mlflow

IUM_08_mlflow.py

@@ -0,0 +1,78 @@
+import torch
+import sys
+from torch import nn
+import numpy as np
+import pandas as pd
+import mlflow
+from sklearn.metrics import accuracy_score
+
+np.set_printoptions(suppress=False)
+
+
+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)
+
+
+
+def readAndtrain(epochs, batch_size):
+    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
+
+    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()
+
+
+    torch.save(model.state_dict(), 'DEATH_EVENT.pth')
+    prediction= model(xTest)
+
+    return prediction, loss.item(), yTest
+
+    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))
+
+
+batch_size = int(sys.argv[1]) if len(sys.argv) > 1 else 10
+epochs = int(sys.argv[2]) if len(sys.argv) > 2 else 5
+with mlflow.start_run():
+
+    prediction, loss, yTest = readAndtrain(epochs, batch_size)
+
+    mlflow.log_metric("accuracy_score", accuracy_score(yTest, np.argmax(prediction.detach().numpy(), axis=1)))
+    mlflow.log_param("Last loss", loss)
+    mlflow.log_param("epochs", epochs)
+    mlflow.log_param("batch size", batch_size)

MLproject

@@ -0,0 +1,11 @@
+name: ium_s434766_mlflow_lab8
+
+docker_env: my_env.yaml
+    image:  s434732/ium:latest
+
+entry_points:
+  main:
+    parameters:
+        epochs = 5
+        batch_size = 10
+    command: "python mlflow.py -r {train_size_param} {test_size_param} {epochs} {batch_size}"

fileObserver.py

@@ -9,7 +9,7 @@ from sacred.observers import FileStorageObserver
 
 np.set_printoptions(suppress=False)
 
-ex = Experiment("434732", interactive=False, save_git_info=False)
+ex = Experiment("ium_s434732", interactive=False, save_git_info=False)
 ex.observers.append(FileStorageObserver('ium_s434732/my_runs'))
 
 @ex.config

mlruns/0/9031aa80d3b948fca55268f6f0b0c117/meta.yaml

@@ -0,0 +1,15 @@
+artifact_uri: file:///mnt/c/Users/micha/Desktop/MAGISTER/In%C5%BCyniera%20uczenia%20maszynowego/git/ium_434732/mlruns/0/9031aa80d3b948fca55268f6f0b0c117/artifacts
+end_time: 1621170671245
+entry_point_name: ''
+experiment_id: '0'
+lifecycle_stage: active
+name: ''
+run_id: 9031aa80d3b948fca55268f6f0b0c117
+run_uuid: 9031aa80d3b948fca55268f6f0b0c117
+source_name: ''
+source_type: 4
+source_version: ''
+start_time: 1621170670730
+status: 4
+tags: []
+user_id: naapster

mlruns/0/9031aa80d3b948fca55268f6f0b0c117/metrics/accuracy_score

@@ -0,0 +1 @@
+1621170671205 0.55 0

mlruns/0/9031aa80d3b948fca55268f6f0b0c117/tags/mlflow.source.git.commit

@@ -0,0 +1 @@
+8e853f42dcd05f226e6f42086c9778fc3c9adcbf

mlruns/0/9031aa80d3b948fca55268f6f0b0c117/tags/mlflow.source.name

@@ -0,0 +1 @@
+IUM_08_mlflow.py

mlruns/0/9031aa80d3b948fca55268f6f0b0c117/tags/mlflow.source.type

@@ -0,0 +1 @@
+LOCAL

mlruns/0/9031aa80d3b948fca55268f6f0b0c117/tags/mlflow.user

@@ -0,0 +1 @@
+naapster

mlruns/0/b5ca2a8b58714cbdb4203ff66b024557/meta.yaml

@@ -0,0 +1,15 @@
+artifact_uri: file:///mnt/c/Users/micha/Desktop/MAGISTER/In%C5%BCyniera%20uczenia%20maszynowego/git/ium_434732/mlruns/0/b5ca2a8b58714cbdb4203ff66b024557/artifacts
+end_time: 1621171451245
+entry_point_name: ''
+experiment_id: '0'
+lifecycle_stage: active
+name: ''
+run_id: b5ca2a8b58714cbdb4203ff66b024557
+run_uuid: b5ca2a8b58714cbdb4203ff66b024557
+source_name: ''
+source_type: 4
+source_version: ''
+start_time: 1621171450331
+status: 3
+tags: []
+user_id: naapster

mlruns/0/b5ca2a8b58714cbdb4203ff66b024557/metrics/accuracy_score

@@ -0,0 +1 @@
+1621171451102 0.55 0

mlruns/0/b5ca2a8b58714cbdb4203ff66b024557/params/Last loss

@@ -0,0 +1 @@
+27.758575439453125

mlruns/0/b5ca2a8b58714cbdb4203ff66b024557/params/batch size

@@ -0,0 +1 @@
+10

mlruns/0/b5ca2a8b58714cbdb4203ff66b024557/params/epochs

@@ -0,0 +1 @@
+5

mlruns/0/b5ca2a8b58714cbdb4203ff66b024557/tags/mlflow.source.git.commit

@@ -0,0 +1 @@
+8e853f42dcd05f226e6f42086c9778fc3c9adcbf

mlruns/0/b5ca2a8b58714cbdb4203ff66b024557/tags/mlflow.source.name

@@ -0,0 +1 @@
+IUM_08_mlflow.py

mlruns/0/b5ca2a8b58714cbdb4203ff66b024557/tags/mlflow.source.type

@@ -0,0 +1 @@
+LOCAL

mlruns/0/b5ca2a8b58714cbdb4203ff66b024557/tags/mlflow.user

@@ -0,0 +1 @@
+naapster

mlruns/0/meta.yaml

@@ -0,0 +1,4 @@
+artifact_location: file:///mnt/c/Users/micha/Desktop/MAGISTER/In%C5%BCyniera%20uczenia%20maszynowego/git/ium_434732/mlruns/0
+experiment_id: '0'
+lifecycle_stage: active
+name: Default