Predict_news_sentiment.ipynb

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    "colab": {
      "name": "Predict news sentiment.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm"
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    "kernelspec": {
      "name": "python3",
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      "cell_type": "code",
      "metadata": {
        "id": "jyDODoaWC6KI",
        "outputId": "50471cd7-735d-4d6e-dce4-abf982cfdf32",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive',force_remount=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fCSZglOfOYyg",
        "outputId": "54e5d233-e19d-4e0b-aae9-796240faad06",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "!ls drive/My\\ Drive/ML-work/NLP/predict-news-sentiment/"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "data  glove.6B.50d.txt\tglove.6B.50d.txt.zip\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h1UgWh2AOZOl",
        "outputId": "7d0b3f44-b6b3-44fd-ada3-ea0b7dc246a8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "%cd drive/My\\ Drive/ML-work/NLP/predict-news-sentiment/"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive/ML-work/NLP/predict-news-sentiment\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AlzZ2oJRug0e",
        "outputId": "63958743-d5d1-4be3-e95e-7472b67e4566",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import scipy.stats as stats\n",
        "from sklearn.externals import joblib \n",
        "import nltk\n",
        "# nltk.download('stopwords')\n",
        "# print('Downloaded Stopwords')\n",
        "from nltk.corpus import stopwords\n",
        "import re\n",
        "from xgboost import XGBRegressor\n",
        "import xgboost as xgb\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.feature_extraction.text import TfidfTransformer\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "import seaborn as sns\n",
        "from sklearn.multioutput import MultiOutputRegressor\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from spacy.lang.en import English\n",
        "from spacy.lang.en.stop_words import STOP_WORDS\n",
        "from sklearn.base import TransformerMixin\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer \n",
        "from sklearn.feature_extraction.text import TfidfTransformer\n",
        "stop_words = STOP_WORDS\n",
        "import string\n",
        "punctuations = string.punctuation\n",
        "from sklearn.feature_extraction.text import HashingVectorizer"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/__init__.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gXN4jSqLug6-"
      },
      "source": [
        "train = pd.read_csv('./data/train.csv')\n",
        "test = pd.read_csv('./data/test.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "llh9avH_uhBZ",
        "outputId": "913c097a-f019-43aa-ed02-b2569ff87397",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "train.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <th>3</th>\n",
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              "       IDLink  ... SentimentHeadline\n",
              "0  Tr3CMgRv1N  ...         -0.053300\n",
              "1  Wc81vGp8qZ  ...         -0.156386\n",
              "2  zNGH03CrZH  ...          0.139754\n",
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              "\n",
              "[5 rows x 11 columns]"
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        "colab": {
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          "height": 35
        }
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      "source": [
        "train.loc[0,'Headline']"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Obama Lays Wreath at Arlington National Cemetery. President Barack Obama has laid a wreath at the Tomb of the Unknowns to honor'"
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            "tags": []
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          "execution_count": 7
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        "outputId": "6d2e1189-72a3-4d4e-839b-bfc343acd711",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "train.loc[0,'Title']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Obama Lays Wreath at Arlington National Cemetery'"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 8
        }
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      },
      "source": [
        "test.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th>LinkedIn</th>\n",
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              "      <th>0</th>\n",
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              "      <td>A recent Microsoft Research video shows how th...</td>\n",
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              "      <th>2</th>\n",
              "      <td>OT9UIZm5M2</td>\n",
              "      <td>Microsoft’s Twitter Robot Praises Hitler, Trum...</td>\n",
              "      <td>* Microsoft teamed with Bing to create TayTwee...</td>\n",
              "      <td>EURweb</td>\n",
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              "      <th>4</th>\n",
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              "      <td>USD/JPY: bears lining up on mixed U.S. economy...</td>\n",
              "      <td>However, this streak of seven-day gains might ...</td>\n",
              "      <td>FXStreet</td>\n",
              "      <td>economy</td>\n",
              "      <td>2016-03-29 02:01:07</td>\n",
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              "       IDLink  ... LinkedIn\n",
              "0  tFrqIR6Chj  ...        1\n",
              "1  DVAaGErjlF  ...       13\n",
              "2  OT9UIZm5M2  ...        0\n",
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              "\n",
              "[5 rows x 9 columns]"
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          "execution_count": 9
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        }
      },
      "source": [
        "missing_val = pd.DataFrame(train.isnull().sum())\n",
        "#Reset index\n",
        "missing_val = missing_val.reset_index()\n",
        "missing_val"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>8</th>\n",
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            "text/plain": [
              "                index    0\n",
              "0              IDLink    0\n",
              "1               Title    0\n",
              "2            Headline    0\n",
              "3              Source  175\n",
              "4               Topic    0\n",
              "5         PublishDate    0\n",
              "6            Facebook    0\n",
              "7          GooglePlus    0\n",
              "8            LinkedIn    0\n",
              "9      SentimentTitle    0\n",
              "10  SentimentHeadline    0"
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          "height": 570
        }
      },
      "source": [
        "train[train['Source'].isna()]"
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      "execution_count": null,
      "outputs": [
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          "output_type": "execute_result",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>lKg2pImhCl</td>\n",
              "      <td>Monday, 29 Feb 2016</td>\n",
              "      <td>RAMALLAH, February 25, 2016 (WAFA) - Palestine...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>palestine</td>\n",
              "      <td>2016-02-28 14:03:00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.005906</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>ZOKue2cqfO</td>\n",
              "      <td>Monday, 29 Feb 2016</td>\n",
              "      <td>RAMALLAH, February 29, 2016 (WAFA) - The Gover...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>palestine</td>\n",
              "      <td>2016-03-01 09:29:00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.048546</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>111</th>\n",
              "      <td>JXfRvxs0R6</td>\n",
              "      <td>Tuesday, 1 Mar 2016</td>\n",
              "      <td>RAMALLAH, February 29, 2016 (WAFA) - The Gover...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>palestine</td>\n",
              "      <td>2016-03-01 00:15:00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.243068</td>\n",
              "      <td>0.048546</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>350</th>\n",
              "      <td>A1YOXDsuOO</td>\n",
              "      <td>Human rights situation in Palestinian territor...</td>\n",
              "      <td>In a special meeting convened by the UN Human ...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>palestine</td>\n",
              "      <td>2015-10-28 17:24:00</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-0.228299</td>\n",
              "      <td>-0.124931</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>409</th>\n",
              "      <td>fO2mYsUg5P</td>\n",
              "      <td>Japan Contributes $5.73M To UNRWA’s Food Assis...</td>\n",
              "      <td>JERUSALEM, October 31, 2015 (WAFA) – The Japan...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>palestine</td>\n",
              "      <td>2015-10-31 04:30:00</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>-0.042878</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55482</th>\n",
              "      <td>FJZonPtvqe</td>\n",
              "      <td>Monday, 28 Mar 2016</td>\n",
              "      <td>JERICHO, March 28, 2016 (WAFA) – Secretary Gen...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>palestine</td>\n",
              "      <td>2016-03-28 04:35:00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.243068</td>\n",
              "      <td>0.068465</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55483</th>\n",
              "      <td>NanKfuNzsO</td>\n",
              "      <td>Saturday, 16 Apr 2016</td>\n",
              "      <td>JERICHO, March 28, 2016 (WAFA) – Secretary Gen...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>palestine</td>\n",
              "      <td>2016-03-28 04:36:00</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.068465</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55486</th>\n",
              "      <td>ovFBb4XFt4</td>\n",
              "      <td>Sunday, 17 Apr 2016</td>\n",
              "      <td>JERICHO, March 28, 2016 (WAFA) – Secretary Gen...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>palestine</td>\n",
              "      <td>2016-03-28 05:07:00</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.068465</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55620</th>\n",
              "      <td>H2PlLqiX9U</td>\n",
              "      <td>Tuesday, 29 Mar 2016</td>\n",
              "      <td>JERICHO, March 28, 2016 (WAFA) – Secretary Gen...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>palestine</td>\n",
              "      <td>2016-03-28 14:06:00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.243068</td>\n",
              "      <td>0.068465</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55726</th>\n",
              "      <td>WRLC4C8Nvj</td>\n",
              "      <td>Monday, 18 Apr 2016</td>\n",
              "      <td>JERICHO, March 28, 2016 (WAFA) – Secretary Gen...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>palestine</td>\n",
              "      <td>2016-03-28 17:35:00</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.068465</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>175 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           IDLink  ... SentimentHeadline\n",
              "6      lKg2pImhCl  ...         -0.005906\n",
              "56     ZOKue2cqfO  ...          0.048546\n",
              "111    JXfRvxs0R6  ...          0.048546\n",
              "350    A1YOXDsuOO  ...         -0.124931\n",
              "409    fO2mYsUg5P  ...         -0.042878\n",
              "...           ...  ...               ...\n",
              "55482  FJZonPtvqe  ...          0.068465\n",
              "55483  NanKfuNzsO  ...          0.068465\n",
              "55486  ovFBb4XFt4  ...          0.068465\n",
              "55620  H2PlLqiX9U  ...          0.068465\n",
              "55726  WRLC4C8Nvj  ...          0.068465\n",
              "\n",
              "[175 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4V19Y5JYuiI8"
      },
      "source": [
        "train.dropna(inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jk81-CQ9Pr8i"
      },
      "source": [
        "test['Source'].fillna(value=test['Source'].mean(), inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VYcggJkH1VfN"
      },
      "source": [
        "### **EDA - Data Visualization**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u7xyvGVCuiOY"
      },
      "source": [
        "sns.set(style='darkgrid',palette='Set1')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7oTRQdmWuiVh",
        "outputId": "9fbf5b61-4ca9-4434-df49-8c20bf20bff2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 615
        }
      },
      "source": [
        "_ = sns.jointplot(x='SentimentTitle',y='SentimentHeadline',data=train,kind = 'reg')\n",
        "_.annotate(stats.pearsonr)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-14-682f3648cba9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjointplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'SentimentTitle'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'SentimentHeadline'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mkind\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'reg'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0m_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mannotate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstats\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpearsonr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'JointGrid' object has no attribute 'annotate'"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d_WI_rUPuibw",
        "outputId": "89fe0d9c-2a2a-4482-82bc-03b333bf6d84",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 850
        }
      },
      "source": [
        "plt.figure(figsize=(15,15))\n",
        "_ = sns.heatmap(train[['Facebook','GooglePlus','LinkedIn','SentimentTitle','SentimentHeadline']].corr(), square=True, cmap='RdYlGn',linewidths=0.5,linecolor='w',annot=True)\n",
        "plt.title('Correlation matrix ')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dERGTDeYuii_",
        "outputId": "c1d43f42-d791-49fe-ff73-0708a6722130",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "train.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['IDLink', 'Title', 'Headline', 'Source', 'Topic', 'PublishDate',\n",
              "       'Facebook', 'GooglePlus', 'LinkedIn', 'SentimentTitle',\n",
              "       'SentimentHeadline'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bj1p0ty9uipz",
        "outputId": "d095480b-521f-4292-810e-b2875da62cb1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "train.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(55757, 11)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RaNrBtALuiww",
        "outputId": "99e1334a-5f8a-471e-d1fb-9130acd6ad9d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        }
      },
      "source": [
        "train.index"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Int64Index([    0,     1,     2,     3,     4,     5,     7,     8,     9,\n",
              "               10,\n",
              "            ...\n",
              "            55922, 55923, 55924, 55925, 55926, 55927, 55928, 55929, 55930,\n",
              "            55931],\n",
              "           dtype='int64', length=55757)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9bS9nozZ5YiL"
      },
      "source": [
        "### Loading spacy English model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O6OSK5Hkui4H"
      },
      "source": [
        "nlp = English()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MGdXKbHB5eJ0"
      },
      "source": [
        "### Custom Tokenizer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cHcDaF0Uui_X"
      },
      "source": [
        "# Creating our tokenizer function\n",
        "def spacy_tokenizer(sentence):\n",
        "    # Creating our token object, which is used to create documents with linguistic annotations.\n",
        "    mytokens = nlp(sentence)\n",
        "\n",
        "    # here the token is converted into lowercase if it is a Pronoun and if it is not a Pronoun then it is lemmatized and lowercased    \n",
        "    mytokens = [ word.lemma_.lower().strip() if word.lemma_ != \"-PRON-\" else word.lower_ for word in mytokens ]\n",
        "\n",
        "    # Removing stop words using stopword from spacy library and punctuations from string library\n",
        "    mytokens = [ word for word in mytokens if word not in stop_words and word not in punctuations ]\n",
        "\n",
        "    # return preprocessed list of tokens\n",
        "    return mytokens"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H-RyuXyFAf17"
      },
      "source": [
        "### Custom Transformer and text cleaner "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "25ML_GgtujFX"
      },
      "source": [
        "class predictors(TransformerMixin):\n",
        "    def transform(self, X, **transform_params):\n",
        "        \n",
        "        return [clean_text(text) for text in X]\n",
        "\n",
        "    def fit(self, X, y, **fit_params):\n",
        "        return self\n",
        "\n",
        "    def get_params(self, deep=True):\n",
        "        return {}\n",
        "\n",
        "\n",
        "def clean_text(text):\n",
        "   \n",
        "    return text.strip().lower()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uxQKrD3LujL2"
      },
      "source": [
        "bow_vector = CountVectorizer(max_features = 100,tokenizer = spacy_tokenizer,ngram_range=(1,2))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IsdROfSeujSr"
      },
      "source": [
        "X_train_title = train.loc[:,'Title'].values\n",
        "y_train_title = train.loc[:,['SentimentTitle']].values\n",
        "\n",
        "X_train_headline = train.loc[:,'Headline'].values\n",
        "y_train_headline = train.loc[:,['SentimentHeadline']].values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MaKdcaDXEj8F"
      },
      "source": [
        "X_test_title = test.loc[:,'Title'].values\n",
        "X_test_headline = test.loc[:,'Headline'].values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GKIZncGIBQlI",
        "outputId": "13a38e41-1f08-4dad-c6f3-8c9eb7b9a5dd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "X_train_title.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(55757,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g-48LWt3BQvL",
        "outputId": "ac29a6f5-7701-42e3-98d3-d6b2af32f94b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "X_train_headline.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(55757,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oGGMkJliBQ3g",
        "outputId": "2165d94e-d64d-41e2-9d35-76b41e09aa29",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "y_train_headline.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(55757, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0IDm74eWFnrg"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train_title, x_valid_title, Y_train_title, y_valid_title = train_test_split(X_train_title, y_train_title, shuffle = True, test_size = 0.1)\n",
        "x_train_headline, x_valid_headline, Y_train_headline, y_valid_headline = train_test_split(X_train_headline, y_train_headline, shuffle = True, test_size = 0.1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7OlNMXzcCojq"
      },
      "source": [
        "abs_y_train_title=np.sign(Y_train_title)\n",
        "abs_y_train_headline=np.sign(Y_train_headline)\n",
        "abs_y_valid_title=np.sign(y_valid_title)\n",
        "abs_y_valid_headline=np.sign(y_valid_headline)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nRcKGYwPCFeU"
      },
      "source": [
        "### XGBoost Regressor\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eO91VbkEBQ-o"
      },
      "source": [
        "xgboost = MultiOutputRegressor(XGBRegressor())\n",
        "regr_multirf = MultiOutputRegressor(RandomForestRegressor(n_estimators=100,\n",
        "                                                          max_depth=None,\n",
        "                                                          random_state=0))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Hkv3A3wBRGZ"
      },
      "source": [
        "pipe1 = Pipeline([(\"cleaner\", predictors()),\n",
        "                 ('vectorizer', bow_vector),\n",
        "                 ('tfidf',TfidfTransformer()),\n",
        "                 ('regressor', regr_multirf)])\n",
        "\n",
        "pipe2 = Pipeline([(\"cleaner\", predictors()),\n",
        "                 ('vectorizer', bow_vector),\n",
        "                 ('tfidf',TfidfTransformer()),\n",
        "                 ('regressor', regr_multirf)])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LxL_zoDbCZAe"
      },
      "source": [
        "#### Title model generation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "imoZLVylBRN7",
        "outputId": "d9d894f6-ce3c-466f-fec7-773e36d50cb5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 538
        }
      },
      "source": [
        "pipe1.fit(x_train_title,Y_train_title)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(memory=None,\n",
              "         steps=[('cleaner', <__main__.predictors object at 0x7fba32571908>),\n",
              "                ('vectorizer',\n",
              "                 CountVectorizer(analyzer='word', binary=False,\n",
              "                                 decode_error='strict',\n",
              "                                 dtype=<class 'numpy.int64'>, encoding='utf-8',\n",
              "                                 input='content', lowercase=True, max_df=1.0,\n",
              "                                 max_features=100, min_df=1, ngram_range=(1, 2),\n",
              "                                 preprocessor=None, stop_words=None,\n",
              "                                 strip_accents=None,\n",
              "                                 to...\n",
              "                 MultiOutputRegressor(estimator=RandomForestRegressor(bootstrap=True,\n",
              "                                                                      ccp_alpha=0.0,\n",
              "                                                                      criterion='mse',\n",
              "                                                                      max_depth=None,\n",
              "                                                                      max_features='auto',\n",
              "                                                                      max_leaf_nodes=None,\n",
              "                                                                      max_samples=None,\n",
              "                                                                      min_impurity_decrease=0.0,\n",
              "                                                                      min_impurity_split=None,\n",
              "                                                                      min_samples_leaf=1,\n",
              "                                                                      min_samples_split=2,\n",
              "                                                                      min_weight_fraction_leaf=0.0,\n",
              "                                                                      n_estimators=100,\n",
              "                                                                      n_jobs=None,\n",
              "                                                                      oob_score=False,\n",
              "                                                                      random_state=0,\n",
              "                                                                      verbose=0,\n",
              "                                                                      warm_start=False),\n",
              "                                      n_jobs=None))],\n",
              "         verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PJttwiJsBRVy",
        "outputId": "83f8c8f8-a0cb-40ed-d11c-15b5d5d50cc3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 538
        }
      },
      "source": [
        "pipe2.fit(x_train_headline,Y_train_headline)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(memory=None,\n",
              "         steps=[('cleaner', <__main__.predictors object at 0x7fba325713c8>),\n",
              "                ('vectorizer',\n",
              "                 CountVectorizer(analyzer='word', binary=False,\n",
              "                                 decode_error='strict',\n",
              "                                 dtype=<class 'numpy.int64'>, encoding='utf-8',\n",
              "                                 input='content', lowercase=True, max_df=1.0,\n",
              "                                 max_features=100, min_df=1, ngram_range=(1, 2),\n",
              "                                 preprocessor=None, stop_words=None,\n",
              "                                 strip_accents=None,\n",
              "                                 to...\n",
              "                 MultiOutputRegressor(estimator=RandomForestRegressor(bootstrap=True,\n",
              "                                                                      ccp_alpha=0.0,\n",
              "                                                                      criterion='mse',\n",
              "                                                                      max_depth=None,\n",
              "                                                                      max_features='auto',\n",
              "                                                                      max_leaf_nodes=None,\n",
              "                                                                      max_samples=None,\n",
              "                                                                      min_impurity_decrease=0.0,\n",
              "                                                                      min_impurity_split=None,\n",
              "                                                                      min_samples_leaf=1,\n",
              "                                                                      min_samples_split=2,\n",
              "                                                                      min_weight_fraction_leaf=0.0,\n",
              "                                                                      n_estimators=100,\n",
              "                                                                      n_jobs=None,\n",
              "                                                                      oob_score=False,\n",
              "                                                                      random_state=0,\n",
              "                                                                      verbose=0,\n",
              "                                                                      warm_start=False),\n",
              "                                      n_jobs=None))],\n",
              "         verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KlCfzk83N-Zk"
      },
      "source": [
        "test_pred_title=pipe1.predict(x_valid_title)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1VQPzBfCN-Dw"
      },
      "source": [
        "test_pred_headline=pipe2.predict(x_valid_headline)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PhPkpaFIBRdd"
      },
      "source": [
        "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bKEHO_ZbBRkg"
      },
      "source": [
        "mae_title=mean_absolute_error(y_valid_title,test_pred_title)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vyd-11iFBRsl"
      },
      "source": [
        "mae_headline=mean_absolute_error(y_valid_headline,test_pred_headline)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eqbZofQMNZ5-"
      },
      "source": [
        "score=1-((0.4*mae_title)+(0.6*mae_headline))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hLwFkJOmNaZM",
        "outputId": "6031ea38-68c7-4412-a608-86376fa3a8ae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "score"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9000100102350481"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b-7XwOfRYF0_"
      },
      "source": [
        "pred_title=pipe1.predict(X_test_title)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZedfKjY1YGFL"
      },
      "source": [
        "pred_headline=pipe2.predict(X_test_headline)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aUdcFbpFNawJ"
      },
      "source": [
        "submission=pd.DataFrame()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uP0eIlbyNa_t"
      },
      "source": [
        "submission['IDLink']=test['IDLink'].to_list()\n",
        "submission['SentimentTitle']=pred_title\n",
        "submission['SentimentHeadline']=pred_headline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZQh7VU9UNarD",
        "outputId": "1c20398c-9674-497b-d5b3-6faaa015752d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "submission.head()"
      ],
      "execution_count": null,
      "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>IDLink</th>\n",
              "      <th>SentimentTitle</th>\n",
              "      <th>SentimentHeadline</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>tFrqIR6Chj</td>\n",
              "      <td>-0.030225</td>\n",
              "      <td>-0.009514</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>DVAaGErjlF</td>\n",
              "      <td>-0.018410</td>\n",
              "      <td>-0.035337</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>OT9UIZm5M2</td>\n",
              "      <td>-0.018410</td>\n",
              "      <td>-0.053826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>lflGp3q2Fj</td>\n",
              "      <td>0.052213</td>\n",
              "      <td>-0.033969</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>zDYG0SoovZ</td>\n",
              "      <td>-0.124802</td>\n",
              "      <td>-0.005911</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       IDLink  SentimentTitle  SentimentHeadline\n",
              "0  tFrqIR6Chj       -0.030225          -0.009514\n",
              "1  DVAaGErjlF       -0.018410          -0.035337\n",
              "2  OT9UIZm5M2       -0.018410          -0.053826\n",
              "3  lflGp3q2Fj        0.052213          -0.033969\n",
              "4  zDYG0SoovZ       -0.124802          -0.005911"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "79WUSHtDNaSj"
      },
      "source": [
        "submission.to_csv('./data/submission3.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "daemC4qsNZxD"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}