Machine-learning-deep-learning-projects/Genetic algorithm.py at master · Munaze/Machine-learning-deep-learning-projects · GitHub

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from tpot import TPOT
from sklearn.cross_validation import train_test_split
import pandas as pd
import numpy as np

telescope = pd.read_csv("MAGIC Gamma Telescope Data.csv")

telescope_shuffle = telescope.iloc[np.random.permutation(len[telescope])]
tele = telescope_shuffle.reset_index(drop = True)

tele['Class'] = tele['Class'].map({'g':0, 'h':1})
tele_class = tele['Class'].values

training_indices, validation_indices = training_indices,testing_indices = train_test_split(tele.index,
                                                                                           stratify = tele_class,train_size = 0.75,test_size = 0.25)
tpot = TPOT(generation = 5,verbosity =2)
tpot.fit(tele.drop('Class',axis =1).loc[training_indices].values,
         tele.loc[training_indices,'Class'].values)
tpot.score(tele.drop('Class', axis =1).loc[validation_indices].values,
           tele.loc[validation_indices,'Class'].values)
'''


#load the data
telescope=pd.read_csv('MAGIC Gamma Telescope Data.csv')

#clean the data
telescope_shuffle=telescope.iloc[np.random.permutation(len(telescope))]
tele=telescope_shuffle.reset_index(drop=True)

#Store 2 classes
tele['Class']=tele['Class'].map({'g':0, 'h':1})
tele_class = tele['Class'].values

#Split training, testing, and validation data
training_indices, validation_indices = training_indices, testing_indices = train_test_split(tele.index,
	stratify= tele_class, train_size=0.75, test_size=0.25)

#Let Genetic Programming find best ML model and hyperparameters
tpot = TPOTClassifier(generations=5, verbosity=2)
tpot.fit(tele.drop('Class', axis=1).loc[training_indices].values,
	tele.loc[training_indicss, 'Class'].values)

#Score the accuracy
tpot.score(tele.drop('Class', axis=1).loc[validation_indices].values,
	tele.loc[validation_indices, 'Class'].values)

#Export the generated code
tpot.export('pipeline.py')
'''