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@julian-carpenter julian-carpenter/predict_survival_rate_titanic.ipynb
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predict_survival_rate_titanic.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# The imports we are using\n",
"import pandas as pd\n",
"import graphviz\n",
"from sklearn import tree"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Get the data. Which is in our case very easy, because it's from kaggle: https://www.kaggle.com/c/titanic/data\n",
"data_raw = pd.read_csv('train.csv')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"scrolled": false
},
"outputs": [
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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",
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" <td>male</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
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" <td>A/5 21171</td>\n",
" <td>7.2500</td>\n",
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" <td>3</td>\n",
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" 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",
"5 6 0 3 \n",
"6 7 0 1 \n",
"7 8 0 3 \n",
"8 9 1 3 \n",
"9 10 1 2 \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",
"5 Moran, Mr. James male NaN 0 \n",
"6 McCarthy, Mr. Timothy J male 54.0 0 \n",
"7 Palsson, Master. Gosta Leonard male 2.0 3 \n",
"8 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27.0 0 \n",
"9 Nasser, Mrs. Nicholas (Adele Achem) female 14.0 1 \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 \n",
"5 0 330877 8.4583 NaN Q \n",
"6 0 17463 51.8625 E46 S \n",
"7 1 349909 21.0750 NaN S \n",
"8 2 347742 11.1333 NaN S \n",
"9 0 237736 30.0708 NaN C "
]
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],
"source": [
"# Lets have a look at it\n",
"# We use the iloc method so see the first 10 entries\n",
"data_raw.iloc[:10]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Next we need to convert the 'sex' column to binary values.\n",
"# For that we use an if/then clause within list comprehension\n",
"data_raw['SexNumerical'] = [1 if x=='male' else 0 for x in data_raw['Sex']]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"PassengerId 0.005007\n",
"SibSp 0.035322\n",
"Age 0.077221\n",
"Parch 0.081629\n",
"Fare 0.257307\n",
"Pclass 0.338481\n",
"SexNumerical 0.543351\n",
"Survived 1.000000\n",
"Name: Survived, dtype: float64"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# a good idea for predicting survival is look at the correlations\n",
"# with the 'Survived' feature. The stronger the correlation the more\n",
"# the corresponding feature explains the survival.\n",
"data_raw.corr()['Survived'].abs().sort_values()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"233 women and 109 men survived\n"
]
}
],
"source": [
"print('{:.0f} women and {:.0f} men survived'.format(data_raw.where(data_raw['Sex']=='female')['Survived'].sum(),data_raw.where(data_raw['Sex']=='male')['Survived'].sum()))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"PassengerId 0.008790\n",
"Age 0.116109\n",
"Fare 0.218466\n",
"Parch 0.223644\n",
"SibSp 0.263284\n",
"Pclass 0.477114\n",
"Survived 1.000000\n",
"SexNumerical NaN\n",
"Name: Survived, dtype: float64"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Since the sex has such a strong correlation we split the dataset\n",
"# and look at all correlations for female and male passengers.\n",
"data_raw.where(data_raw['Sex']=='female').corr()['Survived'].abs().sort_values()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"scrolled": false
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"outputs": [
{
"data": {
"text/plain": [
"SibSp 0.020238\n",
"PassengerId 0.040477\n",
"Parch 0.096318\n",
"Age 0.119618\n",
"Fare 0.171288\n",
"Pclass 0.220618\n",
"Survived 1.000000\n",
"SexNumerical NaN\n",
"Name: Survived, dtype: float64"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_raw.where(data_raw['Sex']=='male').corr()['Survived'].abs().sort_values()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# We want to use only four classes: 'sex', 'age', 'Pclass' and 'survived'.\n",
"# Since some of them may contain NaNs, we need to filter these rows out\n",
"data_raw = data_raw.dropna(axis=0, how='any', subset = ['Sex', 'Age', 'Pclass','Survived'])"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Now we split the data in a training set and an evaluation set.\n",
"# we use 90% of all data for the training and 10% for the evaluation\n",
"idx = int(data_raw.shape[0]*.9)\n",
"train_data = data_raw.iloc[:idx].copy()\n",
"eval_data = data_raw.iloc[idx:].copy()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# We now prepare our three feature columns for the decision tree routine\n",
"# by stacking them all together\n",
"samples = pd.concat([train_data['SexNumerical'], train_data['Pclass'], train_data['Age']],axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Now to the fitting part. Define the classifier and fit it to the data\n",
"clf = tree.DecisionTreeClassifier(criterion='gini', max_depth=3, max_leaf_nodes=7)\n",
"clf = clf.fit(samples, train_data['Survived'])"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"scrolled": false
},
"outputs": [
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"<text text-anchor=\"start\" x=\"331.5\" y=\"-290.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">Class ≤ 2.5</text>\n",
"<text text-anchor=\"start\" x=\"328\" y=\"-275.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.487</text>\n",
"<text text-anchor=\"start\" x=\"323.5\" y=\"-260.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 31</text>\n",
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"<text text-anchor=\"start\" x=\"313\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = Survived</text>\n",
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"<title>6</title>\n",
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"<text text-anchor=\"start\" x=\"486.5\" y=\"-290.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">Class ≤ 1.5</text>\n",
"<text text-anchor=\"start\" x=\"483\" y=\"-275.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.296</text>\n",
"<text text-anchor=\"start\" x=\"474\" y=\"-260.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 377</text>\n",
"<text text-anchor=\"start\" x=\"465\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [309, 68]</text>\n",
"<text text-anchor=\"start\" x=\"483\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = Died</text>\n",
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"<text text-anchor=\"start\" x=\"193\" y=\"-164.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.0</text>\n",
"<text text-anchor=\"start\" x=\"179.5\" y=\"-149.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 10</text>\n",
"<text text-anchor=\"start\" x=\"175\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0, 10]</text>\n",
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"</g>\n",
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"<title>5&#45;&gt;9</title>\n",
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"<title>10</title>\n",
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"<text text-anchor=\"start\" x=\"330\" y=\"-164.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.472</text>\n",
"<text text-anchor=\"start\" x=\"325.5\" y=\"-149.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 21</text>\n",
"<text text-anchor=\"start\" x=\"321\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [13, 8]</text>\n",
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"<!-- 5&#45;&gt;10 -->\n",
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"<title>5&#45;&gt;10</title>\n",
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"<text text-anchor=\"start\" x=\"480\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.471</text>\n",
"<text text-anchor=\"start\" x=\"475.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 92</text>\n",
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"<text text-anchor=\"start\" x=\"625\" y=\"-149.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 285</text>\n",
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"text/plain": [
"<graphviz.files.Source at 0x7f4623126e80>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# To visualize our graph we use graphviz:\n",
"dot_data = tree.export_graphviz(clf, out_file=None, \n",
" feature_names=['Sex','Class','Age'], \n",
" class_names=['Died','Survived'], \n",
" filled=True, rounded=True, \n",
" special_characters=True,\n",
" leaves_parallel=False) \n",
"graph = graphviz.Source(dot_data)\n",
"graph"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"11.58 %\n"
]
}
],
"source": [
"# We'll use the \"predict_proba\" method to get predictions\n",
"prob = clf.predict_proba([[1,2,33]])[0,1]\n",
"print('{:.02f} %'.format(100*prob))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For me, a 33 year old man who hasn't the money to travel first class, the odds to survive were: **11.58%** ... yikes \n",
"\n",
"**But wait** ! How good is our model anyway ? We need to test it. *So let's write an evaluation routine.*"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true,
"scrolled": true
},
"outputs": [],
"source": [
"# we define a lambda function for that\n",
"probs = lambda x: clf.predict([x])\n",
"# now use the eval data !\n",
"eval_samples = pd.concat([eval_data['SexNumerical'], eval_data['Pclass'], eval_data['Age']],axis=1)\n",
"eval_data['Predicted_Probablity'] = [probs(idx) for idx in eval_samples.values]"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def f(x): \n",
" return 0 if x['Predicted_Probablity'] != x['Survived'] else 1\n",
"\n",
"eval_data['prediction_was_correct'] = eval_data.apply(f, axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"80.56 % was correctly predicted\n"
]
}
],
"source": [
"precision = eval_data['prediction_was_correct'].sum(axis=0)/eval_data.shape[0]\n",
"# Get the predictions and print it out nicely\n",
"print('{:.02f} % was correctly predicted'.format(100*precision))"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"######################################################################################\n",
"###### This is the second part of the article. It is only for tutorial purposes ######\n",
"######################################################################################\n",
"\n",
"# we need linspace and isscalar from numpy\n",
"from numpy import linspace, isscalar\n",
"\n",
"#### we calculate everything for the root node as an example\n",
"\n",
"# The number of samples in the root node\n",
"n_all = len(train_data)\n",
"\n",
"# the proportional probabilites\n",
"p_mk = lambda n_m, indicator: (1/n_m)*indicator\n",
"# gini metric\n",
"gini = lambda pmk_0, pmk_1: pmk_0*(1-pmk_0)+pmk_1*(1-pmk_1)\n",
"# cost function\n",
"cost_fun = lambda n_0, n_1, gini_0, gini_1: (1/n_all)*(n_0*gini_0+n_1*gini_1)\n",
"# all the subsets\n",
"data_provider = lambda cond: train_data.where(cond).dropna(axis=0, how='any', \n",
" subset = ['Sex', 'Age', 'Pclass','Survived'])\n",
"\n",
"def get_subset(t_m, feat):\n",
" # smaller than t_m\n",
" subsets = [data_provider((train_data['Survived']==cat[0]) & (train_data[feat]<=cat[1])) \n",
" for cat in [[0,t_m],[1,t_m]]]\n",
" # larger than t_m\n",
" subsets.extend([data_provider((train_data['Survived']==cat[0]) & (train_data[feat]>=cat[1])) \n",
" for cat in [[0,t_m],[1,t_m]]])\n",
" return subsets\n",
"\n",
"def calculate_cost(data_subsets):\n",
" # the indicator function is just the length of each subset\n",
" n_k = [len(subset) for subset in data_subsets]\n",
" # splitting into smaller/larger\n",
" n_1 = sum(n_k[:2])\n",
" n_2 = sum(n_k[2:])\n",
"\n",
" # intermediate probabilities\n",
" pmk_1 = p_mk(n_1, n_k[0])\n",
" pmk_2 = p_mk(n_1, n_k[1])\n",
" pmk_3 = p_mk(n_2, n_k[2])\n",
" pmk_4 = p_mk(n_2, n_k[3])\n",
"\n",
" # gini for binary choices\n",
" gini_1 = gini(pmk_1, pmk_2)\n",
" gini_2 = gini(pmk_3, pmk_4)\n",
"\n",
" # impurity cost function\n",
" return cost_fun(n_1, n_2, gini_1, gini_2),gini_1 , gini_2"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The best results for each feature are:\n",
"'SexNumerical' -> Cost: 0.346, Threshold: 0.50, Gini_Left: 0.369, Gini_Right: 0.333\n",
"The best results for each feature are:\n",
"'Age' -> Cost: 0.470, Threshold: 6.05, Gini_Left: 0.393, Gini_Right: 0.475\n",
"The best results for each feature are:\n",
"'Pclass' -> Cost: 0.572, Threshold: 2.00, Gini_Left: 0.490, Gini_Right: 0.439\n"
]
}
],
"source": [
"# Tuple to account for all classes, we'll use 100 datapoints to rasterize Age\n",
"it_tuple = ((.5, 'SexNumerical'),((linspace(train_data['Age'].min(),train_data['Age'].max(),100)),'Age'), ((1,2,3),'Pclass'))\n",
"\n",
"# we store the results in a dict\n",
"results = {}\n",
"# helper variable\n",
"cost_start = 1\n",
"# iterate over all values in it_tuple\n",
"for item in it_tuple:\n",
" # if we don't have a scalar then iterate over to first item\n",
" if not isscalar(item[0]):\n",
" for thresholds in item[0]:\n",
" # get all four subsets\n",
" data_subsets = get_subset(thresholds, item[1])\n",
" # calculate the cost function, return it as well as both gini scores\n",
" cost, gini_imp_1 , gini_imp_2 = calculate_cost(data_subsets)\n",
" # here we need the helper, write only in the dict if the\n",
" # value is smaller then everything before it\n",
" if cost < cost_start:\n",
" results.update({item[1]: (thresholds, cost, gini_imp_1 , gini_imp_2)})\n",
" cost_start = cost\n",
" # reset cost_start\n",
" cost_start = 1\n",
" else:\n",
" # the same for scalars\n",
" data_subsets = get_subset(item[0], item[1])\n",
" cost, gini_imp_1 , gini_imp_2 = calculate_cost(data_subsets)\n",
" results.update({item[1]: (item[0], cost, gini_imp_1 , gini_imp_2)})\n",
"\n",
"# print out results\n",
"for feature, vals in results.items():\n",
" print('The best results for each feature are:')\n",
" print('\\'{}\\' -> Cost: {:.03f}, Threshold: {:.02f}, Gini_Left: {:.03f}, Gini_Right: {:.03f}'. format(feature, vals[1], vals[0], vals[2], vals[3]))"
]
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