update samples - test

Revert automatic overwrite of databricks content

how-to-use-azureml/monitor-models/data-drift/azure-ml-datadrift.ipynb

@@ -361,7 +361,7 @@
       "outputs": [],
       "source": [
         "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn', 'joblib', 'lightgbm', 'pandas'],\n",
-        "                                 pip_packages=['azureml-monitoring', 'azureml-sdk[automl]'])\n",
+        "                                 pip_packages=['azureml-monitoring', 'azureml-defaults'])\n",
         "\n",
         "with open(\"myenv.yml\",\"w\") as f:\n",
         "    f.write(myenv.serialize_to_string())"
@@ -626,7 +626,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "target_date = datetime.today()\n",
+        "now = datetime.utcnow()\n",
+        "target_date = datetime(now.year, now.month, now.day)\n",
         "run = datadrift.run(target_date, services, feature_list=feature_list, create_compute_target=True)"
       ]
     },
@@ -655,7 +656,7 @@
       "source": [
         "child_run.wait_for_completion(wait_post_processing=True)\n",
         "\n",
-        "drift_metrics = datadrift.get_output(start_time=start, end_time=end)\n",
+        "drift_metrics = datadrift.get_output(run_id=run.id)\n",
         "drift_metrics"
       ]
     },
@@ -668,7 +669,7 @@
         "# Show all drift figures, one per serivice.\n",
         "# If setting with_details is False (by default), only drift will be shown; if it's True, all details will be shown.\n",
         "\n",
-        "drift_figures = datadrift.show(with_details=True)"
+        "drift_figures = datadrift.show()"
       ]
     },
     {
@@ -691,7 +692,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "rafarmah"
+        "name": "dmdatadrift"
       }
     ],
     "kernelspec": {

how-to-use-azureml/monitor-models/data-drift/score.py

@@ -1,14 +1,10 @@
-import pickle
 import json
-import numpy
-import azureml.train.automl
-from sklearn.externals import joblib
-from sklearn.linear_model import Ridge
-from azureml.core.model import Model
-from azureml.core.run import Run
-from azureml.monitoring import ModelDataCollector
 import time
+
 import pandas as pd
+from azureml.core.model import Model
+from azureml.monitoring import ModelDataCollector
+from sklearn.externals import joblib
 
 
 def init():
@@ -25,11 +21,11 @@ def init():
     categorical_features = ["usaf", "wban", "p_k", "station_name"]
 
     inputs_dc = ModelDataCollector(model_name="driftmodel",
-                                   identifier="inputs",
+                                   designation="inputs",
                                    feature_names=feature_names)
 
-    prediction_dc = ModelDataCollector("driftmodel",
-                                       identifier="predictions",
+    prediction_dc = ModelDataCollector(model_name="driftmodel",
+                                       designation="predictions",
                                        feature_names=["temperature"])