keyword_test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from distinct_keywords.keywords import DistinctKeywords "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "doc = \"\"\"\n",
    "         Supervised learning is the machine learning task of learning a function that\n",
    "         maps an input to an output based on example input-output pairs. It infers a\n",
    "         function from labeled training data consisting of a set of training examples.\n",
    "         In supervised learning, each example is a pair consisting of an input object\n",
    "         (typically a vector) and a desired output value (also called the supervisory signal). \n",
    "         A supervised learning algorithm analyzes the training data and produces an inferred function, \n",
    "         which can be used for mapping new examples. An optimal scenario will allow for the \n",
    "         algorithm to correctly determine the class labels for unseen instances. This requires \n",
    "         the learning algorithm to generalize from the training data to unseen situations in a \n",
    "         'reasonable' way (see inductive bias).\n",
    "      \"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "distinct_keywords=DistinctKeywords()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['supervisory',\n",
       " ' Supervised learning',\n",
       " 'the machine',\n",
       " 'mapping',\n",
       " 'vector',\n",
       " 'training',\n",
       " 'supervised',\n",
       " 'a function',\n",
       " 'the training data',\n",
       " 'task',\n",
       " 'unseen',\n",
       " 'algorithm',\n",
       " 'typically',\n",
       " 'machine learning',\n",
       " 'bias',\n",
       " 'pairs']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distinct_keywords.get_keywords(doc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'24098176703961584660'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distinct_keywords.hilbert_lookup_dictionary['april']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'24098176703961584661'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distinct_keywords.hilbert_lookup_dictionary['july']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'24098176695545132848'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distinct_keywords.hilbert_lookup_dictionary['january']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'10361698322165241623'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distinct_keywords.hilbert_lookup_dictionary['machine_learning']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'12050206188136005184'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distinct_keywords.hilbert_lookup_dictionary['mathematics']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'12062515252859527804'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distinct_keywords.hilbert_lookup_dictionary['deep_learning']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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