WeakSupervision/Fin_SA_WeakSupervision.ipynb

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      "execution_count": 1,
      "metadata": {
        "id": "I2tWTUvyqN_2"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install snorkel"
      ],
      "metadata": {
        "id": "4ZQdlmfN22mA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import snorkel\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report"
      ],
      "metadata": {
        "id": "r85x0GdEqmvS"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from nltk.stem.porter import PorterStemmer\n",
        "stemmer = PorterStemmer()"
      ],
      "metadata": {
        "id": "_Zk3DxmM0SqJ"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "#nltk.download('all')"
      ],
      "metadata": {
        "id": "hUN2Ga0m7Cbj"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def stem(word):\n",
        "  word = word.lower()\n",
        "  n_word = \"\"\n",
        "  for w in word.split():\n",
        "    n_word +=\" \"+stemmer.stem(w)\n",
        "  return n_word\n"
      ],
      "metadata": {
        "id": "WObMN0P90acv"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "path = '/content/drive/MyDrive/Datasets/Sentences_50Agree.txt'"
      ],
      "metadata": {
        "id": "BWWY33OF2q1W"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(path, delimiter='@',encoding='latin-1')\n",
        "df.head()"
      ],
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        "colab": {
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          "height": 206
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>sentiment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>According to Gran , the company has no plans t...</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Technopolis plans to develop in stages an area...</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>The international electronic industry company ...</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>With the new production plant the company woul...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>'According to the company ''s updated strategy...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            "text/plain": [
              "                                                text sentiment\n",
              "0  According to Gran , the company has no plans t...   neutral\n",
              "1  Technopolis plans to develop in stages an area...   neutral\n",
              "2  The international electronic industry company ...  negative\n",
              "3  With the new production plant the company woul...  positive\n",
              "4  'According to the company ''s updated strategy...  positive"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['text'] = df['text'].map(stem)"
      ],
      "metadata": {
        "id": "yIQgT4X10bmU"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#df['text'] = df['text'].map(lemmat)"
      ],
      "metadata": {
        "id": "G4hBdxT46155"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "df[\"sentiment\"] = LabelEncoder().fit_transform(df['sentiment'])\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "kpq6WT7vq4Tv",
        "outputId": "ff4f773d-f9e8-4885-847a-624bc3d594e2"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>sentiment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>accord to gran , the compani ha no plan to mo...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>technopoli plan to develop in stage an area o...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>the intern electron industri compani elcoteq ...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>with the new product plant the compani would ...</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>'accord to the compani '' updat strategi for ...</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<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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              "  "
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            "text/plain": [
              "                                                text  sentiment\n",
              "0   accord to gran , the compani ha no plan to mo...          1\n",
              "1   technopoli plan to develop in stage an area o...          1\n",
              "2   the intern electron industri compani elcoteq ...          0\n",
              "3   with the new product plant the compani would ...          2\n",
              "4   'accord to the compani '' updat strategi for ...          2"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"sentiment\"].value_counts()\n",
        "\n",
        "# 28.12% data is positive\n",
        "# 12.46% data is negative\n",
        "# 59.40% data is neurtal"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hny3I24j1lKW",
        "outputId": "a0d1510a-565b-436a-9b6a-4b7caf9dd0bb"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    2879\n",
              "2    1363\n",
              "0     604\n",
              "Name: sentiment, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df[['text']]\n",
        "y = df[['sentiment']]"
      ],
      "metadata": {
        "id": "4odZwzUorRfv"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ABSTAIN = 1\n",
        "NEGATIVE = 0\n",
        "POSITIVE = 2"
      ],
      "metadata": {
        "id": "dXojvLaJthZS"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "    * Polarity: The set of unique labels this LF outputs (excluding abstains)\n",
        "    * Coverage: The fraction of the dataset the LF labels\n",
        "    * Overlaps: The fraction of the dataset where this LF and at least one other LF label\n",
        "    * Conflicts: The fraction of the dataset where this LF and at least one other LF label and disagree\n",
        "    * Correct: The number of data points this LF labels correctly (if gold labels are provided)\n",
        "    * Incorrect: The number of data points this LF labels incorrectly (if gold labels are provided)\n",
        "    * Empirical Accuracy: The empirical accuracy of this LF (if gold labels are provided)\n"
      ],
      "metadata": {
        "id": "ucU40QkL1rWK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ABSTAIN = 1\n",
        "NEGATIVE = 0\n",
        "POSITIVE = 2"
      ],
      "metadata": {
        "id": "WnO36iji3NVk"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from snorkel.labeling import labeling_function\n",
        "\n",
        "@labeling_function()\n",
        "def check_increase(x):\n",
        "  output = ABSTAIN\n",
        "  if \"increas\" in str(x.text): output = POSITIVE\n",
        "  if \"won\" in str(x.text): output = POSITIVE\n",
        "  if \"rais\" in str(x.text): output = POSITIVE\n",
        "  if \"good\" in str(x.text): output = POSITIVE\n",
        "  if \"doubl\" in str(x.text): output = POSITIVE\n",
        "  if \"profit\" in str(x.text): output = POSITIVE\n",
        "  if \"improv\" in str(x.text): output = POSITIVE\n",
        "  if \"grown\" in str(x.text): output = POSITIVE\n",
        "  if \"promot\" in str(x.text): output = POSITIVE\n",
        "  if \"build\" in str(x.text): output = POSITIVE\n",
        "  if \"up\" in str(x.text): output = POSITIVE\n",
        "  if \"boost\" in str(x.text): output = POSITIVE\n",
        "  if \"applaud\" in str(x.text): output = POSITIVE\n",
        "  return output\n",
        "\n",
        "\n",
        "@labeling_function()\n",
        "def check_decrease(x):\n",
        "  output = ABSTAIN\n",
        "\n",
        "  if \"decreas\" in str(x.text): output = NEGATIVE\n",
        "  if \"bad\" in str(x.text): output = NEGATIVE\n",
        "  if \"declin\" in str(x.text): output = NEGATIVE\n",
        "  if \"loss\" in str(x.text): output = NEGATIVE\n",
        "  if \"halv\" in str(x.text): output = NEGATIVE\n",
        "  if \"laid\" in str(x.text): output = NEGATIVE\n",
        "  if \"down\" in str(x.text): output = NEGATIVE\n",
        "  if \"weaken\" in str(x.text): output = NEGATIVE\n",
        "  if \"weak\" in str(x.text): output = NEGATIVE\n",
        "  if \"low\" in str(x.text): output = NEGATIVE\n",
        "\n",
        "  return output\n",
        "  \n",
        "#Just assuming that the words after but are mostly positive, will imporve later\n",
        "@labeling_function()\n",
        "def check_but(x):\n",
        "  output = ABSTAIN\n",
        "\n",
        "  if \"but\" in str(x.text): output = POSITIVE\n",
        "  if \"however\" in str(x.text): output = POSITIVE\n",
        "\n",
        "  return output\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "55st9c2ivJ2A"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from snorkel.labeling import PandasLFApplier\n",
        "\n",
        "lfs = [check_increase,check_decrease,check_but]\n",
        "\n",
        "applier = PandasLFApplier(lfs=lfs)\n",
        "L_train = applier.apply(df=X)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UAwD24FY1IoX",
        "outputId": "c60636fb-6e60-48a4-8967-1c5fd0f5ae9b"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 4846/4846 [00:01<00:00, 4603.94it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Check out the coverage\n",
        "coverage_check_increase, coverage_check_decrease,coverage_check_but = (L_train != ABSTAIN).mean(axis=0)\n",
        "print(f\"check_out increase: {coverage_check_increase * 100:.1f}%\")\n",
        "print(f\"check_out decrease: {coverage_check_decrease * 100:.1f}%\")\n",
        "print(f\"check_out but: {coverage_check_but * 100:.1f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VIFHhGCP1OqZ",
        "outputId": "e98149d2-2757-44ad-e0b7-815ab2750da6"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "check_out increase: 31.2%\n",
            "check_out decrease: 11.6%\n",
            "check_out but: 1.9%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from snorkel.labeling import LFAnalysis\n",
        "LFAnalysis(L=L_train, lfs=lfs).lf_summary()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "JFyDUh2a4kFs",
        "outputId": "1159dd05-cd08-49e0-b6ae-c36d0adcff5a"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<style scoped>\n",
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              "        vertical-align: middle;\n",
              "    }\n",
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              "    .dataframe tbody tr th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>j</th>\n",
              "      <th>Polarity</th>\n",
              "      <th>Coverage</th>\n",
              "      <th>Overlaps</th>\n",
              "      <th>Conflicts</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>check_increase</th>\n",
              "      <td>0</td>\n",
              "      <td>[1, 2]</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>0.393314</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>check_decrease</th>\n",
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              "    </tr>\n",
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              "      <td>2</td>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-eb11716a-6b2b-4078-94b4-fe1849a621e0');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ],
            "text/plain": [
              "                j Polarity  Coverage  Overlaps  Conflicts\n",
              "check_increase  0   [1, 2]       1.0       1.0   0.393314\n",
              "check_decrease  1   [0, 1]       1.0       1.0   0.393314\n",
              "check_but       2   [1, 2]       1.0       1.0   0.393314"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Combining label outputs with label model\n",
        "L_train.shape[0]\n",
        "majority  =[]\n",
        "for arr in L_train:\n",
        "  if arr[2]==2:\n",
        "    majority.append(2)\n",
        "  else:\n",
        "    if arr[0]==2:\n",
        "      majority.append(2)\n",
        "    elif arr[1]==1:\n",
        "      majority.append(1)\n",
        "    else:\n",
        "      majority.append(0)\n"
      ],
      "metadata": {
        "id": "EpcoKNNp0-Qr"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "print(\"Accuracy Score with simple mode {:.2f}%\".format(100*accuracy_score(majority, y)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c1yJ4SdZPUB_",
        "outputId": "537f225d-8650-4157-9575-1a01288542b9"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy Score with simple mode 61.02%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "cf_matrix = confusion_matrix(majority,y)\n",
        "print(cf_matrix)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OoejSyorUAah",
        "outputId": "6e755239-6685-40ac-d13d-c9a019d5d90d"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 154  112   58]\n",
            " [ 208 2115  617]\n",
            " [ 242  652  688]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "ax = sns.heatmap(cf_matrix, annot=True, cmap='Blues')\n",
        "ax.set_title('Seaborn Confusion Matrix with labels\\n\\n');\n",
        "ax.set_xlabel('\\nPredicted Values')\n",
        "ax.set_ylabel('Actual Values ');\n",
        "## Ticket labels - List must be in alphabetical order\n",
        "ax.xaxis.set_ticklabels(['Negative','Neutral','Positive'])\n",
        "ax.yaxis.set_ticklabels(['Negative','Neutral','Positive'])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "J2kkZP1fU4KY",
        "outputId": "216b5973-111b-41c9-8ecd-713a2b145122"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0, 0.5, 'Negative'), Text(0, 1.5, 'Neutral'), Text(0, 2.5, 'Positive')]"
            ]
          },
          "metadata": {},
          "execution_count": 91
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "kM3KzCexWHXe"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}