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edwardeasling/santander.ipynb

Created February 19, 2019 08:19
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santander.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Initial setup / imports:**"
]
},
{
"cell_type": "code",
"execution_count": 361,
"metadata": {},
"outputs": [],
"source": [
"from fastai.tabular import *\n",
"import zipfile\n",
"import csv"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Getting the data:**"
]
},
{
"cell_type": "code",
"execution_count": 362,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"PosixPath('/storage/santander')"
]
},
"execution_count": 362,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"path = Config.data_path()/'santander'\n",
"path.mkdir(parents=True, exist_ok=True)\n",
"path"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"404 - Not Found\r\n"
]
}
],
"source": [
"#! kaggle competitions download -c santander-customer-transaction-prediction"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"zip_ref = zipfile.ZipFile(path/'train.csv.zip', 'r')\n",
"zip_ref.extractall(path)\n",
"zip_ref = zipfile.ZipFile(path/'test.csv.zip', 'r')\n",
"zip_ref.extractall(path)\n",
"zip_ref = zipfile.ZipFile(path/'sample_submission.csv.zip', 'r')\n",
"zip_ref.extractall(path)\n",
"zip_ref.close()"
]
},
{
"cell_type": "code",
"execution_count": 363,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[PosixPath('/storage/santander/models'),\n",
" PosixPath('/storage/santander/test.csv'),\n",
" PosixPath('/storage/santander/train.csv.zip'),\n",
" PosixPath('/storage/santander/sample_submission.csv.zip'),\n",
" PosixPath('/storage/santander/test.csv.zip'),\n",
" PosixPath('/storage/santander/sample_submission.csv'),\n",
" PosixPath('/storage/santander/train.csv'),\n",
" PosixPath('/storage/santander/.ipynb_checkpoints'),\n",
" PosixPath('/storage/santander/submission.csv')]"
]
},
"execution_count": 363,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"path.ls()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Looking at the data:**"
]
},
{
"cell_type": "code",
"execution_count": 364,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>ID_code</th>\n",
" <th>target</th>\n",
" <th>var_0</th>\n",
" <th>var_1</th>\n",
" <th>var_2</th>\n",
" <th>var_3</th>\n",
" <th>var_4</th>\n",
" <th>var_5</th>\n",
" <th>var_6</th>\n",
" <th>var_7</th>\n",
" <th>...</th>\n",
" <th>var_190</th>\n",
" <th>var_191</th>\n",
" <th>var_192</th>\n",
" <th>var_193</th>\n",
" <th>var_194</th>\n",
" <th>var_195</th>\n",
" <th>var_196</th>\n",
" <th>var_197</th>\n",
" <th>var_198</th>\n",
" <th>var_199</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>train_0</td>\n",
" <td>0</td>\n",
" <td>8.9255</td>\n",
" <td>-6.7863</td>\n",
" <td>11.9081</td>\n",
" <td>5.0930</td>\n",
" <td>11.4607</td>\n",
" <td>-9.2834</td>\n",
" <td>5.1187</td>\n",
" <td>18.6266</td>\n",
" <td>...</td>\n",
" <td>4.4354</td>\n",
" <td>3.9642</td>\n",
" <td>3.1364</td>\n",
" <td>1.6910</td>\n",
" <td>18.5227</td>\n",
" <td>-2.3978</td>\n",
" <td>7.8784</td>\n",
" <td>8.5635</td>\n",
" <td>12.7803</td>\n",
" <td>-1.0914</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>train_1</td>\n",
" <td>0</td>\n",
" <td>11.5006</td>\n",
" <td>-4.1473</td>\n",
" <td>13.8588</td>\n",
" <td>5.3890</td>\n",
" <td>12.3622</td>\n",
" <td>7.0433</td>\n",
" <td>5.6208</td>\n",
" <td>16.5338</td>\n",
" <td>...</td>\n",
" <td>7.6421</td>\n",
" <td>7.7214</td>\n",
" <td>2.5837</td>\n",
" <td>10.9516</td>\n",
" <td>15.4305</td>\n",
" <td>2.0339</td>\n",
" <td>8.1267</td>\n",
" <td>8.7889</td>\n",
" <td>18.3560</td>\n",
" <td>1.9518</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>train_2</td>\n",
" <td>0</td>\n",
" <td>8.6093</td>\n",
" <td>-2.7457</td>\n",
" <td>12.0805</td>\n",
" <td>7.8928</td>\n",
" <td>10.5825</td>\n",
" <td>-9.0837</td>\n",
" <td>6.9427</td>\n",
" <td>14.6155</td>\n",
" <td>...</td>\n",
" <td>2.9057</td>\n",
" <td>9.7905</td>\n",
" <td>1.6704</td>\n",
" <td>1.6858</td>\n",
" <td>21.6042</td>\n",
" <td>3.1417</td>\n",
" <td>-6.5213</td>\n",
" <td>8.2675</td>\n",
" <td>14.7222</td>\n",
" <td>0.3965</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>train_3</td>\n",
" <td>0</td>\n",
" <td>11.0604</td>\n",
" <td>-2.1518</td>\n",
" <td>8.9522</td>\n",
" <td>7.1957</td>\n",
" <td>12.5846</td>\n",
" <td>-1.8361</td>\n",
" <td>5.8428</td>\n",
" <td>14.9250</td>\n",
" <td>...</td>\n",
" <td>4.4666</td>\n",
" <td>4.7433</td>\n",
" <td>0.7178</td>\n",
" <td>1.4214</td>\n",
" <td>23.0347</td>\n",
" <td>-1.2706</td>\n",
" <td>-2.9275</td>\n",
" <td>10.2922</td>\n",
" <td>17.9697</td>\n",
" <td>-8.9996</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>train_4</td>\n",
" <td>0</td>\n",
" <td>9.8369</td>\n",
" <td>-1.4834</td>\n",
" <td>12.8746</td>\n",
" <td>6.6375</td>\n",
" <td>12.2772</td>\n",
" <td>2.4486</td>\n",
" <td>5.9405</td>\n",
" <td>19.2514</td>\n",
" <td>...</td>\n",
" <td>-1.4905</td>\n",
" <td>9.5214</td>\n",
" <td>-0.1508</td>\n",
" <td>9.1942</td>\n",
" <td>13.2876</td>\n",
" <td>-1.5121</td>\n",
" <td>3.9267</td>\n",
" <td>9.5031</td>\n",
" <td>17.9974</td>\n",
" <td>-8.8104</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 202 columns</p>\n",
"</div>"
],
"text/plain": [
" ID_code target var_0 var_1 var_2 var_3 var_4 var_5 var_6 \\\n",
"0 train_0 0 8.9255 -6.7863 11.9081 5.0930 11.4607 -9.2834 5.1187 \n",
"1 train_1 0 11.5006 -4.1473 13.8588 5.3890 12.3622 7.0433 5.6208 \n",
"2 train_2 0 8.6093 -2.7457 12.0805 7.8928 10.5825 -9.0837 6.9427 \n",
"3 train_3 0 11.0604 -2.1518 8.9522 7.1957 12.5846 -1.8361 5.8428 \n",
"4 train_4 0 9.8369 -1.4834 12.8746 6.6375 12.2772 2.4486 5.9405 \n",
"\n",
" var_7 ... var_190 var_191 var_192 var_193 var_194 var_195 \\\n",
"0 18.6266 ... 4.4354 3.9642 3.1364 1.6910 18.5227 -2.3978 \n",
"1 16.5338 ... 7.6421 7.7214 2.5837 10.9516 15.4305 2.0339 \n",
"2 14.6155 ... 2.9057 9.7905 1.6704 1.6858 21.6042 3.1417 \n",
"3 14.9250 ... 4.4666 4.7433 0.7178 1.4214 23.0347 -1.2706 \n",
"4 19.2514 ... -1.4905 9.5214 -0.1508 9.1942 13.2876 -1.5121 \n",
"\n",
" var_196 var_197 var_198 var_199 \n",
"0 7.8784 8.5635 12.7803 -1.0914 \n",
"1 8.1267 8.7889 18.3560 1.9518 \n",
"2 -6.5213 8.2675 14.7222 0.3965 \n",
"3 -2.9275 10.2922 17.9697 -8.9996 \n",
"4 3.9267 9.5031 17.9974 -8.8104 \n",
"\n",
"[5 rows x 202 columns]"
]
},
"execution_count": 364,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv(path/'train.csv')\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 365,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>ID_code</th>\n",
" <th>var_0</th>\n",
" <th>var_1</th>\n",
" <th>var_2</th>\n",
" <th>var_3</th>\n",
" <th>var_4</th>\n",
" <th>var_5</th>\n",
" <th>var_6</th>\n",
" <th>var_7</th>\n",
" <th>var_8</th>\n",
" <th>...</th>\n",
" <th>var_190</th>\n",
" <th>var_191</th>\n",
" <th>var_192</th>\n",
" <th>var_193</th>\n",
" <th>var_194</th>\n",
" <th>var_195</th>\n",
" <th>var_196</th>\n",
" <th>var_197</th>\n",
" <th>var_198</th>\n",
" <th>var_199</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>test_0</td>\n",
" <td>11.0656</td>\n",
" <td>7.7798</td>\n",
" <td>12.9536</td>\n",
" <td>9.4292</td>\n",
" <td>11.4327</td>\n",
" <td>-2.3805</td>\n",
" <td>5.8493</td>\n",
" <td>18.2675</td>\n",
" <td>2.1337</td>\n",
" <td>...</td>\n",
" <td>-2.1556</td>\n",
" <td>11.8495</td>\n",
" <td>-1.4300</td>\n",
" <td>2.4508</td>\n",
" <td>13.7112</td>\n",
" <td>2.4669</td>\n",
" <td>4.3654</td>\n",
" <td>10.7200</td>\n",
" <td>15.4722</td>\n",
" <td>-8.7197</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>test_1</td>\n",
" <td>8.5304</td>\n",
" <td>1.2543</td>\n",
" <td>11.3047</td>\n",
" <td>5.1858</td>\n",
" <td>9.1974</td>\n",
" <td>-4.0117</td>\n",
" <td>6.0196</td>\n",
" <td>18.6316</td>\n",
" <td>-4.4131</td>\n",
" <td>...</td>\n",
" <td>10.6165</td>\n",
" <td>8.8349</td>\n",
" <td>0.9403</td>\n",
" <td>10.1282</td>\n",
" <td>15.5765</td>\n",
" <td>0.4773</td>\n",
" <td>-1.4852</td>\n",
" <td>9.8714</td>\n",
" <td>19.1293</td>\n",
" <td>-20.9760</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>test_2</td>\n",
" <td>5.4827</td>\n",
" <td>-10.3581</td>\n",
" <td>10.1407</td>\n",
" <td>7.0479</td>\n",
" <td>10.2628</td>\n",
" <td>9.8052</td>\n",
" <td>4.8950</td>\n",
" <td>20.2537</td>\n",
" <td>1.5233</td>\n",
" <td>...</td>\n",
" <td>-0.7484</td>\n",
" <td>10.9935</td>\n",
" <td>1.9803</td>\n",
" <td>2.1800</td>\n",
" <td>12.9813</td>\n",
" <td>2.1281</td>\n",
" <td>-7.1086</td>\n",
" <td>7.0618</td>\n",
" <td>19.8956</td>\n",
" <td>-23.1794</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>test_3</td>\n",
" <td>8.5374</td>\n",
" <td>-1.3222</td>\n",
" <td>12.0220</td>\n",
" <td>6.5749</td>\n",
" <td>8.8458</td>\n",
" <td>3.1744</td>\n",
" <td>4.9397</td>\n",
" <td>20.5660</td>\n",
" <td>3.3755</td>\n",
" <td>...</td>\n",
" <td>9.5702</td>\n",
" <td>9.0766</td>\n",
" <td>1.6580</td>\n",
" <td>3.5813</td>\n",
" <td>15.1874</td>\n",
" <td>3.1656</td>\n",
" <td>3.9567</td>\n",
" <td>9.2295</td>\n",
" <td>13.0168</td>\n",
" <td>-4.2108</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>test_4</td>\n",
" <td>11.7058</td>\n",
" <td>-0.1327</td>\n",
" <td>14.1295</td>\n",
" <td>7.7506</td>\n",
" <td>9.1035</td>\n",
" <td>-8.5848</td>\n",
" <td>6.8595</td>\n",
" <td>10.6048</td>\n",
" <td>2.9890</td>\n",
" <td>...</td>\n",
" <td>4.2259</td>\n",
" <td>9.1723</td>\n",
" <td>1.2835</td>\n",
" <td>3.3778</td>\n",
" <td>19.5542</td>\n",
" <td>-0.2860</td>\n",
" <td>-5.1612</td>\n",
" <td>7.2882</td>\n",
" <td>13.9260</td>\n",
" <td>-9.1846</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 201 columns</p>\n",
"</div>"
],
"text/plain": [
" ID_code var_0 var_1 var_2 var_3 var_4 var_5 var_6 \\\n",
"0 test_0 11.0656 7.7798 12.9536 9.4292 11.4327 -2.3805 5.8493 \n",
"1 test_1 8.5304 1.2543 11.3047 5.1858 9.1974 -4.0117 6.0196 \n",
"2 test_2 5.4827 -10.3581 10.1407 7.0479 10.2628 9.8052 4.8950 \n",
"3 test_3 8.5374 -1.3222 12.0220 6.5749 8.8458 3.1744 4.9397 \n",
"4 test_4 11.7058 -0.1327 14.1295 7.7506 9.1035 -8.5848 6.8595 \n",
"\n",
" var_7 var_8 ... var_190 var_191 var_192 var_193 var_194 \\\n",
"0 18.2675 2.1337 ... -2.1556 11.8495 -1.4300 2.4508 13.7112 \n",
"1 18.6316 -4.4131 ... 10.6165 8.8349 0.9403 10.1282 15.5765 \n",
"2 20.2537 1.5233 ... -0.7484 10.9935 1.9803 2.1800 12.9813 \n",
"3 20.5660 3.3755 ... 9.5702 9.0766 1.6580 3.5813 15.1874 \n",
"4 10.6048 2.9890 ... 4.2259 9.1723 1.2835 3.3778 19.5542 \n",
"\n",
" var_195 var_196 var_197 var_198 var_199 \n",
"0 2.4669 4.3654 10.7200 15.4722 -8.7197 \n",
"1 0.4773 -1.4852 9.8714 19.1293 -20.9760 \n",
"2 2.1281 -7.1086 7.0618 19.8956 -23.1794 \n",
"3 3.1656 3.9567 9.2295 13.0168 -4.2108 \n",
"4 -0.2860 -5.1612 7.2882 13.9260 -9.1846 \n",
"\n",
"[5 rows x 201 columns]"
]
},
"execution_count": 365,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_df = pd.read_csv(path/'test.csv')\n",
"test_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Creating the model:**"
]
},
{
"cell_type": "code",
"execution_count": 366,
"metadata": {},
"outputs": [],
"source": [
"dep_var = 'target'\n",
"cont_names = [] #creating empty list, to be filled with loop below\n",
"var_counter = 0 #creating a counter\n",
"num_of_cont_vars = len(df.columns) - 2\n",
"for _ in range(num_of_cont_vars):\n",
" name = 'var_' + str(var_counter)\n",
" cont_names.append(name)\n",
" var_counter+=1\n",
"procs = [FillMissing, Normalize]"
]
},
{
"cell_type": "code",
"execution_count": 367,
"metadata": {},
"outputs": [],
"source": [
"test = TabularList.from_df(test_df, path=path, cont_names=cont_names, procs=procs)"
]
},
{
"cell_type": "code",
"execution_count": 368,
"metadata": {},
"outputs": [],
"source": [
"data = (TabularList.from_df(df, path=path, cont_names=cont_names, procs=procs)\n",
" .random_split_by_pct(valid_pct=0.05)\n",
" .label_from_df(cols=dep_var)\n",
" .add_test(test)\n",
" .databunch())"
]
},
{
"cell_type": "code",
"execution_count": 369,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
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" <th>var_1</th>\n",
" <th>var_2</th>\n",
" <th>var_3</th>\n",
" <th>var_4</th>\n",
" <th>var_5</th>\n",
" <th>var_6</th>\n",
" <th>var_7</th>\n",
" <th>var_8</th>\n",
" <th>var_9</th>\n",
" <th>var_10</th>\n",
" <th>var_11</th>\n",
" <th>var_12</th>\n",
" <th>var_13</th>\n",
" <th>var_14</th>\n",
" <th>var_15</th>\n",
" <th>var_16</th>\n",
" <th>var_17</th>\n",
" <th>var_18</th>\n",
" <th>var_19</th>\n",
" <th>var_20</th>\n",
" <th>var_21</th>\n",
" <th>var_22</th>\n",
" <th>var_23</th>\n",
" <th>var_24</th>\n",
" <th>var_25</th>\n",
" <th>var_26</th>\n",
" <th>var_27</th>\n",
" <th>var_28</th>\n",
" <th>var_29</th>\n",
" <th>var_30</th>\n",
" <th>var_31</th>\n",
" <th>var_32</th>\n",
" <th>var_33</th>\n",
" <th>var_34</th>\n",
" <th>var_35</th>\n",
" <th>var_36</th>\n",
" <th>var_37</th>\n",
" <th>var_38</th>\n",
" <th>var_39</th>\n",
" <th>var_40</th>\n",
" <th>var_41</th>\n",
" <th>var_42</th>\n",
" <th>var_43</th>\n",
" <th>var_44</th>\n",
" <th>var_45</th>\n",
" <th>var_46</th>\n",
" <th>var_47</th>\n",
" <th>var_48</th>\n",
" <th>var_49</th>\n",
" <th>var_50</th>\n",
" <th>var_51</th>\n",
" <th>var_52</th>\n",
" <th>var_53</th>\n",
" <th>var_54</th>\n",
" <th>var_55</th>\n",
" <th>var_56</th>\n",
" <th>var_57</th>\n",
" <th>var_58</th>\n",
" <th>var_59</th>\n",
" <th>var_60</th>\n",
" <th>var_61</th>\n",
" <th>var_62</th>\n",
" <th>var_63</th>\n",
" <th>var_64</th>\n",
" <th>var_65</th>\n",
" <th>var_66</th>\n",
" <th>var_67</th>\n",
" <th>var_68</th>\n",
" <th>var_69</th>\n",
" <th>var_70</th>\n",
" <th>var_71</th>\n",
" <th>var_72</th>\n",
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" <th>var_80</th>\n",
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" <th>var_84</th>\n",
" <th>var_85</th>\n",
" <th>var_86</th>\n",
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" <th>var_88</th>\n",
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" <th>var_124</th>\n",
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" <th>var_138</th>\n",
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" <th>var_198</th>\n",
" <th>var_199</th>\n",
" <th>target</th>\n",
" </tr>\n",
" <tr>\n",
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" <th>-2.1152</th>\n",
" <th>0.9240</th>\n",
" <th>-2.2236</th>\n",
" <th>1.1821</th>\n",
" <th>0.0922</th>\n",
" <th>1.2952</th>\n",
" <th>-1.4487</th>\n",
" <th>0.6667</th>\n",
" <th>-0.4005</th>\n",
" <th>0.2825</th>\n",
" <th>0.8668</th>\n",
" <th>-1.7161</th>\n",
" <th>-0.5232</th>\n",
" <th>0.1792</th>\n",
" <th>0.9189</th>\n",
" <th>-0.3719</th>\n",
" <th>-2.1742</th>\n",
" <th>1.2377</th>\n",
" <th>0.1529</th>\n",
" <th>-1.1848</th>\n",
" <th>-0.3950</th>\n",
" <th>1.0913</th>\n",
" <th>1.1104</th>\n",
" <th>0.6258</th>\n",
" <th>-0.1391</th>\n",
" <th>-0.4704</th>\n",
" <th>1.4569</th>\n",
" <th>-0.3702</th>\n",
" <th>2.0020</th>\n",
" <th>-0.0621</th>\n",
" <th>-1.2032</th>\n",
" <th>-1.4355</th>\n",
" <th>-0.2599</th>\n",
" <th>-1.0895</th>\n",
" <th>1.3355</th>\n",
" <th>-0.8929</th>\n",
" <th>1.0158</th>\n",
" <th>-1.4777</th>\n",
" <th>-0.1439</th>\n",
" <th>0.4449</th>\n",
" <th>-1.5205</th>\n",
" <th>-0.7415</th>\n",
" <th>0.1915</th>\n",
" <th>-0.7045</th>\n",
" <th>-0.8602</th>\n",
" <th>-1.1148</th>\n",
" <th>-1.0880</th>\n",
" <th>-1.6275</th>\n",
" <th>-1.1248</th>\n",
" <th>0.6928</th>\n",
" <th>1.4966</th>\n",
" <th>1.1561</th>\n",
" <th>0.2349</th>\n",
" <th>0.1628</th>\n",
" <th>-1.6375</th>\n",
" <th>-1.3085</th>\n",
" <th>-0.1145</th>\n",
" <th>-0.9880</th>\n",
" <th>0.8688</th>\n",
" <th>0.8143</th>\n",
" <th>-0.1710</th>\n",
" <th>-0.6231</th>\n",
" <th>-0.4099</th>\n",
" <th>-1.0337</th>\n",
" <th>0.6030</th>\n",
" <th>-0.7529</th>\n",
" <th>1.4528</th>\n",
" <th>0.9898</th>\n",
" <th>-0.2724</th>\n",
" <th>0.3640</th>\n",
" <th>0.4484</th>\n",
" <th>-0.1692</th>\n",
" <th>0.0251</th>\n",
" <th>1.1082</th>\n",
" <th>-1.1622</th>\n",
" <th>-0.7329</th>\n",
" <th>-1.2749</th>\n",
" <th>1.7611</th>\n",
" <th>-0.7094</th>\n",
" <th>-2.1625</th>\n",
" <th>-0.1928</th>\n",
" <th>1.1228</th>\n",
" <th>-1.6140</th>\n",
" <th>0.3990</th>\n",
" <th>0.2126</th>\n",
" <th>-0.0290</th>\n",
" <th>0.9725</th>\n",
" <th>-2.0003</th>\n",
" <th>0.3787</th>\n",
" <th>0.3063</th>\n",
" <th>0.1251</th>\n",
" <th>0.0373</th>\n",
" <th>-0.1658</th>\n",
" <th>1.2330</th>\n",
" <th>0.4516</th>\n",
" <th>0.7298</th>\n",
" <th>-0.8404</th>\n",
" <th>0.4131</th>\n",
" <th>1.2410</th>\n",
" <th>0.9679</th>\n",
" <th>0.5101</th>\n",
" <th>0.8262</th>\n",
" <th>-0.9395</th>\n",
" <th>-0.3617</th>\n",
" <th>-0.2150</th>\n",
" <th>1.0829</th>\n",
" <th>-0.3702</th>\n",
" <th>0.4883</th>\n",
" <th>-1.6527</th>\n",
" <th>-0.9440</th>\n",
" <th>0.5835</th>\n",
" <th>1.0979</th>\n",
" <th>-0.3008</th>\n",
" <th>2.4326</th>\n",
" <th>-1.5901</th>\n",
" <th>1.4272</th>\n",
" <th>-1.3549</th>\n",
" <th>1.2758</th>\n",
" <th>1.5044</th>\n",
" <th>0.2173</th>\n",
" <th>-1.2969</th>\n",
" <th>0</th>\n",
" </tr>\n",
"</table>\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data.show_batch(rows=10)"
]
},
{
"cell_type": "code",
"execution_count": 370,
"metadata": {},
"outputs": [],
"source": [
"learn = tabular_learner(data, layers=[200,100], metrics=accuracy)\n",
"# right now I don't understand what the layer numbers do (and how to optimize)\n",
"# ideally will add in ROC_AUC as a metric (this is what Kaggle scores this competition on)"
]
},
{
"cell_type": "code",
"execution_count": 371,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n",
"Min numerical gradient: 2.29E-02\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"learn.lr_find()\n",
"learn.recorder.plot()"
]
},
{
"cell_type": "code",
"execution_count": 372,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"Total time: 01:24 <p><table style='width:300px; margin-bottom:10px'>\n",
" <tr>\n",
" <th>epoch</th>\n",
" <th>train_loss</th>\n",
" <th>valid_loss</th>\n",
" <th>accuracy</th>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <th>0.239046</th>\n",
" <th>0.230086</th>\n",
" <th>0.914900</th>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <th>0.244681</th>\n",
" <th>0.654876</th>\n",
" <th>0.908800</th>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <th>0.224277</th>\n",
" <th>0.223173</th>\n",
" <th>0.917100</th>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <th>0.213654</th>\n",
" <th>0.237945</th>\n",
" <th>0.915700</th>\n",
" </tr>\n",
"</table>\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"learn.fit_one_cycle(4, 5e-3)"
]
},
{
"cell_type": "code",
"execution_count": 373,
"metadata": {},
"outputs": [],
"source": [
"learn.save('initial-run')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Looking at the sample submission format:**"
]
},
{
"cell_type": "code",
"execution_count": 374,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
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" 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>ID_code</th>\n",
" <th>target</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>test_0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>test_1</td>\n",
" <td>0</td>\n",
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" <th>2</th>\n",
" <td>test_2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>test_3</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>test_4</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ID_code target\n",
"0 test_0 0\n",
"1 test_1 0\n",
"2 test_2 0\n",
"3 test_3 0\n",
"4 test_4 0"
]
},
"execution_count": 374,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sample = pd.read_csv(path/'sample_submission.csv')\n",
"sample.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Running the model on our test set and submitting to Kaggle:**"
]
},
{
"cell_type": "code",
"execution_count": 375,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"preds = learn.get_preds(ds_type=DatasetType.Test)"
]
},
{
"cell_type": "code",
"execution_count": 378,
"metadata": {},
"outputs": [],
"source": [
"target_preds = preds[0][:,1]\n",
"test_df['target'] = target_preds\n",
"test_df.to_csv(path/'submission.csv', columns=['ID_code', 'target'], index=False)"
]
},
{
"cell_type": "code",
"execution_count": 379,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" </tr>\n",
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" <td>0.051695</td>\n",
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" <td>0.109581</td>\n",
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" <tr>\n",
" <th>4</th>\n",
" <td>test_4</td>\n",
" <td>0.025509</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ID_code target\n",
"0 test_0 0.222986\n",
"1 test_1 0.076002\n",
"2 test_2 0.051695\n",
"3 test_3 0.109581\n",
"4 test_4 0.025509"
]
},
"execution_count": 379,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sub = pd.read_csv(path/'submission.csv')\n",
"sub.head()"
]
},
{
"cell_type": "code",
"execution_count": 381,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"100%|██████████████████████████████████████| 4.39M/4.39M [00:00<00:00, 4.79MB/s]\n",
"Successfully submitted to Santander Customer Transaction Prediction"
]
}
],
"source": [
"! kaggle competitions submit -c santander-customer-transaction-prediction -f {path/'submission.csv'} -m \"My submission\""
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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