diff --git a/CHANGELOG.md b/CHANGELOG.md index 3eb41f9aab..509488ef9b 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -3,7 +3,7 @@ Changes Unreleased: -None +* FastText wrapper added, can be used for training FastText word representations and performing word2vec operations over it 0.13.4.1, 2017-01-04 diff --git a/docs/notebooks/FastText_Tutorial.ipynb b/docs/notebooks/FastText_Tutorial.ipynb new file mode 100644 index 0000000000..31144c3b96 --- /dev/null +++ b/docs/notebooks/FastText_Tutorial.ipynb @@ -0,0 +1,607 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Using FastText via Gensim" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This tutorial is about using the Gensim wrapper for the [FastText](https://github.com/facebookresearch/fastText) library for training FastText models, loading them and performing similarity operations and vector lookups analogous to Word2Vec." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## When to use FastText?\n", + "The main principle behind FastText is that the morphological structure of a word carries important information about the meaning of the word, which is not taken into account by traditional word embeddings, which train a unique word embedding for every individual word. This is especially significant for morphologically rich languages (German, Turkish) in which a single word can have a large number of morphological forms, each of which might occur rarely, thus making it hard to train good word embeddings. \n", + "FastText attempts to solve this by treating each word as the aggregation of its subwords. For the sake of simplicity and language-independence, subwords are taken to the character ngrams of the word. The vector for a word is simply taken to be the sum of all vectors of its component char-ngrams. \n", + "According to a detailed comparison of Word2Vec and FastText in [this notebook](https://github.com/RaRe-Technologies/gensim/blob/develop/docs/notebooks/Word2Vec_FastText_Comparison.ipynb), FastText does significantly better on syntactic tasks as compared to the original Word2Vec, especially when the size of the training corpus is small. Word2Vec slightly outperforms FastText on semantic tasks though. The differences grow smaller as the size of training corpus increases. \n", + "Training time for FastText is significantly higher than the Gensim version of Word2Vec (`15min 42s` vs `6min 42s` on text8, 17 mil tokens, 5 epochs, and a vector size of 100). \n", + "FastText can be used to obtain vectors for out-of-vocabulary (oov) words, by summing up vectors for its component char-ngrams, provided at least one of the char-ngrams was present in the training data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Training models" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the following examples, we'll use the Lee Corpus (which you already have if you've installed gensim)\n", + "\n", + "You need to have FastText setup locally to be able to train models. See [installation instructions for FastText](https://github.com/facebookresearch/fastText/#requirements) if you don't have FastText installed." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FastText(vocab=1762, size=100, alpha=0.025)\n" + ] + } + ], + "source": [ + "import gensim, os\n", + "from gensim.models.wrappers.fasttext import FastText\n", + "\n", + "# Set FastText home to the path to the FastText executable\n", + "ft_home = '/home/jayant/Projects/fastText/fasttext'\n", + "\n", + "# Set file names for train and test data\n", + "data_dir = '{}'.format(os.sep).join([gensim.__path__[0], 'test', 'test_data']) + os.sep\n", + "lee_train_file = data_dir + 'lee_background.cor'\n", + "\n", + "model = FastText.train(ft_home, lee_train_file)\n", + "\n", + "print(model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Hyperparameters for training the model follow the same pattern as Word2Vec. FastText supports the folllowing parameters from the original word2vec - \n", + " - model: Training architecture. Allowed values: `cbow`, `skipgram` (Default `cbow`)\n", + " - size: Size of embeddings to be learnt (Default 100)\n", + " - alpha: Initial learning rate (Default 0.025)\n", + " - window: Context window size (Default 5)\n", + " - min_count: Ignore words with number of occurrences below this (Default 5)\n", + " - loss: Training objective. Allowed values: `ns`, `hs`, `softmax` (Default `ns`)\n", + " - sample: Threshold for downsampling higher-frequency words (Default 0.001)\n", + " - negative: Number of negative words to sample, for `ns` (Default 5)\n", + " - iter: Number of epochs (Default 5)\n", + " - sorted_vocab: Sort vocab by descending frequency (Default 1)\n", + " - threads: Number of threads to use (Default 12)\n", + " \n", + "In addition, FastText has two additional parameters - \n", + " - min_n: min length of char ngrams to be used (Default 3)\n", + " - max_n: max length of char ngrams to be used for (Default 6)\n", + "These control the lengths of character ngrams that each word is broken down into while training and looking up embeddings. If `max_n` is set to 0, or to be lesser than `min_n`, no character ngrams are used, and the model effectively reduces to Word2Vec." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FastText(vocab=816, size=50, alpha=0.025)\n" + ] + } + ], + "source": [ + "model = FastText.train(ft_home, lee_train_file, size=50, alpha=0.05, min_count=10)\n", + "print(model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Continuation of training with FastText models is not supported." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Saving/loading models" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Models can be saved and loaded via the `load` and `save` methods." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FastText(vocab=816, size=50, alpha=0.025)\n" + ] + } + ], + "source": [ + "model.save('saved_fasttext_model')\n", + "loaded_model = FastText.load('saved_fasttext_model')\n", + "print(loaded_model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `save_word2vec_method` causes the vectors for ngrams to be lost. As a result, a model loaded in this way will behave as a regular word2vec model. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Word vector lookup\n", + "FastText models support vector lookups for out-of-vocabulary words by summing up character ngrams belonging to the word." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n", + "False\n", + "[-0.47196999 -0.17528 0.19518 -0.31948 0.42835999 0.083281\n", + " -0.15183 0.43415001 0.41251001 -0.10186 -0.54948997 0.12667\n", + " 0.14816 -0.065804 -0.21105 -0.42304999 0.011494 0.53068\n", + " -0.57410997 -0.53930998 -0.33537999 0.16154 0.12377 -0.23537\n", + " -0.14629 -0.34777001 0.27304 0.20597 0.12581 0.36671999\n", + " 0.32075 0.27351999 -0.13311 -0.04975 -0.52293003 -0.2766\n", + " 0.11863 -0.009231 -0.66074997 0.018031 0.57145 0.35547\n", + " 0.21588001 0.14431 -0.31865999 0.32027 0.55005002 0.19374999\n", + " 0.36609 -0.54184002]\n", + "[-0.4256132 -0.11521876 0.20166218 -0.34812452 0.30932881 0.02802653\n", + " -0.18951961 0.4175721 0.41008326 -0.09026544 -0.50756483 0.07746826\n", + " 0.09458492 0.01440104 -0.17157355 -0.35189211 0.00103696 0.50923289\n", + " -0.49944138 -0.38334864 -0.34287725 0.18023167 0.18014225 -0.22820314\n", + " -0.08267317 -0.31241801 0.26023088 0.20673522 0.07008089 0.31678561\n", + " 0.31590793 0.16198126 -0.09287339 -0.1722331 -0.43232849 -0.26644917\n", + " 0.10019614 0.08444232 -0.57080398 0.07581607 0.50339428 0.28109486\n", + " 0.05507131 0.10023506 -0.17840675 0.18620458 0.42583067 0.00790601\n", + " 0.2036875 -0.4925791 ]\n" + ] + } + ], + "source": [ + "print('night' in model.wv.vocab)\n", + "print('nights' in model.wv.vocab)\n", + "print(model['night'])\n", + "print(model['nights'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The word vector lookup operation only works if atleast one of the component character ngrams is present in the training corpus. For example -" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "KeyError", + "evalue": "'all ngrams for word axe absent from model'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Raises a KeyError since none of the character ngrams of the word `axe` are present in the training data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'axe'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/home/jayant/Projects/gensim/gensim/models/word2vec.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, words)\u001b[0m\n\u001b[1;32m 1304\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1305\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwords\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1306\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwords\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1307\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1308\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mstaticmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/home/jayant/Projects/gensim/gensim/models/keyedvectors.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, words)\u001b[0m\n\u001b[1;32m 363\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwords\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstring_types\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 364\u001b[0m \u001b[0;31m# allow calls like trained_model['office'], as a shorthand for trained_model[['office']]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 365\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mword_vec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwords\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 366\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 367\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mvstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mword_vec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mword\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mword\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mwords\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/home/jayant/Projects/gensim/gensim/models/wrappers/fasttext.pyc\u001b[0m in \u001b[0;36mword_vec\u001b[0;34m(self, word, use_norm)\u001b[0m\n\u001b[1;32m 89\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mword_vec\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mngrams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 90\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# No ngrams of the word are present in self.ngrams\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 91\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'all ngrams for word %s absent from model'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mword\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 92\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 93\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0minit_sims\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: 'all ngrams for word axe absent from model'" + ] + } + ], + "source": [ + "# Raises a KeyError since none of the character ngrams of the word `axe` are present in the training data\n", + "model['axe']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `in` operation works slightly differently from the original word2vec. It tests whether a vector for the given word exists or not, not whether the word is present in the word vocabulary. To test whether a word is present in the training word vocabulary -" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "False\n", + "True\n" + ] + } + ], + "source": [ + "# Tests if word present in vocab\n", + "print(\"word\" in model.wv.vocab)\n", + "# Tests if vector present for word\n", + "print(\"word\" in model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Similarity operations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similarity operations work the same way as word2vec. Out-of-vocabulary words can also be used, provided they have atleast one character ngram present in the training data." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "False\n", + "True\n" + ] + }, + { + "data": { + "text/plain": [ + "0.97944545147919504" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(\"nights\" in model.wv.vocab)\n", + "print(\"night\" in model.wv.vocab)\n", + "model.similarity(\"night\", \"nights\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Syntactically similar words generally have high similarity in FastText models, since a large number of the component char-ngrams will be the same. As a result, FastText generally does better at syntactic tasks than Word2Vec. A detailed comparison is provided [here](https://github.com/RaRe-Technologies/gensim/blob/develop/docs/notebooks/Word2Vec_FastText_Comparison.ipynb).\n", + "\n", + "Other similarity operations -" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[(u'12', 0.9912641048431396),\n", + " (u'across', 0.990070641040802),\n", + " (u'few', 0.9840448498725891),\n", + " (u'deaths', 0.9840392470359802),\n", + " (u'parts', 0.9835165739059448),\n", + " (u'One', 0.9833074808120728),\n", + " (u'running', 0.9832631349563599),\n", + " (u'2', 0.982011079788208),\n", + " (u'victory', 0.9806963801383972),\n", + " (u'each', 0.9789758920669556)]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# The example training corpus is a toy corpus, results are not expected to be good, for proof-of-concept only\n", + "model.most_similar(\"nights\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.97543218704680112" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.n_similarity(['sushi', 'shop'], ['japanese', 'restaurant'])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'lunch'" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.doesnt_match(\"breakfast cereal dinner lunch\".split())" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[(u'against', 0.94775390625),\n", + " (u'after', 0.923099935054779),\n", + " (u'West', 0.910752534866333),\n", + " (u'again', 0.903070867061615),\n", + " (u'arrest', 0.8878517150878906),\n", + " (u'suicide', 0.8750319480895996),\n", + " (u'After', 0.8682445287704468),\n", + " (u'innings', 0.859328031539917),\n", + " (u'Test', 0.8542338609695435),\n", + " (u'during', 0.852535605430603)]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.most_similar(positive=['baghdad', 'england'], negative=['london'])" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'correct': [], 'incorrect': [], 'section': u'capital-common-countries'},\n", + " {'correct': [], 'incorrect': [], 'section': u'capital-world'},\n", + " {'correct': [], 'incorrect': [], 'section': u'currency'},\n", + " {'correct': [], 'incorrect': [], 'section': u'city-in-state'},\n", + " {'correct': [],\n", + " 'incorrect': [(u'HE', u'SHE', u'HIS', u'HER'),\n", + " (u'HIS', u'HER', u'HE', u'SHE')],\n", + " 'section': u'family'},\n", + " {'correct': [], 'incorrect': [], 'section': u'gram1-adjective-to-adverb'},\n", + " {'correct': [], 'incorrect': [], 'section': u'gram2-opposite'},\n", + " {'correct': [], 'incorrect': [], 'section': u'gram3-comparative'},\n", + " {'correct': [(u'BIG', u'BIGGEST', u'GOOD', u'BEST')],\n", + " 'incorrect': [(u'GOOD', u'BEST', u'BIG', u'BIGGEST')],\n", + " 'section': u'gram4-superlative'},\n", + " {'correct': [(u'GO', u'GOING', u'SAY', u'SAYING'),\n", + " (u'LOOK', u'LOOKING', u'SAY', u'SAYING'),\n", + " (u'RUN', u'RUNNING', u'SAY', u'SAYING'),\n", + " (u'SAY', u'SAYING', u'LOOK', u'LOOKING')],\n", + " 'incorrect': [(u'GO', u'GOING', u'LOOK', u'LOOKING'),\n", + " (u'GO', u'GOING', u'RUN', u'RUNNING'),\n", + " (u'LOOK', u'LOOKING', u'RUN', u'RUNNING'),\n", + " (u'LOOK', u'LOOKING', u'GO', u'GOING'),\n", + " (u'RUN', u'RUNNING', u'GO', u'GOING'),\n", + " (u'RUN', u'RUNNING', u'LOOK', u'LOOKING'),\n", + " (u'SAY', u'SAYING', u'GO', u'GOING'),\n", + " (u'SAY', u'SAYING', u'RUN', u'RUNNING')],\n", + " 'section': u'gram5-present-participle'},\n", + " {'correct': [(u'AUSTRALIA', u'AUSTRALIAN', u'ISRAEL', u'ISRAELI'),\n", + " (u'INDIA', u'INDIAN', u'ISRAEL', u'ISRAELI'),\n", + " (u'INDIA', u'INDIAN', u'AUSTRALIA', u'AUSTRALIAN')],\n", + " 'incorrect': [(u'AUSTRALIA', u'AUSTRALIAN', u'INDIA', u'INDIAN'),\n", + " (u'ISRAEL', u'ISRAELI', u'AUSTRALIA', u'AUSTRALIAN'),\n", + " (u'ISRAEL', u'ISRAELI', u'INDIA', u'INDIAN')],\n", + " 'section': u'gram6-nationality-adjective'},\n", + " {'correct': [],\n", + " 'incorrect': [(u'GOING', u'WENT', u'SAYING', u'SAID'),\n", + " (u'GOING', u'WENT', u'TAKING', u'TOOK'),\n", + " (u'SAYING', u'SAID', u'TAKING', u'TOOK'),\n", + " (u'SAYING', u'SAID', u'GOING', u'WENT'),\n", + " (u'TAKING', u'TOOK', u'GOING', u'WENT'),\n", + " (u'TAKING', u'TOOK', u'SAYING', u'SAID')],\n", + " 'section': u'gram7-past-tense'},\n", + " {'correct': [],\n", + " 'incorrect': [(u'BUILDING', u'BUILDINGS', u'CHILD', u'CHILDREN'),\n", + " (u'BUILDING', u'BUILDINGS', u'MAN', u'MEN'),\n", + " (u'CHILD', u'CHILDREN', u'MAN', u'MEN'),\n", + " (u'CHILD', u'CHILDREN', u'BUILDING', u'BUILDINGS'),\n", + " (u'MAN', u'MEN', u'BUILDING', u'BUILDINGS'),\n", + " (u'MAN', u'MEN', u'CHILD', u'CHILDREN')],\n", + " 'section': u'gram8-plural'},\n", + " {'correct': [], 'incorrect': [], 'section': u'gram9-plural-verbs'},\n", + " {'correct': [(u'BIG', u'BIGGEST', u'GOOD', u'BEST'),\n", + " (u'GO', u'GOING', u'SAY', u'SAYING'),\n", + " (u'LOOK', u'LOOKING', u'SAY', u'SAYING'),\n", + " (u'RUN', u'RUNNING', u'SAY', u'SAYING'),\n", + " (u'SAY', u'SAYING', u'LOOK', u'LOOKING'),\n", + " (u'AUSTRALIA', u'AUSTRALIAN', u'ISRAEL', u'ISRAELI'),\n", + " (u'INDIA', u'INDIAN', u'ISRAEL', u'ISRAELI'),\n", + " (u'INDIA', u'INDIAN', u'AUSTRALIA', u'AUSTRALIAN')],\n", + " 'incorrect': [(u'HE', u'SHE', u'HIS', u'HER'),\n", + " (u'HIS', u'HER', u'HE', u'SHE'),\n", + " (u'GOOD', u'BEST', u'BIG', u'BIGGEST'),\n", + " (u'GO', u'GOING', u'LOOK', u'LOOKING'),\n", + " (u'GO', u'GOING', u'RUN', u'RUNNING'),\n", + " (u'LOOK', u'LOOKING', u'RUN', u'RUNNING'),\n", + " (u'LOOK', u'LOOKING', u'GO', u'GOING'),\n", + " (u'RUN', u'RUNNING', u'GO', u'GOING'),\n", + " (u'RUN', u'RUNNING', u'LOOK', u'LOOKING'),\n", + " (u'SAY', u'SAYING', u'GO', u'GOING'),\n", + " (u'SAY', u'SAYING', u'RUN', u'RUNNING'),\n", + " (u'AUSTRALIA', u'AUSTRALIAN', u'INDIA', u'INDIAN'),\n", + " (u'ISRAEL', u'ISRAELI', u'AUSTRALIA', u'AUSTRALIAN'),\n", + " (u'ISRAEL', u'ISRAELI', u'INDIA', u'INDIAN'),\n", + " (u'GOING', u'WENT', u'SAYING', u'SAID'),\n", + " (u'GOING', u'WENT', u'TAKING', u'TOOK'),\n", + " (u'SAYING', u'SAID', u'TAKING', u'TOOK'),\n", + " (u'SAYING', u'SAID', u'GOING', u'WENT'),\n", + " (u'TAKING', u'TOOK', u'GOING', u'WENT'),\n", + " (u'TAKING', u'TOOK', u'SAYING', u'SAID'),\n", + " (u'BUILDING', u'BUILDINGS', u'CHILD', u'CHILDREN'),\n", + " (u'BUILDING', u'BUILDINGS', u'MAN', u'MEN'),\n", + " (u'CHILD', u'CHILDREN', u'MAN', u'MEN'),\n", + " (u'CHILD', u'CHILDREN', u'BUILDING', u'BUILDINGS'),\n", + " (u'MAN', u'MEN', u'BUILDING', u'BUILDINGS'),\n", + " (u'MAN', u'MEN', u'CHILD', u'CHILDREN')],\n", + " 'section': 'total'}]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.accuracy(questions='questions-words.txt')" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.7756597547632444" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Word Movers distance\n", + "sentence_obama = 'Obama speaks to the media in Illinois'.lower().split()\n", + "sentence_president = 'The president greets the press in Chicago'.lower().split()\n", + "\n", + "# Remove their stopwords.\n", + "from nltk.corpus import stopwords\n", + "stopwords = stopwords.words('english')\n", + "sentence_obama = [w for w in sentence_obama if w not in stopwords]\n", + "sentence_president = [w for w in sentence_president if w not in stopwords]\n", + "\n", + "# Compute WMD.\n", + "distance = model.wmdistance(sentence_obama, sentence_president)\n", + "distance" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/gensim/models/keyedvectors.py b/gensim/models/keyedvectors.py index 369060a4f8..d97a5ae497 100644 --- a/gensim/models/keyedvectors.py +++ b/gensim/models/keyedvectors.py @@ -46,6 +46,25 @@ def save(self, *args, **kwargs): kwargs['ignore'] = kwargs.get('ignore', ['syn0norm']) super(KeyedVectors, self).save(*args, **kwargs) + def word_vec(self, word, use_norm=False): + """ + Accept a single word as input. + Returns the word's representations in vector space, as a 1D numpy array. + + Example:: + + >>> trained_model.word_vec('office', use_norm=True) + array([ -1.40128313e-02, ...]) + + """ + if word in self.vocab: + if use_norm: + return self.syn0norm[self.vocab[word].index] + else: + return self.syn0[self.vocab[word].index] + else: + raise KeyError("word '%s' not in vocabulary" % word) + def most_similar(self, positive=[], negative=[], topn=10, restrict_vocab=None, indexer=None): """ Find the top-N most similar words. Positive words contribute positively towards the @@ -90,11 +109,10 @@ def most_similar(self, positive=[], negative=[], topn=10, restrict_vocab=None, i for word, weight in positive + negative: if isinstance(word, ndarray): mean.append(weight * word) - elif word in self.vocab: - mean.append(weight * self.syn0norm[self.vocab[word].index]) - all_words.add(self.vocab[word].index) else: - raise KeyError("word '%s' not in vocabulary" % word) + mean.append(weight * self.word_vec(word, use_norm=True)) + if word in self.vocab: + all_words.add(self.vocab[word].index) if not mean: raise ValueError("cannot compute similarity with no input") mean = matutils.unitvec(array(mean).mean(axis=0)).astype(REAL) @@ -230,22 +248,14 @@ def most_similar_cosmul(self, positive=[], negative=[], topn=10): # allow calls like most_similar_cosmul('dog'), as a shorthand for most_similar_cosmul(['dog']) positive = [positive] - all_words = set() - def word_vec(word): - if isinstance(word, ndarray): - return word - elif word in self.vocab: - all_words.add(self.vocab[word].index) - return self.syn0norm[self.vocab[word].index] - else: - raise KeyError("word '%s' not in vocabulary" % word) - - positive = [word_vec(word) for word in positive] - negative = [word_vec(word) for word in negative] + positive = [self.word_vec(word, use_norm=True) for word in positive] + negative = [self.word_vec(word, use_norm=True) for word in negative] if not positive: raise ValueError("cannot compute similarity with no input") + all_words = set([self.vocab[word].index for word in positive+negative if word in self.vocab]) + # equation (4) of Levy & Goldberg "Linguistic Regularities...", # with distances shifted to [0,1] per footnote (7) pos_dists = [((1 + dot(self.syn0norm, term)) / 2) for term in positive] @@ -311,14 +321,16 @@ def doesnt_match(self, words): """ self.init_sims() - words = [word for word in words if word in self.vocab] # filter out OOV words - logger.debug("using words %s" % words) - if not words: + used_words = [word for word in words if word in self] + if len(used_words) != len(words): + ignored_words = set(words) - set(used_words) + logger.warning("vectors for words %s are not present in the model, ignoring these words", ignored_words) + if not used_words: raise ValueError("cannot select a word from an empty list") - vectors = vstack(self.syn0norm[self.vocab[word].index] for word in words).astype(REAL) + vectors = vstack(self.word_vec(word, use_norm=True) for word in used_words).astype(REAL) mean = matutils.unitvec(vectors.mean(axis=0)).astype(REAL) dists = dot(vectors, mean) - return sorted(zip(dists, words))[0][1] + return sorted(zip(dists, used_words))[0][1] def __getitem__(self, words): @@ -345,9 +357,9 @@ def __getitem__(self, words): """ if isinstance(words, string_types): # allow calls like trained_model['office'], as a shorthand for trained_model[['office']] - return self.syn0[self.vocab[words].index] + return self.word_vec(words) - return vstack([self.syn0[self.vocab[word].index] for word in words]) + return vstack([self.word_vec(word) for word in words]) def __contains__(self, word): return word in self.vocab diff --git a/gensim/models/word2vec.py b/gensim/models/word2vec.py index 31d7353919..ccdf93c8a1 100644 --- a/gensim/models/word2vec.py +++ b/gensim/models/word2vec.py @@ -435,7 +435,7 @@ def __init__( else: logger.debug('Fast version of {0} is being used'.format(__name__)) - self.wv = KeyedVectors() # wv --> KeyedVectors + self.initialize_word_vectors() self.sg = int(sg) self.cum_table = None # for negative sampling self.vector_size = int(size) @@ -469,6 +469,9 @@ def __init__( self.build_vocab(sentences, trim_rule=trim_rule) self.train(sentences) + def initialize_word_vectors(self): + self.wv = KeyedVectors() + def make_cum_table(self, power=0.75, domain=2**31 - 1): """ Create a cumulative-distribution table using stored vocabulary word counts for diff --git a/gensim/models/wrappers/__init__.py b/gensim/models/wrappers/__init__.py index a9027170e7..8933171250 100644 --- a/gensim/models/wrappers/__init__.py +++ b/gensim/models/wrappers/__init__.py @@ -5,4 +5,5 @@ from .ldamallet import LdaMallet from .dtmmodel import DtmModel from .ldavowpalwabbit import LdaVowpalWabbit -from .wordrank import Wordrank +from .fasttext import FastText +from .wordrank import Wordrank \ No newline at end of file diff --git a/gensim/models/wrappers/fasttext.py b/gensim/models/wrappers/fasttext.py new file mode 100644 index 0000000000..81bb6ccce4 --- /dev/null +++ b/gensim/models/wrappers/fasttext.py @@ -0,0 +1,355 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +# +# Copyright (C) 2013 Radim Rehurek +# Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html + + +""" +Python wrapper around word representation learning from FastText, a library for efficient learning +of word representations and sentence classification [1]. + +This module allows training a word embedding from a training corpus with the additional ability +to obtain word vectors for out-of-vocabulary words, using the fastText C implementation. + +The wrapped model can NOT be updated with new documents for online training -- use gensim's +`Word2Vec` for that. + +Example: + +>>> from gensim.models.wrappers import FastText +>>> model = fasttext.FastText.train('/Users/kofola/fastText/fasttext', corpus_file='text8') +>>> print model['forests'] # prints vector for given out-of-vocabulary word + +.. [1] https://github.com/facebookresearch/fastText#enriching-word-vectors-with-subword-information + +""" + + +import logging +import tempfile +import os +import struct + +import numpy as np +from numpy import float32 as REAL, sqrt, newaxis +from gensim import utils +from gensim.models.keyedvectors import KeyedVectors +from gensim.models.word2vec import Word2Vec + +from six import string_types + +logger = logging.getLogger(__name__) + + +class FastTextKeyedVectors(KeyedVectors): + """ + Class to contain vectors, vocab and ngrams for the FastText training class and other methods not directly + involved in training such as most_similar(). + Subclasses KeyedVectors to implement oov lookups, storing ngrams and other FastText specific methods + + """ + def __init__(self): + super(FastTextKeyedVectors, self).__init__() + self.syn0_all_norm = None + self.ngrams = {} + + def save(self, *args, **kwargs): + # don't bother storing the cached normalized vectors + kwargs['ignore'] = kwargs.get('ignore', ['syn0norm', 'syn0_all_norm']) + super(FastTextKeyedVectors, self).save(*args, **kwargs) + + def word_vec(self, word, use_norm=False): + """ + Accept a single word as input. + Returns the word's representations in vector space, as a 1D numpy array. + + The word can be out-of-vocabulary as long as ngrams for the word are present. + For words with all ngrams absent, a KeyError is raised. + + Example:: + + >>> trained_model['office'] + array([ -1.40128313e-02, ...]) + + """ + if word in self.vocab: + return super(FastTextKeyedVectors, self).word_vec(word, use_norm) + else: + word_vec = np.zeros(self.syn0_all.shape[1]) + ngrams = FastText.compute_ngrams(word, self.min_n, self.max_n) + if use_norm: + ngram_weights = self.syn0_all_norm + else: + ngram_weights = self.syn0_all + for ngram in ngrams: + if ngram in self.ngrams: + word_vec += ngram_weights[self.ngrams[ngram]] + if word_vec.any(): + return word_vec / len(ngrams) + else: # No ngrams of the word are present in self.ngrams + raise KeyError('all ngrams for word %s absent from model' % word) + + def init_sims(self, replace=False): + """ + Precompute L2-normalized vectors. + + If `replace` is set, forget the original vectors and only keep the normalized + ones = saves lots of memory! + + Note that you **cannot continue training** after doing a replace. The model becomes + effectively read-only = you can only call `most_similar`, `similarity` etc. + + """ + super(FastTextKeyedVectors, self).init_sims(replace) + if getattr(self, 'syn0_all_norm', None) is None or replace: + logger.info("precomputing L2-norms of ngram weight vectors") + if replace: + for i in xrange(self.syn0_all.shape[0]): + self.syn0_all[i, :] /= sqrt((self.syn0_all[i, :] ** 2).sum(-1)) + self.syn0_all_norm = self.syn0_all + else: + self.syn0_all_norm = (self.syn0_all / sqrt((self.syn0_all ** 2).sum(-1))[..., newaxis]).astype(REAL) + + def __contains__(self, word): + """ + Check if word is present in the vocabulary, or if any word ngrams are present. A vector for the word is + guaranteed to exist if `__contains__` returns True. + + """ + if word in self.vocab: + return True + else: + word_ngrams = set(FastText.compute_ngrams(word, self.min_n, self.max_n)) + if len(word_ngrams & set(self.ngrams.keys())): + return True + else: + return False + + +class FastText(Word2Vec): + """ + Class for word vector training using FastText. Communication between FastText and Python + takes place by working with data files on disk and calling the FastText binary with + subprocess.call(). + Implements functionality similar to [fasttext.py](https://github.com/salestock/fastText.py), + improving speed and scope of functionality like `most_similar`, `similarity` by extracting vectors + into numpy matrix. + + """ + + def initialize_word_vectors(self): + self.wv = FastTextKeyedVectors() + + @classmethod + def train(cls, ft_path, corpus_file, output_file=None, model='cbow', size=100, alpha=0.025, window=5, min_count=5, + loss='ns', sample=1e-3, negative=5, iter=5, min_n=3, max_n=6, sorted_vocab=1, threads=12): + """ + `ft_path` is the path to the FastText executable, e.g. `/home/kofola/fastText/fasttext`. + + `corpus_file` is the filename of the text file to be used for training the FastText model. + Expects file to contain utf-8 encoded text. + + `model` defines the training algorithm. By default, cbow is used. Accepted values are + 'cbow', 'skipgram'. + + `size` is the dimensionality of the feature vectors. + + `window` is the maximum distance between the current and predicted word within a sentence. + + `alpha` is the initial learning rate. + + `min_count` = ignore all words with total occurrences lower than this. + + `loss` = defines training objective. Allowed values are `hs` (hierarchical softmax), + `ns` (negative sampling) and `softmax`. Defaults to `ns` + + `sample` = threshold for configuring which higher-frequency words are randomly downsampled; + default is 1e-3, useful range is (0, 1e-5). + + `negative` = the value for negative specifies how many "noise words" should be drawn + (usually between 5-20). Default is 5. If set to 0, no negative samping is used. + Only relevant when `loss` is set to `ns` + + `iter` = number of iterations (epochs) over the corpus. Default is 5. + + `min_n` = min length of char ngrams to be used for training word representations. Default is 3. + + `max_n` = max length of char ngrams to be used for training word representations. Set `max_n` to be + lesser than `min_n` to avoid char ngrams being used. Default is 6. + + `sorted_vocab` = if 1 (default), sort the vocabulary by descending frequency before + assigning word indexes. + + `threads` = number of threads to use. Default is 12. + + """ + ft_path = ft_path + output_file = output_file or os.path.join(tempfile.gettempdir(), 'ft_model') + ft_args = { + 'input': corpus_file, + 'output': output_file, + 'lr': alpha, + 'dim': size, + 'ws': window, + 'epoch': iter, + 'minCount': min_count, + 'neg': negative, + 'loss': loss, + 'minn': min_n, + 'maxn': max_n, + 'thread': threads, + 't': sample + } + cmd = [ft_path, model] + for option, value in ft_args.items(): + cmd.append("-%s" % option) + cmd.append(str(value)) + + output = utils.check_output(args=cmd) + model = cls.load_fasttext_format(output_file) + cls.delete_training_files(output_file) + return model + + def save(self, *args, **kwargs): + # don't bother storing the cached normalized vectors + kwargs['ignore'] = kwargs.get('ignore', ['syn0norm', 'syn0_all_norm']) + super(FastText, self).save(*args, **kwargs) + + @classmethod + def load_fasttext_format(cls, model_file): + """ + Load the input-hidden weight matrix from the fast text output files. + + Note that due to limitations in the FastText API, you cannot continue training + with a model loaded this way, though you can query for word similarity etc. + + `model_file` is the path to the FastText output files. + FastText outputs two training files - `/path/to/train.vec` and `/path/to/train.bin` + Expected value for this example: `/path/to/train` + + """ + model = cls.load_word2vec_format('%s.vec' % model_file) + model.load_binary_data('%s.bin' % model_file) + return model + + @classmethod + def delete_training_files(cls, model_file): + """Deletes the files created by FastText training""" + try: + os.remove('%s.vec' % model_file) + os.remove('%s.bin' % model_file) + except FileNotFoundError: + logger.debug('Training files %s not found when attempting to delete', model_file) + pass + + def load_binary_data(self, model_binary_file): + """Loads data from the output binary file created by FastText training""" + with utils.smart_open(model_binary_file, 'rb') as f: + self.load_model_params(f) + self.load_dict(f) + self.load_vectors(f) + + def load_model_params(self, file_handle): + (dim, ws, epoch, minCount, neg, _, loss, model, bucket, minn, maxn, _, t) = self.struct_unpack(file_handle, '@12i1d') + # Parameters stored by [Args::save](https://github.com/facebookresearch/fastText/blob/master/src/args.cc) + self.size = dim + self.window = ws + self.iter = epoch + self.min_count = minCount + self.negative = neg + self.hs = loss == 1 + self.sg = model == 2 + self.bucket = bucket + self.wv.min_n = minn + self.wv.max_n = maxn + self.sample = t + + def load_dict(self, file_handle): + (vocab_size, nwords, _) = self.struct_unpack(file_handle, '@3i') + # Vocab stored by [Dictionary::save](https://github.com/facebookresearch/fastText/blob/master/src/dictionary.cc) + assert len(self.wv.vocab) == nwords, 'mismatch between vocab sizes' + assert len(self.wv.vocab) == vocab_size, 'mismatch between vocab sizes' + ntokens, = self.struct_unpack(file_handle, '@q') + for i in range(nwords): + word = '' + char, = self.struct_unpack(file_handle, '@c') + char = char.decode() + # Read vocab word + while char != '\x00': + word += char + char, = self.struct_unpack(file_handle, '@c') + char = char.decode() + count, _ = self.struct_unpack(file_handle, '@ib') + _ = self.struct_unpack(file_handle, '@i') + assert self.wv.vocab[word].index == i, 'mismatch between gensim word index and fastText word index' + self.wv.vocab[word].count = count + + def load_vectors(self, file_handle): + num_vectors, dim = self.struct_unpack(file_handle, '@2q') + # Vectors stored by [Matrix::save](https://github.com/facebookresearch/fastText/blob/master/src/matrix.cc) + assert self.size == dim, 'mismatch between model sizes' + float_size = struct.calcsize('@f') + if float_size == 4: + dtype = np.dtype(np.float32) + elif float_size == 8: + dtype = np.dtype(np.float64) + + self.num_original_vectors = num_vectors + self.wv.syn0_all = np.fromstring(file_handle.read(num_vectors * dim * float_size), dtype=dtype) + self.wv.syn0_all = self.wv.syn0_all.reshape((num_vectors, dim)) + assert self.wv.syn0_all.shape == (self.bucket + len(self.wv.vocab), self.size), \ + 'mismatch between weight matrix shape and vocab/model size' + self.init_ngrams() + + def struct_unpack(self, file_handle, fmt): + num_bytes = struct.calcsize(fmt) + return struct.unpack(fmt, file_handle.read(num_bytes)) + + def init_ngrams(self): + """ + Computes ngrams of all words present in vocabulary and stores vectors for only those ngrams. + Vectors for other ngrams are initialized with a random uniform distribution in FastText. These + vectors are discarded here to save space. + + """ + self.wv.ngrams = {} + all_ngrams = [] + for w, v in self.wv.vocab.items(): + all_ngrams += self.compute_ngrams(w, self.wv.min_n, self.wv.max_n) + all_ngrams = set(all_ngrams) + self.num_ngram_vectors = len(all_ngrams) + ngram_indices = [] + for i, ngram in enumerate(all_ngrams): + ngram_hash = self.ft_hash(ngram) + ngram_indices.append((len(self.wv.vocab) + ngram_hash) % self.bucket) + self.wv.ngrams[ngram] = i + self.wv.syn0_all = self.wv.syn0_all.take(ngram_indices, axis=0) + + @staticmethod + def compute_ngrams(word, min_n, max_n): + ngram_indices = [] + BOW, EOW = ('<', '>') # Used by FastText to attach to all words as prefix and suffix + extended_word = BOW + word + EOW + ngrams = set() + for i in range(len(extended_word) - min_n + 1): + for j in range(min_n, max(len(extended_word) - max_n, max_n + 1)): + ngrams.add(extended_word[i:i+j]) + return ngrams + + @staticmethod + def ft_hash(string): + """ + Reproduces [hash method](https://github.com/facebookresearch/fastText/blob/master/src/dictionary.cc) + used in fastText. + + """ + # Runtime warnings for integer overflow are raised, this is expected behaviour. 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+tree -0.098798 -0.0036365 -0.14983 -0.3772 0.016228 -1.0777 -0.17989 1.0082 0.27513 0.64743 +hundred -0.57144 -0.0085561 0.15748 -0.67855 -0.25459 -0.58077 -0.09913 1.1447 0.23418 0.060007 diff --git a/gensim/test/test_fasttext_wrapper.py b/gensim/test/test_fasttext_wrapper.py new file mode 100644 index 0000000000..3b93d6c57c --- /dev/null +++ b/gensim/test/test_fasttext_wrapper.py @@ -0,0 +1,213 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +# +# Copyright (C) 2010 Radim Rehurek +# Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html + +""" +Automated tests for checking transformation algorithms (the models package). +""" + + +import logging +import unittest +import os +import tempfile + +import numpy + +from gensim.models.wrappers import fasttext +from gensim.models import keyedvectors + +module_path = os.path.dirname(__file__) # needed because sample data files are located in the same folder +datapath = lambda fname: os.path.join(module_path, 'test_data', fname) +logger = logging.getLogger(__name__) + + +def testfile(): + # temporary data will be stored to this file + return os.path.join(tempfile.gettempdir(), 'gensim_fasttext.tst') + +class TestFastText(unittest.TestCase): + @classmethod + def setUp(self): + ft_home = os.environ.get('FT_HOME', None) + self.ft_path = os.path.join(ft_home, 'fasttext') if ft_home else None + self.corpus_file = datapath('lee_background.cor') + self.test_model_file = datapath('lee_fasttext') + # Load pre-trained model to perform tests in case FastText binary isn't available in test environment + self.test_model = fasttext.FastText.load_fasttext_format(self.test_model_file) + + def model_sanity(self, model): + """Even tiny models trained on any corpus should pass these sanity checks""" + self.assertEqual(model.wv.syn0.shape, (len(model.wv.vocab), model.size)) + self.assertEqual(model.wv.syn0_all.shape, (model.num_ngram_vectors, model.size)) + + def models_equal(self, model1, model2): + self.assertEqual(len(model1.wv.vocab), len(model2.wv.vocab)) + self.assertEqual(set(model1.wv.vocab.keys()), set(model2.wv.vocab.keys())) + self.assertTrue(numpy.allclose(model1.wv.syn0, model2.wv.syn0)) + self.assertTrue(numpy.allclose(model1.wv.syn0_all, model2.wv.syn0_all)) + + def testTraining(self): + """Test self.test_model successfully trained, parameters and weights correctly loaded""" + if self.ft_path is None: + logger.info("FT_HOME env variable not set, skipping test") + return # Use self.skipTest once python < 2.7 is no longer supported + vocab_size, model_size = 1762, 10 + trained_model = fasttext.FastText.train( + self.ft_path, self.corpus_file, size=model_size, output_file=testfile()) + + self.assertEqual(trained_model.wv.syn0.shape, (vocab_size, model_size)) + self.assertEqual(len(trained_model.wv.vocab), vocab_size) + self.assertEqual(trained_model.wv.syn0_all.shape[1], model_size) + self.model_sanity(trained_model) + + # Tests temporary training files deleted + self.assertFalse(os.path.exists('%s.vec' % testfile())) + self.assertFalse(os.path.exists('%s.bin' % testfile())) + + def testMinCount(self): + """Tests words with frequency less than `min_count` absent from vocab""" + if self.ft_path is None: + logger.info("FT_HOME env variable not set, skipping test") + return # Use self.skipTest once python < 2.7 is no longer supported + self.assertTrue('forests' not in self.test_model.wv.vocab) + test_model_min_count_1 = fasttext.FastText.train( + self.ft_path, self.corpus_file, output_file=testfile(), size=10, min_count=1) + self.assertTrue('forests' in test_model_min_count_1.wv.vocab) + + def testModelSize(self): + """Tests output vector dimensions are the same as the value for `size` param""" + if self.ft_path is None: + logger.info("FT_HOME env variable not set, skipping test") + return # Use self.skipTest once python < 2.7 is no longer supported + test_model_size_20 = fasttext.FastText.train( + self.ft_path, self.corpus_file, output_file=testfile(), size=20) + self.assertEqual(test_model_size_20.size, 20) + self.assertEqual(test_model_size_20.wv.syn0.shape[1], 20) + self.assertEqual(test_model_size_20.wv.syn0_all.shape[1], 20) + + def testPersistence(self): + """Test storing/loading the entire model.""" + self.test_model.save(testfile()) + loaded = fasttext.FastText.load(testfile()) + self.models_equal(self.test_model, loaded) + + self.test_model.save(testfile(), sep_limit=0) + self.models_equal(self.test_model, fasttext.FastText.load(testfile())) + + def testNormalizedVectorsNotSaved(self): + """Test syn0norm/syn0_all_norm aren't saved in model file""" + self.test_model.init_sims() + self.test_model.save(testfile()) + loaded = fasttext.FastText.load(testfile()) + self.assertTrue(loaded.wv.syn0norm is None) + self.assertTrue(loaded.wv.syn0_all_norm is None) + + wv = self.test_model.wv + wv.save(testfile()) + loaded_kv = keyedvectors.KeyedVectors.load(testfile()) + self.assertTrue(loaded_kv.syn0norm is None) + self.assertTrue(loaded_kv.syn0_all_norm is None) + + def testLoadFastTextFormat(self): + """Test model successfully loaded from fastText .vec and .bin files""" + model = fasttext.FastText.load_fasttext_format(self.test_model_file) + vocab_size, model_size = 1762, 10 + self.assertEqual(self.test_model.wv.syn0.shape, (vocab_size, model_size)) + self.assertEqual(len(self.test_model.wv.vocab), vocab_size, model_size) + self.assertEqual(self.test_model.wv.syn0_all.shape, (self.test_model.num_ngram_vectors, model_size)) + self.model_sanity(model) + + def testNSimilarity(self): + """Test n_similarity for in-vocab and out-of-vocab words""" + # In vocab, sanity check + self.assertTrue(numpy.allclose(self.test_model.n_similarity(['the', 'and'], ['and', 'the']), 1.0)) + self.assertEqual(self.test_model.n_similarity(['the'], ['and']), self.test_model.n_similarity(['and'], ['the'])) + # Out of vocab check + self.assertTrue(numpy.allclose(self.test_model.n_similarity(['night', 'nights'], ['nights', 'night']), 1.0)) + self.assertEqual(self.test_model.n_similarity(['night'], ['nights']), self.test_model.n_similarity(['nights'], ['night'])) + + def testSimilarity(self): + """Test similarity for in-vocab and out-of-vocab words""" + # In vocab, sanity check + self.assertTrue(numpy.allclose(self.test_model.similarity('the', 'the'), 1.0)) + self.assertEqual(self.test_model.similarity('the', 'and'), self.test_model.similarity('and', 'the')) + # Out of vocab check + self.assertTrue(numpy.allclose(self.test_model.similarity('nights', 'nights'), 1.0)) + self.assertEqual(self.test_model.similarity('night', 'nights'), self.test_model.similarity('nights', 'night')) + + def testMostSimilar(self): + """Test most_similar for in-vocab and out-of-vocab words""" + # In vocab, sanity check + self.assertEqual(len(self.test_model.most_similar(positive=['the', 'and'], topn=5)), 5) + self.assertEqual(self.test_model.most_similar('the'), self.test_model.most_similar(positive=['the'])) + # Out of vocab check + self.assertEqual(len(self.test_model.most_similar(['night', 'nights'], topn=5)), 5) + self.assertEqual(self.test_model.most_similar('nights'), self.test_model.most_similar(positive=['nights'])) + + def testMostSimilarCosmul(self): + """Test most_similar_cosmul for in-vocab and out-of-vocab words""" + # In vocab, sanity check + self.assertEqual(len(self.test_model.most_similar(positive=['the', 'and'], topn=5)), 5) + self.assertEqual(self.test_model.most_similar('the'), self.test_model.most_similar(positive=['the'])) + # Out of vocab check + self.assertEqual(len(self.test_model.most_similar(['night', 'nights'], topn=5)), 5) + self.assertEqual(self.test_model.most_similar('nights'), self.test_model.most_similar(positive=['nights'])) + + def testLookup(self): + """Tests word vector lookup for in-vocab and out-of-vocab words""" + # In vocab, sanity check + self.assertTrue('night' in self.test_model.wv.vocab) + self.assertTrue(numpy.allclose(self.test_model['night'], self.test_model[['night']])) + # Out of vocab check + self.assertFalse('nights' in self.test_model.wv.vocab) + self.assertTrue(numpy.allclose(self.test_model['nights'], self.test_model[['nights']])) + # Word with no ngrams in model + self.assertRaises(KeyError, lambda: self.test_model['a!@']) + + def testContains(self): + """Tests __contains__ for in-vocab and out-of-vocab words""" + # In vocab, sanity check + self.assertTrue('night' in self.test_model.wv.vocab) + self.assertTrue('night' in self.test_model) + # Out of vocab check + self.assertFalse('nights' in self.test_model.wv.vocab) + self.assertTrue('night' in self.test_model) + # Word with no ngrams in model + self.assertFalse('a!@' in self.test_model.wv.vocab) + self.assertFalse('a!@' in self.test_model) + + def testWmdistance(self): + """Tests wmdistance for docs with in-vocab and out-of-vocab words""" + doc = ['night', 'payment'] + oov_doc = ['nights', 'forests', 'payments'] + ngrams_absent_doc = ['a!@', 'b#$'] + + dist = self.test_model.wmdistance(doc, oov_doc) + self.assertNotEqual(float('inf'), dist) + dist = self.test_model.wmdistance(doc, ngrams_absent_doc) + self.assertEqual(float('inf'), dist) + + def testDoesntMatch(self): + """Tests doesnt_match for list of out-of-vocab words""" + oov_words = ['nights', 'forests', 'payments'] + # Out of vocab check + for word in oov_words: + self.assertFalse(word in self.test_model.wv.vocab) + try: + self.test_model.doesnt_match(oov_words) + except Exception: + self.fail('model.doesnt_match raises exception for oov words') + + def testHash(self): + # Tests FastText.ft_hash method return values to those obtained from original C implementation + ft_hash = fasttext.FastText.ft_hash('test') + self.assertEqual(ft_hash, 2949673445) + ft_hash = fasttext.FastText.ft_hash('word') + self.assertEqual(ft_hash, 1788406269) + +if __name__ == '__main__': + logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.DEBUG) + unittest.main() \ No newline at end of file diff --git a/gensim/test/test_similarities.py b/gensim/test/test_similarities.py index e973aa49a8..88addc6d9c 100644 --- a/gensim/test/test_similarities.py +++ b/gensim/test/test_similarities.py @@ -20,6 +20,7 @@ from gensim.corpora import mmcorpus, Dictionary from gensim.models import word2vec from gensim.models import doc2vec +from gensim.models.wrappers import fasttext from gensim import matutils, utils, similarities from gensim.models import Word2Vec @@ -450,52 +451,75 @@ def setUp(self): raise unittest.SkipTest("Annoy library is not available") from gensim.similarities.index import AnnoyIndexer + self.indexer = AnnoyIndexer + + def testWord2Vec(self): + model = word2vec.Word2Vec(texts, min_count=1) + model.init_sims() + index = self.indexer(model, 10) + + self.assertVectorIsSimilarToItself(model, index) + self.assertApproxNeighborsMatchExact(model, index) + self.assertIndexSaved(index) + self.assertLoadedIndexEqual(index, model) + + def testFastText(self): + ft_home = os.environ.get('FT_HOME', None) + ft_path = os.path.join(ft_home, 'fasttext') if ft_home else None + if not ft_path: + return + corpus_file = datapath('lee.cor') + model = fasttext.FastText.train(ft_path, corpus_file) + model.init_sims() + index = self.indexer(model, 10) - self.model = word2vec.Word2Vec(texts, min_count=1) - self.model.init_sims() - self.index = AnnoyIndexer(self.model, 10) - self.vector = self.model.wv.syn0norm[0] + self.assertVectorIsSimilarToItself(model, index) + self.assertApproxNeighborsMatchExact(model, index) + self.assertIndexSaved(index) + self.assertLoadedIndexEqual(index, model) - def testVectorIsSimilarToItself(self): - label = self.model.index2word[0] - approx_neighbors = self.index.most_similar(self.vector, 1) + def testLoadMissingRaisesError(self): + from gensim.similarities.index import AnnoyIndexer + test_index = AnnoyIndexer() + + self.assertRaises(IOError, test_index.load, fname='test-index') + + def assertVectorIsSimilarToItself(self, model, index): + vector = model.syn0norm[0] + label = model.index2word[0] + approx_neighbors = index.most_similar(vector, 1) word, similarity = approx_neighbors[0] self.assertEqual(word, label) self.assertEqual(similarity, 1.0) - def testApproxNeighborsMatchExact(self): - approx_neighbors = self.model.most_similar([self.vector], topn=5, indexer=self.index) - exact_neighbors = self.model.most_similar(positive=[self.vector], topn=5) + def assertApproxNeighborsMatchExact(self, model, index): + vector = model.syn0norm[0] + approx_neighbors = model.most_similar([vector], topn=5, indexer=index) + exact_neighbors = model.most_similar(positive=[vector], topn=5) approx_words = [neighbor[0] for neighbor in approx_neighbors] exact_words = [neighbor[0] for neighbor in exact_neighbors] self.assertEqual(approx_words, exact_words) - def testSave(self): - self.index.save('index') + def assertIndexSaved(self, index): + index.save('index') self.assertTrue(os.path.exists('index')) self.assertTrue(os.path.exists('index.d')) - def testLoadNotExist(self): + def assertLoadedIndexEqual(self, index, model): from gensim.similarities.index import AnnoyIndexer - self.test_index = AnnoyIndexer() - self.assertRaises(IOError, self.test_index.load, fname='test-index') + index.save('index') - def testSaveLoad(self): - from gensim.similarities.index import AnnoyIndexer - - self.index.save('index') + index2 = AnnoyIndexer() + index2.load('index') + index2.model = model - self.index2 = AnnoyIndexer() - self.index2.load('index') - self.index2.model = self.model - - self.assertEqual(self.index.index.f, self.index2.index.f) - self.assertEqual(self.index.labels, self.index2.labels) - self.assertEqual(self.index.num_trees, self.index2.num_trees) + self.assertEqual(index.index.f, index2.index.f) + self.assertEqual(index.labels, index2.labels) + self.assertEqual(index.num_trees, index2.num_trees) class TestDoc2VecAnnoyIndexer(unittest.TestCase): diff --git a/gensim/test/test_word2vec.py b/gensim/test/test_word2vec.py index 200335c7cd..6c96652c28 100644 --- a/gensim/test/test_word2vec.py +++ b/gensim/test/test_word2vec.py @@ -34,8 +34,6 @@ module_path = os.path.dirname(__file__) # needed because sample data files are located in the same folder datapath = lambda fname: os.path.join(module_path, 'test_data', fname) -logger = logging.getLogger() -logger.level = logging.ERROR class LeeCorpus(object): def __iter__(self): @@ -88,14 +86,14 @@ def testOnlineLearning(self): vocabulary and to a trained model when using a sorted vocabulary""" model_hs = word2vec.Word2Vec(sentences, size=10, min_count=0, seed=42, hs=1, negative=0) model_neg = word2vec.Word2Vec(sentences, size=10, min_count=0, seed=42, hs=0, negative=5) - self.assertTrue(len(model_hs.vocab), 12) - self.assertTrue(model_hs.vocab['graph'].count, 3) + self.assertTrue(len(model_hs.wv.vocab), 12) + self.assertTrue(model_hs.wv.vocab['graph'].count, 3) model_hs.build_vocab(new_sentences, update=True) model_neg.build_vocab(new_sentences, update=True) - self.assertTrue(model_hs.vocab['graph'].count, 4) - self.assertTrue(model_hs.vocab['artificial'].count, 4) - self.assertEqual(len(model_hs.vocab), 14) - self.assertEqual(len(model_neg.vocab), 14) + self.assertTrue(model_hs.wv.vocab['graph'].count, 4) + self.assertTrue(model_hs.wv.vocab['artificial'].count, 4) + self.assertEqual(len(model_hs.wv.vocab), 14) + self.assertEqual(len(model_neg.wv.vocab), 14) def onlineSanity(self, model): terro, others = [], [] @@ -107,12 +105,12 @@ def onlineSanity(self, model): self.assertTrue(all(['terrorism' not in l for l in others])) model.build_vocab(others) model.train(others) - self.assertFalse('terrorism' in model.vocab) + self.assertFalse('terrorism' in model.wv.vocab) model.build_vocab(terro, update=True) - self.assertTrue('terrorism' in model.vocab) - orig0 = np.copy(model.syn0) + self.assertTrue('terrorism' in model.wv.vocab) + orig0 = np.copy(model.wv.syn0) model.train(terro) - self.assertFalse(np.allclose(model.syn0, orig0)) + self.assertFalse(np.allclose(model.wv.syn0, orig0)) sim = model.n_similarity(['war'], ['terrorism']) self.assertLess(0., sim) @@ -159,21 +157,21 @@ def testPersistenceWithConstructorRule(self): def testRuleWithMinCount(self): """Test that returning RULE_DEFAULT from trim_rule triggers min_count.""" model = word2vec.Word2Vec(sentences + [["occurs_only_once"]], min_count=2, trim_rule=_rule) - self.assertTrue("human" not in model.vocab) - self.assertTrue("occurs_only_once" not in model.vocab) - self.assertTrue("interface" in model.vocab) + self.assertTrue("human" not in model.wv.vocab) + self.assertTrue("occurs_only_once" not in model.wv.vocab) + self.assertTrue("interface" in model.wv.vocab) def testRule(self): """Test applying vocab trim_rule to build_vocab instead of constructor.""" model = word2vec.Word2Vec(min_count=1) model.build_vocab(sentences, trim_rule=_rule) - self.assertTrue("human" not in model.vocab) + self.assertTrue("human" not in model.wv.vocab) def testLambdaRule(self): """Test that lambda trim_rule works.""" rule = lambda word, count, min_count: utils.RULE_DISCARD if word == "human" else utils.RULE_DEFAULT model = word2vec.Word2Vec(sentences, min_count=1, trim_rule=rule) - self.assertTrue("human" not in model.vocab) + self.assertTrue("human" not in model.wv.vocab) def testSyn0NormNotSaved(self): """Test syn0norm isn't saved in model file""" @@ -203,6 +201,7 @@ def testLoadPreKeyedVectorModel(self): model = word2vec.Word2Vec.load(datapath(model_file)) self.assertTrue(model.wv.syn0.shape == (len(model.wv.vocab), model.vector_size)) self.assertTrue(model.syn1neg.shape == (len(model.wv.vocab), model.vector_size)) + # Model stored in multiple files model_file = 'word2vec_pre_kv_sep%s' % model_file_suffix model = word2vec.Word2Vec.load(datapath(model_file)) @@ -225,7 +224,7 @@ def testPersistenceWord2VecFormat(self): norm_only_model = word2vec.Word2Vec.load_word2vec_format(testfile(), binary=True) norm_only_model.init_sims(replace=True) self.assertFalse(np.allclose(model['human'], norm_only_model['human'])) - self.assertTrue(np.allclose(model.wv.syn0norm[model.vocab['human'].index], norm_only_model['human'])) + self.assertTrue(np.allclose(model.wv.syn0norm[model.wv.vocab['human'].index], norm_only_model['human'])) limited_model = word2vec.Word2Vec.load_word2vec_format(testfile(), binary=True, limit=3) self.assertEquals(len(limited_model.wv.syn0), 3) half_precision_model = word2vec.Word2Vec.load_word2vec_format(testfile(), binary=True, datatype=np.float16) @@ -270,7 +269,7 @@ def testPersistenceWord2VecFormatNonBinary(self): norm_only_model = word2vec.Word2Vec.load_word2vec_format(testfile(), binary=False) norm_only_model.init_sims(True) self.assertFalse(np.allclose(model['human'], norm_only_model['human'], atol=1e-6)) - self.assertTrue(np.allclose(model.wv.syn0norm[model.vocab['human'].index], norm_only_model['human'], atol=1e-4)) + self.assertTrue(np.allclose(model.wv.syn0norm[model.wv.vocab['human'].index], norm_only_model['human'], atol=1e-4)) def testPersistenceWord2VecFormatWithVocab(self): """Test storing/loading the entire model and vocabulary in word2vec format.""" @@ -279,7 +278,7 @@ def testPersistenceWord2VecFormatWithVocab(self): testvocab = os.path.join(tempfile.gettempdir(), 'gensim_word2vec.vocab') model.save_word2vec_format(testfile(), testvocab, binary=True) binary_model_with_vocab = word2vec.Word2Vec.load_word2vec_format(testfile(), testvocab, binary=True) - self.assertEqual(model.vocab['human'].count, binary_model_with_vocab.vocab['human'].count) + self.assertEqual(model.wv.vocab['human'].count, binary_model_with_vocab.wv.vocab['human'].count) def testPersistenceWord2VecFormatCombinationWithStandardPersistence(self): """Test storing/loading the entire model and vocabulary in word2vec format chained with @@ -291,7 +290,7 @@ def testPersistenceWord2VecFormatCombinationWithStandardPersistence(self): binary_model_with_vocab = word2vec.Word2Vec.load_word2vec_format(testfile(), testvocab, binary=True) binary_model_with_vocab.save(testfile()) binary_model_with_vocab = word2vec.Word2Vec.load(testfile()) - self.assertEqual(model.vocab['human'].count, binary_model_with_vocab.vocab['human'].count) + self.assertEqual(model.wv.vocab['human'].count, binary_model_with_vocab.wv.vocab['human'].count) def testLargeMmap(self): """Test storing/loading the entire model.""" @@ -312,17 +311,17 @@ def testVocab(self): # try vocab building explicitly, using all words model = word2vec.Word2Vec(min_count=1, hs=1, negative=0) model.build_vocab(corpus) - self.assertTrue(len(model.vocab) == 6981) + self.assertTrue(len(model.wv.vocab) == 6981) # with min_count=1, we're not throwing away anything, so make sure the word counts add up to be the entire corpus - self.assertEqual(sum(v.count for v in model.vocab.values()), total_words) + self.assertEqual(sum(v.count for v in model.wv.vocab.values()), total_words) # make sure the binary codes are correct - np.allclose(model.vocab['the'].code, [1, 1, 0, 0]) + np.allclose(model.wv.vocab['the'].code, [1, 1, 0, 0]) # test building vocab with default params model = word2vec.Word2Vec(hs=1, negative=0) model.build_vocab(corpus) - self.assertTrue(len(model.vocab) == 1750) - np.allclose(model.vocab['the'].code, [1, 1, 1, 0]) + self.assertTrue(len(model.wv.vocab) == 1750) + np.allclose(model.wv.vocab['the'].code, [1, 1, 1, 0]) # no input => "RuntimeError: you must first build vocabulary before training the model" self.assertRaises(RuntimeError, word2vec.Word2Vec, []) @@ -336,15 +335,15 @@ def testTraining(self): model = word2vec.Word2Vec(size=2, min_count=1, hs=1, negative=0) model.build_vocab(sentences) - self.assertTrue(model.wv.syn0.shape == (len(model.vocab), 2)) - self.assertTrue(model.syn1.shape == (len(model.vocab), 2)) + self.assertTrue(model.wv.syn0.shape == (len(model.wv.vocab), 2)) + self.assertTrue(model.syn1.shape == (len(model.wv.vocab), 2)) model.train(sentences) sims = model.most_similar('graph', topn=10) # self.assertTrue(sims[0][0] == 'trees', sims) # most similar # test querying for "most similar" by vector - graph_vector = model.wv.syn0norm[model.vocab['graph'].index] + graph_vector = model.wv.syn0norm[model.wv.vocab['graph'].index] sims2 = model.most_similar(positive=[graph_vector], topn=11) sims2 = [(w, sim) for w, sim in sims2 if w != 'graph'] # ignore 'graph' itself self.assertEqual(sims, sims2) @@ -443,15 +442,15 @@ def testTrainingCbow(self): # build vocabulary, don't train yet model = word2vec.Word2Vec(size=2, min_count=1, sg=0, hs=1, negative=0) model.build_vocab(sentences) - self.assertTrue(model.wv.syn0.shape == (len(model.vocab), 2)) - self.assertTrue(model.syn1.shape == (len(model.vocab), 2)) + self.assertTrue(model.wv.syn0.shape == (len(model.wv.vocab), 2)) + self.assertTrue(model.syn1.shape == (len(model.wv.vocab), 2)) model.train(sentences) sims = model.most_similar('graph', topn=10) # self.assertTrue(sims[0][0] == 'trees', sims) # most similar # test querying for "most similar" by vector - graph_vector = model.wv.syn0norm[model.vocab['graph'].index] + graph_vector = model.wv.syn0norm[model.wv.vocab['graph'].index] sims2 = model.most_similar(positive=[graph_vector], topn=11) sims2 = [(w, sim) for w, sim in sims2 if w != 'graph'] # ignore 'graph' itself self.assertEqual(sims, sims2) @@ -466,15 +465,15 @@ def testTrainingSgNegative(self): # build vocabulary, don't train yet model = word2vec.Word2Vec(size=2, min_count=1, hs=0, negative=2) model.build_vocab(sentences) - self.assertTrue(model.wv.syn0.shape == (len(model.vocab), 2)) - self.assertTrue(model.syn1neg.shape == (len(model.vocab), 2)) + self.assertTrue(model.wv.syn0.shape == (len(model.wv.vocab), 2)) + self.assertTrue(model.syn1neg.shape == (len(model.wv.vocab), 2)) model.train(sentences) sims = model.most_similar('graph', topn=10) # self.assertTrue(sims[0][0] == 'trees', sims) # most similar # test querying for "most similar" by vector - graph_vector = model.wv.syn0norm[model.vocab['graph'].index] + graph_vector = model.wv.syn0norm[model.wv.vocab['graph'].index] sims2 = model.most_similar(positive=[graph_vector], topn=11) sims2 = [(w, sim) for w, sim in sims2 if w != 'graph'] # ignore 'graph' itself self.assertEqual(sims, sims2) @@ -489,15 +488,15 @@ def testTrainingCbowNegative(self): # build vocabulary, don't train yet model = word2vec.Word2Vec(size=2, min_count=1, sg=0, hs=0, negative=2) model.build_vocab(sentences) - self.assertTrue(model.wv.syn0.shape == (len(model.vocab), 2)) - self.assertTrue(model.syn1neg.shape == (len(model.vocab), 2)) + self.assertTrue(model.wv.syn0.shape == (len(model.wv.vocab), 2)) + self.assertTrue(model.syn1neg.shape == (len(model.wv.vocab), 2)) model.train(sentences) sims = model.most_similar('graph', topn=10) # self.assertTrue(sims[0][0] == 'trees', sims) # most similar # test querying for "most similar" by vector - graph_vector = model.wv.syn0norm[model.vocab['graph'].index] + graph_vector = model.wv.syn0norm[model.wv.vocab['graph'].index] sims2 = model.most_similar(positive=[graph_vector], topn=11) sims2 = [(w, sim) for w, sim in sims2 if w != 'graph'] # ignore 'graph' itself self.assertEqual(sims, sims2) @@ -551,13 +550,13 @@ def testRNG(self): self.models_equal(model, model2) def models_equal(self, model, model2): - self.assertEqual(len(model.vocab), len(model2.vocab)) + self.assertEqual(len(model.wv.vocab), len(model2.wv.vocab)) self.assertTrue(np.allclose(model.wv.syn0, model2.wv.syn0)) if model.hs: self.assertTrue(np.allclose(model.syn1, model2.syn1)) if model.negative: self.assertTrue(np.allclose(model.syn1neg, model2.syn1neg)) - most_common_word = max(model.vocab.items(), key=lambda item: item[1].count)[0] + most_common_word = max(model.wv.vocab.items(), key=lambda item: item[1].count)[0] self.assertTrue(np.allclose(model[most_common_word], model2[most_common_word])) def testDeleteTemporaryTrainingData(self): @@ -572,8 +571,8 @@ def testDeleteTemporaryTrainingData(self): self.assertTrue(hasattr(model, 'syn0_lockf')) model.delete_temporary_training_data(replace_word_vectors_with_normalized=True) self.assertTrue(len(model['human']), 10) - self.assertTrue(len(model.vocab), 12) - self.assertTrue(model.vocab['graph'].count, 3) + self.assertTrue(len(model.wv.vocab), 12) + self.assertTrue(model.wv.vocab['graph'].count, 3) self.assertTrue(not hasattr(model, 'syn1')) self.assertTrue(not hasattr(model, 'syn1neg')) self.assertTrue(not hasattr(model, 'syn0_lockf'))