98 lines
3.1 KiB
Python
98 lines
3.1 KiB
Python
from sklearn.model_selection import train_test_split
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import torch
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import torch.nn as nn
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import pandas as pd
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import numpy as np
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import torch.nn.functional as F
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from torch.utils.data import DataLoader, TensorDataset, random_split
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from sklearn import preprocessing
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import sys
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from sacred import Experiment
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from sacred.observers import MongoObserver
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from sklearn.metrics import accuracy_score
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from sklearn.metrics import f1_score
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from sklearn.metrics import mean_squared_error
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np.set_printoptions(suppress=False)
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ex = Experiment("ium_s440058", interactive=False, save_git_info=False)
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ex.observers.append(MongoObserver(url='mongodb://mongo_user:mongo_password_IUM_2021@172.17.0.1:27017',
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db_name='sacred'))
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@ex.config
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def my_config():
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num_epochs = 10
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batch_size = 20
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class LogisticRegressionModel(torch.nn.Module):
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def __init__(self, input_dim, output_dim):
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super(LogisticRegressionModel, self).__init__()
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self.linear = nn.Linear(input_dim, output_dim)
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self.sigmoid = nn.Sigmoid()
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def forward(self, x):
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out = self.linear(x)
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return self.sigmoid(out)
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@ex.capture
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def script(num_epochs, batch_size, _run):
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results = pd.read_csv('diabetes2.csv')
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results.dropna()
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data_train, data_valid, data_test = np.split(results.sample(frac=1), [int(.6*len(results)), int(.8*len(results))])
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columns_to_train = ['Glucose', 'BloodPressure', 'Insulin', 'Age']
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x_train = data_train[columns_to_train].astype(np.float32)
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y_train = data_train['Outcome'].astype(np.float32)
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x_test = data_test[columns_to_train].astype(np.float32)
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y_test = data_test['Outcome'].astype(np.float32)
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fTrain = torch.from_numpy(x_train.values)
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tTrain = torch.from_numpy(y_train.values.reshape(460,1))
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fTest= torch.from_numpy(x_test.values)
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tTest = torch.from_numpy(y_test.values)
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_run.log_scalar("Batch", str(batch_size))
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_run.log_scalar("epoch", str(num_epochs))
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learning_rate = 0.005
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input_dim = 4
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output_dim = 1
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model = LogisticRegressionModel(input_dim, output_dim)
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criterion = torch.nn.BCELoss(reduction='mean')
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optimizer = torch.optim.SGD(model.parameters(), lr = learning_rate)
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for epoch in range(num_epochs):
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print ("Epoch - ",epoch)
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model.train()
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optimizer.zero_grad()
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# Forward pass
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y_pred = model(fTrain)
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# Compute Loss
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loss = criterion(y_pred, tTrain)
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print(loss.item())
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# Backward pass
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loss.backward()
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optimizer.step()
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_run.log_scalar("Lost", str(loss.item()))
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torch.save(model.state_dict(), 'diabetes.pth')
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pred = model(fTest)
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accuracy = accuracy_score(tTest, np.argmax(pred.detach().numpy(), axis = 1))
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f1 = f1_score(tTest, np.argmax(pred.detach().numpy(), axis = 1), average = None)
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rmse = mean_squared_error(tTest, pred.detach().numpy())
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_run.log_scalar("accuracy", accuracy)
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_run.log_scalar("f1", f1)
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_run.log_scalar("rmse", rmse)
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@ex.automain
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def my_main(num_epochs, batch_size, _run):
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script()
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ex.run()
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ex.add_artifact('diabetes_model/diabetes.pth') |