Thesis-NoiseDetection-26-attributes/models.py

# models imports
import numpy as np

from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier, VotingClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.feature_selection import RFECV
import sklearn.svm as svm
from sklearn.metrics import accuracy_score
# from thundersvm import SVC
from sklearn.model_selection import KFold, cross_val_score
            

# variables and parameters
# n_predict = 0

# def my_accuracy_scorer(*args):
#         global n_predict
#         score = accuracy_score(*args)
#         print('{0} - Score is {1}'.format(n_predict, score))
#         n_predict += 1
#         return score

def _get_best_model(X_train, y_train):

    Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]
    gammas = [0.001, 0.01, 0.1, 5, 10, 100]
    param_grid = {'kernel':['rbf'], 'C': Cs, 'gamma' : gammas}

    svc = svm.SVC(probability=True, class_weight='balanced')
    #clf = GridSearchCV(svc, param_grid, cv=5, verbose=1, scoring=my_accuracy_scorer, n_jobs=-1)
    clf = GridSearchCV(svc, param_grid, cv=5, verbose=1, n_jobs=-1)

    clf.fit(X_train, y_train)

    model = clf.best_estimator_

    return model

def svm_model(X_train, y_train):

    return _get_best_model(X_train, y_train)


# def _get_best_gpu_model(X_train, y_train):

#     Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]
#     gammas = [0.001, 0.01, 0.1, 5, 10, 100]
#     param_grid = {'kernel':['rbf'], 'C': Cs, 'gamma' : gammas}

#     svc = svm.SVC(probability=True, class_weight='balanced')
#     #clf = GridSearchCV(svc, param_grid, cv=5, verbose=1, scoring=my_accuracy_scorer, n_jobs=-1)
#     clf = GridSearchCV(svc, param_grid, cv=5, verbose=1, n_jobs=-1)

#     clf.fit(X_train, y_train)

#     model = clf.best_estimator_

#     return model

# def svm_gpu(X_train, y_train):

#     return _get_best_gpu_model(X_train, y_train)


def ensemble_model(X_train, y_train):

    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)

    return ensemble_model


def ensemble_model_v2(X_train, y_train):

    svm_model = _get_best_model(X_train, y_train)
    knc_model = KNeighborsClassifier(n_neighbors=2)
    gbc_model = GradientBoostingClassifier(n_estimators=100, learning_rate=1.0, max_depth=1, random_state=0)
    lr_model = LogisticRegression(solver='liblinear', multi_class='ovr', random_state=1)
    rf_model = RandomForestClassifier(n_estimators=100, random_state=1)

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

    ensemble_model.fit(X_train, y_train)

    return ensemble_model



def get_trained_model(choice, X_train, y_train):

    if choice == 'svm_model':
        return svm_model(X_train, y_train)

    # if choice == 'svm_gpu':
        # return svm_gpu(X_train, y_train)

    if choice == 'ensemble_model':
        return ensemble_model(X_train, y_train)

    if choice == 'ensemble_model_v2':
        return ensemble_model_v2(X_train, y_train)