cancer_classification.py

# -*- coding: utf-8 -*-
"""
Created on Sat Dec 22 15:18:59 2018

@author: Koffi Moïse AGBENYA

In this notebook, you will use SVM (Support Vector Machines) to build and train 
a model using human cell records, and classify cells to whether the samples are 
benign or malignant.

SVM works by mapping data to a high-dimensional feature space so that data 
points can be categorized, even when the data are not otherwise linearly 
separable. A separator between the categories is found, then the data are 
transformed in such a way that the separator could be drawn as a hyperplane. 
Following this, characteristics of new data can be used to predict the group to 
which a new record should belong.

About dataset:
The example is based on a dataset that is publicly available from the UCI 
Machine Learning Repository (Asuncion and Newman, 2007)
[http://mlearn.ics.uci.edu/MLRepository.html]. The dataset consists of several 
hundred human cell sample records, each of which contains the values of a set 
of cell characteristics.

The fields in each record are:

Field       name	Description
ID	          Clump thickness
Clump	       Clump thickness
UnifSize	   Uniformity of cell size
UnifShape	   Uniformity of cell shape
MargAdh	   Marginal adhesion
SingEpiSize  Single epithelial cell size
BareNuc	   Bare nuclei
BlandChrom	   Bland chromatin
NormNucl	   Normal nucleoli
Mit	          Mitoses
Class	      Benign or malignant

"""

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt

path = "https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/cell_samples.csv"

cell_df = pd.read_csv(path)
print(cell_df.head())

#The ID field contains the patient identifiers. The characteristics of the cell 
#samples from each patient are contained in fields Clump to Mit. The values are 
#graded from 1 to 10, with 1 being the closest to benign.
#The Class field contains the diagnosis, as confirmed by separate medical 
#procedures, as to whether the samples are benign (value = 2) or malignant 
#(value = 4).
#Lets look at the distribution of the classes based on Clump thickness and 
#Uniformity of cell size:

ax = cell_df[cell_df['Class'] == 4][0:50].plot(kind='scatter', x='Clump', y='UnifSize', color='DarkBlue', label='malignant');
cell_df[cell_df['Class'] == 2][0:50].plot(kind='scatter', x='Clump', y='UnifSize', color='Yellow', label='benign', ax=ax);
plt.show()

#Data pre-processing and selection
#Lets first look at columns data types:
print(cell_df.dtypes)

#It looks like the BareNuc column includes some values that are not numerical. 
#We can drop those rows:

cell_df = cell_df[pd.to_numeric(cell_df['BareNuc'], errors='coerce').notnull()]
cell_df['BareNuc'] = cell_df['BareNuc'].astype('int')
print(cell_df.dtypes)

feature_df = cell_df[['Clump', 'UnifSize', 'UnifShape', 'MargAdh', 'SingEpiSize', 'BareNuc', 'BlandChrom', 'NormNucl', 'Mit']]
X = np.asarray(feature_df)
X[0:5]

#We want the model to predict the value of Class (that is, benign (=2) or 
#malignant (=4)). As this field can have one of only two possible values, we 
#need to change its measurement level to reflect this.

cell_df['Class'] = cell_df['Class'].astype('int')
y = np.asarray(cell_df['Class'])
y [0:5]

#Train/Test dataset
X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=4)
print ('Train set:', X_train.shape,  y_train.shape)
print ('Test set:', X_test.shape,  y_test.shape)

#Modeling SVM
#The SVM algorithm offers a choice of kernel functions for performing its 
#processing. Basically, mapping data into a higher dimensional space is called 
#kernelling. The mathematical function used for the transformation is known as 
#the kernel function, and can be of different types, such as:
# 1.Linear
# 2.Polynomial
# 3.Radial basis function (RBF)
# 4.Sigmoid
#Each of these functions has its characteristics, its pros and cons, and its 
#equation, but as there's no easy way of knowing which function performs best 
#with any given dataset, we usually choose different functions in turn and 
#compare the results. Let's just use the default, RBF (Radial Basis Function) 
#for this set.

from sklearn import svm
clf = svm.SVC(kernel='rbf')
clf.fit(X_train, y_train)

#After being fitted, the model can then be used to predict new values:
yhat = clf.predict(X_test)
yhat [0:5]

#Evaluation
from sklearn.metrics import classification_report, confusion_matrix
import itertools

def plot_confusion_matrix(cm, classes,
                          normalize=False,
                          title='Confusion matrix',
                          cmap=plt.cm.Blues):
    """
    This function prints and plots the confusion matrix.
    Normalization can be applied by setting `normalize=True`.
    """
    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
        print("Normalized confusion matrix")
    else:
        print('Confusion matrix, without normalization')

    print(cm)

    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title)
    plt.colorbar()
    tick_marks = np.arange(len(classes))
    plt.xticks(tick_marks, classes, rotation=45)
    plt.yticks(tick_marks, classes)

    fmt = '.2f' if normalize else 'd'
    thresh = cm.max() / 2.
    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(j, i, format(cm[i, j], fmt),
                 horizontalalignment="center",
                 color="white" if cm[i, j] > thresh else "black")

    plt.tight_layout()
    plt.ylabel('True label')
    plt.xlabel('Predicted label')

# Compute confusion matrix
cnf_matrix = confusion_matrix(y_test, yhat, labels=[2,4])
np.set_printoptions(precision=2)

print (classification_report(y_test, yhat))

# Plot non-normalized confusion matrix
plt.figure()
plot_confusion_matrix(cnf_matrix, classes=['Benign(2)','Malignant(4)'],normalize= False,  title='Confusion matrix')

#F1 Score
from sklearn.metrics import f1_score
f1_score(y_test, yhat, average='weighted') 

#Jaccard index
from sklearn.metrics import jaccard_similarity_score
jaccard_similarity_score(y_test, yhat)