src/feature_mapping.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "#Problem Statement\n",
        "\n",
        "The goal is to extract a named entity from movie trivia. For example, the tags are for movie name, actor name, director name, and movie plot. General libraries might extract the names but don't differentiate between actor and director, and it would be challenging to extract movie plots. We need to build the customer model that predicts these tags for the sentences on movies.\n",
        "\n",
        "Essentially, we have a data set that talks about the movies. It consists of sentences or questions on movies, and each of those words in the sentence has a predefined tag. We need to build the NER model to predict these tags.\n",
        "\n",
        "Along the way, we need to understand these concepts.\n",
        "\n",
        "1. Build the model using various algorithms.\n",
        "\n",
        "2. Design a metric to measure the performance of the model.\n",
        "\n",
        "3. Understand where the model fails and what might be the reason for the failure.\n",
        "\n",
        "4. Fine-tune the model.\n",
        "\n",
        "5. Repeat these steps until we achieve the best accuracy on the test data."
      ],
      "metadata": {
        "id": "DSBjDZmoNbXi"
      },
      "id": "DSBjDZmoNbXi"
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "\n",
        "#Methodology and Approach\n",
        "\n",
        "NER identifies the entities in a text and classifies them into predefined categories such as location, a person's name, organization name, and more. But for this problem, we need to tag the director's name, actor's name, genre, and movie character (likewise, there are 25 such tags defined in the data set) for the entities in a sentence. So NER alone does not suffice. Here let's build custom models and train them using conditional random fields and BERT.\n",
        "\n",
        "The steps to solve this problem are as follows.\n",
        "\n",
        "1. Data collection\n",
        "\n",
        "2. Data understanding\n",
        "\n",
        "3. Data preprocessing\n",
        "\n",
        "4. Feature mapping\n",
        "\n",
        "5. Model Building\n",
        "* Conditional random fields\n",
        "*   BERT\n",
        "\n",
        "6. Hyperparameter tuning\n",
        "\n",
        "7. Evaluating the model\n",
        "\n",
        "8. Prediction on random sentences\n",
        "\n",
        " Figure 2 shows how the product works at a high level.\n",
        "\n",
        " ![Task4_2.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "LUqjrTc7Ngg3"
      },
      "id": "LUqjrTc7Ngg3"
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Implementation\n",
        "\n",
        "We understood the problem statement as well as various approached to solve it. Let's start the implementation of the project. We begin with importing and understanding the data.\n",
        "\n",
        "# Data\n",
        "\n",
        "We used data from the MIT movie corpus, which is in .bio format. Download the trivia10k13train.bio and trivia10k13test.bio data sets from https://groups.csail.mit.edu/sls/downloads/movie/.\n",
        "\n",
        "Now let's convert the data into a pandas data frame using the following code."
      ],
      "metadata": {
        "id": "CZjcaunIO6KP"
      },
      "id": "CZjcaunIO6KP"
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "bade7ba8",
      "metadata": {
        "id": "bade7ba8"
      },
      "outputs": [],
      "source": [
        "# create a function to add a column sentence that indicates the sentence u=id for each txt file as a preprocessing step.\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "def data_conversion(file_name):\n",
        "\n",
        "  df_eng=pd.read_csv(file_name, delimiter='\\t', header=None, skip_blank_lines=False)\n",
        "\n",
        "  df_eng.columns=['tag', 'tokens']\n",
        "\n",
        "  tempTokens=list(df_eng['tokens'])\n",
        "\n",
        "  tempSentence = list()\n",
        "\n",
        "  count = 1\n",
        "\n",
        "  for i in tempTokens:\n",
        "\n",
        "    tempSentence.append(\"Sentence\" + str(count))\n",
        "\n",
        "    tempTokens = list(df_eng['tokens'])\n",
        "\n",
        "    if str(i) == 'nan':\n",
        "\n",
        "      count = count+1\n",
        "\n",
        "  dfSentence =  pd.DataFrame (tempSentence, columns=['Sentence'])\n",
        "\n",
        "  result = pd.concat([df_eng, dfSentence], axis=1, join='inner')\n",
        "\n",
        "  return result"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HnzuWIFxxAXh",
        "outputId": "6b8031d2-7603-4ab5-ac63-3b6e7bf1bdf3"
      },
      "id": "HnzuWIFxxAXh",
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "9095d37b",
      "metadata": {
        "id": "9095d37b"
      },
      "outputs": [],
      "source": [
        "# passing the text files to function\n",
        "\n",
        "trivia_train=data_conversion('/content/drive/MyDrive/trivia10k13train.bio.txt')\n",
        "\n",
        "trivia_test=data_conversion('/content/drive/MyDrive/trivia10k13test.bio.txt')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Train Data Preparation\n",
        "\n",
        "Let's look at the first five rows of the training data."
      ],
      "metadata": {
        "id": "iWNfm5XGQzwe"
      },
      "id": "iWNfm5XGQzwe"
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      "execution_count": 4,
      "id": "f88688aa",
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          "height": 206
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        "outputId": "9640e915-ce07-4709-92e9-d5f9ad6ae935"
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      "outputs": [
        {
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          "data": {
            "text/plain": [
              "       tag     tokens   Sentence\n",
              "0  B-Actor      steve  Sentence1\n",
              "1  I-Actor    mcqueen  Sentence1\n",
              "2        O   provided  Sentence1\n",
              "3        O          a  Sentence1\n",
              "4   B-Plot  thrilling  Sentence1"
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              "      <td>Sentence1</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>thrilling</td>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "trivia_train.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, we check the number of rows and columns present in the training data set."
      ],
      "metadata": {
        "id": "ZsWfBBvkRFnf"
      },
      "id": "ZsWfBBvkRFnf"
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "c0033f94",
      "metadata": {
        "id": "c0033f94",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "70a145fe-f95c-4aae-c7bf-f8c332c99a53"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(166639, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "trivia_train.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "There are a total of 166,639 rows and three columns. Let's check how many unique words are present in the training data set"
      ],
      "metadata": {
        "id": "I2NbSlAFRMUu"
      },
      "id": "I2NbSlAFRMUu"
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "df420206",
      "metadata": {
        "id": "df420206",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b9993338-f319-4051-847d-333808351838"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10986"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "trivia_train.tokens.nunique()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are 10,986 unique words for the training data. Now, let's check if there are any null values present in the training data set."
      ],
      "metadata": {
        "id": "Iiebv0LsRUoj"
      },
      "id": "Iiebv0LsRUoj"
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "c77aa13d",
      "metadata": {
        "id": "c77aa13d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e424fe2b-a704-457c-f091-4f8c064c924b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tag         7816\n",
              "tokens      7817\n",
              "Sentence       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "trivia_train.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are 7817 null rows.Let's drop the null rows using the following code."
      ],
      "metadata": {
        "id": "lbLIhS9jR-PE"
      },
      "id": "lbLIhS9jR-PE"
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "1ccef4fb",
      "metadata": {
        "id": "1ccef4fb"
      },
      "outputs": [],
      "source": [
        "trivia_train.dropna(inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Test Data Preparation\n",
        "\n",
        "Let's look at the first five rows of the test data."
      ],
      "metadata": {
        "id": "LnM4YHDJSRRy"
      },
      "id": "LnM4YHDJSRRy"
    },
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      "execution_count": 9,
      "id": "60a144ab",
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  tag tokens   Sentence\n",
              "0   O      i  Sentence1\n",
              "1   O   need  Sentence1\n",
              "2   O   that  Sentence1\n",
              "3   O  movie  Sentence1\n",
              "4   O  which  Sentence1"
            ],
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "trivia_test.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "Next, we check the number of rows and columns present in the test data set.\n"
      ],
      "metadata": {
        "id": "ls5xooEoSVMi"
      },
      "id": "ls5xooEoSVMi"
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "06a5b2d7",
      "metadata": {
        "id": "06a5b2d7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "57441691-c71f-40c4-b2ae-11d1a75d92b8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(40988, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "trivia_test.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "There are a total of 40,988 rows and three columns. Let's check how many unique words are present in the test data set."
      ],
      "metadata": {
        "id": "q1j7iZqCSayO"
      },
      "id": "q1j7iZqCSayO"
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "9f72435a",
      "metadata": {
        "id": "9f72435a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cd9e8796-b3ac-490f-c9a8-822af5f3e250"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5786"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "trivia_test.tokens.nunique()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are 5786 unique words for test data. Now, let's check if there are any null values present in the test data set."
      ],
      "metadata": {
        "id": "R3UpY-z0SqwI"
      },
      "id": "R3UpY-z0SqwI"
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "03cd0ad1",
      "metadata": {
        "id": "03cd0ad1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "07a086c2-f9f7-4309-8c30-2139c367abee"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tag         1953\n",
              "tokens      1953\n",
              "Sentence       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "trivia_test.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are 1953 null rows.Let's drop the null rows using the following code."
      ],
      "metadata": {
        "id": "Kd96CkN6S0lK"
      },
      "id": "Kd96CkN6S0lK"
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "0796bb8b",
      "metadata": {
        "id": "0796bb8b"
      },
      "outputs": [],
      "source": [
        "trivia_test.dropna(inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "The data set consists of three columns after extraction.\n",
        "\n",
        "\n",
        "\n",
        "*   **tag** is the category of words\n",
        "*   **tokens** consist of words\n",
        "*   **sentence** is the sentence number in which a word belongs\n",
        "\n",
        "\n",
        "\n",
        "There are a total of 24 (excluding the O tag) unique tags in the given training data set.\n",
        "\n",
        "Their distribution is as follows."
      ],
      "metadata": {
        "id": "CQhTBXFHTDoH"
      },
      "id": "CQhTBXFHTDoH"
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "86bf4e80",
      "metadata": {
        "id": "86bf4e80",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 580
        },
        "outputId": "4bfc81e7-5dac-4197-bd43-03aac1c4539c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# get the distribution plot for the tags.\n",
        "\n",
        "trivia_train[trivia_train [\"tag\"]!=\"O\"][\"tag\"].value_counts(). plot (kind=\"bar\", figsize=(10,5))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, let's create a copy of train and test data for the further analysis and model build."
      ],
      "metadata": {
        "id": "E1n-lCamUMgU"
      },
      "id": "E1n-lCamUMgU"
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "418e991e",
      "metadata": {
        "id": "418e991e"
      },
      "outputs": [],
      "source": [
        "data=trivia_train.copy()\n",
        "\n",
        "data1=trivia_test.copy()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, let's rename the columns using the following code"
      ],
      "metadata": {
        "id": "toDqPr6lURiS"
      },
      "id": "toDqPr6lURiS"
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "850dbbd1",
      "metadata": {
        "id": "850dbbd1"
      },
      "outputs": [],
      "source": [
        "data.rename(columns={\"Sentence\": \"sentence_id\", \"tokens\": \"words\",\"tag\": \"labels\"}, inplace =True)\n",
        "\n",
        "data1.rename(columns={\"Sentence\": \"sentence_id\", \"tokens\": \"words\",\"tag\": \"labels\"}, inplace =True)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Model Building\n",
        "\n",
        "##Conditional Random Fields (CRF)\n",
        "\n",
        "CRF is a conditional model class best suited to prediction tasks where the state of neighbors or contextual information affects the current prediction.\n",
        "\n",
        "The main applications of CRFS are named-entity recognition, part of speech tagging,gene prediction, noise reduction, and object detection problems, to name a few.\n",
        "\n",
        "In a sequence classification problem, the final goal is to find the probability of y(target) given input of sequence vector x.\n",
        "\n",
        "Because conditional random fields are conditional models, they apply logistic regression to sequence data.\n",
        "\n",
        "Conditional distribution is basically\n",
        "\n",
        "                                                Y = argmax P(y|x)\n",
        "\n",
        "This finds the best output (probability) given sequence.x.\n",
        "\n",
        "In CRF, the input data is expected to be sequential, so we have to label the data as position i for the data point we are predicting.\n",
        "\n",
        "We define feature functions for each variable; in this case, there is no POS tag. We only use one feature function.\n",
        "\n",
        "The feature function's main purpose is to express a characteristic of the sequence that the data point represents.\n",
        "\n",
        "Each feature function is relatively based on the label of the previous word and the current word. It is either a 0 or a 1.\n",
        "\n",
        "So, to build CRI as we do in other models, assign some weights to each feature\n",
        "function and let weights update by optimization algorithm like gradient descent.\n",
        "\n",
        "Maximum likelihood estimation is used to estimate parameters, where we take the negative log of distribution to make the derivative easier to calculate.\n",
        "\n",
        "In general,\n",
        "\n",
        "1. Define feature function.\n",
        "\n",
        "2. Initialize weights to random values.\n",
        "\n",
        "3. Apply gradient descent to parameter values to converge.\n",
        "\n",
        "CRF's are more likely similar to logistic regression since they use *conditional probability distribution*. But, we extend the algorithm by applying feature functions as the sequential input.\n",
        "\n",
        "We aim to extract entities from the given sentence and identifying their types using the CRF Model. Now let's import the required libraries.\n",
        "\n",
        "We used the following libraries."
      ],
      "metadata": {
        "id": "TMI6B56KUfVV"
      },
      "id": "TMI6B56KUfVV"
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "22016272",
      "metadata": {
        "id": "22016272",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4a41f11e-3dc5-420c-d17a-425b856f4d09"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Successfully installed python-crfsuite-0.9.9 sklearn_crfsuite-0.3.6\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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          ]
        }
      ],
      "source": [
        "# For visualization\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import seaborn as sns\n",
        "\n",
        "sns.set(color_codes=True)\n",
        "\n",
        "sns.set(font_scale=1)\n",
        "\n",
        "%matplotlib inline\n",
        "\n",
        "%config InlineBackend. figure_format = 'svg'\n",
        "\n",
        "# For Modeling\n",
        "\n",
        "!pip install sklearn_crfsuite\n",
        "\n",
        "!pip install eli5\n",
        "\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "from sklearn_crfsuite import CRF, scorers, metrics\n",
        "\n",
        "from sklearn_crfsuite.metrics import flat_classification_report\n",
        "\n",
        "from sklearn.metrics import classification_report, make_scorer\n",
        "\n",
        "import scipy.stats\n",
        "\n",
        "import eli5"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's create grouped words and their corresponding tags as a tuple. Also, let's store the words of the same sentence in one list using the following sentence generator function."
      ],
      "metadata": {
        "id": "Tjb6OIGxWRwp"
      },
      "id": "Tjb6OIGxWRwp"
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "282351e8",
      "metadata": {
        "id": "282351e8"
      },
      "outputs": [],
      "source": [
        "class Get_Sent(object):\n",
        "\n",
        "  def __init__(self, dataset):\n",
        "\n",
        "    self.n_sent = 1\n",
        "\n",
        "    self.dataset = dataset\n",
        "\n",
        "    self.empty = False\n",
        "\n",
        "    agg_func = lambda s: [(a, b) for a,b in zip (s[\"words\"].values.tolist(),s[\"labels\"].values.tolist())]\n",
        "\n",
        "    self.grouped = self.dataset.groupby(\"sentence_id\").apply(agg_func)\n",
        "\n",
        "    self.sentences = [x for x in self.grouped]\n",
        "\n",
        "def get_next(self):\n",
        "\n",
        "  try:\n",
        "\n",
        "    s = self.grouped [\"Sentence: {}\".format(self.n_sent)]\n",
        "\n",
        "    self.n_sent += 1\n",
        "\n",
        "  except:\n",
        "\n",
        "    return None\n",
        "\n",
        "# calling the Get_Sent function and passing the train dataset\n",
        "\n",
        "Sent_get= Get_Sent(data)\n",
        "\n",
        "sentences=Sent_get.sentences"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "cd448bc2",
      "metadata": {
        "id": "cd448bc2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d96b3cc4-3c5b-45d4-c22b-cb53b333d3cc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[('i', 'O'), ('need', 'O'), ('that', 'O'), ('movie', 'O'), ('which', 'O'), ('involves', 'O'), ('aliens', 'B-Plot'), ('invading', 'I-Plot'), ('earth', 'I-Plot'), ('in', 'I-Plot'), ('a', 'I-Plot'), ('particular', 'I-Plot'), ('united', 'I-Plot'), ('states', 'I-Plot'), ('place', 'I-Plot'), ('in', 'I-Plot'), ('california', 'I-Plot')]\n"
          ]
        }
      ],
      "source": [
        "# calling the Get_Sent function and passing the test dataset\n",
        "\n",
        "Sent_get= Get_Sent(data1)\n",
        "\n",
        "sentences1 = Sent_get.sentences\n",
        "\n",
        "# This is what a sentence will look like.\n",
        "\n",
        "print (sentences1[0])\n",
        "\n",
        "# shows the output of the Gent_set function for test data."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "For converting text into numeric arrays, we use simple and complex features.\n",
        "\n",
        "# Simple Feature Mapping\n",
        "\n",
        "In this mapping, we have considered simple mapping of words and considered only six features of each word.\n",
        "\n",
        "\n",
        "*   word title\n",
        "\n",
        "*   word  title lower string\n",
        "\n",
        "*   word  title upper string\n",
        "\n",
        "*   length of word\n",
        "\n",
        "*   word numeric\n",
        "\n",
        "*   word alphabet\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "Let's look at the code for simple feature mapping."
      ],
      "metadata": {
        "id": "9_jxQLfDXH3n"
      },
      "id": "9_jxQLfDXH3n"
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "a3725482",
      "metadata": {
        "id": "a3725482",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b6e363c0-2a0b-4f19-f823-c0a3adf91b3e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[array([0, 1, 0, 5, 0, 1]), array([0, 1, 0, 7, 0, 1]), array([0, 1, 0, 8, 0, 1]), array([0, 1, 0, 1, 0, 1]), array([0, 1, 0, 9, 0, 1])]\n"
          ]
        }
      ],
      "source": [
        "# feature mapping for the classifier.\n",
        "import numpy as np\n",
        "\n",
        "def create_ft(txt):\n",
        "\n",
        "  return np.array([txt.istitle(), txt.islower(), txt.isupper(), len(txt), txt.isdigit(), txt.isalpha()])\n",
        "\n",
        "# using the above function created to get the mapping of words for train data.\n",
        "\n",
        "words  = [create_ft(x) for x in data [\"words\"].values.tolist()]\n",
        "\n",
        "# lets take unique labels\n",
        "\n",
        "target = data[\"labels\"].values.tolist()\n",
        "\n",
        "# print few words with array\n",
        "\n",
        "print (words[:5])\n",
        "\n",
        "# we get mapping of words as below (for first five words)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Likewise, we use the function created for the test data."
      ],
      "metadata": {
        "id": "MBtHL-NlYZwv"
      },
      "id": "MBtHL-NlYZwv"
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "0461bedf",
      "metadata": {
        "id": "0461bedf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b990d841-b742-419b-ab0e-5561489fcb84"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                  precision    recall  f1-score   support\n",
            "\n",
            "         B-Actor       1.00      0.00      0.00      5010\n",
            "         B-Award       1.00      0.00      0.00       309\n",
            "B-Character_Name       1.00      0.00      0.00      1024\n",
            "      B-Director       1.00      0.00      0.00      1787\n",
            "         B-Genre       1.00      0.00      0.00      3384\n",
            "       B-Opinion       1.00      0.00      0.00       810\n",
            "        B-Origin       1.00      0.00      0.00       779\n",
            "          B-Plot       1.00      0.00      0.00      6468\n",
            "         B-Quote       1.00      0.00      0.00       126\n",
            "  B-Relationship       1.00      0.00      0.00       580\n",
            "    B-Soundtrack       1.00      0.00      0.00        50\n",
            "          B-Year       0.88      0.99      0.93      2702\n",
            "         I-Actor       0.51      0.01      0.01      6121\n",
            "         I-Award       1.00      0.00      0.00       719\n",
            "I-Character_Name       1.00      0.00      0.00       760\n",
            "      I-Director       1.00      0.00      0.00      1653\n",
            "         I-Genre       1.00      0.00      0.00      2283\n",
            "       I-Opinion       1.00      0.00      0.00       539\n",
            "        I-Origin       1.00      0.00      0.00      3340\n",
            "          I-Plot       0.45      0.50      0.47     62107\n",
            "         I-Quote       1.00      0.00      0.00       817\n",
            "  I-Relationship       1.00      0.00      0.00      1206\n",
            "    I-Soundtrack       1.00      0.00      0.00       158\n",
            "          I-Year       1.00      0.00      0.00       195\n",
            "               O       0.44      0.67      0.53     55895\n",
            "\n",
            "        accuracy                           0.45    158822\n",
            "       macro avg       0.93      0.09      0.08    158822\n",
            "    weighted avg       0.56      0.45      0.39    158822\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# using the above function created to get the mapping of words for test data.\n",
        "\n",
        "words1 = [create_ft(x) for x in data1[\"words\"].values.tolist()]\n",
        "\n",
        "target1 = data1[\"labels\"].values.tolist()\n",
        "\n",
        "# Apply five-fold cross validation for the random classifier model and get the results as follows. Next, the cross_val_predict function is used. It is defined in sklearn.\n",
        "\n",
        "# importing package\n",
        "\n",
        "from sklearn.model_selection import cross_val_predict\n",
        "\n",
        "# train the RF model\n",
        "\n",
        "Ner_prediction = cross_val_predict(RandomForestClassifier(n_estimators=20), X=words, y=target, cv=10)\n",
        "\n",
        "# import library\n",
        "\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "Accuracy_rpt = classification_report(y_pred=Ner_prediction, y_true=target, zero_division=1)\n",
        "\n",
        "print(Accuracy_rpt)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Accuracy is 45%, and the overall F1 score is 0.39. The performance is not so good. Now let's further improve the accuracy, leveraging more features, like \"pre\" and \"post\" words."
      ],
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        "id": "xmjBhXxcY4CK"
      },
      "id": "xmjBhXxcY4CK"
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