Zindi_mental_health_nb.ipynb

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  "metadata": {
    "colab": {
      "name": "Zindi_mental_health_nb.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "slImbTSfC2b_",
        "colab_type": "text"
      },
      "source": [
        "#Notebook on Zindi Mental Health Competition"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a1IcMWRfCw9h",
        "colab_type": "code",
        "outputId": "45282419-e5d4-4895-ec77-f0edee0ecd31",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F0cnpqAKDpPl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import os\n",
        "import random\n",
        "os.chdir('/content/drive/My Drive/Zindi_mental_health/')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_jB-DFP5D3SC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FRT_fwEVEEyi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train=pd.read_csv('Train.csv')\n",
        "test=pd.read_csv('Test.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7z4qt42HELhW",
        "colab_type": "code",
        "outputId": "7b24bebf-38fc-481f-9a8b-34a414a8d2c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "source": [
        "print('Train shape:',train.shape,'and number of null values are:',train.isnull().sum())\n",
        "train.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train shape: (616, 3) and number of null values are: ID       0\n",
            "text     0\n",
            "label    0\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "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>ID</th>\n",
              "      <th>text</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>SUAVK39Z</td>\n",
              "      <td>I feel that it was better I dieAm happy</td>\n",
              "      <td>Depression</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9JDAGUV3</td>\n",
              "      <td>Why do I get hallucinations?</td>\n",
              "      <td>Drugs</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>419WR1LQ</td>\n",
              "      <td>I am stresseed due to lack of financial suppor...</td>\n",
              "      <td>Depression</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6UY7DX6Q</td>\n",
              "      <td>Why is life important?</td>\n",
              "      <td>Suicide</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>FYC0FTFB</td>\n",
              "      <td>How could I be helped to go through the depres...</td>\n",
              "      <td>Depression</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         ID                                               text       label\n",
              "0  SUAVK39Z            I feel that it was better I dieAm happy  Depression\n",
              "1  9JDAGUV3                       Why do I get hallucinations?       Drugs\n",
              "2  419WR1LQ  I am stresseed due to lack of financial suppor...  Depression\n",
              "3  6UY7DX6Q                             Why is life important?     Suicide\n",
              "4  FYC0FTFB  How could I be helped to go through the depres...  Depression"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OZvhbAkfEp7N",
        "colab_type": "code",
        "outputId": "9fac6b78-a9d3-4a43-ca77-f77483e54569",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "print('Test shape:',test.shape,'and number of null values are:',test.isnull().sum())\n",
        "test.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test shape: (309, 2) and number of null values are: ID      0\n",
            "text    0\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "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",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>02V56KMO</td>\n",
              "      <td>How to overcome bad feelings and emotions</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>03BMGTOK</td>\n",
              "      <td>I feel like giving up in life</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>03LZVFM6</td>\n",
              "      <td>I was so depressed feel like got no strength t...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0EPULUM5</td>\n",
              "      <td>I feel so low especially since I had no one to...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0GM4C5GD</td>\n",
              "      <td>can i be successful when I am a drug addict?</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         ID                                               text\n",
              "0  02V56KMO          How to overcome bad feelings and emotions\n",
              "1  03BMGTOK                     I feel like giving up in life \n",
              "2  03LZVFM6  I was so depressed feel like got no strength t...\n",
              "3  0EPULUM5  I feel so low especially since I had no one to...\n",
              "4  0GM4C5GD       can i be successful when I am a drug addict?"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZO8iaDu2ExVn",
        "colab_type": "text"
      },
      "source": [
        "#Lets try a CountVectorizer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J5VBhzFBEwkW",
        "colab_type": "code",
        "outputId": "fed4d8bb-aed3-46eb-a998-384a94173e4b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        }
      },
      "source": [
        "vect = CountVectorizer()\n",
        "X_train_dtm=vect.fit_transform(train['text'])\n",
        "pd.DataFrame(X_train_dtm.toarray(), columns=vect.get_feature_names())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>you</th>\n",
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              "      <th>youths</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
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              "    <tr>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>615</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>616 rows × 834 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     abandoned  able  about  absent  abuse  ...  yet  you  young  your  youths\n",
              "0            0     0      0       0      0  ...    0    0      0     0       0\n",
              "1            0     0      0       0      0  ...    0    0      0     0       0\n",
              "2            0     0      0       0      0  ...    0    0      0     0       0\n",
              "3            0     0      0       0      0  ...    0    0      0     0       0\n",
              "4            0     0      0       0      0  ...    0    0      0     0       0\n",
              "..         ...   ...    ...     ...    ...  ...  ...  ...    ...   ...     ...\n",
              "611          0     0      0       0      0  ...    0    0      0     0       0\n",
              "612          0     0      0       0      0  ...    0    0      0     0       0\n",
              "613          0     0      0       0      0  ...    0    0      0     0       0\n",
              "614          0     0      0       0      0  ...    0    0      0     0       0\n",
              "615          0     0      0       0      0  ...    0    0      0     0       0\n",
              "\n",
              "[616 rows x 834 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OIkutG8rFO9_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_test_dtm=vect.transform(test['text'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ki17WcBTGDmg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_train=train['label'].factorize()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vXkB_-PjGIig",
        "colab_type": "code",
        "outputId": "681462f5-7e7a-458f-8bfd-6d6af35e5f6f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "y_train[1]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Depression', 'Drugs', 'Suicide', 'Alcohol'], dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I9uIES-xHyQH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import xgboost as xgb\n",
        "from sklearn.model_selection import cross_validate\n",
        "model=xgb.XGBClassifier()\n",
        "cv_results = cross_validate(model, X_train_dtm, y_train[0], cv=3,scoring=('accuracy', 'neg_log_loss'),return_train_score=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hhv4rogdJoty",
        "colab_type": "code",
        "outputId": "bb790f69-1f30-4646-b17d-7c92d10e6440",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "sorted(cv_results.keys())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['fit_time',\n",
              " 'score_time',\n",
              " 'test_accuracy',\n",
              " 'test_neg_log_loss',\n",
              " 'train_accuracy',\n",
              " 'train_neg_log_loss']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d1K5MpJxJu80",
        "colab_type": "code",
        "outputId": "9d091521-8315-4909-e4a4-95138529aee2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "cv_results['test_neg_log_loss'],cv_results['train_neg_log_loss']"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([-0.44084763, -0.5742301 , -0.55187825]),\n",
              " array([-0.33764278, -0.27203583, -0.29129758]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-rLNm20qJ5X6",
        "colab_type": "code",
        "outputId": "fd0bd79e-9eb6-4a51-909e-646cda6c57ac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "cv_results['test_accuracy'],cv_results['train_accuracy']"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0.85436893, 0.80487805, 0.8097561 ]),\n",
              " array([0.89512195, 0.9026764 , 0.90024331]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "suN5kB_0KTjj",
        "colab_type": "text"
      },
      "source": [
        "#Now let us try TF-IDF"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y_yEK1GxKSI0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tf_vect=TfidfVectorizer()\n",
        "X_train_tdtm=tf_vect.fit_transform(train['text'])\n",
        "X_test_tdtm=tf_vect.transform(test['text'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SssMUyAjK_xx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model1=xgb.XGBClassifier()\n",
        "cv_results_tf = cross_validate(model1, X_train_tdtm, y_train[0], cv=3,scoring=('accuracy', 'neg_log_loss'),return_train_score=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wcdJXOFDLGsv",
        "colab_type": "code",
        "outputId": "4761b735-69ec-4230-de1d-9d14458f1e33",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "cv_results_tf['test_neg_log_loss'],cv_results_tf['train_neg_log_loss']"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([-0.48974735, -0.60569255, -0.56573606]),\n",
              " array([-0.26534189, -0.20991587, -0.22979633]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I8AXTeFELOyq",
        "colab_type": "code",
        "outputId": "6279ff04-ee57-47cf-ba42-5c98992a3c38",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "cv_results_tf['test_accuracy'],cv_results_tf['train_accuracy']"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0.82038835, 0.8097561 , 0.79512195]),\n",
              " array([0.92439024, 0.94890511, 0.94403893]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w8qAgXX8LXZT",
        "colab_type": "text"
      },
      "source": [
        "#Now let us try to combine TF-IDF & CountVectorizer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SqcBsLD4LdGu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import scipy.sparse\n",
        "X_train = scipy.sparse.hstack([X_train_dtm, X_train_tdtm])\n",
        "model_comb=xgb.XGBClassifier()\n",
        "cv_results_comb = cross_validate(model_comb, X_train, y_train[0], cv=3,scoring=('accuracy', 'neg_log_loss'),return_train_score=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z-NTW3-QMG07",
        "colab_type": "code",
        "outputId": "a0e81df1-8998-4e62-bbd3-2807612a00fc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "cv_results_comb['test_accuracy'],cv_results_comb['train_accuracy']"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0.83009709, 0.8097561 , 0.79512195]),\n",
              " array([0.92439024, 0.94890511, 0.94647202]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "23aJT-yFMSDv",
        "colab_type": "code",
        "outputId": "e46edfdc-3eb8-4e3d-e028-42382c7b0814",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "cv_results_comb['test_neg_log_loss'],cv_results_comb['train_neg_log_loss']"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([-0.49207947, -0.60488936, -0.55899634]),\n",
              " array([-0.26643764, -0.20981581, -0.22932442]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8d_0C-86Mke0",
        "colab_type": "text"
      },
      "source": [
        "#Lets do a prediction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fYoBwXxFMo4g",
        "colab_type": "code",
        "outputId": "5cfe85d8-3159-4321-cfd6-1c998515fef5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "sub=pd.read_csv('SampleSubmission.csv')\n",
        "sub.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>ID</th>\n",
              "      <th>Depression</th>\n",
              "      <th>Alcohol</th>\n",
              "      <th>Suicide</th>\n",
              "      <th>Drugs</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>02V56KMO</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>03BMGTOK</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>03LZVFM6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0EPULUM5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0GM4C5GD</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         ID  Depression  Alcohol  Suicide  Drugs\n",
              "0  02V56KMO           0        0        0      0\n",
              "1  03BMGTOK           0        0        0      0\n",
              "2  03LZVFM6           0        0        0      0\n",
              "3  0EPULUM5           0        0        0      0\n",
              "4  0GM4C5GD           0        0        0      0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4d2tmy3zMzd8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_test = scipy.sparse.hstack([X_test_dtm, X_test_tdtm])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "soAaNLi4M7bc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model_comb.fit(X_train,y_train[0])\n",
        "preds=model_comb.predict_proba(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CpQ1i-mdOJ5B",
        "colab_type": "code",
        "outputId": "a8d55196-8481-4f29-c13c-a1aabcc3f79f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "preds.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(309, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tkYTWdo2NLTG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sub['Depression']=preds[:,0]\n",
        "sub['Alcohol']=preds[:,3]\n",
        "sub['Suicide']=preds[:,2]\n",
        "sub['Drugs']=preds[:,1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uxdag9IJOQh1",
        "colab_type": "code",
        "outputId": "9d840025-4861-4c91-eda0-eeca30a502fa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "sub.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>ID</th>\n",
              "      <th>Depression</th>\n",
              "      <th>Alcohol</th>\n",
              "      <th>Suicide</th>\n",
              "      <th>Drugs</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>02V56KMO</td>\n",
              "      <td>0.520719</td>\n",
              "      <td>0.146882</td>\n",
              "      <td>0.261715</td>\n",
              "      <td>0.070684</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>03BMGTOK</td>\n",
              "      <td>0.974787</td>\n",
              "      <td>0.005327</td>\n",
              "      <td>0.016541</td>\n",
              "      <td>0.003345</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>03LZVFM6</td>\n",
              "      <td>0.985065</td>\n",
              "      <td>0.005750</td>\n",
              "      <td>0.006193</td>\n",
              "      <td>0.002992</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0EPULUM5</td>\n",
              "      <td>0.845713</td>\n",
              "      <td>0.039992</td>\n",
              "      <td>0.087932</td>\n",
              "      <td>0.026362</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0GM4C5GD</td>\n",
              "      <td>0.411991</td>\n",
              "      <td>0.080869</td>\n",
              "      <td>0.039919</td>\n",
              "      <td>0.467221</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         ID  Depression   Alcohol   Suicide     Drugs\n",
              "0  02V56KMO    0.520719  0.146882  0.261715  0.070684\n",
              "1  03BMGTOK    0.974787  0.005327  0.016541  0.003345\n",
              "2  03LZVFM6    0.985065  0.005750  0.006193  0.002992\n",
              "3  0EPULUM5    0.845713  0.039992  0.087932  0.026362\n",
              "4  0GM4C5GD    0.411991  0.080869  0.039919  0.467221"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QKqAB1YLOT6e",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sub.to_csv('Sub_CountVect_TFIdf.csv',index=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_l6E4veXOdwi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}