)\n", + "predmodel = model.predict(x_test)\n", + "print(\"N-S RESULTS\")\n", + "# print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "# print(confusion_matrix(y_test,predmodel))\n", + "# print(\"Score:\",round(accuracy_score(y_test,predmodel)*100,2))\n", + "# print(\"Classification Report:\",classification_report(y_test,predmodel))\n", + "val_loss, val_acc = model.evaluate(X_test, y_test, verbose=0)\n", + "print(f'Test loss: {val_loss}')\n", + "print(f'Test accuracy: {val_acc}')\n", + "print(\"*\" * 100)\n", + "\n", + "# TF\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y_TF, test_size=0.2, random_state=10)\n", + "model.fit(X_train, y_train, epochs = 10, batch_size= 25)\n", + "predmodel = model.predict(x_test)\n", + "print(\"T-F Results\")\n", + "# print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "# print(confusion_matrix(y_test,predmodel))\n", + "# print(\"Score:\",round(accuracy_score(y_test,predmodel)*100,2))\n", + "# print(\"Classification Report:\",classification_report(y_test,predmodel))\n", + "val_loss, val_acc = model.evaluate(X_test, y_test, verbose=0)\n", + "print(f'Test loss: {val_loss}')\n", + "print(f'Test accuracy: {val_acc}')\n", + "print(\"*\" * 100)\n", + "\n", + "# JP\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y_JP, test_size=0.2, random_state=10)\n", + "model.fit(X_train, y_train, epochs = 10, batch_size= 25)\n", + "predmodel = model.predict(x_test)\n", + "print(\"J-P Results\")\n", + "# print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "# print(confusion_matrix(y_test,predmodel))\n", + "# print(\"Score:\",round(accuracy_score(y_test,predmodel)*100,2))\n", + "# print(\"Classification Report:\",classification_report(y_test,predmodel))\n", + "val_loss, val_acc = model.evaluate(X_test, y_test, verbose=0)\n", + "print(f'Test loss: {val_loss}')\n", + "print(f'Test accuracy: {val_acc}')\n", + "print(\"*\" * 100)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "XG BOOST" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.naive_bayes import MultinomialNB\n", + "\n", + "mnb = MultinomialNB()\n", + "mnb.fit(x_train, y_train)\n", + "\n", + "\n", + "# IE\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_IE, test_size=0.2, random_state=10)\n", + "mnb.fit(x_train, y_train)\n", + "ieb_train = mnb.score (x_train,y_train)\n", + "ieb_test = mnb.score (x_train,y_train)\n", + "predmnb = mnb.predict(x_test)\n", + "print(\"I-E RESULTS\")\n", + "print('Label I-E train score is :',ieb_train)\n", + "print('Label I-E test score is :',ieb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predmnb))\n", + "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", + "print(\"*\" * 100)\n", + "\n", + "# NS\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_NS, test_size=0.2, random_state=10)\n", + "mnb.fit(x_train, y_train)\n", + "nsb_train = mnb.score (x_train,y_train)\n", + "nsb_test = mnb.score (x_train,y_train)\n", + "predmnb = mnb.predict(x_test)\n", + "print(\"N-S RESULTS\")\n", + "print('Label N-S train score is :',nsb_train)\n", + "print('Label N-S test score is :',nsb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predmnb))\n", + "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", + "print(\"*\" * 100)\n", + "\n", + "# TF\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_TF, test_size=0.2, random_state=10)\n", + "mnb.fit(x_train, y_train)\n", + "tfb_train = mnb.score (x_train,y_train)\n", + "tfb_test = mnb.score (x_train,y_train)\n", + "predmnb = mnb.predict(x_test)\n", + "print(\"T-F Results\")\n", + "print('Label T-F train score is :',tfb_train)\n", + "print('Label T-F test score is :',tfb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predmnb))\n", + "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", + "print(\"*\" * 100)\n", + "\n", + "# JP\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_JP, test_size=0.2, random_state=10)\n", + "mnb.fit(x_train, y_train)\n", + "jpb_train = mnb.score (x_train,y_train)\n", + "jpb_test = mnb.score (x_train,y_train)\n", + "predmnb = mnb.predict(x_test)\n", + "print(\"J-P Results\")\n", + "print('Label J-P train score is :',jpb_train)\n", + "print('Label J-P test score is :',jpb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predmnb))\n", + "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", + "print(\"*\" * 100)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/your-project/code/Code 4 - Extending Data.ipynb b/your-project/code/Code 4 - Extending Data.ipynb new file mode 100644 index 00000000..9221454a --- /dev/null +++ b/your-project/code/Code 4 - Extending Data.ipynb @@ -0,0 +1,892 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## Extending the Dataframe by Quantitative Data" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "import re\n", + "\n", + "from sklearn.model_selection import cross_validate\n", + "from sklearn.model_selection import StratifiedKFold\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.model_selection import learning_curve\n", + "from sklearn.ensemble import ExtraTreesClassifier\n", + "from sklearn.decomposition import TruncatedSVD\n", + "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.naive_bayes import MultinomialNB" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": {}, + "outputs": [], + "source": [ + "# clean data differently" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "data = pd.read_csv(r'../data/mbti_1.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + 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typeposts
0INFJ'http://www.youtube.com/watch?v=qsXHcwe3krw|||...
1ENTP'I'm finding the lack of me in these posts ver...
2INTP'Good one _____ https://www.youtube.com/wat...
3INTJ'Dear INTP, I enjoyed our conversation the o...
4ENTJ'You're fired.|||That's another silly misconce...
\n", + "
" + ], + "text/plain": [ + " type posts\n", + "0 INFJ 'http://www.youtube.com/watch?v=qsXHcwe3krw|||...\n", + "1 ENTP 'I'm finding the lack of me in these posts ver...\n", + "2 INTP 'Good one _____ https://www.youtube.com/wat...\n", + "3 INTJ 'Dear INTP, I enjoyed our conversation the o...\n", + "4 ENTJ 'You're fired.|||That's another silly misconce..." + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import re\n", + "\n", + "def var_posts(string):\n", + " lengths = []\n", + " for post in string:\n", + " pattern2 = '\\w{1,}'\n", + " bag = re.findall(pattern2, post)\n", + " count_post = len(bag)\n", + " lengths.append(count_post)\n", + " return np.var(lengths)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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typepostslength_commentvar_lengthlinks_countqm_countexm_countimg_countmusic_countdots_countht_countme_count
0INFJ'http://www.youtube.com/watch?v=qsXHcwe3krw|||...11.120.1797500.480.360.060.120.020.300.01.18
1ENTP'I'm finding the lack of me in these posts ver...23.400.1844690.200.100.000.020.000.380.03.06
2INTP'Good one _____ https://www.youtube.com/wat...16.720.1840100.100.240.080.000.000.260.01.44
3INTJ'Dear INTP, I enjoyed our conversation the o...21.280.1866400.040.220.060.000.020.520.02.18
4ENTJ'You're fired.|||That's another silly misconce...19.340.1787230.120.200.020.040.020.420.01.48
\n", + "
" + ], + "text/plain": [ + " type posts length_comment \\\n", + "0 INFJ 'http://www.youtube.com/watch?v=qsXHcwe3krw|||... 11.12 \n", + "1 ENTP 'I'm finding the lack of me in these posts ver... 23.40 \n", + "2 INTP 'Good one _____ https://www.youtube.com/wat... 16.72 \n", + "3 INTJ 'Dear INTP, I enjoyed our conversation the o... 21.28 \n", + "4 ENTJ 'You're fired.|||That's another silly misconce... 19.34 \n", + "\n", + " var_length links_count qm_count exm_count img_count music_count \\\n", + "0 0.179750 0.48 0.36 0.06 0.12 0.02 \n", + "1 0.184469 0.20 0.10 0.00 0.02 0.00 \n", + "2 0.184010 0.10 0.24 0.08 0.00 0.00 \n", + "3 0.186640 0.04 0.22 0.06 0.00 0.02 \n", + "4 0.178723 0.12 0.20 0.02 0.04 0.02 \n", + "\n", + " dots_count ht_count me_count \n", + "0 0.30 0.0 1.18 \n", + "1 0.38 0.0 3.06 \n", + "2 0.26 0.0 1.44 \n", + "3 0.52 0.0 2.18 \n", + "4 0.42 0.0 1.48 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# average number of words per scentence\n", + "\n", + "# std number of words per scentence\n", + "\n", + "# number of adjectives used, dictionairy import, english dict of adj\n", + "\n", + "# number of commas in a scentence\n", + "\n", + "\n", + "data['length_comment'] = data['posts'].apply(lambda x: len(x.split())/50)\n", + "data['var_length'] = data['posts'].apply(lambda x: var_posts(x))\n", + "data['links_count'] = data['posts'].apply(lambda x: x.count('http')/50)\n", + "data['qm_count'] = data['posts'].apply(lambda x: x.count('?')/50)\n", + "data['exm_count'] = data['posts'].apply(lambda x: x.count('!')/50) # normalize by the amount of scentences/ percent of number of scentences\n", + "data['img_count'] = data['posts'].apply(lambda x: x.count('jpg')/50)\n", + "# data['music_count'] = data['posts'].apply(lambda x: x.count('music')/50)\n", + "data['dots_count'] = data['posts'].apply(lambda x: x.count('...')/50)\n", + "data['ht_count'] = data['posts'].apply(lambda x: x.count('#')/50)\n", + "data['me_count'] = data['posts'].apply(lambda x: (x.lower.count('me') + x.lower.count('i') + x.lower.count('myself'))/50) # add uppercase\n", + "\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(15,10))\n", + "sns.swarmplot(\"type\", \"length_comment\", data=data)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(15,10))\n", + "sns.violinplot(x='type', y='length_comment', data=data, inner=None, color='lightgray')\n", + "sns.stripplot(x='type', y='length_comment', data=data, size=4, jitter=True)\n", + "plt.ylabel('Words per comment')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(15,10))\n", + "sns.jointplot(x='length_comment', y='dots_count', data=data, kind='kde')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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typelength_commentvar_lengthlinks_countqm_countexm_countimg_countmusic_countdots_countht_countme_count
0INFJ11.120.1797500.480.360.060.120.020.300.01.18
1ENTP23.400.1844690.200.100.000.020.000.380.03.06
2INTP16.720.1840100.100.240.080.000.000.260.01.44
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4ENTJ19.340.1787230.120.200.020.040.020.420.01.48
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1s 4ms/step - loss: 9.7968e-07 - accuracy: 0.0225\n", + "Epoch 2/10\n", + "278/278 [==============================] - 1s 2ms/step - loss: 9.7968e-07 - accuracy: 0.0225\n", + "Epoch 3/10\n", + "278/278 [==============================] - 1s 2ms/step - loss: 9.7968e-07 - accuracy: 0.0225\n", + "Epoch 4/10\n", + "278/278 [==============================] - 1s 2ms/step - loss: 9.7968e-07 - accuracy: 0.0225\n", + "Epoch 5/10\n", + "278/278 [==============================] - 1s 2ms/step - loss: 9.7968e-07 - accuracy: 0.0225\n", + "Epoch 6/10\n", + "278/278 [==============================] - 1s 2ms/step - loss: 9.7968e-07 - accuracy: 0.0225: 0s - loss: 9.8123e-07 - accuracy: \n", + "Epoch 7/10\n", + "278/278 [==============================] - 1s 2ms/step - loss: 9.7968e-07 - accuracy: 0.0225\n", + "Epoch 8/10\n", + "278/278 [==============================] - 1s 2ms/step - loss: 9.7968e-07 - accuracy: 0.0225\n", + "Epoch 9/10\n", + "278/278 [==============================] - 1s 2ms/step - loss: 9.7968e-07 - accuracy: 0.0225\n", + "Epoch 10/10\n", + "278/278 [==============================] - 1s 3ms/step - loss: 9.7968e-07 - accuracy: 0.0225\n", + "Test loss: 9.869979749055346e-07\n", + "Test accuracy: 0.019596541300415993\n" + ] + } + ], + "source": [ + "model = tf.keras.models.Sequential()\n", + "model.add(tf.keras.layers.Dense(units=256, kernel_initializer='uniform', activation='relu', input_dim=10))\n", + "model.add(tf.keras.layers.Dense(units=128, kernel_initializer='uniform', activation='relu'))\n", + "model.add(tf.keras.layers.Dense(units=64, kernel_initializer='uniform', activation='relu'))\n", + "model.add(tf.keras.layers.Dense(activation = 'sigmoid', units = 1, kernel_initializer = 'uniform')) # output layer has number of outputs not number of neurons\n", + "\n", + "model.compile(optimizer = 'adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])\n", + "# calculating loss, model is always trying to minimize loss, adam is the default optimize\n", + "\n", + "\n", + "model.fit(X_train, y_train, epochs = 10, batch_size= 25)\n", + "\n", + "val_loss, val_acc = model.evaluate(X_test, y_test, verbose=0)\n", + "print(f'Test loss: {val_loss}')\n", + "print(f'Test accuracy: {val_acc}')" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "55/55 [==============================] - 0s 2ms/step - loss: 9.8700e-07 - accuracy: 0.0202\n", + "9.869979749055346e-07 0.020172910764813423\n" + ] + } + ], + "source": [ + "val_loss, val_acc = model.evaluate(X_test, y_test)\n", + "print(val_loss, val_acc)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# neural network on seperate targets" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " length_comment var_length links_count qm_count exm_count img_count \\\n", + "0 11.12 0.179750 0.48 0.36 0.06 0.12 \n", + "1 23.40 0.184469 0.20 0.10 0.00 0.02 \n", + "2 16.72 0.184010 0.10 0.24 0.08 0.00 \n", + "3 21.28 0.186640 0.04 0.22 0.06 0.00 \n", + "4 19.34 0.178723 0.12 0.20 0.02 0.04 \n", + "\n", + " music_count dots_count ht_count me_count I-E N-S T-F J-P \n", + "0 0.02 0.30 0.0 1.18 1 0 0 0 \n", + "1 0.00 0.38 0.0 3.06 0 0 1 1 \n", + "2 0.00 0.26 0.0 1.44 1 0 1 1 \n", + "3 0.02 0.52 0.0 2.18 1 0 1 0 \n", + "4 0.02 0.42 0.0 1.48 0 0 1 0 " + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data2 = sep_targets.drop(columns = ['type'])\n", + "data2.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "#data2['I-E'] = data2['I-E'].astype('category')\n", + "#data2['N-S'] = data2['N-S'].astype('category')\n", + "#data2['T-F'] = data2['T-F'].astype('category')\n", + "#data2['J-P'] = data2['J-P'].astype('category')\n", + "\n", + "#cat_columns = data2.select_dtypes(['category']).columns\n", + "\n", + "# data2[cat_columns] = data2[cat_columns].apply(lambda x: x.cat.codes)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "X = data2.drop(columns = ['I-E', 'N-S' , 'T-F', 'J-P'])\n", + "\n", + "y_IE = data2['I-E']\n", + "y_NS = data2['N-S']\n", + "y_TF = data2['T-F']\n", + "y_JP = data2['J-P']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + " # X = tf.keras.utils.normalize(X, axis = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "model = tf.keras.models.Sequential()\n", + "model.add(tf.keras.layers.Dense(units=256, kernel_initializer='uniform', activation='relu', input_dim=10))\n", + "model.add(tf.keras.layers.Dense(units=128, kernel_initializer='uniform', activation='relu'))\n", + "model.add(tf.keras.layers.Dense(units=64, kernel_initializer='uniform', activation='relu'))\n", + "model.add(tf.keras.layers.Dense(activation = 'sigmoid', units = 1, kernel_initializer = 'uniform')) # output layer has number of outputs not number of neurons\n", + "\n", + "model.compile(optimizer = 'adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])\n", + "# calculating loss, model is always trying to minimize loss, adam is the default optimize\n", + "\n", + "\n", + "# model.fit(X_train, y_train, epochs = 10, batch_size= 25)\n", + "\n", + "# val_loss, val_acc = model.evaluate(X_test, y_test, verbose=0)\n", + "# print(f'Test loss: {val_loss}')\n", + "# print(f'Test accuracy: {val_acc}')" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "278/278 [==============================] - 1s 3ms/step - loss: 9.1520e-08 - accuracy: 0.7677\n", + "Epoch 2/10\n", + "278/278 [==============================] - 1s 3ms/step - loss: 9.1520e-08 - accuracy: 0.7677\n", + "Epoch 3/10\n", + "278/278 [==============================] - 1s 3ms/step - loss: 9.1520e-08 - accuracy: 0.7677\n", + "Epoch 4/10\n", + "278/278 [==============================] - 1s 4ms/step - loss: 9.1520e-08 - accuracy: 0.7677\n", + "Epoch 5/10\n", + "278/278 [==============================] - 1s 3ms/step - loss: 9.1520e-08 - accuracy: 0.7677: 0s - loss: 9.287\n", + "Epoch 6/10\n", + "278/278 [==============================] - 1s 3ms/step - loss: 9.1520e-08 - accuracy: 0.7677\n", + "Epoch 7/10\n", + "278/278 [==============================] - 1s 3ms/step - loss: 9.1520e-08 - accuracy: 0.7677\n", + "Epoch 8/10\n", + "278/278 [==============================] - 1s 3ms/step - loss: 9.1520e-08 - accuracy: 0.7677\n", + "Epoch 9/10\n", + "278/278 [==============================] - 1s 5ms/step - loss: 9.1520e-08 - accuracy: 0.7677\n", + "Epoch 10/10\n", + "278/278 [==============================] - 2s 8ms/step - loss: 9.1520e-08 - accuracy: 0.7677\n", + "I-E RESULTS\n", + "Test loss: 9.261908928692719e-08\n", + "Test accuracy: 0.7769452333450317\n", + "****************************************************************************************************\n", + "Epoch 1/10\n", + "278/278 [==============================] - 1s 3ms/step - loss: 1.6284e-08 - accuracy: 0.1366\n", + "Epoch 2/10\n", + "278/278 [==============================] - 1s 4ms/step - loss: 1.6284e-08 - accuracy: 0.1366\n", + "Epoch 3/10\n", + "278/278 [==============================] - 1s 4ms/step - loss: 1.6284e-08 - accuracy: 0.1366\n", + "Epoch 4/10\n", + "278/278 [==============================] - 1s 4ms/step - loss: 1.6284e-08 - accuracy: 0.1366\n", + "Epoch 5/10\n", + "278/278 [==============================] - 2s 6ms/step - loss: 1.6284e-08 - accuracy: 0.1366\n", + "Epoch 6/10\n", + "278/278 [==============================] - 1s 3ms/step - loss: 1.6284e-08 - accuracy: 0.1366\n", + "Epoch 7/10\n", + "278/278 [==============================] - 1s 4ms/step - loss: 1.6284e-08 - accuracy: 0.1366: 0s - loss: 1.677\n", + "Epoch 8/10\n", + "278/278 [==============================] - 1s 4ms/step - loss: 1.6284e-08 - accuracy: 0.1366\n", + "Epoch 9/10\n", + "278/278 [==============================] - 1s 4ms/step - loss: 1.6284e-08 - accuracy: 0.1366\n", + "Epoch 10/10\n", + "278/278 [==============================] - 2s 6ms/step - loss: 1.6284e-08 - accuracy: 0.1366\n", + "N-S RESULTS\n", + "Test loss: 1.710842312263594e-08\n", + "Test accuracy: 0.14351585507392883\n", + "****************************************************************************************************\n", + "Epoch 1/10\n", + "278/278 [==============================] - 1s 4ms/step - loss: 5.4829e-08 - accuracy: 0.4599: 0s - loss:\n", + "Epoch 2/10\n", + " 1/278 [..............................] - ETA: 0s - loss: 6.6757e-08 - accuracy: 0.5600WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.119151). Check your callbacks.\n", + "278/278 [==============================] - 2s 7ms/step - loss: 5.4829e-08 - accuracy: 0.4599\n", + "Epoch 3/10\n", + "278/278 [==============================] - 1s 4ms/step - loss: 5.4829e-08 - accuracy: 0.4599\n", + "Epoch 4/10\n", + "278/278 [==============================] - 1s 4ms/step - loss: 5.4829e-08 - accuracy: 0.4599\n", + "Epoch 5/10\n", + "278/278 [==============================] - 1s 4ms/step - loss: 5.4829e-08 - accuracy: 0.4599\n", + "Epoch 6/10\n", + "278/278 [==============================] - 1s 4ms/step - loss: 5.4829e-08 - accuracy: 0.4599: \n", + "Epoch 7/10\n", + "278/278 [==============================] - 1s 5ms/step - loss: 5.4829e-08 - accuracy: 0.4599\n", + "Epoch 8/10\n", + "278/278 [==============================] - 1s 5ms/step - loss: 5.4829e-08 - accuracy: 0.4599\n", + "Epoch 9/10\n", + "278/278 [==============================] - 1s 5ms/step - loss: 5.4829e-08 - accuracy: 0.4599\n", + "Epoch 10/10\n", + "278/278 [==============================] - 1s 5ms/step - loss: 5.4829e-08 - accuracy: 0.4599\n", + "T-F Results\n", + "Test loss: 5.421102500235975e-08\n", + "Test accuracy: 0.454755038022995\n", + "****************************************************************************************************\n", + "Epoch 1/10\n", + "278/278 [==============================] - 1s 4ms/step - loss: 7.2230e-08 - accuracy: 0.6059\n", + "Epoch 2/10\n", + "278/278 [==============================] - 1s 4ms/step - loss: 7.2230e-08 - accuracy: 0.6059\n", + "Epoch 3/10\n", + "278/278 [==============================] - 1s 5ms/step - loss: 7.2230e-08 - accuracy: 0.6059\n", + "Epoch 4/10\n", + "278/278 [==============================] - 1s 4ms/step - loss: 7.2230e-08 - accuracy: 0.6059\n", + "Epoch 5/10\n", + "278/278 [==============================] - 1s 4ms/step - loss: 7.2230e-08 - accuracy: 0.6059\n", + "Epoch 6/10\n", + "278/278 [==============================] - 1s 4ms/step - loss: 7.2230e-08 - accuracy: 0.6059\n", + "Epoch 7/10\n", + "278/278 [==============================] - 1s 5ms/step - loss: 7.2230e-08 - accuracy: 0.6059\n", + "Epoch 8/10\n", + "278/278 [==============================] - ETA: 0s - loss: 7.2296e-08 - accuracy: 0.60 - 1s 4ms/step - loss: 7.2230e-08 - accuracy: 0.6059\n", + "Epoch 9/10\n", + "278/278 [==============================] - 1s 4ms/step - loss: 7.2230e-08 - accuracy: 0.6059\n", + "Epoch 10/10\n", + "278/278 [==============================] - 1s 4ms/step - loss: 7.2230e-08 - accuracy: 0.6059\n", + "J-P Results\n", + "Test loss: 7.118202915989968e-08\n", + "Test accuracy: 0.5971181392669678\n", + "****************************************************************************************************\n" + ] + } + ], + "source": [ + "\n", + "\n", + "# IE\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y_IE, test_size=0.2, random_state=10)\n", + "model.fit(X_train, y_train, epochs = 10, batch_size= 25)\n", + "predmodel = model.predict(x_test)\n", + "print(\"I-E RESULTS\")\n", + "# print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "# print(confusion_matrix(y_test,predmodel))\n", + "# print(\"Score:\",round(accuracy_score(y_test,predmodel)*100,2))\n", + "# print(\"Classification Report:\",classification_report(y_test,predmodel))\n", + "val_loss, val_acc = model.evaluate(X_test, y_test, verbose=0)\n", + "print(f'Test loss: {val_loss}')\n", + "print(f'Test accuracy: {val_acc}')\n", + "print(\"*\" * 100)\n", + "\n", + "# NS\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y_NS, test_size=0.2, random_state=10)\n", + "model.fit(X_train, y_train, epochs = 10, batch_size= 25)\n", + "predmodel = model.predict(x_test)\n", + "print(\"N-S RESULTS\")\n", + "# print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "# print(confusion_matrix(y_test,predmodel))\n", + "# print(\"Score:\",round(accuracy_score(y_test,predmodel)*100,2))\n", + "# print(\"Classification Report:\",classification_report(y_test,predmodel))\n", + "val_loss, val_acc = model.evaluate(X_test, y_test, verbose=0)\n", + "print(f'Test loss: {val_loss}')\n", + "print(f'Test accuracy: {val_acc}')\n", + "print(\"*\" * 100)\n", + "\n", + "# TF\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y_TF, test_size=0.2, random_state=10)\n", + "model.fit(X_train, y_train, epochs = 10, batch_size= 25)\n", + "predmodel = model.predict(x_test)\n", + "print(\"T-F Results\")\n", + "# print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "# print(confusion_matrix(y_test,predmodel))\n", + "# print(\"Score:\",round(accuracy_score(y_test,predmodel)*100,2))\n", + "# print(\"Classification Report:\",classification_report(y_test,predmodel))\n", + "val_loss, val_acc = model.evaluate(X_test, y_test, verbose=0)\n", + "print(f'Test loss: {val_loss}')\n", + "print(f'Test accuracy: {val_acc}')\n", + "print(\"*\" * 100)\n", + "\n", + "# JP\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y_JP, test_size=0.2, random_state=10)\n", + "model.fit(X_train, y_train, epochs = 10, batch_size= 25)\n", + "predmodel = model.predict(x_test)\n", + "print(\"J-P Results\")\n", + "# print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "# print(confusion_matrix(y_test,predmodel))\n", + "# print(\"Score:\",round(accuracy_score(y_test,predmodel)*100,2))\n", + "# print(\"Classification Report:\",classification_report(y_test,predmodel))\n", + "val_loss, val_acc = model.evaluate(X_test, y_test, verbose=0)\n", + "print(f'Test loss: {val_loss}')\n", + "print(f'Test accuracy: {val_acc}')\n", + "print(\"*\" * 100)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/your-project/code/Code 6 - Piecing together by Accuracy.ipynb b/your-project/code/Code 6 - Piecing together by Accuracy.ipynb new file mode 100644 index 00000000..c085e09e --- /dev/null +++ b/your-project/code/Code 6 - Piecing together by Accuracy.ipynb @@ -0,0 +1,453 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating new Model by Algorithm's Accuracy" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import re\n", + "import random" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from sklearn.model_selection import train_test_split, cross_val_score\n", + "from sklearn.model_selection import cross_validate\n", + "from sklearn.model_selection import StratifiedKFold\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.model_selection import learning_curve\n", + "from sklearn.ensemble import ExtraTreesClassifier\n", + "from sklearn.decomposition import TruncatedSVD\n", + "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.naive_bayes import MultinomialNB\n", + "from sklearn.metrics import classification_report\n", + "from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score, roc_curve" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "data = pd.read_csv(r'../data/personalities_cleaned.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "data['I-E'] = data['type'].astype(str).str[0]\n", + "data['N-S'] = data['type'].astype(str).str[1]\n", + "data['T-F'] = data['type'].astype(str).str[2]\n", + "data['J-P'] = data['type'].astype(str).str[3]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "data = data.drop(columns = ['Unnamed: 0', 'posts', 'text_processed'])\n", + "data.drop(data.index[3559], inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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typetext_readyI-EN-ST-FJ-P
0INFJintj moment sportscent top ten play prank ha l...INFJ
1ENTPfind lack post veri alarm sex bore posit often...ENTP
2INTPgood one cours say know bless cur doe absolut ...INTP
3INTJdear intp enjoy convers day esoter gab natur u...INTJ
4ENTJfire anoth silli misconcept approach logic go ...ENTJ
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" + ], + "text/plain": [ + " type text_ready I-E N-S T-F J-P\n", + "0 INFJ intj moment sportscent top ten play prank ha l... I N F J\n", + "1 ENTP find lack post veri alarm sex bore posit often... E N T P\n", + "2 INTP good one cours say know bless cur doe absolut ... I N T P\n", + "3 INTJ dear intp enjoy convers day esoter gab natur u... I N T J\n", + "4 ENTJ fire anoth silli misconcept approach logic go ... E N T J" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "x = data['text_ready']\n", + "\n", + "y_IE = data['I-E']\n", + "y_NS = data['N-S']\n", + "y_TF = data['T-F']\n", + "y_JP = data['J-P']" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.feature_extraction.text import CountVectorizer\n", + "\n", + "vector = CountVectorizer(ngram_range=(2, 2)).fit(x) \n", + "X = vector.transform(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split, cross_val_score" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I-E RESULTS\n", + "Label I-E train score is : 0.7685545467646635\n", + "Label I-E test score is : 0.7685545467646635\n", + "Confusion Matrix for K-Nearest Neighbors:\n", + "[[ 0 393]\n", + " [ 0 1342]]\n", + "Score: 77.35\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " E 0.00 0.00 0.00 393\n", + " I 0.77 1.00 0.87 1342\n", + "\n", + " accuracy 0.77 1735\n", + " macro avg 0.39 0.50 0.44 1735\n", + "weighted avg 0.60 0.77 0.67 1735\n", + "\n", + "****************************************************************************************************\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "N-S RESULTS\n", + "Label N-S train score is : 0.8617956477878657\n", + "Label N-S test score is : 0.8617956477878657\n", + "Confusion Matrix for Random Forests:\n", + "[[1497 0]\n", + " [ 238 0]]\n", + "Score: 86.28\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " N 0.86 1.00 0.93 1497\n", + " S 0.00 0.00 0.00 238\n", + "\n", + " accuracy 0.86 1735\n", + " macro avg 0.43 0.50 0.46 1735\n", + "weighted avg 0.74 0.86 0.80 1735\n", + "\n", + "****************************************************************************************************\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "T-F Results\n", + "Label T-F train score is : 1.0\n", + "Label T-F test score is : 1.0\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[895 65]\n", + " [363 412]]\n", + "Score: 75.33\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " F 0.71 0.93 0.81 960\n", + " T 0.86 0.53 0.66 775\n", + "\n", + " accuracy 0.75 1735\n", + " macro avg 0.79 0.73 0.73 1735\n", + "weighted avg 0.78 0.75 0.74 1735\n", + "\n", + "****************************************************************************************************\n", + "J-P Results\n", + "Label J-P train score is : 1.0\n", + "Label J-P test score is : 1.0\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[311 369]\n", + " [193 862]]\n", + "Score: 67.61\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " J 0.62 0.46 0.53 680\n", + " P 0.70 0.82 0.75 1055\n", + "\n", + " accuracy 0.68 1735\n", + " macro avg 0.66 0.64 0.64 1735\n", + "weighted avg 0.67 0.68 0.66 1735\n", + "\n", + "****************************************************************************************************\n" + ] + } + ], + "source": [ + "# making the model according to Accuracies in Code \n", + "\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "knn = KNeighborsClassifier(n_neighbors=9) # see if can tweak accuracy\n", + "\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "rfn = RandomForestClassifier(max_depth=5, random_state=0) # see if can tweak accuracy\n", + "\n", + "from sklearn.naive_bayes import MultinomialNB\n", + "mnb = MultinomialNB()\n", + "\n", + "\n", + "\n", + "# IE\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_IE, test_size=0.2, random_state=10)\n", + "knn.fit(x_train, y_train)\n", + "ieb_train = knn.score (x_train,y_train)\n", + "ieb_test = knn.score (x_train,y_train)\n", + "predknn = knn.predict(x_test)\n", + "print(\"I-E RESULTS\")\n", + "print('Label I-E train score is :',ieb_train)\n", + "print('Label I-E test score is :',ieb_test)\n", + "print(\"Confusion Matrix for K-Nearest Neighbors:\")\n", + "print(confusion_matrix(y_test,predknn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predknn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predknn))\n", + "print(\"*\" * 100)\n", + "\n", + "# NS\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_NS, test_size=0.2, random_state=10)\n", + "rfn.fit(x_train, y_train)\n", + "nsb_train = rfn.score (x_train,y_train)\n", + "nsb_test = rfn.score (x_train,y_train)\n", + "predrfn = rfn.predict(x_test)\n", + "print(\"N-S RESULTS\")\n", + "print('Label N-S train score is :',nsb_train)\n", + "print('Label N-S test score is :',nsb_test)\n", + "print(\"Confusion Matrix for Random Forests:\")\n", + "print(confusion_matrix(y_test,predrfn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predrfn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predrfn))\n", + "print(\"*\" * 100)\n", + "\n", + "# TF\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_TF, test_size=0.2, random_state=10)\n", + "mnb.fit(x_train, y_train)\n", + "tfb_train = mnb.score (x_train,y_train)\n", + "tfb_test = mnb.score (x_train,y_train)\n", + "predmnb = mnb.predict(x_test)\n", + "print(\"T-F Results\")\n", + "print('Label T-F train score is :',tfb_train)\n", + "print('Label T-F test score is :',tfb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predmnb))\n", + "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", + "print(\"*\" * 100)\n", + "\n", + "# JP\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_JP, test_size=0.2, random_state=10)\n", + "mnb.fit(x_train, y_train)\n", + "jpb_train = mnb.score (x_train,y_train)\n", + "jpb_test = mnb.score (x_train,y_train)\n", + "predmnb = mnb.predict(x_test)\n", + "print(\"J-P Results\")\n", + "print('Label J-P train score is :',jpb_train)\n", + "print('Label J-P test score is :',jpb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predmnb))\n", + "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", + "print(\"*\" * 100)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/your-project/code/Code 8 - Trying Models on Quant Data.ipynb b/your-project/code/Code 8 - Trying Models on Quant Data.ipynb new file mode 100644 index 00000000..ee2b4d7c --- /dev/null +++ b/your-project/code/Code 8 - Trying Models on Quant Data.ipynb @@ -0,0 +1,1071 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Splitting the target" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from sklearn.model_selection import train_test_split, cross_val_score\n", + "from sklearn.model_selection import cross_validate\n", + "from sklearn.model_selection import StratifiedKFold\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.model_selection import learning_curve\n", + "from sklearn.ensemble import ExtraTreesClassifier\n", + "from sklearn.decomposition import TruncatedSVD\n", + "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.naive_bayes import MultinomialNB\n", + "from sklearn.metrics import classification_report\n", + "from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score, roc_curve" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "data = pd.read_csv(r'../data/quant_personalities.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "data2 = data.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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44ENTJ19.340.1787230.120.200.020.040.020.420.01.480010
\n", + "
" + ], + "text/plain": [ + " Unnamed: 0 type length_comment var_length links_count qm_count \\\n", + "0 0 INFJ 11.12 0.179750 0.48 0.36 \n", + "1 1 ENTP 23.40 0.184469 0.20 0.10 \n", + "2 2 INTP 16.72 0.184010 0.10 0.24 \n", + "3 3 INTJ 21.28 0.186640 0.04 0.22 \n", + "4 4 ENTJ 19.34 0.178723 0.12 0.20 \n", + "\n", + " exm_count img_count music_count dots_count ht_count me_count I-E \\\n", + "0 0.06 0.12 0.02 0.30 0.0 1.18 1 \n", + "1 0.00 0.02 0.00 0.38 0.0 3.06 0 \n", + "2 0.08 0.00 0.00 0.26 0.0 1.44 1 \n", + "3 0.06 0.00 0.02 0.52 0.0 2.18 1 \n", + "4 0.02 0.04 0.02 0.42 0.0 1.48 0 \n", + "\n", + " N-S T-F J-P \n", + "0 0 0 0 \n", + "1 0 1 1 \n", + "2 0 1 1 \n", + "3 0 1 0 \n", + "4 0 1 0 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data2.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "data2 = data2.drop(columns=['Unnamed: 0'])" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# learning with the model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# defining learnings for each \n", + "\n", + "\n", + "X = data.drop(columns = ['type', 'I-E', 'N-S', 'T-F', 'J-P'])\n", + "\n", + "y_IE = data['I-E']\n", + "y_NS = data['N-S']\n", + "y_TF = data['T-F']\n", + "y_JP = data['J-P']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### MultinomialNB" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I-E RESULTS\n", + "Label I-E train score is : 0.7678674351585014\n", + "Label I-E test score is : 0.7678674351585014\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[ 0 387]\n", + " [ 0 1348]]\n", + "Score: 77.69\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " 0 0.00 0.00 0.00 387\n", + " 1 0.78 1.00 0.87 1348\n", + "\n", + " accuracy 0.78 1735\n", + " macro avg 0.39 0.50 0.44 1735\n", + "weighted avg 0.60 0.78 0.68 1735\n", + "\n", + "****************************************************************************************************\n", + "N-S RESULTS\n", + "Label N-S train score is : 0.8570605187319885\n", + "Label N-S test score is : 0.8570605187319885\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[1470 16]\n", + " [ 244 5]]\n", + "Score: 85.01\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " 0 0.86 0.99 0.92 1486\n", + " 1 0.24 0.02 0.04 249\n", + "\n", + " accuracy 0.85 1735\n", + " macro avg 0.55 0.50 0.48 1735\n", + "weighted avg 0.77 0.85 0.79 1735\n", + "\n", + "****************************************************************************************************\n", + "T-F Results\n", + "Label T-F train score is : 0.5236311239193083\n", + "Label T-F test score is : 0.5236311239193083\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[528 418]\n", + " [417 372]]\n", + "Score: 51.87\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " 0 0.56 0.56 0.56 946\n", + " 1 0.47 0.47 0.47 789\n", + "\n", + " accuracy 0.52 1735\n", + " macro avg 0.51 0.51 0.51 1735\n", + "weighted avg 0.52 0.52 0.52 1735\n", + "\n", + "****************************************************************************************************\n", + "J-P Results\n", + "Label J-P train score is : 0.570028818443804\n", + "Label J-P test score is : 0.570028818443804\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[126 573]\n", + " [194 842]]\n", + "Score: 55.79\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " 0 0.39 0.18 0.25 699\n", + " 1 0.60 0.81 0.69 1036\n", + "\n", + " accuracy 0.56 1735\n", + " macro avg 0.49 0.50 0.47 1735\n", + "weighted avg 0.51 0.56 0.51 1735\n", + "\n", + "****************************************************************************************************\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n" + ] + } + ], + "source": [ + "from sklearn.naive_bayes import MultinomialNB\n", + "\n", + "mnb = MultinomialNB()\n", + "\n", + "\n", + "# IE\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_IE, test_size=0.2, random_state=10)\n", + "mnb.fit(x_train, y_train)\n", + "ieb_train = mnb.score (x_train,y_train)\n", + "ieb_test = mnb.score (x_train,y_train)\n", + "predmnb = mnb.predict(x_test)\n", + "print(\"I-E RESULTS\")\n", + "print('Label I-E train score is :',ieb_train)\n", + "print('Label I-E test score is :',ieb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predmnb))\n", + "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", + "print(\"*\" * 100)\n", + "\n", + "# NS\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_NS, test_size=0.2, random_state=10)\n", + "mnb.fit(x_train, y_train)\n", + "nsb_train = mnb.score (x_train,y_train)\n", + "nsb_test = mnb.score (x_train,y_train)\n", + "predmnb = mnb.predict(x_test)\n", + "print(\"N-S RESULTS\")\n", + "print('Label N-S train score is :',nsb_train)\n", + "print('Label N-S test score is :',nsb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predmnb))\n", + "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", + "print(\"*\" * 100)\n", + "\n", + "# TF\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_TF, test_size=0.2, random_state=10)\n", + "mnb.fit(x_train, y_train)\n", + "tfb_train = mnb.score (x_train,y_train)\n", + "tfb_test = mnb.score (x_train,y_train)\n", + "predmnb = mnb.predict(x_test)\n", + "print(\"T-F Results\")\n", + "print('Label T-F train score is :',tfb_train)\n", + "print('Label T-F test score is :',tfb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predmnb))\n", + "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", + "print(\"*\" * 100)\n", + "\n", + "# JP\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_JP, test_size=0.2, random_state=10)\n", + "mnb.fit(x_train, y_train)\n", + "jpb_train = mnb.score (x_train,y_train)\n", + "jpb_test = mnb.score (x_train,y_train)\n", + "predmnb = mnb.predict(x_test)\n", + "print(\"J-P Results\")\n", + "print('Label J-P train score is :',jpb_train)\n", + "print('Label J-P test score is :',jpb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predmnb))\n", + "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", + "print(\"*\" * 100)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Decision Tree" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I-E RESULTS\n", + "Label I-E train score is : 1.0\n", + "Label I-E test score is : 1.0\n", + "Confusion Matrix for Decision Tree:\n", + "[[ 104 283]\n", + " [ 320 1028]]\n", + "Score: 65.24\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " 0 0.25 0.27 0.26 387\n", + " 1 0.78 0.76 0.77 1348\n", + "\n", + " accuracy 0.65 1735\n", + " macro avg 0.51 0.52 0.51 1735\n", + "weighted avg 0.66 0.65 0.66 1735\n", + "\n", + "****************************************************************************************************\n", + "N-S RESULTS\n", + "Label N-S train score is : 1.0\n", + "Label N-S test score is : 1.0\n", + "Confusion Matrix for Decision Tree:\n", + "[[1225 261]\n", + " [ 203 46]]\n", + "Score: 73.26\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " 0 0.86 0.82 0.84 1486\n", + " 1 0.15 0.18 0.17 249\n", + "\n", + " accuracy 0.73 1735\n", + " macro avg 0.50 0.50 0.50 1735\n", + "weighted avg 0.76 0.73 0.74 1735\n", + "\n", + "****************************************************************************************************\n", + "T-F Results\n", + "Label T-F train score is : 1.0\n", + "Label T-F test score is : 1.0\n", + "Confusion Matrix for Decision Tree:\n", + "[[588 358]\n", + " [389 400]]\n", + "Score: 56.95\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " 0 0.60 0.62 0.61 946\n", + " 1 0.53 0.51 0.52 789\n", + "\n", + " accuracy 0.57 1735\n", + " macro avg 0.56 0.56 0.56 1735\n", + "weighted avg 0.57 0.57 0.57 1735\n", + "\n", + "****************************************************************************************************\n", + "J-P Results\n", + "Label J-P train score is : 1.0\n", + "Label J-P test score is : 1.0\n", + "Confusion Matrix for Decision Tree:\n", + "[[299 400]\n", + " [441 595]]\n", + "Score: 51.53\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " 0 0.40 0.43 0.42 699\n", + " 1 0.60 0.57 0.59 1036\n", + "\n", + " accuracy 0.52 1735\n", + " macro avg 0.50 0.50 0.50 1735\n", + "weighted avg 0.52 0.52 0.52 1735\n", + "\n", + "****************************************************************************************************\n" + ] + } + ], + "source": [ + "from sklearn.tree import DecisionTreeClassifier\n", + "\n", + "dt = DecisionTreeClassifier()\n", + "\n", + "\n", + "# IE\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_IE, test_size=0.2, random_state=10)\n", + "dt.fit(x_train, y_train)\n", + "ieb_train = dt.score (x_train,y_train)\n", + "ieb_test = dt.score (x_train,y_train)\n", + "preddt = dt.predict(x_test)\n", + "print(\"I-E RESULTS\")\n", + "print('Label I-E train score is :',ieb_train)\n", + "print('Label I-E test score is :',ieb_test)\n", + "print(\"Confusion Matrix for Decision Tree:\")\n", + "print(confusion_matrix(y_test,preddt))\n", + "print(\"Score:\",round(accuracy_score(y_test,preddt)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,preddt))\n", + "print(\"*\" * 100)\n", + "\n", + "# NS\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_NS, test_size=0.2, random_state=10)\n", + "dt.fit(x_train, y_train)\n", + "nsb_train = dt.score (x_train,y_train)\n", + "nsb_test = dt.score (x_train,y_train)\n", + "preddt = dt.predict(x_test)\n", + "print(\"N-S RESULTS\")\n", + "print('Label N-S train score is :',nsb_train)\n", + "print('Label N-S test score is :',nsb_test)\n", + "print(\"Confusion Matrix for Decision Tree:\")\n", + "print(confusion_matrix(y_test,preddt))\n", + "print(\"Score:\",round(accuracy_score(y_test,preddt)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,preddt))\n", + "print(\"*\" * 100)\n", + "\n", + "# TF\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_TF, test_size=0.2, random_state=10)\n", + "dt.fit(x_train, y_train)\n", + "tfb_train = dt.score (x_train,y_train)\n", + "tfb_test = dt.score (x_train,y_train)\n", + "preddt = dt.predict(x_test)\n", + "print(\"T-F Results\")\n", + "print('Label T-F train score is :',tfb_train)\n", + "print('Label T-F test score is :',tfb_test)\n", + "print(\"Confusion Matrix for Decision Tree:\")\n", + "print(confusion_matrix(y_test,preddt))\n", + "print(\"Score:\",round(accuracy_score(y_test,preddt)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,preddt))\n", + "print(\"*\" * 100)\n", + "\n", + "# JP\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_JP, test_size=0.2, random_state=10)\n", + "dt.fit(x_train, y_train)\n", + "jpb_train = dt.score (x_train,y_train)\n", + "jpb_test = dt.score (x_train,y_train)\n", + "preddt = dt.predict(x_test)\n", + "print(\"J-P Results\")\n", + "print('Label J-P train score is :',jpb_train)\n", + "print('Label J-P test score is :',jpb_test)\n", + "print(\"Confusion Matrix for Decision Tree:\")\n", + "print(confusion_matrix(y_test,preddt))\n", + "print(\"Score:\",round(accuracy_score(y_test,preddt)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,preddt))\n", + "print(\"*\" * 100)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### K Nearest Neighbour" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I-E RESULTS\n", + "Label I-E train score is : 0.7791066282420749\n", + "Label I-E test score is : 0.7791066282420749\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[ 30 357]\n", + " [ 78 1270]]\n", + "Score: 74.93\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " 0 0.28 0.08 0.12 387\n", + " 1 0.78 0.94 0.85 1348\n", + "\n", + " accuracy 0.75 1735\n", + " macro avg 0.53 0.51 0.49 1735\n", + "weighted avg 0.67 0.75 0.69 1735\n", + "\n", + "****************************************************************************************************\n", + "N-S RESULTS\n", + "Label N-S train score is : 0.8636887608069165\n", + "Label N-S test score is : 0.8636887608069165\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[1483 3]\n", + " [ 249 0]]\n", + "Score: 85.48\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " 0 0.86 1.00 0.92 1486\n", + " 1 0.00 0.00 0.00 249\n", + "\n", + " accuracy 0.85 1735\n", + " macro avg 0.43 0.50 0.46 1735\n", + "weighted avg 0.73 0.85 0.79 1735\n", + "\n", + "****************************************************************************************************\n", + "T-F Results\n", + "Label T-F train score is : 0.632420749279539\n", + "Label T-F test score is : 0.632420749279539\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[699 247]\n", + " [539 250]]\n", + "Score: 54.7\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " 0 0.56 0.74 0.64 946\n", + " 1 0.50 0.32 0.39 789\n", + "\n", + " accuracy 0.55 1735\n", + " macro avg 0.53 0.53 0.51 1735\n", + "weighted avg 0.54 0.55 0.53 1735\n", + "\n", + "****************************************************************************************************\n", + "J-P Results\n", + "Label J-P train score is : 0.6478386167146974\n", + "Label J-P test score is : 0.6478386167146974\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[248 451]\n", + " [373 663]]\n", + "Score: 52.51\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " 0 0.40 0.35 0.38 699\n", + " 1 0.60 0.64 0.62 1036\n", + "\n", + " accuracy 0.53 1735\n", + " macro avg 0.50 0.50 0.50 1735\n", + "weighted avg 0.52 0.53 0.52 1735\n", + "\n", + "****************************************************************************************************\n" + ] + } + ], + "source": [ + "from sklearn.neighbors import KNeighborsClassifier\n", + "\n", + "knn = KNeighborsClassifier(n_neighbors=10)\n", + "\n", + "\n", + "# IE\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_IE, test_size=0.2, random_state=10)\n", + "knn.fit(x_train, y_train)\n", + "ieb_train = knn.score (x_train,y_train)\n", + "ieb_test = knn.score (x_train,y_train)\n", + "predknn = knn.predict(x_test)\n", + "print(\"I-E RESULTS\")\n", + "print('Label I-E train score is :',ieb_train)\n", + "print('Label I-E test score is :',ieb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predknn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predknn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predknn))\n", + "print(\"*\" * 100)\n", + "\n", + "# NS\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_NS, test_size=0.2, random_state=10)\n", + "knn.fit(x_train, y_train)\n", + "nsb_train = knn.score (x_train,y_train)\n", + "nsb_test = knn.score (x_train,y_train)\n", + "predknn = knn.predict(x_test)\n", + "print(\"N-S RESULTS\")\n", + "print('Label N-S train score is :',nsb_train)\n", + "print('Label N-S test score is :',nsb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predknn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predknn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predknn))\n", + "print(\"*\" * 100)\n", + "\n", + "# TF\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_TF, test_size=0.2, random_state=10)\n", + "knn.fit(x_train, y_train)\n", + "tfb_train = knn.score (x_train,y_train)\n", + "tfb_test = knn.score (x_train,y_train)\n", + "predknn = knn.predict(x_test)\n", + "print(\"T-F Results\")\n", + "print('Label T-F train score is :',tfb_train)\n", + "print('Label T-F test score is :',tfb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predknn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predknn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predknn))\n", + "print(\"*\" * 100)\n", + "\n", + "# JP\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_JP, test_size=0.2, random_state=10)\n", + "knn.fit(x_train, y_train)\n", + "jpb_train = knn.score (x_train,y_train)\n", + "jpb_test = knn.score (x_train,y_train)\n", + "predknn = knn.predict(x_test)\n", + "print(\"J-P Results\")\n", + "print('Label J-P train score is :',jpb_train)\n", + "print('Label J-P test score is :',jpb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predknn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predknn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predknn))\n", + "print(\"*\" * 100)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### XGBoost" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.naive_bayes import MultinomialNB\n", + "\n", + "mnb = MultinomialNB()\n", + "mnb.fit(x_train, y_train)\n", + "\n", + "\n", + "# IE\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_IE, test_size=0.2, random_state=10)\n", + "mnb.fit(x_train, y_train)\n", + "ieb_train = mnb.score (x_train,y_train)\n", + "ieb_test = mnb.score (x_train,y_train)\n", + "predmnb = mnb.predict(x_test)\n", + "print(\"I-E RESULTS\")\n", + "print('Label I-E train score is :',ieb_train)\n", + "print('Label I-E test score is :',ieb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predmnb))\n", + "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", + "print(\"*\" * 100)\n", + "\n", + "# NS\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_NS, test_size=0.2, random_state=10)\n", + "mnb.fit(x_train, y_train)\n", + "nsb_train = mnb.score (x_train,y_train)\n", + "nsb_test = mnb.score (x_train,y_train)\n", + "predmnb = mnb.predict(x_test)\n", + "print(\"N-S RESULTS\")\n", + "print('Label N-S train score is :',nsb_train)\n", + "print('Label N-S test score is :',nsb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predmnb))\n", + "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", + "print(\"*\" * 100)\n", + "\n", + "# TF\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_TF, test_size=0.2, random_state=10)\n", + "mnb.fit(x_train, y_train)\n", + "tfb_train = mnb.score (x_train,y_train)\n", + "tfb_test = mnb.score (x_train,y_train)\n", + "predmnb = mnb.predict(x_test)\n", + "print(\"T-F Results\")\n", + "print('Label T-F train score is :',tfb_train)\n", + "print('Label T-F test score is :',tfb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predmnb))\n", + "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", + "print(\"*\" * 100)\n", + "\n", + "# JP\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_JP, test_size=0.2, random_state=10)\n", + "mnb.fit(x_train, y_train)\n", + "jpb_train = mnb.score (x_train,y_train)\n", + "jpb_test = mnb.score (x_train,y_train)\n", + "predmnb = mnb.predict(x_test)\n", + "print(\"J-P Results\")\n", + "print('Label J-P train score is :',jpb_train)\n", + "print('Label J-P test score is :',jpb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predmnb))\n", + "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", + "print(\"*\" * 100)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Random Forest" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "\n", + "rfn = RandomForestClassifier(max_depth=5, random_state=0)\n", + "rfn.fit(x_train,y_train)\n", + "predrf = rfn.predict(x_test)\n", + "print(\"Confusion Matrix for Random Forest Classifier:\")\n", + "print(confusion_matrix(y_test,predknn))\n", + "print(\"Score: \",round(accuracy_score(y_test,predrf)*100,2))\n", + "print(\"Classification Report:\")\n", + "print(classification_report(y_test,predrf))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I-E RESULTS\n", + "Label I-E train score is : 0.7688760806916427\n", + "Label I-E test score is : 0.7688760806916427\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[ 0 387]\n", + " [ 0 1348]]\n", + "Score: 77.69\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " 0 0.00 0.00 0.00 387\n", + " 1 0.78 1.00 0.87 1348\n", + "\n", + " accuracy 0.78 1735\n", + " macro avg 0.39 0.50 0.44 1735\n", + "weighted avg 0.60 0.78 0.68 1735\n", + "\n", + "****************************************************************************************************\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "N-S RESULTS\n", + "Label N-S train score is : 0.8636887608069165\n", + "Label N-S test score is : 0.8636887608069165\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[1486 0]\n", + " [ 249 0]]\n", + "Score: 85.65\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " 0 0.86 1.00 0.92 1486\n", + " 1 0.00 0.00 0.00 249\n", + "\n", + " accuracy 0.86 1735\n", + " macro avg 0.43 0.50 0.46 1735\n", + "weighted avg 0.73 0.86 0.79 1735\n", + "\n", + "****************************************************************************************************\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "T-F Results\n", + "Label T-F train score is : 0.6605187319884727\n", + "Label T-F test score is : 0.6605187319884727\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[667 279]\n", + " [370 419]]\n", + "Score: 62.59\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " 0 0.64 0.71 0.67 946\n", + " 1 0.60 0.53 0.56 789\n", + "\n", + " accuracy 0.63 1735\n", + " macro avg 0.62 0.62 0.62 1735\n", + "weighted avg 0.62 0.63 0.62 1735\n", + "\n", + "****************************************************************************************************\n", + "J-P Results\n", + "Label J-P train score is : 0.6131123919308358\n", + "Label J-P test score is : 0.6131123919308358\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[ 7 692]\n", + " [ 9 1027]]\n", + "Score: 59.6\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " 0 0.44 0.01 0.02 699\n", + " 1 0.60 0.99 0.75 1036\n", + "\n", + " accuracy 0.60 1735\n", + " macro avg 0.52 0.50 0.38 1735\n", + "weighted avg 0.53 0.60 0.45 1735\n", + "\n", + "****************************************************************************************************\n" + ] + } + ], + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "\n", + "rfn = RandomForestClassifier(max_depth=5, random_state=0)\n", + "\n", + "\n", + "# IE\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_IE, test_size=0.2, random_state=10)\n", + "rfn.fit(x_train, y_train)\n", + "ieb_train = rfn.score (x_train,y_train)\n", + "ieb_test = rfn.score (x_train,y_train)\n", + "predrfn = rfn.predict(x_test)\n", + "print(\"I-E RESULTS\")\n", + "print('Label I-E train score is :',ieb_train)\n", + "print('Label I-E test score is :',ieb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predrfn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predrfn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predrfn))\n", + "print(\"*\" * 100)\n", + "\n", + "# NS\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_NS, test_size=0.2, random_state=10)\n", + "rfn.fit(x_train, y_train)\n", + "nsb_train = rfn.score (x_train,y_train)\n", + "nsb_test = rfn.score (x_train,y_train)\n", + "predrfn = rfn.predict(x_test)\n", + "print(\"N-S RESULTS\")\n", + "print('Label N-S train score is :',nsb_train)\n", + "print('Label N-S test score is :',nsb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predrfn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predrfn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predrfn))\n", + "print(\"*\" * 100)\n", + "\n", + "# TF\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_TF, test_size=0.2, random_state=10)\n", + "rfn.fit(x_train, y_train)\n", + "tfb_train = rfn.score (x_train,y_train)\n", + "tfb_test = rfn.score (x_train,y_train)\n", + "predrfn = rfn.predict(x_test)\n", + "print(\"T-F Results\")\n", + "print('Label T-F train score is :',tfb_train)\n", + "print('Label T-F test score is :',tfb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predrfn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predrfn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predrfn))\n", + "print(\"*\" * 100)\n", + "\n", + "# JP\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_JP, test_size=0.2, random_state=10)\n", + "rfn.fit(x_train, y_train)\n", + "jpb_train = rfn.score (x_train,y_train)\n", + "jpb_test = rfn.score (x_train,y_train)\n", + "predrfn = rfn.predict(x_test)\n", + "print(\"J-P Results\")\n", + "print('Label J-P train score is :',jpb_train)\n", + "print('Label J-P test score is :',jpb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predrfn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predrfn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predrfn))\n", + "print(\"*\" * 100)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/your-project/code/testing nlp on Darya.ipynb b/your-project/code/testing nlp on Darya.ipynb new file mode 100644 index 00000000..9225f46e --- /dev/null +++ b/your-project/code/testing nlp on Darya.ipynb @@ -0,0 +1,972 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# putting together friends dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import re\n", + "\n", + "import nltk\n", + "from nltk.stem import PorterStemmer\n", + "from nltk.stem import SnowballStemmer\n", + "from nltk import wordnet\n", + "from nltk.corpus import stopwords\n", + "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n", + "from sklearn.model_selection import train_test_split, cross_val_score\n", + "from sklearn.metrics import classification_report\n", + "from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score, roc_curve\n", + "\n", + "from sklearn.model_selection import train_test_split, cross_val_score\n", + "from sklearn.model_selection import cross_validate\n", + "from sklearn.model_selection import StratifiedKFold\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.model_selection import learning_curve\n", + "from sklearn.ensemble import ExtraTreesClassifier\n", + "from sklearn.decomposition import TruncatedSVD\n", + "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.naive_bayes import MultinomialNB\n", + "from sklearn.metrics import classification_report\n", + "from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score, roc_curve" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [], + "source": [ + "Darya = [\"Quarantine expectations. Swipe for reality\", \n", + " \"22 in 2020 - swipe to see a photo from yesterday. Age affects me.\", \n", + " \"hello? Santa? All I wish for Christmas is my mind back... \", \n", + " \"girl with a hat - a series. \", \n", + " \"casual day at uni. swipe for more idiots\", \n", + " \"thanking Steve for a nice iPhone front camera.\", \n", + " \"Missing this\", \n", + " \"*edit: still can‘t believe a sunscreen for 7.95 prevented me from having a huge sunburn\",\n", + " \"where I‘d rather be part 2\",\n", + " \"when people ask me if I‘ve got free time - I do. Except I don’t.\",\n", + " \n", + " \"just a small town girl, living in a capitalist world...\",\n", + " \"clueless\",\n", + " \"Might be serious, might be hungry. \",\n", + " \"you want blue? you want yellow?\",\n", + " \"While the wall fell nearly 30 years ago, I constantly fall on my face...\",\n", + " \"Im pretty ok with exams being over... \",\n", + " \"unpopular opinion: university can be fun. exhibit a\",\n", + " \"christmas is around the corner but all I‘m wishing for is an extention of deadlines...\",\n", + " \"on some occasions I even get out of the valley and LA to go sightseeing \",\n", + " \"diamonds are a girls best friend. Especially when you trade ‘em for plane tickets and uber rides\",\n", + " \n", + " \"not a threat, it’s a warning. (talking about the beginning of semester tomorrow)\",\n", + " \"come fly with me, let’s fly let’s fly away...\",\n", + " \"saltier than the Pacific\",\n", + " \"me, when I’m a) hangry or b) am studying @ nyfalosangeles @ williamroyal98\",\n", + " \"this whole you-gotta-study-while-the-sun-is-shining gets on my nerve. here’s an example of inner rage\",\n", + " \"you know you might have been spontaneous when while booking your flights they asked you if ou would want to check in for the flight back aswell.\",\n", + " \"Vitamins\",\n", + " \"finished the first semerster at HSG and I still can’t afford a porsche 911.\",\n", + " \"Me: you should be studying. Also me: enjoy the holidays\",\n", + " \"just kidding - I’m still a short ass little human\",\n", + " \n", + " \"week 2 at uni and my icloud and google drive are asking me to pay for more storage.\",\n", + " \"uni starts tomorrow and I already see myself either a) being lost cause I can't find the classroom or b) looking like Annabelle cause sleep is for the weak\",\n", + " \"oh boy\",\n", + " \"this should be my last week of summer but I think the weather decided to give me an early taste of fall already.\",\n", + " \"water falls, darya falls and just casually sit on floors cause standing is overrated \",\n", + " \"I promise I counted all the lights, but then I saw a squirrel and lost count \",\n", + " \"is it a set? Is it a real house? guess.\",\n", + " \"quiet on set\",\n", + " \"Baywatch, are you hiring?\",\n", + " \"What if perfume is actually bugspray but they want us to buy the nasty smelly stuff just to have a good laugh?\",\n", + " \n", + " \"I'm starting to wonder if spotlight can give you a tan cause the sun isn't coming to just yet. All jokes asid tho, I am beyond grateful to share the stage with so many talented people. Do what you love and you'll feel limitless \",\n", + " \"that second when the person you've been straing at looks at you and you quickly turn away, trying to look as casual as possible\",\n", + " \"at that moment I probably thought out a plan to convince my mom to paint one of my walls pink. even though I know it won't happen, happy #internationalwomensday ladies\",\n", + " \"Tell me about it, stud\",\n", + " \"nice to be back. bring it on 2017\"\n", + " \"2016. A year that had probably more faces than facebook. Victories and dissapointment, new friendships and new experiences; I was lucky enough to travel the several times and can't thank the people that made 2016 the year it was enough for all their kind words, laughter and happy tears.\",\n", + " \"don't mess with us - we have RG clubs\",\n", + " \"Summer is over and you're like: pardon me, what?\",\n", + " \"Looking back at those 5 weeks full of new friends, experiences and emotions puts a smile on my face. The teachers I've worked with inspired me every day, the people made my summer the best one yet. I love all of you and you'll always have a special place in my heart #obgottheheat and happy National Dance Day\",\n", + " \"everybody needs some attitude in their life #punintended #obgottheheat\"\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [], + "source": [ + "darya_test = {'name': ['Darya'], 'type': ['ESFP']}" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [], + "source": [ + "darya_row = pd.DataFrame(darya_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [], + "source": [ + "darya_row['text'] = ' '.join(Darya)" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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0DaryaESFPQuarantine expectations. Swipe for reality 22 ...
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" + ], + "text/plain": [ + " name type text\n", + "0 Darya ESFP Quarantine expectations. Swipe for reality 22 ..." + ] + }, + "execution_count": 102, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "darya_row" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [], + "source": [ + "def processed(x):\n", + " pattern = r'''(?i)\\b((?:https?://|www\\d{0,3}[.]|[a-z0-9.\\-]+[.][a-z]{2,4}/)(?:[^\\s()<>]+|\\(([^\\s()<>]+|(\\([^\\s()<>]+\\)))*\\))+(?:\\(([^\\s()<>]+|(\\([^\\s()<>]+\\)))*\\)|[^\\s`!()\\[\\]{};:'\".,<>?«»“”‘’]))'''\n", + " cleaned = re.sub(pattern, ' ', str(x)) \n", + " cleaned = re.sub(r\"[^A-Za-z0-9]+\", \" \", cleaned)\n", + " cleaned = re.sub(\" \\d+\", \" \", cleaned)\n", + " cleaned = cleaned.lower().strip()\n", + "\n", + " pattern2 = '\\w{1,}'\n", + " bag = re.findall(pattern2, cleaned)\n", + "\n", + " porter = PorterStemmer()\n", + " porter_bag = [porter.stem(word) for word in bag]\n", + "\n", + " lemmatizer = wordnet.WordNetLemmatizer()\n", + " bag_of_words = [lemmatizer.lemmatize(word) for word in porter_bag]\n", + "\n", + " stopwords_e = stopwords.words('english')\n", + " bag_of_words = [word for word in bag_of_words if word not in stopwords_e]\n", + "\n", + " return bag_of_words" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [], + "source": [ + "text_processed = [processed(i) for i in darya_row.text]" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [], + "source": [ + "darya_row['text_processed'] = text_processed" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [], + "source": [ + "darya_row['text_ready'] = darya_row.text_processed.apply(' '.join)" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, + "outputs": [], + "source": [ + "darya_row = darya_row.drop(columns = ['name', 'text', 'text_processed'])" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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0ESFPquarantin expect swipe realiti swipe see photo...
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" + ], + "text/plain": [ + " type text_ready\n", + "0 ESFP quarantin expect swipe realiti swipe see photo..." + ] + }, + "execution_count": 108, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "darya_row" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [], + "source": [ + "data = pd.read_csv(r'../data/personalities_cleaned.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [], + "source": [ + "data = data.drop(columns = ['Unnamed: 0', 'posts', 'text_processed'])" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": {}, + "outputs": [], + "source": [ + "data.drop(data.index[3559], inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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0INFJintj moment sportscent top ten play prank ha l...
1ENTPfind lack post veri alarm sex bore posit often...
2INTPgood one cours say know bless cur doe absolut ...
3INTJdear intp enjoy convers day esoter gab natur u...
4ENTJfire anoth silli misconcept approach logic go ...
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" + ], + "text/plain": [ + " type text_ready\n", + "0 INFJ intj moment sportscent top ten play prank ha l...\n", + "1 ENTP find lack post veri alarm sex bore posit often...\n", + "2 INTP good one cours say know bless cur doe absolut ...\n", + "3 INTJ dear intp enjoy convers day esoter gab natur u...\n", + "4 ENTJ fire anoth silli misconcept approach logic go ..." + ] + }, + "execution_count": 121, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": {}, + "outputs": [], + "source": [ + "data = data.append(darya_row, ignore_index = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": {}, + "outputs": [], + "source": [ + "data2 = data.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": {}, + "outputs": [], + "source": [ + "# cleaning and processing\n", + "\n", + "data2['I-E'] = data2['type'].astype(str).str[0]\n", + "data2['N-S'] = data2['type'].astype(str).str[1]\n", + "data2['T-F'] = data2['type'].astype(str).str[2]\n", + "data2['J-P'] = data2['type'].astype(str).str[3]" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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0INFJintj moment sportscent top ten play prank ha l...INFJ
1ENTPfind lack post veri alarm sex bore posit often...ENTP
2INTPgood one cours say know bless cur doe absolut ...INTP
3INTJdear intp enjoy convers day esoter gab natur u...INTJ
4ENTJfire anoth silli misconcept approach logic go ...ENTJ
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" + ], + "text/plain": [ + " type text_ready I-E N-S T-F J-P\n", + "0 INFJ intj moment sportscent top ten play prank ha l... I N F J\n", + "1 ENTP find lack post veri alarm sex bore posit often... E N T P\n", + "2 INTP good one cours say know bless cur doe absolut ... I N T P\n", + "3 INTJ dear intp enjoy convers day esoter gab natur u... I N T J\n", + "4 ENTJ fire anoth silli misconcept approach logic go ... E N T J" + ] + }, + "execution_count": 125, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data2.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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typetext_readyI-EN-ST-FJ-P
8671INTPmani question thing would take purpl pill pick...INTP
8672INFPveri conflict right come want child honestli m...INFP
8673INFPha long sinc personalitycaf although seem chan...INFP
8674ESFPquarantin expect swipe realiti swipe see photo...ESFP
8675ESFPquarantin expect swipe realiti swipe see photo...ESFP
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" + ], + "text/plain": [ + " type text_ready I-E N-S T-F J-P\n", + "8671 INTP mani question thing would take purpl pill pick... I N T P\n", + "8672 INFP veri conflict right come want child honestli m... I N F P\n", + "8673 INFP ha long sinc personalitycaf although seem chan... I N F P\n", + "8674 ESFP quarantin expect swipe realiti swipe see photo... E S F P\n", + "8675 ESFP quarantin expect swipe realiti swipe see photo... E S F P" + ] + }, + "execution_count": 126, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data2.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": {}, + "outputs": [], + "source": [ + "x = data2['text_ready']\n", + "\n", + "y_IE = data2['I-E']\n", + "y_NS = data2['N-S']\n", + "y_TF = data2['T-F']\n", + "y_JP = data2['J-P']" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.feature_extraction.text import CountVectorizer\n", + "\n", + "vector = CountVectorizer(ngram_range=(2, 2)).fit(x) \n", + "X = vector.transform(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": {}, + "outputs": [], + "source": [ + "y_test = list(y_IE[8675])" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'E'" + ] + }, + "execution_count": 130, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_test" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split, cross_val_score" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I-E RESULTS\n", + "Label I-E train score is : 0.7696564445469218\n", + "Label I-E test score is : 0.7696564445469218\n", + "Confusion Matrix for K-Nearest Neighbors:\n", + "[[0 1]\n", + " [0 0]]\n", + "Score: 0.0\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " E 0.00 0.00 0.00 1.0\n", + " I 0.00 0.00 0.00 0.0\n", + "\n", + " accuracy 0.00 1.0\n", + " macro avg 0.00 0.00 0.00 1.0\n", + "weighted avg 0.00 0.00 0.00 1.0\n", + "\n", + "Prediction is: ['I']\n", + "Actual is: ['E']\n", + "****************************************************************************************************\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n", + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "N-S RESULTS\n", + "Label N-S train score is : 0.8620013834447775\n", + "Label N-S test score is : 0.8620013834447775\n", + "Confusion Matrix for Random Forests:\n", + "[[0 0]\n", + " [1 0]]\n", + "Score: 0.0\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " N 0.00 0.00 0.00 0.0\n", + " S 0.00 0.00 0.00 1.0\n", + "\n", + " accuracy 0.00 1.0\n", + " macro avg 0.00 0.00 0.00 1.0\n", + "weighted avg 0.00 0.00 0.00 1.0\n", + "\n", + "Prediction is: ['N']\n", + "Actual is: ['S']\n", + "****************************************************************************************************\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n", + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "T-F Results\n", + "Label T-F train score is : 1.0\n", + "Label T-F test score is : 1.0\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[1]]\n", + "Score: 100.0\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " F 1.00 1.00 1.00 1\n", + "\n", + " accuracy 1.00 1\n", + " macro avg 1.00 1.00 1.00 1\n", + "weighted avg 1.00 1.00 1.00 1\n", + "\n", + "Prediction is: ['F']\n", + "Actual is: ['F']\n", + "****************************************************************************************************\n", + "J-P Results\n", + "Label J-P train score is : 1.0\n", + "Label J-P test score is : 1.0\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[0 0]\n", + " [1 0]]\n", + "Score: 0.0\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " J 0.00 0.00 0.00 0.0\n", + " P 0.00 0.00 0.00 1.0\n", + "\n", + " accuracy 0.00 1.0\n", + " macro avg 0.00 0.00 0.00 1.0\n", + "weighted avg 0.00 0.00 0.00 1.0\n", + "\n", + "Prediction is: ['J']\n", + "Actual is: ['P']\n", + "****************************************************************************************************\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n", + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n" + ] + } + ], + "source": [ + "from sklearn.neighbors import KNeighborsClassifier\n", + "knn = KNeighborsClassifier(n_neighbors=10) # see if can tweak accuracy\n", + "\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "rfn = RandomForestClassifier(max_depth=5, random_state=0) # see if can tweak accuracy\n", + "\n", + "from sklearn.naive_bayes import MultinomialNB\n", + "mnb = MultinomialNB()\n", + "\n", + "\n", + "\n", + "# IE\n", + "x_train = X[0:8674]\n", + "x_test = X[8675]\n", + "y_train = y_IE[0:8674]\n", + "y_test = list(y_IE[8675])\n", + "knn.fit(x_train, y_train)\n", + "ieb_train = knn.score (x_train,y_train)\n", + "ieb_test = knn.score (x_train,y_train)\n", + "predknn = knn.predict(x_test)\n", + "print(\"I-E RESULTS\")\n", + "print('Label I-E train score is :',ieb_train)\n", + "print('Label I-E test score is :',ieb_test)\n", + "print(\"Confusion Matrix for K-Nearest Neighbors:\")\n", + "print(confusion_matrix(y_test,predknn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predknn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predknn))\n", + "print('Prediction is: ', predknn)\n", + "print('Actual is: ', y_test)\n", + "print(\"*\" * 100)\n", + "\n", + "# NS\n", + "x_train = X[0:8674]\n", + "x_test = X[8675]\n", + "y_train = y_NS[0:8674]\n", + "y_test = list(y_NS[8675])\n", + "rfn.fit(x_train, y_train)\n", + "nsb_train = rfn.score (x_train,y_train)\n", + "nsb_test = rfn.score (x_train,y_train)\n", + "predrfn = rfn.predict(x_test)\n", + "print(\"N-S RESULTS\")\n", + "print('Label N-S train score is :',nsb_train)\n", + "print('Label N-S test score is :',nsb_test)\n", + "print(\"Confusion Matrix for Random Forests:\")\n", + "print(confusion_matrix(y_test,predrfn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predrfn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predrfn))\n", + "print('Prediction is: ', predrfn)\n", + "print('Actual is: ', y_test)\n", + "print(\"*\" * 100)\n", + "\n", + "# TF\n", + "x_train = X[0:8674]\n", + "x_test = X[8675]\n", + "y_train = y_TF[0:8674]\n", + "y_test = list(y_TF[8675])\n", + "mnb.fit(x_train, y_train)\n", + "tfb_train = mnb.score (x_train,y_train)\n", + "tfb_test = mnb.score (x_train,y_train)\n", + "predmnb = mnb.predict(x_test)\n", + "print(\"T-F Results\")\n", + "print('Label T-F train score is :',tfb_train)\n", + "print('Label T-F test score is :',tfb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predmnb))\n", + "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", + "print('Prediction is: ', predmnb)\n", + "print('Actual is: ', y_test)\n", + "print(\"*\" * 100)\n", + "\n", + "# JP\n", + "x_train = X[0:8674]\n", + "x_test = X[8675]\n", + "y_train = y_JP[0:8674]\n", + "y_test = list(y_JP[8675])\n", + "mnb.fit(x_train, y_train)\n", + "jpb_train = mnb.score (x_train,y_train)\n", + "jpb_test = mnb.score (x_train,y_train)\n", + "predmnb = mnb.predict(x_test)\n", + "print(\"J-P Results\")\n", + "print('Label J-P train score is :',jpb_train)\n", + "print('Label J-P test score is :',jpb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predmnb))\n", + "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", + "print('Prediction is: ', predmnb)\n", + "print('Actual is: ', y_test)\n", + "print(\"*\" * 100)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/your-project/code/testing nlp on Laurents.ipynb b/your-project/code/testing nlp on Laurents.ipynb new file mode 100644 index 00000000..fca549fb --- /dev/null +++ b/your-project/code/testing nlp on Laurents.ipynb @@ -0,0 +1,972 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# putting together friends dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import re\n", + "\n", + "import nltk\n", + "from nltk.stem import PorterStemmer\n", + "from nltk.stem import SnowballStemmer\n", + "from nltk import wordnet\n", + "from nltk.corpus import stopwords\n", + "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n", + "from sklearn.model_selection import train_test_split, cross_val_score\n", + "from sklearn.metrics import classification_report\n", + "from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score, roc_curve\n", + "\n", + "from sklearn.model_selection import train_test_split, cross_val_score\n", + "from sklearn.model_selection import cross_validate\n", + "from sklearn.model_selection import StratifiedKFold\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.model_selection import learning_curve\n", + "from sklearn.ensemble import ExtraTreesClassifier\n", + "from sklearn.decomposition import TruncatedSVD\n", + "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.naive_bayes import MultinomialNB\n", + "from sklearn.metrics import classification_report\n", + "from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score, roc_curve" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "Laurents = [\"Haven't posted in a while, so this random picture is now filling the void. Been going within a lot in the past year and sorted out some things for myself. Happy to say, that I am refocused, re-energized and working on great things.\", \n", + " \"Impressions from Serfaus. 2020 is off to a good start. Swipe right for the best drinking game\", \n", + " \"Dogs of Berlin\", \n", + " \"Not my boat, still flexing \", \n", + " \"Lads\", \n", + " \"A weekend in Madrid is always a good idea! Hanging out the hangover at some historical sites \", \n", + " \"Drip too hard, don’t stand too close\", \n", + " \"I miss you bro. We pushed each other to be the best every day. There never was a better duo. „The Golden Boys“ Rest Easy\",\n", + " \"Getting settled in Berlin\",\n", + " \"It’s been three tough but fun years at WHU. Last weekend I finally graduated with a degree from the best business school in Germany, but more importantly with new friends for life and another place I can call home.\",\n", + " \n", + " \"Graduated\",\n", + " \"WHU boys - Class of 2017. Had so much fun with you. Now let's get the moolah\",\n", + " \"The pic is blurry but the day was sharp\",\n", + " \"Probably the most spectacular view I ever had was driving along the highway no. 1 between LA and SF\",\n", + " \"He acting all loyal in this pic, but that lil b* bit me the next minute\",\n", + " \"Probably the most spectacular view I ever had was driving along the highway no. 1 between LA and SF.\",\n", + " \"Hey @google, Hire me, if you want your bike back! #mountainview #google #siliconvalley\",\n", + " \"Throwback to an amazing road trip this fall. What a view! Def. worth the hike #hike #nature #travel #roadtrip #yosemite\",\n", + " \"Keep your eyes on the stars and keep your feet on the ground\",\n", + " \"You can clearly see I enjoyed being back in SoCal. That place is so beautiful...\",\n", + " \n", + " \"he Pacific #beers #beach #chilling #california #home #uglyfeet @goldclapmusic good day dude\",\n", + " \"Never hold back #weekendmood\",\n", + " \"Thank you @kaleandme for this awesome party! #crewlove @reiseminister_coco\",\n", + " \"tb to one of my best games ever Still holding the club record for most receptions per game 2013 vs Cottbus W31:15 13 receptions, 193 yards, 3 TDs Thank you @zbreez_ for the trust and the passes !\",\n", + " \"Throwback to the best winter ever. My kids group. I gave us the name the lil crackheads, because they got me cracking up all the time! #fuckhashtags\",\n", + " \"Won this one big time 60:20, but it cost us some good men... #hospitalreunion\",\n", + " \"#Repost @luebeckcougars Much way for improvement though personally. #getbettereveryday Gamestats #cougars #cougarnation #cougarspride #luebeck #luebeckcougars #football #americanfootball #gfl #amgid #afvd #gridiron #awaygame #ritterhude #badgers\",\n", + " \"100 days credits @gridiron_design_s\",\n", + " \"New Year's Eve in the alps with the crew. #illuminati\",\n", + " \"#tb to the good old times when sunday meant gameday and mondays newspaper Sitting in the library studying right now I wish I could go back in time\",\n", + " \n", + " \"What you believe becomes reality. Create your own world #sundaymood\",\n", + " \"Mom, can we have one?\",\n", + " \"Trying to get my surf game up. I think I have a much better form on the beach than in the water though haha #germansaintmermaids @lennartmhr\",\n", + " \"Table Mountain from Lion's Head @lennartmhr\",\n", + " \"I hate off-season... \",\n", + " \"This shoutout is overdue. Best musical speaker @realcoleworld!\",\n", + " \"#tb to when my bro @enzeocha_11 was making the Swedish defense look like 8th graders.\",\n", + " \"Hoffentlich liegt das in der Familie! #crossingfingers #toodamnhot\",\n", + " \"Touchdown 70:46 crazy game!\",\n", + " \"Some work by @dani_piedrabuena. Trying to look smart in that suit haha \",\n", + " \n", + " \"Back at it\",\n", + " \"Received this at work. Now my colleagues think I'm famous lol Thank you so much Colin!\",\n", + " \"#tb to the toughest job I ever had to do good times\",\n", + " \"office break \",\n", + " \"Beach is better. #barcelona #barca #beach #playa #sun #smile #spain #sleepyface\"\n", + " \"Thank you for such a lovely birthday night.\",\n", + " \"My new car... I wish. M4 Test Drive. Still having goosebumps #bmw #m4 #madrid #racing\",\n", + " \"Don't mess with NFL Runningbacks\",\n", + " \"perfect day at el Retiro\",\n", + " \"Morning routine. Scooter Ride #madrid #work #watch #scooter #sun #suit\"\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "laurents_test = {'name': ['Laurents'], 'type': ['ENFP']}" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "laurents_row = pd.DataFrame(laurents_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "laurents_row['text'] = ' '.join(Laurents)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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0LaurentsENFPHaven't posted in a while, so this random pict...
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0ENFPpost thi random pictur fill void go within lot...
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0INFJintj moment sportscent top ten play prank ha l...
1ENTPfind lack post veri alarm sex bore posit often...
2INTPgood one cours say know bless cur doe absolut ...
3INTJdear intp enjoy convers day esoter gab natur u...
4ENTJfire anoth silli misconcept approach logic go ...
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0INFJintj moment sportscent top ten play prank ha l...INFJ
1ENTPfind lack post veri alarm sex bore posit often...ENTP
2INTPgood one cours say know bless cur doe absolut ...INTP
3INTJdear intp enjoy convers day esoter gab natur u...INTJ
4ENTJfire anoth silli misconcept approach logic go ...ENTJ
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" + ], + "text/plain": [ + " type text_ready I-E N-S T-F J-P\n", + "0 INFJ intj moment sportscent top ten play prank ha l... I N F J\n", + "1 ENTP find lack post veri alarm sex bore posit often... E N T P\n", + "2 INTP good one cours say know bless cur doe absolut ... I N T P\n", + "3 INTJ dear intp enjoy convers day esoter gab natur u... I N T J\n", + "4 ENTJ fire anoth silli misconcept approach logic go ... E N T J" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data2.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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typetext_readyI-EN-ST-FJ-P
8670ENFPthi thread alreadi exist someplac el doe heck ...ENFP
8671INTPmani question thing would take purpl pill pick...INTP
8672INFPveri conflict right come want child honestli m...INFP
8673INFPha long sinc personalitycaf although seem chan...INFP
8674ENFPpost thi random pictur fill void go within lot...ENFP
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" + ], + "text/plain": [ + " type text_ready I-E N-S T-F J-P\n", + "8670 ENFP thi thread alreadi exist someplac el doe heck ... E N F P\n", + "8671 INTP mani question thing would take purpl pill pick... I N T P\n", + "8672 INFP veri conflict right come want child honestli m... I N F P\n", + "8673 INFP ha long sinc personalitycaf although seem chan... I N F P\n", + "8674 ENFP post thi random pictur fill void go within lot... E N F P" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data2.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "x = data2['text_ready']\n", + "\n", + "y_IE = data2['I-E']\n", + "y_NS = data2['N-S']\n", + "y_TF = data2['T-F']\n", + "y_JP = data2['J-P']" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.feature_extraction.text import CountVectorizer\n", + "\n", + "vector = CountVectorizer(ngram_range=(2, 2)).fit(x) \n", + "X = vector.transform(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "y_test = list(y_IE[8674])" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['E']" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_test" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split, cross_val_score" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I-E RESULTS\n", + "Label I-E train score is : 0.7696298858526461\n", + "Label I-E test score is : 0.7696298858526461\n", + "Confusion Matrix for K-Nearest Neighbors:\n", + "[[0 1]\n", + " [0 0]]\n", + "Score: 0.0\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " E 0.00 0.00 0.00 1.0\n", + " I 0.00 0.00 0.00 0.0\n", + "\n", + " accuracy 0.00 1.0\n", + " macro avg 0.00 0.00 0.00 1.0\n", + "weighted avg 0.00 0.00 0.00 1.0\n", + "\n", + "Prediction is: ['I']\n", + "Actual is: ['E']\n", + "****************************************************************************************************\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n", + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "N-S RESULTS\n", + "Label N-S train score is : 0.8619854721549637\n", + "Label N-S test score is : 0.8619854721549637\n", + "Confusion Matrix for Random Forests:\n", + "[[1]]\n", + "Score: 100.0\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " N 1.00 1.00 1.00 1\n", + "\n", + " accuracy 1.00 1\n", + " macro avg 1.00 1.00 1.00 1\n", + "weighted avg 1.00 1.00 1.00 1\n", + "\n", + "Prediction is: ['N']\n", + "Actual is: ['N']\n", + "****************************************************************************************************\n", + "T-F Results\n", + "Label T-F train score is : 1.0\n", + "Label T-F test score is : 1.0\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[0 1]\n", + " [0 0]]\n", + "Score: 0.0\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " F 0.00 0.00 0.00 1.0\n", + " T 0.00 0.00 0.00 0.0\n", + "\n", + " accuracy 0.00 1.0\n", + " macro avg 0.00 0.00 0.00 1.0\n", + "weighted avg 0.00 0.00 0.00 1.0\n", + "\n", + "Prediction is: ['T']\n", + "Actual is: ['F']\n", + "****************************************************************************************************\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n", + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "J-P Results\n", + "Label J-P train score is : 1.0\n", + "Label J-P test score is : 1.0\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[0 0]\n", + " [1 0]]\n", + "Score: 0.0\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " J 0.00 0.00 0.00 0.0\n", + " P 0.00 0.00 0.00 1.0\n", + "\n", + " accuracy 0.00 1.0\n", + " macro avg 0.00 0.00 0.00 1.0\n", + "weighted avg 0.00 0.00 0.00 1.0\n", + "\n", + "Prediction is: ['J']\n", + "Actual is: ['P']\n", + "****************************************************************************************************\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n", + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n" + ] + } + ], + "source": [ + "from sklearn.neighbors import KNeighborsClassifier\n", + "knn = KNeighborsClassifier(n_neighbors=10) # see if can tweak accuracy\n", + "\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "rfn = RandomForestClassifier(max_depth=5, random_state=0) # see if can tweak accuracy\n", + "\n", + "from sklearn.naive_bayes import MultinomialNB\n", + "mnb = MultinomialNB()\n", + "\n", + "\n", + "\n", + "# IE\n", + "x_train = X[0:8673]\n", + "x_test = X[8674]\n", + "y_train = y_IE[0:8673]\n", + "y_test = list(y_IE[8674])\n", + "knn.fit(x_train, y_train)\n", + "ieb_train = knn.score (x_train,y_train)\n", + "ieb_test = knn.score (x_train,y_train)\n", + "predknn = knn.predict(x_test)\n", + "print(\"I-E RESULTS\")\n", + "print('Label I-E train score is :',ieb_train)\n", + "print('Label I-E test score is :',ieb_test)\n", + "print(\"Confusion Matrix for K-Nearest Neighbors:\")\n", + "print(confusion_matrix(y_test,predknn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predknn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predknn))\n", + "print('Prediction is: ', predknn)\n", + "print('Actual is: ', y_test)\n", + "print(\"*\" * 100)\n", + "\n", + "# NS\n", + "x_train = X[0:8673]\n", + "x_test = X[8674]\n", + "y_train = y_NS[0:8673]\n", + "y_test = list(y_NS[8674])\n", + "rfn.fit(x_train, y_train)\n", + "nsb_train = rfn.score (x_train,y_train)\n", + "nsb_test = rfn.score (x_train,y_train)\n", + "predrfn = rfn.predict(x_test)\n", + "print(\"N-S RESULTS\")\n", + "print('Label N-S train score is :',nsb_train)\n", + "print('Label N-S test score is :',nsb_test)\n", + "print(\"Confusion Matrix for Random Forests:\")\n", + "print(confusion_matrix(y_test,predrfn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predrfn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predrfn))\n", + "print('Prediction is: ', predrfn)\n", + "print('Actual is: ', y_test)\n", + "print(\"*\" * 100)\n", + "\n", + "# TF\n", + "x_train = X[0:8673]\n", + "x_test = X[8674]\n", + "y_train = y_TF[0:8673]\n", + "y_test = list(y_TF[8674])\n", + "mnb.fit(x_train, y_train)\n", + "tfb_train = mnb.score (x_train,y_train)\n", + "tfb_test = mnb.score (x_train,y_train)\n", + "predmnb = mnb.predict(x_test)\n", + "print(\"T-F Results\")\n", + "print('Label T-F train score is :',tfb_train)\n", + "print('Label T-F test score is :',tfb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predmnb))\n", + "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", + "print('Prediction is: ', predmnb)\n", + "print('Actual is: ', y_test)\n", + "print(\"*\" * 100)\n", + "\n", + "# JP\n", + "x_train = X[0:8673]\n", + "x_test = X[8674]\n", + "y_train = y_JP[0:8673]\n", + "y_test = list(y_JP[8674])\n", + "mnb.fit(x_train, y_train)\n", + "jpb_train = mnb.score (x_train,y_train)\n", + "jpb_test = mnb.score (x_train,y_train)\n", + "predmnb = mnb.predict(x_test)\n", + "print(\"J-P Results\")\n", + "print('Label J-P train score is :',jpb_train)\n", + "print('Label J-P test score is :',jpb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predmnb))\n", + "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", + "print('Prediction is: ', predmnb)\n", + "print('Actual is: ', y_test)\n", + "print(\"*\" * 100)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/your-project/code/testing nlp on Theresa.ipynb b/your-project/code/testing nlp on Theresa.ipynb new file mode 100644 index 00000000..7ddde563 --- /dev/null +++ b/your-project/code/testing nlp on Theresa.ipynb @@ -0,0 +1,972 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# putting together friends dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import re\n", + "\n", + "import nltk\n", + "from nltk.stem import PorterStemmer\n", + "from nltk.stem import SnowballStemmer\n", + "from nltk import wordnet\n", + "from nltk.corpus import stopwords\n", + "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n", + "from sklearn.model_selection import train_test_split, cross_val_score\n", + "from sklearn.metrics import classification_report\n", + "from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score, roc_curve\n", + "\n", + "from sklearn.model_selection import train_test_split, cross_val_score\n", + "from sklearn.model_selection import cross_validate\n", + "from sklearn.model_selection import StratifiedKFold\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.model_selection import learning_curve\n", + "from sklearn.ensemble import ExtraTreesClassifier\n", + "from sklearn.decomposition import TruncatedSVD\n", + "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.naive_bayes import MultinomialNB\n", + "from sklearn.metrics import classification_report\n", + "from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score, roc_curve" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "Theresa = [\"light times\", \n", + " \"simplicité.\", \n", + " \"chapter 1\", \n", + " \"*waiter pours up wine without being asked for*\", \n", + " \"what a year\", \n", + " \"it‘s friday i‘m in loved.\", \n", + " \"herzallerliebst.\", \n", + " \"wie sommerferien für erwachsene irgendwie.\",\n", + " \"sommer und sonne und zitroneneis und stella und ach man was soll ich sagen - das beste leben @stellamarieluise\",\n", + " \"das ist einfach Glück, wenn man euch hat\",\n", + " \n", + " \"an diesem Bild ist so Vieles richtig.\",\n", + " \"ernst gucken geht also doch. @shishaclothing #supportyourlocalbrand #shishaclothing\",\n", + " \"casual durch die gegend düsen halt\",\n", + " \"Liebe für alle Frauen. Aber für Diese am meisten! #happyweltfrauentag\",\n", + " \"meine entscheidungsfreude captured by @stellamarieluise\",\n", + " \"keine termine und leicht im dispo\",\n", + " \"future\",\n", + " \"Zukunftsprognose 2080: alle glücklich.\",\n", + " \"marilyn monroe momente beim brötchen holen\",\n", + " \"reflections.\",\n", + " \n", + " \"keine sorgen, keine wellen, kein conditioner\",\n", + " \"liebenswichtig\",\n", + " \"alles wie immer\",\n", + " \"gute gespräche und blöde ideen\",\n", + " \"klassisches arbeiten halt\",\n", + " \"mag ich\",\n", + " \"lieb ich\",\n", + " \"viel zu schön\",\n", + " \"kaltes bier who dis\",\n", + " \"guess i‘m just in love with sunrises and moonlight and rainstorms and everything that has soul\",\n", + " \n", + " \"semi zufrieden mit dem frühstück\",\n", + " \"ich hab das mit dem ernst gucken nicht mitbekommen\",\n", + " \"verliebtverliebt\",\n", + " \"trouble never looked so goddamn fine\",\n", + " \"und wenn das abendlicht in genau dieser farbe ist\",\n", + " \"say something in dutch\",\n", + " \"superlekker\",\n", + " \"Pass auf deine Füße auf \",\n", + " \"herz euch behalt ich\",\n", + " \"find ich gut\",\n", + " \n", + " \"wenn @lisbz meine wäsche aufhängt\",\n", + " \"find your fire\",\n", + " \"umziehen mit lieblingspizza und lieblingswg\",\n", + " \"lieb ich\",\n", + " \"ist das Kunst oder kann das weg?\"\n", + " \"kennt ihr diese Menschen, die stehen bleiben um den Himmel zu fotografieren?\",\n", + " \"no one is you and that is your power\",\n", + " \"HAPPY WELTFRAUENTAG\",\n", + " \"gründe warum ich kein Blogger bin\",\n", + " \"sommer\"\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "theresa_test = {'name': ['Theresa'], 'type': ['ENFP']}" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "theresa_row = pd.DataFrame(theresa_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "theresa_row['text'] = ' '.join(Theresa)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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0ENFPlight time simplicit chapter waiter pour wine ...
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0INFJintj moment sportscent top ten play prank ha l...
1ENTPfind lack post veri alarm sex bore posit often...
2INTPgood one cours say know bless cur doe absolut ...
3INTJdear intp enjoy convers day esoter gab natur u...
4ENTJfire anoth silli misconcept approach logic go ...
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" + ], + "text/plain": [ + " type text_ready\n", + "0 INFJ intj moment sportscent top ten play prank ha l...\n", + "1 ENTP find lack post veri alarm sex bore posit often...\n", + "2 INTP good one cours say know bless cur doe absolut ...\n", + "3 INTJ dear intp enjoy convers day esoter gab natur u...\n", + "4 ENTJ fire anoth silli misconcept approach logic go ..." + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "data = data.append(theresa_row, ignore_index = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "data2 = data.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "# cleaning and processing\n", + "\n", + "data2['I-E'] = data2['type'].astype(str).str[0]\n", + "data2['N-S'] = data2['type'].astype(str).str[1]\n", + "data2['T-F'] = data2['type'].astype(str).str[2]\n", + "data2['J-P'] = data2['type'].astype(str).str[3]" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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typetext_readyI-EN-ST-FJ-P
0INFJintj moment sportscent top ten play prank ha l...INFJ
1ENTPfind lack post veri alarm sex bore posit often...ENTP
2INTPgood one cours say know bless cur doe absolut ...INTP
3INTJdear intp enjoy convers day esoter gab natur u...INTJ
4ENTJfire anoth silli misconcept approach logic go ...ENTJ
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" + ], + "text/plain": [ + " type text_ready I-E N-S T-F J-P\n", + "0 INFJ intj moment sportscent top ten play prank ha l... I N F J\n", + "1 ENTP find lack post veri alarm sex bore posit often... E N T P\n", + "2 INTP good one cours say know bless cur doe absolut ... I N T P\n", + "3 INTJ dear intp enjoy convers day esoter gab natur u... I N T J\n", + "4 ENTJ fire anoth silli misconcept approach logic go ... E N T J" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data2.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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typetext_readyI-EN-ST-FJ-P
8670ENFPthi thread alreadi exist someplac el doe heck ...ENFP
8671INTPmani question thing would take purpl pill pick...INTP
8672INFPveri conflict right come want child honestli m...INFP
8673INFPha long sinc personalitycaf although seem chan...INFP
8674ENFPlight time simplicit chapter waiter pour wine ...ENFP
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" + ], + "text/plain": [ + " type text_ready I-E N-S T-F J-P\n", + "8670 ENFP thi thread alreadi exist someplac el doe heck ... E N F P\n", + "8671 INTP mani question thing would take purpl pill pick... I N T P\n", + "8672 INFP veri conflict right come want child honestli m... I N F P\n", + "8673 INFP ha long sinc personalitycaf although seem chan... I N F P\n", + "8674 ENFP light time simplicit chapter waiter pour wine ... E N F P" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data2.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "x = data2['text_ready']\n", + "\n", + "y_IE = data2['I-E']\n", + "y_NS = data2['N-S']\n", + "y_TF = data2['T-F']\n", + "y_JP = data2['J-P']" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.feature_extraction.text import CountVectorizer\n", + "\n", + "vector = CountVectorizer(ngram_range=(2, 2)).fit(x) \n", + "X = vector.transform(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "y_test = list(y_IE[8674])" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['E']" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_test" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split, cross_val_score" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I-E RESULTS\n", + "Label I-E train score is : 0.7696298858526461\n", + "Label I-E test score is : 0.7696298858526461\n", + "Confusion Matrix for K-Nearest Neighbors:\n", + "[[0 1]\n", + " [0 0]]\n", + "Score: 0.0\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " E 0.00 0.00 0.00 1.0\n", + " I 0.00 0.00 0.00 0.0\n", + "\n", + " accuracy 0.00 1.0\n", + " macro avg 0.00 0.00 0.00 1.0\n", + "weighted avg 0.00 0.00 0.00 1.0\n", + "\n", + "Prediction is: ['I']\n", + "Actual is: ['E']\n", + "****************************************************************************************************\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n", + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "N-S RESULTS\n", + "Label N-S train score is : 0.8619854721549637\n", + "Label N-S test score is : 0.8619854721549637\n", + "Confusion Matrix for Random Forests:\n", + "[[1]]\n", + "Score: 100.0\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " N 1.00 1.00 1.00 1\n", + "\n", + " accuracy 1.00 1\n", + " macro avg 1.00 1.00 1.00 1\n", + "weighted avg 1.00 1.00 1.00 1\n", + "\n", + "Prediction is: ['N']\n", + "Actual is: ['N']\n", + "****************************************************************************************************\n", + "T-F Results\n", + "Label T-F train score is : 1.0\n", + "Label T-F test score is : 1.0\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[0 1]\n", + " [0 0]]\n", + "Score: 0.0\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " F 0.00 0.00 0.00 1.0\n", + " T 0.00 0.00 0.00 0.0\n", + "\n", + " accuracy 0.00 1.0\n", + " macro avg 0.00 0.00 0.00 1.0\n", + "weighted avg 0.00 0.00 0.00 1.0\n", + "\n", + "Prediction is: ['T']\n", + "Actual is: ['F']\n", + "****************************************************************************************************\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n", + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "J-P Results\n", + "Label J-P train score is : 1.0\n", + "Label J-P test score is : 1.0\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[0 0]\n", + " [1 0]]\n", + "Score: 0.0\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " J 0.00 0.00 0.00 0.0\n", + " P 0.00 0.00 0.00 1.0\n", + "\n", + " accuracy 0.00 1.0\n", + " macro avg 0.00 0.00 0.00 1.0\n", + "weighted avg 0.00 0.00 0.00 1.0\n", + "\n", + "Prediction is: ['J']\n", + "Actual is: ['P']\n", + "****************************************************************************************************\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n", + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n" + ] + } + ], + "source": [ + "from sklearn.neighbors import KNeighborsClassifier\n", + "knn = KNeighborsClassifier(n_neighbors=10) # see if can tweak accuracy\n", + "\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "rfn = RandomForestClassifier(max_depth=5, random_state=0) # see if can tweak accuracy\n", + "\n", + "from sklearn.naive_bayes import MultinomialNB\n", + "mnb = MultinomialNB()\n", + "\n", + "\n", + "\n", + "# IE\n", + "x_train = X[0:8673]\n", + "x_test = X[8674]\n", + "y_train = y_IE[0:8673]\n", + "y_test = list(y_IE[8674])\n", + "knn.fit(x_train, y_train)\n", + "ieb_train = knn.score (x_train,y_train)\n", + "ieb_test = knn.score (x_train,y_train)\n", + "predknn = knn.predict(x_test)\n", + "print(\"I-E RESULTS\")\n", + "print('Label I-E train score is :',ieb_train)\n", + "print('Label I-E test score is :',ieb_test)\n", + "print(\"Confusion Matrix for K-Nearest Neighbors:\")\n", + "print(confusion_matrix(y_test,predknn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predknn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predknn))\n", + "print('Prediction is: ', predknn)\n", + "print('Actual is: ', y_test)\n", + "print(\"*\" * 100)\n", + "\n", + "# NS\n", + "x_train = X[0:8673]\n", + "x_test = X[8674]\n", + "y_train = y_NS[0:8673]\n", + "y_test = list(y_NS[8674])\n", + "rfn.fit(x_train, y_train)\n", + "nsb_train = rfn.score (x_train,y_train)\n", + "nsb_test = rfn.score (x_train,y_train)\n", + "predrfn = rfn.predict(x_test)\n", + "print(\"N-S RESULTS\")\n", + "print('Label N-S train score is :',nsb_train)\n", + "print('Label N-S test score is :',nsb_test)\n", + "print(\"Confusion Matrix for Random Forests:\")\n", + "print(confusion_matrix(y_test,predrfn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predrfn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predrfn))\n", + "print('Prediction is: ', predrfn)\n", + "print('Actual is: ', y_test)\n", + "print(\"*\" * 100)\n", + "\n", + "# TF\n", + "x_train = X[0:8673]\n", + "x_test = X[8674]\n", + "y_train = y_TF[0:8673]\n", + "y_test = list(y_TF[8674])\n", + "mnb.fit(x_train, y_train)\n", + "tfb_train = mnb.score (x_train,y_train)\n", + "tfb_test = mnb.score (x_train,y_train)\n", + "predmnb = mnb.predict(x_test)\n", + "print(\"T-F Results\")\n", + "print('Label T-F train score is :',tfb_train)\n", + "print('Label T-F test score is :',tfb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predmnb))\n", + "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", + "print('Prediction is: ', predmnb)\n", + "print('Actual is: ', y_test)\n", + "print(\"*\" * 100)\n", + "\n", + "# JP\n", + "x_train = X[0:8673]\n", + "x_test = X[8674]\n", + "y_train = y_JP[0:8673]\n", + "y_test = list(y_JP[8674])\n", + "mnb.fit(x_train, y_train)\n", + "jpb_train = mnb.score (x_train,y_train)\n", + "jpb_test = mnb.score (x_train,y_train)\n", + "predmnb = mnb.predict(x_test)\n", + "print(\"J-P Results\")\n", + "print('Label J-P train score is :',jpb_train)\n", + "print('Label J-P test score is :',jpb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predmnb))\n", + "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", + "print('Prediction is: ', predmnb)\n", + "print('Actual is: ', y_test)\n", + "print(\"*\" * 100)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/your-project/code/testing quant on Darya.ipynb b/your-project/code/testing quant on Darya.ipynb new file mode 100644 index 00000000..9225f46e --- /dev/null +++ b/your-project/code/testing quant on Darya.ipynb @@ -0,0 +1,972 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# putting together friends dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import re\n", + "\n", + "import nltk\n", + "from nltk.stem import PorterStemmer\n", + "from nltk.stem import SnowballStemmer\n", + "from nltk import wordnet\n", + "from nltk.corpus import stopwords\n", + "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n", + "from sklearn.model_selection import train_test_split, cross_val_score\n", + "from sklearn.metrics import classification_report\n", + "from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score, roc_curve\n", + "\n", + "from sklearn.model_selection import train_test_split, cross_val_score\n", + "from sklearn.model_selection import cross_validate\n", + "from sklearn.model_selection import StratifiedKFold\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.model_selection import learning_curve\n", + "from sklearn.ensemble import ExtraTreesClassifier\n", + "from sklearn.decomposition import TruncatedSVD\n", + "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.naive_bayes import MultinomialNB\n", + "from sklearn.metrics import classification_report\n", + "from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score, roc_curve" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [], + "source": [ + "Darya = [\"Quarantine expectations. Swipe for reality\", \n", + " \"22 in 2020 - swipe to see a photo from yesterday. Age affects me.\", \n", + " \"hello? Santa? All I wish for Christmas is my mind back... \", \n", + " \"girl with a hat - a series. \", \n", + " \"casual day at uni. swipe for more idiots\", \n", + " \"thanking Steve for a nice iPhone front camera.\", \n", + " \"Missing this\", \n", + " \"*edit: still can‘t believe a sunscreen for 7.95 prevented me from having a huge sunburn\",\n", + " \"where I‘d rather be part 2\",\n", + " \"when people ask me if I‘ve got free time - I do. Except I don’t.\",\n", + " \n", + " \"just a small town girl, living in a capitalist world...\",\n", + " \"clueless\",\n", + " \"Might be serious, might be hungry. \",\n", + " \"you want blue? you want yellow?\",\n", + " \"While the wall fell nearly 30 years ago, I constantly fall on my face...\",\n", + " \"Im pretty ok with exams being over... \",\n", + " \"unpopular opinion: university can be fun. exhibit a\",\n", + " \"christmas is around the corner but all I‘m wishing for is an extention of deadlines...\",\n", + " \"on some occasions I even get out of the valley and LA to go sightseeing \",\n", + " \"diamonds are a girls best friend. Especially when you trade ‘em for plane tickets and uber rides\",\n", + " \n", + " \"not a threat, it’s a warning. (talking about the beginning of semester tomorrow)\",\n", + " \"come fly with me, let’s fly let’s fly away...\",\n", + " \"saltier than the Pacific\",\n", + " \"me, when I’m a) hangry or b) am studying @ nyfalosangeles @ williamroyal98\",\n", + " \"this whole you-gotta-study-while-the-sun-is-shining gets on my nerve. here’s an example of inner rage\",\n", + " \"you know you might have been spontaneous when while booking your flights they asked you if ou would want to check in for the flight back aswell.\",\n", + " \"Vitamins\",\n", + " \"finished the first semerster at HSG and I still can’t afford a porsche 911.\",\n", + " \"Me: you should be studying. Also me: enjoy the holidays\",\n", + " \"just kidding - I’m still a short ass little human\",\n", + " \n", + " \"week 2 at uni and my icloud and google drive are asking me to pay for more storage.\",\n", + " \"uni starts tomorrow and I already see myself either a) being lost cause I can't find the classroom or b) looking like Annabelle cause sleep is for the weak\",\n", + " \"oh boy\",\n", + " \"this should be my last week of summer but I think the weather decided to give me an early taste of fall already.\",\n", + " \"water falls, darya falls and just casually sit on floors cause standing is overrated \",\n", + " \"I promise I counted all the lights, but then I saw a squirrel and lost count \",\n", + " \"is it a set? Is it a real house? guess.\",\n", + " \"quiet on set\",\n", + " \"Baywatch, are you hiring?\",\n", + " \"What if perfume is actually bugspray but they want us to buy the nasty smelly stuff just to have a good laugh?\",\n", + " \n", + " \"I'm starting to wonder if spotlight can give you a tan cause the sun isn't coming to just yet. All jokes asid tho, I am beyond grateful to share the stage with so many talented people. Do what you love and you'll feel limitless \",\n", + " \"that second when the person you've been straing at looks at you and you quickly turn away, trying to look as casual as possible\",\n", + " \"at that moment I probably thought out a plan to convince my mom to paint one of my walls pink. even though I know it won't happen, happy #internationalwomensday ladies\",\n", + " \"Tell me about it, stud\",\n", + " \"nice to be back. bring it on 2017\"\n", + " \"2016. A year that had probably more faces than facebook. Victories and dissapointment, new friendships and new experiences; I was lucky enough to travel the several times and can't thank the people that made 2016 the year it was enough for all their kind words, laughter and happy tears.\",\n", + " \"don't mess with us - we have RG clubs\",\n", + " \"Summer is over and you're like: pardon me, what?\",\n", + " \"Looking back at those 5 weeks full of new friends, experiences and emotions puts a smile on my face. The teachers I've worked with inspired me every day, the people made my summer the best one yet. I love all of you and you'll always have a special place in my heart #obgottheheat and happy National Dance Day\",\n", + " \"everybody needs some attitude in their life #punintended #obgottheheat\"\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [], + "source": [ + "darya_test = {'name': ['Darya'], 'type': ['ESFP']}" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [], + "source": [ + "darya_row = pd.DataFrame(darya_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [], + "source": [ + "darya_row['text'] = ' '.join(Darya)" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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nametypetext
0DaryaESFPQuarantine expectations. Swipe for reality 22 ...
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" + ], + "text/plain": [ + " name type text\n", + "0 Darya ESFP Quarantine expectations. Swipe for reality 22 ..." + ] + }, + "execution_count": 102, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "darya_row" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [], + "source": [ + "def processed(x):\n", + " pattern = r'''(?i)\\b((?:https?://|www\\d{0,3}[.]|[a-z0-9.\\-]+[.][a-z]{2,4}/)(?:[^\\s()<>]+|\\(([^\\s()<>]+|(\\([^\\s()<>]+\\)))*\\))+(?:\\(([^\\s()<>]+|(\\([^\\s()<>]+\\)))*\\)|[^\\s`!()\\[\\]{};:'\".,<>?«»“”‘’]))'''\n", + " cleaned = re.sub(pattern, ' ', str(x)) \n", + " cleaned = re.sub(r\"[^A-Za-z0-9]+\", \" \", cleaned)\n", + " cleaned = re.sub(\" \\d+\", \" \", cleaned)\n", + " cleaned = cleaned.lower().strip()\n", + "\n", + " pattern2 = '\\w{1,}'\n", + " bag = re.findall(pattern2, cleaned)\n", + "\n", + " porter = PorterStemmer()\n", + " porter_bag = [porter.stem(word) for word in bag]\n", + "\n", + " lemmatizer = wordnet.WordNetLemmatizer()\n", + " bag_of_words = [lemmatizer.lemmatize(word) for word in porter_bag]\n", + "\n", + " stopwords_e = stopwords.words('english')\n", + " bag_of_words = [word for word in bag_of_words if word not in stopwords_e]\n", + "\n", + " return bag_of_words" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [], + "source": [ + "text_processed = [processed(i) for i in darya_row.text]" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [], + "source": [ + "darya_row['text_processed'] = text_processed" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [], + "source": [ + "darya_row['text_ready'] = darya_row.text_processed.apply(' '.join)" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, + "outputs": [], + "source": [ + "darya_row = darya_row.drop(columns = ['name', 'text', 'text_processed'])" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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0ESFPquarantin expect swipe realiti swipe see photo...
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" + ], + "text/plain": [ + " type text_ready\n", + "0 ESFP quarantin expect swipe realiti swipe see photo..." + ] + }, + "execution_count": 108, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "darya_row" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [], + "source": [ + "data = pd.read_csv(r'../data/personalities_cleaned.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [], + "source": [ + "data = data.drop(columns = ['Unnamed: 0', 'posts', 'text_processed'])" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": {}, + "outputs": [], + "source": [ + "data.drop(data.index[3559], inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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0INFJintj moment sportscent top ten play prank ha l...
1ENTPfind lack post veri alarm sex bore posit often...
2INTPgood one cours say know bless cur doe absolut ...
3INTJdear intp enjoy convers day esoter gab natur u...
4ENTJfire anoth silli misconcept approach logic go ...
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" + ], + "text/plain": [ + " type text_ready\n", + "0 INFJ intj moment sportscent top ten play prank ha l...\n", + "1 ENTP find lack post veri alarm sex bore posit often...\n", + "2 INTP good one cours say know bless cur doe absolut ...\n", + "3 INTJ dear intp enjoy convers day esoter gab natur u...\n", + "4 ENTJ fire anoth silli misconcept approach logic go ..." + ] + }, + "execution_count": 121, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": {}, + "outputs": [], + "source": [ + "data = data.append(darya_row, ignore_index = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": {}, + "outputs": [], + "source": [ + "data2 = data.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": {}, + "outputs": [], + "source": [ + "# cleaning and processing\n", + "\n", + "data2['I-E'] = data2['type'].astype(str).str[0]\n", + "data2['N-S'] = data2['type'].astype(str).str[1]\n", + "data2['T-F'] = data2['type'].astype(str).str[2]\n", + "data2['J-P'] = data2['type'].astype(str).str[3]" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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typetext_readyI-EN-ST-FJ-P
0INFJintj moment sportscent top ten play prank ha l...INFJ
1ENTPfind lack post veri alarm sex bore posit often...ENTP
2INTPgood one cours say know bless cur doe absolut ...INTP
3INTJdear intp enjoy convers day esoter gab natur u...INTJ
4ENTJfire anoth silli misconcept approach logic go ...ENTJ
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" + ], + "text/plain": [ + " type text_ready I-E N-S T-F J-P\n", + "0 INFJ intj moment sportscent top ten play prank ha l... I N F J\n", + "1 ENTP find lack post veri alarm sex bore posit often... E N T P\n", + "2 INTP good one cours say know bless cur doe absolut ... I N T P\n", + "3 INTJ dear intp enjoy convers day esoter gab natur u... I N T J\n", + "4 ENTJ fire anoth silli misconcept approach logic go ... E N T J" + ] + }, + "execution_count": 125, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data2.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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typetext_readyI-EN-ST-FJ-P
8671INTPmani question thing would take purpl pill pick...INTP
8672INFPveri conflict right come want child honestli m...INFP
8673INFPha long sinc personalitycaf although seem chan...INFP
8674ESFPquarantin expect swipe realiti swipe see photo...ESFP
8675ESFPquarantin expect swipe realiti swipe see photo...ESFP
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" + ], + "text/plain": [ + " type text_ready I-E N-S T-F J-P\n", + "8671 INTP mani question thing would take purpl pill pick... I N T P\n", + "8672 INFP veri conflict right come want child honestli m... I N F P\n", + "8673 INFP ha long sinc personalitycaf although seem chan... I N F P\n", + "8674 ESFP quarantin expect swipe realiti swipe see photo... E S F P\n", + "8675 ESFP quarantin expect swipe realiti swipe see photo... E S F P" + ] + }, + "execution_count": 126, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data2.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": {}, + "outputs": [], + "source": [ + "x = data2['text_ready']\n", + "\n", + "y_IE = data2['I-E']\n", + "y_NS = data2['N-S']\n", + "y_TF = data2['T-F']\n", + "y_JP = data2['J-P']" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.feature_extraction.text import CountVectorizer\n", + "\n", + "vector = CountVectorizer(ngram_range=(2, 2)).fit(x) \n", + "X = vector.transform(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": {}, + "outputs": [], + "source": [ + "y_test = list(y_IE[8675])" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'E'" + ] + }, + "execution_count": 130, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_test" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split, cross_val_score" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I-E RESULTS\n", + "Label I-E train score is : 0.7696564445469218\n", + "Label I-E test score is : 0.7696564445469218\n", + "Confusion Matrix for K-Nearest Neighbors:\n", + "[[0 1]\n", + " [0 0]]\n", + "Score: 0.0\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " E 0.00 0.00 0.00 1.0\n", + " I 0.00 0.00 0.00 0.0\n", + "\n", + " accuracy 0.00 1.0\n", + " macro avg 0.00 0.00 0.00 1.0\n", + "weighted avg 0.00 0.00 0.00 1.0\n", + "\n", + "Prediction is: ['I']\n", + "Actual is: ['E']\n", + "****************************************************************************************************\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n", + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "N-S RESULTS\n", + "Label N-S train score is : 0.8620013834447775\n", + "Label N-S test score is : 0.8620013834447775\n", + "Confusion Matrix for Random Forests:\n", + "[[0 0]\n", + " [1 0]]\n", + "Score: 0.0\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " N 0.00 0.00 0.00 0.0\n", + " S 0.00 0.00 0.00 1.0\n", + "\n", + " accuracy 0.00 1.0\n", + " macro avg 0.00 0.00 0.00 1.0\n", + "weighted avg 0.00 0.00 0.00 1.0\n", + "\n", + "Prediction is: ['N']\n", + "Actual is: ['S']\n", + "****************************************************************************************************\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n", + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "T-F Results\n", + "Label T-F train score is : 1.0\n", + "Label T-F test score is : 1.0\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[1]]\n", + "Score: 100.0\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " F 1.00 1.00 1.00 1\n", + "\n", + " accuracy 1.00 1\n", + " macro avg 1.00 1.00 1.00 1\n", + "weighted avg 1.00 1.00 1.00 1\n", + "\n", + "Prediction is: ['F']\n", + "Actual is: ['F']\n", + "****************************************************************************************************\n", + "J-P Results\n", + "Label J-P train score is : 1.0\n", + "Label J-P test score is : 1.0\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[0 0]\n", + " [1 0]]\n", + "Score: 0.0\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " J 0.00 0.00 0.00 0.0\n", + " P 0.00 0.00 0.00 1.0\n", + "\n", + " accuracy 0.00 1.0\n", + " macro avg 0.00 0.00 0.00 1.0\n", + "weighted avg 0.00 0.00 0.00 1.0\n", + "\n", + "Prediction is: ['J']\n", + "Actual is: ['P']\n", + "****************************************************************************************************\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n", + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n" + ] + } + ], + "source": [ + "from sklearn.neighbors import KNeighborsClassifier\n", + "knn = KNeighborsClassifier(n_neighbors=10) # see if can tweak accuracy\n", + "\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "rfn = RandomForestClassifier(max_depth=5, random_state=0) # see if can tweak accuracy\n", + "\n", + "from sklearn.naive_bayes import MultinomialNB\n", + "mnb = MultinomialNB()\n", + "\n", + "\n", + "\n", + "# IE\n", + "x_train = X[0:8674]\n", + "x_test = X[8675]\n", + "y_train = y_IE[0:8674]\n", + "y_test = list(y_IE[8675])\n", + "knn.fit(x_train, y_train)\n", + "ieb_train = knn.score (x_train,y_train)\n", + "ieb_test = knn.score (x_train,y_train)\n", + "predknn = knn.predict(x_test)\n", + "print(\"I-E RESULTS\")\n", + "print('Label I-E train score is :',ieb_train)\n", + "print('Label I-E test score is :',ieb_test)\n", + "print(\"Confusion Matrix for K-Nearest Neighbors:\")\n", + "print(confusion_matrix(y_test,predknn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predknn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predknn))\n", + "print('Prediction is: ', predknn)\n", + "print('Actual is: ', y_test)\n", + "print(\"*\" * 100)\n", + "\n", + "# NS\n", + "x_train = X[0:8674]\n", + "x_test = X[8675]\n", + "y_train = y_NS[0:8674]\n", + "y_test = list(y_NS[8675])\n", + "rfn.fit(x_train, y_train)\n", + "nsb_train = rfn.score (x_train,y_train)\n", + "nsb_test = rfn.score (x_train,y_train)\n", + "predrfn = rfn.predict(x_test)\n", + "print(\"N-S RESULTS\")\n", + "print('Label N-S train score is :',nsb_train)\n", + "print('Label N-S test score is :',nsb_test)\n", + "print(\"Confusion Matrix for Random Forests:\")\n", + "print(confusion_matrix(y_test,predrfn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predrfn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predrfn))\n", + "print('Prediction is: ', predrfn)\n", + "print('Actual is: ', y_test)\n", + "print(\"*\" * 100)\n", + "\n", + "# TF\n", + "x_train = X[0:8674]\n", + "x_test = X[8675]\n", + "y_train = y_TF[0:8674]\n", + "y_test = list(y_TF[8675])\n", + "mnb.fit(x_train, y_train)\n", + "tfb_train = mnb.score (x_train,y_train)\n", + "tfb_test = mnb.score (x_train,y_train)\n", + "predmnb = mnb.predict(x_test)\n", + "print(\"T-F Results\")\n", + "print('Label T-F train score is :',tfb_train)\n", + "print('Label T-F test score is :',tfb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predmnb))\n", + "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", + "print('Prediction is: ', predmnb)\n", + "print('Actual is: ', y_test)\n", + "print(\"*\" * 100)\n", + "\n", + "# JP\n", + "x_train = X[0:8674]\n", + "x_test = X[8675]\n", + "y_train = y_JP[0:8674]\n", + "y_test = list(y_JP[8675])\n", + "mnb.fit(x_train, y_train)\n", + "jpb_train = mnb.score (x_train,y_train)\n", + "jpb_test = mnb.score (x_train,y_train)\n", + "predmnb = mnb.predict(x_test)\n", + "print(\"J-P Results\")\n", + "print('Label J-P train score is :',jpb_train)\n", + "print('Label J-P test score is :',jpb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predmnb))\n", + "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", + "print('Prediction is: ', predmnb)\n", + "print('Actual is: ', y_test)\n", + "print(\"*\" * 100)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/your-project/code/testing quant on Laurents.ipynb b/your-project/code/testing quant on Laurents.ipynb new file mode 100644 index 00000000..9225f46e --- /dev/null +++ b/your-project/code/testing quant on Laurents.ipynb @@ -0,0 +1,972 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# putting together friends dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import re\n", + "\n", + "import nltk\n", + "from nltk.stem import PorterStemmer\n", + "from nltk.stem import SnowballStemmer\n", + "from nltk import wordnet\n", + "from nltk.corpus import stopwords\n", + "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n", + "from sklearn.model_selection import train_test_split, cross_val_score\n", + "from sklearn.metrics import classification_report\n", + "from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score, roc_curve\n", + "\n", + "from sklearn.model_selection import train_test_split, cross_val_score\n", + "from sklearn.model_selection import cross_validate\n", + "from sklearn.model_selection import StratifiedKFold\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.model_selection import learning_curve\n", + "from sklearn.ensemble import ExtraTreesClassifier\n", + "from sklearn.decomposition import TruncatedSVD\n", + "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.naive_bayes import MultinomialNB\n", + "from sklearn.metrics import classification_report\n", + "from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score, roc_curve" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [], + "source": [ + "Darya = [\"Quarantine expectations. Swipe for reality\", \n", + " \"22 in 2020 - swipe to see a photo from yesterday. Age affects me.\", \n", + " \"hello? Santa? All I wish for Christmas is my mind back... \", \n", + " \"girl with a hat - a series. \", \n", + " \"casual day at uni. swipe for more idiots\", \n", + " \"thanking Steve for a nice iPhone front camera.\", \n", + " \"Missing this\", \n", + " \"*edit: still can‘t believe a sunscreen for 7.95 prevented me from having a huge sunburn\",\n", + " \"where I‘d rather be part 2\",\n", + " \"when people ask me if I‘ve got free time - I do. Except I don’t.\",\n", + " \n", + " \"just a small town girl, living in a capitalist world...\",\n", + " \"clueless\",\n", + " \"Might be serious, might be hungry. \",\n", + " \"you want blue? you want yellow?\",\n", + " \"While the wall fell nearly 30 years ago, I constantly fall on my face...\",\n", + " \"Im pretty ok with exams being over... \",\n", + " \"unpopular opinion: university can be fun. exhibit a\",\n", + " \"christmas is around the corner but all I‘m wishing for is an extention of deadlines...\",\n", + " \"on some occasions I even get out of the valley and LA to go sightseeing \",\n", + " \"diamonds are a girls best friend. Especially when you trade ‘em for plane tickets and uber rides\",\n", + " \n", + " \"not a threat, it’s a warning. (talking about the beginning of semester tomorrow)\",\n", + " \"come fly with me, let’s fly let’s fly away...\",\n", + " \"saltier than the Pacific\",\n", + " \"me, when I’m a) hangry or b) am studying @ nyfalosangeles @ williamroyal98\",\n", + " \"this whole you-gotta-study-while-the-sun-is-shining gets on my nerve. here’s an example of inner rage\",\n", + " \"you know you might have been spontaneous when while booking your flights they asked you if ou would want to check in for the flight back aswell.\",\n", + " \"Vitamins\",\n", + " \"finished the first semerster at HSG and I still can’t afford a porsche 911.\",\n", + " \"Me: you should be studying. Also me: enjoy the holidays\",\n", + " \"just kidding - I’m still a short ass little human\",\n", + " \n", + " \"week 2 at uni and my icloud and google drive are asking me to pay for more storage.\",\n", + " \"uni starts tomorrow and I already see myself either a) being lost cause I can't find the classroom or b) looking like Annabelle cause sleep is for the weak\",\n", + " \"oh boy\",\n", + " \"this should be my last week of summer but I think the weather decided to give me an early taste of fall already.\",\n", + " \"water falls, darya falls and just casually sit on floors cause standing is overrated \",\n", + " \"I promise I counted all the lights, but then I saw a squirrel and lost count \",\n", + " \"is it a set? Is it a real house? guess.\",\n", + " \"quiet on set\",\n", + " \"Baywatch, are you hiring?\",\n", + " \"What if perfume is actually bugspray but they want us to buy the nasty smelly stuff just to have a good laugh?\",\n", + " \n", + " \"I'm starting to wonder if spotlight can give you a tan cause the sun isn't coming to just yet. All jokes asid tho, I am beyond grateful to share the stage with so many talented people. Do what you love and you'll feel limitless \",\n", + " \"that second when the person you've been straing at looks at you and you quickly turn away, trying to look as casual as possible\",\n", + " \"at that moment I probably thought out a plan to convince my mom to paint one of my walls pink. even though I know it won't happen, happy #internationalwomensday ladies\",\n", + " \"Tell me about it, stud\",\n", + " \"nice to be back. bring it on 2017\"\n", + " \"2016. A year that had probably more faces than facebook. Victories and dissapointment, new friendships and new experiences; I was lucky enough to travel the several times and can't thank the people that made 2016 the year it was enough for all their kind words, laughter and happy tears.\",\n", + " \"don't mess with us - we have RG clubs\",\n", + " \"Summer is over and you're like: pardon me, what?\",\n", + " \"Looking back at those 5 weeks full of new friends, experiences and emotions puts a smile on my face. The teachers I've worked with inspired me every day, the people made my summer the best one yet. I love all of you and you'll always have a special place in my heart #obgottheheat and happy National Dance Day\",\n", + " \"everybody needs some attitude in their life #punintended #obgottheheat\"\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [], + "source": [ + "darya_test = {'name': ['Darya'], 'type': ['ESFP']}" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [], + "source": [ + "darya_row = pd.DataFrame(darya_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [], + "source": [ + "darya_row['text'] = ' '.join(Darya)" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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0DaryaESFPQuarantine expectations. Swipe for reality 22 ...
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" + ], + "text/plain": [ + " name type text\n", + "0 Darya ESFP Quarantine expectations. Swipe for reality 22 ..." + ] + }, + "execution_count": 102, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "darya_row" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [], + "source": [ + "def processed(x):\n", + " pattern = r'''(?i)\\b((?:https?://|www\\d{0,3}[.]|[a-z0-9.\\-]+[.][a-z]{2,4}/)(?:[^\\s()<>]+|\\(([^\\s()<>]+|(\\([^\\s()<>]+\\)))*\\))+(?:\\(([^\\s()<>]+|(\\([^\\s()<>]+\\)))*\\)|[^\\s`!()\\[\\]{};:'\".,<>?«»“”‘’]))'''\n", + " cleaned = re.sub(pattern, ' ', str(x)) \n", + " cleaned = re.sub(r\"[^A-Za-z0-9]+\", \" \", cleaned)\n", + " cleaned = re.sub(\" \\d+\", \" \", cleaned)\n", + " cleaned = cleaned.lower().strip()\n", + "\n", + " pattern2 = '\\w{1,}'\n", + " bag = re.findall(pattern2, cleaned)\n", + "\n", + " porter = PorterStemmer()\n", + " porter_bag = [porter.stem(word) for word in bag]\n", + "\n", + " lemmatizer = wordnet.WordNetLemmatizer()\n", + " bag_of_words = [lemmatizer.lemmatize(word) for word in porter_bag]\n", + "\n", + " stopwords_e = stopwords.words('english')\n", + " bag_of_words = [word for word in bag_of_words if word not in stopwords_e]\n", + "\n", + " return bag_of_words" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [], + "source": [ + "text_processed = [processed(i) for i in darya_row.text]" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [], + "source": [ + "darya_row['text_processed'] = text_processed" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [], + "source": [ + "darya_row['text_ready'] = darya_row.text_processed.apply(' '.join)" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, + "outputs": [], + "source": [ + "darya_row = darya_row.drop(columns = ['name', 'text', 'text_processed'])" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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0ESFPquarantin expect swipe realiti swipe see photo...
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" + ], + "text/plain": [ + " type text_ready\n", + "0 ESFP quarantin expect swipe realiti swipe see photo..." + ] + }, + "execution_count": 108, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "darya_row" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [], + "source": [ + "data = pd.read_csv(r'../data/personalities_cleaned.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [], + "source": [ + "data = data.drop(columns = ['Unnamed: 0', 'posts', 'text_processed'])" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": {}, + "outputs": [], + "source": [ + "data.drop(data.index[3559], inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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0INFJintj moment sportscent top ten play prank ha l...
1ENTPfind lack post veri alarm sex bore posit often...
2INTPgood one cours say know bless cur doe absolut ...
3INTJdear intp enjoy convers day esoter gab natur u...
4ENTJfire anoth silli misconcept approach logic go ...
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" + ], + "text/plain": [ + " type text_ready\n", + "0 INFJ intj moment sportscent top ten play prank ha l...\n", + "1 ENTP find lack post veri alarm sex bore posit often...\n", + "2 INTP good one cours say know bless cur doe absolut ...\n", + "3 INTJ dear intp enjoy convers day esoter gab natur u...\n", + "4 ENTJ fire anoth silli misconcept approach logic go ..." + ] + }, + "execution_count": 121, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": {}, + "outputs": [], + "source": [ + "data = data.append(darya_row, ignore_index = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": {}, + "outputs": [], + "source": [ + "data2 = data.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": {}, + "outputs": [], + "source": [ + "# cleaning and processing\n", + "\n", + "data2['I-E'] = data2['type'].astype(str).str[0]\n", + "data2['N-S'] = data2['type'].astype(str).str[1]\n", + "data2['T-F'] = data2['type'].astype(str).str[2]\n", + "data2['J-P'] = data2['type'].astype(str).str[3]" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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typetext_readyI-EN-ST-FJ-P
0INFJintj moment sportscent top ten play prank ha l...INFJ
1ENTPfind lack post veri alarm sex bore posit often...ENTP
2INTPgood one cours say know bless cur doe absolut ...INTP
3INTJdear intp enjoy convers day esoter gab natur u...INTJ
4ENTJfire anoth silli misconcept approach logic go ...ENTJ
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" + ], + "text/plain": [ + " type text_ready I-E N-S T-F J-P\n", + "0 INFJ intj moment sportscent top ten play prank ha l... I N F J\n", + "1 ENTP find lack post veri alarm sex bore posit often... E N T P\n", + "2 INTP good one cours say know bless cur doe absolut ... I N T P\n", + "3 INTJ dear intp enjoy convers day esoter gab natur u... I N T J\n", + "4 ENTJ fire anoth silli misconcept approach logic go ... E N T J" + ] + }, + "execution_count": 125, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data2.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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typetext_readyI-EN-ST-FJ-P
8671INTPmani question thing would take purpl pill pick...INTP
8672INFPveri conflict right come want child honestli m...INFP
8673INFPha long sinc personalitycaf although seem chan...INFP
8674ESFPquarantin expect swipe realiti swipe see photo...ESFP
8675ESFPquarantin expect swipe realiti swipe see photo...ESFP
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" + ], + "text/plain": [ + " type text_ready I-E N-S T-F J-P\n", + "8671 INTP mani question thing would take purpl pill pick... I N T P\n", + "8672 INFP veri conflict right come want child honestli m... I N F P\n", + "8673 INFP ha long sinc personalitycaf although seem chan... I N F P\n", + "8674 ESFP quarantin expect swipe realiti swipe see photo... E S F P\n", + "8675 ESFP quarantin expect swipe realiti swipe see photo... E S F P" + ] + }, + "execution_count": 126, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data2.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": {}, + "outputs": [], + "source": [ + "x = data2['text_ready']\n", + "\n", + "y_IE = data2['I-E']\n", + "y_NS = data2['N-S']\n", + "y_TF = data2['T-F']\n", + "y_JP = data2['J-P']" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.feature_extraction.text import CountVectorizer\n", + "\n", + "vector = CountVectorizer(ngram_range=(2, 2)).fit(x) \n", + "X = vector.transform(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": {}, + "outputs": [], + "source": [ + "y_test = list(y_IE[8675])" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'E'" + ] + }, + "execution_count": 130, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_test" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split, cross_val_score" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I-E RESULTS\n", + "Label I-E train score is : 0.7696564445469218\n", + "Label I-E test score is : 0.7696564445469218\n", + "Confusion Matrix for K-Nearest Neighbors:\n", + "[[0 1]\n", + " [0 0]]\n", + "Score: 0.0\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " E 0.00 0.00 0.00 1.0\n", + " I 0.00 0.00 0.00 0.0\n", + "\n", + " accuracy 0.00 1.0\n", + " macro avg 0.00 0.00 0.00 1.0\n", + "weighted avg 0.00 0.00 0.00 1.0\n", + "\n", + "Prediction is: ['I']\n", + "Actual is: ['E']\n", + "****************************************************************************************************\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n", + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "N-S RESULTS\n", + "Label N-S train score is : 0.8620013834447775\n", + "Label N-S test score is : 0.8620013834447775\n", + "Confusion Matrix for Random Forests:\n", + "[[0 0]\n", + " [1 0]]\n", + "Score: 0.0\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " N 0.00 0.00 0.00 0.0\n", + " S 0.00 0.00 0.00 1.0\n", + "\n", + " accuracy 0.00 1.0\n", + " macro avg 0.00 0.00 0.00 1.0\n", + "weighted avg 0.00 0.00 0.00 1.0\n", + "\n", + "Prediction is: ['N']\n", + "Actual is: ['S']\n", + "****************************************************************************************************\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n", + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "T-F Results\n", + "Label T-F train score is : 1.0\n", + "Label T-F test score is : 1.0\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[1]]\n", + "Score: 100.0\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " F 1.00 1.00 1.00 1\n", + "\n", + " accuracy 1.00 1\n", + " macro avg 1.00 1.00 1.00 1\n", + "weighted avg 1.00 1.00 1.00 1\n", + "\n", + "Prediction is: ['F']\n", + "Actual is: ['F']\n", + "****************************************************************************************************\n", + "J-P Results\n", + "Label J-P train score is : 1.0\n", + "Label J-P test score is : 1.0\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[0 0]\n", + " [1 0]]\n", + "Score: 0.0\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " J 0.00 0.00 0.00 0.0\n", + " P 0.00 0.00 0.00 1.0\n", + "\n", + " accuracy 0.00 1.0\n", + " macro avg 0.00 0.00 0.00 1.0\n", + "weighted avg 0.00 0.00 0.00 1.0\n", + "\n", + "Prediction is: ['J']\n", + "Actual is: ['P']\n", + "****************************************************************************************************\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n", + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n" + ] + } + ], + "source": [ + "from sklearn.neighbors import KNeighborsClassifier\n", + "knn = KNeighborsClassifier(n_neighbors=10) # see if can tweak accuracy\n", + "\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "rfn = RandomForestClassifier(max_depth=5, random_state=0) # see if can tweak accuracy\n", + "\n", + "from sklearn.naive_bayes import MultinomialNB\n", + "mnb = MultinomialNB()\n", + "\n", + "\n", + "\n", + "# IE\n", + "x_train = X[0:8674]\n", + "x_test = X[8675]\n", + "y_train = y_IE[0:8674]\n", + "y_test = list(y_IE[8675])\n", + "knn.fit(x_train, y_train)\n", + "ieb_train = knn.score (x_train,y_train)\n", + "ieb_test = knn.score (x_train,y_train)\n", + "predknn = knn.predict(x_test)\n", + "print(\"I-E RESULTS\")\n", + "print('Label I-E train score is :',ieb_train)\n", + "print('Label I-E test score is :',ieb_test)\n", + "print(\"Confusion Matrix for K-Nearest Neighbors:\")\n", + "print(confusion_matrix(y_test,predknn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predknn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predknn))\n", + "print('Prediction is: ', predknn)\n", + "print('Actual is: ', y_test)\n", + "print(\"*\" * 100)\n", + "\n", + "# NS\n", + "x_train = X[0:8674]\n", + "x_test = X[8675]\n", + "y_train = y_NS[0:8674]\n", + "y_test = list(y_NS[8675])\n", + "rfn.fit(x_train, y_train)\n", + "nsb_train = rfn.score (x_train,y_train)\n", + "nsb_test = rfn.score (x_train,y_train)\n", + "predrfn = rfn.predict(x_test)\n", + "print(\"N-S RESULTS\")\n", + "print('Label N-S train score is :',nsb_train)\n", + "print('Label N-S test score is :',nsb_test)\n", + "print(\"Confusion Matrix for Random Forests:\")\n", + "print(confusion_matrix(y_test,predrfn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predrfn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predrfn))\n", + "print('Prediction is: ', predrfn)\n", + "print('Actual is: ', y_test)\n", + "print(\"*\" * 100)\n", + "\n", + "# TF\n", + "x_train = X[0:8674]\n", + "x_test = X[8675]\n", + "y_train = y_TF[0:8674]\n", + "y_test = list(y_TF[8675])\n", + "mnb.fit(x_train, y_train)\n", + "tfb_train = mnb.score (x_train,y_train)\n", + "tfb_test = mnb.score (x_train,y_train)\n", + "predmnb = mnb.predict(x_test)\n", + "print(\"T-F Results\")\n", + "print('Label T-F train score is :',tfb_train)\n", + "print('Label T-F test score is :',tfb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predmnb))\n", + "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", + "print('Prediction is: ', predmnb)\n", + "print('Actual is: ', y_test)\n", + "print(\"*\" * 100)\n", + "\n", + "# JP\n", + "x_train = X[0:8674]\n", + "x_test = X[8675]\n", + "y_train = y_JP[0:8674]\n", + "y_test = list(y_JP[8675])\n", + "mnb.fit(x_train, y_train)\n", + "jpb_train = mnb.score (x_train,y_train)\n", + "jpb_test = mnb.score (x_train,y_train)\n", + "predmnb = mnb.predict(x_test)\n", + "print(\"J-P Results\")\n", + "print('Label J-P train score is :',jpb_train)\n", + "print('Label J-P test score is :',jpb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predmnb))\n", + "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", + "print('Prediction is: ', predmnb)\n", + "print('Actual is: ', y_test)\n", + "print(\"*\" * 100)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/your-project/data/quant_personalities.csv b/your-project/data/quant_personalities.csv new file mode 100644 index 00000000..38776a3a --- /dev/null +++ b/your-project/data/quant_personalities.csv @@ -0,0 +1,8676 @@ +,type,length_comment,var_length,links_count,qm_count,exm_count,img_count,music_count,dots_count,ht_count,me_count,I-E,N-S,T-F,J-P +0,INFJ,11.12,0.17974993327512315,0.48,0.36,0.06,0.12,0.02,0.3,0.0,1.18,1,0,0,0 +1,ENTP,23.4,0.184468936246192,0.2,0.1,0.0,0.02,0.0,0.38,0.0,3.06,0,0,1,1 +2,INTP,16.72,0.1840100504234724,0.1,0.24,0.08,0.0,0.0,0.26,0.0,1.44,1,0,1,1 +3,INTJ,21.28,0.18663994130415476,0.04,0.22,0.06,0.0,0.02,0.52,0.0,2.18,1,0,1,0 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+8632,ISTJ,19.98,0.19258924589927884,0.12,0.26,0.16,0.08,0.0,0.48,0.02,1.86,1,1,1,0 +8633,ENTP,31.88,0.182730461571733,0.0,0.22,0.14,0.0,0.0,0.76,0.0,3.8,0,0,1,1 +8634,INFP,24.66,0.18260595121793205,0.04,0.28,0.06,0.02,0.02,0.68,0.0,1.96,1,0,0,1 +8635,INFJ,25.58,0.19222579191663425,0.0,0.18,0.08,0.0,0.0,0.56,0.0,2.7,1,0,0,0 +8636,INFP,31.28,0.18367728844478787,0.02,0.02,0.1,0.0,0.06,0.7,0.0,3.52,1,0,0,1 +8637,INTJ,32.22,0.18198286048009277,0.02,0.2,0.26,0.0,0.0,0.9,0.0,3.76,1,0,1,0 +8638,INFP,18.28,0.1824661638511809,0.0,0.38,0.22,0.0,0.0,0.3,0.0,1.46,1,0,0,1 +8639,ENTP,27.82,0.19446765681494393,0.04,0.52,0.36,0.02,0.0,1.36,0.0,3.26,0,0,1,1 +8640,INTP,24.48,0.1836360882156383,0.0,0.1,0.04,0.0,0.02,0.74,0.0,2.64,1,0,1,1 +8641,INFP,27.14,0.1832478821464601,0.02,0.1,0.16,0.0,0.02,0.56,0.0,3.3,1,0,0,1 +8642,INFJ,26.02,0.1811412469829075,0.08,0.1,0.1,0.0,0.02,0.58,0.0,2.26,1,0,0,0 +8643,ENTP,23.02,0.1807069509387542,0.0,0.4,0.24,0.0,0.0,0.32,0.0,2.78,0,0,1,1 +8644,INFJ,29.8,0.17959735245241162,0.02,0.08,0.12,0.0,0.0,0.68,0.02,3.1,1,0,0,0 +8645,INFJ,29.8,0.18418355945490292,0.2,0.3,0.42,0.02,0.0,0.8,0.0,3.22,1,0,0,0 +8646,INFP,27.16,0.1850431497960104,0.02,0.08,0.3,0.0,0.08,0.62,0.0,1.86,1,0,0,1 +8647,INTP,27.66,0.18725960019622231,0.0,0.16,0.04,0.0,0.0,0.58,0.02,3.4,1,0,1,1 +8648,INFP,25.06,0.1841573429881064,0.18,0.24,0.26,0.12,0.04,0.7,0.1,2.9,1,0,0,1 +8649,INFP,5.32,0.18788687619846342,0.74,0.66,0.02,0.0,0.0,0.2,0.0,0.5,1,0,0,1 +8650,INFJ,22.64,0.18464236271220386,0.1,0.24,0.12,0.06,0.02,0.42,0.0,2.38,1,0,0,0 +8651,ISTP,26.36,0.18470222204030526,0.02,0.3,0.0,0.0,0.0,0.72,0.0,2.62,1,1,1,1 +8652,ISFJ,28.6,0.17545516662159855,0.04,0.12,0.26,0.0,0.0,0.7,0.0,2.54,1,1,0,0 +8653,INFP,32.72,0.18537392023930618,0.06,0.18,0.06,0.0,0.0,0.8,0.0,3.9,1,0,0,1 +8654,ISTJ,31.28,0.1867194228336967,0.0,0.24,0.28,0.0,0.0,0.7,0.0,3.78,1,1,1,0 +8655,INFJ,26.16,0.1842586830917353,0.14,0.44,0.12,0.0,0.06,0.76,0.0,2.86,1,0,0,0 +8656,INFJ,30.52,0.1818836355970476,0.02,0.36,0.24,0.0,0.02,0.86,0.0,2.84,1,0,0,0 +8657,INTJ,27.58,0.17884007398652574,0.0,0.06,0.04,0.0,0.0,0.58,0.0,1.4,1,0,1,0 +8658,ESFJ,35.18,0.17566165552535348,0.0,0.16,0.06,0.0,0.0,1.0,0.0,3.3,0,1,0,0 +8659,ENFP,26.24,0.18887549650981078,0.02,0.2,0.04,0.0,0.04,0.58,0.0,3.12,0,0,0,1 +8660,INFP,21.74,0.17792113500915882,0.08,0.22,0.0,0.0,0.0,0.74,0.0,1.78,1,0,0,1 +8661,ENTP,31.52,0.19419433983404946,0.0,0.22,0.06,0.0,0.0,1.4,0.0,3.96,0,0,1,1 +8662,INTJ,26.54,0.18370862663923432,0.04,0.12,0.04,0.0,0.0,0.54,0.0,3.5,1,0,1,0 +8663,INTP,15.36,0.17813657294424518,0.04,0.12,0.0,0.0,0.0,0.16,0.02,1.34,1,0,1,1 +8664,INTP,22.68,0.1851030618857531,0.06,0.08,0.0,0.0,0.0,0.74,0.0,3.42,1,0,1,1 +8665,ENTP,27.4,0.18490749547484506,0.02,0.16,0.18,0.0,0.0,0.86,0.0,2.38,0,0,1,1 +8666,INTJ,22.82,0.18923119302944216,0.08,0.28,0.22,0.0,0.08,0.36,0.0,2.68,1,0,1,0 +8667,ENTP,28.66,0.1810649821263962,0.02,0.32,0.28,0.0,0.0,0.72,0.0,2.18,0,0,1,1 +8668,INTJ,22.42,0.1779959049195191,0.12,0.28,0.1,0.0,0.02,0.38,0.0,2.12,1,0,1,0 +8669,INFJ,23.8,0.18421415366904026,0.3,0.28,0.22,0.18,0.02,0.8,0.02,2.7,1,0,0,0 +8670,ISFP,15.92,0.1805570749738721,0.14,0.18,0.12,0.02,0.0,0.14,0.0,1.78,1,1,0,1 +8671,ENFP,26.18,0.19151461900306085,0.04,0.2,0.66,0.0,0.0,0.82,0.02,3.28,0,0,0,1 +8672,INTP,18.96,0.18654349215679777,0.04,0.18,0.02,0.0,0.0,0.38,0.0,1.48,1,0,1,1 +8673,INFP,34.1,0.18756591783541904,0.0,0.18,0.06,0.0,0.0,0.94,0.0,4.18,1,0,0,1 +8674,INFP,27.22,0.18793735567467182,0.06,0.12,0.1,0.0,0.0,0.48,0.0,3.54,1,0,0,1 From cf46a380a3b28e6c872abcb0a51df8a19ea91238 Mon Sep 17 00:00:00 2001 From: Vicky Zauner Date: Thu, 28 May 2020 11:24:33 +0200 Subject: [PATCH 2/9] cleaned up and created rl dataframe --- ...- Splitting Targets | NLP Analysis 2.ipynb | 1059 ++++++++++++--- .../code/Code 7 - Real Life Data.ipynb | 1130 +++++++++++++++++ your-project/code/MASTER NOTEBOOK.ipynb | 32 + your-project/code/testing nlp on Darya.ipynb | 972 -------------- .../code/testing nlp on Laurents.ipynb | 972 -------------- .../code/testing nlp on Theresa.ipynb | 972 -------------- .../code/testing quant on Darya.ipynb | 972 -------------- .../code/testing quant on Laurents.ipynb | 972 -------------- your-project/data/reallife_data_insta.csv | 8 + 9 files changed, 2028 insertions(+), 5061 deletions(-) create mode 100644 your-project/code/Code 7 - Real Life Data.ipynb create mode 100644 your-project/code/MASTER NOTEBOOK.ipynb delete mode 100644 your-project/code/testing nlp on Darya.ipynb delete mode 100644 your-project/code/testing nlp on Laurents.ipynb delete mode 100644 your-project/code/testing nlp on Theresa.ipynb delete mode 100644 your-project/code/testing quant on Darya.ipynb delete mode 100644 your-project/code/testing quant on Laurents.ipynb create mode 100644 your-project/data/reallife_data_insta.csv diff --git a/your-project/code/Code 3 - Splitting Targets | NLP Analysis 2.ipynb b/your-project/code/Code 3 - Splitting Targets | NLP Analysis 2.ipynb index 79721b08..2d0ca262 100644 --- a/your-project/code/Code 3 - Splitting Targets | NLP Analysis 2.ipynb +++ b/your-project/code/Code 3 - Splitting Targets | NLP Analysis 2.ipynb @@ -336,7 +336,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -389,45 +389,70 @@ "output_type": "stream", "text": [ "I-E Accuracy for 1 neigbours.\n", - "Label I-E test score is : 0.7636887608069164\n", + "Label I-E test score is : 0.6789625360230548\n", "Confusion Matrix for KNeighbors:\n", - "[[ 1 410]\n", - " [ 0 1324]]\n", + "[[ 99 284]\n", + " [ 273 1079]]\n", "I-E Accuracy for 3 neigbours.\n", - "Label I-E test score is : 0.768299711815562\n", + "Label I-E test score is : 0.7152737752161383\n", "Confusion Matrix for KNeighbors:\n", - "[[ 1 400]\n", - " [ 2 1332]]\n", + "[[ 74 335]\n", + " [ 159 1167]]\n", "I-E Accuracy for 5 neigbours.\n", - "Label I-E test score is : 0.7648414985590778\n", + "Label I-E test score is : 0.7527377521613833\n", "Confusion Matrix for KNeighbors:\n", - "[[ 0 408]\n", - " [ 0 1327]]\n", + "[[ 41 346]\n", + " [ 83 1265]]\n", "I-E Accuracy for 7 neigbours.\n", - "Label I-E test score is : 0.7769452449567723\n", + "Label I-E test score is : 0.7440922190201729\n", "Confusion Matrix for KNeighbors:\n", - "[[ 0 385]\n", - " [ 2 1348]]\n", + "[[ 26 388]\n", + " [ 56 1265]]\n", "I-E Accuracy for 9 neigbours.\n", - "Label I-E test score is : 0.7711815561959654\n", + "Label I-E test score is : 0.7510086455331412\n", "Confusion Matrix for KNeighbors:\n", - "[[ 0 397]\n", - " [ 0 1338]]\n", + "[[ 21 383]\n", + " [ 49 1282]]\n", "I-E Accuracy for 11 neigbours.\n", - "Label I-E test score is : 0.7757925072046109\n", + "Label I-E test score is : 0.7538904899135447\n", "Confusion Matrix for KNeighbors:\n", - "[[ 0 389]\n", - " [ 0 1346]]\n", + "[[ 17 387]\n", + " [ 40 1291]]\n", "I-E Accuracy for 13 neigbours.\n", - "Label I-E test score is : 0.7694524495677233\n", + "Label I-E test score is : 0.7619596541786744\n", "Confusion Matrix for KNeighbors:\n", - "[[ 0 400]\n", - " [ 0 1335]]\n", + "[[ 9 393]\n", + " [ 20 1313]]\n", "I-E Accuracy for 15 neigbours.\n", - "Label I-E test score is : 0.7769452449567723\n", + "Label I-E test score is : 0.7665706051873199\n", + "Confusion Matrix for KNeighbors:\n", + "[[ 6 389]\n", + " [ 16 1324]]\n", + "I-E Accuracy for 17 neigbours.\n", + "Label I-E test score is : 0.7631123919308357\n", + "Confusion Matrix for KNeighbors:\n", + "[[ 8 398]\n", + " [ 13 1316]]\n", + "I-E Accuracy for 19 neigbours.\n", + "Label I-E test score is : 0.7648414985590778\n", + "Confusion Matrix for KNeighbors:\n", + "[[ 8 400]\n", + " [ 8 1319]]\n", + "I-E Accuracy for 21 neigbours.\n", + "Label I-E test score is : 0.7596541786743516\n", + "Confusion Matrix for KNeighbors:\n", + "[[ 6 411]\n", + " [ 6 1312]]\n", + "I-E Accuracy for 23 neigbours.\n", + "Label I-E test score is : 0.7636887608069164\n", "Confusion Matrix for KNeighbors:\n", - "[[ 0 387]\n", - " [ 0 1348]]\n" + "[[ 2 402]\n", + " [ 8 1323]]\n", + "I-E Accuracy for 25 neigbours.\n", + "Label I-E test score is : 0.7711815561959654\n", + "Confusion Matrix for KNeighbors:\n", + "[[ 2 393]\n", + " [ 4 1336]]\n" ] } ], @@ -435,7 +460,7 @@ "from sklearn.neighbors import KNeighborsClassifier\n", "\n", "\n", - "for x in range(1, 16, 2):\n", + "for x in range(1, 26, 2):\n", " x_train, x_test, y_train, y_test = train_test_split(reduced, y_IE, test_size=0.2)\n", " knn = KNeighborsClassifier(n_neighbors = x)\n", " clf = knn.fit(x_train, y_train)\n", @@ -451,7 +476,192 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I-E Accuracy for 1 neigbours.\n", + "Label I-E test score is : 0.6651296829971182\n", + "Confusion Matrix for KNeighbors:\n", + "[[ 96 300]\n", + " [ 281 1058]]\n", + "I-E Accuracy for 3 neigbours.\n", + "Label I-E test score is : 0.6927953890489914\n", + "Confusion Matrix for KNeighbors:\n", + "[[ 56 382]\n", + " [ 151 1146]]\n", + "I-E Accuracy for 5 neigbours.\n", + "Label I-E test score is : 0.7446685878962536\n", + "Confusion Matrix for KNeighbors:\n", + "[[ 34 368]\n", + " [ 75 1258]]\n", + "I-E Accuracy for 7 neigbours.\n", + "Label I-E test score is : 0.7544668587896254\n", + "Confusion Matrix for KNeighbors:\n", + "[[ 24 362]\n", + " [ 64 1285]]\n", + "I-E Accuracy for 9 neigbours.\n", + "Label I-E test score is : 0.7463976945244957\n", + "Confusion Matrix for KNeighbors:\n", + "[[ 12 399]\n", + " [ 41 1283]]\n", + "I-E Accuracy for 11 neigbours.\n", + "Label I-E test score is : 0.7619596541786744\n", + "Confusion Matrix for KNeighbors:\n", + "[[ 11 380]\n", + " [ 33 1311]]\n", + "I-E Accuracy for 13 neigbours.\n", + "Label I-E test score is : 0.7487031700288185\n", + "Confusion Matrix for KNeighbors:\n", + "[[ 9 422]\n", + " [ 14 1290]]\n", + "I-E Accuracy for 15 neigbours.\n", + "Label I-E test score is : 0.7706051873198847\n", + "Confusion Matrix for KNeighbors:\n", + "[[ 7 384]\n", + " [ 14 1330]]\n", + "I-E Accuracy for 17 neigbours.\n", + "Label I-E test score is : 0.7665706051873199\n", + "Confusion Matrix for KNeighbors:\n", + "[[ 7 393]\n", + " [ 12 1323]]\n", + "I-E Accuracy for 19 neigbours.\n", + "Label I-E test score is : 0.7590778097982709\n", + "Confusion Matrix for KNeighbors:\n", + "[[ 3 414]\n", + " [ 4 1314]]\n", + "I-E Accuracy for 21 neigbours.\n", + "Label I-E test score is : 0.7544668587896254\n", + "Confusion Matrix for KNeighbors:\n", + "[[ 7 419]\n", + " [ 7 1302]]\n", + "I-E Accuracy for 23 neigbours.\n", + "Label I-E test score is : 0.777521613832853\n", + "Confusion Matrix for KNeighbors:\n", + "[[ 2 380]\n", + " [ 6 1347]]\n", + "I-E Accuracy for 25 neigbours.\n", + "Label I-E test score is : 0.7648414985590778\n", + "Confusion Matrix for KNeighbors:\n", + "[[ 5 406]\n", + " [ 2 1322]]\n" + ] + } + ], + "source": [ + "from sklearn.neighbors import KNeighborsClassifier\n", + "\n", + "\n", + "for x in range(1, 26, 2):\n", + " x_train, x_test, y_train, y_test = train_test_split(scaled, y_IE, test_size=0.2)\n", + " knn = KNeighborsClassifier(n_neighbors = x)\n", + " clf = knn.fit(x_train, y_train)\n", + " predknn = clf.predict(x_test)\n", + " print(\"I-E Accuracy for \", x, \" neigbours.\")\n", + " print('Label I-E test score is :',accuracy_score(y_test, predknn))\n", + " print(\"Confusion Matrix for KNeighbors:\")\n", + " print(confusion_matrix(y_test, predknn))" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I-E Accuracy for 1 max_depth.\n", + "[[1492 0]\n", + " [ 243 0]]\n", + "Score: 85.99\n", + "I-E Accuracy for 2 max_depth.\n", + "[[1511 0]\n", + " [ 224 0]]\n", + "Score: 87.09\n", + "I-E Accuracy for 3 max_depth.\n", + "[[1494 0]\n", + " [ 241 0]]\n", + "Score: 86.11\n", + "I-E Accuracy for 4 max_depth.\n", + "[[1484 0]\n", + " [ 251 0]]\n", + "Score: 85.53\n", + "I-E Accuracy for 5 max_depth.\n", + "[[1502 0]\n", + " [ 233 0]]\n", + "Score: 86.57\n", + "I-E Accuracy for 6 max_depth.\n", + "[[1521 0]\n", + " [ 214 0]]\n", + "Score: 87.67\n", + "I-E Accuracy for 7 max_depth.\n", + "[[1479 0]\n", + " [ 256 0]]\n", + "Score: 85.24\n", + "I-E Accuracy for 8 max_depth.\n", + "[[1489 0]\n", + " [ 246 0]]\n", + "Score: 85.82\n", + "I-E Accuracy for 9 max_depth.\n", + "[[1505 0]\n", + " [ 230 0]]\n", + "Score: 86.74\n", + "I-E Accuracy for 10 max_depth.\n", + "[[1482 3]\n", + " [ 250 0]]\n", + "Score: 85.42\n", + "I-E Accuracy for 11 max_depth.\n", + "[[1521 3]\n", + " [ 210 1]]\n", + "Score: 87.72\n", + "I-E Accuracy for 12 max_depth.\n", + "[[1499 0]\n", + " [ 235 1]]\n", + "Score: 86.46\n", + "I-E Accuracy for 13 max_depth.\n", + "[[1472 0]\n", + " [ 263 0]]\n", + "Score: 84.84\n", + "I-E Accuracy for 14 max_depth.\n", + "[[1494 6]\n", + " [ 234 1]]\n", + "Score: 86.17\n", + "I-E Accuracy for 15 max_depth.\n", + "[[1507 3]\n", + " [ 225 0]]\n", + "Score: 86.86\n" + ] + } + ], + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "\n", + "\n", + "for x in range(1, 16):\n", + " rfn = RandomForestClassifier(max_depth= x, random_state=0)\n", + "\n", + " # NS\n", + " x_train, x_test, y_train, y_test = train_test_split(reduced, y_NS, test_size=0.2)\n", + " rfn.fit(x_train, y_train)\n", + "# ieb_train = knn.score(x_train,y_train)\n", + "# ieb_test = knn.score (x_train,y_train)\n", + " predrfn = rfn.predict(x_test)\n", + " print(\"I-E Accuracy for \", x, \" max_depth.\")\n", + "# print('Label I-E train score is :',ieb_train)\n", + " print(confusion_matrix(y_test,predrfn))\n", + " print(\"Score:\",round(accuracy_score(y_test,predrfn)*100,2))\n", + " #print(\"Score:\",round(accuracy_score(predknn, y_test)*100,2))\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -495,29 +705,29 @@ " [ 238 0]]\n", "Score: 86.28\n", "I-E Accuracy for 10 max_depth.\n", - "[[1497 0]\n", + "[[1496 1]\n", " [ 238 0]]\n", - "Score: 86.28\n", + "Score: 86.22\n", "I-E Accuracy for 11 max_depth.\n", - "[[1497 0]\n", + "[[1495 2]\n", " [ 238 0]]\n", - "Score: 86.28\n", + "Score: 86.17\n", "I-E Accuracy for 12 max_depth.\n", - "[[1497 0]\n", - " [ 238 0]]\n", + "[[1495 2]\n", + " [ 236 2]]\n", "Score: 86.28\n", "I-E Accuracy for 13 max_depth.\n", - "[[1497 0]\n", - " [ 238 0]]\n", - "Score: 86.28\n", + "[[1495 2]\n", + " [ 237 1]]\n", + "Score: 86.22\n", "I-E Accuracy for 14 max_depth.\n", - "[[1497 0]\n", - " [ 238 0]]\n", - "Score: 86.28\n", + "[[1492 5]\n", + " [ 236 2]]\n", + "Score: 86.11\n", "I-E Accuracy for 15 max_depth.\n", - "[[1497 0]\n", - " [ 238 0]]\n", - "Score: 86.28\n" + "[[1494 3]\n", + " [ 237 1]]\n", + "Score: 86.17\n" ] } ], @@ -529,7 +739,7 @@ " rfn = RandomForestClassifier(max_depth= x, random_state=0)\n", "\n", " # NS\n", - " x_train, x_test, y_train, y_test = train_test_split(reduced, y_NS, test_size=0.2, random_state=10)\n", + " x_train, x_test, y_train, y_test = train_test_split(scaled, y_NS, test_size=0.2, random_state=10)\n", " rfn.fit(x_train, y_train)\n", "# ieb_train = knn.score(x_train,y_train)\n", "# ieb_test = knn.score (x_train,y_train)\n", @@ -538,8 +748,7 @@ "# print('Label I-E train score is :',ieb_train)\n", " print(confusion_matrix(y_test,predrfn))\n", " print(\"Score:\",round(accuracy_score(y_test,predrfn)*100,2))\n", - " #print(\"Score:\",round(accuracy_score(predknn, y_test)*100,2))\n", - "\n" + " #print(\"Score:\",round(accuracy_score(predknn, y_test)*100,2))" ] }, { @@ -552,20 +761,108 @@ { "cell_type": "code", "execution_count": 23, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { - "ename": "ValueError", - "evalue": "Negative values in data passed to MultinomialNB (input X)", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;31m# IE\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreduced\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_IE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mmnb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 10\u001b[0m \u001b[0mieb_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmnb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0mieb_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmnb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/naive_bayes.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m 633\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 634\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_init_counters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_effective_classes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_features\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 635\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 636\u001b[0m \u001b[0malpha\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_alpha\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 637\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_feature_log_prob\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/naive_bayes.py\u001b[0m in \u001b[0;36m_count\u001b[0;34m(self, X, Y)\u001b[0m\n\u001b[1;32m 754\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 755\u001b[0m \u001b[0;34m\"\"\"Count and smooth feature occurrences.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 756\u001b[0;31m \u001b[0mcheck_non_negative\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"MultinomialNB (input X)\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 757\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeature_count_\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0msafe_sparse_dot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mY\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 758\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclass_count_\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_non_negative\u001b[0;34m(X, whom)\u001b[0m\n\u001b[1;32m 992\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 993\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mX_min\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 994\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Negative values in data passed to %s\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mwhom\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 995\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 996\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mValueError\u001b[0m: Negative values in data passed to MultinomialNB (input X)" + "name": "stdout", + "output_type": "stream", + "text": [ + "I-E RESULTS\n", + "Label I-E train score is : 0.7685545467646635\n", + "Label I-E test score is : 0.7685545467646635\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[ 0 393]\n", + " [ 0 1342]]\n", + "Score: 77.35\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " E 0.00 0.00 0.00 393\n", + " I 0.77 1.00 0.87 1342\n", + "\n", + " accuracy 0.77 1735\n", + " macro avg 0.39 0.50 0.44 1735\n", + "weighted avg 0.60 0.77 0.67 1735\n", + "\n", + "****************************************************************************************************\n", + "N-S RESULTS\n", + "Label N-S train score is : 0.8617956477878657\n", + "Label N-S test score is : 0.8617956477878657\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[1497 0]\n", + " [ 238 0]]\n", + "Score: 86.28\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " N 0.86 1.00 0.93 1497\n", + " S 0.00 0.00 0.00 238\n", + "\n", + " accuracy 0.86 1735\n", + " macro avg 0.43 0.50 0.46 1735\n", + "weighted avg 0.74 0.86 0.80 1735\n", + "\n", + "****************************************************************************************************\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n", + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "T-F Results\n", + "Label T-F train score is : 0.5379737714368065\n", + "Label T-F test score is : 0.5379737714368065\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[960 0]\n", + " [775 0]]\n", + "Score: 55.33\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " F 0.55 1.00 0.71 960\n", + " T 0.00 0.00 0.00 775\n", + "\n", + " accuracy 0.55 1735\n", + " macro avg 0.28 0.50 0.36 1735\n", + "weighted avg 0.31 0.55 0.39 1735\n", + "\n", + "****************************************************************************************************\n", + "J-P Results\n", + "Label J-P train score is : 0.603112840466926\n", + "Label J-P test score is : 0.603112840466926\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[ 0 680]\n", + " [ 0 1055]]\n", + "Score: 60.81\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " J 0.00 0.00 0.00 680\n", + " P 0.61 1.00 0.76 1055\n", + "\n", + " accuracy 0.61 1735\n", + " macro avg 0.30 0.50 0.38 1735\n", + "weighted avg 0.37 0.61 0.46 1735\n", + "\n", + "****************************************************************************************************\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n", + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n" ] } ], @@ -577,7 +874,7 @@ "\n", "\n", "# IE\n", - "x_train, x_test, y_train, y_test = train_test_split(reduced, y_IE, test_size=0.2, random_state=10)\n", + "x_train, x_test, y_train, y_test = train_test_split(scaled, y_IE, test_size=0.2, random_state=10)\n", "mnb.fit(x_train, y_train)\n", "ieb_train = mnb.score (x_train,y_train)\n", "ieb_test = mnb.score (x_train,y_train)\n", @@ -592,61 +889,209 @@ "print(\"*\" * 100)\n", "\n", "# NS\n", + "x_train, x_test, y_train, y_test = train_test_split(scaled, y_NS, test_size=0.2, random_state=10)\n", + "mnb.fit(x_train, y_train)\n", + "nsb_train = mnb.score (x_train,y_train)\n", + "nsb_test = mnb.score (x_train,y_train)\n", + "predmnb = mnb.predict(x_test)\n", + "print(\"N-S RESULTS\")\n", + "print('Label N-S train score is :',nsb_train)\n", + "print('Label N-S test score is :',nsb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predmnb))\n", + "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", + "print(\"*\" * 100)\n", + "\n", + "# TF\n", + "x_train, x_test, y_train, y_test = train_test_split(scaled, y_TF, test_size=0.2, random_state=10)\n", + "mnb.fit(x_train, y_train)\n", + "tfb_train = mnb.score (x_train,y_train)\n", + "tfb_test = mnb.score (x_train,y_train)\n", + "predmnb = mnb.predict(x_test)\n", + "print(\"T-F Results\")\n", + "print('Label T-F train score is :',tfb_train)\n", + "print('Label T-F test score is :',tfb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predmnb))\n", + "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", + "print(\"*\" * 100)\n", + "\n", + "# JP\n", + "x_train, x_test, y_train, y_test = train_test_split(scaled, y_JP, test_size=0.2, random_state=10)\n", + "mnb.fit(x_train, y_train)\n", + "jpb_train = mnb.score (x_train,y_train)\n", + "jpb_test = mnb.score (x_train,y_train)\n", + "predmnb = mnb.predict(x_test)\n", + "print(\"J-P Results\")\n", + "print('Label J-P train score is :',jpb_train)\n", + "print('Label J-P test score is :',jpb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predmnb))\n", + "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", + "print(\"*\" * 100)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Decision Tree" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I-E RESULTS\n", + "Label I-E train score is : 1.0\n", + "Label I-E test score is : 1.0\n", + "Confusion Matrix for Decision Tree:\n", + "[[ 121 272]\n", + " [ 333 1009]]\n", + "Score: 65.13\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " E 0.27 0.31 0.29 393\n", + " I 0.79 0.75 0.77 1342\n", + "\n", + " accuracy 0.65 1735\n", + " macro avg 0.53 0.53 0.53 1735\n", + "weighted avg 0.67 0.65 0.66 1735\n", + "\n", + "****************************************************************************************************\n", + "N-S RESULTS\n", + "Label N-S train score is : 1.0\n", + "Label N-S test score is : 1.0\n", + "Confusion Matrix for Decision Tree:\n", + "[[1239 258]\n", + " [ 196 42]]\n", + "Score: 73.83\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " N 0.86 0.83 0.85 1497\n", + " S 0.14 0.18 0.16 238\n", + "\n", + " accuracy 0.74 1735\n", + " macro avg 0.50 0.50 0.50 1735\n", + "weighted avg 0.76 0.74 0.75 1735\n", + "\n", + "****************************************************************************************************\n", + "T-F Results\n", + "Label T-F train score is : 1.0\n", + "Label T-F test score is : 1.0\n", + "Confusion Matrix for Decision Tree:\n", + "[[579 381]\n", + " [373 402]]\n", + "Score: 56.54\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " F 0.61 0.60 0.61 960\n", + " T 0.51 0.52 0.52 775\n", + "\n", + " accuracy 0.57 1735\n", + " macro avg 0.56 0.56 0.56 1735\n", + "weighted avg 0.57 0.57 0.57 1735\n", + "\n", + "****************************************************************************************************\n", + "J-P Results\n", + "Label J-P train score is : 1.0\n", + "Label J-P test score is : 1.0\n", + "Confusion Matrix for Decision Tree:\n", + "[[306 374]\n", + " [438 617]]\n", + "Score: 53.2\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " J 0.41 0.45 0.43 680\n", + " P 0.62 0.58 0.60 1055\n", + "\n", + " accuracy 0.53 1735\n", + " macro avg 0.52 0.52 0.52 1735\n", + "weighted avg 0.54 0.53 0.54 1735\n", + "\n", + "****************************************************************************************************\n" + ] + } + ], + "source": [ + "from sklearn.tree import DecisionTreeClassifier\n", + "\n", + "dt = DecisionTreeClassifier()\n", + "dt.fit(x_train, y_train)\n", + "\n", + "\n", + "# IE\n", + "x_train, x_test, y_train, y_test = train_test_split(reduced, y_IE, test_size=0.2, random_state=10)\n", + "dt.fit(x_train, y_train)\n", + "ieb_train = dt.score (x_train,y_train)\n", + "ieb_test = dt.score (x_train,y_train)\n", + "preddt = dt.predict(x_test)\n", + "print(\"I-E RESULTS\")\n", + "print('Label I-E train score is :',ieb_train)\n", + "print('Label I-E test score is :',ieb_test)\n", + "print(\"Confusion Matrix for Decision Tree:\")\n", + "print(confusion_matrix(y_test,preddt))\n", + "print(\"Score:\",round(accuracy_score(y_test,preddt)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,preddt))\n", + "print(\"*\" * 100)\n", + "\n", + "# NS\n", "x_train, x_test, y_train, y_test = train_test_split(reduced, y_NS, test_size=0.2, random_state=10)\n", - "mnb.fit(x_train, y_train)\n", - "nsb_train = mnb.score (x_train,y_train)\n", - "nsb_test = mnb.score (x_train,y_train)\n", - "predmnb = mnb.predict(x_test)\n", + "dt.fit(x_train, y_train)\n", + "nsb_train = dt.score (x_train,y_train)\n", + "nsb_test = dt.score (x_train,y_train)\n", + "preddt = dt.predict(x_test)\n", "print(\"N-S RESULTS\")\n", "print('Label N-S train score is :',nsb_train)\n", "print('Label N-S test score is :',nsb_test)\n", - "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", - "print(confusion_matrix(y_test,predmnb))\n", - "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", - "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", + "print(\"Confusion Matrix for Decision Tree:\")\n", + "print(confusion_matrix(y_test,preddt))\n", + "print(\"Score:\",round(accuracy_score(y_test,preddt)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,preddt))\n", "print(\"*\" * 100)\n", "\n", "# TF\n", "x_train, x_test, y_train, y_test = train_test_split(reduced, y_TF, test_size=0.2, random_state=10)\n", - "mnb.fit(x_train, y_train)\n", - "tfb_train = mnb.score (x_train,y_train)\n", - "tfb_test = mnb.score (x_train,y_train)\n", - "predmnb = mnb.predict(x_test)\n", + "dt.fit(x_train, y_train)\n", + "tfb_train = dt.score (x_train,y_train)\n", + "tfb_test = dt.score (x_train,y_train)\n", + "preddt = dt.predict(x_test)\n", "print(\"T-F Results\")\n", "print('Label T-F train score is :',tfb_train)\n", "print('Label T-F test score is :',tfb_test)\n", - "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", - "print(confusion_matrix(y_test,predmnb))\n", - "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", - "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", + "print(\"Confusion Matrix for Decision Tree:\")\n", + "print(confusion_matrix(y_test,preddt))\n", + "print(\"Score:\",round(accuracy_score(y_test,preddt)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,preddt))\n", "print(\"*\" * 100)\n", "\n", "# JP\n", "x_train, x_test, y_train, y_test = train_test_split(reduced, y_JP, test_size=0.2, random_state=10)\n", - "mnb.fit(x_train, y_train)\n", - "jpb_train = mnb.score (x_train,y_train)\n", - "jpb_test = mnb.score (x_train,y_train)\n", - "predmnb = mnb.predict(x_test)\n", + "dt.fit(x_train, y_train)\n", + "jpb_train = dt.score (x_train,y_train)\n", + "jpb_test = dt.score (x_train,y_train)\n", + "preddt = dt.predict(x_test)\n", "print(\"J-P Results\")\n", "print('Label J-P train score is :',jpb_train)\n", "print('Label J-P test score is :',jpb_test)\n", - "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", - "print(confusion_matrix(y_test,predmnb))\n", - "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", - "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", + "print(\"Confusion Matrix for Decision Tree:\")\n", + "print(confusion_matrix(y_test,preddt))\n", + "print(\"Score:\",round(accuracy_score(y_test,preddt)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,preddt))\n", "print(\"*\" * 100)" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Decision Tree" - ] - }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -657,50 +1102,50 @@ "Label I-E train score is : 1.0\n", "Label I-E test score is : 1.0\n", "Confusion Matrix for Decision Tree:\n", - "[[ 91 302]\n", - " [353 989]]\n", - "Score: 62.25\n", + "[[116 277]\n", + " [357 985]]\n", + "Score: 63.46\n", "Classification Report: precision recall f1-score support\n", "\n", - " E 0.20 0.23 0.22 393\n", - " I 0.77 0.74 0.75 1342\n", + " E 0.25 0.30 0.27 393\n", + " I 0.78 0.73 0.76 1342\n", "\n", - " accuracy 0.62 1735\n", - " macro avg 0.49 0.48 0.48 1735\n", - "weighted avg 0.64 0.62 0.63 1735\n", + " accuracy 0.63 1735\n", + " macro avg 0.51 0.51 0.51 1735\n", + "weighted avg 0.66 0.63 0.65 1735\n", "\n", "****************************************************************************************************\n", "N-S RESULTS\n", "Label N-S train score is : 1.0\n", "Label N-S test score is : 1.0\n", "Confusion Matrix for Decision Tree:\n", - "[[1260 237]\n", - " [ 186 52]]\n", - "Score: 75.62\n", + "[[1253 244]\n", + " [ 198 40]]\n", + "Score: 74.52\n", "Classification Report: precision recall f1-score support\n", "\n", - " N 0.87 0.84 0.86 1497\n", - " S 0.18 0.22 0.20 238\n", + " N 0.86 0.84 0.85 1497\n", + " S 0.14 0.17 0.15 238\n", "\n", - " accuracy 0.76 1735\n", - " macro avg 0.53 0.53 0.53 1735\n", - "weighted avg 0.78 0.76 0.77 1735\n", + " accuracy 0.75 1735\n", + " macro avg 0.50 0.50 0.50 1735\n", + "weighted avg 0.76 0.75 0.75 1735\n", "\n", "****************************************************************************************************\n", "T-F Results\n", "Label T-F train score is : 1.0\n", "Label T-F test score is : 1.0\n", "Confusion Matrix for Decision Tree:\n", - "[[583 377]\n", - " [359 416]]\n", - "Score: 57.58\n", + "[[589 371]\n", + " [357 418]]\n", + "Score: 58.04\n", "Classification Report: precision recall f1-score support\n", "\n", - " F 0.62 0.61 0.61 960\n", - " T 0.52 0.54 0.53 775\n", + " F 0.62 0.61 0.62 960\n", + " T 0.53 0.54 0.53 775\n", "\n", " accuracy 0.58 1735\n", - " macro avg 0.57 0.57 0.57 1735\n", + " macro avg 0.58 0.58 0.58 1735\n", "weighted avg 0.58 0.58 0.58 1735\n", "\n", "****************************************************************************************************\n", @@ -708,17 +1153,17 @@ "Label J-P train score is : 1.0\n", "Label J-P test score is : 1.0\n", "Confusion Matrix for Decision Tree:\n", - "[[304 376]\n", - " [418 637]]\n", - "Score: 54.24\n", + "[[309 371]\n", + " [432 623]]\n", + "Score: 53.72\n", "Classification Report: precision recall f1-score support\n", "\n", " J 0.42 0.45 0.43 680\n", - " P 0.63 0.60 0.62 1055\n", + " P 0.63 0.59 0.61 1055\n", "\n", " accuracy 0.54 1735\n", - " macro avg 0.52 0.53 0.52 1735\n", - "weighted avg 0.55 0.54 0.54 1735\n", + " macro avg 0.52 0.52 0.52 1735\n", + "weighted avg 0.54 0.54 0.54 1735\n", "\n", "****************************************************************************************************\n" ] @@ -732,7 +1177,7 @@ "\n", "\n", "# IE\n", - "x_train, x_test, y_train, y_test = train_test_split(reduced, y_IE, test_size=0.2, random_state=10)\n", + "x_train, x_test, y_train, y_test = train_test_split(scaled, y_IE, test_size=0.2, random_state=10)\n", "dt.fit(x_train, y_train)\n", "ieb_train = dt.score (x_train,y_train)\n", "ieb_test = dt.score (x_train,y_train)\n", @@ -747,7 +1192,7 @@ "print(\"*\" * 100)\n", "\n", "# NS\n", - "x_train, x_test, y_train, y_test = train_test_split(reduced, y_NS, test_size=0.2, random_state=10)\n", + "x_train, x_test, y_train, y_test = train_test_split(scaled, y_NS, test_size=0.2, random_state=10)\n", "dt.fit(x_train, y_train)\n", "nsb_train = dt.score (x_train,y_train)\n", "nsb_test = dt.score (x_train,y_train)\n", @@ -762,7 +1207,7 @@ "print(\"*\" * 100)\n", "\n", "# TF\n", - "x_train, x_test, y_train, y_test = train_test_split(reduced, y_TF, test_size=0.2, random_state=10)\n", + "x_train, x_test, y_train, y_test = train_test_split(scaled, y_TF, test_size=0.2, random_state=10)\n", "dt.fit(x_train, y_train)\n", "tfb_train = dt.score (x_train,y_train)\n", "tfb_test = dt.score (x_train,y_train)\n", @@ -777,7 +1222,7 @@ "print(\"*\" * 100)\n", "\n", "# JP\n", - "x_train, x_test, y_train, y_test = train_test_split(reduced, y_JP, test_size=0.2, random_state=10)\n", + "x_train, x_test, y_train, y_test = train_test_split(scaled, y_JP, test_size=0.2, random_state=10)\n", "dt.fit(x_train, y_train)\n", "jpb_train = dt.score (x_train,y_train)\n", "jpb_test = dt.score (x_train,y_train)\n", @@ -956,6 +1401,154 @@ "print(\"*\" * 100)" ] }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I-E RESULTS\n", + "Label I-E train score is : 0.788586251621271\n", + "Label I-E test score is : 0.788586251621271\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[ 27 366]\n", + " [ 73 1269]]\n", + "Score: 74.7\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " E 0.27 0.07 0.11 393\n", + " I 0.78 0.95 0.85 1342\n", + "\n", + " accuracy 0.75 1735\n", + " macro avg 0.52 0.51 0.48 1735\n", + "weighted avg 0.66 0.75 0.68 1735\n", + "\n", + "****************************************************************************************************\n", + "N-S RESULTS\n", + "Label N-S train score is : 0.8642455685257242\n", + "Label N-S test score is : 0.8642455685257242\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[1479 18]\n", + " [ 234 4]]\n", + "Score: 85.48\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " N 0.86 0.99 0.92 1497\n", + " S 0.18 0.02 0.03 238\n", + "\n", + " accuracy 0.85 1735\n", + " macro avg 0.52 0.50 0.48 1735\n", + "weighted avg 0.77 0.85 0.80 1735\n", + "\n", + "****************************************************************************************************\n", + "T-F Results\n", + "Label T-F train score is : 0.7114858048710189\n", + "Label T-F test score is : 0.7114858048710189\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[510 450]\n", + " [295 480]]\n", + "Score: 57.06\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " F 0.63 0.53 0.58 960\n", + " T 0.52 0.62 0.56 775\n", + "\n", + " accuracy 0.57 1735\n", + " macro avg 0.57 0.58 0.57 1735\n", + "weighted avg 0.58 0.57 0.57 1735\n", + "\n", + "****************************************************************************************************\n", + "J-P Results\n", + "Label J-P train score is : 0.6931834558293702\n", + "Label J-P test score is : 0.6931834558293702\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[201 479]\n", + " [286 769]]\n", + "Score: 55.91\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " J 0.41 0.30 0.34 680\n", + " P 0.62 0.73 0.67 1055\n", + "\n", + " accuracy 0.56 1735\n", + " macro avg 0.51 0.51 0.51 1735\n", + "weighted avg 0.54 0.56 0.54 1735\n", + "\n", + "****************************************************************************************************\n" + ] + } + ], + "source": [ + "from sklearn.neighbors import KNeighborsClassifier\n", + "\n", + "knn = KNeighborsClassifier(n_neighbors=7)\n", + "\n", + "\n", + "\n", + "# IE\n", + "x_train, x_test, y_train, y_test = train_test_split(reduced, y_IE, test_size=0.2, random_state=10)\n", + "knn.fit(x_train, y_train)\n", + "ieb_train = knn.score (x_train,y_train)\n", + "ieb_test = knn.score (x_train,y_train)\n", + "predknn = knn.predict(x_test)\n", + "print(\"I-E RESULTS\")\n", + "print('Label I-E train score is :',ieb_train)\n", + "print('Label I-E test score is :',ieb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predknn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predknn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predknn))\n", + "print(\"*\" * 100)\n", + "\n", + "# NS\n", + "x_train, x_test, y_train, y_test = train_test_split(reduced, y_NS, test_size=0.2, random_state=10)\n", + "knn.fit(x_train, y_train)\n", + "nsb_train = knn.score (x_train,y_train)\n", + "nsb_test = knn.score (x_train,y_train)\n", + "predknn = knn.predict(x_test)\n", + "print(\"N-S RESULTS\")\n", + "print('Label N-S train score is :',nsb_train)\n", + "print('Label N-S test score is :',nsb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predknn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predknn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predknn))\n", + "print(\"*\" * 100)\n", + "\n", + "# TF\n", + "x_train, x_test, y_train, y_test = train_test_split(reduced, y_TF, test_size=0.2, random_state=10)\n", + "knn.fit(x_train, y_train)\n", + "tfb_train = knn.score (x_train,y_train)\n", + "tfb_test = knn.score (x_train,y_train)\n", + "predknn = knn.predict(x_test)\n", + "print(\"T-F Results\")\n", + "print('Label T-F train score is :',tfb_train)\n", + "print('Label T-F test score is :',tfb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predknn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predknn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predknn))\n", + "print(\"*\" * 100)\n", + "\n", + "# JP\n", + "x_train, x_test, y_train, y_test = train_test_split(reduced, y_JP, test_size=0.2, random_state=10)\n", + "knn.fit(x_train, y_train)\n", + "jpb_train = knn.score (x_train,y_train)\n", + "jpb_test = knn.score (x_train,y_train)\n", + "predknn = knn.predict(x_test)\n", + "print(\"J-P Results\")\n", + "print('Label J-P train score is :',jpb_train)\n", + "print('Label J-P test score is :',jpb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predknn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predknn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predknn))\n", + "print(\"*\" * 100)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -965,7 +1558,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -973,8 +1566,8 @@ "output_type": "stream", "text": [ "I-E RESULTS\n", - "Label I-E train score is : 0.769275111687563\n", - "Label I-E test score is : 0.769275111687563\n", + "Label I-E train score is : 0.7691309987029832\n", + "Label I-E test score is : 0.7691309987029832\n", "Confusion Matrix for Multinomial Naive Bayes:\n", "[[ 0 393]\n", " [ 0 1342]]\n", @@ -1004,8 +1597,8 @@ "output_type": "stream", "text": [ "N-S RESULTS\n", - "Label N-S train score is : 0.8622279867416054\n", - "Label N-S test score is : 0.8622279867416054\n", + "Label N-S train score is : 0.8619397607724456\n", + "Label N-S test score is : 0.8619397607724456\n", "Confusion Matrix for Multinomial Naive Bayes:\n", "[[1497 0]\n", " [ 238 0]]\n", @@ -1035,37 +1628,37 @@ "output_type": "stream", "text": [ "T-F Results\n", - "Label T-F train score is : 0.7238795215448912\n", - "Label T-F test score is : 0.7238795215448912\n", + "Label T-F train score is : 0.7169620982850555\n", + "Label T-F test score is : 0.7169620982850555\n", "Confusion Matrix for Multinomial Naive Bayes:\n", - "[[726 234]\n", - " [361 414]]\n", - "Score: 65.71\n", + "[[725 235]\n", + " [369 406]]\n", + "Score: 65.19\n", "Classification Report: precision recall f1-score support\n", "\n", - " F 0.67 0.76 0.71 960\n", - " T 0.64 0.53 0.58 775\n", + " F 0.66 0.76 0.71 960\n", + " T 0.63 0.52 0.57 775\n", "\n", - " accuracy 0.66 1735\n", - " macro avg 0.65 0.65 0.65 1735\n", - "weighted avg 0.65 0.66 0.65 1735\n", + " accuracy 0.65 1735\n", + " macro avg 0.65 0.64 0.64 1735\n", + "weighted avg 0.65 0.65 0.65 1735\n", "\n", "****************************************************************************************************\n", "J-P Results\n", - "Label J-P train score is : 0.6235768842772734\n", - "Label J-P test score is : 0.6235768842772734\n", + "Label J-P train score is : 0.6281884997838305\n", + "Label J-P test score is : 0.6281884997838305\n", "Confusion Matrix for Multinomial Naive Bayes:\n", - "[[ 9 671]\n", - " [ 3 1052]]\n", - "Score: 61.15\n", + "[[ 11 669]\n", + " [ 4 1051]]\n", + "Score: 61.21\n", "Classification Report: precision recall f1-score support\n", "\n", - " J 0.75 0.01 0.03 680\n", + " J 0.73 0.02 0.03 680\n", " P 0.61 1.00 0.76 1055\n", "\n", " accuracy 0.61 1735\n", - " macro avg 0.68 0.51 0.39 1735\n", - "weighted avg 0.67 0.61 0.47 1735\n", + " macro avg 0.67 0.51 0.39 1735\n", + "weighted avg 0.66 0.61 0.47 1735\n", "\n", "****************************************************************************************************\n" ] @@ -1079,7 +1672,7 @@ "\n", "\n", "# IE\n", - "x_train, x_test, y_train, y_test = train_test_split(reduced, y_IE, test_size=0.2, random_state=10)\n", + "x_train, x_test, y_train, y_test = train_test_split(scaled, y_IE, test_size=0.2, random_state=10)\n", "rfn.fit(x_train, y_train)\n", "ieb_train = rfn.score (x_train,y_train)\n", "ieb_test = rfn.score (x_train,y_train)\n", @@ -1094,7 +1687,7 @@ "print(\"*\" * 100)\n", "\n", "# NS\n", - "x_train, x_test, y_train, y_test = train_test_split(reduced, y_NS, test_size=0.2, random_state=10)\n", + "x_train, x_test, y_train, y_test = train_test_split(scaled, y_NS, test_size=0.2, random_state=10)\n", "rfn.fit(x_train, y_train)\n", "nsb_train = rfn.score (x_train,y_train)\n", "nsb_test = rfn.score (x_train,y_train)\n", @@ -1109,7 +1702,7 @@ "print(\"*\" * 100)\n", "\n", "# TF\n", - "x_train, x_test, y_train, y_test = train_test_split(reduced, y_TF, test_size=0.2, random_state=10)\n", + "x_train, x_test, y_train, y_test = train_test_split(scaled, y_TF, test_size=0.2, random_state=10)\n", "rfn.fit(x_train, y_train)\n", "tfb_train = rfn.score (x_train,y_train)\n", "tfb_test = rfn.score (x_train,y_train)\n", @@ -1124,7 +1717,7 @@ "print(\"*\" * 100)\n", "\n", "# JP\n", - "x_train, x_test, y_train, y_test = train_test_split(reduced, y_JP, test_size=0.2, random_state=10)\n", + "x_train, x_test, y_train, y_test = train_test_split(scaled, y_JP, test_size=0.2, random_state=10)\n", "rfn.fit(x_train, y_train)\n", "jpb_train = rfn.score (x_train,y_train)\n", "jpb_test = rfn.score (x_train,y_train)\n", @@ -1148,7 +1741,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -1180,7 +1773,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -1204,10 +1797,7 @@ " \n", " \n", " \n", - " Unnamed: 0\n", " type\n", - " posts\n", - " text_processed\n", " text_ready\n", " I-E\n", " N-S\n", @@ -1218,98 +1808,145 @@ " \n", " \n", " 0\n", - " 0\n", " INFJ\n", - " 'http://www.youtube.com/watch?v=qsXHcwe3krw|||...\n", - " ['intj', 'moment', 'sportscent', 'top', 'ten',...\n", " intj moment sportscent top ten play prank ha l...\n", + " 1\n", " 0\n", " 0\n", - " 1\n", " 0\n", " \n", " \n", " 1\n", - " 1\n", " ENTP\n", - " 'I'm finding the lack of me in these posts ver...\n", - " ['find', 'lack', 'post', 'veri', 'alarm', 'sex...\n", " find lack post veri alarm sex bore posit often...\n", - " 1\n", " 0\n", " 0\n", " 1\n", + " 1\n", " \n", " \n", " 2\n", - " 2\n", " INTP\n", - " 'Good one _____ https://www.youtube.com/wat...\n", - " ['good', 'one', 'cours', 'say', 'know', 'bless...\n", " good one cours say know bless cur doe absolut ...\n", - " 0\n", - " 0\n", + " 1\n", " 0\n", " 1\n", + " 1\n", " \n", " \n", " 3\n", - " 3\n", " INTJ\n", - " 'Dear INTP, I enjoyed our conversation the o...\n", - " ['dear', 'intp', 'enjoy', 'convers', 'day', 'e...\n", " dear intp enjoy convers day esoter gab natur u...\n", + " 1\n", " 0\n", - " 0\n", - " 0\n", + " 1\n", " 0\n", " \n", " \n", " 4\n", - " 4\n", " ENTJ\n", - " 'You're fired.|||That's another silly misconce...\n", - " ['fire', 'anoth', 'silli', 'misconcept', 'appr...\n", " fire anoth silli misconcept approach logic go ...\n", + " 0\n", + " 0\n", + " 1\n", + " 0\n", + " \n", + " \n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " \n", + " \n", + " 8670\n", + " ISFP\n", + " becaus alway think cat fi dom reason websit be...\n", + " 1\n", + " 1\n", + " 0\n", + " 1\n", + " \n", + " \n", + " 8671\n", + " ENFP\n", + " thi thread alreadi exist someplac el doe heck ...\n", + " 0\n", + " 0\n", + " 0\n", + " 1\n", + " \n", + " \n", + " 8672\n", + " INTP\n", + " mani question thing would take purpl pill pick...\n", + " 1\n", + " 0\n", + " 1\n", + " 1\n", + " \n", + " \n", + " 8673\n", + " INFP\n", + " veri conflict right come want child honestli m...\n", " 1\n", " 0\n", " 0\n", + " 1\n", + " \n", + " \n", + " 8674\n", + " INFP\n", + " ha long sinc personalitycaf although seem chan...\n", + " 1\n", + " 0\n", " 0\n", + " 1\n", " \n", " \n", "\n", + "

8674 rows × 6 columns

\n", "" ], "text/plain": [ - " Unnamed: 0 type posts \\\n", - "0 0 INFJ 'http://www.youtube.com/watch?v=qsXHcwe3krw|||... \n", - "1 1 ENTP 'I'm finding the lack of me in these posts ver... \n", - "2 2 INTP 'Good one _____ https://www.youtube.com/wat... \n", - "3 3 INTJ 'Dear INTP, I enjoyed our conversation the o... \n", - "4 4 ENTJ 'You're fired.|||That's another silly misconce... \n", + " type text_ready I-E N-S T-F \\\n", + "0 INFJ intj moment sportscent top ten play prank ha l... 1 0 0 \n", + "1 ENTP find lack post veri alarm sex bore posit often... 0 0 1 \n", + "2 INTP good one cours say know bless cur doe absolut ... 1 0 1 \n", + "3 INTJ dear intp enjoy convers day esoter gab natur u... 1 0 1 \n", + "4 ENTJ fire anoth silli misconcept approach logic go ... 0 0 1 \n", + "... ... ... ... ... ... \n", + "8670 ISFP becaus alway think cat fi dom reason websit be... 1 1 0 \n", + "8671 ENFP thi thread alreadi exist someplac el doe heck ... 0 0 0 \n", + "8672 INTP mani question thing would take purpl pill pick... 1 0 1 \n", + "8673 INFP veri conflict right come want child honestli m... 1 0 0 \n", + "8674 INFP ha long sinc personalitycaf although seem chan... 1 0 0 \n", "\n", - " text_processed \\\n", - "0 ['intj', 'moment', 'sportscent', 'top', 'ten',... \n", - "1 ['find', 'lack', 'post', 'veri', 'alarm', 'sex... \n", - "2 ['good', 'one', 'cours', 'say', 'know', 'bless... \n", - "3 ['dear', 'intp', 'enjoy', 'convers', 'day', 'e... \n", - "4 ['fire', 'anoth', 'silli', 'misconcept', 'appr... \n", + " J-P \n", + "0 0 \n", + "1 1 \n", + "2 1 \n", + "3 0 \n", + "4 0 \n", + "... ... \n", + "8670 1 \n", + "8671 1 \n", + "8672 1 \n", + "8673 1 \n", + "8674 1 \n", "\n", - " text_ready I-E N-S T-F J-P \n", - "0 intj moment sportscent top ten play prank ha l... 0 0 1 0 \n", - "1 find lack post veri alarm sex bore posit often... 1 0 0 1 \n", - "2 good one cours say know bless cur doe absolut ... 0 0 0 1 \n", - "3 dear intp enjoy convers day esoter gab natur u... 0 0 0 0 \n", - "4 fire anoth silli misconcept approach logic go ... 1 0 0 0 " + "[8674 rows x 6 columns]" ] }, - "execution_count": 31, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "data_neural = data.copy()\n", + "data_neural = data2.copy()\n", "\n", "data_neural['I-E'] = data_neural['I-E'].astype('category')\n", "data_neural['N-S'] = data_neural['N-S'].astype('category')\n", @@ -1325,7 +1962,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ @@ -1345,7 +1982,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ @@ -1368,7 +2005,27 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(6939, 1986297)" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_train.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 48, "metadata": {}, "outputs": [ { @@ -1381,12 +2038,12 @@ }, { "ename": "InvalidArgumentError", - "evalue": " TypeError: 'SparseTensor' object is not subscriptable\nTraceback (most recent call last):\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/ops/script_ops.py\", line 241, in __call__\n return func(device, token, args)\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/ops/script_ops.py\", line 130, in __call__\n ret = self._func(*args)\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/autograph/impl/api.py\", line 309, in wrapper\n return func(*args, **kwargs)\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/data_adapter.py\", line 513, in py_method\n return [slice_array(inp) for inp in flat_inputs]\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/data_adapter.py\", line 513, in \n return [slice_array(inp) for inp in flat_inputs]\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/data_adapter.py\", line 512, in slice_array\n contiguous=contiguous)\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/training_utils.py\", line 391, in slice_arrays\n entries = [[x[i:i + 1] for i in indices] for x in arrays]\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/training_utils.py\", line 391, in \n entries = [[x[i:i + 1] for i in indices] for x in arrays]\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/training_utils.py\", line 391, in \n entries = [[x[i:i + 1] for i in indices] for x in arrays]\n\nTypeError: 'SparseTensor' object is not subscriptable\n\n\n\t [[{{node EagerPyFunc}}]]\n\t [[IteratorGetNext]] [Op:__inference_train_function_1799]\n\nFunction call stack:\ntrain_function\n", + "evalue": " TypeError: 'SparseTensor' object is not subscriptable\nTraceback (most recent call last):\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/ops/script_ops.py\", line 241, in __call__\n return func(device, token, args)\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/ops/script_ops.py\", line 130, in __call__\n ret = self._func(*args)\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/autograph/impl/api.py\", line 309, in wrapper\n return func(*args, **kwargs)\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/data_adapter.py\", line 513, in py_method\n return [slice_array(inp) for inp in flat_inputs]\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/data_adapter.py\", line 513, in \n return [slice_array(inp) for inp in flat_inputs]\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/data_adapter.py\", line 512, in slice_array\n contiguous=contiguous)\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/training_utils.py\", line 391, in slice_arrays\n entries = [[x[i:i + 1] for i in indices] for x in arrays]\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/training_utils.py\", line 391, in \n entries = [[x[i:i + 1] for i in indices] for x in arrays]\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/training_utils.py\", line 391, in \n entries = [[x[i:i + 1] for i in indices] for x in arrays]\n\nTypeError: 'SparseTensor' object is not subscriptable\n\n\n\t [[{{node EagerPyFunc}}]]\n\t [[IteratorGetNext]] [Op:__inference_train_function_2113]\n\nFunction call stack:\ntrain_function\n", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mInvalidArgumentError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# IE\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_IE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;36m25\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mpredmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"I-E RESULTS\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# IE\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_IE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;36m25\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mpredmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"I-E RESULTS\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36m_method_wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_method_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 65\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_in_multi_worker_mode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[0;31m# Running inside `run_distribute_coordinator` already.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m 846\u001b[0m batch_size=batch_size):\n\u001b[1;32m 847\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 848\u001b[0;31m \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 849\u001b[0m \u001b[0;31m# Catch OutOfRangeError for Datasets of unknown size.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 850\u001b[0m \u001b[0;31m# This blocks until the batch has finished executing.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 578\u001b[0m \u001b[0mxla_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mExit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 579\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 580\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 581\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 582\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtracing_count\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", @@ -1396,7 +2053,7 @@ "\u001b[0;32m/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m 1744\u001b[0m \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1745\u001b[0m return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1746\u001b[0;31m ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m 1747\u001b[0m forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m 1748\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m 596\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 597\u001b[0m \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 598\u001b[0;31m ctx=ctx)\n\u001b[0m\u001b[1;32m 599\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 600\u001b[0m outputs = execute.execute_with_cancellation(\n", "\u001b[0;32m/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m 58\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 59\u001b[0m tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 60\u001b[0;31m inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m 61\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mInvalidArgumentError\u001b[0m: TypeError: 'SparseTensor' object is not subscriptable\nTraceback (most recent call last):\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/ops/script_ops.py\", line 241, in __call__\n return func(device, token, args)\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/ops/script_ops.py\", line 130, in __call__\n ret = self._func(*args)\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/autograph/impl/api.py\", line 309, in wrapper\n return func(*args, **kwargs)\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/data_adapter.py\", line 513, in py_method\n return [slice_array(inp) for inp in flat_inputs]\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/data_adapter.py\", line 513, in \n return [slice_array(inp) for inp in flat_inputs]\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/data_adapter.py\", line 512, in slice_array\n contiguous=contiguous)\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/training_utils.py\", line 391, in slice_arrays\n entries = [[x[i:i + 1] for i in indices] for x in arrays]\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/training_utils.py\", line 391, in \n entries = [[x[i:i + 1] for i in indices] for x in arrays]\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/training_utils.py\", line 391, in \n entries = [[x[i:i + 1] for i in indices] for x in arrays]\n\nTypeError: 'SparseTensor' object is not subscriptable\n\n\n\t [[{{node EagerPyFunc}}]]\n\t [[IteratorGetNext]] [Op:__inference_train_function_1799]\n\nFunction call stack:\ntrain_function\n" + "\u001b[0;31mInvalidArgumentError\u001b[0m: TypeError: 'SparseTensor' object is not subscriptable\nTraceback (most recent call last):\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/ops/script_ops.py\", line 241, in __call__\n return func(device, token, args)\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/ops/script_ops.py\", line 130, in __call__\n ret = self._func(*args)\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/autograph/impl/api.py\", line 309, in wrapper\n return func(*args, **kwargs)\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/data_adapter.py\", line 513, in py_method\n return [slice_array(inp) for inp in flat_inputs]\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/data_adapter.py\", line 513, in \n return [slice_array(inp) for inp in flat_inputs]\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/data_adapter.py\", line 512, in slice_array\n contiguous=contiguous)\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/training_utils.py\", line 391, in slice_arrays\n entries = [[x[i:i + 1] for i in indices] for x in arrays]\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/training_utils.py\", line 391, in \n entries = [[x[i:i + 1] for i in indices] for x in arrays]\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/training_utils.py\", line 391, in \n entries = [[x[i:i + 1] for i in indices] for x in arrays]\n\nTypeError: 'SparseTensor' object is not subscriptable\n\n\n\t [[{{node EagerPyFunc}}]]\n\t [[IteratorGetNext]] [Op:__inference_train_function_2113]\n\nFunction call stack:\ntrain_function\n" ] } ], diff --git a/your-project/code/Code 7 - Real Life Data.ipynb b/your-project/code/Code 7 - Real Life Data.ipynb new file mode 100644 index 00000000..22b5d736 --- /dev/null +++ b/your-project/code/Code 7 - Real Life Data.ipynb @@ -0,0 +1,1130 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import re" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "Darya = [\"Quarantine expectations. Swipe for reality\", \n", + " \"22 in 2020 - swipe to see a photo from yesterday. Age affects me.\", \n", + " \"hello? Santa? All I wish for Christmas is my mind back... \", \n", + " \"girl with a hat - a series. \", \n", + " \"casual day at uni. swipe for more idiots\", \n", + " \"thanking Steve for a nice iPhone front camera.\", \n", + " \"Missing this\", \n", + " \"*edit: still can‘t believe a sunscreen for 7.95 prevented me from having a huge sunburn\",\n", + " \"where I‘d rather be part 2\",\n", + " \"when people ask me if I‘ve got free time - I do. Except I don’t.\",\n", + " \n", + " \"just a small town girl, living in a capitalist world...\",\n", + " \"clueless\",\n", + " \"Might be serious, might be hungry. \",\n", + " \"you want blue? you want yellow?\",\n", + " \"While the wall fell nearly 30 years ago, I constantly fall on my face...\",\n", + " \"Im pretty ok with exams being over... \",\n", + " \"unpopular opinion: university can be fun. exhibit a\",\n", + " \"christmas is around the corner but all I‘m wishing for is an extention of deadlines...\",\n", + " \"on some occasions I even get out of the valley and LA to go sightseeing \",\n", + " \"diamonds are a girls best friend. Especially when you trade ‘em for plane tickets and uber rides\",\n", + " \n", + " \"not a threat, it’s a warning. (talking about the beginning of semester tomorrow)\",\n", + " \"come fly with me, let’s fly let’s fly away...\",\n", + " \"saltier than the Pacific\",\n", + " \"me, when I’m a) hangry or b) am studying @ nyfalosangeles @ williamroyal98\",\n", + " \"this whole you-gotta-study-while-the-sun-is-shining gets on my nerve. here’s an example of inner rage\",\n", + " \"you know you might have been spontaneous when while booking your flights they asked you if ou would want to check in for the flight back aswell.\",\n", + " \"Vitamins\",\n", + " \"finished the first semerster at HSG and I still can’t afford a porsche 911.\",\n", + " \"Me: you should be studying. Also me: enjoy the holidays\",\n", + " \"just kidding - I’m still a short ass little human\",\n", + " \n", + " \"week 2 at uni and my icloud and google drive are asking me to pay for more storage.\",\n", + " \"uni starts tomorrow and I already see myself either a) being lost cause I can't find the classroom or b) looking like Annabelle cause sleep is for the weak\",\n", + " \"oh boy\",\n", + " \"this should be my last week of summer but I think the weather decided to give me an early taste of fall already.\",\n", + " \"water falls, darya falls and just casually sit on floors cause standing is overrated \",\n", + " \"I promise I counted all the lights, but then I saw a squirrel and lost count \",\n", + " \"is it a set? Is it a real house? guess.\",\n", + " \"quiet on set\",\n", + " \"Baywatch, are you hiring?\",\n", + " \"What if perfume is actually bugspray but they want us to buy the nasty smelly stuff just to have a good laugh?\",\n", + " \n", + " \"I'm starting to wonder if spotlight can give you a tan cause the sun isn't coming to just yet. All jokes asid tho, I am beyond grateful to share the stage with so many talented people. Do what you love and you'll feel limitless \",\n", + " \"that second when the person you've been straing at looks at you and you quickly turn away, trying to look as casual as possible\",\n", + " \"at that moment I probably thought out a plan to convince my mom to paint one of my walls pink. even though I know it won't happen, happy #internationalwomensday ladies\",\n", + " \"Tell me about it, stud\",\n", + " \"nice to be back. bring it on 2017\"\n", + " \"2016. A year that had probably more faces than facebook. Victories and dissapointment, new friendships and new experiences; I was lucky enough to travel the several times and can't thank the people that made 2016 the year it was enough for all their kind words, laughter and happy tears.\",\n", + " \"don't mess with us - we have RG clubs\",\n", + " \"Summer is over and you're like: pardon me, what?\",\n", + " \"Looking back at those 5 weeks full of new friends, experiences and emotions puts a smile on my face. The teachers I've worked with inspired me every day, the people made my summer the best one yet. I love all of you and you'll always have a special place in my heart #obgottheheat and happy National Dance Day\",\n", + " \"everybody needs some attitude in their life #punintended #obgottheheat\"\n", + "]\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "Theresa = [\"light times\", \n", + " \"simplicité.\", \n", + " \"chapter 1\", \n", + " \"*waiter pours up wine without being asked for*\", \n", + " \"what a year\", \n", + " \"it‘s friday i‘m in loved.\", \n", + " \"herzallerliebst.\", \n", + " \"wie sommerferien für erwachsene irgendwie.\",\n", + " \"sommer und sonne und zitroneneis und stella und ach man was soll ich sagen - das beste leben @stellamarieluise\",\n", + " \"das ist einfach Glück, wenn man euch hat\",\n", + " \n", + " \"an diesem Bild ist so Vieles richtig.\",\n", + " \"ernst gucken geht also doch. @shishaclothing #supportyourlocalbrand #shishaclothing\",\n", + " \"casual durch die gegend düsen halt\",\n", + " \"Liebe für alle Frauen. Aber für Diese am meisten! #happyweltfrauentag\",\n", + " \"meine entscheidungsfreude captured by @stellamarieluise\",\n", + " \"keine termine und leicht im dispo\",\n", + " \"future\",\n", + " \"Zukunftsprognose 2080: alle glücklich.\",\n", + " \"marilyn monroe momente beim brötchen holen\",\n", + " \"reflections.\",\n", + " \n", + " \"keine sorgen, keine wellen, kein conditioner\",\n", + " \"liebenswichtig\",\n", + " \"alles wie immer\",\n", + " \"gute gespräche und blöde ideen\",\n", + " \"klassisches arbeiten halt\",\n", + " \"mag ich\",\n", + " \"lieb ich\",\n", + " \"viel zu schön\",\n", + " \"kaltes bier who dis\",\n", + " \"guess i‘m just in love with sunrises and moonlight and rainstorms and everything that has soul\",\n", + " \n", + " \"semi zufrieden mit dem frühstück\",\n", + " \"ich hab das mit dem ernst gucken nicht mitbekommen\",\n", + " \"verliebtverliebt\",\n", + " \"trouble never looked so goddamn fine\",\n", + " \"und wenn das abendlicht in genau dieser farbe ist\",\n", + " \"say something in dutch\",\n", + " \"superlekker\",\n", + " \"Pass auf deine Füße auf \",\n", + " \"herz euch behalt ich\",\n", + " \"find ich gut\",\n", + " \n", + " \"wenn @lisbz meine wäsche aufhängt\",\n", + " \"find your fire\",\n", + " \"umziehen mit lieblingspizza und lieblingswg\",\n", + " \"lieb ich\",\n", + " \"ist das Kunst oder kann das weg?\"\n", + " \"kennt ihr diese Menschen, die stehen bleiben um den Himmel zu fotografieren?\",\n", + " \"no one is you and that is your power\",\n", + " \"HAPPY WELTFRAUENTAG\",\n", + " \"gründe warum ich kein Blogger bin\",\n", + " \"sommer\"\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "Nico = [\"Haven't posted in a while, so this random picture is now filling the void. Been going within a lot in the past year and sorted out some things for myself. Happy to say, that I am refocused, re-energized and working on great things.\", \n", + " \"Impressions from Serfaus. 2020 is off to a good start. Swipe right for the best drinking game\", \n", + " \"Dogs of Berlin\", \n", + " \"Not my boat, still flexing \", \n", + " \"Lads\", \n", + " \"A weekend in Madrid is always a good idea! Hanging out the hangover at some historical sites \", \n", + " \"Drip too hard, don’t stand too close\", \n", + " \"I miss you bro. We pushed each other to be the best every day. There never was a better duo. „The Golden Boys“ Rest Easy\",\n", + " \"Getting settled in Berlin\",\n", + " \"It’s been three tough but fun years at WHU. Last weekend I finally graduated with a degree from the best business school in Germany, but more importantly with new friends for life and another place I can call home.\",\n", + " \n", + " \"Graduated\",\n", + " \"WHU boys - Class of 2017. Had so much fun with you. Now let's get the moolah\",\n", + " \"The pic is blurry but the day was sharp\",\n", + " \"Probably the most spectacular view I ever had was driving along the highway no. 1 between LA and SF\",\n", + " \"He acting all loyal in this pic, but that lil b* bit me the next minute\",\n", + " \"Probably the most spectacular view I ever had was driving along the highway no. 1 between LA and SF.\",\n", + " \"Hey @google, Hire me, if you want your bike back! #mountainview #google #siliconvalley\",\n", + " \"Throwback to an amazing road trip this fall. What a view! Def. worth the hike #hike #nature #travel #roadtrip #yosemite\",\n", + " \"Keep your eyes on the stars and keep your feet on the ground\",\n", + " \"You can clearly see I enjoyed being back in SoCal. That place is so beautiful...\",\n", + " \n", + " \"he Pacific #beers #beach #chilling #california #home #uglyfeet @goldclapmusic good day dude\",\n", + " \"Never hold back #weekendmood\",\n", + " \"Thank you @kaleandme for this awesome party! #crewlove @reiseminister_coco\",\n", + " \"tb to one of my best games ever Still holding the club record for most receptions per game 2013 vs Cottbus W31:15 13 receptions, 193 yards, 3 TDs Thank you @zbreez_ for the trust and the passes !\",\n", + " \"Throwback to the best winter ever. My kids group. I gave us the name the lil crackheads, because they got me cracking up all the time! #fuckhashtags\",\n", + " \"Won this one big time 60:20, but it cost us some good men... #hospitalreunion\",\n", + " \"#Repost @luebeckcougars Much way for improvement though personally. #getbettereveryday Gamestats #cougars #cougarnation #cougarspride #luebeck #luebeckcougars #football #americanfootball #gfl #amgid #afvd #gridiron #awaygame #ritterhude #badgers\",\n", + " \"100 days credits @gridiron_design_s\",\n", + " \"New Year's Eve in the alps with the crew. #illuminati\",\n", + " \"#tb to the good old times when sunday meant gameday and mondays newspaper Sitting in the library studying right now I wish I could go back in time\",\n", + " \n", + " \"What you believe becomes reality. Create your own world #sundaymood\",\n", + " \"Mom, can we have one?\",\n", + " \"Trying to get my surf game up. I think I have a much better form on the beach than in the water though haha #germansaintmermaids @lennartmhr\",\n", + " \"Table Mountain from Lion's Head @lennartmhr\",\n", + " \"I hate off-season... \",\n", + " \"This shoutout is overdue. Best musical speaker @realcoleworld!\",\n", + " \"#tb to when my bro @enzeocha_11 was making the Swedish defense look like 8th graders.\",\n", + " \"Hoffentlich liegt das in der Familie! #crossingfingers #toodamnhot\",\n", + " \"Touchdown 70:46 crazy game!\",\n", + " \"Some work by @dani_piedrabuena. Trying to look smart in that suit haha \",\n", + " \n", + " \"Back at it\",\n", + " \"Received this at work. Now my colleagues think I'm famous lol Thank you so much Colin!\",\n", + " \"#tb to the toughest job I ever had to do good times\",\n", + " \"office break \",\n", + " \"Beach is better. #barcelona #barca #beach #playa #sun #smile #spain #sleepyface\"\n", + " \"Thank you for such a lovely birthday night.\",\n", + " \"My new car... I wish. M4 Test Drive. Still having goosebumps #bmw #m4 #madrid #racing\",\n", + " \"Don't mess with NFL Runningbacks\",\n", + " \"perfect day at el Retiro\",\n", + " \"Morning routine. Scooter Ride #madrid #work #watch #scooter #sun #suit\"\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "Dylan = [\n", + "'Happy golden Birthday Kayla! 22 years on Earth. The photos say it all... you’re one of a kind, and I feel lucky to call you sister. I’m so tremendously proud of you and all that you’ve accomplished this past year. Your unceasing desire to travel and explore is inspiring, and I can’t wait to see what this coming year brings! I know you’ll finish college strong and jump into your career with all the passion you have for everything you do. Happy Kayla Day, love you',\n", + "'Blood is thicker than water. This mud on the other hand... Alabama',\n", + "'A rocky start to the weekend. Alabam',\n", + "'DANGER - Propellers chop heads off.',\n", + "'Memorial Day marked the first Winter —> Summer change at the cabin.',\n", + "\"You're not the only one with great pics of us happy national sibling day squirt, love you and so proud of you.\",\n", + "\"You're not the only one with great pics of us happy national sibling day squirt, love you and so proud of you. #siblingsforthewin #finelookingduo #mynumerouno #lilsismayhem #lookitthempearlywhites\",\n", + "\"Missing my little beast. Sleep tight #audi #tt #3.2 #quattro #purrofakitty #growlofatiger #mapetiteblanchevoiture\",\n", + "\"Officially my view for the next year! #switzerland #valais #balconyview #lesrochesstudent #hospitality #naturalswissbeauty #lifeinthemountains #lastyear\",\n", + "\"Officially my view for the next year! #switzerland #valais #balconyview #lesrochesstudent #hospitality #naturalswissbeauty #lifeinthemountains #lastyear\",\n", + "\"I had the great fortune today of spotting the incredibly rare Bentley Continental GTZ Zagato! No. 1 of 9 in the world! #Lausanne #Switzerland #Bentley #Continental #Zagato #rare #1of9 #dreamcar #themcurves #iwanttoseetheinsurancebill #supercarsofswitzerland #carlove\",\n", + "\"The best respite from finals? Deep in the Swiss Alps will suffice #switzerland #finalsweek #audi #tt #3.2 #quattro #swissalps #freedom #squintyeyes\",\n", + "\"Welcome to my crib #castlehopping #neverstopexploring #switzerland #sion #notreallymycrib #schoolstartstomorrow #lovinlife #naturalbeauty\",\n", + "\"The Matterhorn in all its glory #matterhorn #zermatt #swissalps #neverstopexploring #lovinlife #myhandwasfreezing\",\n", + "\"Bienvenue Suisse #Switzerland #Mountaindriving #Somuchspace #Sierre #lesrochesswitzerland #lovinlife\",\n", + "\"A great day on the water comes to a close, as my summer day count winds down to 3 #Chevytrucks #2500HD #Mastercraft #Lakedays #Minnesota #Summerbummin #mastercraft2015\",\n", + "\"Stormy days at the lake #Stormydays #IronRange #NorthernMN #Nimbusamongus #Summertime\",\n", + "\"The land of the free and the home of the brave. Murica! #freedom #summer #homesweethome #sightforsoreeyes #freedomwithachanceofliberty #letskickit\",\n", + "\"It's a bit late, but it was great to have you here bro! Missing you already @bryce.gordon, until next time keep it real! :P #Shanghai #Xintiandi #Brotime #WhenImetyouinthesummer #Keepitreal\",\n", + "\"The PuLi hotel, Jingan Temple area #Shanghai #PuLi #Hotelschooldays #Urbanresort #LHW #Shouldhavebroughtmyswimsuit\",\n", + "\"Fun in the sun #Boracay #TheDistrict #letsgodiving #architectureonpoint #paradise\",\n", + "\"Beautiful Boracay sunset #Boracay#Philippines#Missionwork#AGIF#nobeachtimejustpreachtime\",\n", + "\"Have a great Valentines Day everyone! @lovelivalove this is waiting for you when you get back ;) #Valentinesday #chocolates #loveisintheair #wouldyoubemyvalentine? #girlslovechocolate\",\n", + "\"#tbt to being cute and devious :P now I'm just devious #youngme #grinchonesie #santahats #christmascheer #kaylathatface #attemptedsmile #saocute #ihategrowingup #takemeback\",\n", + "\"New addition to the family, ready to log some dive time. #Guam #G-Shock #divewatch #adventuretime\",\n", + "\"Speed is a blessing. #Chevrolet #Camaro #ifyourenotfirstyourelast #Guam #lastday #iwannagofast #transformer #niceparkingjob…\",\n", + "\"Time to go wreck diving! #Guam #diving #wreckdiving #WWII #deepblue #dontworrygotmytetanusshot\",\n", + "\"The smiles of two PADI certified advanced open water divers :D #success #guamdayfive #growinggills #GTDS #firstachievementof2015 #crazyeyes\",\n", + "\"A successful first day of diving on the beautiful island of Guam! #GTDS #icanbreatheunderwater #60ftclub #buoyancyisanissue #Guam #Padicertification #SCUBA #getoffthegrass\",\n", + "\"TBT to New Years. My first semester of college has been real with you guys! #bratansandbratishkas#russianmob#TheNest#firstmomentsof2015#livinglarge#americanamongstrussians\",\n", + "\"A big car for a big city #Cadillac #Escalade #Shanghai #JinQiao #Bigblackride #blueskies #weekendendeavours\",\n", + "\"Just another night in Hanoi. #Hanoi #bikes #people #cityviewcafe #yourebeingwatched #eyesinthesky #driving #youngandfree\",\n", + "\"I guess work isnt so bad ;p #shanghai #sunny #internship #Hyattonthebund #livingthedream #showmethemoney\",\n", + "\"About to start my internship! #Internship #firstdayofwork #livingthelife #gettingthingsdone #pathtosuccess #idkwhatimdoing #noobthings #wishmeluck\",\n", + "\"Florida in the summertime #Florida #boatsnhoes #bromance @johnnygmcdonald\",\n", + "\"Rainy dayz\",\n", + "\"Risk... Back to the old days #Risk#theguys#boardgames#Sweden#strategy#hipster\",\n", + "\"Finally get to see this kid again #powerranger #superfly #southafrican #hashtag #18621613390 #hmu #oneandonly\",\n", + "\"No he's not stealing it. #Latino#diy\",\n", + "\"My main bitchz #sosexy #immaluckyguy\",\n", + "\"#sunnyday#shanghaiskyline\",\n", + "\"#tbt #lastyr #swagdawg #flathatsnsunglasses #selfie #swaggy'd before justin bieber was even born.\",\n", + "\"Happy golden Birthday Kayla! 22 years on Earth. The photos say it all... you’re one of a kind, and I feel lucky to call you sister. I’m so tremendously proud of you and all that you’ve accomplished this past year. Your unceasing desire to travel and explore is inspiring, and I can’t wait to see what this coming year brings! I know you’ll finish college strong and jump into your career with all the passion you have for everything you do. Happy Kayla Day, love you\",\n", + "\"Lol #bluefrog#openwide#whatisthischeese#schooluniformreppin\",\n", + "\"A great day on the water comes to a close, as my summer day count winds down to 3 #Chevytrucks #2500HD #Mastercraft #Lakedays #Minnesota #Summerbummin #mastercraft2015\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "Katie = [\n", + "\"Sonndorf VI #autumn #fall #colors #igersaustria #outdoors #loveaustria #beautiful #nature #naturelovers #naturephotography #memories #sun #afternoon #garden\",\n", + "\"Sonndorf V #autumn #fall #colors #igersaustria #outdoors #loveaustria #beautiful #nature #naturelovers #naturephotography #loveaustria #window #sun #afternoon\",\n", + "\"Sonndorf IV #autumn #fall #colors #igersaustria #outdoors #loveaustria #beautiful #nature #naturelovers #naturephotography #loveaustria #window #sun #afternoon\",\n", + "\"Sonndorf III #autumn #fall #colors #igersaustria #outdoors #loveaustria #beautiful #nature #naturelovers #naturephotography #loveaustria #window #sun #afternoon\",\n", + "\"Sonndorf II #autumn #fall #colors #igersaustria #outdoors #loveaustria #beautiful #nature #naturelovers #naturephotography #loveaustria #window #sun #afternoon\",\n", + "\"Sonndorf I #autumn #fall #colors #igersaustria #outdoors #loveaustria #beautiful #nature #naturelovers #naturephotography #loveaustria #window #sun #afternoon\",\n", + "\"he beauty of fall | Lech am Arlberg\",\n", + "\"Mystic Fog | Lech am Arlberg #fog #igersaustria #outdoors #loveaustria #beautiful #nature #naturelovers #naturephotography #mountains #fall #autumn #colors #arlberg\",\n", + "\"Naxos, Greece\",\n", + "\"vom Winde verweht | #schoten #fieren #raumerwind #mannlee #mannluv #übervorn #schotenlos #reffen #fock #groß #bugmann #heckmann\",\n", + "\"sibling goals\",\n", + "\"lovely team #ferienhort #ferienhortamwolfgangsee #campelicious #love #my #team\",\n", + "\"wishing for another awesome breakfast like today | @the_motto_is_diversity #themottoisdiversity #mottoamfluss #lovely #breakfast #acceptance #liveyourlife\",\n", + "\"the beauty of books & architecture | Stift Admont #austria #igersaustria #beautiful #monastery #stiftadmont #library #books #architecture #aesthetic #symmetry #colors #rose #blue #hiddengems\", \n", + "\"excited for spring #igersaustria #outdoors #beautiful #nature #naturelovers #naturephotography #outdoors #colors #spring #exploreaustria #austria #hallstatt #nofilterneeded\", \n", + "\"excited for spring #igersaustria #outdoors #beautiful #nature #naturelovers #naturephotography #outdoors #colors #spring #exploreaustria #austria #hallstatt #nofilterneeded\", \n", + "\"I think I've found Willy Wonka's chocolate mountain\", \n", + "\"beautiful switzerland\",\n", + "\"#mountains #colors #blue #white #snow #beautiful #nature #naturelovers #naturephotography #outdoors #moretocome #igersaustria #nofilter #travellover #switzerland #jungfraujoch #topofeurope #chocolatemountain #charlieandthechocolatefactory #willywonka\",\n", + "\"#switzerland #suisse #schweiz #beautiful #nature #lake #mountains #colors #blue #autumn #fall #favouriteseason #igersswitzerland #igersaustria #outdoor #walk\",\n", + "\"summer | Attersee, Austria #igersaustria #outdoors #naturephotography #macrophotography #naturelovers #plants #green #sun #details #leaf #beautiful #garden ##memories #missingsummer #anticipating #autumn\",\n", + "\"Kinkaku-ji, Kyoto, Japan #japan #kyoto #asia #travel #temple #traditional #throwback #memories #beautiful #garden #goldenhour #igersaustria #outdoors #travellover #travelwithfriends #pond #nature #naturelovers #naturephotography #reflections #japan_focus\",\n", + "\"Kyoto, Japan 2016 #igersaustria #japan #kyoto #asia #travel #faraway #autumn #fall #temple #traditional #awesome #architecture #wood #ancient #travelwithfriends #island #travellover #throwback #memories\", \"spring love #spring #flower #pink #beautiful #nature #love #instagood #instaspring #garden #tree #enjoy#love #warm #weather #nofilterneeded\",\n", + "\"on top of the world #mountains #Arlberg #Austria #skiing #snowboarding #enjoy #life #holidays #fun #family #friends #fog #clouds #beautiful #blue #white #naturephotography #outdoors\",\n", + "\"autumn 2017 III #fall #autumn #leaves #falltime #season #seasons #instafall #instagood #instaautumn #photooftheday #sunset #colorful #orange #red #autumnweather #fallweather #nature #nofilter #brotherandsister #family #fun\",\n", + "\"autumn 2017 II #autumn #fall #enjoy #light #sunset #beautiful #family #brother #photography #model #nature #instagood #instafall\",\n", + "\"autumn 2017 I #fall #autumn #leaves #falltime #season #seasons #instafall #instagood #instaautumn #photooftheday #colorful #orange #blue #autumnweather #fallweather #nature #sunset #family #brother\",\n", + "\"beautiful sunset at the Acropolis\",\n", + "\"spring #beautiful #nature #Butterfly #red #blue #black #white #rose #pink #colors #spring #concrete #animals #love #cute #fly #away #springtime #instagood #instaspring #instalove161w\",\n", + "\"I need more sunshine right now #travel #traveling #vacation #visiting #traveler #instatravel #instago #instagood #trip #holiday #photooftheday #fun #travelling #tourism #tourist #instapassport #instatraveling\",\n", + "\"missing the beach & the warm weather #japan #fukuoka #temple #bigcity #skyscraper #red #traditional #travel #3weeks #japanrailpass #jrpass #friends #park #beautiful #sightseeing #love #asia\",\n", + "\"Fukuoka, Japan #japan #fukuoka #temple #bigcity #skyscraper #red #traditional #travel #3weeks #japanrailpass #jrpass #friends #park #beautiful #sightseeing #love #asia\",\n", + "\"finally I've put all the pics from japan on my phone this was taken in Nara by @fabiakailphotography #Asia #asian # #Tokyo #Japan #Nara #nature #deer #cute #cheeky #animal #woods #temple #famous #throwback #travel #summer\",\n", + "\"early morning train ride #japan #landscape #woods #river #mirror #clouds #water #green #blue #nofilter #early #morning #train #beautiful #nature #love #backpacking\",\n", + "\"Okinawan beauty #okinawa #beach #sand #sea #blue #skies #summer #hot #sesokojima #beautiful #photography #nature\",\n", + "\"favourite #nofiltr #outdoors #ferienhort #green #fog #mountains #morning #happy #friends #love #holidays\",\n", + "\"miss my little tiger #nature #sky #sun #summer #garden #beautiful #pretty #sunset #cat #cats #catsagram #catstagram #instagood #kitten #kitty #kittens #pet #love #home\",\n", + "\"I am already looking forward to spending my summer holidays there\",\n", + "\"spring at home #nature #sky #sun #spring #beautiful #pretty #sunset #landscape #amazing #view #trip #tree #mountains #landscape_lovers #landscapelovers #landscape_lover #igersaustria #austria #canon\",\n", + "\"already miss this see you next year\",\n", + "\"favourite place\",\n", + "\"#arlberg #austria\",\n", + "\"need this again... #summer #austria #brother #fun\",\n", + "\"cloudy day in the mountains\",\n", + "\"one of these days\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "Gustav= [\"optiimal GNTM watching with the best #teammaureen #lovemaureen #marryme? #throwbackto2013 #gntm #nureinekann #men's night #goodphoto #rockclimbing #heclimbs #climber #climberlife #boulderer #bouldering #mountaineering #mountains #mountainlover #slowandsteadywinstherace #hashtag #like4like #follow4follow #instalife #igerslinz #instaboi\",\n", + "\"Thanks mom für 20 years child support\",\n", + "\"Finally, the lockdown is over. And even though I haven't really been productive, at least my GI tract has. Litterally.\",\n", + "\"#hepoops #pooper #pooping #humanneeds #humanwaste #humansaregross #gross #toiletpaper #corona #lockdown #climber #heclimbs #boulderer #bouldering #rockclimbing #climberlife\",\n", + "\"Can you believe this? Well, it's true either way. I can't believe so many people out there live their lives unaware of this fact! #facts #spreadtheknowledge #educated #educateyourself #climber #climbing #rockclimber #heclimbs #bouldering #boulderersofinstagram #water #bamboo\",\n", + "\"Gotta get those PROUDEINS in, man! Oh yeah, and with all that proudein getting I nearly forgot to take a #foodporn picture, hence the half finished serving\",\n", + "\"#proudeins #nocarb #highprotein #limitierendeaminosäurewas #lowcarb #proteins #food #foodporn #eggs #protein #nutrition #heclimbs #climber #rockclimber #rockclimbing #boulderer #bouldering\",\n", + "\"Who would of thought that? I think this is a great point for eating more of this delicious food so many of us crave during times like these\",\n", + "\"#chocolate #science #research #facts #truth #food #nobodytellsyouthat #heclimbs #rockclimber #rockclimbing #mountaineering #bouldering #boulderer #climber #climberlife\",\n", + "\"You better start working on that beach body now to be in shape in time, and this also involves the right diet. Btw, this avocado I just ate has about 500 000 calories of fat in it. Yeah, you read that right, 500 000. That's more fat than @dkm14 has in his entire body, so Kudos to him!!\",\n", + "\"Come to think of it, I really don't understand how people still think this is a healthy food given these stats...\",\n", + "\"Oh yeah, and I managed to soil myself twice during the process of devouring this delicious, yet sinful treat\",\n", + "\"#helsi #avocado #facts #truth #nofakenews #food #foodporn #beachbody #nocarb #lowprotein #dietfood #diet #veggie #heclimbs #climber #rockclimbing #bouldering #workout de\",\n", + "\"I joyfully did my part today. Thanks wikipedia for carrying me and probably almost every other student of the last 15 years through school and some even through university, too!\",\n", + "\"#wikipedia #education #knowledge #enzyklopedia #thankful #climber #heclimbs #rockclimbing #rockclimber #wikipediaisavalidsource #wikipediaisnotacrediblesource #serious\",\n", + "\"#1 Offseason-Snack: Schwedenbombensemmerl #yummy #niemetz #snack #climbing #heclimbs #mountaineering #gottagetdemcaloriesin\",\n", + "\"Less tape than last time, but also less skin #heclimbs #climbing #bouldering #mountaineering #mountain #outdoor #training #nopainnogain #nohandsphotography\",\n", + "\"One of the #salzkammergut classics: The Mahdlgupf via ferrata. It was nice to take this quite long and thus exhausting challenge again! The struggle paid off, as the view on top was breathtaking\",\n", + "\"#viaferrata #mountain #heclimbs #mountaineering #beautiful\",\n", + "\"Did I use enough tape, @niemanddaheim #heclimbs #climbing #sportsclimbing #hands #training #nopainnogain\",\n", + "\"Hard routes being sent in Gloxwald\"\n", + "\"Beautiful climbs with even more beautiful facial expressions\"\n", + "\"Keep rolling @niemanddaheim @bauki_flo @sonnleitnerjulia\"\n", + "\"#heclimbs #climber #rockclimbing #mountain #mountaineering #granitecrew\",\n", + "\"5th via ferrata done! #geilerhengst #wilderritt #heclimbs #climber #mountain #mountaineering\",\n", + "\"@misternemo and me having some fun dressed up as romans at #petrinum\"]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "Jonas= [\"Hanging Tree\",\n", + "\"When in Rome @vivi.duenwald\",\n", + "\"Shoutout to the best family I have ever had so far #proudmember #loveyou\",\n", + "\"Yep, the weather was nice #imshreddingonsunshine @janabrahms\",\n", + "\"I will find you, and I will kiss you #80s\",\n", + "\"Real or fake?\",\n", + "\"Top of Santiago #trek #manquehue\",\n", + "\"START Summit 2018 is over now for a week already and it was amazing! During my last two years at START, I have met so many inspiring and interesting people and gained so much of experience, which you could never get through studying. being a supporter two years ago, responsible for speakers and content last year and leading this year's project and bringing 2'500 students, founders, investors and more to st Gallen was the the best experience I've had in my life so far\",\n", + "\"Thanks for the whole START Team who made this event possible and now I'm looking forward for a new episode of my life (even though there is still enough to do for START) #STARTSummit\",\n", + "\"AUFSTIEG!!! Erste Liga here we come #wasfüreinteam #ersteliga #grizzlies #stgallen\",\n", + "\"Guck mal, da ist Wasser im Stein #washabichdaeigentlichfüreinenhutauf? #hutcrew #nachuntengucker #lachdochmal #nö #suchen\",\n", + "\"It was a blast!! At START Summit 2017 we welcomed 2'200 founders, students, investors and corporates. \",\n", + "\"Thanks a lot to all partners, that made this event possible, to all participants, that made this event such a great success (and contributed in this lit after show party) and to the rest of the team, that made me having such a good time preparing this!! Now it's time to be #mobile for a little. \",\n", + "\"It was awesome to be part of this team! #hoibese #maschinebrätscher\",\n", + "\"Visiting my old love Boston #boston #goodtobeback #möchtegernmodel #newburystreet #spontan\",\n", + "\"Climbing up the cristo in the sunset was for sure one of the best moments of the Magellan-posttrip in brasil #cristo #rio #brasil #sunset #posttrip #magellan\",\n", + "\"Light house in colonia de suiza #magellan #lighthouse #uruguay #exchange #view\",\n", + "\"It was an amazing time @peterescardo ! 10 days of exploring Switzerland and showing the culture, having fun and meeting awesome people from Uruguay! Looking forward to the second part in Uruguay #exchange #switzerland #uruguay #10days #zurich #nosleep #2minutes\",\n", + "\"Milano ! #tuttobene #ac\",\n", + "\"HSG-Ball 2015 @annamaria2662 #warnice #staytilltheend #hsg #prom\",\n", + "\"Bali #incredible #horizon #infinity #pool #gettingsometanon #bali #seminyak\",\n", + "\"Cruising in a nutshell around Mallorca with the banana crew #yachtlife\",\n", + "\"Step 2 on the journey: Washington, D.C. @scaryder #washington #dc #mamorial #national #mall #lincoln #whitehouse #capital #usa #travel #eastcoast\",\n", + "\"Nice days in Philadelphia with @scaryder #philadelphia #libertybell #rocky #skyline #philly #usa #travel #fun #steps\",\n", + "\"Memorial holidays in cape cod with the kings friends @matzee49 @cesar77muse @shion_wtnb @tiziana_cristina @lenajmay @scaryder @niqqi18 @lilita2824 #cape #cod #capecod #massachusetts #memorial #holidays #nice #wheather #beach #kingsboston #sky\",\n", + "\"Baseball lessons with our pro @jdagreda and @matzee49 #baseball #lessons #bestteacherever #jesus #kingscollege #boston\",\n", + "\"Sunset and sunrise at the Acadia national park, Maine #sunset #sunrise #acadia #national #park #maine #usa #beautiful #sun #sky #horizon #barharbor\",\n", + "\"Boston celtics - Cleveland Cavaliers playoffs game 3 #basketball #nba #boston #celtics #vs #cleveland #cavaliers #playoffs #third #game #lost #tdgarden #letsgoceltics\",\n", + "\"Kings College Boston !!! #kings #college #boston #friends\",\n", + "\"Fucking freezing at the rainy boston marathon #bostonmarathon #boston #marathon #rainy #shitwheather #cold #lastmile\",\n", + "\"Kings Friends at stanway stadium for Boston red sox - Washington nationals #kings #college #stanway #boston #redsox #vs #washington #nationals #baseball #stadium #sunglassesmusthave\",\n", + "\"Season is over #vergingwieimflug #freestyle #ski #funpark #kicker #valdifassa #italy\",\n", + "\"War ne geile ausbildungs zeit in #alpbach !\",\n", + "\"Skilehrer in Obertauern #Traumjob #bluecrew\",\n", + "\"Los hombres alemanes en Costa Rica ;D #christmastree\",\n", + "\"having a great time in costa rica and nicaragua with @alemurillov\",\n", + "\"Party, Palmen Weiber und n Bier @janabrahms #sister #mallorca #malle #chill #pool #mar #sol #Beer #sun #españa #nais #holiday\",\n", + "\"Deutschland 2 - Ghana 2 - good luck today vs #castelao #fortaleza #wm #wc #2014 #deutschland #germany #alemanha #ghana #gana #todomundo #good #luck #jogapramim #heissaufheuteabend #football #stadium #viertelfinale #vs #france\",\n", + "\"Ich bin all das wovor deine Eltern dich immer gewarnt haben #mottowoche #tag #3 #sido #kiffer #harzer #penner #fun #sörenderlund #sidosmaske\",\n", + "\"Die Moral von der geschicht... Bremen ist geil, Hamburg nicht #Derbysieg #einszunull #werder #heftigste #choreo #ultras #bremen #stadion #hundertstes #derby #sitdownhsv #stimmung #fans #happy #nice #day #schadehamburg\",\n", + "\"#skifoan #saalbach #hinterglemm #snow #mountains #oleoleole #saalbacherschneekatzen #fun\",\n", + "\"#fehmarn #isschongeil #sea #occean #sky #clouds #sailing #follow #your #instinct #vacations\",\n", + "\"#chil #sandiego #beach #sun #blue #sky #follow #sea #waves ##vacation #good #time\",\n", + "\"#sandiego #missionbeach #beach #hot #sun #sunset #follow #sea #pacific #cali #california #red #sky #clouds #cloudporn #boy\",\n", + "\"#baseball #game #losangeles #dodgers #vs #Cincinnati #reds #stadium #sky #clouds #la #california #cali #fun #dodgerstadium\",\n", + "\"Highway No. 1 #california #highway #sun #vacation #happy\",\n", + "\"@trolflex #party #friends #old #but #gold #fun #fact #club #night #music #sunglasses #möve #l4l\",\n", + "\"#saalbach#ski#schnee#snow#fun#mountains\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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nametypetext
0DaryaESFPQuarantine expectations. Swipe for reality 22 ...
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0TheresaENFPlight times simplicité. chapter 1 *waiter pour...
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0NicoENFPHaven't posted in a while, so this random pict...
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0DylanENTJHappy golden Birthday Kayla! 22 years on Earth...
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0KatieINFJSonndorf VI #autumn #fall #colors #igersaustri...
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0GustavISFJoptiimal GNTM watching with the best #teammaur...
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0JonasESFJHanging Tree When in Rome @vivi.duenwald Shou...
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0DaryaESFPQuarantine expectations. Swipe for reality 22 ...
0TheresaENFPlight times simplicité. chapter 1 *waiter pour...
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0KatieINFJSonndorf VI #autumn #fall #colors #igersaustri...
0DylanENTJHappy golden Birthday Kayla! 22 years on Earth...
0GustavISFJoptiimal GNTM watching with the best #teammaur...
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nametypetext_ready
0DaryaESFPquarantin expect swipe realiti swipe see photo...
0TheresaENFPlight time simplicit chapter waiter pour wine ...
0NicoENFPpost thi random pictur fill void go within lot...
0KatieINFJsonndorf vi autumn fall color igersaustria out...
0DylanENTJhappi golden birthday kayla year earth photo s...
0GustavISFJoptiim gntm watch best teammaureen lovemaureen...
0JonasESFJhang tree rome vivi duenwald shoutout best fam...
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Swipe for reality\", \n", - " \"22 in 2020 - swipe to see a photo from yesterday. Age affects me.\", \n", - " \"hello? Santa? All I wish for Christmas is my mind back... \", \n", - " \"girl with a hat - a series. \", \n", - " \"casual day at uni. swipe for more idiots\", \n", - " \"thanking Steve for a nice iPhone front camera.\", \n", - " \"Missing this\", \n", - " \"*edit: still can‘t believe a sunscreen for 7.95 prevented me from having a huge sunburn\",\n", - " \"where I‘d rather be part 2\",\n", - " \"when people ask me if I‘ve got free time - I do. Except I don’t.\",\n", - " \n", - " \"just a small town girl, living in a capitalist world...\",\n", - " \"clueless\",\n", - " \"Might be serious, might be hungry. \",\n", - " \"you want blue? you want yellow?\",\n", - " \"While the wall fell nearly 30 years ago, I constantly fall on my face...\",\n", - " \"Im pretty ok with exams being over... \",\n", - " \"unpopular opinion: university can be fun. exhibit a\",\n", - " \"christmas is around the corner but all I‘m wishing for is an extention of deadlines...\",\n", - " \"on some occasions I even get out of the valley and LA to go sightseeing \",\n", - " \"diamonds are a girls best friend. Especially when you trade ‘em for plane tickets and uber rides\",\n", - " \n", - " \"not a threat, it’s a warning. (talking about the beginning of semester tomorrow)\",\n", - " \"come fly with me, let’s fly let’s fly away...\",\n", - " \"saltier than the Pacific\",\n", - " \"me, when I’m a) hangry or b) am studying @ nyfalosangeles @ williamroyal98\",\n", - " \"this whole you-gotta-study-while-the-sun-is-shining gets on my nerve. here’s an example of inner rage\",\n", - " \"you know you might have been spontaneous when while booking your flights they asked you if ou would want to check in for the flight back aswell.\",\n", - " \"Vitamins\",\n", - " \"finished the first semerster at HSG and I still can’t afford a porsche 911.\",\n", - " \"Me: you should be studying. Also me: enjoy the holidays\",\n", - " \"just kidding - I’m still a short ass little human\",\n", - " \n", - " \"week 2 at uni and my icloud and google drive are asking me to pay for more storage.\",\n", - " \"uni starts tomorrow and I already see myself either a) being lost cause I can't find the classroom or b) looking like Annabelle cause sleep is for the weak\",\n", - " \"oh boy\",\n", - " \"this should be my last week of summer but I think the weather decided to give me an early taste of fall already.\",\n", - " \"water falls, darya falls and just casually sit on floors cause standing is overrated \",\n", - " \"I promise I counted all the lights, but then I saw a squirrel and lost count \",\n", - " \"is it a set? Is it a real house? guess.\",\n", - " \"quiet on set\",\n", - " \"Baywatch, are you hiring?\",\n", - " \"What if perfume is actually bugspray but they want us to buy the nasty smelly stuff just to have a good laugh?\",\n", - " \n", - " \"I'm starting to wonder if spotlight can give you a tan cause the sun isn't coming to just yet. All jokes asid tho, I am beyond grateful to share the stage with so many talented people. Do what you love and you'll feel limitless \",\n", - " \"that second when the person you've been straing at looks at you and you quickly turn away, trying to look as casual as possible\",\n", - " \"at that moment I probably thought out a plan to convince my mom to paint one of my walls pink. even though I know it won't happen, happy #internationalwomensday ladies\",\n", - " \"Tell me about it, stud\",\n", - " \"nice to be back. bring it on 2017\"\n", - " \"2016. A year that had probably more faces than facebook. Victories and dissapointment, new friendships and new experiences; I was lucky enough to travel the several times and can't thank the people that made 2016 the year it was enough for all their kind words, laughter and happy tears.\",\n", - " \"don't mess with us - we have RG clubs\",\n", - " \"Summer is over and you're like: pardon me, what?\",\n", - " \"Looking back at those 5 weeks full of new friends, experiences and emotions puts a smile on my face. The teachers I've worked with inspired me every day, the people made my summer the best one yet. I love all of you and you'll always have a special place in my heart #obgottheheat and happy National Dance Day\",\n", - " \"everybody needs some attitude in their life #punintended #obgottheheat\"\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 99, - "metadata": {}, - "outputs": [], - "source": [ - "darya_test = {'name': ['Darya'], 'type': ['ESFP']}" - ] - }, - { - "cell_type": "code", - "execution_count": 100, - "metadata": {}, - "outputs": [], - "source": [ - "darya_row = pd.DataFrame(darya_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 101, - "metadata": {}, - "outputs": [], - "source": [ - "darya_row['text'] = ' '.join(Darya)" - ] - }, - { - "cell_type": "code", - "execution_count": 102, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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8675ESFPquarantin expect swipe realiti swipe see photo...ESFP
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" - ], - "text/plain": [ - " type text_ready I-E N-S T-F J-P\n", - "8671 INTP mani question thing would take purpl pill pick... I N T P\n", - "8672 INFP veri conflict right come want child honestli m... I N F P\n", - "8673 INFP ha long sinc personalitycaf although seem chan... I N F P\n", - "8674 ESFP quarantin expect swipe realiti swipe see photo... E S F P\n", - "8675 ESFP quarantin expect swipe realiti swipe see photo... E S F P" - ] - }, - "execution_count": 126, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data2.tail()" - ] - }, - { - "cell_type": "code", - "execution_count": 127, - "metadata": {}, - "outputs": [], - "source": [ - "x = data2['text_ready']\n", - "\n", - "y_IE = data2['I-E']\n", - "y_NS = data2['N-S']\n", - "y_TF = data2['T-F']\n", - "y_JP = data2['J-P']" - ] - }, - { - "cell_type": "code", - "execution_count": 128, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.feature_extraction.text import CountVectorizer\n", - "\n", - "vector = CountVectorizer(ngram_range=(2, 2)).fit(x) \n", - "X = vector.transform(x)" - ] - }, - { - "cell_type": "code", - "execution_count": 140, - "metadata": {}, - "outputs": [], - "source": [ - "y_test = list(y_IE[8675])" - ] - }, - { - "cell_type": "code", - "execution_count": 130, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'E'" - ] - }, - "execution_count": 130, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y_test" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.model_selection import train_test_split, cross_val_score" - ] - }, - { - "cell_type": "code", - "execution_count": 141, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "I-E RESULTS\n", - "Label I-E train score is : 0.7696564445469218\n", - "Label I-E test score is : 0.7696564445469218\n", - "Confusion Matrix for K-Nearest Neighbors:\n", - "[[0 1]\n", - " [0 0]]\n", - "Score: 0.0\n", - "Classification Report: precision recall f1-score support\n", - "\n", - " E 0.00 0.00 0.00 1.0\n", - " I 0.00 0.00 0.00 0.0\n", - "\n", - " accuracy 0.00 1.0\n", - " macro avg 0.00 0.00 0.00 1.0\n", - "weighted avg 0.00 0.00 0.00 1.0\n", - "\n", - "Prediction is: ['I']\n", - "Actual is: ['E']\n", - "****************************************************************************************************\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", - " _warn_prf(average, modifier, msg_start, len(result))\n", - "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n", - " _warn_prf(average, modifier, msg_start, len(result))\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "N-S RESULTS\n", - "Label N-S train score is : 0.8620013834447775\n", - "Label N-S test score is : 0.8620013834447775\n", - "Confusion Matrix for Random Forests:\n", - "[[0 0]\n", - " [1 0]]\n", - "Score: 0.0\n", - "Classification Report: precision recall f1-score support\n", - "\n", - " N 0.00 0.00 0.00 0.0\n", - " S 0.00 0.00 0.00 1.0\n", - "\n", - " accuracy 0.00 1.0\n", - " macro avg 0.00 0.00 0.00 1.0\n", - "weighted avg 0.00 0.00 0.00 1.0\n", - "\n", - "Prediction is: ['N']\n", - "Actual is: ['S']\n", - "****************************************************************************************************\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", - " _warn_prf(average, modifier, msg_start, len(result))\n", - "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n", - " _warn_prf(average, modifier, msg_start, len(result))\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "T-F Results\n", - "Label T-F train score is : 1.0\n", - "Label T-F test score is : 1.0\n", - "Confusion Matrix for Multinomial Naive Bayes:\n", - "[[1]]\n", - "Score: 100.0\n", - "Classification Report: precision recall f1-score support\n", - "\n", - " F 1.00 1.00 1.00 1\n", - "\n", - " accuracy 1.00 1\n", - " macro avg 1.00 1.00 1.00 1\n", - "weighted avg 1.00 1.00 1.00 1\n", - "\n", - "Prediction is: ['F']\n", - "Actual is: ['F']\n", - "****************************************************************************************************\n", - "J-P Results\n", - "Label J-P train score is : 1.0\n", - "Label J-P test score is : 1.0\n", - "Confusion Matrix for Multinomial Naive Bayes:\n", - "[[0 0]\n", - " [1 0]]\n", - "Score: 0.0\n", - "Classification Report: precision recall f1-score support\n", - "\n", - " J 0.00 0.00 0.00 0.0\n", - " P 0.00 0.00 0.00 1.0\n", - "\n", - " accuracy 0.00 1.0\n", - " macro avg 0.00 0.00 0.00 1.0\n", - "weighted avg 0.00 0.00 0.00 1.0\n", - "\n", - "Prediction is: ['J']\n", - "Actual is: ['P']\n", - "****************************************************************************************************\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", - " _warn_prf(average, modifier, msg_start, len(result))\n", - "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n", - " _warn_prf(average, modifier, msg_start, len(result))\n" - ] - } - ], - "source": [ - "from sklearn.neighbors import KNeighborsClassifier\n", - "knn = KNeighborsClassifier(n_neighbors=10) # see if can tweak accuracy\n", - "\n", - "from sklearn.ensemble import RandomForestClassifier\n", - "rfn = RandomForestClassifier(max_depth=5, random_state=0) # see if can tweak accuracy\n", - "\n", - "from sklearn.naive_bayes import MultinomialNB\n", - "mnb = MultinomialNB()\n", - "\n", - "\n", - "\n", - "# IE\n", - "x_train = X[0:8674]\n", - "x_test = X[8675]\n", - "y_train = y_IE[0:8674]\n", - "y_test = list(y_IE[8675])\n", - "knn.fit(x_train, y_train)\n", - "ieb_train = knn.score (x_train,y_train)\n", - "ieb_test = knn.score (x_train,y_train)\n", - "predknn = knn.predict(x_test)\n", - "print(\"I-E RESULTS\")\n", - "print('Label I-E train score is :',ieb_train)\n", - "print('Label I-E test score is :',ieb_test)\n", - "print(\"Confusion Matrix for K-Nearest Neighbors:\")\n", - "print(confusion_matrix(y_test,predknn))\n", - "print(\"Score:\",round(accuracy_score(y_test,predknn)*100,2))\n", - "print(\"Classification Report:\",classification_report(y_test,predknn))\n", - "print('Prediction is: ', predknn)\n", - "print('Actual is: ', y_test)\n", - "print(\"*\" * 100)\n", - "\n", - "# NS\n", - "x_train = X[0:8674]\n", - "x_test = X[8675]\n", - "y_train = y_NS[0:8674]\n", - "y_test = list(y_NS[8675])\n", - "rfn.fit(x_train, y_train)\n", - "nsb_train = rfn.score (x_train,y_train)\n", - "nsb_test = rfn.score (x_train,y_train)\n", - "predrfn = rfn.predict(x_test)\n", - "print(\"N-S RESULTS\")\n", - "print('Label N-S train score is :',nsb_train)\n", - "print('Label N-S test score is :',nsb_test)\n", - "print(\"Confusion Matrix for Random Forests:\")\n", - "print(confusion_matrix(y_test,predrfn))\n", - "print(\"Score:\",round(accuracy_score(y_test,predrfn)*100,2))\n", - "print(\"Classification Report:\",classification_report(y_test,predrfn))\n", - "print('Prediction is: ', predrfn)\n", - "print('Actual is: ', y_test)\n", - "print(\"*\" * 100)\n", - "\n", - "# TF\n", - "x_train = X[0:8674]\n", - "x_test = X[8675]\n", - "y_train = y_TF[0:8674]\n", - "y_test = list(y_TF[8675])\n", - "mnb.fit(x_train, y_train)\n", - "tfb_train = mnb.score (x_train,y_train)\n", - "tfb_test = mnb.score (x_train,y_train)\n", - "predmnb = mnb.predict(x_test)\n", - "print(\"T-F Results\")\n", - "print('Label T-F train score is :',tfb_train)\n", - "print('Label T-F test score is :',tfb_test)\n", - "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", - "print(confusion_matrix(y_test,predmnb))\n", - "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", - "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", - "print('Prediction is: ', predmnb)\n", - "print('Actual is: ', y_test)\n", - "print(\"*\" * 100)\n", - "\n", - "# JP\n", - "x_train = X[0:8674]\n", - "x_test = X[8675]\n", - "y_train = y_JP[0:8674]\n", - "y_test = list(y_JP[8675])\n", - "mnb.fit(x_train, y_train)\n", - "jpb_train = mnb.score (x_train,y_train)\n", - "jpb_test = mnb.score (x_train,y_train)\n", - "predmnb = mnb.predict(x_test)\n", - "print(\"J-P Results\")\n", - "print('Label J-P train score is :',jpb_train)\n", - "print('Label J-P test score is :',jpb_test)\n", - "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", - "print(confusion_matrix(y_test,predmnb))\n", - "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", - "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", - "print('Prediction is: ', predmnb)\n", - "print('Actual is: ', y_test)\n", - "print(\"*\" * 100)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/your-project/code/testing nlp on Laurents.ipynb b/your-project/code/testing nlp on Laurents.ipynb deleted file mode 100644 index fca549fb..00000000 --- a/your-project/code/testing nlp on Laurents.ipynb +++ /dev/null @@ -1,972 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# putting together friends dataframe" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "import re\n", - "\n", - "import nltk\n", - "from nltk.stem import PorterStemmer\n", - "from nltk.stem import SnowballStemmer\n", - "from nltk import wordnet\n", - "from nltk.corpus import stopwords\n", - "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n", - "from sklearn.model_selection import train_test_split, cross_val_score\n", - "from sklearn.metrics import classification_report\n", - "from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score, roc_curve\n", - "\n", - "from sklearn.model_selection import train_test_split, cross_val_score\n", - "from sklearn.model_selection import cross_validate\n", - "from sklearn.model_selection import StratifiedKFold\n", - "from sklearn.linear_model import LogisticRegression\n", - "from sklearn.model_selection import learning_curve\n", - "from sklearn.ensemble import ExtraTreesClassifier\n", - "from sklearn.decomposition import TruncatedSVD\n", - "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n", - "from sklearn.pipeline import Pipeline\n", - "from sklearn.naive_bayes import MultinomialNB\n", - "from sklearn.metrics import classification_report\n", - "from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score, roc_curve" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "Laurents = [\"Haven't posted in a while, so this random picture is now filling the void. Been going within a lot in the past year and sorted out some things for myself. Happy to say, that I am refocused, re-energized and working on great things.\", \n", - " \"Impressions from Serfaus. 2020 is off to a good start. Swipe right for the best drinking game\", \n", - " \"Dogs of Berlin\", \n", - " \"Not my boat, still flexing \", \n", - " \"Lads\", \n", - " \"A weekend in Madrid is always a good idea! Hanging out the hangover at some historical sites \", \n", - " \"Drip too hard, don’t stand too close\", \n", - " \"I miss you bro. We pushed each other to be the best every day. There never was a better duo. „The Golden Boys“ Rest Easy\",\n", - " \"Getting settled in Berlin\",\n", - " \"It’s been three tough but fun years at WHU. Last weekend I finally graduated with a degree from the best business school in Germany, but more importantly with new friends for life and another place I can call home.\",\n", - " \n", - " \"Graduated\",\n", - " \"WHU boys - Class of 2017. Had so much fun with you. Now let's get the moolah\",\n", - " \"The pic is blurry but the day was sharp\",\n", - " \"Probably the most spectacular view I ever had was driving along the highway no. 1 between LA and SF\",\n", - " \"He acting all loyal in this pic, but that lil b* bit me the next minute\",\n", - " \"Probably the most spectacular view I ever had was driving along the highway no. 1 between LA and SF.\",\n", - " \"Hey @google, Hire me, if you want your bike back! #mountainview #google #siliconvalley\",\n", - " \"Throwback to an amazing road trip this fall. What a view! Def. worth the hike #hike #nature #travel #roadtrip #yosemite\",\n", - " \"Keep your eyes on the stars and keep your feet on the ground\",\n", - " \"You can clearly see I enjoyed being back in SoCal. That place is so beautiful...\",\n", - " \n", - " \"he Pacific #beers #beach #chilling #california #home #uglyfeet @goldclapmusic good day dude\",\n", - " \"Never hold back #weekendmood\",\n", - " \"Thank you @kaleandme for this awesome party! #crewlove @reiseminister_coco\",\n", - " \"tb to one of my best games ever Still holding the club record for most receptions per game 2013 vs Cottbus W31:15 13 receptions, 193 yards, 3 TDs Thank you @zbreez_ for the trust and the passes !\",\n", - " \"Throwback to the best winter ever. My kids group. I gave us the name the lil crackheads, because they got me cracking up all the time! #fuckhashtags\",\n", - " \"Won this one big time 60:20, but it cost us some good men... #hospitalreunion\",\n", - " \"#Repost @luebeckcougars Much way for improvement though personally. #getbettereveryday Gamestats #cougars #cougarnation #cougarspride #luebeck #luebeckcougars #football #americanfootball #gfl #amgid #afvd #gridiron #awaygame #ritterhude #badgers\",\n", - " \"100 days credits @gridiron_design_s\",\n", - " \"New Year's Eve in the alps with the crew. #illuminati\",\n", - " \"#tb to the good old times when sunday meant gameday and mondays newspaper Sitting in the library studying right now I wish I could go back in time\",\n", - " \n", - " \"What you believe becomes reality. Create your own world #sundaymood\",\n", - " \"Mom, can we have one?\",\n", - " \"Trying to get my surf game up. I think I have a much better form on the beach than in the water though haha #germansaintmermaids @lennartmhr\",\n", - " \"Table Mountain from Lion's Head @lennartmhr\",\n", - " \"I hate off-season... \",\n", - " \"This shoutout is overdue. Best musical speaker @realcoleworld!\",\n", - " \"#tb to when my bro @enzeocha_11 was making the Swedish defense look like 8th graders.\",\n", - " \"Hoffentlich liegt das in der Familie! #crossingfingers #toodamnhot\",\n", - " \"Touchdown 70:46 crazy game!\",\n", - " \"Some work by @dani_piedrabuena. Trying to look smart in that suit haha \",\n", - " \n", - " \"Back at it\",\n", - " \"Received this at work. Now my colleagues think I'm famous lol Thank you so much Colin!\",\n", - " \"#tb to the toughest job I ever had to do good times\",\n", - " \"office break \",\n", - " \"Beach is better. #barcelona #barca #beach #playa #sun #smile #spain #sleepyface\"\n", - " \"Thank you for such a lovely birthday night.\",\n", - " \"My new car... I wish. M4 Test Drive. Still having goosebumps #bmw #m4 #madrid #racing\",\n", - " \"Don't mess with NFL Runningbacks\",\n", - " \"perfect day at el Retiro\",\n", - " \"Morning routine. Scooter Ride #madrid #work #watch #scooter #sun #suit\"\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "laurents_test = {'name': ['Laurents'], 'type': ['ENFP']}" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "laurents_row = pd.DataFrame(laurents_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "laurents_row['text'] = ' '.join(Laurents)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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nametypetext
0LaurentsENFPHaven't posted in a while, so this random pict...
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" - ], - "text/plain": [ - " name type text\n", - "0 Laurents ENFP Haven't posted in a while, so this random pict..." - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "laurents_row" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "def processed(x):\n", - " pattern = r'''(?i)\\b((?:https?://|www\\d{0,3}[.]|[a-z0-9.\\-]+[.][a-z]{2,4}/)(?:[^\\s()<>]+|\\(([^\\s()<>]+|(\\([^\\s()<>]+\\)))*\\))+(?:\\(([^\\s()<>]+|(\\([^\\s()<>]+\\)))*\\)|[^\\s`!()\\[\\]{};:'\".,<>?«»“”‘’]))'''\n", - " cleaned = re.sub(pattern, ' ', str(x)) \n", - " cleaned = re.sub(r\"[^A-Za-z0-9]+\", \" \", cleaned)\n", - " cleaned = re.sub(\" \\d+\", \" \", cleaned)\n", - " cleaned = cleaned.lower().strip()\n", - "\n", - " pattern2 = '\\w{1,}'\n", - " bag = re.findall(pattern2, cleaned)\n", - "\n", - " porter = PorterStemmer()\n", - " porter_bag = [porter.stem(word) for word in bag]\n", - "\n", - " lemmatizer = wordnet.WordNetLemmatizer()\n", - " bag_of_words = [lemmatizer.lemmatize(word) for word in porter_bag]\n", - "\n", - " stopwords_e = stopwords.words('english')\n", - " bag_of_words = [word for word in bag_of_words if word not in stopwords_e]\n", - "\n", - " return bag_of_words" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "text_processed = [processed(i) for i in laurents_row.text]" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "laurents_row['text_processed'] = text_processed" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "laurents_row['text_ready'] = laurents_row.text_processed.apply(' '.join)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "laurents_row = laurents_row.drop(columns = ['name', 'text', 'text_processed'])" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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0ENFPpost thi random pictur fill void go within lot...
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" - ], - "text/plain": [ - " type text_ready\n", - "0 ENFP post thi random pictur fill void go within lot..." - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "laurents_row" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "data = pd.read_csv(r'../data/personalities_cleaned.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "data = data.drop(columns = ['Unnamed: 0', 'posts', 'text_processed'])" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "data.drop(data.index[3559], inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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0INFJintj moment sportscent top ten play prank ha l...
1ENTPfind lack post veri alarm sex bore posit often...
2INTPgood one cours say know bless cur doe absolut ...
3INTJdear intp enjoy convers day esoter gab natur u...
4ENTJfire anoth silli misconcept approach logic go ...
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" - ], - "text/plain": [ - " type text_ready\n", - "0 INFJ intj moment sportscent top ten play prank ha l...\n", - "1 ENTP find lack post veri alarm sex bore posit often...\n", - "2 INTP good one cours say know bless cur doe absolut ...\n", - "3 INTJ dear intp enjoy convers day esoter gab natur u...\n", - "4 ENTJ fire anoth silli misconcept approach logic go ..." - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "data = data.append(laurents_row, ignore_index = True)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "data2 = data.copy()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# cleaning and processing\n", - "\n", - "data2['I-E'] = data2['type'].astype(str).str[0]\n", - "data2['N-S'] = data2['type'].astype(str).str[1]\n", - "data2['T-F'] = data2['type'].astype(str).str[2]\n", - "data2['J-P'] = data2['type'].astype(str).str[3]" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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typetext_readyI-EN-ST-FJ-P
0INFJintj moment sportscent top ten play prank ha l...INFJ
1ENTPfind lack post veri alarm sex bore posit often...ENTP
2INTPgood one cours say know bless cur doe absolut ...INTP
3INTJdear intp enjoy convers day esoter gab natur u...INTJ
4ENTJfire anoth silli misconcept approach logic go ...ENTJ
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" - ], - "text/plain": [ - " type text_ready I-E N-S T-F J-P\n", - "0 INFJ intj moment sportscent top ten play prank ha l... I N F J\n", - "1 ENTP find lack post veri alarm sex bore posit often... E N T P\n", - "2 INTP good one cours say know bless cur doe absolut ... I N T P\n", - "3 INTJ dear intp enjoy convers day esoter gab natur u... I N T J\n", - "4 ENTJ fire anoth silli misconcept approach logic go ... E N T J" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data2.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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typetext_readyI-EN-ST-FJ-P
8670ENFPthi thread alreadi exist someplac el doe heck ...ENFP
8671INTPmani question thing would take purpl pill pick...INTP
8672INFPveri conflict right come want child honestli m...INFP
8673INFPha long sinc personalitycaf although seem chan...INFP
8674ENFPpost thi random pictur fill void go within lot...ENFP
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" - ], - "text/plain": [ - " type text_ready I-E N-S T-F J-P\n", - "8670 ENFP thi thread alreadi exist someplac el doe heck ... E N F P\n", - "8671 INTP mani question thing would take purpl pill pick... I N T P\n", - "8672 INFP veri conflict right come want child honestli m... I N F P\n", - "8673 INFP ha long sinc personalitycaf although seem chan... I N F P\n", - "8674 ENFP post thi random pictur fill void go within lot... E N F P" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data2.tail()" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "x = data2['text_ready']\n", - "\n", - "y_IE = data2['I-E']\n", - "y_NS = data2['N-S']\n", - "y_TF = data2['T-F']\n", - "y_JP = data2['J-P']" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.feature_extraction.text import CountVectorizer\n", - "\n", - "vector = CountVectorizer(ngram_range=(2, 2)).fit(x) \n", - "X = vector.transform(x)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "y_test = list(y_IE[8674])" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['E']" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y_test" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.model_selection import train_test_split, cross_val_score" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "I-E RESULTS\n", - "Label I-E train score is : 0.7696298858526461\n", - "Label I-E test score is : 0.7696298858526461\n", - "Confusion Matrix for K-Nearest Neighbors:\n", - "[[0 1]\n", - " [0 0]]\n", - "Score: 0.0\n", - "Classification Report: precision recall f1-score support\n", - "\n", - " E 0.00 0.00 0.00 1.0\n", - " I 0.00 0.00 0.00 0.0\n", - "\n", - " accuracy 0.00 1.0\n", - " macro avg 0.00 0.00 0.00 1.0\n", - "weighted avg 0.00 0.00 0.00 1.0\n", - "\n", - "Prediction is: ['I']\n", - "Actual is: ['E']\n", - "****************************************************************************************************\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", - " _warn_prf(average, modifier, msg_start, len(result))\n", - "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n", - " _warn_prf(average, modifier, msg_start, len(result))\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "N-S RESULTS\n", - "Label N-S train score is : 0.8619854721549637\n", - "Label N-S test score is : 0.8619854721549637\n", - "Confusion Matrix for Random Forests:\n", - "[[1]]\n", - "Score: 100.0\n", - "Classification Report: precision recall f1-score support\n", - "\n", - " N 1.00 1.00 1.00 1\n", - "\n", - " accuracy 1.00 1\n", - " macro avg 1.00 1.00 1.00 1\n", - "weighted avg 1.00 1.00 1.00 1\n", - "\n", - "Prediction is: ['N']\n", - "Actual is: ['N']\n", - "****************************************************************************************************\n", - "T-F Results\n", - "Label T-F train score is : 1.0\n", - "Label T-F test score is : 1.0\n", - "Confusion Matrix for Multinomial Naive Bayes:\n", - "[[0 1]\n", - " [0 0]]\n", - "Score: 0.0\n", - "Classification Report: precision recall f1-score support\n", - "\n", - " F 0.00 0.00 0.00 1.0\n", - " T 0.00 0.00 0.00 0.0\n", - "\n", - " accuracy 0.00 1.0\n", - " macro avg 0.00 0.00 0.00 1.0\n", - "weighted avg 0.00 0.00 0.00 1.0\n", - "\n", - "Prediction is: ['T']\n", - "Actual is: ['F']\n", - "****************************************************************************************************\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", - " _warn_prf(average, modifier, msg_start, len(result))\n", - "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n", - " _warn_prf(average, modifier, msg_start, len(result))\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "J-P Results\n", - "Label J-P train score is : 1.0\n", - "Label J-P test score is : 1.0\n", - "Confusion Matrix for Multinomial Naive Bayes:\n", - "[[0 0]\n", - " [1 0]]\n", - "Score: 0.0\n", - "Classification Report: precision recall f1-score support\n", - "\n", - " J 0.00 0.00 0.00 0.0\n", - " P 0.00 0.00 0.00 1.0\n", - "\n", - " accuracy 0.00 1.0\n", - " macro avg 0.00 0.00 0.00 1.0\n", - "weighted avg 0.00 0.00 0.00 1.0\n", - "\n", - "Prediction is: ['J']\n", - "Actual is: ['P']\n", - "****************************************************************************************************\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", - " _warn_prf(average, modifier, msg_start, len(result))\n", - "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n", - " _warn_prf(average, modifier, msg_start, len(result))\n" - ] - } - ], - "source": [ - "from sklearn.neighbors import KNeighborsClassifier\n", - "knn = KNeighborsClassifier(n_neighbors=10) # see if can tweak accuracy\n", - "\n", - "from sklearn.ensemble import RandomForestClassifier\n", - "rfn = RandomForestClassifier(max_depth=5, random_state=0) # see if can tweak accuracy\n", - "\n", - "from sklearn.naive_bayes import MultinomialNB\n", - "mnb = MultinomialNB()\n", - "\n", - "\n", - "\n", - "# IE\n", - "x_train = X[0:8673]\n", - "x_test = X[8674]\n", - "y_train = y_IE[0:8673]\n", - "y_test = list(y_IE[8674])\n", - "knn.fit(x_train, y_train)\n", - "ieb_train = knn.score (x_train,y_train)\n", - "ieb_test = knn.score (x_train,y_train)\n", - "predknn = knn.predict(x_test)\n", - "print(\"I-E RESULTS\")\n", - "print('Label I-E train score is :',ieb_train)\n", - "print('Label I-E test score is :',ieb_test)\n", - "print(\"Confusion Matrix for K-Nearest Neighbors:\")\n", - "print(confusion_matrix(y_test,predknn))\n", - "print(\"Score:\",round(accuracy_score(y_test,predknn)*100,2))\n", - "print(\"Classification Report:\",classification_report(y_test,predknn))\n", - "print('Prediction is: ', predknn)\n", - "print('Actual is: ', y_test)\n", - "print(\"*\" * 100)\n", - "\n", - "# NS\n", - "x_train = X[0:8673]\n", - "x_test = X[8674]\n", - "y_train = y_NS[0:8673]\n", - "y_test = list(y_NS[8674])\n", - "rfn.fit(x_train, y_train)\n", - "nsb_train = rfn.score (x_train,y_train)\n", - "nsb_test = rfn.score (x_train,y_train)\n", - "predrfn = rfn.predict(x_test)\n", - "print(\"N-S RESULTS\")\n", - "print('Label N-S train score is :',nsb_train)\n", - "print('Label N-S test score is :',nsb_test)\n", - "print(\"Confusion Matrix for Random Forests:\")\n", - "print(confusion_matrix(y_test,predrfn))\n", - "print(\"Score:\",round(accuracy_score(y_test,predrfn)*100,2))\n", - "print(\"Classification Report:\",classification_report(y_test,predrfn))\n", - "print('Prediction is: ', predrfn)\n", - "print('Actual is: ', y_test)\n", - "print(\"*\" * 100)\n", - "\n", - "# TF\n", - "x_train = X[0:8673]\n", - "x_test = X[8674]\n", - "y_train = y_TF[0:8673]\n", - "y_test = list(y_TF[8674])\n", - "mnb.fit(x_train, y_train)\n", - "tfb_train = mnb.score (x_train,y_train)\n", - "tfb_test = mnb.score (x_train,y_train)\n", - "predmnb = mnb.predict(x_test)\n", - "print(\"T-F Results\")\n", - "print('Label T-F train score is :',tfb_train)\n", - "print('Label T-F test score is :',tfb_test)\n", - "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", - "print(confusion_matrix(y_test,predmnb))\n", - "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", - "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", - "print('Prediction is: ', predmnb)\n", - "print('Actual is: ', y_test)\n", - "print(\"*\" * 100)\n", - "\n", - "# JP\n", - "x_train = X[0:8673]\n", - "x_test = X[8674]\n", - "y_train = y_JP[0:8673]\n", - "y_test = list(y_JP[8674])\n", - "mnb.fit(x_train, y_train)\n", - "jpb_train = mnb.score (x_train,y_train)\n", - "jpb_test = mnb.score (x_train,y_train)\n", - "predmnb = mnb.predict(x_test)\n", - "print(\"J-P Results\")\n", - "print('Label J-P train score is :',jpb_train)\n", - "print('Label J-P test score is :',jpb_test)\n", - "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", - "print(confusion_matrix(y_test,predmnb))\n", - "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", - "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", - "print('Prediction is: ', predmnb)\n", - "print('Actual is: ', y_test)\n", - "print(\"*\" * 100)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/your-project/code/testing nlp on Theresa.ipynb b/your-project/code/testing nlp on Theresa.ipynb deleted file mode 100644 index 7ddde563..00000000 --- a/your-project/code/testing nlp on Theresa.ipynb +++ /dev/null @@ -1,972 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# putting together friends dataframe" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "import re\n", - "\n", - "import nltk\n", - "from nltk.stem import PorterStemmer\n", - "from nltk.stem import SnowballStemmer\n", - "from nltk import wordnet\n", - "from nltk.corpus import stopwords\n", - "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n", - "from sklearn.model_selection import train_test_split, cross_val_score\n", - "from sklearn.metrics import classification_report\n", - "from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score, roc_curve\n", - "\n", - "from sklearn.model_selection import train_test_split, cross_val_score\n", - "from sklearn.model_selection import cross_validate\n", - "from sklearn.model_selection import StratifiedKFold\n", - "from sklearn.linear_model import LogisticRegression\n", - "from sklearn.model_selection import learning_curve\n", - "from sklearn.ensemble import ExtraTreesClassifier\n", - "from sklearn.decomposition import TruncatedSVD\n", - "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n", - "from sklearn.pipeline import Pipeline\n", - "from sklearn.naive_bayes import MultinomialNB\n", - "from sklearn.metrics import classification_report\n", - "from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score, roc_curve" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "Theresa = [\"light times\", \n", - " \"simplicité.\", \n", - " \"chapter 1\", \n", - " \"*waiter pours up wine without being asked for*\", \n", - " \"what a year\", \n", - " \"it‘s friday i‘m in loved.\", \n", - " \"herzallerliebst.\", \n", - " \"wie sommerferien für erwachsene irgendwie.\",\n", - " \"sommer und sonne und zitroneneis und stella und ach man was soll ich sagen - das beste leben @stellamarieluise\",\n", - " \"das ist einfach Glück, wenn man euch hat\",\n", - " \n", - " \"an diesem Bild ist so Vieles richtig.\",\n", - " \"ernst gucken geht also doch. @shishaclothing #supportyourlocalbrand #shishaclothing\",\n", - " \"casual durch die gegend düsen halt\",\n", - " \"Liebe für alle Frauen. Aber für Diese am meisten! #happyweltfrauentag\",\n", - " \"meine entscheidungsfreude captured by @stellamarieluise\",\n", - " \"keine termine und leicht im dispo\",\n", - " \"future\",\n", - " \"Zukunftsprognose 2080: alle glücklich.\",\n", - " \"marilyn monroe momente beim brötchen holen\",\n", - " \"reflections.\",\n", - " \n", - " \"keine sorgen, keine wellen, kein conditioner\",\n", - " \"liebenswichtig\",\n", - " \"alles wie immer\",\n", - " \"gute gespräche und blöde ideen\",\n", - " \"klassisches arbeiten halt\",\n", - " \"mag ich\",\n", - " \"lieb ich\",\n", - " \"viel zu schön\",\n", - " \"kaltes bier who dis\",\n", - " \"guess i‘m just in love with sunrises and moonlight and rainstorms and everything that has soul\",\n", - " \n", - " \"semi zufrieden mit dem frühstück\",\n", - " \"ich hab das mit dem ernst gucken nicht mitbekommen\",\n", - " \"verliebtverliebt\",\n", - " \"trouble never looked so goddamn fine\",\n", - " \"und wenn das abendlicht in genau dieser farbe ist\",\n", - " \"say something in dutch\",\n", - " \"superlekker\",\n", - " \"Pass auf deine Füße auf \",\n", - " \"herz euch behalt ich\",\n", - " \"find ich gut\",\n", - " \n", - " \"wenn @lisbz meine wäsche aufhängt\",\n", - " \"find your fire\",\n", - " \"umziehen mit lieblingspizza und lieblingswg\",\n", - " \"lieb ich\",\n", - " \"ist das Kunst oder kann das weg?\"\n", - " \"kennt ihr diese Menschen, die stehen bleiben um den Himmel zu fotografieren?\",\n", - " \"no one is you and that is your power\",\n", - " \"HAPPY WELTFRAUENTAG\",\n", - " \"gründe warum ich kein Blogger bin\",\n", - " \"sommer\"\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "theresa_test = {'name': ['Theresa'], 'type': ['ENFP']}" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "theresa_row = pd.DataFrame(theresa_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "theresa_row['text'] = ' '.join(Theresa)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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nametypetext
0TheresaENFPlight times simplicité. chapter 1 *waiter pour...
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" - ], - "text/plain": [ - " name type text\n", - "0 Theresa ENFP light times simplicité. chapter 1 *waiter pour..." - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "theresa_row" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "def processed(x):\n", - " pattern = r'''(?i)\\b((?:https?://|www\\d{0,3}[.]|[a-z0-9.\\-]+[.][a-z]{2,4}/)(?:[^\\s()<>]+|\\(([^\\s()<>]+|(\\([^\\s()<>]+\\)))*\\))+(?:\\(([^\\s()<>]+|(\\([^\\s()<>]+\\)))*\\)|[^\\s`!()\\[\\]{};:'\".,<>?«»“”‘’]))'''\n", - " cleaned = re.sub(pattern, ' ', str(x)) \n", - " cleaned = re.sub(r\"[^A-Za-z0-9]+\", \" \", cleaned)\n", - " cleaned = re.sub(\" \\d+\", \" \", cleaned)\n", - " cleaned = cleaned.lower().strip()\n", - "\n", - " pattern2 = '\\w{1,}'\n", - " bag = re.findall(pattern2, cleaned)\n", - "\n", - " porter = PorterStemmer()\n", - " porter_bag = [porter.stem(word) for word in bag]\n", - "\n", - " lemmatizer = wordnet.WordNetLemmatizer()\n", - " bag_of_words = [lemmatizer.lemmatize(word) for word in porter_bag]\n", - "\n", - " stopwords_e = stopwords.words('english')\n", - " bag_of_words = [word for word in bag_of_words if word not in stopwords_e]\n", - "\n", - " return bag_of_words" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "text_processed = [processed(i) for i in theresa_row.text]" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "theresa_row['text_processed'] = text_processed" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "theresa_row['text_ready'] = theresa_row.text_processed.apply(' '.join)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "theresa_row = theresa_row.drop(columns = ['name', 'text', 'text_processed'])" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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typetext_ready
0ENFPlight time simplicit chapter waiter pour wine ...
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" - ], - "text/plain": [ - " type text_ready\n", - "0 ENFP light time simplicit chapter waiter pour wine ..." - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "theresa_row" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [], - "source": [ - "data = pd.read_csv(r'../data/personalities_cleaned.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [], - "source": [ - "data = data.drop(columns = ['Unnamed: 0', 'posts', 'text_processed'])" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [], - "source": [ - "data.drop(data.index[3559], inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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0INFJintj moment sportscent top ten play prank ha l...
1ENTPfind lack post veri alarm sex bore posit often...
2INTPgood one cours say know bless cur doe absolut ...
3INTJdear intp enjoy convers day esoter gab natur u...
4ENTJfire anoth silli misconcept approach logic go ...
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" - ], - "text/plain": [ - " type text_ready\n", - "0 INFJ intj moment sportscent top ten play prank ha l...\n", - "1 ENTP find lack post veri alarm sex bore posit often...\n", - "2 INTP good one cours say know bless cur doe absolut ...\n", - "3 INTJ dear intp enjoy convers day esoter gab natur u...\n", - "4 ENTJ fire anoth silli misconcept approach logic go ..." - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data.tail()" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [], - "source": [ - "data = data.append(theresa_row, ignore_index = True)" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [], - "source": [ - "data2 = data.copy()" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [], - "source": [ - "# cleaning and processing\n", - "\n", - "data2['I-E'] = data2['type'].astype(str).str[0]\n", - "data2['N-S'] = data2['type'].astype(str).str[1]\n", - "data2['T-F'] = data2['type'].astype(str).str[2]\n", - "data2['J-P'] = data2['type'].astype(str).str[3]" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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typetext_readyI-EN-ST-FJ-P
0INFJintj moment sportscent top ten play prank ha l...INFJ
1ENTPfind lack post veri alarm sex bore posit often...ENTP
2INTPgood one cours say know bless cur doe absolut ...INTP
3INTJdear intp enjoy convers day esoter gab natur u...INTJ
4ENTJfire anoth silli misconcept approach logic go ...ENTJ
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" - ], - "text/plain": [ - " type text_ready I-E N-S T-F J-P\n", - "0 INFJ intj moment sportscent top ten play prank ha l... I N F J\n", - "1 ENTP find lack post veri alarm sex bore posit often... E N T P\n", - "2 INTP good one cours say know bless cur doe absolut ... I N T P\n", - "3 INTJ dear intp enjoy convers day esoter gab natur u... I N T J\n", - "4 ENTJ fire anoth silli misconcept approach logic go ... E N T J" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data2.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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typetext_readyI-EN-ST-FJ-P
8670ENFPthi thread alreadi exist someplac el doe heck ...ENFP
8671INTPmani question thing would take purpl pill pick...INTP
8672INFPveri conflict right come want child honestli m...INFP
8673INFPha long sinc personalitycaf although seem chan...INFP
8674ENFPlight time simplicit chapter waiter pour wine ...ENFP
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" - ], - "text/plain": [ - " type text_ready I-E N-S T-F J-P\n", - "8670 ENFP thi thread alreadi exist someplac el doe heck ... E N F P\n", - "8671 INTP mani question thing would take purpl pill pick... I N T P\n", - "8672 INFP veri conflict right come want child honestli m... I N F P\n", - "8673 INFP ha long sinc personalitycaf although seem chan... I N F P\n", - "8674 ENFP light time simplicit chapter waiter pour wine ... E N F P" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data2.tail()" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [], - "source": [ - "x = data2['text_ready']\n", - "\n", - "y_IE = data2['I-E']\n", - "y_NS = data2['N-S']\n", - "y_TF = data2['T-F']\n", - "y_JP = data2['J-P']" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.feature_extraction.text import CountVectorizer\n", - "\n", - "vector = CountVectorizer(ngram_range=(2, 2)).fit(x) \n", - "X = vector.transform(x)" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [], - "source": [ - "y_test = list(y_IE[8674])" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['E']" - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y_test" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.model_selection import train_test_split, cross_val_score" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "I-E RESULTS\n", - "Label I-E train score is : 0.7696298858526461\n", - "Label I-E test score is : 0.7696298858526461\n", - "Confusion Matrix for K-Nearest Neighbors:\n", - "[[0 1]\n", - " [0 0]]\n", - "Score: 0.0\n", - "Classification Report: precision recall f1-score support\n", - "\n", - " E 0.00 0.00 0.00 1.0\n", - " I 0.00 0.00 0.00 0.0\n", - "\n", - " accuracy 0.00 1.0\n", - " macro avg 0.00 0.00 0.00 1.0\n", - "weighted avg 0.00 0.00 0.00 1.0\n", - "\n", - "Prediction is: ['I']\n", - "Actual is: ['E']\n", - "****************************************************************************************************\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", - " _warn_prf(average, modifier, msg_start, len(result))\n", - "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n", - " _warn_prf(average, modifier, msg_start, len(result))\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "N-S RESULTS\n", - "Label N-S train score is : 0.8619854721549637\n", - "Label N-S test score is : 0.8619854721549637\n", - "Confusion Matrix for Random Forests:\n", - "[[1]]\n", - "Score: 100.0\n", - "Classification Report: precision recall f1-score support\n", - "\n", - " N 1.00 1.00 1.00 1\n", - "\n", - " accuracy 1.00 1\n", - " macro avg 1.00 1.00 1.00 1\n", - "weighted avg 1.00 1.00 1.00 1\n", - "\n", - "Prediction is: ['N']\n", - "Actual is: ['N']\n", - "****************************************************************************************************\n", - "T-F Results\n", - "Label T-F train score is : 1.0\n", - "Label T-F test score is : 1.0\n", - "Confusion Matrix for Multinomial Naive Bayes:\n", - "[[0 1]\n", - " [0 0]]\n", - "Score: 0.0\n", - "Classification Report: precision recall f1-score support\n", - "\n", - " F 0.00 0.00 0.00 1.0\n", - " T 0.00 0.00 0.00 0.0\n", - "\n", - " accuracy 0.00 1.0\n", - " macro avg 0.00 0.00 0.00 1.0\n", - "weighted avg 0.00 0.00 0.00 1.0\n", - "\n", - "Prediction is: ['T']\n", - "Actual is: ['F']\n", - "****************************************************************************************************\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", - " _warn_prf(average, modifier, msg_start, len(result))\n", - "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n", - " _warn_prf(average, modifier, msg_start, len(result))\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "J-P Results\n", - "Label J-P train score is : 1.0\n", - "Label J-P test score is : 1.0\n", - "Confusion Matrix for Multinomial Naive Bayes:\n", - "[[0 0]\n", - " [1 0]]\n", - "Score: 0.0\n", - "Classification Report: precision recall f1-score support\n", - "\n", - " J 0.00 0.00 0.00 0.0\n", - " P 0.00 0.00 0.00 1.0\n", - "\n", - " accuracy 0.00 1.0\n", - " macro avg 0.00 0.00 0.00 1.0\n", - "weighted avg 0.00 0.00 0.00 1.0\n", - "\n", - "Prediction is: ['J']\n", - "Actual is: ['P']\n", - "****************************************************************************************************\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", - " _warn_prf(average, modifier, msg_start, len(result))\n", - "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n", - " _warn_prf(average, modifier, msg_start, len(result))\n" - ] - } - ], - "source": [ - "from sklearn.neighbors import KNeighborsClassifier\n", - "knn = KNeighborsClassifier(n_neighbors=10) # see if can tweak accuracy\n", - "\n", - "from sklearn.ensemble import RandomForestClassifier\n", - "rfn = RandomForestClassifier(max_depth=5, random_state=0) # see if can tweak accuracy\n", - "\n", - "from sklearn.naive_bayes import MultinomialNB\n", - "mnb = MultinomialNB()\n", - "\n", - "\n", - "\n", - "# IE\n", - "x_train = X[0:8673]\n", - "x_test = X[8674]\n", - "y_train = y_IE[0:8673]\n", - "y_test = list(y_IE[8674])\n", - "knn.fit(x_train, y_train)\n", - "ieb_train = knn.score (x_train,y_train)\n", - "ieb_test = knn.score (x_train,y_train)\n", - "predknn = knn.predict(x_test)\n", - "print(\"I-E RESULTS\")\n", - "print('Label I-E train score is :',ieb_train)\n", - "print('Label I-E test score is :',ieb_test)\n", - "print(\"Confusion Matrix for K-Nearest Neighbors:\")\n", - "print(confusion_matrix(y_test,predknn))\n", - "print(\"Score:\",round(accuracy_score(y_test,predknn)*100,2))\n", - "print(\"Classification Report:\",classification_report(y_test,predknn))\n", - "print('Prediction is: ', predknn)\n", - "print('Actual is: ', y_test)\n", - "print(\"*\" * 100)\n", - "\n", - "# NS\n", - "x_train = X[0:8673]\n", - "x_test = X[8674]\n", - "y_train = y_NS[0:8673]\n", - "y_test = list(y_NS[8674])\n", - "rfn.fit(x_train, y_train)\n", - "nsb_train = rfn.score (x_train,y_train)\n", - "nsb_test = rfn.score (x_train,y_train)\n", - "predrfn = rfn.predict(x_test)\n", - "print(\"N-S RESULTS\")\n", - "print('Label N-S train score is :',nsb_train)\n", - "print('Label N-S test score is :',nsb_test)\n", - "print(\"Confusion Matrix for Random Forests:\")\n", - "print(confusion_matrix(y_test,predrfn))\n", - "print(\"Score:\",round(accuracy_score(y_test,predrfn)*100,2))\n", - "print(\"Classification Report:\",classification_report(y_test,predrfn))\n", - "print('Prediction is: ', predrfn)\n", - "print('Actual is: ', y_test)\n", - "print(\"*\" * 100)\n", - "\n", - "# TF\n", - "x_train = X[0:8673]\n", - "x_test = X[8674]\n", - "y_train = y_TF[0:8673]\n", - "y_test = list(y_TF[8674])\n", - "mnb.fit(x_train, y_train)\n", - "tfb_train = mnb.score (x_train,y_train)\n", - "tfb_test = mnb.score (x_train,y_train)\n", - "predmnb = mnb.predict(x_test)\n", - "print(\"T-F Results\")\n", - "print('Label T-F train score is :',tfb_train)\n", - "print('Label T-F test score is :',tfb_test)\n", - "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", - "print(confusion_matrix(y_test,predmnb))\n", - "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", - "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", - "print('Prediction is: ', predmnb)\n", - "print('Actual is: ', y_test)\n", - "print(\"*\" * 100)\n", - "\n", - "# JP\n", - "x_train = X[0:8673]\n", - "x_test = X[8674]\n", - "y_train = y_JP[0:8673]\n", - "y_test = list(y_JP[8674])\n", - "mnb.fit(x_train, y_train)\n", - "jpb_train = mnb.score (x_train,y_train)\n", - "jpb_test = mnb.score (x_train,y_train)\n", - "predmnb = mnb.predict(x_test)\n", - "print(\"J-P Results\")\n", - "print('Label J-P train score is :',jpb_train)\n", - "print('Label J-P test score is :',jpb_test)\n", - "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", - "print(confusion_matrix(y_test,predmnb))\n", - "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", - "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", - "print('Prediction is: ', predmnb)\n", - "print('Actual is: ', y_test)\n", - "print(\"*\" * 100)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/your-project/code/testing quant on Darya.ipynb b/your-project/code/testing quant on Darya.ipynb deleted file mode 100644 index 9225f46e..00000000 --- a/your-project/code/testing quant on Darya.ipynb +++ /dev/null @@ -1,972 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# putting together friends dataframe" - ] - }, - { - "cell_type": "code", - "execution_count": 97, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "import re\n", - "\n", - "import nltk\n", - "from nltk.stem import PorterStemmer\n", - "from nltk.stem import SnowballStemmer\n", - "from nltk import wordnet\n", - "from nltk.corpus import stopwords\n", - "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n", - "from sklearn.model_selection import train_test_split, cross_val_score\n", - "from sklearn.metrics import classification_report\n", - "from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score, roc_curve\n", - "\n", - "from sklearn.model_selection import train_test_split, cross_val_score\n", - "from sklearn.model_selection import cross_validate\n", - "from sklearn.model_selection import StratifiedKFold\n", - "from sklearn.linear_model import LogisticRegression\n", - "from sklearn.model_selection import learning_curve\n", - "from sklearn.ensemble import ExtraTreesClassifier\n", - "from sklearn.decomposition import TruncatedSVD\n", - "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n", - "from sklearn.pipeline import Pipeline\n", - "from sklearn.naive_bayes import MultinomialNB\n", - "from sklearn.metrics import classification_report\n", - "from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score, roc_curve" - ] - }, - { - "cell_type": "code", - "execution_count": 98, - "metadata": {}, - "outputs": [], - "source": [ - "Darya = [\"Quarantine expectations. Swipe for reality\", \n", - " \"22 in 2020 - swipe to see a photo from yesterday. Age affects me.\", \n", - " \"hello? Santa? All I wish for Christmas is my mind back... \", \n", - " \"girl with a hat - a series. \", \n", - " \"casual day at uni. swipe for more idiots\", \n", - " \"thanking Steve for a nice iPhone front camera.\", \n", - " \"Missing this\", \n", - " \"*edit: still can‘t believe a sunscreen for 7.95 prevented me from having a huge sunburn\",\n", - " \"where I‘d rather be part 2\",\n", - " \"when people ask me if I‘ve got free time - I do. Except I don’t.\",\n", - " \n", - " \"just a small town girl, living in a capitalist world...\",\n", - " \"clueless\",\n", - " \"Might be serious, might be hungry. \",\n", - " \"you want blue? you want yellow?\",\n", - " \"While the wall fell nearly 30 years ago, I constantly fall on my face...\",\n", - " \"Im pretty ok with exams being over... \",\n", - " \"unpopular opinion: university can be fun. exhibit a\",\n", - " \"christmas is around the corner but all I‘m wishing for is an extention of deadlines...\",\n", - " \"on some occasions I even get out of the valley and LA to go sightseeing \",\n", - " \"diamonds are a girls best friend. Especially when you trade ‘em for plane tickets and uber rides\",\n", - " \n", - " \"not a threat, it’s a warning. (talking about the beginning of semester tomorrow)\",\n", - " \"come fly with me, let’s fly let’s fly away...\",\n", - " \"saltier than the Pacific\",\n", - " \"me, when I’m a) hangry or b) am studying @ nyfalosangeles @ williamroyal98\",\n", - " \"this whole you-gotta-study-while-the-sun-is-shining gets on my nerve. here’s an example of inner rage\",\n", - " \"you know you might have been spontaneous when while booking your flights they asked you if ou would want to check in for the flight back aswell.\",\n", - " \"Vitamins\",\n", - " \"finished the first semerster at HSG and I still can’t afford a porsche 911.\",\n", - " \"Me: you should be studying. Also me: enjoy the holidays\",\n", - " \"just kidding - I’m still a short ass little human\",\n", - " \n", - " \"week 2 at uni and my icloud and google drive are asking me to pay for more storage.\",\n", - " \"uni starts tomorrow and I already see myself either a) being lost cause I can't find the classroom or b) looking like Annabelle cause sleep is for the weak\",\n", - " \"oh boy\",\n", - " \"this should be my last week of summer but I think the weather decided to give me an early taste of fall already.\",\n", - " \"water falls, darya falls and just casually sit on floors cause standing is overrated \",\n", - " \"I promise I counted all the lights, but then I saw a squirrel and lost count \",\n", - " \"is it a set? Is it a real house? guess.\",\n", - " \"quiet on set\",\n", - " \"Baywatch, are you hiring?\",\n", - " \"What if perfume is actually bugspray but they want us to buy the nasty smelly stuff just to have a good laugh?\",\n", - " \n", - " \"I'm starting to wonder if spotlight can give you a tan cause the sun isn't coming to just yet. All jokes asid tho, I am beyond grateful to share the stage with so many talented people. Do what you love and you'll feel limitless \",\n", - " \"that second when the person you've been straing at looks at you and you quickly turn away, trying to look as casual as possible\",\n", - " \"at that moment I probably thought out a plan to convince my mom to paint one of my walls pink. even though I know it won't happen, happy #internationalwomensday ladies\",\n", - " \"Tell me about it, stud\",\n", - " \"nice to be back. bring it on 2017\"\n", - " \"2016. A year that had probably more faces than facebook. Victories and dissapointment, new friendships and new experiences; I was lucky enough to travel the several times and can't thank the people that made 2016 the year it was enough for all their kind words, laughter and happy tears.\",\n", - " \"don't mess with us - we have RG clubs\",\n", - " \"Summer is over and you're like: pardon me, what?\",\n", - " \"Looking back at those 5 weeks full of new friends, experiences and emotions puts a smile on my face. The teachers I've worked with inspired me every day, the people made my summer the best one yet. I love all of you and you'll always have a special place in my heart #obgottheheat and happy National Dance Day\",\n", - " \"everybody needs some attitude in their life #punintended #obgottheheat\"\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 99, - "metadata": {}, - "outputs": [], - "source": [ - "darya_test = {'name': ['Darya'], 'type': ['ESFP']}" - ] - }, - { - "cell_type": "code", - "execution_count": 100, - "metadata": {}, - "outputs": [], - "source": [ - "darya_row = pd.DataFrame(darya_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 101, - "metadata": {}, - "outputs": [], - "source": [ - "darya_row['text'] = ' '.join(Darya)" - ] - }, - { - "cell_type": "code", - "execution_count": 102, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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0DaryaESFPQuarantine expectations. Swipe for reality 22 ...
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" - ], - "text/plain": [ - " name type text\n", - "0 Darya ESFP Quarantine expectations. Swipe for reality 22 ..." - ] - }, - "execution_count": 102, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "darya_row" - ] - }, - { - "cell_type": "code", - "execution_count": 103, - "metadata": {}, - "outputs": [], - "source": [ - "def processed(x):\n", - " pattern = r'''(?i)\\b((?:https?://|www\\d{0,3}[.]|[a-z0-9.\\-]+[.][a-z]{2,4}/)(?:[^\\s()<>]+|\\(([^\\s()<>]+|(\\([^\\s()<>]+\\)))*\\))+(?:\\(([^\\s()<>]+|(\\([^\\s()<>]+\\)))*\\)|[^\\s`!()\\[\\]{};:'\".,<>?«»“”‘’]))'''\n", - " cleaned = re.sub(pattern, ' ', str(x)) \n", - " cleaned = re.sub(r\"[^A-Za-z0-9]+\", \" \", cleaned)\n", - " cleaned = re.sub(\" \\d+\", \" \", cleaned)\n", - " cleaned = cleaned.lower().strip()\n", - "\n", - " pattern2 = '\\w{1,}'\n", - " bag = re.findall(pattern2, cleaned)\n", - "\n", - " porter = PorterStemmer()\n", - " porter_bag = [porter.stem(word) for word in bag]\n", - "\n", - " lemmatizer = wordnet.WordNetLemmatizer()\n", - " bag_of_words = [lemmatizer.lemmatize(word) for word in porter_bag]\n", - "\n", - " stopwords_e = stopwords.words('english')\n", - " bag_of_words = [word for word in bag_of_words if word not in stopwords_e]\n", - "\n", - " return bag_of_words" - ] - }, - { - "cell_type": "code", - "execution_count": 104, - "metadata": {}, - "outputs": [], - "source": [ - "text_processed = [processed(i) for i in darya_row.text]" - ] - }, - { - "cell_type": "code", - "execution_count": 105, - "metadata": {}, - "outputs": [], - "source": [ - "darya_row['text_processed'] = text_processed" - ] - }, - { - "cell_type": "code", - "execution_count": 106, - "metadata": {}, - "outputs": [], - "source": [ - "darya_row['text_ready'] = darya_row.text_processed.apply(' '.join)" - ] - }, - { - "cell_type": "code", - "execution_count": 107, - "metadata": {}, - "outputs": [], - "source": [ - "darya_row = darya_row.drop(columns = ['name', 'text', 'text_processed'])" - ] - }, - { - "cell_type": "code", - "execution_count": 108, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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typetext_ready
0ESFPquarantin expect swipe realiti swipe see photo...
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" - ], - "text/plain": [ - " type text_ready\n", - "0 ESFP quarantin expect swipe realiti swipe see photo..." - ] - }, - "execution_count": 108, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "darya_row" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 109, - "metadata": {}, - "outputs": [], - "source": [ - "data = pd.read_csv(r'../data/personalities_cleaned.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 110, - "metadata": {}, - "outputs": [], - "source": [ - "data = data.drop(columns = ['Unnamed: 0', 'posts', 'text_processed'])" - ] - }, - { - "cell_type": "code", - "execution_count": 120, - "metadata": {}, - "outputs": [], - "source": [ - "data.drop(data.index[3559], inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 121, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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0INFJintj moment sportscent top ten play prank ha l...
1ENTPfind lack post veri alarm sex bore posit often...
2INTPgood one cours say know bless cur doe absolut ...
3INTJdear intp enjoy convers day esoter gab natur u...
4ENTJfire anoth silli misconcept approach logic go ...
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" - ], - "text/plain": [ - " type text_ready\n", - "0 INFJ intj moment sportscent top ten play prank ha l...\n", - "1 ENTP find lack post veri alarm sex bore posit often...\n", - "2 INTP good one cours say know bless cur doe absolut ...\n", - "3 INTJ dear intp enjoy convers day esoter gab natur u...\n", - "4 ENTJ fire anoth silli misconcept approach logic go ..." - ] - }, - "execution_count": 121, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 122, - "metadata": {}, - "outputs": [], - "source": [ - "data = data.append(darya_row, ignore_index = True)" - ] - }, - { - "cell_type": "code", - "execution_count": 123, - "metadata": {}, - "outputs": [], - "source": [ - "data2 = data.copy()" - ] - }, - { - "cell_type": "code", - "execution_count": 124, - "metadata": {}, - "outputs": [], - "source": [ - "# cleaning and processing\n", - "\n", - "data2['I-E'] = data2['type'].astype(str).str[0]\n", - "data2['N-S'] = data2['type'].astype(str).str[1]\n", - "data2['T-F'] = data2['type'].astype(str).str[2]\n", - "data2['J-P'] = data2['type'].astype(str).str[3]" - ] - }, - { - "cell_type": "code", - "execution_count": 125, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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typetext_readyI-EN-ST-FJ-P
0INFJintj moment sportscent top ten play prank ha l...INFJ
1ENTPfind lack post veri alarm sex bore posit often...ENTP
2INTPgood one cours say know bless cur doe absolut ...INTP
3INTJdear intp enjoy convers day esoter gab natur u...INTJ
4ENTJfire anoth silli misconcept approach logic go ...ENTJ
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" - ], - "text/plain": [ - " type text_ready I-E N-S T-F J-P\n", - "0 INFJ intj moment sportscent top ten play prank ha l... I N F J\n", - "1 ENTP find lack post veri alarm sex bore posit often... E N T P\n", - "2 INTP good one cours say know bless cur doe absolut ... I N T P\n", - "3 INTJ dear intp enjoy convers day esoter gab natur u... I N T J\n", - "4 ENTJ fire anoth silli misconcept approach logic go ... E N T J" - ] - }, - "execution_count": 125, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data2.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 126, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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typetext_readyI-EN-ST-FJ-P
8671INTPmani question thing would take purpl pill pick...INTP
8672INFPveri conflict right come want child honestli m...INFP
8673INFPha long sinc personalitycaf although seem chan...INFP
8674ESFPquarantin expect swipe realiti swipe see photo...ESFP
8675ESFPquarantin expect swipe realiti swipe see photo...ESFP
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" - ], - "text/plain": [ - " type text_ready I-E N-S T-F J-P\n", - "8671 INTP mani question thing would take purpl pill pick... I N T P\n", - "8672 INFP veri conflict right come want child honestli m... I N F P\n", - "8673 INFP ha long sinc personalitycaf although seem chan... I N F P\n", - "8674 ESFP quarantin expect swipe realiti swipe see photo... E S F P\n", - "8675 ESFP quarantin expect swipe realiti swipe see photo... E S F P" - ] - }, - "execution_count": 126, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data2.tail()" - ] - }, - { - "cell_type": "code", - "execution_count": 127, - "metadata": {}, - "outputs": [], - "source": [ - "x = data2['text_ready']\n", - "\n", - "y_IE = data2['I-E']\n", - "y_NS = data2['N-S']\n", - "y_TF = data2['T-F']\n", - "y_JP = data2['J-P']" - ] - }, - { - "cell_type": "code", - "execution_count": 128, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.feature_extraction.text import CountVectorizer\n", - "\n", - "vector = CountVectorizer(ngram_range=(2, 2)).fit(x) \n", - "X = vector.transform(x)" - ] - }, - { - "cell_type": "code", - "execution_count": 140, - "metadata": {}, - "outputs": [], - "source": [ - "y_test = list(y_IE[8675])" - ] - }, - { - "cell_type": "code", - "execution_count": 130, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'E'" - ] - }, - "execution_count": 130, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y_test" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.model_selection import train_test_split, cross_val_score" - ] - }, - { - "cell_type": "code", - "execution_count": 141, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "I-E RESULTS\n", - "Label I-E train score is : 0.7696564445469218\n", - "Label I-E test score is : 0.7696564445469218\n", - "Confusion Matrix for K-Nearest Neighbors:\n", - "[[0 1]\n", - " [0 0]]\n", - "Score: 0.0\n", - "Classification Report: precision recall f1-score support\n", - "\n", - " E 0.00 0.00 0.00 1.0\n", - " I 0.00 0.00 0.00 0.0\n", - "\n", - " accuracy 0.00 1.0\n", - " macro avg 0.00 0.00 0.00 1.0\n", - "weighted avg 0.00 0.00 0.00 1.0\n", - "\n", - "Prediction is: ['I']\n", - "Actual is: ['E']\n", - "****************************************************************************************************\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", - " _warn_prf(average, modifier, msg_start, len(result))\n", - "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n", - " _warn_prf(average, modifier, msg_start, len(result))\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "N-S RESULTS\n", - "Label N-S train score is : 0.8620013834447775\n", - "Label N-S test score is : 0.8620013834447775\n", - "Confusion Matrix for Random Forests:\n", - "[[0 0]\n", - " [1 0]]\n", - "Score: 0.0\n", - "Classification Report: precision recall f1-score support\n", - "\n", - " N 0.00 0.00 0.00 0.0\n", - " S 0.00 0.00 0.00 1.0\n", - "\n", - " accuracy 0.00 1.0\n", - " macro avg 0.00 0.00 0.00 1.0\n", - "weighted avg 0.00 0.00 0.00 1.0\n", - "\n", - "Prediction is: ['N']\n", - "Actual is: ['S']\n", - "****************************************************************************************************\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", - " _warn_prf(average, modifier, msg_start, len(result))\n", - "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n", - " _warn_prf(average, modifier, msg_start, len(result))\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "T-F Results\n", - "Label T-F train score is : 1.0\n", - "Label T-F test score is : 1.0\n", - "Confusion Matrix for Multinomial Naive Bayes:\n", - "[[1]]\n", - "Score: 100.0\n", - "Classification Report: precision recall f1-score support\n", - "\n", - " F 1.00 1.00 1.00 1\n", - "\n", - " accuracy 1.00 1\n", - " macro avg 1.00 1.00 1.00 1\n", - "weighted avg 1.00 1.00 1.00 1\n", - "\n", - "Prediction is: ['F']\n", - "Actual is: ['F']\n", - "****************************************************************************************************\n", - "J-P Results\n", - "Label J-P train score is : 1.0\n", - "Label J-P test score is : 1.0\n", - "Confusion Matrix for Multinomial Naive Bayes:\n", - "[[0 0]\n", - " [1 0]]\n", - "Score: 0.0\n", - "Classification Report: precision recall f1-score support\n", - "\n", - " J 0.00 0.00 0.00 0.0\n", - " P 0.00 0.00 0.00 1.0\n", - "\n", - " accuracy 0.00 1.0\n", - " macro avg 0.00 0.00 0.00 1.0\n", - "weighted avg 0.00 0.00 0.00 1.0\n", - "\n", - "Prediction is: ['J']\n", - "Actual is: ['P']\n", - "****************************************************************************************************\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", - " _warn_prf(average, modifier, msg_start, len(result))\n", - "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n", - " _warn_prf(average, modifier, msg_start, len(result))\n" - ] - } - ], - "source": [ - "from sklearn.neighbors import KNeighborsClassifier\n", - "knn = KNeighborsClassifier(n_neighbors=10) # see if can tweak accuracy\n", - "\n", - "from sklearn.ensemble import RandomForestClassifier\n", - "rfn = RandomForestClassifier(max_depth=5, random_state=0) # see if can tweak accuracy\n", - "\n", - "from sklearn.naive_bayes import MultinomialNB\n", - "mnb = MultinomialNB()\n", - "\n", - "\n", - "\n", - "# IE\n", - "x_train = X[0:8674]\n", - "x_test = X[8675]\n", - "y_train = y_IE[0:8674]\n", - "y_test = list(y_IE[8675])\n", - "knn.fit(x_train, y_train)\n", - "ieb_train = knn.score (x_train,y_train)\n", - "ieb_test = knn.score (x_train,y_train)\n", - "predknn = knn.predict(x_test)\n", - "print(\"I-E RESULTS\")\n", - "print('Label I-E train score is :',ieb_train)\n", - "print('Label I-E test score is :',ieb_test)\n", - "print(\"Confusion Matrix for K-Nearest Neighbors:\")\n", - "print(confusion_matrix(y_test,predknn))\n", - "print(\"Score:\",round(accuracy_score(y_test,predknn)*100,2))\n", - "print(\"Classification Report:\",classification_report(y_test,predknn))\n", - "print('Prediction is: ', predknn)\n", - "print('Actual is: ', y_test)\n", - "print(\"*\" * 100)\n", - "\n", - "# NS\n", - "x_train = X[0:8674]\n", - "x_test = X[8675]\n", - "y_train = y_NS[0:8674]\n", - "y_test = list(y_NS[8675])\n", - "rfn.fit(x_train, y_train)\n", - "nsb_train = rfn.score (x_train,y_train)\n", - "nsb_test = rfn.score (x_train,y_train)\n", - "predrfn = rfn.predict(x_test)\n", - "print(\"N-S RESULTS\")\n", - "print('Label N-S train score is :',nsb_train)\n", - "print('Label N-S test score is :',nsb_test)\n", - "print(\"Confusion Matrix for Random Forests:\")\n", - "print(confusion_matrix(y_test,predrfn))\n", - "print(\"Score:\",round(accuracy_score(y_test,predrfn)*100,2))\n", - "print(\"Classification Report:\",classification_report(y_test,predrfn))\n", - "print('Prediction is: ', predrfn)\n", - "print('Actual is: ', y_test)\n", - "print(\"*\" * 100)\n", - "\n", - "# TF\n", - "x_train = X[0:8674]\n", - "x_test = X[8675]\n", - "y_train = y_TF[0:8674]\n", - "y_test = list(y_TF[8675])\n", - "mnb.fit(x_train, y_train)\n", - "tfb_train = mnb.score (x_train,y_train)\n", - "tfb_test = mnb.score (x_train,y_train)\n", - "predmnb = mnb.predict(x_test)\n", - "print(\"T-F Results\")\n", - "print('Label T-F train score is :',tfb_train)\n", - "print('Label T-F test score is :',tfb_test)\n", - "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", - "print(confusion_matrix(y_test,predmnb))\n", - "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", - "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", - "print('Prediction is: ', predmnb)\n", - "print('Actual is: ', y_test)\n", - "print(\"*\" * 100)\n", - "\n", - "# JP\n", - "x_train = X[0:8674]\n", - "x_test = X[8675]\n", - "y_train = y_JP[0:8674]\n", - "y_test = list(y_JP[8675])\n", - "mnb.fit(x_train, y_train)\n", - "jpb_train = mnb.score (x_train,y_train)\n", - "jpb_test = mnb.score (x_train,y_train)\n", - "predmnb = mnb.predict(x_test)\n", - "print(\"J-P Results\")\n", - "print('Label J-P train score is :',jpb_train)\n", - "print('Label J-P test score is :',jpb_test)\n", - "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", - "print(confusion_matrix(y_test,predmnb))\n", - "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", - "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", - "print('Prediction is: ', predmnb)\n", - "print('Actual is: ', y_test)\n", - "print(\"*\" * 100)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/your-project/code/testing quant on Laurents.ipynb b/your-project/code/testing quant on Laurents.ipynb deleted file mode 100644 index 9225f46e..00000000 --- a/your-project/code/testing quant on Laurents.ipynb +++ /dev/null @@ -1,972 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# putting together friends dataframe" - ] - }, - { - "cell_type": "code", - "execution_count": 97, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "import re\n", - "\n", - "import nltk\n", - "from nltk.stem import PorterStemmer\n", - "from nltk.stem import SnowballStemmer\n", - "from nltk import wordnet\n", - "from nltk.corpus import stopwords\n", - "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n", - "from sklearn.model_selection import train_test_split, cross_val_score\n", - "from sklearn.metrics import classification_report\n", - "from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score, roc_curve\n", - "\n", - "from sklearn.model_selection import train_test_split, cross_val_score\n", - "from sklearn.model_selection import cross_validate\n", - "from sklearn.model_selection import StratifiedKFold\n", - "from sklearn.linear_model import LogisticRegression\n", - "from sklearn.model_selection import learning_curve\n", - "from sklearn.ensemble import ExtraTreesClassifier\n", - "from sklearn.decomposition import TruncatedSVD\n", - "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n", - "from sklearn.pipeline import Pipeline\n", - "from sklearn.naive_bayes import MultinomialNB\n", - "from sklearn.metrics import classification_report\n", - "from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score, roc_curve" - ] - }, - { - "cell_type": "code", - "execution_count": 98, - "metadata": {}, - "outputs": [], - "source": [ - "Darya = [\"Quarantine expectations. Swipe for reality\", \n", - " \"22 in 2020 - swipe to see a photo from yesterday. Age affects me.\", \n", - " \"hello? Santa? All I wish for Christmas is my mind back... \", \n", - " \"girl with a hat - a series. \", \n", - " \"casual day at uni. swipe for more idiots\", \n", - " \"thanking Steve for a nice iPhone front camera.\", \n", - " \"Missing this\", \n", - " \"*edit: still can‘t believe a sunscreen for 7.95 prevented me from having a huge sunburn\",\n", - " \"where I‘d rather be part 2\",\n", - " \"when people ask me if I‘ve got free time - I do. Except I don’t.\",\n", - " \n", - " \"just a small town girl, living in a capitalist world...\",\n", - " \"clueless\",\n", - " \"Might be serious, might be hungry. \",\n", - " \"you want blue? you want yellow?\",\n", - " \"While the wall fell nearly 30 years ago, I constantly fall on my face...\",\n", - " \"Im pretty ok with exams being over... \",\n", - " \"unpopular opinion: university can be fun. exhibit a\",\n", - " \"christmas is around the corner but all I‘m wishing for is an extention of deadlines...\",\n", - " \"on some occasions I even get out of the valley and LA to go sightseeing \",\n", - " \"diamonds are a girls best friend. Especially when you trade ‘em for plane tickets and uber rides\",\n", - " \n", - " \"not a threat, it’s a warning. (talking about the beginning of semester tomorrow)\",\n", - " \"come fly with me, let’s fly let’s fly away...\",\n", - " \"saltier than the Pacific\",\n", - " \"me, when I’m a) hangry or b) am studying @ nyfalosangeles @ williamroyal98\",\n", - " \"this whole you-gotta-study-while-the-sun-is-shining gets on my nerve. here’s an example of inner rage\",\n", - " \"you know you might have been spontaneous when while booking your flights they asked you if ou would want to check in for the flight back aswell.\",\n", - " \"Vitamins\",\n", - " \"finished the first semerster at HSG and I still can’t afford a porsche 911.\",\n", - " \"Me: you should be studying. Also me: enjoy the holidays\",\n", - " \"just kidding - I’m still a short ass little human\",\n", - " \n", - " \"week 2 at uni and my icloud and google drive are asking me to pay for more storage.\",\n", - " \"uni starts tomorrow and I already see myself either a) being lost cause I can't find the classroom or b) looking like Annabelle cause sleep is for the weak\",\n", - " \"oh boy\",\n", - " \"this should be my last week of summer but I think the weather decided to give me an early taste of fall already.\",\n", - " \"water falls, darya falls and just casually sit on floors cause standing is overrated \",\n", - " \"I promise I counted all the lights, but then I saw a squirrel and lost count \",\n", - " \"is it a set? Is it a real house? guess.\",\n", - " \"quiet on set\",\n", - " \"Baywatch, are you hiring?\",\n", - " \"What if perfume is actually bugspray but they want us to buy the nasty smelly stuff just to have a good laugh?\",\n", - " \n", - " \"I'm starting to wonder if spotlight can give you a tan cause the sun isn't coming to just yet. All jokes asid tho, I am beyond grateful to share the stage with so many talented people. Do what you love and you'll feel limitless \",\n", - " \"that second when the person you've been straing at looks at you and you quickly turn away, trying to look as casual as possible\",\n", - " \"at that moment I probably thought out a plan to convince my mom to paint one of my walls pink. even though I know it won't happen, happy #internationalwomensday ladies\",\n", - " \"Tell me about it, stud\",\n", - " \"nice to be back. bring it on 2017\"\n", - " \"2016. A year that had probably more faces than facebook. Victories and dissapointment, new friendships and new experiences; I was lucky enough to travel the several times and can't thank the people that made 2016 the year it was enough for all their kind words, laughter and happy tears.\",\n", - " \"don't mess with us - we have RG clubs\",\n", - " \"Summer is over and you're like: pardon me, what?\",\n", - " \"Looking back at those 5 weeks full of new friends, experiences and emotions puts a smile on my face. The teachers I've worked with inspired me every day, the people made my summer the best one yet. I love all of you and you'll always have a special place in my heart #obgottheheat and happy National Dance Day\",\n", - " \"everybody needs some attitude in their life #punintended #obgottheheat\"\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 99, - "metadata": {}, - "outputs": [], - "source": [ - "darya_test = {'name': ['Darya'], 'type': ['ESFP']}" - ] - }, - { - "cell_type": "code", - "execution_count": 100, - "metadata": {}, - "outputs": [], - "source": [ - "darya_row = pd.DataFrame(darya_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 101, - "metadata": {}, - "outputs": [], - "source": [ - "darya_row['text'] = ' '.join(Darya)" - ] - }, - { - "cell_type": "code", - "execution_count": 102, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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0DaryaESFPQuarantine expectations. Swipe for reality 22 ...
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0ESFPquarantin expect swipe realiti swipe see photo...
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0INFJintj moment sportscent top ten play prank ha l...
1ENTPfind lack post veri alarm sex bore posit often...
2INTPgood one cours say know bless cur doe absolut ...
3INTJdear intp enjoy convers day esoter gab natur u...
4ENTJfire anoth silli misconcept approach logic go ...
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0INFJintj moment sportscent top ten play prank ha l...INFJ
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2INTPgood one cours say know bless cur doe absolut ...INTP
3INTJdear intp enjoy convers day esoter gab natur u...INTJ
4ENTJfire anoth silli misconcept approach logic go ...ENTJ
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8671INTPmani question thing would take purpl pill pick...INTP
8672INFPveri conflict right come want child honestli m...INFP
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" - ], - "text/plain": [ - " type text_ready I-E N-S T-F J-P\n", - "8671 INTP mani question thing would take purpl pill pick... I N T P\n", - "8672 INFP veri conflict right come want child honestli m... I N F P\n", - "8673 INFP ha long sinc personalitycaf although seem chan... I N F P\n", - "8674 ESFP quarantin expect swipe realiti swipe see photo... E S F P\n", - "8675 ESFP quarantin expect swipe realiti swipe see photo... E S F P" - ] - }, - "execution_count": 126, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data2.tail()" - ] - }, - { - "cell_type": "code", - "execution_count": 127, - "metadata": {}, - "outputs": [], - "source": [ - "x = data2['text_ready']\n", - "\n", - "y_IE = data2['I-E']\n", - "y_NS = data2['N-S']\n", - "y_TF = data2['T-F']\n", - "y_JP = data2['J-P']" - ] - }, - { - "cell_type": "code", - "execution_count": 128, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.feature_extraction.text import CountVectorizer\n", - "\n", - "vector = CountVectorizer(ngram_range=(2, 2)).fit(x) \n", - "X = vector.transform(x)" - ] - }, - { - "cell_type": "code", - "execution_count": 140, - "metadata": {}, - "outputs": [], - "source": [ - "y_test = list(y_IE[8675])" - ] - }, - { - "cell_type": "code", - "execution_count": 130, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'E'" - ] - }, - "execution_count": 130, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y_test" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.model_selection import train_test_split, cross_val_score" - ] - }, - { - "cell_type": "code", - "execution_count": 141, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "I-E RESULTS\n", - "Label I-E train score is : 0.7696564445469218\n", - "Label I-E test score is : 0.7696564445469218\n", - "Confusion Matrix for K-Nearest Neighbors:\n", - "[[0 1]\n", - " [0 0]]\n", - "Score: 0.0\n", - "Classification Report: precision recall f1-score support\n", - "\n", - " E 0.00 0.00 0.00 1.0\n", - " I 0.00 0.00 0.00 0.0\n", - "\n", - " accuracy 0.00 1.0\n", - " macro avg 0.00 0.00 0.00 1.0\n", - "weighted avg 0.00 0.00 0.00 1.0\n", - "\n", - "Prediction is: ['I']\n", - "Actual is: ['E']\n", - "****************************************************************************************************\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", - " _warn_prf(average, modifier, msg_start, len(result))\n", - "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n", - " _warn_prf(average, modifier, msg_start, len(result))\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "N-S RESULTS\n", - "Label N-S train score is : 0.8620013834447775\n", - "Label N-S test score is : 0.8620013834447775\n", - "Confusion Matrix for Random Forests:\n", - "[[0 0]\n", - " [1 0]]\n", - "Score: 0.0\n", - "Classification Report: precision recall f1-score support\n", - "\n", - " N 0.00 0.00 0.00 0.0\n", - " S 0.00 0.00 0.00 1.0\n", - "\n", - " accuracy 0.00 1.0\n", - " macro avg 0.00 0.00 0.00 1.0\n", - "weighted avg 0.00 0.00 0.00 1.0\n", - "\n", - "Prediction is: ['N']\n", - "Actual is: ['S']\n", - "****************************************************************************************************\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", - " _warn_prf(average, modifier, msg_start, len(result))\n", - "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n", - " _warn_prf(average, modifier, msg_start, len(result))\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "T-F Results\n", - "Label T-F train score is : 1.0\n", - "Label T-F test score is : 1.0\n", - "Confusion Matrix for Multinomial Naive Bayes:\n", - "[[1]]\n", - "Score: 100.0\n", - "Classification Report: precision recall f1-score support\n", - "\n", - " F 1.00 1.00 1.00 1\n", - "\n", - " accuracy 1.00 1\n", - " macro avg 1.00 1.00 1.00 1\n", - "weighted avg 1.00 1.00 1.00 1\n", - "\n", - "Prediction is: ['F']\n", - "Actual is: ['F']\n", - "****************************************************************************************************\n", - "J-P Results\n", - "Label J-P train score is : 1.0\n", - "Label J-P test score is : 1.0\n", - "Confusion Matrix for Multinomial Naive Bayes:\n", - "[[0 0]\n", - " [1 0]]\n", - "Score: 0.0\n", - "Classification Report: precision recall f1-score support\n", - "\n", - " J 0.00 0.00 0.00 0.0\n", - " P 0.00 0.00 0.00 1.0\n", - "\n", - " accuracy 0.00 1.0\n", - " macro avg 0.00 0.00 0.00 1.0\n", - "weighted avg 0.00 0.00 0.00 1.0\n", - "\n", - "Prediction is: ['J']\n", - "Actual is: ['P']\n", - "****************************************************************************************************\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", - " _warn_prf(average, modifier, msg_start, len(result))\n", - "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n", - " _warn_prf(average, modifier, msg_start, len(result))\n" - ] - } - ], - "source": [ - "from sklearn.neighbors import KNeighborsClassifier\n", - "knn = KNeighborsClassifier(n_neighbors=10) # see if can tweak accuracy\n", - "\n", - "from sklearn.ensemble import RandomForestClassifier\n", - "rfn = RandomForestClassifier(max_depth=5, random_state=0) # see if can tweak accuracy\n", - "\n", - "from sklearn.naive_bayes import MultinomialNB\n", - "mnb = MultinomialNB()\n", - "\n", - "\n", - "\n", - "# IE\n", - "x_train = X[0:8674]\n", - "x_test = X[8675]\n", - "y_train = y_IE[0:8674]\n", - "y_test = list(y_IE[8675])\n", - "knn.fit(x_train, y_train)\n", - "ieb_train = knn.score (x_train,y_train)\n", - "ieb_test = knn.score (x_train,y_train)\n", - "predknn = knn.predict(x_test)\n", - "print(\"I-E RESULTS\")\n", - "print('Label I-E train score is :',ieb_train)\n", - "print('Label I-E test score is :',ieb_test)\n", - "print(\"Confusion Matrix for K-Nearest Neighbors:\")\n", - "print(confusion_matrix(y_test,predknn))\n", - "print(\"Score:\",round(accuracy_score(y_test,predknn)*100,2))\n", - "print(\"Classification Report:\",classification_report(y_test,predknn))\n", - "print('Prediction is: ', predknn)\n", - "print('Actual is: ', y_test)\n", - "print(\"*\" * 100)\n", - "\n", - "# NS\n", - "x_train = X[0:8674]\n", - "x_test = X[8675]\n", - "y_train = y_NS[0:8674]\n", - "y_test = list(y_NS[8675])\n", - "rfn.fit(x_train, y_train)\n", - "nsb_train = rfn.score (x_train,y_train)\n", - "nsb_test = rfn.score (x_train,y_train)\n", - "predrfn = rfn.predict(x_test)\n", - "print(\"N-S RESULTS\")\n", - "print('Label N-S train score is :',nsb_train)\n", - "print('Label N-S test score is :',nsb_test)\n", - "print(\"Confusion Matrix for Random Forests:\")\n", - "print(confusion_matrix(y_test,predrfn))\n", - "print(\"Score:\",round(accuracy_score(y_test,predrfn)*100,2))\n", - "print(\"Classification Report:\",classification_report(y_test,predrfn))\n", - "print('Prediction is: ', predrfn)\n", - "print('Actual is: ', y_test)\n", - "print(\"*\" * 100)\n", - "\n", - "# TF\n", - "x_train = X[0:8674]\n", - "x_test = X[8675]\n", - "y_train = y_TF[0:8674]\n", - "y_test = list(y_TF[8675])\n", - "mnb.fit(x_train, y_train)\n", - "tfb_train = mnb.score (x_train,y_train)\n", - "tfb_test = mnb.score (x_train,y_train)\n", - "predmnb = mnb.predict(x_test)\n", - "print(\"T-F Results\")\n", - "print('Label T-F train score is :',tfb_train)\n", - "print('Label T-F test score is :',tfb_test)\n", - "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", - "print(confusion_matrix(y_test,predmnb))\n", - "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", - "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", - "print('Prediction is: ', predmnb)\n", - "print('Actual is: ', y_test)\n", - "print(\"*\" * 100)\n", - "\n", - "# JP\n", - "x_train = X[0:8674]\n", - "x_test = X[8675]\n", - "y_train = y_JP[0:8674]\n", - "y_test = list(y_JP[8675])\n", - "mnb.fit(x_train, y_train)\n", - "jpb_train = mnb.score (x_train,y_train)\n", - "jpb_test = mnb.score (x_train,y_train)\n", - "predmnb = mnb.predict(x_test)\n", - "print(\"J-P Results\")\n", - "print('Label J-P train score is :',jpb_train)\n", - "print('Label J-P test score is :',jpb_test)\n", - "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", - "print(confusion_matrix(y_test,predmnb))\n", - "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", - "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", - "print('Prediction is: ', predmnb)\n", - "print('Actual is: ', y_test)\n", - "print(\"*\" * 100)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/your-project/data/reallife_data_insta.csv b/your-project/data/reallife_data_insta.csv new file mode 100644 index 00000000..02cdc734 --- /dev/null +++ b/your-project/data/reallife_data_insta.csv @@ -0,0 +1,8 @@ +,name,type,text_ready +0,Darya,ESFP,quarantin expect swipe realiti swipe see photo yesterday age affect hello santa wish christma mind back girl hat seri casual day uni swipe idiot thank steve nice iphon front camera miss thi edit still believ sunscreen prevent huge sunburn rather part peopl ask got free time except small town girl live capitalist world clueless might seriou might hungri want blue want yellow wall fell nearli year ago constantli fall face im pretti ok exam unpopular opinion univers fun exhibit christma around corner wish extent deadlin occas even get valley la go sightse diamond girl best friend especi trade em plane ticket uber ride threat warn talk begin semest tomorrow come fli let fli let fli away saltier pacif hangri b studi nyfalosangel williamroyal98 thi whole gotta studi sun shine get nerv exampl inner rage know might spontan book flight ask ou would want check flight back aswel vitamin finish first semerst hsg still afford porsch studi also enjoy holiday kid still short littl human week uni icloud googl drive ask pay storag uni start tomorrow alreadi see either lost caus find classroom b look like annabel caus sleep weak oh boy thi last week summer think weather decid give earli tast fall alreadi water fall darya fall casual sit floor caus stand overr promis count light saw squirrel lost count set real hous guess quiet set baywatch hire perfum actual bugspray want u buy nasti smelli stuff good laugh start wonder spotlight give tan caus sun come 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bleiben um den himmel zu fotografieren one power happi weltfrauentag gr nde warum ich kein blogger bin sommer +0,Nico,ENFP,post thi random pictur fill void go within lot past year sort thing happi say refocus energ work great thing impress serfau good start swipe right best drink game dog berlin boat still flex lad weekend madrid alway good idea hang hangov histor site drip hard stand close miss bro push best everi day never wa better duo golden boy rest easi get settl berlin three tough fun year whu last weekend final graduat degre best busi school germani importantli new friend life anoth place call home graduat whu boy class much fun let get moolah pic blurri day wa sharp probabl spectacular view ever wa drive along highway la sf act loyal thi pic lil b bit next minut probabl spectacular view ever wa drive along highway la sf hey googl hire want bike back mountainview googl siliconvalley throwback amaz road trip thi fall view def worth hike hike natur travel roadtrip yosemit keep eye star keep foot ground clearli see enjoy back socal place beauti pacif beer beach chill california home uglyfeet goldclapmus good day dude never hold back weekendmood thank kaleandm thi awesom parti crewlov reiseminist coco tb one best game ever still hold club record recept per game v cottbu w31 recept yard td thank zbreez trust pas throwback best winter ever kid group gave u name lil crackhead becaus got crack time fuckhashtag thi one big time cost u good men hospitalreunion repost luebeckcougar much way improv though person getbettereveryday gamestat cougar cougarn cougarsprid luebeck luebeckcougar footbal americanfootbal gfl amgid afvd gridiron awaygam ritterhud badger day credit gridiron design new year eve alp crew illuminati tb good old time sunday meant gameday monday newspap sit librari studi right wish could go back time believ becom realiti creat world sundaymood mom one tri get surf game think much better form beach water though haha germansaintmermaid lennartmhr tabl 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marathon bostonmarathon boston marathon raini shitwheath cold lastmil king friend stanway stadium boston red sox washington nation king colleg stanway boston redsox v washington nation basebal stadium sunglassesmusthav season vergingwieimflug freestyl ski funpark kicker valdifassa itali war ne geil ausbildung zeit alpbach skilehr obertauern traumjob bluecrew lo hombr aleman en costa rica christmastre great time costa rica nicaragua alemurillov parti palmen weiber und n bier janabrahm sister mallorca mall chill pool mar sol beer sun espa nai holiday deutschland ghana good luck today v castelao fortaleza wm wc deutschland germani alemanha ghana gana todomundo good luck jogapramim heissaufheuteabend footbal stadium viertelfinal v franc ich bin da wovor dein eltern dich immer gewarnt haben mottowoch tag sido kiffer harzer penner fun renderlund sidosmask die moral von der geschicht bremen ist geil hamburg nicht derbysieg einszunul werder heftigst choreo ultra bremen stadion hundertst derbi sitdownhsv stimmung fan happi nice day schadehamburg skifoan saalbach hinterglemm snow mountain oleoleol saalbacherschneekatzen fun fehmarn isschongeil sea occean sky cloud sail follow instinct vacat chil sandiego beach sun blue sky follow sea wave vacat good time sandiego missionbeach beach hot sun sunset follow sea pacif cali california red sky cloud cloudporn boy basebal game losangel dodger v cincinnati red stadium sky cloud la california cali fun dodgerstadium highway california highway sun vacat happi trolflex parti friend old gold fun fact club night music sunglass l4l saalbach ski schnee snow fun mountain From 84a97cec8a32ce985453c2f100142a2698488b96 Mon Sep 17 00:00:00 2001 From: Vicky Zauner Date: Thu, 28 May 2020 14:59:26 +0200 Subject: [PATCH 3/9] readme done and code sorted --- README.md | 117 ++++ your-project/README.md | 72 --- ...- Splitting Targets | NLP Analysis 2.ipynb | 589 +++++++++--------- ...d Tool.ipynb => Code 9 - Build Tool.ipynb} | 0 your-project/code/RF_IE.sav | Bin 0 -> 6441545 bytes your-project/code/RF_JP.sav | Bin 0 -> 8475833 bytes your-project/code/RF_NS.sav | Bin 0 -> 3798851 bytes your-project/code/RF_TF.sav | Bin 0 -> 9906905 bytes 8 files changed, 399 insertions(+), 379 deletions(-) create mode 100644 README.md delete mode 100644 your-project/README.md rename your-project/code/{Code 10 - Build Tool.ipynb => Code 9 - Build Tool.ipynb} (100%) create mode 100644 your-project/code/RF_IE.sav create mode 100644 your-project/code/RF_JP.sav create mode 100644 your-project/code/RF_NS.sav create mode 100644 your-project/code/RF_TF.sav diff --git a/README.md b/README.md new file mode 100644 index 00000000..2aeacfa8 --- /dev/null +++ b/README.md @@ -0,0 +1,117 @@ +Ironhack Logo + +# Title of My Project +*[Your Name]* + +*[Your Cohort, Campus & Date]* + +## Content +- [Project Description](#project-description) +- [Hypotheses / Questions](#hypotheses-questions) +- [Dataset](#dataset) +- [Cleaning](#cleaning) +- [Analysis](#analysis) +- [Model Training and Evaluation](#model-training-and-evaluation) +- [Conclusion](#conclusion) +- [Future Work](#future-work) +- [Workflow](#workflow) +- [Organization](#organization) +- [Links](#links) + +## Project Description +Out of personal interest for the topic, I chose to work on determining personality types according to the Myer-Briggs-Type-Indicator Theory from the text of comments on Social Media. I applied two different approaches to the problem: First I used the NLTK library to determine the personality types with Natural Language Processing, second I tried a quantitative approach by taking factors into account with include: the average length of a comment, the variance in length per comment, the average number of links used, the average number of questionmarks used, the average number of excalamtion marks used, the average number of dots used, the the average number of hashtags used and the average number of the words "me", "myself" or "I" used per comment. A variety of Algorithms was tested to maximize accuracy. In the end, the best model was tested on real life data. + +## Hypotheses / Questions +* Can the personality type of a person be determined merely by the language they use when posting on social media? +* In what ways can the ability of this determination or even of singular traits be useful for HR, Consulting and team building purposes? +* What are the highly correlated properties/features/words of posts with personality types/traits? +* What are the main indicators to determine the personality traits of a person? + +## Dataset + +[Dataset](https://www.kaggle.com/datasnaek/mbti-type?select=mbti_1.csv) + +* The data was retreived publicly from Kaggle. +* The original table contained 8675 clean rows with two columns: The personality Type classified with the four letter indicator frame and the text from 50 comments of the same person. +* From this data, I extracted quantitative characteristics for the second part of my analysis. +* To retreive the real life test data I chose not to scrape Instagram but to build the Dataframe manually due to the network's protection. +* To improve the success of the model, data retrieved particularly from the network it would be applied to later to accurately regard the vibe of the context reflected in the tone of the posts would have been very helpful. Also interesting insights could be gained with further in depth analysis of images matching the text and general activity and behavior in form of dates, comments, likes, follows, followers, fluctuations etc. + +## Cleaning +* For the natural language processing I cleaned the Data using regular expressions and the nltk library to apply the PorterStemmer, lemmatize and remove stopwords from the text. +* For the quantitative data I used string operations to extend the dataframe by the features desired. +* In both cases the target in form of the 4 letter indicator was seperated in order to allow dicisions and determinations by 2 each and not by 16. + +## Analysis + +Description is in the [Workflow](#workflow). + +## Model Training and Evaluation + +Description is in the [Workflow](#workflow). + +## Conclusion + +* Personality Types and traits can be roughly determined by language only. +* The accuracy can still be improved and would significantly profit from the model being trained on the same network as it is being used on. +* The model could be used on profiles of companies, on the character of their social media marketing as well as on the companies followers to determine buyer personas, which would allow adapting and consequently improving successful marketing. +* According to experience and by analysing the personality type of current employees and their performance and the way they flourish in the company culture can allow HR departments to detemrine a new employee's desired personality type. + +## Future Work + +Adding furter data would help to make the model more universally applicable: Images, body language, behavior on the platform would certainly be of interest. Also the model could be further improved by finding and cleaning misleading words. + +## Workflow + +1. After initial alaysis of the data (Code 1), I cleaned and modified the dataframe to prepare it for NLP (Code 2). + +2. In the following step I vectorized the text using the Count Vectorizer for feature extraction and I tried a variety of algorithms on the data including Multinomial Naive Bayes, the Decision Tree and K-Nearest Neighbor. In this case the Decision Tree Algorithm gave me the hightest overall accuracy of 49% for determining one of the 16 personalities correctly. (Code 2) + +3. Next, I decided to split the target into the four traits concluding the personality type in order to be able to make a decision between two options each rather than 16 at the same time. (Code 3) + +Following this I tried a variety of algorithms on the four traits seperately including Multinomial Naive Bayes, the Decision Tree, K-Nearest Neighbor and Random Forests. + +4. At first, I looked for the best performing algorithm for every trait and pieced a model together accordingly. (Code 4): +IE: K-Nearest Neighbors +NS: Random Forests +TF: Multinomial Naive Bayes +JP: Multinomial Naive Bayes + +5. In order to inrease accuracy I used PCA on the features extracted by the vectorizer and also used MinMaxScaler to scale the reduced features. +Additionally, I tested every algorithm on different parameters (n_neighbors, max_depth etc.) to insure optimized modelling. After this process, I decided to use the Random Forest Model on the fully reduced and scales training data to maximize accuracy. + +6. Parallel to this process, I was working on my extended dataframe, which featured quatitative characteristics of the posts such as the average length of a comment, the variance in length per comment, the average number of links used, the average number of questionmarks used, the average number of excalamtion marks used, the average number of dots used, the the average number of hashtags used and the average number of the words "me", "myself" or "I" used per comment and inspected the data for correlations with the personality types. (Code 4) + +7. In the following step I created a Neural Network Model for the Quantitative Data which resulted in decent accuracy for two of the four traits, IE and JP. (Code 5) + +8. When trying the other algorithms on the quantitative data, the Random Forests and Multinomial Naive Bayes Models performed best but not significantly better - if even - than when run on NLP. (Code 8) + +9. To test the model on real life data, I asked friends for permission to let me analyse their posts on Instagram and to take the test [online](https://www.16personalities.com) and retrieved a dataframe from it which I tested my algorithms on. (Code 7) + +10. In the Jupyter Notbook file Code 9 I started building an Instagram scraper, that should retreive verbal captions form people's profile, process the text, feed it to the model and return the user's personality type. It is still in progress. + + +## Organization + +I used a Trello Board to keep an overview of pending tasks and kept notes on paper. + +My repository is organized in two folders: data and code. + +*Data* +* The remote Repo does not contain the larger files, those can be found zipped in my [Google Drive](https://drive.google.com/drive/u/1/my-drive). +* The file quant_personalities.csv holds the dataframe with the quantitative measures I created. +* The file reallife_data_insta.csv includes the caption of the friends that allowed me to use them. + + +*Code* + +Includes the 9 Jupyter Notbooks mentioned above as well as one Master Notebook, which includes the most important snippits of code condensed alongside careful detailes markdown. + +Also you find the saved model (Random Forests for each of the four Personality Features on the reduced and scaled nlp data) + + +## Links + +[Repository](https://github.com/VickyZauner/Project-Week-8-Final-Project) +[Slides](https://docs.google.com/presentation/d/10Yu48XyE_rS521vREW13zlOzsgoRrV4sjKo4MVCjjs0/edit#slide=id.g85e9335f2e_0_70) +[Trello](https://trello.com/b/b9H0SKTx/project-5) diff --git a/your-project/README.md b/your-project/README.md deleted file mode 100644 index 9563cd70..00000000 --- a/your-project/README.md +++ /dev/null @@ -1,72 +0,0 @@ -Ironhack Logo - -# Title of My Project -*[Your Name]* - -*[Your Cohort, Campus & Date]* - -## Content -- [Project Description](#project-description) -- [Hypotheses / Questions](#hypotheses-questions) -- [Dataset](#dataset) -- [Cleaning](#cleaning) -- [Analysis](#analysis) -- [Model Training and Evaluation](#model-training-and-evaluation) -- [Conclusion](#conclusion) -- [Future Work](#future-work) -- [Workflow](#workflow) -- [Organization](#organization) -- [Links](#links) - -## Project Description -Write a short description of your project: 3-5 sentences about what your project is about, why you chose this topic (if relevant), and what you are trying to show. - -## Hypotheses / Questions -* What data/business/research/personal question you would like to answer? -* What is the context for the question and the possible scientific or business application? -* What are the hypotheses you would like to test in order to answer your question? -Frame your hypothesis with statistical/data languages (i.e. define Null and Alternative Hypothesis). You can use formulas if you want but that is not required. - -## Dataset -* Where did you get your data? If you downloaded a dataset (either public or private), describe where you downloaded it and include the command to load the dataset. -* Did you build your own datset? If so, did you use an API or a web scraper? PRovide the relevant scripts in your repo. -* For all types of datasets, provide a description of the size, complexity, and data types included in your dataset, as well as a schema of the tables if necessary. -* If the question cannot be answered with the available data, why not? What data would you need to answer it better? - -## Cleaning -Describe your full process of data wrangling and cleaning. Document why you chose to fill missing values, extract outliers, or create the variables you did as well as your reasoning behind the process. - -## Analysis -* Overview the general steps you went through to analyze your data in order to test your hypothesis. -* Document each step of your data exploration and analysis. -* Include charts to demonstrate the effect of your work. -* If you used Machine Learning in your final project, describe your feature selection process. - -## Model Training and Evaluation -*Include this section only if you chose to include ML in your project.* -* Describe how you trained your model, the results you obtained, and how you evaluated those results. - -## Conclusion -* Summarize your results. What do they mean? -* What can you say about your hypotheses? -* Interpret your findings in terms of the questions you try to answer. - -## Future Work -Address any questions you were unable to answer, or any next steps or future extensions to your project. - -## Workflow -Outline the workflow you used in your project. What were the steps? -How did you test the accuracy of your analysis and/or machine learning algorithm? - -## Organization -How did you organize your work? Did you use any tools like a trello or kanban board? - -What does your repository look like? Explain your folder and file structure. - -## Links -Include links to your repository, slides and trello/kanban board. Feel free to include any other links associated with your project. - - -[Repository](https://github.com/) -[Slides](https://slides.com/) -[Trello](https://trello.com/en) diff --git a/your-project/code/Code 3 - Splitting Targets | NLP Analysis 2.ipynb b/your-project/code/Code 3 - Splitting Targets | NLP Analysis 2.ipynb index 2d0ca262..39c4319e 100644 --- a/your-project/code/Code 3 - Splitting Targets | NLP Analysis 2.ipynb +++ b/your-project/code/Code 3 - Splitting Targets | NLP Analysis 2.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -34,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -43,7 +43,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -52,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -64,7 +64,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -73,7 +73,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -88,7 +88,7 @@ "dtype: int64" ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -99,7 +99,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -150,7 +150,7 @@ "3559 INFP NaN I N F P" ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -162,7 +162,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -171,7 +171,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -186,7 +186,7 @@ "dtype: int64" ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -197,7 +197,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -288,7 +288,7 @@ "4 ENTJ fire anoth silli misconcept approach logic go ... E N T J" ] }, - "execution_count": 10, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -299,7 +299,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -308,7 +308,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -323,7 +323,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -336,7 +336,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -359,7 +359,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -370,7 +370,7 @@ "Name: I-E, dtype: int64" ] }, - "execution_count": 19, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -389,70 +389,70 @@ "output_type": "stream", "text": [ "I-E Accuracy for 1 neigbours.\n", - "Label I-E test score is : 0.6789625360230548\n", + "Label I-E test score is : 0.6610951008645534\n", "Confusion Matrix for KNeighbors:\n", - "[[ 99 284]\n", - " [ 273 1079]]\n", + "[[ 88 293]\n", + " [ 295 1059]]\n", "I-E Accuracy for 3 neigbours.\n", - "Label I-E test score is : 0.7152737752161383\n", + "Label I-E test score is : 0.7066282420749279\n", "Confusion Matrix for KNeighbors:\n", - "[[ 74 335]\n", - " [ 159 1167]]\n", + "[[ 64 373]\n", + " [ 136 1162]]\n", "I-E Accuracy for 5 neigbours.\n", - "Label I-E test score is : 0.7527377521613833\n", + "Label I-E test score is : 0.7360230547550433\n", "Confusion Matrix for KNeighbors:\n", - "[[ 41 346]\n", - " [ 83 1265]]\n", + "[[ 45 352]\n", + " [ 106 1232]]\n", "I-E Accuracy for 7 neigbours.\n", - "Label I-E test score is : 0.7440922190201729\n", + "Label I-E test score is : 0.7636887608069164\n", "Confusion Matrix for KNeighbors:\n", - "[[ 26 388]\n", - " [ 56 1265]]\n", + "[[ 17 363]\n", + " [ 47 1308]]\n", "I-E Accuracy for 9 neigbours.\n", - "Label I-E test score is : 0.7510086455331412\n", + "Label I-E test score is : 0.7688760806916427\n", "Confusion Matrix for KNeighbors:\n", - "[[ 21 383]\n", - " [ 49 1282]]\n", + "[[ 18 357]\n", + " [ 44 1316]]\n", "I-E Accuracy for 11 neigbours.\n", - "Label I-E test score is : 0.7538904899135447\n", + "Label I-E test score is : 0.7475504322766571\n", "Confusion Matrix for KNeighbors:\n", - "[[ 17 387]\n", - " [ 40 1291]]\n", + "[[ 14 403]\n", + " [ 35 1283]]\n", "I-E Accuracy for 13 neigbours.\n", - "Label I-E test score is : 0.7619596541786744\n", + "Label I-E test score is : 0.7734870317002882\n", "Confusion Matrix for KNeighbors:\n", - "[[ 9 393]\n", - " [ 20 1313]]\n", + "[[ 7 375]\n", + " [ 18 1335]]\n", "I-E Accuracy for 15 neigbours.\n", - "Label I-E test score is : 0.7665706051873199\n", + "Label I-E test score is : 0.7798270893371758\n", "Confusion Matrix for KNeighbors:\n", - "[[ 6 389]\n", - " [ 16 1324]]\n", + "[[ 3 367]\n", + " [ 15 1350]]\n", "I-E Accuracy for 17 neigbours.\n", - "Label I-E test score is : 0.7631123919308357\n", + "Label I-E test score is : 0.7613832853025937\n", "Confusion Matrix for KNeighbors:\n", - "[[ 8 398]\n", - " [ 13 1316]]\n", + "[[ 3 403]\n", + " [ 11 1318]]\n", "I-E Accuracy for 19 neigbours.\n", - "Label I-E test score is : 0.7648414985590778\n", + "Label I-E test score is : 0.7642651296829971\n", "Confusion Matrix for KNeighbors:\n", - "[[ 8 400]\n", - " [ 8 1319]]\n", + "[[ 6 401]\n", + " [ 8 1320]]\n", "I-E Accuracy for 21 neigbours.\n", - "Label I-E test score is : 0.7596541786743516\n", + "Label I-E test score is : 0.7688760806916427\n", "Confusion Matrix for KNeighbors:\n", - "[[ 6 411]\n", - " [ 6 1312]]\n", + "[[ 6 393]\n", + " [ 8 1328]]\n", "I-E Accuracy for 23 neigbours.\n", - "Label I-E test score is : 0.7636887608069164\n", + "Label I-E test score is : 0.7717579250720461\n", "Confusion Matrix for KNeighbors:\n", - "[[ 2 402]\n", - " [ 8 1323]]\n", + "[[ 2 393]\n", + " [ 3 1337]]\n", "I-E Accuracy for 25 neigbours.\n", - "Label I-E test score is : 0.7711815561959654\n", + "Label I-E test score is : 0.7659942363112392\n", "Confusion Matrix for KNeighbors:\n", - "[[ 2 393]\n", - " [ 4 1336]]\n" + "[[ 2 404]\n", + " [ 2 1327]]\n" ] } ], @@ -476,7 +476,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -484,70 +484,70 @@ "output_type": "stream", "text": [ "I-E Accuracy for 1 neigbours.\n", - "Label I-E test score is : 0.6651296829971182\n", + "Label I-E test score is : 0.6570605187319885\n", "Confusion Matrix for KNeighbors:\n", - "[[ 96 300]\n", - " [ 281 1058]]\n", + "[[ 91 308]\n", + " [ 287 1049]]\n", "I-E Accuracy for 3 neigbours.\n", - "Label I-E test score is : 0.6927953890489914\n", + "Label I-E test score is : 0.7066282420749279\n", "Confusion Matrix for KNeighbors:\n", - "[[ 56 382]\n", - " [ 151 1146]]\n", + "[[ 63 356]\n", + " [ 153 1163]]\n", "I-E Accuracy for 5 neigbours.\n", - "Label I-E test score is : 0.7446685878962536\n", + "Label I-E test score is : 0.729106628242075\n", "Confusion Matrix for KNeighbors:\n", - "[[ 34 368]\n", - " [ 75 1258]]\n", + "[[ 38 374]\n", + " [ 96 1227]]\n", "I-E Accuracy for 7 neigbours.\n", - "Label I-E test score is : 0.7544668587896254\n", + "Label I-E test score is : 0.7596541786743516\n", "Confusion Matrix for KNeighbors:\n", - "[[ 24 362]\n", - " [ 64 1285]]\n", + "[[ 25 367]\n", + " [ 50 1293]]\n", "I-E Accuracy for 9 neigbours.\n", - "Label I-E test score is : 0.7463976945244957\n", + "Label I-E test score is : 0.7613832853025937\n", "Confusion Matrix for KNeighbors:\n", - "[[ 12 399]\n", - " [ 41 1283]]\n", + "[[ 17 382]\n", + " [ 32 1304]]\n", "I-E Accuracy for 11 neigbours.\n", - "Label I-E test score is : 0.7619596541786744\n", + "Label I-E test score is : 0.7538904899135447\n", "Confusion Matrix for KNeighbors:\n", - "[[ 11 380]\n", - " [ 33 1311]]\n", + "[[ 19 394]\n", + " [ 33 1289]]\n", "I-E Accuracy for 13 neigbours.\n", - "Label I-E test score is : 0.7487031700288185\n", + "Label I-E test score is : 0.768299711815562\n", "Confusion Matrix for KNeighbors:\n", - "[[ 9 422]\n", - " [ 14 1290]]\n", + "[[ 10 381]\n", + " [ 21 1323]]\n", "I-E Accuracy for 15 neigbours.\n", - "Label I-E test score is : 0.7706051873198847\n", + "Label I-E test score is : 0.7654178674351585\n", "Confusion Matrix for KNeighbors:\n", - "[[ 7 384]\n", - " [ 14 1330]]\n", + "[[ 10 386]\n", + " [ 21 1318]]\n", "I-E Accuracy for 17 neigbours.\n", - "Label I-E test score is : 0.7665706051873199\n", + "Label I-E test score is : 0.7780979827089337\n", "Confusion Matrix for KNeighbors:\n", - "[[ 7 393]\n", - " [ 12 1323]]\n", + "[[ 7 376]\n", + " [ 9 1343]]\n", "I-E Accuracy for 19 neigbours.\n", - "Label I-E test score is : 0.7590778097982709\n", + "Label I-E test score is : 0.7642651296829971\n", "Confusion Matrix for KNeighbors:\n", - "[[ 3 414]\n", - " [ 4 1314]]\n", + "[[ 5 400]\n", + " [ 9 1321]]\n", "I-E Accuracy for 21 neigbours.\n", - "Label I-E test score is : 0.7544668587896254\n", + "Label I-E test score is : 0.7757925072046109\n", "Confusion Matrix for KNeighbors:\n", - "[[ 7 419]\n", - " [ 7 1302]]\n", + "[[ 5 383]\n", + " [ 6 1341]]\n", "I-E Accuracy for 23 neigbours.\n", - "Label I-E test score is : 0.777521613832853\n", + "Label I-E test score is : 0.7694524495677233\n", "Confusion Matrix for KNeighbors:\n", - "[[ 2 380]\n", - " [ 6 1347]]\n", + "[[ 0 395]\n", + " [ 5 1335]]\n", "I-E Accuracy for 25 neigbours.\n", - "Label I-E test score is : 0.7648414985590778\n", + "Label I-E test score is : 0.777521613832853\n", "Confusion Matrix for KNeighbors:\n", - "[[ 5 406]\n", - " [ 2 1322]]\n" + "[[ 5 384]\n", + " [ 2 1344]]\n" ] } ], @@ -568,7 +568,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -580,61 +580,61 @@ " [ 243 0]]\n", "Score: 85.99\n", "I-E Accuracy for 2 max_depth.\n", - "[[1511 0]\n", - " [ 224 0]]\n", - "Score: 87.09\n", + "[[1510 0]\n", + " [ 225 0]]\n", + "Score: 87.03\n", "I-E Accuracy for 3 max_depth.\n", - "[[1494 0]\n", - " [ 241 0]]\n", - "Score: 86.11\n", + "[[1503 0]\n", + " [ 232 0]]\n", + "Score: 86.63\n", "I-E Accuracy for 4 max_depth.\n", - "[[1484 0]\n", - " [ 251 0]]\n", - "Score: 85.53\n", + "[[1487 0]\n", + " [ 248 0]]\n", + "Score: 85.71\n", "I-E Accuracy for 5 max_depth.\n", "[[1502 0]\n", " [ 233 0]]\n", "Score: 86.57\n", "I-E Accuracy for 6 max_depth.\n", - "[[1521 0]\n", - " [ 214 0]]\n", - "Score: 87.67\n", + "[[1499 0]\n", + " [ 236 0]]\n", + "Score: 86.4\n", "I-E Accuracy for 7 max_depth.\n", - "[[1479 0]\n", - " [ 256 0]]\n", - "Score: 85.24\n", + "[[1498 0]\n", + " [ 237 0]]\n", + "Score: 86.34\n", "I-E Accuracy for 8 max_depth.\n", - "[[1489 0]\n", - " [ 246 0]]\n", - "Score: 85.82\n", + "[[1525 0]\n", + " [ 210 0]]\n", + "Score: 87.9\n", "I-E Accuracy for 9 max_depth.\n", - "[[1505 0]\n", - " [ 230 0]]\n", - "Score: 86.74\n", + "[[1511 0]\n", + " [ 224 0]]\n", + "Score: 87.09\n", "I-E Accuracy for 10 max_depth.\n", - "[[1482 3]\n", - " [ 250 0]]\n", - "Score: 85.42\n", + "[[1490 0]\n", + " [ 244 1]]\n", + "Score: 85.94\n", "I-E Accuracy for 11 max_depth.\n", - "[[1521 3]\n", - " [ 210 1]]\n", - "Score: 87.72\n", + "[[1504 2]\n", + " [ 228 1]]\n", + "Score: 86.74\n", "I-E Accuracy for 12 max_depth.\n", - "[[1499 0]\n", - " [ 235 1]]\n", - "Score: 86.46\n", + "[[1470 3]\n", + " [ 262 0]]\n", + "Score: 84.73\n", "I-E Accuracy for 13 max_depth.\n", - "[[1472 0]\n", - " [ 263 0]]\n", - "Score: 84.84\n", + "[[1494 1]\n", + " [ 240 0]]\n", + "Score: 86.11\n", "I-E Accuracy for 14 max_depth.\n", - "[[1494 6]\n", - " [ 234 1]]\n", - "Score: 86.17\n", + "[[1471 5]\n", + " [ 257 2]]\n", + "Score: 84.9\n", "I-E Accuracy for 15 max_depth.\n", - "[[1507 3]\n", - " [ 225 0]]\n", - "Score: 86.86\n" + "[[1501 1]\n", + " [ 232 1]]\n", + "Score: 86.57\n" ] } ], @@ -661,7 +661,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -669,65 +669,65 @@ "output_type": "stream", "text": [ "I-E Accuracy for 1 max_depth.\n", - "[[1497 0]\n", - " [ 238 0]]\n", - "Score: 86.28\n", + "[[1490 0]\n", + " [ 245 0]]\n", + "Score: 85.88\n", "I-E Accuracy for 2 max_depth.\n", - "[[1497 0]\n", - " [ 238 0]]\n", - "Score: 86.28\n", + "[[1500 0]\n", + " [ 235 0]]\n", + "Score: 86.46\n", "I-E Accuracy for 3 max_depth.\n", - "[[1497 0]\n", - " [ 238 0]]\n", - "Score: 86.28\n", + "[[1502 0]\n", + " [ 233 0]]\n", + "Score: 86.57\n", "I-E Accuracy for 4 max_depth.\n", - "[[1497 0]\n", - " [ 238 0]]\n", - "Score: 86.28\n", + "[[1503 0]\n", + " [ 232 0]]\n", + "Score: 86.63\n", "I-E Accuracy for 5 max_depth.\n", - "[[1497 0]\n", - " [ 238 0]]\n", - "Score: 86.28\n", + "[[1478 0]\n", + " [ 257 0]]\n", + "Score: 85.19\n", "I-E Accuracy for 6 max_depth.\n", - "[[1497 0]\n", - " [ 238 0]]\n", - "Score: 86.28\n", + "[[1503 0]\n", + " [ 232 0]]\n", + "Score: 86.63\n", "I-E Accuracy for 7 max_depth.\n", "[[1497 0]\n", " [ 238 0]]\n", "Score: 86.28\n", "I-E Accuracy for 8 max_depth.\n", - "[[1497 0]\n", - " [ 238 0]]\n", - "Score: 86.28\n", + "[[1507 0]\n", + " [ 228 0]]\n", + "Score: 86.86\n", "I-E Accuracy for 9 max_depth.\n", - "[[1497 0]\n", - " [ 238 0]]\n", - "Score: 86.28\n", + "[[1510 1]\n", + " [ 224 0]]\n", + "Score: 87.03\n", "I-E Accuracy for 10 max_depth.\n", - "[[1496 1]\n", - " [ 238 0]]\n", - "Score: 86.22\n", + "[[1492 0]\n", + " [ 243 0]]\n", + "Score: 85.99\n", "I-E Accuracy for 11 max_depth.\n", - "[[1495 2]\n", - " [ 238 0]]\n", - "Score: 86.17\n", + "[[1484 1]\n", + " [ 250 0]]\n", + "Score: 85.53\n", "I-E Accuracy for 12 max_depth.\n", - "[[1495 2]\n", - " [ 236 2]]\n", - "Score: 86.28\n", + "[[1493 2]\n", + " [ 240 0]]\n", + "Score: 86.05\n", "I-E Accuracy for 13 max_depth.\n", - "[[1495 2]\n", - " [ 237 1]]\n", - "Score: 86.22\n", + "[[1505 2]\n", + " [ 227 1]]\n", + "Score: 86.8\n", "I-E Accuracy for 14 max_depth.\n", - "[[1492 5]\n", - " [ 236 2]]\n", - "Score: 86.11\n", + "[[1516 1]\n", + " [ 216 2]]\n", + "Score: 87.49\n", "I-E Accuracy for 15 max_depth.\n", - "[[1494 3]\n", - " [ 237 1]]\n", - "Score: 86.17\n" + "[[1492 4]\n", + " [ 239 0]]\n", + "Score: 85.99\n" ] } ], @@ -739,7 +739,7 @@ " rfn = RandomForestClassifier(max_depth= x, random_state=0)\n", "\n", " # NS\n", - " x_train, x_test, y_train, y_test = train_test_split(scaled, y_NS, test_size=0.2, random_state=10)\n", + " x_train, x_test, y_train, y_test = train_test_split(scaled, y_NS, test_size=0.2)\n", " rfn.fit(x_train, y_train)\n", "# ieb_train = knn.score(x_train,y_train)\n", "# ieb_test = knn.score (x_train,y_train)\n", @@ -1263,71 +1263,71 @@ "output_type": "stream", "text": [ "I-E RESULTS\n", - "Label I-E train score is : 0.7867127828217323\n", - "Label I-E test score is : 0.7867127828217323\n", + "Label I-E train score is : 0.7731661622712206\n", + "Label I-E test score is : 0.7731661622712206\n", "Confusion Matrix for Multinomial Naive Bayes:\n", - "[[ 39 354]\n", - " [ 58 1284]]\n", - "Score: 76.25\n", + "[[ 8 385]\n", + " [ 14 1328]]\n", + "Score: 77.0\n", "Classification Report: precision recall f1-score support\n", "\n", - " E 0.40 0.10 0.16 393\n", - " I 0.78 0.96 0.86 1342\n", + " E 0.36 0.02 0.04 393\n", + " I 0.78 0.99 0.87 1342\n", "\n", - " accuracy 0.76 1735\n", - " macro avg 0.59 0.53 0.51 1735\n", - "weighted avg 0.70 0.76 0.70 1735\n", + " accuracy 0.77 1735\n", + " macro avg 0.57 0.50 0.45 1735\n", + "weighted avg 0.68 0.77 0.68 1735\n", "\n", "****************************************************************************************************\n", "N-S RESULTS\n", - "Label N-S train score is : 0.8648220204640438\n", - "Label N-S test score is : 0.8648220204640438\n", + "Label N-S train score is : 0.8622279867416054\n", + "Label N-S test score is : 0.8622279867416054\n", "Confusion Matrix for Multinomial Naive Bayes:\n", - "[[1475 22]\n", - " [ 233 5]]\n", - "Score: 85.3\n", + "[[1496 1]\n", + " [ 238 0]]\n", + "Score: 86.22\n", "Classification Report: precision recall f1-score support\n", "\n", - " N 0.86 0.99 0.92 1497\n", - " S 0.19 0.02 0.04 238\n", + " N 0.86 1.00 0.93 1497\n", + " S 0.00 0.00 0.00 238\n", "\n", - " accuracy 0.85 1735\n", - " macro avg 0.52 0.50 0.48 1735\n", - "weighted avg 0.77 0.85 0.80 1735\n", + " accuracy 0.86 1735\n", + " macro avg 0.43 0.50 0.46 1735\n", + "weighted avg 0.74 0.86 0.80 1735\n", "\n", "****************************************************************************************************\n", "T-F Results\n", - "Label T-F train score is : 0.7122063697939184\n", - "Label T-F test score is : 0.7122063697939184\n", + "Label T-F train score is : 0.6813661910938176\n", + "Label T-F test score is : 0.6813661910938176\n", "Confusion Matrix for Multinomial Naive Bayes:\n", - "[[509 451]\n", - " [282 493]]\n", - "Score: 57.75\n", + "[[496 464]\n", + " [257 518]]\n", + "Score: 58.44\n", "Classification Report: precision recall f1-score support\n", "\n", - " F 0.64 0.53 0.58 960\n", - " T 0.52 0.64 0.57 775\n", + " F 0.66 0.52 0.58 960\n", + " T 0.53 0.67 0.59 775\n", "\n", " accuracy 0.58 1735\n", - " macro avg 0.58 0.58 0.58 1735\n", - "weighted avg 0.59 0.58 0.58 1735\n", + " macro avg 0.59 0.59 0.58 1735\n", + "weighted avg 0.60 0.58 0.58 1735\n", "\n", "****************************************************************************************************\n", "J-P Results\n", - "Label J-P train score is : 0.6921746649373108\n", - "Label J-P test score is : 0.6921746649373108\n", + "Label J-P train score is : 0.6536964980544747\n", + "Label J-P test score is : 0.6536964980544747\n", "Confusion Matrix for Multinomial Naive Bayes:\n", - "[[196 484]\n", - " [282 773]]\n", - "Score: 55.85\n", + "[[147 533]\n", + " [188 867]]\n", + "Score: 58.44\n", "Classification Report: precision recall f1-score support\n", "\n", - " J 0.41 0.29 0.34 680\n", - " P 0.61 0.73 0.67 1055\n", + " J 0.44 0.22 0.29 680\n", + " P 0.62 0.82 0.71 1055\n", "\n", - " accuracy 0.56 1735\n", - " macro avg 0.51 0.51 0.50 1735\n", - "weighted avg 0.53 0.56 0.54 1735\n", + " accuracy 0.58 1735\n", + " macro avg 0.53 0.52 0.50 1735\n", + "weighted avg 0.55 0.58 0.54 1735\n", "\n", "****************************************************************************************************\n" ] @@ -1336,7 +1336,7 @@ "source": [ "from sklearn.neighbors import KNeighborsClassifier\n", "\n", - "knn = KNeighborsClassifier(n_neighbors=7)\n", + "knn = KNeighborsClassifier(n_neighbors=15)\n", "\n", "\n", "\n", @@ -1566,43 +1566,29 @@ "output_type": "stream", "text": [ "I-E RESULTS\n", - "Label I-E train score is : 0.7691309987029832\n", - "Label I-E test score is : 0.7691309987029832\n", + "Label I-E train score is : 0.9211701974347889\n", + "Label I-E test score is : 0.9211701974347889\n", "Confusion Matrix for Multinomial Naive Bayes:\n", - "[[ 0 393]\n", - " [ 0 1342]]\n", - "Score: 77.35\n", + "[[ 4 389]\n", + " [ 6 1336]]\n", + "Score: 77.23\n", "Classification Report: precision recall f1-score support\n", "\n", - " E 0.00 0.00 0.00 393\n", + " E 0.40 0.01 0.02 393\n", " I 0.77 1.00 0.87 1342\n", "\n", " accuracy 0.77 1735\n", - " macro avg 0.39 0.50 0.44 1735\n", - "weighted avg 0.60 0.77 0.67 1735\n", + " macro avg 0.59 0.50 0.45 1735\n", + "weighted avg 0.69 0.77 0.68 1735\n", "\n", - "****************************************************************************************************\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", - " _warn_prf(average, modifier, msg_start, len(result))\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "****************************************************************************************************\n", "N-S RESULTS\n", - "Label N-S train score is : 0.8619397607724456\n", - "Label N-S test score is : 0.8619397607724456\n", + "Label N-S train score is : 0.8975356679636836\n", + "Label N-S test score is : 0.8975356679636836\n", "Confusion Matrix for Multinomial Naive Bayes:\n", - "[[1497 0]\n", + "[[1493 4]\n", " [ 238 0]]\n", - "Score: 86.28\n", + "Score: 86.05\n", "Classification Report: precision recall f1-score support\n", "\n", " N 0.86 1.00 0.93 1497\n", @@ -1612,53 +1598,39 @@ " macro avg 0.43 0.50 0.46 1735\n", "weighted avg 0.74 0.86 0.80 1735\n", "\n", - "****************************************************************************************************\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", - " _warn_prf(average, modifier, msg_start, len(result))\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "****************************************************************************************************\n", "T-F Results\n", - "Label T-F train score is : 0.7169620982850555\n", - "Label T-F test score is : 0.7169620982850555\n", + "Label T-F train score is : 0.9975500792621416\n", + "Label T-F test score is : 0.9975500792621416\n", "Confusion Matrix for Multinomial Naive Bayes:\n", - "[[725 235]\n", - " [369 406]]\n", - "Score: 65.19\n", + "[[736 224]\n", + " [349 426]]\n", + "Score: 66.97\n", "Classification Report: precision recall f1-score support\n", "\n", - " F 0.66 0.76 0.71 960\n", - " T 0.63 0.52 0.57 775\n", + " F 0.68 0.77 0.72 960\n", + " T 0.66 0.55 0.60 775\n", "\n", - " accuracy 0.65 1735\n", - " macro avg 0.65 0.64 0.64 1735\n", - "weighted avg 0.65 0.65 0.65 1735\n", + " accuracy 0.67 1735\n", + " macro avg 0.67 0.66 0.66 1735\n", + "weighted avg 0.67 0.67 0.67 1735\n", "\n", "****************************************************************************************************\n", "J-P Results\n", - "Label J-P train score is : 0.6281884997838305\n", - "Label J-P test score is : 0.6281884997838305\n", + "Label J-P train score is : 0.9809770860354518\n", + "Label J-P test score is : 0.9809770860354518\n", "Confusion Matrix for Multinomial Naive Bayes:\n", - "[[ 11 669]\n", - " [ 4 1051]]\n", - "Score: 61.21\n", + "[[ 80 600]\n", + " [ 69 986]]\n", + "Score: 61.44\n", "Classification Report: precision recall f1-score support\n", "\n", - " J 0.73 0.02 0.03 680\n", - " P 0.61 1.00 0.76 1055\n", + " J 0.54 0.12 0.19 680\n", + " P 0.62 0.93 0.75 1055\n", "\n", " accuracy 0.61 1735\n", - " macro avg 0.67 0.51 0.39 1735\n", - "weighted avg 0.66 0.61 0.47 1735\n", + " macro avg 0.58 0.53 0.47 1735\n", + "weighted avg 0.59 0.61 0.53 1735\n", "\n", "****************************************************************************************************\n" ] @@ -1666,14 +1638,16 @@ ], "source": [ "from sklearn.ensemble import RandomForestClassifier\n", + "import joblib\n", "\n", - "rfn = RandomForestClassifier(max_depth=5, random_state=0)\n", + "rfn = RandomForestClassifier(max_depth=14, random_state=0)\n", "rfn.fit(x_train, y_train)\n", "\n", "\n", "# IE\n", "x_train, x_test, y_train, y_test = train_test_split(scaled, y_IE, test_size=0.2, random_state=10)\n", "rfn.fit(x_train, y_train)\n", + "model = rfn.fit(x_train, y_train)\n", "ieb_train = rfn.score (x_train,y_train)\n", "ieb_test = rfn.score (x_train,y_train)\n", "predrfn = rfn.predict(x_test)\n", @@ -1684,11 +1658,14 @@ "print(confusion_matrix(y_test,predrfn))\n", "print(\"Score:\",round(accuracy_score(y_test,predrfn)*100,2))\n", "print(\"Classification Report:\",classification_report(y_test,predrfn))\n", + "filename = 'RF_IE.sav'\n", + "joblib.dump(model, filename)\n", "print(\"*\" * 100)\n", "\n", "# NS\n", "x_train, x_test, y_train, y_test = train_test_split(scaled, y_NS, test_size=0.2, random_state=10)\n", "rfn.fit(x_train, y_train)\n", + "model = rfn.fit(x_train, y_train)\n", "nsb_train = rfn.score (x_train,y_train)\n", "nsb_test = rfn.score (x_train,y_train)\n", "predrfn = rfn.predict(x_test)\n", @@ -1699,11 +1676,14 @@ "print(confusion_matrix(y_test,predrfn))\n", "print(\"Score:\",round(accuracy_score(y_test,predrfn)*100,2))\n", "print(\"Classification Report:\",classification_report(y_test,predrfn))\n", + "filename = 'RF_NS.sav'\n", + "joblib.dump(model, filename)\n", "print(\"*\" * 100)\n", "\n", "# TF\n", "x_train, x_test, y_train, y_test = train_test_split(scaled, y_TF, test_size=0.2, random_state=10)\n", "rfn.fit(x_train, y_train)\n", + "model = rfn.fit(x_train, y_train)\n", "tfb_train = rfn.score (x_train,y_train)\n", "tfb_test = rfn.score (x_train,y_train)\n", "predrfn = rfn.predict(x_test)\n", @@ -1714,11 +1694,14 @@ "print(confusion_matrix(y_test,predrfn))\n", "print(\"Score:\",round(accuracy_score(y_test,predrfn)*100,2))\n", "print(\"Classification Report:\",classification_report(y_test,predrfn))\n", + "filename = 'RF_TF.sav'\n", + "joblib.dump(model, filename)\n", "print(\"*\" * 100)\n", "\n", "# JP\n", "x_train, x_test, y_train, y_test = train_test_split(scaled, y_JP, test_size=0.2, random_state=10)\n", "rfn.fit(x_train, y_train)\n", + "model = rfn.fit(x_train, y_train)\n", "jpb_train = rfn.score (x_train,y_train)\n", "jpb_test = rfn.score (x_train,y_train)\n", "predrfn = rfn.predict(x_test)\n", @@ -1729,6 +1712,8 @@ "print(confusion_matrix(y_test,predrfn))\n", "print(\"Score:\",round(accuracy_score(y_test,predrfn)*100,2))\n", "print(\"Classification Report:\",classification_report(y_test,predrfn))\n", + "filename = 'RF_JP.sav'\n", + "joblib.dump(model, filename)\n", "print(\"*\" * 100)" ] }, @@ -1741,17 +1726,9 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 33, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Using TensorFlow backend.\n" - ] - } - ], + "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", @@ -1766,14 +1743,12 @@ "from keras.layers import Flatten\n", "from keras.layers import Dense\n", "from sklearn import preprocessing\n", - "from tensorflow.keras.models import load_model\n", - "import tensorflow as tf\n", "from sklearn.model_selection import train_test_split, cross_val_score" ] }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -1940,7 +1915,7 @@ "[8674 rows x 6 columns]" ] }, - "execution_count": 41, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -1962,7 +1937,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -1982,7 +1957,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -2005,7 +1980,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -2014,7 +1989,7 @@ "(6939, 1986297)" ] }, - "execution_count": 47, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -2025,7 +2000,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -2038,12 +2013,12 @@ }, { "ename": "InvalidArgumentError", - "evalue": " TypeError: 'SparseTensor' object is not subscriptable\nTraceback (most recent call last):\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/ops/script_ops.py\", line 241, in __call__\n return func(device, token, args)\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/ops/script_ops.py\", line 130, in __call__\n ret = self._func(*args)\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/autograph/impl/api.py\", line 309, in wrapper\n return func(*args, **kwargs)\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/data_adapter.py\", line 513, in py_method\n return [slice_array(inp) for inp in flat_inputs]\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/data_adapter.py\", line 513, in \n return [slice_array(inp) for inp in flat_inputs]\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/data_adapter.py\", line 512, in slice_array\n contiguous=contiguous)\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/training_utils.py\", line 391, in slice_arrays\n entries = [[x[i:i + 1] for i in indices] for x in arrays]\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/training_utils.py\", line 391, in \n entries = [[x[i:i + 1] for i in indices] for x in arrays]\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/training_utils.py\", line 391, in \n entries = [[x[i:i + 1] for i in indices] for x in arrays]\n\nTypeError: 'SparseTensor' object is not subscriptable\n\n\n\t [[{{node EagerPyFunc}}]]\n\t [[IteratorGetNext]] [Op:__inference_train_function_2113]\n\nFunction call stack:\ntrain_function\n", + "evalue": " TypeError: 'SparseTensor' object is not subscriptable\nTraceback (most recent call last):\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/ops/script_ops.py\", line 241, in __call__\n return func(device, token, args)\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/ops/script_ops.py\", line 130, in __call__\n ret = self._func(*args)\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/autograph/impl/api.py\", line 309, in wrapper\n return func(*args, **kwargs)\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/data_adapter.py\", line 513, in py_method\n return [slice_array(inp) for inp in flat_inputs]\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/data_adapter.py\", line 513, in \n return [slice_array(inp) for inp in flat_inputs]\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/data_adapter.py\", line 512, in slice_array\n contiguous=contiguous)\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/training_utils.py\", line 391, in slice_arrays\n entries = [[x[i:i + 1] for i in indices] for x in arrays]\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/training_utils.py\", line 391, in \n entries = [[x[i:i + 1] for i in indices] for x in arrays]\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/training_utils.py\", line 391, in \n entries = [[x[i:i + 1] for i in indices] for x in arrays]\n\nTypeError: 'SparseTensor' object is not subscriptable\n\n\n\t [[{{node EagerPyFunc}}]]\n\t [[IteratorGetNext]] [Op:__inference_train_function_1612]\n\nFunction call stack:\ntrain_function\n", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mInvalidArgumentError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# IE\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_IE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;36m25\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mpredmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"I-E RESULTS\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# IE\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_IE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;36m25\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mpredmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"I-E RESULTS\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36m_method_wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_method_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 65\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_in_multi_worker_mode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[0;31m# Running inside `run_distribute_coordinator` already.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m 846\u001b[0m batch_size=batch_size):\n\u001b[1;32m 847\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 848\u001b[0;31m \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 849\u001b[0m \u001b[0;31m# Catch OutOfRangeError for Datasets of unknown size.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 850\u001b[0m \u001b[0;31m# This blocks until the batch has finished executing.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 578\u001b[0m \u001b[0mxla_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mExit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 579\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 580\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 581\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 582\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtracing_count\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", @@ -2053,7 +2028,7 @@ "\u001b[0;32m/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m 1744\u001b[0m \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1745\u001b[0m return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1746\u001b[0;31m ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m 1747\u001b[0m forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m 1748\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m 596\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 597\u001b[0m \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 598\u001b[0;31m ctx=ctx)\n\u001b[0m\u001b[1;32m 599\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 600\u001b[0m outputs = execute.execute_with_cancellation(\n", "\u001b[0;32m/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m 58\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 59\u001b[0m tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 60\u001b[0;31m inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m 61\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mInvalidArgumentError\u001b[0m: TypeError: 'SparseTensor' object is not subscriptable\nTraceback (most recent call last):\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/ops/script_ops.py\", line 241, in __call__\n return func(device, token, args)\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/ops/script_ops.py\", line 130, in __call__\n ret = self._func(*args)\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/autograph/impl/api.py\", line 309, in wrapper\n return func(*args, **kwargs)\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/data_adapter.py\", line 513, in py_method\n return [slice_array(inp) for inp in flat_inputs]\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/data_adapter.py\", line 513, in \n return [slice_array(inp) for inp in flat_inputs]\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/data_adapter.py\", line 512, in slice_array\n contiguous=contiguous)\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/training_utils.py\", line 391, in slice_arrays\n entries = [[x[i:i + 1] for i in indices] for x in arrays]\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/training_utils.py\", line 391, in \n entries = [[x[i:i + 1] for i in indices] for x in arrays]\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/training_utils.py\", line 391, in \n entries = [[x[i:i + 1] for i in indices] for x in arrays]\n\nTypeError: 'SparseTensor' object is not subscriptable\n\n\n\t [[{{node EagerPyFunc}}]]\n\t [[IteratorGetNext]] [Op:__inference_train_function_2113]\n\nFunction call stack:\ntrain_function\n" + "\u001b[0;31mInvalidArgumentError\u001b[0m: TypeError: 'SparseTensor' object is not subscriptable\nTraceback (most recent call last):\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/ops/script_ops.py\", line 241, in __call__\n return func(device, token, args)\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/ops/script_ops.py\", line 130, in __call__\n ret = self._func(*args)\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/autograph/impl/api.py\", line 309, in wrapper\n return func(*args, **kwargs)\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/data_adapter.py\", line 513, in py_method\n return [slice_array(inp) for inp in flat_inputs]\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/data_adapter.py\", line 513, in \n return [slice_array(inp) for inp in flat_inputs]\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/data_adapter.py\", line 512, in slice_array\n contiguous=contiguous)\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/training_utils.py\", line 391, in slice_arrays\n entries = [[x[i:i + 1] for i in indices] for x in arrays]\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/training_utils.py\", line 391, in \n entries = [[x[i:i + 1] for i in indices] for x in arrays]\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/training_utils.py\", line 391, in \n entries = [[x[i:i + 1] for i in indices] for x in arrays]\n\nTypeError: 'SparseTensor' object is not subscriptable\n\n\n\t [[{{node EagerPyFunc}}]]\n\t [[IteratorGetNext]] [Op:__inference_train_function_1612]\n\nFunction call stack:\ntrain_function\n" ] } ], diff --git a/your-project/code/Code 10 - Build Tool.ipynb b/your-project/code/Code 9 - Build Tool.ipynb similarity index 100% rename from your-project/code/Code 10 - Build Tool.ipynb rename to your-project/code/Code 9 - Build Tool.ipynb diff --git a/your-project/code/RF_IE.sav b/your-project/code/RF_IE.sav new file mode 100644 index 0000000000000000000000000000000000000000..38fcc5b426abc0fc6b8a20db096ac5f879c3ff28 GIT binary patch literal 6441545 zcmdSCcYG98+c!RdKGoDr)SY zh!JUmH%7YD1d@=RMeH?pZ|i$@=X=eZGyMGQxc77a-sjyv_B+?P%DGN2GjqmQC3(^9 z^HLHQrglw9U6e9EXpc!z}}Qa#&SC%RI6GccB<%(=~- zmN0u^Vse^rAt5MGcZA>ufAO^i1mc>Vl9;ww4RNg6EPF|QIGSAz6NO!-X9#Jqa6LdGdjbh@f z3u%vVWWmBTZ{zryf)d%VFeO>|G0WRT&(LH~LgGA+JJH+Jj3tm02`Ki2J5m+~%Ej9( 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