#!/usr/bin/python

import pickle
import numpy
numpy.random.seed(42)


### The words (features) and authors (labels), already largely processed.
### These files should have been created from the previous (Lesson 10)
### mini-project.
words_file = "../text_learning/your_word_data_fixed.pkl"
authors_file = "../text_learning/your_email_authors_fixed.pkl"
word_data = pickle.load( open(words_file, "rb") )
authors = pickle.load( open(authors_file, "rb") )


### test_size is the percentage of events assigned to the test set (the
### remainder go into training)
### feature matrices changed to dense representations for compatibility with
### classifier functions in versions 0.15.2 and earlier
from sklearn.model_selection import train_test_split
features_train, features_test, labels_train, labels_test = train_test_split(word_data, authors, test_size=0.1, random_state=42)

from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer(sublinear_tf=True, max_df=0.5,
                             stop_words='english')
features_train = vectorizer.fit_transform(features_train)
features_test  = vectorizer.transform(features_test).toarray()


### a classic way to overfit is to use a small number
### of data points and a large number of features;
### train on only 150 events to put ourselves in this regime
features_train = features_train[:150].toarray()
labels_train   = labels_train[:150]

print(len(features_train))

### your code goes here

from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score

d3 = DecisionTreeClassifier()
d3.fit_transform(features_train, labels_train)
d3_acc = accuracy_score(d3.predict(features_test), labels_test)

print(d3_acc)

features_importance = d3.feature_importances_

counter = 0

range_features = d3.n_features_

for i in range(range_features):
    if features_importance[i] > 0.2:
        print(i, ":", features_importance[i], ":", vectorizer.get_feature_names()[i])


# print(vectorizer.get_feature_names()[21323])

# print(features_importance)

