MLP_melhores_params

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon May 27 17:52:43 2019

@author: thales
"""

import pandas as pd
from sklearn.neural_network import MLPClassifier
from sklearn.preprocessing import LabelEncoder
#from sklearn.model_selection import train_test_split
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score, StratifiedKFold
from sklearn.metrics import precision_score, confusion_matrix, accuracy_score, jaccard_similarity_score, mean_squared_error
#from sklearn.preprocessing import MinMaxScaler, normalize, StandardScaler
from sklearn.multiclass import OneVsRestClassifier
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import MaxAbsScaler
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import RobustScaler
from sklearn.preprocessing import Normalizer, normalize
from sklearn.preprocessing import QuantileTransformer
def conjunto_treinamento(X, y):
#    X = normalize(X)
#    StandardScaler().fit_transform(X)
#    MinMaxScaler().fit_transform(X)
#    MaxAbsScaler().fit_transform(X)
#    RobustScaler(quantile_range=(25, 75)).fit_transform(X)
#    QuantileTransformer(output_distribution='normal').fit_transform(X)
#    QuantileTransformer(output_distribution='uniform').fit_transform(X)
    Normalizer().fit_transform(X)
    kf = KFold(n_splits=3)
    X1, X2, y_1, y_2 = [], [], [], []
    for train_index, test_index in kf.split(X):
        X1, X2, y_1, y_2 = X[train_index], X[test_index], y[train_index], y[test_index]
    return X1, X2, y_1, y_2
#from sklearn.model_selection import ShuffleSplit
#from sklearn.pipeline import make_pipeline
def previsao_validacao_cruzada(mlp, X, y):
#    cv = ShuffleSplit(n_splits=5, test_size=0.3, random_state=0)
    cv_results = cross_val_score(mlp, X, y, cv=5, scoring='accuracy')
    print("Precisão\n %f (%f)" % (cv_results.mean(), cv_results.std()))
    


base = pd.read_csv('./testes_outros_algoritmos.csv')

array = base.values
X = array[:, :14]
y = array[:, 14:15]
label = LabelEncoder() #Transformando as classes em valores numéricos
y = label.fit_transform(y)
#y = label.inverse_transform(y)
y = y.ravel()

#mlp = MLPClassifier(solver='sgd', activation='tanh', learning_rate='adaptive', learning_rate_init=0.01, 
#                   hidden_layer_sizes=(100, 260), 
#                   momentum=0.9, nesterovs_momentum=False, 
#                   early_stopping=True, max_iter=10000, random_state=0)
mlp = MLPClassifier(verbose=0, random_state=5, 
                        hidden_layer_sizes=(100, 100, 100, 100), 
                        learning_rate_init=0.2,
                        beta_1=0.096, beta_2=0.999999999, epsilon=1e-06, early_stopping=False)






X_train, X_test, y_train, y_test = conjunto_treinamento(X, y)

X_train = MinMaxScaler().fit_transform(X_train)
X_test = MinMaxScaler().fit_transform(X_test)
mlp.fit(X_train, y_train)
y_pred = mlp.predict(X_test)

print("Score de treinamento: %f" % mlp.score(X_train, y_train))
print('Acerto na previsão (accuracy_score): %f' % accuracy_score(y_test, y_pred))
print('Erro quadrático médio: %f' % mean_squared_error(y_test, y_pred))
print('Índice de Jaccard (multiclass): %f' % jaccard_similarity_score(y_test, y_pred))
matrix = confusion_matrix(y_test, y_pred)

print(confusion_matrix(y_test, y_pred))

#print(precision_score(y_test, y_pred, average=None))
#print('Acerto na previsão (accuracy_score): %f' % accuracy_score(y_test, y_pred))