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

    # clf.fit(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]

    bestModel = None
    bestScore = 0.

    n_eval = 1
    k_fold = KFold(n_splits=5)

    for c in Cs:
        for g in gammas:

            svc = SVC(probability=True, class_weight='balanced', kernel='rbf', gamma=g, C=c)
            svc.fit(X_train, y_train)

            score = cross_val_score(svc, X_train, y_train, cv=k_fold, n_jobs=-1)
            score = np.mean(score)

            # keep track of best model
            if score > bestScore:
                bestScore = score
                bestModel = svc

            print('Eval n° {} [C: {}, gamma: {}] => [score: {}, bestScore: {}]'.format(n_eval, c, g, score, bestScore))
            n_eval += 1

    return bestModel

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)