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titanic-ml.ipynb
{
"cells": [
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "import fastai; fastai.__version__",
"execution_count": 1,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 1,
"data": {
"text/plain": "'2.0.10'"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "import numpy as np \nimport pandas as pd ",
"execution_count": 2,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "from fastcore import *",
"execution_count": 3,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "from fastai.tabular.all import *",
"execution_count": 4,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "from sklearn.ensemble import RandomForestClassifier",
"execution_count": 5,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "#import kaggle",
"execution_count": 6,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "#!kaggle competitions download -c titanic",
"execution_count": 7,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "import os",
"execution_count": 8,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "#print('getcwd: ', os.getcwd())",
"execution_count": 9,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "cwd = os.getcwd()",
"execution_count": 10,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "path = Path(cwd)",
"execution_count": 11,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "path = path/'data/titanic'",
"execution_count": 12,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "#path.ls()",
"execution_count": 13,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "train_data = pd.read_csv(path/'train.csv')",
"execution_count": 14,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "train_data.head()",
"execution_count": 15,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 15,
"data": {
"text/plain": " PassengerId Survived Pclass \\\n0 1 0 3 \n1 2 1 1 \n2 3 1 3 \n3 4 1 1 \n4 5 0 3 \n\n Name Sex Age SibSp \\\n0 Braund, Mr. Owen Harris male 22.0 1 \n1 Cumings, Mrs. John Bradley (Florence Briggs Thayer) female 38.0 1 \n2 Heikkinen, Miss. Laina female 26.0 0 \n3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n4 Allen, Mr. William Henry male 35.0 0 \n\n Parch Ticket Fare Cabin Embarked \n0 0 A/5 21171 7.2500 NaN S \n1 0 PC 17599 71.2833 C85 C \n2 0 STON/O2. 3101282 7.9250 NaN S \n3 0 113803 53.1000 C123 S \n4 0 373450 8.0500 NaN S ",
"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>PassengerId</th>\n <th>Survived</th>\n <th>Pclass</th>\n <th>Name</th>\n <th>Sex</th>\n <th>Age</th>\n <th>SibSp</th>\n <th>Parch</th>\n <th>Ticket</th>\n <th>Fare</th>\n <th>Cabin</th>\n <th>Embarked</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1</td>\n <td>0</td>\n <td>3</td>\n <td>Braund, Mr. Owen Harris</td>\n <td>male</td>\n <td>22.0</td>\n <td>1</td>\n <td>0</td>\n <td>A/5 21171</td>\n <td>7.2500</td>\n <td>NaN</td>\n <td>S</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2</td>\n <td>1</td>\n <td>1</td>\n <td>Cumings, Mrs. John Bradley (Florence Briggs Thayer)</td>\n <td>female</td>\n <td>38.0</td>\n <td>1</td>\n <td>0</td>\n <td>PC 17599</td>\n <td>71.2833</td>\n <td>C85</td>\n <td>C</td>\n </tr>\n <tr>\n <th>2</th>\n <td>3</td>\n <td>1</td>\n <td>3</td>\n <td>Heikkinen, Miss. Laina</td>\n <td>female</td>\n <td>26.0</td>\n <td>0</td>\n <td>0</td>\n <td>STON/O2. 3101282</td>\n <td>7.9250</td>\n <td>NaN</td>\n <td>S</td>\n </tr>\n <tr>\n <th>3</th>\n <td>4</td>\n <td>1</td>\n <td>1</td>\n <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n <td>female</td>\n <td>35.0</td>\n <td>1</td>\n <td>0</td>\n <td>113803</td>\n <td>53.1000</td>\n <td>C123</td>\n <td>S</td>\n </tr>\n <tr>\n <th>4</th>\n <td>5</td>\n <td>0</td>\n <td>3</td>\n <td>Allen, Mr. William Henry</td>\n <td>male</td>\n <td>35.0</td>\n <td>0</td>\n <td>0</td>\n <td>373450</td>\n <td>8.0500</td>\n <td>NaN</td>\n <td>S</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "#from pandas_profiling import ProfileReport\n#prof = ProfileReport(train_data)\n#prof.to_file(output_file='output.html')",
"execution_count": 16,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "test_data = pd.read_csv(path/'test.csv')",
"execution_count": 17,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "test_data.head()",
"execution_count": 18,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 18,
"data": {
"text/plain": " PassengerId Pclass Name Sex \\\n0 892 3 Kelly, Mr. James male \n1 893 3 Wilkes, Mrs. James (Ellen Needs) female \n2 894 2 Myles, Mr. Thomas Francis male \n3 895 3 Wirz, Mr. Albert male \n4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female \n\n Age SibSp Parch Ticket Fare Cabin Embarked \n0 34.5 0 0 330911 7.8292 NaN Q \n1 47.0 1 0 363272 7.0000 NaN S \n2 62.0 0 0 240276 9.6875 NaN Q \n3 27.0 0 0 315154 8.6625 NaN S \n4 22.0 1 1 3101298 12.2875 NaN S ",
"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>PassengerId</th>\n <th>Pclass</th>\n <th>Name</th>\n <th>Sex</th>\n <th>Age</th>\n <th>SibSp</th>\n <th>Parch</th>\n <th>Ticket</th>\n <th>Fare</th>\n <th>Cabin</th>\n <th>Embarked</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>892</td>\n <td>3</td>\n <td>Kelly, Mr. James</td>\n <td>male</td>\n <td>34.5</td>\n <td>0</td>\n <td>0</td>\n <td>330911</td>\n <td>7.8292</td>\n <td>NaN</td>\n <td>Q</td>\n </tr>\n <tr>\n <th>1</th>\n <td>893</td>\n <td>3</td>\n <td>Wilkes, Mrs. James (Ellen Needs)</td>\n <td>female</td>\n <td>47.0</td>\n <td>1</td>\n <td>0</td>\n <td>363272</td>\n <td>7.0000</td>\n <td>NaN</td>\n <td>S</td>\n </tr>\n <tr>\n <th>2</th>\n <td>894</td>\n <td>2</td>\n <td>Myles, Mr. Thomas Francis</td>\n <td>male</td>\n <td>62.0</td>\n <td>0</td>\n <td>0</td>\n <td>240276</td>\n <td>9.6875</td>\n <td>NaN</td>\n <td>Q</td>\n </tr>\n <tr>\n <th>3</th>\n <td>895</td>\n <td>3</td>\n <td>Wirz, Mr. Albert</td>\n <td>male</td>\n <td>27.0</td>\n <td>0</td>\n <td>0</td>\n <td>315154</td>\n <td>8.6625</td>\n <td>NaN</td>\n <td>S</td>\n </tr>\n <tr>\n <th>4</th>\n <td>896</td>\n <td>3</td>\n <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n <td>female</td>\n <td>22.0</td>\n <td>1</td>\n <td>1</td>\n <td>3101298</td>\n <td>12.2875</td>\n <td>NaN</td>\n <td>S</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "dep_var = 'Survived'",
"execution_count": 19,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "cat_vars = ['PassengerId', 'Pclass', 'Sex', 'SibSp', 'Parch', 'Cabin', 'Embarked']",
"execution_count": 20,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "cont_vars = ['Age', 'Fare']",
"execution_count": 21,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "#cont,cat = cont_cat_split(train_data, 1, dep_var=dep_var)",
"execution_count": 22,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "procs = [Categorify, FillMissing, Normalize]",
"execution_count": 23,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "len(train_data)",
"execution_count": 24,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 24,
"data": {
"text/plain": "891"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "splits = IndexSplitter(list(range(710,891)))(range_of(train_data))",
"execution_count": 25,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "#splits = RandomSplitter(valid_pct=0.2)(range_of(train_data))",
"execution_count": 26,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "#splits = RandomSplitter()(range_of(train_data))",
"execution_count": 27,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "to = TabularPandas(train_data, procs, cat_vars, cont_vars, y_names=dep_var, splits=splits)",
"execution_count": 29,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "to.show()",
"execution_count": 30,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<IPython.core.display.HTML object>",
"text/html": "<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>PassengerId</th>\n <th>Pclass</th>\n <th>Sex</th>\n <th>SibSp</th>\n <th>Parch</th>\n <th>Cabin</th>\n <th>Embarked</th>\n <th>Age_na</th>\n <th>Age</th>\n <th>Fare</th>\n <th>Survived</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1</td>\n <td>3</td>\n <td>male</td>\n <td>1</td>\n <td>0</td>\n <td>#na#</td>\n <td>S</td>\n <td>False</td>\n <td>22.0</td>\n <td>7.250000</td>\n <td>0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2</td>\n <td>1</td>\n <td>female</td>\n <td>1</td>\n <td>0</td>\n <td>C85</td>\n <td>C</td>\n <td>False</td>\n <td>38.0</td>\n <td>71.283302</td>\n <td>1</td>\n </tr>\n <tr>\n <th>2</th>\n <td>3</td>\n <td>3</td>\n <td>female</td>\n <td>0</td>\n <td>0</td>\n <td>#na#</td>\n <td>S</td>\n <td>False</td>\n <td>26.0</td>\n <td>7.925000</td>\n <td>1</td>\n </tr>\n <tr>\n <th>3</th>\n <td>4</td>\n <td>1</td>\n <td>female</td>\n <td>1</td>\n <td>0</td>\n <td>C123</td>\n <td>S</td>\n <td>False</td>\n <td>35.0</td>\n <td>53.099998</td>\n <td>1</td>\n </tr>\n <tr>\n <th>4</th>\n <td>5</td>\n <td>3</td>\n <td>male</td>\n <td>0</td>\n <td>0</td>\n <td>#na#</td>\n <td>S</td>\n <td>False</td>\n <td>35.0</td>\n <td>8.050000</td>\n <td>0</td>\n </tr>\n <tr>\n <th>5</th>\n <td>6</td>\n <td>3</td>\n <td>male</td>\n <td>0</td>\n <td>0</td>\n <td>#na#</td>\n <td>Q</td>\n <td>True</td>\n <td>28.0</td>\n <td>8.458300</td>\n <td>0</td>\n </tr>\n <tr>\n <th>6</th>\n <td>7</td>\n <td>1</td>\n <td>male</td>\n <td>0</td>\n <td>0</td>\n <td>E46</td>\n <td>S</td>\n <td>False</td>\n <td>54.0</td>\n <td>51.862499</td>\n <td>0</td>\n </tr>\n <tr>\n <th>7</th>\n <td>8</td>\n <td>3</td>\n <td>male</td>\n <td>3</td>\n <td>1</td>\n <td>#na#</td>\n <td>S</td>\n <td>False</td>\n <td>2.0</td>\n <td>21.075001</td>\n <td>0</td>\n </tr>\n <tr>\n <th>8</th>\n <td>9</td>\n <td>3</td>\n <td>female</td>\n <td>0</td>\n <td>2</td>\n <td>#na#</td>\n <td>S</td>\n <td>False</td>\n <td>27.0</td>\n <td>11.133300</td>\n <td>1</td>\n </tr>\n <tr>\n <th>9</th>\n <td>10</td>\n <td>2</td>\n <td>female</td>\n <td>1</td>\n <td>0</td>\n <td>#na#</td>\n <td>C</td>\n <td>False</td>\n <td>14.0</td>\n <td>30.070801</td>\n <td>1</td>\n </tr>\n </tbody>\n</table>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "to.valid.xs #I believe PassengerId is set to 0/#na# by the Categorify proc",
"execution_count": 31,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 31,
"data": {
"text/plain": " PassengerId Pclass Sex SibSp Parch Cabin Embarked Age_na \\\n710 0 1 1 1 1 85 1 1 \n711 0 1 2 1 1 57 3 2 \n712 0 1 2 2 1 59 3 1 \n713 0 3 2 1 1 0 3 1 \n714 0 2 2 1 1 0 3 1 \n.. ... ... ... ... ... ... ... ... \n886 0 2 2 1 1 0 3 1 \n887 0 1 1 1 1 31 3 1 \n888 0 3 1 2 3 0 3 2 \n889 0 1 2 1 1 61 1 1 \n890 0 3 2 1 1 0 2 1 \n\n Age Fare \n710 -0.431126 0.349269 \n711 -0.124355 -0.122048 \n712 1.409496 0.400515 \n713 -0.047663 -0.472477 \n714 1.716266 -0.400269 \n.. ... ... \n886 -0.201048 -0.400269 \n887 -0.814588 -0.051210 \n888 -0.124355 -0.185700 \n889 -0.277740 -0.051210 \n890 0.182415 -0.508067 \n\n[181 rows x 10 columns]",
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>PassengerId</th>\n <th>Pclass</th>\n <th>Sex</th>\n <th>SibSp</th>\n <th>Parch</th>\n <th>Cabin</th>\n <th>Embarked</th>\n <th>Age_na</th>\n <th>Age</th>\n <th>Fare</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>710</th>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>85</td>\n <td>1</td>\n <td>1</td>\n <td>-0.431126</td>\n <td>0.349269</td>\n </tr>\n <tr>\n <th>711</th>\n <td>0</td>\n <td>1</td>\n <td>2</td>\n <td>1</td>\n <td>1</td>\n <td>57</td>\n <td>3</td>\n <td>2</td>\n <td>-0.124355</td>\n <td>-0.122048</td>\n </tr>\n <tr>\n <th>712</th>\n <td>0</td>\n <td>1</td>\n <td>2</td>\n <td>2</td>\n <td>1</td>\n <td>59</td>\n <td>3</td>\n <td>1</td>\n <td>1.409496</td>\n <td>0.400515</td>\n </tr>\n <tr>\n <th>713</th>\n <td>0</td>\n <td>3</td>\n <td>2</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>3</td>\n <td>1</td>\n <td>-0.047663</td>\n <td>-0.472477</td>\n </tr>\n <tr>\n <th>714</th>\n <td>0</td>\n <td>2</td>\n <td>2</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>3</td>\n <td>1</td>\n <td>1.716266</td>\n <td>-0.400269</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 </tr>\n <tr>\n <th>886</th>\n <td>0</td>\n <td>2</td>\n <td>2</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>3</td>\n <td>1</td>\n <td>-0.201048</td>\n <td>-0.400269</td>\n </tr>\n <tr>\n <th>887</th>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>31</td>\n <td>3</td>\n <td>1</td>\n <td>-0.814588</td>\n <td>-0.051210</td>\n </tr>\n <tr>\n <th>888</th>\n <td>0</td>\n <td>3</td>\n <td>1</td>\n <td>2</td>\n <td>3</td>\n <td>0</td>\n <td>3</td>\n <td>2</td>\n <td>-0.124355</td>\n <td>-0.185700</td>\n </tr>\n <tr>\n <th>889</th>\n <td>0</td>\n <td>1</td>\n <td>2</td>\n <td>1</td>\n <td>1</td>\n <td>61</td>\n <td>1</td>\n <td>1</td>\n <td>-0.277740</td>\n <td>-0.051210</td>\n </tr>\n <tr>\n <th>890</th>\n <td>0</td>\n <td>3</td>\n <td>2</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>2</td>\n <td>1</td>\n <td>0.182415</td>\n <td>-0.508067</td>\n </tr>\n </tbody>\n</table>\n<p>181 rows × 10 columns</p>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "dls = to.dataloaders(bs=64)",
"execution_count": 32,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "dls.train.xs",
"execution_count": 33,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 33,
"data": {
"text/plain": " PassengerId Pclass Sex SibSp Parch Cabin Embarked Age_na \\\n0 1 3 2 2 1 0 3 1 \n1 2 1 1 2 1 82 1 1 \n2 3 3 1 1 1 0 3 1 \n3 4 1 1 2 1 56 3 1 \n4 5 3 2 1 1 0 3 1 \n.. ... ... ... ... ... ... ... ... \n705 706 2 2 1 1 0 3 1 \n706 707 2 1 1 1 0 3 1 \n707 708 1 2 1 1 121 3 1 \n708 709 1 1 1 1 0 3 1 \n709 710 3 2 2 2 0 1 2 \n\n Age Fare \n0 -0.584511 -0.518334 \n1 0.642570 0.796457 \n2 -0.277740 -0.504474 \n3 0.412492 0.423101 \n4 0.412492 -0.501907 \n.. ... ... \n705 0.719263 -0.133341 \n706 1.179418 -0.390003 \n707 0.949340 -0.127438 \n708 -0.584511 2.444567 \n709 -0.124355 -0.354156 \n\n[710 rows x 10 columns]",
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>PassengerId</th>\n <th>Pclass</th>\n <th>Sex</th>\n <th>SibSp</th>\n <th>Parch</th>\n <th>Cabin</th>\n <th>Embarked</th>\n <th>Age_na</th>\n <th>Age</th>\n <th>Fare</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1</td>\n <td>3</td>\n <td>2</td>\n <td>2</td>\n <td>1</td>\n <td>0</td>\n <td>3</td>\n <td>1</td>\n <td>-0.584511</td>\n <td>-0.518334</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2</td>\n <td>1</td>\n <td>1</td>\n <td>2</td>\n <td>1</td>\n <td>82</td>\n <td>1</td>\n <td>1</td>\n <td>0.642570</td>\n <td>0.796457</td>\n </tr>\n <tr>\n <th>2</th>\n <td>3</td>\n <td>3</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>3</td>\n <td>1</td>\n <td>-0.277740</td>\n <td>-0.504474</td>\n </tr>\n <tr>\n <th>3</th>\n <td>4</td>\n <td>1</td>\n <td>1</td>\n <td>2</td>\n <td>1</td>\n <td>56</td>\n <td>3</td>\n <td>1</td>\n <td>0.412492</td>\n <td>0.423101</td>\n </tr>\n <tr>\n <th>4</th>\n <td>5</td>\n <td>3</td>\n <td>2</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>3</td>\n <td>1</td>\n <td>0.412492</td>\n <td>-0.501907</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 </tr>\n <tr>\n <th>705</th>\n <td>706</td>\n <td>2</td>\n <td>2</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>3</td>\n <td>1</td>\n <td>0.719263</td>\n <td>-0.133341</td>\n </tr>\n <tr>\n <th>706</th>\n <td>707</td>\n <td>2</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>3</td>\n <td>1</td>\n <td>1.179418</td>\n <td>-0.390003</td>\n </tr>\n <tr>\n <th>707</th>\n <td>708</td>\n <td>1</td>\n <td>2</td>\n <td>1</td>\n <td>1</td>\n <td>121</td>\n <td>3</td>\n <td>1</td>\n <td>0.949340</td>\n <td>-0.127438</td>\n </tr>\n <tr>\n <th>708</th>\n <td>709</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>3</td>\n <td>1</td>\n <td>-0.584511</td>\n <td>2.444567</td>\n </tr>\n <tr>\n <th>709</th>\n <td>710</td>\n <td>3</td>\n <td>2</td>\n <td>2</td>\n <td>2</td>\n <td>0</td>\n <td>1</td>\n <td>2</td>\n <td>-0.124355</td>\n <td>-0.354156</td>\n </tr>\n </tbody>\n</table>\n<p>710 rows × 10 columns</p>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "dls.valid.show_batch() #I believe PassengerId is set to 0/#na# by the Categorify proc in TabularPandas",
"execution_count": 34,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<IPython.core.display.HTML object>",
"text/html": "<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>PassengerId</th>\n <th>Pclass</th>\n <th>Sex</th>\n <th>SibSp</th>\n <th>Parch</th>\n <th>Cabin</th>\n <th>Embarked</th>\n <th>Age_na</th>\n <th>Age</th>\n <th>Fare</th>\n <th>Survived</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>#na#</td>\n <td>1</td>\n <td>female</td>\n <td>0</td>\n <td>0</td>\n <td>C90</td>\n <td>C</td>\n <td>False</td>\n <td>24.000000</td>\n <td>49.504200</td>\n <td>1.0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>#na#</td>\n <td>1</td>\n <td>male</td>\n <td>0</td>\n <td>0</td>\n <td>C124</td>\n <td>S</td>\n <td>True</td>\n <td>28.000000</td>\n <td>26.549999</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>#na#</td>\n <td>1</td>\n <td>male</td>\n <td>1</td>\n <td>0</td>\n <td>C126</td>\n <td>S</td>\n <td>False</td>\n <td>48.000000</td>\n <td>52.000000</td>\n <td>1.0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>#na#</td>\n <td>3</td>\n <td>male</td>\n <td>0</td>\n <td>0</td>\n <td>#na#</td>\n <td>S</td>\n <td>False</td>\n <td>29.000000</td>\n <td>9.483300</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>#na#</td>\n <td>2</td>\n <td>male</td>\n <td>0</td>\n <td>0</td>\n <td>#na#</td>\n <td>S</td>\n <td>False</td>\n <td>52.000001</td>\n <td>12.999999</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>5</th>\n <td>#na#</td>\n <td>3</td>\n <td>male</td>\n <td>0</td>\n <td>0</td>\n <td>F G73</td>\n <td>S</td>\n <td>False</td>\n <td>19.000000</td>\n <td>7.649999</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>6</th>\n <td>#na#</td>\n <td>1</td>\n <td>female</td>\n <td>0</td>\n <td>0</td>\n <td>C45</td>\n <td>C</td>\n <td>False</td>\n <td>38.000000</td>\n <td>227.525004</td>\n <td>1.0</td>\n </tr>\n <tr>\n <th>7</th>\n <td>#na#</td>\n <td>2</td>\n <td>female</td>\n <td>0</td>\n <td>0</td>\n <td>E101</td>\n <td>S</td>\n <td>False</td>\n <td>27.000000</td>\n <td>10.499999</td>\n <td>1.0</td>\n </tr>\n <tr>\n <th>8</th>\n <td>#na#</td>\n <td>3</td>\n <td>male</td>\n <td>0</td>\n <td>0</td>\n <td>#na#</td>\n <td>Q</td>\n <td>True</td>\n <td>28.000000</td>\n <td>15.500001</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>9</th>\n <td>#na#</td>\n <td>3</td>\n <td>male</td>\n <td>0</td>\n <td>0</td>\n <td>#na#</td>\n <td>S</td>\n <td>False</td>\n <td>33.000000</td>\n <td>7.775000</td>\n <td>0.0</td>\n </tr>\n </tbody>\n</table>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "dls.show_batch()",
"execution_count": 35,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<IPython.core.display.HTML object>",
"text/html": "<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>PassengerId</th>\n <th>Pclass</th>\n <th>Sex</th>\n <th>SibSp</th>\n <th>Parch</th>\n <th>Cabin</th>\n <th>Embarked</th>\n <th>Age_na</th>\n <th>Age</th>\n <th>Fare</th>\n <th>Survived</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>453</td>\n <td>1</td>\n <td>male</td>\n <td>0</td>\n <td>0</td>\n <td>C111</td>\n <td>C</td>\n <td>False</td>\n <td>30.000000</td>\n <td>27.750000</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>168</td>\n <td>3</td>\n <td>female</td>\n <td>1</td>\n <td>4</td>\n <td>#na#</td>\n <td>S</td>\n <td>False</td>\n <td>45.000001</td>\n <td>27.900000</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>41</td>\n <td>3</td>\n <td>female</td>\n <td>1</td>\n <td>0</td>\n <td>#na#</td>\n <td>S</td>\n <td>False</td>\n <td>40.000000</td>\n <td>9.475000</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>105</td>\n <td>3</td>\n <td>male</td>\n <td>2</td>\n <td>0</td>\n <td>#na#</td>\n <td>S</td>\n <td>False</td>\n <td>37.000000</td>\n <td>7.925001</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>50</td>\n <td>3</td>\n <td>female</td>\n <td>1</td>\n <td>0</td>\n <td>#na#</td>\n <td>S</td>\n <td>False</td>\n <td>18.000000</td>\n <td>17.799999</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>5</th>\n <td>21</td>\n <td>2</td>\n <td>male</td>\n <td>0</td>\n <td>0</td>\n <td>#na#</td>\n <td>S</td>\n <td>False</td>\n <td>35.000000</td>\n <td>26.000000</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>6</th>\n <td>305</td>\n <td>3</td>\n <td>male</td>\n <td>0</td>\n <td>0</td>\n <td>#na#</td>\n <td>S</td>\n <td>True</td>\n <td>28.000000</td>\n <td>8.049999</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>7</th>\n <td>208</td>\n <td>3</td>\n <td>male</td>\n <td>0</td>\n <td>0</td>\n <td>#na#</td>\n <td>C</td>\n <td>False</td>\n <td>26.000000</td>\n <td>18.787500</td>\n <td>1.0</td>\n </tr>\n <tr>\n <th>8</th>\n <td>214</td>\n <td>2</td>\n <td>male</td>\n <td>0</td>\n <td>0</td>\n <td>#na#</td>\n <td>S</td>\n <td>False</td>\n <td>30.000000</td>\n <td>12.999999</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>9</th>\n <td>40</td>\n <td>3</td>\n <td>female</td>\n <td>1</td>\n <td>0</td>\n <td>#na#</td>\n <td>C</td>\n <td>False</td>\n <td>14.000000</td>\n <td>11.241700</td>\n <td>1.0</td>\n </tr>\n </tbody>\n</table>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "learn = tabular_learner(dls, layers=[200,100], metrics=accuracy)",
"execution_count": 36,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "learn.lr_find()",
"execution_count": 37,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<IPython.core.display.HTML object>",
"text/html": ""
},
"metadata": {}
},
{
"output_type": "execute_result",
"execution_count": 37,
"data": {
"text/plain": "SuggestedLRs(lr_min=0.010000000149011612, lr_steep=0.0010000000474974513)"
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": "<Figure size 432x288 with 1 Axes>",
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "learn.fit(15, 1e-2/2)",
"execution_count": 38,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<IPython.core.display.HTML object>",
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"data": {
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