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Titanic Kaggle w/ fast.ai
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titanic.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%reload_ext autoreload\n",
"%autoreload 2\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"sys.path.append(\"/home/tonygentilcore/fastai/courses/dl1/\")\n",
"from fastai.structured import *\n",
"from fastai.column_data import *\n",
"np.set_printoptions(threshold=50, edgeitems=20)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"PATH = '/home/tonygentilcore/.kaggle/competitions/titanic'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"** Explore data **"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"gender_submission.csv models test.csv tmp train.csv\r\n"
]
}
],
"source": [
"!ls {PATH}"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"train = pd.read_csv(f'{PATH}/train.csv')\n",
"test = pd.read_csv(f'{PATH}/test.csv')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
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" vertical-align: middle;\n",
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" 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>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Fare</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>891.000000</td>\n",
" <td>891.000000</td>\n",
" <td>891.000000</td>\n",
" <td>714.000000</td>\n",
" <td>891.000000</td>\n",
" <td>891.000000</td>\n",
" <td>891.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>446.000000</td>\n",
" <td>0.383838</td>\n",
" <td>2.308642</td>\n",
" <td>29.699118</td>\n",
" <td>0.523008</td>\n",
" <td>0.381594</td>\n",
" <td>32.204208</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>257.353842</td>\n",
" <td>0.486592</td>\n",
" <td>0.836071</td>\n",
" <td>14.526497</td>\n",
" <td>1.102743</td>\n",
" <td>0.806057</td>\n",
" <td>49.693429</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.420000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>223.500000</td>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>20.125000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>7.910400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>446.000000</td>\n",
" <td>0.000000</td>\n",
" <td>3.000000</td>\n",
" <td>28.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>14.454200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>668.500000</td>\n",
" <td>1.000000</td>\n",
" <td>3.000000</td>\n",
" <td>38.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>31.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>891.000000</td>\n",
" <td>1.000000</td>\n",
" <td>3.000000</td>\n",
" <td>80.000000</td>\n",
" <td>8.000000</td>\n",
" <td>6.000000</td>\n",
" <td>512.329200</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass Age SibSp \\\n",
"count 891.000000 891.000000 891.000000 714.000000 891.000000 \n",
"mean 446.000000 0.383838 2.308642 29.699118 0.523008 \n",
"std 257.353842 0.486592 0.836071 14.526497 1.102743 \n",
"min 1.000000 0.000000 1.000000 0.420000 0.000000 \n",
"25% 223.500000 0.000000 2.000000 20.125000 0.000000 \n",
"50% 446.000000 0.000000 3.000000 28.000000 0.000000 \n",
"75% 668.500000 1.000000 3.000000 38.000000 1.000000 \n",
"max 891.000000 1.000000 3.000000 80.000000 8.000000 \n",
"\n",
" Parch Fare \n",
"count 891.000000 891.000000 \n",
"mean 0.381594 32.204208 \n",
"std 0.806057 49.693429 \n",
"min 0.000000 0.000000 \n",
"25% 0.000000 7.910400 \n",
"50% 0.000000 14.454200 \n",
"75% 0.000000 31.000000 \n",
"max 6.000000 512.329200 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train.describe()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
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" <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 Th...</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>"
],
"text/plain": [
" PassengerId Survived Pclass \\\n",
"0 1 0 3 \n",
"1 2 1 1 \n",
"2 3 1 3 \n",
"3 4 1 1 \n",
"4 5 0 3 \n",
"\n",
" Name Sex Age SibSp \\\n",
"0 Braund, Mr. Owen Harris male 22.0 1 \n",
"1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
"2 Heikkinen, Miss. Laina female 26.0 0 \n",
"3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n",
"4 Allen, Mr. William Henry male 35.0 0 \n",
"\n",
" Parch Ticket Fare Cabin Embarked \n",
"0 0 A/5 21171 7.2500 NaN S \n",
"1 0 PC 17599 71.2833 C85 C \n",
"2 0 STON/O2. 3101282 7.9250 NaN S \n",
"3 0 113803 53.1000 C123 S \n",
"4 0 373450 8.0500 NaN S "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train.head()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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" <thead>\n",
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" <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",
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" <th>Cabin</th>\n",
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" <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",
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" </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>"
],
"text/plain": [
" PassengerId Pclass Name Sex \\\n",
"0 892 3 Kelly, Mr. James male \n",
"1 893 3 Wilkes, Mrs. James (Ellen Needs) female \n",
"2 894 2 Myles, Mr. Thomas Francis male \n",
"3 895 3 Wirz, Mr. Albert male \n",
"4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female \n",
"\n",
" Age SibSp Parch Ticket Fare Cabin Embarked \n",
"0 34.5 0 0 330911 7.8292 NaN Q \n",
"1 47.0 1 0 363272 7.0000 NaN S \n",
"2 62.0 0 0 240276 9.6875 NaN Q \n",
"3 27.0 0 0 315154 8.6625 NaN S \n",
"4 22.0 1 1 3101298 12.2875 NaN S "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"** Prepare data **"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"index = 'PassengerId'\n",
"dep = 'Survived'\n",
"cat_vars = ['Pclass', 'Sex', 'SibSp', 'Parch', 'Embarked']\n",
"contin_vars = ['Age']\n",
"drop_vars = ['Name', 'Ticket', 'Cabin', 'Fare']\n",
"\n",
"test.set_index(index)\n",
"train.set_index(index)\n",
"\n",
"for v in cat_vars:\n",
" test[v] = test[v].astype('category').cat.as_ordered()\n",
" train[v] = train[v].astype('category').cat.as_ordered()\n",
"\n",
"for v in contin_vars:\n",
" test[v] = test[v].astype('float32')\n",
" train[v] = train[v].astype('float32')\n",
" \n",
"for v in drop_vars:\n",
" if v in test:\n",
" test.drop(v, axis=1, inplace=True)\n",
" train.drop(v, axis=1, inplace=True)\n",
"\n",
"test[dep] = np.nan\n",
" \n",
"apply_cats(test, train)\n",
"\n",
"df, y, nas, mapper = proc_df(train, dep, do_scale=True, skip_flds=[index])\n",
"df_test, _, nas, mapper = proc_df(test, dep, do_scale=True, skip_flds=[index], mapper=mapper, na_dict=nas)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Pclass</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Embarked</th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>3</td>\n",
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" <td>-0.565736</td>\n",
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" <td>1</td>\n",
" <td>3</td>\n",
" <td>-0.497895</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.663861</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>-0.497895</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>-0.258337</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>-0.497895</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.433312</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>-0.497895</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>0.433312</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>-0.497895</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Pclass Sex Age SibSp Parch Embarked Age_na\n",
"0 3 2 -0.565736 2 1 3 -0.497895\n",
"1 1 1 0.663861 2 1 1 -0.497895\n",
"2 3 1 -0.258337 1 1 3 -0.497895\n",
"3 1 1 0.433312 2 1 3 -0.497895\n",
"4 3 2 0.433312 1 1 3 -0.497895"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
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" <tr>\n",
" <th>1</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>1.355510</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>-0.497895</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2.508257</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>-0.497895</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>-0.181487</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>-0.497895</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>-0.565736</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>-0.497895</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Pclass Sex Age SibSp Parch Embarked Age_na\n",
"0 3 2 0.394887 1 1 2 -0.497895\n",
"1 3 1 1.355510 2 1 3 -0.497895\n",
"2 2 2 2.508257 1 1 2 -0.497895\n",
"3 3 2 -0.181487 1 1 3 -0.497895\n",
"4 3 1 -0.565736 2 2 3 -0.497895"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_test.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"** Create model/learner **"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"cat_sz = [(c, len(train[c].cat.categories)+1) for c in cat_vars]\n",
"emb_szs = [(c, min(50, (c+1)//2)) for _,c in cat_sz]"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"n = len(train)\n",
"val_idxs = get_cv_idxs(n)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"md = ColumnarModelData.from_data_frame(PATH, val_idxs, df, y.astype(np.float32),\n",
" cat_flds=cat_vars, bs=128, test_df=df_test)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"m = md.get_learner(emb_szs, len(df.columns)-len(cat_vars),\n",
" 0.04, 1, [1000,500], [0.001,0.01], y_range=[0, 1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"** Train **"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
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"model_id": "8b2f29c343ee4ceb9de09bac7e4074dd",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
"<p>\n",
" If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
" that the widgets JavaScript is still loading. If this message persists, it\n",
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"metadata": {},
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{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch trn_loss val_loss \n",
" 0 0.26849 0.589888 \n",
"\n"
]
},
{
"data": {
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],
"source": [
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]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"lr = 1e-3"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
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"text": [
"epoch trn_loss val_loss \n",
" 0 0.29958 0.289071 \n",
" 1 0.266482 0.225575 \n",
" 2 0.243882 0.230261 \n",
" 3 0.230642 0.224803 \n",
" 4 0.220913 0.205747 \n",
" 5 0.210903 0.209014 \n",
" 6 0.202561 0.203815 \n",
" 7 0.194041 0.199481 \n",
" 8 0.18833 0.194539 \n",
" 9 0.182456 0.188606 \n",
" 10 0.177253 0.184244 \n",
" 11 0.173428 0.183238 \n",
" 12 0.169191 0.14376 \n",
" 13 0.164396 0.151117 \n",
" 14 0.159777 0.136738 \n",
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" 23 0.13165 0.137135 \n",
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" 25 0.127662 0.137821 \n",
" 26 0.126263 0.137893 \n",
" 27 0.124427 0.137869 \n",
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"execution_count": 18,
"metadata": {},
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],
"source": [
"m.fit(lr, 3, cycle_len=4, cycle_mult=2)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"m.save('val0')"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"m.load('val0')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"** Prepare submission **"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"x,y=m.predict_with_targs()"
]
},
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"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
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"178"
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},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(y)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"pred_test = m.predict(True)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"test[dep] = pred_test"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"csv_fn=f'{PATH}/tmp/sub.csv'"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Pclass</th>\n",
" <th>Sex</th>\n",
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" <th>SibSp</th>\n",
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" <td>893</td>\n",
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" <td>S</td>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>3</td>\n",
" <td>male</td>\n",
" <td>27.0</td>\n",
" <td>0</td>\n",
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" </tr>\n",
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" <td>0.355572</td>\n",
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],
"text/plain": [
" PassengerId Pclass Sex Age SibSp Parch Embarked Survived\n",
"0 892 3 male 34.5 0 0 Q 0.124961\n",
"1 893 3 female 47.0 1 0 S 0.326619\n",
"2 894 2 male 62.0 0 0 Q 0.191005\n",
"3 895 3 male 27.0 0 0 S 0.127235\n",
"4 896 3 female 22.0 1 1 S 0.355572"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test.head()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
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],
"text/plain": [
" PassengerId Survived\n",
"0 892 0.124961\n",
"1 893 0.326619\n",
"2 894 0.191005\n",
"3 895 0.127235\n",
"4 896 0.355572"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sub = test[[index, dep]]\n",
"sub.head()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<a href='/home/tonygentilcore/.kaggle/competitions/titanic/tmp/sub.csv' target='_blank'>/home/tonygentilcore/.kaggle/competitions/titanic/tmp/sub.csv</a><br>"
],
"text/plain": [
"/home/tonygentilcore/.kaggle/competitions/titanic/tmp/sub.csv"
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"source": [
"sub.to_csv(csv_fn, index=False)\n",
"FileLink(csv_fn)"
]
}
],
"metadata": {
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