94 lines
2.7 KiB
Python
94 lines
2.7 KiB
Python
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import datetime
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import pandas as pd
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import numpy as np
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from torch.autograd import Variable
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import torch
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import torch.nn as nn
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from sacred import Experiment
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from sacred.observers import MongoObserver
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import csv
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ex = Experiment("434700-file", interactive=False, save_git_info=False)
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ex.observers.append(MongoObserver(
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url='mongodb://mongo_user:mongo_password_IUM_2021@172.17.0.1:27017', db_name='sacred'))
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@ex.config
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def my_config():
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epochs = 10
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batch_size = 16
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@ex.capture
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def prepare_model(epochs, batch_size, _run):
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INPUT_DIM = 1
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OUTPUT_DIM = 1
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LEARNING_RATE = 0.01
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EPOCHS = epochs
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dataset = pd.read_csv('datasets/train_set.csv')
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x_values = [datetime.datetime.strptime(
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item, "%Y-%m-%d").month for item in dataset['date'].values]
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x_train = np.array(x_values, dtype=np.float32)
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x_train = x_train.reshape(-1, 1)
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y_values = [min(dataset['result_1'].values[i]/dataset['result_2'].values[i], dataset['result_2'].values[i] /
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dataset['result_1'].values[i]) for i in range(len(dataset['result_1'].values))]
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y_train = np.array(y_values, dtype=np.float32)
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y_train = y_train.reshape(-1, 1)
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class LinearRegression(torch.nn.Module):
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def __init__(self, inputSize, outputSize):
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super(LinearRegression, self).__init__()
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self.linear = torch.nn.Linear(inputSize, outputSize)
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def forward(self, x):
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out = self.linear(x)
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return out
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model = LinearRegression(INPUT_DIM, OUTPUT_DIM)
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criterion = torch.nn.MSELoss()
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optimizer = torch.optim.SGD(model.parameters(), lr=LEARNING_RATE)
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for epoch in range(EPOCHS):
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inputs = Variable(torch.from_numpy(x_train))
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labels = Variable(torch.from_numpy(y_train))
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optimizer.zero_grad()
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outputs = model(inputs)
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loss = criterion(outputs, labels)
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print(loss)
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loss.backward()
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optimizer.step()
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print('epoch {}, loss {}'.format(epoch, loss.item()))
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torch.save(model.state_dict(), 'model-experiment.pt')
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with torch.no_grad(): # we don't need gradients in the testing phase
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predicted = model(Variable(torch.from_numpy(x_train))).data.numpy()
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with open('model_experiment_results.csv', mode='w') as filee:
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writer = csv.writer(filee, delimiter=',', quotechar='"',
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quoting=csv.QUOTE_MINIMAL)
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writer.writerow(['x', 'y', 'predicted_y'])
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for i in range(len(x_train)):
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writer.writerow([x_train[i][0], y_train[i][0], predicted[i][0]])
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@ex.automain
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def my_main(epochs, batch_size):
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print(prepare_model())
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ex.run()
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ex.add_artifact('model-experiment.pt')
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ex.add_artifact('model_experiment_results.csv')
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