{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(**Click the icon below to open this notebook in Colab**)\n",
"\n",
"[](https://colab.research.google.com/github/xiangshiyin/machine-learning-for-actuarial-science/blob/main/2025-spring/week06/notebook/demo.ipynb)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Overview\n",
"\n",
"In our last class, we explored the Titanic dataset, examined it from multiple perspectives, and applied various feature engineering techniques to enhance its explanatory variables. Today, we will continue working with the Titanic dataset, focusing on model training and evaluation techniques to gain deeper insights into predictive modeling."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Load the dataset\n",
"\n",
"https://www.kaggle.com/competitions/titanic/data\n",
"- **The Titanic** https://en.wikipedia.org/wiki/Titanic\n",
"\n",
"| Variable | Definition | Key |\n",
"|------------|-------------------------------------------|--------------------------------------|\n",
"| survival | Survival | 0 = No, 1 = Yes |\n",
"| pclass | Ticket class | 1 = 1st, 2 = 2nd, 3 = 3rd |\n",
"| sex | Sex | |\n",
"| Age | Age in years | |\n",
"| sibsp | # of siblings / spouses aboard the Titanic | |\n",
"| parch | # of parents / children aboard the Titanic | |\n",
"| ticket | Ticket number | |\n",
"| fare | Passenger fare | |\n",
"| cabin | Cabin number | |\n",
"| embarked | Port of Embarkation | C = Cherbourg, Q = Queenstown, S = Southampton |"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"pd.set_option('display.max_rows', None)\n",
"pd.set_option('display.max_columns', None)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"train = pd.read_csv('../data/titanic/train.csv')\n",
"test = pd.read_csv('../data/titanic/test.csv')\n",
"\n",
"# convert all column names to lower cases\n",
"train.columns = train.columns.str.lower()\n",
"test.columns = test.columns.str.lower()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"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>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",
" </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",
"\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",
"\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 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"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>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",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" passengerid pclass name sex age sibsp \\\n",
"0 892 3 Kelly, Mr. James male 34.5 0 \n",
"1 893 3 Wilkes, Mrs. James (Ellen Needs) female 47.0 1 \n",
"2 894 2 Myles, Mr. Thomas Francis male 62.0 0 \n",
"\n",
" parch ticket fare cabin embarked \n",
"0 0 330911 7.8292 NaN Q \n",
"1 0 363272 7.0000 NaN S \n",
"2 0 240276 9.6875 NaN Q "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test.head(3)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"vscode": {
"languageId": "plaintext"
}
},
"source": [
"# Streamline the data transformations\n",
"\n",
"Here are the data exploration and transformation strategies we used so far:\n",
"* Quick survey across key variables\n",
"* Detect and address data anomalies\n",
" * Missing values\n",
" * Outliers\n",
"* Feature engineering\n",
" * Encode categorical variables\n",
" * Normalize numerical variables\n",
" * Create new features with stronger predictive power\n",
"\n",
"Data exploration process is typically iterative and complex. Once we have a good understanding of the data and some potential strategies to apply in the feature engineering process, we need to make sure these transformation strategies can be easily and consistently applied to new datasets, such as the test set and new batches of data for model retraining. This requires a systematic approach to streamline the data transformations so that we don't need to start from scratch and repeat the same steps for each new dataset. This is especially important in the real-world scenario where we want to productionalize and automate the data transformation process."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"passengerid 0\n",
"survived 0\n",
"pclass 0\n",
"name 0\n",
"sex 0\n",
"age 177\n",
"sibsp 0\n",
"parch 0\n",
"ticket 0\n",
"fare 0\n",
"cabin 687\n",
"embarked 2\n",
"dtype: int64"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train.isnull().sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Missing value imputation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def impute_missing_num_values(df):\n",
" \"\"\"\n",
" Impute missing values in numerical columns of a DataFrame using the median of each column.\n",
"\n",
" Args:\n",
" df (pandas.DataFrame): The DataFrame to impute missing values in.\n",
"\n",
" Returns:\n",
" pandas.DataFrame: The DataFrame with missing values imputed.\n",
" \"\"\"\n",
" # Select only the numerical columns\n",
" num_cols = df.select_dtypes(include=['float64', 'int64']).columns\n",
" # Impute missing values with the median of each column\n",
" for col in num_cols:\n",
" df[col] = df[col].fillna(df[col].median())\n",
" return df\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"passengerid 0\n",
"survived 0\n",
"pclass 0\n",
"name 0\n",
"sex 0\n",
"age 0\n",
"sibsp 0\n",
"parch 0\n",
"ticket 0\n",
"fare 0\n",
"cabin 687\n",
"embarked 2\n",
"dtype: int64"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train = impute_missing_num_values(train)\n",
"train.isnull().sum()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# categorical variables could have missing values too\n",
"# https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.mode.html\n",
"\n",
"def impute_missing_cat_values(df, ignore_list):\n",
" \"\"\"\n",
" Impute missing categorical values with the most frequent value.\n",
"\n",
" Args:\n",
" df (pd.DataFrame): DataFrame containing the data.\n",
" ignore_list (list): List of column names to ignore. \n",
" Returns:\n",
" pd.DataFrame: DataFrame with imputed missing categorical values.\n",
" \"\"\"\n",
" for col in df.columns:\n",
" if col not in ignore_list:\n",
" if df[col].dtype == 'object':\n",
" df[col] = df[col].fillna(df[col].mode()[0])\n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"passengerid 0\n",
"survived 0\n",
"pclass 0\n",
"name 0\n",
"sex 0\n",
"age 0\n",
"sibsp 0\n",
"parch 0\n",
"ticket 0\n",
"fare 0\n",
"cabin 687\n",
"embarked 0\n",
"dtype: int64"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train = impute_missing_cat_values(train, ignore_list=['cabin'])\n",
"train.isnull().sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"⚠️ **Attention:** We will treat the missing values in `cabin` in the feature engineering step!!\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Feature Engineering\n",
"- Encode categorical features\n",
"- Normalize numerical features\n",
"- Create new features"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['name', 'sex', 'ticket', 'cabin', 'embarked']\n"
]
}
],
"source": [
"# The categorical variables in the datasets\n",
"\n",
"cat_cols = [\n",
" col\n",
" for col in train.columns if train[col].dtype == \"object\"\n",
"] \n",
"print(cat_cols)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"# apply onehot encoding to the categorical columns\n",
"# use the sklearn library\n",
"\n",
"from sklearn.preprocessing import OneHotEncoder\n",
"\n",
"def onehot_encode(df, ignore_list):\n",
" cat_cols = [\n",
" col for col in df.columns if col not in ignore_list and df[col].dtype == 'object'\n",
" ]\n",
" encoder = OneHotEncoder()\n",
" encoded = encoder.fit_transform(df[cat_cols])\n",
" encoded_df = pd.DataFrame(encoded.toarray(), columns=encoder.get_feature_names_out(cat_cols))\n",
" df = pd.concat([df, encoded_df], axis=1)\n",
" df = df.drop(cat_cols, axis=1)\n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"train = onehot_encode(train, ignore_list=['cabin', 'name', 'ticket'])"
]
},
{
"cell_type": "code",
"execution_count": 13,
"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>passengerid</th>\n",
" <th>survived</th>\n",
" <th>pclass</th>\n",
" <th>name</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>sex_female</th>\n",
" <th>sex_male</th>\n",
" <th>embarked_C</th>\n",
" <th>embarked_Q</th>\n",
" <th>embarked_S</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>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>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</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>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>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</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>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>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</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",
"\n",
" name age sibsp parch \\\n",
"0 Braund, Mr. Owen Harris 22.0 1 0 \n",
"1 Cumings, Mrs. John Bradley (Florence Briggs Th... 38.0 1 0 \n",
"2 Heikkinen, Miss. Laina 26.0 0 0 \n",
"\n",
" ticket fare cabin sex_female sex_male embarked_C \\\n",
"0 A/5 21171 7.2500 NaN 0.0 1.0 0.0 \n",
"1 PC 17599 71.2833 C85 1.0 0.0 1.0 \n",
"2 STON/O2. 3101282 7.9250 NaN 1.0 0.0 0.0 \n",
"\n",
" embarked_Q embarked_S \n",
"0 0.0 1.0 \n",
"1 0.0 0.0 \n",
"2 0.0 1.0 "
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train.head(3)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"# transform numeric features, log transform `fare`\n",
"import numpy as np\n",
"\n",
"def log_transform(df, features, drop=False):\n",
" for feature in features:\n",
" df[feature+'_log'] = np.log1p(df[feature]) \n",
" if drop:\n",
" df = df.drop(features, axis=1)\n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['passengerid', 'survived', 'pclass', 'name', 'age', 'sibsp', 'parch',\n",
" 'ticket', 'fare', 'cabin', 'sex_female', 'sex_male', 'embarked_C',\n",
" 'embarked_Q', 'embarked_S', 'fare_log'],\n",
" dtype='object')"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train = log_transform(train, features=['fare'])\n",
"train.columns"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"# Create new features\n",
"import re\n",
"\n",
"def create_features(df):\n",
" df['has_cabin'] = df['cabin'].apply(lambda x: 0 if type(x) == float else 1)\n",
" df['family_size'] = df['sibsp'] + df['parch'] + 1\n",
" df['is_alone'] = df['family_size'].apply(lambda x: 1 if x == 1 else 0)\n",
" df['title'] = df['name'].apply(lambda x: re.search('([A-Z][a-z]+)\\\\.', x).group(1))\n",
" df['cabin'] = df['cabin'].fillna('U0')\n",
" df['deck'] = df['cabin'].apply(lambda x: re.search('([A-Z]+)', x).group(1))\n",
" df['name_len_cat'] = df['name'].apply(lambda x: 0 if len(x) <= 23 else 1 if len(x) <= 28 else 2 if len(x) <= 40 else 3)\n",
" df['age_cat'] = df['age'].apply(lambda x: 0 if x <= 14 else 1 if x <= 30 else 2 if x <= 40 else 3 if x <= 50 else 4 if x <= 60 else 5)\n",
" df['fare_log_cat'] = df['fare_log'].apply(lambda x: 0 if x <= 2.7 else 1 if x <= 3.2 else 2 if x <= 3.6 else 3)\n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['passengerid', 'survived', 'pclass', 'name', 'age', 'sibsp', 'parch',\n",
" 'ticket', 'fare', 'cabin', 'sex_female', 'sex_male', 'embarked_C',\n",
" 'embarked_Q', 'embarked_S', 'fare_log', 'has_cabin', 'family_size',\n",
" 'is_alone', 'title', 'deck', 'name_len_cat', 'age_cat', 'fare_log_cat'],\n",
" dtype='object')"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train = create_features(train)\n",
"train.columns"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Put all together"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"import re\n",
"import pandas as pd\n",
"import numpy as np\n",
"from sklearn.preprocessing import OneHotEncoder\n",
"\n",
"def impute_missing_num_values(df):\n",
" \"\"\"\n",
" Impute missing values in numerical columns of a DataFrame using the median of each column.\n",
"\n",
" Args:\n",
" df (pandas.DataFrame): The DataFrame to impute missing values in.\n",
"\n",
" Returns:\n",
" pandas.DataFrame: The DataFrame with missing values imputed.\n",
" \"\"\"\n",
" # Select only the numerical columns\n",
" num_cols = df.select_dtypes(include=['float64', 'int64']).columns\n",
" # Impute missing values with the median of each column\n",
" for col in num_cols:\n",
" df[col] = df[col].fillna(df[col].median())\n",
" return df\n",
"\n",
"def impute_missing_cat_values(df, ignore_list):\n",
" \"\"\"\n",
" Impute missing categorical values with the most frequent value.\n",
"\n",
" Args:\n",
" df (pd.DataFrame): DataFrame containing the data.\n",
" ignore_list (list): List of column names to ignore. \n",
" Returns:\n",
" pd.DataFrame: DataFrame with imputed missing categorical values.\n",
" \"\"\"\n",
" for col in df.columns:\n",
" if col not in ignore_list:\n",
" if df[col].dtype == 'object':\n",
" df[col] = df[col].fillna(df[col].mode()[0])\n",
" return df\n",
"\n",
"def log_transform(df, features, drop=False):\n",
" for feature in features:\n",
" df[feature+'_log'] = np.log1p(df[feature]) \n",
" if drop:\n",
" df = df.drop(features, axis=1)\n",
" return df\n",
"\n",
"def create_features(df):\n",
" df['has_cabin'] = df['cabin'].apply(lambda x: 0 if type(x) == float else 1)\n",
" df['family_size'] = df['sibsp'] + df['parch'] + 1\n",
" df['is_alone'] = df['family_size'].apply(lambda x: 1 if x == 1 else 0)\n",
" df['title'] = df['name'].apply(lambda x: re.search('([A-Z][a-z]+)\\\\.', x).group(1))\n",
" df['cabin'] = df['cabin'].fillna('U0')\n",
" df['deck'] = df['cabin'].apply(lambda x: re.search('([A-Z]+)', x).group(1))\n",
" df['name_len_cat'] = df['name'].apply(lambda x: 0 if len(x) <= 23 else 1 if len(x) <= 28 else 2 if len(x) <= 40 else 3)\n",
" df['age_cat'] = df['age'].apply(lambda x: 0 if x <= 14 else 1 if x <= 30 else 2 if x <= 40 else 3 if x <= 50 else 4 if x <= 60 else 5)\n",
" df['fare_log_cat'] = df['fare_log'].apply(lambda x: 0 if x <= 2.7 else 1 if x <= 3.2 else 2 if x <= 3.6 else 3)\n",
" return df\n",
"\n",
"def load_data():\n",
" train = pd.read_csv('../data/titanic/train.csv')\n",
" test = pd.read_csv('../data/titanic/test.csv')\n",
" # convert all column names to lower cases\n",
" train.columns = train.columns.str.lower()\n",
" test.columns = test.columns.str.lower() \n",
" return train, test\n",
"\n",
"def transform_data(df, encoder=None):\n",
" df = impute_missing_num_values(df)\n",
" df = impute_missing_cat_values(df, ['cabin', 'embarked'])\n",
" df = log_transform(df, ['fare'])\n",
" df = create_features(df)\n",
" \n",
" cat_attributes = ['sex', 'embarked', 'title', 'deck']\n",
" if not encoder:\n",
" encoder = OneHotEncoder(handle_unknown='ignore', sparse_output=False)\n",
" encoder.fit(df[cat_attributes])\n",
" encoded = encoder.transform(df[cat_attributes])\n",
" df = pd.concat([df, pd.DataFrame(encoded, columns=encoder.get_feature_names_out(cat_attributes))], axis=1)\n",
" \n",
" # drop columns that are not needed\n",
" df = df.drop([\n",
" 'name', 'ticket', 'cabin', 'fare'\n",
" # , 'age', 'fare', 'sibsp', 'parch'\n",
" ] + cat_attributes, axis=1)\n",
" return df, encoder\n",
"\n",
"train, test = load_data()\n",
"train, encoder = transform_data(train)\n",
"test, _ = transform_data(test, encoder)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"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>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_log</th>\n",
" <th>has_cabin</th>\n",
" <th>family_size</th>\n",
" <th>is_alone</th>\n",
" <th>name_len_cat</th>\n",
" <th>age_cat</th>\n",
" <th>fare_log_cat</th>\n",
" <th>sex_female</th>\n",
" <th>sex_male</th>\n",
" <th>embarked_C</th>\n",
" <th>embarked_Q</th>\n",
" <th>embarked_S</th>\n",
" <th>embarked_nan</th>\n",
" <th>title_Capt</th>\n",
" <th>title_Col</th>\n",
" <th>title_Countess</th>\n",
" <th>title_Don</th>\n",
" <th>title_Dr</th>\n",
" <th>title_Jonkheer</th>\n",
" <th>title_Lady</th>\n",
" <th>title_Major</th>\n",
" <th>title_Master</th>\n",
" <th>title_Miss</th>\n",
" <th>title_Mlle</th>\n",
" <th>title_Mme</th>\n",
" <th>title_Mr</th>\n",
" <th>title_Mrs</th>\n",
" <th>title_Ms</th>\n",
" <th>title_Rev</th>\n",
" <th>title_Sir</th>\n",
" <th>deck_A</th>\n",
" <th>deck_B</th>\n",
" <th>deck_C</th>\n",
" <th>deck_D</th>\n",
" <th>deck_E</th>\n",
" <th>deck_F</th>\n",
" <th>deck_G</th>\n",
" <th>deck_T</th>\n",
" <th>deck_U</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>22.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>2.110213</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>4.280593</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" passengerid survived pclass age sibsp parch fare_log has_cabin \\\n",
"0 1 0 3 22.0 1 0 2.110213 0 \n",
"1 2 1 1 38.0 1 0 4.280593 1 \n",
"\n",
" family_size is_alone name_len_cat age_cat fare_log_cat sex_female \\\n",
"0 2 0 0 1 0 0.0 \n",
"1 2 0 3 2 3 1.0 \n",
"\n",
" sex_male embarked_C embarked_Q embarked_S embarked_nan title_Capt \\\n",
"0 1.0 0.0 0.0 1.0 0.0 0.0 \n",
"1 0.0 1.0 0.0 0.0 0.0 0.0 \n",
"\n",
" title_Col title_Countess title_Don title_Dr title_Jonkheer title_Lady \\\n",
"0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"1 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
" title_Major title_Master title_Miss title_Mlle title_Mme title_Mr \\\n",
"0 0.0 0.0 0.0 0.0 0.0 1.0 \n",
"1 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
" title_Mrs title_Ms title_Rev title_Sir deck_A deck_B deck_C deck_D \\\n",
"0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"1 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 \n",
"\n",
" deck_E deck_F deck_G deck_T deck_U \n",
"0 0.0 0.0 0.0 0.0 1.0 \n",
"1 0.0 0.0 0.0 0.0 0.0 "
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(418, 44)"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test.shape"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"</style>\n",
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" <th>pclass</th>\n",
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" <th>sibsp</th>\n",
" <th>parch</th>\n",
" <th>fare_log</th>\n",
" <th>has_cabin</th>\n",
" <th>family_size</th>\n",
" <th>is_alone</th>\n",
" <th>name_len_cat</th>\n",
" <th>age_cat</th>\n",
" <th>fare_log_cat</th>\n",
" <th>sex_female</th>\n",
" <th>sex_male</th>\n",
" <th>embarked_C</th>\n",
" <th>embarked_Q</th>\n",
" <th>embarked_S</th>\n",
" <th>embarked_nan</th>\n",
" <th>title_Capt</th>\n",
" <th>title_Col</th>\n",
" <th>title_Countess</th>\n",
" <th>title_Don</th>\n",
" <th>title_Dr</th>\n",
" <th>title_Jonkheer</th>\n",
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" <th>title_Major</th>\n",
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" <th>deck_B</th>\n",
" <th>deck_C</th>\n",
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" <th>deck_E</th>\n",
" <th>deck_F</th>\n",
" <th>deck_G</th>\n",
" <th>deck_T</th>\n",
" <th>deck_U</th>\n",
" </tr>\n",
" </thead>\n",
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" <th>1</th>\n",
" <td>893</td>\n",
" <td>3</td>\n",
" <td>47.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>2.079442</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
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" <td>1.0</td>\n",
" <td>0.0</td>\n",
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" <td>0.0</td>\n",
" <td>1.0</td>\n",
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" <td>894</td>\n",
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],
"text/plain": [
" passengerid pclass age sibsp parch fare_log has_cabin family_size \\\n",
"0 892 3 34.5 0 0 2.178064 0 1 \n",
"1 893 3 47.0 1 0 2.079442 0 2 \n",
"2 894 2 62.0 0 0 2.369075 0 1 \n",
"\n",
" is_alone name_len_cat age_cat fare_log_cat sex_female sex_male \\\n",
"0 1 0 2 0 0.0 1.0 \n",
"1 0 2 3 0 1.0 0.0 \n",
"2 1 1 5 0 0.0 1.0 \n",
"\n",
" embarked_C embarked_Q embarked_S embarked_nan title_Capt title_Col \\\n",
"0 0.0 1.0 0.0 0.0 0.0 0.0 \n",
"1 0.0 0.0 1.0 0.0 0.0 0.0 \n",
"2 0.0 1.0 0.0 0.0 0.0 0.0 \n",
"\n",
" title_Countess title_Don title_Dr title_Jonkheer title_Lady \\\n",
"0 0.0 0.0 0.0 0.0 0.0 \n",
"1 0.0 0.0 0.0 0.0 0.0 \n",
"2 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
" title_Major title_Master title_Miss title_Mlle title_Mme title_Mr \\\n",
"0 0.0 0.0 0.0 0.0 0.0 1.0 \n",
"1 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"2 0.0 0.0 0.0 0.0 0.0 1.0 \n",
"\n",
" title_Mrs title_Ms title_Rev title_Sir deck_A deck_B deck_C deck_D \\\n",
"0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"1 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
" deck_E deck_F deck_G deck_T deck_U \n",
"0 0.0 0.0 0.0 0.0 1.0 \n",
"1 0.0 0.0 0.0 0.0 1.0 \n",
"2 0.0 0.0 0.0 0.0 1.0 "
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test.head(3)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For standard transfromations, you could also use the `pipeline` modules from `sklearn`\n",
"- https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train a simple model"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/xiangshiyin/Documents/Teaching/machine-learning-for-actuarial-science/.venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
"STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT.\n",
"\n",
"Increase the number of iterations (max_iter) or scale the data as shown in:\n",
" https://scikit-learn.org/stable/modules/preprocessing.html\n",
"Please also refer to the documentation for alternative solver options:\n",
" https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
" n_iter_i = _check_optimize_result(\n"
]
}
],
"source": [
"# train a simple logistic regression model to predict the survival label\n",
"from sklearn.linear_model import LogisticRegression\n",
"\n",
"lr = LogisticRegression()\n",
"lr.fit(\n",
" train.drop(columns=['survived', 'passengerid']), # everything except the survival label\n",
" train['survived'] # the survival label\n",
")\n",
"\n",
"pred = lr.predict(test.drop(columns=['passengerid']))"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"numpy.ndarray"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(pred)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(418,)"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pred.shape"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 1, 0, 0, 1, 0, 1, 0, 1, 0])"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pred[:10]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 26,
"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>passengerid</th>\n",
" <th>survived</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>892</td>\n",
" <td>0</td>\n",
" </tr>\n",
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" <th>1</th>\n",
" <td>893</td>\n",
" <td>1</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>894</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" passengerid survived\n",
"0 892 0\n",
"1 893 1\n",
"2 894 0"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_submission = pd.concat([test['passengerid'], pd.DataFrame(pred, columns=['survived'])], axis=1)\n",
"df_submission.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"df_submission.to_csv('../data/titanic/submission.csv', index=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Evaluate the prediction results\n",
"\n",
"https://www.kaggle.com/competitions/titanic/overview/evaluation\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## AutoML exploration"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"X_train = train.drop(columns=['survived', 'passengerid'])\n",
"y_train = train['survived']"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" 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>pclass</th>\n",
" <th>age</th>\n",
" <th>sibsp</th>\n",
" <th>parch</th>\n",
" <th>fare_log</th>\n",
" <th>has_cabin</th>\n",
" <th>family_size</th>\n",
" <th>is_alone</th>\n",
" <th>name_len_cat</th>\n",
" <th>age_cat</th>\n",
" <th>fare_log_cat</th>\n",
" <th>sex_female</th>\n",
" <th>sex_male</th>\n",
" <th>embarked_C</th>\n",
" <th>embarked_Q</th>\n",
" <th>embarked_S</th>\n",
" <th>embarked_nan</th>\n",
" <th>title_Capt</th>\n",
" <th>title_Col</th>\n",
" <th>title_Countess</th>\n",
" <th>title_Don</th>\n",
" <th>title_Dr</th>\n",
" <th>title_Jonkheer</th>\n",
" <th>title_Lady</th>\n",
" <th>title_Major</th>\n",
" <th>title_Master</th>\n",
" <th>title_Miss</th>\n",
" <th>title_Mlle</th>\n",
" <th>title_Mme</th>\n",
" <th>title_Mr</th>\n",
" <th>title_Mrs</th>\n",
" <th>title_Ms</th>\n",
" <th>title_Rev</th>\n",
" <th>title_Sir</th>\n",
" <th>deck_A</th>\n",
" <th>deck_B</th>\n",
" <th>deck_C</th>\n",
" <th>deck_D</th>\n",
" <th>deck_E</th>\n",
" <th>deck_F</th>\n",
" <th>deck_G</th>\n",
" <th>deck_T</th>\n",
" <th>deck_U</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>3</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>2.110213</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>4.280593</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" pclass age sibsp parch fare_log has_cabin family_size is_alone \\\n",
"0 3 22.0 1 0 2.110213 0 2 0 \n",
"1 1 38.0 1 0 4.280593 1 2 0 \n",
"\n",
" name_len_cat age_cat fare_log_cat sex_female sex_male embarked_C \\\n",
"0 0 1 0 0.0 1.0 0.0 \n",
"1 3 2 3 1.0 0.0 1.0 \n",
"\n",
" embarked_Q embarked_S embarked_nan title_Capt title_Col \\\n",
"0 0.0 1.0 0.0 0.0 0.0 \n",
"1 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
" title_Countess title_Don title_Dr title_Jonkheer title_Lady \\\n",
"0 0.0 0.0 0.0 0.0 0.0 \n",
"1 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
" title_Major title_Master title_Miss title_Mlle title_Mme title_Mr \\\n",
"0 0.0 0.0 0.0 0.0 0.0 1.0 \n",
"1 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
" title_Mrs title_Ms title_Rev title_Sir deck_A deck_B deck_C deck_D \\\n",
"0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"1 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 \n",
"\n",
" deck_E deck_F deck_G deck_T deck_U \n",
"0 0.0 0.0 0.0 0.0 1.0 \n",
"1 0.0 0.0 0.0 0.0 0.0 "
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_train.head(2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**FLAML** - https://github.com/microsoft/FLAML/tree/main\n",
"- `pip install flaml[automl]`\n",
"- [Documentation](https://microsoft.github.io/FLAML/docs/Getting-Started)\n",
"- Best practices [[link](https://learn.microsoft.com/en-us/fabric/data-science/automated-machine-learning-fabric#automl-workflow)]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[flaml.automl.logger: 02-17 20:34:43] {1728} INFO - task = classification\n",
"[flaml.automl.logger: 02-17 20:34:43] {1739} INFO - Evaluation method: cv\n",
"[flaml.automl.logger: 02-17 20:34:43] {1838} INFO - Minimizing error metric: 1-accuracy\n",
"[flaml.automl.logger: 02-17 20:34:43] {1955} INFO - List of ML learners in AutoML Run: ['lgbm', 'rf', 'xgboost', 'extra_tree', 'xgb_limitdepth', 'sgd', 'lrl1']\n",
"[flaml.automl.logger: 02-17 20:34:43] {2258} INFO - iteration 0, current learner lgbm\n",
"[flaml.automl.logger: 02-17 20:34:43] {2393} INFO - Estimated sufficient time budget=1181s. Estimated necessary time budget=27s.\n",
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"[flaml.automl.logger: 02-17 20:35:43] {2685} INFO - retrain lgbm for 0.1s\n",
"[flaml.automl.logger: 02-17 20:35:43] {2688} INFO - retrained model: LGBMClassifier(colsample_bytree=np.float64(0.9263510142224147),\n",
" learning_rate=np.float64(0.11073419548910175), max_bin=1023,\n",
" min_child_samples=7, n_estimators=26, n_jobs=-1, num_leaves=35,\n",
" reg_alpha=np.float64(0.8491669558916534),\n",
" reg_lambda=np.float64(0.08792333670079354), verbose=-1)\n",
"[flaml.automl.logger: 02-17 20:35:43] {1985} INFO - fit succeeded\n",
"[flaml.automl.logger: 02-17 20:35:43] {1986} INFO - Time taken to find the best model: 57.45935106277466\n"
]
}
],
"source": [
"from flaml import AutoML\n",
"\n",
"automl = AutoML()\n",
"settings = {\n",
" \"time_budget\": 60, # total running time in seconds\n",
" \"metric\": 'accuracy', \n",
" # check the documentation for options of metrics (https://microsoft.github.io/FLAML/docs/Use-Cases/Task-Oriented-AutoML#optimization-metric)\n",
" \"task\": 'classification', # task type\n",
" \"log_file_name\": 'automl_experiment.log', # flaml log file\n",
" \"seed\": 7654321, # random seed\n",
" \"ensemble\": False,\n",
"}\n",
"automl.fit(X_train, y_train, **settings)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"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>passengerid</th>\n",
" <th>survived</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>892</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>893</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>894</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" passengerid survived\n",
"0 892 0\n",
"1 893 0\n",
"2 894 0"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_test = test.drop(columns=['passengerid'])\n",
"y_test_pred = automl.predict(X_test)\n",
"df_submission = pd.concat([test['passengerid'], pd.DataFrame(y_test_pred, columns=['survived'])], axis=1)\n",
"df_submission.head(3)\n"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"df_submission.to_csv('../data/titanic/submission.csv', index=False)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'lgbm'"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"automl.best_estimator"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'n_estimators': 26,\n",
" 'num_leaves': 35,\n",
" 'min_child_samples': 7,\n",
" 'learning_rate': np.float64(0.11073419548910175),\n",
" 'log_max_bin': 10,\n",
" 'colsample_bytree': np.float64(0.9263510142224147),\n",
" 'reg_alpha': np.float64(0.8491669558916534),\n",
" 'reg_lambda': np.float64(0.08792333670079354)}"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"automl.best_config"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 32, 262, 1, 4, 292, 16, 28, 0, 29, 1, 2, 17, 3,\n",
" 14, 0, 24, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 1, 0, 0, 26, 1, 0, 0, 0, 2, 0, 8, 3, 8,\n",
" 0, 0, 0, 5], dtype=int32)"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"automl.model.estimator.feature_importances_"
]
},
{
"cell_type": "code",
"execution_count": 36,
"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>Feature</th>\n",
" <th>Importance</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>fare_log</td>\n",
" <td>292</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>age</td>\n",
" <td>262</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>pclass</td>\n",
" <td>32</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>name_len_cat</td>\n",
" <td>29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>family_size</td>\n",
" <td>28</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>title_Mr</td>\n",
" <td>26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>embarked_S</td>\n",
" <td>24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>sex_female</td>\n",
" <td>17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>has_cabin</td>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>embarked_C</td>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>deck_C</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>deck_E</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>deck_U</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>parch</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>sex_male</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>deck_D</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>deck_A</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>fare_log_cat</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>sibsp</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>title_Miss</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>age_cat</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>title_Mrs</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>is_alone</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>title_Col</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>embarked_Q</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>title_Capt</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>embarked_nan</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>title_Master</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>title_Major</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>title_Lady</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>title_Jonkheer</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>title_Don</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>title_Dr</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>title_Countess</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>title_Mme</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>title_Sir</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>title_Rev</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>title_Ms</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>title_Mlle</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>deck_B</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>deck_F</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>deck_G</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>deck_T</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Feature Importance\n",
"4 fare_log 292\n",
"1 age 262\n",
"0 pclass 32\n",
"8 name_len_cat 29\n",
"6 family_size 28\n",
"29 title_Mr 26\n",
"15 embarked_S 24\n",
"11 sex_female 17\n",
"5 has_cabin 16\n",
"13 embarked_C 14\n",
"36 deck_C 8\n",
"38 deck_E 8\n",
"42 deck_U 5\n",
"3 parch 4\n",
"12 sex_male 3\n",
"37 deck_D 3\n",
"34 deck_A 2\n",
"10 fare_log_cat 2\n",
"2 sibsp 1\n",
"26 title_Miss 1\n",
"9 age_cat 1\n",
"30 title_Mrs 1\n",
"7 is_alone 0\n",
"18 title_Col 0\n",
"14 embarked_Q 0\n",
"17 title_Capt 0\n",
"16 embarked_nan 0\n",
"25 title_Master 0\n",
"24 title_Major 0\n",
"23 title_Lady 0\n",
"22 title_Jonkheer 0\n",
"20 title_Don 0\n",
"21 title_Dr 0\n",
"19 title_Countess 0\n",
"28 title_Mme 0\n",
"33 title_Sir 0\n",
"32 title_Rev 0\n",
"31 title_Ms 0\n",
"27 title_Mlle 0\n",
"35 deck_B 0\n",
"39 deck_F 0\n",
"40 deck_G 0\n",
"41 deck_T 0"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Get feature importance values\n",
"importances = automl.model.estimator.feature_importances_\n",
"# Get feature names from the dataset\n",
"feature_names = X_train.columns\n",
"# Create a DataFrame for better visualization\n",
"feature_importance_df = pd.DataFrame({'Feature': feature_names, 'Importance': importances})\n",
"# Sort by importance (highest first)\n",
"feature_importance_df = feature_importance_df.sort_values(by='Importance', ascending=False)\n",
"\n",
"feature_importance_df"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"# Plot feature importance\n",
"plt.figure(figsize=(10, 6)) # Adjust figure size for better readability\n",
"plt.bar(feature_importance_df['Feature'], feature_importance_df['Importance'], color='skyblue')\n",
"\n",
"# Rotate feature names for readability\n",
"plt.xticks(rotation=45, ha='right') # Tilt labels 45 degrees and align to the right\n",
"\n",
"# Labels and title\n",
"plt.xlabel('Feature Name')\n",
"plt.ylabel('Importance Score')\n",
"plt.title('Feature Importance')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Model Evaluations\n",
"\n",
"Model evaluations can be approached from multiple perspectives:\n",
"\n",
"- From the Perspective of Evaluation Metrics:\n",
" - **Prediction Quality**: How well does the model predict or classify new data?\n",
" - **Interpretability**: How easily can the model’s predictions be understood and explained?\n",
"- From the Perspective of the ML Workflow:\n",
" - **Offline Evaluations**: Assessment of model performance on the training and test datasets.\n",
" - **Online Evaluations**: Evaluation of model performance using live, production data.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prediction Quality\n",
"\n",
"### Classification Problems\n",
"#### Accuracy\n",
"$$\n",
"\\mathrm{Accuracy} = \\frac{\\mathrm{TP} + \\mathrm{TN}}{\\mathrm{TP} + \\mathrm{FP} + \\mathrm{FN} + \\mathrm{TN}}\n",
"$$"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 79.22%\n"
]
}
],
"source": [
"# Cross Validation Classification Accuracy\n",
"import pandas as pd\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.metrics import accuracy_score\n",
"from sklearn.linear_model import LogisticRegression\n",
"url = \"https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv\"\n",
"names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']\n",
"dataframe = pd.read_csv(url, names=names)\n",
"array = dataframe.values\n",
"X = array[:,0:8]\n",
"Y = array[:,8]\n",
"\n",
"X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=7)\n",
"lr = LogisticRegression()\n",
"lr.fit(X_train, Y_train)\n",
"\n",
"Y_test_pred = lr.predict(X_test)\n",
"accuracy = accuracy_score(Y_test, Y_test_pred)\n",
"print(\"Accuracy: %.2f%%\" % (accuracy * 100.0))"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"np.float64(0.7922077922077922)"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.sum(Y_test == Y_test_pred) / len(Y_test)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"precision: 0.7906976744186046\n",
"recall: 0.5964912280701754\n"
]
}
],
"source": [
"from sklearn.metrics import precision_score, recall_score\n",
"\n",
"precision = precision_score(Y_test, Y_test_pred)\n",
"recall = recall_score(Y_test, Y_test_pred)\n",
"\n",
"print(f\"precision: {precision}\")\n",
"print(f\"recall: {recall}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"f1 score: 0.68\n"
]
}
],
"source": [
"from sklearn.metrics import f1_score\n",
"f1_score = f1_score(Y_test, Y_test_pred)\n",
"print(f\"f1 score: {f1_score}\")"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/78/njcscll93_s6cc27zw_h0pmr0000gn/T/ipykernel_50750/2920054557.py:7: RuntimeWarning: invalid value encountered in divide\n",
" 2 * precision * recall / (precision + recall)\n"
]
},
{
"data": {
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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"precision = np.linspace(0, 1, 1000)\n",
"recalls = np.linspace(0, 1, 10)\n",
"f1s = [\n",
" 2 * precision * recall / (precision + recall)\n",
" for recall in recalls\n",
"]\n",
"for f1, recall in zip(f1s, recalls):\n",
" plt.plot(precision, f1, label=f\"recall={recall:.1f}%\")\n",
"\n",
"plt.xlabel(\"Precision\")\n",
"plt.ylabel(\"F1 Score\")\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Logistic Loss (Log Loss)\n",
"\n",
"For binary classification problem, the log loss is defined as:\n",
"$$\n",
"L(y, p) = - \\frac{1}{N} \\sum_{i=1}^N \\left[ y_i \\log(p_i) + (1 - y_i) \\log(1 - p_i) \\right]\n",
"$$"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Calculated Log Loss Score: 0.495027137138449\n"
]
}
],
"source": [
"# Calculate the score from scratch\n",
"import numpy as np\n",
"Y_test_pred_prob = lr.predict_proba(X_test)[:, 1]\n",
"log_loss_score = np.sum(-np.log(np.where(Y_test == 1, Y_test_pred_prob, 1 - Y_test_pred_prob))) / len(Y_test)\n",
"print(f\"Calculated Log Loss Score: {log_loss_score}\")"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Log Loss Score: 0.4950\n"
]
}
],
"source": [
"from sklearn.metrics import log_loss\n",
"\n",
"log_loss_score2 = log_loss(Y_test, Y_test_pred_prob)\n",
"print(f\"Log Loss Score: {log_loss_score2:.4f}\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Area under ROC curve"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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IWgog1FvYsmVLe/vtt93p2mCPkAaxJPxCCqVy5cquDuipbqOC8AoiUnPqN1Tgq8FjSmfQoDCdYgwO4hD1aigg1hfIqdqbVqpzqu2jgWqhAl89p3pf1OOoL+yE1OuSlAaWBHtC07KdU6IBTAr4X3vttfh1GkynXvCETmfbn0pwMgL1XusYCtKgHw1QSvqDIykFYDoGP/3007AOEtJAKPWI6UdSwiApLad4U/sYOr5F74OUfhAmt/3P9P2RklKlSrnBeLqol08BpX5EBgPf1D5f8Fg91Xs9pQBx3bp1qbq9jhn9iFVwpx+FwW2v96Lq7uq9+NRTT510P30ujB492j1fcD8o/Un30fGlQYepoWNXgbLSLcJ57OozIul7UtI6k50+h7RfdeZLnRZKl9JAxYw6puAP5Pgiqmn0v3qBNVuSAiaNjtc6ffFs3br1pNvrNGSQTrvpA1a9Ssn1vqnnSafgdToz1Gn6YHWC5KinR70X+qDXRR/6CYNQffmoHQqMQ30xJ2xvWjVt2tQFpepRDTUzlL5wFMw+8sgjJ/XQqKc4YY6upmxVzmUw6EjLdk6JXn/SHtj//e9/J/UkqWdNQn35ni6dOVCPnvatAoYgTSiQXA9f0p4n5QerR1JtTko9VwroVXkgLYI9wknTPrQfw/0Y7dq1cz+89ANJ75+EEt5X2z9U6smZvj9C0b5P+lw63kqXLu2C+VO1KSn9uNR7TqlR6nFNS++/UnW0n9Myi5neTzp9n7DHUuMR1JOrSiNJH0vHSZ8+fdwxp9SUhPfRZ4eCfaWrJKU0haRBoyqhaD/qvR/OY1fBqLZ1sFda9L4P9dmZElVZ0evSD7NPPvnEve8Spjmk1zEFf6HHF1FPU4BqViMNKNHAIeUBqzdBXxr6cFePj0qG6ctDH+TBKT11v+CMYBr4olxCDdpSL9mgQYPc6Tf1huh0nB5XvWXqGdUXswa1aP2kSZPiUy+Sow92lfBS3p9yffXhn5C+DPXYGjym9uoLUu3Q4K6pU6e6/58u9TCp50SlkpQSotPHCh50ynjmzJmubdoOSanXSdtQX8i6vX5YKEjUl3pQardzStQTpi9ApTjodeu+es3BU8xBOh2rYE6nu/UFrPxA5UkrIDpdOvWv2dSUbqHH0heuenp1HOmLPjW9TQoOlFagcmvapno96h1TgKVBOjpOEvbwp4aCUbVNPX4qUaayewoC9FpD/cg4k8dQrqdSH2677TZr2LChO0bUfu075QWrvJbovaEfbg888IC7ndKK9NjheH+ECug0WFUBUnCaXh0TKn2X8MxAcm0KRWkHOlbVa6wSWcpB1b4eP368S0FIid47CvBSkzsrOo6Vk6wydcp/17GsfaHPGr0X1Y6EM7fprIve6w8++GCiY0VnU3RM6cerAncdn9q+Wq9SgsGzNQnLsWmgmEoAtm3bNqzHrv5qSmQNBNbtdWyoXJ7y/tM6CFWfOQq2FeTrsyNpWcL0OKbgMyFrPQCZTHITWARnBlKhc12C5bLWrFkTuPHGGwMlS5Z0JcU0icFll13mSqAltGvXrsA999zjrg8WSlfpnoSlxTTr0n/+8x9XrF6l01SIXZMF9O/f3xVfT66cWZAmLAgW2Z89e3bI17d9+3ZXvqlcuXKuvWq3yoUNHjz4pDJdKu2VFprF6ZlnnnHtV5kylXW66KKLXNmupOWcEk5g8dprr7n26DU3b97cFcdPKjXbOaV9p1JrmrhDkwpo8gSV21JZqFDbcsiQIa7sUbZs2VI1gUXS7ZTcxAYq5abn0+ts1KiRK5ek/duhQ4dUbV8dc++//77bRio/pe2gx9PrSlguKrmZ24LbJ+GkHSppVbt2bTfRQoUKFdzxpwkpkt4uOIFFKKl9jOBtVYZOx4cm1dB20KQLCWfau/baa10pvqQTWKT2/RGc2CCUhOXMVGZOZcE0a5mOVc1Epv8nnXwjuTYlt5+XLVvmSsfp9tom5557buDpp58OnMrixYvd4yUtr5XcBBaiyVqSlmiTHTt2BB544IFAlSpV3LZSWzQxR7CEWSh6j6j0okoHahY3tV2zwKlc4tatWxPdVmXprr/++kBqpfbYlcmTJ7vn1eektp0mqEhpAovk6DNHnyu6nSaYCSW1xxQQShb943XwDSBzUC+YesM0mEg5in6k07w6Pd69e/eQp1vhP+qpVaqFzk5EKvVcq0dbPbBpGWwJRBtyfAEgGcqHTNo3oPQQpZcohxmQF154waVVpHUwV0ZSypTSQwh64Xfk+AJAMubPn++mKlaet3Ix1Vv2wQcfuJqvWgeI8u+PHTtmkezLL7/0uglARCDwBYBkqDybRrhr8JN6ec866yw3UYJ6zxLO+gYAyBzI8QUAAIAvkOMLAAAAXyDwBQAAgC9k92Mpoi1btrjZiJjuEAAAIPIoE1cT1qhUYNKJnc6E7wJfBb0arAIAAIDItmnTJjdbY7j4LvBVT6+sW7fOjdBGdIuNjXXzzWuKVk3liejG/vYX9re/sL/95a+//nITJgXjtnDxXeAbTG/QhtQ89Ij+D0rNTa99zQdl9GN/+wv721/Y3/6i/S3hTktlcBsAAAB8gcAXAAAAvkDgCwAAAF8g8AUAAIAvEPgCAADAFwh8AQAA4AsEvgAAAPAFAl8AAAD4AoEvAAAAfIHAFwAAAL5A4AsAAABfIPAFAACALxD4AgAAwBcIfAEAAOALBL4AAADwBU8D3++//946d+5spUuXtixZstioUaNOeZ+ZM2faBRdcYLly5bIqVarYsGHDMqStAAAAyNw8DXwPHjxoderUsYEDB6bq9uvWrbNOnTpZy5YtbcmSJfbvf//bbrvtNps0aVK6txUAAACZW3Yvn/zSSy91l9QaNGiQVaxY0V577TW3fN5559ns2bPtjTfesPbt26djSwEAAJDZeRr4ptW8efOsTZs2idYp4FXPb3KOHj3qLkH79u1zf2NjY90F0S24j9nX/sD+9hf2t7+wv6PTN99ksf79s9mBA/+/LkuW4xYXly1dni9TBb7btm2zEiVKJFqnZQWzhw8ftjx58px0nxdffNH69+9/0voZM2ZY3rx507W9iBxTpkzxugnIQOxvf2F/+wv7O7o88kgri4kp4P6fI0estWs3yQoX3muffXZZujxfpgp8T8fjjz9uDzzwQPyyguRy5cq5POGiRYt62jakP/UM6EOybdu2liNHDq+bg3TG/vYX9re/sL+jUyDwdyhauvQWu+qqEVa48C63XKfORvvlF58HviVLlrTt27cnWqflggULhuztFVV/0CUpvWl44/gH+9tf2N/+wv72F/Z3tAlY06ZzrHXr6ZYt2wkrUKCAde3a1e65p5AVK+bzwLdJkyY2YcKEROv060/rAQAAkHns27fPOnYcZWXKrHPL1atXd2VulYq6a9ffPb9RVc7swIEDriyZLsFyZfr/xo0b49MUbrzxxvjb33nnnbZ27Vp75JFHbMWKFfbOO+/Y119/bffff79nrwEAAABpEwgEXAynoPfYsRz2/fed7aqrrkr38VeeBr6LFi2yevXquYsoF1f/79u3r1veunVrfBAsKmU2fvx418ur+r8qa/b+++9TygwAACATyZIliytpu21bORs06A5bufICty69eZrqcMkll7iIPzmhZmXTfX7++ed0bhkAAEBkGj7cTH2E+/dbplK8eIwVKrTL/vijzj9rytjWrTfbiRNZrEyZjGlDpsrxBQAA8DsFvStWWKaRNesJa978B2vRYpadOJHVfv9dxQqC5Wn/7uUt8HdFs3RH4AsAAJCJBHt6s2Y1K1XKIlqBArvtkktGWsmSm9zy2rU1LG/eQol6eBX0DhiQMe0h8AUAAMiEFPTGxFhECgQC9uuvv7pqXMeOHXOlZTt27Gi1a9f2tF0EvgAAAAhr0Dt69Gj75Z8ZKDRxWPfu3a1w4cLmNQJfAAAAhI2qMxQrVsz9VVGCZs2aWVblZUQAAl8AAACckePHj7v5GQoVKuSWmzZtalWrVrUSJYKD2CIDgS8AAABO286dO23EiBEWFxdnt99+u5tSWj28kRb0CoEvAAA+lnlqwma3I0faWe7chC5bt1rE5PIuXrzYJk2aZLGxsZY7d277888/rXTp0hapOHoAAPCxzFMTVvVe83jdiIiSUbVvQzl06JCNHTvWVvxz8Gh23a5du1rBggUtkhH4AgDgY5mnJmzAjhw54noVg5Me+FlG1r5Nas2aNTZq1CiX06uUhtatW1uTJk0yZMrhM0XgCwAAIromrMTGxtmECZNdLVjlkMK79IY5c+a4oFeVG3r06GElS5a0zILAFwAAAKmiXt0uXbrYggULrGXLlpnuR0hkFFUDAABARPbwLliwwA1gC1LJsnbt2mW6oFfo8QUAAMBJlM6gGdhWr17tlmvUqOFmYcvMCHwBAACQyMqVK23MmDGuekP27Nmtbdu2VrZsWcvsCHwBwCcyT73W1KKuazTVhEVkiI2NtcmTJ9uiRYvcsiah6N69uxUvXtyiAZ8WAOATmadea2pR1zVaasIicvJ5P/nkE9u0aZNbVomyVq1auR7faBE9rwQAECX1WlOLuq7RUBMWkVWx4cILL7Q9e/a4ySgqVapk0YbAFwB8JtLrtaYWdV2BM7dv3z7bvXu3lS9fPn4AW5UqVSxnzpwWjQh8AQAAfGj58uU2btw4N/tanz59LH/+/G59tAa9QuALAADgI0ePHrWJEyfakiVL3HLp0qXdoDY/IPAFAADwiZiYGBsxYoRLb5DmzZtbixYtLFu2bOYHBL4AAAA+qNjw/fff26xZs9z/Nftat27d4nN7/YLAFwB8UhOXeq2Avys27N271wW9tWrVcoNC/66I4i8EvgDgs5q41GsF/EFBblxcXHzVkw4dOriKDarc4FcEvgDgo5q41GsF/EE1rsePH++mHL7++utdj2/OnDl9HfQKgS8AnIFoqYkLIHps2LDBRo4c6VIbFPBu3rzZypYt63WzIgKBLwAAQBQ4fvy4zZw502bPnu2WixQpYt27dyfoTYDAFwAAIJPbuXOnK1O29Z9RrHXr1nU5vbly5fK6aRGFwBcAACCTD2ILBr2q1NC5c2ff5/Imh8AXAE6jbBmlwQBECuXxXnbZZTZjxgwX9BYsWNDrJkUsAl8AOIOyZZQGA+CFNWvW2J49e6x+/frx0w5fd911Xjcr4hH4AsBpli2jNBiAjKa6vFOnTrUFCxa4aYY1cK1EiRJeNyvTIPAFgFSgbBkAr+3YscO+/fZb91fq1atnZ511ltfNylQIfAEAACJ88NrChQttypQprmRZ3rx5rUuXLlatWjWvm5bpEPgCAABEcND71Vdf2cqVK92yphxW0Js/f36vm5YpEfgCAABEcMWGcuXKucFsbdu2tYYNG7p1OD0EvgAAABEkNjbWDhw44GZek6ZNm9p5551HPm8YEPgC8L3kavUK9XoBZCRNQqHJKKR3796WI0cO18NL0BseBL4AfO9UtXqFer0A0juXd+7cuTZ9+nQ7ceKEy+HdvXu3FS9e3OumRRUCXwC+l1KtXqFeL4D0tG/fPhs5cqStX7/eLVevXt3NwKbqDQgvAl8A+Ae1egFktOXLl9u4cePsyJEjLq2hQ4cOrj4vA9jSB4EvAACAR+kNixcvdkGvphzu3r27FS1a1OtmRTUCXwAAgAwOeNWjq4tq8v7888/WrFkzNwUx0ldWrxsAAADgBxq0NmvWLPvuu+/i1xUsWNBatGhB0JtB6PEFAABIZ6rQoAFsmzZtcst16tSxMmXKeN0s3yHwBQAASMe0hqVLl9r48ePt2LFjlitXLuvYsSNBr0cIfAEAANKBBq0p4F22bJlb1tTDGsBWuHBhr5vmWwS+AAAA6dDT+/HHH7uZ2DSI7ZJLLnED2LKqYDg8Q+ALAAAQZgp2L774YpsyZYp169bNypYt63WTQOALAAAQHrt27bK9e/dapUqV4mdgq1KlimXPTrgVKdgTAAAAYZiIYtKkSS7I7dOnjxXQXOcKtAh6Iwp7AwAA4DQdOnTIxo4daytWrHDLpDRENgJfAL4xfLhZ375m+/cnXr91q1ctApCZrVmzxkaNGmUHDhxwg9Zat25tTZo0cfm9iEwEvgB8Q0HvP50yIf1zZhIATpnaMHnyZJs/f75bLlasmCtTVqpUKa+bhlMg8AXgG8GeXlUTSvr9pKB3wABPmgUgk1GPbmxsrPt/gwYNrF27dpYjRw6vm4VUIPAF4DsKemNivG4FgMzWyxuceU0U7J533nlWuXJlr5uGNCDwBQAASIFyeEePHm3Hjx+3G264wfX45syZk6A3EyLwBQAASMaqVatc0KvqDSpNtm3bNnJ5MzECXwAAgCSUw6sBbIsWLXLLJUqUcAPYihcv7nXTcAYIfAEAABLYunWrjRgxwnbu3OmWL7zwQleqjMkoMj/2IICo8803WezZZ6nXC+D0BrEptUFBb/78+a1r167k8kYRAl8AUad//2y2cmXy11OvF0ByNHCtS5cuNmfOHOvYsaPlzZvX6yYhjAh8AUSdAwf+/ku9XgCp8dtvv7nKDY0aNXLLGrx2xRVXeN0spAMCXwBRi3q9AFJy9OhRmzhxoi1ZssRNOVy+fHk3iA3Ri8AXAAD4TkxMjBvAtnv3brfctGlTN/UwohuBLwAA8I0TJ07YDz/8YLNmzXID2QoVKmTdunVzvb2IfgS+AADAFxTofvLJJ7Z+/Xq3XLNmTevUqZPlzp3b66YhgxD4AgAA31RsqFatmqvTq4oNtWvX9rpJyGAEvgAyzPDhZn37nlxfN3yy25Ej7eyflD0AsCNHjriKDcH8XU1Gcf7551vBggW9bho8QOALIMMo6F2xIj2fIYuZ5Ylfol4v4G8bNmywkSNHuhnXevfubTlz5nS9vgS9/kXgCyDDBHt6Q9XXDY+A691Rvl6BAlmo1wv41PHjx23mzJk2e/Zst1ykSBHbv3+/FS1a1OumwWMEvgCipr5ubGycTZgw2eXu5ciRI/xPACDi7dq1y5Up27Jli1uuW7eudejQwXLlyuV10xABCHwBAEBUVGxYvHixTZo0yWJjY92Zn86dO1uNGjW8bhoiCIEvAACICr///rsLeitWrGhdu3YllxcnIfAFAACZuqdXA9Z06dKliy1fvtwaN27sloGksp60BgAAIMLFxcXZxIkTbdy4cfHrChQo4MqVEfQiYgPfgQMHWoUKFVwujn6hLVy4MMXbv/nmm3buuedanjx5rFy5cnb//fe7UdwAAMAfduzYYUOGDLEFCxa4vN5t27Z53SRkEp6mOnz11Vf2wAMP2KBBg1zQq6C2ffv2tnLlSitevPhJt//888/tscces6FDh1rTpk1t1apVdtNNN7lfdq+//ronrwEAAGRcWsOPP/5o06dPdyXL8ubN69IbSpYs6XXTkEl4GvgqWL399tvt5ptvdssKgMePH+8CWwW4Sc2dO9cuuugiu/baa92yeoqvueYa94sPAABEL82+tnbtWvvll1/ccpUqVVzQmz9/fq+bhkzEs8D32LFj9tNPP9njjz8evy5r1qzWpk0bmzdvXsj7qJf3008/dekQjRo1cm+ACRMm2A033JDs8xw9etRdgvbt2+f+atSnLohuwX3Mvo6kjxzl3gVczd1wY3/7C/vbXz29OuurSSg0C1urVq2sfv367owv+z86xabTfvUs8N25c6c7TVGiRIlE67W8Ipk5TdXTq/s1a9bMvQmU2H7nnXfaE088kezzvPjii9a/f/+T1s+YMcOdIoE/TJkyxesmwMyOHGnnphRWXr4mmkgv7G9/YX/7gwauqde3fPnyLsf3u+++87pJSEeHDh1Kl8fNVOXMNP3gCy+8YO+8847LCV69erXdd999NmDAAHv66adD3kc9ysojTtjjq0FxLVu2ZOpCn/xi1Jdi27ZtmckrAuTO/fdHjgazana1cGN/+wv7O7ppwNrBgwetcuXK8ft78uTJ1q5dO/a3T2bgi6rAt1ixYpYtWzbbvn17ovVaTi5JXcGt0hpuu+02t1yrVi33pujdu7c9+eSTLlUiKU1RGGqaQr1peOP4B/s70mRJ1/3B/vYX9nd00RldjenRALacOXNanz594ieiUGoD+9sfcqTTPvYs8NXBrPycadOmudlV5MSJE275nnvuSbbbO2lwq+A5+EYBzsTw4WZ9+5rt3+91S6LX1q1etwBAJNu7d6+NGjXK1q9fHz+IXTm9QLh4ejQpBaFXr17WoEEDN1hN5czUgxus8nDjjTdamTJlXJ6uaM5tVYKoV69efKqDeoG1PhgAA6dLQW8y6eUIswIFvG4BgEijGdc0GYXGAKi3r0OHDu77nskoEDWBb8+ePe3PP/+0vn37ulyeunXrullYggPeNm7cmKiH96mnnnJvAP3dvHmznX322S7off755z18FYgWwZ5eHXKlSnndmugOegcM8LoVACKFztiOGTPGlixZ4pZLly5t3bt3ZxwO0oXn5w+U1pBcaoMGsyWk0x39+vVzFyC9KOiNifG6FQDgD+rQ0ve7/qpqU4sWLTiLi+gNfAEAgL9oTI9q7OfJk8ctq1JD7dq1XdUlID0R+AIAgAyze/duGzlypEtl1Fge/VVOL0EvMgKBLwAAyJBc3l9//dXNuKrZW1VqVJNSFS9e3OumwUcIfAEAQLpSpYbx48fbsmXL3LJ6dzWArXDhwl43DT5D4Isor7ub3U2TG5wxLCXUmAWA8FNNXqU2aOZUDWC75JJL3CC2UJNOAemNwBdRXndX9R//HjyRWtSYBYDwpTeoTKmC3iJFirhe3rJly3rdLPgYgS+ivO5uwJ1iy5079z9BcMqoMQsA4aMeXs3O+uOPP1r79u3drK2Alwh8EdV1d2Nj42zChMnWsWNH5nYHgAzo4V28eLEbvNakSRO3rmTJkm6yKSASEPgCAIAzdujQIRs7dqytWLHC5e9WrlyZig2IOAS+AADgjKxZs8ZGjRplBw4ccEFv69at7eyzz/a6WcBJCHwBAMBpiYuLs6lTp9qCBQvccrFixaxHjx4uvQGIRAS+AADgtKYd/vDDD23Lli1uuWHDhta2bVvGUyCiEfgi09brpe4uAHhHKQ21atWyPXv2WJcuXaxatWpeNwk4JQJfZPp6vdTdBYCMoRxeDWILDlpr3Lix1a5d2/Lmzet104BUIfBFxEupXi91dwEgY6xcudLGjBnj6qLfcccdriav6vQS9CIzIfBFVNTrBQCkj9jYWJs8ebItWrTILRcoUMD1+jIZBTIjAl8AABDS1q1bbcSIEbZz5063rEkpWrVqZdmzEz4gc+LIBQAAJ83ANnfuXJs+fbqr3pA/f37r1q2bVapUyeumAWeEwBcAAJxk/fr1LuitXr26m3KYXF5EAwJfAADgKNBVmTINWlOJstWrV1udOnXcMhANCHwRETV5U0K9XgBIX0ePHrWJEye6/yvgFaU31K1b1+OWAeFF4IuIqsmbEur1AkD4xcTEuAFsu3fvdj27GsAWrNMLRBsCX0RMTd6UUK8XAMKf1vDDDz/YrFmz3GC2QoUKuQFsBL2IZgS+8AQ1eQHAO+rdHTlypG3atMkt16xZ0zp16uQmpwCiGYEvAAA+6+n99NNP7a+//rJcuXJZx44d3bTDgB8Q+AIA4COq2tChQwebPXu2S20oXLiw100CMgyBLwAAUW7Dhg125MgRO/fcc91y1apVrUqVKpQpg+8Q+OK0UZoMACLb8ePHbebMma53V/m7d955pxvEJgS98CMCX5w2SpMBQOTauXOnK1O29Z8eB83AxuA1+B2BL04bpckAIPKoNNnixYtt0qRJFhsb64JdTTlco0YNr5sGeI7AF2eM0mQAEDkVG4YPH24r/jkdV7FiRevatasVLFjQ66YBEYHAFwCAKKrYoCBXf1u3bu1mYSOXF/h/BL4AAGRicXFxdvToUcuXL59bbtOmjV1wwQVWokQJr5sGRBwCXwAAMqkdO3a4AWzK473xxhtdT2+OHDkIeoFkEPgCAJAJB7AtXLjQpkyZ4kqW5c2b101DXLRoUa+bBkQ0Al+cdr1eavICQMY7cOCAjR492lavXu2WNRFFly5dLH/+/F43DYh4BL4443q91OQFgIyxcuVKGzNmjB06dMiyZ89ubdu2tYYNGzKADUglAl+cUb1eavICQMaVKps+fboLepXD2717dytevLjXzQIyFQJfpBr1egHAOxq4pmD3119/tZYtW7oeXwBpw7sGAIAIHcA2d+5c97dZs2ZunXp6ld4A4PQQ+AIAEGH27dtno0aNsnXr1rn83erVq1uxYsW8bhaQ6RH4AgAQQZYvX27jxo2zI0eOuJq8HTp0oEwZECYEvgAARADNvjZx4kRbsmSJWy5durTL6SXoBcKHwBcAgAio2DB06FA3E5s0b97cWrRoYdmyZfO6aUBUIfAFACACKjZccMEFNm/ePOvWrZuVL1/e6yYBUYnAFwAAD2iKYaU3lCxZ0i03atTI6tata7ly5fK6aUDUIvAFACADqTzZ0qVLbfz48ZYvXz674447XLCr6g0EvUD6IvAFACCDqFKDAt5ly5bF1+U9duwYAS+QQQh8AQDIABs2bLCRI0fa3r17Xe/uJZdc4iamUH4vgIxB4AsAQDpXbJgxY4bNnj3bLRcpUsSVKStbtqzXTQN8h8AX8YYPN+vb12z//sTrt271qkUAkPmpd3f79u3u/xq8pgkpSG0AvEHgi3gKelesSP76AgUysjUAkLkHsB0/ftyyZ8/uAt8uXbrYxo0b7bzzzvO6aYCvEfgiXrCnV+lmpUqdHPQOGOBJswAgUzl06JCNHTvW9ep27drVrVP1BoJeIJMHvhqdmjt37vC1BhFBQW9MjNetAIDMZ82aNTZq1Cg7cOCAG7SmGdiYchiIHFlPJ0l/wIABVqZMGcufP7+tXbvWrX/66aftgw8+SI82AgAQ0eLi4mzSpEn26aefuqC3WLFidttttxH0Apk98H3uueds2LBh9vLLL1vOnDnj19esWdPef//9cLcPAICItmPHDvf9N3/+fLfcoEED6927t5VKmjMGIPMFvh9//LENHjzYrrvuOsuWLVv8+jp16tiKlEZGAQAQZXQW9IsvvnBVG/LmzWvXXHONderUyXLkyOF10wCEI8d38+bNVqVKlZBv/tjY2LQ+HAAAmZbyeBXoLly40C6//HKXAgggigLfGjVq2A8//GDly5dPtP6bb76xevXqhbNtAABEnFWrVrlSZcEqDeoMqly5sitbBiDKAt++fftar169XM+venlHjBhhK1eudCkQ48aNS59WAgDgMZ3VnDx5si1atMiVKitdurQVKlTIXUfQC0Rpjq+KcKs+4dSpU11dQgXCv//+u1vXtm3b9GklAAAe2rp1qxvfoqBXdIZT34EAfFDHV3UJp0yZEv7WAAAQYTOwzZ0716ZPn+7OciqHV5NSKLUBgA96fCtVqmS7du06af2ePXvcdQAARAPl8X7yySfuDKeC3urVq1ufPn0IegE/9fiuX7/efRgkdfToUZf3CwBANFDJzuLFi1tMTIx16NDBpTeQywv4JPAdM2ZM/P81O00woV8UCE+bNs0qVKgQ/hYCAJBB1Ilz7NgxK1CggFtu06aNNWrUyM466yyvmwYgIwNf5TSJfu2qqkNCKtStoPe1114LR5sAAMhw6tlVpSLl8d50002uRm/27NkJegE/Br7Kb5KKFSvajz/+6OYhR+YzfLhK0pnt33/ydVu3etEiAPCWvt9Un37WrFluMJuW9+7da0WKFPG6aQC8zvFdt25duNuADKSg91QzS/9zhg8Aot7u3btt5MiRtmnTJrdcs2ZNNxNb7ty5vW4agEgpZ3bw4EH3y3jjxo0uFyqhf/3rX+FqG9JBsKc3a1azUqVCB70DBmR4swAgQ6lnd+nSpTZ+/Hj3PZYzZ04X8NauXdvrpgGIpMD3559/to4dO9qhQ4dcAKzcp507d1revHnd6FcC38xBQW9MjNetAABvKJ1B9XkV9JYrV866detGagPgA2mu43v//fdb586d3emhPHny2Pz5823Dhg1Wv359e/XVV9OnlQAAhLlUWY8ePaxly5ZuIBtBL+APaQ58lyxZYg8++KAb7aoPDpV+0a/ll19+2Z544on0aSUAAGcgWHbz+++/j1939tln28UXX+y+zwD4Q5pTHVS6LPghodQG5fmed955rq5vcHAAAACRQrONqkzZli1bXElODWCjRBngT2kOfDVzjcqZVa1a1Vq0aGF9+/Z1Ob6a1lEfJgAARMoAtsWLF7tJl2JjY12lBqXqEfQC/pXmwPeFF16w/f+UBnj++eftxhtvdHOXKxD+4IMP0qONAACkiQZgjx071lb8U79RNeg1EVPBggW9bhqAzBT4NmjQIP7/SnWYOHFiuNsEAMAZ5fO+//77bhC2UvNat25tTZo0cWkOAPwtbBn9Op102WWXhevhAAA4LRp4rUBXM4zedttt1rRpU4JeAGkPfJUn9dBDD7nqDWvXrnXrdBpJp48aNmwYP61xWgwcONAqVKjgcq8aN25sCxcuTPH2e/bssbvvvttKlSpluXLlsmrVqtmECRPS/LwAgOixY8cO27x5c6Kzk71793bfFQCQ5lQH5e/efvvtblCATh/pNNLrr79u9957r/Xs2dOWLVvmqjukxVdffWUPPPCADRo0yAW9b775prVv395Wrlzp0iiSUqHxtm3buuu++eYbK1OmjKshXLhw4TQ9LwAgegawacD19OnTrUCBAnbnnXe6ThH18KoKEQCcVuD71ltv2X/+8x97+OGH7dtvv7Urr7zS3nnnHTflY9myZe10KHBWMH3zzTe7ZQXAmj5y6NCh9thjj510e63/66+/3Gw7wQ809RYDAPznwIED7uzjL7/84paV2qD8XgA448B3zZo1LtiV7t27W/bs2e2VV1457aBXvbc//fSTPf744/HrNAihTZs2Nm/evJD3GTNmjMvbUqrD6NGjXfHxa6+91h599FGX0xWKJtjQJWjfvn3ur0rb6OLPXa5ct4DFxsZZtAvuY3/ua/9hf/vHH3/84TpKVL1B30etWrVyM4iqp5f9H514f/tLbDrt51QHvocPH7a8efO6/+uDRaeSziR3SrV/9cu8RIkSidZrOVh+Jin9stfprOuuu87l9a5evdruuusut3H69esX8j4vvvii9e/f/6T1M2bMiH890WjOnNL2xRfV7fDhxLt49+6/l48cOWITJkw2v5gyZYrXTUAGYn9Hd2pDTEyMm5RCND6kfPnyLsf3u+++87p5yAC8v/3h0KFD3pczU15v/vz53f/j4uJs2LBh7tRSQv/6178svWjwnPJ7Bw8e7Hp49etegxnU85xc4KseZeURJ+zx1RTLmp+9aNGiFq0efTS7xcQkP4q5WLHc1rFjR4t2+lGkD0nlhpPvF/3Y3/4IfDXGQ4GvBrDp7KHGhrC/ox/vb3/Z9c+PW88C33POOceGDBkSv1yyZEk3W1tC6glObeCrgFnB6/bt2xOt17IeOxT1MOtgT5jWoAF127Ztcx9+OXPmPOk+6pnWJSk9TjS/cQ4c+PuvZpdO2jFfoIDZgAH+GvgR7fsbibG/oy/YVWdLcJ926dLFfVeoE0Nn/9jf/sL+9occ6bSPUx34rl+/PqxPrCBVPbbTpk1z5dCCPbpavueee0Le56KLLrLPP//c3U75wLJq1SoXEIcKevF30BsT43UrAOD07N2710aNGuUqNmh8ieTLl88qVapEricA7yawOB1KQVAv8kcffWS///67m/r44MGD8VUeNB1ywsFvul5VHe677z4X8Gpgg6ZQ1mA3AEB0Wb58uav2o44Xjf1QKU0AyNApi8NJ9X///PNP69u3r0tXqFu3rpsCOTjgbePGjfE9u6LTWppE4/7777fatWu7Or4KglXVAQAQHVSJRwPVgmXKSpcu7Xp7ixQp4nXTAGRynga+orSG5FIbZs6cedI6lTObP39+BrQMAJDRVLFhxIgRrndX40aaNWtmLVq0SLZkJQBkqsAXAABRicvhw4e76juFChWybt26uVJlABAuBL6Z2PDhZn37mu3ff/J1W7d60SIAOH3q1b388stdioPKLapGLwB4HvhqFrcPP/zQ/dVUxqqtq3wslTw7//zzw9pAJE9BbzJzfSQqXQYAkVqm7Ndff3UBb82aNd26ypUruwsARERVh1mzZlmtWrVswYIFLg9Lc6WLfqEnN4kE0kewp1fj/8qUOflSvbrq9XrdSgA4mWaP1HeISpWNHTvWlS0DgIjr8X3sscfsueeec6XIVFcxSPOkv/322+FuH1KBWr0AMhOVJxs5cqTL5dUANtVoT/h9AgARE/guXbrUTSKRlNIddu7cGa52AQCicPCaqvXMnj3bLas8mcqUlS1b1uumAfCJNAe+hQsXtq1bt1rFihUTrf/5559dXV0AAJLSlMMaG7Jlyxa3rLrtl156KbNuAojsHN+rr77aTRihCSd0ikrTB8+ZM8ceeughN9MaAABJZc+e3ZUmU6WGK6+80rp06ULQCyDye3yDUwRrFjWdtqpRo4b7e+2119pTTz2VPq0EAGQ6hw4dstjYWFeTNzgW5MILL7SCBQt63TQAPpXmwFe/0IcMGWJPP/20LVu2zFV1qFevnlWtWjV9WggAyHRU7lIVG5Qed/PNN7vp59XrS9ALIFMFvhqUoCkkVbNXFwAAEubyTp061ZW8FKU2qIOEgBdApgx8dapKg9iuueYau/76612qAwAAO3bssG+//db9lQYNGli7du0sR44cXjcNAE5vcJtG5D744INuIgvNtKORua+88orFUEgWAHw7A5t6eAcPHuyC3rx587rOkU6dOhH0AsjcgW+xYsXsnnvucZUclMOl0bkfffSRVahQwfUGAwD8RdV9lixZ4gY6V6lSxfr06WPVqlXzulkAcOapDgmplq9mcqtTp44b7KZeYACAf3p6VdYyW7Zs1qNHD1u7dq01bNjQrQOAqOjxDVKP71133WWlSpVypcyU9jB+/Pjwtg4AEHFUomzcuHFuFraEZwMbNWpE0Asgunp8H3/8cfvyyy9drm/btm3trbfecoXIldMFAIhumrlzxIgRbop6lShTOUuVLAOAqAx8v//+e3v44Yftqquucr/wAQD+SGuYO3euTZ8+3eX05s+f37p27UrQCyC6A1+lOAAA/GPv3r1uMor169e75erVq1vnzp050wcgOgPfMWPG2KWXXurK0uj/Kbn88svD1TYAgMc0IcXQoUNt37597jugQ4cOLr2BXF4AURv46nTWtm3brHjx4u7/ydEHocrZAACig6YZvvjii23x4sXWvXt3K1q0qNdNAoD0DXyVzxXq/wCA6KMJiZTTW65cObd8wQUXuMmKVLYMAHxVzuzjjz+2o0ePnrT+2LFj7joAQOakjg3VY1dqg6YePnLkiFsfrNULAL4LfG+++WY30CGp/fv3u+sAAJnP7t27bdiwYa42b8LeXgDwdVWH4Ew9oU6NFSpUKFztAgBkAH2m//rrrzZhwgR35i5XrlzWsWNHq127ttdNAwDvAt/gKF5dWrdu7QY8BGlA27p169xoXwBA5qnYMHr0aFu2bJlbVi+vBrBRmxeA+T3wDVZzWLJkibVv394VLw/KmTOnVahQwc3VDgDIHJS3q+BXHRqXXHKJNWvWzM3GBgDm98C3X79+7q8C3J49e1ru3LnTs10AgHSgM3QKdpXSoIBXE1Eo4C1TpozXTQOAyMvx7dWrV/q0BACQrnbt2mUjRoywIkWKuDN0Cnw1+xozsAHwi1QFvmeddZatWrXKihUr5j4wU5qx56+//gpn+wAAYRjApgkoJk2aZLGxse5zWjOxMSAZgN+kKvB94403rECBAvH/Z6pKAMgcDh06ZGPHjrUVK1a45YoVK7oxGwULFvS6aQAQmYFvwvSGm266KT3bAwAIkzVr1tioUaPswIEDbtCaKvI0adKEzgsAvpXm4bs6XbZ06dL4ZZXCUe/BE0884WpAIvyGDzc77zyzsmUTX7Zu9bplACKVBrCNGTPGBb1KU7vtttusadOmBL0AfC3Nge8dd9zh8n1l7dq1rsKDBkYMHz7cHnnkkfRoo+/17Wums5SbNye+nDjx9/X/ZKEAQDzVWlenRIMGDax3795WqlQpr5sEAJmvqoOC3rp167r/K9ht0aKFff755zZnzhy7+uqr7c0330yPdvra/v1//1V5zaTfXQp6BwzwpFkAImwA28KFCy1Pnjzxs64pn1cXAMAZTFl84p+uxqlTp9pll10WP+PPzp070/pwSAMFvTExXrcCQKRROoPSzlavXh0/oRCD1wAgDIGvTps999xz1qZNG5s1a5a9++67br2mLC5RokRaHw4AcAZWrlzpcnlVvUHpDRrAFqzCAwA4w8BXqQzXXXedGyn85JNPWpUqVdz6b775xg2cAACkP9XjnTx5si1atMgtq+Ohe/fuVrx4ca+bBgDRE/gqdyxhVYegV155xc37DgBI/6B3yJAh9ueff7pllShr1aqV6/EFACTvtD8lf/rpJ/v999/d/2vUqGEXXHDB6T4UACANcuTIYVWrVrXDhw+7yg2VK1f2ukkAEJ2B744dO1wJM+X3Fi5c2K3bs2ePtWzZ0r788ks7++yz06OdvqnXq9JlwSoOQdTrBaApho8fP+6mjRf18F500UWunCQAIJ3q+N57771uBPHy5cvdfO+6LFu2zH0o/+tf/0rrwyEB6vUCCEWftxpI/O2337rgV5RaRtALAOnc4ztx4kRXxuw8TSX2D6U6DBw40Nq1a5fWh0MC1OsFkNDRo0fdZ+6SJUviy0kqvSF//vxeNw0A/BH4qoav8suS0rpgfV+cGer1AoiJibERI0bY7t273XLz5s3dhEEMIgaADAx8lVd233332RdffGGlS5d26zZv3mz333+/qx8JADh96kD44Ycf3DgK9fAWKlTIunXrZuXLl/e6aQDgv8D37bfftssvv9zNDKTZ2mTTpk1Ws2ZN+/TTT9OjjQDgGwp2NSmF/upztVOnTpY7d26vmwUA/gx8FewuXrzYpk2bFl/OTPm+mskNAJB2CnIlS5YsLpVBE1Fs2bLF1U0HAHgU+H711Vduasxjx465tAZVeAAAnL4jR47Y+PHjXZkypZJJsWLF3AUA4FHgq1I6d999tyuanidPHjfoYs2aNW7GNgBA2m3YsMFGjhxpe/fudT29DRo0sIIFC3rdLACIWlnTktvbr18/l3um0jofffSRvfPOO+nbOgCIQqrFq3SxYcOGuaBXvb033XQTQS8ARErgu3btWuvVq1f88rXXXmtxcXG2lWnFACDVdu3aZUOHDrXZs2e75bp169odd9xhZcuW9bppABD1sqelkHq+fPnil7NmzWo5c+Z0xdQBAKcWGxtrH374oR08eNBVaujcubObAAgAEIGD255++ulEU2RqkNvzzz/v6kwGvf766+FtIQBECU30owFsmua9a9eupDYAQKQGvhdffLHL702oadOmLgUiSKV4AAD/T4OAFfCec845brlevXruwuclAERw4Dtz5sz0bQkARBGNgdAAtvnz57ue3TvvvNNVxCHgBYBMNIEFACBlO3bscCUft2/f7parVatm2bPzcQsAXuOTGADCOAPbwoULbcqUKa5kmcZEdOnSxQW+AADvEfgCQJgqNnz99de2evVqt1ylShUX9ObPn9/rpgEA/kHgCwBhoFQGlXjUDGzt2rWzhg0bks8LABGGwBcAzqCXVykNqsmrIPeyyy6zFi1aWPHixb1uGgDgTGZuS+iHH36w66+/3po0aWKbN2926z755JP4mYgAINpp1srBgwfb2LFjXW6vqGoDQS8ARFHg++2331r79u3dB/zPP//sZnQTzTf/wgsvpEcbASBiKMidM2eOvf/++7Zz507buHGjHThwwOtmAQDSI/B97rnnbNCgQTZkyBBXlD3ooosussWLF6f14QAg09i3b587uzV16lQ7ceKEVa9e3fr06WMFChTwumkAgPTI8dXsbZrFLSlNW7xnz560PlzUGj7crG9fs/37U3+frVvTs0UAzsRvv/3m0hqOHDnifvR36NCBGdgAINoD35IlS7pyPRUqVEi0Xvm9lSpVCmfbMjUFvStWnN596TwCIm8Q26RJk1zQW7p0aevevbsVLVrU62YBANI78L399tvtvvvus6FDh7qeji1btti8efPsoYcesqeffjqtDxe1gj29WbOalSqVtqB3wIB0axaA06Ae3q5du9ratWvtkksucSXLAAA+CHwfe+wxl9vWunVrO3TokEt7yJUrlwt877333vRpZSamoDcmxutWAEgLfcapeo1SuOrWrevWVaxY0V0AAD4KfNXL++STT9rDDz/sUh40mrlGjRrMTgQgKuzevdtGjhxpmzZtcj29lStXZvAaAPh9AgvNUKSAFwCipUzZ0qVLbfz48Xbs2DF3Jqtjx44EvQDg58C3ZcuWKY5inj59+pm2CQAylAatKeBdtmyZWy5XrpwbwFa4cGGvmwYA8DLwDea7JRztvGTJEveF0atXr3C2DQDSnT7D3nvvPVeOUT/qNXitWbNmllUjUwEA/g5833jjjZDrn3nmGWYvApDpKI/3/PPPd3V61ctbtmxZr5sEAEgnYevSuP76612JMwCIdLt27bK//vorUQrXHXfcQdALAFHutAe3JaVavrlz5w7XwwFAugxg09Tqmozi7LPPtltuucXV5A1eAADRLc2Br04FJv0i2bp1qy1atIgJLABELNUd15TDK/6ZUlFVG44ePWp58+b1umkAgEgNfFXQPSENADn33HPt2WeftXbt2oWzbQAQFmvWrLFRo0a5cQj6zNIEPE2aNEmxQg0AwOeB7/Hjx+3mm2+2WrVqWZEiRdKvVQAQBnFxcTZt2jSbP3++Wy5WrJj16NHDSpYs6XXTAACRPrhNOXDq1VXZn3AaOHCgVahQweUIN27c2BYuXJiq+3355Zeux6Zr165hbQ+A6KDPh40bN7r/N2zY0Hr37k3QCwA+luaqDjVr1rS1a9eGrQFfffWVPfDAA9avXz836KROnTrWvn1727FjR4r3W79+vT300EPWvHnzsLUFQOancQcnTpyI/7GucQnXXHONm4VNpcsAAP6V5sD3ueeecwHnuHHj3KC2ffv2Jbqk1euvv2633367S6HQFMiDBg1yg01SKo2mlIvrrrvO+vfvb5UqVUrzcwKITsrh1Q/zWbNmxa8rWrSoVatWzdN2AQAyWY6vBq89+OCDrtdELr/88kQDQ9TLomUFpal17Ngx++mnn+zxxx+PX6eBJ23atHHl0VJqS/Hixe3WW2+1H374IcXn0KhtXYKCwblma9IlfTettk/AYmPj0vF5kJLgPk7ffY1I8Mcff7gf5IcPH3ZVZpTakD9/fq+bhXTE+9tf2N/+EptO+znVga96V++8806bMWNG2J58586dLlAuUaJEovVaDpYcSmr27Nn2wQcfuGmSU+PFF190bU9KryM9yxgdOaIKF3nsyJEjNmHC5HR7HqTOlClTvG4C0onSGjZv3uwmpRCNFShfvrx9//33XjcNGYT3t7+wv/1TgtLTwFc9utKiRQvzyv79++2GG26wIUOGuNHZqaHeZOUQJ+zxLVeunJupSadA00vu3Nnjv4SDveTw5hejPiTbtm1LfmcU2rZtmytTFpyFrUGDBu5MksYJsL+jH+9vf2F/+8uufzozPC1nFu6alwpeNfhk+/btidZrOdTIa9Xi1KC2zp07x68LDmLJnj27rVy50ipXrpzoPipSr0tSetNkzBsnC2/QCJBx+xsZRQHuF1984VIbChQo4Kq76EfthAkT2N8+w/72F/a3P+RIp32cpsBXA0ROFfwGe15SI2fOnFa/fn1XZzNYkkyBrJbvueeek25fvXp1W7p0aaJ1Tz31lOsJfuutt9yXHgB/0OeHyivqB69+DCt1idw/AEDYAl/lyiadue1MKQ2hV69e7hRlo0aN7M0337SDBw+6Kg9y4403WpkyZVyurtIGVE4tocKFC7u/SdcDiD7Lly+3fPnyubrfovKHujADGwAg7IHv1Vdf7aophFPPnj3tzz//tL59+7p8vbp169rEiRPjB7yp+LwqPQDwL1Vm0eeCBrUqraFPnz6WJ08eAl4AQPoEvun5BaO0hlCpDTJz5swU7zts2LB0ahWASBATE2MjRoyw3bt3u2X9OFaaAwAA6V7VAQAygvL9Vadbk1Ho80dpVt26dXOlygAASNfAN1g9AQAyomLDp59+aps2bXLLtWrVcmUBlecPAECG5PgCQEaVsSlYsKArRaiAt3bt2l43CQAQBQh8AUQEzXKolIbgoLVOnTq5dUWKFPG6aQCAKEG5BACe08Q07777ro0ZMyZ+PIECYIJeAEA40eMLwDPHjx93lVtmz57tljWTo+ZnV61eAADCjcAXgCd27tzpypRt3bo1vkxZhw4dQk4xDgBAOBD4AshQSmVYvHixTZo0yU0xrEoNmnK4Ro0aXjcNABDlCHwBZCgFu6rPq78VK1a0rl27ugoOAACkNwJfABlKs65pIorNmzdbkyZNmHYYAJBhCHwBpKu4uDibNm2aFStWzOrXr+/WafY1ZmADAGQ0Al8A6WbHjh327bffur+alKJ69epUbAAAeIbAF0C6DGBbuHChTZkyxZUsy5s3r3Xp0oWgFwDgKQJfAGF14MABGz16tK1evdotV6lSxQW9+fPn97ppAACfI/AFEDZHjx619957zwW/2bNnt7Zt21rDhg0ZwAYAiAgEvgDCRpNP1KtXz1atWmXdu3e34sWLe90kAADiEfgCOCOaeU0D11S1QVq0aGEXX3yx6/EFACCSZPW6AQAy7wC2OXPm2Pvvv++mHtYgNsmWLRtBLwAgIvHtBCDN9u3bZ6NGjbJ169a55UKFCrmZ2BT0AgAQqQh8AaTJ8uXLbdy4cXbkyBGX4tChQweX18sANgBApCPwBZAq6tGdMGGCLVmyxC2XLl3aDWArWrSo100DACBVCHzPwPDhZn37mu3ff/J1W7d60SIg/SiNYefOne7/zZs3d4PYSG0AAGQmBL5nQEHvihUp36ZAgYxqDRB+J06ccIPYFOBmzZrVunXrZvv377fy5ct73TQAANKMwPcMBHt6s2Y1K1UqdNA7YECGNwsIi927d9vIkSOtXLlybiIKOeuss9wFAIDMiMA3DBT0xsR43QogPNTD++uvv7p83mPHjtmff/5pF110keXNm9frpgEAcEYIfAHEU6WG8ePH27Jly9yyens1gI2gFwAQDQh8ATjr1693qQ2q0avSZJdccok1a9bM5fYCABANCHwBuJ7eL7/80o4ePWpFihRxvbxly5b1ulkAAIQVgS8Ay507t1166aWu11cTUuTKlcvrJgEAEHYEvoBPB7AtXrzY9e5WqlTJratTp467AAAQrQh8AZ85dOiQjR071lasWGH58+e3u+66y/LkyeN1swAASHcEvoCPrFmzxkaNGmUHDhxwg9aaNGni0hwAAPADAl/AB+Li4mzq1Km2YMECt1ysWDE3gK1UqJlXAACIUgS+gA8qNnz44Ye2Y8cOt9ygQQNr166d5ciRw+umAQCQoQh8gSinCg3Fixd36Q1dunSxatWqed0kAAA8QeALRKFgDq9mXNNkFJ06dXLpDhrMBgCAXxH4psLw4WZ9+5rt3594/datXrUISN7KlSttzJgxbrrhnj17usCXAWwAABD4poqC3hUrkr++QIGMbA0QWmxsrE2ePNkWLVrklvfs2ePyeylVBgDA3wh8UyHY05s1q1nSQfAKegcM8KRZQLytW7faiBEjbOfOnW75wgsvtNatW1v27LzFAQAI4lsxDRT0xsR43Qog8Qxsc+fOtenTp9uJEydcDm/Xrl2tcuXKXjcNAICIQ+ALZGLHjh2zH3/80QW91atXt86dO7sBbQAA4GQEvkAm7enVoDWVKtNEFEpxqFevnlsHAABCy5rMegAR6OjRozZ69Gj76aef4tedc845dsEFFxD0AgBwCvT4AplETEyMG8C2e/du++233+z888+nYgMAAGlA4AtEOOXv/vDDDzZr1iyX4lCoUCHr1q0bQS8AAGlE4AtEMPXujhw50jZt2uSWa9as6WZhY0IKAADSjsAXiFCafGLw4MHub86cOV3AW7t2ba+bBQBApkXgC0Qo9eo2btzY1q5d61IbihQp4nWTAADI1Ah8gQiyYcMGV4f37LPPdssXX3yxu2TVtIEAAOCM8G0KRIDjx4/btGnTbNiwYa5yQ1xcnFuvgJegFwCA8KDHF/DYrl27XLC7ZcsWt1yyZElXyQEAAIQXgS/gEZUmW7x4sU2aNMliY2NdTq+mHK5Ro4bXTQMAICoR+AIezcA2atQoW7FihVuuWLGide3a1QoWLOh10wAAiFoEvoAHcuTIYQcPHnT5u61bt7YmTZow5TAAAOmMwBfIIMEBa9mzZ3cBr0qUqUZvqVKlvG4aAAC+QOALZIAdO3a4AWxKaWjfvr1bR11eAAAyFoEvkM4D2BYuXGhTp051Pb4HDhxwdXnz5MnjddMAAPAdAl8gnSjIHT16tK1evdotV6lSxbp06ULQCwCARwh8gXSwatUqF/QeOnTIsmXLZu3atbOGDRsygA0AAA8R+AJhdvjwYZfPq5JlJUqUsO7du1vx4sW9bhYAAL5H4AuEmVIZOnXq5GZiU6kyVXEAAADe4xsZCMMAtrlz57reXeXxSq1atdwFAABEDgJf4Azs27fPzcC2bt06y58/v919991u6mEAABB5CHyB07R8+XIbN26cm4RCM7G1atXKcuXK5XWzAABAMgh8gTTSoLWJEyfakiVL3HLp0qXdALaiRYt63TQAAJACAl8gjRUbhgwZYrt373bLzZs3txYtWriSZQAAILIR+AJprNhQrlw5O3HihHXr1s3Kly/vdZMAAEAqEfgCp6De3Zw5c1q+fPnccseOHV0lBwaxAQCQuWT1ugFApFJw+8svv9igQYNszJgxblk0gI2gFwCAzIceXyAEVWoYP368LVu2LH5Zg9oIeAEAyLwIfIEkNmzYYCNHjrS9e/dalixZ7JJLLrFmzZpZ1qycIAEAIDMj8AX+cfz4cZs5c6bNnj3bLRcpUsSVKStbtqzXTQMAAGFA4Av8Iy4uLj61oV69etahQwc3qA0AAEQHAl/4WnDAmlIaNGitR48ebhriGjVqeN00AAAQZgS+8K1Dhw65ag2VK1e2hg0bunWkNQAAEL0IfOFLa9assVGjRtmBAwds/fr1VqtWLSo2AAAQ5Qh84bs83qlTp9qCBQvccrFixVx6A0EvAADRLyLqMw0cONAqVKjggo/GjRvbwoULk73tkCFDrHnz5m7EvS5t2rRJ8fZA0I4dO9zxEwx6GzRoYL1797aSJUt63TQAAOCHwPerr76yBx54wPr162eLFy+2OnXqWPv27V2QEorKTV1zzTU2Y8YMmzdvnpUrV87atWtnmzdvzvC2I3Pl837wwQfuuMqbN687hjp16mQ5cuTwumkAAMAvge/rr79ut99+u918881uJL2mh1VgMnTo0JC3/+yzz+yuu+6yunXrWvXq1e3999+3EydO2LRp0zK87cg8dEw1bdrUqlSpYn369LFq1ap53SQAAOCnHN9jx47ZTz/9ZI8//nj8Os2OpfQF9eamticvNjbWzjrrrJDXa5pZXYJUqkp0H11Sv5myqPiVxcbGpfI+8Noff/xh+fLlc//Xvr7wwgtd2TJdUr/vkZkE9yv71x/Y3/7C/vaX2HTaz54Gvjt37nSzZZUoUSLRei2vWLEiVY/x6KOPWunSpV2wHMqLL75o/fv3P2m9UiXUC5gaR460M7M8duTIEZswYXKq7gPv6AyAUl927drl8sbVuztlyhSvm4UMxP72F/a3v7C//eHQoUPp8riZuqrDSy+9ZF9++aXL+01uVL56k5VDnLDHV3nBLVu2tKJFi6bqeXLn/nsz6Tk6duwYptYjPWzbts2VKfvrr7/ccs2aNd2ZhbZt25LP65MeAn0psr/9gf3tL+xvf9m1a1f0Bb4qJZUtWzbbvn17ovVaPtVI+1dffdUFvipNVbt27WRvp9m4dElKb5q0v3Gy8GaL4BnY5s6da9OnT3c9vvnz57du3bq5HzkTJkw4zf2NzIr97S/sb39hf/tDjnTax54ObsuZM6fVr18/0cC04EC1Jk2aJHu/l19+2QYMGGATJ050Jangb4cPH7aPP/7Y/QjS8aNBjxrAVqlSJa+bBgAAIojnqQ5KQ+jVq5cLYBs1amRvvvmmHTx40FV5kBtvvNHKlCnjcnXlP//5j/Xt29c+//xzV/tXp7ZFPXy6wH/Uo6+AV78OO3ToYPXq1XMD2AAAACIq8O3Zs6f9+eefLphVEKsyZerJDQ5427hxo6v0EPTuu++6nM0rrrgi0eOoDvAzzzyT4e2HN1SpQ2ky2bNnd8dH9+7d3axsqc3bBgAA/uN54Cv33HOPu4SigWsJrV+/PoNahUgVExNjI0aMcNUa1MMrhQoV8rpZAAAgwnk+gQWQWkpnmDVrlpvcZPfu3a7kXcIazQAAABHf4wucigLdkSNH2qZNm9xyrVq1XGm5UBU7AAAAQiHwRcSXKfv1119dSTLldivQVcCbUgk7AACAUAh8EfGlyr777jsX9KomrwaxFS5c2OtmAQCATIjAFxFN00pfdtllbia2Zs2aJarwAQAAkBYEvogox48fd5U8zjnnHKtatWr8tMMAAABnisAXEWPnzp2uTNnWrVstX758du+99zJ4DQAAhA2BLyJiANvixYtt0qRJFhsba7lz56ZiAwAACDsCX3jq0KFDNnbsWFeTVypWrGhdu3a1ggULet00AAAQZQh84ZmDBw/aoEGD7MCBA27QWuvWra1JkyaWJUsWr5sGAACiEIEvPKM83sqVK9vmzZtdmbJSpUp53SQAABDFCHyRoXbs2OFKlOXPn98tK5dXPbw5cuTwumkAACDKURQVGTaAbcGCBTZ48GAbM2aMW5acOXMS9AIAgAxBjy/SnXJ4R48ebatXr45fp+oNCnoBAAAyCoEv0tXKlStdD6+qN2TPnt3atm1rDRs2ZAAbAADIcAS+SBfq0VVd3p9++sktlyhRwg1gK168uNdNAwAAPkXgi3Rx4sQJW7t2rfu/SpS1atXK9fgCAAB4hUgEYRMcsKY0Bs261qNHDzt69KhVqlTJ66YBAAAQ+CI89u3bZ6NGjbJzzz3XGjdu7NaVKVPG62YBAADEI/DFGVu+fLmNGzfOjhw5Ytu2bbN69epRsQEAAEQcAl+cNqUxTJw40ZYsWeKWS5cu7QawEfQCAIBIROCL0xITE2MjRoyw3bt3u+XmzZtbixYtLFu2bF43DQAAICQCX5zWhBQfffSRxcXFWaFChaxbt25Wvnx5r5sFAACQIgJfpFn+/Pnt4osvth07dlinTp0sd+7cXjcJAADglAh8kaoyZb/++quVLFnSTUQhzZo1Y/Y1AACQqRD4IkWq1DB+/HhbtmyZnX322Xb77bdbjhw5CHoBAECmQ+CLZK1fv95GjhzpavQq0K1ZsyaD1wAAQKZF4IuTHD9+3GbOnGmzZ892y0WKFHFlysqWLet10wAAAE4bgS8SOXjwoH3++ee2ZcsWt1y3bl3r0KGDm4IYAAAgMyPwRSJ58uRxObyq1NC5c2erUaOG100CAAAICwJf2KFDh1ywq0vWrFldWoMULFjQ66YBAACETdbwPRQyozVr1ti7775rU6ZMiV+ngJegFwAARBt6fH1Ks65NmzbN5s+f75bXrVtnx44ds5w5c3rdNAAAgHRB4OtDmnFtxIgRtn37drfcoEEDa9eunUt1AAAAiFYEvj6bgW3hwoUurUEly/LmzWtdunSxatWqed00AACAdEfg67NSZarPq6C3SpUqLujNnz+/180CAADIEAS+PqIgVyXKDhw4YA0bNmTaYQAA4CsEvlEsNjbWJk+ebFWrVo1PZ6AuLwAA8CvKmUWprVu32uDBg23RokU2ZswYV7EBAADAz+jxjcIBbHPnzrXp06fbiRMnXHpD165dKVMGAAB8j8A3iuzbt89GjRrlavJK9erVXU6vqjcAAAD4HYFvlNi/f7+bge3IkSOuHm+HDh2sXr16DGADAAD4B4FvlChQoIDr4dXkFN27d7eiRYt63SQAAICIQuCbicXExFihQoVc0CsdO3a0rFmzWrZs2bxuGgAAQMShqkMmpEFrs2bNsqFDh9ro0aPdgDZRigNBLwAAQGj0+GYyu3fvtpEjR9qmTZvccp48eSwuLs4FvQAAAEgegW8moV7dpUuX2vjx411N3ly5crnUhtq1a3vdNAAAgEyBwDcTOHr0qI0bN86WLVvmlsuVK2fdunWzIkWKeN00AACATIPANxNQSbItW7a4vy1atLDmzZu7QWwAgPQ5w6YUsuPHj3vdFCQQGxtr2bNnd2U72TfRIYcHY5MIfCOU3tQKbhXsata1K664wq0rW7as100DgKilVDJN+X7o0CGvm4IQP0hKlizpxrhQoz46ZMmSxcU1mmU2oxD4RqBdu3bZiBEjrFatWnbhhRe6daVKlfK6WQAQ9RVzNPOleqBKly7tOh0IsCJr/xw4cMAFSZz1jI4fMn/++acrzVq1atUM6/kl8I2wg2Dx4sU2adIkd0pHUxDXr1+fig0AkEG9vQquNI6Cqd4jj/aN9lHu3LkJfKPE2WefbevXr3cxD4Gvz+i02tixY23FihVuuWLFita1a1eCXgDIYARVQMbw4owKgW8EWLNmjY0aNcqdwtEHbuvWra1JkyacYgMAAAgjAl+P7d+/37744gs3cK1YsWLWvXt38nkBAADSAedzPFagQAG75JJLrEGDBta7d2+CXgAAPBhUXrx4cZdvivC4+uqr7bXXXrNIQ+DrwQC2hQsX2rZt2+LXXXTRRdapUyfyeQEAaXbTTTe51Dhd9D2iMSKPPPKIq3eblCZDUj14dbpoAF/Dhg1t2LBhIR/322+/dR0zhQoVcpUUNFPos88+a3/99VeK7ZkxY4abWbRo0aLuOWrUqGEPPvigbd682SLV888/b126dLEKFSqcdF379u3dwKsff/zxpOu0ff7973+ftF7btHDhwonWacD6k08+adWrV3cD9FSarU2bNq6Kk2KD9DJz5ky74IIL3IyvVapUSXZ/J6RB9qoqpeNEA9B69OiR7I+COXPmuPrKdevWTbT+qaeectt17969FkkIfDOQcng///xz++6779wHigqkC7m8AIAz0aFDB1d/eO3atfbGG2/Ye++9Z/369Ut0m//9738uuFNny4IFC+zXX391vXJ33nmnPfTQQ4luqwCtZ8+eLjDWd5ZmDlXv3S+//GKffPJJsu3Q8yqYU1Cn77nffvvNBg0a5IKfM+n9UzWH9Bxc/sEHH9itt9560nUbN260uXPn2j333GNDhw497efYs2ePNW3a1D7++GN7/PHHXQWn77//3m1j/UhJr+BQ5fnUsdayZUtbsmSJC9Jvu+02F9imdB8dJ61atXL30W137tzpUjFDva4bb7zRjU1KqmbNmla5cmX79NNPLaIEfGbv3r36WRXYuXNnqu9Tpox+iv3993StXLky8PLLLweeeeaZwIABAwILFiwInDhx4vQfEKly7NixwKhRo9xfRD/2t7+Ee38fPnw48Ntvv7m/mUmvXr0CXbp0SbSue/fugXr16sUvb9y4MZAjR47AAw88cNL9//vf/7rvxfnz57tlfT9p+c033wz5fLt37w65ftOmTYGcOXMG/v3vf6d4v379+gXq1KmT6Lo33ngjUL58+ZNe03PPPRcoVapUoEKFCoHHHnssUL9+/cDx48cT3bd27dqB/v37xy8PGTIkUL169UCuXLkC5557bmDgwIGBlAwfPjxw9tlnh7xO39lXX3114Pfffw8UKlQocOjQoUTXt2jRInDfffeddL8PP/zQ3T6oT58+gXz58gU2b9580m33798fiI2NDaSHRx55JHD++ecnWtezZ89A+/btU9we2bNnT7Sdx4wZE8iSJctJ7zU91lNPPRVyn4r2S7NmzU7rPac4Tceh4rZwYnBbOlNtusmTJ9uiRYvccokSJdyvJuUSAQAiX4MGZgmy0zJEyZJm/3xtpJl6Z9VLWb58+fh133zzjfs+StqzK3fccYc98cQTbqB148aN7bPPPnOpDXfddVfIx096Cj9o+PDhrmdWPZhpuV9ypk2bZgULFrQpU6bE1/F96aWXXCUkTXggy5cvdz3X6l0Wtb1v37729ttvW7169eznn3+222+/3fLly2e9evUK+Tw//PCDq5mflNIPPvzwQxs4cKBLT1CagLbjDTfckKbXoXZ/+eWXdt1117mJUZJKadYyte3SSy9N8fHVy67HDmXevHmuBz5p6sa/Q6RnBGlbqMKUXrvSaHS2Wr38epyEKZm6XmcY1KP73HPPhXysRo0auXSHo0ePulSLSEDgm84VG3RaQ6cIRPkyOh2gXBgAQOagoDeC01Pjc3cVQCmFTkGGAhcFf0GrVq1yubqhBlBrhrpKlSq528gff/zhltM67kT3U6AarkHaClbff/99175gAKnT5wrQFdwGA10F6wpKRekdSqkInpZXvrPSLRQcJhf4btiwIWRAOnXqVJcGoUBRrr/+epcSkdbAVzHA7t27XfCcVhr4rnSDlKhDLTkaT5T0+hIlSrh848OHD1uePHlOuo+2mTrsrrrqKvejSFWnVGJ1woQJifb1Y4895gLzlGIabVf9GFI7Ev4Q8xIRWDrSh5AuGmCgySiU6wIAyFzU+xrpz6kcznfffdcOHjzocnwVjGhA0uk43YFWul84x6zUqlUrPugNuvLKK+MDXz2f/v/AAw+46/Ta1RusXF318gbpx4CC/uQoANRgs6SU06sc3GBgd80119jDDz/sniMt3+dnMnBNgWkwqM8o27Ztc9tPPxT0mtWJp+19xRVXuN53/QC59tprrX///latWrUUHysYWOsHRKQg8A0z/YrSjtYvZX0A6FenRoMy/SUAZE6nm3KQkdQ7GgyQFLDVqVMn0YAtBSgaQLVly5aTejfVI6dgTsFz8LazZ892qRFp6fUNPocG2aXU66ve6KTBoJ4r1GtKSsH8M8884waHKWDdtGmTC05Fp+RlyJAhrhc4oZSmw1UNffXIJqTKFSNHjnTt0g+KIPV+avvq9L2ohzvUwDQN+goG26qKoDSP4MysaXGmqQ4aZLh9+/ZE67Zv3+7aHaq3V5Taoba//PLL8euUzqCpvDUoUj3XSt9UGokG/YmCYe1T/UhQb7EGxkmwAoi2QaSgqkMYKddIbxDt9KBgyRgAADKCAkvl7KqclILDYMCoIDZUZQVVXVBvqXr3RL15CiLfeeedkI+voC4U9QiqhzZhwBTqfgqC1KuYMPg91en8oDJlyrhybEpx0KVt27bxY2Z0Cl9BvfJO9SMg4UWn75OjXGClQySkxy5btqyrYqG2BS/afioHpgBYzj33XBeEJ6V1wd5Q7Q9Vz9Bj6odHUtrWwSpPyaU6pHS5/PLLk31tSlFQrnRCU6ZMceuTo97ZpNN2B384KMBV0Lx06dJEbVBlEG0L/T/hjw7lm2s76sdFxAj4TEpVHb7+OhCoXv3v6g0JL1mzplzV4ciRI25ksUZ/6qIRpYwqjwyM8vcX9re/UNUh+aoOqhJQpkyZwCuvvJKockLWrFkDTzzxhKtSsHr16sBrr73mqh88+OCDJ1UDyJYtW+Dhhx8OzJ07N7B+/frA1KlTA1dccUWy1R5EFRQ0+v+WW24JzJw5091v9uzZgd69e8dXlNA21m1eeukl14a33347UKRIkZBVHRJSlQFVhnjvvfcCpUuXDhQrVizwySefJLqNvn/z5MkTeOutt1w1pV9//TUwdOhQ9zqTo9uoisFff/0Vv04VCh599NGTbrtnzx5XuWLcuHFuec2aNYHcuXMH7r333sAvv/wSWLFihXsuPd53330Xf79du3a5ShNly5YNfPTRR4Hly5cHVq1aFfjggw8CVapUSbZSxplau3ZtIG/evG4/ap8PHDjQ7deJEyfG3+Z///tfoFWrVvHL06ZNc/tHFRnUxp9++slVgdD+SVrVIii5qg7ajzoWIqmqA4FvAgp6FeAmd9H1ocq36A0WDHp1wMTFxWXMi8EpEQj5C/vbXwh8kw8S5cUXX3Rlug4cOBC/bvTo0YHmzZu70loK2FQeTIFhKF999VXg4osvDhQoUMDdXmXDnn322VMGaVOmTHGBkoJZPYcCvoceeiiwZcuW+Nu8++67gXLlyrnHvfHGGwPPP/98qgNfBZEK1hXQqRRYUp999lmgbt26LkBVG/QaRowYkWKbGzVqFBg0aJD7/6JFi1ycsHDhwpC3vfTSSwPdunWLX9bt2rZt67a1Spg1btw4MHLkyJBBs0qyVa1a1bWtRIkSgTZt2rjbpmd50xkzZsRvj0qVKrlSa0mD1oTbXr744gtXDk/7R6/r8ssvd4FzckIFvnofaXvMmzcvogLfLPrHfJaDq9wVjbLUrDIJlS3798hd9fAnTU8qUMBswACdyrH47n7l3syaNcudrtFjduvWLWJGLeJvys/SSFTNIsTMeNGP/e0v4d7fGois4v06LR5qsBO8pe9dfYfrVHvSU/Fnavz48W7gmk7Nh/ux/erdd991edIJ0z/T8p7TNNJKkVAOtfZ5uDC4LQQFvTExKd9G+VBK8lbQq/IqmhmFD0oAADIffYerRJemVdYgLpw5/RjVbIGRhsD3NGnQmhLKNRpW85cDAIDMK6VJHZB2mho5EhH4ppK643Uq5Pzzz48vQn06xagBAADgDQLfVFi/fr3LU1Fukf6v0ijMvgYAAJC5EL2lQHX6ZsyYYXPmzHHLRYoUcRNSEPQCQPTy2ZhvwFfvNSK4ZKjqw4gRI9wMNFK3bl03e0rS6RMBANEhWBlCBfyTm9UKQPhonNSpZtYLNwLfEPLl22uDBw92pXJUqaFz585Wo0YNr5sFAEhH+vLV1LI7duxwy5p1U1PPI3LKmSlQ0pgbSo5Fx/78888/3fssI8+kE/iGcPBgIatVq5abu7tr165hrR8HAIhcJUuWdH+DwS8i67S4pmBWbzw/SKJD1qxZ7ZxzzsnQ/Ung+481a9ZY3rya77uAW1Zag3798+YCAP/QZ36pUqWsePHi7qwfIof2x/fff28XX3wxE9REiZw5c2Z4773vA9+4uDibOnWqm4yiRYtKtnr19froYwAbAPiYOj4yMu8Qp6b9oe9spSAS+OJ0RUSSzMCBA61ChQruYG7cuLEtXLgwxdsPHz7c1dDV7ZWSoCkrT4dOZQ0ZMsQFvbJ371mWNevx03osAAAARDbPA9+vvvrKHnjgAevXr58tXrzY6tSpY+3bt082v2ru3Ll2zTXX2K233mo///yzy8HVRfNrp4WeSwPY9DxKrNZjzp3byY4fp6cXAAAgGnke+L7++ut2++2328033+wqJwwaNMgFokOHDg15+7feess6dOhgDz/8sJ133nk2YMAAu+CCC+ztt99O0/NOnz7d1enVZBR9+vSxatWqhekVAQAAIBJ52r2psiQ//fSTPf744/HrlOTcpk0bmzdvXsj7aL16iBNSD/GoUaNC3v7o0aPuErR3717399ChOFuwoLm9994F9vDDSm/Ya9u26ZosduJEwHbtigvPi4TngyFUk3PXrl3khPkA+9tf2N/+wv72l7/++itdJrnI7vUkEep1LVGiRKL1Wl6xYkXI+2zbti3k7bU+lBdffNH69+9/0vr//vdVM9PlZJqzolixNLwQAAAAhJ1+6BQqVChsjxf1Ca3qTU7YQ7xnzx4rX768bdy4MawbEpFp3759Vq5cOdu0aRP1mH2A/e0v7G9/YX/7y969e12N37POOiusj+tp4FusWDFXnmT79u2J1ms5WEQ8Ka1Py+1z5crlLkkp6OWN4x/a1+xv/2B/+wv721/Y3/6SNcx1frN6Xbi4fv36Nm3atERT2Gm5SZMmIe+j9QlvL1OmTEn29gAAAEBEpDooDaFXr17WoEEDa9Sokb355pt28OBBV+VBbrzxRitTpozL1ZX77rvPWrRoYa+99pp16tTJvvzyS1u0aJErTQYAAABEbODbs2dP+/PPP61v375ugFrdunVt4sSJ8QPYlIubsJu7adOm9vnnn9tTTz1lTzzxhFWtWtVVdKhZs2aqnk9pD6oZHCr9AdGH/e0v7G9/YX/7C/vbX3Kl0/7OEgh3nQgAAAAgAnk+gQUAAACQEQh8AQAA4AsEvgAAAPAFAl8AAAD4QlQGvgMHDrQKFSpY7ty5rXHjxrZw4cIUbz98+HCrXr26u32tWrVswoQJGdZWZOz+HjJkiDVv3tyKFCniLm3atDnl8YHM/f4OUunDLFmyWNeuXdO9jfBuf2t2zrvvvttKlSrlRoNXq1aNz/Qo3t8qgXruuedanjx53Kxu999/vx05ciTD2ovT9/3331vnzp2tdOnS7rNZFbpOZebMmXbBBRe493aVKlVs2LBhaX/iQJT58ssvAzlz5gwMHTo0sHz58sDtt98eKFy4cGD79u0hbz9nzpxAtmzZAi+//HLgt99+Czz11FOBHDlyBJYuXZrhbUf67+9rr702MHDgwMDPP/8c+P333wM33XRToFChQoGYmJgMbzvSf38HrVu3LlCmTJlA8+bNA126dMmw9iJj9/fRo0cDDRo0CHTs2DEwe/Zst99nzpwZWLJkSYa3Hem/vz/77LNArly53F/t60mTJgVKlSoVuP/++zO87Ui7CRMmBJ588snAiBEjVF0sMHLkyBRvv3bt2kDevHkDDzzwgIvX/ve//7n4beLEiWl63qgLfBs1ahS4++6745ePHz8eKF26dODFF18Mefurrroq0KlTp0TrGjduHLjjjjvSva3I+P2dVFxcXKBAgQKBjz76KB1bCS/3t/Zx06ZNA++//36gV69eBL5RvL/ffffdQKVKlQLHjh3LwFbCq/2t27Zq1SrROgVFF110Ubq3FeGVmsD3kUceCZx//vmJ1vXs2TPQvn37ND1XVKU6HDt2zH766Sd3+jpIk19oed68eSHvo/UJby/t27dP9vbI3Ps7qUOHDllsbKydddZZ6dhSeLm/n332WStevLjdeuutGdRSeLW/x4wZ46avV6qDJkHSxEYvvPCCHT9+PANbjoza35rQSvcJpkOsXbvWpbV07Ngxw9qNjBOueM3zmdvCaefOne4DLjjrW5CWV6xYEfI+mi0u1O21HtG3v5N69NFHXX5R0jcTomN/z5492z744ANbsmRJBrUSXu5vBT7Tp0+36667zgVAq1evtrvuusv9uNUMUIiu/X3ttde6+zVr1kxnry0uLs7uvPNON6sros+2ZOK1ffv22eHDh12ed2pEVY8vkBYvvfSSG/A0cuRIN5AC0WX//v12ww03uAGNxYoV87o5yAAnTpxwvfuDBw+2+vXrW8+ePe3JJ5+0QYMGed00pAMNdFKP/jvvvGOLFy+2ESNG2Pjx423AgAFeNw0RLKp6fPXlli1bNtu+fXui9VouWbJkyPtofVpuj8y9v4NeffVVF/hOnTrVateunc4thRf7e82aNbZ+/Xo3ajhhYCTZs2e3lStXWuXKlTOg5cio97cqOeTIkcPdL+i8885zPUU6lZ4zZ850bzcybn8//fTT7sftbbfd5pZVlengwYPWu3dv94NHqRKIHiWTidcKFiyY6t5eiaqjQh9q+pU/bdq0RF90WlbeVyhan/D2MmXKlGRvj8y9v+Xll192PQITJ060Bg0aZFBrkdH7WyUKly5d6tIcgpfLL7/cWrZs6f6v0keIrvf3RRdd5NIbgj9wZNWqVS4gJuiNvv2tMRpJg9vgj56/x0shmjQJV7wWiMJyKCpvMmzYMFfuonfv3q4cyrZt29z1N9xwQ+Cxxx5LVM4se/bsgVdffdWVt+rXrx/lzKJ4f7/00kuuXM4333wT2Lp1a/xl//79Hr4KpNf+ToqqDtG9vzdu3OiqtNxzzz2BlStXBsaNGxcoXrx44LnnnvPwVSC99re+r7W/v/jiC1fqavLkyYHKlSu7ak2IfPv373elRXVROPr666+7/2/YsMFdr32tfZ60nNnDDz/s4jWVJqWc2T9U2+2cc85xAY7Ko8yfPz/+uhYtWrgvv4S+/vrrQLVq1dztVSpj/PjxHrQaGbG/y5cv795gSS/6AEV0vr8TIvCN/v09d+5cV5JSAZRKmz3//POupB2ib3/HxsYGnnnmGRfs5s6dO1CuXLnAXXfdFdi9e7dHrUdazJgxI+T3cXAf66/2edL71K1b1x0fen9/+OGHgbTKon/C2xkNAAAARJ6oyvEFAAAAkkPgCwAAAF8g8AUAAIAvEPgCAADAFwh8AQAA4AsEvgAAAPAFAl8AAAD4AoEvAAAAfIHAFwDMbNiwYVa4cGHLrLJkyWKjRo1K8TY33XSTde3aNcPaBACRhsAXQNRQYKcAMOll9erVERFYB9uTNWtWK1u2rN188822Y8eOsDz+1q1b7dJLL3X/X79+vXueJUuWJLrNW2+95dqRnp555pn415ktWzYrV66c9e7d2/766680PQ5BOoD0kD1dHhUAPNKhQwf78MMPE607++yzLRIULFjQVq5caSdOnLBffvnFBb5btmyxSZMmnfFjlyxZ8pS3KVSokGWE888/36ZOnWrHjx+333//3W655Rbbu3evffXVVxny/ACQHHp8AUSVXLlyuSAw4UU9j6+//rrVqlXL8uXL53oh77rrLjtw4ECyj6PAtGXLllagQAEXsNavX98WLVoUf/3s2bOtefPmlidPHvd4//rXv+zgwYMptk29oGpP6dKlXe+s7qMA8fDhwy4YfvbZZ11PsF5D3bp1beLEifH3PXbsmN1zzz1WqlQpy507t5UvX95efPHFkKkOFStWdH/r1avn1l9yySUn9aIOHjzYtUPPm1CXLl1coBo0evRou+CCC9xzVqpUyfr3729xcXEpvs7s2bO711mmTBlr06aNXXnllTZlypT46xUQ33rrra6d2n7nnnuu641O2Gv80UcfuecO9h7PnDnTXbdp0ya76qqrXFrKWWed5dqrHm4ASA0CXwC+oPSC//73v7Z8+XIXVE2fPt0eeeSRZG9/3XXXuSD0xx9/tJ9++skee+wxy5Ejh7tuzZo1rme5R48e9uuvv7qeTAXCCkzTQkGfAk8Fkgr8XnvtNXv11VfdY7Zv394uv/xy++OPP9xt1fYxY8bY119/7XqNP/vsM6tQoULIx124cKH7q6BaKRAjRow46TYKRnft2mUzZsyIX6d0BAXbeu3yww8/2I033mj33Xef/fbbb/bee++5VInnn38+1a9RQal6tHPmzBm/Tq9Z23b48OHucfv27WtPPPGEe23y0EMPueBW21jt16Vp06YWGxvrtot+jKhtc+bMsfz587vb6YcBAJxSAACiRK9evQLZsmUL5MuXL/5yxRVXhLzt8OHDA0WLFo1f/vDDDwOFChWKXy5QoEBg2LBhIe976623Bnr37p1o3Q8//BDImjVr4PDhwyHvk/TxV61aFahWrVqgQYMGbrl06dKB559/PtF9GjZsGLjrrrvc/++9995Aq1atAidOnAj5+Po4HzlypPv/unXr3PLPP/980vbp0qVL/LL+f8stt8Qvv/fee64dx48fd8utW7cOvPDCC4ke45NPPgmUKlUqkJx+/fq57aBtnzt3btcOXV5//fVASu6+++5Ajx49km1r8LnPPffcRNvg6NGjgTx58gQmTZqU4uMDgJDjCyCqKD3h3XffjV9WakOw91OpAStWrLB9+/a5XtYjR47YoUOHLG/evCc9zgMPPGC33XabffLJJ/Gn6ytXrhyfBqFeWfW6Bin2VE/munXr7LzzzgvZNuW5qodSt9NzN2vWzN5//33XHuX6XnTRRYlur2U9VzBNoW3bti4tQD2cl112mbVr1+6MtpV6dm+//XZ75513XHqFXs/VV1/teseDr1O9qgl7eJWmkNJ2E7VRvdO63aeffuoG2d17772JbjNw4EAbOnSobdy40aV6qMdW6R0pUXs0UFE9vgnpedQLDwCnQuALIKoo0K1SpcpJp9sVKPbp08cFccoNVWqC8kwVcIUK4JRneu2119r48ePtu+++s379+tmXX35p3bp1c7nBd9xxh8vRTeqcc85Jtm0K2BYvXuwCS+XqKtVBFPieivJsFVSrLQrilQqggPybb76x09W5c2cXsOs1NmzY0KUPvPHGG/HX63Uqp7d79+4n3Vc5v8lRWkNwH7z00kvWqVMn9zgDBgxw67Qdlc6g1I4mTZq47fLKK6/YggULUmyv2qNc64Q/OCJtACOAyEbgCyDqKUdXvawKtIK9mcF80pRUq1bNXe6//3675pprXLUIBb4KQpWbmjTAPhU9d6j7aPCcBpqpd7VFixbx67XcqFGjRLfr2bOnu1xxxRWu51d5uQrkEwrm06p3NiUKXhXUKpBUT6p6avXagvR/5ROn9XUm9dRTT1mrVq3cD4/g61TOrgYYBiXtsdVrSNp+tUf51MWLF3fbAgDSisFtAKKeAjcNjPrf//5na9eudekLgwYNSvb2OvWugWqqJLBhwwYXqGmQWzCF4dFHH7W5c+e62+g0vgagqQJBWge3JfTwww/bf/7zHxfYKdjUYDo9tgaWiapSfPHFFy5VY9WqVW5gmConhJp0Q4GhepM1UG379u0uxSKldAf1+CrtIDioLUiDzj7++GPXW6tBgSpNpt5aBbJpoV7d2rVr2wsvvOCWq1at6ipkaNCbXsvTTz/ttm9CGrindBJti507d7r9p/YVK1bMVXJQ77R6wLWP1PMeExOTpjYB8CcCXwBRr06dOi5wVGBZs2ZN18OZsBRYUip/pooHqmigHl+lFaj8mAJAURA3a9YsF7SppJnKhilIVG/m6VLwprziBx980JVdU9CqPFkFiaJ0gJdfftkaNGjg0hKUvjFhwoT4Huyk5cRUBUJVGNQmBYrJUU+seowVYCq1IyFVUBg3bpxNnjzZPeeFF17oUiFUSi2t1GuufGaVI1OaiHqa1XPduHFjt60T9v6Kco/VA63XqzQG/fhQSsr333/v0kl0f/0QUbqKcnzpAQaQGlk0wi1VtwQAAAAyMXp8AQAA4AsEvgAAAPAFAl8AAAD4AoEvAAAAfIHAFwAAAL5A4AsAAABfIPAFAACALxD4AgAAwBcIfAEAAOALBL4AAADwBQJfAAAAmB/8H/OjV0i8UzPVAAAAAElFTkSuQmCC",
"text/plain": [
"<Figure size 800x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from sklearn.metrics import roc_curve, auc\n",
"\n",
"fpr, tpr, _ = roc_curve(Y_test, Y_test_pred_prob)\n",
"roc_auc = auc(fpr, tpr)\n",
"\n",
"# plot the ROC curve\n",
"plt.figure(figsize=(8, 6))\n",
"plt.plot(fpr, tpr, color='blue', lw=2, label=f'ROC Curve (AUC = {roc_auc:.2f})')\n",
"plt.plot([0, 1], [0, 1], color='gray', linestyle='--') # Diagonal line for random guessing\n",
"plt.xlim([0.0, 1.0])\n",
"plt.ylim([0.0, 1.05])\n",
"plt.xlabel('False Positive Rate')\n",
"plt.ylabel('True Positive Rate')\n",
"plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
"plt.legend(loc='lower right')\n",
"plt.grid()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Confusion Matrix\n",
"\n",
"- https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html\n",
"- https://scikit-learn.org/stable/modules/model_evaluation.html#confusion-matrix"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[88 9]\n",
" [23 34]]\n"
]
}
],
"source": [
"from sklearn.metrics import confusion_matrix\n",
"\n",
"matrix = confusion_matrix(Y_test, Y_test_pred)\n",
"print(matrix)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Classification Report\n",
"- https://scikit-learn.org/stable/modules/model_evaluation.html#classification-report"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" 0.0 0.79 0.91 0.85 97\n",
" 1.0 0.79 0.60 0.68 57\n",
"\n",
" accuracy 0.79 154\n",
" macro avg 0.79 0.75 0.76 154\n",
"weighted avg 0.79 0.79 0.78 154\n",
"\n"
]
}
],
"source": [
"from sklearn.metrics import classification_report\n",
"\n",
"report = classification_report(Y_test, Y_test_pred)\n",
"print(report)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Regression Problems"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean Squared Error: 104.20\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.datasets import make_regression\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.metrics import mean_squared_error\n",
"\n",
"# Generate a synthetic regression dataset\n",
"X, y = make_regression(n_samples=100, n_features=1, noise=10, random_state=42)\n",
"\n",
"# Split data into training and test sets\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
"\n",
"# Train a linear regression model\n",
"model = LinearRegression()\n",
"model.fit(X_train, y_train)\n",
"\n",
"# Make predictions\n",
"y_pred = model.predict(X_test)\n",
"\n",
"# Calculate Mean Squared Error\n",
"mse = mean_squared_error(y_test, y_pred)\n",
"\n",
"# Print the result\n",
"print(f\"Mean Squared Error: {mse:.2f}\")\n",
"\n",
"# Plot the regression line\n",
"plt.scatter(X_test, y_test, color='blue', label=\"Actual values\")\n",
"plt.plot(X_test, y_pred, color='red', linewidth=2, label=\"Predicted values\")\n",
"plt.xlabel(\"Feature\")\n",
"plt.ylabel(\"Target\")\n",
"plt.title(\"Linear Regression with MSE Calculation\")\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MSE: 10.207949183448665\n"
]
}
],
"source": [
"print(f\"MSE: {np.sqrt(mse)}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MAE: 8.416659922209051\n"
]
}
],
"source": [
"from sklearn.metrics import mean_absolute_error\n",
"\n",
"mae = mean_absolute_error(y_test, y_pred)\n",
"print(f\"MAE: {mae}\")"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"R2 score: 0.9374151607623286\n"
]
}
],
"source": [
"from sklearn.metrics import r2_score\n",
"\n",
"r2 = r2_score(y_test, y_pred)\n",
"print(f\"R2 score: {r2}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### `k-fold` cross-validation"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fold 1: MSE = 104.20\n",
"Fold 2: MSE = 66.52\n",
"Fold 3: MSE = 63.17\n",
"Fold 4: MSE = 69.47\n",
"Fold 5: MSE = 105.17\n",
"\n",
"Overall Mean Squared Error across all folds: 81.71\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 800x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.datasets import make_regression\n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.metrics import mean_squared_error\n",
"from sklearn.model_selection import KFold\n",
"\n",
"# Generate a synthetic regression dataset\n",
"X, y = make_regression(n_samples=100, n_features=1, noise=10, random_state=42)\n",
"\n",
"# Define the number of folds\n",
"kf = KFold(n_splits=5, shuffle=True, random_state=42)\n",
"\n",
"# Store MSE for each fold\n",
"mse_values = []\n",
"\n",
"# Perform K-Fold Cross-Validation\n",
"for fold, (train_index, test_index) in enumerate(kf.split(X), 1):\n",
" X_train, X_test = X[train_index], X[test_index]\n",
" y_train, y_test = y[train_index], y[test_index]\n",
"\n",
" # Train a linear regression model\n",
" model = LinearRegression()\n",
" model.fit(X_train, y_train)\n",
"\n",
" # Make predictions\n",
" y_pred = model.predict(X_test)\n",
"\n",
" # Calculate Mean Squared Error\n",
" mse = mean_squared_error(y_test, y_pred)\n",
" mse_values.append(mse)\n",
"\n",
" print(f\"Fold {fold}: MSE = {mse:.2f}\")\n",
"\n",
"# Calculate overall mean MSE\n",
"overall_mse = np.mean(mse_values)\n",
"print(f\"\\nOverall Mean Squared Error across all folds: {overall_mse:.2f}\")\n",
"\n",
"# Plot MSE values for each fold\n",
"plt.figure(figsize=(8, 5))\n",
"plt.plot(range(1, len(mse_values) + 1), mse_values, marker='o', linestyle='-', color='b', label=\"MSE per fold\")\n",
"plt.axhline(y=overall_mse, color='r', linestyle='--', label=f\"Overall Mean MSE = {overall_mse:.2f}\")\n",
"plt.xlabel(\"Fold Number\")\n",
"plt.ylabel(\"Mean Squared Error\")\n",
"plt.title(\"K-Fold Cross-Validation MSE\")\n",
"plt.legend()\n",
"plt.grid()\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MSE: 81.709 (18.871)\n"
]
}
],
"source": [
"from sklearn import model_selection\n",
"\n",
"# Define the number of folds\n",
"kf = KFold(n_splits=5, shuffle=True, random_state=42)\n",
"\n",
"LR = LinearRegression()\n",
"k_fold_mse = model_selection.cross_val_score(\n",
" LR, X, y, cv=kf, scoring='neg_mean_squared_error')\n",
"print(\"MSE: %.3f (%.3f)\" % (-1 * k_fold_mse.mean(), k_fold_mse.std()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Model interpretation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Random Forest model"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"\n",
"X = train.drop(columns=['survived', 'passengerid'])\n",
"y = train['survived']\n"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(891, 43)"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X.shape"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 80.00%\n"
]
}
],
"source": [
"from sklearn.ensemble import RandomForestClassifier\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=123)\n",
"rf = RandomForestClassifier(n_estimators=5, max_depth=3, random_state=234)\n",
"rf.fit(X_train, y_train)\n",
"y_pred = rf.predict(X_test)\n",
"\n",
"accuracy = accuracy_score(y_test, y_pred)\n",
"print(\"Accuracy: %.2f%%\" % (accuracy * 100.0))\n"
]
},
{
"cell_type": "code",
"execution_count": 62,
"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>Feature</th>\n",
" <th>Importance</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>sex_male</td>\n",
" <td>0.463926</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>title_Mr</td>\n",
" <td>0.136208</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>title_Mrs</td>\n",
" <td>0.133414</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>has_cabin</td>\n",
" <td>0.078299</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>fare_log_cat</td>\n",
" <td>0.057256</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>age</td>\n",
" <td>0.022328</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>deck_U</td>\n",
" <td>0.021360</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>deck_D</td>\n",
" <td>0.019512</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>pclass</td>\n",
" <td>0.015956</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>family_size</td>\n",
" <td>0.013974</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>embarked_C</td>\n",
" <td>0.013360</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>name_len_cat</td>\n",
" <td>0.008225</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>deck_F</td>\n",
" <td>0.004893</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>age_cat</td>\n",
" <td>0.002641</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>is_alone</td>\n",
" <td>0.002355</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>title_Major</td>\n",
" <td>0.001872</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>embarked_Q</td>\n",
" <td>0.001702</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>parch</td>\n",
" <td>0.001431</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>deck_G</td>\n",
" <td>0.001100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>deck_E</td>\n",
" <td>0.000188</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>sibsp</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>fare_log</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>title_Col</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>embarked_S</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>embarked_nan</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>title_Capt</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>sex_female</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>title_Miss</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>title_Master</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>title_Lady</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>title_Jonkheer</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>title_Don</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>title_Dr</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>title_Countess</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>title_Mme</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>deck_A</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>title_Sir</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>title_Rev</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>title_Ms</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>title_Mlle</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>deck_C</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>deck_B</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>deck_T</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Feature Importance\n",
"12 sex_male 0.463926\n",
"29 title_Mr 0.136208\n",
"30 title_Mrs 0.133414\n",
"5 has_cabin 0.078299\n",
"10 fare_log_cat 0.057256\n",
"1 age 0.022328\n",
"42 deck_U 0.021360\n",
"37 deck_D 0.019512\n",
"0 pclass 0.015956\n",
"6 family_size 0.013974\n",
"13 embarked_C 0.013360\n",
"8 name_len_cat 0.008225\n",
"39 deck_F 0.004893\n",
"9 age_cat 0.002641\n",
"7 is_alone 0.002355\n",
"24 title_Major 0.001872\n",
"14 embarked_Q 0.001702\n",
"3 parch 0.001431\n",
"40 deck_G 0.001100\n",
"38 deck_E 0.000188\n",
"2 sibsp 0.000000\n",
"4 fare_log 0.000000\n",
"18 title_Col 0.000000\n",
"15 embarked_S 0.000000\n",
"16 embarked_nan 0.000000\n",
"17 title_Capt 0.000000\n",
"11 sex_female 0.000000\n",
"26 title_Miss 0.000000\n",
"25 title_Master 0.000000\n",
"23 title_Lady 0.000000\n",
"22 title_Jonkheer 0.000000\n",
"20 title_Don 0.000000\n",
"21 title_Dr 0.000000\n",
"19 title_Countess 0.000000\n",
"28 title_Mme 0.000000\n",
"34 deck_A 0.000000\n",
"33 title_Sir 0.000000\n",
"32 title_Rev 0.000000\n",
"31 title_Ms 0.000000\n",
"27 title_Mlle 0.000000\n",
"36 deck_C 0.000000\n",
"35 deck_B 0.000000\n",
"41 deck_T 0.000000"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"importances = rf.feature_importances_\n",
"df_importances = pd.DataFrame({\n",
" 'Feature': X.columns,\n",
" 'Importance': importances\n",
"})\n",
"df_importances = df_importances.sort_values(by='Importance', ascending=False)\n",
"df_importances"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
"X_test2 = test.drop(columns=['passengerid'])\n",
"y_pred2 = automl.predict(X_test2)\n",
"df_submission = pd.concat([test['passengerid'], pd.DataFrame(y_pred2, columns=['survived'])], axis=1)\n",
"df_submission.to_csv('../data/titanic/submission.csv', index=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### SHAP"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/xiangshiyin/Documents/Teaching/machine-learning-for-actuarial-science/.venv/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n"
]
},
{
"data": {
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",
"text/plain": [
"<Figure size 800x550 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import xgboost\n",
"import shap\n",
"\n",
"# train an XGBoost model\n",
"X, y = shap.datasets.california()\n",
"model = xgboost.XGBRegressor().fit(X, y)\n",
"\n",
"# explain the model's predictions using SHAP\n",
"# (same syntax works for LightGBM, CatBoost, scikit-learn, transformers, Spark, etc.)\n",
"explainer = shap.Explainer(model)\n",
"shap_values = explainer(X)\n",
"\n",
"# visualize the first prediction's explanation\n",
"shap.plots.waterfall(shap_values[0])"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([4.526, 3.585, 3.521, 3.413, 3.422])"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y[:5]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 800x950 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import shap\n",
"\n",
"explainer = shap.Explainer(rf)\n",
"shap_values = explainer.shap_values(X_test)\n",
"\n",
"# Extract SHAP values for the positive class (class 1)\n",
"shap_values_class1 = shap_values[:,:,1]\n",
"\n",
"# Visualize global feature importance for class 1\n",
"shap.summary_plot(shap_values_class1, X_test, feature_names=feature_names, plot_type=\"bar\")"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 800x650 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"explainer = shap.Explainer(rf)\n",
"shap_values = explainer.shap_values(X_test)\n",
"\n",
"shap_values_class1 = shap_values[:,:,1]\n",
"explanation = shap.Explanation(\n",
" values=shap_values_class1,\n",
" base_values=explainer.expected_value[1], # Base value for class 1\n",
" data=X_test.values, # Input data (as a NumPy array)\n",
" feature_names=X_test.columns.tolist() # Feature names\n",
")\n",
"shap.plots.waterfall(explanation[1])"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"np.float64(0.16546512151307932)"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_pred_prob = rf.predict_proba(X_test)\n",
"y_pred_prob[1,1]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.12.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}