+ Bash template article +
+ +
+
+
+
++ Article Recommander +
+ +
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+import pandas as pd
+import numpy as np
+%matplotlib inline
+Loading data and preprocessing
+we first learn the pickled article database. We will be cleaning it and separating the interesting articles from the uninteresting ones.
+df = pd.read_pickle('./article.pkl')
+del df["html"]
+del df["image"]
+del df["URL"]
+del df["hash"]
+del df["source"]
+
+df["label"] = df["note"].apply(lambda x: 0 if x <= 0 else 1)
+df.head(5)
+
+
+
+
+
+
+
+| + | authors | +note | +resume | +texte | +titre | +label | +
|---|---|---|---|---|---|---|
| 0 | +[Danny Bradbury, Marco Santori, Adam Draper, M... | +-10.0 | +Black Market Reloaded, a black market site tha... | +Black Market Reloaded, a black market site tha... | +Black Market Reloaded back online after source... | +0 | +
| 1 | +[Emily Spaven, Stan Higgins, Emilyspaven] | +1.0 | +The UK Home Office believes the government sho... | +The UK Home Office believes the government sho... | +Home Office: UK Should Create a Crime-Fighting... | +1 | +
| 2 | +[Pete Rizzo, Alex Batlin, Yessi Bello Perez, P... | +-10.0 | +Though lofty in its ideals, lead developer Dan... | +A new social messaging app is aiming to disrup... | +Gems Bitcoin App Lets Users Earn Money From So... | +0 | +
| 3 | +[Nermin Hajdarbegovic, Stan Higgins, Pete Rizz... | +3.0 | +US satellite service provider DISH Network has... | +US satellite service provider DISH Network has... | +DISH Becomes World's Largest Company to Accept... | +1 | +
| 4 | +[Stan Higgins, Bailey Reutzel, Garrett Keirns,... | +-10.0 | +An unidentified 28-year-old man was robbed of ... | +An unidentified 28-year-old man was robbed of ... | +Bitcoin Stolen at Gunpoint in New York City Ro... | +0 | +
Basic statistics on the dataset
+let's explore the dataset and extract some numbers : +* the number of article liked/disliked
+df["label"].value_counts()
+0 879
+1 324
+Name: label, dtype: int64
+Create the full content column
+df['full_content'] = df.titre + ' ' + df.resume #exclude the full texte of the article for the moment
+df.head(1)
+
+
+
+
+
+
+
+| + | authors | +note | +resume | +texte | +titre | +label | +full_content | +
|---|---|---|---|---|---|---|---|
| 0 | +[Danny Bradbury, Marco Santori, Adam Draper, M... | +-10.0 | +Black Market Reloaded, a black market site tha... | +Black Market Reloaded, a black market site tha... | +Black Market Reloaded back online after source... | +0 | +Black Market Reloaded back online after source... | +
from sklearn.model_selection import train_test_split
+training, testing = train_test_split(
+ df, # The dataset we want to split
+ train_size=0.75, # The proportional size of our training set
+ stratify=df.label, # The labels are used for stratification
+ random_state=400 # Use the same random state for reproducibility
+)
+
+training.head(5)
+
+
+
+
+
+
+
+| + | authors | +note | +resume | +texte | +titre | +label | +full_content | +
|---|---|---|---|---|---|---|---|
| 748 | +[Jon Brodkin] | +-10.0 | +Amazon, Reddit, Mozilla, and other Internet co... | +Amazon, Reddit, Mozilla, and other Internet co... | +Amazon and Reddit try to save net neutrality r... | +0 | +Amazon and Reddit try to save net neutrality r... | +
| 1183 | +[Jon Brodkin] | +-10.0 | +(The Time Warner involved in this transaction ... | +A group of mostly Democratic senators led by A... | +Democrats urge Trump administration to block A... | +0 | +Democrats urge Trump administration to block A... | +
| 769 | +[Joseph Brogan] | +-10.0 | +On Twitter, bad news comes at all hours, with ... | +On Twitter, bad news comes at all hours, with ... | +Some of the best art on Twitter comes from the... | +0 | +Some of the best art on Twitter comes from the... | +
| 57 | +[Michael Del Castillo, Pete Rizzo, Trond Vidar... | +-10.0 | +Publicly traded online travel service Webjet i... | +Publicly traded online travel service Webjet i... | +Webjet Ethereum Pilot Targets Hotel Industry's... | +0 | +Webjet Ethereum Pilot Targets Hotel Industry's... | +
| 892 | +[Andrew Cunningham] | +10.0 | +What has changed on the 2017 MacBook, then?\nI... | +Andrew Cunningham\n\nAndrew Cunningham\n\nAndr... | +Mini-review: The 2017 MacBook could actually b... | +1 | +Mini-review: The 2017 MacBook could actually b... | +
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
+from sklearn.svm import LinearSVC, SVC
+from sklearn.pipeline import Pipeline
+from sklearn.model_selection import cross_val_predict
+from utils.plotting import pipeline_performance
+
+steps = (
+ ('vectorizer', TfidfVectorizer()),
+ ('classifier', LinearSVC())
+)
+pipeline = Pipeline(steps)
+
+predicted_labels = cross_val_predict(pipeline, training.full_content, training.label)
+pipeline_performance(training.label, predicted_labels)
+
+pipeline = pipeline.fit(training.titre, training.label)
+Accuracy = 80.6%
+Confusion matrix, without normalization
+[[624 35]
+ [140 103]]
+
import re
+from utils.plotting import print_top_features
+from sklearn.model_selection import GridSearchCV
+
+def mask_integers(s):
+ return re.sub(r'\d+', 'INTMASK', s)
+steps = (
+ ('vectorizer', TfidfVectorizer()),
+ ('classifier', LinearSVC())
+)
+
+pipeline = Pipeline(steps)
+
+gs_params = {
+ #'vectorizer__use_idf': (True, False),
+ 'vectorizer__lowercase': [True, False],
+ 'vectorizer__stop_words': ['english', None],
+ 'vectorizer__ngram_range': [(1, 1), (1, 2), (2, 2)],
+ 'vectorizer__preprocessor': [mask_integers, None],
+ 'classifier__C': np.linspace(5,20,25)
+}
+
+
+gs = GridSearchCV(pipeline, gs_params, n_jobs=1)
+gs.fit(training.full_content, training.label)
+
+print(gs.best_params_)
+print(gs.best_score_)
+
+pipeline1 = gs.best_estimator_
+predicted_labels = pipeline1.predict(testing.full_content)
+pipeline_performance(testing.label, predicted_labels)
+
+print_top_features(pipeline1, n_features=10)
+aaa = gs.predict(testing.full_content) == testing.label
+
+aaa = aaa[testing.label == 1]
+
+testing["titre"].iloc[~aaa.values]
+
+#pipeline1.predict(["windows xbox bitcoin"])
+from sklearn.externals import joblib
+joblib.dump(pipeline1, 'classifier.pkl')
+gs.predict(['Google'])
+array([1], dtype=int64)
+steps = (
+ ('vectorizer', TfidfVectorizer()),
+ ('classifier', SVC())
+)
+
+pipeline = Pipeline(steps)
+
+gs_params = {
+ #'vectorizer__use_idf': (True, False),
+ 'vectorizer__stop_words': ['english', None],
+ 'vectorizer__ngram_range': [(1, 1), (1, 2), (2, 2)],
+ 'vectorizer__preprocessor': [mask_integers, None],
+ 'classifier__C': np.linspace(5,20,25)
+}
+
+
+gs = GridSearchCV(pipeline, gs_params, n_jobs=1)
+gs.fit(training.full_content, training.label)
+
+print(gs.best_params_)
+print(gs.best_score_)
+
+pipeline1 = gs.best_estimator_
+predicted_labels = pipeline1.predict(testing.full_content)
+pipeline_performance(testing.label, predicted_labels)
+
+print_top_features(pipeline1, n_features=10)
+{'classifier__C': 5.0, 'vectorizer__ngram_range': (1, 1), 'vectorizer__preprocessor': <function mask_integers at 0x00000237491B67B8>, 'vectorizer__stop_words': 'english'}
+0.711180124224
+Accuracy = 71.2%
+Confusion matrix, without normalization
+[[153 0]
+ [ 62 0]]
+
+
+
+---------------------------------------------------------------------------
+
+ValueError Traceback (most recent call last)
+
+<ipython-input-9-3e0781e307fb> in <module>()
+ 25 pipeline_performance(testing.label, predicted_labels)
+ 26
+---> 27 print_top_features(pipeline1, n_features=10)
+
+
+C:\Users\Guillaume\Documents\Code\recommandation\utils\plotting.py in print_top_features(pipeline, vectorizer_name, classifier_name, n_features)
+ 81 def print_top_features(pipeline, vectorizer_name='vectorizer', classifier_name='classifier', n_features=7):
+ 82 vocabulary = np.array(pipeline.named_steps[vectorizer_name].get_feature_names())
+---> 83 coefs = pipeline.named_steps[classifier_name].coef_[0]
+ 84 top_feature_idx = np.argsort(coefs)
+ 85 top_features = vocabulary[top_feature_idx]
+
+
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\svm\base.py in coef_(self)
+ 483 def coef_(self):
+ 484 if self.kernel != 'linear':
+--> 485 raise ValueError('coef_ is only available when using a '
+ 486 'linear kernel')
+ 487
+
+
+ValueError: coef_ is only available when using a linear kernel
+
from sklearn.naive_bayes import BernoulliNB
+
+
+steps = (
+ ('vectorizer', TfidfVectorizer()),
+ ('classifier', BernoulliNB())
+)
+
+pipeline2 = Pipeline(steps)
+
+gs_params = {
+ 'vectorizer__stop_words': ['english', None],
+ 'vectorizer__ngram_range': [(1, 1), (1, 2), (2, 2)],
+ 'vectorizer__preprocessor': [mask_integers, None],
+ 'classifier__alpha': np.linspace(0,1,5),
+ 'classifier__fit_prior': [True, False]
+}
+
+gs = GridSearchCV(pipeline2, gs_params, n_jobs=1)
+gs.fit(training.full_content, training.label)
+
+print(gs.best_params_)
+print(gs.best_score_)
+
+pipeline2 = gs.best_estimator_
+predicted_labels = pipeline2.predict(testing.full_content)
+pipeline_performance(testing.label, predicted_labels)
+
+print_top_features(pipeline2, n_features=10)
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:820: RuntimeWarning: divide by zero encountered in log
+ neg_prob = np.log(1 - np.exp(self.feature_log_prob_))
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:823: RuntimeWarning: invalid value encountered in add
+ jll += self.class_log_prior_ + neg_prob.sum(axis=1)
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:820: RuntimeWarning: divide by zero encountered in log
+ neg_prob = np.log(1 - np.exp(self.feature_log_prob_))
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:823: RuntimeWarning: invalid value encountered in add
+ jll += self.class_log_prior_ + neg_prob.sum(axis=1)
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:820: RuntimeWarning: divide by zero encountered in log
+ neg_prob = np.log(1 - np.exp(self.feature_log_prob_))
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:823: RuntimeWarning: invalid value encountered in add
+ jll += self.class_log_prior_ + neg_prob.sum(axis=1)
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:820: RuntimeWarning: divide by zero encountered in log
+ neg_prob = np.log(1 - np.exp(self.feature_log_prob_))
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:823: RuntimeWarning: invalid value encountered in add
+ jll += self.class_log_prior_ + neg_prob.sum(axis=1)
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:820: RuntimeWarning: divide by zero encountered in log
+ neg_prob = np.log(1 - np.exp(self.feature_log_prob_))
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:823: RuntimeWarning: invalid value encountered in add
+ jll += self.class_log_prior_ + neg_prob.sum(axis=1)
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:820: RuntimeWarning: divide by zero encountered in log
+ neg_prob = np.log(1 - np.exp(self.feature_log_prob_))
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:823: RuntimeWarning: invalid value encountered in add
+ jll += self.class_log_prior_ + neg_prob.sum(axis=1)
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:820: RuntimeWarning: divide by zero encountered in log
+ neg_prob = np.log(1 - np.exp(self.feature_log_prob_))
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:823: RuntimeWarning: invalid value encountered in add
+ jll += self.class_log_prior_ + neg_prob.sum(axis=1)
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:820: RuntimeWarning: divide by zero encountered in log
+ neg_prob = np.log(1 - np.exp(self.feature_log_prob_))
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:823: RuntimeWarning: invalid value encountered in add
+ jll += self.class_log_prior_ + neg_prob.sum(axis=1)
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:820: RuntimeWarning: divide by zero encountered in log
+ neg_prob = np.log(1 - np.exp(self.feature_log_prob_))
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:823: RuntimeWarning: invalid value encountered in add
+ jll += self.class_log_prior_ + neg_prob.sum(axis=1)
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:820: RuntimeWarning: divide by zero encountered in log
+ neg_prob = np.log(1 - np.exp(self.feature_log_prob_))
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:823: RuntimeWarning: invalid value encountered in add
+ jll += self.class_log_prior_ + neg_prob.sum(axis=1)
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:820: RuntimeWarning: divide by zero encountered in log
+ neg_prob = np.log(1 - np.exp(self.feature_log_prob_))
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:823: RuntimeWarning: invalid value encountered in add
+ jll += self.class_log_prior_ + neg_prob.sum(axis=1)
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:820: RuntimeWarning: divide by zero encountered in log
+ neg_prob = np.log(1 - np.exp(self.feature_log_prob_))
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:823: RuntimeWarning: invalid value encountered in add
+ jll += self.class_log_prior_ + neg_prob.sum(axis=1)
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:820: RuntimeWarning: divide by zero encountered in log
+ neg_prob = np.log(1 - np.exp(self.feature_log_prob_))
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:823: RuntimeWarning: invalid value encountered in add
+ jll += self.class_log_prior_ + neg_prob.sum(axis=1)
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:820: RuntimeWarning: divide by zero encountered in log
+ neg_prob = np.log(1 - np.exp(self.feature_log_prob_))
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:823: RuntimeWarning: invalid value encountered in add
+ jll += self.class_log_prior_ + neg_prob.sum(axis=1)
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:820: RuntimeWarning: divide by zero encountered in log
+ neg_prob = np.log(1 - np.exp(self.feature_log_prob_))
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:823: RuntimeWarning: invalid value encountered in add
+ jll += self.class_log_prior_ + neg_prob.sum(axis=1)
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:820: RuntimeWarning: divide by zero encountered in log
+ neg_prob = np.log(1 - np.exp(self.feature_log_prob_))
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:823: RuntimeWarning: invalid value encountered in add
+ jll += self.class_log_prior_ + neg_prob.sum(axis=1)
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:820: RuntimeWarning: divide by zero encountered in log
+ neg_prob = np.log(1 - np.exp(self.feature_log_prob_))
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:823: RuntimeWarning: invalid value encountered in add
+ jll += self.class_log_prior_ + neg_prob.sum(axis=1)
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:820: RuntimeWarning: divide by zero encountered in log
+ neg_prob = np.log(1 - np.exp(self.feature_log_prob_))
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:823: RuntimeWarning: invalid value encountered in add
+ jll += self.class_log_prior_ + neg_prob.sum(axis=1)
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:820: RuntimeWarning: divide by zero encountered in log
+ neg_prob = np.log(1 - np.exp(self.feature_log_prob_))
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:823: RuntimeWarning: invalid value encountered in add
+ jll += self.class_log_prior_ + neg_prob.sum(axis=1)
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:820: RuntimeWarning: divide by zero encountered in log
+ neg_prob = np.log(1 - np.exp(self.feature_log_prob_))
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:823: RuntimeWarning: invalid value encountered in add
+ jll += self.class_log_prior_ + neg_prob.sum(axis=1)
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:820: RuntimeWarning: divide by zero encountered in log
+ neg_prob = np.log(1 - np.exp(self.feature_log_prob_))
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:823: RuntimeWarning: invalid value encountered in add
+ jll += self.class_log_prior_ + neg_prob.sum(axis=1)
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:820: RuntimeWarning: divide by zero encountered in log
+ neg_prob = np.log(1 - np.exp(self.feature_log_prob_))
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:823: RuntimeWarning: invalid value encountered in add
+ jll += self.class_log_prior_ + neg_prob.sum(axis=1)
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:820: RuntimeWarning: divide by zero encountered in log
+ neg_prob = np.log(1 - np.exp(self.feature_log_prob_))
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:823: RuntimeWarning: invalid value encountered in add
+ jll += self.class_log_prior_ + neg_prob.sum(axis=1)
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:820: RuntimeWarning: divide by zero encountered in log
+ neg_prob = np.log(1 - np.exp(self.feature_log_prob_))
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:823: RuntimeWarning: invalid value encountered in add
+ jll += self.class_log_prior_ + neg_prob.sum(axis=1)
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:801: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+
+
+{'classifier__alpha': 0.25, 'classifier__fit_prior': True, 'vectorizer__ngram_range': (1, 1), 'vectorizer__preprocessor': <function mask_integers at 0x00000237491B67B8>, 'vectorizer__stop_words': 'english'}
+0.805900621118
+Accuracy = 78.1%
+Confusion matrix, without normalization
+[[140 13]
+ [ 34 28]]
+Top like features:
+['use' 'just' 'year' 'price' 'time' 'Bitcoin' 'bitcoin' 'new' 'The'
+ 'INTMASK']
+---
+Top dislike features:
+['ABBA' 'cable' 'cab' 'byte' 'publication' 'bye' 'publications' 'publicity'
+ 'buyer' 'publicizing']
+
from sklearn.naive_bayes import MultinomialNB
+
+
+steps = (
+ ('vectorizer', TfidfVectorizer()),
+ ('classifier', MultinomialNB())
+)
+
+pipeline3 = Pipeline(steps)
+
+gs_params = {
+ 'vectorizer__stop_words': ['english', None],
+ 'vectorizer__ngram_range': [(1, 1), (1, 2), (2, 2)],
+ 'vectorizer__preprocessor': [mask_integers, None],
+ 'classifier__alpha': np.linspace(0,1,5),
+ 'classifier__fit_prior': [True, False]
+}
+
+gs = GridSearchCV(pipeline3, gs_params, n_jobs=1)
+gs.fit(training.full_content, training.label)
+
+print(gs.best_params_)
+print(gs.best_score_)
+
+pipeline3 = gs.best_estimator_
+predicted_labels = pipeline3.predict(testing.full_content)
+pipeline_performance(testing.label, predicted_labels)
+
+print_top_features(pipeline3, n_features=10)
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+C:\Users\Guillaume\Anaconda3\lib\site-packages\sklearn\naive_bayes.py:699: RuntimeWarning: divide by zero encountered in log
+ self.feature_log_prob_ = (np.log(smoothed_fc) -
+
+
+{'classifier__alpha': 0.5, 'classifier__fit_prior': False, 'vectorizer__ngram_range': (1, 1), 'vectorizer__preprocessor': <function mask_integers at 0x00000237491B67B8>, 'vectorizer__stop_words': 'english'}
+0.80900621118
+Accuracy = 79.1%
+Confusion matrix, without normalization
+[[141 12]
+ [ 33 29]]
+Top like features:
+['time' 'Google' 'Pro' 'Apple' 'new' 'The' 'Bitcoin' 'price' 'bitcoin'
+ 'INTMASK']
+---
+Top dislike features:
+['ABBA' 'categories' 'catching' 'catalyst' 'catalog' 'casually' 'casts'
+ 'cast' 'cashier' 'ran']
+
