"""

import pandas as pd

from sklearn import svm

file = 'data/train.csv'

train_data = pd.read_csv(file)

print(train_data.head())

print(train_data.columns)

#features = Sex, Age, Pclass, Cabin, SibSp, Parch, Embarked, Name, Ticket

#label = Survived

#'PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp','Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'

#SVM

#Bayesian logisitic regression

kernel = 'rbf'

svm.SVC()

"""

# Extract features using sliding window and form the training dataset, test dataset

from sklearn.ensemble import RandomForestClassifier

from sklearn.datasets import make_classification

from sklearn.model_selection import train_test_split

from sklearn.mixture import GaussianMixture

import numpy as np

X, y = make_classification(n_samples=10000, n_features=6,

                            n_informative=3, n_redundant=0,

                            random_state=0, shuffle=True)

print(X.shape)  # 10000x6

print(y.shape)  # 10000

# TODO: Feature extraction using sliding window

train_features, test_features, train_labels, test_labels = train_test_split(X, y,

                                                                            test_size=0.25, random_state=42)

# TODO: K-fold cross validation

print('Training Features Shape:', train_features.shape)

print('Training Labels Shape:', train_labels.shape)

print('Testing Features Shape:', test_features.shape)

print('Testing Labels Shape:', test_labels.shape)

clf = RandomForestClassifier(n_estimators=100, max_depth=3, oob_score=True

                             )

clf.fit(X, y)

print(clf.feature_importances_)

#print(clf.oob_decision_function_)

print(clf.oob_score_)

predictions = clf.predict(test_features)

errors = abs(predictions - test_labels)

print("M A E: ", round(np.mean(errors), 2))

# Visualization

feature_list = [1, 2, 3, 4, 5, 6]

from sklearn.tree import export_graphviz

import pydot

# Pull out one tree from the forest

tree = clf.estimators_[5]

# Export the image to a dot file

export_graphviz(tree, out_file='tree.dot', feature_names=feature_list, rounded=True, precision=1)

# Use dot file to create a graph

(graph, ) = pydot.graph_from_dot_file('tree.dot')

# Write graph to a png file

#graph.write_png('tree_.png')

# TODO: Confusion matrix, Accuracy

# GMM

gmm = GaussianMixture(n_components=3, covariance_type='full')

gmm.fit(X, y)