Sacred training

jenkins/training.Jenkinsfile

@@ -37,6 +37,7 @@ pipeline {
             steps {
                 archiveArtifacts artifacts: '*data/predictions.csv', onlyIfSuccessful: true
                 archiveArtifacts artifacts: '*data/model_scripted*', onlyIfSuccessful: true
+                archiveArtifacts artifacts: '*data/training_runs/*', onlyIfSuccessful: true
             }
         }
     }

scripts/sacred_train.py

@@ -0,0 +1,210 @@
+from audioop import rms
+from cgi import test
+from multiprocessing.spawn import prepare
+from xml.etree.ElementPath import prepare_star
+from sacred import Experiment
+from sacred.observers import FileStorageObserver, MongoObserver
+
+import argparse
+import pandas as pd
+import numpy as np
+from sklearn.metrics import mean_squared_error, mean_absolute_error
+
+import torch
+from torch import nn
+from torch.utils import data as t_u_data
+
+
+ex = Experiment("478841 sacred_scopes", interactive=True, save_git_info=False)
+ex.observers.append(MongoObserver(
+    url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
+ex.observers.append(FileStorageObserver('./data/training_runs'))
+
+
+@ex.config
+def my_config():
+    parser = argparse.ArgumentParser(description="Script performing logistic regression model training",
+                                     formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+    parser.add_argument(
+        "-e", "--epochs", default=100, help="Number of epochs the model will be trained for")
+    parser.add_argument(
+        "-s", "--step", default=10, help="Number of steps to repeat logging loss values on")
+    parser.add_argument("--save", action="store_true",
+                        help="Save trained model to file 'trained_model.h5'")
+
+    args = vars(parser.parse_args())
+
+    epochs = int(args['epochs'])
+    save_model = args['save']
+    log_step = int(args['step'])
+
+
+# * Customized Dataset class (base provided by PyTorch)
+class AvocadoDataset(t_u_data.Dataset):
+    def __init__(self, path: str, target: str = 'AveragePrice'):
+        data = pd.read_csv(path)
+        y = data[target].values.astype('float32')
+        self.y = y.reshape((len(y), 1))
+        self.x_data = data.drop(
+            [target], axis=1).values.astype('float32')
+        self.x_shape = data.drop([target], axis=1).shape
+        # print("Data shape is: ", self.x_data.shape)
+
+    def __len__(self):
+        return len(self.x_data)
+
+    def __getitem__(self, idx):
+        return [self.x_data[idx], self.y[idx]]
+
+    def get_shape(self):
+        return self.x_shape
+
+    def get_splits(self, n_test=0.33):
+        test_size = round(n_test * len(self.x_data))
+        train_size = len(self.x_data) - test_size
+        return t_u_data.random_split(self, [train_size, test_size])
+
+
+class AvocadoRegressor(nn.Module):
+    def __init__(self, input_dim):
+        super(AvocadoRegressor, self).__init__()
+        self.hidden1 = nn.Linear(input_dim, 32)
+        nn.init.xavier_uniform_(self.hidden1.weight)
+        self.act1 = nn.ReLU()
+        self.hidden2 = nn.Linear(32, 8)
+        nn.init.xavier_uniform_(self.hidden2.weight)
+        self.act2 = nn.ReLU()
+        self.hidden3 = nn.Linear(8, 1)
+        nn.init.xavier_uniform_(self.hidden3.weight)
+
+    def forward(self, x):
+        x = self.hidden1(x)
+        x = self.act1(x)
+        x = self.hidden2(x)
+        x = self.act2(x)
+        x = self.hidden3(x)
+        return x
+
+
+def prepare_data(paths):
+    train_dl = t_u_data.DataLoader(AvocadoDataset(
+        paths[0]), batch_size=32, shuffle=True)
+    validate_dl = t_u_data.DataLoader(AvocadoDataset(
+        paths[1]), batch_size=128, shuffle=True)
+    test_dl = t_u_data.DataLoader(AvocadoDataset(
+        paths[2]), batch_size=1, shuffle=False)
+    return train_dl, validate_dl, test_dl
+
+
+@ex.capture
+def train_model(train_dl, model, epochs, log_step, _run):
+    criterion = nn.MSELoss()
+    optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
+    to_compare = None
+
+    for epoch in range(1, epochs+1):
+        for _, (inputs, targets) in enumerate(train_dl):
+            optimizer.zero_grad()
+            yhat = model(inputs)
+            # * For loss value inspection
+            to_compare = (yhat, targets)
+            loss = criterion(yhat, targets)
+
+            loss.backward()
+            optimizer.step()
+
+        if epoch == 1 or (epoch) % log_step == 0:
+            result, target = to_compare[0].detach(
+            ).numpy(), to_compare[1].detach().numpy()
+            mse = mean_squared_error(target, result)
+            mae = mean_absolute_error(target, result)
+            _run.log_scalar("training.RMSE", np.sqrt(mse), epoch)
+            _run.log_scalar("training.MAE", mae, epoch)
+            _run.log_scalar('training.MSE', mse, epoch)
+
+            print(
+                f"Epoch {epoch}\t→\tMSE: {mse},\tRMSE: {np.sqrt(mse)},\tMAE: {mae}")
+
+
+def evaluate_model(test_dl, model):
+    predictions, actuals = list(), list()
+    for _, (inputs, targets) in enumerate(test_dl):
+        yhat = model(inputs)
+        # * retrieve numpy array
+        yhat = yhat.detach().numpy()
+        actual = targets.numpy()
+        actual = actual.reshape((len(actual), 1))
+        # * store predictions
+        predictions.append(yhat)
+        actuals.append(actual)
+    predictions, actuals = np.vstack(predictions), np.vstack(actuals)
+    # * return MSE value
+    mse = mean_squared_error(actuals, predictions)
+    rmse = mean_squared_error(actuals, predictions, squared=False)
+    mae = mean_absolute_error(actuals, predictions)
+    return mse, rmse, mae
+
+
+def predict(row, model):
+    row = row[0].flatten()
+    yhat = model(row)
+    yhat = yhat.detach().numpy()
+    return yhat
+
+
+@ex.main
+def main(epochs, save_model, log_step, _run):
+    print(
+        f"Your model will be trained for {epochs} epochs. Trained model will {'not ' if save_model else ''}be saved.")
+
+    # * Paths to data
+    avocado_data = ['./data/avocado.data.train',
+                    './data/avocado.data.valid',
+                    './data/avocado.data.test']
+
+    # * Data preparation
+    train_dl, validate_dl, test_dl = prepare_data(paths=avocado_data)
+    print(f"""
+          Train set size: {len(train_dl.dataset)},
+          Validate set size: {len(validate_dl.dataset)}
+          Test set size: {len(test_dl.dataset)}
+          """)
+
+    # * Model definition
+    # ! 66 - in case only regions and type are used (among all the categorical vals)
+    model = AvocadoRegressor(235)
+
+    # * Train model
+    print("Let's start the training, mate!")
+    train_model(train_dl=train_dl, model=model,
+                epochs=epochs, log_step=log_step)
+
+    # * Evaluate model
+    mse, rmse, mae = evaluate_model(validate_dl, model)
+    print(
+        f"\nEvaluation on validation set\t→\tMSE: {mse}, RMSE: {rmse}, MAE: {mae}")
+
+    _run.log_scalar("validation.RMSE", rmse, epochs+1)
+    _run.log_scalar("validation.MAE", mae, epochs+1)
+    _run.log_scalar('validation.MSE', mse, epochs+1)
+
+    # * Prediction
+    predictions = [(predict(row, model)[0], row[1].item()) for row in test_dl]
+    preds_df = pd.DataFrame(predictions, columns=["Prediction", "Target"])
+    test_loss = evaluate_model(test_dl, model)
+
+    print("\nNow predictions - hey ho, let's go!\n", preds_df.head(),
+          f"\nLoss values for test data: \t→\tMSE: {test_loss[0]}, RMSE: {test_loss[1]}, MAE: {test_loss[2]}")
+    print("\n...let's save them\ndum...\ndum...\ndum dum dum...\n\tDUM\n")
+
+    preds_df.to_csv("./data/predictions.csv", index=False)
+
+    # * Save the trained model
+    if save_model:
+        print("Your model has been saved - have a nice day!")
+        scripted_model = torch.jit.script(model)
+        scripted_model.save('./data/model_scripted.pt')
+        ex.add_artifact('./data/model_scripted.pt')
+
+
+ex.run()