ium_478831/IUM_main.ipynb

!pip install kaggle
!pip install pandas
!pip install seaborn

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!kaggle datasets download -d wenruliu/adult-income-dataset

    

401 - Unauthorized

!unzip -o adult-income-dataset.zip

'unzip' is not recognized as an internal or external command,
operable program or batch file.

import pandas as pd
df=pd.read_csv('adult-income-dataset.csv')
df

	age	workclass	fnlwgt	education	educational-num	marital-status	occupation	relationship	race	gender	capital-gain	capital-loss	hours-per-week	native-country	income
0	25	Private	226802	11th	7	Never-married	Machine-op-inspct	Own-child	Black	Male	0	0	40	United-States	<=50K
1	38	Private	89814	HS-grad	9	Married-civ-spouse	Farming-fishing	Husband	White	Male	0	0	50	United-States	<=50K
2	28	Local-gov	336951	Assoc-acdm	12	Married-civ-spouse	Protective-serv	Husband	White	Male	0	0	40	United-States	>50K
3	44	Private	160323	Some-college	10	Married-civ-spouse	Machine-op-inspct	Husband	Black	Male	7688	0	40	United-States	>50K
4	18	?	103497	Some-college	10	Never-married	?	Own-child	White	Female	0	0	30	United-States	<=50K
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
48837	27	Private	257302	Assoc-acdm	12	Married-civ-spouse	Tech-support	Wife	White	Female	0	0	38	United-States	<=50K
48838	40	Private	154374	HS-grad	9	Married-civ-spouse	Machine-op-inspct	Husband	White	Male	0	0	40	United-States	>50K
48839	58	Private	151910	HS-grad	9	Widowed	Adm-clerical	Unmarried	White	Female	0	0	40	United-States	<=50K
48840	22	Private	201490	HS-grad	9	Never-married	Adm-clerical	Own-child	White	Male	0	0	20	United-States	<=50K
48841	52	Self-emp-inc	287927	HS-grad	9	Married-civ-spouse	Exec-managerial	Wife	White	Female	15024	0	40	United-States	>50K

48842 rows × 15 columns

df['income_if_<=50k'] = df['income'].apply(lambda x: True if x == '<=50K' else False)
df['if_male'] = df['gender'].apply(lambda x: True if x == 'Male' else False)
df['income_if_<=50k']= df['income_if_<=50k'].astype(int)
df['if_male']= df['if_male'].astype(int)

#usunięcie nie pełnych danych 
df = df[df.workclass != '?']
df = df.reset_index()

import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
X, y = df[['age']], df['income_if_<=50k']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=37)
n_samples, n_features = X.shape

X_train = np.array(X_train).reshape(-1,1)
X_test = np.array(X_test).reshape(-1,1)
y_train = np.array(y_train).reshape(-1,1)
y_test = np.array(y_test).reshape(-1,1)

sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.fit_transform(X_test)

import torch
torch.from_file
X_train = torch.from_numpy(X_train.astype(np.float32))
X_test = torch.from_numpy(X_test.astype(np.float32))
y_train = torch.from_numpy(y_train.astype(np.float32))
y_test = torch.from_numpy(y_test.astype(np.float32))

y_train = y_train.view(y_train.shape[0], 1)
y_test= y_test.view(y_test.shape[0], 1)

import torch.nn as nn
class LogisticRegresion(nn.Module):
    def __init__(self, n_input_featuers):
        super(LogisticRegresion, self).__init__()
        self.linear = nn.Linear(n_input_featuers, 1)
    
    def forward(self, x):
        y_predicted = torch.sigmoid(self.linear(x))
        return y_predicted

model = LogisticRegresion(n_features)

criterion = nn.BCELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

num_epochs = 1500
for epoch in range(num_epochs):
    y_predicted = model(X_train)
    loss = criterion(y_predicted,y_train)
    loss.backward()
    optimizer.step()
    optimizer.zero_grad()

    if (epoch%100==0):
        print(f'epoch:{epoch+1},loss = {loss.item():.4f}')

epoch:1,loss = 1.0032
epoch:101,loss = 0.8295
epoch:201,loss = 0.7194
epoch:301,loss = 0.6511
epoch:401,loss = 0.6088
epoch:501,loss = 0.5823
epoch:601,loss = 0.5656
epoch:701,loss = 0.5548
epoch:801,loss = 0.5478
epoch:901,loss = 0.5431
epoch:1001,loss = 0.5400
epoch:1101,loss = 0.5378
epoch:1201,loss = 0.5363
epoch:1301,loss = 0.5353
epoch:1401,loss = 0.5346

with torch.no_grad():
    y_predicted = model(X_test)
    y_predicted_cls = y_predicted.round()
    acc = y_predicted_cls.eq(y_test).sum()/float(y_test.shape[0])
    print(f'{acc:.4f}')

0.7395

result = open("result_pytorch",'w+')
result.write(f'acc:{acc:.4f}')

10