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from __future__ import division
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np

# Some nice default configuration for plots
plt.rcParams['figure.figsize'] = 10, 7.5
plt.rcParams['axes.grid'] = True
plt.gray();



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%run ../fetch_data.py

Text Feature Extraction for Classification and Clustering

Outline of this section:

Turn a corpus of text documents into feature vectors using a Bag of Words representation,
Train a simple text classifier on the feature vectors,
Wrap the vectorizer and the classifier with a pipeline,
Cross-validation and model selection on the pipeline.

Text Classification in 20 lines of Python

Let's start by implementing a canonical text classification example:

The 20 newsgroups dataset: around 18000 text posts from 20 newsgroups forums
Bag of Words features extraction with TF-IDF weighting
Naive Bayes classifier or Linear Support Vector Machine for the classifier itself



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from sklearn.datasets import load_files
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB

# Load the text data
categories = [
    'alt.atheism',
    'talk.religion.misc',
    'comp.graphics',
    'sci.space',
]
twenty_train_small = load_files('../datasets/20news-bydate-train/',
    categories=categories, encoding='latin-1')
twenty_test_small = load_files('../datasets/20news-bydate-test/',
    categories=categories, encoding='latin-1')

# Turn the text documents into vectors of word frequencies
vectorizer = TfidfVectorizer(min_df=2)
X_train = vectorizer.fit_transform(twenty_train_small.data)
y_train = twenty_train_small.target

# Fit a classifier on the training set
classifier = MultinomialNB().fit(X_train, y_train)
print("Training score: {0:.1f}%".format(
    classifier.score(X_train, y_train) * 100))

# Evaluate the classifier on the testing set
X_test = vectorizer.transform(twenty_test_small.data)
y_test = twenty_test_small.target
print("Testing score: {0:.1f}%".format(
    classifier.score(X_test, y_test) * 100))

Here is a workflow diagram summary of what happened previously:

Let's now decompose what we just did to understand and customize each step.

Loading the Dataset

Let's explore the dataset loading utility without passing a list of categories: in this case we load the full 20 newsgroups dataset in memory. The source website for the 20 newsgroups already provides a date-based train / test split that is made available using the subset keyword argument:



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ls -lh ../datasets/



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ls -lh ../datasets/20news-bydate-train



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# ls -lh ../datasets/20news-bydate-train/alt.atheism/

The load_files function can load text files from a 2 levels folder structure assuming folder names represent categories:



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# print(load_files.__doc__)



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all_twenty_train = load_files('../datasets/20news-bydate-train/',
    encoding='latin-1', random_state=42)
all_twenty_test = load_files('../datasets/20news-bydate-test/',
    encoding='latin-1', random_state=42)



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list(all_twenty_train.keys())



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all_target_names = all_twenty_train.target_names
all_target_names



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all_twenty_train.target



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all_twenty_train.target.shape



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all_twenty_test.target.shape



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len(all_twenty_train.data)



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type(all_twenty_train.data[0])



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def display_sample(i, dataset):
    target_id = dataset.target[i]
    print("Class id: %d" % target_id)
    print("Class name: " + dataset.target_names[target_id])
    print("Text content:\n")
    print(dataset.data[i])



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display_sample(0, all_twenty_train)



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display_sample(1, all_twenty_train)

Let's compute the (uncompressed, in-memory) size of the training and test sets in MB assuming an 8-bit encoding (in this case, all chars can be encoded using the latin-1 charset).



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def text_size(text, charset='iso-8859-1'):
    return len(text.encode(charset)) * 8 * 1e-6

train_size_mb = sum(text_size(text) for text in all_twenty_train.data) 
test_size_mb = sum(text_size(text) for text in all_twenty_test.data)

print("Training set size: {0} MB".format(int(train_size_mb)))
print("Testing set size: {0} MB".format(int(test_size_mb)))

If we only consider a small subset of the 4 categories selected from the initial example:



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twenty_train_small.target_names



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train_small_size_mb = sum(text_size(text) for text in twenty_train_small.data) 
test_small_size_mb = sum(text_size(text) for text in twenty_test_small.data)

print("Training set size: {0} MB".format(int(train_small_size_mb)))
print("Testing set size: {0} MB".format(int(test_small_size_mb)))

Extracting Text Features



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from sklearn.feature_extraction.text import TfidfVectorizer

TfidfVectorizer()



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vectorizer = TfidfVectorizer(min_df=1)

%time X_train_small = vectorizer.fit_transform(twenty_train_small.data)

The results is not a numpy.array but instead a scipy.sparse matrix. This datastructure is quite similar to a 2D numpy array but it does not store the zeros.



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X_train_small

scipy.sparse matrices also have a shape attribute to access the dimensions:



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n_samples, n_features = X_train_small.shape

This dataset has around 2000 samples (the rows of the data matrix):



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n_samples

This is the same value as the number of strings in the original list of text documents:



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len(twenty_train_small.data)

The columns represent the individual token occurrences:



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n_features

This number is the size of the vocabulary of the model extracted during fit in a Python dictionary:



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type(vectorizer.vocabulary_)



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len(vectorizer.vocabulary_)

The keys of the vocabulary_ attribute are also called feature names and can be accessed as a list of strings.



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len(vectorizer.get_feature_names())

Here are the first 10 elements (sorted in lexicographical order):



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vectorizer.get_feature_names()[:10]

Let's have a look at the features from the middle:



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vectorizer.get_feature_names()[n_features // 2:n_features // 2 + 10]

Now that we have extracted a vector representation of the data, it's a good idea to project the data on the first 2D of a Principal Component Analysis to get a feel of the data. Note that the TruncatedSVD class can accept scipy.sparse matrices as input (as an alternative to numpy arrays):



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from sklearn.decomposition import TruncatedSVD

%time X_train_small_pca = TruncatedSVD(n_components=2).fit_transform(X_train_small)



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from itertools import cycle

colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k']
for i, c in zip(np.unique(y_train), cycle(colors)):
    plt.scatter(X_train_small_pca[y_train == i, 0],
               X_train_small_pca[y_train == i, 1],
               c=c, label=twenty_train_small.target_names[i], alpha=0.5)
    
plt.legend(loc='best');

We can observe that there is a large overlap of the samples from different categories. This is to be expected as the PCA linear projection projects data from a 34118 dimensional space down to 2 dimensions: data that is linearly separable in 34118D is often no longer linearly separable in 2D.

Still we can notice an interesting pattern: the newsgroups on religion and atheism occupy the much the same region and computer graphics and space science / space overlap more together than they do with the religion or atheism newsgroups.

Training a Classifier on Text Features

We have previously extracted a vector representation of the training corpus and put it into a variable name X_train_small. To train a supervised model, in this case a classifier, we also need



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y_train_small = twenty_train_small.target



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y_train_small.shape



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y_train_small

We can shape that we have the same number of samples for the input data and the labels:



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X_train_small.shape[0] == y_train_small.shape[0]

We can now train a classifier, for instance a Multinomial Naive Bayesian classifier:



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from sklearn.naive_bayes import MultinomialNB

clf = MultinomialNB(alpha=0.1)
clf



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clf.fit(X_train_small, y_train_small)

We can now evaluate the classifier on the testing set. Let's first use the builtin score function, which is the rate of correct classification in the test set:



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X_test_small = vectorizer.transform(twenty_test_small.data)
y_test_small = twenty_test_small.target



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X_test_small.shape



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y_test_small.shape



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clf.score(X_test_small, y_test_small)

We can also compute the score on the test set and observe that the model is both overfitting and underfitting a bit at the same time:



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clf.score(X_train_small, y_train_small)

Introspecting the Behavior of the Text Vectorizer

The text vectorizer has many parameters to customize it's behavior, in particular how it extracts tokens:



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TfidfVectorizer()



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print(TfidfVectorizer.__doc__)

The easiest way to introspect what the vectorizer is actually doing for a given test of parameters is call the vectorizer.build_analyzer() to get an instance of the text analyzer it uses to process the text:



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analyzer = TfidfVectorizer().build_analyzer()
analyzer("I love scikit-learn: this is a cool Python lib!")

You can notice that all the tokens are lowercase, that the single letter word "I" was dropped, and that hyphenation is used. Let's change some of that default behavior:



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analyzer = TfidfVectorizer(
    preprocessor=lambda text: text,  # disable lowercasing
    token_pattern=r'(?u)\b[\w-]+\b', # treat hyphen as a letter
                                      # do not exclude single letter tokens
).build_analyzer()

analyzer("I love scikit-learn: this is a cool Python lib!")

The analyzer name comes from the Lucene parlance: it wraps the sequential application of:

text preprocessing (processing the text documents as a whole, e.g. lowercasing)
text tokenization (splitting the document into a sequence of tokens)
token filtering and recombination (e.g. n-grams extraction, see later)

The analyzer system of scikit-learn is much more basic than lucene's though.

Exercise:

Write a pre-processor callable (e.g. a python function) to remove the headers of the text a newsgroup post.
Vectorize the data again and measure the impact on performance of removing the header info from the dataset.
Do you expect the performance of the model to improve or decrease? What is the score of a uniform random classifier on the same dataset?

Hint: the TfidfVectorizer class can accept python functions to customize the preprocessor, tokenizer or analyzer stages of the vectorizer.

type TfidfVectorizer() alone in a cell to see the default value of the parameters
type TfidfVectorizer.__doc__ to print the constructor parameters doc or ? suffix operator on a any Python class or method to read the docstring or even the ?? operator to read the source code.



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%load solutions/07A_1_strip_headers.py



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%load solutions/07A_2_evaluate_model.py

Model Selection of the Naive Bayes Classifier Parameter Alone

The MultinomialNB class is a good baseline classifier for text as it's fast and has few parameters to tweak:



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MultinomialNB()



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print(MultinomialNB.__doc__)

By reading the doc we can see that the alpha parameter is a good candidate to adjust the model for the bias (underfitting) vs variance (overfitting) trade-off.

Exercise:

use the sklearn.grid_search.GridSearchCV or the model_selection.RandomizedGridSeach utility function from the previous chapters to find a good value for the parameter alpha
plots the validation scores (and optionally the training scores) for each value of alpha and identify the areas where model overfits or underfits.

Hints:

you can search for values of alpha in the range [0.00001 - 1] using a logarithmic scale
RandomizedGridSearch also has a launch_for_arrays method as an alternative to launch_for_splits in case the CV splits have not been precomputed in advance. 1



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%load solutions/07B_grid_search_alpha_nb.py



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%load solutions/07C_validation_curves_alpha.py

Setting Up a Pipeline for Cross Validation and Model Selection of the Feature Extraction parameters

The feature extraction class has many options to customize its behavior:



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print(TfidfVectorizer.__doc__)

In order to evaluate the impact of the parameters of the feature extraction one can chain a configured feature extraction and linear classifier (as an alternative to the naive Bayes model):



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from sklearn.linear_model import PassiveAggressiveClassifier
from sklearn.pipeline import Pipeline

pipeline = Pipeline((
    ('vec', TfidfVectorizer(min_df=1, max_df=0.8, use_idf=True)),
    ('clf', PassiveAggressiveClassifier(C=1)),
))

Such a pipeline can then be cross validated or even grid searched:



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from sklearn.cross_validation import cross_val_score
from scipy.stats import sem

scores = cross_val_score(pipeline, twenty_train_small.data,
                         twenty_train_small.target, cv=3, n_jobs=-1)
scores.mean(), sem(scores)

For the grid search, the parameters names are prefixed with the name of the pipeline step using "__" as a separator:



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from sklearn.grid_search import GridSearchCV

parameters = {
    #'vec__min_df': [1, 2],
    'vec__max_df': [0.8, 1.0],
    'vec__ngram_range': [(1, 1), (1, 2)],
    'vec__use_idf': [True, False],
}

gs = GridSearchCV(pipeline, parameters, verbose=2, refit=False)
_ = gs.fit(twenty_train_small.data, twenty_train_small.target)



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gs.best_score_



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gs.best_params_

Introspecting Model Performance

Displaying the Most Discriminative Features

Let's fit a model on the small dataset and collect info on the fitted components:



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_ = pipeline.fit(twenty_train_small.data, twenty_train_small.target)



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vec_name, vec = pipeline.steps[0]
clf_name, clf = pipeline.steps[1]

feature_names = vec.get_feature_names()
target_names = twenty_train_small.target_names

feature_weights = clf.coef_

feature_weights.shape

By sorting the feature weights on the linear model and asking the vectorizer what their names is, one can get a clue on what the model did actually learn on the data:



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def display_important_features(feature_names, target_names, weights, n_top=30):
    for i, target_name in enumerate(target_names):
        print("Class: " + target_name)
        print("")
        
        sorted_features_indices = weights[i].argsort()[::-1]
        
        most_important = sorted_features_indices[:n_top]
        print(", ".join("{0}: {1:.4f}".format(feature_names[j], weights[i, j])
                        for j in most_important))
        print("...")
        
        least_important = sorted_features_indices[-n_top:]
        print(", ".join("{0}: {1:.4f}".format(feature_names[j], weights[i, j])
                        for j in least_important))
        print("")
        
display_important_features(feature_names, target_names, feature_weights)

Displaying the per-class Classification Reports



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from sklearn.metrics import classification_report

predicted = pipeline.predict(twenty_test_small.data)



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print(classification_report(twenty_test_small.target, predicted,
                            target_names=twenty_test_small.target_names))

Printing the Confusion Matrix

The confusion matrix summarize which class where by having a look at off-diagonal entries: here we can see that articles about atheism have been wrongly classified as being about religion 57 times for instance:



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from sklearn.metrics import confusion_matrix

cm = confusion_matrix(twenty_test_small.target, predicted)
cm



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twenty_test_small.target_names



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def plot_confusion(cm, target_names, title='Confusion matrix'):
    plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)
    plt.title(title)
    plt.colorbar()

    tick_marks = np.arange(len(target_names))
    plt.xticks(tick_marks, target_names, rotation=60)
    plt.yticks(tick_marks, target_names)
    plt.ylabel('True label')
    plt.xlabel('Predicted label')
    # Convenience function to adjust plot parameters for a clear layout.
    plt.tight_layout()

plot_confusion(cm, twenty_test_small.target_names)

Final exercise: adding a non-descriminative "junk" class

As we have seen previously, the negative features of a specific class tend.

To mitigate this issue we can try to generate some random text documents by randomly assembling snippets from all the other classes (or ideally from random text collected on the web) and give it the target name "junk" or "unknown".

Then we can retrain a model with on a new dataset with the 4 previously used classes along with our new 5-th "junk" class. We can then examine the impact on the quality of the model and the negative features for each class.



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%load solutions/07D_junk_class.py