207 lines
7.0 KiB
Python
207 lines
7.0 KiB
Python
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from sacred import Experiment
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from sacred.observers import FileStorageObserver, MongoObserver
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import argparse
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import pandas as pd
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import numpy as np
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from sklearn.metrics import mean_squared_error, mean_absolute_error
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import torch
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from torch import nn
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from torch.utils import data as t_u_data
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ex = Experiment("478841 sacred_scopes", interactive=True, save_git_info=False)
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ex.observers.append(MongoObserver(
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url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
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ex.observers.append(FileStorageObserver('./data/training_runs'))
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@ex.config
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def my_config():
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parser = argparse.ArgumentParser(description="Script performing logistic regression model training",
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formatter_class=argparse.ArgumentDefaultsHelpFormatter)
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parser.add_argument(
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"-e", "--epochs", default=100, help="Number of epochs the model will be trained for")
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parser.add_argument(
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"-s", "--step", default=10, help="Number of steps to repeat logging loss values on")
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parser.add_argument("--save", action="store_true",
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help="Save trained model to file 'trained_model.h5'")
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args = vars(parser.parse_args())
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epochs = int(args['epochs'])
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save_model = args['save']
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log_step = int(args['step'])
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# * Customized Dataset class (base provided by PyTorch)
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class AvocadoDataset(t_u_data.Dataset):
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def __init__(self, path: str, target: str = 'AveragePrice'):
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data = pd.read_csv(path)
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y = data[target].values.astype('float32')
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self.y = y.reshape((len(y), 1))
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self.x_data = data.drop(
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[target], axis=1).values.astype('float32')
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self.x_shape = data.drop([target], axis=1).shape
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# print("Data shape is: ", self.x_data.shape)
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def __len__(self):
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return len(self.x_data)
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def __getitem__(self, idx):
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return [self.x_data[idx], self.y[idx]]
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def get_shape(self):
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return self.x_shape
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def get_splits(self, n_test=0.33):
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test_size = round(n_test * len(self.x_data))
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train_size = len(self.x_data) - test_size
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return t_u_data.random_split(self, [train_size, test_size])
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class AvocadoRegressor(nn.Module):
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def __init__(self, input_dim):
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super(AvocadoRegressor, self).__init__()
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self.hidden1 = nn.Linear(input_dim, 32)
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nn.init.xavier_uniform_(self.hidden1.weight)
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self.act1 = nn.ReLU()
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self.hidden2 = nn.Linear(32, 8)
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nn.init.xavier_uniform_(self.hidden2.weight)
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self.act2 = nn.ReLU()
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self.hidden3 = nn.Linear(8, 1)
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nn.init.xavier_uniform_(self.hidden3.weight)
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def forward(self, x):
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x = self.hidden1(x)
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x = self.act1(x)
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x = self.hidden2(x)
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x = self.act2(x)
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x = self.hidden3(x)
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return x
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def prepare_data(paths):
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train_dl = t_u_data.DataLoader(AvocadoDataset(
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paths[0]), batch_size=32, shuffle=True)
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validate_dl = t_u_data.DataLoader(AvocadoDataset(
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paths[1]), batch_size=128, shuffle=True)
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test_dl = t_u_data.DataLoader(AvocadoDataset(
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paths[2]), batch_size=1, shuffle=False)
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return train_dl, validate_dl, test_dl
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@ex.capture
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def train_model(train_dl, model, epochs, log_step, _run):
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criterion = nn.MSELoss()
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optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
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to_compare = None
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for epoch in range(1, epochs+1):
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for _, (inputs, targets) in enumerate(train_dl):
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optimizer.zero_grad()
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yhat = model(inputs)
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# * For loss value inspection
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to_compare = (yhat, targets)
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loss = criterion(yhat, targets)
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loss.backward()
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optimizer.step()
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if epoch == 1 or (epoch) % log_step == 0:
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result, target = to_compare[0].detach(
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).numpy(), to_compare[1].detach().numpy()
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mse = mean_squared_error(target, result)
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mae = mean_absolute_error(target, result)
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_run.log_scalar("training.RMSE", np.sqrt(mse), epoch)
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_run.log_scalar("training.MAE", mae, epoch)
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_run.log_scalar('training.MSE', mse, epoch)
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print(
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f"Epoch {epoch}\t→\tMSE: {mse},\tRMSE: {np.sqrt(mse)},\tMAE: {mae}")
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def evaluate_model(test_dl, model):
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predictions, actuals = list(), list()
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for _, (inputs, targets) in enumerate(test_dl):
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yhat = model(inputs)
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# * retrieve numpy array
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yhat = yhat.detach().numpy()
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actual = targets.numpy()
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actual = actual.reshape((len(actual), 1))
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# * store predictions
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predictions.append(yhat)
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actuals.append(actual)
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predictions, actuals = np.vstack(predictions), np.vstack(actuals)
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# * return MSE value
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mse = mean_squared_error(actuals, predictions)
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rmse = mean_squared_error(actuals, predictions, squared=False)
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mae = mean_absolute_error(actuals, predictions)
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return mse, rmse, mae
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def predict(row, model):
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row = row[0].flatten()
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yhat = model(row)
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yhat = yhat.detach().numpy()
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return yhat
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@ex.main
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def main(epochs, save_model, log_step, _run):
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print(
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f"Your model will be trained for {epochs} epochs. Trained model will {'not ' if save_model else ''}be saved.")
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# * Paths to data
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avocado_data = ['./data/avocado.data.train',
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'./data/avocado.data.valid',
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'./data/avocado.data.test']
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# * Data preparation
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train_dl, validate_dl, test_dl = prepare_data(paths=avocado_data)
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print(f"""
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Train set size: {len(train_dl.dataset)},
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Validate set size: {len(validate_dl.dataset)}
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Test set size: {len(test_dl.dataset)}
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""")
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# * Model definition
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# ! 66 - in case only regions and type are used (among all the categorical vals)
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model = AvocadoRegressor(235)
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# * Train model
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print("Let's start the training, mate!")
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train_model(train_dl=train_dl, model=model,
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epochs=epochs, log_step=log_step)
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# * Evaluate model
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mse, rmse, mae = evaluate_model(validate_dl, model)
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print(
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f"\nEvaluation on validation set\t→\tMSE: {mse}, RMSE: {rmse}, MAE: {mae}")
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_run.log_scalar("validation.RMSE", rmse, epochs+1)
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_run.log_scalar("validation.MAE", mae, epochs+1)
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_run.log_scalar('validation.MSE', mse, epochs+1)
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# * Prediction
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predictions = [(predict(row, model)[0], row[1].item()) for row in test_dl]
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preds_df = pd.DataFrame(predictions, columns=["Prediction", "Target"])
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test_loss = evaluate_model(test_dl, model)
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print("\nNow predictions - hey ho, let's go!\n", preds_df.head(),
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f"\nLoss values for test data: \t→\tMSE: {test_loss[0]}, RMSE: {test_loss[1]}, MAE: {test_loss[2]}")
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print("\n...let's save them\ndum...\ndum...\ndum dum dum...\n\tDUM\n")
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preds_df.to_csv("./data/predictions.csv", index=False)
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# * Save the trained model
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if save_model:
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print("Your model has been saved - have a nice day!")
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scripted_model = torch.jit.script(model)
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scripted_model.save('./data/model_scripted.pt')
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ex.add_artifact('./data/model_scripted.pt')
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ex.run()
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