1 import pandas as pd
2 import numpy as np
3
4 # 数据读取
5 df = pd.read_csv('Amazon_Unlocked_Mobile.csv')
6
7 # Sample the data to speed up computation
8 # Comment out this line to match with lecture
9 df = df.sample(frac=0.1, random_state=10)
10
11 df.head()
1 # Drop missing values
2 df.dropna(inplace=True)
3
4 # Remove any 'neutral' ratings equal to 3
5 df = df[df['Rating'] != 3]
6
7 # Encode 4s and 5s as 1 (rated positively)
8 # Encode 1s and 2s as 0 (rated poorly)
9 df['Positively Rated'] = np.where(df['Rating'] > 3, 1, 0)
10 df.head(10)
1 # Most ratings are positive
2 df['Positively Rated'].mean()
0.7471776686078667
1 from sklearn.model_selection import train_test_split
2
3 # Split data into training and test sets
4 X_train, X_test, y_train, y_test = train_test_split(df['Reviews'],
5 df['Positively Rated'],
6 random_state=0)
7 print('X_train first entry:\n\n', X_train.iloc[0])
8 print('\n\nX_train shape: ', X_train.shape)
X_train first entry:
Everything about it is awesome!
X_train shape: (23052,)
CountVectorizer
1 from sklearn.feature_extraction.text import CountVectorizer
2
3 # Fit the CountVectorizer to the training data
4 vect = CountVectorizer().fit(X_train)
5 vect.get_feature_names()[::2000]
['00',
'arroja',
'comapañias',
'dvds',
'golden',
'lands',
'oil',
'razonable',
'smallsliver',
'tweak']
1 len(vect.get_feature_names())
19601
1 # transform the documents in the training data to a document-term matrix
2 X_train_vectorized = vect.transform(X_train)
3
4 X_train_vectorized
<23052x19601 sparse matrix of type '<class 'numpy.int64'>'
with 613289 stored elements in Compressed Sparse Row format>
1 from sklearn.linear_model import LogisticRegression
2
3 # Train the model
4 model = LogisticRegression()
5 model.fit(X_train_vectorized, y_train)
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
verbose=0, warm_start=False)
1 from sklearn.metrics import roc_auc_score
2
3 # Predict the transformed test documents
4 predictions = model.predict(vect.transform(X_test))
5
6 print('AUC: ', roc_auc_score(y_test, predictions))
AUC: 0.897433277667
1 # get the feature names as numpy array
2 feature_names = np.array(vect.get_feature_names())
3
4 # Sort the coefficients from the model
5 sorted_coef_index = model.coef_[0].argsort()
6
7 # Find the 10 smallest and 10 largest coefficients
8 # The 10 largest coefficients are being indexed using [:-11:-1]
9 # so the list returned is in order of largest to smallest
10 print('Smallest Coefs:\n{}\n'.format(feature_names[sorted_coef_index[:10]]))
11 print('Largest Coefs: \n{}'.format(feature_names[sorted_coef_index[:-11:-1]]))
Smallest Coefs:
['worst' 'terrible' 'slow' 'junk' 'poor' 'sucks' 'horrible' 'useless'
'waste' 'disappointed']
Largest Coefs:
['excelent' 'excelente' 'excellent' 'perfectly' 'love' 'perfect' 'exactly'
'great' 'best' 'awesome']
IF-idf
1 from sklearn.feature_extraction.text import TfidfVectorizer
2
3 # Fit the TfidfVectorizer to the training data specifiying a minimum document frequency of 5
4 vect = TfidfVectorizer(min_df=5).fit(X_train)
5 len(vect.get_feature_names())
5442
1 X_train_vectorized = vect.transform(X_train)
2
3 model = LogisticRegression()
4 model.fit(X_train_vectorized, y_train)
5
6 predictions = model.predict(vect.transform(X_test))
7
8 print('AUC: ', roc_auc_score(y_test, predictions))
