ium_444421/classification_net.py

#!/usr/bin/env python
# coding: utf-8

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# get_ipython().system('kaggle datasets download -d kukuroo3/body-performance-data')


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get_ipython().system('unzip -o body-performance-data.zip')


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import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
import torch
from torch import nn, optim
import torch.nn.functional as F


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df = pd.read_csv('bodyPerformance.csv')
df.shape


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df.head()


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cols = ['gender', 'height_cm', 'weight_kg', 'body fat_%', 'sit-ups counts', 'broad jump_cm']
df = df[cols]

# male - 0, female - 1
df['gender'].replace({'M': 0, 'F': 1}, inplace = True)
df = df.dropna(how='any')


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df.gender.value_counts() / df.shape[0]


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X = df[['height_cm', 'weight_kg', 'body fat_%', 'sit-ups counts', 'broad jump_cm']]
y = df[['gender']]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)


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X_train = torch.from_numpy(np.array(X_train)).float()
y_train = torch.squeeze(torch.from_numpy(y_train.values).float())

X_test = torch.from_numpy(np.array(X_test)).float()
y_test = torch.squeeze(torch.from_numpy(y_test.values).float())

print(X_train.shape, y_train.shape)
print(X_test.shape, y_test.shape)


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class Net(nn.Module):
  def __init__(self, n_features):
    super(Net, self).__init__()
    self.fc1 = nn.Linear(n_features, 5)
    self.fc2 = nn.Linear(5, 3)
    self.fc3 = nn.Linear(3, 1)
  def forward(self, x):
    x = F.relu(self.fc1(x))
    x = F.relu(self.fc2(x))
    return torch.sigmoid(self.fc3(x))
net = Net(X_train.shape[1])


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criterion = nn.BCELoss()


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optimizer = optim.Adam(net.parameters(), lr=0.001)


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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")


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X_train = X_train.to(device)
y_train = y_train.to(device)
X_test = X_test.to(device)
y_test = y_test.to(device)


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net = net.to(device)
criterion = criterion.to(device)


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def calculate_accuracy(y_true, y_pred):
  predicted = y_pred.ge(.5).view(-1)
  return (y_true == predicted).sum().float() / len(y_true)


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def round_tensor(t, decimal_places=3):
  return round(t.item(), decimal_places)
for epoch in range(1000):
    y_pred = net(X_train)
    y_pred = torch.squeeze(y_pred)
    train_loss = criterion(y_pred, y_train)
    if epoch % 100 == 0:
      train_acc = calculate_accuracy(y_train, y_pred)
      y_test_pred = net(X_test)
      y_test_pred = torch.squeeze(y_test_pred)
      test_loss = criterion(y_test_pred, y_test)
      test_acc = calculate_accuracy(y_test, y_test_pred)
      print(
f'''epoch {epoch}
Train set - loss: {round_tensor(train_loss)}, accuracy: {round_tensor(train_acc)}
Test  set - loss: {round_tensor(test_loss)}, accuracy: {round_tensor(test_acc)}
''')
    optimizer.zero_grad()
    train_loss.backward()
    optimizer.step()


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# torch.save(net, 'model.pth')


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# net = torch.load('model.pth')


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classes = ['Male', 'Female']
y_pred = net(X_test)
y_pred = y_pred.ge(.5).view(-1).cpu()
y_test = y_test.cpu()
print(classification_report(y_test, y_pred, target_names=classes))


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with open('test_out.csv', 'w') as file:
    for y in y_pred:
        file.write(classes[y.item()])
        file.write('\n')