abstract_svn_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "%store -r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(590, 1179)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(unique_rejected_clean_training), len(unique_accepted_clean_training_abstracts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "random.shuffle(unique_accepted_clean_training_abstracts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "unique_accepted_clean_training_abstracts_short = unique_accepted_clean_training_abstracts[0:590]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "590"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(unique_accepted_clean_training_abstracts_short)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine our data into two lists, one with abstracts, and the other with the labels. \n",
    "labels_clean_training = []\n",
    "abstracts_clean_training = [] \n",
    "for abstract in unique_accepted_clean_training_abstracts_short:\n",
    "    abstracts_clean_training.append(abstract)\n",
    "    labels_clean_training.append(\"selected\")\n",
    "for abstract in unique_rejected_clean_training:\n",
    "    abstracts_clean_training.append(abstract)\n",
    "    labels_clean_training.append(\"rejected\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stored 'abstracts_clean_training' (list)\n"
     ]
    }
   ],
   "source": [
    "%store abstracts_clean_training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stored 'labels_clean_training' (list)\n"
     ]
    }
   ],
   "source": [
    "%store labels_clean_training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create the data frame\n",
    "trainCleanBalancedDF = pandas.DataFrame()\n",
    "trainCleanBalancedDF['text'] = abstracts_clean_training\n",
    "trainCleanBalancedDF['label'] = labels_clean_training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Background:The posterior cruciate ligament (PC...</td>\n",
       "      <td>selected</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Background: An initial and vital important ste...</td>\n",
       "      <td>selected</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Objectives: Reliable epidemiologic estimates o...</td>\n",
       "      <td>selected</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "                                                text     label\n",
       "0  Background:The posterior cruciate ligament (PC...  selected\n",
       "1  Background: An initial and vital important ste...  selected\n",
       "2  Objectives: Reliable epidemiologic estimates o...  selected"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainCleanBalancedDF[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "# now we do the model creation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Features and Labels\n",
    "X = trainCleanBalancedDF['text']\n",
    "ylabels = trainCleanBalancedDF['label']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, ylabels, test_size=0.25, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "def spacy_tokenizer(sentence):\n",
    "    mytokens = parser(sentence)\n",
    "    mytokens = [ word.lemma_.lower().strip() if word.lemma_ != \"-PRON-\" else word.lower_ for word in mytokens ]\n",
    "    mytokens = [ word for word in mytokens if word not in stopwords and word not in punctuations ]\n",
    "    return mytokens"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# show the core model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Vectorization\n",
    "vectorizer = CountVectorizer(tokenizer = spacy_tokenizer, ngram_range=(1,1)) \n",
    "classifier = LinearSVC()\n",
    "# classifier = SVC(probability=True, class_weight=\"balanced\",C=0.1, gamma= 0.01)\n",
    "# classifier = SVC(probability=True, \n",
    "#                  class_weight=\"balanced\",\n",
    "#                  C=0.01, \n",
    "#                  gamma= 0.1\n",
    "#                 )\n",
    "# Using Tfidf\n",
    "tfvectorizer = TfidfVectorizer(tokenizer = spacy_tokenizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for out clearner, vectorize and classifier defined earlier, we are using the full abstract texxt here. \n",
    "pipe = Pipeline([(\"cleaner\", predictors()),\n",
    "                 ('vectorizer', tfvectorizer),\n",
    "                 ('classifier', classifier)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('cleaner', <__main__.predictors object at 0x1c66e2e3c8>), ('vectorizer', TfidfVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
       "        dtype=<class 'numpy.float64'>, encoding='utf-8', input='content',\n",
       "        lowercase=True, max_df=1.0, max_features=None, min_df=1,\n",
       "        ng...ax_iter=1000,\n",
       "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
       "     verbose=0))])"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit the data\n",
    "# Fit our data\n",
    "pipe.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get predictions\n",
    "y_pred = pipe.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get pred_proba as we are using \n",
    "# classifier = SVC(probability=True, class_weight=\"balanced\",C=0.1, gamma= 0.01) and not Linear SVC \n",
    "ye_pred_prob = pipe.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Show how poor the model preforms "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[83, 53],\n",
       "        [65, 94]]), 0.6)"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look at the preformacne of this model\n",
    "metrics.confusion_matrix(y_pred, y_test), metrics.accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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