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ex1
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{
"cells": [
{
"cell_type": "code",
pytorch
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"execution_count": 53,
ex1
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: kaggle in c:\\users\\user\\anaconda3\\lib\\site-packages (1.5.12)\n",
"Requirement already satisfied: python-dateutil in c:\\users\\user\\anaconda3\\lib\\site-packages (from kaggle) (2.8.2)\n",
"Requirement already satisfied: python-slugify in c:\\users\\user\\anaconda3\\lib\\site-packages (from kaggle) (5.0.2)\n",
pytorch
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"Requirement already satisfied: urllib3 in c:\\users\\user\\anaconda3\\lib\\site-packages (from kaggle) (1.26.7)\n",
"Requirement already satisfied: certifi in c:\\users\\user\\anaconda3\\lib\\site-packages (from kaggle) (2021.10.8)\n",
"Requirement already satisfied: tqdm in c:\\users\\user\\anaconda3\\lib\\site-packages (from kaggle) (4.62.3)\n",
ex1
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"Requirement already satisfied: requests in c:\\users\\user\\anaconda3\\lib\\site-packages (from kaggle) (2.26.0)\n",
"Requirement already satisfied: six>=1.10 in c:\\users\\user\\anaconda3\\lib\\site-packages (from kaggle) (1.16.0)\n",
"Requirement already satisfied: text-unidecode>=1.3 in c:\\users\\user\\anaconda3\\lib\\site-packages (from python-slugify->kaggle) (1.3)\n",
"Requirement already satisfied: charset-normalizer~=2.0.0 in c:\\users\\user\\anaconda3\\lib\\site-packages (from requests->kaggle) (2.0.4)\n",
"Requirement already satisfied: idna<4,>=2.5 in c:\\users\\user\\anaconda3\\lib\\site-packages (from requests->kaggle) (3.2)\n",
"Requirement already satisfied: colorama in c:\\users\\user\\anaconda3\\lib\\site-packages (from tqdm->kaggle) (0.4.4)\n",
"Requirement already satisfied: pandas in c:\\users\\user\\anaconda3\\lib\\site-packages (1.3.4)\n",
"Requirement already satisfied: pytz>=2017.3 in c:\\users\\user\\anaconda3\\lib\\site-packages (from pandas) (2021.3)\n",
"Requirement already satisfied: numpy>=1.17.3 in c:\\users\\user\\anaconda3\\lib\\site-packages (from pandas) (1.20.3)\n",
pytorch
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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",
ex1
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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",
"Requirement already satisfied: seaborn in c:\\users\\user\\anaconda3\\lib\\site-packages (0.11.2)\n",
pytorch
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"Requirement already satisfied: scipy>=1.0 in c:\\users\\user\\anaconda3\\lib\\site-packages (from seaborn) (1.7.1)\n",
ex1
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"Requirement already satisfied: numpy>=1.15 in c:\\users\\user\\anaconda3\\lib\\site-packages (from seaborn) (1.20.3)\n",
"Requirement already satisfied: matplotlib>=2.2 in c:\\users\\user\\anaconda3\\lib\\site-packages (from seaborn) (3.4.3)\n",
"Requirement already satisfied: pandas>=0.23 in c:\\users\\user\\anaconda3\\lib\\site-packages (from seaborn) (1.3.4)\n",
pytorch
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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",
ex1
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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",
"Requirement already satisfied: pyparsing>=2.2.1 in c:\\users\\user\\anaconda3\\lib\\site-packages (from matplotlib>=2.2->seaborn) (3.0.4)\n",
"Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\user\\anaconda3\\lib\\site-packages (from matplotlib>=2.2->seaborn) (2.8.2)\n",
pytorch
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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",
ex1
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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",
"Requirement already satisfied: pytz>=2017.3 in c:\\users\\user\\anaconda3\\lib\\site-packages (from pandas>=0.23->seaborn) (2021.3)\n"
]
}
],
"source": [
"!pip install kaggle\n",
"!pip install pandas\n",
"!pip install seaborn"
]
},
{
"cell_type": "code",
pytorch
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"execution_count": 54,
ex1
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
pytorch
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"401 - Unauthorized\n"
ex1
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]
}
],
"source": [
"!kaggle datasets download -d wenruliu/adult-income-dataset\n",
"\n",
" "
]
},
{
"cell_type": "code",
pytorch
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"execution_count": 55,
ex1
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"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"'unzip' is not recognized as an internal or external command,\n",
"operable program or batch file.\n"
]
}
],
"source": [
"!unzip -o adult-income-dataset.zip"
]
},
{
"cell_type": "code",
pytorch
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"execution_count": 56,
ex1
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"metadata": {},
"outputs": [
{
"data": {
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" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>age</th>\n",
" <th>workclass</th>\n",
" <th>fnlwgt</th>\n",
" <th>education</th>\n",
" <th>educational-num</th>\n",
" <th>marital-status</th>\n",
" <th>occupation</th>\n",
" <th>relationship</th>\n",
" <th>race</th>\n",
" <th>gender</th>\n",
" <th>capital-gain</th>\n",
" <th>capital-loss</th>\n",
" <th>hours-per-week</th>\n",
" <th>native-country</th>\n",
" <th>income</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>25</td>\n",
" <td>Private</td>\n",
" <td>226802</td>\n",
" <td>11th</td>\n",
" <td>7</td>\n",
" <td>Never-married</td>\n",
" <td>Machine-op-inspct</td>\n",
" <td>Own-child</td>\n",
" <td>Black</td>\n",
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" <td>40</td>\n",
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" <td>38</td>\n",
" <td>Private</td>\n",
" <td>89814</td>\n",
" <td>HS-grad</td>\n",
" <td>9</td>\n",
" <td>Married-civ-spouse</td>\n",
" <td>Farming-fishing</td>\n",
" <td>Husband</td>\n",
" <td>White</td>\n",
" <td>Male</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>50</td>\n",
" <td>United-States</td>\n",
" <td>&lt;=50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>28</td>\n",
" <td>Local-gov</td>\n",
" <td>336951</td>\n",
" <td>Assoc-acdm</td>\n",
" <td>12</td>\n",
" <td>Married-civ-spouse</td>\n",
" <td>Protective-serv</td>\n",
" <td>Husband</td>\n",
" <td>White</td>\n",
" <td>Male</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" <td>United-States</td>\n",
" <td>&gt;50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>44</td>\n",
" <td>Private</td>\n",
" <td>160323</td>\n",
" <td>Some-college</td>\n",
" <td>10</td>\n",
" <td>Married-civ-spouse</td>\n",
" <td>Machine-op-inspct</td>\n",
" <td>Husband</td>\n",
" <td>Black</td>\n",
" <td>Male</td>\n",
" <td>7688</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" <td>United-States</td>\n",
" <td>&gt;50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>18</td>\n",
" <td>?</td>\n",
" <td>103497</td>\n",
" <td>Some-college</td>\n",
" <td>10</td>\n",
" <td>Never-married</td>\n",
" <td>?</td>\n",
" <td>Own-child</td>\n",
" <td>White</td>\n",
" <td>Female</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>30</td>\n",
" <td>United-States</td>\n",
" <td>&lt;=50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
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" <tr>\n",
" <th>48837</th>\n",
" <td>27</td>\n",
" <td>Private</td>\n",
" <td>257302</td>\n",
" <td>Assoc-acdm</td>\n",
" <td>12</td>\n",
" <td>Married-civ-spouse</td>\n",
" <td>Tech-support</td>\n",
" <td>Wife</td>\n",
" <td>White</td>\n",
" <td>Female</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>38</td>\n",
" <td>United-States</td>\n",
" <td>&lt;=50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48838</th>\n",
" <td>40</td>\n",
" <td>Private</td>\n",
" <td>154374</td>\n",
" <td>HS-grad</td>\n",
" <td>9</td>\n",
" <td>Married-civ-spouse</td>\n",
" <td>Machine-op-inspct</td>\n",
" <td>Husband</td>\n",
" <td>White</td>\n",
" <td>Male</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" <td>United-States</td>\n",
" <td>&gt;50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48839</th>\n",
" <td>58</td>\n",
" <td>Private</td>\n",
" <td>151910</td>\n",
" <td>HS-grad</td>\n",
" <td>9</td>\n",
" <td>Widowed</td>\n",
" <td>Adm-clerical</td>\n",
" <td>Unmarried</td>\n",
" <td>White</td>\n",
" <td>Female</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" <td>United-States</td>\n",
" <td>&lt;=50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48840</th>\n",
" <td>22</td>\n",
" <td>Private</td>\n",
" <td>201490</td>\n",
" <td>HS-grad</td>\n",
" <td>9</td>\n",
" <td>Never-married</td>\n",
" <td>Adm-clerical</td>\n",
" <td>Own-child</td>\n",
" <td>White</td>\n",
" <td>Male</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>20</td>\n",
" <td>United-States</td>\n",
" <td>&lt;=50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48841</th>\n",
" <td>52</td>\n",
" <td>Self-emp-inc</td>\n",
" <td>287927</td>\n",
" <td>HS-grad</td>\n",
" <td>9</td>\n",
" <td>Married-civ-spouse</td>\n",
" <td>Exec-managerial</td>\n",
" <td>Wife</td>\n",
" <td>White</td>\n",
" <td>Female</td>\n",
" <td>15024</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" <td>United-States</td>\n",
" <td>&gt;50K</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>48842 rows × 15 columns</p>\n",
"</div>"
],
"text/plain": [
" age workclass fnlwgt education educational-num \\\n",
"0 25 Private 226802 11th 7 \n",
"1 38 Private 89814 HS-grad 9 \n",
"2 28 Local-gov 336951 Assoc-acdm 12 \n",
"3 44 Private 160323 Some-college 10 \n",
"4 18 ? 103497 Some-college 10 \n",
"... ... ... ... ... ... \n",
"48837 27 Private 257302 Assoc-acdm 12 \n",
"48838 40 Private 154374 HS-grad 9 \n",
"48839 58 Private 151910 HS-grad 9 \n",
"48840 22 Private 201490 HS-grad 9 \n",
"48841 52 Self-emp-inc 287927 HS-grad 9 \n",
"\n",
" marital-status occupation relationship race gender \\\n",
"0 Never-married Machine-op-inspct Own-child Black Male \n",
"1 Married-civ-spouse Farming-fishing Husband White Male \n",
"2 Married-civ-spouse Protective-serv Husband White Male \n",
"3 Married-civ-spouse Machine-op-inspct Husband Black Male \n",
"4 Never-married ? Own-child White Female \n",
"... ... ... ... ... ... \n",
"48837 Married-civ-spouse Tech-support Wife White Female \n",
"48838 Married-civ-spouse Machine-op-inspct Husband White Male \n",
"48839 Widowed Adm-clerical Unmarried White Female \n",
"48840 Never-married Adm-clerical Own-child White Male \n",
"48841 Married-civ-spouse Exec-managerial Wife White Female \n",
"\n",
" capital-gain capital-loss hours-per-week native-country income \n",
"0 0 0 40 United-States <=50K \n",
"1 0 0 50 United-States <=50K \n",
"2 0 0 40 United-States >50K \n",
"3 7688 0 40 United-States >50K \n",
"4 0 0 30 United-States <=50K \n",
"... ... ... ... ... ... \n",
"48837 0 0 38 United-States <=50K \n",
"48838 0 0 40 United-States >50K \n",
"48839 0 0 40 United-States <=50K \n",
"48840 0 0 20 United-States <=50K \n",
"48841 15024 0 40 United-States >50K \n",
"\n",
"[48842 rows x 15 columns]"
]
},
pytorch
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"execution_count": 56,
ex1
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"df=pd.read_csv('adult-income-dataset.csv')\n",
"df\n"
]
},
{
"cell_type": "code",
pytorch
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"execution_count": 57,
"metadata": {},
"outputs": [],
"source": [
"df['income_if_<=50k'] = df['income'].apply(lambda x: True if x == '<=50K' else False)\n",
"df['if_male'] = df['gender'].apply(lambda x: True if x == 'Male' else False)\n",
"df['income_if_<=50k']= df['income_if_<=50k'].astype(int)\n",
"df['if_male']= df['if_male'].astype(int)\n"
]
},
{
"cell_type": "code",
"execution_count": 58,
ex1
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"metadata": {},
"outputs": [],
"source": [
"#usunięcie nie pełnych danych \n",
pytorch
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"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"
ex1
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]
},
{
"cell_type": "code",
pytorch
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"execution_count": 62,
ex1
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"metadata": {},
"outputs": [],
"source": [
"import torch\n",
pytorch
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"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",
ex1
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"\n",
pytorch
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"y_train = y_train.view(y_train.shape[0], 1)\n",
"y_test= y_test.view(y_test.shape[0], 1)\n"
ex1
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]
},
{
"cell_type": "code",
pytorch
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"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,
ex1
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
pytorch
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"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"
ex1
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]
pytorch
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}
],
"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": [
ex1
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{
pytorch
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"name": "stdout",
"output_type": "stream",
"text": [
"0.7395\n"
]
ex1
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}
],
"source": [
pytorch
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"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}')"
ex1
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]
},
{
"cell_type": "code",
pytorch
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"execution_count": 72,
ex1
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
pytorch
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"10"
ex1
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]
},
pytorch
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"execution_count": 72,
ex1
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
pytorch
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"result = open(\"result_pytorch\",'w+')\n",
"result.write(f'acc:{acc:.4f}')"
ex1
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]
}
],
"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
}
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