NLP Cross-Val Pipe

CV_Study.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm\n",
    "import textacy\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "mlp\\preprocess.py:34: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df[\"labels\"][df[\"labels\"] == missing_label] = -1  # to make a \"non-category\"\n",
      "mlp\\preprocess.py:37: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df[\"labels\"][df[\"labels\"] > missing_label] = df[\"labels\"][df[\"labels\"] > missing_label].values - 1\n"
     ]
    }
   ],
   "source": [
    "import mlp.preprocess as pre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from mlp.embeddings import TopicVectors, SemanticVectors\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.preprocessing import FunctionTransformer\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "corpus = pre.get_corpus()\n",
    "df = pre.get_df()\n",
    "\n",
    "y = pre.get_labeled_data(df['labels'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: 91.407407%\n"
     ]
    }
   ],
   "source": [
    "sgd_w2v = Pipeline([\n",
    "    ('word2vec embedding', SemanticVectors()),\n",
    "    ('extract_labeled', FunctionTransformer(pre.get_labeled_data, validate=False)),  # extract labeled points\n",
    "    ('SGD Lin-SVC w/ElasticNet', SGDClassifier(class_weight='balanced',  # compensate for class freqs\n",
    "                                               penalty='elasticnet',   # L1 + L2 regularized\n",
    "                                               alpha=0.001,\n",
    "                                               n_iter=10))\n",
    "])\n",
    "\n",
    "# sgd_w2v.set_params(anova__k=10, svc__C=.1).fit(corpus, y)\n",
    "sgd_w2v.fit(corpus, y)\n",
    "\n",
    "print \"score: {:2f}%\".format((sgd_w2v.score(corpus, y))*100.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pipe = Pipeline([\n",
    "    ('embedding', SemanticVectors()),\n",
    "    ('extract_labeled', FunctionTransformer(pre.get_labeled_data, validate=False)),  # extract labeled points\n",
    "    ('classify', LinearSVC())\n",
    "])\n",
    "\n",
    "# SGDClassifier(class_weight='balanced',  # compensate for class freqs\n",
    "#                                                penalty='elasticnet',   # L1 + L2 regularized\n",
    "#                                                alpha=0.001,\n",
    "#                                                n_iter=10)\n",
    "\n",
    "N_FEATURES_OPTIONS = [100, 200, 300, 400, 500]\n",
    "# C_OPTIONS = [1, 10, 100, 1000]\n",
    "TOPIC_OPTIONS = ['lda', 'lsa', 'nmf']\n",
    "param_grid = [\n",
    "    {\n",
    "        'embedding': [TopicVectors()],\n",
    "        'embedding__n_topics': N_FEATURES_OPTIONS,\n",
    "        'embedding__model': TOPIC_OPTIONS,\n",
    "        'embedding__bow_kws': [{\n",
    "                                'weighting': 'tfidf',  # change to tf if using LDA\n",
    "                                'normalize': True,  # turn off if using LDA\n",
    "                                'smooth_idf': True,  # turn off if using LDA\n",
    "                               },\n",
    "                                {\n",
    "                                'weighting': 'tf',  # change to tf if using LDA\n",
    "                                'normalize': False,  # turn off if using LDA\n",
    "                                'smooth_idf': False,  # turn off if using LDA\n",
    "                                }]\n",
    "    },\n",
    "    {\n",
    "        'embedding': [SemanticVectors()]\n",
    "    },\n",
    "]\n",
    "# reducer_labels = ['PCA', 'NMF', 'KBest(chi2)']\n",
    "\n",
    "grid = GridSearchCV(pipe, cv=3, n_jobs=3, param_grid=param_grid)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Found input variables with inconsistent numbers of samples: [3435, 675]",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-18-545120320836>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgrid\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcorpus\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32mC:\\Users\\tbs4\\AppData\\Local\\Continuum\\Anaconda2\\lib\\site-packages\\sklearn\\model_selection\\_search.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, groups)\u001b[0m\n\u001b[1;32m    943\u001b[0m             \u001b[0mtrain\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mtest\u001b[0m \u001b[0mset\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    944\u001b[0m         \"\"\"\n\u001b[0;32m--> 945\u001b[0;31m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgroups\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mParameterGrid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparam_grid\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    946\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    947\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\tbs4\\AppData\\Local\\Continuum\\Anaconda2\\lib\\site-packages\\sklearn\\model_selection\\_search.pyc\u001b[0m in \u001b[0;36m_fit\u001b[0;34m(self, X, y, groups, parameter_iterable)\u001b[0m\n\u001b[1;32m    540\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscorer_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_scoring\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscoring\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscoring\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    541\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 542\u001b[0;31m         \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgroups\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mindexable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgroups\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    543\u001b[0m         \u001b[0mn_splits\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_n_splits\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgroups\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    544\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mverbose\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mparameter_iterable\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mSized\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\tbs4\\AppData\\Local\\Continuum\\Anaconda2\\lib\\site-packages\\sklearn\\utils\\validation.pyc\u001b[0m in \u001b[0;36mindexable\u001b[0;34m(*iterables)\u001b[0m\n\u001b[1;32m    204\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    205\u001b[0m             \u001b[0mresult\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 206\u001b[0;31m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    207\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    208\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\tbs4\\AppData\\Local\\Continuum\\Anaconda2\\lib\\site-packages\\sklearn\\utils\\validation.pyc\u001b[0m in \u001b[0;36mcheck_consistent_length\u001b[0;34m(*arrays)\u001b[0m\n\u001b[1;32m    179\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0muniques\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m         raise ValueError(\"Found input variables with inconsistent numbers of\"\n\u001b[0;32m--> 181\u001b[0;31m                          \" samples: %r\" % [int(l) for l in lengths])\n\u001b[0m\u001b[1;32m    182\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    183\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Found input variables with inconsistent numbers of samples: [3435, 675]"
     ]
    }
   ],
   "source": [
    "grid.fit(corpus, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [default]",
   "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": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}