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import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from sklearn import metrics
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier
import itertools

AllData = pd.read_csv('all_data.csv')  #帶入資料

CleanData = AllData.dropna()
CleanData = CleanData.reset_index(drop=True)
CleanData = CleanData.drop('mdate', axis = 1)
CleanData = CleanData.drop('pmkt', axis = 1)

X = CleanData.drop('Y', axis = 1)
y = CleanData['Y']
y = y.astype(int)

sc = StandardScaler()
X = sc.fit_transform(X)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
MLP= MLPClassifier(solver='adam',learning_rate_init=0.01,hidden_layer_sizes=(40,100),
                   random_state=1)
MLP.fit(X_train, y_train)
print(metrics.classification_report(y_test, MLP.predict(X_test)))#預測出來的結果報告
accuracy = metrics.accuracy_score(y_test, MLP.predict(X_test))
print(accuracy)

def plot_confusion_matrix(cm, classes,
                          normalize=False,
                          title='Confusion matrix',
                          cmap=plt.cm.Blues):
    """
    This function prints and plots the confusion matrix.
    Normalization can be applied by setting `normalize=True`.
    """
    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title)
    plt.colorbar()
    tick_marks = np.arange(len(classes))
    plt.xticks(tick_marks, classes, rotation=45)
    plt.yticks(tick_marks, classes)

    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
        print("Normalized confusion matrix")
    else:
        print('Confusion matrix, without normalization')

    print(cm)

    thresh = cm.max() / 2.
    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(j, i, cm[i, j],
                 horizontalalignment="center",
                 color="white" if cm[i, j] > thresh else "black")

    plt.tight_layout()
    plt.ylabel('True label')
    plt.xlabel('Predicted label')

cnf_matrix = metrics.confusion_matrix(y_test,MLP.predict(X_test))
target_names = ['0', '1']
plot_confusion_matrix(cnf_matrix, classes=target_names)
plt.show()