Note that the optional watermark extension is a small IPython notebook plugin that I developed to make the code reproducible. You can just skip the following line(s).
%load_ext watermark
%watermark -a 'Sebastian Raschka' -u -d -v -p numpy,pandas,matplotlib,scikit-learn,nltk
Sebastian Raschka Last updated: 09/10/2015 CPython 3.4.3 IPython 4.0.0 numpy 1.9.2 pandas 0.16.2 matplotlib 1.4.3 scikit-learn 0.16.1 nltk 3.0.4
# to install watermark just uncomment the following line:
#%install_ext https://raw.githubusercontent.com/rasbt/watermark/master/watermark.py
The IMDB movie review set can be downloaded from http://ai.stanford.edu/~amaas/data/sentiment/. After downloading the dataset, decompress the files.
A) If you are working with Linux or MacOS X, open a new terminal windowm cd into the download directory and execute
tar -zxf aclImdb_v1.tar.gz
B) If you are working with Windows, download an archiver such as 7Zip to extract the files from the download archive.
import pyprind
import pandas as pd
import os
labels = {'pos':1, 'neg':0}
pbar = pyprind.ProgBar(50000)
df = pd.DataFrame()
for s in ('test', 'train'):
for l in ('pos', 'neg'):
path ='./aclImdb/%s/%s' % (s, l)
for file in os.listdir(path):
with open(os.path.join(path, file), 'r') as infile:
txt = infile.read()
df = df.append([[txt, labels[l]]], ignore_index=True)
pbar.update()
df.columns = ['review', 'sentiment']
0% 100% [##############################] | ETA[sec]: 0.000 Total time elapsed: 725.001 sec
Shuffling the DataFrame:
import numpy as np
np.random.seed(0)
df = df.reindex(np.random.permutation(df.index))
Optional: Saving the assembled data as CSV file:
df.to_csv('./movie_data.csv', index=False)
import pandas as pd
df = pd.read_csv('./movie_data.csv')
df.head(3)
| review | sentiment | |
|---|---|---|
| 0 | In 1974, the teenager Martha Moxley (Maggie Gr... | 1 |
| 1 | OK... so... I really like Kris Kristofferson a... | 0 |
| 2 | ***SPOILER*** Do not read this, if you think a... | 0 |
...
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
count = CountVectorizer()
docs = np.array([
'The sun is shining',
'The weather is sweet',
'The sun is shining and the weather is sweet'])
bag = count.fit_transform(docs)
print(count.vocabulary_)
{'sweet': 4, 'is': 1, 'shining': 2, 'weather': 6, 'sun': 3, 'the': 5, 'and': 0}
print(bag.toarray())
[[0 1 1 1 0 1 0] [0 1 0 0 1 1 1] [1 2 1 1 1 2 1]]
np.set_printoptions(precision=2)
from sklearn.feature_extraction.text import TfidfTransformer
tfidf = TfidfTransformer(use_idf=True, norm='l2', smooth_idf=True)
print(tfidf.fit_transform(count.fit_transform(docs)).toarray())
[[ 0. 0.43 0.56 0.56 0. 0.43 0. ] [ 0. 0.43 0. 0. 0.56 0.43 0.56] [ 0.4 0.48 0.31 0.31 0.31 0.48 0.31]]
tf_is = 2
n_docs = 3
idf_is = np.log((n_docs+1) / (3+1) )
tfidf_is = tf_is * (idf_is + 1)
print('tf-idf of term "is" = %.2f' % tfidf_is)
tf-idf of term "is" = 2.00
tfidf = TfidfTransformer(use_idf=True, norm=None, smooth_idf=True)
raw_tfidf = tfidf.fit_transform(count.fit_transform(docs)).toarray()[-1]
raw_tfidf
array([ 1.69, 2. , 1.29, 1.29, 1.29, 2. , 1.29])
l2_tfidf = raw_tfidf / np.sqrt(np.sum(raw_tfidf**2))
l2_tfidf
array([ 0.4 , 0.48, 0.31, 0.31, 0.31, 0.48, 0.31])
df.loc[0, 'review'][-50:]
'is seven.<br /><br />Title (Brazil): Not Available'
import re
def preprocessor(text):
text = re.sub('<[^>]*>', '', text)
emoticons = re.findall('(?::|;|=)(?:-)?(?:\)|\(|D|P)', text)
text = re.sub('[\W]+', ' ', text.lower()) + \
' '.join(emoticons).replace('-', '')
return text
preprocessor(df.loc[0, 'review'][-50:])
'is seven title brazil not available'
preprocessor("</a>This :) is :( a test :-)!")
'this is a test :) :( :)'
df['review'] = df['review'].apply(preprocessor)
from nltk.stem.porter import PorterStemmer
porter = PorterStemmer()
def tokenizer(text):
return text.split()
def tokenizer_porter(text):
return [porter.stem(word) for word in text.split()]
tokenizer('runners like running and thus they run')
['runners', 'like', 'running', 'and', 'thus', 'they', 'run']
tokenizer_porter('runners like running and thus they run')
['runner', 'like', 'run', 'and', 'thu', 'they', 'run']
import nltk
nltk.download('stopwords')
[nltk_data] Downloading package stopwords to [nltk_data] /Users/sebastian/nltk_data... [nltk_data] Package stopwords is already up-to-date!
True
from nltk.corpus import stopwords
stop = stopwords.words('english')
[w for w in tokenizer_porter('a runner likes running and runs a lot')[-10:] if w not in stop]
['runner', 'like', 'run', 'run', 'lot']
Strip HTML and punctuation to speed up the GridSearch later:
X_train = df.loc[:25000, 'review'].values
y_train = df.loc[:25000, 'sentiment'].values
X_test = df.loc[25000:, 'review'].values
y_test = df.loc[25000:, 'sentiment'].values
from sklearn.grid_search import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression
from sklearn.feature_extraction.text import TfidfVectorizer
tfidf = TfidfVectorizer(strip_accents=None,
lowercase=False,
preprocessor=None)
param_grid = [{'vect__ngram_range': [(1,1)],
'vect__stop_words': [stop, None],
'vect__tokenizer': [tokenizer, tokenizer_porter],
'clf__penalty': ['l1', 'l2'],
'clf__C': [1.0, 10.0, 100.0]},
{'vect__ngram_range': [(1,1)],
'vect__stop_words': [stop, None],
'vect__tokenizer': [tokenizer, tokenizer_porter],
'vect__use_idf':[False],
'vect__norm':[None],
'clf__penalty': ['l1', 'l2'],
'clf__C': [1.0, 10.0, 100.0]},
]
lr_tfidf = Pipeline([('vect', tfidf),
('clf', LogisticRegression(random_state=0))])
gs_lr_tfidf = GridSearchCV(lr_tfidf, param_grid,
scoring='accuracy',
cv=5, verbose=1,
n_jobs=-1)
gs_lr_tfidf.fit(X_train, y_train)
Fitting 5 folds for each of 48 candidates, totalling 240 fits
[Parallel(n_jobs=-1)]: Done 1 jobs | elapsed: 28.9s [Parallel(n_jobs=-1)]: Done 50 jobs | elapsed: 8.9min [Parallel(n_jobs=-1)]: Done 200 jobs | elapsed: 34.1min [Parallel(n_jobs=-1)]: Done 226 out of 240 | elapsed: 38.9min remaining: 2.4min [Parallel(n_jobs=-1)]: Done 240 out of 240 | elapsed: 40.7min finished
GridSearchCV(cv=5,
estimator=Pipeline(steps=[('vect', TfidfVectorizer(analyzer='word', binary=False, charset=None,
charset_error=None, decode_error='strict',
dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',
lowercase=False, max_df=1.0, max_features=None, min_df=1,
ngram_range=(1, 1), norm='...alse, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=0, tol=0.0001))]),
fit_params={}, iid=True, loss_func=None, n_jobs=-1,
param_grid=[{'clf__C': [1.0, 10.0, 100.0], 'vect__stop_words': [['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', 'her', 'hers', 'herself', 'it', 'its', 'itself', 'they', 'them', 'their', 'theirs', 't...okenizer': [<function tokenizer at 0x7f6c704948c8>, <function tokenizer_porter at 0x7f6c70494950>]}],
pre_dispatch='2*n_jobs', refit=True, score_func=None,
scoring='accuracy', verbose=1)
print('Best parameter set: %s ' % gs_lr_tfidf.best_params_)
print('CV Accuracy: %.3f' % gs_lr_tfidf.best_score_)
Best parameter set: {'clf__C': 10.0, 'vect__stop_words': None, 'clf__penalty': 'l2', 'vect__tokenizer': <function tokenizer at 0x7f6c704948c8>, 'vect__ngram_range': (1, 1)}
CV Accuracy: 0.897
clf = gs_lr_tfidf.best_estimator_
print('Test Accuracy: %.3f' % clf.score(X_test, y_test))
Test Accuracy: 0.899
import numpy as np
import re
from nltk.corpus import stopwords
stop = stopwords.words('english')
def tokenizer(text):
text = re.sub('<[^>]*>', '', text)
emoticons = re.findall('(?::|;|=)(?:-)?(?:\)|\(|D|P)', text.lower())
text = re.sub('[\W]+', ' ', text.lower()) + ' '.join(emoticons).replace('-', '')
tokenized = [w for w in text.split() if w not in stop]
return tokenized
def stream_docs(path):
with open(path, 'r') as csv:
next(csv) # skip header
for line in csv:
text, label = line[:-3], int(line[-2])
yield text, label
next(stream_docs(path='./movie_data.csv'))
('"In 1974, the teenager Martha Moxley (Maggie Grace) moves to the high-class area of Belle Haven, Greenwich, Connecticut. On the Mischief Night, eve of Halloween, she was murdered in the backyard of her house and her murder remained unsolved. Twenty-two years later, the writer Mark Fuhrman (Christopher Meloni), who is a former LA detective that has fallen in disgrace for perjury in O.J. Simpson trial and moved to Idaho, decides to investigate the case with his partner Stephen Weeks (Andrew Mitchell) with the purpose of writing a book. The locals squirm and do not welcome them, but with the support of the retired detective Steve Carroll (Robert Forster) that was in charge of the investigation in the 70\'s, they discover the criminal and a net of power and money to cover the murder.<br /><br />""Murder in Greenwich"" is a good TV movie, with the true story of a murder of a fifteen years old girl that was committed by a wealthy teenager whose mother was a Kennedy. The powerful and rich family used their influence to cover the murder for more than twenty years. However, a snoopy detective and convicted perjurer in disgrace was able to disclose how the hideous crime was committed. The screenplay shows the investigation of Mark and the last days of Martha in parallel, but there is a lack of the emotion in the dramatization. My vote is seven.<br /><br />Title (Brazil): Not Available"',
1)
def get_minibatch(doc_stream, size):
docs, y = [], []
try:
for _ in range(size):
text, label = next(doc_stream)
docs.append(text)
y.append(label)
except StopIteration:
return None, None
return docs, y
from sklearn.feature_extraction.text import HashingVectorizer
from sklearn.linear_model import SGDClassifier
vect = HashingVectorizer(decode_error='ignore',
n_features=2**21,
preprocessor=None,
tokenizer=tokenizer)
clf = SGDClassifier(loss='log', random_state=1, n_iter=1)
doc_stream = stream_docs(path='./movie_data.csv')
import pyprind
pbar = pyprind.ProgBar(45)
classes = np.array([0, 1])
for _ in range(45):
X_train, y_train = get_minibatch(doc_stream, size=1000)
if not X_train:
break
X_train = vect.transform(X_train)
clf.partial_fit(X_train, y_train, classes=classes)
pbar.update()
0% 100% [##############################] | ETA[sec]: 0.000 Total time elapsed: 50.063 sec
X_test, y_test = get_minibatch(doc_stream, size=5000)
X_test = vect.transform(X_test)
print('Accuracy: %.3f' % clf.score(X_test, y_test))
Accuracy: 0.868
clf = clf.partial_fit(X_test, y_test)