Work in progress

Author: jainr

code/scoring/score.py

@@ -23,10 +23,8 @@
 ARISING IN ANY WAY OUT OF THE USE OF THE SOFTWARE CODE, EVEN IF ADVISED OF THE
 POSSIBILITY OF SUCH DAMAGE.
 """
-import pickle
 import json
 import numpy
-from sklearn.ensemble import RandomForestClassifier
 from azureml.core.model import Model
 
 

code/training/train.py

@@ -23,8 +23,6 @@
 ARISING IN ANY WAY OUT OF THE USE OF THE SOFTWARE CODE, EVEN IF ADVISED OF THE
 POSSIBILITY OF SUCH DAMAGE.
 """
-import pickle
-from azureml.core import Workspace
 from azureml.core.run import Run
 import os
 from sklearn.datasets import load_diabetes
@@ -33,55 +31,43 @@
 from sklearn.model_selection import train_test_split
 from sklearn.externals import joblib
 import numpy as np
-import json
-import subprocess
-from typing import Tuple, List
-
-# run_history_name = 'devops-ai'
-# os.makedirs('./outputs', exist_ok=True)
-# #ws.get_details()
-# Start recording results to AML
-# run = Run.start_logging(workspace = ws, history_name = run_history_name)
-run = Run.get_submitted_run()
-
 
+# using diabetes dataset from scikit-learn
 X, y = load_diabetes(return_X_y=True)
-columns = ["age", "gender", "bmi", "bp", "s1", "s2", "s3", "s4", "s5", "s6"]
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
 data = {"train": {"X": X_train, "y": y_train}, "test": {"X": X_test, "y": y_test}}
 
-print("Running train.py")
-
-# Randomly pic alpha
-alphas = np.arange(0.0, 1.0, 0.05)
-alpha = alphas[np.random.choice(alphas.shape[0], 1, replace=False)][0]
-print(alpha)
-run.log("alpha", alpha)
-reg = Ridge(alpha=alpha)
-reg.fit(data["train"]["X"], data["train"]["y"])
-preds = reg.predict(data["test"]["X"])
-run.log("mse", mean_squared_error(preds, data["test"]["y"]))
-
-
-# Save model as part of the run history
-model_name = "sklearn_regression_model.pkl"
-# model_name = "."
 
-with open(model_name, "wb") as file:
-    joblib.dump(value=reg, filename=model_name)
+def experiment_code(data_split):
+    run = Run.get_submitted_run()
+    # Randomly pic alpha
+    alphas = np.arange(0.0, 1.0, 0.05)
+    alpha = alphas[np.random.choice(alphas.shape[0], 1, replace=False)][0]
+    print(alpha)
+    # Log alpha metric
+    run.log("alpha", alpha)
+    # train the model with selected value of alpha and log mse
+    reg = Ridge(alpha=alpha)
+    reg.fit(data["train"]["X"], data_split["train"]["y"])
+    preds = reg.predict(data["test"]["X"])
+    run.log("mse", mean_squared_error(preds, data_split["test"]["y"]))
 
-# upload the model file explicitly into artifacts
-run.upload_file(name="./outputs/" + model_name, path_or_stream=model_name)
-print("Uploaded the model {} to experiment {}".format(model_name, run.experiment.name))
-dirpath = os.getcwd()
-print(dirpath)
+    # Write model name to the config file
+    model_name = "sklearn_regression_model.pkl"
+    with open(model_name, "wb"):
+        joblib.dump(value=reg, filename=model_name)
 
+    # upload the model file explicitly into artifacts
+    run.upload_file(name="./outputs/" + model_name, path_or_stream=model_name)
+    print("Uploaded the model {} to experiment {}".format(model_name, run.experiment.name))
+    dirpath = os.getcwd()
+    print(dirpath)
 
-# register the model
-# run.log_model(file_name = model_name)
-# print('Registered the model {} to run history {}'.format(model_name, run.history.name))
+    print("Following files are uploaded ")
+    print(run.get_file_names())
+    run.complete()
 
 
-print("Following files are uploaded ")
-print(run.get_file_names())
-run.complete()
+if __name__ == "__main__":
+    print("Running train.py")
+    experiment_code(data)