3.1. sentiment linear regression.ipynb

{"cells":[{"cell_type":"code","metadata":{"tags":[],"cell_id":"00000-9a8ca4fd-c6cf-4ca0-856d-32580d653a67","output_cleared":false,"source_hash":"7e19de34","execution_start":1602210038027,"execution_millis":651},"source":"import numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn import metrics\nimport pandas as pd","execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"tags":[],"cell_id":"00001-d6142473-f345-473e-9d9c-25aa825ed56b","output_cleared":false,"source_hash":"3db5a5da","execution_start":1602210040862,"execution_millis":2494},"source":"target = np.load('target.npy')/4\nsent_input = np.load('sentiment_input.npy')\n\nprint(sent_input.shape)\nprint(target.shape)","execution_count":null,"outputs":[{"name":"stdout","text":"(1600000, 100)\n(1600000, 1)\n","output_type":"stream"}]},{"cell_type":"code","metadata":{"tags":[],"cell_id":"00002-67a8d994-b030-4fda-bbc9-07c46aadead8"},"source":"","execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Linear Regression","metadata":{"tags":[],"cell_id":"00002-7d21910d-34cb-4d68-bf9b-8672d8cda275","output_cleared":false}},{"cell_type":"code","metadata":{"tags":[],"cell_id":"00002-3a224bdc-1f9d-4ccb-b4ca-250345c3fe3d","output_cleared":false},"source":"lm = LinearRegression()\n\nX = sent_input\ny = target\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=7)\n\nlm.fit(X_train,y_train)","execution_count":null,"outputs":[{"output_type":"execute_result","execution_count":3,"data":{"text/plain":"LinearRegression()"},"metadata":{}}]},{"cell_type":"code","metadata":{"tags":[],"cell_id":"00003-c0d91794-7a0d-46c0-b636-426d5a3a7773","output_cleared":false},"source":"predicts = lm.predict(X_test)\n\nprint(y_test[:100])\n\nprint(\"\"\"\n        Mean Squared Error: {}\n        R2 Score: {}\n        Mean Absolute Error: {}\n     \"\"\".format(\n        metrics.mean_squared_error(y_test, predicts),\n        metrics.r2_score(y_test,predicts),\n        metrics.mean_absolute_error(y_test,predicts)\n        ))","execution_count":null,"outputs":[{"name":"stdout","text":"[[0]\n [0]\n [0]\n [0]\n [4]\n [0]\n [4]\n [0]\n [0]\n [4]\n [0]\n [4]\n [0]\n [0]\n [0]\n [0]\n [0]\n [4]\n [4]\n [4]\n [4]\n [0]\n [4]\n [4]\n [0]\n [4]\n [0]\n [4]\n [4]\n [0]\n [4]\n [0]\n [4]\n [0]\n [0]\n [4]\n [0]\n [0]\n [4]\n [0]\n [0]\n [0]\n [0]\n [4]\n [0]\n [4]\n [4]\n [4]\n [4]\n [0]\n [0]\n [4]\n [4]\n [4]\n [4]\n [0]\n [0]\n [4]\n [0]\n [4]\n [0]\n [4]\n [4]\n [0]\n [0]\n [0]\n [4]\n [4]\n [0]\n [0]\n [4]\n [0]\n [4]\n [4]\n [0]\n [4]\n [0]\n [4]\n [0]\n [0]\n [0]\n [4]\n [4]\n [0]\n [0]\n [4]\n [0]\n [0]\n [0]\n [0]\n [4]\n [0]\n [0]\n [0]\n [4]\n [4]\n [4]\n [4]\n [0]\n [0]]\n\n        Mean Squared Error: 3.238367890439902\n        R2 Score: 0.1903876654576483\n        Mean Absolute Error: 1.6568375733531429\n     \n","output_type":"stream"}]},{"cell_type":"code","metadata":{"tags":[],"cell_id":"00004-dfb734c4-534c-4e6f-9825-d5e60ed60209","output_cleared":false},"source":"error_sent = pd.DataFrame({\n        'Actual Values': np.array(y_test).flatten(),\n        'Predicted Values': predicts.flatten()})\n\nerror_sent","execution_count":null,"outputs":[{"output_type":"execute_result","execution_count":5,"data":{"application/vnd.deepnote.dataframe.v2+json":{"row_count":400000,"column_count":2,"columns":[{"name":"Actual Values","dtype":"int64"},{"name":"Predicted Values","dtype":"float64"},{"name":"_deepnote_index_column","dtype":"int64"}],"rows_top":[{"Actual Values":0,"Predicted Values":1.0420138107266437,"_deepnote_index_column":0},{"Actual Values":0,"Predicted Values":1.5658952433450202,"_deepnote_index_column":1},{"Actual Values":0,"Predicted Values":3.009561326516373,"_deepnote_index_column":2},{"Actual 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Values":2.372633324618815,"_deepnote_index_column":399998},{"Actual Values":4,"Predicted Values":2.5126581631842195,"_deepnote_index_column":399999}]},"text/plain":"        Actual Values  Predicted Values\n0                   0          1.042014\n1                   0          1.565895\n2                   0          3.009561\n3                   0          1.641086\n4                   4          2.446340\n...               ...               ...\n399995              0          2.004272\n399996              0          1.763731\n399997              0          2.575088\n399998              0          2.372633\n399999              4          2.512658\n\n[400000 rows x 2 columns]","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>Actual Values</th>\n      <th>Predicted Values</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>1.042014</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>1.565895</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>3.009561</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>1.641086</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>2.446340</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>399995</th>\n      <td>0</td>\n      <td>2.004272</td>\n    </tr>\n    <tr>\n      <th>399996</th>\n      <td>0</td>\n      <td>1.763731</td>\n    </tr>\n    <tr>\n      <th>399997</th>\n      <td>0</td>\n      <td>2.575088</td>\n    </tr>\n    <tr>\n      <th>399998</th>\n      <td>0</td>\n      <td>2.372633</td>\n    </tr>\n    <tr>\n      <th>399999</th>\n      <td>4</td>\n      <td>2.512658</td>\n    </tr>\n  </tbody>\n</table>\n<p>400000 rows × 2 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","metadata":{"tags":[],"cell_id":"00012-fe1b69e6-7489-4fdc-8633-d0adc77dac59","output_cleared":false},"source":"# PCA Analysis\n\nfrom sklearn.decomposition import PCA\npca = PCA(n_components=1)\npca_X = pca.fit_transform(X)\npca_X","execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"tags":[],"cell_id":"00014-e9652818-093a-4c07-8d39-ff1a564da64b","output_cleared":false},"source":"","execution_count":null,"outputs":[]}],"nbformat":4,"nbformat_minor":2,"metadata":{"orig_nbformat":2,"deepnote_notebook_id":"518c1096-ef14-4add-9c87-44eb374f7260","deepnote_execution_queue":[]}}