Merge pull request #108 from tmu-nlp/osada

'knock57'

osada/chapter06/knock57.ipynb

@@ -0,0 +1 @@
+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyMcG2EMeaB6UHNpyEkURBE4"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["57\n","52で学習したロジスティック回帰モデルの中で，重みの高い特徴量トップ10と，重みの低い特徴量トップ10を確認せよ．"],"metadata":{"id":"iwdLcsj3Q0Rx"}},{"cell_type":"code","source":["#57\n","from sklearn.metrics import *\n","\n","import numpy as np\n","\n","feat = X_train.columns.values\n","ind = [i for i in range(1, 11)]\n","for c, coef in zip(lr.classes_, lr.coef_):\n","    top10 = pd.DataFrame(feat[np.argsort(-coef)[:10]],\n","                         columns=[f\"top10 : {c}\"], index=ind)\n","    worst10 = pd.DataFrame(feat[np.argsort(coef)[:10]], columns=[\n","                           f\"worst10 : {c}\"], index=ind)\n","    print(f\"{c}'s weight\")\n","    print(top10)\n","    print(worst10)\n","    print(\"---------------------\")"],"metadata":{"id":"gHCmtKtyQzh3"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["2       update\n","3       google\n","4        study\n","5         says\n","6      billion\n","7          ceo\n","8        china\n","9         data\n","10          gm\n","---------------------\n","m's weight\n","   top10 : m\n","1      ebola\n","2      study\n","3     cancer\n","4        fda\n","5       mers\n","6       drug\n","7    doctors\n","8        cdc\n","9     health\n","10     cases\n","   worst10 : m\n","1           gm\n","2         deal\n","3     facebook\n","4      climate\n","5          ceo\n","6         bank\n","7        sales\n","8        apple\n","9       google\n","10          tv\n","---------------------\n","t's weight\n","     top10 : t\n","1       google\n","2     facebook\n","3        apple\n","4    microsoft\n","5      climate\n","6   heartbleed\n","7        tesla\n","8      tmobile\n","9      googles\n","10        mars\n","   worst10 : t\n","1       shares\n","2          fed\n","3     american\n","4          her\n","5       cancer\n","6         drug\n","7       stocks\n","8          but\n","9          his\n","10       movie\n","---------------------"],"metadata":{"id":"XCyu1HdlRFb0"},"execution_count":null,"outputs":[]}]}