From 97cd64fad0507dca121164ed45789cfca1d20460 Mon Sep 17 00:00:00 2001 From: Kris Singh Date: Wed, 22 Mar 2017 00:20:36 +0530 Subject: [PATCH] Sklearn LDA wrapper now works in sklearn pipeline (#1213) --- .travis.yml | 1 + docs/notebooks/sklearn_wrapper.ipynb | 197 +++++++++++++----- .../sklearn_wrapper_gensim_ldamodel.py | 43 +++- gensim/test/test_data/mini_newsgroup | Bin 0 -> 196956 bytes gensim/test/test_sklearn_integration.py | 42 +++- 5 files changed, 223 insertions(+), 60 deletions(-) create mode 100644 gensim/test/test_data/mini_newsgroup diff --git a/.travis.yml b/.travis.yml index 663f506869..7e67dc09fe 100644 --- a/.travis.yml +++ b/.travis.yml @@ -18,6 +18,7 @@ install: - pip install annoy - pip install testfixtures - pip install unittest2 + - pip install scikit-learn - pip install Morfessor==2.0.2a4 - python setup.py install script: python setup.py test diff --git a/docs/notebooks/sklearn_wrapper.ipynb b/docs/notebooks/sklearn_wrapper.ipynb index 34110916dd..0d28429ecf 100644 --- a/docs/notebooks/sklearn_wrapper.ipynb +++ b/docs/notebooks/sklearn_wrapper.ipynb @@ -38,13 +38,13 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "from gensim.sklearn_integration.sklearn_wrapper_gensim_ldaModel import SklearnWrapperLdaModel" + "from gensim.sklearn_integration.sklearn_wrapper_gensim_ldamodel import SklearnWrapperLdaModel" ] }, { @@ -56,7 +56,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 21, "metadata": { "collapsed": true }, @@ -85,7 +85,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 22, "metadata": { "collapsed": false }, @@ -100,13 +100,18 @@ { "data": { "text/plain": [ - "[(0,\n", - " u'0.164*\"computer\" + 0.117*\"system\" + 0.105*\"graph\" + 0.061*\"server\" + 0.057*\"tree\" + 0.046*\"malfunction\" + 0.045*\"kernel\" + 0.045*\"complier\" + 0.043*\"loading\" + 0.039*\"hamiltonian\"'),\n", - " (1,\n", - " u'0.102*\"graph\" + 0.083*\"system\" + 0.072*\"tree\" + 0.064*\"server\" + 0.059*\"user\" + 0.059*\"computer\" + 0.057*\"trees\" + 0.056*\"eulerian\" + 0.055*\"node\" + 0.052*\"flow\"')]" + "array([[ 0.85275314, 0.14724686],\n", + " [ 0.12390183, 0.87609817],\n", + " [ 0.4612995 , 0.5387005 ],\n", + " [ 0.84924177, 0.15075823],\n", + " [ 0.49180096, 0.50819904],\n", + " [ 0.40086923, 0.59913077],\n", + " [ 0.28454427, 0.71545573],\n", + " [ 0.88776198, 0.11223802],\n", + " [ 0.84210373, 0.15789627]])" ] }, - "execution_count": 3, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -114,7 +119,8 @@ "source": [ "model=SklearnWrapperLdaModel(num_topics=2,id2word=dictionary,iterations=20, random_state=1)\n", "model.fit(corpus)\n", - "model.print_topics(2)" + "model.print_topics(2)\n", + "model.transform(corpus)" ] }, { @@ -135,9 +141,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 23, "metadata": { - "collapsed": true + "collapsed": false }, "outputs": [], "source": [ @@ -146,14 +152,14 @@ "from gensim.models.ldamodel import LdaModel\n", "from sklearn.datasets import fetch_20newsgroups\n", "from sklearn.feature_extraction.text import CountVectorizer\n", - "from gensim.sklearn_integration.sklearn_wrapper_gensim_ldaModel import SklearnWrapperLdaModel" + "from gensim.sklearn_integration.sklearn_wrapper_gensim_ldamodel import SklearnWrapperLdaModel" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 24, "metadata": { - "collapsed": true + "collapsed": false }, "outputs": [], "source": [ @@ -173,9 +179,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 25, "metadata": { - "collapsed": true + "collapsed": false }, "outputs": [], "source": [ @@ -196,7 +202,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 26, "metadata": { "collapsed": false }, @@ -205,18 +211,18 @@ "data": { "text/plain": [ "[(0,\n", - " u'0.018*\"cryptography\" + 0.018*\"face\" + 0.017*\"fierkelab\" + 0.008*\"abuse\" + 0.007*\"constitutional\" + 0.007*\"collection\" + 0.007*\"finish\" + 0.007*\"150\" + 0.007*\"fast\" + 0.006*\"difference\"'),\n", + " u'0.085*\"abroad\" + 0.053*\"ciphertext\" + 0.042*\"arithmetic\" + 0.037*\"facts\" + 0.031*\"courtesy\" + 0.025*\"amolitor\" + 0.023*\"argue\" + 0.021*\"asking\" + 0.020*\"agree\" + 0.018*\"classified\"'),\n", " (1,\n", - " u'0.022*\"corporate\" + 0.022*\"accurate\" + 0.012*\"chance\" + 0.008*\"decipher\" + 0.008*\"example\" + 0.008*\"basically\" + 0.008*\"dawson\" + 0.008*\"cases\" + 0.008*\"consideration\" + 0.008*\"follow\"'),\n", + " u'0.098*\"asking\" + 0.075*\"cryptography\" + 0.068*\"abroad\" + 0.033*\"456\" + 0.025*\"argue\" + 0.022*\"bitnet\" + 0.017*\"false\" + 0.014*\"digex\" + 0.014*\"effort\" + 0.013*\"disk\"'),\n", " (2,\n", - " u'0.034*\"argue\" + 0.031*\"456\" + 0.031*\"arithmetic\" + 0.024*\"courtesy\" + 0.020*\"beastmaster\" + 0.019*\"bitnet\" + 0.015*\"false\" + 0.015*\"classified\" + 0.014*\"cubs\" + 0.014*\"digex\"'),\n", + " u'0.023*\"accurate\" + 0.021*\"corporate\" + 0.013*\"clark\" + 0.012*\"chance\" + 0.009*\"consideration\" + 0.008*\"authentication\" + 0.008*\"dawson\" + 0.008*\"candidates\" + 0.008*\"basically\" + 0.008*\"assess\"'),\n", " (3,\n", - " u'0.108*\"abroad\" + 0.089*\"asking\" + 0.060*\"cryptography\" + 0.035*\"certain\" + 0.030*\"ciphertext\" + 0.030*\"book\" + 0.028*\"69\" + 0.028*\"demand\" + 0.028*\"87\" + 0.027*\"cracking\"'),\n", + " u'0.016*\"cryptography\" + 0.007*\"evans\" + 0.006*\"considering\" + 0.006*\"forgot\" + 0.006*\"built\" + 0.005*\"constitutional\" + 0.005*\"fly\" + 0.004*\"cellular\" + 0.004*\"computed\" + 0.004*\"digitized\"'),\n", " (4,\n", - " u'0.022*\"clark\" + 0.019*\"authentication\" + 0.017*\"candidates\" + 0.016*\"decryption\" + 0.015*\"attempt\" + 0.013*\"creation\" + 0.013*\"1993apr5\" + 0.013*\"acceptable\" + 0.013*\"algorithms\" + 0.013*\"employer\"')]" + " u'0.028*\"certain\" + 0.022*\"69\" + 0.021*\"book\" + 0.020*\"demand\" + 0.020*\"cracking\" + 0.020*\"87\" + 0.017*\"farm\" + 0.017*\"fierkelab\" + 0.015*\"face\" + 0.009*\"constitutional\"')]" ] }, - "execution_count": 7, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -233,72 +239,163 @@ "collapsed": true }, "source": [ - "#### Using together with Scikit learn's Logistic Regression" + "### Example for Using Grid Search" ] }, { - "cell_type": "markdown", - "metadata": {}, + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [], "source": [ - "Now lets try Sklearn's logistic classifier to classify the given categories into two types.Ideally we should get postive weights when cryptography is talked about and negative when baseball is talked about." + "from sklearn.model_selection import GridSearchCV\n", + "from gensim.models.coherencemodel import CoherenceModel" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 31, "metadata": { "collapsed": true }, "outputs": [], "source": [ - "from sklearn import linear_model" + "def scorer(estimator, X,y=None):\n", + " goodcm = CoherenceModel(model=estimator, texts= texts, dictionary=estimator.id2word, coherence='c_v')\n", + " return goodcm.get_coherence()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "GridSearchCV(cv=5, error_score='raise',\n", + " estimator=SklearnWrapperLdaModel(alpha='symmetric', chunksize=2000, corpus=None,\n", + " decay=0.5, eta=None, eval_every=10, gamma_threshold=0.001,\n", + " id2word=,\n", + " iterations=50, minimum_probability=0.01, num_topics=5,\n", + " offset=1.0, passes=20, random_state=None, update_every=1),\n", + " fit_params={}, iid=True, n_jobs=1,\n", + " param_grid={'num_topics': (2, 3, 5, 10), 'iterations': (1, 20, 50)},\n", + " pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n", + " scoring=, verbose=0)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "obj=SklearnWrapperLdaModel(id2word=dictionary,num_topics=5,passes=20)\n", + "parameters = {'num_topics':(2, 3, 5, 10), 'iterations':(1,20,50)}\n", + "model = GridSearchCV(obj, parameters, scoring=scorer, cv=5)\n", + "model.fit(corpus)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'iterations': 50, 'num_topics': 3}" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.best_params_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Example of Using Pipeline" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 34, "metadata": { "collapsed": true }, "outputs": [], "source": [ - "def print_features(clf, vocab, n=10):\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn import linear_model\n", + "def print_features_pipe(clf, vocab, n=10):\n", " ''' Better printing for sorted list '''\n", - " coef = clf.coef_[0]\n", + " coef = clf.named_steps['classifier'].coef_[0]\n", + " print coef\n", " print 'Positive features: %s' % (' '.join(['%s:%.2f' % (vocab[j], coef[j]) for j in np.argsort(coef)[::-1][:n] if coef[j] > 0]))\n", - " print 'Negative features: %s' % (' '.join(['%s:%.2f' % (vocab[j], coef[j]) for j in np.argsort(coef)[:n] if coef[j] < 0]))" + " print 'Negative features: %s' % (' '.join(['%s:%.2f' % (vocab[j], coef[j]) for j in np.argsort(coef)[:n] if coef[j] < 0]))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "id2word=Dictionary(map(lambda x : x.split(),data.data))\n", + "corpus = [id2word.doc2bow(i.split()) for i in data.data]" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:gensim.models.ldamodel:too few updates, training might not converge; consider increasing the number of passes or iterations to improve accuracy\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "Positive features: clipper:1.50 code:1.24 key:1.04 encryption:0.95 chip:0.37 nsa:0.37 government:0.36 uk:0.36 org:0.23 cryptography:0.23\n", - "Negative features: baseball:-1.32 game:-0.71 year:-0.61 team:-0.38 edu:-0.27 games:-0.26 players:-0.23 ball:-0.17 season:-0.14 phillies:-0.11\n" + "[ -2.95020466e-01 -1.04115352e-01 5.19570267e-01 1.03817059e-01\n", + " 2.72881013e-02 1.35738501e-02 1.89246630e-13 1.89246630e-13\n", + " 1.89246630e-13 1.89246630e-13 1.89246630e-13 1.89246630e-13\n", + " 1.89246630e-13 1.89246630e-13 1.89246630e-13]\n", + "Positive features: Fame,:0.52 Keach:0.10 comp.org.eff.talk,:0.03 comp.org.eff.talk.:0.01 >Pat:0.00 dome.:0.00 internet...:0.00 trawling:0.00 hanging:0.00 red@redpoll.neoucom.edu:0.00\n", + "Negative features: Fame.:-0.30 considered,:-0.10\n", + "0.531040268456\n" ] } ], "source": [ - "clf=linear_model.LogisticRegression(penalty='l1', C=0.1) #l1 penalty used\n", - "clf.fit(X,data.target)\n", - "print_features(clf,vocab)" + "model=SklearnWrapperLdaModel(num_topics=15,id2word=id2word,iterations=50, random_state=37)\n", + "clf=linear_model.LogisticRegression(penalty='l2', C=0.1) #l2 penalty used\n", + "pipe = Pipeline((('features', model,), ('classifier', clf)))\n", + "pipe.fit(corpus, data.target)\n", + "print_features_pipe(pipe, id2word.values())\n", + "print pipe.score(corpus, data.target)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] } ], "metadata": { @@ -317,7 +414,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.6" + "version": "2.7.13" } }, "nbformat": 4, diff --git a/gensim/sklearn_integration/sklearn_wrapper_gensim_ldamodel.py b/gensim/sklearn_integration/sklearn_wrapper_gensim_ldamodel.py index e27a29f886..003d313e6d 100644 --- a/gensim/sklearn_integration/sklearn_wrapper_gensim_ldamodel.py +++ b/gensim/sklearn_integration/sklearn_wrapper_gensim_ldamodel.py @@ -8,12 +8,17 @@ Scikit learn interface for gensim for easy use of gensim with scikit-learn follows on scikit learn API conventions """ +import numpy as np + from gensim import models from gensim import matutils from scipy import sparse +from sklearn.base import TransformerMixin, BaseEstimator + -class SklearnWrapperLdaModel(models.LdaModel): + +class SklearnWrapperLdaModel(models.LdaModel, TransformerMixin, BaseEstimator): """ Base LDA module """ @@ -56,15 +61,14 @@ def get_params(self, deep=True): """ Returns all parameters as dictionary. """ - if deep: - return { - "corpus": self.corpus, "num_topics": self.num_topics, "id2word": self.id2word, + return {"corpus": self.corpus, "num_topics": self.num_topics, "id2word": self.id2word, "chunksize": self.chunksize, "passes": self.passes, - "update_every": self.update_every, "alpha": self.alpha, " eta": self.eta, " decay": self.decay, - "offset": self.offset, "eval_every": self.eval_every, " iterations": self.iterations, + "update_every": self.update_every, "alpha": self.alpha, "eta": self.eta, "decay": self.decay, + "offset": self.offset, "eval_every": self.eval_every, "iterations": self.iterations, "gamma_threshold": self.gamma_threshold, "minimum_probability": self.minimum_probability, "random_state": self.random_state} + def set_params(self, **parameters): """ Set all parameters. @@ -73,7 +77,7 @@ def set_params(self, **parameters): self.parameter = value return self - def fit(self, X): + def fit(self, X, y=None): """ For fitting corpus into the class object. Calls gensim.model.LdaModel: @@ -93,7 +97,28 @@ def fit(self, X): random_state=self.random_state) return self - def transform(self, bow, minimum_probability=None, minimum_phi_value=None, per_word_topics=False): + def transform(self, docs, minimum_probability=None): + """ + Takes as an list of input a documents (documents). + Returns matrix of topic distribution for the given document bow, where a_ij + indicates (topic_i, topic_probability_j). + """ + # The input as array of array + check = lambda x: [x] if isinstance(x[0], tuple) else x + docs = check(docs) + X = [[] for i in range(0,len(docs))]; + for k,v in enumerate(docs): + + doc_topics = self.get_document_topics(v, minimum_probability=minimum_probability) + probs_docs = list(map(lambda x: x[1], doc_topics)) + # Everything should be equal in length + if len(probs_docs) != self.num_topics: + probs_docs.extend([1e-12]*(self.num_topics - len(probs_docs))) + X[k] = probs_docs + probs_docs = [] + return np.reshape(np.array(X), (len(docs), self.num_topics)) + + def get_topic_dist(self, bow, minimum_probability=None, minimum_phi_value=None, per_word_topics=False): """ Takes as an input a new document (bow). Returns the topic distribution for the given document bow, as a list of (topic_id, topic_probability) 2-tuples. @@ -109,4 +134,4 @@ def partial_fit(self, X): if sparse.issparse(X): X = matutils.Sparse2Corpus(X) - self.update(corpus=X) + self.update(corpus=X) \ No newline at end of file diff --git a/gensim/test/test_data/mini_newsgroup b/gensim/test/test_data/mini_newsgroup new file mode 100644 index 0000000000000000000000000000000000000000..65f80e66205aa2b052a41d7dfa0ec6f118fde501 GIT binary patch literal 196956 zcmV(%K;pl6oaDWEciTvoFZg%mrwA#1+LD`u32qWq-A+-9ZKbu9D5tZ!rk)2RgCr~v zU}BL>Prmx>bAR_nU?C~l?yPz}=Zve;mViL4H}1ZSM$uyVG1k)`=&w-}CTX0F(kT96 z(fDC!l-vc{i$iDZrCy@b#2L~*KOAM@X!gUx{bBU+<1q7s)DJ&?{9){m(jOMhA9luz z)(?Z{C#Qqso990)+CN+-gXidge(C(M6D+zv{Bo%ul4%@ei{wCgLF#zvO#8|F|H>MT zhIdWB9jK8Prd~izw3Ed5&M0w4^UTrXY-<#ILvOJN)?Q4KJx7mp{=3fNXyG|JoI2A~ zPm-}SisoCh=ppdJu@mbgjY7u@6DQQ^R!CDybbPO4=hJ;S;%7^b20>4L>^a$;=ZrPC zbH%?E2G^-C2ygF;Sorc?RWl68&XnvsVv%~fFYUfCYdhFk& z>LyBb=w-X|iXHqf%*@(d(_`QcH3R& 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e8ae26bb82..3a6401962b 100644 --- a/gensim/test/test_sklearn_integration.py +++ b/gensim/test/test_sklearn_integration.py @@ -1,12 +1,22 @@ import six import unittest import numpy +import os +import codecs +import pickle from scipy import sparse +from sklearn.pipeline import Pipeline +from sklearn.feature_extraction.text import CountVectorizer +from sklearn.datasets import load_files +from sklearn import linear_model from gensim.sklearn_integration.sklearn_wrapper_gensim_ldamodel import SklearnWrapperLdaModel from gensim.corpora import Dictionary from gensim import matutils +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) + texts = [['complier', 'system', 'computer'], ['eulerian', 'node', 'cycle', 'graph', 'tree', 'path'], ['graph', 'flow', 'network', 'graph'], @@ -35,7 +45,21 @@ def testPrintTopic(self): def testTransform(self): texts_new = ['graph','eulerian'] bow = self.model.id2word.doc2bow(texts_new) - doc_topics, word_topics, phi_values = self.model.transform(bow,per_word_topics=True) + X = self.model.transform(bow) + self.assertTrue(X.shape[0], 1) + self.assertTrue(X.shape[1], self.model.num_topics) + texts_new = [['graph','eulerian'],['server', 'flow'], ['path', 'system']] + bow = [] + for i in texts_new: + bow.append(self.model.id2word.doc2bow(i)) + X = self.model.transform(bow) + self.assertTrue(X.shape[0], 3) + self.assertTrue(X.shape[1], self.model.num_topics) + + def testGetTopicDist(self): + texts_new = ['graph','eulerian'] + bow = self.model.id2word.doc2bow(texts_new) + doc_topics, word_topics, phi_values = self.model.get_topic_dist(bow,per_word_topics=True) for k,v in word_topics: self.assertTrue(isinstance(v, list)) @@ -67,5 +91,21 @@ def testCSRMatrixConversion(self): self.assertTrue(isinstance(v, six.string_types)) self.assertTrue(isinstance(k, int)) + def testPipeline(self): + model = SklearnWrapperLdaModel(num_topics=2, passes=10, minimum_probability=0, random_state=numpy.random.seed(0)) + with open(datapath('mini_newsgroup'),'rb') as f: + compressed_content = f.read() + uncompressed_content = codecs.decode(compressed_content, 'zlib_codec') + cache = pickle.loads(uncompressed_content) + data = cache + id2word=Dictionary(map(lambda x : x.split(), data.data)) + corpus = [id2word.doc2bow(i.split()) for i in data.data] + rand = numpy.random.mtrand.RandomState(1) # set seed for getting same result + clf=linear_model.LogisticRegression(penalty='l2', C=0.1) + text_lda = Pipeline((('features', model,), ('classifier', clf))) + text_lda.fit(corpus, data.target) + score = text_lda.score(corpus, data.target) + self.assertGreater(score, 0.50) + if __name__ == '__main__': unittest.main()