Added machine learning models

from sklearn.model_selection import train_test_split

from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score

from sklearn.model_selection import GridSearchCV

from sklearn.linear_model import Perceptron, SGDClassifier

from sklearn.neural_network import MLPClassifier

from sklearn.tree import DecisionTreeClassifier

from joblib import dump, load

from warnings import simplefilter

simplefilter(action='ignore')

# preprocessing

X_train = []

Y_train = []

X = []

Y = []

import preprocess

def printMetrics(y_actual, y_pred):

    print('Accuracy: ', accuracy_score(y_actual, y_pred))

    print('Precision:', precision_score(y_actual, y_pred, average='macro'))

    print('Recall:   ', recall_score(y_actual, y_pred, average='macro'))

    print('F1:       ', f1_score(y_actual, y_pred, average='macro'))

def preprocessing():

    #this function does nothing

    XYList = preprocess.loadXY()

    X = XYList[0]

    Y = XYList[1]

def trainModels():

    XYList = preprocess.loadXY()

    X = XYList[0]

    Y = XYList[1]

    X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=0)

    print('\n==================================================\n')

    ### train perceptron

    # 'n_jobs': [16]

    params = {'tol': [.1, .01, .001, .0001]}

    pla = GridSearchCV(Perceptron(), params, cv=5)

    pla.fit(X_train, Y_train)

    pred_y_train = pla.predict(X_train)

    pred_y_test = pla.predict(X_test)

    print('Perceptron CV parameters:')

    print('Best: ', pla.best_params_)

    accuracy = pla.cv_results_['mean_test_score']

    for acc, param in zip(accuracy, pla.cv_results_['params']):

        print("%0.5f - %r"% (acc, param))

    print('\nPerceptron (training data):')

    printMetrics(Y_train, pred_y_train)

    print('\nPerceptron (testing data):')

    printMetrics(Y_test, pred_y_test)

    dump(pla.best_estimator_, 'pla.joblib')

    print('\n==================================================\n')

    ### train logistic regression

    # 'n_jobs': [16]

    params = {'alpha': [.00001, .0001, .001, .01, .1]}

    sgc = GridSearchCV(SGDClassifier(), params, cv=5)

    sgc.fit(X_train, Y_train)

    pred_y_train = sgc.predict(X_train)

    pred_y_test = sgc.predict(X_test)

    print('Stochastic Gradient Descent CV parameters:')

    print('Best: ', sgc.best_params_)

    accuracy = sgc.cv_results_['mean_test_score']

    for acc, param in zip(accuracy, sgc.cv_results_['params']):

        print("%0.5f - %r"% (acc, param))

    print('\nSGD (training data):')

    printMetrics(Y_train, pred_y_train)

    print('\nSGD (testing data):')

    printMetrics(Y_test, pred_y_test)

    dump(sgc.best_estimator_, 'sgc.joblib')

    print('\n==================================================\n')

    ### train neural network

    #params = {'hidden_layer_sizes': [(500,), (1000,), (1500,)], 'alpha': [.0001, .001, .01]}

    params = {'hidden_layer_sizes': [(100,)], 'alpha': [.001]}

    nn = GridSearchCV(MLPClassifier(), params, cv=5)

    nn.fit(X_train, Y_train)

    pred_y_train = nn.predict(X_train)

    pred_y_test = nn.predict(X_test)

    print('Neural Network CV parameters:')

    print('Best: ', nn.best_params_)

    accuracy = nn.cv_results_['mean_test_score']

    for acc, param in zip(accuracy, nn.cv_results_['params']):

        print("%0.5f - %r"% (acc, param))

    print('\nNeural Network (training data):')

    printMetrics(Y_train, pred_y_train)

    print('\nNeural Network (testing data):')

    printMetrics(Y_test, pred_y_test)

    dump(nn.best_estimator_, 'nn.joblib')

    print('\n==================================================\n')

    ### train decision tree

    tree = DecisionTreeClassifier()

    tree.fit(X_train, Y_train)

    pred_y_train = tree.predict(X_train)

    pred_y_test = tree.predict(X_test)

    print('Decision Tree (training data):')

    printMetrics(Y_train, pred_y_train)

    print('\nDecision Tree (testing data):')

    printMetrics(Y_test, pred_y_test)

    dump(tree, 'tree.joblib')

    print('\n==================================================\n')

def testModels():

    XYList = preprocess.loadXY()

    X_train = XYList[0]

    Y_train = XYList[1]

    X = XYList[0]

    Y = XYList[1]

    pla = load('pla.joblib')

    pred_y = pla.predict(X)

    print('Perceptron (all data):')

    printMetrics(Y, pred_y)

    sgc = load('sgc.joblib')

    pred_y = sgc.predict(X)

    print('\nStochastic Gradient Descent (all data):')

    printMetrics(Y, pred_y)

    nn = load('nn.joblib')

    pred_y = nn.predict(X)

    print('\nNeural Network (all data):')

    printMetrics(Y, pred_y)

    tree = load('tree.joblib')

    pred_y = tree.predict(X)

    print('\nDecision Tree (all data):')

    printMetrics(Y, pred_y)

# declaring, training, fitting each algorithm

def mlinit():

def switchCurrentAlgorithm():

    pass