Easily extract features from texts, and run machine learning algorithms on them. Write your own features, use ours, or do both!

https://www.qcrit.org

Installation

With pip:

pip install qcrit

With pipenv

pipenv install qcrit

About

The qcrit package contains utilities to facilitate processing and analyzing literature.

To try extracting some features, just replace 'your-directory-name' with the name of a directory of .txt files. Everything else is taken care of!

from qcrit.extract_features import main
from qcrit.textual_feature import setup_tokenizers
import qcrit.features.universal_features
setup_tokenizers(terminal_punctuation=('.', '!', '?'))
main(
	corpus_dir='your-directory-name', file_extension_to_parse_function={'txt': lambda filename: open(filename).read()}
)

Writing Your Own Features

A feature is a number that results from processing literature. An example of a feature might be the number of definite articles, the mean sentence length, or the fraction of interrogative sentences. The word "feature" can also refer to a python function that computes such a value.

Normally to compute features, you must 1) obtain a corpus of texts, 2) traverse each text in the corpus, 3) parse the text into tokens, 4) write logic to calculate features, and 5) output the results to the console or to a file. Also, this will run slowly unless you 6) cache tokenized text for features that use the same tokens.

With the textual_feature decorator, steps (2), (3), (5), and (6) are abstracted away - you just need (1) to supply the corpus and (4) to write logic to calculate features.

Once you have written a feature as a python function, label it with the decorator textual_feature. Your feature must have exactly one parameter which is assumed to be the parsed text of a file.

from qcrit.textual_feature import textual_feature
@textual_feature()
def count_definite_article(text):
	return text.count('the')

The textual_feature decorator takes an argument that represents the type of tokenization.

There are four supported tokenization_types: 'sentences', 'words', 'sentence_words' and None. This tells the function in what format it will receive the 'text' parameter.

• If None, the function will receive the text parameter as a string.
• If 'sentences', the function will receive the text parameter as a list of sentences, each as a string
• If 'words', the function will receive the text parameter as a list of words
• If 'sentence_words', the function will recieve the text parameter as a list of sentences, each as a list of words

from functools import reduce
@textual_feature(tokenize_type='sentences')
def mean_sentence_len(text):
	sen_len = reduce(lambda cur_len, cur_sen: cur_len + len(cur_sen), text, 0)
	num_sentences = len(text)
	return sen_len / num_sentences

Extracting Features

Use qcrit.extract_features.main to run all the functions labeled with the @textual_feature decorator and output results into a file.

corpus_dir - the directory to search for files containing texts, this will traverse all sub-directories as well

file_extension_to_parse_function - map from file extension (e.g. 'txt', 'tess') of texts that you would like to parse to a function directing how to parse it

output_file - the file to output the results into, created to be analyzed during machine learning phase

In order for sentence tokenization to work correctly, setup_tokenizers() must be called with the terminal punctuation marks of the language being analyzed. You can also optionally supply the name of the language as well. If data exists about how to parse the language, this may improve sentence tokenization.

from qcrit.extract_features import main, parse_tess
from qcrit.textual_feature import setup_tokenizers
from somewhere_else import count_definite_article, mean_sentence_len
setup_tokenizers(terminal_punctuation=('.', '!', '?'), language='greek')
main(
	corpus_dir='demo', file_extension_to_parse_function={'tess': parse_tess}, output_file='output.pickle'
)

Output:

Extracting features from .tess files in demo/
100%|██████████████████████████████████████████| 4/4 [00:00<00:00,  8.67it/s]
Feature mining complete. Attempting to write feature results to "output.pickle"...
Success!


Feature mining elapsed time: 1.4919 seconds

Analysis

Use the @model_analyzer() decorator to label functions that analyze machine learning models

Invoke analyze_models.main('output.pickle', 'classifications.csv') to run all functions labeled with the @model_analyzer() decorator. To run only one function, include the name of the function as the third parameter to analyze_models.main()

output.pickle: Now that the features have been extracted and output into output.pickle, we can use machine learning models on them.

classifications.csv: The file classifications.csv contains the name of the file in the first column and the particular classification (prose or verse) in the second column for every file in the corpus.

import qcrit.analyze_models
from qcrit.model_analyzer import model_analyzer
from sklearn import ensemble
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

@model_analyzer()
def feature_rankings(data, target, file_names, feature_names, labels_key):
	print('-' * 40 + '\nRandom Forest Classifier feature rankings\n')
	features_train, features_test, labels_train, _ = train_test_split(data, target, test_size=0.5, random_state=0)
	clf = ensemble.RandomForestClassifier(random_state=0, n_estimators=10)
	clf.fit(features_train, labels_train)
	clf.predict(features_test)

	#Display features in order of importance
	print('Feature importances:')
	for tup in sorted(zip(feature_names, clf.feature_importances_), key=lambda s: -s[1]):
		print('\t%f: %s' % (tup[1], tup[0]))

@model_analyzer()
def classifier_accuracy(data, target, file_names, feature_names, labels_key):
	print('-' * 40 + '\nRandom Forest Classifier accuracy\n')
	features_train, features_test, labels_train, labels_test = train_test_split(
		data, target, test_size=0.5, random_state=0
	)
	clf = ensemble.RandomForestClassifier(random_state=0, n_estimators=10)
	clf.fit(features_train, labels_train)
	results = clf.predict(features_test)

	print('Stats:')
	print(
		'\tNumber correct: ' + str(accuracy_score(labels_test, results, normalize=False)) +
		' / ' + str(len(results))
	)
	print('\tPercentage correct: ' + str(accuracy_score(labels_test, results) * 100) + '%')

@model_analyzer()
def misclassified_texts(data, target, file_names, feature_names, labels_key):
	print('-' * 40 + '\nRandom Forest Classifier misclassified texts\n')
	features_train, features_test, labels_train, labels_test, idx_train, idx_test = train_test_split(
		data, target, range(len(target)), test_size=0.5, random_state=0
	)
	print('Train texts:\n\t' + '\n\t'.join(file_names[i] for i in idx_train) + '\n')
	print('Test texts:\n\t' + '\n\t'.join(file_names[i] for i in idx_test) + '\n')
	clf = ensemble.RandomForestClassifier(random_state=0, n_estimators=10)
	clf.fit(features_train, labels_train)
	results = clf.predict(features_test)

	print('Misclassifications:')
	for i, _ in enumerate(results):
		if results[i] != labels_test[i]:
			print('\t' + file_names[idx_test[i]])

qcrit.analyze_models.main(
	'output.pickle', 'classifications.csv'
)

Output:

----------------------------------------
Random Forest Classifier feature rankings

Feature importances:
	0.400000: num_conjunctions
	0.400000: num_interrogatives
	0.200000: mean_sentence_length


Elapsed time: 0.0122 seconds

----------------------------------------
Random Forest Classifier accuracy

Stats:
	Number correct: 1 / 2
	Percentage correct: 50.0%


Elapsed time: 0.0085 seconds

----------------------------------------
Random Forest Classifier misclassified texts

Train texts:
	demo/aristotle.poetics.tess
	demo/aristophanes.ecclesiazusae.tess

Test texts:
	demo/euripides.heracles.tess
	demo/plato.respublica.part.1.tess

Misclassifications:
	demo/plato.respublica.part.1.tess


Elapsed time: 0.0082 seconds

Development

1. Ensure that you have pipenv installed. Also, ensure that you have a version of python installed that matches the version in the Pipfile.
2. Setup a virtual environment and install the necessary dependencies:

   PIPENV_VENV_IN_PROJECT=true pipenv install --dev

3. Activate the virtual environment:

   pipenv shell

   Now, python commands will use the dependencies and python version from the virtual environment. Use exit to leave the virtual environment, and use pipenv shell while in the project directory to activate it again.

Demo

python demo/demo.py

Submission

The following commands will submit the package to the Python Package Index. Before running them, it may be necessary to increment the version number in __init__.py and to delete any previously generated dist/, build/, and egg-info directories if they exist.

python setup.py bdist_wheel sdist
twine upload dist/*