part2_automl_nhl_regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AutoML Experiments\n",
    "\n",
    "_**Note:** this notebook takes a significant amount of time to execute_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'DataScience'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the workspace information from config.json using the Azure ML SDK\n",
    "from azureml.core import Workspace\n",
    "\n",
    "ws = Workspace.from_config()\n",
    "ws.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NHL-Penalties-2020 v7: A breakdown of penalty minutes per game matchup\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>penaltyMinutes</th>\n",
       "      <th>type</th>\n",
       "      <th>homeTeam</th>\n",
       "      <th>awayTeam</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12.0</td>\n",
       "      <td>R</td>\n",
       "      <td>Maple Leafs</td>\n",
       "      <td>Canadiens</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12.0</td>\n",
       "      <td>R</td>\n",
       "      <td>Maple Leafs</td>\n",
       "      <td>Canadiens</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>76.0</td>\n",
       "      <td>R</td>\n",
       "      <td>Maple Leafs</td>\n",
       "      <td>Canadiens</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12.0</td>\n",
       "      <td>R</td>\n",
       "      <td>Maple Leafs</td>\n",
       "      <td>Canadiens</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10.0</td>\n",
       "      <td>R</td>\n",
       "      <td>Maple Leafs</td>\n",
       "      <td>Canadiens</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   penaltyMinutes type     homeTeam   awayTeam\n",
       "0            12.0    R  Maple Leafs  Canadiens\n",
       "1            12.0    R  Maple Leafs  Canadiens\n",
       "2            76.0    R  Maple Leafs  Canadiens\n",
       "3            12.0    R  Maple Leafs  Canadiens\n",
       "4            10.0    R  Maple Leafs  Canadiens"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Next, grab our dataset from Azure. \n",
    "from azureml.core import Dataset\n",
    "\n",
    "ds = Dataset.get_by_name(workspace=ws, name='NHL-Penalties-2020') # This is the version we registered in the last notebook\n",
    "print(ds.name + ' v' + str(ds.version) + ': ' + ds.description)\n",
    "\n",
    "# Display the data structure here for verification\n",
    "ds.to_pandas_dataframe().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using existing compute: Low-End-Compute-Cluster\n",
      "Succeeded\n",
      "AmlCompute wait for completion finished\n",
      "\n",
      "Minimum number of nodes requested have been provisioned\n"
     ]
    }
   ],
   "source": [
    "from azureml.core.compute import ComputeTarget, AmlCompute\n",
    "from azureml.core.compute_target import ComputeTargetException\n",
    "\n",
    "# Now let's make sure we have a compute resource created\n",
    "cluster_name = \"Low-End-Compute-Cluster\"\n",
    "max_nodes = 4\n",
    "\n",
    "# Fetch or create the compute resource\n",
    "try:\n",
    "    cpu_cluster = ComputeTarget(workspace=ws, name=cluster_name) # This will throw a ComputeTargetException if this doesn't exist\n",
    "    print('Using existing compute: ' + cluster_name)\n",
    "except ComputeTargetException:\n",
    "    # Create the cluster\n",
    "    print('Provisioning cluster...')\n",
    "    compute_config = AmlCompute.provisioning_configuration(vm_size=\"Standard_D2DS_V4\", min_nodes=0, max_nodes=max_nodes)\n",
    "    cpu_cluster = ComputeTarget.create(ws, cluster_name, compute_config)\n",
    "\n",
    "# Ensure the cluster is ready to go\n",
    "cpu_cluster.wait_for_completion(show_output=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the configuration for the experiment\n",
    "from azureml.train.automl import AutoMLConfig\n",
    "\n",
    "# See https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.automlconfig?view=azure-ml-py for details\n",
    "automl_config = AutoMLConfig(\n",
    "        task='regression',                                  # regression, classification, or forecasting\n",
    "        training_data=ds,                                   # The data to use to train the model\n",
    "        label_column_name='penaltyMinutes',                 # The column we're trying to predict\n",
    "        n_cross_validations=3,                              # How many cross-validation sets to use\n",
    "        primary_metric='normalized_root_mean_squared_error',# The metric we use to compare model performance\n",
    "        compute_target=cpu_cluster,                         # Where the experiment should be run\n",
    "        max_concurrent_iterations=max_nodes,                # How many models can be trained simultaneously\n",
    "        iterations=8,                                       # The total number of models to train\n",
    "        iteration_timeout_minutes=5                         # The amount of time before giving up on a single model training run\n",
    "    )            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['normalized_root_mean_squared_error',\n",
       " 'spearman_correlation',\n",
       " 'normalized_mean_absolute_error',\n",
       " 'r2_score']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List all available primary regression metrics, for reference\n",
    "from azureml.train.automl.utilities import get_primary_metrics\n",
    "\n",
    "get_primary_metrics('regression')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.experiment import Experiment\n",
    "\n",
    "# Find or Create a Machine Learning Experiment in Azure Machine Learning Studio\n",
    "experiment_name = 'NHL-Penalty-Minute-Prediction'\n",
    "experiment=Experiment(ws, experiment_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Submitting remote run.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table style=\"width:100%\"><tr><th>Experiment</th><th>Id</th><th>Type</th><th>Status</th><th>Details Page</th><th>Docs Page</th></tr><tr><td>NHL-Penalty-Minute-Prediction</td><td>AutoML_b5b9df0c-c05b-4ff1-b9fa-1c19ef68c631</td><td>automl</td><td>NotStarted</td><td><a href=\"https://ml.azure.com/runs/AutoML_b5b9df0c-c05b-4ff1-b9fa-1c19ef68c631?wsid=/subscriptions/efba8785-116c-4443-9a05-764c75c7bb0d/resourcegroups/datascience/workspaces/DataScience&amp;tid=542fe8ad-7ac8-4b9e-898f-d6de53312581\" target=\"_blank\" rel=\"noopener\">Link to Azure Machine Learning studio</a></td><td><a href=\"https://docs.microsoft.com/en-us/python/api/overview/azure/ml/intro?view=azure-ml-py\" target=\"_blank\" rel=\"noopener\">Link to Documentation</a></td></tr></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from azureml.widgets import RunDetails\n",
    "\n",
    "# Start running the experiment\n",
    "run = experiment.submit(automl_config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "model_id": "4f9b19f25c884f0d83fd559a0ae66d91",
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      },
      "text/plain": [
       "_AutoMLWidget(widget_settings={'childWidgetDisplay': 'popup', 'send_telemetry': False, 'log_level': 'INFO', 's…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/aml.mini.widget.v1": "{\"status\": \"Completed\", \"workbench_run_details_uri\": \"https://ml.azure.com/runs/AutoML_b5b9df0c-c05b-4ff1-b9fa-1c19ef68c631?wsid=/subscriptions/efba8785-116c-4443-9a05-764c75c7bb0d/resourcegroups/datascience/workspaces/DataScience&tid=542fe8ad-7ac8-4b9e-898f-d6de53312581\", \"run_id\": \"AutoML_b5b9df0c-c05b-4ff1-b9fa-1c19ef68c631\", \"run_properties\": {\"run_id\": \"AutoML_b5b9df0c-c05b-4ff1-b9fa-1c19ef68c631\", \"created_utc\": \"2022-06-20T03:24:25.920676Z\", \"properties\": {\"num_iterations\": \"8\", \"training_type\": \"TrainFull\", \"acquisition_function\": \"EI\", \"primary_metric\": \"normalized_root_mean_squared_error\", \"train_split\": \"0\", \"acquisition_parameter\": \"0\", \"num_cross_validation\": \"3\", \"target\": \"Low-End-Compute-Cluster\", \"AMLSettingsJsonString\": 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     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/plain": [
       "{'runId': 'AutoML_b5b9df0c-c05b-4ff1-b9fa-1c19ef68c631',\n",
       " 'target': 'Low-End-Compute-Cluster',\n",
       " 'status': 'Completed',\n",
       " 'startTimeUtc': '2022-06-20T03:24:40.509401Z',\n",
       " 'endTimeUtc': '2022-06-20T03:31:15.422532Z',\n",
       " 'services': {},\n",
       " 'properties': {'num_iterations': '8',\n",
       "  'training_type': 'TrainFull',\n",
       "  'acquisition_function': 'EI',\n",
       "  'primary_metric': 'normalized_root_mean_squared_error',\n",
       "  'train_split': '0',\n",
       "  'acquisition_parameter': '0',\n",
       "  'num_cross_validation': '3',\n",
       "  'target': 'Low-End-Compute-Cluster',\n",
       "  'AMLSettingsJsonString': '{\"path\":null,\"name\":\"NHL-Penalty-Minute-Prediction\",\"subscription_id\":\"efba8785-116c-4443-9a05-764c75c7bb0d\",\"resource_group\":\"datascience\",\"workspace_name\":\"DataScience\",\"region\":\"northcentralus\",\"compute_target\":\"Low-End-Compute-Cluster\",\"spark_service\":null,\"azure_service\":\"remote\",\"many_models\":false,\"pipeline_fetch_max_batch_size\":1,\"enable_batch_run\":true,\"enable_run_restructure\":false,\"start_auxiliary_runs_before_parent_complete\":false,\"enable_code_generation\":false,\"iterations\":8,\"primary_metric\":\"normalized_root_mean_squared_error\",\"task_type\":\"regression\",\"positive_label\":null,\"data_script\":null,\"test_size\":0.0,\"test_include_predictions_only\":false,\"validation_size\":0.0,\"n_cross_validations\":3,\"y_min\":null,\"y_max\":null,\"num_classes\":null,\"featurization\":\"auto\",\"_ignore_package_version_incompatibilities\":false,\"is_timeseries\":false,\"max_cores_per_iteration\":1,\"max_concurrent_iterations\":4,\"iteration_timeout_minutes\":5,\"mem_in_mb\":null,\"enforce_time_on_windows\":true,\"experiment_timeout_minutes\":8640,\"experiment_exit_score\":null,\"partition_column_names\":null,\"whitelist_models\":null,\"blacklist_algos\":[\"TensorFlowDNN\",\"TensorFlowLinearRegressor\"],\"supported_models\":[\"RandomForest\",\"ElasticNet\",\"DecisionTree\",\"ExtremeRandomTrees\",\"XGBoostRegressor\",\"TensorFlowDNN\",\"GradientBoosting\",\"LassoLars\",\"TensorFlowLinearRegressor\",\"TabnetRegressor\",\"FastLinearRegressor\",\"OnlineGradientDescentRegressor\",\"LightGBM\",\"KNN\",\"SGD\"],\"private_models\":[],\"auto_blacklist\":true,\"blacklist_samples_reached\":false,\"exclude_nan_labels\":true,\"verbosity\":20,\"_debug_log\":\"azureml_automl.log\",\"show_warnings\":false,\"model_explainability\":true,\"service_url\":null,\"sdk_url\":null,\"sdk_packages\":null,\"enable_onnx_compatible_models\":false,\"enable_split_onnx_featurizer_estimator_models\":false,\"vm_type\":\"STANDARD_D2DS_V4\",\"telemetry_verbosity\":20,\"send_telemetry\":true,\"enable_dnn\":false,\"scenario\":\"SDK-1.13.0\",\"environment_label\":null,\"save_mlflow\":false,\"enable_categorical_indicators\":false,\"force_text_dnn\":false,\"enable_feature_sweeping\":true,\"enable_early_stopping\":true,\"early_stopping_n_iters\":10,\"arguments\":null,\"dataset_id\":\"8b25d268-7a7a-40d4-b496-4c016aeaaca0\",\"hyperdrive_config\":null,\"validation_dataset_id\":null,\"run_source\":null,\"metrics\":null,\"enable_metric_confidence\":false,\"enable_ensembling\":true,\"enable_stack_ensembling\":true,\"ensemble_iterations\":8,\"enable_tf\":false,\"enable_subsampling\":false,\"subsample_seed\":null,\"enable_nimbusml\":false,\"enable_streaming\":false,\"force_streaming\":false,\"track_child_runs\":true,\"allowed_private_models\":[],\"label_column_name\":\"penaltyMinutes\",\"weight_column_name\":null,\"cv_split_column_names\":null,\"enable_local_managed\":false,\"_local_managed_run_id\":null,\"cost_mode\":1,\"lag_length\":0,\"metric_operation\":\"minimize\",\"preprocess\":true}',\n",
       "  'DataPrepJsonString': '{\\\\\"training_data\\\\\": {\\\\\"datasetId\\\\\": \\\\\"8b25d268-7a7a-40d4-b496-4c016aeaaca0\\\\\"}, \\\\\"datasets\\\\\": 0}',\n",
       "  'EnableSubsampling': 'False',\n",
       "  'runTemplate': 'AutoML',\n",
       "  'azureml.runsource': 'automl',\n",
       "  'display_task_type': 'regression',\n",
       "  'dependencies_versions': '{\"azureml-widgets\": \"1.40.0\", \"azureml-training-tabular\": \"1.40.0\", \"azureml-train\": \"1.40.0\", \"azureml-train-restclients-hyperdrive\": \"1.40.0\", \"azureml-train-core\": \"1.40.0\", \"azureml-train-automl-runtime\": \"1.40.0.post1\", \"azureml-train-automl-client\": \"1.40.0\", \"azureml-telemetry\": \"1.40.0\", \"azureml-pipeline\": \"1.40.0\", \"azureml-pipeline-steps\": \"1.40.0\", \"azureml-pipeline-core\": \"1.40.0\", \"azureml-mlflow\": \"1.40.0\", \"azureml-interpret\": \"1.40.0\", \"azureml-inference-server-http\": \"0.4.13\", \"azureml-defaults\": \"1.40.0\", \"azureml-dataset-runtime\": \"1.40.0\", \"azureml-dataprep\": \"3.0.2\", \"azureml-dataprep-rslex\": \"2.4.2\", \"azureml-dataprep-native\": \"38.0.0\", \"azureml-core\": \"1.40.0\", \"azureml-automl-runtime\": \"1.40.0\", \"azureml-automl-core\": \"1.40.0\"}',\n",
       "  '_aml_system_scenario_identification': 'Remote.Parent',\n",
       "  'ClientType': 'SDK',\n",
       "  'environment_cpu_name': 'AzureML-AutoML',\n",
       "  'environment_cpu_label': 'prod',\n",
       "  'environment_gpu_name': 'AzureML-AutoML-GPU',\n",
       "  'environment_gpu_label': 'prod',\n",
       "  'root_attribution': 'automl',\n",
       "  'attribution': 'AutoML',\n",
       "  'Orchestrator': 'AutoML',\n",
       "  'CancelUri': 'https://northcentralus.api.azureml.ms/jasmine/v1.0/subscriptions/efba8785-116c-4443-9a05-764c75c7bb0d/resourceGroups/datascience/providers/Microsoft.MachineLearningServices/workspaces/DataScience/experimentids/035c4945-9be2-4c17-954e-b063ddd656c2/cancel/AutoML_b5b9df0c-c05b-4ff1-b9fa-1c19ef68c631',\n",
       "  'azureml.git.repository_uri': 'https://github.com/IntegerMan/AutoMLwithPythonAndAzure.git',\n",
       "  'mlflow.source.git.repoURL': 'https://github.com/IntegerMan/AutoMLwithPythonAndAzure.git',\n",
       "  'azureml.git.branch': 'main',\n",
       "  'mlflow.source.git.branch': 'main',\n",
       "  'azureml.git.commit': '4f4da08a809bdfa8aade5854f3834a3ebe76ca44',\n",
       "  'mlflow.source.git.commit': '4f4da08a809bdfa8aade5854f3834a3ebe76ca44',\n",
       "  'azureml.git.dirty': 'True',\n",
       "  'ClientSdkVersion': '1.42.0.post1',\n",
       "  'snapshotId': '00000000-0000-0000-0000-000000000000',\n",
       "  'SetupRunId': 'AutoML_b5b9df0c-c05b-4ff1-b9fa-1c19ef68c631_setup',\n",
       "  'SetupRunContainerId': 'dcid.AutoML_b5b9df0c-c05b-4ff1-b9fa-1c19ef68c631_setup',\n",
       "  'FeaturizationRunJsonPath': 'featurizer_container.json',\n",
       "  'FeaturizationRunId': 'AutoML_b5b9df0c-c05b-4ff1-b9fa-1c19ef68c631_featurize',\n",
       "  'ProblemInfoJsonString': '{\"dataset_num_categorical\": 0, \"is_sparse\": true, \"subsampling\": false, \"has_extra_col\": true, \"dataset_classes\": 112, \"dataset_features\": 63, \"dataset_samples\": 9142, \"single_frequency_class_detected\": false}',\n",
       "  'ModelExplainRunId': 'AutoML_b5b9df0c-c05b-4ff1-b9fa-1c19ef68c631_ModelExplain'},\n",
       " 'inputDatasets': [{'dataset': {'id': '8b25d268-7a7a-40d4-b496-4c016aeaaca0'}, 'consumptionDetails': {'type': 'RunInput', 'inputName': 'training_data', 'mechanism': 'Direct'}}],\n",
       " 'outputDatasets': [],\n",
       " 'logFiles': {},\n",
       " 'submittedBy': 'Matt Eland'}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Wait for the experiment to complete (displays active details about the run)\n",
    "RunDetails(run).show()\n",
    "run.wait_for_completion(show_output=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:The version of the SDK does not match the version the model was trained on.\n",
      "WARNING:root:The consistency in the result may not be guaranteed.\n",
      "WARNING:root:Package:azureml-automl-core, training version:1.42.0.post1, current version:1.40.0\n",
      "Package:azureml-automl-runtime, training version:1.42.0.post1, current version:1.40.0\n",
      "Package:azureml-core, training version:1.42.0, current version:1.40.0\n",
      "Package:azureml-dataprep, training version:4.0.3, current version:3.0.2\n",
      "Package:azureml-dataprep-rslex, training version:2.6.3, current version:2.4.2\n",
      "Package:azureml-dataset-runtime, training version:1.42.0, current version:1.40.0\n",
      "Package:azureml-defaults, training version:1.42.0, current version:1.40.0\n",
      "Package:azureml-interpret, training version:1.42.0, current version:1.40.0\n",
      "Package:azureml-mlflow, training version:1.42.0, current version:1.40.0\n",
      "Package:azureml-pipeline-core, training version:1.42.0, current version:1.40.0\n",
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      "Package:azureml-training-tabular, training version:1.42.0.post1, current version:1.40.0\n",
      "WARNING:root:Below packages were used for model training but missing in current environment:\n",
      "WARNING:root:Package:azureml-responsibleai, training version:1.42.0\n",
      "WARNING:root:Please ensure the version of your local conda dependencies match the version on which your model was trained in order to properly retrieve your model.\n"
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INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3048,), 'y_pred': (3048,), 'y_test_unique': 87, 'y_pred_unique': 991, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.571 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3048,), 'y_pred': (3048,), 'y_test_unique': 87, 'y_pred_unique': 991, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.573 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3047,), 'y_pred': (3047,), 'y_test_unique': 87, 'y_pred_unique': 1021, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.573 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3047,), 'y_pred': (3047,), 'y_test_unique': 87, 'y_pred_unique': 1021, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.575 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3047,), 'y_pred': (3047,), 'y_test_unique': 84, 'y_pred_unique': 1001, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.575 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3047,), 'y_pred': (3047,), 'y_test_unique': 84, 'y_pred_unique': 1001, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.576 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3048,), 'y_pred': (3048,), 'y_test_unique': 87, 'y_pred_unique': 995, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.576 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3048,), 'y_pred': (3048,), 'y_test_unique': 87, 'y_pred_unique': 995, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.577 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3047,), 'y_pred': (3047,), 'y_test_unique': 87, 'y_pred_unique': 1024, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.577 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3047,), 'y_pred': (3047,), 'y_test_unique': 87, 'y_pred_unique': 1024, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.579 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3047,), 'y_pred': (3047,), 'y_test_unique': 84, 'y_pred_unique': 1004, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.579 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3047,), 'y_pred': (3047,), 'y_test_unique': 84, 'y_pred_unique': 1004, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.580 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3048,), 'y_pred': (3048,), 'y_test_unique': 87, 'y_pred_unique': 995, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.580 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3048,), 'y_pred': (3048,), 'y_test_unique': 87, 'y_pred_unique': 995, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.581 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3047,), 'y_pred': (3047,), 'y_test_unique': 87, 'y_pred_unique': 1024, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.581 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3047,), 'y_pred': (3047,), 'y_test_unique': 87, 'y_pred_unique': 1024, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.583 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3047,), 'y_pred': (3047,), 'y_test_unique': 84, 'y_pred_unique': 1004, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.583 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3047,), 'y_pred': (3047,), 'y_test_unique': 84, 'y_pred_unique': 1004, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.584 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3048,), 'y_pred': (3048,), 'y_test_unique': 87, 'y_pred_unique': 995, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.584 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3048,), 'y_pred': (3048,), 'y_test_unique': 87, 'y_pred_unique': 995, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.585 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3047,), 'y_pred': (3047,), 'y_test_unique': 87, 'y_pred_unique': 1024, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.585 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3047,), 'y_pred': (3047,), 'y_test_unique': 87, 'y_pred_unique': 1024, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.587 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3047,), 'y_pred': (3047,), 'y_test_unique': 84, 'y_pred_unique': 1004, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.587 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3047,), 'y_pred': (3047,), 'y_test_unique': 84, 'y_pred_unique': 1004, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.588 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3048,), 'y_pred': (3048,), 'y_test_unique': 87, 'y_pred_unique': 995, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.588 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3048,), 'y_pred': (3048,), 'y_test_unique': 87, 'y_pred_unique': 995, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.589 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3047,), 'y_pred': (3047,), 'y_test_unique': 87, 'y_pred_unique': 1024, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.589 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3047,), 'y_pred': (3047,), 'y_test_unique': 87, 'y_pred_unique': 1024, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.590 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3047,), 'y_pred': (3047,), 'y_test_unique': 84, 'y_pred_unique': 1004, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.590 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3047,), 'y_pred': (3047,), 'y_test_unique': 84, 'y_pred_unique': 1004, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.592 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3048,), 'y_pred': (3048,), 'y_test_unique': 87, 'y_pred_unique': 994, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.592 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3048,), 'y_pred': (3048,), 'y_test_unique': 87, 'y_pred_unique': 994, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.593 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3047,), 'y_pred': (3047,), 'y_test_unique': 87, 'y_pred_unique': 1024, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.593 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3047,), 'y_pred': (3047,), 'y_test_unique': 87, 'y_pred_unique': 1024, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.595 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3047,), 'y_pred': (3047,), 'y_test_unique': 84, 'y_pred_unique': 1004, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.595 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3047,), 'y_pred': (3047,), 'y_test_unique': 84, 'y_pred_unique': 1004, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.596 - INFO - 44 - azureml.automl.runtime._ml_engine.ensemble.ensemble_selector.select:139 - Single models scores: [0.09572821 0.09676371 0.09676665 0.09676926 0.09677231 0.09680151]\\n2022-06-20 03:30:57.596 - INFO - 44 - azureml.automl.runtime._ml_engine.ensemble.ensemble_selector.select:139 - Single models scores: [0.09572821 0.09676371 0.09676665 0.09676926 0.09677231 0.09680151]\\n2022-06-20 03:30:57.597 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3048,), 'y_pred': (3048,), 'y_test_unique': 87, 'y_pred_unique': 995, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.597 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3048,), 'y_pred': (3048,), 'y_test_unique': 87, 'y_pred_unique': 995, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.599 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3047,), 'y_pred': (3047,), 'y_test_unique': 87, 'y_pred_unique': 1024, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.599 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3047,), 'y_pred': (3047,), 'y_test_unique': 87, 'y_pred_unique': 1024, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.600 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3047,), 'y_pred': (3047,), 'y_test_unique': 84, 'y_pred_unique': 1004, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.600 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3047,), 'y_pred': (3047,), 'y_test_unique': 84, 'y_pred_unique': 1004, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.601 - INFO - 44 - azureml.automl.runtime._ml_engine.ensemble.ensemble_selector.select:171 - Starting ensemble hill climbing. Current ensemble: [0, 1, 2, 3], current score: 0.09634150100602172\\n2022-06-20 03:30:57.601 - INFO - 44 - azureml.automl.runtime._ml_engine.ensemble.ensemble_selector.select:171 - Starting ensemble hill climbing. Current ensemble: [0, 1, 2, 3], current score: 0.09634150100602172\\n2022-06-20 03:30:57.602 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3048,), 'y_pred': (3048,), 'y_test_unique': 87, 'y_pred_unique': 995, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.602 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3048,), 'y_pred': (3048,), 'y_test_unique': 87, 'y_pred_unique': 995, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.603 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3047,), 'y_pred': (3047,), 'y_test_unique': 87, 'y_pred_unique': 1024, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.603 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3047,), 'y_pred': (3047,), 'y_test_unique': 87, 'y_pred_unique': 1024, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.604 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3047,), 'y_pred': (3047,), 'y_test_unique': 84, 'y_pred_unique': 1004, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.604 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3047,), 'y_pred': (3047,), 'y_test_unique': 84, 'y_pred_unique': 1004, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.606 - INFO - 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Regression metrics debug: {'y_test': (3047,), 'y_pred': (3047,), 'y_test_unique': 87, 'y_pred_unique': 1024, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.608 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3047,), 'y_pred': (3047,), 'y_test_unique': 84, 'y_pred_unique': 1004, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.608 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3047,), 'y_pred': (3047,), 'y_test_unique': 84, 'y_pred_unique': 1004, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.610 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3048,), 'y_pred': (3048,), 'y_test_unique': 87, 'y_pred_unique': 995, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.610 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3048,), 'y_pred': (3048,), 'y_test_unique': 87, 'y_pred_unique': 995, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.611 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3047,), 'y_pred': (3047,), 'y_test_unique': 87, 'y_pred_unique': 1024, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.611 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3047,), 'y_pred': (3047,), 'y_test_unique': 87, 'y_pred_unique': 1024, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.613 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3047,), 'y_pred': (3047,), 'y_test_unique': 84, 'y_pred_unique': 1004, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.613 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3047,), 'y_pred': (3047,), 'y_test_unique': 84, 'y_pred_unique': 1004, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.614 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3048,), 'y_pred': (3048,), 'y_test_unique': 87, 'y_pred_unique': 995, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.614 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3048,), 'y_pred': (3048,), 'y_test_unique': 87, 'y_pred_unique': 995, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.615 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3047,), 'y_pred': (3047,), 'y_test_unique': 87, 'y_pred_unique': 1024, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.615 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3047,), 'y_pred': (3047,), 'y_test_unique': 87, 'y_pred_unique': 1024, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.617 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3047,), 'y_pred': (3047,), 'y_test_unique': 84, 'y_pred_unique': 1004, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.617 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3047,), 'y_pred': (3047,), 'y_test_unique': 84, 'y_pred_unique': 1004, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.618 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3048,), 'y_pred': (3048,), 'y_test_unique': 87, 'y_pred_unique': 995, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.618 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3048,), 'y_pred': (3048,), 'y_test_unique': 87, 'y_pred_unique': 995, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.619 - INFO - 44 - 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INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3048,), 'y_pred': (3048,), 'y_test_unique': 87, 'y_pred_unique': 995, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.715 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3048,), 'y_pred': (3048,), 'y_test_unique': 87, 'y_pred_unique': 995, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.716 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3047,), 'y_pred': (3047,), 'y_test_unique': 87, 'y_pred_unique': 1024, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.716 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3047,), 'y_pred': (3047,), 'y_test_unique': 87, 'y_pred_unique': 1024, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.717 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3047,), 'y_pred': (3047,), 'y_test_unique': 84, 'y_pred_unique': 1004, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.717 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3047,), 'y_pred': (3047,), 'y_test_unique': 84, 'y_pred_unique': 1004, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:57.718 - INFO - 44 - azureml.automl.runtime._ml_engine.ensemble.ensemble_selector.select:205 - Iteration 3, new best ensemble: [0, 1, 2, 3, 5, 5, 5, 5], new best score :0.0948382682978563\\n2022-06-20 03:30:57.718 - INFO - 44 - azureml.automl.runtime._ml_engine.ensemble.ensemble_selector.select:205 - Iteration 3, new best ensemble: [0, 1, 2, 3, 5, 5, 5, 5], new best score :0.0948382682978563\\n2022-06-20 03:30:57.718 - INFO - 44 - azureml.automl.runtime._ml_engine.ensemble.ensemble_selector.select:209 - Final ensemble: [0, 1, 2, 3, 5, 5, 5, 5], score: 0.0948382682978563. 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INFO - 44 - azureml.automl.runtime.shared.cache_store._log_activity:55 - ActivityStarted: get\\n2022-06-20 03:30:58.583 - INFO - 44 - azureml.automl.runtime.shared.cache_store._log_activity:55 - ActivityStarted: get\\n2022-06-20 03:30:58.583 - INFO - 44 - azureml.automl.runtime.shared.lazy_file_cache_store.get:426 - Getting data for key: featurized_cv_split_1\\n2022-06-20 03:30:58.583 - INFO - 44 - azureml.automl.runtime.shared.lazy_file_cache_store.get:426 - Getting data for key: featurized_cv_split_1\\n2022-06-20 03:30:58.584 - INFO - 44 - azureml.automl.runtime.shared.cache_store._log_activity:93 - ActivityCompleted: Activity=get, HowEnded=Success, Duration=1.03[ms]\\n2022-06-20 03:30:58.584 - INFO - 44 - azureml.automl.runtime.shared.cache_store._log_activity:93 - ActivityCompleted: Activity=get, HowEnded=Success, Duration=1.03[ms]\\n2022-06-20 03:30:58.585 - INFO - 44 - azureml.automl.runtime.shared.metrics_utilities._log_activity:55 - ActivityStarted: Predict\\n2022-06-20 03:30:58.585 - INFO - 44 - azureml.automl.runtime.shared.metrics_utilities._log_activity:55 - ActivityStarted: Predict\\n2022-06-20 03:30:58.616 - INFO - 44 - azureml.automl.runtime.shared.metrics_utilities._log_activity:93 - ActivityCompleted: Activity=Predict, HowEnded=Success, Duration=31.53[ms]\\n2022-06-20 03:30:58.616 - INFO - 44 - azureml.automl.runtime.shared.metrics_utilities._log_activity:93 - ActivityCompleted: Activity=Predict, HowEnded=Success, Duration=31.53[ms]\\n2022-06-20 03:30:58.617 - INFO - 44 - azureml.automl.runtime.shared.metrics_utilities._log_activity:55 - ActivityStarted: ComputeMetrics\\n2022-06-20 03:30:58.617 - INFO - 44 - azureml.automl.runtime.shared.metrics_utilities._log_activity:55 - ActivityStarted: ComputeMetrics\\n2022-06-20 03:30:58.618 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3047,), 'y_pred': (3047,), 'y_test_unique': 87, 'y_pred_unique': 1024, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:58.618 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3047,), 'y_pred': (3047,), 'y_test_unique': 87, 'y_pred_unique': 1024, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:58.625 - INFO - 44 - azureml.automl.runtime.shared.metrics_utilities._log_activity:93 - ActivityCompleted: Activity=ComputeMetrics, HowEnded=Success, Duration=8.13[ms]\\n2022-06-20 03:30:58.625 - INFO - 44 - azureml.automl.runtime.shared.metrics_utilities._log_activity:93 - ActivityCompleted: Activity=ComputeMetrics, HowEnded=Success, Duration=8.13[ms]\\n2022-06-20 03:30:58.626 - INFO - 44 - azureml.automl.runtime.shared.cache_store._log_activity:55 - ActivityStarted: get\\n2022-06-20 03:30:58.626 - INFO - 44 - azureml.automl.runtime.shared.cache_store._log_activity:55 - ActivityStarted: get\\n2022-06-20 03:30:58.626 - INFO - 44 - azureml.automl.runtime.shared.lazy_file_cache_store.get:426 - Getting data for key: featurized_cv_split_2\\n2022-06-20 03:30:58.626 - INFO - 44 - azureml.automl.runtime.shared.lazy_file_cache_store.get:426 - Getting data for key: featurized_cv_split_2\\n2022-06-20 03:30:58.626 - INFO - 44 - azureml.automl.runtime.shared.cache_store._log_activity:93 - ActivityCompleted: Activity=get, HowEnded=Success, Duration=0.72[ms]\\n2022-06-20 03:30:58.626 - INFO - 44 - azureml.automl.runtime.shared.cache_store._log_activity:93 - ActivityCompleted: Activity=get, HowEnded=Success, Duration=0.72[ms]\\n2022-06-20 03:30:58.627 - INFO - 44 - azureml.automl.runtime.shared.metrics_utilities._log_activity:55 - ActivityStarted: Predict\\n2022-06-20 03:30:58.627 - INFO - 44 - azureml.automl.runtime.shared.metrics_utilities._log_activity:55 - ActivityStarted: Predict\\n2022-06-20 03:30:58.658 - INFO - 44 - azureml.automl.runtime.shared.metrics_utilities._log_activity:93 - ActivityCompleted: Activity=Predict, HowEnded=Success, Duration=31.49[ms]\\n2022-06-20 03:30:58.658 - INFO - 44 - azureml.automl.runtime.shared.metrics_utilities._log_activity:93 - ActivityCompleted: Activity=Predict, HowEnded=Success, Duration=31.49[ms]\\n2022-06-20 03:30:58.659 - INFO - 44 - azureml.automl.runtime.shared.metrics_utilities._log_activity:55 - ActivityStarted: ComputeMetrics\\n2022-06-20 03:30:58.659 - INFO - 44 - azureml.automl.runtime.shared.metrics_utilities._log_activity:55 - ActivityStarted: ComputeMetrics\\n2022-06-20 03:30:58.660 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3047,), 'y_pred': (3047,), 'y_test_unique': 84, 'y_pred_unique': 1004, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:58.660 - INFO - 44 - azureml.automl.runtime.shared.score._validation.log_regression_debug:218 - Regression metrics debug: {'y_test': (3047,), 'y_pred': (3047,), 'y_test_unique': 84, 'y_pred_unique': 1004, 'y_test_has_negative': False, 'y_pred_has_negative': False, 'min_max_equal': False, 'sample_weight': None}\\n2022-06-20 03:30:58.667 - INFO - 44 - azureml.automl.runtime.shared.metrics_utilities._log_activity:93 - ActivityCompleted: Activity=ComputeMetrics, HowEnded=Success, Duration=8.2[ms]\\n2022-06-20 03:30:58.667 - INFO - 44 - azureml.automl.runtime.shared.metrics_utilities._log_activity:93 - ActivityCompleted: Activity=ComputeMetrics, HowEnded=Success, Duration=8.2[ms]\\n2022-06-20 03:30:58.670 - INFO - 44 - azureml.automl.runtime.shared.runner._log_activity:93 - ActivityCompleted: Activity=RunEnsembling, HowEnded=Success, Duration=3892.09[ms]\\n2022-06-20 03:30:58.670 - INFO - 44 - azureml.automl.runtime.shared.runner._log_activity:93 - ActivityCompleted: Activity=RunEnsembling, HowEnded=Success, Duration=3892.09[ms]\\n\\nRun is completed.\", \"graph\": {\"module_nodes\": {\"04b1b6b6\": {\"node_id\": \"04b1b6b6\", \"name\": \"prefittedsoftvotingregressor\", \"status\": \"model\"}, \"ba635eba\": {\"node_id\": \"ba635eba\", \"name\": \"Categorical\", \"status\": \"dataType\"}, \"54694616\": {\"node_id\": \"54694616\", \"name\": \"ModeCatImputer-StringCast-LabelEncoder\", \"status\": \"operation\"}, \"96919eb8\": {\"node_id\": \"96919eb8\", \"name\": \"StringCast-CharGramCountVectorizer\", \"status\": \"operation\"}}, \"edges\": [{\"source_node_id\": \"082faca3\", \"source_node_name\": \"data_source\", \"source_name\": \"\", \"target_name\": \"3 col\", \"dst_node_id\": \"ba635eba\", \"dst_node_name\": \"Categorical\"}, {\"source_node_id\": \"ba635eba\", \"source_node_name\": \"\", \"source_name\": \"\", \"target_name\": \"\", \"dst_node_id\": \"54694616\", \"dst_node_name\": \"\"}, {\"source_node_id\": \"54694616\", \"source_node_name\": \"\", \"source_name\": \"\", \"target_name\": \"31 col\", \"dst_node_id\": \"04b1b6b6\", \"dst_node_name\": \"\"}, {\"source_node_id\": \"ba635eba\", \"source_node_name\": \"\", \"source_name\": \"\", \"target_name\": \"\", \"dst_node_id\": \"96919eb8\", \"dst_node_name\": \"\"}, {\"source_node_id\": \"96919eb8\", \"source_node_name\": \"\", \"source_name\": \"\", \"target_name\": \"31 col\", \"dst_node_id\": \"04b1b6b6\", \"dst_node_name\": \"\"}], \"child_runs\": [], \"datasource_nodes\": {\"082faca3\": {\"node_id\": \"082faca3\", \"name\": \"data_source - 3 col\"}}}, \"widget_settings\": {\"childWidgetDisplay\": \"popup\", \"send_telemetry\": false, \"log_level\": \"INFO\", \"sdk_version\": \"1.40.0\"}, \"loading\": false}"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Grab the resulting model and best run\n",
    "best_auto_run, automl_model = run.get_output()\n",
    "\n",
    "# Display details about the best run\n",
    "print('Best Run: ' + str(best_auto_run.id))\n",
    "RunDetails(best_auto_run).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Model(workspace=Workspace.create(name='DataScience', subscription_id='efba8785-116c-4443-9a05-764c75c7bb0d', resource_group='datascience'), name=NHL-Penalties-AutoML, id=NHL-Penalties-AutoML:6, version=6, tags={}, properties={})"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Register the model in Azure\n",
    "best_auto_run.register_model(\n",
    "    model_name='NHL-Penalties-AutoML', \n",
    "    model_path='outputs/model.pkl', \n",
    "    description='NHL Game Penalty Prediction Best AutoML Run')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_auto_run.download_files(output_directory='automl-output')"
   ]
  }
 ],
 "metadata": {
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   "display_name": "Python 3.8.8 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
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