syntactic-text-similarity.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matching Click Sequences"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scope"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The aim of the task is the matching between contact and respond json segments according to the syntactic similarity or even better the semantic similarity of their content. However, human language is complex, and the semantic meaning of an utterance is usually dependent upon the context at a given time and learnt experience of the meaning of the words that are used. This is particularly challenging when automatically understanding the meaning of texting, which can contain non-standard language."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data content approach"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The body of the json files seems to be separated into actions, each of these presents information about event type, element name and text value. The first two fields are indicating mostly the user-computer interaction and the third text value reveals a lot of information about the main content/reason of the contact or the respond which can help us to decide where it belongs. Thus, the algorithm who implemented collects and groups into one big string the values of the text_value elements. For example:"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Contact[0] = 2005X-3045B-1569J 67A23B Dear Ms. A, can you confirm your purchase of a plane ticket to Hong Kong?\n",
    "Respond[2] = Ms. A approved purchase of a plane ticket to Hong Kong, purchase number: 2005X-3045B-1569J."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing data manipulation libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import json\n",
    "import matplotlib.pyplot as plt\n",
    "import os "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Natural language Processing libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from nltk.tokenize import RegexpTokenizer\n",
    "from nltk.corpus import stopwords\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function implementation section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_value_text(data):\n",
    "    \"\"\"\n",
    "    Description:\n",
    "        Extracts the values of each field of the json, but returns \n",
    "        only a unified string of the text_values after grouping.\n",
    "    Input Parameters\n",
    "        data: json data [contact or respond data] \n",
    "    Output Parameters\n",
    "        _txt_str: String \n",
    "    \"\"\"\n",
    "    _txt = []\n",
    "    _evnt = []\n",
    "    _elm = []\n",
    "    _misc =[]\n",
    "    for i in range(len(data)): \n",
    "        for key, value in data[i].items(): \n",
    "            if key == 'text_value':\n",
    "                _txt.append(value)\n",
    "            else:\n",
    "                _misc.append(value)\n",
    "    _txt_str=' '.join(_txt) \n",
    "\n",
    "    return _txt_str"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def string_constractor(data, kind):\n",
    "    \"\"\"\n",
    "    Description:\n",
    "        Composes the unified strings into lists and gives\n",
    "        a tag according to the kind of data that red\n",
    "    Input Parameters\n",
    "        data: json data [contact or respond data] \n",
    "        kind: String \n",
    "    Output Parameters\n",
    "        text: List\n",
    "        tag: List \n",
    "    \"\"\"\n",
    "    text = []\n",
    "    tag = []\n",
    "    for i in range(len(data)):\n",
    "        text.append(extract_value_text(data[i]))\n",
    "        tag.append(kind + str(i))\n",
    "    return text , tag\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def df_constructor(text_c, tag_c, text_r, tag_r):\n",
    "    \"\"\"\n",
    "    Description:\n",
    "        Composes a dataframe from lists \n",
    "    Input Parameters\n",
    "        text_c: List\n",
    "        tag_c: List \n",
    "        text_r: List\n",
    "        tag_r: List\n",
    "    Output Parameters\n",
    "        df: Dataframe\n",
    "    \"\"\"\n",
    "    text = []\n",
    "    tag = []\n",
    "    text = text_c + text_r\n",
    "    tag = tag_c + tag_r\n",
    "\n",
    "    df = pd.DataFrame({'text': text, 'tag': tag })\n",
    "    return df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def heatmap(df, mtx):\n",
    "    \"\"\"\n",
    "    Description:\n",
    "        Visualizes the cosine similarity between the inputs \n",
    "    Input Parameters\n",
    "        df: Dataframe\n",
    "        mtx: 2D Array \n",
    "    \"\"\"\n",
    "    x_labels = df['text'].tolist()\n",
    "    y_labels = df['text'].tolist()\n",
    "\n",
    "    fig, ax = plt.subplots()\n",
    "    im = ax.imshow(mtx)\n",
    "\n",
    "    ax.set_xticks(np.arange(len(x_labels)))\n",
    "    ax.set_yticks(np.arange(len(y_labels)))\n",
    "    ax.set_xticklabels(x_labels)\n",
    "    ax.set_yticklabels(y_labels)\n",
    "\n",
    "    plt.setp(ax.get_xticklabels(), rotation=45, ha='right', rotation_mode='anchor')\n",
    "\n",
    "    for i in range(len(y_labels)):\n",
    "        for j in range(len(x_labels)):\n",
    "            text = ax.text(j, i, '%.2f'%mtx[i, j], ha='center', va='center', color='w', fontsize=6)\n",
    "\n",
    "   # fig.tight_layout()\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_text(doc):\n",
    "    \"\"\"\n",
    "    Description:\n",
    "        Preprocesses each document into a proper \n",
    "        format to to pass it on TfidfVectorizer\n",
    "    Input Parameters\n",
    "        doc: String\n",
    "    \"\"\"\n",
    "    tokeniser = RegexpTokenizer(r'\\w+')\n",
    "    tokens = tokeniser.tokenize(doc)\n",
    "    \n",
    "    lemmatiser = WordNetLemmatizer()\n",
    "    lemmas = [lemmatiser.lemmatize(token.lower(), pos='v') for token in tokens]\n",
    "    \n",
    "    keywords= [lemma for lemma in lemmas if lemma not in stopwords.words('english')]\n",
    "    return keywords\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def feature_matrix_tf_idf(df):\n",
    "    \"\"\"\n",
    "    Description:\n",
    "        Preprocess texts and Constracts a feature matrix\n",
    "    Input Parameters\n",
    "        df: Dataframe\n",
    "    Output Parameters\n",
    "        X_ftr: Compressed sparse row matrix \n",
    "    \"\"\"\n",
    "    corpus = df['text'].tolist()\n",
    "\n",
    "    vectoriser = TfidfVectorizer(analyzer=preprocess_text)\n",
    "    X_ftr = vectoriser.fit_transform(corpus)\n",
    "\n",
    "    return X_ftr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pairwise_cosine_similarity(X_ftr):\n",
    "    \"\"\"\n",
    "    Description:\n",
    "        Measures the similarity between two non-zero \n",
    "        vectors of an inner product space\n",
    "    Input Parameters\n",
    "        X_ftr: Compressed sparse row matrix \n",
    "    Output Parameters\n",
    "        res: 2d Array\n",
    "    \"\"\"\n",
    "    pairwise_similarity = X_ftr * X_ftr.T\n",
    "    res = pairwise_similarity.toarray()\n",
    "\n",
    "    return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def query_res(corr, df):\n",
    "    \"\"\"\n",
    "    Description:\n",
    "        Finds the index of the most similar document \n",
    "    Input Parameters\n",
    "        corr: 2d Array\n",
    "        df: Dataframe\n",
    "    Output Parameters\n",
    "        qdf: Dataframe\n",
    "    \"\"\"\n",
    "    corpus = df['text'].tolist()\n",
    "\n",
    "    np.fill_diagonal(corr, np.nan)\n",
    "    flt = df[df['tag'].str.contains('contact',regex = False)]\n",
    "    n = len(flt)\n",
    "    lst = []\n",
    "    for idx in range(len(flt)):\n",
    "\n",
    "        query_idx = corpus.index(flt['text'][idx])       \n",
    "        result_idx = np.nanargmax(corr[query_idx])\n",
    "        score = corr.item(query_idx,result_idx)\n",
    "\n",
    "        lst.append([df['tag'][query_idx],df['tag'][result_idx],score ])\n",
    "        res = pd.DataFrame(lst, columns =['contact', 'response', 'score'])    \n",
    "        qdf = res[res['response'].str.contains('respond ',regex = False)]\n",
    "\n",
    "        #if result_idx >= n:\n",
    "        #    score = corr.item(query_idx,result_idx)\n",
    "        #    lst.append([df['tag'][query_idx],df['tag'][result_idx],score ])\n",
    "        #    res = pd.DataFrame(lst, columns =['contact', 'response', 'score'])    \n",
    "\n",
    "    return qdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Main body of the algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " - [ ? ] Loading test data\n",
      "   [ V ] Successfully complete\n"
     ]
    }
   ],
   "source": [
    "if __name__ == '__main__':\n",
    "    \n",
    "    cd = os.getcwd()\n",
    "\n",
    "    try:\n",
    "        print(' - [ ? ] Loading test data')\n",
    "        with open(cd+r\"\\data.json\") as json_file: \n",
    "            data = json.load(json_file)\n",
    "\n",
    "        contact =[]\n",
    "        respond = []\n",
    "        contact = data['contact']\n",
    "        respond = data['respond']\n",
    "        print('   [ V ] Successfully complete')\n",
    "    except Exception as e:\n",
    "        print(e)\n",
    "        print('   [ X ] Failed. Application exits...')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pre-processing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " - [ ? ] Preparing the text_value into a uniform string\n",
      "   [ V ] Successfully complete\n"
     ]
    }
   ],
   "source": [
    "    try:\n",
    "        print(' - [ ? ] Preparing the text_value into a uniform string')\n",
    "        text_c, tag_c = string_constractor(contact, 'contact ')\n",
    "        text_r, tag_r = string_constractor(respond, 'respond ')\n",
    "        print('   [ V ] Successfully complete')\n",
    "    except Exception as e:\n",
    "        print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " - [ ? ] Constructing a dataframe\n",
      "                                                text        tag\n",
      "0  2005X-3045B-1569J 67A23B Dear Ms. A, can you c...  contact 0\n",
      "1  3865T-0665J-5443D E43AC2 Dear Mr. B, can you c...  contact 1\n",
      "2  9834K-1239Y-8733N 983D1C Dear Mrs. C, can you ...  contact 2\n",
      "3  Mrs. C approved purchase of a plane ticket to ...  respond 0\n",
      "4  Mr. B rejected purchase of a meal in Hong Kong...  respond 1\n",
      "5  Ms. A approved purchase of a plane ticket to H...  respond 2\n",
      "   [ V ] Successfully complete\n"
     ]
    }
   ],
   "source": [
    "    try:\n",
    "        print(' - [ ? ] Constructing a dataframe')\n",
    "        df = df_constructor(text_c, tag_c, text_r, tag_r)\n",
    "        print(df)\n",
    "        print('   [ V ] Successfully complete')\n",
    "    except Exception as e:\n",
    "        print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Term Frequency–Inverse Document Frequency (tf-idf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to proceed into count vectorize and tf-idf representation and finally, to pairwise cosine similarity, the input corpus needs to be pre-processed with tokenizing, normalizing, and stop words removing, then we can fit to the data and transform to a feature matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " - [ ? ] Creating a feature matrix of words's frequencies\n",
      "   [ V ] Successfully complete\n"
     ]
    }
   ],
   "source": [
    "    try:\n",
    "        print(' - [ ? ] Creating a feature matrix of words\\'s frequencies')\n",
    "        X_train = feature_matrix_tf_idf(df)\n",
    "        print('   [ V ] Successfully complete')\n",
    "    except Exception as e:\n",
    "        print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pairwise Cosine Similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " - [ ? ] Calculating the cosine similarity array\n",
      "[[1.         0.28321752 0.27034024 0.1619527  0.16589065 0.73101562]\n",
      " [0.28321752 1.         0.16000584 0.05535968 0.73851827 0.17019244]\n",
      " [0.27034024 0.16000584 1.         0.77836839 0.05412749 0.16245416]\n",
      " [0.1619527  0.05535968 0.77836839 1.         0.17662095 0.39686157]\n",
      " [0.16589065 0.73851827 0.05412749 0.17662095 1.         0.30312016]\n",
      " [0.73101562 0.17019244 0.16245416 0.39686157 0.30312016 1.        ]]\n",
      "   [ V ] Successfully complete\n"
     ]
    }
   ],
   "source": [
    "    try:\n",
    "        print(' - [ ? ] Calculating the cosine similarity array')\n",
    "        corr = pairwise_cosine_similarity(X_train) \n",
    "        print(corr)\n",
    "        print('   [ V ] Successfully complete')\n",
    "    except Exception as e:\n",
    "        print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " - [ ? ] Vissualizing the similarity array\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   [ V ] Successfully complete\n"
     ]
    }
   ],
   "source": [
    "    try:\n",
    "        print(' - [ ? ] Vissualizing the similarity array')\n",
    "        heatmap(df, corr)\n",
    "        print('   [ V ] Successfully complete')\n",
    "    except Exception as e:\n",
    "        print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " - [ ? ] Matching the contact/respond segments with the max similarity\n",
      "     contact   response     score\n",
      "0  contact 0  respond 2  0.731016\n",
      "1  contact 1  respond 1  0.738518\n",
      "2  contact 2  respond 0  0.778368\n",
      "   [ V ] Successfully complete\n",
      "\n"
     ]
    }
   ],
   "source": [
    "    try:\n",
    "        print(' - [ ? ] Matching the contact/respond segments with the max similarity')\n",
    "        print(query_res(corr, df))\n",
    "        print('   [ V ] Successfully complete')\n",
    "        print('')\n",
    "    except Exception as e:\n",
    "        print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Limitations of the implementation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.\tIt relies mainly in syntactic similarity\n",
    "2.\tIn case of no match we don’t get result like: contact 2  | X |  0.0000\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}