added onehot encoding for factors and scaling for numeric columns
mahehsma
parent
8e80949a38
commit
e2b6e45cc6
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@ -2,7 +2,7 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 2,
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"id": "initial_id",
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"metadata": {
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"jupyter": {
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@ -11,12 +11,13 @@
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},
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"outputs": [],
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"source": [
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"import pandas as pd"
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"import pandas as pd\n",
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"from sklearn.preprocessing import MinMaxScaler, StandardScaler"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"execution_count": 14,
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"id": "67503952-9074-4cdb-9d7e-d9142f7c319c",
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"metadata": {},
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"outputs": [
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@ -42,128 +43,180 @@
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>age</th>\n",
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" <th>sex</th>\n",
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" <th>cp</th>\n",
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" <th>trestbps</th>\n",
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" <th>chol</th>\n",
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" <th>fbs</th>\n",
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" <th>restecg</th>\n",
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" <th>thalach</th>\n",
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" <th>exang</th>\n",
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" <th>oldpeak</th>\n",
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" <th>slope</th>\n",
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" <th>ca</th>\n",
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" <th>thal</th>\n",
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" <th>goal</th>\n",
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" <th>sex_0</th>\n",
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" <th>sex_1</th>\n",
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" <th>cp_1</th>\n",
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" <th>cp_2</th>\n",
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" <th>cp_3</th>\n",
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" <th>...</th>\n",
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" <th>slope_1</th>\n",
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" <th>slope_2</th>\n",
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" <th>slope_3</th>\n",
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" <th>thal_3.0</th>\n",
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" <th>thal_6.0</th>\n",
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" <th>thal_7.0</th>\n",
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" <th>ca_0.0</th>\n",
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" <th>ca_1.0</th>\n",
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" <th>ca_2.0</th>\n",
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" <th>ca_3.0</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>63</td>\n",
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" <td>1</td>\n",
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" <td>1</td>\n",
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" <td>145</td>\n",
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" <td>233</td>\n",
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" <td>1</td>\n",
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" <td>2</td>\n",
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" <td>150</td>\n",
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" <td>0</td>\n",
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" <td>2.3</td>\n",
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" <td>3</td>\n",
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" <td>0.0</td>\n",
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" <td>6.0</td>\n",
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" <td>0</td>\n",
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" <td>0.708333</td>\n",
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" <td>0.481132</td>\n",
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" <td>0.244292</td>\n",
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" <td>0.603053</td>\n",
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" <td>0.370968</td>\n",
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" <td>False</td>\n",
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" <td>True</td>\n",
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" <td>True</td>\n",
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" <td>False</td>\n",
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" <td>False</td>\n",
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" <td>...</td>\n",
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" <td>False</td>\n",
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" <td>False</td>\n",
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" <td>True</td>\n",
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" <td>False</td>\n",
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" <td>True</td>\n",
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" <td>False</td>\n",
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" <td>True</td>\n",
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" <td>False</td>\n",
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" <td>False</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>67</td>\n",
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" <td>1</td>\n",
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" <td>4</td>\n",
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" <td>160</td>\n",
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" <td>286</td>\n",
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" <td>0</td>\n",
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" <td>2</td>\n",
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" <td>108</td>\n",
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" <td>1</td>\n",
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" <td>1.5</td>\n",
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" <td>2</td>\n",
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" <td>3.0</td>\n",
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" <td>3.0</td>\n",
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" <td>2</td>\n",
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" <td>0.791667</td>\n",
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" <td>0.622642</td>\n",
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" <td>0.365297</td>\n",
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" <td>0.282443</td>\n",
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" <td>0.241935</td>\n",
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" <td>False</td>\n",
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" <td>True</td>\n",
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" <td>False</td>\n",
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" <td>False</td>\n",
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" <td>False</td>\n",
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" <td>...</td>\n",
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" <td>False</td>\n",
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" <td>True</td>\n",
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" <td>False</td>\n",
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" <td>True</td>\n",
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" <td>False</td>\n",
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" <td>False</td>\n",
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" <td>False</td>\n",
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" <td>False</td>\n",
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" <td>False</td>\n",
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" <td>True</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>67</td>\n",
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" <td>1</td>\n",
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" <td>4</td>\n",
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" <td>120</td>\n",
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" <td>229</td>\n",
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" <td>0</td>\n",
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" <td>2</td>\n",
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" <td>129</td>\n",
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" <td>1</td>\n",
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" <td>2.6</td>\n",
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" <td>2</td>\n",
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" <td>2.0</td>\n",
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" <td>7.0</td>\n",
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" <td>1</td>\n",
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" <td>0.791667</td>\n",
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" <td>0.245283</td>\n",
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" <td>0.235160</td>\n",
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" <td>0.442748</td>\n",
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" <td>0.419355</td>\n",
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" <td>False</td>\n",
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" <td>True</td>\n",
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" <td>False</td>\n",
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" <td>False</td>\n",
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" <td>False</td>\n",
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" <td>...</td>\n",
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" <td>False</td>\n",
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" <td>True</td>\n",
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" <td>False</td>\n",
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" <td>False</td>\n",
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" <td>False</td>\n",
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" <td>True</td>\n",
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" <td>False</td>\n",
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" <td>False</td>\n",
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" <td>True</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>37</td>\n",
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" <td>1</td>\n",
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" <td>3</td>\n",
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" <td>130</td>\n",
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" <td>250</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>187</td>\n",
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" <td>0</td>\n",
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" <td>3.5</td>\n",
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" <td>3</td>\n",
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" <td>0.0</td>\n",
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" <td>3.0</td>\n",
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" <td>0</td>\n",
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" <td>0.166667</td>\n",
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" <td>0.339623</td>\n",
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" <td>0.283105</td>\n",
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" <td>0.885496</td>\n",
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" <td>0.564516</td>\n",
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" <td>False</td>\n",
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" <td>True</td>\n",
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" <td>False</td>\n",
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" <td>False</td>\n",
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" <td>True</td>\n",
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" <td>...</td>\n",
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" <td>False</td>\n",
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" <td>False</td>\n",
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" <td>True</td>\n",
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" <td>True</td>\n",
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" <td>False</td>\n",
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" <td>False</td>\n",
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" <td>True</td>\n",
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" <td>False</td>\n",
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" <td>False</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>41</td>\n",
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" <td>0</td>\n",
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" <td>2</td>\n",
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" <td>130</td>\n",
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" <td>204</td>\n",
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" <td>0</td>\n",
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" <td>2</td>\n",
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" <td>172</td>\n",
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" <td>0</td>\n",
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" <td>1.4</td>\n",
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" <td>1</td>\n",
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" <td>0.0</td>\n",
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" <td>3.0</td>\n",
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" <td>0</td>\n",
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" <td>0.250000</td>\n",
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" <td>0.339623</td>\n",
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" <td>0.178082</td>\n",
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" <td>0.770992</td>\n",
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" <td>0.225806</td>\n",
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" <td>True</td>\n",
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" <td>False</td>\n",
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" <td>False</td>\n",
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" <td>True</td>\n",
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" <td>False</td>\n",
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" <td>...</td>\n",
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" <td>True</td>\n",
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" <td>False</td>\n",
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" <td>False</td>\n",
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" <td>True</td>\n",
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" <td>False</td>\n",
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" <td>False</td>\n",
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" <td>True</td>\n",
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" <td>False</td>\n",
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" <td>False</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"<p>5 rows × 28 columns</p>\n",
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"</div>"
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],
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"text/plain": [
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" age sex cp trestbps chol fbs restecg thalach exang oldpeak slope \\\n",
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"0 63 1 1 145 233 1 2 150 0 2.3 3 \n",
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"1 67 1 4 160 286 0 2 108 1 1.5 2 \n",
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"2 67 1 4 120 229 0 2 129 1 2.6 2 \n",
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"3 37 1 3 130 250 0 0 187 0 3.5 3 \n",
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"4 41 0 2 130 204 0 2 172 0 1.4 1 \n",
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" age trestbps chol thalach oldpeak sex_0 sex_1 cp_1 \\\n",
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"0 0.708333 0.481132 0.244292 0.603053 0.370968 False True True \n",
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"1 0.791667 0.622642 0.365297 0.282443 0.241935 False True False \n",
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"2 0.791667 0.245283 0.235160 0.442748 0.419355 False True False \n",
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"3 0.166667 0.339623 0.283105 0.885496 0.564516 False True False \n",
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"4 0.250000 0.339623 0.178082 0.770992 0.225806 True False False \n",
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"\n",
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" ca thal goal \n",
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"0 0.0 6.0 0 \n",
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"1 3.0 3.0 2 \n",
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"2 2.0 7.0 1 \n",
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"3 0.0 3.0 0 \n",
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"4 0.0 3.0 0 "
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" cp_2 cp_3 ... slope_1 slope_2 slope_3 thal_3.0 thal_6.0 thal_7.0 \\\n",
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"0 False False ... False False True False True False \n",
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"1 False False ... False True False True False False \n",
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"2 False False ... False True False False False True \n",
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"3 False True ... False False True True False False \n",
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"4 True False ... True False False True False False \n",
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"\n",
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" ca_0.0 ca_1.0 ca_2.0 ca_3.0 \n",
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"0 True False False False \n",
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"1 False False False True \n",
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"2 False False True False \n",
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"3 True False False False \n",
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"4 True False False False \n",
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"\n",
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"[5 rows x 28 columns]"
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]
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},
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"execution_count": 25,
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"execution_count": 14,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -171,31 +224,7 @@
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"source": [
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"df = pd.read_csv('./data/dataset_cleaned.csv')\n",
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"df.dropna(inplace=True)\n",
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"df.head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 60,
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"id": "8fa945ef-34d4-4e4c-a1cd-f1e1e6da79e7",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[0],\n",
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" [1],\n",
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" [1],\n",
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" [0],\n",
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" [0]], dtype=int64)"
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]
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},
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"execution_count": 60,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"\n",
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"# extract all columns except 'goal' --> X\n",
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"X = df.loc[:, df.columns != 'goal']\n",
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"# extract only the column 'goal' --> y\n",
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@ -206,12 +235,24 @@
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"y = y.reshape((len(y),1))\n",
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"\n",
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"# binarize y\n",
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"y[y>0] = 1"
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"y[y>0] = 1\n",
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"\n",
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"factor_columns = ['sex', 'cp', 'fbs', 'restecg', 'exang', 'slope', 'thal', 'ca']\n",
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"numeric_columns = [column for column in X.columns if column not in factor_columns]\n",
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"\n",
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"# transform factors into onehot vectors\n",
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"X = pd.get_dummies(X, columns=factor_columns)\n",
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"\n",
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"# min max scaling of numeric columns\n",
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"scaler = MinMaxScaler()\n",
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"X[numeric_columns] = scaler.fit_transform(X[numeric_columns])\n",
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"\n",
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"X.head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 91,
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"execution_count": 18,
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"id": "2bbee865-c000-43da-84d9-ce7e04874110",
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"metadata": {},
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"outputs": [],
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@ -221,7 +262,6 @@
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" tf.keras.layers.InputLayer(shape=(n_features,)),\n",
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" tf.keras.layers.Dense(30, activation='relu'),\n",
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" tf.keras.layers.Dense(30, activation='relu'),\n",
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" tf.keras.layers.Dense(30, activation='relu'),\n",
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" tf.keras.layers.Dense(1, activation='sigmoid')\n",
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" ], name='test')\n",
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" model.compile(optimizer=tf.keras.optimizers.Adam(), \n",
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@ -231,7 +271,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 97,
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"execution_count": 20,
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"id": "38eb4f87-ca3c-4ecf-a8ca-29422822d933",
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"metadata": {},
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"outputs": [
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@ -239,27 +279,47 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Training fold 0 for 5 epochs\n",
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"Train samples:\t237\n",
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"Test samples:\t60\n",
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"Accuracy of fold 0: 0.6166666666666667\n",
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"Training fold 1 for 5 epochs\n",
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"Train samples:\t237\n",
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"Test samples:\t60\n",
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"Accuracy of fold 1: 0.75\n",
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"Training fold 2 for 5 epochs\n",
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"Train samples:\t238\n",
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"Test samples:\t59\n",
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"Accuracy of fold 2: 0.6949152542372882\n",
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"Training fold 3 for 5 epochs\n",
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"Train samples:\t238\n",
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"Test samples:\t59\n",
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"Accuracy of fold 3: 0.7457627118644068\n",
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"Training fold 4 for 5 epochs\n",
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"Train samples:\t238\n",
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"Test samples:\t59\n",
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"Accuracy of fold 4: 0.6610169491525424\n",
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"Avg accuracy 0.6936723163841808\n"
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"Training fold 0 for 20 epochs\n",
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"Train samples:\t267\n",
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"Test samples:\t30\n",
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"Accuracy of fold 0: 0.8666666666666667\n",
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"Training fold 1 for 20 epochs\n",
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"Train samples:\t267\n",
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"Test samples:\t30\n",
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"Accuracy of fold 1: 0.8666666666666667\n",
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"Training fold 2 for 20 epochs\n",
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"Train samples:\t267\n",
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"Test samples:\t30\n",
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"Accuracy of fold 2: 0.8666666666666667\n",
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"Training fold 3 for 20 epochs\n",
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"Train samples:\t267\n",
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"Test samples:\t30\n",
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"Accuracy of fold 3: 0.9333333333333333\n",
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"Training fold 4 for 20 epochs\n",
|
||||
"Train samples:\t267\n",
|
||||
"Test samples:\t30\n",
|
||||
"Accuracy of fold 4: 0.8666666666666667\n",
|
||||
"Training fold 5 for 20 epochs\n",
|
||||
"Train samples:\t267\n",
|
||||
"Test samples:\t30\n",
|
||||
"Accuracy of fold 5: 0.8333333333333334\n",
|
||||
"Training fold 6 for 20 epochs\n",
|
||||
"Train samples:\t267\n",
|
||||
"Test samples:\t30\n",
|
||||
"Accuracy of fold 6: 0.8666666666666667\n",
|
||||
"Training fold 7 for 20 epochs\n",
|
||||
"Train samples:\t268\n",
|
||||
"Test samples:\t29\n",
|
||||
"Accuracy of fold 7: 0.896551724137931\n",
|
||||
"Training fold 8 for 20 epochs\n",
|
||||
"Train samples:\t268\n",
|
||||
"Test samples:\t29\n",
|
||||
"Accuracy of fold 8: 0.7931034482758621\n",
|
||||
"Training fold 9 for 20 epochs\n",
|
||||
"Train samples:\t268\n",
|
||||
"Test samples:\t29\n",
|
||||
"Accuracy of fold 9: 0.7931034482758621\n",
|
||||
"Avg accuracy 0.8582758620689654\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
|
@ -269,12 +329,13 @@
|
|||
"import tensorflow as tf\n",
|
||||
"\n",
|
||||
"# number of components extracted from the pca\n",
|
||||
"n_features = 5 \n",
|
||||
"n_features = 8\n",
|
||||
"\n",
|
||||
"epochs = 5\n",
|
||||
"epochs = 20\n",
|
||||
"k_folds = 10\n",
|
||||
"\n",
|
||||
"# used to split the dataset into k folds\n",
|
||||
"kf = KFold(n_splits=5)\n",
|
||||
"kf = KFold(n_splits=k_folds)\n",
|
||||
"\n",
|
||||
"accuracies = []\n",
|
||||
"for i, (train_idx, test_idx) in enumerate(kf.split(X)):\n",
|
||||
|
|
|
|||
Loading…
Reference in New Issue