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        "author": "Tim Gianitsos",
        "author_email": "contact@qcrit.org",
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            "License :: OSI Approved :: MIT License",
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        "description": "Utilities for The Quantitative Criticism Lab\nhttps://www.qcrit.org\n\n## Installation\nWith `pip`:\n```bash\npip install qcrit\n```\nWith `pipenv`\n```bash\npipenv install qcrit\n```\n## About\n\nThe qcrit package contains utilities to facilitate processing and analyzing literature.\n\n### Feature extraction\n\nA 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.\n\nTo compute features, you must 1) traverse each text in a corpus, 2) parse the text into tokens, 3) write logic to calculate features, and 4) output the results to the console or to a file. Also, this will run slowly unless you 5) cache tokenized text for features that use the same tokens.\n\nWith the `textual_feature` decorator, steps (1), (2), (4), and (5) are abstracted away - you just need to implement (3) the logic to calculate each feature.\n\nOnce 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.\n```python\nfrom qcrit.textual_feature import textual_feature\n@textual_feature()\ndef count_definite_article(text):\n\treturn text.count('the')\n```\n\nThe `textual_feature` module takes an argument that represents the type of tokenization.\n\nThere are four supported tokenization_types: 'sentences', 'words', 'sentence_words' and None. This tells the function in \nwhat format it will receive the 'text' parameter.\n- If None, the function will receive the text parameter as a string. \n- If 'sentences', the function will receive the text parameter as a list of sentences, each as a string\n- If 'words', the function will receive the text parameter as a list of words\n- If 'sentence_words', the function will recieve the text parameter as a list of sentences, each as a list of words\n\n```python\nfrom functools import reduce\n@textual_feature(tokenize_type='sentences')\ndef mean_sentence_len(text):\n\tsen_len = reduce(lambda cur_len, cur_sen: cur_len + len(cur_sen))\n\tnum_sentences = len(text)\n\treturn sen_len / num_sentences\n```\n\nUse `qcrit.extract_features.main` to run all the functions labeled with the decorators and output results into a file.\n\ncorpus_dir - the directory to search for files containing texts, this will traverse all sub-directories as well\n\nfile_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\n\noutput_file - the file to output the results into, created to be analyzed during machine learning phase\n\nIn order for sentence tokenization to work correctly, setup_tokenizers() must be set to the \nterminal punctuation marks of the language being analyzed. Make sure this is done before features are declared.\n\n```python\nfrom qcrit.extract_features import main, parse_tess\nfrom qcrit.textual_feature import setup_tokenizers\nsetup_tokenizers(terminal_punctuation=('.', '?'))\nfrom somewhere_else import count_definite_article, mean_sentence_len\nmain(\n\tcorpus_dir='demo', file_extension_to_parse_function={'tess': parse_tess}, output_file='output.pickle'\n)\n\n```\nOutput:\n```bash\nExtracting features from .tess files in demo/\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 4/4 [00:00<00:00,  8.67it/s]\nFeature mining complete. Attempting to write feature results to \"output.pickle\"...\nSuccess!\n\n\nFeature mining elapsed time: 1.4919 seconds\n\n```\n\n### Analysis\n\nUse the `@model_analyzer()` decorator to label functions that analyze machine learning models\n\nInvoke `analyze_models.main('output.pickle', 'classifications.csv')` to\nrun all functions labeled with the `@model_analyzer()` decorator. To run only one function, include\nthe name of the function as the third parameter to analyze_models.main()\n\noutput.pickle: Now that the features have been extracted and output into output.pickle, we\ncan use machine learning models on them.\n\nclassifications.csv: The file classifications.csv contains the name of the file in the first column\nand the particular classification (prose or verse) in the second column for every file in the corpus.\n\n```python\nimport qcrit.analyze_models\nfrom qcrit.model_analyzer import model_analyzer\nfrom sklearn import ensemble\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score\n\n@model_analyzer()\ndef feature_rankings(data, target, file_names, feature_names, labels_key):\n\tprint('-' * 40 + '\\nRandom Forest Classifier feature rankings\\n')\n\tfeatures_train, features_test, labels_train, _ = train_test_split(data, target, test_size=0.5, random_state=0)\n\tclf = ensemble.RandomForestClassifier(random_state=0, n_estimators=10)\n\tclf.fit(features_train, labels_train)\n\tclf.predict(features_test)\n\n\t#Display features in order of importance\n\tprint('Feature importances:')\n\tfor tup in sorted(zip(feature_names, clf.feature_importances_), key=lambda s: -s[1]):\n\t\tprint('\\t%f: %s' % (tup[1], tup[0]))\n\n@model_analyzer()\ndef classifier_accuracy(data, target, file_names, feature_names, labels_key):\n\tprint('-' * 40 + '\\nRandom Forest Classifier accuracy\\n')\n\tfeatures_train, features_test, labels_train, labels_test = train_test_split(\n\t\tdata, target, test_size=0.5, random_state=0\n\t)\n\tclf = ensemble.RandomForestClassifier(random_state=0, n_estimators=10)\n\tclf.fit(features_train, labels_train)\n\tresults = clf.predict(features_test)\n\n\tprint('Stats:')\n\tprint(\n\t\t'\\tNumber correct: ' + str(accuracy_score(labels_test, results, normalize=False)) +\n\t\t' / ' + str(len(results))\n\t)\n\tprint('\\tPercentage correct: ' + str(accuracy_score(labels_test, results) * 100) + '%')\n\n@model_analyzer()\ndef misclassified_texts(data, target, file_names, feature_names, labels_key):\n\tprint('-' * 40 + '\\nRandom Forest Classifier misclassified texts\\n')\n\tfeatures_train, features_test, labels_train, labels_test, idx_train, idx_test = train_test_split(\n\t\tdata, target, range(len(target)), test_size=0.5, random_state=0\n\t)\n\tprint('Train texts:\\n\\t' + '\\n\\t'.join(file_names[i] for i in idx_train) + '\\n')\n\tprint('Test texts:\\n\\t' + '\\n\\t'.join(file_names[i] for i in idx_test) + '\\n')\n\tclf = ensemble.RandomForestClassifier(random_state=0, n_estimators=10)\n\tclf.fit(features_train, labels_train)\n\tresults = clf.predict(features_test)\n\n\tprint('Misclassifications:')\n\tfor i, _ in enumerate(results):\n\t\tif results[i] != labels_test[i]:\n\t\t\tprint('\\t' + file_names[idx_test[i]])\n\nqcrit.analyze_models.main(\n\t'output.pickle', 'classifications.csv'\n)\n```\nOutput:\n```\n----------------------------------------\nRandom Forest Classifier feature rankings\n\nFeature importances:\n\t0.400000: num_conjunctions\n\t0.400000: num_interrogatives\n\t0.200000: mean_sentence_length\n\n\nElapsed time: 0.0122 seconds\n\n----------------------------------------\nRandom Forest Classifier accuracy\n\nStats:\n\tNumber correct: 1 / 2\n\tPercentage correct: 50.0%\n\n\nElapsed time: 0.0085 seconds\n\n----------------------------------------\nRandom Forest Classifier misclassified texts\n\nTrain texts:\n\tdemo/aristotle.poetics.tess\n\tdemo/aristophanes.ecclesiazusae.tess\n\nTest texts:\n\tdemo/euripides.heracles.tess\n\tdemo/plato.respublica.part.1.tess\n\nMisclassifications:\n\tdemo/plato.respublica.part.1.tess\n\n\nElapsed time: 0.0082 seconds\n```\n\n## Development\nTo activate the virtual environment, ensure that you have `pipenv` installed and `python` version 3.6 installed. Run the following:\n```bash\npipenv --python 3.6\npipenv shell\npipenv install --dev\n```\n\n### Demo\n```bash\npython demo/demo.py\n```\n\n## Submission\nThe following commands will submit the package to the `Python Package Index`. It may be necessary to increment the version number in `setup.py` and to delete any previously generated `dist/` and `build/` directories if they exist.\n```bash\npython setup.py bdist_wheel sdist\ntwine upload dist/*\n```\n\n\n",
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