604 lines
21 KiB
Plaintext
604 lines
21 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 53,
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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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"Requirement already satisfied: kaggle in c:\\users\\user\\anaconda3\\lib\\site-packages (1.5.12)\n",
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"Requirement already satisfied: python-dateutil in c:\\users\\user\\anaconda3\\lib\\site-packages (from kaggle) (2.8.2)\n",
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"Requirement already satisfied: python-slugify in c:\\users\\user\\anaconda3\\lib\\site-packages (from kaggle) (5.0.2)\n",
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"Requirement already satisfied: urllib3 in c:\\users\\user\\anaconda3\\lib\\site-packages (from kaggle) (1.26.7)\n",
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"Requirement already satisfied: certifi in c:\\users\\user\\anaconda3\\lib\\site-packages (from kaggle) (2021.10.8)\n",
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"Requirement already satisfied: tqdm in c:\\users\\user\\anaconda3\\lib\\site-packages (from kaggle) (4.62.3)\n",
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"Requirement already satisfied: requests in c:\\users\\user\\anaconda3\\lib\\site-packages (from kaggle) (2.26.0)\n",
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"Requirement already satisfied: six>=1.10 in c:\\users\\user\\anaconda3\\lib\\site-packages (from kaggle) (1.16.0)\n",
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"Requirement already satisfied: text-unidecode>=1.3 in c:\\users\\user\\anaconda3\\lib\\site-packages (from python-slugify->kaggle) (1.3)\n",
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"Requirement already satisfied: charset-normalizer~=2.0.0 in c:\\users\\user\\anaconda3\\lib\\site-packages (from requests->kaggle) (2.0.4)\n",
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"Requirement already satisfied: idna<4,>=2.5 in c:\\users\\user\\anaconda3\\lib\\site-packages (from requests->kaggle) (3.2)\n",
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"Requirement already satisfied: colorama in c:\\users\\user\\anaconda3\\lib\\site-packages (from tqdm->kaggle) (0.4.4)\n",
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"Requirement already satisfied: pandas in c:\\users\\user\\anaconda3\\lib\\site-packages (1.3.4)\n",
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"Requirement already satisfied: pytz>=2017.3 in c:\\users\\user\\anaconda3\\lib\\site-packages (from pandas) (2021.3)\n",
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"Requirement already satisfied: numpy>=1.17.3 in c:\\users\\user\\anaconda3\\lib\\site-packages (from pandas) (1.20.3)\n",
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"Requirement already satisfied: python-dateutil>=2.7.3 in c:\\users\\user\\anaconda3\\lib\\site-packages (from pandas) (2.8.2)\n",
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"Requirement already satisfied: six>=1.5 in c:\\users\\user\\anaconda3\\lib\\site-packages (from python-dateutil>=2.7.3->pandas) (1.16.0)\n",
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"Requirement already satisfied: seaborn in c:\\users\\user\\anaconda3\\lib\\site-packages (0.11.2)\n",
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"Requirement already satisfied: scipy>=1.0 in c:\\users\\user\\anaconda3\\lib\\site-packages (from seaborn) (1.7.1)\n",
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"Requirement already satisfied: numpy>=1.15 in c:\\users\\user\\anaconda3\\lib\\site-packages (from seaborn) (1.20.3)\n",
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"Requirement already satisfied: matplotlib>=2.2 in c:\\users\\user\\anaconda3\\lib\\site-packages (from seaborn) (3.4.3)\n",
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"Requirement already satisfied: pandas>=0.23 in c:\\users\\user\\anaconda3\\lib\\site-packages (from seaborn) (1.3.4)\n",
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"Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\user\\anaconda3\\lib\\site-packages (from matplotlib>=2.2->seaborn) (1.3.1)\n",
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"Requirement already satisfied: pillow>=6.2.0 in c:\\users\\user\\anaconda3\\lib\\site-packages (from matplotlib>=2.2->seaborn) (8.4.0)\n",
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"Requirement already satisfied: pyparsing>=2.2.1 in c:\\users\\user\\anaconda3\\lib\\site-packages (from matplotlib>=2.2->seaborn) (3.0.4)\n",
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"Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\user\\anaconda3\\lib\\site-packages (from matplotlib>=2.2->seaborn) (2.8.2)\n",
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"Requirement already satisfied: cycler>=0.10 in c:\\users\\user\\anaconda3\\lib\\site-packages (from matplotlib>=2.2->seaborn) (0.10.0)\n",
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"Requirement already satisfied: six in c:\\users\\user\\anaconda3\\lib\\site-packages (from cycler>=0.10->matplotlib>=2.2->seaborn) (1.16.0)\n",
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"Requirement already satisfied: pytz>=2017.3 in c:\\users\\user\\anaconda3\\lib\\site-packages (from pandas>=0.23->seaborn) (2021.3)\n"
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]
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}
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],
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"source": [
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"!pip install kaggle\n",
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"!pip install pandas\n",
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"!pip install seaborn"
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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": 54,
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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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"401 - Unauthorized\n"
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]
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}
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],
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"source": [
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"!kaggle datasets download -d wenruliu/adult-income-dataset\n",
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"\n",
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" "
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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": 55,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"'unzip' is not recognized as an internal or external command,\n",
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"operable program or batch file.\n"
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]
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}
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],
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"source": [
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"!unzip -o adult-income-dataset.zip"
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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": 56,
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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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" .dataframe tbody tr th {\n",
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" vertical-align: top;\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>age</th>\n",
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" <th>workclass</th>\n",
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" <th>fnlwgt</th>\n",
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" <th>education</th>\n",
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" <th>educational-num</th>\n",
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" <th>marital-status</th>\n",
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" <th>occupation</th>\n",
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" <th>relationship</th>\n",
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" <th>race</th>\n",
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" <th>gender</th>\n",
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" <th>capital-gain</th>\n",
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" <th>capital-loss</th>\n",
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" <th>hours-per-week</th>\n",
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" <th>native-country</th>\n",
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" <th>income</th>\n",
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" <td>11th</td>\n",
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" <td>7</td>\n",
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" <td>Never-married</td>\n",
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" <td>Machine-op-inspct</td>\n",
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" <td><=50K</td>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td><=50K</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>28</td>\n",
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" <td>Local-gov</td>\n",
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" <td>336951</td>\n",
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" <td>Assoc-acdm</td>\n",
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" <td>12</td>\n",
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" <td>Married-civ-spouse</td>\n",
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" <td>Protective-serv</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>40</td>\n",
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" <td>>50K</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>44</td>\n",
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" <td>Private</td>\n",
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" <td>160323</td>\n",
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" <td>Some-college</td>\n",
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" <td>10</td>\n",
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" <td>Married-civ-spouse</td>\n",
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" <td>Machine-op-inspct</td>\n",
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" <td>Husband</td>\n",
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" <td>Black</td>\n",
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" <td>Male</td>\n",
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" <td>7688</td>\n",
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" <td>0</td>\n",
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" <td>40</td>\n",
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" <td>United-States</td>\n",
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" <td>>50K</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>18</td>\n",
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" <td>?</td>\n",
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" <td>103497</td>\n",
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" <td>Some-college</td>\n",
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" <td>10</td>\n",
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" <td>Never-married</td>\n",
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" <td>0</td>\n",
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" <td>30</td>\n",
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" <td>United-States</td>\n",
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" <td><=50K</td>\n",
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" </tr>\n",
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" <tr>\n",
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||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
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||
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|
||
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|
||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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" <td>27</td>\n",
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||
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||
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" <td>Assoc-acdm</td>\n",
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||
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||
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" <td>38</td>\n",
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" <td>United-States</td>\n",
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" <td><=50K</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>48838</th>\n",
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" <td>40</td>\n",
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" <td>Private</td>\n",
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" <td>154374</td>\n",
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" <td>HS-grad</td>\n",
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" <td>9</td>\n",
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" <td>Married-civ-spouse</td>\n",
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" <td>Machine-op-inspct</td>\n",
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" <td>Husband</td>\n",
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" <td>White</td>\n",
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" <td>Male</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>40</td>\n",
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" <td>United-States</td>\n",
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" <td>>50K</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>48839</th>\n",
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" <td>58</td>\n",
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" <td>Private</td>\n",
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" <td>151910</td>\n",
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" <td>HS-grad</td>\n",
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" <td>9</td>\n",
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" <td>Widowed</td>\n",
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" <td>Adm-clerical</td>\n",
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" <td>Unmarried</td>\n",
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" <td>White</td>\n",
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" <td>Female</td>\n",
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" <td>0</td>\n",
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||
" <td>0</td>\n",
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||
" <td>40</td>\n",
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||
" <td>United-States</td>\n",
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" <td><=50K</td>\n",
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||
" </tr>\n",
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" <tr>\n",
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" <th>48840</th>\n",
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" <td>22</td>\n",
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||
" <td>Private</td>\n",
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||
" <td>201490</td>\n",
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" <td>HS-grad</td>\n",
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" <td>9</td>\n",
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" <td>Never-married</td>\n",
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" <td>Adm-clerical</td>\n",
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||
" <td>Own-child</td>\n",
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||
" <td>White</td>\n",
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||
" <td>Male</td>\n",
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||
" <td>0</td>\n",
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||
" <td>0</td>\n",
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||
" <td>20</td>\n",
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||
" <td>United-States</td>\n",
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" <td><=50K</td>\n",
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||
" </tr>\n",
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" <tr>\n",
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" <th>48841</th>\n",
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" <td>52</td>\n",
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" <td>Self-emp-inc</td>\n",
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||
" <td>287927</td>\n",
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||
" <td>HS-grad</td>\n",
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||
" <td>9</td>\n",
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||
" <td>Married-civ-spouse</td>\n",
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||
" <td>Exec-managerial</td>\n",
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||
" <td>Wife</td>\n",
|
||
" <td>White</td>\n",
|
||
" <td>Female</td>\n",
|
||
" <td>15024</td>\n",
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||
" <td>0</td>\n",
|
||
" <td>40</td>\n",
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||
" <td>United-States</td>\n",
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" <td>>50K</td>\n",
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||
" </tr>\n",
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||
" </tbody>\n",
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||
"</table>\n",
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||
"<p>48842 rows × 15 columns</p>\n",
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||
"</div>"
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||
],
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"text/plain": [
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" age workclass fnlwgt education educational-num \\\n",
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"0 25 Private 226802 11th 7 \n",
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"1 38 Private 89814 HS-grad 9 \n",
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"2 28 Local-gov 336951 Assoc-acdm 12 \n",
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"3 44 Private 160323 Some-college 10 \n",
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"4 18 ? 103497 Some-college 10 \n",
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||
"... ... ... ... ... ... \n",
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"48837 27 Private 257302 Assoc-acdm 12 \n",
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"48838 40 Private 154374 HS-grad 9 \n",
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"48839 58 Private 151910 HS-grad 9 \n",
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"48840 22 Private 201490 HS-grad 9 \n",
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"48841 52 Self-emp-inc 287927 HS-grad 9 \n",
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"\n",
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" marital-status occupation relationship race gender \\\n",
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"0 Never-married Machine-op-inspct Own-child Black Male \n",
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"1 Married-civ-spouse Farming-fishing Husband White Male \n",
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"2 Married-civ-spouse Protective-serv Husband White Male \n",
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"3 Married-civ-spouse Machine-op-inspct Husband Black Male \n",
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"4 Never-married ? Own-child White Female \n",
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||
"... ... ... ... ... ... \n",
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"48837 Married-civ-spouse Tech-support Wife White Female \n",
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"48838 Married-civ-spouse Machine-op-inspct Husband White Male \n",
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"48839 Widowed Adm-clerical Unmarried White Female \n",
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"48840 Never-married Adm-clerical Own-child White Male \n",
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"48841 Married-civ-spouse Exec-managerial Wife White Female \n",
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"\n",
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" capital-gain capital-loss hours-per-week native-country income \n",
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"0 0 0 40 United-States <=50K \n",
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"1 0 0 50 United-States <=50K \n",
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"2 0 0 40 United-States >50K \n",
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"3 7688 0 40 United-States >50K \n",
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"4 0 0 30 United-States <=50K \n",
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"... ... ... ... ... ... \n",
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"48837 0 0 38 United-States <=50K \n",
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"48838 0 0 40 United-States >50K \n",
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"48839 0 0 40 United-States <=50K \n",
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"48840 0 0 20 United-States <=50K \n",
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"48841 15024 0 40 United-States >50K \n",
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"\n",
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"[48842 rows x 15 columns]"
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||
]
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||
},
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||
"execution_count": 56,
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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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"import pandas as pd\n",
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"df=pd.read_csv('adult-income-dataset.csv')\n",
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"df\n"
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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": 57,
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"metadata": {},
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"outputs": [],
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"source": [
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"df['income_if_<=50k'] = df['income'].apply(lambda x: True if x == '<=50K' else False)\n",
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"df['if_male'] = df['gender'].apply(lambda x: True if x == 'Male' else False)\n",
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"df['income_if_<=50k']= df['income_if_<=50k'].astype(int)\n",
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"df['if_male']= df['if_male'].astype(int)\n"
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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": 58,
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||
"metadata": {},
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||
"outputs": [],
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||
"source": [
|
||
"#usunięcie nie pełnych danych \n",
|
||
"df = df[df.workclass != '?']\n",
|
||
"df = df.reset_index()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 59,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"from sklearn.preprocessing import StandardScaler\n",
|
||
"from sklearn.model_selection import train_test_split\n",
|
||
"X, y = df[['age']], df['income_if_<=50k']\n",
|
||
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=37)\n",
|
||
"n_samples, n_features = X.shape"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 60,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"X_train = np.array(X_train).reshape(-1,1)\n",
|
||
"X_test = np.array(X_test).reshape(-1,1)\n",
|
||
"y_train = np.array(y_train).reshape(-1,1)\n",
|
||
"y_test = np.array(y_test).reshape(-1,1)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 61,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"sc = StandardScaler()\n",
|
||
"X_train = sc.fit_transform(X_train)\n",
|
||
"X_test = sc.fit_transform(X_test)\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 62,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import torch\n",
|
||
"torch.from_file\n",
|
||
"X_train = torch.from_numpy(X_train.astype(np.float32))\n",
|
||
"X_test = torch.from_numpy(X_test.astype(np.float32))\n",
|
||
"y_train = torch.from_numpy(y_train.astype(np.float32))\n",
|
||
"y_test = torch.from_numpy(y_test.astype(np.float32))\n",
|
||
"\n",
|
||
"y_train = y_train.view(y_train.shape[0], 1)\n",
|
||
"y_test= y_test.view(y_test.shape[0], 1)\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 63,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import torch.nn as nn\n",
|
||
"class LogisticRegresion(nn.Module):\n",
|
||
" def __init__(self, n_input_featuers):\n",
|
||
" super(LogisticRegresion, self).__init__()\n",
|
||
" self.linear = nn.Linear(n_input_featuers, 1)\n",
|
||
" \n",
|
||
" def forward(self, x):\n",
|
||
" y_predicted = torch.sigmoid(self.linear(x))\n",
|
||
" return y_predicted\n",
|
||
"\n",
|
||
"model = LogisticRegresion(n_features)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 64,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"criterion = nn.BCELoss()\n",
|
||
"optimizer = torch.optim.SGD(model.parameters(), lr=0.01)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 65,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"epoch:1,loss = 1.0032\n",
|
||
"epoch:101,loss = 0.8295\n",
|
||
"epoch:201,loss = 0.7194\n",
|
||
"epoch:301,loss = 0.6511\n",
|
||
"epoch:401,loss = 0.6088\n",
|
||
"epoch:501,loss = 0.5823\n",
|
||
"epoch:601,loss = 0.5656\n",
|
||
"epoch:701,loss = 0.5548\n",
|
||
"epoch:801,loss = 0.5478\n",
|
||
"epoch:901,loss = 0.5431\n",
|
||
"epoch:1001,loss = 0.5400\n",
|
||
"epoch:1101,loss = 0.5378\n",
|
||
"epoch:1201,loss = 0.5363\n",
|
||
"epoch:1301,loss = 0.5353\n",
|
||
"epoch:1401,loss = 0.5346\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"num_epochs = 1500\n",
|
||
"for epoch in range(num_epochs):\n",
|
||
" y_predicted = model(X_train)\n",
|
||
" loss = criterion(y_predicted,y_train)\n",
|
||
" loss.backward()\n",
|
||
" optimizer.step()\n",
|
||
" optimizer.zero_grad()\n",
|
||
"\n",
|
||
" if (epoch%100==0):\n",
|
||
" print(f'epoch:{epoch+1},loss = {loss.item():.4f}')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 66,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"0.7395\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"with torch.no_grad():\n",
|
||
" y_predicted = model(X_test)\n",
|
||
" y_predicted_cls = y_predicted.round()\n",
|
||
" acc = y_predicted_cls.eq(y_test).sum()/float(y_test.shape[0])\n",
|
||
" print(f'{acc:.4f}')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 72,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"10"
|
||
]
|
||
},
|
||
"execution_count": 72,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"result = open(\"result_pytorch\",'w+')\n",
|
||
"result.write(f'acc:{acc:.4f}')"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"interpreter": {
|
||
"hash": "2647ea34e536f865ab67ff9ddee7fd78773d956cec0cab53c79b32cd10da5d83"
|
||
},
|
||
"kernelspec": {
|
||
"display_name": "Python 3.9.11 64-bit",
|
||
"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": "3.9.7"
|
||
},
|
||
"orig_nbformat": 2
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 2
|
||
}
|