scared mongo observer

evaluation/Jenkinsfile

@@ -28,6 +28,7 @@ pipeline {
                 sh 'python ./evaluation/eval.py'
                 sh 'python ./evaluation/plot.py'
                 sh 'python ./evaluation/scared-fileobserver.py'
+                sh 'python ./evaluation/scared-mongoobserver.py'
             }
         }
         stage('archiveArtifacts') {

evaluation/scared-mongoobserver.py

@@ -0,0 +1,93 @@
+import datetime
+import pandas as pd
+import numpy as np
+from torch.autograd import Variable
+import torch
+import torch.nn as nn
+from sacred import Experiment
+from sacred.observers import MongoObserver
+import csv
+
+ex = Experiment("434700-file", interactive=False, save_git_info=False)
+ex.observers.append(MongoObserver(
+    url='mongodb://mongo_user:mongo_password_IUM_2021@172.17.0.1:27017', db_name='sacred'))
+
+
+@ex.config
+def my_config():
+    epochs = 10
+    batch_size = 16
+
+
+@ex.capture
+def prepare_model(epochs, batch_size, _run):
+    INPUT_DIM = 1
+    OUTPUT_DIM = 1
+    LEARNING_RATE = 0.01
+    EPOCHS = epochs
+
+    dataset = pd.read_csv('datasets/train_set.csv')
+
+    x_values = [datetime.datetime.strptime(
+        item, "%Y-%m-%d").month for item in dataset['date'].values]
+    x_train = np.array(x_values, dtype=np.float32)
+    x_train = x_train.reshape(-1, 1)
+
+    y_values = [min(dataset['result_1'].values[i]/dataset['result_2'].values[i], dataset['result_2'].values[i] /
+                    dataset['result_1'].values[i]) for i in range(len(dataset['result_1'].values))]
+    y_train = np.array(y_values, dtype=np.float32)
+    y_train = y_train.reshape(-1, 1)
+
+    class LinearRegression(torch.nn.Module):
+        def __init__(self, inputSize, outputSize):
+            super(LinearRegression, self).__init__()
+            self.linear = torch.nn.Linear(inputSize, outputSize)
+
+        def forward(self, x):
+            out = self.linear(x)
+            return out
+
+    model = LinearRegression(INPUT_DIM, OUTPUT_DIM)
+
+    criterion = torch.nn.MSELoss()
+    optimizer = torch.optim.SGD(model.parameters(), lr=LEARNING_RATE)
+
+    for epoch in range(EPOCHS):
+        inputs = Variable(torch.from_numpy(x_train))
+        labels = Variable(torch.from_numpy(y_train))
+
+        optimizer.zero_grad()
+
+        outputs = model(inputs)
+
+        loss = criterion(outputs, labels)
+        print(loss)
+        loss.backward()
+
+        optimizer.step()
+
+        print('epoch {}, loss {}'.format(epoch, loss.item()))
+
+    torch.save(model.state_dict(), 'model-experiment.pt')
+
+    with torch.no_grad():  # we don't need gradients in the testing phase
+        predicted = model(Variable(torch.from_numpy(x_train))).data.numpy()
+
+    with open('model_experiment_results.csv', mode='w') as filee:
+        writer = csv.writer(filee, delimiter=',', quotechar='"',
+                            quoting=csv.QUOTE_MINIMAL)
+
+        writer.writerow(['x', 'y', 'predicted_y'])
+
+        for i in range(len(x_train)):
+            writer.writerow([x_train[i][0], y_train[i][0], predicted[i][0]])
+
+
+@ex.automain
+def my_main(epochs, batch_size):
+    print(prepare_model())
+
+
+ex.run()
+ex.add_artifact('model-experiment.pt')
+ex.add_artifact('model_experiment_results.csv')