Infrared-spectrum-classification.ipynb

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PCA"],"metadata":{"id":"q1pBqZOQmKSu"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df = pd.read_csv('/content/data.csv', header=None)\n","df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":488},"id":"M1lEQPh5mULn","executionInfo":{"status":"ok","timestamp":1662051471603,"user_tz":420,"elapsed":162,"user":{"displayName":"Cao Jinpu","userId":"11416302596004484254"}},"outputId":"547e64dd-c243-49fd-aab7-71db1bd29c4c"},"execution_count":123,"outputs":[{"output_type":"execute_result","data":{"text/plain":["     0         1         2         3         4         5         6         7   \\\n","0     1  0.025000 -0.017969  0.023438 -0.042969 -0.001563  0.041406  0.027734   \n","1     1  0.007422 -0.110550  0.003516 -0.117970 -0.003906  0.114060  0.004297   \n","2     1  0.000781 -0.007422 -0.001953 -0.008203 -0.002734  0.005469  0.004687   \n","3     1  0.040625 -0.077734  0.038281 -0.118360 -0.002344  0.116020  0.045703   \n","4     1  0.017578  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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","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":123}]},{"cell_type":"code","source":["df.shape[1]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hNCCmjYe8TRe","executionInfo":{"status":"ok","timestamp":1662050741126,"user_tz":420,"elapsed":165,"user":{"displayName":"Cao Jinpu","userId":"11416302596004484254"}},"outputId":"4807335c-0b6d-4246-d5d1-6074a39afd27"},"execution_count":89,"outputs":[{"output_type":"execute_result","data":{"text/plain":["26"]},"metadata":{},"execution_count":89}]},{"cell_type":"markdown","source":["## 2.1&nbsp; X distribution "],"metadata":{"id":"auR3XXIyqm26"}},{"cell_type":"code","source":["for i in range(1,df.shape[1]-1):\n","  plt.hist(df.iloc[:, i], alpha= 0.1)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":265},"id":"oWvilcbpmjAN","executionInfo":{"status":"ok","timestamp":1662051480002,"user_tz":420,"elapsed":1243,"user":{"displayName":"Cao Jinpu","userId":"11416302596004484254"}},"outputId":"7234cec1-9d43-4168-c833-0685446d4108"},"execution_count":124,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAXcAAAD4CAYAAAAXUaZHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAZK0lEQVR4nO3dfaxk933X8ff3zMN92PV6d73r9dZ2ui1y/jAgnLBKg6CqSxpI80cdBDKuKHWrCCOSSiBAqiF/FBVVckEtapUSMCSKg0ibUJpmpQaIYxpFIJx6owa3dki8dZN4nd313af7NDPn6ffljzm7d+7debzzdO+5n5d0NWfOOTPzvaM5n/u7v/Ob3zF3R0REyiWadwEiIjJ5CncRkRJSuIuIlJDCXUSkhBTuIiIlVJ13AQAnTpzwM2fOzLsMEZF95Wtf+9pVdz/ZbdueCPczZ85w/vz5eZchIrKvmNl3em1Tt4yISAkp3EVESkjhLiJSQgp3EZESUriLiJSQwl1EpIQU7iIiJaRwFxEpIYW7iEgJKdxF+lm71P4R2WcU7iIiJaRwFxEpIYW7iEgJKdxFREpI4S4iUkIKdxGRElK4i4iUkMJdRKSEFO4iIiWkcBcRKSGFu4hICSncRURKSOEuIlJCA8PdzB40s983s1fN7BUz+4fF+uNm9ryZvVbcHivWm5n9upldMLOXzeyd0/4lRERku2Fa7hnwT9z9YeDdwIfN7GHgaeAFd38IeKG4D/DjwEPFz1PAxyZetYiI9FUdtIO7XwIuFcvrZvYN4H7gMeDRYrfngC8DP1+s/5S7O/CimR01s9PF84jsSVfWWl3XRxsxAIHu23c6dWRxYjWJjGOkPnczOwO8A/gqcKojsC8Dp4rl+4E3Oh52sVi387meMrPzZnZ+ZWVlxLJFRKSfocPdzA4D/xX4R+6+1rmtaKX7KC/s7s+6+1l3P3vy5MlRHioiIgMMFe5mVqMd7P/Z3X+nWH3FzE4X208DbxXr3wQe7Hj4A8U6ERGZkWFGyxjwceAb7v6rHZvOAU8Wy08Cn+9Y/9PFqJl3A6vqbxcRma2BJ1SBvwz8XeCPzOzrxbp/DjwDfNbMPgh8B3i82PYF4P3ABaAB/OxEKxaZoM2b7ROmjfWk6/bKZnt9nnffDrB8d33yhYmMaZjRMv8LsB6b39Nlfwc+PGZdIiIyBn1DVUSkhBTuIiIlpHAXESkhhbuISAkp3EVESmiYoZAiB9br1zcB+P5Dcy5EZERquYuIlJDCXUSkhBTuIiIlpHAXESkhnVAV6eONJAVgOcs4WdXhIvuHWu4iIiWkcBcRKSGFu4hICSncRURKSOEuIlJCOv0v0ke19V0AQnY3VE/PuRqR4anlLiJSQgp3EZESUriLiJSQwl1EpIR0QlVKLV+L+24P60n7djPput2z0N7eTGFxsrWJTJPCXaQPY23eJYjsirplRERKSOEuIlJC6pYR6aOR+rxLENkVhbtIH5v651b2KX1yRfpodB9EI7LnKdxFREpI4S4iUkIKdxGRElK4i4iUkMJdRKSEFO4ig+T956cR2Ys0zl2kj5ZHQGXeZYiMTC13EZESUriL9FHLNuddgsiuKNxFBohozLsEkZENDHcz+4SZvWVmf9yx7l+Y2Ztm9vXi5/0d2/6ZmV0ws2+a2V+fVuEiItLbMC33TwLv67L+37j7I8XPFwDM7GHgCeDPFo/5t2ams1EiIjM2MNzd/SvA9SGf7zHgt9w9dvc/BS4A7xqjPhER2YVx+tx/zsxeLrptjhXr7gfe6NjnYrHuDmb2lJmdN7PzKysrY5QhIiI77TbcPwb8GeAR4BLwK6M+gbs/6+5n3f3syZMnd1mGiIh0s6twd/cr7p67ewD+A1tdL28CD3bs+kCxTmTfCnlOq9mcdxkiI9lVuJvZ6Y67fwO4NZLmHPCEmS2Y2Q8ADwF/MF6JIiIyqoHTD5jZbwKPAifM7CLwC8CjZvYI4MC3gb8P4O6vmNlngVeBDPiwu+fTKV1ERHoZGO7u/pNdVn+8z/6/BPzSOEWJiMh49A1VEZES0qyQIj2sZDmpt/se1/LASpZ23W8pttvLHnf/zt59C7VplCjSk1ruIj00mg3y4IQ8EMcxjUaDRkPzzMj+oHAXESkhhbvIAE427xJERqZwFxEpIYW7iEgJKdxFREpI4S4iUkIKdxGRElK4i4iUkMJdRKSEFO4iIiWkuWWk9FbW457bWpvt+WJuNO6cN2Y9Tgmek+VOo8e8MiJ7lVruIiIlpHAXESkhhbuISAmpz12kQ5Zfu72chwaOrhIp+5Na7iIiJaRwFxEpIYW7iEgJKdxFREpI4S4iUkIKdxGRElK4i4iUkMJdRKSEFO4iIiWkcBcRKSGFu4hICSncRURKSOEuIlJCCncRkRJSuIuIlJDCXUSkhHSxDpEOUXxzazmJsZBBZY4FieySWu4iIiWkcBcRKSGFu4hICSncRURKaGC4m9knzOwtM/vjjnXHzex5M3utuD1WrDcz+3Uzu2BmL5vZO6dZvIiIdDfMaJlPAh8FPtWx7mngBXd/xsyeLu7/PPDjwEPFzw8BHytuReYmXLvae9tm3r5tZgDkrfWtbUmO5wEqAW80yVdXqdx993SLFZmQgS13d/8KcH3H6seA54rl54APdKz/lLe9CBw1s9OTKlZERIaz2z73U+5+qVi+DJwqlu8H3ujY72Kx7g5m9pSZnTez8ysrK7ssQ2S6PASC+7zLEBnZ2CdU3d2BkT/97v6su59197MnT54ctwwREemw23C/cqu7pbh9q1j/JvBgx34PFOtERGSGdhvu54Ani+Ungc93rP/pYtTMu4HVju4bERGZkYGjZczsN4FHgRNmdhH4BeAZ4LNm9kHgO8Djxe5fAN4PXAAawM9OoWaRmaum7Z7HfHWVLE62bcvircMojetdH5/Wtk9QUzt174QrFNluYLi7+0/22PSeLvs68OFxixIRkfHoG6oiIiWkcBcRKSGFu4hICSncRQZYynWYyP6jT62ISAkp3EVESkjhLtJH5BFRbvMuQ2RkCncRkRJSuIuIlJDCXUSkhBTuIiIlNMxl9kRKrxa3LxgTJZ0XHdMl9WT/UstdRKSEFO4iQ6imTqUV5l2GyNAU7iIiJaRwFxEpIYW7yBDy1ua8SxAZicJdRKSEFO4iIiWkce4iQ9popaREJB2HTeXGdZLFrTZStFTbesA999xerFa39jlxeHG6hYqgcBe5QzNrtG+tjoecEOWQrePxFRwneI1o6W1zrlKkP4W7SIc0bpGmLQCySozj4A6a9Vf2GYW7yIiyJkSeA+Atp65eFtmDFO5SKpfjdNv9PEm5Gnp/s7Tl7W2btG9jq9CyCR0W167dXgwdfe4cvn8yzy/Sh0bLiAwSDM8hjyFvzbsYkeEo3EV6WMpuYp5jBBZbOYcaG/MuSWRoCneRITVCk/VcAS/7g8JdZIBq0GEi+48+tSJDCFlCiGNCs0HegqyZz7skkb4U7iKDFKNt8kyDy2T/ULiLDCFPNExG9heFu4hICSncRYaQ5il5nMy7DJGhKdxFREpI4S4iUkI6/S8yTTduANvnlskOLQBQO3XvXEqSg0EtdxGRElK4i4iU0FjdMmb2bWAdyIHM3c+a2XHgM8AZ4NvA4+5+Y7wyReYoBKI4pUJCdKNJlB0hWlimkvS5gkfRHcPqavspqkZ099EZFCvSNomW+4+6+yPufra4/zTwgrs/BLxQ3BfZ9zYt7ro+bYbbP3Gj99zxIrM0jW6Zx4DniuXngA9M4TVE5mbDN9nw1XmXIdLXuOHuwBfN7Gtm9lSx7pS7XyqWLwOnuj3QzJ4ys/Nmdn5lZWXMMkRmo5HGZClksVrosreNOxTyr7j7m2Z2L/C8mf2/zo3u7mbm3R7o7s8CzwKcPXu26z4ie0GVFFiYdxkiIxmr5e7ubxa3bwGfA94FXDGz0wDF7VvjFikyTc24SZzExElMkqakIScNHVP65oHgTvAM8jrNOKbpm6znN+dXtMgAu265m9khIHL39WL5rwG/CJwDngSeKW4/P4lCRSZtNV4jjQOtJCZOmwBkoftJU4CQ12DK0/6mV7q3hfSFJxnVOJ/UU8DnzOzW83za3f+7mb0EfNbMPgh8B3h8/DJF5qdbx+Kmb2CtwFLlLmoL+qK37D27/lS6++vAX+iy/hrwnnGKEtmLMgK5O5XivicReTT86aKw2u7GyZbrgFrjMl1qcsiB09xsX+Q6STZJmoEkSUjj9sU4+nwtiZDGNOqBzMA4NINKRXZP4S4yiiwFlm7fjTbXiFa3xiVYqwZxdPubqSLzonAXGYKTE/IqHpyQpJglZMSERgR3dbTiLYMogiTb/vhs6w9AaGRwvPvJ0+zq1dvL1RMnJv+LyIGhcBcZwHFycjzP4HaP+5bQag+bjBZ3bFtf23qOaCvc88UaPKD+dpkuzQopMqLYU2JvzrsMkb7UchfZhYXVhJo1qaQ5USUhHD4y75JEtlHLXWSAZl4lqjexWmvepYgMTS13kSFU6i2Iu8wv02xBNSVqblKtgdUiqPd/rnxtjez69ekUKlJQuIsM4s5a5MS1lChqUiWmEhaoWpO658Bd865Q5A7qlhEZQqO2QKO6veWehGRO1YgMppa7yAB1z/AQ4Ti1UCFPcvKQk4WcJHNC2p5sLE6hvrSIAdV6v++6DqdzzPtOmrpABlHLXWQITiBQXKAj37pQR8tatGjMqSqR3tRyFxmChclfeanbSdXq8eMTfx05mNRyFxnCoi+w83BpRS0ald7zv4vMk8JdZAiBiKxjzkgPKQCpp/MqSaQvdcuIjCGEKmshZjFanunr9rpi0y064SpquYuMqE6NWqjNuwyRvtRyFxkoELxoB6UpLGwP9hCy2900eRKRuOMOtYXFWRcqcpvCXQ6E1Xhr+t0k2Rz58RaqBIw0iu6Y9DfPnbQYJpnljuGkgHVcfDW1rdE2cchhY/sXoBYPD5izQGRECnfZs66sjT5R10qy/QSnb+qEpxxMCnc5sIKv4gTcEtzWgf7XUHUc963WuIcUz3OobT+MVrMmFYcj1u6WqatRLnOgcJfSaq7dhEZO2NzYVVdMp/XaMp63sJC3+9OziDyq4J6SpzVSr1Cr5xOqXGR8Cnc5UOrxtdvL7utEScCyhGGmADsUFoA7u3kyMhKPqY1wOOWrN7ffz4Zr3uu6qjIshbscWJasYylEeU6U9+6QSchpkrNMBOSYRbhnEFLiKCG3CtAe595InBYBM6hm7ZOosTnWMeo4J6Pa3H4B7bjYHNXbf2aOLm8P+37zvyvwpRuFu+xJ6ZW3CBujn1AN2daolLCxCi3vs/dkVDyh6i3qWYV6NSOjBTEEnKg2/uyQIruhcBcZQkQORPT6UxFbTNUiMtTvLnuDwl1kAPNAlDuVNKOW5gSMWgJV0p5hLzJvCnc5MFpxgneOg89y8iEb2nVPwTr7wQNGjnvAaE8JnIdFIqtRiXSFJpk/hbvIAL6jfZ4kGbA40sRMa2ydC0iDUfPtf1VqadE3n7ZPtHrSo6/+8hWO1bvPa6MTq9JJ4S57Ur6R4ZvZ4B136ryoxqROpjoc9g1WaveRt2rEFnFPHjAPeDEXwWYUyCsxyzmEYhrgEG9QyZwodRY6r79aXSbb2D7u3tLiiTY2AEgX2oemHTlyRzl5bfsECLrAh3SjcJd9r7mxNW9Mc0qd4M0ds0BWajEVj8lCu6smjhIW6d6iDpmRawJWmTGFu5TORrI+ledNvcIhDlGprkIOSVQh8QpLRbdN+8IdC/2fRGRGFO4iQ6rVmzgVciJyAlggW3AsFJfa8+1fPMpDjTxAJUpncqRlV6/23KaLdxw8CnfZl1qb7ROSV5sZcXNrdMp1YDPp3lefpDlL6daJTAujf8Ho+vJhlov/DBZ8gcbIzyAyG6UJ953Tw66+fnlXz3Pvoa1+00NH2stq9QhAnhuVSkZSrdH0iChJqATwPOAhEIjIQ3sfgOjW9VXH6KnxtbVt97udYO1GrfjJ2s30072cOjKbi7js+3C/HLcPoJV4+4RO62H0bwqeiHZehkEEqkmGYURkVLOURVsljY6QVaFVqeNkLN/qmgFyb6d5r1Oo2Y5h8LUxpwTuO+9Ml5E0uv7q+Hx9999lyHdMLF05Mp3zNPs+3EU6WbxO1lzrui0ComyrI8V3M4DFA5sLyyw1oVmpUfUqoV6BSkTeas8nM6iJkKYJ1jmqJzh4+1CsLerSfDIZCncphXrS7oZYSDdZytp94s3qsYm+RhUnruUc9hqLtRusxcdYrKRUvU7NA41KhllgwZYAOBaAWoOo6uQhg+wQlTFb6b62Rl7d/lepcvToeE8qpaRwl9Jaym5M/DkXivncG769hR0wUm/SrCaE/Ag1rxPyBKsmZFkDq9Zwb1HhntuPyYqexGr34fG3JfGOwfsjHLW9umz0bdbym1q4m9n7gF8DKsB/dPdnpvVasr9dju+8AEac5dzs/LbpDi1vb7tOIAFm2ZlxPNzkzegEUWUBy66RV2usVhfwYISKsRAbWZQTW8DIup5P3Uwb5Dks2zJp0+DWVMVpoFoH75hd0uPtjw0TODU0ygnXzj76nY/b+UdC/fV7x1TC3cwqwG8A7wUuAi+Z2Tl3f3UarzcpV0OO5x3/8t4oDrAb3x35uU5V2kfgrRE3oA/+OJqbG9vuJ82cxvolVovzWp5utdI9unOAooXlsWu4i60pA46xxvfs+1i6axFv5tTqLbKwSJrXSTEWay0uE/FgMJp2lGbcJM+MamgQRQlRllKtVomBauTkuUF1iSzKqEZQsY65aHb87duItz6jhxcGNPuHsLN133xlTx+mI+kc5TLOSdD9aFot93cBF9z9dQAz+y3gMWDPf2quNe5sRe7qeYp/35fyrTHX1Wj0b06ePLLIfRM4gPeLzZvtkE4212g22+9hM26ymWwP7LQVWCqWg63j1hH+XaYg6Bb4u9Wg3XF+b/Uyy1nGjUM1QlJhY/Ewh5JNiHK+53cTLOO410hCFbOEPIqo0+DuvEmeJxAiqF3HQgXiQ5DHVKNlKgSqVMhqd3V9/bijJV/Fqd3cfsm+bn3w/UbUjLLvPOaxydfiwTv1cNACvdO0wv1+4I2O+xeBH+rcwcyeAp4q7m6Y2TenVEsvJ4De/5vuXap7tvZr3bB/a1fdw/v+XhvmdkLV3Z8Fnp3X65vZeXc/O6/X3y3VPVv7tW7Yv7Wr7smY1lR1bwIPdtx/oFgnIiIzMK1wfwl4yMx+wMzqwBPAuSm9loiI7DCVbhl3z8zs54D/QXso5Cfc/ZVpvNYY5tYlNCbVPVv7tW7Yv7Wr7gkwd13iV0SkbHR5GBGRElK4i4iU0IEJdzM7bmbPm9lrxW3XWaXM7G1m9kUz+4aZvWpmZ2Zb6R31DFV3se8RM7toZh+dZY09ahlYt5k9Ymb/x8xeMbOXzexvz6PWopb3mdk3zeyCmT3dZfuCmX2m2P7VeX8ubhmi7n9cfI5fNrMXzKznuOhZG1R7x35/08zczPbEMMNh6jazx4v3/RUz+/SsawTA3Q/ED/CvgKeL5aeBX+6x35eB9xbLh4Hl/VB3sf3XgE8DH90P7zfwduChYvn7gEvA0TnUWgH+BPhBoA78X+DhHft8CPh3xfITwGf2wHs8TN0/euszDPyDvVD3sLUX+90FfAV4ETi7H+oGHgL+EDhW3L93HrUemJY77ekPniuWnwM+sHMHM3sYqLr78wDuvuHu876S2sC6AczsLwKngC/OqK5BBtbt7t9y99eK5e8BbwEnZ1bhltvTZbh7AtyaLqNT5+/z28B7zGz06/RN1sC63f33Oz7DL9L+zsleMMx7DvAvgV8GJncppPEMU/ffA37D3W8AuHv/q6NMyUEK91PufqlYvkw7CHd6O3DTzH7HzP7QzP51MQnaPA2s28wi4FeAfzrLwgYY5v2+zczeRbsl9CfTLqyLbtNl3N9rH3fPgFXomL93Poapu9MHgf821YqGN7B2M3sn8KC7/94sCxtgmPf87cDbzex/m9mLxQy5M1eq+dzN7EvAfV02faTzjru7mXUbA1oFfhh4B/Bd4DPAzwAfn2yl202g7g8BX3D3i7NsTE6g7lvPcxr4T8CT7t57nl/ZNTP7KeAs8CPzrmUYRYPlV2kff/tNlXbXzKO0/1P6ipn9eXe/2fdRUyiiNNz9x3ptM7MrZnba3S8VYdLtX6WLwNd9azbL3wXezZTDfQJ1/yXgh83sQ7TPE9TNbMPde56kmoQJ1I2ZHQF+D/iIu784pVIHGWa6jFv7XDSzKnA3cG025fU01DQfZvZjtP/g/oj7ztnh52ZQ7XcBfw74ctFguQ84Z2Y/4e7nZ1blnYZ5zy8CX3X3FPhTM/sW7bB/aTYlth2kbplzwJPF8pPA57vs8xJw1Mxu9fv+VeY/TfHAut3977j729z9DO2umU9NO9iHMLDuYmqKz9Gu97dnWNtOw0yX0fn7/C3gf3pxtmyOBtZtZu8A/j3wE/Pq++2hb+3uvuruJ9z9TPG5fpH27zDPYIfhPiu/S7vVjpmdoN1N8/osiwQO1GiZe4AXgNeALwHHi/VnaV8p6tZ+7wVeBv4I+CRQ3w91d+z/M+yN0TID6wZ+CkiBr3f8PDKnet8PfIt2n/9HinW/SDtQoH2xp/8CXAD+APjBeb/HQ9b9JeBKx/t7bt41D1v7jn2/zB4YLTPke260u5ReLXLkiXnUqekHRERK6CB1y4iIHBgKdxGRElK4i4iUkMJdRKSEFO4iIiWkcBcRKSGFu4hICf1/bLv1oT2gbAwAAAAASUVORK5CYII=\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["# Normalization   - rescales the values into a range of [0,1]\n","min = df.iloc[:,:-1].min(axis=0)\n","max = df.iloc[:,:-1].max(axis=0)\n","df_n = df.copy()\n","df_n.iloc[:,:-1] = (df.iloc[:,:-1] - min) / (max - min)\n","df_n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":488},"id":"6Fc6EM-9pCL6","executionInfo":{"status":"ok","timestamp":1662051481009,"user_tz":420,"elapsed":143,"user":{"displayName":"Cao Jinpu","userId":"11416302596004484254"}},"outputId":"0bf91288-d0be-4667-9f05-25f886d32cbb"},"execution_count":125,"outputs":[{"output_type":"execute_result","data":{"text/plain":["      0         1         2         3         4         5         6   \\\n","0    0.0  0.216525  0.409343  0.676372  0.730043  0.849023  0.723004   \n","1    0.0  0.194125  0.000000  0.589041  0.637153  0.845867  0.987200   \n","2    0.0  0.185663  0.455976  0.565069  0.773101  0.847445  0.592323   \n","3    0.0  0.236435  0.145094  0.741437  0.636670  0.847971  0.994327   \n","4    0.0  0.207067  0.511244  0.642123  0.767779  0.848497  0.610789   \n","..   ...       ...       ...       ...       ...       ...       ...   \n","235  1.0  0.185663  0.404161  0.534247  0.758587  0.837976  0.609367   \n","236  1.0  0.113488  0.499154  0.448629  0.855345  0.887950  0.460222   \n","237  1.0  0.109506  0.383437  0.392125  0.826801  0.874799  0.508516   \n","238  1.0  0.142358  0.609692  0.619864  0.858248  0.910043  0.511356   \n","239  1.0  0.110501  0.366167  0.345892  0.820997  0.859544  0.484367   \n","\n","           7         8         9   ...        44        45        46  \\\n","0    0.219913  0.584873  0.614535  ...  0.844353  0.396786  0.185958   \n","1    0.190914  0.000000  0.474874  ...  0.466455  0.312099  0.338189   \n","2    0.191398  0.395054  0.504669  ...  0.474110  0.616412  0.263463   \n","3    0.242147  0.378078  0.711370  ...  0.415942  0.199764  0.126006   \n","4    0.220397  0.395054  0.528877  ...  0.341736  0.218439  0.260235   \n","..        ...    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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","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":125}]},{"cell_type":"code","source":["# X distribution after normalization\n","for i in range(1,df.shape[1]-1):\n","  plt.hist(df_n.iloc[:, i], alpha= 0.1)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":265},"id":"rHJmSw-3pW_F","executionInfo":{"status":"ok","timestamp":1662051483476,"user_tz":420,"elapsed":1536,"user":{"displayName":"Cao Jinpu","userId":"11416302596004484254"}},"outputId":"a93b2811-3153-452a-fd70-6802556d4be5"},"execution_count":126,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["# Standardization -  rescales data to have a mean of 0 and a standard deviation of 1\n","mean = df.iloc[:,:-1].mean(axis=0)\n","std = df.iloc[:,:-1].std(axis=0)\n","df_s = df.copy()\n","df_s.iloc[:,:-1] = (df.iloc[:,:-1] - mean) / std\n","df_s"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":488},"id":"aW6H-al4oDnn","executionInfo":{"status":"ok","timestamp":1662051483477,"user_tz":420,"elapsed":4,"user":{"displayName":"Cao Jinpu","userId":"11416302596004484254"}},"outputId":"2b66f98d-2024-41c5-81bd-dda4d9585dc4"},"execution_count":127,"outputs":[{"output_type":"execute_result","data":{"text/plain":["           0         1         2         3         4         5         6   \\\n","0   -1.222191  0.084462 -0.636197  0.736171 -0.213651  0.130889  0.979077   \n","1   -1.222191 -0.051320 -4.064024  0.191645 -0.783557  0.111715  2.591507   \n","2   -1.222191 -0.102616 -0.245689  0.042173  0.050522  0.121301  0.181514   \n","3   -1.222191  0.205158 -2.849006  1.141865 -0.786520  0.124498  2.635006   \n","4   -1.222191  0.027131  0.217125  0.522624  0.017871  0.127693  0.294215   \n","..        ...       ...       ...       ...       ...       ...       ...   \n","235  1.222191 -0.102616 -0.679591 -0.150009 -0.038526  0.063780  0.285538   \n","236  1.222191 -0.540136  0.115884 -0.683853  0.555117  0.367370 -0.624719   \n","237  1.222191 -0.564275 -0.853128 -1.036167  0.379992  0.287475 -0.329970   \n","238  1.222191 -0.365129  1.041524  0.383830  0.572929  0.501585 -0.312637   \n","239  1.222191 -0.558242 -0.997748 -1.324441  0.344377  0.194802 -0.477356   \n","\n","           7         8         9   ...        44        45        46  \\\n","0    0.131542  0.661921  1.404661  ...  2.528397  0.474726 -0.822067   \n","1   -0.054640 -5.527143 -0.059288  ...  0.456104  0.020985 -0.000598   \n","2   -0.051537 -1.346725  0.253024  ...  0.498081  1.651456 -0.403833   \n","3    0.274286 -1.526365  2.419700  ...  0.179101 -0.580887 -1.145581   \n","4    0.134648 -1.346725  0.506776  ... -0.227825 -0.480831 -0.421257   \n","..        ...       ...       ...  ...       ...       ...       ...   \n","235 -0.228413  0.041371 -0.605829  ... -1.240463 -0.927850  0.820074   \n","236 -0.191180  0.400638 -0.371600  ...  0.255831 -0.080287  1.012299   \n","237 -0.464252  0.123022  0.116386  ... -0.278832 -1.035844 -1.507306   \n","238 -0.228413  0.890557  0.018790  ... -0.395821 -0.145263  0.013948   \n","239 -0.299789 -0.138264 -0.508238  ...  0.479030  1.234140  0.912511   \n","\n","           47        48        49        50        51        52  53  \n","0    0.411805  0.014145  0.152612 -0.225762 -0.384763 -0.210307   1  \n","1   -0.556166 -0.585824 -0.209620 -0.326968  0.076930 -0.184161   1  \n","2   -0.612572 -0.597485 -0.355586 -0.387192 -0.155591 -0.152317   1  \n","3   -0.569097 -0.605000 -0.192882 -0.121199 -0.149238 -0.221476   1  \n","4   -0.499617 -0.595412 -0.306105 -0.084782  0.168505  0.075884   1  \n","..        ...       ...  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<th>0</th>\n","      <th>1</th>\n","      <th>2</th>\n","      <th>3</th>\n","      <th>4</th>\n","      <th>5</th>\n","      <th>6</th>\n","      <th>7</th>\n","      <th>8</th>\n","      <th>9</th>\n","      <th>...</th>\n","      <th>44</th>\n","      <th>45</th>\n","      <th>46</th>\n","      <th>47</th>\n","      <th>48</th>\n","      <th>49</th>\n","      <th>50</th>\n","      <th>51</th>\n","      <th>52</th>\n","      <th>53</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>-1.222191</td>\n","      <td>0.084462</td>\n","      <td>-0.636197</td>\n","      <td>0.736171</td>\n","      <td>-0.213651</td>\n","      <td>0.130889</td>\n","      <td>0.979077</td>\n","      <td>0.131542</td>\n","      <td>0.661921</td>\n","      <td>1.404661</td>\n","      <td>...</td>\n","      <td>2.528397</td>\n","      <td>0.474726</td>\n","      <td>-0.822067</td>\n","      <td>0.411805</td>\n","      <td>0.014145</td>\n","      <td>0.152612</td>\n","  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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","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":127}]},{"cell_type":"code","source":["# X distribution after Standardization \n","for i in range(1,df.shape[1]-1):\n","  plt.hist(df_s.iloc[:, i], alpha= 0.1)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":265},"id":"TtdTGxiUopH7","executionInfo":{"status":"ok","timestamp":1662051484627,"user_tz":420,"elapsed":1153,"user":{"displayName":"Cao Jinpu","userId":"11416302596004484254"}},"outputId":"cd3744a7-f53b-4a04-e58c-6782ab7da486"},"execution_count":128,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["## 2.2 &nbsp;y"],"metadata":{"id":"pi_FimGBrdid"}},{"cell_type":"code","source":["categories = [1,2,3,4,5,6,7,8,9,10]\n","cat_count = [sum(df.iloc[:,-1]==ele) for ele in categories]\n","print('categories:', categories)\n","print('cat_count:', cat_count)\n","print('sum:', sum(cat_count))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"DWffO0nlrlsv","executionInfo":{"status":"ok","timestamp":1662017389272,"user_tz":420,"elapsed":130,"user":{"displayName":"Cao Jinpu","userId":"11416302596004484254"}},"outputId":"5b2d3f92-4ec6-4402-b5a4-09d4edd9085a"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["categories: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n","cat_count: [24, 24, 24, 24, 24, 24, 24, 24, 24, 24]\n","sum: 240\n"]}]},{"cell_type":"code","source":["plt.bar(categories, cat_count)\n","plt.ylabel('Count')\n","plt.xlabel('Category')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":306},"id":"joqwKqoDsaLP","executionInfo":{"status":"ok","timestamp":1662017389581,"user_tz":420,"elapsed":192,"user":{"displayName":"Cao Jinpu","userId":"11416302596004484254"}},"outputId":"62e3d67c-c21e-4a45-c85f-a6510d76fe08"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0.5, 0, 'Category')"]},"metadata":{},"execution_count":115},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["# 3.&nbsp;Base Model"],"metadata":{"id":"bkzjudRLs0oV"}},{"cell_type":"markdown","source":["## 3.1. &nbsp; X, y"],"metadata":{"id":"po-WwbE_tE37"}},{"cell_type":"code","source":["X = df.iloc[:, :-1]\n","X"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":488},"id":"mMnptNwwsz_L","executionInfo":{"status":"ok","timestamp":1662051488710,"user_tz":420,"elapsed":149,"user":{"displayName":"Cao Jinpu","userId":"11416302596004484254"}},"outputId":"d1f748ae-be10-4118-ac68-a52a4fc139dd"},"execution_count":129,"outputs":[{"output_type":"execute_result","data":{"text/plain":["     0         1         2         3         4         5         6         7   \\\n","0     1  0.025000 -0.017969  0.023438 -0.042969 -0.001563  0.041406  0.027734   \n","1     1  0.007422 -0.110550  0.003516 -0.117970 -0.003906  0.114060  0.004297   \n","2     1  0.000781 -0.007422 -0.001953 -0.008203 -0.002734  0.005469  0.004687   \n","3     1  0.040625 -0.077734  0.038281 -0.118360 -0.002344  0.116020  0.045703   \n","4     1  0.017578  0.005078  0.015625 -0.012500 -0.001953  0.010547  0.028125   \n","..   ..       ...       ...       ...       ...       ...       ...       ...   \n","235   3  0.000781 -0.019141 -0.008984 -0.019922 -0.009766  0.010156 -0.017578   \n","236   3 -0.055859  0.002344 -0.028516  0.058203  0.027344 -0.030859 -0.012891   \n","237   3 -0.058984 -0.023828 -0.041406  0.035156  0.017578 -0.017578 -0.047266   \n","238   3 -0.033203  0.027344  0.010547  0.060547  0.043750 -0.016797 -0.017578   \n","239   3 -0.058203 -0.027734 -0.051953  0.030469  0.006250 -0.024219 -0.026563   \n","\n","           8         9   ...        43        44        45        46  \\\n","0    0.014453  0.025781  ...  0.038673  0.163250  0.037954  0.007015   \n","1   -0.133590 -0.003516  ...  0.033866  0.092112  0.030866  0.012267   \n","2   -0.033594  0.002734  ...  0.028156  0.093553  0.056336  0.009689   \n","3   -0.037891  0.046094  ...  0.033878  0.082603  0.021464  0.004947   \n","4   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<th>2</th>\n","      <th>3</th>\n","      <th>4</th>\n","      <th>5</th>\n","      <th>6</th>\n","      <th>7</th>\n","      <th>8</th>\n","      <th>9</th>\n","      <th>...</th>\n","      <th>43</th>\n","      <th>44</th>\n","      <th>45</th>\n","      <th>46</th>\n","      <th>47</th>\n","      <th>48</th>\n","      <th>49</th>\n","      <th>50</th>\n","      <th>51</th>\n","      <th>52</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>0.025000</td>\n","      <td>-0.017969</td>\n","      <td>0.023438</td>\n","      <td>-0.042969</td>\n","      <td>-0.001563</td>\n","      <td>0.041406</td>\n","      <td>0.027734</td>\n","      <td>0.014453</td>\n","      <td>0.025781</td>\n","      <td>...</td>\n","      <td>0.038673</td>\n","      <td>0.163250</td>\n","      <td>0.037954</td>\n","      <td>0.007015</td>\n","      <td>0.011026</td>\n","      <td>0.006441</td>\n","      <td>0.006690</td>\n","      <td>0.003455</td>\n","      <td>0.001945</td>\n","      <td>0.001913</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>0.007422</td>\n","      <td>-0.110550</td>\n","      <td>0.003516</td>\n","      <td>-0.117970</td>\n","      <td>-0.003906</td>\n","      <td>0.114060</td>\n","      <td>0.004297</td>\n","      <td>-0.133590</td>\n","      <td>-0.003516</td>\n","      <td>...</td>\n","      <td>0.033866</td>\n","      <td>0.092112</td>\n","      <td>0.030866</td>\n","      <td>0.012267</td>\n","      <td>0.004948</td>\n","      <td>0.002649</td>\n","      <td>0.004028</td>\n","      <td>0.002719</td>\n","      <td>0.004620</td>\n","      <td>0.002064</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1</td>\n","      <td>0.000781</td>\n","      <td>-0.007422</td>\n","      <td>-0.001953</td>\n","      <td>-0.008203</td>\n","      <td>-0.002734</td>\n","      <td>0.005469</td>\n","      <td>0.004687</td>\n","      <td>-0.033594</td>\n","      <td>0.002734</td>\n","      <td>...</td>\n","      <td>0.028156</td>\n","      <td>0.093553</td>\n","      <td>0.056336</td>\n","      <td>0.009689</td>\n","      <td>0.004593</td>\n","      <td>0.002575</td>\n","      <td>0.002956</td>\n","      <td>0.002281</td>\n","      <td>0.003273</td>\n","      <td>0.002248</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1</td>\n","      <td>0.040625</td>\n","      <td>-0.077734</td>\n","      <td>0.038281</td>\n","      <td>-0.118360</td>\n","      <td>-0.002344</td>\n","      <td>0.116020</td>\n","      <td>0.045703</td>\n","      <td>-0.037891</td>\n","      <td>0.046094</td>\n","      <td>...</td>\n","      <td>0.033878</td>\n","      <td>0.082603</td>\n","      <td>0.021464</td>\n","      <td>0.004947</td>\n","      <td>0.004866</td>\n","      <td>0.002528</td>\n","      <td>0.004151</td>\n","      <td>0.004215</td>\n","      <td>0.003310</td>\n","      <td>0.001849</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1</td>\n","      <td>0.017578</td>\n","      <td>0.005078</td>\n","      <td>0.015625</td>\n","      <td>-0.012500</td>\n","      <td>-0.001953</td>\n","      <td>0.010547</td>\n","      <td>0.028125</td>\n","      <td>-0.033594</td>\n","      <td>0.007812</td>\n","      <td>...</td>\n","      <td>0.028536</td>\n","      <td>0.068634</td>\n","      <td>0.023027</td>\n","      <td>0.009577</td>\n","      <td>0.005303</td>\n","      <td>0.002588</td>\n","      <td>0.003319</td>\n","      <td>0.004480</td>\n","      <td>0.005150</td>\n","      <td>0.003566</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","      <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>235</th>\n","      <td>3</td>\n","      <td>0.000781</td>\n","      <td>-0.019141</td>\n","      <td>-0.008984</td>\n","      <td>-0.019922</td>\n","      <td>-0.009766</td>\n","      <td>0.010156</td>\n","      <td>-0.017578</td>\n","      <td>-0.000391</td>\n","      <td>-0.014453</td>\n","      <td>...</td>\n","      <td>0.013007</td>\n","      <td>0.033872</td>\n","      <td>0.016044</td>\n","      <td>0.017514</td>\n","      <td>0.011223</td>\n","      <td>0.005518</td>\n","      <td>0.001604</td>\n","      <td>0.003699</td>\n","      <td>0.002921</td>\n","      <td>0.003194</td>\n","    </tr>\n","    <tr>\n","      <th>236</th>\n","      <td>3</td>\n","      <td>-0.055859</td>\n","      <td>0.002344</td>\n","      <td>-0.028516</td>\n","      <td>0.058203</td>\n","      <td>0.027344</td>\n","      <td>-0.030859</td>\n","      <td>-0.012891</td>\n","      <td>0.008203</td>\n","      <td>-0.009766</td>\n","      <td>...</td>\n","      <td>0.009010</td>\n","      <td>0.085237</td>\n","      <td>0.029284</td>\n","      <td>0.018743</td>\n","      <td>0.004081</td>\n","      <td>0.008107</td>\n","      <td>0.004727</td>\n","      <td>0.013117</td>\n","      <td>0.005518</td>\n","      <td>0.002529</td>\n","    </tr>\n","    <tr>\n","      <th>237</th>\n","      <td>3</td>\n","      <td>-0.058984</td>\n","      <td>-0.023828</td>\n","      <td>-0.041406</td>\n","      <td>0.035156</td>\n","      <td>0.017578</td>\n","      <td>-0.017578</td>\n","      <td>-0.047266</td>\n","      <td>0.001563</td>\n","      <td>0.000000</td>\n","      <td>...</td>\n","      <td>0.011660</td>\n","      <td>0.066883</td>\n","      <td>0.014357</td>\n","      <td>0.002634</td>\n","      <td>0.003580</td>\n","      <td>0.006042</td>\n","      <td>0.003435</td>\n","      <td>0.013687</td>\n","      <td>0.005000</td>\n","      <td>0.001929</td>\n","    </tr>\n","    <tr>\n","      <th>238</th>\n","      <td>3</td>\n","      <td>-0.033203</td>\n","      <td>0.027344</td>\n","      <td>0.010547</td>\n","      <td>0.060547</td>\n","      <td>0.043750</td>\n","      <td>-0.016797</td>\n","      <td>-0.017578</td>\n","      <td>0.019922</td>\n","      <td>-0.001953</td>\n","      <td>...</td>\n","      <td>0.006871</td>\n","      <td>0.062867</td>\n","      <td>0.028269</td>\n","      <td>0.012360</td>\n","      <td>0.008847</td>\n","      <td>0.006768</td>\n","      <td>0.002351</td>\n","      <td>0.005865</td>\n","      <td>0.003046</td>\n","      <td>0.002324</td>\n","    </tr>\n","    <tr>\n","      <th>239</th>\n","      <td>3</td>\n","      <td>-0.058203</td>\n","      <td>-0.027734</td>\n","      <td>-0.051953</td>\n","      <td>0.030469</td>\n","      <td>0.006250</td>\n","      <td>-0.024219</td>\n","      <td>-0.026563</td>\n","      <td>-0.004687</td>\n","      <td>-0.012500</td>\n","      <td>...</td>\n","      <td>0.020026</td>\n","      <td>0.092899</td>\n","      <td>0.049817</td>\n","      <td>0.018105</td>\n","      <td>0.008627</td>\n","      <td>0.011240</td>\n","      <td>0.008818</td>\n","      <td>0.021069</td>\n","      <td>0.008565</td>\n","      <td>0.004841</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>240 rows × 53 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-83ef7b6a-ca1a-4277-98fe-ddce07dce5d0')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 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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","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":129}]},{"cell_type":"code","source":["y = df.iloc[:,-1]\n","y"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FhJaCj7dtRFy","executionInfo":{"status":"ok","timestamp":1662051490562,"user_tz":420,"elapsed":137,"user":{"displayName":"Cao Jinpu","userId":"11416302596004484254"}},"outputId":"598ade30-f69e-471f-a2e2-ca3615679f87"},"execution_count":130,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0       1\n","1       1\n","2       1\n","3       1\n","4       1\n","       ..\n","235    10\n","236    10\n","237    10\n","238    10\n","239    10\n","Name: 53, Length: 240, dtype: int64"]},"metadata":{},"execution_count":130}]},{"cell_type":"markdown","source":["## 3.2 &nbsp;Split train and test set"],"metadata":{"id":"RzYoRRCWuMuE"}},{"cell_type":"code","source":["from sklearn.model_selection import train_test_split\n","print('X.shape:', X.shape)\n","sample_num = X.shape[0]\n","train_ratio = 2/3\n","\n","idx = range(sample_num)\n","train_size =int(train_ratio * sample_num)\n","X_train, X_test, y_train, y_test, idx_train, idx_test = train_test_split(\n","    X, y, idx, train_size=train_size, \n","    stratify=y, #  https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html\n","    shuffle=True,\n","    random_state=42)\n","\n","print('X_train.shape:', X_train.shape)\n","print('y_train.shape:', y_train.shape)\n","print('X_test.shape:', X_test.shape)\n","print('y_test.shape:', y_test.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Xcfmp0d5tU1B","executionInfo":{"status":"ok","timestamp":1662017409537,"user_tz":420,"elapsed":120,"user":{"displayName":"Cao Jinpu","userId":"11416302596004484254"}},"outputId":"2f35d007-3eca-44f8-81ea-95a95cff1386"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["X.shape: (240, 7)\n","X_train.shape: (160, 7)\n","y_train.shape: (160,)\n","X_test.shape: (80, 7)\n","y_test.shape: (80,)\n"]}]},{"cell_type":"markdown","source":["## 3.3 &nbsp; random forest model"],"metadata":{"id":"0JOhTa6ewcDs"}},{"cell_type":"code","source":["from sklearn.ensemble import RandomForestClassifier\n","clf = RandomForestClassifier(max_depth=10, random_state=5)\n","\n","# train\n","clf.fit(X_train, y_train)\n","\n","# predict\n","y_pred = clf.predict(X_test)\n","\n","# Accuracy\n","accuracy = sum(y_pred==y_test)  / len(y_test)\n","accuracy"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UK91j070wSAs","executionInfo":{"status":"ok","timestamp":1662017413621,"user_tz":420,"elapsed":280,"user":{"displayName":"Cao Jinpu","userId":"11416302596004484254"}},"outputId":"69dca3b8-cd91-41b9-cd9a-0e379a77f604"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.4125"]},"metadata":{},"execution_count":119}]},{"cell_type":"code","source":["# 识别准确率 = score\n","score = clf.score(X_test, y_test)\n","score"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"NgoN-uJBxfs0","executionInfo":{"status":"ok","timestamp":1662017415847,"user_tz":420,"elapsed":122,"user":{"displayName":"Cao Jinpu","userId":"11416302596004484254"}},"outputId":"11b728ca-df40-49d9-fa17-d833aa37403a"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.4125"]},"metadata":{},"execution_count":120}]},{"cell_type":"markdown","source":["## 3.4 &nbsp; cross validation"],"metadata":{"id":"4JyRlxhIyLTC"}},{"cell_type":"code","source":["from sklearn.model_selection import cross_val_score\n","clf = RandomForestClassifier(max_depth=10, random_state=0)\n","print(cross_val_score(clf, X, y, cv=3))\n","#  cross_val_score (randomly split without stratify)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0NZlHTdGyH6r","executionInfo":{"status":"ok","timestamp":1662017418730,"user_tz":420,"elapsed":437,"user":{"displayName":"Cao Jinpu","userId":"11416302596004484254"}},"outputId":"e42e7a75-013f-4c5a-e47c-5da653ebe7e2"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[0.325  0.3875 0.4125]\n"]}]},{"cell_type":"code","source":["np.unique(y)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"yuivnIiwsqyW","executionInfo":{"status":"ok","timestamp":1662017420733,"user_tz":420,"elapsed":112,"user":{"displayName":"Cao Jinpu","userId":"11416302596004484254"}},"outputId":"0bd9253c-41e5-45c0-ba1a-fda6d635313a"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])"]},"metadata":{},"execution_count":122}]},{"cell_type":"code","source":["# Manually split\n","from sklearn.model_selection import StratifiedKFold\n","import numpy as np\n","# https://scikit-learn.org/stable/modules/cross_validation.html#stratified-k-fold\n","\n","cv = 3\n","skf = StratifiedKFold(n_splits=cv, random_state=32, shuffle=True)\n","val_score = []\n","\n","k = 1\n","for train_index, test_index in skf.split(X, y):\n","  # count sample number in each class\n","  print('train -  {}   |   test -  {}'.format(\n","      np.bincount(y[train_index]), np.bincount(y[test_index])))\n","  \n","  # print(test_index)\n","  X_train, X_test = X.iloc[train_index,:], X.iloc[test_index,:]\n","  y_train, y_test = y[train_index], y[test_index]\n","  \n","  # Train Model\n","  clf = RandomForestClassifier(max_depth=5, random_state=0)\n","  clf.fit(X_train, y_train)\n","  y_pred = clf.predict(X_test)\n","  accuracy = sum(y_pred==y_test)  / len(y_test)\n","  val_score.append(accuracy)\n","\n","  print(f'Cross Validation: {k} - Accuracy: {accuracy}')\n","  k += 1\n","\n","val_score = pd.DataFrame(data = np.array([val_score+[np.mean(val_score)]]), \n","                             index=['accuracy'],\n","                             # CV -- Cross Validation\n","                            columns=['CV_'+str(i) for i in range(1, cv+1)]+['Mean'])\n","val_score"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":185},"id":"r2U9b7xGzuqb","executionInfo":{"status":"ok","timestamp":1662017428001,"user_tz":420,"elapsed":643,"user":{"displayName":"Cao Jinpu","userId":"11416302596004484254"}},"outputId":"09e59938-da9f-405b-caf0-59aa237d86bb"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["train -  [ 0 16 16 16 16 16 16 16 16 16 16]   |   test -  [0 8 8 8 8 8 8 8 8 8 8]\n","Cross Validation: 1 - Accuracy: 0.4\n","train -  [ 0 16 16 16 16 16 16 16 16 16 16]   |   test -  [0 8 8 8 8 8 8 8 8 8 8]\n","Cross Validation: 2 - Accuracy: 0.425\n","train -  [ 0 16 16 16 16 16 16 16 16 16 16]   |   test -  [0 8 8 8 8 8 8 8 8 8 8]\n","Cross Validation: 3 - Accuracy: 0.4375\n"]},{"output_type":"execute_result","data":{"text/plain":["          CV_1   CV_2    CV_3      Mean\n","accuracy   0.4  0.425  0.4375  0.420833"],"text/html":["\n","  <div id=\"df-1b6f31fd-ffe5-4a01-a851-e9589ad121b7\">\n","    <div class=\"colab-df-container\">\n","      <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>CV_1</th>\n","      <th>CV_2</th>\n","      <th>CV_3</th>\n","      <th>Mean</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>accuracy</th>\n","      <td>0.4</td>\n","      <td>0.425</td>\n","      <td>0.4375</td>\n","      <td>0.420833</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1b6f31fd-ffe5-4a01-a851-e9589ad121b7')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-1b6f31fd-ffe5-4a01-a851-e9589ad121b7 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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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","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":123}]},{"cell_type":"code","source":["# cross_val function\n","def cross_val(X, y, clf, cv, random_state=32, shuffle=True):\n","  \n","  skf = StratifiedKFold(n_splits=cv, random_state=random_state, shuffle=shuffle)\n","  val_score = []\n","\n","  k = 1\n","  X = np.array(X)\n","  for train_index, test_index in skf.split(X, y):\n","    # count sample number in each class\n","    # print('train -  {}   |   test -  {}'.format(\n","    #     np.bincount(y[train_index]), np.bincount(y[test_index])))\n","    X_train, X_test = X[train_index,:], X[test_index,:]\n","    y_train, y_test = y[train_index], y[test_index]\n","    \n","    # Train Model\n","    clf.fit(X_train, y_train)\n","    y_pred = clf.predict(X_test)\n","    accuracy = sum(y_pred==y_test)  / len(y_test)\n","    val_score.append(accuracy)\n","\n","    print(f'Cross Validation: {k} - Accuracy: {accuracy}')\n","    k += 1\n","\n","  val_score = pd.DataFrame(data = np.array([val_score+[np.mean(val_score)]]), \n","                              index=['accuracy'],\n","                              # CV -- Cross Validation\n","                              columns=['CV_'+str(i) for i in range(1, cv+1)]+['Mean'])\n","  return val_score"],"metadata":{"id":"Fhsk9k7lCdWt"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["clf = RandomForestClassifier(max_depth=5, random_state=0)\n","cv = 3\n","val_score = cross_val(X, y, clf, cv, random_state=32, shuffle=True)\n","val_score"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":133},"id":"XiH8PnE2DZpn","executionInfo":{"status":"ok","timestamp":1662017436158,"user_tz":420,"elapsed":453,"user":{"displayName":"Cao Jinpu","userId":"11416302596004484254"}},"outputId":"da9e4258-304a-4a7d-9756-8bdfeff2424b"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Cross Validation: 1 - Accuracy: 0.4\n","Cross Validation: 2 - Accuracy: 0.425\n","Cross Validation: 3 - Accuracy: 0.4375\n"]},{"output_type":"execute_result","data":{"text/plain":["          CV_1   CV_2    CV_3      Mean\n","accuracy   0.4  0.425  0.4375  0.420833"],"text/html":["\n","  <div id=\"df-0752e7fc-e720-477d-8638-2e4ebf8f9660\">\n","    <div class=\"colab-df-container\">\n","      <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>CV_1</th>\n","      <th>CV_2</th>\n","      <th>CV_3</th>\n","      <th>Mean</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>accuracy</th>\n","      <td>0.4</td>\n","      <td>0.425</td>\n","      <td>0.4375</td>\n","      <td>0.420833</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0752e7fc-e720-477d-8638-2e4ebf8f9660')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-0752e7fc-e720-477d-8638-2e4ebf8f9660 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html\n","\n","n_components = 3 # The number of principal components\n","pca = PCA(n_components = n_components, random_state=3)\n","pca.fit(X)\n","variance = pca.explained_variance_ratio_\n","print('explained variance of each component：', variance)\n","print('Cumulative variance explained by each principal component：', sum(variance))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"SE0dadrh1HQl","executionInfo":{"status":"ok","timestamp":1662017445578,"user_tz":420,"elapsed":120,"user":{"displayName":"Cao Jinpu","userId":"11416302596004484254"}},"outputId":"f7280e66-4a83-40af-8a74-1306407fd93d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["explained variance of each component： [0.93110709 0.06173154 0.0054942 ]\n","Cumulative variance explained by each principal component： 0.9983328271616548\n"]}]},{"cell_type":"code","source":["plt.plot(variance, label='Variance of Principal Components')\n","plt.plot(np.cumsum(variance), label='Variance of Principal Components (Cumulative)')\n","plt.xlabel('The number of principal components')\n","plt.legend()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":302},"id":"6T7W8sjR7qKr","executionInfo":{"status":"ok","timestamp":1662017448882,"user_tz":420,"elapsed":545,"user":{"displayName":"Cao Jinpu","userId":"11416302596004484254"}},"outputId":"2cb46ef6-3e42-4b97-b3ae-94ff83342413"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.legend.Legend at 0x7f2f6fc09490>"]},"metadata":{},"execution_count":127},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["# plot scatter plot with the 1st and 2nd principal components\n","comp_X = pca.transform(X)\n","print('comp_X.shape:', comp_X.shape)\n","\n","colors = ['blue', 'green', 'red', 'cyan', 'magenta', 'yellow', 'black','pink','gray','olive']\n","for label, color in zip(np.unique(df.iloc[:,-1]), colors):\n","    class_embeddings = comp_X[df.iloc[:,-1] == label]\n","    plt.rcParams.update({'font.size': 12})\n","    plt.scatter(class_embeddings[:, 0], class_embeddings[:,1], c=color, label=label)\n","    plt.legend(ncol=3)\n","    plt.xlabel(\"Principal Component 1\")\n","    plt.ylabel(\"Principal Component 2\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":302},"id":"6v5pDKJc8AKn","executionInfo":{"status":"ok","timestamp":1662017453514,"user_tz":420,"elapsed":386,"user":{"displayName":"Cao Jinpu","userId":"11416302596004484254"}},"outputId":"31929ddf-9f75-4c28-e70a-47408b7c8f7f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["comp_X.shape: (240, 3)\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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4.2 &nbsp;Use Principal Components as inputs"],"metadata":{"id":"k8avqNwo9WYG"}},{"cell_type":"code","source":["# cross validation\n","clf = RandomForestClassifier(max_depth=5, random_state=0)\n","cv = 3\n","val_score = cross_val(comp_X, y, clf, cv, random_state=32, shuffle=True)\n","val_score"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":133},"id":"WNTmFfLyDsJk","executionInfo":{"status":"ok","timestamp":1662017468377,"user_tz":420,"elapsed":465,"user":{"displayName":"Cao Jinpu","userId":"11416302596004484254"}},"outputId":"b6c25c9e-34c3-469e-feed-5a5da5a398c1"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Cross Validation: 1 - Accuracy: 0.325\n","Cross Validation: 2 - Accuracy: 0.3375\n","Cross Validation: 3 - Accuracy: 0.35\n"]},{"output_type":"execute_result","data":{"text/plain":["           CV_1    CV_2  CV_3    Mean\n","accuracy  0.325  0.3375  0.35  0.3375"],"text/html":["\n","  <div 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'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-8d0a8137-46f6-4442-8030-d40812867ea4');\n","          const dataTable =\n","            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","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":129}]},{"cell_type":"markdown","source":[],"metadata":{"id":"7i7wIAhJ_HEf"}},{"cell_type":"markdown","source":["## 4.3 &nbsp; use features normalized / standarized as inputs "],"metadata":{"id":"EnasozJy_c0s"}},{"cell_type":"markdown","source":["CV Accuracy（RandomForestClassifier）：<br>\n","Cross Validation: 1 - Accuracy: 0.4752475247524752 <br>\n","Cross Validation: 2 - Accuracy: 0.42<br>\n","Cross Validation: 3 - Accuracy: 0.44<br>"],"metadata":{"id":"g7Ft9B8PBwOk"}},{"cell_type":"markdown","source":["### normalized features\n","\n"],"metadata":{"id":"XwO4YyRa_1g-"}},{"cell_type":"code","source":["X = df_n.iloc[:, :-1]\n","clf = RandomForestClassifier(max_depth=5, random_state=0)\n","cv = 3\n","val_score = cross_val(X, y, clf, cv, random_state=32, shuffle=True)\n","val_score"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":133},"id":"GCsPWhUGEffc","executionInfo":{"status":"ok","timestamp":1662017483378,"user_tz":420,"elapsed":464,"user":{"displayName":"Cao Jinpu","userId":"11416302596004484254"}},"outputId":"96bc616c-55df-4503-eeb5-eefa862716fc"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Cross Validation: 1 - Accuracy: 0.4125\n","Cross Validation: 2 - Accuracy: 0.425\n","Cross Validation: 3 - Accuracy: 0.425\n"]},{"output_type":"execute_result","data":{"text/plain":["            CV_1   CV_2   CV_3      Mean\n","accuracy  0.4125  0.425  0.425  0.420833"],"text/html":["\n","  <div id=\"df-1f7fcb9a-b2c8-47b9-8e27-ebc34aabdcaa\">\n","    <div class=\"colab-df-container\">\n","      <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>CV_1</th>\n","      <th>CV_2</th>\n","      <th>CV_3</th>\n","      <th>Mean</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>accuracy</th>\n","      <td>0.4125</td>\n","      <td>0.425</td>\n","      <td>0.425</td>\n","      <td>0.420833</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1f7fcb9a-b2c8-47b9-8e27-ebc34aabdcaa')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-1f7fcb9a-b2c8-47b9-8e27-ebc34aabdcaa button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-1f7fcb9a-b2c8-47b9-8e27-ebc34aabdcaa');\n","          const dataTable =\n","            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","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":134}]},{"cell_type":"markdown","source":[],"metadata":{"id":"61S-alNZB5ka"}},{"cell_type":"code","source":["X = df_n.iloc[:, :-1]\n","\n","# PCA\n","n_components = 3\n","pca = PCA(n_components = n_components, random_state=3)\n","pca.fit(X)\n","variance = pca.explained_variance_ratio_\n","comp_X = pca.transform(X)\n","\n","clf = RandomForestClassifier(max_depth=5, random_state=0)\n","cv = 3\n","val_score = cross_val(comp_X, y, clf, cv, random_state=32, shuffle=True)\n","val_score"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":133},"id":"_j4-2-r6EqIi","executionInfo":{"status":"ok","timestamp":1662017493156,"user_tz":420,"elapsed":395,"user":{"displayName":"Cao Jinpu","userId":"11416302596004484254"}},"outputId":"2753bd2a-337c-48c7-a3c0-f20d456ec32f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Cross Validation: 1 - Accuracy: 0.2875\n","Cross Validation: 2 - Accuracy: 0.3125\n","Cross Validation: 3 - Accuracy: 0.3375\n"]},{"output_type":"execute_result","data":{"text/plain":["            CV_1    CV_2    CV_3    Mean\n","accuracy  0.2875  0.3125  0.3375  0.3125"],"text/html":["\n","  <div id=\"df-f3bfd90a-ba8f-4ec1-b7fa-1d7d0df8df83\">\n","    <div class=\"colab-df-container\">\n","      <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>CV_1</th>\n","      <th>CV_2</th>\n","      <th>CV_3</th>\n","      <th>Mean</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>accuracy</th>\n","      <td>0.2875</td>\n","      <td>0.3125</td>\n","      <td>0.3375</td>\n","      <td>0.3125</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f3bfd90a-ba8f-4ec1-b7fa-1d7d0df8df83')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-f3bfd90a-ba8f-4ec1-b7fa-1d7d0df8df83 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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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","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":135}]},{"cell_type":"markdown","source":[],"metadata":{"id":"RwNYX0xaE58c"}},{"cell_type":"markdown","source":["### Standarized features"],"metadata":{"id":"IjsNM8o7FDWk"}},{"cell_type":"code","source":["X = df_s.iloc[:, :-1]\n","clf = RandomForestClassifier(max_depth=5, random_state=0)\n","cv = 3\n","val_score = cross_val(X, y, clf, cv, random_state=32, shuffle=True)\n","val_score"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":133},"id":"VJRHMzz1E4v6","executionInfo":{"status":"ok","timestamp":1662017498417,"user_tz":420,"elapsed":434,"user":{"displayName":"Cao Jinpu","userId":"11416302596004484254"}},"outputId":"b9bfe2bc-bac8-41de-e41c-42122ae05e57"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Cross Validation: 1 - Accuracy: 0.4\n","Cross Validation: 2 - Accuracy: 0.4125\n","Cross Validation: 3 - Accuracy: 0.425\n"]},{"output_type":"execute_result","data":{"text/plain":["          CV_1    CV_2   CV_3    Mean\n","accuracy   0.4  0.4125  0.425  0.4125"],"text/html":["\n","  <div id=\"df-c706451d-75ec-46da-870f-634b1d3fa0c5\">\n","    <div class=\"colab-df-container\">\n","      <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>CV_1</th>\n","      <th>CV_2</th>\n","      <th>CV_3</th>\n","      <th>Mean</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>accuracy</th>\n","      <td>0.4</td>\n","      <td>0.4125</td>\n","      <td>0.425</td>\n","      <td>0.4125</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c706451d-75ec-46da-870f-634b1d3fa0c5')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-c706451d-75ec-46da-870f-634b1d3fa0c5 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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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","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":136}]},{"cell_type":"markdown","source":[],"metadata":{"id":"6tSpdR2fFLFy"}},{"cell_type":"code","source":["X = df_s.iloc[:, :-1]\n","\n","# PCA\n","n_components = 3 \n","pca = PCA(n_components = n_components, random_state=3)\n","pca.fit(X)\n","variance = pca.explained_variance_ratio_\n","comp_X = pca.transform(X)\n","\n","clf = RandomForestClassifier(max_depth=5, random_state=0)\n","cv = 3\n","val_score = cross_val(comp_X, y, clf, cv, random_state=32, shuffle=True)\n","val_score"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":133},"id":"UQRY00zPA_ov","executionInfo":{"status":"ok","timestamp":1662017513927,"user_tz":420,"elapsed":400,"user":{"displayName":"Cao Jinpu","userId":"11416302596004484254"}},"outputId":"b83c27e0-119e-4024-c8ff-96c20871757d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Cross Validation: 1 - Accuracy: 0.275\n","Cross Validation: 2 - Accuracy: 0.4\n","Cross Validation: 3 - Accuracy: 0.2875\n"]},{"output_type":"execute_result","data":{"text/plain":["           CV_1  CV_2    CV_3      Mean\n","accuracy  0.275   0.4  0.2875  0.320833"],"text/html":["\n","  <div id=\"df-51ec7ac7-7e14-4123-912d-29619a3f51d5\">\n","    <div class=\"colab-df-container\">\n","      <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>CV_1</th>\n","      <th>CV_2</th>\n","      <th>CV_3</th>\n","      <th>Mean</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>accuracy</th>\n","      <td>0.275</td>\n","      <td>0.4</td>\n","      <td>0.2875</td>\n","      <td>0.320833</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-51ec7ac7-7e14-4123-912d-29619a3f51d5')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-51ec7ac7-7e14-4123-912d-29619a3f51d5 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-51ec7ac7-7e14-4123-912d-29619a3f51d5');\n","          const dataTable =\n","            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","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":137}]},{"cell_type":"markdown","source":[],"metadata":{"id":"EX_GZa_LFWhy"}},{"cell_type":"markdown","source":["## 4.4 &nbsp;imbalanced y label"],"metadata":{"id":"6DduU844FpzS"}},{"cell_type":"code","source":["from imblearn.over_sampling import RandomOverSampler\n","# https://imbalanced-learn.org/stable/references/generated/imblearn.over_sampling.RandomOverSampler.html\n","\n","X = df.iloc[:, :-1]\n","cv = 3\n","skf = StratifiedKFold(n_splits=cv, random_state=32, shuffle=True)\n","val_score = []\n","\n","k = 1\n","X = np.array(X)\n","for train_index, test_index in skf.split(X, y):\n","  # print('train -  {}   |   test -  {}'.format(\n","  #     np.bincount(y[train_index]), np.bincount(y[test_index])))\n","  X_train, X_test = X[train_index,:], X[test_index,:]\n","  y_train, y_test = y[train_index], y[test_index]\n","  \n","  if k == 1:\n","    print('X_train.shape (before random over sampler): ', X_train.shape)\n","    categories = np.arange(1,11)\n","    cat_count = [sum(y_train==ele) for ele in categories]\n","    fig, axs = plt.subplots(2,1, sharex=True)\n","    axs[0].bar(categories, cat_count)\n","    axs[0].set_xlabel('Category')\n","    axs[0].set_ylabel('Count')\n","    axs[0].set_title('Before Random over Sampler')\n","\n","  # RandomOverSampler\n","  ros = RandomOverSampler(sampling_strategy='not majority',\n","                          random_state = 3)\n","  X_train, y_train = ros.fit_resample(X_train, y_train)\n","\n","  if k == 1:\n","    print('X_train.shape (after random over sampler): ', X_train.shape)\n","    cat_count = [sum(y_train==ele) for ele in categories]\n","    axs[1].bar(categories, cat_count)\n","    axs[1].set_xlabel('Category')\n","    axs[1].set_ylabel('Count')\n","    axs[1].set_title('After Random over Sampler')\n","    plt.tight_layout()\n","\n","  # Train\n","  clf = RandomForestClassifier(max_depth=5, random_state=0)\n","  clf.fit(X_train, y_train)\n","  y_pred = clf.predict(X_test)\n","  accuracy = sum(y_pred==y_test)  / len(y_test)\n","  val_score.append(accuracy)\n","\n","  print(f'Cross Validation: {k} - Accuracy: {accuracy}')\n","  k += 1\n","\n","val_score = pd.DataFrame(data = np.array([val_score+[np.mean(val_score)]]), \n","                             index=['accuracy'],\n","                             # CV -- Cross Validation\n","                            columns=['CV_'+str(i) for i in range(1, cv+1)]+['Mean'])\n","val_score"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":444},"id":"SY6EAZAJFyfl","executionInfo":{"status":"ok","timestamp":1662017518913,"user_tz":420,"elapsed":949,"user":{"displayName":"Cao Jinpu","userId":"11416302596004484254"}},"outputId":"16811b92-e194-4349-eb1c-03834235a7cb"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["X_train.shape (before random over sampler):  (160, 7)\n","X_train.shape (after random over sampler):  (160, 7)\n","Cross Validation: 1 - Accuracy: 0.4\n","Cross Validation: 2 - Accuracy: 0.425\n","Cross Validation: 3 - Accuracy: 0.4375\n"]},{"output_type":"execute_result","data":{"text/plain":["          CV_1   CV_2    CV_3      Mean\n","accuracy   0.4  0.425  0.4375  0.420833"],"text/html":["\n","  <div id=\"df-4af5a197-71fb-47af-962e-6c694aaf0662\">\n","    <div class=\"colab-df-container\">\n","      <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>CV_1</th>\n","      <th>CV_2</th>\n","      <th>CV_3</th>\n","      <th>Mean</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>accuracy</th>\n","      <td>0.4</td>\n","      <td>0.425</td>\n","      <td>0.4375</td>\n","      <td>0.420833</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4af5a197-71fb-47af-962e-6c694aaf0662')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-4af5a197-71fb-47af-962e-6c694aaf0662 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-4af5a197-71fb-47af-962e-6c694aaf0662');\n","          const dataTable =\n","            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","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":138},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 2 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":[],"metadata":{"id":"YUJI8QbeJXDC"}},{"cell_type":"code","source":["# RandomOverSampler + PCA\n","from imblearn.over_sampling import RandomOverSampler\n","# https://imbalanced-learn.org/stable/references/generated/imblearn.over_sampling.RandomOverSampler.html\n","\n","X = df.iloc[:, :-1]\n","cv = 3\n","skf = StratifiedKFold(n_splits=cv, random_state=32, shuffle=True)\n","val_score = []\n","\n","# PCA\n","n_components = 3 \n","pca = PCA(n_components = n_components, random_state=3)\n","pca.fit(X)\n","variance = pca.explained_variance_ratio_\n","comp_X = pca.transform(X)\n","\n","k = 1\n","for train_index, test_index in skf.split(comp_X, y):\n","  # print('train -  {}   |   test -  {}'.format(\n","  #     np.bincount(y[train_index]), np.bincount(y[test_index])))\n","  X_train, X_test = comp_X[train_index,:], comp_X[test_index,:]\n","  y_train, y_test = y[train_index], y[test_index]\n","  \n","  if k == 1:\n","    print('X_train.shape (before random over sampler): ', X_train.shape)\n","\n","  # RandomOverSampler\n","  ros = RandomOverSampler(sampling_strategy='not majority',\n","                          random_state = 3)\n","  X_train, y_train = ros.fit_resample(X_train, y_train)\n","\n","  if k == 1:\n","    print('X_train.shape (after random over sampler): ', X_train.shape)\n","\n","  # Train\n","  clf = RandomForestClassifier(max_depth=5, random_state=0)\n","  clf.fit(X_train, y_train)\n","  y_pred = clf.predict(X_test)\n","  accuracy = sum(y_pred==y_test)  / len(y_test)\n","  val_score.append(accuracy)\n","\n","  print(f'Cross Validation: {k} - Accuracy: {accuracy}')\n","  k += 1\n","\n","val_score = pd.DataFrame(data = np.array([val_score+[np.mean(val_score)]]), \n","                             index=['accuracy'],\n","                             # CV -- Cross Validation\n","                            columns=['CV_'+str(i) for i in range(1, cv+1)]+['Mean'])\n","val_score"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":168},"id":"daGFTa9UJbbe","executionInfo":{"status":"ok","timestamp":1662017531468,"user_tz":420,"elapsed":422,"user":{"displayName":"Cao Jinpu","userId":"11416302596004484254"}},"outputId":"33b24775-dc2e-4cfe-d749-4419e7f81679"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["X_train.shape (before random over sampler):  (160, 3)\n","X_train.shape (after random over sampler):  (160, 3)\n","Cross Validation: 1 - Accuracy: 0.325\n","Cross Validation: 2 - Accuracy: 0.3375\n","Cross Validation: 3 - Accuracy: 0.35\n"]},{"output_type":"execute_result","data":{"text/plain":["           CV_1    CV_2  CV_3    Mean\n","accuracy  0.325  0.3375  0.35  0.3375"],"text/html":["\n","  <div id=\"df-7c518e05-6903-498c-9318-e7934178d235\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        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0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-7c518e05-6903-498c-9318-e7934178d235 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-7c518e05-6903-498c-9318-e7934178d235');\n","          const dataTable =\n","            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","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":139}]},{"cell_type":"markdown","source":[],"metadata":{"id":"OhFqa0xIu8bK"}},{"cell_type":"code","source":["#  PCA + RandomOverSampler + CV function\n","def cross_val_pca_ros(X, y, clf, cv, \n","                      n_components=3, # \n","                      pca_random_state=3,\n","                      ros_random_state=3, \n","                      split_random_state=32, split_shuffle=True):\n","\n","  # PCA\n","  # pca = PCA(n_components = n_components, random_state=pca_random_state)\n","  # pca.fit(X)\n","  # comp_X = pca.transform(X)\n","\n","  comp_X = np.array(X)\n","\n","  # CV\n","  skf = StratifiedKFold(n_splits=cv, random_state=split_random_state, shuffle=split_shuffle)\n","  val_score = []\n","\n","  k = 1\n","  for train_index, test_index in skf.split(comp_X, y):\n","    # print('train -  {}   |   test -  {}'.format(\n","    #     np.bincount(y[train_index]), np.bincount(y[test_index])))\n","    X_train, X_test = comp_X[train_index,:], comp_X[test_index,:]\n","    y_train, y_test = y[train_index], y[test_index]\n","    \n","    if k == 1:\n","      print('X_train.shape (before random over sampler): ', X_train.shape)\n","\n","    # RandomOverSampler\n","    # ros = RandomOverSampler(sampling_strategy='not majority',\n","    #                         random_state = ros_random_state)\n","    # X_train, y_train = ros.fit_resample(X_train, y_train)\n","\n","    if k == 1:\n","      print('X_train.shape (after random over sampler): ', X_train.shape)\n","\n","    # Train\n","    clf.fit(X_train, y_train)\n","    y_pred = clf.predict(X_test)\n","    accuracy = sum(y_pred==y_test)  / len(y_test)\n","    val_score.append(accuracy)\n","\n","    print(f'Cross Validation: {k} - Accuracy: {accuracy}')\n","    k += 1\n","\n","  val_score = pd.DataFrame(data = np.array([val_score+[np.mean(val_score)]]), \n","                              index=['accuracy'],\n","                              # CV -- Cross Validation\n","                              columns=['CV_'+str(i) for i in range(1, cv+1)]+['Mean'])\n","  return val_score\n","    "],"metadata":{"id":"DFluyorVvgGn"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["X = df.iloc[:,:-1]\n","clf = RandomForestClassifier(max_depth=5, random_state=0)\n","cv=3\n","val_score = cross_val_pca_ros(X, y, clf, cv)\n","val_score"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":168},"id":"kxtbffycxPvm","executionInfo":{"status":"ok","timestamp":1662017540369,"user_tz":420,"elapsed":406,"user":{"displayName":"Cao Jinpu","userId":"11416302596004484254"}},"outputId":"0f0270be-312b-4dec-9b78-a4e58ab0f942"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["X_train.shape (before random over sampler):  (160, 7)\n","X_train.shape (after random over sampler):  (160, 7)\n","Cross Validation: 1 - Accuracy: 0.4\n","Cross Validation: 2 - Accuracy: 0.425\n","Cross Validation: 3 - Accuracy: 0.4375\n"]},{"output_type":"execute_result","data":{"text/plain":["          CV_1   CV_2    CV_3      Mean\n","accuracy   0.4  0.425  0.4375  0.420833"],"text/html":["\n","  <div id=\"df-eea71bf8-a72e-4fb0-848b-020357ae910f\">\n","    <div class=\"colab-df-container\">\n","      <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>CV_1</th>\n","      <th>CV_2</th>\n","      <th>CV_3</th>\n","      <th>Mean</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>accuracy</th>\n","      <td>0.4</td>\n","      <td>0.425</td>\n","      <td>0.4375</td>\n","      <td>0.420833</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-eea71bf8-a72e-4fb0-848b-020357ae910f')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-eea71bf8-a72e-4fb0-848b-020357ae910f button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-eea71bf8-a72e-4fb0-848b-020357ae910f');\n","          const dataTable =\n","            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","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":141}]},{"cell_type":"markdown","source":["## 4.5 &nbsp;Other Models\n","https://scikit-learn.org/stable/supervised_learning.html"],"metadata":{"id":"5CP_m6nivSY_"}},{"cell_type":"code","source":["X = df.iloc[:,:-1]"],"metadata":{"id":"Hqu1aHJU0sKk"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["X = df_n.iloc[:,:-1]"],"metadata":{"id":"P0RRtkn_99in"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from sklearn.linear_model import LogisticRegression\n","clf = LogisticRegression(random_state=2)\n","val_score = cross_val_pca_ros(X, y, clf, cv=3)\n","val_score"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":168},"id":"J_vTRq3J0jVN","executionInfo":{"status":"ok","timestamp":1662017599325,"user_tz":420,"elapsed":354,"user":{"displayName":"Cao Jinpu","userId":"11416302596004484254"}},"outputId":"4ab67a91-6cef-4a45-92a0-955e7909e4ea"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["X_train.shape (before random over sampler):  (160, 7)\n","X_train.shape (after random over sampler):  (160, 7)\n","Cross Validation: 1 - Accuracy: 0.25\n","Cross Validation: 2 - Accuracy: 0.175\n","Cross Validation: 3 - Accuracy: 0.1875\n"]},{"output_type":"execute_result","data":{"text/plain":["          CV_1   CV_2    CV_3      Mean\n","accuracy  0.25  0.175  0.1875  0.204167"],"text/html":["\n","  <div id=\"df-a5c265c0-753f-41d5-b51a-4a958e4d700b\">\n","    <div class=\"colab-df-container\">\n","      <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>CV_1</th>\n","      <th>CV_2</th>\n","      <th>CV_3</th>\n","      <th>Mean</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>accuracy</th>\n","      <td>0.25</td>\n","      <td>0.175</td>\n","      <td>0.1875</td>\n","      <td>0.204167</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a5c265c0-753f-41d5-b51a-4a958e4d700b')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-a5c265c0-753f-41d5-b51a-4a958e4d700b button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-a5c265c0-753f-41d5-b51a-4a958e4d700b');\n","          const dataTable =\n","            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","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":150}]},{"cell_type":"code","source":["from sklearn import svm\n","clf = svm.SVC(kernel='rbf')\n","val_score = cross_val_pca_ros(X, y, clf, cv=3)\n","val_score"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":168},"id":"xz445Ftx0kSI","executionInfo":{"status":"ok","timestamp":1662017602751,"user_tz":420,"elapsed":286,"user":{"displayName":"Cao Jinpu","userId":"11416302596004484254"}},"outputId":"2a981325-1688-46c5-a44a-9c2a94fa6a5f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["X_train.shape (before random over sampler):  (160, 7)\n","X_train.shape (after random over sampler):  (160, 7)\n","Cross Validation: 1 - Accuracy: 0.225\n","Cross Validation: 2 - Accuracy: 0.2\n","Cross Validation: 3 - Accuracy: 0.1875\n"]},{"output_type":"execute_result","data":{"text/plain":["           CV_1  CV_2    CV_3      Mean\n","accuracy  0.225   0.2  0.1875  0.204167"],"text/html":["\n","  <div id=\"df-26415e2d-40ac-4bff-a53b-8d5134e6d993\">\n","    <div class=\"colab-df-container\">\n","      <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>CV_1</th>\n","      <th>CV_2</th>\n","      <th>CV_3</th>\n","      <th>Mean</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>accuracy</th>\n","      <td>0.225</td>\n","      <td>0.2</td>\n","      <td>0.1875</td>\n","      <td>0.204167</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-26415e2d-40ac-4bff-a53b-8d5134e6d993')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-26415e2d-40ac-4bff-a53b-8d5134e6d993 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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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","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":151}]},{"cell_type":"code","source":["from sklearn.neural_network import MLPClassifier\n","clf = MLPClassifier(hidden_layer_sizes=(20),\n","                    random_state=1, max_iter=2000)\n","val_score = cross_val_pca_ros(X, y, clf, cv=3, n_components=5)\n","val_score"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":292},"id":"fUL20Q-J0lvh","executionInfo":{"status":"ok","timestamp":1662017609087,"user_tz":420,"elapsed":3370,"user":{"displayName":"Cao Jinpu","userId":"11416302596004484254"}},"outputId":"13da8364-0d71-4c31-d65d-dd2cc992a31d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["X_train.shape (before random over sampler):  (160, 7)\n","X_train.shape (after random over sampler):  (160, 7)\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n","  ConvergenceWarning,\n"]},{"output_type":"stream","name":"stdout","text":["Cross Validation: 1 - Accuracy: 0.3375\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n","  ConvergenceWarning,\n"]},{"output_type":"stream","name":"stdout","text":["Cross Validation: 2 - Accuracy: 0.325\n","Cross Validation: 3 - Accuracy: 0.2625\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n","  ConvergenceWarning,\n"]},{"output_type":"execute_result","data":{"text/plain":["            CV_1   CV_2    CV_3      Mean\n","accuracy  0.3375  0.325  0.2625  0.308333"],"text/html":["\n","  <div id=\"df-23c66b29-62c4-4f2c-adbf-9ded801ae797\">\n","    <div class=\"colab-df-container\">\n","      <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>CV_1</th>\n","      <th>CV_2</th>\n","      <th>CV_3</th>\n","      <th>Mean</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>accuracy</th>\n","      <td>0.3375</td>\n","      <td>0.325</td>\n","      <td>0.2625</td>\n","      <td>0.308333</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-23c66b29-62c4-4f2c-adbf-9ded801ae797')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-23c66b29-62c4-4f2c-adbf-9ded801ae797 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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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","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":152}]},{"cell_type":"code","source":["from sklearn.ensemble import GradientBoostingClassifier\n","clf = GradientBoostingClassifier(n_estimators=50, learning_rate=0.1,\n","     max_depth=3, random_state=0)\n","val_score = cross_val_pca_ros(X, y, clf, cv=3)\n","val_score"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":168},"id":"_c6tTqlT0U3e","executionInfo":{"status":"ok","timestamp":1662017612269,"user_tz":420,"elapsed":1172,"user":{"displayName":"Cao Jinpu","userId":"11416302596004484254"}},"outputId":"2c328779-932c-4689-f888-d33abf40bab4"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["X_train.shape (before random over sampler):  (160, 7)\n","X_train.shape (after random over sampler):  (160, 7)\n","Cross Validation: 1 - Accuracy: 0.3375\n","Cross Validation: 2 - Accuracy: 0.375\n","Cross Validation: 3 - Accuracy: 0.3375\n"]},{"output_type":"execute_result","data":{"text/plain":["            CV_1   CV_2    CV_3  Mean\n","accuracy  0.3375  0.375  0.3375  0.35"],"text/html":["\n","  <div id=\"df-ea4a7429-e9d8-4283-a4aa-07c70abf5fe3\">\n","    <div class=\"colab-df-container\">\n","      <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>CV_1</th>\n","      <th>CV_2</th>\n","      <th>CV_3</th>\n","      <th>Mean</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>accuracy</th>\n","      <td>0.3375</td>\n","      <td>0.375</td>\n","      <td>0.3375</td>\n","      <td>0.35</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ea4a7429-e9d8-4283-a4aa-07c70abf5fe3')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-ea4a7429-e9d8-4283-a4aa-07c70abf5fe3 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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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","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":153}]},{"cell_type":"code","source":["from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n","clf = LinearDiscriminantAnalysis()\n","val_score = cross_val_pca_ros(X, y, clf, cv=3)\n","val_score"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":168},"id":"upUjhj9j32sU","executionInfo":{"status":"ok","timestamp":1662017614106,"user_tz":420,"elapsed":154,"user":{"displayName":"Cao Jinpu","userId":"11416302596004484254"}},"outputId":"4b0f60b4-f2fa-4ecd-caa7-a93b1e672e14"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["X_train.shape (before random over sampler):  (160, 7)\n","X_train.shape (after random over sampler):  (160, 7)\n","Cross Validation: 1 - Accuracy: 0.2625\n","Cross Validation: 2 - Accuracy: 0.2375\n","Cross Validation: 3 - Accuracy: 0.2125\n"]},{"output_type":"execute_result","data":{"text/plain":["            CV_1    CV_2    CV_3    Mean\n","accuracy  0.2625  0.2375  0.2125  0.2375"],"text/html":["\n","  <div id=\"df-2fbecb62-dd35-4b67-b8d3-b7af708f628d\">\n","    <div class=\"colab-df-container\">\n","      <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>CV_1</th>\n","      <th>CV_2</th>\n","      <th>CV_3</th>\n","      <th>Mean</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>accuracy</th>\n","      <td>0.2625</td>\n","      <td>0.2375</td>\n","      <td>0.2125</td>\n","      <td>0.2375</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2fbecb62-dd35-4b67-b8d3-b7af708f628d')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-2fbecb62-dd35-4b67-b8d3-b7af708f628d button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-2fbecb62-dd35-4b67-b8d3-b7af708f628d');\n","          const dataTable =\n","            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","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":154}]},{"cell_type":"markdown","source":["## 4.6 &nbsp;AutoGluon\n","http://lucky521.github.io/blog/machinelearning/2019/07/20/AutoML.html#ray-tune <br>\n","https://auto.gluon.ai/stable/index.html"],"metadata":{"id":"BFItXsOT5_cC"}},{"cell_type":"code","source":["!pip install --upgrade mxnet\n","!pip install autogluon\n","from autogluon.tabular import TabularPredictor\n","# https://auto.gluon.ai/dev/api/autogluon.task.html"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"cHkShTh75_GL","executionInfo":{"status":"ok","timestamp":1662049013812,"user_tz":420,"elapsed":35911,"user":{"displayName":"Cao Jinpu","userId":"11416302596004484254"}},"outputId":"c4b5ccb5-c633-42b1-d489-03cc437c7cd8"},"execution_count":60,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: mxnet in /usr/local/lib/python3.7/dist-packages (1.9.1)\n","Requirement already satisfied: numpy<2.0.0,>1.16.0 in /usr/local/lib/python3.7/dist-packages (from mxnet) (1.21.6)\n","Requirement already satisfied: graphviz<0.9.0,>=0.8.1 in /usr/local/lib/python3.7/dist-packages (from mxnet) (0.8.4)\n","Requirement already satisfied: requests<3,>=2.20.0 in /usr/local/lib/python3.7/dist-packages (from mxnet) (2.23.0)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.20.0->mxnet) (2022.6.15)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.20.0->mxnet) (2.10)\n","Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.20.0->mxnet) (1.25.11)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.20.0->mxnet) (3.0.4)\n","Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: autogluon in /usr/local/lib/python3.7/dist-packages (0.5.2)\n","Requirement already satisfied: autogluon.text==0.5.2 in /usr/local/lib/python3.7/dist-packages (from autogluon) (0.5.2)\n","Requirement already satisfied: autogluon.multimodal==0.5.2 in 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StratifiedKFold(n_splits=cv, random_state=split_random_state, shuffle=split_shuffle)\n","\n","train_idx_list = []\n","test_idx_list = []\n","for train_index, test_index in skf.split(comp_X, y):\n","  train_idx_list.append(train_index)\n","  test_idx_list.append(test_index)"],"metadata":{"id":"__QX6sfjEfMn"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["train_idx_list"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"jTKC18N0GX3o","executionInfo":{"status":"ok","timestamp":1662017805615,"user_tz":420,"elapsed":122,"user":{"displayName":"Cao Jinpu","userId":"11416302596004484254"}},"outputId":"b16d56fc-b81d-49b4-85d3-f4e4b74f1805"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[array([  0,   1,   2,   3,   4,   6,   8,   9,  10,  13,  14,  15,  16,\n","         17,  20,  22,  26,  27,  28,  29,  31,  34,  35,  36,  38,  39,\n","         40,  41,  44,  46,  47,  48,  50,  51,  53,  54,  55,  56,  57,\n","         58,  59,  62,  63,  66,  68,  69,  70,  71,  72,  74,  75,  77,\n","         78,  79,  80,  82,  83,  85,  87,  88,  91,  92,  93,  94,  95,\n","         96,  97,  98, 100, 103, 104, 105, 107, 108, 110, 112, 115, 116,\n","        117, 119, 120, 121, 123, 124, 125, 127, 128, 129, 130, 131, 133,\n","        134, 138, 139, 140, 141, 142, 144, 145, 146, 148, 149, 150, 152,\n","        154, 155, 156, 159, 160, 161, 164, 166, 167, 169, 170, 171, 172,\n","        176, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 189, 190,\n","        192, 194, 195, 196, 197, 198, 203, 204, 205, 206, 207, 208, 209,\n","        210, 211, 216, 218, 219, 221, 222, 227, 228, 229, 230, 231, 232,\n","        234, 235, 237, 238]),\n"," array([  0,   3,   4,   5,   7,   8,  10,  11,  12,  18,  19,  20,  21,\n","         22,  23,  24,  25,  27,  28,  30,  31,  32,  33,  34,  37,  38,\n","         42,  43,  45,  47,  49,  52,  53,  54,  55,  56,  57,  58,  60,\n","         61,  64,  65,  67,  68,  70,  72,  73,  74,  76,  81,  83,  84,\n","         85,  86,  88,  89,  90,  91,  93,  94,  95,  97,  99, 101, 102,\n","        103, 104, 106, 108, 109, 110, 111, 112, 113, 114, 118, 119, 122,\n","        124, 125, 126, 127, 130, 132, 133, 134, 135, 136, 137, 138, 139,\n","        140, 141, 143, 145, 146, 147, 148, 150, 151, 153, 154, 156, 157,\n","        158, 159, 161, 162, 163, 164, 165, 167, 168, 170, 173, 174, 175,\n","        177, 178, 179, 180, 182, 186, 188, 189, 191, 192, 193, 194, 195,\n","        196, 199, 200, 201, 202, 203, 205, 206, 207, 210, 211, 212, 213,\n","        214, 215, 217, 220, 221, 223, 224, 225, 226, 227, 229, 232, 233,\n","        235, 236, 237, 239]),\n"," array([  1,   2,   5,   6,   7,   9,  11,  12,  13,  14,  15,  16,  17,\n","         18,  19,  21,  23,  24,  25,  26,  29,  30,  32,  33,  35,  36,\n","         37,  39,  40,  41,  42,  43,  44,  45,  46,  48,  49,  50,  51,\n","         52,  59,  60,  61,  62,  63,  64,  65,  66,  67,  69,  71,  73,\n","         75,  76,  77,  78,  79,  80,  81,  82,  84,  86,  87,  89,  90,\n","         92,  96,  98,  99, 100, 101, 102, 105, 106, 107, 109, 111, 113,\n","        114, 115, 116, 117, 118, 120, 121, 122, 123, 126, 128, 129, 131,\n","        132, 135, 136, 137, 142, 143, 144, 147, 149, 151, 152, 153, 155,\n","        157, 158, 160, 162, 163, 165, 166, 168, 169, 171, 172, 173, 174,\n","        175, 176, 177, 181, 183, 184, 185, 187, 188, 190, 191, 193, 197,\n","        198, 199, 200, 201, 202, 204, 208, 209, 212, 213, 214, 215, 216,\n","        217, 218, 219, 220, 222, 223, 224, 225, 226, 228, 230, 231, 233,\n","        234, 236, 238, 239])]"]},"metadata":{},"execution_count":173}]},{"cell_type":"code","source":["test_idx_list"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"aFUdncmiGU34","executionInfo":{"status":"ok","timestamp":1662017808830,"user_tz":420,"elapsed":121,"user":{"displayName":"Cao Jinpu","userId":"11416302596004484254"}},"outputId":"9de0cb98-01e5-4976-d877-4285d2fcb4f9"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[array([  5,   7,  11,  12,  18,  19,  21,  23,  24,  25,  30,  32,  33,\n","         37,  42,  43,  45,  49,  52,  60,  61,  64,  65,  67,  73,  76,\n","         81,  84,  86,  89,  90,  99, 101, 102, 106, 109, 111, 113, 114,\n","        118, 122, 126, 132, 135, 136, 137, 143, 147, 151, 153, 157, 158,\n","        162, 163, 165, 168, 173, 174, 175, 177, 188, 191, 193, 199, 200,\n","        201, 202, 212, 213, 214, 215, 217, 220, 223, 224, 225, 226, 233,\n","        236, 239]),\n"," array([  1,   2,   6,   9,  13,  14,  15,  16,  17,  26,  29,  35,  36,\n","         39,  40,  41,  44,  46,  48,  50,  51,  59,  62,  63,  66,  69,\n","         71,  75,  77,  78,  79,  80,  82,  87,  92,  96,  98, 100, 105,\n","        107, 115, 116, 117, 120, 121, 123, 128, 129, 131, 142, 144, 149,\n","        152, 155, 160, 166, 169, 171, 172, 176, 181, 183, 184, 185, 187,\n","        190, 197, 198, 204, 208, 209, 216, 218, 219, 222, 228, 230, 231,\n","        234, 238]),\n"," array([  0,   3,   4,   8,  10,  20,  22,  27,  28,  31,  34,  38,  47,\n","         53,  54,  55,  56,  57,  58,  68,  70,  72,  74,  83,  85,  88,\n","         91,  93,  94,  95,  97, 103, 104, 108, 110, 112, 119, 124, 125,\n","        127, 130, 133, 134, 138, 139, 140, 141, 145, 146, 148, 150, 154,\n","        156, 159, 161, 164, 167, 170, 178, 179, 180, 182, 186, 189, 192,\n","        194, 195, 196, 203, 205, 206, 207, 210, 211, 221, 227, 229, 232,\n","        235, 237])]"]},"metadata":{},"execution_count":174}]},{"cell_type":"markdown","source":["### Step_by_step"],"metadata":{"id":"Z5qYAno7C0WY"}},{"cell_type":"code","source":["# 1st CV\n","k = 0\n","train_index = train_idx_list[k]\n","test_index = test_idx_list[k]\n","X_train, X_test = comp_X[train_index,:], comp_X[test_index,:]\n","y_train, y_test = y[train_index], y[test_index]\n","\n","# RandomOverSampler\n","# ros_random_state = 3\n","# ros = RandomOverSampler(sampling_strategy='not majority',\n","#                         random_state = ros_random_state)\n","# X_train, y_train = ros.fit_resample(X_train, y_train)\n","\n","# features to DataFrame\n","columns = [i for i in range(X_train.shape[1])] + ['class']\n","df_train = pd.DataFrame(data = np.c_[X_train,y_train], columns=columns)\n","df_test = pd.DataFrame(data = np.c_[X_test,y_test], columns=columns)\n","print('df_train.shape: ', df_train.shape)\n","print('df_test.shape: ', df_test.shape)\n","df_train"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":458},"id":"AskxXhDi-1HK","executionInfo":{"status":"ok","timestamp":1662017812899,"user_tz":420,"elapsed":4,"user":{"displayName":"Cao Jinpu","userId":"11416302596004484254"}},"outputId":"12b558c3-076a-4ccd-e1fb-74f34dbb64ba"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["df_train.shape:  (160, 7)\n","df_test.shape:  (80, 7)\n"]},{"output_type":"execute_result","data":{"text/plain":["            0         1         2         3         4         5  class\n","0    0.025000 -0.017969  0.023438 -0.042969 -0.001563  0.041406    1.0\n","1    0.007422 -0.110550  0.003516 -0.117970 -0.003906  0.114060    1.0\n","2    0.000781 -0.007422 -0.001953 -0.008203 -0.002734  0.005469    1.0\n","3    0.040625 -0.077734  0.038281 -0.118360 -0.002344  0.116020    1.0\n","4    0.017578  0.005078  0.015625 -0.012500 -0.001953  0.010547    1.0\n","..        ...       ...       ...       ...       ...       ...    ...\n","155 -0.056250 -0.016016 -0.001953  0.040234  0.054297  0.014063   10.0\n","156 -0.066406 -0.025000 -0.021094  0.041406  0.045312  0.003906   10.0\n","157  0.000781 -0.019141 -0.008984 -0.019922 -0.009766  0.010156   10.0\n","158 -0.058984 -0.023828 -0.041406  0.035156  0.017578 -0.017578   10.0\n","159 -0.033203  0.027344  0.010547  0.060547  0.043750 -0.016797   10.0\n","\n","[160 rows x 7 columns]"],"text/html":["\n","  <div id=\"df-ec613ee3-9a72-4c72-bf23-d0d86169f464\">\n","    <div class=\"colab-df-container\">\n","      <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>0</th>\n","      <th>1</th>\n","      <th>2</th>\n","      <th>3</th>\n","      <th>4</th>\n","      <th>5</th>\n","      <th>class</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.025000</td>\n","      <td>-0.017969</td>\n","      <td>0.023438</td>\n","      <td>-0.042969</td>\n","      <td>-0.001563</td>\n","      <td>0.041406</td>\n","      <td>1.0</td>\n"," 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Models will be saved in: \"AutogluonModels/ag-20220901_073657/\"\n","INFO:autogluon.tabular.learner.default_learner:Beginning AutoGluon training ...\n","INFO:autogluon.tabular.learner.default_learner:AutoGluon will save models to \"AutogluonModels/ag-20220901_073657/\"\n","INFO:autogluon.tabular.learner.default_learner:AutoGluon Version:  0.5.2\n","INFO:autogluon.tabular.learner.default_learner:Python Version:     3.7.13\n","INFO:autogluon.tabular.learner.default_learner:Operating System:   Linux\n","INFO:autogluon.tabular.learner.default_learner:Train Data Rows:    160\n","INFO:autogluon.tabular.learner.default_learner:Train Data Columns: 6\n","INFO:autogluon.tabular.learner.default_learner:Label Column: class\n","INFO:autogluon.tabular.learner.default_learner:Preprocessing data ...\n","INFO:autogluon.tabular.learner.default_learner:Train Data Class Count: 10\n","INFO:autogluon.tabular.learner.default_learner:Using Feature Generators to preprocess the data ...\n","INFO:autogluon.features.generators.abstract:Fitting AutoMLPipelineFeatureGenerator...\n","INFO:autogluon.features.generators.abstract:\tAvailable Memory:                    11594.44 MB\n","INFO:autogluon.features.generators.abstract:\tTrain Data (Original)  Memory Usage: 0.01 MB (0.0% of available memory)\n","INFO:autogluon.features.generators.abstract:\tInferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.\n","INFO:autogluon.features.generators.abstract:\tStage 1 Generators:\n","INFO:autogluon.features.generators.abstract:\t\tFitting AsTypeFeatureGenerator...\n","INFO:autogluon.features.generators.abstract:\tStage 2 Generators:\n","INFO:autogluon.features.generators.abstract:\t\tFitting FillNaFeatureGenerator...\n","INFO:autogluon.features.generators.abstract:\tStage 3 Generators:\n","INFO:autogluon.features.generators.abstract:\t\tFitting IdentityFeatureGenerator...\n","INFO:autogluon.features.generators.abstract:\tStage 4 Generators:\n","INFO:autogluon.features.generators.abstract:\t\tFitting DropUniqueFeatureGenerator...\n","INFO:autogluon.features.generators.abstract:\tTypes of features in original data (raw dtype, special dtypes):\n","INFO:autogluon.common.features.feature_metadata:\t\t('float', []) : 6 | ['0', '1', '2', '3', '4', ...]\n","INFO:autogluon.features.generators.abstract:\tTypes of features in processed data (raw dtype, special dtypes):\n","INFO:autogluon.common.features.feature_metadata:\t\t('float', []) : 6 | ['0', '1', '2', '3', '4', ...]\n","INFO:autogluon.features.generators.abstract:\t0.1s = Fit runtime\n","INFO:autogluon.features.generators.abstract:\t6 features in original data used to generate 6 features in processed data.\n","INFO:autogluon.features.generators.abstract:\tTrain Data (Processed) Memory Usage: 0.01 MB (0.0% of available memory)\n","INFO:autogluon.tabular.learner.default_learner:Data preprocessing and feature engineering runtime = 0.11s ...\n","Level 25:autogluon.core.trainer.abstract_trainer:AutoGluon will gauge predictive performance using evaluation metric: 'accuracy'\n","INFO:autogluon.core.trainer.abstract_trainer:\tTo change this, specify the eval_metric parameter of Predictor()\n","INFO:autogluon.tabular.trainer.auto_trainer:Automatically generating train/validation split with holdout_frac=0.2, Train Rows: 128, Val Rows: 32\n","INFO:autogluon.core.trainer.abstract_trainer:Fitting 13 L1 models ...\n","INFO:autogluon.core.trainer.abstract_trainer:Fitting model: KNeighborsUnif ...\n","INFO:autogluon.core.trainer.abstract_trainer:\t0.3438\t = Validation score   (accuracy)\n","INFO:autogluon.core.trainer.abstract_trainer:\t0.01s\t = Training   runtime\n","INFO:autogluon.core.trainer.abstract_trainer:\t0.1s\t = Validation runtime\n","INFO:autogluon.core.trainer.abstract_trainer:Fitting model: KNeighborsDist ...\n","INFO:autogluon.core.trainer.abstract_trainer:\t0.375\t = Validation score   (accuracy)\n","INFO:autogluon.core.trainer.abstract_trainer:\t0.01s\t = Training   runtime\n","INFO:autogluon.core.trainer.abstract_trainer:\t0.1s\t = Validation runtime\n","INFO:autogluon.core.trainer.abstract_trainer:Fitting model: NeuralNetFastAI ...\n","INFO:autogluon.core.trainer.abstract_trainer:\t0.4688\t = Validation score   (accuracy)\n","INFO:autogluon.core.trainer.abstract_trainer:\t1.48s\t = Training   runtime\n","INFO:autogluon.core.trainer.abstract_trainer:\t0.01s\t = Validation runtime\n","INFO:autogluon.core.trainer.abstract_trainer:Fitting model: LightGBMXT ...\n","INFO:autogluon.core.trainer.abstract_trainer:\t0.4375\t = Validation score   (accuracy)\n","INFO:autogluon.core.trainer.abstract_trainer:\t0.24s\t = Training   runtime\n","INFO:autogluon.core.trainer.abstract_trainer:\t0.0s\t = Validation runtime\n","INFO:autogluon.core.trainer.abstract_trainer:Fitting model: LightGBM ...\n","INFO:autogluon.core.trainer.abstract_trainer:\t0.4688\t = Validation score   (accuracy)\n","INFO:autogluon.core.trainer.abstract_trainer:\t0.37s\t = Training   runtime\n","INFO:autogluon.core.trainer.abstract_trainer:\t0.0s\t = Validation runtime\n","INFO:autogluon.core.trainer.abstract_trainer:Fitting model: RandomForestGini ...\n","INFO:autogluon.core.trainer.abstract_trainer:\t0.4375\t = Validation score   (accuracy)\n","INFO:autogluon.core.trainer.abstract_trainer:\t0.7s\t = Training   runtime\n","INFO:autogluon.core.trainer.abstract_trainer:\t0.1s\t = Validation runtime\n","INFO:autogluon.core.trainer.abstract_trainer:Fitting model: RandomForestEntr ...\n","INFO:autogluon.core.trainer.abstract_trainer:\t0.4688\t = Validation score   (accuracy)\n","INFO:autogluon.core.trainer.abstract_trainer:\t0.7s\t = Training   runtime\n","INFO:autogluon.core.trainer.abstract_trainer:\t0.1s\t = Validation runtime\n","INFO:autogluon.core.trainer.abstract_trainer:Fitting model: CatBoost ...\n","INFO:autogluon.core.trainer.abstract_trainer:\t0.4375\t = Validation score   (accuracy)\n","INFO:autogluon.core.trainer.abstract_trainer:\t1.13s\t = Training   runtime\n","INFO:autogluon.core.trainer.abstract_trainer:\t0.0s\t = Validation runtime\n","INFO:autogluon.core.trainer.abstract_trainer:Fitting model: ExtraTreesGini ...\n","INFO:autogluon.core.trainer.abstract_trainer:\t0.4375\t = Validation score   (accuracy)\n","INFO:autogluon.core.trainer.abstract_trainer:\t0.7s\t = Training   runtime\n","INFO:autogluon.core.trainer.abstract_trainer:\t0.1s\t = Validation runtime\n","INFO:autogluon.core.trainer.abstract_trainer:Fitting model: ExtraTreesEntr ...\n","INFO:autogluon.core.trainer.abstract_trainer:\t0.4688\t = Validation score   (accuracy)\n","INFO:autogluon.core.trainer.abstract_trainer:\t0.7s\t = Training   runtime\n","INFO:autogluon.core.trainer.abstract_trainer:\t0.1s\t = Validation runtime\n","INFO:autogluon.core.trainer.abstract_trainer:Fitting model: XGBoost ...\n","INFO:autogluon.core.trainer.abstract_trainer:\t0.4062\t = Validation score   (accuracy)\n","INFO:autogluon.core.trainer.abstract_trainer:\t0.36s\t = Training   runtime\n","INFO:autogluon.core.trainer.abstract_trainer:\t0.0s\t = Validation runtime\n","INFO:autogluon.core.trainer.abstract_trainer:Fitting model: NeuralNetTorch ...\n","INFO:autogluon.core.trainer.abstract_trainer:\t0.3438\t = Validation score   (accuracy)\n","INFO:autogluon.core.trainer.abstract_trainer:\t0.6s\t = Training   runtime\n","INFO:autogluon.core.trainer.abstract_trainer:\t0.01s\t = Validation runtime\n","INFO:autogluon.core.trainer.abstract_trainer:Fitting model: LightGBMLarge ...\n","INFO:autogluon.core.trainer.abstract_trainer:\t0.4062\t = Validation score   (accuracy)\n","INFO:autogluon.core.trainer.abstract_trainer:\t0.51s\t = Training   runtime\n","INFO:autogluon.core.trainer.abstract_trainer:\t0.0s\t = Validation runtime\n","INFO:autogluon.core.trainer.abstract_trainer:Fitting model: WeightedEnsemble_L2 ...\n","INFO:autogluon.core.trainer.abstract_trainer:\t0.5312\t = Validation score   (accuracy)\n","INFO:autogluon.core.trainer.abstract_trainer:\t0.24s\t = Training   runtime\n","INFO:autogluon.core.trainer.abstract_trainer:\t0.0s\t = Validation runtime\n","INFO:autogluon.tabular.learner.default_learner:AutoGluon training complete, total runtime = 8.82s ... Best model: \"WeightedEnsemble_L2\"\n","INFO:autogluon.tabular.predictor.predictor:TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"AutogluonModels/ag-20220901_073657/\")\n"]}]},{"cell_type":"code","source":["test_result = predictor.leaderboard(df_test, silent=True)\n","test_result"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":552},"id":"MhI6-D7zBQ8z","executionInfo":{"status":"ok","timestamp":1662017829018,"user_tz":420,"elapsed":878,"user":{"displayName":"Cao Jinpu","userId":"11416302596004484254"}},"outputId":"fafa9d8f-b7b7-43d8-964d-e51996d50168"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                  model  score_test  score_val  pred_time_test  pred_time_val  \\\n","0         LightGBMLarge      0.4375    0.40625        0.002950       0.002178   \n","1               XGBoost      0.4375    0.40625        0.008551       0.004875   \n","2        ExtraTreesGini      0.4250    0.43750        0.118301       0.102427   \n","3      RandomForestGini      0.4125    0.43750        0.112145       0.102351   \n","4      RandomForestEntr      0.4125    0.46875        0.113320       0.102500   \n","5              LightGBM      0.4000    0.46875        0.014266       0.004051   \n","6        KNeighborsDist      0.4000    0.37500        0.104449       0.104699   \n","7        ExtraTreesEntr      0.4000    0.46875        0.116542       0.102762   \n","8   WeightedEnsemble_L2      0.3875    0.53125        0.037811       0.012354   \n","9              CatBoost      0.3750    0.43750        0.001587       0.001099   \n","10           LightGBMXT      0.3750    0.43750        0.005422       0.003488   \n","11       KNeighborsUnif      0.3625    0.34375        0.102493       0.102162   \n","12       NeuralNetTorch      0.2750    0.34375        0.009448       0.006745   \n","13      NeuralNetFastAI      0.2750    0.46875        0.019448       0.007968   \n","\n","    fit_time  pred_time_test_marginal  pred_time_val_marginal  \\\n","0   0.511274                 0.002950                0.002178   \n","1   0.363998                 0.008551                0.004875   \n","2   0.700223                 0.118301                0.102427   \n","3   0.697349                 0.112145                0.102351   \n","4   0.697885                 0.113320                0.102500   \n","5   0.365437                 0.014266                0.004051   \n","6   0.005237                 0.104449                0.104699   \n","7   0.703716                 0.116542                0.102762   \n","8   2.093273                 0.004096                0.000335   \n","9   1.132872                 0.001587                0.001099   \n","10  0.238848                 0.005422                0.003488   \n","11  0.011914                 0.102493                0.102162   \n","12  0.595972                 0.009448                0.006745   \n","13  1.483423                 0.019448                0.007968   \n","\n","    fit_time_marginal  stack_level  can_infer  fit_order  \n","0            0.511274            1       True         13  \n","1            0.363998            1       True         11  \n","2            0.700223            1       True          9  \n","3            0.697349            1       True          6  \n","4            0.697885            1       True          7  \n","5            0.365437            1       True          5  \n","6            0.005237            1       True          2  \n","7            0.703716            1       True         10  \n","8            0.244413            2       True         14  \n","9            1.132872            1       True          8  \n","10           0.238848            1       True          4  \n","11           0.011914            1       True          1  \n","12           0.595972            1       True         12  \n","13           1.483423            1       True          3  "],"text/html":["\n","  <div id=\"df-a0c7b0e7-d08b-4052-93fb-53ff7e3ddb2b\">\n","    <div class=\"colab-df-container\">\n","      <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>model</th>\n","      <th>score_test</th>\n","      <th>score_val</th>\n","      <th>pred_time_test</th>\n","      <th>pred_time_val</th>\n","      <th>fit_time</th>\n","      <th>pred_time_test_marginal</th>\n","      <th>pred_time_val_marginal</th>\n","      <th>fit_time_marginal</th>\n","      <th>stack_level</th>\n","      <th>can_infer</th>\n","      <th>fit_order</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>LightGBMLarge</td>\n","      <td>0.4375</td>\n","      <td>0.40625</td>\n","      <td>0.002950</td>\n","      <td>0.002178</td>\n","      <td>0.511274</td>\n","      <td>0.002950</td>\n","      <td>0.002178</td>\n","      <td>0.511274</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>13</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>XGBoost</td>\n","      <td>0.4375</td>\n","      <td>0.40625</td>\n","      <td>0.008551</td>\n","      <td>0.004875</td>\n","      <td>0.363998</td>\n","      <td>0.008551</td>\n","      <td>0.004875</td>\n","      <td>0.363998</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>11</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>ExtraTreesGini</td>\n","      <td>0.4250</td>\n","      <td>0.43750</td>\n","      <td>0.118301</td>\n","      <td>0.102427</td>\n","      <td>0.700223</td>\n","      <td>0.118301</td>\n","      <td>0.102427</td>\n","      <td>0.700223</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>9</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>RandomForestGini</td>\n","      <td>0.4125</td>\n","      <td>0.43750</td>\n","      <td>0.112145</td>\n","      <td>0.102351</td>\n","      <td>0.697349</td>\n","      <td>0.112145</td>\n","      <td>0.102351</td>\n","      <td>0.697349</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>6</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>RandomForestEntr</td>\n","      <td>0.4125</td>\n","      <td>0.46875</td>\n","      <td>0.113320</td>\n","      <td>0.102500</td>\n","      <td>0.697885</td>\n","      <td>0.113320</td>\n","      <td>0.102500</td>\n","      <td>0.697885</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>7</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>LightGBM</td>\n","      <td>0.4000</td>\n","      <td>0.46875</td>\n","      <td>0.014266</td>\n","      <td>0.004051</td>\n","      <td>0.365437</td>\n","      <td>0.014266</td>\n","      <td>0.004051</td>\n","      <td>0.365437</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>KNeighborsDist</td>\n","      <td>0.4000</td>\n","      <td>0.37500</td>\n","      <td>0.104449</td>\n","      <td>0.104699</td>\n","      <td>0.005237</td>\n","      <td>0.104449</td>\n","      <td>0.104699</td>\n","      <td>0.005237</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>ExtraTreesEntr</td>\n","      <td>0.4000</td>\n","      <td>0.46875</td>\n","      <td>0.116542</td>\n","      <td>0.102762</td>\n","      <td>0.703716</td>\n","      <td>0.116542</td>\n","      <td>0.102762</td>\n","      <td>0.703716</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>10</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>WeightedEnsemble_L2</td>\n","      <td>0.3875</td>\n","      <td>0.53125</td>\n","      <td>0.037811</td>\n","      <td>0.012354</td>\n","      <td>2.093273</td>\n","      <td>0.004096</td>\n","      <td>0.000335</td>\n","      <td>0.244413</td>\n","      <td>2</td>\n","      <td>True</td>\n","      <td>14</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>CatBoost</td>\n","      <td>0.3750</td>\n","      <td>0.43750</td>\n","      <td>0.001587</td>\n","      <td>0.001099</td>\n","      <td>1.132872</td>\n","      <td>0.001587</td>\n","      <td>0.001099</td>\n","      <td>1.132872</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>8</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>LightGBMXT</td>\n","      <td>0.3750</td>\n","      <td>0.43750</td>\n","      <td>0.005422</td>\n","      <td>0.003488</td>\n","      <td>0.238848</td>\n","      <td>0.005422</td>\n","      <td>0.003488</td>\n","      <td>0.238848</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>KNeighborsUnif</td>\n","      <td>0.3625</td>\n","      <td>0.34375</td>\n","      <td>0.102493</td>\n","      <td>0.102162</td>\n","      <td>0.011914</td>\n","      <td>0.102493</td>\n","      <td>0.102162</td>\n","      <td>0.011914</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>NeuralNetTorch</td>\n","      <td>0.2750</td>\n","      <td>0.34375</td>\n","      <td>0.009448</td>\n","      <td>0.006745</td>\n","      <td>0.595972</td>\n","      <td>0.009448</td>\n","      <td>0.006745</td>\n","      <td>0.595972</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>12</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>NeuralNetFastAI</td>\n","      <td>0.2750</td>\n","      <td>0.46875</td>\n","      <td>0.019448</td>\n","      <td>0.007968</td>\n","      <td>1.483423</td>\n","      <td>0.019448</td>\n","      <td>0.007968</td>\n","      <td>1.483423</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>3</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a0c7b0e7-d08b-4052-93fb-53ff7e3ddb2b')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 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Jinpu","userId":"11416302596004484254"}},"outputId":"135a4e2b-51a8-40a1-b7ae-d332e30fea23"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['KNeighborsUnif',\n"," 'KNeighborsDist',\n"," 'NeuralNetFastAI',\n"," 'LightGBMXT',\n"," 'LightGBM',\n"," 'RandomForestGini',\n"," 'RandomForestEntr',\n"," 'CatBoost',\n"," 'ExtraTreesGini',\n"," 'ExtraTreesEntr',\n"," 'XGBoost',\n"," 'NeuralNetTorch',\n"," 'LightGBMLarge',\n"," 'WeightedEnsemble_L2']"]},"metadata":{},"execution_count":161}]},{"cell_type":"code","source":["# best config\n","predictor.info()['model_info']['RandomForestEntr']"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"A_ZQCFrH9R4A","executionInfo":{"status":"ok","timestamp":1662017691304,"user_tz":420,"elapsed":2056,"user":{"displayName":"Cao Jinpu","userId":"11416302596004484254"}},"outputId":"30280e18-ad7b-4fa5-fdd9-2daaf7e9f304"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'name': 'RandomForestEntr',\n"," 'model_type': 'RFModel',\n"," 'problem_type': 'multiclass',\n"," 'eval_metric': 'accuracy',\n"," 'stopping_metric': 'accuracy',\n"," 'fit_time': 0.6948225498199463,\n"," 'num_classes': 10,\n"," 'quantile_levels': None,\n"," 'predict_time': 0.10239505767822266,\n"," 'val_score': 0.34375,\n"," 'hyperparameters': {'n_estimators': 300,\n","  'max_leaf_nodes': 15000,\n","  'n_jobs': -1,\n","  'random_state': 0,\n","  'bootstrap': True,\n","  'criterion': 'entropy'},\n"," 'hyperparameters_fit': {'n_estimators': 300},\n"," 'hyperparameters_nondefault': ['criterion'],\n"," 'ag_args_fit': {'max_memory_usage_ratio': 1.0,\n","  'max_time_limit_ratio': 1.0,\n","  'max_time_limit': None,\n","  'min_time_limit': 0,\n","  'valid_raw_types': ['bool', 'int', 'float', 'category'],\n","  'valid_special_types': None,\n","  'ignored_type_group_special': None,\n","  'ignored_type_group_raw': None,\n","  'get_features_kwargs': None,\n","  'get_features_kwargs_extra': None,\n","  'predict_1_batch_size': None},\n"," 'num_features': 7,\n"," 'features': ['0', '1', '2', '3', '4', '5', '6'],\n"," 'feature_metadata': <autogluon.common.features.feature_metadata.FeatureMetadata at 0x7f2f6f7fe590>,\n"," 'memory_size': 3888632}"]},"metadata":{},"execution_count":162}]},{"cell_type":"code","source":["y_pred = predictor.predict(df_test, model='WeightedEnsemble_L2')\n","y_true = df_test.iloc[:,-1]\n","# Accuracy (= score_test)\n","accuracy = sum(y_pred==y_true)  / len(y_true)\n","accuracy"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sVqVBeE9B_vb","executionInfo":{"status":"ok","timestamp":1662017702378,"user_tz":420,"elapsed":294,"user":{"displayName":"Cao Jinpu","userId":"11416302596004484254"}},"outputId":"a0504b28-a208-411e-8309-f0399caefdcb"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.45"]},"metadata":{},"execution_count":163}]},{"cell_type":"markdown","source":["### Function"],"metadata":{"id":"dgirbehOC6zf"}},{"cell_type":"markdown","source":["#### model 0"],"metadata":{"id":"ctSl02B96B0G"}},{"cell_type":"code","source":["def clf_train_k(X, y, clf, k, train_idx_list, test_idx_list, \n","                n_components=3, pca_random_state=3,\n","                ros_random_state = 3):\n","  # # PCA\n","  # pca = PCA(n_components = n_components, random_state=pca_random_state)\n","  # pca.fit(X)\n","  # comp_X = pca.transform(X)\n","\n","  comp_X = np.array(X)\n","\n","  train_index = train_idx_list[k]\n","  test_index = test_idx_list[k]\n","  X_train, X_test = comp_X[train_index,:], comp_X[test_index,:]\n","  y_train, y_test = y[train_index], y[test_index]\n","\n","  # # RandomOverSampler\n","  \n","  # ros = RandomOverSampler(sampling_strategy='not majority',\n","  #                         random_state = ros_random_state)\n","  # X_train, y_train = ros.fit_resample(X_train, y_train)\n","\n","  # Train\n","  clf.fit(X_train, y_train)\n","  y_pred = clf.predict(X_test)\n","  accuracy = sum(y_pred==y_test)  / len(y_test)\n","  \n","\n","  return clf, accuracy\n"],"metadata":{"id":"jGSwWSaG6Dc6"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["mean_acc = 0\n","cv = 3\n","for k in range(cv):\n","  clf = RandomForestClassifier(max_depth=5, random_state=0)\n","  clf, accuracy = clf_train_k(X, y, clf, k, train_idx_list, test_idx_list)\n","  mean_acc += accuracy\n","  print(f'CV {k+1}：', accuracy)\n","print('Mean Accuracy：', mean_acc/cv)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9IMEXKwq8Ic2","executionInfo":{"status":"ok","timestamp":1662017839404,"user_tz":420,"elapsed":553,"user":{"displayName":"Cao Jinpu","userId":"11416302596004484254"}},"outputId":"2d9ed0b1-3589-4723-875b-5dbc6d03159e"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["CV 1： 0.3375\n","CV 2： 0.475\n","CV 3： 0.4375\n","Mean Accuracy： 0.4166666666666667\n"]}]},{"cell_type":"markdown","source":["#### AutoML Model"],"metadata":{"id":"noJw05rI8jVG"}},{"cell_type":"code","source":["from sklearn.model_selection import train_test_split\n","from sklearn.model_selection import cross_val_score\n","import numpy as np\n","from sklearn.decomposition import PCA\n","from imblearn.over_sampling import RandomOverSampler\n","from sklearn.model_selection import StratifiedKFold"],"metadata":{"id":"tRcs3FLFxXQM","executionInfo":{"status":"ok","timestamp":1662048970573,"user_tz":420,"elapsed":170,"user":{"displayName":"Cao Jinpu","userId":"11416302596004484254"}}},"execution_count":59,"outputs":[]},{"cell_type":"code","source":["def autotrain_k(X, y, k, train_idx_list, test_idx_list, \n","                n_components=10, pca_random_state=3,\n","                ros_random_state = 3):\n","  # # PCA\n","  # pca = PCA(n_components = n_components, random_state=pca_random_state)\n","  # pca.fit(X)\n","  # comp_X = pca.transform(X)\n","\n","  comp_X = np.array(X)\n","\n","  train_index = train_idx_list[k]\n","  test_index = test_idx_list[k]\n","  X_train, X_test = comp_X[train_index,:], comp_X[test_index,:]\n","  y_train, y_test = y[train_index], y[test_index]\n","\n","  # RandomOverSampler\n","  # ros = RandomOverSampler(sampling_strategy='not majority',\n","  #                         random_state = ros_random_state)\n","  # X_train, y_train = ros.fit_resample(X_train, y_train)\n","\n","  # to DataFrame\n","  columns = [i for i in range(X_train.shape[1])] + ['class']\n","  df_train = pd.DataFrame(data = np.c_[X_train,y_train], columns=columns)\n","  df_test = pd.DataFrame(data = np.c_[X_test,y_test], columns=columns)\n","\n","  # AutoML\n","  predictor = TabularPredictor(label='class',\n","                               problem_type = 'multiclass',\n","                              #  eval_metric = 'accuracy',\n","                               eval_metric = 'f1_macro',\n","                               verbosity = 1, \n","                               ).fit(df_train)\n","\n","  return predictor, df_test\n"],"metadata":{"id":"1SDWqw46CSDo","executionInfo":{"status":"ok","timestamp":1662052274032,"user_tz":420,"elapsed":213,"user":{"displayName":"Cao Jinpu","userId":"11416302596004484254"}}},"execution_count":138,"outputs":[]},{"cell_type":"code","source":["# X = df.iloc[:80, 1:-1]\n","# y = df.iloc[:80,-1]\n","\n","# X = df.iloc[80:160, 1:-1]\n","# y = df.iloc[80:160,-1]\n","\n","\n","# X = df.iloc[160:240, 1:-1]\n","# y = df.iloc[160:240,-1]\n","\n","# X.index = np.arange(80)\n","# y.index = np.arange(80)"],"metadata":{"id":"_aC3S3SIBGxw"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# without shoe no\n","# X = df_s.iloc[:, 1:-1]\n","X = df_n.iloc[:, 1:-1]\n","\n","y = df.iloc[:,-1]\n"],"metadata":{"id":"ohpjc8eeEwBS","executionInfo":{"status":"ok","timestamp":1662053511529,"user_tz":420,"elapsed":158,"user":{"displayName":"Cao Jinpu","userId":"11416302596004484254"}}},"execution_count":153,"outputs":[]},{"cell_type":"code","source":["# with shoe no\n","X = df.iloc[:, :-1]\n","y = df.iloc[:,-1]\n","\n","X"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":488},"id":"PiZuwEuG1wrQ","executionInfo":{"status":"ok","timestamp":1662052279897,"user_tz":420,"elapsed":192,"user":{"displayName":"Cao Jinpu","userId":"11416302596004484254"}},"outputId":"806861ab-14cb-48e1-d248-d1e596127fdb"},"execution_count":139,"outputs":[{"output_type":"execute_result","data":{"text/plain":["     0         1         2         3         4         5         6         7   \\\n","0     1  0.025000 -0.017969  0.023438 -0.042969 -0.001563  0.041406  0.027734   \n","1     1  0.007422 -0.110550  0.003516 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Models will be saved in: \"AutogluonModels/ag-20220901_173204/\"\n","Level 25:autogluon.core.trainer.abstract_trainer:AutoGluon will gauge predictive performance using evaluation metric: 'f1_macro'\n"]},{"output_type":"stream","name":"stdout","text":["[17:34:08] WARNING: ../src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"]},{"output_type":"execute_result","data":{"text/plain":["                  model  score_test  score_val  pred_time_test  pred_time_val  \\\n","0        ExtraTreesEntr    0.755126   0.593492        0.118852       0.103904   \n","1        ExtraTreesGini    0.715484   0.569206        0.117150       0.103895   \n","2        NeuralNetTorch    0.707026   0.664444        0.027500       0.024018   \n","3       NeuralNetFastAI    0.694791   0.787619        0.035501       0.023914   \n","4   WeightedEnsemble_L2    0.694791   0.787619        0.038520       0.025053   \n","5              CatBoost    0.693241   0.596190        0.019359       0.006065   \n","6      RandomForestGini    0.686513   0.561111        0.114095       0.103714   \n","7      RandomForestEntr    0.684244   0.569206        0.116164       0.103907   \n","8               XGBoost    0.654521   0.594286        0.013825       0.009635   \n","9              LightGBM    0.616306   0.641111        0.046805       0.009925   \n","10       KNeighborsDist    0.595971   0.458095        0.107662       0.104057   \n","11       KNeighborsUnif    0.531218   0.386905        0.103405       0.104980   \n","12           LightGBMXT    0.518633   0.633333        0.019439       0.003641   \n","13        LightGBMLarge    0.312739   0.496667        0.041023       0.003707   \n","\n","      fit_time  pred_time_test_marginal  pred_time_val_marginal  \\\n","0     0.939179                 0.118852                0.103904   \n","1     0.939883                 0.117150                0.103895   \n","2     1.382238                 0.027500                0.024018   \n","3     2.344780                 0.035501                0.023914   \n","4     3.604844                 0.003019                0.001138   \n","5   114.864405                 0.019359                0.006065   \n","6     0.945948                 0.114095                0.103714   \n","7     1.040783                 0.116164                0.103907   \n","8     1.627688                 0.013825                0.009635   \n","9     1.172553                 0.046805                0.009925   \n","10    0.011748                 0.107662                0.104057   \n","11    0.012764                 0.103405                0.104980   \n","12    0.809743                 0.019439                0.003641   \n","13    2.142125                 0.041023                0.003707   \n","\n","    fit_time_marginal  stack_level  can_infer  fit_order  \n","0            0.939179            1       True         10  \n","1            0.939883            1       True          9  \n","2            1.382238            1       True         12  \n","3            2.344780            1       True          3  \n","4            1.260064            2       True         14  \n","5          114.864405            1       True          8  \n","6            0.945948            1       True          6  \n","7            1.040783            1       True          7  \n","8            1.627688            1       True         11  \n","9            1.172553            1       True          5  \n","10           0.011748            1       True          2  \n","11           0.012764            1       True          1  \n","12           0.809743            1       True          4  \n","13           2.142125            1       True         13  "],"text/html":["\n","  <div id=\"df-179aa20b-08c8-4c98-ae46-21fdb2ac733c\">\n","    <div class=\"colab-df-container\">\n","      <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>model</th>\n","      <th>score_test</th>\n","      <th>score_val</th>\n","      <th>pred_time_test</th>\n","      <th>pred_time_val</th>\n","      <th>fit_time</th>\n","      <th>pred_time_test_marginal</th>\n","      <th>pred_time_val_marginal</th>\n","      <th>fit_time_marginal</th>\n","      <th>stack_level</th>\n","      <th>can_infer</th>\n","      <th>fit_order</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>ExtraTreesEntr</td>\n","      <td>0.755126</td>\n","      <td>0.593492</td>\n","      <td>0.118852</td>\n","      <td>0.103904</td>\n","      <td>0.939179</td>\n","      <td>0.118852</td>\n","      <td>0.103904</td>\n","      <td>0.939179</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>10</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>ExtraTreesGini</td>\n","      <td>0.715484</td>\n","      <td>0.569206</td>\n","      <td>0.117150</td>\n","      <td>0.103895</td>\n","      <td>0.939883</td>\n","      <td>0.117150</td>\n","      <td>0.103895</td>\n","      <td>0.939883</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>9</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>NeuralNetTorch</td>\n","      <td>0.707026</td>\n","      <td>0.664444</td>\n","      <td>0.027500</td>\n","      <td>0.024018</td>\n","      <td>1.382238</td>\n","      <td>0.027500</td>\n","      <td>0.024018</td>\n","      <td>1.382238</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>12</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>NeuralNetFastAI</td>\n","      <td>0.694791</td>\n","      <td>0.787619</td>\n","      <td>0.035501</td>\n","      <td>0.023914</td>\n","      <td>2.344780</td>\n","      <td>0.035501</td>\n","      <td>0.023914</td>\n","      <td>2.344780</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>WeightedEnsemble_L2</td>\n","      <td>0.694791</td>\n","      <td>0.787619</td>\n","      <td>0.038520</td>\n","      <td>0.025053</td>\n","      <td>3.604844</td>\n","      <td>0.003019</td>\n","      <td>0.001138</td>\n","      <td>1.260064</td>\n","      <td>2</td>\n","      <td>True</td>\n","      <td>14</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>CatBoost</td>\n","      <td>0.693241</td>\n","      <td>0.596190</td>\n","      <td>0.019359</td>\n","      <td>0.006065</td>\n","      <td>114.864405</td>\n","      <td>0.019359</td>\n","      <td>0.006065</td>\n","      <td>114.864405</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>8</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>RandomForestGini</td>\n","      <td>0.686513</td>\n","      <td>0.561111</td>\n","      <td>0.114095</td>\n","      <td>0.103714</td>\n","      <td>0.945948</td>\n","      <td>0.114095</td>\n","      <td>0.103714</td>\n","      <td>0.945948</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>6</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>RandomForestEntr</td>\n","      <td>0.684244</td>\n","      <td>0.569206</td>\n","      <td>0.116164</td>\n","      <td>0.103907</td>\n","      <td>1.040783</td>\n","      <td>0.116164</td>\n","      <td>0.103907</td>\n","      <td>1.040783</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>7</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>XGBoost</td>\n","      <td>0.654521</td>\n","      <td>0.594286</td>\n","      <td>0.013825</td>\n","      <td>0.009635</td>\n","      <td>1.627688</td>\n","      <td>0.013825</td>\n","      <td>0.009635</td>\n","      <td>1.627688</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>11</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>LightGBM</td>\n","      <td>0.616306</td>\n","      <td>0.641111</td>\n","      <td>0.046805</td>\n","      <td>0.009925</td>\n","      <td>1.172553</td>\n","      <td>0.046805</td>\n","      <td>0.009925</td>\n","      <td>1.172553</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>KNeighborsDist</td>\n","      <td>0.595971</td>\n","      <td>0.458095</td>\n","      <td>0.107662</td>\n","      <td>0.104057</td>\n","      <td>0.011748</td>\n","      <td>0.107662</td>\n","      <td>0.104057</td>\n","      <td>0.011748</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>KNeighborsUnif</td>\n","      <td>0.531218</td>\n","      <td>0.386905</td>\n","      <td>0.103405</td>\n","      <td>0.104980</td>\n","      <td>0.012764</td>\n","      <td>0.103405</td>\n","      <td>0.104980</td>\n","      <td>0.012764</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>LightGBMXT</td>\n","      <td>0.518633</td>\n","      <td>0.633333</td>\n","      <td>0.019439</td>\n","      <td>0.003641</td>\n","      <td>0.809743</td>\n","      <td>0.019439</td>\n","      <td>0.003641</td>\n","      <td>0.809743</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>LightGBMLarge</td>\n","      <td>0.312739</td>\n","      <td>0.496667</td>\n","      <td>0.041023</td>\n","      <td>0.003707</td>\n","      <td>2.142125</td>\n","      <td>0.041023</td>\n","      <td>0.003707</td>\n","      <td>2.142125</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>13</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-179aa20b-08c8-4c98-ae46-21fdb2ac733c')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" 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Models will be saved in: \"AutogluonModels/ag-20220901_173415/\"\n","Level 25:autogluon.core.trainer.abstract_trainer:AutoGluon will gauge predictive performance using evaluation metric: 'f1_macro'\n"]},{"output_type":"stream","name":"stdout","text":["[17:36:52] WARNING: ../src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"]},{"output_type":"execute_result","data":{"text/plain":["                  model  score_test  score_val  pred_time_test  pred_time_val  \\\n","0        NeuralNetTorch    0.651522   0.713889        0.026926       0.023296   \n","1              CatBoost    0.629111   0.557381        0.020552       0.006005   \n","2       NeuralNetFastAI    0.628189   0.812143        0.036888       0.023832   \n","3      RandomForestGini    0.612912   0.586825        0.114572       0.107793   \n","4   WeightedEnsemble_L2    0.604365   0.819762        0.145206       0.128428   \n","5      RandomForestEntr    0.593444   0.601349        0.116369       0.103765   \n","6        ExtraTreesGini    0.592788   0.492302        0.116506       0.103730   \n","7        ExtraTreesEntr    0.592513   0.625952        0.217110       0.103669   \n","8            LightGBMXT    0.570266   0.649048        0.019843       0.007425   \n","9        KNeighborsUnif    0.557873   0.315000        0.103525       0.103453   \n","10             LightGBM    0.514111   0.617143        0.016244       0.005478   \n","11       KNeighborsDist    0.508663   0.462381        0.105764       0.103482   \n","12              XGBoost    0.493967   0.560952        0.009724       0.009504   \n","13        LightGBMLarge    0.458739   0.554286        0.017208       0.005125   \n","\n","      fit_time  pred_time_test_marginal  pred_time_val_marginal  \\\n","0     1.374417                 0.026926                0.023296   \n","1   148.635633                 0.020552                0.006005   \n","2     1.339181                 0.036888                0.023832   \n","3     0.945031                 0.114572                0.107793   \n","4     2.578304                 0.004793                0.001143   \n","5     1.037750                 0.116369                0.103765   \n","6     0.944165                 0.116506                0.103730   \n","7     0.952491                 0.217110                0.103669   \n","8     1.043048                 0.019843                0.007425   \n","9     0.008351                 0.103525                0.103453   \n","10    1.120204                 0.016244                0.005478   \n","11    0.006904                 0.105764                0.103482   \n","12    1.691779                 0.009724                0.009504   \n","13    2.347328                 0.017208                0.005125   \n","\n","    fit_time_marginal  stack_level  can_infer  fit_order  \n","0            1.374417            1       True         12  \n","1          148.635633            1       True          8  \n","2            1.339181            1       True          3  \n","3            0.945031            1       True          6  \n","4            1.230772            2       True         14  \n","5            1.037750            1       True          7  \n","6            0.944165            1       True          9  \n","7            0.952491            1       True         10  \n","8            1.043048            1       True          4  \n","9            0.008351            1       True          1  \n","10           1.120204            1       True          5  \n","11           0.006904            1       True          2  \n","12           1.691779            1       True         11  \n","13           2.347328            1       True         13  "],"text/html":["\n","  <div id=\"df-07347442-7b14-4ef5-bf57-41610cac89e4\">\n","    <div class=\"colab-df-container\">\n","      <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>model</th>\n","      <th>score_test</th>\n","      <th>score_val</th>\n","      <th>pred_time_test</th>\n","      <th>pred_time_val</th>\n","      <th>fit_time</th>\n","      <th>pred_time_test_marginal</th>\n","      <th>pred_time_val_marginal</th>\n","      <th>fit_time_marginal</th>\n","      <th>stack_level</th>\n","      <th>can_infer</th>\n","      <th>fit_order</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>NeuralNetTorch</td>\n","      <td>0.651522</td>\n","      <td>0.713889</td>\n","      <td>0.026926</td>\n","      <td>0.023296</td>\n","      <td>1.374417</td>\n","      <td>0.026926</td>\n","      <td>0.023296</td>\n","      <td>1.374417</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>12</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>CatBoost</td>\n","      <td>0.629111</td>\n","      <td>0.557381</td>\n","      <td>0.020552</td>\n","      <td>0.006005</td>\n","      <td>148.635633</td>\n","      <td>0.020552</td>\n","      <td>0.006005</td>\n","      <td>148.635633</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>8</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>NeuralNetFastAI</td>\n","      <td>0.628189</td>\n","      <td>0.812143</td>\n","      <td>0.036888</td>\n","      <td>0.023832</td>\n","      <td>1.339181</td>\n","      <td>0.036888</td>\n","      <td>0.023832</td>\n","      <td>1.339181</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>RandomForestGini</td>\n","      <td>0.612912</td>\n","      <td>0.586825</td>\n","      <td>0.114572</td>\n","      <td>0.107793</td>\n","      <td>0.945031</td>\n","      <td>0.114572</td>\n","      <td>0.107793</td>\n","      <td>0.945031</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>6</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>WeightedEnsemble_L2</td>\n","      <td>0.604365</td>\n","      <td>0.819762</td>\n","      <td>0.145206</td>\n","      <td>0.128428</td>\n","      <td>2.578304</td>\n","      <td>0.004793</td>\n","      <td>0.001143</td>\n","      <td>1.230772</td>\n","      <td>2</td>\n","      <td>True</td>\n","      <td>14</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>RandomForestEntr</td>\n","      <td>0.593444</td>\n","      <td>0.601349</td>\n","      <td>0.116369</td>\n","      <td>0.103765</td>\n","      <td>1.037750</td>\n","      <td>0.116369</td>\n","      <td>0.103765</td>\n","      <td>1.037750</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>7</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>ExtraTreesGini</td>\n","      <td>0.592788</td>\n","      <td>0.492302</td>\n","      <td>0.116506</td>\n","      <td>0.103730</td>\n","      <td>0.944165</td>\n","      <td>0.116506</td>\n","      <td>0.103730</td>\n","      <td>0.944165</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>9</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>ExtraTreesEntr</td>\n","      <td>0.592513</td>\n","      <td>0.625952</td>\n","      <td>0.217110</td>\n","      <td>0.103669</td>\n","      <td>0.952491</td>\n","      <td>0.217110</td>\n","      <td>0.103669</td>\n","      <td>0.952491</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>10</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>LightGBMXT</td>\n","      <td>0.570266</td>\n","      <td>0.649048</td>\n","      <td>0.019843</td>\n","      <td>0.007425</td>\n","      <td>1.043048</td>\n","      <td>0.019843</td>\n","      <td>0.007425</td>\n","      <td>1.043048</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>KNeighborsUnif</td>\n","      <td>0.557873</td>\n","      <td>0.315000</td>\n","      <td>0.103525</td>\n","      <td>0.103453</td>\n","      <td>0.008351</td>\n","      <td>0.103525</td>\n","      <td>0.103453</td>\n","      <td>0.008351</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>LightGBM</td>\n","      <td>0.514111</td>\n","      <td>0.617143</td>\n","      <td>0.016244</td>\n","      <td>0.005478</td>\n","      <td>1.120204</td>\n","      <td>0.016244</td>\n","      <td>0.005478</td>\n","      <td>1.120204</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>KNeighborsDist</td>\n","      <td>0.508663</td>\n","      <td>0.462381</td>\n","      <td>0.105764</td>\n","      <td>0.103482</td>\n","      <td>0.006904</td>\n","      <td>0.105764</td>\n","      <td>0.103482</td>\n","      <td>0.006904</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>XGBoost</td>\n","      <td>0.493967</td>\n","      <td>0.560952</td>\n","      <td>0.009724</td>\n","      <td>0.009504</td>\n","      <td>1.691779</td>\n","      <td>0.009724</td>\n","      <td>0.009504</td>\n","      <td>1.691779</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>11</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>LightGBMLarge</td>\n","      <td>0.458739</td>\n","      <td>0.554286</td>\n","      <td>0.017208</td>\n","      <td>0.005125</td>\n","      <td>2.347328</td>\n","      <td>0.017208</td>\n","      <td>0.005125</td>\n","      <td>2.347328</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>13</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-07347442-7b14-4ef5-bf57-41610cac89e4')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg 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Models will be saved in: \"AutogluonModels/ag-20220901_173700/\"\n","Level 25:autogluon.core.trainer.abstract_trainer:AutoGluon will gauge predictive performance using evaluation metric: 'f1_macro'\n"]},{"output_type":"stream","name":"stdout","text":["[17:38:19] WARNING: ../src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"]},{"output_type":"execute_result","data":{"text/plain":["                  model  score_test  score_val  pred_time_test  pred_time_val  \\\n","0       NeuralNetFastAI    0.753210   0.660173        0.038695       0.027844   \n","1   WeightedEnsemble_L2    0.721847   0.723095        0.070365       0.053926   \n","2        NeuralNetTorch    0.679930   0.694524        0.028154       0.024925   \n","3            LightGBMXT    0.631948   0.518968        0.019760       0.007728   \n","4        ExtraTreesEntr    0.623457   0.513968        0.117137       0.103784   \n","5              LightGBM    0.606995   0.432222        0.066783       0.007466   \n","6      RandomForestEntr    0.600016   0.412143        0.115842       0.104131   \n","7      RandomForestGini    0.588869   0.402381        0.116228       0.103729   \n","8              CatBoost    0.587206   0.537143        0.013580       0.004640   \n","9        ExtraTreesGini    0.553707   0.434286        0.117120       0.104049   \n","10              XGBoost    0.539025   0.451349        0.010951       0.007460   \n","11        LightGBMLarge    0.523510   0.468254        0.051597       0.005672   \n","12       KNeighborsDist    0.485801   0.462222        0.103570       0.103772   \n","13       KNeighborsUnif    0.467157   0.497143        0.103411       0.103981   \n","\n","     fit_time  pred_time_test_marginal  pred_time_val_marginal  \\\n","0    1.400502                 0.038695                0.027844   \n","1    3.664292                 0.003516                0.001157   \n","2    0.973423                 0.028154                0.024925   \n","3    1.041913                 0.019760                0.007728   \n","4    1.046594                 0.117137                0.103784   \n","5    1.342636                 0.066783                0.007466   \n","6    1.050684                 0.115842                0.104131   \n","7    0.939747                 0.116228                0.103729   \n","8   70.083525                 0.013580                0.004640   \n","9    0.955296                 0.117120                0.104049   \n","10   1.466841                 0.010951                0.007460   \n","11   2.440979                 0.051597                0.005672   \n","12   0.006842                 0.103570                0.103772   \n","13   0.008335                 0.103411                0.103981   \n","\n","    fit_time_marginal  stack_level  can_infer  fit_order  \n","0            1.400502            1       True          3  \n","1            1.290367            2       True         14  \n","2            0.973423            1       True         12  \n","3            1.041913            1       True          4  \n","4            1.046594            1       True         10  \n","5            1.342636            1       True          5  \n","6            1.050684            1       True          7  \n","7            0.939747            1       True          6  \n","8           70.083525            1       True          8  \n","9            0.955296            1       True          9  \n","10           1.466841            1       True         11  \n","11           2.440979            1       True         13  \n","12           0.006842            1       True          2  \n","13           0.008335            1       True          1  "],"text/html":["\n","  <div id=\"df-ebcbed2f-9d1e-46c1-a0b0-d9f30b504cf1\">\n","    <div class=\"colab-df-container\">\n","      <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>model</th>\n","      <th>score_test</th>\n","      <th>score_val</th>\n","      <th>pred_time_test</th>\n","      <th>pred_time_val</th>\n","      <th>fit_time</th>\n","      <th>pred_time_test_marginal</th>\n","      <th>pred_time_val_marginal</th>\n","      <th>fit_time_marginal</th>\n","      <th>stack_level</th>\n","      <th>can_infer</th>\n","      <th>fit_order</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>NeuralNetFastAI</td>\n","      <td>0.753210</td>\n","      <td>0.660173</td>\n","      <td>0.038695</td>\n","      <td>0.027844</td>\n","      <td>1.400502</td>\n","      <td>0.038695</td>\n","      <td>0.027844</td>\n","      <td>1.400502</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>WeightedEnsemble_L2</td>\n","      <td>0.721847</td>\n","      <td>0.723095</td>\n","      <td>0.070365</td>\n","      <td>0.053926</td>\n","      <td>3.664292</td>\n","      <td>0.003516</td>\n","      <td>0.001157</td>\n","      <td>1.290367</td>\n","      <td>2</td>\n","      <td>True</td>\n","      <td>14</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>NeuralNetTorch</td>\n","      <td>0.679930</td>\n","      <td>0.694524</td>\n","      <td>0.028154</td>\n","      <td>0.024925</td>\n","      <td>0.973423</td>\n","      <td>0.028154</td>\n","      <td>0.024925</td>\n","      <td>0.973423</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>12</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>LightGBMXT</td>\n","      <td>0.631948</td>\n","      <td>0.518968</td>\n","      <td>0.019760</td>\n","      <td>0.007728</td>\n","      <td>1.041913</td>\n","      <td>0.019760</td>\n","      <td>0.007728</td>\n","      <td>1.041913</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>ExtraTreesEntr</td>\n","      <td>0.623457</td>\n","      <td>0.513968</td>\n","      <td>0.117137</td>\n","      <td>0.103784</td>\n","      <td>1.046594</td>\n","      <td>0.117137</td>\n","      <td>0.103784</td>\n","      <td>1.046594</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>10</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>LightGBM</td>\n","      <td>0.606995</td>\n","      <td>0.432222</td>\n","      <td>0.066783</td>\n","      <td>0.007466</td>\n","      <td>1.342636</td>\n","      <td>0.066783</td>\n","      <td>0.007466</td>\n","      <td>1.342636</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>RandomForestEntr</td>\n","      <td>0.600016</td>\n","      <td>0.412143</td>\n","      <td>0.115842</td>\n","      <td>0.104131</td>\n","      <td>1.050684</td>\n","      <td>0.115842</td>\n","      <td>0.104131</td>\n","      <td>1.050684</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>7</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>RandomForestGini</td>\n","      <td>0.588869</td>\n","      <td>0.402381</td>\n","      <td>0.116228</td>\n","      <td>0.103729</td>\n","      <td>0.939747</td>\n","      <td>0.116228</td>\n","      <td>0.103729</td>\n","      <td>0.939747</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>6</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>CatBoost</td>\n","      <td>0.587206</td>\n","      <td>0.537143</td>\n","      <td>0.013580</td>\n","      <td>0.004640</td>\n","      <td>70.083525</td>\n","      <td>0.013580</td>\n","      <td>0.004640</td>\n","      <td>70.083525</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>8</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>ExtraTreesGini</td>\n","      <td>0.553707</td>\n","      <td>0.434286</td>\n","      <td>0.117120</td>\n","      <td>0.104049</td>\n","      <td>0.955296</td>\n","      <td>0.117120</td>\n","      <td>0.104049</td>\n","      <td>0.955296</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>9</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>XGBoost</td>\n","      <td>0.539025</td>\n","      <td>0.451349</td>\n","      <td>0.010951</td>\n","      <td>0.007460</td>\n","      <td>1.466841</td>\n","      <td>0.010951</td>\n","      <td>0.007460</td>\n","      <td>1.466841</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>11</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>LightGBMLarge</td>\n","      <td>0.523510</td>\n","      <td>0.468254</td>\n","      <td>0.051597</td>\n","      <td>0.005672</td>\n","      <td>2.440979</td>\n","      <td>0.051597</td>\n","      <td>0.005672</td>\n","      <td>2.440979</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>13</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>KNeighborsDist</td>\n","      <td>0.485801</td>\n","      <td>0.462222</td>\n","      <td>0.103570</td>\n","      <td>0.103772</td>\n","      <td>0.006842</td>\n","      <td>0.103570</td>\n","      <td>0.103772</td>\n","      <td>0.006842</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>KNeighborsUnif</td>\n","      <td>0.467157</td>\n","      <td>0.497143</td>\n","      <td>0.103411</td>\n","      <td>0.103981</td>\n","      <td>0.008335</td>\n","      <td>0.103411</td>\n","      <td>0.103981</td>\n","      <td>0.008335</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ebcbed2f-9d1e-46c1-a0b0-d9f30b504cf1')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       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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","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":157}]},{"cell_type":"code","source":["# sort models alphabetical\n","test_result1 = test_result1.sort_values('model')\n","test_result2 = test_result2.sort_values('model')\n","test_result3 = test_result3.sort_values('model')"],"metadata":{"id":"4J-QLQj_-0ei","executionInfo":{"status":"ok","timestamp":1662053906197,"user_tz":420,"elapsed":12,"user":{"displayName":"Cao Jinpu","userId":"11416302596004484254"}}},"execution_count":158,"outputs":[]},{"cell_type":"code","source":["test_result_all = pd.concat((test_result1, test_result2,test_result3))\n","test_result_all = test_result_all.groupby(['model']).mean()\n","test_result_all.sort_values(by=['score_test'], ascending=False)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":0},"id":"btN2ZjOS-nEB","executionInfo":{"status":"ok","timestamp":1662053906197,"user_tz":420,"elapsed":11,"user":{"displayName":"Cao Jinpu","userId":"11416302596004484254"}},"outputId":"f6658586-155a-45d5-ed45-30eec0738e49"},"execution_count":159,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                     score_test  score_val  pred_time_test  pred_time_val  \\\n","model                                                                       \n","NeuralNetFastAI        0.692063   0.753312        0.037028       0.025197   \n","NeuralNetTorch         0.679493   0.690952        0.027526       0.024080   \n","WeightedEnsemble_L2    0.673668   0.776825        0.084697       0.069135   \n","ExtraTreesEntr         0.657032   0.577804        0.151033       0.103786   \n","CatBoost               0.636519   0.563571        0.017831       0.005570   \n","RandomForestGini       0.629431   0.516772        0.114965       0.105079   \n","RandomForestEntr       0.625901   0.527566        0.116125       0.103934   \n","ExtraTreesGini         0.620660   0.498598        0.116925       0.103891   \n","LightGBM               0.579137   0.563492        0.043278       0.007623   \n","LightGBMXT             0.573616   0.600450        0.019681       0.006264   \n","XGBoost                0.562505   0.535529        0.011500       0.008866   \n","KNeighborsDist         0.530145   0.460899        0.105666       0.103770   \n","KNeighborsUnif         0.518749   0.399683        0.103447       0.104138   \n","LightGBMLarge          0.431663   0.506402        0.036609       0.004835   \n","\n","                       fit_time  pred_time_test_marginal  \\\n","model                                                      \n","NeuralNetFastAI        1.694821                 0.037028   \n","NeuralNetTorch         1.243359                 0.027526   \n","WeightedEnsemble_L2    3.282480                 0.003776   \n","ExtraTreesEntr         0.979422                 0.151033   \n","CatBoost             111.194521                 0.017831   \n","RandomForestGini       0.943576                 0.114965   \n","RandomForestEntr       1.043072                 0.116125   \n","ExtraTreesGini         0.946448                 0.116925   \n","LightGBM               1.211798                 0.043278   \n","LightGBMXT             0.964901                 0.019681   \n","XGBoost                1.595436                 0.011500   \n","KNeighborsDist         0.008498                 0.105666   \n","KNeighborsUnif         0.009817                 0.103447   \n","LightGBMLarge          2.310144                 0.036609   \n","\n","                     pred_time_val_marginal  fit_time_marginal  stack_level  \\\n","model                                                                         \n","NeuralNetFastAI                    0.025197           1.694821          1.0   \n","NeuralNetTorch                     0.024080           1.243359          1.0   \n","WeightedEnsemble_L2                0.001146           1.260401          2.0   \n","ExtraTreesEntr                     0.103786           0.979422          1.0   \n","CatBoost                           0.005570         111.194521          1.0   \n","RandomForestGini                   0.105079           0.943576          1.0   \n","RandomForestEntr                   0.103934           1.043072          1.0   \n","ExtraTreesGini                     0.103891           0.946448          1.0   \n","LightGBM                           0.007623           1.211798          1.0   \n","LightGBMXT                         0.006264           0.964901          1.0   \n","XGBoost                            0.008866           1.595436          1.0   \n","KNeighborsDist                     0.103770           0.008498          1.0   \n","KNeighborsUnif                     0.104138           0.009817          1.0   \n","LightGBMLarge                      0.004835           2.310144          1.0   \n","\n","                     can_infer  fit_order  \n","model                                      \n","NeuralNetFastAI            1.0        3.0  \n","NeuralNetTorch             1.0       12.0  \n","WeightedEnsemble_L2        1.0       14.0  \n","ExtraTreesEntr             1.0       10.0  \n","CatBoost                   1.0        8.0  \n","RandomForestGini           1.0        6.0  \n","RandomForestEntr           1.0        7.0  \n","ExtraTreesGini             1.0        9.0  \n","LightGBM                   1.0        5.0  \n","LightGBMXT                 1.0        4.0  \n","XGBoost                    1.0       11.0  \n","KNeighborsDist             1.0        2.0  \n","KNeighborsUnif             1.0        1.0  \n","LightGBMLarge              1.0       13.0  "],"text/html":["\n","  <div id=\"df-643331be-d33b-4d40-a7ab-bf40359bcc24\">\n","    <div class=\"colab-df-container\">\n","      <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>score_test</th>\n","      <th>score_val</th>\n","      <th>pred_time_test</th>\n","      <th>pred_time_val</th>\n","      <th>fit_time</th>\n","      <th>pred_time_test_marginal</th>\n","      <th>pred_time_val_marginal</th>\n","      <th>fit_time_marginal</th>\n","      <th>stack_level</th>\n","      <th>can_infer</th>\n","      <th>fit_order</th>\n","    </tr>\n","    <tr>\n","      <th>model</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>NeuralNetFastAI</th>\n","      <td>0.692063</td>\n","      <td>0.753312</td>\n","      <td>0.037028</td>\n","      <td>0.025197</td>\n","      <td>1.694821</td>\n","      <td>0.037028</td>\n","      <td>0.025197</td>\n","      <td>1.694821</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>3.0</td>\n","    </tr>\n","    <tr>\n","      <th>NeuralNetTorch</th>\n","      <td>0.679493</td>\n","      <td>0.690952</td>\n","      <td>0.027526</td>\n","      <td>0.024080</td>\n","      <td>1.243359</td>\n","      <td>0.027526</td>\n","      <td>0.024080</td>\n","      <td>1.243359</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>12.0</td>\n","    </tr>\n","    <tr>\n","      <th>WeightedEnsemble_L2</th>\n","      <td>0.673668</td>\n","      <td>0.776825</td>\n","      <td>0.084697</td>\n","      <td>0.069135</td>\n","      <td>3.282480</td>\n","      <td>0.003776</td>\n","      <td>0.001146</td>\n","      <td>1.260401</td>\n","      <td>2.0</td>\n","      <td>1.0</td>\n","      <td>14.0</td>\n","    </tr>\n","    <tr>\n","      <th>ExtraTreesEntr</th>\n","      <td>0.657032</td>\n","      <td>0.577804</td>\n","      <td>0.151033</td>\n","      <td>0.103786</td>\n","      <td>0.979422</td>\n","      <td>0.151033</td>\n","      <td>0.103786</td>\n","      <td>0.979422</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>10.0</td>\n","    </tr>\n","    <tr>\n","      <th>CatBoost</th>\n","      <td>0.636519</td>\n","      <td>0.563571</td>\n","      <td>0.017831</td>\n","      <td>0.005570</td>\n","      <td>111.194521</td>\n","      <td>0.017831</td>\n","      <td>0.005570</td>\n","      <td>111.194521</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>8.0</td>\n","    </tr>\n","    <tr>\n","      <th>RandomForestGini</th>\n","      <td>0.629431</td>\n","      <td>0.516772</td>\n","      <td>0.114965</td>\n","      <td>0.105079</td>\n","      <td>0.943576</td>\n","      <td>0.114965</td>\n","      <td>0.105079</td>\n","      <td>0.943576</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>6.0</td>\n","    </tr>\n","    <tr>\n","      <th>RandomForestEntr</th>\n","      <td>0.625901</td>\n","      <td>0.527566</td>\n","      <td>0.116125</td>\n","      <td>0.103934</td>\n","      <td>1.043072</td>\n","      <td>0.116125</td>\n","      <td>0.103934</td>\n","      <td>1.043072</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>7.0</td>\n","    </tr>\n","    <tr>\n","      <th>ExtraTreesGini</th>\n","      <td>0.620660</td>\n","      <td>0.498598</td>\n","      <td>0.116925</td>\n","      <td>0.103891</td>\n","      <td>0.946448</td>\n","      <td>0.116925</td>\n","      <td>0.103891</td>\n","      <td>0.946448</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>9.0</td>\n","    </tr>\n","    <tr>\n","      <th>LightGBM</th>\n","      <td>0.579137</td>\n","      <td>0.563492</td>\n","      <td>0.043278</td>\n","      <td>0.007623</td>\n","      <td>1.211798</td>\n","      <td>0.043278</td>\n","      <td>0.007623</td>\n","      <td>1.211798</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>5.0</td>\n","    </tr>\n","    <tr>\n","      <th>LightGBMXT</th>\n","      <td>0.573616</td>\n","      <td>0.600450</td>\n","      <td>0.019681</td>\n","      <td>0.006264</td>\n","      <td>0.964901</td>\n","      <td>0.019681</td>\n","      <td>0.006264</td>\n","      <td>0.964901</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>4.0</td>\n","    </tr>\n","    <tr>\n","      <th>XGBoost</th>\n","      <td>0.562505</td>\n","      <td>0.535529</td>\n","      <td>0.011500</td>\n","      <td>0.008866</td>\n","      <td>1.595436</td>\n","      <td>0.011500</td>\n","      <td>0.008866</td>\n","      <td>1.595436</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>11.0</td>\n","    </tr>\n","    <tr>\n","      <th>KNeighborsDist</th>\n","      <td>0.530145</td>\n","      <td>0.460899</td>\n","      <td>0.105666</td>\n","      <td>0.103770</td>\n","      <td>0.008498</td>\n","      <td>0.105666</td>\n","      <td>0.103770</td>\n","      <td>0.008498</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>2.0</td>\n","    </tr>\n","    <tr>\n","      <th>KNeighborsUnif</th>\n","      <td>0.518749</td>\n","      <td>0.399683</td>\n","      <td>0.103447</td>\n","      <td>0.104138</td>\n","      <td>0.009817</td>\n","      <td>0.103447</td>\n","      <td>0.104138</td>\n","      <td>0.009817</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>LightGBMLarge</th>\n","      <td>0.431663</td>\n","      <td>0.506402</td>\n","      <td>0.036609</td>\n","      <td>0.004835</td>\n","      <td>2.310144</td>\n","      <td>0.036609</td>\n","      <td>0.004835</td>\n","      <td>2.310144</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>13.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-643331be-d33b-4d40-a7ab-bf40359bcc24')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 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Models will be saved in: \"AutogluonModels/ag-20220811_000149/\"\n","Level 25:autogluon.core.trainer.abstract_trainer:AutoGluon will gauge predictive performance using evaluation metric: 'accuracy'\n"]}]},{"cell_type":"code","source":["# 测试训练数据\n","predictor.leaderboard(df_test, silent=True)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":552},"id":"n5mXpW3WC_u-","executionInfo":{"status":"ok","timestamp":1660176157188,"user_tz":420,"elapsed":1400,"user":{"displayName":"Cao Jinpu","userId":"11416302596004484254"}},"outputId":"ab5dbb23-3284-4e24-fb6c-5b52859ffc9f"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                  model  score_test  score_val  pred_time_test  pred_time_val  \\\n","0   WeightedEnsemble_L2    0.970100   0.914286        0.236515       0.110237   \n","1        ExtraTreesEntr    0.963455   0.895238        0.218377       0.102695   \n","2              LightGBM    0.960133   0.876190        0.059111       0.011930   \n","3              CatBoost    0.956811   0.876190        0.005124       0.003579   \n","4        NeuralNetTorch    0.956811   0.876190        0.010516       0.005255   \n","5               XGBoost    0.956811   0.876190        0.014203       0.007023   \n","6            LightGBMXT    0.956811   0.866667        0.068116       0.012377   \n","7         LightGBMLarge    0.956811   0.866667        0.118110       0.017116   \n","8      RandomForestGini    0.953488   0.866667        0.216934       0.102543   \n","9        ExtraTreesGini    0.950166   0.857143        0.223722       0.102630   \n","10     RandomForestEntr    0.946844   0.847619        0.128788       0.102636   \n","11      NeuralNetFastAI    0.943522   0.857143        0.031110       0.019327   \n","12       KNeighborsDist    0.916944   0.752381        0.109229       0.106175   \n","13       KNeighborsUnif    0.534884   0.514286        0.106212       0.103776   \n","\n","     fit_time  pred_time_test_marginal  pred_time_val_marginal  \\\n","0    2.974036                 0.003935                0.000520   \n","1    0.930031                 0.218377                0.102695   \n","2    1.913142                 0.059111                0.011930   \n","3   24.038169                 0.005124                0.003579   \n","4    2.180157                 0.010516                0.005255   \n","5    1.702427                 0.014203                0.007023   \n","6    2.440791                 0.068116                0.012377   \n","7    7.282402                 0.118110                0.017116   \n","8    0.923740                 0.216934                0.102543   \n","9    0.934250                 0.223722                0.102630   \n","10   1.225385                 0.128788                0.102636   \n","11   0.666510                 0.031110                0.019327   \n","12   0.008056                 0.109229                0.106175   \n","13   0.008478                 0.106212                0.103776   \n","\n","    fit_time_marginal  stack_level  can_infer  fit_order  \n","0            0.341578            2       True         14  \n","1            0.930031            1       True         10  \n","2            1.913142            1       True          5  \n","3           24.038169            1       True          8  \n","4            2.180157            1       True         12  \n","5            1.702427            1       True         11  \n","6            2.440791            1       True          4  \n","7            7.282402            1       True         13  \n","8            0.923740            1       True          6  \n","9            0.934250            1       True          9  \n","10           1.225385            1       True          7  \n","11           0.666510            1       True          3  \n","12           0.008056            1       True          2  \n","13           0.008478            1       True          1  "],"text/html":["\n","  <div id=\"df-c5adcf68-03b2-465d-95c1-cb01dcc94e2c\">\n","    <div class=\"colab-df-container\">\n","      <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>model</th>\n","      <th>score_test</th>\n","      <th>score_val</th>\n","      <th>pred_time_test</th>\n","      <th>pred_time_val</th>\n","      <th>fit_time</th>\n","      <th>pred_time_test_marginal</th>\n","      <th>pred_time_val_marginal</th>\n","      <th>fit_time_marginal</th>\n","      <th>stack_level</th>\n","      <th>can_infer</th>\n","      <th>fit_order</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>WeightedEnsemble_L2</td>\n","      <td>0.970100</td>\n","      <td>0.914286</td>\n","      <td>0.236515</td>\n","      <td>0.110237</td>\n","      <td>2.974036</td>\n","      <td>0.003935</td>\n","      <td>0.000520</td>\n","      <td>0.341578</td>\n","      <td>2</td>\n","      <td>True</td>\n","      <td>14</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>ExtraTreesEntr</td>\n","      <td>0.963455</td>\n","      <td>0.895238</td>\n","      <td>0.218377</td>\n","      <td>0.102695</td>\n","      <td>0.930031</td>\n","      <td>0.218377</td>\n","      <td>0.102695</td>\n","      <td>0.930031</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>10</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>LightGBM</td>\n","      <td>0.960133</td>\n","      <td>0.876190</td>\n","      <td>0.059111</td>\n","      <td>0.011930</td>\n","      <td>1.913142</td>\n","      <td>0.059111</td>\n","      <td>0.011930</td>\n","      <td>1.913142</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>CatBoost</td>\n","      <td>0.956811</td>\n","      <td>0.876190</td>\n","      <td>0.005124</td>\n","      <td>0.003579</td>\n","      <td>24.038169</td>\n","      <td>0.005124</td>\n","      <td>0.003579</td>\n","      <td>24.038169</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>8</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>NeuralNetTorch</td>\n","      <td>0.956811</td>\n","      <td>0.876190</td>\n","      <td>0.010516</td>\n","      <td>0.005255</td>\n","      <td>2.180157</td>\n","      <td>0.010516</td>\n","      <td>0.005255</td>\n","      <td>2.180157</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>12</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>XGBoost</td>\n","      <td>0.956811</td>\n","      <td>0.876190</td>\n","      <td>0.014203</td>\n","      <td>0.007023</td>\n","      <td>1.702427</td>\n","      <td>0.014203</td>\n","      <td>0.007023</td>\n","      <td>1.702427</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>11</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>LightGBMXT</td>\n","      <td>0.956811</td>\n","      <td>0.866667</td>\n","      <td>0.068116</td>\n","      <td>0.012377</td>\n","      <td>2.440791</td>\n","      <td>0.068116</td>\n","      <td>0.012377</td>\n","      <td>2.440791</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>LightGBMLarge</td>\n","      <td>0.956811</td>\n","      <td>0.866667</td>\n","      <td>0.118110</td>\n","      <td>0.017116</td>\n","      <td>7.282402</td>\n","      <td>0.118110</td>\n","      <td>0.017116</td>\n","      <td>7.282402</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>13</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>RandomForestGini</td>\n","      <td>0.953488</td>\n","      <td>0.866667</td>\n","      <td>0.216934</td>\n","      <td>0.102543</td>\n","      <td>0.923740</td>\n","      <td>0.216934</td>\n","      <td>0.102543</td>\n","      <td>0.923740</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>6</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>ExtraTreesGini</td>\n","      <td>0.950166</td>\n","      <td>0.857143</td>\n","      <td>0.223722</td>\n","      <td>0.102630</td>\n","      <td>0.934250</td>\n","      <td>0.223722</td>\n","      <td>0.102630</td>\n","      <td>0.934250</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>9</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>RandomForestEntr</td>\n","      <td>0.946844</td>\n","      <td>0.847619</td>\n","      <td>0.128788</td>\n","      <td>0.102636</td>\n","      <td>1.225385</td>\n","      <td>0.128788</td>\n","      <td>0.102636</td>\n","      <td>1.225385</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>7</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>NeuralNetFastAI</td>\n","      <td>0.943522</td>\n","      <td>0.857143</td>\n","      <td>0.031110</td>\n","      <td>0.019327</td>\n","      <td>0.666510</td>\n","      <td>0.031110</td>\n","      <td>0.019327</td>\n","      <td>0.666510</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>KNeighborsDist</td>\n","      <td>0.916944</td>\n","      <td>0.752381</td>\n","      <td>0.109229</td>\n","      <td>0.106175</td>\n","      <td>0.008056</td>\n","      <td>0.109229</td>\n","      <td>0.106175</td>\n","      <td>0.008056</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>KNeighborsUnif</td>\n","      <td>0.534884</td>\n","      <td>0.514286</td>\n","      <td>0.106212</td>\n","      <td>0.103776</td>\n","      <td>0.008478</td>\n","      <td>0.106212</td>\n","      <td>0.103776</td>\n","      <td>0.008478</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c5adcf68-03b2-465d-95c1-cb01dcc94e2c')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 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Jinpu","userId":"11416302596004484254"}},"outputId":"798410c7-dbc7-47bc-9d53-8d09c2712a15"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["         1         2         3         4         5         6         7    \\\n","0                                                                          \n","1   0.128543  0.128437  0.128335  0.128242  0.128168  0.128117  0.128095   \n","2   0.122109  0.122045  0.121987  0.121941  0.121909  0.121890  0.121884   \n","3   0.134325  0.134297  0.134276  0.134268  0.134277  0.134302  0.134341   \n","4   0.147585  0.147581  0.147577  0.147570  0.147565  0.147568  0.147582   \n","5   0.118670  0.118687  0.118702  0.118716  0.118735  0.118765  0.118813   \n","..       ...       ...       ...       ...       ...       ...       ...   \n","72  0.127848  0.127808  0.127785  0.127797  0.127838  0.127899  0.127964   \n","73  0.131379  0.131342  0.131304  0.131269  0.131246  0.131246  0.131276   \n","74  0.130476  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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","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":62}]},{"cell_type":"code","source":["X_submit = df_submit\n","# 主成分降维\n","comp_X_submit = pca.transform(X_submit)\n","print('comp_X_submit.shape:', comp_X_submit.shape)\n","\n","# 转化为dataframe格式，以供预测\n","columns = [i for i in range(comp_X_submit.shape[1])] \n","comp_X_submit_df = pd.DataFrame(data = comp_X_submit, columns=columns)\n","comp_X_submit_df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":679},"id":"nVF44zgsEH5I","executionInfo":{"status":"ok","timestamp":1660176157402,"user_tz":420,"elapsed":215,"user":{"displayName":"Cao Jinpu","userId":"11416302596004484254"}},"outputId":"32a324aa-ddd6-48d2-fd14-7923f44dc25a"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["comp_X_submit.shape: (76, 30)\n"]},{"output_type":"execute_result","data":{"text/plain":["          0         1         2         3         4         5         6   \\\n","0   0.175082 -0.146153  0.035632  0.021096  0.027883 -0.007445 -0.000693   \n","1   0.152232 -0.255989  0.031277  0.048899 -0.014380 -0.004634 -0.006077   \n","2  -0.058556  0.217477 -0.038830  0.014121  0.003520 -0.004664 -0.003018   \n","3   0.290734  0.397307 -0.031447  0.012655  0.013854 -0.003125 -0.001606   \n","4  -0.195989 -0.029469 -0.056392 -0.006877  0.003874 -0.007249  0.001571   \n","..       ...       ...       ...       ...       ...       ...       ...   \n","71  0.301612  0.038319 -0.050459 -0.019406 -0.013843  0.004146  0.000340   \n","72  0.004095  0.107762 -0.021578 -0.010109 -0.004354  0.006813 -0.000120   \n","73  0.185800  0.079107 -0.025451 -0.021266 -0.008537 -0.000157 -0.002816   \n","74  0.416043 -0.052026 -0.009459 -0.000678 -0.027302  0.000557  0.000815   \n","75  0.827611  0.022046  0.006677 -0.008546  0.026838  0.010054  0.001102   \n","\n","          7         8         9   ...            20        21        22  \\\n","0  -0.002013 -0.002765  0.000467  ... -6.800040e-05 -0.000193  0.000108   \n","1   0.001551 -0.001293  0.000430  ... -1.998098e-04 -0.000005  0.000103   \n","2   0.003804 -0.002605 -0.001931  ... -3.185941e-04  0.000184 -0.000106   \n","3   0.005386 -0.001695 -0.002036  ... -2.567786e-04  0.000024 -0.000035   \n","4   0.001458 -0.000960 -0.000666  ...  1.992224e-04  0.000025 -0.000222   \n","..       ...       ...       ...  ...           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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","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":63}]},{"cell_type":"code","source":["# 使用自动优化后确定的模型ExtraTreesEntr来作最终预测\n","y_pred_submit = predictor.predict(comp_X_submit_df, model='ExtraTreesEntr')\n","y_pred_submit"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"LfnnbHZCDxAB","executionInfo":{"status":"ok","timestamp":1660176157564,"user_tz":420,"elapsed":164,"user":{"displayName":"Cao Jinpu","userId":"11416302596004484254"}},"outputId":"903d4017-7e6d-4df3-f590-8fc2e85eadb7"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0     5.0\n","1     4.0\n","2     2.0\n","3     3.0\n","4     4.0\n","     ... \n","71    1.0\n","72    1.0\n","73    1.0\n","74    1.0\n","75    2.0\n","Name: class, Length: 76, dtype: float64"]},"metadata":{},"execution_count":64}]},{"cell_type":"code","source":["# 使用['A', 'B', 'C', ...] 代替 [0,1,2,...]\n","categories = ['A', 'B', 'C', 'D', 'E', 'F', 'G']\n","for i, cat in enumerate(categories):\n","    y_pred_submit = y_pred_submit.replace(i, cat)\n","y_pred_submit"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xoTW101LHQlN","executionInfo":{"status":"ok","timestamp":1660176157565,"user_tz":420,"elapsed":6,"user":{"displayName":"Cao Jinpu","userId":"11416302596004484254"}},"outputId":"fc9d5e69-8ed4-4ac8-9fa4-c2d6eff730b0"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0     F\n","1     E\n","2     C\n","3     D\n","4     E\n","     ..\n","71    B\n","72    B\n","73    B\n","74    B\n","75    C\n","Name: class, Length: 76, dtype: object"]},"metadata":{},"execution_count":65}]},{"cell_type":"code","source":["cat_count = [sum(y_pred_submit==ele) for ele in categories]\n","# 绘制测试类别分布图\n","print('categories:', categories)\n","print('cat_count:', cat_count)\n","print('sum:', sum(cat_count))\n","plt.bar(categories, cat_count)\n","plt.ylabel('Count')\n","plt.xlabel('Category')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":354},"id":"LAGJXiDpGsQ7","executionInfo":{"status":"ok","timestamp":1660176157757,"user_tz":420,"elapsed":196,"user":{"displayName":"Cao Jinpu","userId":"11416302596004484254"}},"outputId":"d3e7e82c-bc60-491f-895c-c796db96014f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["categories: ['A', 'B', 'C', 'D', 'E', 'F', 'G']\n","cat_count: [7, 6, 13, 3, 9, 20, 18]\n","sum: 76\n"]},{"output_type":"execute_result","data":{"text/plain":["Text(0.5, 0, 'Category')"]},"metadata":{},"execution_count":66},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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保存预测结果\n","y_pred_submit = pd.DataFrame(y_pred_submit)\n","y_pred_submit.index.name = '预测集样本序号'\n","y_pred_submit.index = y_pred_submit.index+1\n","y_pred_submit.columns = ['类别预测值']\n","y_pred_submit"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":455},"id":"szIIvNBtH315","executionInfo":{"status":"ok","timestamp":1660176157757,"user_tz":420,"elapsed":4,"user":{"displayName":"Cao Jinpu","userId":"11416302596004484254"}},"outputId":"0391d41e-9765-4074-f9e8-ce80ecbe5252"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["        类别预测值\n","预测集样本序号      \n","1           F\n","2           E\n","3           C\n","4           D\n","5           E\n","...       ...\n","72          B\n","73          B\n","74          B\n","75          B\n","76          C\n","\n","[76 rows x 1 columns]"],"text/html":["\n","  <div id=\"df-5d0cd110-7f23-4e97-9f66-9a6bac05dbdf\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style 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<td>B</td>\n","    </tr>\n","    <tr>\n","      <th>73</th>\n","      <td>B</td>\n","    </tr>\n","    <tr>\n","      <th>74</th>\n","      <td>B</td>\n","    </tr>\n","    <tr>\n","      <th>75</th>\n","      <td>B</td>\n","    </tr>\n","    <tr>\n","      <th>76</th>\n","      <td>C</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>76 rows × 1 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5d0cd110-7f23-4e97-9f66-9a6bac05dbdf')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 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