├── LICENSE ├── README.md ├── docs ├── .nojekyll ├── Makefile ├── _sources │ ├── index.rst.txt │ ├── torchfm.dataset.rst.txt │ ├── torchfm.model.rst.txt │ └── torchfm.rst.txt ├── _static │ ├── basic.css │ ├── css │ │ ├── badge_only.css │ │ └── theme.css │ ├── doctools.js │ ├── documentation_options.js │ ├── file.png │ ├── fonts │ │ ├── Inconsolata-Bold.ttf │ │ ├── Inconsolata-Regular.ttf │ │ ├── Inconsolata.ttf │ │ ├── Lato-Bold.ttf │ │ ├── Lato-Regular.ttf │ │ ├── Lato │ │ │ ├── lato-bold.eot │ │ │ ├── lato-bold.ttf │ │ │ ├── lato-bold.woff │ │ │ ├── lato-bold.woff2 │ │ │ ├── lato-bolditalic.eot │ │ │ ├── lato-bolditalic.ttf │ │ │ ├── lato-bolditalic.woff │ │ │ ├── lato-bolditalic.woff2 │ │ │ ├── lato-italic.eot │ │ │ ├── lato-italic.ttf │ │ │ ├── lato-italic.woff │ │ │ ├── lato-italic.woff2 │ │ │ ├── lato-regular.eot │ │ │ ├── lato-regular.ttf │ │ │ ├── lato-regular.woff │ │ │ └── lato-regular.woff2 │ │ ├── RobotoSlab-Bold.ttf │ │ ├── RobotoSlab-Regular.ttf │ │ ├── RobotoSlab │ │ │ ├── roboto-slab-v7-bold.eot │ │ │ ├── roboto-slab-v7-bold.ttf │ │ │ ├── roboto-slab-v7-bold.woff │ │ │ ├── roboto-slab-v7-bold.woff2 │ │ │ ├── roboto-slab-v7-regular.eot │ │ │ ├── roboto-slab-v7-regular.ttf │ │ │ ├── roboto-slab-v7-regular.woff │ │ │ └── roboto-slab-v7-regular.woff2 │ │ ├── fontawesome-webfont.eot │ │ ├── fontawesome-webfont.svg │ │ ├── fontawesome-webfont.ttf │ │ ├── fontawesome-webfont.woff │ │ └── fontawesome-webfont.woff2 │ ├── jquery-3.2.1.js │ ├── jquery.js │ ├── js │ │ ├── modernizr.min.js │ │ └── theme.js │ ├── language_data.js │ ├── minus.png │ ├── plus.png │ ├── pygments.css │ ├── searchtools.js │ ├── underscore-1.3.1.js │ └── underscore.js ├── conf.py ├── genindex.html ├── index.html ├── index.rst ├── make.bat ├── objects.inv ├── py-modindex.html ├── search.html ├── searchindex.js ├── torchfm.dataset.html ├── torchfm.dataset.rst ├── torchfm.html ├── torchfm.model.html ├── torchfm.model.rst └── torchfm.rst ├── examples └── main.py ├── requirements.txt ├── setup.py ├── test └── test_layers.py └── torchfm ├── __init__.py ├── dataset ├── __init__.py ├── avazu.py ├── criteo.py └── movielens.py ├── layer.py └── model ├── __init__.py ├── afi.py ├── afm.py ├── afn.py ├── dcn.py ├── dfm.py ├── ffm.py ├── fm.py ├── fnfm.py ├── fnn.py ├── hofm.py ├── lr.py ├── ncf.py ├── nfm.py ├── pnn.py ├── wd.py └── xdfm.py /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2019 rixwew 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Factorization Machine models in PyTorch 2 | 3 | This package provides a PyTorch implementation of factorization machine models and common datasets in CTR prediction. 4 | 5 | 6 | ## Available Datasets 7 | 8 | * [MovieLens Dataset](https://grouplens.org/datasets/movielens) 9 | * [Criteo Display Advertising Challenge](https://www.kaggle.com/c/criteo-display-ad-challenge) 10 | * [Avazu Click-Through Rate Prediction](https://www.kaggle.com/c/avazu-ctr-prediction) 11 | 12 | 13 | ## Available Models 14 | 15 | | Model | Reference | 16 | |-------|-----------| 17 | | Logistic Regression | | 18 | | Factorization Machine | [S Rendle, Factorization Machines, 2010.](https://www.csie.ntu.edu.tw/~b97053/paper/Rendle2010FM.pdf) | 19 | | Field-aware Factorization Machine | [Y Juan, et al. Field-aware Factorization Machines for CTR Prediction, 2015.](https://www.csie.ntu.edu.tw/~cjlin/papers/ffm.pdf) | 20 | | Higher-Order Factorization Machines | [ M Blondel, et al. Higher-Order Factorization Machines, 2016.](https://dl.acm.org/doi/10.5555/3157382.3157473) | 21 | | Factorization-Supported Neural Network | [W Zhang, et al. Deep Learning over Multi-field Categorical Data - A Case Study on User Response Prediction, 2016.](https://arxiv.org/abs/1601.02376) | 22 | | Wide&Deep | [HT Cheng, et al. Wide & Deep Learning for Recommender Systems, 2016.](https://arxiv.org/abs/1606.07792) | 23 | | Attentional Factorization Machine | [J Xiao, et al. Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks, 2017.](https://arxiv.org/abs/1708.04617) | 24 | | Neural Factorization Machine | [X He and TS Chua, Neural Factorization Machines for Sparse Predictive Analytics, 2017.](https://arxiv.org/abs/1708.05027) | 25 | | Neural Collaborative Filtering | [X He, et al. Neural Collaborative Filtering, 2017.](https://arxiv.org/abs/1708.05031) | 26 | | Field-aware Neural Factorization Machine | [L Zhang, et al. Field-aware Neural Factorization Machine for Click-Through Rate Prediction, 2019.](https://arxiv.org/abs/1902.09096) | 27 | | Product Neural Network | [Y Qu, et al. Product-based Neural Networks for User Response Prediction, 2016.](https://arxiv.org/abs/1611.00144) | 28 | | Deep Cross Network | [R Wang, et al. Deep & Cross Network for Ad Click Predictions, 2017.](https://arxiv.org/abs/1708.05123) | 29 | | DeepFM | [H Guo, et al. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction, 2017.](https://arxiv.org/abs/1703.04247) | 30 | | xDeepFM | [J Lian, et al. xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems, 2018.](https://arxiv.org/abs/1803.05170) | 31 | | AutoInt (Automatic Feature Interaction Model) | [W Song, et al. AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks, 2018.](https://arxiv.org/abs/1810.11921) | 32 | | AFN(AdaptiveFactorizationNetwork Model) | [Cheng W, et al. Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions, AAAI'20.](https://arxiv.org/pdf/1909.03276.pdf) | 33 | 34 | Each model's AUC values are about 0.80 for criteo dataset, and about 0.78 for avazu dataset. (please see [example code](examples/main.py)) 35 | 36 | 37 | ## Installation 38 | 39 | pip install torchfm 40 | 41 | 42 | ## API Documentation 43 | 44 | https://rixwew.github.io/pytorch-fm 45 | 46 | 47 | ## Licence 48 | 49 | MIT 50 | -------------------------------------------------------------------------------- /docs/.nojekyll: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /docs/Makefile: -------------------------------------------------------------------------------- 1 | # Minimal makefile for Sphinx documentation 2 | # 3 | 4 | # You can set these variables from the command line. 5 | SPHINXOPTS = 6 | SPHINXBUILD = sphinx-build 7 | SOURCEDIR = . 8 | BUILDDIR = _build 9 | 10 | # Put it first so that "make" without argument is like "make help". 11 | help: 12 | @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) 13 | 14 | .PHONY: help Makefile 15 | 16 | # Catch-all target: route all unknown targets to Sphinx using the new 17 | # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). 18 | %: Makefile 19 | @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) -------------------------------------------------------------------------------- /docs/_sources/index.rst.txt: -------------------------------------------------------------------------------- 1 | pytorch-fm 2 | =========== 3 | 4 | *Factorization Machine models in PyTorch.* 5 | 6 | This package provides an implementation of various factorization machine models and common datasets in PyTorch. 7 | 8 | 9 | 10 | Minimal requirements 11 | ==================== 12 | 13 | * Python 3.x 14 | * PyTorch 1.1.0 15 | * numpy 16 | * lmdb 17 | 18 | Installation 19 | ============ 20 | 21 | Install with pip:: 22 | 23 | pip install torchfm 24 | 25 | 26 | API documentation 27 | ================= 28 | 29 | .. toctree:: 30 | torchfm 31 | 32 | 33 | Indices and tables 34 | ================== 35 | 36 | * :ref:`genindex` 37 | * :ref:`modindex` 38 | * :ref:`search` 39 | -------------------------------------------------------------------------------- /docs/_sources/torchfm.dataset.rst.txt: -------------------------------------------------------------------------------- 1 | torchfm.dataset 2 | ======================= 3 | 4 | torchfm.dataset.avazu 5 | ---------------------------- 6 | 7 | .. automodule:: torchfm.dataset.avazu 8 | :members: 9 | 10 | torchfm.dataset.criteo 11 | ----------------------------- 12 | 13 | .. automodule:: torchfm.dataset.criteo 14 | :members: 15 | 16 | torchfm.dataset.movielens 17 | -------------------------------- 18 | 19 | .. automodule:: torchfm.dataset.movielens 20 | :members: 21 | -------------------------------------------------------------------------------- /docs/_sources/torchfm.model.rst.txt: -------------------------------------------------------------------------------- 1 | torchfm.model 2 | ===================== 3 | 4 | torchfm.model.afi 5 | ------------------------ 6 | 7 | .. automodule:: torchfm.model.afi 8 | :members: 9 | 10 | torchfm.model.afm 11 | ------------------------ 12 | 13 | .. automodule:: torchfm.model.afm 14 | :members: 15 | 16 | torchfm.model.dcn 17 | ------------------------ 18 | 19 | .. automodule:: torchfm.model.dcn 20 | :members: 21 | 22 | torchfm.model.dfm 23 | ------------------------ 24 | 25 | .. automodule:: torchfm.model.dfm 26 | :members: 27 | 28 | torchfm.model.ffm 29 | ------------------------ 30 | 31 | .. automodule:: torchfm.model.ffm 32 | :members: 33 | 34 | torchfm.model.fm 35 | ----------------------- 36 | 37 | .. automodule:: torchfm.model.fm 38 | :members: 39 | 40 | torchfm.model.fnfm 41 | ------------------------- 42 | 43 | .. automodule:: torchfm.model.fnfm 44 | :members: 45 | 46 | torchfm.model.fnn 47 | ------------------------ 48 | 49 | .. automodule:: torchfm.model.fnn 50 | :members: 51 | 52 | torchfm.model.lr 53 | ----------------------- 54 | 55 | .. automodule:: torchfm.model.lr 56 | :members: 57 | 58 | torchfm.model.nfm 59 | ------------------------ 60 | 61 | .. automodule:: torchfm.model.nfm 62 | :members: 63 | 64 | torchfm.model.pnn 65 | ------------------------ 66 | 67 | .. automodule:: torchfm.model.pnn 68 | :members: 69 | 70 | torchfm.model.wd 71 | ----------------------- 72 | 73 | .. automodule:: torchfm.model.wd 74 | :members: 75 | 76 | torchfm.model.xdfm 77 | ------------------------- 78 | 79 | .. automodule:: torchfm.model.xdfm 80 | :members: 81 | -------------------------------------------------------------------------------- /docs/_sources/torchfm.rst.txt: -------------------------------------------------------------------------------- 1 | torchfm package 2 | =============== 3 | 4 | 5 | 6 | .. toctree:: 7 | 8 | torchfm.dataset 9 | torchfm.model 10 | 11 | 12 | torchfm.layer 13 | -------------------- 14 | 15 | .. automodule:: torchfm.layer 16 | :members: 17 | 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rv:([\w.]+)|)/.exec(ua) || 132 | []; 133 | 134 | return { 135 | browser: match[ 1 ] || "", 136 | version: match[ 2 ] || "0" 137 | }; 138 | }; 139 | jQuery.browser = {}; 140 | jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true; 141 | } 142 | 143 | /** 144 | * Small JavaScript module for the documentation. 145 | */ 146 | var Documentation = { 147 | 148 | init : function() { 149 | this.fixFirefoxAnchorBug(); 150 | this.highlightSearchWords(); 151 | this.initIndexTable(); 152 | if (DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) { 153 | this.initOnKeyListeners(); 154 | } 155 | }, 156 | 157 | /** 158 | * i18n support 159 | */ 160 | TRANSLATIONS : {}, 161 | PLURAL_EXPR : function(n) { return n === 1 ? 0 : 1; }, 162 | LOCALE : 'unknown', 163 | 164 | // gettext and ngettext don't access this so that the functions 165 | // can safely bound to a different name (_ = Documentation.gettext) 166 | gettext : function(string) { 167 | var translated = Documentation.TRANSLATIONS[string]; 168 | if (typeof translated === 'undefined') 169 | return string; 170 | return (typeof translated === 'string') ? translated : translated[0]; 171 | }, 172 | 173 | ngettext : function(singular, plural, n) { 174 | var translated = Documentation.TRANSLATIONS[singular]; 175 | if (typeof translated === 'undefined') 176 | return (n == 1) ? singular : plural; 177 | return translated[Documentation.PLURALEXPR(n)]; 178 | }, 179 | 180 | addTranslations : function(catalog) { 181 | for (var key in catalog.messages) 182 | this.TRANSLATIONS[key] = catalog.messages[key]; 183 | this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')'); 184 | this.LOCALE = catalog.locale; 185 | }, 186 | 187 | /** 188 | * add context elements like header anchor links 189 | */ 190 | addContextElements : function() { 191 | $('div[id] > :header:first').each(function() { 192 | $('\u00B6'). 193 | attr('href', '#' + this.id). 194 | attr('title', _('Permalink to this headline')). 195 | appendTo(this); 196 | }); 197 | $('dt[id]').each(function() { 198 | $('\u00B6'). 199 | attr('href', '#' + this.id). 200 | attr('title', _('Permalink to this definition')). 201 | appendTo(this); 202 | }); 203 | }, 204 | 205 | /** 206 | * workaround a firefox stupidity 207 | * see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075 208 | */ 209 | fixFirefoxAnchorBug : function() { 210 | if (document.location.hash && $.browser.mozilla) 211 | window.setTimeout(function() { 212 | document.location.href += ''; 213 | }, 10); 214 | }, 215 | 216 | /** 217 | * highlight the search words provided in the url in the text 218 | */ 219 | highlightSearchWords : function() { 220 | var params = $.getQueryParameters(); 221 | var terms = (params.highlight) ? params.highlight[0].split(/\s+/) : []; 222 | if (terms.length) { 223 | var body = $('div.body'); 224 | if (!body.length) { 225 | body = $('body'); 226 | } 227 | window.setTimeout(function() { 228 | $.each(terms, function() { 229 | body.highlightText(this.toLowerCase(), 'highlighted'); 230 | }); 231 | }, 10); 232 | $('

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DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL; 269 | }, 270 | 271 | /** 272 | * get the current relative url 273 | */ 274 | getCurrentURL : function() { 275 | var path = document.location.pathname; 276 | var parts = path.split(/\//); 277 | $.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\//), function() { 278 | if (this === '..') 279 | parts.pop(); 280 | }); 281 | var url = parts.join('/'); 282 | return path.substring(url.lastIndexOf('/') + 1, path.length - 1); 283 | }, 284 | 285 | initOnKeyListeners: function() { 286 | $(document).keyup(function(event) { 287 | var activeElementType = document.activeElement.tagName; 288 | // don't navigate when in search box or textarea 289 | if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT') { 290 | switch (event.keyCode) { 291 | case 37: // left 292 | var prevHref = $('link[rel="prev"]').prop('href'); 293 | if (prevHref) { 294 | window.location.href = prevHref; 295 | return false; 296 | } 297 | 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-------------------------------------------------------------------------------- 1 | # Configuration file for the Sphinx documentation builder. 2 | # 3 | # This file only contains a selection of the most common options. For a full 4 | # list see the documentation: 5 | # http://www.sphinx-doc.org/en/master/config 6 | 7 | # -- Path setup -------------------------------------------------------------- 8 | 9 | # If extensions (or modules to document with autodoc) are in another directory, 10 | # add these directories to sys.path here. If the directory is relative to the 11 | # documentation root, use os.path.abspath to make it absolute, like shown here. 12 | # 13 | import os 14 | import sys 15 | sys.path.insert(0, os.path.abspath('..')) 16 | 17 | 18 | # -- Project information ----------------------------------------------------- 19 | 20 | project = 'pytorch-fm' 21 | copyright = '2019, rixwew@gmail.com' 22 | author = 'rixwew@gmail.com' 23 | 24 | # The full version, including alpha/beta/rc tags 25 | release = '0.1' 26 | 27 | # -- General configuration --------------------------------------------------- 28 | 29 | # Add any Sphinx extension module names here, as strings. They can be 30 | # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom 31 | # ones. 32 | extensions = [ 33 | 'sphinx.ext.todo', 34 | 'sphinx.ext.autosummary', 35 | 'sphinx.ext.viewcode', 36 | 'sphinx.ext.autodoc' 37 | ] 38 | 39 | # Add any paths that contain templates here, relative to this directory. 40 | templates_path = ['_templates'] 41 | 42 | # List of patterns, relative to source directory, that match files and 43 | # directories to ignore when looking for source files. 44 | # This pattern also affects html_static_path and html_extra_path. 45 | exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] 46 | 47 | # -- Options for HTML output ------------------------------------------------- 48 | 49 | # The theme to use for HTML and HTML Help pages. See the documentation for 50 | # a list of builtin themes. 51 | # 52 | html_theme = 'sphinx_rtd_theme' 53 | 54 | # Add any paths that contain custom static files (such as style sheets) here, 55 | # relative to this directory. They are copied after the builtin static files, 56 | # so a file named "default.css" will overwrite the builtin "default.css". 57 | html_static_path = ['_static'] 58 | -------------------------------------------------------------------------------- /docs/genindex.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | Index — pytorch-fm 0.1 documentation 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 |
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Index

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150 | A 151 | | C 152 | | D 153 | | E 154 | | F 155 | | I 156 | | L 157 | | M 158 | | N 159 | | O 160 | | P 161 | | T 162 | | W 163 | 164 |
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401 | © Copyright 2019, rixwew@gmail.com 402 | 403 |

404 |
405 | Built with Sphinx using a theme provided by Read the Docs. 406 | 407 |
408 | 409 |
410 |
411 | 412 |
413 | 414 |
415 | 416 | 417 | 418 | 423 | 424 | 425 | 426 | 427 | 428 | 429 | 430 | -------------------------------------------------------------------------------- /docs/index.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | pytorch-fm — pytorch-fm 0.1 documentation 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 |
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pytorch-fm

150 |

Factorization Machine models in PyTorch.

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This package provides an implementation of various factorization machine models and common datasets in PyTorch.

152 |
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Minimal requirements

155 |
    156 |

  • Python 3.x

    • 157 |

  • PyTorch 1.1.0

    • 158 |

  • numpy

    • 159 |

  • lmdb

    • 160 |
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Installation

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Install with pip:

165 | 
pip install torchfm
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API documentation

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Indices and tables

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229 | © Copyright 2019, rixwew@gmail.com 230 | 231 |

232 |
233 | Built with Sphinx using a theme provided by Read the Docs. 234 | 235 |
236 | 237 |
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241 | 242 |
243 | 244 | 245 | 246 | 251 | 252 | 253 | 254 | 255 | 256 | 257 | 258 | -------------------------------------------------------------------------------- /docs/index.rst: -------------------------------------------------------------------------------- 1 | pytorch-fm 2 | =========== 3 | 4 | *Factorization Machine models in PyTorch.* 5 | 6 | This package provides an implementation of various factorization machine models and common datasets in PyTorch. 7 | 8 | 9 | 10 | Minimal requirements 11 | ==================== 12 | 13 | * Python 3.x 14 | * PyTorch 1.1.0 15 | * numpy 16 | * lmdb 17 | 18 | Installation 19 | ============ 20 | 21 | Install with pip:: 22 | 23 | pip install torchfm 24 | 25 | 26 | API documentation 27 | ================= 28 | 29 | .. toctree:: 30 | torchfm 31 | 32 | 33 | Indices and tables 34 | ================== 35 | 36 | * :ref:`genindex` 37 | * :ref:`modindex` 38 | * :ref:`search` 39 | -------------------------------------------------------------------------------- /docs/make.bat: -------------------------------------------------------------------------------- 1 | @ECHO OFF 2 | 3 | pushd %~dp0 4 | 5 | REM Command file for Sphinx documentation 6 | 7 | if "%SPHINXBUILD%" == "" ( 8 | set SPHINXBUILD=sphinx-build 9 | ) 10 | set SOURCEDIR=. 11 | set BUILDDIR=_build 12 | 13 | if "%1" == "" goto help 14 | 15 | %SPHINXBUILD% >NUL 2>NUL 16 | if errorlevel 9009 ( 17 | echo. 18 | echo.The 'sphinx-build' command was not found. Make sure you have Sphinx 19 | echo.installed, then set the SPHINXBUILD environment variable to point 20 | echo.to the full path of the 'sphinx-build' executable. Alternatively you 21 | echo.may add the Sphinx directory to PATH. 22 | echo. 23 | echo.If you don't have Sphinx installed, grab it from 24 | echo.http://sphinx-doc.org/ 25 | exit /b 1 26 | ) 27 | 28 | %SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% 29 | goto end 30 | 31 | :help 32 | %SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% 33 | 34 | :end 35 | popd 36 | -------------------------------------------------------------------------------- /docs/objects.inv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rixwew/pytorch-fm/f74ad19771eda104e99874d19dc892e988ec53fa/docs/objects.inv -------------------------------------------------------------------------------- /docs/py-modindex.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | Python Module Index — pytorch-fm 0.1 documentation 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 |
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Python Module Index

148 | 149 |
150 | t 151 |
152 | 153 | 154 | 155 | 157 | 158 | 160 | 163 | 164 | 165 | 168 | 169 | 170 | 173 | 174 | 175 | 178 | 179 | 180 | 183 | 184 | 185 | 188 | 189 | 190 | 193 | 194 | 195 | 198 | 199 | 200 | 203 | 204 | 205 | 208 | 209 | 210 | 213 | 214 | 215 | 218 | 219 | 220 | 223 | 224 | 225 | 228 | 229 | 230 | 233 | 234 | 235 | 238 | 239 | 240 | 243 | 244 | 245 | 248 |
 
156 | t
161 | torchfm 162 |
    166 | torchfm.dataset.avazu 167 |
    171 | torchfm.dataset.criteo 172 |
    176 | torchfm.dataset.movielens 177 |
    181 | torchfm.layer 182 |
    186 | torchfm.model.afi 187 |
    191 | torchfm.model.afm 192 |
    196 | torchfm.model.dcn 197 |
    201 | torchfm.model.dfm 202 |
    206 | torchfm.model.ffm 207 |
    211 | torchfm.model.fm 212 |
    216 | torchfm.model.fnfm 217 |
    221 | torchfm.model.fnn 222 |
    226 | torchfm.model.lr 227 |
    231 | torchfm.model.nfm 232 |
    236 | torchfm.model.pnn 237 |
    241 | torchfm.model.wd 242 |
    246 | torchfm.model.xdfm 247 |
249 | 250 | 251 |
252 | 253 |
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255 | 256 | 257 |
258 | 259 |
260 |

261 | © Copyright 2019, rixwew@gmail.com 262 | 263 |

264 |
265 | Built with Sphinx using a theme provided by Read the Docs. 266 | 267 |
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275 | 276 | 277 | 278 | 283 | 284 | 285 | 286 | 287 | 288 | 289 | 290 | -------------------------------------------------------------------------------- /docs/search.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | Search — pytorch-fm 0.1 documentation 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 |
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184 | 185 | 186 | 187 | 192 | 193 | 194 | 195 | 196 | 197 | 200 | 201 | 202 | 203 | 204 | 205 | 206 | -------------------------------------------------------------------------------- /docs/searchindex.js: -------------------------------------------------------------------------------- 1 | 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162 |

torchfm.dataset

163 |
164 |

torchfm.dataset.avazu

165 |
166 |
167 | class torchfm.dataset.avazu.AvazuDataset(dataset_path=None, cache_path='.avazu', rebuild_cache=False, min_threshold=4)[source]
168 |

Avazu Click-Through Rate Prediction Dataset

169 |
170 |
Dataset preparation

Remove the infrequent features (appearing in less than threshold instances) and treat them as a single feature

171 |
172 |
173 |
174 |
Parameters
175 |
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  • dataset_path – avazu train path

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  • cache_path – lmdb cache path

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  • rebuild_cache – If True, lmdb cache is refreshed

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  • min_threshold – infrequent feature threshold

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Reference

https://www.kaggle.com/c/avazu-ctr-prediction

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torchfm.dataset.criteo

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194 | class torchfm.dataset.criteo.CriteoDataset(dataset_path=None, cache_path='.criteo', rebuild_cache=False, min_threshold=10)[source]
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Criteo Display Advertising Challenge Dataset

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Data prepration:
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  • Remove the infrequent features (appearing in less than threshold instances) and treat them as a single feature

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  • Discretize numerical values by log2 transformation which is proposed by the winner of Criteo Competition

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Parameters
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  • dataset_path – criteo train.txt path.

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  • cache_path – lmdb cache path.

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  • rebuild_cache – If True, lmdb cache is refreshed.

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  • min_threshold – infrequent feature threshold.

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Reference:

https://labs.criteo.com/2014/02/kaggle-display-advertising-challenge-dataset 215 | https://www.csie.ntu.edu.tw/~r01922136/kaggle-2014-criteo.pdf

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torchfm.dataset.movielens

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225 | class torchfm.dataset.movielens.MovieLens1MDataset(dataset_path)[source]
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MovieLens 1M Dataset

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Data preparation

treat samples with a rating less than 3 as negative samples

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Parameters
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dataset_path – MovieLens dataset path

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Reference:

https://grouplens.org/datasets/movielens

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244 | class torchfm.dataset.movielens.MovieLens20MDataset(dataset_path, sep=', ')[source]
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MovieLens 20M Dataset

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Data preparation

treat samples with a rating less than 3 as negative samples

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Parameters
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dataset_path – MovieLens dataset path

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Reference:

https://grouplens.org/datasets/movielens

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284 | © Copyright 2019, rixwew@gmail.com 285 | 286 |

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288 | Built with Sphinx using a theme provided by Read the Docs. 289 | 290 |
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298 | 299 | 300 | 301 | 306 | 307 | 308 | 309 | 310 | 311 | 312 | 313 | -------------------------------------------------------------------------------- /docs/torchfm.dataset.rst: -------------------------------------------------------------------------------- 1 | torchfm.dataset 2 | ======================= 3 | 4 | torchfm.dataset.avazu 5 | ---------------------------- 6 | 7 | .. automodule:: torchfm.dataset.avazu 8 | :members: 9 | 10 | torchfm.dataset.criteo 11 | ----------------------------- 12 | 13 | .. automodule:: torchfm.dataset.criteo 14 | :members: 15 | 16 | torchfm.dataset.movielens 17 | -------------------------------- 18 | 19 | .. automodule:: torchfm.dataset.movielens 20 | :members: 21 | -------------------------------------------------------------------------------- /docs/torchfm.model.rst: -------------------------------------------------------------------------------- 1 | torchfm.model 2 | ===================== 3 | 4 | torchfm.model.afi 5 | ------------------------ 6 | 7 | .. automodule:: torchfm.model.afi 8 | :members: 9 | 10 | torchfm.model.afm 11 | ------------------------ 12 | 13 | .. automodule:: torchfm.model.afm 14 | :members: 15 | 16 | torchfm.model.dcn 17 | ------------------------ 18 | 19 | .. automodule:: torchfm.model.dcn 20 | :members: 21 | 22 | torchfm.model.dfm 23 | ------------------------ 24 | 25 | .. automodule:: torchfm.model.dfm 26 | :members: 27 | 28 | torchfm.model.ffm 29 | ------------------------ 30 | 31 | .. automodule:: torchfm.model.ffm 32 | :members: 33 | 34 | torchfm.model.fm 35 | ----------------------- 36 | 37 | .. automodule:: torchfm.model.fm 38 | :members: 39 | 40 | torchfm.model.fnfm 41 | ------------------------- 42 | 43 | .. automodule:: torchfm.model.fnfm 44 | :members: 45 | 46 | torchfm.model.fnn 47 | ------------------------ 48 | 49 | .. automodule:: torchfm.model.fnn 50 | :members: 51 | 52 | torchfm.model.lr 53 | ----------------------- 54 | 55 | .. automodule:: torchfm.model.lr 56 | :members: 57 | 58 | torchfm.model.nfm 59 | ------------------------ 60 | 61 | .. automodule:: torchfm.model.nfm 62 | :members: 63 | 64 | torchfm.model.pnn 65 | ------------------------ 66 | 67 | .. automodule:: torchfm.model.pnn 68 | :members: 69 | 70 | torchfm.model.wd 71 | ----------------------- 72 | 73 | .. automodule:: torchfm.model.wd 74 | :members: 75 | 76 | torchfm.model.xdfm 77 | ------------------------- 78 | 79 | .. automodule:: torchfm.model.xdfm 80 | :members: 81 | -------------------------------------------------------------------------------- /docs/torchfm.rst: -------------------------------------------------------------------------------- 1 | torchfm package 2 | =============== 3 | 4 | 5 | 6 | .. toctree:: 7 | 8 | torchfm.dataset 9 | torchfm.model 10 | 11 | 12 | torchfm.layer 13 | -------------------- 14 | 15 | .. automodule:: torchfm.layer 16 | :members: 17 | -------------------------------------------------------------------------------- /examples/main.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import tqdm 3 | from sklearn.metrics import roc_auc_score 4 | from torch.utils.data import DataLoader 5 | 6 | from torchfm.dataset.avazu import AvazuDataset 7 | from torchfm.dataset.criteo import CriteoDataset 8 | from torchfm.dataset.movielens import MovieLens1MDataset, MovieLens20MDataset 9 | from torchfm.model.afi import AutomaticFeatureInteractionModel 10 | from torchfm.model.afm import AttentionalFactorizationMachineModel 11 | from torchfm.model.dcn import DeepCrossNetworkModel 12 | from torchfm.model.dfm import DeepFactorizationMachineModel 13 | from torchfm.model.ffm import FieldAwareFactorizationMachineModel 14 | from torchfm.model.fm import FactorizationMachineModel 15 | from torchfm.model.fnfm import FieldAwareNeuralFactorizationMachineModel 16 | from torchfm.model.fnn import FactorizationSupportedNeuralNetworkModel 17 | from torchfm.model.hofm import HighOrderFactorizationMachineModel 18 | from torchfm.model.lr import LogisticRegressionModel 19 | from torchfm.model.ncf import NeuralCollaborativeFiltering 20 | from torchfm.model.nfm import NeuralFactorizationMachineModel 21 | from torchfm.model.pnn import ProductNeuralNetworkModel 22 | from torchfm.model.wd import WideAndDeepModel 23 | from torchfm.model.xdfm import ExtremeDeepFactorizationMachineModel 24 | from torchfm.model.afn import AdaptiveFactorizationNetwork 25 | 26 | 27 | def get_dataset(name, path): 28 | if name == 'movielens1M': 29 | return MovieLens1MDataset(path) 30 | elif name == 'movielens20M': 31 | return MovieLens20MDataset(path) 32 | elif name == 'criteo': 33 | return CriteoDataset(path) 34 | elif name == 'avazu': 35 | return AvazuDataset(path) 36 | else: 37 | raise ValueError('unknown dataset name: ' + name) 38 | 39 | 40 | def get_model(name, dataset): 41 | """ 42 | Hyperparameters are empirically determined, not opitmized. 43 | """ 44 | field_dims = dataset.field_dims 45 | if name == 'lr': 46 | return LogisticRegressionModel(field_dims) 47 | elif name == 'fm': 48 | return FactorizationMachineModel(field_dims, embed_dim=16) 49 | elif name == 'hofm': 50 | return HighOrderFactorizationMachineModel(field_dims, order=3, embed_dim=16) 51 | elif name == 'ffm': 52 | return FieldAwareFactorizationMachineModel(field_dims, embed_dim=4) 53 | elif name == 'fnn': 54 | return FactorizationSupportedNeuralNetworkModel(field_dims, embed_dim=16, mlp_dims=(16, 16), dropout=0.2) 55 | elif name == 'wd': 56 | return WideAndDeepModel(field_dims, embed_dim=16, mlp_dims=(16, 16), dropout=0.2) 57 | elif name == 'ipnn': 58 | return ProductNeuralNetworkModel(field_dims, embed_dim=16, mlp_dims=(16,), method='inner', dropout=0.2) 59 | elif name == 'opnn': 60 | return ProductNeuralNetworkModel(field_dims, embed_dim=16, mlp_dims=(16,), method='outer', dropout=0.2) 61 | elif name == 'dcn': 62 | return DeepCrossNetworkModel(field_dims, embed_dim=16, num_layers=3, mlp_dims=(16, 16), dropout=0.2) 63 | elif name == 'nfm': 64 | return NeuralFactorizationMachineModel(field_dims, embed_dim=64, mlp_dims=(64,), dropouts=(0.2, 0.2)) 65 | elif name == 'ncf': 66 | # only supports MovieLens dataset because for other datasets user/item colums are indistinguishable 67 | assert isinstance(dataset, MovieLens20MDataset) or isinstance(dataset, MovieLens1MDataset) 68 | return NeuralCollaborativeFiltering(field_dims, embed_dim=16, mlp_dims=(16, 16), dropout=0.2, 69 | user_field_idx=dataset.user_field_idx, 70 | item_field_idx=dataset.item_field_idx) 71 | elif name == 'fnfm': 72 | return FieldAwareNeuralFactorizationMachineModel(field_dims, embed_dim=4, mlp_dims=(64,), dropouts=(0.2, 0.2)) 73 | elif name == 'dfm': 74 | return DeepFactorizationMachineModel(field_dims, embed_dim=16, mlp_dims=(16, 16), dropout=0.2) 75 | elif name == 'xdfm': 76 | return ExtremeDeepFactorizationMachineModel( 77 | field_dims, embed_dim=16, cross_layer_sizes=(16, 16), split_half=False, mlp_dims=(16, 16), dropout=0.2) 78 | elif name == 'afm': 79 | return AttentionalFactorizationMachineModel(field_dims, embed_dim=16, attn_size=16, dropouts=(0.2, 0.2)) 80 | elif name == 'afi': 81 | return AutomaticFeatureInteractionModel( 82 | field_dims, embed_dim=16, atten_embed_dim=64, num_heads=2, num_layers=3, mlp_dims=(400, 400), dropouts=(0, 0, 0)) 83 | elif name == 'afn': 84 | print("Model:AFN") 85 | return AdaptiveFactorizationNetwork( 86 | field_dims, embed_dim=16, LNN_dim=1500, mlp_dims=(400, 400, 400), dropouts=(0, 0, 0)) 87 | else: 88 | raise ValueError('unknown model name: ' + name) 89 | 90 | 91 | class EarlyStopper(object): 92 | 93 | def __init__(self, num_trials, save_path): 94 | self.num_trials = num_trials 95 | self.trial_counter = 0 96 | self.best_accuracy = 0 97 | self.save_path = save_path 98 | 99 | def is_continuable(self, model, accuracy): 100 | if accuracy > self.best_accuracy: 101 | self.best_accuracy = accuracy 102 | self.trial_counter = 0 103 | torch.save(model, self.save_path) 104 | return True 105 | elif self.trial_counter + 1 < self.num_trials: 106 | self.trial_counter += 1 107 | return True 108 | else: 109 | return False 110 | 111 | 112 | def train(model, optimizer, data_loader, criterion, device, log_interval=100): 113 | model.train() 114 | total_loss = 0 115 | tk0 = tqdm.tqdm(data_loader, smoothing=0, mininterval=1.0) 116 | for i, (fields, target) in enumerate(tk0): 117 | fields, target = fields.to(device), target.to(device) 118 | y = model(fields) 119 | loss = criterion(y, target.float()) 120 | model.zero_grad() 121 | loss.backward() 122 | optimizer.step() 123 | total_loss += loss.item() 124 | if (i + 1) % log_interval == 0: 125 | tk0.set_postfix(loss=total_loss / log_interval) 126 | total_loss = 0 127 | 128 | 129 | def test(model, data_loader, device): 130 | model.eval() 131 | targets, predicts = list(), list() 132 | with torch.no_grad(): 133 | for fields, target in tqdm.tqdm(data_loader, smoothing=0, mininterval=1.0): 134 | fields, target = fields.to(device), target.to(device) 135 | y = model(fields) 136 | targets.extend(target.tolist()) 137 | predicts.extend(y.tolist()) 138 | return roc_auc_score(targets, predicts) 139 | 140 | 141 | def main(dataset_name, 142 | dataset_path, 143 | model_name, 144 | epoch, 145 | learning_rate, 146 | batch_size, 147 | weight_decay, 148 | device, 149 | save_dir): 150 | device = torch.device(device) 151 | dataset = get_dataset(dataset_name, dataset_path) 152 | train_length = int(len(dataset) * 0.8) 153 | valid_length = int(len(dataset) * 0.1) 154 | test_length = len(dataset) - train_length - valid_length 155 | train_dataset, valid_dataset, test_dataset = torch.utils.data.random_split( 156 | dataset, (train_length, valid_length, test_length)) 157 | train_data_loader = DataLoader(train_dataset, batch_size=batch_size, num_workers=8) 158 | valid_data_loader = DataLoader(valid_dataset, batch_size=batch_size, num_workers=8) 159 | test_data_loader = DataLoader(test_dataset, batch_size=batch_size, num_workers=8) 160 | model = get_model(model_name, dataset).to(device) 161 | criterion = torch.nn.BCELoss() 162 | optimizer = torch.optim.Adam(params=model.parameters(), lr=learning_rate, weight_decay=weight_decay) 163 | early_stopper = EarlyStopper(num_trials=2, save_path=f'{save_dir}/{model_name}.pt') 164 | for epoch_i in range(epoch): 165 | train(model, optimizer, train_data_loader, criterion, device) 166 | auc = test(model, valid_data_loader, device) 167 | print('epoch:', epoch_i, 'validation: auc:', auc) 168 | if not early_stopper.is_continuable(model, auc): 169 | print(f'validation: best auc: {early_stopper.best_accuracy}') 170 | break 171 | auc = test(model, test_data_loader, device) 172 | print(f'test auc: {auc}') 173 | 174 | 175 | if __name__ == '__main__': 176 | import argparse 177 | 178 | parser = argparse.ArgumentParser() 179 | parser.add_argument('--dataset_name', default='criteo') 180 | parser.add_argument('--dataset_path', help='criteo/train.txt, avazu/train, or ml-1m/ratings.dat') 181 | parser.add_argument('--model_name', default='afi') 182 | parser.add_argument('--epoch', type=int, default=100) 183 | parser.add_argument('--learning_rate', type=float, default=0.001) 184 | parser.add_argument('--batch_size', type=int, default=2048) 185 | parser.add_argument('--weight_decay', type=float, default=1e-6) 186 | parser.add_argument('--device', default='cuda:0') 187 | parser.add_argument('--save_dir', default='chkpt') 188 | args = parser.parse_args() 189 | main(args.dataset_name, 190 | args.dataset_path, 191 | args.model_name, 192 | args.epoch, 193 | args.learning_rate, 194 | args.batch_size, 195 | args.weight_decay, 196 | args.device, 197 | args.save_dir) 198 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | torch>=1.1 2 | scikit-learn 3 | numpy 4 | pandas 5 | tqdm 6 | lmdb -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | from pathlib import Path 2 | 3 | from setuptools import setup, find_packages 4 | 5 | 6 | if __name__ == '__main__': 7 | 8 | with open(Path(__file__).parent / 'README.md', encoding='utf-8') as f: 9 | long_description = f.read() 10 | 11 | setup( 12 | name="torchfm", 13 | version="0.7.0", 14 | description="PyTorch implementation of Factorization Machine Models", 15 | long_description=long_description, 16 | long_description_content_type="text/markdown", 17 | url="https://github.com/rixwew/torchfm", 18 | author="rixwew", 19 | author_email="rixwew@gmail.com", 20 | packages=find_packages(exclude=["examples", "docs"]), 21 | ) 22 | -------------------------------------------------------------------------------- /test/test_layers.py: -------------------------------------------------------------------------------- 1 | import unittest 2 | 3 | import numpy as np 4 | import torch 5 | 6 | from torchfm.layer import AnovaKernel 7 | 8 | 9 | class TestAnovaKernel(unittest.TestCase): 10 | 11 | def test_forward_order_2(self): 12 | batch_size, num_fields, embed_dim = 32, 16, 16 13 | kernel = AnovaKernel(order=2, reduce_sum=True).eval() 14 | with torch.no_grad(): 15 | x = torch.FloatTensor(np.random.randn(batch_size, num_fields, embed_dim)) 16 | y_true = 0 17 | for i in range(num_fields - 1): 18 | for j in range(i + 1, num_fields): 19 | y_true = x[:, i, :] * x[:, j, :] + y_true 20 | y_true = torch.sum(y_true, dim=1, keepdim=True).numpy() 21 | y_pred = kernel(x).numpy() 22 | np.testing.assert_almost_equal(y_pred, y_true, 3) 23 | 24 | def test_forward_order_3(self): 25 | batch_size, num_fields, embed_dim = 32, 16, 16 26 | kernel = AnovaKernel(order=3, reduce_sum=True).eval() 27 | with torch.no_grad(): 28 | x = torch.FloatTensor(np.random.randn(batch_size, num_fields, embed_dim)) 29 | y_true = 0 30 | for i in range(num_fields - 2): 31 | for j in range(i + 1, num_fields - 1): 32 | for k in range(j + 1, num_fields): 33 | y_true = x[:, i, :] * x[:, j, :] * x[:, k, :] + y_true 34 | y_true = torch.sum(y_true, dim=1, keepdim=True).numpy() 35 | y_pred = kernel(x).numpy() 36 | np.testing.assert_almost_equal(y_pred, y_true, 3) 37 | 38 | def test_forward_order_4(self): 39 | batch_size, num_fields, embed_dim = 32, 16, 16 40 | kernel = AnovaKernel(order=4, reduce_sum=True).eval() 41 | with torch.no_grad(): 42 | x = torch.FloatTensor(np.random.randn(batch_size, num_fields, embed_dim)) 43 | y_true = 0 44 | for i in range(num_fields - 3): 45 | for j in range(i + 1, num_fields - 2): 46 | for k in range(j + 1, num_fields - 1): 47 | for l in range(k + 1, num_fields): 48 | y_true = x[:, i, :] * x[:, j, :] * x[:, k, :] * x[:, l, :] + y_true 49 | y_true = torch.sum(y_true, dim=1, keepdim=True).numpy() 50 | y_pred = kernel(x).numpy() 51 | np.testing.assert_almost_equal(y_pred, y_true, 3) 52 | 53 | 54 | if __name__ == '__main__': 55 | unittest.main(verbosity=2) 56 | -------------------------------------------------------------------------------- /torchfm/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rixwew/pytorch-fm/f74ad19771eda104e99874d19dc892e988ec53fa/torchfm/__init__.py -------------------------------------------------------------------------------- /torchfm/dataset/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rixwew/pytorch-fm/f74ad19771eda104e99874d19dc892e988ec53fa/torchfm/dataset/__init__.py -------------------------------------------------------------------------------- /torchfm/dataset/avazu.py: -------------------------------------------------------------------------------- 1 | import shutil 2 | import struct 3 | from collections import defaultdict 4 | from pathlib import Path 5 | 6 | import lmdb 7 | import numpy as np 8 | import torch.utils.data 9 | from tqdm import tqdm 10 | 11 | 12 | class AvazuDataset(torch.utils.data.Dataset): 13 | """ 14 | Avazu Click-Through Rate Prediction Dataset 15 | 16 | Dataset preparation 17 | Remove the infrequent features (appearing in less than threshold instances) and treat them as a single feature 18 | 19 | :param dataset_path: avazu train path 20 | :param cache_path: lmdb cache path 21 | :param rebuild_cache: If True, lmdb cache is refreshed 22 | :param min_threshold: infrequent feature threshold 23 | 24 | Reference 25 | https://www.kaggle.com/c/avazu-ctr-prediction 26 | """ 27 | 28 | def __init__(self, dataset_path=None, cache_path='.avazu', rebuild_cache=False, min_threshold=4): 29 | self.NUM_FEATS = 22 30 | self.min_threshold = min_threshold 31 | if rebuild_cache or not Path(cache_path).exists(): 32 | shutil.rmtree(cache_path, ignore_errors=True) 33 | if dataset_path is None: 34 | raise ValueError('create cache: failed: dataset_path is None') 35 | self.__build_cache(dataset_path, cache_path) 36 | self.env = lmdb.open(cache_path, create=False, lock=False, readonly=True) 37 | with self.env.begin(write=False) as txn: 38 | self.length = txn.stat()['entries'] - 1 39 | self.field_dims = np.frombuffer(txn.get(b'field_dims'), dtype=np.uint32) 40 | 41 | def __getitem__(self, index): 42 | with self.env.begin(write=False) as txn: 43 | np_array = np.frombuffer( 44 | txn.get(struct.pack('>I', index)), dtype=np.uint32).astype(dtype=np.long) 45 | return np_array[1:], np_array[0] 46 | 47 | def __len__(self): 48 | return self.length 49 | 50 | def __build_cache(self, path, cache_path): 51 | feat_mapper, defaults = self.__get_feat_mapper(path) 52 | with lmdb.open(cache_path, map_size=int(1e11)) as env: 53 | field_dims = np.zeros(self.NUM_FEATS, dtype=np.uint32) 54 | for i, fm in feat_mapper.items(): 55 | field_dims[i - 1] = len(fm) + 1 56 | with env.begin(write=True) as txn: 57 | txn.put(b'field_dims', field_dims.tobytes()) 58 | for buffer in self.__yield_buffer(path, feat_mapper, defaults): 59 | with env.begin(write=True) as txn: 60 | for key, value in buffer: 61 | txn.put(key, value) 62 | 63 | def __get_feat_mapper(self, path): 64 | feat_cnts = defaultdict(lambda: defaultdict(int)) 65 | with open(path) as f: 66 | f.readline() 67 | pbar = tqdm(f, mininterval=1, smoothing=0.1) 68 | pbar.set_description('Create avazu dataset cache: counting features') 69 | for line in pbar: 70 | values = line.rstrip('\n').split(',') 71 | if len(values) != self.NUM_FEATS + 2: 72 | continue 73 | for i in range(1, self.NUM_FEATS + 1): 74 | feat_cnts[i][values[i + 1]] += 1 75 | feat_mapper = {i: {feat for feat, c in cnt.items() if c >= self.min_threshold} for i, cnt in feat_cnts.items()} 76 | feat_mapper = {i: {feat: idx for idx, feat in enumerate(cnt)} for i, cnt in feat_mapper.items()} 77 | defaults = {i: len(cnt) for i, cnt in feat_mapper.items()} 78 | return feat_mapper, defaults 79 | 80 | def __yield_buffer(self, path, feat_mapper, defaults, buffer_size=int(1e5)): 81 | item_idx = 0 82 | buffer = list() 83 | with open(path) as f: 84 | f.readline() 85 | pbar = tqdm(f, mininterval=1, smoothing=0.1) 86 | pbar.set_description('Create avazu dataset cache: setup lmdb') 87 | for line in pbar: 88 | values = line.rstrip('\n').split(',') 89 | if len(values) != self.NUM_FEATS + 2: 90 | continue 91 | np_array = np.zeros(self.NUM_FEATS + 1, dtype=np.uint32) 92 | np_array[0] = int(values[1]) 93 | for i in range(1, self.NUM_FEATS + 1): 94 | np_array[i] = feat_mapper[i].get(values[i+1], defaults[i]) 95 | buffer.append((struct.pack('>I', item_idx), np_array.tobytes())) 96 | item_idx += 1 97 | if item_idx % buffer_size == 0: 98 | yield buffer 99 | buffer.clear() 100 | yield buffer 101 | -------------------------------------------------------------------------------- /torchfm/dataset/criteo.py: -------------------------------------------------------------------------------- 1 | import math 2 | import shutil 3 | import struct 4 | from collections import defaultdict 5 | from functools import lru_cache 6 | from pathlib import Path 7 | 8 | import lmdb 9 | import numpy as np 10 | import torch.utils.data 11 | from tqdm import tqdm 12 | 13 | 14 | class CriteoDataset(torch.utils.data.Dataset): 15 | """ 16 | Criteo Display Advertising Challenge Dataset 17 | 18 | Data prepration: 19 | * Remove the infrequent features (appearing in less than threshold instances) and treat them as a single feature 20 | * Discretize numerical values by log2 transformation which is proposed by the winner of Criteo Competition 21 | 22 | :param dataset_path: criteo train.txt path. 23 | :param cache_path: lmdb cache path. 24 | :param rebuild_cache: If True, lmdb cache is refreshed. 25 | :param min_threshold: infrequent feature threshold. 26 | 27 | Reference: 28 | https://labs.criteo.com/2014/02/kaggle-display-advertising-challenge-dataset 29 | https://www.csie.ntu.edu.tw/~r01922136/kaggle-2014-criteo.pdf 30 | """ 31 | 32 | def __init__(self, dataset_path=None, cache_path='.criteo', rebuild_cache=False, min_threshold=10): 33 | self.NUM_FEATS = 39 34 | self.NUM_INT_FEATS = 13 35 | self.min_threshold = min_threshold 36 | if rebuild_cache or not Path(cache_path).exists(): 37 | shutil.rmtree(cache_path, ignore_errors=True) 38 | if dataset_path is None: 39 | raise ValueError('create cache: failed: dataset_path is None') 40 | self.__build_cache(dataset_path, cache_path) 41 | self.env = lmdb.open(cache_path, create=False, lock=False, readonly=True) 42 | with self.env.begin(write=False) as txn: 43 | self.length = txn.stat()['entries'] - 1 44 | self.field_dims = np.frombuffer(txn.get(b'field_dims'), dtype=np.uint32) 45 | 46 | def __getitem__(self, index): 47 | with self.env.begin(write=False) as txn: 48 | np_array = np.frombuffer( 49 | txn.get(struct.pack('>I', index)), dtype=np.uint32).astype(dtype=np.long) 50 | return np_array[1:], np_array[0] 51 | 52 | def __len__(self): 53 | return self.length 54 | 55 | def __build_cache(self, path, cache_path): 56 | feat_mapper, defaults = self.__get_feat_mapper(path) 57 | with lmdb.open(cache_path, map_size=int(1e11)) as env: 58 | field_dims = np.zeros(self.NUM_FEATS, dtype=np.uint32) 59 | for i, fm in feat_mapper.items(): 60 | field_dims[i - 1] = len(fm) + 1 61 | with env.begin(write=True) as txn: 62 | txn.put(b'field_dims', field_dims.tobytes()) 63 | for buffer in self.__yield_buffer(path, feat_mapper, defaults): 64 | with env.begin(write=True) as txn: 65 | for key, value in buffer: 66 | txn.put(key, value) 67 | 68 | def __get_feat_mapper(self, path): 69 | feat_cnts = defaultdict(lambda: defaultdict(int)) 70 | with open(path) as f: 71 | pbar = tqdm(f, mininterval=1, smoothing=0.1) 72 | pbar.set_description('Create criteo dataset cache: counting features') 73 | for line in pbar: 74 | values = line.rstrip('\n').split('\t') 75 | if len(values) != self.NUM_FEATS + 1: 76 | continue 77 | for i in range(1, self.NUM_INT_FEATS + 1): 78 | feat_cnts[i][convert_numeric_feature(values[i])] += 1 79 | for i in range(self.NUM_INT_FEATS + 1, self.NUM_FEATS + 1): 80 | feat_cnts[i][values[i]] += 1 81 | feat_mapper = {i: {feat for feat, c in cnt.items() if c >= self.min_threshold} for i, cnt in feat_cnts.items()} 82 | feat_mapper = {i: {feat: idx for idx, feat in enumerate(cnt)} for i, cnt in feat_mapper.items()} 83 | defaults = {i: len(cnt) for i, cnt in feat_mapper.items()} 84 | return feat_mapper, defaults 85 | 86 | def __yield_buffer(self, path, feat_mapper, defaults, buffer_size=int(1e5)): 87 | item_idx = 0 88 | buffer = list() 89 | with open(path) as f: 90 | pbar = tqdm(f, mininterval=1, smoothing=0.1) 91 | pbar.set_description('Create criteo dataset cache: setup lmdb') 92 | for line in pbar: 93 | values = line.rstrip('\n').split('\t') 94 | if len(values) != self.NUM_FEATS + 1: 95 | continue 96 | np_array = np.zeros(self.NUM_FEATS + 1, dtype=np.uint32) 97 | np_array[0] = int(values[0]) 98 | for i in range(1, self.NUM_INT_FEATS + 1): 99 | np_array[i] = feat_mapper[i].get(convert_numeric_feature(values[i]), defaults[i]) 100 | for i in range(self.NUM_INT_FEATS + 1, self.NUM_FEATS + 1): 101 | np_array[i] = feat_mapper[i].get(values[i], defaults[i]) 102 | buffer.append((struct.pack('>I', item_idx), np_array.tobytes())) 103 | item_idx += 1 104 | if item_idx % buffer_size == 0: 105 | yield buffer 106 | buffer.clear() 107 | yield buffer 108 | 109 | 110 | @lru_cache(maxsize=None) 111 | def convert_numeric_feature(val: str): 112 | if val == '': 113 | return 'NULL' 114 | v = int(val) 115 | if v > 2: 116 | return str(int(math.log(v) ** 2)) 117 | else: 118 | return str(v - 2) 119 | -------------------------------------------------------------------------------- /torchfm/dataset/movielens.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import pandas as pd 3 | import torch.utils.data 4 | 5 | 6 | class MovieLens20MDataset(torch.utils.data.Dataset): 7 | """ 8 | MovieLens 20M Dataset 9 | 10 | Data preparation 11 | treat samples with a rating less than 3 as negative samples 12 | 13 | :param dataset_path: MovieLens dataset path 14 | 15 | Reference: 16 | https://grouplens.org/datasets/movielens 17 | """ 18 | 19 | def __init__(self, dataset_path, sep=',', engine='c', header='infer'): 20 | data = pd.read_csv(dataset_path, sep=sep, engine=engine, header=header).to_numpy()[:, :3] 21 | self.items = data[:, :2].astype(np.int) - 1 # -1 because ID begins from 1 22 | self.targets = self.__preprocess_target(data[:, 2]).astype(np.float32) 23 | self.field_dims = np.max(self.items, axis=0) + 1 24 | self.user_field_idx = np.array((0, ), dtype=np.long) 25 | self.item_field_idx = np.array((1,), dtype=np.long) 26 | 27 | def __len__(self): 28 | return self.targets.shape[0] 29 | 30 | def __getitem__(self, index): 31 | return self.items[index], self.targets[index] 32 | 33 | def __preprocess_target(self, target): 34 | target[target <= 3] = 0 35 | target[target > 3] = 1 36 | return target 37 | 38 | 39 | class MovieLens1MDataset(MovieLens20MDataset): 40 | """ 41 | MovieLens 1M Dataset 42 | 43 | Data preparation 44 | treat samples with a rating less than 3 as negative samples 45 | 46 | :param dataset_path: MovieLens dataset path 47 | 48 | Reference: 49 | https://grouplens.org/datasets/movielens 50 | """ 51 | 52 | def __init__(self, dataset_path): 53 | super().__init__(dataset_path, sep='::', engine='python', header=None) 54 | -------------------------------------------------------------------------------- /torchfm/layer.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import torch 3 | import torch.nn.functional as F 4 | 5 | 6 | class FeaturesLinear(torch.nn.Module): 7 | 8 | def __init__(self, field_dims, output_dim=1): 9 | super().__init__() 10 | self.fc = torch.nn.Embedding(sum(field_dims), output_dim) 11 | self.bias = torch.nn.Parameter(torch.zeros((output_dim,))) 12 | self.offsets = np.array((0, *np.cumsum(field_dims)[:-1]), dtype=np.long) 13 | 14 | def forward(self, x): 15 | """ 16 | :param x: Long tensor of size ``(batch_size, num_fields)`` 17 | """ 18 | x = x + x.new_tensor(self.offsets).unsqueeze(0) 19 | return torch.sum(self.fc(x), dim=1) + self.bias 20 | 21 | 22 | class FeaturesEmbedding(torch.nn.Module): 23 | 24 | def __init__(self, field_dims, embed_dim): 25 | super().__init__() 26 | self.embedding = torch.nn.Embedding(sum(field_dims), embed_dim) 27 | self.offsets = np.array((0, *np.cumsum(field_dims)[:-1]), dtype=np.long) 28 | torch.nn.init.xavier_uniform_(self.embedding.weight.data) 29 | 30 | def forward(self, x): 31 | """ 32 | :param x: Long tensor of size ``(batch_size, num_fields)`` 33 | """ 34 | x = x + x.new_tensor(self.offsets).unsqueeze(0) 35 | return self.embedding(x) 36 | 37 | 38 | class FieldAwareFactorizationMachine(torch.nn.Module): 39 | 40 | def __init__(self, field_dims, embed_dim): 41 | super().__init__() 42 | self.num_fields = len(field_dims) 43 | self.embeddings = torch.nn.ModuleList([ 44 | torch.nn.Embedding(sum(field_dims), embed_dim) for _ in range(self.num_fields) 45 | ]) 46 | self.offsets = np.array((0, *np.cumsum(field_dims)[:-1]), dtype=np.long) 47 | for embedding in self.embeddings: 48 | torch.nn.init.xavier_uniform_(embedding.weight.data) 49 | 50 | def forward(self, x): 51 | """ 52 | :param x: Long tensor of size ``(batch_size, num_fields)`` 53 | """ 54 | x = x + x.new_tensor(self.offsets).unsqueeze(0) 55 | xs = [self.embeddings[i](x) for i in range(self.num_fields)] 56 | ix = list() 57 | for i in range(self.num_fields - 1): 58 | for j in range(i + 1, self.num_fields): 59 | ix.append(xs[j][:, i] * xs[i][:, j]) 60 | ix = torch.stack(ix, dim=1) 61 | return ix 62 | 63 | 64 | class FactorizationMachine(torch.nn.Module): 65 | 66 | def __init__(self, reduce_sum=True): 67 | super().__init__() 68 | self.reduce_sum = reduce_sum 69 | 70 | def forward(self, x): 71 | """ 72 | :param x: Float tensor of size ``(batch_size, num_fields, embed_dim)`` 73 | """ 74 | square_of_sum = torch.sum(x, dim=1) ** 2 75 | sum_of_square = torch.sum(x ** 2, dim=1) 76 | ix = square_of_sum - sum_of_square 77 | if self.reduce_sum: 78 | ix = torch.sum(ix, dim=1, keepdim=True) 79 | return 0.5 * ix 80 | 81 | 82 | class MultiLayerPerceptron(torch.nn.Module): 83 | 84 | def __init__(self, input_dim, embed_dims, dropout, output_layer=True): 85 | super().__init__() 86 | layers = list() 87 | for embed_dim in embed_dims: 88 | layers.append(torch.nn.Linear(input_dim, embed_dim)) 89 | layers.append(torch.nn.BatchNorm1d(embed_dim)) 90 | layers.append(torch.nn.ReLU()) 91 | layers.append(torch.nn.Dropout(p=dropout)) 92 | input_dim = embed_dim 93 | if output_layer: 94 | layers.append(torch.nn.Linear(input_dim, 1)) 95 | self.mlp = torch.nn.Sequential(*layers) 96 | 97 | def forward(self, x): 98 | """ 99 | :param x: Float tensor of size ``(batch_size, embed_dim)`` 100 | """ 101 | return self.mlp(x) 102 | 103 | 104 | class InnerProductNetwork(torch.nn.Module): 105 | 106 | def forward(self, x): 107 | """ 108 | :param x: Float tensor of size ``(batch_size, num_fields, embed_dim)`` 109 | """ 110 | num_fields = x.shape[1] 111 | row, col = list(), list() 112 | for i in range(num_fields - 1): 113 | for j in range(i + 1, num_fields): 114 | row.append(i), col.append(j) 115 | return torch.sum(x[:, row] * x[:, col], dim=2) 116 | 117 | 118 | class OuterProductNetwork(torch.nn.Module): 119 | 120 | def __init__(self, num_fields, embed_dim, kernel_type='mat'): 121 | super().__init__() 122 | num_ix = num_fields * (num_fields - 1) // 2 123 | if kernel_type == 'mat': 124 | kernel_shape = embed_dim, num_ix, embed_dim 125 | elif kernel_type == 'vec': 126 | kernel_shape = num_ix, embed_dim 127 | elif kernel_type == 'num': 128 | kernel_shape = num_ix, 1 129 | else: 130 | raise ValueError('unknown kernel type: ' + kernel_type) 131 | self.kernel_type = kernel_type 132 | self.kernel = torch.nn.Parameter(torch.zeros(kernel_shape)) 133 | torch.nn.init.xavier_uniform_(self.kernel.data) 134 | 135 | def forward(self, x): 136 | """ 137 | :param x: Float tensor of size ``(batch_size, num_fields, embed_dim)`` 138 | """ 139 | num_fields = x.shape[1] 140 | row, col = list(), list() 141 | for i in range(num_fields - 1): 142 | for j in range(i + 1, num_fields): 143 | row.append(i), col.append(j) 144 | p, q = x[:, row], x[:, col] 145 | if self.kernel_type == 'mat': 146 | kp = torch.sum(p.unsqueeze(1) * self.kernel, dim=-1).permute(0, 2, 1) 147 | return torch.sum(kp * q, -1) 148 | else: 149 | return torch.sum(p * q * self.kernel.unsqueeze(0), -1) 150 | 151 | 152 | class CrossNetwork(torch.nn.Module): 153 | 154 | def __init__(self, input_dim, num_layers): 155 | super().__init__() 156 | self.num_layers = num_layers 157 | self.w = torch.nn.ModuleList([ 158 | torch.nn.Linear(input_dim, 1, bias=False) for _ in range(num_layers) 159 | ]) 160 | self.b = torch.nn.ParameterList([ 161 | torch.nn.Parameter(torch.zeros((input_dim,))) for _ in range(num_layers) 162 | ]) 163 | 164 | def forward(self, x): 165 | """ 166 | :param x: Float tensor of size ``(batch_size, num_fields, embed_dim)`` 167 | """ 168 | x0 = x 169 | for i in range(self.num_layers): 170 | xw = self.w[i](x) 171 | x = x0 * xw + self.b[i] + x 172 | return x 173 | 174 | 175 | class AttentionalFactorizationMachine(torch.nn.Module): 176 | 177 | def __init__(self, embed_dim, attn_size, dropouts): 178 | super().__init__() 179 | self.attention = torch.nn.Linear(embed_dim, attn_size) 180 | self.projection = torch.nn.Linear(attn_size, 1) 181 | self.fc = torch.nn.Linear(embed_dim, 1) 182 | self.dropouts = dropouts 183 | 184 | def forward(self, x): 185 | """ 186 | :param x: Float tensor of size ``(batch_size, num_fields, embed_dim)`` 187 | """ 188 | num_fields = x.shape[1] 189 | row, col = list(), list() 190 | for i in range(num_fields - 1): 191 | for j in range(i + 1, num_fields): 192 | row.append(i), col.append(j) 193 | p, q = x[:, row], x[:, col] 194 | inner_product = p * q 195 | attn_scores = F.relu(self.attention(inner_product)) 196 | attn_scores = F.softmax(self.projection(attn_scores), dim=1) 197 | attn_scores = F.dropout(attn_scores, p=self.dropouts[0], training=self.training) 198 | attn_output = torch.sum(attn_scores * inner_product, dim=1) 199 | attn_output = F.dropout(attn_output, p=self.dropouts[1], training=self.training) 200 | return self.fc(attn_output) 201 | 202 | 203 | class CompressedInteractionNetwork(torch.nn.Module): 204 | 205 | def __init__(self, input_dim, cross_layer_sizes, split_half=True): 206 | super().__init__() 207 | self.num_layers = len(cross_layer_sizes) 208 | self.split_half = split_half 209 | self.conv_layers = torch.nn.ModuleList() 210 | prev_dim, fc_input_dim = input_dim, 0 211 | for i in range(self.num_layers): 212 | cross_layer_size = cross_layer_sizes[i] 213 | self.conv_layers.append(torch.nn.Conv1d(input_dim * prev_dim, cross_layer_size, 1, 214 | stride=1, dilation=1, bias=True)) 215 | if self.split_half and i != self.num_layers - 1: 216 | cross_layer_size //= 2 217 | prev_dim = cross_layer_size 218 | fc_input_dim += prev_dim 219 | self.fc = torch.nn.Linear(fc_input_dim, 1) 220 | 221 | def forward(self, x): 222 | """ 223 | :param x: Float tensor of size ``(batch_size, num_fields, embed_dim)`` 224 | """ 225 | xs = list() 226 | x0, h = x.unsqueeze(2), x 227 | for i in range(self.num_layers): 228 | x = x0 * h.unsqueeze(1) 229 | batch_size, f0_dim, fin_dim, embed_dim = x.shape 230 | x = x.view(batch_size, f0_dim * fin_dim, embed_dim) 231 | x = F.relu(self.conv_layers[i](x)) 232 | if self.split_half and i != self.num_layers - 1: 233 | x, h = torch.split(x, x.shape[1] // 2, dim=1) 234 | else: 235 | h = x 236 | xs.append(x) 237 | return self.fc(torch.sum(torch.cat(xs, dim=1), 2)) 238 | 239 | 240 | class AnovaKernel(torch.nn.Module): 241 | 242 | def __init__(self, order, reduce_sum=True): 243 | super().__init__() 244 | self.order = order 245 | self.reduce_sum = reduce_sum 246 | 247 | def forward(self, x): 248 | """ 249 | :param x: Float tensor of size ``(batch_size, num_fields, embed_dim)`` 250 | """ 251 | batch_size, num_fields, embed_dim = x.shape 252 | a_prev = torch.ones((batch_size, num_fields + 1, embed_dim), dtype=torch.float).to(x.device) 253 | for t in range(self.order): 254 | a = torch.zeros((batch_size, num_fields + 1, embed_dim), dtype=torch.float).to(x.device) 255 | a[:, t+1:, :] += x[:, t:, :] * a_prev[:, t:-1, :] 256 | a = torch.cumsum(a, dim=1) 257 | a_prev = a 258 | if self.reduce_sum: 259 | return torch.sum(a[:, -1, :], dim=-1, keepdim=True) 260 | else: 261 | return a[:, -1, :] 262 | -------------------------------------------------------------------------------- /torchfm/model/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rixwew/pytorch-fm/f74ad19771eda104e99874d19dc892e988ec53fa/torchfm/model/__init__.py -------------------------------------------------------------------------------- /torchfm/model/afi.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn.functional as F 3 | 4 | from torchfm.layer import FeaturesEmbedding, FeaturesLinear, MultiLayerPerceptron 5 | 6 | 7 | class AutomaticFeatureInteractionModel(torch.nn.Module): 8 | """ 9 | A pytorch implementation of AutoInt. 10 | 11 | Reference: 12 | W Song, et al. AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks, 2018. 13 | """ 14 | 15 | def __init__(self, field_dims, embed_dim, atten_embed_dim, num_heads, num_layers, mlp_dims, dropouts, has_residual=True): 16 | super().__init__() 17 | self.num_fields = len(field_dims) 18 | self.linear = FeaturesLinear(field_dims) 19 | self.embedding = FeaturesEmbedding(field_dims, embed_dim) 20 | self.atten_embedding = torch.nn.Linear(embed_dim, atten_embed_dim) 21 | self.embed_output_dim = len(field_dims) * embed_dim 22 | self.atten_output_dim = len(field_dims) * atten_embed_dim 23 | self.has_residual = has_residual 24 | self.mlp = MultiLayerPerceptron(self.embed_output_dim, mlp_dims, dropouts[1]) 25 | self.self_attns = torch.nn.ModuleList([ 26 | torch.nn.MultiheadAttention(atten_embed_dim, num_heads, dropout=dropouts[0]) for _ in range(num_layers) 27 | ]) 28 | self.attn_fc = torch.nn.Linear(self.atten_output_dim, 1) 29 | if self.has_residual: 30 | self.V_res_embedding = torch.nn.Linear(embed_dim, atten_embed_dim) 31 | 32 | def forward(self, x): 33 | """ 34 | :param x: Long tensor of size ``(batch_size, num_fields)`` 35 | """ 36 | embed_x = self.embedding(x) 37 | atten_x = self.atten_embedding(embed_x) 38 | cross_term = atten_x.transpose(0, 1) 39 | for self_attn in self.self_attns: 40 | cross_term, _ = self_attn(cross_term, cross_term, cross_term) 41 | cross_term = cross_term.transpose(0, 1) 42 | if self.has_residual: 43 | V_res = self.V_res_embedding(embed_x) 44 | cross_term += V_res 45 | cross_term = F.relu(cross_term).contiguous().view(-1, self.atten_output_dim) 46 | x = self.linear(x) + self.attn_fc(cross_term) + self.mlp(embed_x.view(-1, self.embed_output_dim)) 47 | return torch.sigmoid(x.squeeze(1)) 48 | -------------------------------------------------------------------------------- /torchfm/model/afm.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | from torchfm.layer import FeaturesEmbedding, FeaturesLinear, AttentionalFactorizationMachine 4 | 5 | 6 | class AttentionalFactorizationMachineModel(torch.nn.Module): 7 | """ 8 | A pytorch implementation of Attentional Factorization Machine. 9 | 10 | Reference: 11 | J Xiao, et al. Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks, 2017. 12 | """ 13 | 14 | def __init__(self, field_dims, embed_dim, attn_size, dropouts): 15 | super().__init__() 16 | self.num_fields = len(field_dims) 17 | self.embedding = FeaturesEmbedding(field_dims, embed_dim) 18 | self.linear = FeaturesLinear(field_dims) 19 | self.afm = AttentionalFactorizationMachine(embed_dim, attn_size, dropouts) 20 | 21 | def forward(self, x): 22 | """ 23 | :param x: Long tensor of size ``(batch_size, num_fields)`` 24 | """ 25 | x = self.linear(x) + self.afm(self.embedding(x)) 26 | return torch.sigmoid(x.squeeze(1)) 27 | -------------------------------------------------------------------------------- /torchfm/model/afn.py: -------------------------------------------------------------------------------- 1 | import math 2 | import torch 3 | import torch.nn.functional as F 4 | 5 | from torchfm.layer import FeaturesEmbedding, FeaturesLinear, MultiLayerPerceptron 6 | 7 | class LNN(torch.nn.Module): 8 | """ 9 | A pytorch implementation of LNN layer 10 | Input shape 11 | - A 3D tensor with shape: ``(batch_size,field_size,embedding_size)``. 12 | Output shape 13 | - 2D tensor with shape:``(batch_size,LNN_dim*embedding_size)``. 14 | Arguments 15 | - **in_features** : Embedding of feature. 16 | - **num_fields**: int.The field size of feature. 17 | - **LNN_dim**: int.The number of Logarithmic neuron. 18 | - **bias**: bool.Whether or not use bias in LNN. 19 | """ 20 | def __init__(self, num_fields, embed_dim, LNN_dim, bias=False): 21 | super(LNN, self).__init__() 22 | self.num_fields = num_fields 23 | self.embed_dim = embed_dim 24 | self.LNN_dim = LNN_dim 25 | self.lnn_output_dim = LNN_dim * embed_dim 26 | self.weight = torch.nn.Parameter(torch.Tensor(LNN_dim, num_fields)) 27 | if bias: 28 | self.bias = torch.nn.Parameter(torch.Tensor(LNN_dim, embed_dim)) 29 | else: 30 | self.register_parameter('bias', None) 31 | self.reset_parameters() 32 | 33 | def reset_parameters(self): 34 | stdv = 1. / math.sqrt(self.weight.size(1)) 35 | self.weight.data.uniform_(-stdv, stdv) 36 | if self.bias is not None: 37 | self.bias.data.uniform_(-stdv, stdv) 38 | 39 | def forward(self, x): 40 | """ 41 | :param x: Long tensor of size ``(batch_size, num_fields, embedding_size)`` 42 | """ 43 | embed_x_abs = torch.abs(x) # Computes the element-wise absolute value of the given input tensor. 44 | embed_x_afn = torch.add(embed_x_abs, 1e-7) 45 | # Logarithmic Transformation 46 | embed_x_log = torch.log1p(embed_x_afn) # torch.log1p and torch.expm1 47 | lnn_out = torch.matmul(self.weight, embed_x_log) 48 | if self.bias is not None: 49 | lnn_out += self.bias 50 | lnn_exp = torch.expm1(lnn_out) 51 | output = F.relu(lnn_exp).contiguous().view(-1, self.lnn_output_dim) 52 | return output 53 | 54 | 55 | 56 | 57 | 58 | 59 | class AdaptiveFactorizationNetwork(torch.nn.Module): 60 | """ 61 | A pytorch implementation of AFN. 62 | 63 | Reference: 64 | Cheng W, et al. Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions, 2019. 65 | """ 66 | def __init__(self, field_dims, embed_dim, LNN_dim, mlp_dims, dropouts): 67 | super().__init__() 68 | self.num_fields = len(field_dims) 69 | self.linear = FeaturesLinear(field_dims) # Linear 70 | self.embedding = FeaturesEmbedding(field_dims, embed_dim) # Embedding 71 | self.LNN_dim = LNN_dim 72 | self.LNN_output_dim = self.LNN_dim * embed_dim 73 | self.LNN = LNN(self.num_fields, embed_dim, LNN_dim) 74 | self.mlp = MultiLayerPerceptron(self.LNN_output_dim, mlp_dims, dropouts[0]) 75 | 76 | def forward(self, x): 77 | """ 78 | :param x: Long tensor of size ``(batch_size, num_fields)`` 79 | """ 80 | embed_x = self.embedding(x) 81 | lnn_out = self.LNN(embed_x) 82 | x = self.linear(x) + self.mlp(lnn_out) 83 | return torch.sigmoid(x.squeeze(1)) 84 | 85 | -------------------------------------------------------------------------------- /torchfm/model/dcn.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | from torchfm.layer import FeaturesEmbedding, CrossNetwork, MultiLayerPerceptron 4 | 5 | 6 | class DeepCrossNetworkModel(torch.nn.Module): 7 | """ 8 | A pytorch implementation of Deep & Cross Network. 9 | 10 | Reference: 11 | R Wang, et al. Deep & Cross Network for Ad Click Predictions, 2017. 12 | """ 13 | 14 | def __init__(self, field_dims, embed_dim, num_layers, mlp_dims, dropout): 15 | super().__init__() 16 | self.embedding = FeaturesEmbedding(field_dims, embed_dim) 17 | self.embed_output_dim = len(field_dims) * embed_dim 18 | self.cn = CrossNetwork(self.embed_output_dim, num_layers) 19 | self.mlp = MultiLayerPerceptron(self.embed_output_dim, mlp_dims, dropout, output_layer=False) 20 | self.linear = torch.nn.Linear(mlp_dims[-1] + self.embed_output_dim, 1) 21 | 22 | def forward(self, x): 23 | """ 24 | :param x: Long tensor of size ``(batch_size, num_fields)`` 25 | """ 26 | embed_x = self.embedding(x).view(-1, self.embed_output_dim) 27 | x_l1 = self.cn(embed_x) 28 | h_l2 = self.mlp(embed_x) 29 | x_stack = torch.cat([x_l1, h_l2], dim=1) 30 | p = self.linear(x_stack) 31 | return torch.sigmoid(p.squeeze(1)) 32 | -------------------------------------------------------------------------------- /torchfm/model/dfm.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | from torchfm.layer import FactorizationMachine, FeaturesEmbedding, FeaturesLinear, MultiLayerPerceptron 4 | 5 | 6 | class DeepFactorizationMachineModel(torch.nn.Module): 7 | """ 8 | A pytorch implementation of DeepFM. 9 | 10 | Reference: 11 | H Guo, et al. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction, 2017. 12 | """ 13 | 14 | def __init__(self, field_dims, embed_dim, mlp_dims, dropout): 15 | super().__init__() 16 | self.linear = FeaturesLinear(field_dims) 17 | self.fm = FactorizationMachine(reduce_sum=True) 18 | self.embedding = FeaturesEmbedding(field_dims, embed_dim) 19 | self.embed_output_dim = len(field_dims) * embed_dim 20 | self.mlp = MultiLayerPerceptron(self.embed_output_dim, mlp_dims, dropout) 21 | 22 | def forward(self, x): 23 | """ 24 | :param x: Long tensor of size ``(batch_size, num_fields)`` 25 | """ 26 | embed_x = self.embedding(x) 27 | x = self.linear(x) + self.fm(embed_x) + self.mlp(embed_x.view(-1, self.embed_output_dim)) 28 | return torch.sigmoid(x.squeeze(1)) 29 | -------------------------------------------------------------------------------- /torchfm/model/ffm.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | from torchfm.layer import FeaturesLinear, FieldAwareFactorizationMachine 4 | 5 | 6 | class FieldAwareFactorizationMachineModel(torch.nn.Module): 7 | """ 8 | A pytorch implementation of Field-aware Factorization Machine. 9 | 10 | Reference: 11 | Y Juan, et al. Field-aware Factorization Machines for CTR Prediction, 2015. 12 | """ 13 | 14 | def __init__(self, field_dims, embed_dim): 15 | super().__init__() 16 | self.linear = FeaturesLinear(field_dims) 17 | self.ffm = FieldAwareFactorizationMachine(field_dims, embed_dim) 18 | 19 | def forward(self, x): 20 | """ 21 | :param x: Long tensor of size ``(batch_size, num_fields)`` 22 | """ 23 | ffm_term = torch.sum(torch.sum(self.ffm(x), dim=1), dim=1, keepdim=True) 24 | x = self.linear(x) + ffm_term 25 | return torch.sigmoid(x.squeeze(1)) 26 | -------------------------------------------------------------------------------- /torchfm/model/fm.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | from torchfm.layer import FactorizationMachine, FeaturesEmbedding, FeaturesLinear 4 | 5 | 6 | class FactorizationMachineModel(torch.nn.Module): 7 | """ 8 | A pytorch implementation of Factorization Machine. 9 | 10 | Reference: 11 | S Rendle, Factorization Machines, 2010. 12 | """ 13 | 14 | def __init__(self, field_dims, embed_dim): 15 | super().__init__() 16 | self.embedding = FeaturesEmbedding(field_dims, embed_dim) 17 | self.linear = FeaturesLinear(field_dims) 18 | self.fm = FactorizationMachine(reduce_sum=True) 19 | 20 | def forward(self, x): 21 | """ 22 | :param x: Long tensor of size ``(batch_size, num_fields)`` 23 | """ 24 | x = self.linear(x) + self.fm(self.embedding(x)) 25 | return torch.sigmoid(x.squeeze(1)) 26 | -------------------------------------------------------------------------------- /torchfm/model/fnfm.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | from torchfm.layer import FieldAwareFactorizationMachine, MultiLayerPerceptron, FeaturesLinear 4 | 5 | 6 | class FieldAwareNeuralFactorizationMachineModel(torch.nn.Module): 7 | """ 8 | A pytorch implementation of Field-aware Neural Factorization Machine. 9 | 10 | Reference: 11 | L Zhang, et al. Field-aware Neural Factorization Machine for Click-Through Rate Prediction, 2019. 12 | """ 13 | 14 | def __init__(self, field_dims, embed_dim, mlp_dims, dropouts): 15 | super().__init__() 16 | self.linear = FeaturesLinear(field_dims) 17 | self.ffm = FieldAwareFactorizationMachine(field_dims, embed_dim) 18 | self.ffm_output_dim = len(field_dims) * (len(field_dims) - 1) // 2 * embed_dim 19 | self.bn = torch.nn.BatchNorm1d(self.ffm_output_dim) 20 | self.dropout = torch.nn.Dropout(dropouts[0]) 21 | self.mlp = MultiLayerPerceptron(self.ffm_output_dim, mlp_dims, dropouts[1]) 22 | 23 | def forward(self, x): 24 | """ 25 | :param x: Long tensor of size ``(batch_size, num_fields)`` 26 | """ 27 | cross_term = self.ffm(x).view(-1, self.ffm_output_dim) 28 | cross_term = self.bn(cross_term) 29 | cross_term = self.dropout(cross_term) 30 | x = self.linear(x) + self.mlp(cross_term) 31 | return torch.sigmoid(x.squeeze(1)) 32 | -------------------------------------------------------------------------------- /torchfm/model/fnn.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | from torchfm.layer import FeaturesEmbedding, MultiLayerPerceptron 4 | 5 | 6 | class FactorizationSupportedNeuralNetworkModel(torch.nn.Module): 7 | """ 8 | A pytorch implementation of Neural Factorization Machine. 9 | 10 | Reference: 11 | W Zhang, et al. Deep Learning over Multi-field Categorical Data - A Case Study on User Response Prediction, 2016. 12 | """ 13 | 14 | def __init__(self, field_dims, embed_dim, mlp_dims, dropout): 15 | super().__init__() 16 | self.embedding = FeaturesEmbedding(field_dims, embed_dim) 17 | self.embed_output_dim = len(field_dims) * embed_dim 18 | self.mlp = MultiLayerPerceptron(self.embed_output_dim, mlp_dims, dropout) 19 | 20 | def forward(self, x): 21 | """ 22 | :param x: Long tensor of size ``(batch_size, num_fields)`` 23 | """ 24 | embed_x = self.embedding(x) 25 | x = self.mlp(embed_x.view(-1, self.embed_output_dim)) 26 | return torch.sigmoid(x.squeeze(1)) 27 | -------------------------------------------------------------------------------- /torchfm/model/hofm.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | from torchfm.layer import FeaturesLinear, FactorizationMachine, AnovaKernel, FeaturesEmbedding 4 | 5 | 6 | class HighOrderFactorizationMachineModel(torch.nn.Module): 7 | """ 8 | A pytorch implementation of Higher-Order Factorization Machines. 9 | 10 | Reference: 11 | M Blondel, et al. Higher-Order Factorization Machines, 2016. 12 | """ 13 | 14 | def __init__(self, field_dims, order, embed_dim): 15 | super().__init__() 16 | if order < 1: 17 | raise ValueError(f'invalid order: {order}') 18 | self.order = order 19 | self.embed_dim = embed_dim 20 | self.linear = FeaturesLinear(field_dims) 21 | if order >= 2: 22 | self.embedding = FeaturesEmbedding(field_dims, embed_dim * (order - 1)) 23 | self.fm = FactorizationMachine(reduce_sum=True) 24 | if order >= 3: 25 | self.kernels = torch.nn.ModuleList([ 26 | AnovaKernel(order=i, reduce_sum=True) for i in range(3, order + 1) 27 | ]) 28 | 29 | def forward(self, x): 30 | """ 31 | :param x: Long tensor of size ``(batch_size, num_fields)`` 32 | """ 33 | y = self.linear(x).squeeze(1) 34 | if self.order >= 2: 35 | x = self.embedding(x) 36 | x_part = x[:, :, :self.embed_dim] 37 | y += self.fm(x_part).squeeze(1) 38 | for i in range(self.order - 2): 39 | x_part = x[:, :, (i + 1) * self.embed_dim: (i + 2) * self.embed_dim] 40 | y += self.kernels[i](x_part).squeeze(1) 41 | return torch.sigmoid(y) 42 | -------------------------------------------------------------------------------- /torchfm/model/lr.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | from torchfm.layer import FeaturesLinear 4 | 5 | 6 | class LogisticRegressionModel(torch.nn.Module): 7 | """ 8 | A pytorch implementation of Logistic Regression. 9 | """ 10 | 11 | def __init__(self, field_dims): 12 | super().__init__() 13 | self.linear = FeaturesLinear(field_dims) 14 | 15 | def forward(self, x): 16 | """ 17 | :param x: Long tensor of size ``(batch_size, num_fields)`` 18 | """ 19 | return torch.sigmoid(self.linear(x).squeeze(1)) 20 | -------------------------------------------------------------------------------- /torchfm/model/ncf.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from torchfm.layer import FeaturesEmbedding, MultiLayerPerceptron 3 | 4 | 5 | class NeuralCollaborativeFiltering(torch.nn.Module): 6 | """ 7 | A pytorch implementation of Neural Collaborative Filtering. 8 | 9 | Reference: 10 | X He, et al. Neural Collaborative Filtering, 2017. 11 | """ 12 | 13 | def __init__(self, field_dims, user_field_idx, item_field_idx, embed_dim, mlp_dims, dropout): 14 | super().__init__() 15 | self.user_field_idx = user_field_idx 16 | self.item_field_idx = item_field_idx 17 | self.embedding = FeaturesEmbedding(field_dims, embed_dim) 18 | self.embed_output_dim = len(field_dims) * embed_dim 19 | self.mlp = MultiLayerPerceptron(self.embed_output_dim, mlp_dims, dropout, output_layer=False) 20 | self.fc = torch.nn.Linear(mlp_dims[-1] + embed_dim, 1) 21 | 22 | def forward(self, x): 23 | """ 24 | :param x: Long tensor of size ``(batch_size, num_user_fields)`` 25 | """ 26 | x = self.embedding(x) 27 | user_x = x[:, self.user_field_idx].squeeze(1) 28 | item_x = x[:, self.item_field_idx].squeeze(1) 29 | x = self.mlp(x.view(-1, self.embed_output_dim)) 30 | gmf = user_x * item_x 31 | x = torch.cat([gmf, x], dim=1) 32 | x = self.fc(x).squeeze(1) 33 | return torch.sigmoid(x) 34 | -------------------------------------------------------------------------------- /torchfm/model/nfm.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | from torchfm.layer import FactorizationMachine, FeaturesEmbedding, MultiLayerPerceptron, FeaturesLinear 4 | 5 | 6 | class NeuralFactorizationMachineModel(torch.nn.Module): 7 | """ 8 | A pytorch implementation of Neural Factorization Machine. 9 | 10 | Reference: 11 | X He and TS Chua, Neural Factorization Machines for Sparse Predictive Analytics, 2017. 12 | """ 13 | 14 | def __init__(self, field_dims, embed_dim, mlp_dims, dropouts): 15 | super().__init__() 16 | self.embedding = FeaturesEmbedding(field_dims, embed_dim) 17 | self.linear = FeaturesLinear(field_dims) 18 | self.fm = torch.nn.Sequential( 19 | FactorizationMachine(reduce_sum=False), 20 | torch.nn.BatchNorm1d(embed_dim), 21 | torch.nn.Dropout(dropouts[0]) 22 | ) 23 | self.mlp = MultiLayerPerceptron(embed_dim, mlp_dims, dropouts[1]) 24 | 25 | def forward(self, x): 26 | """ 27 | :param x: Long tensor of size ``(batch_size, num_fields)`` 28 | """ 29 | cross_term = self.fm(self.embedding(x)) 30 | x = self.linear(x) + self.mlp(cross_term) 31 | return torch.sigmoid(x.squeeze(1)) 32 | -------------------------------------------------------------------------------- /torchfm/model/pnn.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | from torchfm.layer import FeaturesEmbedding, FeaturesLinear, InnerProductNetwork, \ 4 | OuterProductNetwork, MultiLayerPerceptron 5 | 6 | 7 | class ProductNeuralNetworkModel(torch.nn.Module): 8 | """ 9 | A pytorch implementation of inner/outer Product Neural Network. 10 | Reference: 11 | Y Qu, et al. Product-based Neural Networks for User Response Prediction, 2016. 12 | """ 13 | 14 | def __init__(self, field_dims, embed_dim, mlp_dims, dropout, method='inner'): 15 | super().__init__() 16 | num_fields = len(field_dims) 17 | if method == 'inner': 18 | self.pn = InnerProductNetwork() 19 | elif method == 'outer': 20 | self.pn = OuterProductNetwork(num_fields, embed_dim) 21 | else: 22 | raise ValueError('unknown product type: ' + method) 23 | self.embedding = FeaturesEmbedding(field_dims, embed_dim) 24 | self.linear = FeaturesLinear(field_dims, embed_dim) 25 | self.embed_output_dim = num_fields * embed_dim 26 | self.mlp = MultiLayerPerceptron(num_fields * (num_fields - 1) // 2 + self.embed_output_dim, mlp_dims, dropout) 27 | 28 | def forward(self, x): 29 | """ 30 | :param x: Long tensor of size ``(batch_size, num_fields)`` 31 | """ 32 | embed_x = self.embedding(x) 33 | cross_term = self.pn(embed_x) 34 | x = torch.cat([embed_x.view(-1, self.embed_output_dim), cross_term], dim=1) 35 | x = self.mlp(x) 36 | return torch.sigmoid(x.squeeze(1)) 37 | -------------------------------------------------------------------------------- /torchfm/model/wd.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | from torchfm.layer import FeaturesLinear, MultiLayerPerceptron, FeaturesEmbedding 4 | 5 | 6 | class WideAndDeepModel(torch.nn.Module): 7 | """ 8 | A pytorch implementation of wide and deep learning. 9 | 10 | Reference: 11 | HT Cheng, et al. Wide & Deep Learning for Recommender Systems, 2016. 12 | """ 13 | 14 | def __init__(self, field_dims, embed_dim, mlp_dims, dropout): 15 | super().__init__() 16 | self.linear = FeaturesLinear(field_dims) 17 | self.embedding = FeaturesEmbedding(field_dims, embed_dim) 18 | self.embed_output_dim = len(field_dims) * embed_dim 19 | self.mlp = MultiLayerPerceptron(self.embed_output_dim, mlp_dims, dropout) 20 | 21 | def forward(self, x): 22 | """ 23 | :param x: Long tensor of size ``(batch_size, num_fields)`` 24 | """ 25 | embed_x = self.embedding(x) 26 | x = self.linear(x) + self.mlp(embed_x.view(-1, self.embed_output_dim)) 27 | return torch.sigmoid(x.squeeze(1)) 28 | -------------------------------------------------------------------------------- /torchfm/model/xdfm.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | from torchfm.layer import CompressedInteractionNetwork, FeaturesEmbedding, FeaturesLinear, MultiLayerPerceptron 4 | 5 | 6 | class ExtremeDeepFactorizationMachineModel(torch.nn.Module): 7 | """ 8 | A pytorch implementation of xDeepFM. 9 | 10 | Reference: 11 | J Lian, et al. xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems, 2018. 12 | """ 13 | 14 | def __init__(self, field_dims, embed_dim, mlp_dims, dropout, cross_layer_sizes, split_half=True): 15 | super().__init__() 16 | self.embedding = FeaturesEmbedding(field_dims, embed_dim) 17 | self.embed_output_dim = len(field_dims) * embed_dim 18 | self.cin = CompressedInteractionNetwork(len(field_dims), cross_layer_sizes, split_half) 19 | self.mlp = MultiLayerPerceptron(self.embed_output_dim, mlp_dims, dropout) 20 | self.linear = FeaturesLinear(field_dims) 21 | 22 | def forward(self, x): 23 | """ 24 | :param x: Long tensor of size ``(batch_size, num_fields)`` 25 | """ 26 | embed_x = self.embedding(x) 27 | x = self.linear(x) + self.cin(embed_x) + self.mlp(embed_x.view(-1, self.embed_output_dim)) 28 | return torch.sigmoid(x.squeeze(1)) 29 | --------------------------------------------------------------------------------