Datasets:
Razvan27
/
ML4SE-Python

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doc-pypi-links.sh
ilius_pyglossary/scripts/doc-pypi-links.sh
#!/bin/bash grep -roh 'https://pypi.org/project/[^)]*' doc/p/ | sort | uniq --count
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ilius/pyglossary
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dump.py
ilius_pyglossary/scripts/dump.py
#!/usr/bin/env python3 import sys from pprint import pformat from pyglossary.glossary import Glossary glos = Glossary() glos.read(sys.argv[1]) for entry in glos: print("Words: " + pformat(entry.l_word)) print("Definitions: " + pformat(entry.defis)) print("-------------------------")
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ilius/pyglossary
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sort-jsonl.py
ilius_pyglossary/scripts/wiktextract/sort-jsonl.py
#!/usr/bin/env python # read json lines from stdin, # sort them by "word" key and print import operator import sys from json import loads data: "list[tuple[str, str]]" = [] for line in sys.stdin: line = line.strip() # noqa: PLW2901 if not line: continue row = loads(line) data.append((row.get("word"), line)) ...
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ilius/pyglossary
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extract-schema.py
ilius_pyglossary/scripts/wiktextract/extract-schema.py
import json import sys from collections import Counter from collections import OrderedDict as odict from dataclasses import dataclass from typing import Any @dataclass(slots=True) class Node: Type: str = "" Dict: "dict[str, Node] | None" = None KeyScore: "Counter | None" = None ListOf: "Node | None" = None de...
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ilius/pyglossary
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fix-css.py
ilius_pyglossary/scripts/appledict/fix-css.py
import sys from os.path import dirname, realpath, splitext sys.path.insert(0, dirname(dirname(dirname(realpath(__file__))))) from pyglossary.apple_utils import substituteAppleCSS for fpath in sys.argv[1:]: if fpath.endswith("-fixed.css"): continue fpathNoExt, _ = splitext(fpath) fpathNew = fpathNoExt + "-fixed....
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doc-pypi-links.sh
ilius_pyglossary/scripts/doc-pypi-links.sh
#!/bin/bash grep -roh 'https://pypi.org/project/[^)]*' doc/p/ | sort | uniq --count
85
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ilius/pyglossary
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pyglossary.pyw
ilius_pyglossary/pyglossary.pyw
#!/usr/bin/env python3 import sys from os.path import dirname sys.path.insert(0, dirname(__file__)) from pyglossary.ui.main import main main()
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ilius/pyglossary
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__main__.py
codespell-project_codespell/codespell_lib/__main__.py
import sys from ._codespell import _script_main if __name__ == "__main__": sys.exit(_script_main())
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_text_util.py
codespell-project_codespell/codespell_lib/_text_util.py
# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; version 2 of the License. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the impli...
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codespell-project/codespell
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_codespell.py
codespell-project_codespell/codespell_lib/_codespell.py
# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; version 2 of the License. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the impli...
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__init__.py
codespell-project_codespell/codespell_lib/__init__.py
from ._codespell import _script_main, main from ._version import __version__ # type: ignore[import-not-found] __all__ = ["__version__", "_script_main", "main"]
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codespell-project/codespell
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_spellchecker.py
codespell-project_codespell/codespell_lib/_spellchecker.py
# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; version 2 of the License. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the impli...
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codespell-project/codespell
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test_basic.py
codespell-project_codespell/codespell_lib/tests/test_basic.py
import contextlib import inspect import os import os.path as op import re import subprocess import sys from io import StringIO from pathlib import Path from shutil import copyfile from typing import Any, Generator, Optional, Tuple, Union from unittest import mock import pytest import codespell_lib as cs_ from codespe...
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codespell-project/codespell
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test_dictionary.py
codespell-project_codespell/codespell_lib/tests/test_dictionary.py
import glob import os import os.path as op import pathlib import re from typing import Any, Dict, Iterable, Optional, Set, Tuple import pytest from codespell_lib._codespell import ( _builtin_dictionaries, supported_languages, word_regex_def, ) spellers = {} root = pathlib.Path(__file__).parent.parent t...
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codespell-project/codespell
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setup.py
piskvorky_gensim/setup.py
#!/usr/bin/env python # -*- coding: utf-8 -*- # # Copyright (C) 2014 Radim Rehurek <radimrehurek@seznam.cz> # Licensed under the GNU LGPL v2.1 - https://www.gnu.org/licenses/old-licenses/lgpl-2.1.en.html """ Run with:: python ./setup.py install """ import itertools import os import platform import shutil import ...
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gensim Quick Start.ipynb
piskvorky_gensim/gensim Quick Start.ipynb
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial has moved.\n", "\n", "Please see https://radimrehurek.com/gensim/auto_examples/." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "...
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install_wheel.py
piskvorky_gensim/continuous_integration/install_wheel.py
"""Install the built wheel for testing under AppVeyor. Assumes that gensim/dist contains a single wheel to install. """ import os import subprocess curr_dir = os.path.dirname(__file__) dist_path = os.path.join(curr_dir, '..', 'dist') wheels = [ os.path.join(dist_path, f) for f in os.listdir(dist_path) if f.en...
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check_wheels.py
piskvorky_gensim/continuous_integration/check_wheels.py
#!/usr/bin/env python # -*- coding: utf-8 -*- # # Copyright (C) 2019 RaRe Technologies s.r.o. # Licensed under the GNU LGPL v2.1 - https://www.gnu.org/licenses/old-licenses/lgpl-2.1.en.html """Print available wheels for a particular Python package.""" import re import sys import requests def to_int(value): value ...
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upgrade_pip_py310.py
piskvorky_gensim/continuous_integration/upgrade_pip_py310.py
# This script needs to be able run under both Python 2 and 3 without crashing # It only achieves the desired effect under Py3.10 on Linux and MacOS. import subprocess import sys import tempfile if sys.platform in ('linux', 'darwin') and sys.version_info[:2] == (3, 10): import urllib.request with tempfile.NamedT...
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Poincare Evaluation.ipynb
piskvorky_gensim/docs/notebooks/Poincare Evaluation.ipynb
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Evaluation of Poincare Embeddings" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook demonstrates how well Poincare embeddings perform on the tasks detailed in the [original paper](https:/...
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WordRank_wrapper_quickstart.ipynb
piskvorky_gensim/docs/notebooks/WordRank_wrapper_quickstart.ipynb
{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# WordRank wrapper tutorial on Lee Corpus\n", "\n", "WordRank is a new word embedding algorithm which captures the semantic similarities in a text data well. See this [notebook](Wordrank_comparisons.ip...
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downloader_api_tutorial.ipynb
piskvorky_gensim/docs/notebooks/downloader_api_tutorial.ipynb
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Tutorial for using Gensim's API for downloading corpuses/models\n", "Let's start by importing the api module." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ ...
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soft_cosine_benchmark.ipynb
piskvorky_gensim/docs/notebooks/soft_cosine_benchmark.ipynb
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Benchmark: Implement Levenshtein term similarity matrix and fast SCM between corpora ([RaRe-Technologies/gensim PR #2016][#2016])\n", "\n", " [#2016]: https://github.com/RaRe-Technologies/gensim/pull/2016 (Implement Levenshte...
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lda_model_difference.ipynb
piskvorky_gensim/docs/notebooks/lda_model_difference.ipynb
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Comparison of two LDA models & visualize difference" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## In this notebook, I want to show how you can compare models with itself and with other model and...
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summarization_tutorial.ipynb
piskvorky_gensim/docs/notebooks/summarization_tutorial.ipynb
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial has moved.\n", "\n", "Please see https://radimrehurek.com/gensim/auto_examples/." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "...
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Wordrank_comparisons.ipynb
piskvorky_gensim/docs/notebooks/Wordrank_comparisons.ipynb
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Comparison of WordRank, Word2Vec and FastText\n", "\n", "[Wordrank](https://arxiv.org/pdf/1506.02761v3.pdf) is a fresh new approach to the word embeddings, which formulates it as a ranking problem. That is, given a word w, it...
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atmodel_prediction_tutorial.ipynb
piskvorky_gensim/docs/notebooks/atmodel_prediction_tutorial.ipynb
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Authorship prediction with the author-topic model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this tutorial, you will learn how to use the author-topic model in Gensim for authorship predictio...
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FastText_Tutorial.ipynb
piskvorky_gensim/docs/notebooks/FastText_Tutorial.ipynb
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Using FastText via Gensim" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial is about using [fastText](https://github.com/facebookresearch/fastText) model in Gensim. There are two ways you...
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atmodel_tutorial.ipynb
piskvorky_gensim/docs/notebooks/atmodel_tutorial.ipynb
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# The author-topic model: LDA with metadata\n", "\n", "In this tutorial, you will learn how to use the author-topic model in Gensim. We will apply it to a corpus consisting of scientific papers, to get insight about the authors...
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lda_training_tips.ipynb
piskvorky_gensim/docs/notebooks/lda_training_tips.ipynb
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial has moved.\n", "\n", "Please see https://radimrehurek.com/gensim/auto_examples/." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "...
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doc2vec-wikipedia.ipynb
piskvorky_gensim/docs/notebooks/doc2vec-wikipedia.ipynb
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Training Doc2Vec on Wikipedia articles" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook replicates the **Document Embedding with Paragraph Vectors** paper, http://arxiv.org/abs/1507.0799...
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Topic_dendrogram.ipynb
piskvorky_gensim/docs/notebooks/Topic_dendrogram.ipynb
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Visualizing Topic clusters\n", "\n", "In this notebook, we will learn how to visualize topic clusters using dendrogram. Dendrogram is a tree-structured graph which can be used to visualize the result of a hierarchical cluster...
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annoytutorial.ipynb
piskvorky_gensim/docs/notebooks/annoytutorial.ipynb
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial has moved.\n", "\n", "Please see https://radimrehurek.com/gensim/auto_examples/." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "...
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pivoted_document_length_normalisation.ipynb
piskvorky_gensim/docs/notebooks/pivoted_document_length_normalisation.ipynb
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Pivoted Document Length Normalization\n", "\n", "## Background\n", "\n", "In many cases, normalizing the tfidf weights for each term favors weight of terms of the documents with shorter length. The _pivoted document l...
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ensemble_lda_with_opinosis.ipynb
piskvorky_gensim/docs/notebooks/ensemble_lda_with_opinosis.ipynb
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "import logging\n", "from gensim.models import EnsembleLda, LdaMulticore\n", "from gensim.models.ensemblelda import rank_masking\n", "from gensim.corpora im...
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doc2vec-IMDB.ipynb
piskvorky_gensim/docs/notebooks/doc2vec-IMDB.ipynb
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial has moved.\n", "\n", "Please see https://radimrehurek.com/gensim/auto_examples/." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "...
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topic_network.ipynb
piskvorky_gensim/docs/notebooks/topic_network.ipynb
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Topic Networks\n", "\n", "In this notebook, we will learn how to visualize topic model using network graphs. Networks can be a great way to explore topic models. We can use it to navigate that how topics belonging to one cont...
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piskvorky/gensim
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sklearn_api.ipynb
piskvorky_gensim/docs/notebooks/sklearn_api.ipynb
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Using wrappers for Scikit learn API" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial is about using gensim models as a part of your scikit learn workflow with the help of wrappers found...
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Poincare Tutorial.ipynb
piskvorky_gensim/docs/notebooks/Poincare Tutorial.ipynb
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Tutorial on Poincaré Embeddings" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook discusses the basic ideas and use-cases for Poincaré embeddings and demonstrates what kind of operations ...
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piskvorky/gensim
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{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Illustrating common terms usage using Wikinews in english" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## getting data\n", "\n", "We get the cirrussearch dump of wikinews (a dump meant for...
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Word2Vec_FastText_Comparison.ipynb
piskvorky_gensim/docs/notebooks/Word2Vec_FastText_Comparison.ipynb
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Comparison of FastText and Word2Vec " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Facebook Research open sourced a great project recently - [fastText](https://github.com/facebookresearch/fastText)...
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ldaseqmodel.ipynb
piskvorky_gensim/docs/notebooks/ldaseqmodel.ipynb
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "# Dynamic Topic Models Tutorial" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "### What is this tutorial about?\n", "This tutorial will exaplin what Dynamic Topic Models are, a...
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soft_cosine_tutorial.ipynb
piskvorky_gensim/docs/notebooks/soft_cosine_tutorial.ipynb
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Finding similar documents with Word2Vec and Soft Cosine Measure \n", "\n", "Soft Cosine Measure (SCM) [1, 3] is a promising new tool in machine learning that allows us to submit a query and return the most relevant documents....
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topic_coherence_model_selection.ipynb
piskvorky_gensim/docs/notebooks/topic_coherence_model_selection.ipynb
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Performing Model Selection Using Topic Coherence\n", "\n", "This notebook will perform topic modeling on the 20 Newsgroups corpus using LDA. We will perform model selection (over the number of topics) using topic coherence as...
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Any2Vec_Filebased.ipynb
piskvorky_gensim/docs/notebooks/Any2Vec_Filebased.ipynb
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# *2Vec File-based Training: API Tutorial\n", "\n", "This tutorial introduces a new file-based training mode for **`gensim.models.{Word2Vec, FastText, Doc2Vec}`** which leads to (much) faster training on machines with many core...
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nmslibtutorial.ipynb
piskvorky_gensim/docs/notebooks/nmslibtutorial.ipynb
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Similarity Queries using Nmslib Tutorial" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial is about using the ([Non-Metric Space Library (NMSLIB)](https://github.com...
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Training_visualizations.ipynb
piskvorky_gensim/docs/notebooks/Training_visualizations.ipynb
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Setup Visdom\n", "\n", "Install it with:\n", "\n", "`pip install visdom`\n", "\n", "Start the server:\n", "\n", "`python -m visdom.server`\n", "\n", "Visdom now can be accessed at http://localh...
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Corpora_and_Vector_Spaces.ipynb
piskvorky_gensim/docs/notebooks/Corpora_and_Vector_Spaces.ipynb
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial has moved.\n", "\n", "Please see https://radimrehurek.com/gensim/auto_examples/." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "...
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deepir.ipynb
piskvorky_gensim/docs/notebooks/deepir.ipynb
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Deep Inverse Regression with Yelp reviews\n", "\n", "In this note we'll use [gensim](https://radimrehurek.com/gensim/) to turn the Word2Vec machinery into a document classifier, as in [Document Classification by Inversion of...
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dtm_example.ipynb
piskvorky_gensim/docs/notebooks/dtm_example.ipynb
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# DTM Example\n", "\n", "In this example we will present a sample usage of the DTM wrapper. Prior to using this you need to compile the [DTM code](https://github.com/magsilva/dtm) yourself or use one of the [binaries](https://g...
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doc2vec-lee.ipynb
piskvorky_gensim/docs/notebooks/doc2vec-lee.ipynb
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial has moved.\n", "\n", "Please see https://radimrehurek.com/gensim/auto_examples/." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "...
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gensim Quick Start.ipynb
piskvorky_gensim/docs/notebooks/gensim Quick Start.ipynb
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial has moved.\n", "\n", "Please see https://radimrehurek.com/gensim/auto_examples/." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "...
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topic_coherence-movies.ipynb
piskvorky_gensim/docs/notebooks/topic_coherence-movies.ipynb
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Benchmark testing of coherence pipeline on Movies dataset\n", "## How to find how well coherence measure matches your manual annotators" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__Introduct...
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nmf_tutorial.ipynb
piskvorky_gensim/docs/notebooks/nmf_tutorial.ipynb
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Gensim Tutorial on Online Non-Negative Matrix Factorization" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebooks explains basic ideas behind the open source NMF implementation in [Gensim](h...
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distance_metrics.ipynb
piskvorky_gensim/docs/notebooks/distance_metrics.ipynb
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial has moved.\n", "\n", "Please see https://radimrehurek.com/gensim/auto_examples/." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "...
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topic_methods.ipynb
piskvorky_gensim/docs/notebooks/topic_methods.ipynb
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# New Term Topics Methods and Document Coloring" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "from gensim.corpora import Dictionary\n", "from gensim.models...
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Varembed.ipynb
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{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# VarEmbed Tutorial\n", "\n", "Varembed is a word embedding model incorporating morphological information, capturing shared sub-word features. Unlike previous work that constructs word embeddings directly from morphemes, varemb...
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test_notebooks.py
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import os import sys import tempfile from glob import glob import nbformat from nbconvert.preprocessors import ExecutePreprocessor from nbconvert.preprocessors.execute import CellExecutionError "from smart_open import smart_open\n", def _notebook_run(path): """Execute a notebook via nbconvert and collect output....
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Topics_and_Transformations.ipynb
piskvorky_gensim/docs/notebooks/Topics_and_Transformations.ipynb
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial has moved.\n", "\n", "Please see https://radimrehurek.com/gensim/auto_examples/." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "...
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Similarity_Queries.ipynb
piskvorky_gensim/docs/notebooks/Similarity_Queries.ipynb
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial has moved.\n", "\n", "Please see https://radimrehurek.com/gensim/auto_examples/." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "...
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word2vec.ipynb
piskvorky_gensim/docs/notebooks/word2vec.ipynb
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial has moved.\n", "\n", "Please see https://radimrehurek.com/gensim/auto_examples/." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "...
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translation_matrix.ipynb
piskvorky_gensim/docs/notebooks/translation_matrix.ipynb
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Translation Matrix Tutorial" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## What is it ?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Suppose we are given a se...
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gensim_news_classification.ipynb
piskvorky_gensim/docs/notebooks/gensim_news_classification.ipynb
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial has moved.\n", "\n", "Please see https://radimrehurek.com/gensim/auto_examples/." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "...
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Tensorboard_visualizations.ipynb
piskvorky_gensim/docs/notebooks/Tensorboard_visualizations.ipynb
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# TensorBoard Visualizations\n", "\n", "\n", "In this tutorial, we will learn how to visualize different types of NLP based Embeddings via TensorBoard. TensorBoard is a data visualization framework for visualizing and inspe...
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online_w2v_tutorial.ipynb
piskvorky_gensim/docs/notebooks/online_w2v_tutorial.ipynb
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Online word2vec tutorial\n", "\n", "So far, word2vec cannot increase the size of vocabulary after initial training. To handle unknown words, not in word2vec vocaburary, you must retrain updated documents over again.\n", ...
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topic_coherence_tutorial.ipynb
piskvorky_gensim/docs/notebooks/topic_coherence_tutorial.ipynb
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Demonstration of the topic coherence pipeline in Gensim" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ ...
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WMD_tutorial.ipynb
piskvorky_gensim/docs/notebooks/WMD_tutorial.ipynb
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Finding similar documents with Word2Vec and WMD \n", "\n", "Word Mover's Distance is a promising new tool in machine learning that allows us to submit a query and return the most relevant documents. For example, in a blog pos...
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poincare_numpy.patch
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diff --git a/poincare.py b/poincare.py index ecae36e..f85bf22 100644 --- a/poincare.py +++ b/poincare.py @@ -1,160 +1,169 @@ +import argparse +import csv import nltk from nltk.corpus import wordnet as wn from math import * +import pickle import random +import re import numpy as np -import matplotlib.pyplot as plt ...
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conf.py
piskvorky_gensim/docs/src/conf.py
# -*- coding: utf-8 -*- # # gensim documentation build configuration file, created by # sphinx-quickstart on Wed Mar 17 13:42:21 2010. # # This file is execfile()d with the current directory set to its containing dir. # # Note that not all possible configuration values are present in this # autogenerated file. # # All ...
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check_gallery.py
piskvorky_gensim/docs/src/check_gallery.py
"""Check that the cached gallery files are up to date. If they are stale, then Sphinx will attempt to rebuild them from source. When running the documentation build on CI, we want to avoid rebuilding the gallery, because that takes too long. Instead, we use this script to warn the author of the PR that they need to ...
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run_doc.py
piskvorky_gensim/docs/src/gallery/howtos/run_doc.py
r""" How to Author Gensim Documentation ================================== How to author documentation for Gensim. """ ############################################################################### # Background # ---------- # # Gensim is a large project with a wide range of functionality. # Unfortunately, not all of...
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run_doc2vec_imdb.py
piskvorky_gensim/docs/src/gallery/howtos/run_doc2vec_imdb.py
r""" How to reproduce the doc2vec 'Paragraph Vector' paper ===================================================== Shows how to reproduce results of the "Distributed Representation of Sentences and Documents" paper by Le and Mikolov using Gensim. """ import logging logging.basicConfig(format='%(asctime)s : %(levelname...
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run_compare_lda.py
piskvorky_gensim/docs/src/gallery/howtos/run_compare_lda.py
r""" How to Compare LDA Models ========================= Demonstrates how you can visualize and compare trained topic models. """ # sphinx_gallery_thumbnail_number = 2 import logging logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO) #########################################...
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run_downloader_api.py
piskvorky_gensim/docs/src/gallery/howtos/run_downloader_api.py
r""" How to download pre-trained models and corpora ============================================== Demonstrates simple and quick access to common corpora and pretrained models. """ import logging logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO) #############################...
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run_corpora_and_vector_spaces.py
piskvorky_gensim/docs/src/gallery/core/run_corpora_and_vector_spaces.py
r""" Corpora and Vector Spaces ========================= Demonstrates transforming text into a vector space representation. Also introduces corpus streaming and persistence to disk in various formats. """ import logging logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO) ####...
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run_core_concepts.py
piskvorky_gensim/docs/src/gallery/core/run_core_concepts.py
r""" Core Concepts ============= This tutorial introduces Documents, Corpora, Vectors and Models: the basic concepts and terms needed to understand and use gensim. """ import pprint ############################################################################### # The core concepts of ``gensim`` are: # # 1. :ref:`cor...
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run_topics_and_transformations.py
piskvorky_gensim/docs/src/gallery/core/run_topics_and_transformations.py
r""" Topics and Transformations =========================== Introduces transformations and demonstrates their use on a toy corpus. """ import logging logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO) ###########################################################################...
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run_similarity_queries.py
piskvorky_gensim/docs/src/gallery/core/run_similarity_queries.py
r""" Similarity Queries ================== Demonstrates querying a corpus for similar documents. """ import logging logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO) ############################################################################### # # Creating the Corpus # ---...
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run_word2vec.py
piskvorky_gensim/docs/src/gallery/tutorials/run_word2vec.py
r""" Word2Vec Model ============== Introduces Gensim's Word2Vec model and demonstrates its use on the `Lee Evaluation Corpus <https://hekyll.services.adelaide.edu.au/dspace/bitstream/2440/28910/1/hdl_28910.pdf>`_. """ import logging logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging...
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run_lda.py
piskvorky_gensim/docs/src/gallery/tutorials/run_lda.py
r""" LDA Model ========= Introduces Gensim's LDA model and demonstrates its use on the NIPS corpus. """ import logging logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO) ############################################################################### # The purpose of this tut...
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run_scm.py
piskvorky_gensim/docs/src/gallery/tutorials/run_scm.py
r""" Soft Cosine Measure =================== Demonstrates using Gensim's implemenation of the SCM. """ ############################################################################### # Soft Cosine Measure (SCM) is a promising new tool in machine learning that # allows us to submit a query and return the most relevan...
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run_doc2vec_lee.py
piskvorky_gensim/docs/src/gallery/tutorials/run_doc2vec_lee.py
r""" Doc2Vec Model ============= Introduces Gensim's Doc2Vec model and demonstrates its use on the `Lee Corpus <https://hekyll.services.adelaide.edu.au/dspace/bitstream/2440/28910/1/hdl_28910.pdf>`__. """ import logging logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO) ####...
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run_fasttext.py
piskvorky_gensim/docs/src/gallery/tutorials/run_fasttext.py
r""" FastText Model ============== Introduces Gensim's fastText model and demonstrates its use on the Lee Corpus. """ import logging logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO) ############################################################################### # Here, we'l...
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run_annoy.py
piskvorky_gensim/docs/src/gallery/tutorials/run_annoy.py
r""" Fast Similarity Queries with Annoy and Word2Vec =============================================== Introduces the Annoy library for similarity queries on top of vectors learned by Word2Vec. """ LOGS = False # Set to True if you want to see progress in logs. if LOGS: import logging logging.basicConfig(forma...
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run_wmd.py
piskvorky_gensim/docs/src/gallery/tutorials/run_wmd.py
r""" Word Mover's Distance ===================== Demonstrates using Gensim's implemenation of the WMD. """ ############################################################################### # Word Mover's Distance (WMD) is a promising new tool in machine learning that # allows us to submit a query and return the most r...
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run_ensemblelda.py
piskvorky_gensim/docs/src/gallery/tutorials/run_ensemblelda.py
r""" Ensemble LDA ============ Introduces Gensim's EnsembleLda model """ import logging logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO) ############################################################################### # This tutorial will explain how to use the EnsembleLDA ...
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piskvorky/gensim
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__init__.py
piskvorky_gensim/docs/src/sphinx_rtd_theme/__init__.py
""" Sphinx Read the Docs theme. From https://github.com/ryan-roemer/sphinx-bootstrap-theme. """ from os import path import sphinx __version__ = '0.5.0' __version_full__ = __version__ def get_html_theme_path(): """Return list of HTML theme paths.""" cur_dir = path.abspath(path.dirname(path.dirname(__file_...
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run_doc.py
piskvorky_gensim/docs/src/auto_examples/howtos/run_doc.py
r""" How to Author Gensim Documentation ================================== How to author documentation for Gensim. """ ############################################################################### # Background # ---------- # # Gensim is a large project with a wide range of functionality. # Unfortunately, not all of...
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run_doc2vec_imdb.py
piskvorky_gensim/docs/src/auto_examples/howtos/run_doc2vec_imdb.py
r""" How to reproduce the doc2vec 'Paragraph Vector' paper ===================================================== Shows how to reproduce results of the "Distributed Representation of Sentences and Documents" paper by Le and Mikolov using Gensim. """ import logging logging.basicConfig(format='%(asctime)s : %(levelname...
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run_compare_lda.py
piskvorky_gensim/docs/src/auto_examples/howtos/run_compare_lda.py
r""" How to Compare LDA Models ========================= Demonstrates how you can visualize and compare trained topic models. """ # sphinx_gallery_thumbnail_number = 2 import logging logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO) #########################################...
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run_downloader_api.py
piskvorky_gensim/docs/src/auto_examples/howtos/run_downloader_api.py
r""" How to download pre-trained models and corpora ============================================== Demonstrates simple and quick access to common corpora and pretrained models. """ import logging logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO) #############################...
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run_compare_lda.ipynb
piskvorky_gensim/docs/src/auto_examples/howtos/run_compare_lda.ipynb
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run_doc.ipynb
piskvorky_gensim/docs/src/auto_examples/howtos/run_doc.ipynb
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run_downloader_api.ipynb
piskvorky_gensim/docs/src/auto_examples/howtos/run_downloader_api.ipynb
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\nHow to downloa...
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run_doc2vec_imdb.ipynb
piskvorky_gensim/docs/src/auto_examples/howtos/run_doc2vec_imdb.ipynb
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n# How to repro...
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run_corpora_and_vector_spaces.py
piskvorky_gensim/docs/src/auto_examples/core/run_corpora_and_vector_spaces.py
r""" Corpora and Vector Spaces ========================= Demonstrates transforming text into a vector space representation. Also introduces corpus streaming and persistence to disk in various formats. """ import logging logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO) ####...
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run_core_concepts.ipynb
piskvorky_gensim/docs/src/auto_examples/core/run_core_concepts.ipynb
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run_topics_and_transformations.ipynb
piskvorky_gensim/docs/src/auto_examples/core/run_topics_and_transformations.ipynb
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n# Topics and T...
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run_core_concepts.py
piskvorky_gensim/docs/src/auto_examples/core/run_core_concepts.py
r""" Core Concepts ============= This tutorial introduces Documents, Corpora, Vectors and Models: the basic concepts and terms needed to understand and use gensim. """ import pprint ############################################################################### # The core concepts of ``gensim`` are: # # 1. :ref:`cor...
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run_topics_and_transformations.py
piskvorky_gensim/docs/src/auto_examples/core/run_topics_and_transformations.py
r""" Topics and Transformations =========================== Introduces transformations and demonstrates their use on a toy corpus. """ import logging logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO) ###########################################################################...
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