Sections
- Obtaining the IMDb movie review dataset
- Text-feature-extraction
- Cleaning text data
- Processing documents into tokens
- Training a logistic regression model for sentiment classification
- Working with bigger data - online algorithms and out-of-core learning
Obtaining the IMDb movie review dataset
数据可从这下载
解压之后，下面代码可将数据读成 Pandas 的 DataFrame
cd Documents/shanghai_python/data/aclImdb/
/Users/xiaokai/Documents/shanghai_python/data/aclImdb
ls
README imdb.vocab imdbEr.txt [34mtest[m[m/ [34mtrain[m[m/
cd train/pos/
/Users/xiaokai/Documents/shanghai_python/data/aclImdb/train/pos
ls
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cat 0_9.txt
Bromwell High is a cartoon comedy. It ran at the same time as some other programs about school life, such as "Teachers". My 35 years in the teaching profession lead me to believe that Bromwell High's satire is much closer to reality than is "Teachers". The scramble to survive financially, the insightful students who can see right through their pathetic teachers' pomp, the pettiness of the whole situation, all remind me of the schools I knew and their students. When I saw the episode in which a student repeatedly tried to burn down the school, I immediately recalled ......... at .......... High. A classic line: INSPECTOR: I'm here to sack one of your teachers. STUDENT: Welcome to Bromwell High. I expect that many adults of my age think that Bromwell High is far fetched. What a pity that it isn't!
# pip install pyprind
import pyprind # module for Python Progress Indicator
import pandas as pd
import os
labels = {'pos':1, 'neg':0} # 1 = positive and 0 = negative
pbar = pyprind.ProgBar(50000)
df = pd.DataFrame()
for s in ('test', 'train'):
for l in ('pos', 'neg'):
path ='data/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: 00:00:00
Total time elapsed: 00:01:41
df.head(3)
| review | sentiment | |
|---|---|---|
| 0 | I went and saw this movie last night after bei... | 1 |
| 1 | Actor turned director Bill Paxton follows up h... | 1 |
| 2 | As a recreational golfer with some knowledge o... | 1 |
Shuffling the DataFrame:
import numpy as np
np.random.seed(0)
df = df.reindex(np.random.permutation(df.index))
df.head(3)
| review | sentiment | |
|---|---|---|
| 11841 | In 1974, the teenager Martha Moxley (Maggie Gr... | 1 |
| 19602 | OK... so... I really like Kris Kristofferson a... | 0 |
| 45519 | ***SPOILER*** Do not read this, if you think a... | 0 |
Optional: Saving the assembled data as CSV file:
df.to_csv('./data/movie_data.csv', index=False, encoding='utf-8')
import pandas as pd
df = pd.read_csv('./data/movie_data.csv', encoding='utf-8')
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 |
Text feature extraction
Bag-of-words model
Free text with variables length is very far from the fixed length numeric representation that we need to do machine learning with scikit-learn.
However, there is an easy and effective way to go from text data to a numeric representation using the so-called bag-of-words model, which provides a data structure that is compatible with the machine learning aglorithms in scikit-learn.
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, the weather is sweet, and one and one is two'])
bag = count.fit_transform(docs)
count.vocabulary_
{u'and': 0,
u'is': 1,
u'one': 2,
u'shining': 3,
u'sun': 4,
u'sweet': 5,
u'the': 6,
u'two': 7,
u'weather': 8}
count.get_feature_names()
[u'and',
u'is',
u'one',
u'shining',
u'sun',
u'sweet',
u'the',
u'two',
u'weather']
As we can see from executing the preceding command, the vocabulary is stored in a Python dictionary, which maps the unique words that are mapped to integer indices. Next let us print the feature vectors that we just created:
bag.toarray() # 每行对应着一个 document， 每列对应一个 word， 值是 word 对应的计数
array([[0, 1, 0, 1, 1, 0, 1, 0, 0],
[0, 1, 0, 0, 0, 1, 1, 0, 1],
[2, 3, 2, 1, 1, 1, 2, 1, 1]])
count.inverse_transform(bag)
[array([u'the', u'sun', u'is', u'shining'],
dtype='<U7'), array([u'the', u'is', u'weather', u'sweet'],
dtype='<U7'), array([u'the', u'sun', u'is', u'shining', u'weather', u'sweet', u'and',
u'one', u'two'],
dtype='<U7')]
Bigrams and N-Grams
In last section, we used the so-called 1-gram (unigram) tokenization: Each token represents a single element with regard to the splittling criterion.
Entirely discarding word order is not always a good idea, as composite phrases often have specific meaning, and modifiers like "not" can invert the meaning of words.
A simple way to include some word order are n-grams, which don't only look at a single token, but at all pairs of neighborhing tokens. For example, in 2-gram (bigram) tokenization, we would group words together with an overlap of one word; in 3-gram (trigram) splits we would create an overlap two words, and so forth:
- original text: "this is how you get ants"
- 1-gram: "this", "is", "how", "you", "get", "ants"
- 2-gram: "this is", "is how", "how you", "you get", "get ants"
- 3-gram: "this is how", "is how you", "how you get", "you get ants"
Which "n" we choose for "n-gram" tokenization to obtain the optimal performance in our predictive model depends on the learning algorithm, dataset, and task. Or in other words, we have consider "n" in "n-grams" as a tuning parameters.
The CountVectorizer class in scikit-learn allows us to use different n-gram models via its ngram_range parameter. While a 1-gram representation is used by default, we could switch to a 2-gram representation by initializing a new CountVectorizer instance with ngram_range=(2,2)
bigram_vectorizer = CountVectorizer(ngram_range=(2,2))
bigram_vectorizer.fit_transform(docs).toarray()
array([[0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1],
[2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
bigram_vectorizer.vocabulary_
{u'and one': 0,
u'is shining': 1,
u'is sweet': 2,
u'is two': 3,
u'one and': 4,
u'one is': 5,
u'shining the': 6,
u'sun is': 7,
u'sweet and': 8,
u'the sun': 9,
u'the weather': 10,
u'weather is': 11}
Character n-grams
Sometimes it is also helpful not only to look at words, but to consider single characters instead.
That is particularly useful if we have very noisy data and want to identify the language, or if we want to predict something about a single word.
We can simply look at characters instead of words by setting analyzer="char".
X = ['Some say the world will end in fire,', 'Some say in ice.']
char_vectorizer = CountVectorizer(analyzer="char")
char_vectorizer.fit(X)
CountVectorizer(analyzer='char', binary=False, decode_error=u'strict',
dtype=<type 'numpy.int64'>, encoding=u'utf-8', input=u'content',
lowercase=True, max_df=1.0, max_features=None, min_df=1,
ngram_range=(1, 1), preprocessor=None, stop_words=None,
strip_accents=None, token_pattern=u'(?u)\\b\\w\\w+\\b',
tokenizer=None, vocabulary=None)
print(char_vectorizer.get_feature_names())
[u' ', u',', u'.', u'a', u'c', u'd', u'e', u'f', u'h', u'i', u'l', u'm', u'n', u'o', u'r', u's', u't', u'w', u'y']
Tfidf encoding
np.set_printoptions(precision=2)
When we are analyzing text data, we often encounter words that occur across multiple documents from both classes. Those frequently occurring words typically don't contain useful or discriminatory information. In this subsection, we will learn about a useful technique called term frequency-inverse document frequency (tf-idf) that can be used to downweight those frequently occurring words in the feature vectors. The tf-idf can be de ned as the product of the term frequency and the inverse document frequency:
Here the tf(t, d) is the term frequency that we introduced in the previous section, and the inverse document frequency idf(t, d) can be calculated as:
where is the total number of documents, and df(d, t) is the number of documents d that contain the term t. Note that adding the constant 1 to the denominator is optional and serves the purpose of assigning a non-zero value to terms that occur in all training samples; the log is used to ensure that low document frequencies are not given too much weight.
Scikit-learn implements yet another transformer, the TfidfTransformer, that takes the raw term frequencies from CountVectorizer as input and transforms them into tf-idfs:
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. 0.56 0.56 0. 0.43 0. 0. ]
[ 0. 0.43 0. 0. 0. 0.56 0.43 0. 0.56]
[ 0.5 0.45 0.5 0.19 0.19 0.19 0.3 0.25 0.19]]
As we saw in the previous subsection, the word "is" (column 2) had the largest term frequency in the 3rd document, being the most frequently occurring word. However, after transforming the same feature vector into tf-idfs, we see that the word "is" is now associated with a relatively small tf-idf (0.45) in document 3 since it is also contained in documents 1 and 2 and thus is unlikely to contain any useful, discriminatory information.
However, if we'd manually calculated the tf-idfs of the individual terms in our feature vectors, we'd have noticed that the TfidfTransformer calculates the tf-idfs slightly differently compared to the standard textbook equations that we see earlier. The equations for the idf and tf-idf that were implemented in scikit-learn are:
The tf-idf equation that was implemented in scikit-learn is as follows:
While it is also more typical to normalize the raw term frequencies before calculating the tf-idfs, the TfidfTransformer normalizes the tf-idfs directly.
By default (norm='l2'), scikit-learn's TfidfTransformer applies the L2-normalization, which returns a vector of length 1 by dividing an un-normalized feature vector v by its L2-norm:
To make sure that we understand how TfidfTransformer works, let us walk through an example and calculate the tf-idf of the word is in the 3rd document.
The word is has a term frequency of 3 (tf = 3) in document 3, and the document frequency of this term is 3 since the term is occurs in all three documents (df = 3). Thus, we can calculate the idf as follows:
Now in order to calculate the tf-idf, we simply need to add 1 to the inverse document frequency and multiply it by the term frequency:
tf_is = 3
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" = 3.00
If we repeated these calculations for all terms in the 3rd document, we'd obtain the following tf-idf vectors: [3.39, 3.0, 3.39, 1.29, 1.29, 1.29, 2.0 , 1.69, 1.29]. However, we notice that the values in this feature vector are different from the values that we obtained from the TfidfTransformer that we used previously. The step that we are missing in this tf-idf calculation is the L2-normalization, which can be applied as follows:
tfidf = TfidfTransformer(use_idf=True, norm=None, smooth_idf=True)
raw_tfidf = tfidf.fit_transform(count.fit_transform(docs)).toarray()[-1]
raw_tfidf
array([ 3.39, 3. , 3.39, 1.29, 1.29, 1.29, 2. , 1.69, 1.29])
l2_tfidf = raw_tfidf / np.sqrt(np.sum(raw_tfidf**2))
l2_tfidf
array([ 0.5 , 0.45, 0.5 , 0.19, 0.19, 0.19, 0.3 , 0.25, 0.19])
As we can see, the results match the results returned by scikit-learn's TfidfTransformer (below). Since we now understand how tf-idfs are calculated, let us proceed to the next sections and apply those concepts to the movie review dataset.
Cleaning text data
df.loc[0, 'review'][-50:] # last 50 characters from the first document
u'is seven.<br /><br />Title (Brazil): Not Available'
text contains html markup tags, we need clean them. we will now remove all punctuation marks but only keep emoticon characters such as ":)"
import re # Python 中处理正则表达式的模块
def preprocessor(text):
text = re.sub(r'<[^>]*>', '', text) # 去除 HTML 标签
emoticons = re.findall(r'(?::|;|=)(?:-)?(?:\)|\(|D|P)', text)
text = re.sub(r'\W+', ' ', text.lower()) + \
' '.join(emoticons).replace('-', '') # 将表情符号拼接在正文后面
return text
preprocessor(df.loc[0, 'review'][-50:])
u'is seven title brazil not available'
preprocessor("</a>This :) is :( a test :-)!")
'this is a test :) :( :)'
df['review'] = df['review'].apply(preprocessor)
Processing documents into tokens
split the text corpora into individual elements.
tokenize 是把长文本切成一系列单词，最简单的方式就是以 whitespace 切分.
word stemming 是将词转为最原始的形式, root form, (例如 running -> run), 一种算法是 Porter stemmer algorithm
以下需要使用 nltk, 需要先安装: pip install nltk
from nltk.stem.porter import PorterStemmer
porter = PorterStemmer()
def tokenizer(text):
return text.split() # 默认以空格切分
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')
[u'runner', u'like', u'run', u'and', u'thu', u'they', u'run']
Stop-words (停词) 是 最常见的一些单词, 它们的实际意义并不是很大, 大多是起辅助作用的, 但是它们的频次非常高, 所以需要去除, 例如 is, and, has
import nltk
nltk.download('stopwords') # 下载停词
[nltk_data] Downloading package stopwords to /Users/alan/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]
[u'runner', u'like', u'run', u'run', u'lot']
Training a logistic regression model for sentiment classification
Strip HTML and punctuation to speed up the GridSearch later:
# 25,000 documents for training and 25,000 documents for testing, 需要大约40分钟
# 所以先使用5000 documents
X_train = df.loc[:5000, 'review'].values
y_train = df.loc[:5000, 'sentiment'].values
X_test = df.loc[5000:, 'review'].values
y_test = df.loc[5000:, '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
# TfidfVectorizer 等同于 CountVectorizer + TfidfTransformer
tfidf = TfidfVectorizer(strip_accents=None,
lowercase=False,
preprocessor=None)
# grig search
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]},
]
# 先转化为 tfidf matrix
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)
# 数据量减少为5000 后需要时间大约为8分钟
gs_lr_tfidf.fit(X_train, y_train)
Fitting 5 folds for each of 48 candidates, totalling 240 fits
[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 1.4min
[Parallel(n_jobs=-1)]: Done 184 tasks | elapsed: 8.0min
[Parallel(n_jobs=-1)]: Done 240 out of 240 | elapsed: 10.5min finished
GridSearchCV(cv=5, error_score='raise',
estimator=Pipeline(steps=[('vect', TfidfVectorizer(analyzer=u'word', binary=False, decode_error=u'strict',
dtype=<type 'numpy.int64'>, encoding=u'utf-8', input=u'content',
lowercase=False, max_df=1.0, max_features=None, min_df=1,
ngram_range=(1, 1), norm=u'l2', preprocessor=None, smooth_idf=Tru...nalty='l2', random_state=0, solver='liblinear', tol=0.0001,
verbose=0, warm_start=False))]),
fit_params={}, iid=True, n_jobs=-1,
param_grid=[{'vect__ngram_range': [(1, 1)], 'vect__tokenizer': [<function tokenizer at 0x130dca410>, <function tokenizer_porter at 0x130dca488>], 'clf__penalty': ['l1', 'l2'], 'clf__C': [1.0, 10.0, 100.0], 'vect__stop_words': [[u'i', u'me', u'my', u'myself', u'we', u'our', u'ours', u'ourselves', u'y...x130dca488>], 'vect__use_idf': [False], 'clf__C': [1.0, 10.0, 100.0], 'clf__penalty': ['l1', 'l2']}],
pre_dispatch='2*n_jobs', refit=True, 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: {'vect__ngram_range': (1, 1), 'vect__tokenizer': <function tokenizer at 0x130dca410>, 'clf__penalty': 'l2', 'clf__C': 10.0, 'vect__stop_words': [u'i', u'me', u'my', u'myself', u'we', u'our', u'ours', u'ourselves', u'you', u'your', u'yours', u'yourself', u'yourselves', u'he', u'him', u'his', u'himself', u'she', u'her', u'hers', u'herself', u'it', u'its', u'itself', u'they', u'them', u'their', u'theirs', u'themselves', u'what', u'which', u'who', u'whom', u'this', u'that', u'these', u'those', u'am', u'is', u'are', u'was', u'were', u'be', u'been', u'being', u'have', u'has', u'had', u'having', u'do', u'does', u'did', u'doing', u'a', u'an', u'the', u'and', u'but', u'if', u'or', u'because', u'as', u'until', u'while', u'of', u'at', u'by', u'for', u'with', u'about', u'against', u'between', u'into', u'through', u'during', u'before', u'after', u'above', u'below', u'to', u'from', u'up', u'down', u'in', u'out', u'on', u'off', u'over', u'under', u'again', u'further', u'then', u'once', u'here', u'there', u'when', u'where', u'why', u'how', u'all', u'any', u'both', u'each', u'few', u'more', u'most', u'other', u'some', u'such', u'no', u'nor', u'not', u'only', u'own', u'same', u'so', u'than', u'too', u'very', u's', u't', u'can', u'will', u'just', u'don', u'should', u'now', u'd', u'll', u'm', u'o', u're', u've', u'y', u'ain', u'aren', u'couldn', u'didn', u'doesn', u'hadn', u'hasn', u'haven', u'isn', u'ma', u'mightn', u'mustn', u'needn', u'shan', u'shouldn', u'wasn', u'weren', u'won', u'wouldn']}
CV Accuracy: 0.862
clf = gs_lr_tfidf.best_estimator_
print('Test Accuracy: %.3f' % clf.score(X_test, y_test))
Test Accuracy: 0.873
Working with bigger data - online algorithms and out-of-core learning
Out-of-Core learning is the task of training a machine learning model on a dataset that does not fit into memory or RAM. This requires the following conditions:
- a feature extraction layer with fixed output dimensionality
- knowing the list of all classes in advance (in this case we only have positive and negative tweets)
- a machine learning algorithm that supports incremental learning (the
partial_fitmethod in scikit-learn).
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): # 生成器
# generator funciton, reads in and returns one document at a time
with open(path, 'r') as csv:
next(csv) # skip header
for line in csv:
text, label = line[:-3], int(line[-2])
yield text, label
# To verify that our stream_docs function works correctly
gen = stream_docs(path='./data/movie_data.csv')
next(gen)
('"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):
'''
take a document stream from the stream_docs function and
return a particular number of documents
'''
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 # makes use of the Hashing trick
from sklearn.linear_model import SGDClassifier # train a logistic regression model using small minibatches of documents
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='./data/movie_data.csv')
# iterated over 45 minibatches of documents
# where each minibatch consists of 1,000 documents each
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: 00:00:00
Total time elapsed: 00:00:40
#use the last 5,000 documents to evaluate the performance
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: 0.867
虽然准确率略低于前面，但训练速度快了很多，而且使用的内存更少
# use the last 5,000 documents to update our model
# 可以使用 partial_fit 继续训练
clf = clf.partial_fit(X_test, y_test)
Model persistence
训练模型是 expensive 并且耗费时间的, 我们不希望在应用中每次都要重新训练模型, 所以我们需要保存模型, 并且能进行新的预测以及更新。 可以用到 pickle 模块来储存模型, 将 python object 储存为byte code, 可以读取也可以写入
After we trained the logistic regression model as shown above, we can save the classifier along with the stop words, Porter Stemmer, and HashingVectorizer as serialized objects to our local disk so that we can use the fitted classifier in our web application later.
import pickle
import os
# created a movieclassifier directory
# created a pkl_objects subdirectory to save the serialized Python objects to our local drive
dest = os.path.join('movieclassifier', 'pkl_objects')
if not os.path.exists(dest):
os.makedirs(dest)
# 写入
pickle.dump(stop, open(os.path.join(dest, 'stopwords.pkl'), 'wb'), protocol=2)
pickle.dump(clf, open(os.path.join(dest, 'classifier.pkl'), 'wb'), protocol=2)
Next, we save the HashingVectorizer as in a separate file so that we can import it later.
%%writefile movieclassifier/vectorizer.py
from sklearn.feature_extraction.text import HashingVectorizer
import re
import os
import pickle
cur_dir = os.path.dirname(__file__)
stop = pickle.load(open(
os.path.join(cur_dir,
'pkl_objects',
'stopwords.pkl'), 'rb'))
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
vect = HashingVectorizer(decode_error='ignore',
n_features=2**21,
preprocessor=None,
tokenizer=tokenizer)
Overwriting movieclassifier/vectorizer.py
After executing the preceeding code cells, we can now restart the IPython notebook kernel to check if the objects were serialized correctly.
First, change the current Python directory to movieclassifer:
import os
os.chdir('movieclassifier')
import pickle
import re
import os
from vectorizer import vect
clf = pickle.load(open(os.path.join('pkl_objects', 'classifier.pkl'), 'rb'))
import numpy as np
label = {0:'negative', 1:'positive'}
example = ['I love this movie']
X = vect.transform(example)
print('Prediction: %s\nProbability: %.2f%%' %\
(label[clf.predict(X)[0]], np.max(clf.predict_proba(X))*100))
Prediction: positive
Probability: 82.53%
word2vec
word2vec 是 Mikolov et al. 提出一种训练词向量的方法。它有 Continuous Bag-of-Words model (CBOW) and the Skip-Gram model 两种变式，前者是用一个词序列窗口中的其他词来预测中心词，后者则是用中心词来预测其他词。在实际使用 word2vec 时，一般使用 Skip-Gram model 结合 Negative Sampling 进行训练。
# gensim 库中包含了 word2vec 模块
from gensim.models.word2vec import Word2Vec
训练词向量， gensim 训练词向量时可以喂入一个 iterator
class MySentence:
def __init__(self, data):
self.data = data
def __iter__(self):
for line in self.data:
yield line.lower().split()
train_corpus = MySentence(df['review'])
# 或者 train_corpus = [s.split() for s in df['review']]
model = Word2Vec(train_corpus,
size=200, # 词向量的维度
iter=20, # 数据在训练中用到的次数, 即 epoch 数
workers=4) # 调用的进程数
简单查看词向量的结果
model.most_similar('good')
[(u'decent', 0.7367605566978455),
(u'bad', 0.7155213356018066),
(u'great', 0.7067341804504395),
(u'nice', 0.6252745389938354),
(u'cool', 0.5865251421928406),
(u'passable', 0.5830472707748413),
(u'funny', 0.5790721774101257),
(u'fine', 0.5767843127250671),
(u'lousy', 0.5747220516204834),
(u'terrible', 0.5679782629013062)]
获得词对应的词向量
model['good']
array([-1.80578566, 2.59199047, 1.00538623, 0.00376823, -1.63011074,
1.19826996, -0.05748612, 0.34942579, -3.64253092, 0.77269369,
-0.12568648, 0.63311213, -0.29951221, 1.00349665, 0.57139468,
0.26391807, -2.15211248, 1.37436974, 0.72486091, 1.39909697,
1.39214003, -0.55878437, 1.28609359, 1.4021709 , -0.61972415,
1.40909946, 1.23801959, 0.12476239, -2.10969472, -0.16599979,
1.07056391, -0.9283669 , -1.24553549, -1.00224996, 0.85817921,
-2.98209238, 2.52684212, -0.84701031, -1.30793285, -3.52102351,
2.51759624, 1.55442834, -0.81624299, 0.80133152, 0.76279849,
-1.61152196, 0.84180117, 1.95387816, 0.39838204, -2.50793004,
-1.3055681 , 2.4016645 , 2.7259481 , 1.65427065, 2.08329916,
-0.67130673, -2.77032781, -0.86488032, -0.83239168, 0.70329827,
0.1054525 , -0.48466596, 1.93771338, 0.57588094, -0.16703451,
2.06361747, 0.90677142, 1.05519104, 1.84931016, 0.77389133,
2.08386183, -2.16565847, 1.20824552, 0.05443871, 3.20145893,
-2.12376046, -0.46623436, 1.19470978, -0.60041159, -2.01585841,
-1.14179373, 0.25336841, -2.7429111 , 0.19020027, 0.77245057,
1.07972777, 3.45747972, -1.17416704, 0.14808762, -0.04948059,
2.25656533, -1.87219381, -2.38383865, -2.15413165, -0.18855318,
-1.33702457, -0.72171617, -0.28952792, 0.9959411 , 2.43245673,
-0.94892198, 1.32803464, 3.27445745, 0.44902089, 1.76025999,
-0.15520753, -0.42482886, 0.22728981, -0.93469167, 0.87159711,
1.20421445, 1.03957427, 0.56455994, 0.19218144, -1.84992611,
1.75870144, -2.13160086, 0.99597716, -1.92741001, 1.47426069,
-2.23345065, -0.38542071, -0.33888757, -0.50440568, -1.29633522,
1.55562842, 1.08055615, 1.2215786 , -1.75132465, -0.11741698,
0.77109945, -0.68743432, -0.31800482, 0.23696584, 0.77463788,
-1.75956166, 1.77490389, 0.07634074, 2.27028704, 0.66218233,
3.3049078 , 3.30950427, -1.33130598, -0.92713892, 0.15286015,
0.287049 , 0.42833641, -1.46425474, 0.9658314 , 0.40959406,
0.47199422, -0.95312113, -3.23242712, -0.08304592, -1.2138108 ,
0.30118775, -0.96748334, -0.68403792, -2.61612749, -0.97091049,
0.03540362, -1.22498131, 0.23248808, 2.79292464, 1.39140284,
0.35824764, 0.53603202, 2.08097696, 1.61062503, -1.45209515,
-0.87486774, 0.6024124 , 1.00253165, -0.25757971, 3.5850575 ,
0.37494364, 1.26284862, -1.56030715, -2.38174224, -2.99708772,
0.75272441, 0.95936358, 0.24002089, 1.7718761 , 1.28533518,
-2.32952619, 2.59281707, -3.69943428, 0.34991279, -0.52845728,
1.35607052, 0.67054856, -1.21081388, 1.81272793, 0.64519709,
-0.4952068 , 2.65413833, 1.53806341, -2.95830393, 2.38203692], dtype=float32)
存储/读取模型
model.save('data/imdb.d2v')
model = Word2Vec.load('data/imdb.d2v')
利用训练好的词向量来进行情感分析
# 用 word vec 的均值作为 doc vec
def get_doc_vec(sentence, model):
scores = [model[word] for word in sentence.split()
if word in model] # 如果词频小于 min_count, word2vec 不会把这个词放入 vocab 里
return np.mean(scores, axis=0)
X_word2vec_train = np.array([get_doc_vec(sentence, model) for sentence in X_train])
X_word2vec_test = np.array([get_doc_vec(sentence, model) for sentence in X_test])
from sklearn.grid_search import GridSearchCV
from sklearn.linear_model import LogisticRegression
lr = LogisticRegression(random_state=0)
param_grid = [{'penalty': ['l1', 'l2'],
'C': [1.0, 10.0, 100.0]}]
gs = GridSearchCV(lr, param_grid,
scoring='accuracy',
cv=5, verbose=1,
n_jobs=-1)
gs.fit(X_word2vec_train, y_train);
Fitting 5 folds for each of 6 candidates, totalling 30 fits
[Parallel(n_jobs=-1)]: Done 30 out of 30 | elapsed: 14.5s finished
gs.best_params_
{'C': 1.0, 'penalty': 'l2'}
gs.best_score_
0.85642871425714862
clf = gs.best_estimator_
print('Test Accuracy: %.3f' % clf.score(X_word2vec_test, y_test))
Test Accuracy: 0.869
中文新闻分类
from os import path
import os
import re
import pandas as pd
import numpy as np
rootdir = 'data/SogouC.reduced/Reduced'
dirs = os.listdir(rootdir)
dirs = [path.join(rootdir,f) for f in dirs if f.startswith('C')]
dirs
['data/SogouC.reduced/Reduced/C000008',
'data/SogouC.reduced/Reduced/C000010',
'data/SogouC.reduced/Reduced/C000013',
'data/SogouC.reduced/Reduced/C000014',
'data/SogouC.reduced/Reduced/C000016',
'data/SogouC.reduced/Reduced/C000020',
'data/SogouC.reduced/Reduced/C000022',
'data/SogouC.reduced/Reduced/C000023',
'data/SogouC.reduced/Reduced/C000024']
def load_txt(x):
with open(x) as f:
res = [t.decode('gbk','ignore') for t in f]
return ''.join(res)
text_t = {}
for i, d in enumerate(dirs):
files = os.listdir(d)
files = [path.join(d, x) for x in files if x.endswith('txt') and not x.startswith('.')]
text_t[i] = [load_txt(f) for f in files]
print(text_t[0][0][:100])
本报记者陈雪频实习记者唐翔发自上海
一家刚刚成立两年的网络支付公司，它的目标是成为市值100亿美元的上市公司。
这家公司叫做快钱，说这句话的是快钱的CEO关国光。他之前曾任网易的高级副
flen = [len(t) for t in text_t.values()]
labels = np.repeat(text_t.keys(),flen)
# flatter nested list
import itertools
merged = list(itertools.chain.from_iterable(text_t.values()))
df = pd.DataFrame({'label': labels, 'txt': merged})
df.head()
| label | txt | |
|---|---|---|
| 0 | 0 | 本报记者陈雪频实习记者唐翔发自上海\r\n 一家刚刚成立两年的网络支付公司，它的目标是... |
| 1 | 0 | 证券通：百联股份未来5年有能力保持高速增长\r\n\r\n 深度报告 权威内参... |
| 2 | 0 | 5月09日消息快评\r\n\r\n 深度报告 权威内参 来自“证券通”www.... |
| 3 | 0 | 5月09日消息快评\r\n\r\n 深度报告 权威内参 来自“证券通”www.... |
| 4 | 0 | 5月09日消息快评\r\n\r\n 深度报告 权威内参 来自“证券通”www.... |
# cut word
import jieba
jieba.enable_parallel(4)
def cutword_1(x):
words = jieba.cut(x)
return ' '.join(words)
Building prefix dict from the default dictionary ...
Loading model from cache /var/folders/y8/z6ws1f2907vbb33mcp8c63300000gn/T/jieba.cache
Loading model cost 1.119 seconds.
Prefix dict has been built succesfully.
df['seg_word'] = df.txt.map(cutword_1)
df.head()
| label | txt | seg_word | |
|---|---|---|---|
| 0 | 0 | 本报记者陈雪频实习记者唐翔发自上海\r\n 一家刚刚成立两年的网络支付公司，它的目标是... | 本报记者 陈雪频 实习 记者 唐翔 发自 上海 \r\n 一家 刚刚 成立 ... |
| 1 | 0 | 证券通：百联股份未来5年有能力保持高速增长\r\n\r\n 深度报告 权威内参... | 证券 通 ： 百联 股份 未来 5 年 有 能力 保持高速 增长 \r\n ... |
| 2 | 0 | 5月09日消息快评\r\n\r\n 深度报告 权威内参 来自“证券通”www.... | 5 月 09 日 消息 快评 \r\n \r\n 深度 报告... |
| 3 | 0 | 5月09日消息快评\r\n\r\n 深度报告 权威内参 来自“证券通”www.... | 5 月 09 日 消息 快评 \r\n \r\n 深度 报告... |
| 4 | 0 | 5月09日消息快评\r\n\r\n 深度报告 权威内参 来自“证券通”www.... | 5 月 09 日 消息 快评 \r\n \r\n 深度 报告... |
from cPickle import dump,load
dump(df, open('data/tmdf.pickle', 'wb'))
# df = load(open('df.pickle','rb'))
from sklearn.feature_extraction.text import TfidfVectorizer
vect = TfidfVectorizer(ngram_range=(1,1), min_df = 2, max_features = 10000)
xvec = vect.fit_transform(df.seg_word)
xvec.shape
(17910, 10000)
y = df.label
from sklearn.cross_validation import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import MultinomialNB
from sklearn.ensemble import RandomForestClassifier
train_X, test_X, train_y, test_y = train_test_split(xvec, y , train_size=0.7, random_state=1)
clf = MultinomialNB()
clf.fit(train_X, train_y)
MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)
from sklearn import metrics
pre = clf.predict(test_X)
print metrics.classification_report(test_y, pre)
precision recall f1-score support
0 0.90 0.88 0.89 577
1 0.89 0.81 0.85 603
2 0.88 0.82 0.85 619
3 0.98 0.97 0.98 584
4 0.86 0.88 0.87 570
5 0.88 0.79 0.83 600
6 0.77 0.90 0.83 600
7 0.76 0.83 0.80 615
8 0.92 0.93 0.93 605
avg / total 0.87 0.87 0.87 5373
# word2vec
txt = df.seg_word.values
txtlist = []
for sent in txt:
temp = [w for w in sent.split()]
txtlist.append(temp)
num_features = 100
min_word_count = 10
num_workers = 4
context = 5
epoch = 20
sample = 1e-5
from gensim.models import word2vec
model = word2vec.Word2Vec(txtlist, workers = num_workers,
sample = sample,
size = num_features,
min_count=min_word_count,
window = context,
iter = epoch)
model.syn0.shape
(57675, 100)
for w in model.most_similar(u'互联网'):
print w[0], w[1]
网络 0.787674069405
门户网站 0.747487425804
搜索引擎 0.744884610176
无线 0.732329308987
B2B 0.713720798492
网络广告 0.712735056877
腾讯 0.702631175518
MSN 0.701346695423
大旗网 0.69608104229
Google 0.68867880106
#model.save('sogo_wv')
#model = word2vec.Word2Vec.load('sogo_wv')
# 将词向量平均化为文档向量
def sentvec_1(sent,m=num_features,model=model):
res = np.zeros(m)
words = sent.split()
num = 0
for w in words:
if w in model.index2word:
res += model[w]
num += 1.0
if num == 0: return np.zeros(m)
else: return res/num
n = df.shape[0]
sent_matrix = np.zeros([n,num_features],float)
for i ,sent in enumerate(df.seg_word.values):
sent_matrix[i,:] = sentvec_1(sent)
sent_matrix.shape
(17910, 100)
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.cross_validation import train_test_split
train_X, test_X, train_y, test_y = train_test_split(sent_matrix, y , train_size=0.7, random_state=1)
clf = GradientBoostingClassifier()
clf.fit(train_X, train_y)
from sklearn import metrics
pre = clf.predict(test_X)
print metrics.classification_report(test_y, pre)
precision recall f1-score support
0 0.91 0.85 0.88 577
1 0.84 0.82 0.83 603
2 0.86 0.88 0.87 619
3 0.98 0.97 0.98 584
4 0.84 0.86 0.85 570
5 0.84 0.80 0.82 600
6 0.86 0.87 0.87 600
7 0.77 0.83 0.80 615
8 0.93 0.93 0.93 605
avg / total 0.87 0.87 0.87 5373