Thesis-NoiseDetection-attributes/models/ensemble_model_train.py

from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier, VotingClassifier

import sklearn.svm as svm
from sklearn.utils import shuffle
from sklearn.externals import joblib

import numpy as np

import pandas as pd
from sklearn.metrics import accuracy_score

import sys, os, getopt

current_dirpath = os.getcwd()
output_model_folder = os.path.join(current_dirpath, 'saved_models')

def get_best_model(X_train, y_train):
    Cs = [0.001, 0.01, 0.1, 1, 10]
    gammas = [0.001, 0.01, 0.1, 1]
    param_grid = {'kernel':['rbf'], 'C': Cs, 'gamma' : gammas}
    
    parameters = {'kernel':['rbf'], 'C': np.arange(1, 20)}
    svc = svm.SVC(gamma="scale", probability=True)
    clf = GridSearchCV(svc, parameters, cv=5, scoring='accuracy', verbose=10)

    clf.fit(X_train, y_train)

    model = clf.best_estimator_

    return model


def main():

    if len(sys.argv) <= 1:
        print('Run with default parameters...')
        print('python ensemble_model_train.py --data xxxx --output xxxx')
        sys.exit(2)
    try:
        opts, args = getopt.getopt(sys.argv[1:], "hd:o", ["help=", "data=", "output="])
    except getopt.GetoptError:
        # print help information and exit:
        print('python ensemble_model_train.py --data xxxx --output xxxx')
        sys.exit(2)
    for o, a in opts:
        if o == "-h":
            print('python ensemble_model_train.py --data xxxx --output xxxx')
            sys.exit()
        elif o in ("-d", "--data"):
            p_data_file = a
        elif o in ("-o", "--output"):
            p_output = a
        else:
            assert False, "unhandled option"

    if not os.path.exists(output_model_folder):
        os.makedirs(output_model_folder)

    # get and split data
    dataset = pd.read_csv(p_data_file, header=None, sep=";")

     # default first shuffle of data
    dataset = shuffle(dataset)
    
    # get dataset with equal number of classes occurences
    noisy_df = dataset[dataset.ix[:, 0] == 1]
    not_noisy_df = dataset[dataset.ix[:, 0] == 0]
    nb_noisy = len(noisy_df.index)
    
    final_df = pd.concat([not_noisy_df[0:nb_noisy], noisy_df])
    #final_df = pd.concat([not_noisy_df, noisy_df])
    
    # shuffle data another time
    final_df = shuffle(final_df)
    
    print(len(final_df.index))

    y_dataset = final_df.ix[:,0]
    x_dataset = final_df.ix[:,1:]

    X_train, X_test, y_train, y_test = train_test_split(x_dataset, y_dataset, test_size=0.4, random_state=42)

    svm_model = get_best_model(X_train, y_train)

    lr_model = LogisticRegression(solver='liblinear', multi_class='ovr', random_state=1)
    rf_model = RandomForestClassifier(n_estimators=100, random_state=1)

    ensemble_model = VotingClassifier(estimators=[
       ('svm', svm_model), ('lr', lr_model), ('rf', rf_model)],
       voting='soft', weights=[1,1,1])

    ensemble_model.fit(X_train, y_train)

    y_train_model = ensemble_model.predict(X_train)
    print("**Train :** " + str(accuracy_score(y_train, y_train_model)))

    y_pred = ensemble_model.predict(X_test)
    print("**Test :** " + str(accuracy_score(y_test, y_pred)))

    joblib.dump(ensemble_model, output_model_folder + '/' + p_output + '.joblib') 

if __name__== "__main__":
    main()