Delete src/distinct_n

src/distinct_n/.gitignore

@@ -1,58 +0,0 @@
-# Byte-compiled / optimized / DLL files
-__pycache__/
-*.py[cod]
-state.py
-
-# C extensions
-*.so
-
-# Distribution / packaging
-.Python
-env/
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-develop-eggs/
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-eggs/
-.eggs/
-lib/
-lib64/
-parts/
-sdist/
-var/
-*.egg-info/
-.installed.cfg
-*.egg
-
-# PyInstaller
-#  Usually these files are written by a python script from a template
-#  before PyInstaller builds the exe, so as to inject date/other infos into it.
-*.manifest
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-
-# Installer logs
-pip-log.txt
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-
-# Unit test / coverage reports
-htmlcov/
-.tox/
-.coverage
-.coverage.*
-.cache
-nosetests.xml
-coverage.xml
-*,cover
-
-# Translations
-*.mo
-*.pot
-
-# Django stuff:
-*.log
-
-# Sphinx documentation
-docs/_build/
-
-# PyBuilder
-target/

src/distinct_n/.idea/Distinct-N.iml

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-<?xml version="1.0" encoding="UTF-8"?>
-<module type="PYTHON_MODULE" version="4">
-  <component name="NewModuleRootManager">
-    <content url="file://$MODULE_DIR$">
-      <sourceFolder url="file://$MODULE_DIR$/distinct_n" isTestSource="false" />
-      <excludeFolder url="file://$MODULE_DIR$/docs" />
-    </content>
-    <orderEntry type="jdk" jdkName="Python 3.6 (Metrics)" jdkType="Python SDK" />
-    <orderEntry type="sourceFolder" forTests="false" />
-  </component>
-</module>

src/distinct_n/.idea/encodings.xml

@@ -1,4 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="Encoding" addBOMForNewFiles="with NO BOM" />
-</project>

src/distinct_n/.idea/misc.xml

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-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="JavaScriptSettings">
-    <option name="languageLevel" value="ES6" />
-  </component>
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.6 (tensorflow)" project-jdk-type="Python SDK" />
-</project>

src/distinct_n/.idea/modules.xml

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-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="ProjectModuleManager">
-    <modules>
-      <module fileurl="file://$PROJECT_DIR$/.idea/Distinct-N.iml" filepath="$PROJECT_DIR$/.idea/Distinct-N.iml" />
-    </modules>
-  </component>
-</project>

src/distinct_n/.idea/other.xml

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-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="PySciProjectComponent">
-    <option name="PY_SCI_VIEW_SUGGESTED" value="true" />
-  </component>
-</project>

src/distinct_n/.idea/vcs.xml

@@ -1,6 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="VcsDirectoryMappings">
-    <mapping directory="$PROJECT_DIR$" vcs="Git" />
-  </component>
-</project>

src/distinct_n/.idea/webResources.xml

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-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="WebResourcesPaths">
-    <contentEntries>
-      <entry url="file://$PROJECT_DIR$">
-        <entryData>
-          <resourceRoots>
-            <path value="file://$PROJECT_DIR$/testdata" />
-          </resourceRoots>
-        </entryData>
-      </entry>
-    </contentEntries>
-  </component>
-</project>

src/distinct_n/LICENSE.txt

@@ -1,202 +0,0 @@
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src/distinct_n/README.md

@@ -1,30 +0,0 @@
-# Distinct-N
-Distinct-N, most notably distinct-1 and distinct-2, is metric that measures the
-diversity of a sentence. It focuses on the number of *distinct* n-gram of a sentence and thus
-penalizes sentences with lots of repeated words. The metric is free of any *reference* or *ground truth*
-sentence and devotes totally to the property of a sentence (generated by the system).
-It is proposed by Jiwei Li et.al in the paper *A Diversity-Promoting Objective Function for Neural Conversation Models*.
-
-# Definitions
-The original paper coined *Distinct-N* as:
-
-    We report degree of diversity by calculating the number of distinct unigrams and bigrams in generated responses.
-    The value is scaled by total number of generated tokens to avoid favoring long sentences
-    
-which is exactly what we have mentioned before.
-
-# Usage
-```bash
-$ python distinct_metric.py -n N_NGRAMS PREDICTION
-```
-
-
-where `N_GRAMS` is the length of token sequence to count as unique within one sentence.
-`PREDICTION` is the prediction or response your model generates with one utterance (sentence) per line.
-
-
-# Dependencies
-`python>=3.6.1`
-
-# References
-[1] A Diversity-Promoting Objective Function for Neural Conversation Models

src/distinct_n/bin/distinct_metric.py

@@ -1,29 +0,0 @@
-import argparse
-import logging
-
-from distinct_n import distinct_n_sentence_level
-from pathlib import Path
-from agenda.metric_helper import write_score
-
-NAME = 'distinct_n'
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('hypothesis', help="predicted text file, one example per line")
-    parser.add_argument('-n', dest='n_range', type=int, nargs='+', help="n to use as in distinct-N")
-    parser.add_argument('--output_dir')
-    args = parser.parse_args()
-
-    logging.basicConfig(level=logging.INFO)
-    logging.info('loading hypothesis file...')
-    with open(args.hypothesis) as f:
-        hypothesis = [sentence.split() for sentence in f.readlines()]
-
-    output_dir = Path(args.output_dir)
-    for n in args.n_range:
-        write_score(
-            name=NAME,
-            output=output_dir.joinpath(f'{NAME}_{n}').with_suffix('.json'),
-            params={'n': n},
-            scores=[distinct_n_sentence_level(s, n) for s in hypothesis],
-        )

src/distinct_n/bin/score.sh

@@ -1,6 +0,0 @@
-#!/usr/bin/env bash
-
-HYPO=/home/cgsdfc/UbuntuDialogueCorpus/ResponseContextPairs/ModelPredictions/VHRED/First_VHRED_BeamSearch_5_GeneratedTestResponses.txt_First.txt
-DIR=/home/cgsdfc/Result/Test
-
-python bin/distinct_metric.py --output_dir $DIR $HYPO -n 3

src/distinct_n/distinct_n/metrics.py

@@ -1,33 +0,0 @@
-from src.distinct_n.distinct_n.utils import ngrams
-
-__all__ = ["distinct_n_sentence_level", "distinct_n_corpus_level"]
-
-
-def distinct_n_sentence_level(sentence, n):
-    """
-    Compute distinct-N for a single sentence.
-    :param sentence: a list of words.
-    :param n: int, ngram.
-    :return: float, the metric value.
-    """
-    if len(sentence) == 0:
-        return 0.0  # Prevent a zero division
-    # distinct_ngrams = set(ngrams(sentence, n))
-    # print(ngrams(sentence, n))
-    return list(set(ngrams(sentence, n)))
-    # return len(distinct_ngrams) / len(sentence)
-
-
-def distinct_n_corpus_level(sentences, n):
-    """
-    Compute average distinct-N of a list of sentences (the corpus).
-    :param sentences: a list of sentence.
-    :param n: int, ngram.
-    :return: float, the average value.
-    """
-    temp = []
-    length = 0
-    for sentence in sentences:
-        length += len(sentence)
-        temp.extend(distinct_n_sentence_level(sentence, n))
-    return len(set(temp)) / length

src/distinct_n/distinct_n/test.py

@@ -1,32 +0,0 @@
-import unittest
-
-from distinct_n import distinct_n_sentence_level
-from distinct_n import distinct_n_corpus_level
-
-
-class TestDistinctN(unittest.TestCase):
-    def test_unigram(self):
-        sentence = "the the the the the".split()
-        self.assertAlmostEqual(
-            distinct_n_sentence_level(sentence, 1), 0.2
-        )
-        sentence = "the the the the cat".split()
-        self.assertAlmostEqual(
-            distinct_n_sentence_level(sentence, 1), 0.4
-        )
-
-    def test_bigram(self):
-        sentence = "the cat sat on the".split()
-        self.assertAlmostEqual(
-            distinct_n_sentence_level(sentence, 2), 0.8
-        )
-
-    def test_corpus_level(self):
-        sentences = [
-            'the cat sat on the mat'.split(),
-            'mat the on sat cat the'.split(),
-            'i do not know'.split(),
-            'Sorry but i do not know'.split(),
-        ]
-        self.assertAlmostEqual(0.916666, distinct_n_corpus_level(sentences, 1), delta=1e-5)
-        self.assertAlmostEqual(0.8125, distinct_n_corpus_level(sentences, 2), delta=1e-5)

src/distinct_n/distinct_n/utils.py

@@ -1,90 +0,0 @@
-"""
-Copied from nltk.ngrams().
-"""
-from itertools import chain
-
-__all__ = ["ngrams"]
-
-
-def pad_sequence(sequence, n, pad_left=False, pad_right=False,
-                 left_pad_symbol=None, right_pad_symbol=None):
-    """
-    Returns a padded sequence of items before ngram extraction.
-
-        >>> list(pad_sequence([1,2,3,4,5], 2, pad_left=True, pad_right=True, left_pad_symbol='<s>', right_pad_symbol='</s>'))
-        ['<s>', 1, 2, 3, 4, 5, '</s>']
-        >>> list(pad_sequence([1,2,3,4,5], 2, pad_left=True, left_pad_symbol='<s>'))
-        ['<s>', 1, 2, 3, 4, 5]
-        >>> list(pad_sequence([1,2,3,4,5], 2, pad_right=True, right_pad_symbol='</s>'))
-        [1, 2, 3, 4, 5, '</s>']
-
-    :param sequence: the source data to be padded
-    :type sequence: sequence or iter
-    :param n: the degree of the ngrams
-    :type n: int
-    :param pad_left: whether the ngrams should be left-padded
-    :type pad_left: bool
-    :param pad_right: whether the ngrams should be right-padded
-    :type pad_right: bool
-    :param left_pad_symbol: the symbol to use for left padding (default is None)
-    :type left_pad_symbol: any
-    :param right_pad_symbol: the symbol to use for right padding (default is None)
-    :type right_pad_symbol: any
-    :rtype: sequence or iter
-    """
-    sequence = iter(sequence)
-    if pad_left:
-        sequence = chain((left_pad_symbol,) * (n - 1), sequence)
-    if pad_right:
-        sequence = chain(sequence, (right_pad_symbol,) * (n - 1))
-    return sequence
-
-
-def ngrams(sequence, n, pad_left=False, pad_right=False,
-           left_pad_symbol=None, right_pad_symbol=None):
-    """
-    Return the ngrams generated from a sequence of items, as an iterator.
-    For example:
-
-        >>> from nltk.util import ngrams
-        >>> list(ngrams([1,2,3,4,5], 3))
-        [(1, 2, 3), (2, 3, 4), (3, 4, 5)]
-
-    Wrap with list for a list version of this function.  Set pad_left
-    or pad_right to true in order to get additional ngrams:
-
-        >>> list(ngrams([1,2,3,4,5], 2, pad_right=True))
-        [(1, 2), (2, 3), (3, 4), (4, 5), (5, None)]
-        >>> list(ngrams([1,2,3,4,5], 2, pad_right=True, right_pad_symbol='</s>'))
-        [(1, 2), (2, 3), (3, 4), (4, 5), (5, '</s>')]
-        >>> list(ngrams([1,2,3,4,5], 2, pad_left=True, left_pad_symbol='<s>'))
-        [('<s>', 1), (1, 2), (2, 3), (3, 4), (4, 5)]
-        >>> list(ngrams([1,2,3,4,5], 2, pad_left=True, pad_right=True, left_pad_symbol='<s>', right_pad_symbol='</s>'))
-        [('<s>', 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, '</s>')]
-
-
-    :param sequence: the source data to be converted into ngrams
-    :type sequence: sequence or iter
-    :param n: the degree of the ngrams
-    :type n: int
-    :param pad_left: whether the ngrams should be left-padded
-    :type pad_left: bool
-    :param pad_right: whether the ngrams should be right-padded
-    :type pad_right: bool
-    :param left_pad_symbol: the symbol to use for left padding (default is None)
-    :type left_pad_symbol: any
-    :param right_pad_symbol: the symbol to use for right padding (default is None)
-    :type right_pad_symbol: any
-    :rtype: sequence or iter
-    """
-    sequence = pad_sequence(sequence, n, pad_left, pad_right,
-                            left_pad_symbol, right_pad_symbol)
-
-    history = []
-    while n > 1:
-        history.append(next(sequence))
-        n -= 1
-    for item in sequence:
-        history.append(item)
-        yield tuple(history)
-        del history[0]

src/distinct_n/setup.py

@@ -1,29 +0,0 @@
-from setuptools import setup
-
-__version__ = '0.4.0'
-
-setup(
-    name='Distinct_N',
-    version=__version__,
-    description='Distinct-N metric that measures degree of diversity of generated response',
-    url='https://github.com/neural-dialogue-metrics/Distinct-N.git',
-    author='cgsdfc',
-    author_email='[email protected]',
-    keywords=[
-        'NL', 'CL', 'MT',
-        'natural language processing',
-        'computational linguistics',
-        'machine translation',
-    ],
-    packages=['distinct_n'],
-    scripts=['bin/distinct_metric.py'],
-    classifiers=[
-        'Intended Audience :: Science/Research',
-        'License :: OSI Approved :: Apache-v2',
-        'Programming Language :: Python :: 3',
-        'Topic :: Text Processing :: Linguistic',
-    ],
-    license='LICENCE.txt',
-    long_description=open('README.md').read(),
-    install_requires=[],
-)

src/distinct_n/testdata/bigram.txt

@@ -1 +0,0 @@
-the cat sat on the mat

src/distinct_n/testdata/unigram.txt

@@ -1 +0,0 @@
-the the the the a