sacred my runs

ium_s434766/my_runs/1/config.json

@@ -0,0 +1,6 @@
+{
+  "batch_size": 16,
+  "learning_rate": 0.001,
+  "num_epochs": 15,
+  "seed": 397973104
+}

ium_s434766/my_runs/1/cout.txt

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+INFO - ium_s434766 - Running command 'my_main'
+INFO - ium_s434766 - Started run with ID "1"
+INFO - train - Batch size = 16 Epochs = 15
+INFO - train - Last loss = 0.32308459281921387

ium_s434766/my_runs/1/metrics.json

@@ -0,0 +1 @@
+{}

ium_s434766/my_runs/1/run.json

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+{
+  "artifacts": [],
+  "command": "my_main",
+  "experiment": {
+    "base_dir": "/home/przemek/ium_434766",
+    "dependencies": [
+      "numpy==1.20.1",
+      "pandas==1.2.4",
+      "sacred==0.8.2",
+      "scikit-learn==0.24.1",
+      "torch==1.8.1"
+    ],
+    "mainfile": "sacred-pytorch1.py",
+    "name": "ium_s434766",
+    "repositories": [],
+    "sources": [
+      [
+        "sacred-pytorch1.py",
+        "_sources/sacred-pytorch1_37f3ae3f09d3a85faa1cb43617a6d59e.py"
+      ]
+    ]
+  },
+  "heartbeat": "2021-05-16T16:01:49.590955",
+  "host": {
+    "ENV": {},
+    "cpu": "Intel(R) Core(TM) i5-4690 CPU @ 3.50GHz",
+    "hostname": "OwczarPC",
+    "os": [
+      "Linux",
+      "Linux-5.4.72-microsoft-standard-WSL2-x86_64-with-glibc2.29"
+    ],
+    "python_version": "3.8.5"
+  },
+  "meta": {
+    "command": "my_main",
+    "options": {
+      "--beat-interval": null,
+      "--capture": null,
+      "--comment": null,
+      "--debug": false,
+      "--enforce_clean": false,
+      "--file_storage": null,
+      "--force": false,
+      "--help": false,
+      "--loglevel": null,
+      "--mongo_db": null,
+      "--name": null,
+      "--pdb": false,
+      "--print-config": false,
+      "--priority": null,
+      "--queue": false,
+      "--s3": null,
+      "--sql": null,
+      "--tiny_db": null,
+      "--unobserved": false,
+      "COMMAND": null,
+      "UPDATE": [],
+      "help": false,
+      "with": false
+    }
+  },
+  "resources": [],
+  "result": null,
+  "start_time": "2021-05-16T16:01:49.507065",
+  "status": "COMPLETED",
+  "stop_time": "2021-05-16T16:01:49.589890"
+}

ium_s434766/my_runs/2/config.json

@@ -0,0 +1,6 @@
+{
+  "batch_size": 16,
+  "learning_rate": 0.001,
+  "num_epochs": 15,
+  "seed": 9915532
+}

ium_s434766/my_runs/2/cout.txt

@@ -0,0 +1,5 @@
+INFO - ium_s434766 - Running command 'my_main'
+INFO - ium_s434766 - Started run with ID "2"
+INFO - train - Batch size = 16 Epochs = 15
+INFO - train - Last loss = 0.29651644825935364
+INFO - ium_s434766 - Completed after 0:00:00

ium_s434766/my_runs/2/metrics.json

@@ -0,0 +1 @@
+{}

ium_s434766/my_runs/2/run.json

@@ -0,0 +1,63 @@
+{
+  "artifacts": [],
+  "command": "my_main",
+  "experiment": {
+    "base_dir": "/home/przemek/ium_434766",
+    "dependencies": [
+      "numpy==1.20.1",
+      "pandas==1.2.4",
+      "sacred==0.8.2",
+      "scikit-learn==0.24.1",
+      "torch==1.8.1"
+    ],
+    "mainfile": "sacred-pytorch1.py",
+    "name": "ium_s434766",
+    "repositories": [],
+    "sources": [
+      [
+        "sacred-pytorch1.py",
+        "_sources/sacred-pytorch1_37f3ae3f09d3a85faa1cb43617a6d59e.py"
+      ]
+    ]
+  },
+  "heartbeat": "2021-05-16T16:01:49.672141",
+  "host": {
+    "ENV": {},
+    "cpu": "Intel(R) Core(TM) i5-4690 CPU @ 3.50GHz",
+    "hostname": "OwczarPC",
+    "os": [
+      "Linux",
+      "Linux-5.4.72-microsoft-standard-WSL2-x86_64-with-glibc2.29"
+    ],
+    "python_version": "3.8.5"
+  },
+  "meta": {
+    "command": "my_main",
+    "options": {
+      "--beat-interval": null,
+      "--capture": null,
+      "--comment": null,
+      "--debug": false,
+      "--enforce_clean": false,
+      "--file_storage": null,
+      "--force": false,
+      "--help": false,
+      "--loglevel": null,
+      "--mongo_db": null,
+      "--name": null,
+      "--pdb": false,
+      "--print-config": false,
+      "--priority": null,
+      "--queue": false,
+      "--s3": null,
+      "--sql": null,
+      "--tiny_db": null,
+      "--unobserved": false
+    }
+  },
+  "resources": [],
+  "result": null,
+  "start_time": "2021-05-16T16:01:49.643954",
+  "status": "COMPLETED",
+  "stop_time": "2021-05-16T16:01:49.670359"
+}

ium_s434766/my_runs/3/config.json

@@ -0,0 +1,6 @@
+{
+  "batch_size": 16,
+  "learning_rate": 0.001,
+  "num_epochs": 15,
+  "seed": 400864859
+}

ium_s434766/my_runs/3/cout.txt

@@ -0,0 +1,4 @@
+INFO - ium_s434766 - Running command 'my_main'
+INFO - ium_s434766 - Started run with ID "3"
+INFO - train - Batch size = 16 Epochs = 15
+INFO - train - Last loss = 0.8644022345542908

ium_s434766/my_runs/3/metrics.json

@@ -0,0 +1 @@
+{}

ium_s434766/my_runs/3/run.json

@@ -0,0 +1,67 @@
+{
+  "artifacts": [],
+  "command": "my_main",
+  "experiment": {
+    "base_dir": "/home/przemek/ium_434766",
+    "dependencies": [
+      "numpy==1.20.1",
+      "pandas==1.2.4",
+      "sacred==0.8.2",
+      "scikit-learn==0.24.1",
+      "torch==1.8.1"
+    ],
+    "mainfile": "sacred-pytorch1.py",
+    "name": "ium_s434766",
+    "repositories": [],
+    "sources": [
+      [
+        "sacred-pytorch1.py",
+        "_sources/sacred-pytorch1_e0e75cc8f994d35ec0d404b605721131.py"
+      ]
+    ]
+  },
+  "heartbeat": "2021-05-16T16:02:13.422226",
+  "host": {
+    "ENV": {},
+    "cpu": "Intel(R) Core(TM) i5-4690 CPU @ 3.50GHz",
+    "hostname": "OwczarPC",
+    "os": [
+      "Linux",
+      "Linux-5.4.72-microsoft-standard-WSL2-x86_64-with-glibc2.29"
+    ],
+    "python_version": "3.8.5"
+  },
+  "meta": {
+    "command": "my_main",
+    "options": {
+      "--beat-interval": null,
+      "--capture": null,
+      "--comment": null,
+      "--debug": false,
+      "--enforce_clean": false,
+      "--file_storage": null,
+      "--force": false,
+      "--help": false,
+      "--loglevel": null,
+      "--mongo_db": null,
+      "--name": null,
+      "--pdb": false,
+      "--print-config": false,
+      "--priority": null,
+      "--queue": false,
+      "--s3": null,
+      "--sql": null,
+      "--tiny_db": null,
+      "--unobserved": false,
+      "COMMAND": null,
+      "UPDATE": [],
+      "help": false,
+      "with": false
+    }
+  },
+  "resources": [],
+  "result": null,
+  "start_time": "2021-05-16T16:02:13.390881",
+  "status": "COMPLETED",
+  "stop_time": "2021-05-16T16:02:13.420375"
+}

ium_s434766/my_runs/4/config.json

@@ -0,0 +1,6 @@
+{
+  "batch_size": 16,
+  "learning_rate": 0.001,
+  "num_epochs": 15,
+  "seed": 562374018
+}

ium_s434766/my_runs/4/cout.txt

@@ -0,0 +1,4 @@
+INFO - ium_s434766 - Running command 'my_main'
+INFO - ium_s434766 - Started run with ID "4"
+INFO - train - Batch size = 16 Epochs = 15
+INFO - train - Last loss = 0.2932703197002411

ium_s434766/my_runs/4/metrics.json

@@ -0,0 +1 @@
+{}

ium_s434766/my_runs/4/run.json

@@ -0,0 +1,65 @@
+{
+  "artifacts": [
+    "stroke.pth"
+  ],
+  "command": "my_main",
+  "experiment": {
+    "base_dir": "/home/przemek/ium_434766",
+    "dependencies": [
+      "numpy==1.20.1",
+      "pandas==1.2.4",
+      "sacred==0.8.2",
+      "scikit-learn==0.24.1",
+      "torch==1.8.1"
+    ],
+    "mainfile": "sacred-pytorch1.py",
+    "name": "ium_s434766",
+    "repositories": [],
+    "sources": [
+      [
+        "sacred-pytorch1.py",
+        "_sources/sacred-pytorch1_e0e75cc8f994d35ec0d404b605721131.py"
+      ]
+    ]
+  },
+  "heartbeat": "2021-05-16T16:02:13.502327",
+  "host": {
+    "ENV": {},
+    "cpu": "Intel(R) Core(TM) i5-4690 CPU @ 3.50GHz",
+    "hostname": "OwczarPC",
+    "os": [
+      "Linux",
+      "Linux-5.4.72-microsoft-standard-WSL2-x86_64-with-glibc2.29"
+    ],
+    "python_version": "3.8.5"
+  },
+  "meta": {
+    "command": "my_main",
+    "options": {
+      "--beat-interval": null,
+      "--capture": null,
+      "--comment": null,
+      "--debug": false,
+      "--enforce_clean": false,
+      "--file_storage": null,
+      "--force": false,
+      "--help": false,
+      "--loglevel": null,
+      "--mongo_db": null,
+      "--name": null,
+      "--pdb": false,
+      "--print-config": false,
+      "--priority": null,
+      "--queue": false,
+      "--s3": null,
+      "--sql": null,
+      "--tiny_db": null,
+      "--unobserved": false
+    }
+  },
+  "resources": [],
+  "result": null,
+  "start_time": "2021-05-16T16:02:13.476148",
+  "status": "COMPLETED",
+  "stop_time": "2021-05-16T16:02:13.501265"
+}

ium_s434766/my_runs/_sources/sacred-pytorch1_37f3ae3f09d3a85faa1cb43617a6d59e.py

@@ -0,0 +1,85 @@
+import torch
+import sys
+import torch.nn.functional as F
+from torch import nn
+from sklearn.metrics import accuracy_score, mean_squared_error
+import numpy as np
+import pandas as pd
+from sacred import Experiment
+from sacred.observers import FileStorageObserver
+
+np.set_printoptions(suppress=False)
+
+ex = Experiment("ium_s434766", interactive=False, save_git_info=False)
+ex.observers.append(FileStorageObserver("ium_s434766/my_runs"))
+
+@ex.config
+def my_config():
+    num_epochs = 15
+    batch_size = 16
+    learning_rate = 0.001
+
+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 train(num_epochs, batch_size, learning_rate, _log):
+    data_train = pd.read_csv("data_train.csv")
+    data_test  = pd.read_csv("data_test.csv")
+    FEATURES   =  ['age','hypertension','heart_disease','ever_married', 'avg_glucose_level', 'bmi']
+
+    x_train = data_train[FEATURES].astype(np.float32)
+    y_train = data_train['stroke'].astype(np.float32)
+
+    x_test = data_test[FEATURES].astype(np.float32)
+    y_test = data_test['stroke'].astype(np.float32)
+
+    fTrain = torch.from_numpy(x_train.values)
+    tTrain = torch.from_numpy(y_train.values.reshape(2945,1))
+
+    fTest= torch.from_numpy(x_test.values)
+    tTest = torch.from_numpy(y_test.values)
+
+
+    input_dim = 6
+    output_dim = 1
+    info_params = "Batch size = " + str(batch_size) + " Epochs = " + str(num_epochs)
+    _log.info(info_params)
+    model = LogisticRegressionModel(input_dim, output_dim)
+
+    criterion = torch.nn.BCELoss(reduction='mean') 
+    optimizer = torch.optim.SGD(model.parameters(), lr = learning_rate)
+
+    for epoch in range(num_epochs):
+        # print ("Epoch #",epoch)
+        model.train()
+        optimizer.zero_grad()
+        # Forward pass
+        y_pred = model(fTrain)
+        # Compute Loss
+        loss = criterion(y_pred, tTrain)
+        # print(loss.item())
+        # Backward pass
+        loss.backward()
+        optimizer.step()
+        
+    info_loss = "Last loss = " + str(loss.item())
+    _log.info(info_loss)
+    y_pred = model(fTest)
+    # print("predicted Y value: ", y_pred.data)
+
+    torch.save(model.state_dict(), 'stroke.pth')
+
+
+@ex.automain
+def my_main(num_epochs, batch_size, learning_rate, _run):
+    train()
+
+r = ex.run()
+ex.add_artifact("stroke_model/stroke.pth")

ium_s434766/my_runs/_sources/sacred-pytorch1_e0e75cc8f994d35ec0d404b605721131.py

@@ -0,0 +1,85 @@
+import torch
+import sys
+import torch.nn.functional as F
+from torch import nn
+from sklearn.metrics import accuracy_score, mean_squared_error
+import numpy as np
+import pandas as pd
+from sacred import Experiment
+from sacred.observers import FileStorageObserver
+
+np.set_printoptions(suppress=False)
+
+ex = Experiment("ium_s434766", interactive=False, save_git_info=False)
+ex.observers.append(FileStorageObserver("ium_s434766/my_runs"))
+
+@ex.config
+def my_config():
+    num_epochs = 15
+    batch_size = 16
+    learning_rate = 0.001
+
+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 train(num_epochs, batch_size, learning_rate, _log):
+    data_train = pd.read_csv("data_train.csv")
+    data_test  = pd.read_csv("data_test.csv")
+    FEATURES   =  ['age','hypertension','heart_disease','ever_married', 'avg_glucose_level', 'bmi']
+
+    x_train = data_train[FEATURES].astype(np.float32)
+    y_train = data_train['stroke'].astype(np.float32)
+
+    x_test = data_test[FEATURES].astype(np.float32)
+    y_test = data_test['stroke'].astype(np.float32)
+
+    fTrain = torch.from_numpy(x_train.values)
+    tTrain = torch.from_numpy(y_train.values.reshape(2945,1))
+
+    fTest= torch.from_numpy(x_test.values)
+    tTest = torch.from_numpy(y_test.values)
+
+
+    input_dim = 6
+    output_dim = 1
+    info_params = "Batch size = " + str(batch_size) + " Epochs = " + str(num_epochs)
+    _log.info(info_params)
+    model = LogisticRegressionModel(input_dim, output_dim)
+
+    criterion = torch.nn.BCELoss(reduction='mean') 
+    optimizer = torch.optim.SGD(model.parameters(), lr = learning_rate)
+
+    for epoch in range(num_epochs):
+        # print ("Epoch #",epoch)
+        model.train()
+        optimizer.zero_grad()
+        # Forward pass
+        y_pred = model(fTrain)
+        # Compute Loss
+        loss = criterion(y_pred, tTrain)
+        # print(loss.item())
+        # Backward pass
+        loss.backward()
+        optimizer.step()
+        
+    info_loss = "Last loss = " + str(loss.item())
+    _log.info(info_loss)
+    y_pred = model(fTest)
+    # print("predicted Y value: ", y_pred.data)
+
+    torch.save(model.state_dict(), 'stroke.pth')
+
+
+@ex.automain
+def my_main(num_epochs, batch_size, learning_rate, _run):
+    train()
+
+r = ex.run()
+ex.add_artifact("stroke.pth")

sacred-pytorch1.py

@@ -82,4 +82,4 @@ def my_main(num_epochs, batch_size, learning_rate, _run):
     train()
 
 r = ex.run()
-ex.add_artifact("stroke_model/stroke.pth")
+ex.add_artifact("stroke.pth")