from sklearn.linear_model import LogisticRegression

from sklearn.metrics import confusion_matrix

from mlxtend.preprocessing import DenseTransformer

from sklearn.model_selection import train_test_split

from sklearn.feature_extraction.text import CountVectorizer

from data import DataSet

from sklearn.metrics import f1_score

from sklearn.metrics import accuracy_score

from sklearn.pipeline import Pipeline, FeatureUnion

import matplotlib.pyplot as plt

from conf_lib import plot_confusion_matrix

from sklearn.decomposition import TruncatedSVD, PCA

import random

data_set = DataSet()

data, label, class_names = data_set.get_train_data_set()

indexs = random.sample(range(len(data)), 50000)

data = data[indexs]

label = label[indexs]

X_train, X_test, y_train, y_test = train_test_split(data, label, test_size=0.33, random_state=42)

est = [('count_vect',CountVectorizer()),('tr',TruncatedSVD(n_components=10, n_iter=100, random_state=42)),('clf_LG',LogisticRegression())]

pipeline_LG = Pipeline(est)

pipeline_LG = pipeline_LG.fit(X_train, y_train)

y_pred = pipeline_LG.predict(X_test)

print("F1 score - LG:", f1_score(y_test, pipeline_LG.predict(X_test), average='micro'))

print("Accuracy Score - LG:", accuracy_score(y_test, pipeline_LG.predict(X_test)))

cnf_matrix = confusion_matrix(y_test, y_pred)

plt.figure()

plt = plot_confusion_matrix(cnf_matrix, classes=class_names, normalize=True,title=' confusion matrix LogisticRegression')

plt.show()