Scared

Dockerfile

@@ -17,6 +17,7 @@ COPY ./skrypt_stat.py ./
 COPY ./IUM_05.py ./
 COPY ./training.py ./
 COPY ./mongoObserver.py ./
+COPY ./fileObserver.py ./
 
 RUN mkdir /.kaggle
 RUN chmod -R 777 /.kaggle

fileObserver.py

@@ -0,0 +1,86 @@
+import torch
+from torch import nn
+import numpy as np
+import pandas as pd
+from sklearn.metrics import accuracy_score
+from sklearn.metrics import f1_score
+from sacred import Experiment
+from sacred.observers import FileStorageObserver
+
+np.set_printoptions(suppress=False)
+
+ex = Experiment("434732", interactive=False, save_git_info=False)
+ex.observers.append(FileStorageObserver('ium_s434732/my_runs'))
+
+@ex.config
+def my_config():
+    epochs = 5
+    batch_size = 10
+
+class LogisticRegressionModel(nn.Module):
+    def __init__(self, input_dim, output_dim):
+        super(LogisticRegressionModel, self).__init__()
+        self.linear = nn.Linear(input_dim, output_dim)
+        self.sigmoid = nn.Sigmoid()
+    def forward(self, x):
+        out = self.linear(x)
+        return self.sigmoid(out)
+
+
+@ex.capture
+def readAndtrain(epochs, batch_size, _run):
+    train = pd.read_csv("train.csv")
+    test = pd.read_csv("test.csv")
+
+    xtrain = train[['age','anaemia','creatinine_phosphokinase','diabetes', 'ejection_fraction', 'high_blood_pressure', 'platelets', 'serum_creatinine', 'serum_sodium', 'sex', 'smoking']].astype(np.float32)
+    ytrain = train['DEATH_EVENT'].astype(np.float32)
+    xtest = test[['age','anaemia','creatinine_phosphokinase','diabetes', 'ejection_fraction', 'high_blood_pressure', 'platelets', 'serum_creatinine', 'serum_sodium', 'sex', 'smoking']].astype(np.float32)
+    ytest = test['DEATH_EVENT'].astype(np.float32)
+    xTrain = torch.from_numpy(xtrain.values)
+    yTrain = torch.from_numpy(ytrain.values.reshape(179,1))
+    xTest = torch.from_numpy(xtest.values)
+    yTest = torch.from_numpy(ytest.values)
+
+    learning_rate = 0.002
+    input_dim = 11
+    output_dim = 1
+
+    _run.log_scalar("Batch", str(batch_size))
+    _run.log_scalar("epoch",  str(epochs))
+
+    model = LogisticRegressionModel(input_dim, output_dim)
+    model.load_state_dict(torch.load('DEATH_EVENT.pth'))
+
+    criterion = torch.nn.BCELoss(reduction='mean')
+    optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
+
+    for epoch in range(epochs):
+        # print ("Epoch #",epoch)
+        model.train()
+        optimizer.zero_grad()
+        # Forward pass
+        y_pred = model(xTrain)
+        # Compute Loss
+        loss = criterion(y_pred, yTrain)
+        # print(loss.item())
+        # Backward pass
+        loss.backward()
+        optimizer.step()
+
+    _run.log_scalar("Lost", str(loss.item()))
+
+
+    torch.save(model.state_dict(), 'DEATH_EVENT.pth')
+    prediction= model(xTest)
+    _run.log_scalar("accuracy_score", accuracy_score(yTest, np.argmax(prediction.detach().numpy(), axis=1)))
+    # _run.log_scalar("F1", str(f1_score(yTest, np.argmax(prediction.detach().numpy(), axis=1), average=None)))
+
+    print("accuracy_score", accuracy_score(yTest, np.argmax(prediction.detach().numpy(), axis=1)))
+    # print("F1", f1_score(yTest, np.argmax(prediction.detach().numpy(), axis=1), average=None))
+
+@ex.automain
+def my_main(epochs, batch_size, _run):
+    readAndtrain()
+
+ex.run()
+ex.add_artifact('DEATH_EVENT.pth')