Reverting back an accidental commit

Revert "Fixing merge conflicts"

This reverts commit f580ae9fc0, reversing
changes made to 44f2e732f3.

code/scoring/score.py

@@ -23,8 +23,10 @@ IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
 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

@@ -23,6 +23,8 @@ IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
 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
 import argparse
@@ -32,41 +34,69 @@ from sklearn.metrics import mean_squared_error
 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
+
+
+parser = argparse.ArgumentParser("train")
+parser.add_argument(
+    "--config_suffix", type=str, help="Datetime suffix for json config files"
+)
+parser.add_argument(
+    "--json_config",
+    type=str,
+    help="Directory to write all the intermediate json configs",
+)
+args = parser.parse_args()
+
+print("Argument 1: %s" % args.config_suffix)
+print("Argument 2: %s" % args.json_config)
+
+if not (args.json_config is None):
+    os.makedirs(args.json_config, exist_ok=True)
+    print("%s created" % args.json_config)
+
+run = Run.get_context()
+exp = run.experiment
+ws = run.experiment.workspace
 
-# 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")
 
-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"]))
+# 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"]))
 
-    # 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)
+# Save model as part of the run history
+model_name = "sklearn_regression_model.pkl"
+# model_name = "."
 
-    print("Following files are uploaded ")
-    print(run.get_file_names())
-    run.complete()
+with open(model_name, "wb") as file:
+    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)
+print("Following files are uploaded ")
+print(run.get_file_names())
+
+# register the model
+# run.log_model(file_name = model_name)
+# print('Registered the model {} to run history {}'.format(model_name, run.history.name))
 
 run_id = {}
 run_id["run_id"] = run.id
@@ -76,6 +106,4 @@ output_path = os.path.join(args.json_config, filename)
 with open(output_path, "w") as outfile:
     json.dump(run_id, outfile)
 
-if __name__ == "__main__":
-    print("Running train.py")
-    experiment_code(data)
+run.complete()

tests/unit/test_train.py

@@ -1,84 +0,0 @@
-"""
-Copyright (C) Microsoft Corporation. All rights reserved.​
- ​
-Microsoft Corporation (“Microsoft”) grants you a nonexclusive, perpetual,
-royalty-free right to use, copy, and modify the software code provided by us
-("Software Code"). You may not sublicense the Software Code or any use of it
-(except to your affiliates and to vendors to perform work on your behalf)
-through distribution, network access, service agreement, lease, rental, or
-otherwise. This license does not purport to express any claim of ownership over
-data you may have shared with Microsoft in the creation of the Software Code.
-Unless applicable law gives you more rights, Microsoft reserves all other
-rights not expressly granted herein, whether by implication, estoppel or
-otherwise. ​
- ​
-THE SOFTWARE CODE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS
-OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
-MICROSOFT OR ITS LICENSORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
-SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
-PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR
-BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER
-IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-ARISING IN ANY WAY OUT OF THE USE OF THE SOFTWARE CODE, EVEN IF ADVISED OF THE
-POSSIBILITY OF SUCH DAMAGE.
-"""
-
-# test the training script
-
-import os
-import numpy as np
-import pandas as pd
-
-from sklearn.datasets import load_diabetes
-
-
-# using diabetes dataset from scikit-learn
-X, y = load_diabetes(return_X_y=True)
-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}}
-
-
-
-def test_check_schema():
-    datafile = get_absPath("diabetes.csv")
-    # check that file exists
-    assert(os.path.exists(datafile))
-    dataset = pd.read_csv(datafile)
-    header = dataset[dataset.columns[:-1]]
-    actual_columns = header.shape[1]
-    # check header has expected number of columns
-    assert(actual_columns == expected_columns)
-
-
-def test_check_bad_schema():
-    datafile = get_absPath("diabetes_bad_schema.csv")
-    # check that file exists
-    assert(os.path.exists(datafile))
-    dataset = pd.read_csv(datafile)
-    header = dataset[dataset.columns[:-1]]
-    actual_columns = header.shape[1]
-    # check header has expected number of columns
-    assert(actual_columns != expected_columns)
-
-
-def test_check_missing_values():
-    datafile = get_absPath("diabetes_missing_values.csv")
-    # check that file exists
-    assert(os.path.exists(datafile))
-    dataset = pd.read_csv(datafile)
-    n_nan = np.sum(np.isnan(dataset.values))
-    assert(n_nan > 0)
-
-
-def test_check_distribution():
-    datafile = get_absPath("diabetes_bad_dist.csv")
-    # check that file exists
-    assert(os.path.exists(datafile))
-    dataset = pd.read_csv(datafile)
-    mean = np.mean(dataset.values, axis=0)
-    std = np.mean(dataset.values, axis=0)
-    assert(np.sum(abs(mean - historical_mean) > shift_tolerance *
-                  abs(historical_mean)) or
-           np.sum(abs(std - historical_std) > shift_tolerance * 
-                  abs(historical_std)) > 0)