2022-05-15 10:54:30 +02:00
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from urllib.parse import urlparse
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import mlflow
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import mlflow.pytorch as model_logger
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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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2022-05-15 11:13:47 +02:00
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# mlflow.set_tracking_uri("http://localhost:5000")
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mlflow.set_tracking_uri("http://172.17.0.1:5000")
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2022-05-15 10:54:30 +02:00
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mlflow.set_experiment("s478841")
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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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def train_model(train_dl, model, epochs, log_step):
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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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metrics = 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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metrics = {'train.mse': mean_squared_error(target, result),
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'train.mae': mean_absolute_error(target, result),
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'train.rmse': mean_squared_error(target, result, squared=False)}
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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: {metrics['train.mse']},\tRMSE: {metrics['train.rmse']},\tMAE: {metrics['train.mae']}")
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return metrics
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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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def main(epochs, save_model, log_step):
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print(
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f"Your model will be trained for {epochs} epochs, logging every {log_step} steps. 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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with mlflow.start_run() as run:
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print("MLflow run experiment_id: {0}".format(run.info.experiment_id))
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print("MLflow run artifact_uri: {0}".format(run.info.artifact_uri))
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metrics = train_model(train_dl=train_dl, model=model,
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epochs=epochs, log_step=log_step)
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mlflow.log_param('epochs', epochs)
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mlflow.log_metrics(metrics)
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# * Evaluate model
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val_metrics = {key: val for key, val in zip(
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['validate.mse', 'validate.rmse', 'validate.mae'], evaluate_model(validate_dl, model))}
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print(
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f"\nEvaluation on VALIDATION set\t→\tMSE: {val_metrics['validate.mse']}, RMSE: {val_metrics['validate.rmse']}, MAE: {val_metrics['validate.mae']}")
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mlflow.log_metrics(val_metrics)
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test_loss = {key: val for key, val in zip(
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['test.mse', 'test.rmse', 'test.mae'], evaluate_model(test_dl, model))}
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print(
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f"\nEvaluation on TEST set\t→\tMSE: {test_loss['test.mse']}, RMSE: {test_loss['test.rmse']}, MAE: {test_loss['test.mae']}")
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mlflow.log_metrics(test_loss)
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# tracking_url_type_store = urlparse(mlflow.get_tracking_uri()).scheme
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# if tracking_url_type_store != 'file':
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# print('First option')
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# model_logger.log_model(
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# model, "avocados-model", registered_model_name="AvocadoModel_478841")
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# else:
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# print('Second option')
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# model_logger.log_model(model, "model")
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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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if __name__ == '__main__':
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# * Model parameters
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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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main(epochs, save_model, log_step)
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