317 lines
9.2 KiB
Plaintext
317 lines
9.2 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 139,
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"id": "02249c82",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pandas as pd\n",
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"from sklearn.impute import SimpleImputer\n",
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"from sklearn.feature_extraction.text import TfidfVectorizer\n",
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"\n",
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"from sklearn.linear_model import LogisticRegression\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.metrics import precision_recall_fscore_support"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 140,
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"id": "4be6437d",
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"metadata": {},
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"outputs": [],
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"source": [
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"data = pd.read_csv('titanic.tsv',sep='\\t')\n",
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"\n",
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"# formatowanie danych\n",
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"data['Sex'] = data['Sex'].apply(lambda x: 1 if x=='male' else 0)\n",
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"data['Name_to_num'] = data['Name'].apply(\n",
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" lambda x: 1 if 'Mr.' in x else 0\n",
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")\n",
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"del data['Name']\n",
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"\n",
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"data['Cabin'] = data['Cabin'].replace(np.nan,'Undefined')\n",
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"\n",
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"\n",
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"vectorizer = TfidfVectorizer()\n",
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"vectorizer.fit(data['Cabin'])\n",
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"vector = vectorizer.transform(data['Cabin']).toarray()\n",
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"vector_sum = []\n",
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"for v in vector:\n",
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" vector_sum.append(v.sum())\n",
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"data['Cabin']=vector_sum\n",
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"\n",
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"data['Embarked'] = data['Embarked'].replace(np.nan,'Undefined')\n",
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"\n",
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"data = pd.get_dummies(data,columns=['Embarked'])\n",
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"\n",
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"imputer = SimpleImputer(missing_values=np.nan, strategy='median')\n",
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"data[['Age']] = imputer.fit_transform(data[['Age']])\n",
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"\n",
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"data['Ticket'] = data['Ticket'].replace(np.nan,'Undefined')\n",
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"\n",
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"vectorizer = TfidfVectorizer()\n",
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"vectorizer.fit(data['Ticket'])\n",
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"vector = vectorizer.transform(data['Ticket']).toarray()\n",
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"vector_sum = []\n",
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"for v in vector:\n",
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" vector_sum.append(v.sum())\n",
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"data['Ticket']=vector_sum"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 141,
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"id": "618e8841",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Survived</th>\n",
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" <th>PassengerId</th>\n",
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" <th>Pclass</th>\n",
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" <th>Sex</th>\n",
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" <th>Age</th>\n",
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" <th>SibSp</th>\n",
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" <th>Parch</th>\n",
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" <th>Ticket</th>\n",
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" <th>Fare</th>\n",
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" <th>Cabin</th>\n",
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" <th>Name_to_num</th>\n",
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" <th>Embarked_C</th>\n",
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" <th>Embarked_Q</th>\n",
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" <th>Embarked_S</th>\n",
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" <th>Embarked_Undefined</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>0</td>\n",
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" <td>530</td>\n",
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" <td>2</td>\n",
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" <td>1</td>\n",
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" <td>23.0</td>\n",
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" <td>2</td>\n",
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" <td>1</td>\n",
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" <td>1.000000</td>\n",
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" <td>11.5000</td>\n",
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" <td>1.0</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>0</td>\n",
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" <td>466</td>\n",
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" <td>3</td>\n",
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" <td>1</td>\n",
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" <td>38.0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>1.391284</td>\n",
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" <td>7.0500</td>\n",
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" <td>1.0</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>0</td>\n",
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" <td>753</td>\n",
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" <td>3</td>\n",
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" <td>1</td>\n",
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" <td>33.0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>1.000000</td>\n",
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" <td>9.5000</td>\n",
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" <td>1.0</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>0</td>\n",
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" <td>855</td>\n",
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" <td>2</td>\n",
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" <td>0</td>\n",
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" <td>44.0</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>1.000000</td>\n",
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" <td>26.0000</td>\n",
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" <td>1.0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>0</td>\n",
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" <td>333</td>\n",
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" <td>1</td>\n",
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" <td>1</td>\n",
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" <td>38.0</td>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
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" <td>1.365721</td>\n",
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" <td>153.4625</td>\n",
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" <td>1.0</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" Survived PassengerId Pclass Sex Age SibSp Parch Ticket Fare \\\n",
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"0 0 530 2 1 23.0 2 1 1.000000 11.5000 \n",
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"1 0 466 3 1 38.0 0 0 1.391284 7.0500 \n",
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"2 0 753 3 1 33.0 0 0 1.000000 9.5000 \n",
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"3 0 855 2 0 44.0 1 0 1.000000 26.0000 \n",
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"4 0 333 1 1 38.0 0 1 1.365721 153.4625 \n",
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"\n",
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" Cabin Name_to_num Embarked_C Embarked_Q Embarked_S Embarked_Undefined \n",
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"0 1.0 1 0 0 1 0 \n",
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"1 1.0 1 0 0 1 0 \n",
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"2 1.0 1 0 0 1 0 \n",
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"3 1.0 0 0 0 1 0 \n",
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"4 1.0 1 0 0 1 0 "
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]
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},
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"execution_count": 141,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"data.head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 142,
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"id": "7134ea55",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Podział danych na zbiór uczący i zbiór testowy\n",
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"data_train,data_test = train_test_split(data, test_size=0.2)\n",
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"\n",
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"# zdefiniowanie cech\n",
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"FEATURES = ['Sex','Age','Embarked_C','Embarked_Q','Embarked_S']\n",
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"x_train = pd.DataFrame(data_train[FEATURES])\n",
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"y_train = pd.DataFrame(data_train['Survived'])\n",
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"model = LogisticRegression()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 143,
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"id": "1ff85122",
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"metadata": {},
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"outputs": [],
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"source": [
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"# uczenie modelu\n",
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"model.fit(x_train,y_train.values.ravel())\n",
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"\n",
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"# predykcja wynikow\n",
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"x_test = pd.DataFrame(data_test[FEATURES])\n",
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"y_expected = pd.DataFrame(data_test['Survived'])\n",
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"y_predicted = model.predict(x_test)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 144,
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"id": "0dff77b1",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Precision: 0.832\n",
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"Recall: 0.832\n",
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"F-score: 0.832\n",
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"Model score: 0.832\n"
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]
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}
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],
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"source": [
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"# ewaluacja wynikow\n",
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"precision, recall, fscore, support = precision_recall_fscore_support(\n",
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" y_expected, y_predicted, average='micro'\n",
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")\n",
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"\n",
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"print(f\"Precision: {precision}\")\n",
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"print(f\"Recall: {recall}\")\n",
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"print(f\"F-score: {fscore}\")\n",
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"\n",
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"score = model.score(x_test, y_expected)\n",
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"\n",
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"print(f\"Model score: {score}\")"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.10"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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