Important: This notebook will only work with fastai-0.7.x. Do not try to run any fastai-1.x code from this path in the repository because it will load fastai-0.7.x
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%reload_ext autoreload
%autoreload 2
%matplotlib inline
from fastai.nlp import *
from sklearn.linear_model import LogisticRegression
The large movie review dataset contains a collection of 50,000 reviews from IMDB. The dataset contains an even number of positive and negative reviews. The authors considered only highly polarized reviews. A negative review has a score ≤ 4 out of 10, and a positive review has a score ≥ 7 out of 10. Neutral reviews are not included in the dataset. The dataset is divided into training and test sets. The training set is the same 25,000 labeled reviews.
The sentiment classification task consists of predicting the polarity (positive or negative) of a given text.
To get the dataset, in your terminal run the following commands:
wget http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz
gunzip aclImdb_v1.tar.gz
tar -xvf aclImdb_v1.tar
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PATH='data/aclImdb/'
names = ['neg','pos']
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%ls {PATH}
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%ls {PATH}train
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%ls {PATH}train/pos | head
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trn,trn_y = texts_labels_from_folders(f'{PATH}train',names)
val,val_y = texts_labels_from_folders(f'{PATH}test',names)
Here is the text of the first review
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trn[0]
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trn_y[0]
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CountVectorizer converts a collection of text documents to a matrix of token counts (part of sklearn.feature_extraction.text).
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veczr = CountVectorizer(tokenizer=tokenize)
fit_transform(trn) finds the vocabulary in the training set. It also transforms the training set into a term-document matrix. Since we have to apply the same transformation to your validation set, the second line uses just the method transform(val). trn_term_doc and val_term_doc are sparse matrices. trn_term_doc[i] represents training document i and it contains a count of words for each document for each word in the vocabulary.
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trn_term_doc = veczr.fit_transform(trn)
val_term_doc = veczr.transform(val)
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trn_term_doc
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trn_term_doc[0]
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vocab = veczr.get_feature_names(); vocab[5000:5005]
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w0 = set([o.lower() for o in trn[0].split(' ')]); w0
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len(w0)
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veczr.vocabulary_['absurd']
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trn_term_doc[0,1297]
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trn_term_doc[0,5000]
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We define the log-count ratio $r$ for each word $f$:
$r = \log \frac{\text{ratio of feature $f$ in positive documents}}{\text{ratio of feature $f$ in negative documents}}$
where ratio of feature $f$ in positive documents is the number of times a positive document has a feature divided by the number of positive documents.
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def pr(y_i):
p = x[y==y_i].sum(0)
return (p+1) / ((y==y_i).sum()+1)
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x=trn_term_doc
y=trn_y
r = np.log(pr(1)/pr(0))
b = np.log((y==1).mean() / (y==0).mean())
Here is the formula for Naive Bayes.
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pre_preds = val_term_doc @ r.T + b
preds = pre_preds.T>0
(preds==val_y).mean()
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...and binarized Naive Bayes.
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x=trn_term_doc.sign()
r = np.log(pr(1)/pr(0))
pre_preds = val_term_doc.sign() @ r.T + b
preds = pre_preds.T>0
(preds==val_y).mean()
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Here is how we can fit logistic regression where the features are the unigrams.
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m = LogisticRegression(C=1e8, dual=True)
m.fit(x, y)
preds = m.predict(val_term_doc)
(preds==val_y).mean()
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m = LogisticRegression(C=1e8, dual=True)
m.fit(trn_term_doc.sign(), y)
preds = m.predict(val_term_doc.sign())
(preds==val_y).mean()
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...and the regularized version
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m = LogisticRegression(C=0.1, dual=True)
m.fit(x, y)
preds = m.predict(val_term_doc)
(preds==val_y).mean()
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m = LogisticRegression(C=0.1, dual=True)
m.fit(trn_term_doc.sign(), y)
preds = m.predict(val_term_doc.sign())
(preds==val_y).mean()
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Our next model is a version of logistic regression with Naive Bayes features described here. For every document we compute binarized features as described above, but this time we use bigrams and trigrams too. Each feature is a log-count ratio. A logistic regression model is then trained to predict sentiment.
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veczr = CountVectorizer(ngram_range=(1,3), tokenizer=tokenize, max_features=800000)
trn_term_doc = veczr.fit_transform(trn)
val_term_doc = veczr.transform(val)
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trn_term_doc.shape
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vocab = veczr.get_feature_names()
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vocab[200000:200005]
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y=trn_y
x=trn_term_doc.sign()
val_x = val_term_doc.sign()
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r = np.log(pr(1) / pr(0))
b = np.log((y==1).mean() / (y==0).mean())
Here we fit regularized logistic regression where the features are the trigrams.
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m = LogisticRegression(C=0.1, dual=True)
m.fit(x, y);
preds = m.predict(val_x)
(preds.T==val_y).mean()
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Here is the $\text{log-count ratio}$ r.
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r.shape, r
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np.exp(r)
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Here we fit regularized logistic regression where the features are the trigrams' log-count ratios.
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x_nb = x.multiply(r)
m = LogisticRegression(dual=True, C=0.1)
m.fit(x_nb, y);
val_x_nb = val_x.multiply(r)
preds = m.predict(val_x_nb)
(preds.T==val_y).mean()
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sl=2000
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# Here is how we get a model from a bag of words
md = TextClassifierData.from_bow(trn_term_doc, trn_y, val_term_doc, val_y, sl)
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learner = md.dotprod_nb_learner()
learner.fit(0.02, 1, wds=1e-6, cycle_len=1)
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learner.fit(0.02, 2, wds=1e-6, cycle_len=1)
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learner.fit(0.02, 2, wds=1e-6, cycle_len=1)
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