Deployement.ipynb

{
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Loan Amount</th>\n",
              "      <th>Funded Amount</th>\n",
              "      <th>Funded Amount Investor</th>\n",
              "      <th>Term</th>\n",
              "      <th>Batch Enrolled</th>\n",
              "      <th>Interest Rate</th>\n",
              "      <th>Grade</th>\n",
              "      <th>Sub Grade</th>\n",
              "      <th>Employment Duration</th>\n",
              "      <th>Home Ownership</th>\n",
              "      <th>...</th>\n",
              "      <th>Total Received Late Fee</th>\n",
              "      <th>Recoveries</th>\n",
              "      <th>Collection Recovery Fee</th>\n",
              "      <th>Collection 12 months Medical</th>\n",
              "      <th>Application Type</th>\n",
              "      <th>Last week Pay</th>\n",
              "      <th>Total Collection Amount</th>\n",
              "      <th>Total Current Balance</th>\n",
              "      <th>Total Revolving Credit Limit</th>\n",
              "      <th>Loan Status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>10000</td>\n",
              "      <td>32236</td>\n",
              "      <td>12329.36286</td>\n",
              "      <td>59</td>\n",
              "      <td>2522922</td>\n",
              "      <td>11.135007</td>\n",
              "      <td>2</td>\n",
              "      <td>34</td>\n",
              "      <td>0</td>\n",
              "      <td>176346.62670</td>\n",
              "      <td>...</td>\n",
              "      <td>0.102055</td>\n",
              "      <td>2.498291</td>\n",
              "      <td>0.793724</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>31</td>\n",
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              "      <td>12191.99692</td>\n",
              "      <td>59</td>\n",
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              "      <td>3</td>\n",
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              "      <td>...</td>\n",
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              "      <td>6</td>\n",
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              "      <td>91506.69105</td>\n",
              "      <td>...</td>\n",
              "      <td>18.778660</td>\n",
              "      <td>4.316277</td>\n",
              "      <td>1.020075</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>66</td>\n",
              "      <td>34</td>\n",
              "      <td>89801</td>\n",
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              "      <td>0</td>\n",
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              "      <td>3</td>\n",
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              "      <td>3</td>\n",
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              "      <td>44234.82545</td>\n",
              "      <td>...</td>\n",
              "      <td>19.306646</td>\n",
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              "</table>\n",
              "<p>67463 rows × 32 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       Loan Amount  Funded Amount  Funded Amount Investor  Term  \\\n",
              "0            10000          32236             12329.36286    59   \n",
              "1             3609          11940             12191.99692    59   \n",
              "2            28276           9311             21603.22455    59   \n",
              "3            11170           6954             17877.15585    59   \n",
              "4            16890          13226             13539.92667    59   \n",
              "...            ...            ...                     ...   ...   \n",
              "67458        13601           6848             13175.28583    59   \n",
              "67459         8323          11046             15637.46301    59   \n",
              "67460        15897          32921             12329.45775    59   \n",
              "67461        16567           4975             21353.68465    59   \n",
              "67462        15353          29875             14207.44860    59   \n",
              "\n",
              "       Batch Enrolled  Interest Rate  Grade  Sub Grade  Employment Duration  \\\n",
              "0             2522922      11.135007      2         34                    0   \n",
              "1             1586599      12.237563      3         43                    1   \n",
              "2             2136391      12.545884      6         44                    0   \n",
              "3             2428731      16.731201      3         33                    0   \n",
              "4             5341619      15.008300      3         44                    0   \n",
              "...               ...            ...    ...        ...                  ...   \n",
              "67458         3193689       9.408858      3         14                    0   \n",
              "67459         1780517       9.972104      3         23                    1   \n",
              "67460         1761981      19.650943      1         61                    0   \n",
              "67461         2333412      13.169095      4         53                    2   \n",
              "67462         1930365      16.034631      2         41                    0   \n",
              "\n",
              "       Home Ownership  ...  Total Received Late Fee   Recoveries  \\\n",
              "0        176346.62670  ...                 0.102055     2.498291   \n",
              "1         39833.92100  ...                 0.036181     2.377215   \n",
              "2         91506.69105  ...                18.778660     4.316277   \n",
              "3        108286.57590  ...                 0.044131     0.107020   \n",
              "4         44234.82545  ...                19.306646  1294.818751   \n",
              "...               ...  ...                      ...          ...   \n",
              "67458     83961.15003  ...                 0.023478   564.614852   \n",
              "67459     65491.12817  ...                 0.027095     2.015494   \n",
              "67460     34813.96985  ...                 0.028212     5.673092   \n",
              "67461     96938.83564  ...                 0.074508     1.157454   \n",
              "67462    105123.15580  ...                 0.000671     1.856480   \n",
              "\n",
              "       Collection Recovery Fee  Collection 12 months Medical  \\\n",
              "0                     0.793724                             0   \n",
              "1                     0.974821                             0   \n",
              "2                     1.020075                             0   \n",
              "3                     0.749971                             0   \n",
              "4                     0.368953                             0   \n",
              "...                        ...                           ...   \n",
              "67458                 0.865230                             0   \n",
              "67459                 1.403368                             0   \n",
              "67460                 1.607093                             0   \n",
              "67461                 0.207608                             0   \n",
              "67462                 0.366386                             0   \n",
              "\n",
              "       Application Type  Last week Pay  Total Collection Amount  \\\n",
              "0                     0             49                       31   \n",
              "1                     0            109                       53   \n",
              "2                     0             66                       34   \n",
              "3                     0             39                       40   \n",
              "4                     0             18                      430   \n",
              "...                 ...            ...                      ...   \n",
              "67458                 0             69                       48   \n",
              "67459                 0             14                       37   \n",
              "67460                 0            137                       17   \n",
              "67461                 0             73                       61   \n",
              "67462                 0             54                       47   \n",
              "\n",
              "       Total Current Balance  Total Revolving Credit Limit  Loan Status  \n",
              "0                     311301                          6619            0  \n",
              "1                     182610                         20885            0  \n",
              "2                      89801                         26155            0  \n",
              "3                       9189                         60214            0  \n",
              "4                     126029                         22579            0  \n",
              "...                      ...                           ...          ...  \n",
              "67458                 181775                         34301            1  \n",
              "67459                  22692                          8714            0  \n",
              "67460                 176857                         42330            0  \n",
              "67461                 361339                         39075            0  \n",
              "67462                 196960                         66060            0  \n",
              "\n",
              "[67463 rows x 32 columns]"
            ]
          },
          "execution_count": 1,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import azureml.core\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import logging\n",
        "\n",
        "from azureml.core import Workspace, Experiment, Dataset\n",
        "from azureml.core.compute import AmlCompute\n",
        "from azureml.core.compute import ComputeTarget\n",
        "from azureml.train.automl.run import AutoMLRun\n",
        "\n",
        "#Connect to wrokspace and Experiment again\n",
        "ws = Workspace('8c96f3ed-e53f-4400-a002-420605371b18','Azure_Project','FRT_Project')\n",
        "experiment_name = 'automl_loan_approval_prediction'\n",
        "experiment =  Experiment(ws, experiment_name)\n",
        "\n",
        "#Connect to cpu cluster again\n",
        "amlcompute_cluster_name = \"cpu-cluster\"\n",
        "cts=ws.compute_targets\n",
        "compute_target = cts[amlcompute_cluster_name]\n",
        "\n",
        "#Connect to dataset from within te datastore again\n",
        "datastore = ws.get_default_datastore() \n",
        "dataset = Dataset.Tabular.from_delimited_files(path=[(datastore,'dataset/Prepared_train.csv')])\n",
        "dataset.to_pandas_dataframe().reset_index(drop=True)\n",
        "\n",
        "#Connect to most recent run\n",
        "run_id = \"AutoML_9301b394-5cc4-49a7-beee-c3f879f52408\"\n",
        "remote_run = AutoMLRun(experiment,run_id)\n",
        "\n",
        "test_dataset = Dataset.Tabular.from_delimited_files(path=[(datastore,'dataset/Prepared_train.csv')])\n",
        "test_dataset.to_pandas_dataframe().reset_index(drop=True)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "gather": {
          "logged": 1643808377252
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'runId': 'AutoML_9301b394-5cc4-49a7-beee-c3f879f52408',\n",
              " 'target': 'local',\n",
              " 'status': 'Completed',\n",
              " 'startTimeUtc': '2022-02-02T17:27:39.507389Z',\n",
              " 'endTimeUtc': '2022-02-02T18:39:29.690111Z',\n",
              " 'services': {},\n",
              " 'warnings': [{'source': 'JasmineService',\n",
              "   'message': 'No scores improved over last 20 iterations, so experiment stopped early. This early stopping behavior can be disabled by setting enable_early_stopping = False in AutoMLConfig for notebook/python SDK runs.'}],\n",
              " 'properties': {'num_iterations': '1000',\n",
              "  'training_type': 'TrainFull',\n",
              "  'acquisition_function': 'EI',\n",
              "  'primary_metric': 'AUC_weighted',\n",
              "  'train_split': '0',\n",
              "  'acquisition_parameter': '0',\n",
              "  'num_cross_validation': '3',\n",
              "  'target': 'local',\n",
              "  'AMLSettingsJsonString': '{\"path\":null,\"name\":\"automl_loan_approval_prediction\",\"subscription_id\":\"8c96f3ed-e53f-4400-a002-420605371b18\",\"resource_group\":\"Azure_Project\",\"workspace_name\":\"FRT_Project\",\"region\":\"westus\",\"compute_target\":\"local\",\"spark_service\":null,\"azure_service\":\"local\",\"many_models\":false,\"pipeline_fetch_max_batch_size\":1,\"enable_batch_run\":false,\"enable_run_restructure\":false,\"start_auxiliary_runs_before_parent_complete\":false,\"enable_code_generation\":false,\"iterations\":1000,\"primary_metric\":\"AUC_weighted\",\"task_type\":\"classification\",\"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\":10,\"mem_in_mb\":null,\"enforce_time_on_windows\":true,\"experiment_timeout_minutes\":240,\"experiment_exit_score\":null,\"whitelist_models\":null,\"blacklist_algos\":[\"XGBoostClassifier\",\"TensorFlowLinearClassifier\",\"TensorFlowDNN\"],\"supported_models\":[\"KNN\",\"TensorFlowLinearClassifier\",\"RandomForest\",\"SVM\",\"MultinomialNaiveBayes\",\"DecisionTree\",\"GradientBoosting\",\"ExtremeRandomTrees\",\"LinearSVM\",\"LogisticRegression\",\"BernoulliNaiveBayes\",\"XGBoostClassifier\",\"SGD\",\"TensorFlowDNN\",\"LightGBM\",\"AveragedPerceptronClassifier\",\"TabnetClassifier\"],\"private_models\":[],\"auto_blacklist\":true,\"blacklist_samples_reached\":false,\"exclude_nan_labels\":true,\"verbosity\":20,\"_debug_log\":\"automated_ml_errors.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\":null,\"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\":null,\"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\":15,\"enable_tf\":false,\"enable_subsampling\":null,\"subsample_seed\":null,\"enable_nimbusml\":false,\"enable_streaming\":false,\"force_streaming\":false,\"track_child_runs\":true,\"allowed_private_models\":[],\"label_column_name\":\"Loan Status\",\"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\":\"maximize\",\"preprocess\":true}',\n",
              "  'DataPrepJsonString': '{\\\\\"training_data\\\\\": {\\\\\"datasetId\\\\\": \\\\\"873b7b15-bfdc-4c59-ba5b-1eb648fbf60d\\\\\"}, \\\\\"datasets\\\\\": 0}',\n",
              "  'EnableSubsampling': None,\n",
              "  'runTemplate': 'AutoML',\n",
              "  'azureml.runsource': 'automl',\n",
              "  'display_task_type': 'classification',\n",
              "  'dependencies_versions': '{\"azureml-model-management-sdk\": \"1.0.1b6.post1\", \"azureml-widgets\": \"1.38.0\", \"azureml-train-restclients-hyperdrive\": \"1.38.0\", \"azureml-train-core\": \"1.38.0\", \"azureml-train-automl\": \"1.38.0\", \"azureml-train-automl-runtime\": \"1.38.0\", \"azureml-train-automl-client\": \"1.38.0\", \"azureml-telemetry\": \"1.38.0\", \"azureml-sdk\": \"1.38.0\", \"azureml-pipeline\": \"1.38.0\", \"azureml-pipeline-steps\": \"1.38.0\", \"azureml-pipeline-core\": \"1.38.0\", \"azureml-mlflow\": \"1.38.0\", \"azureml-interpret\": \"1.38.0\", \"azureml-inference-server-http\": \"0.4.2\", \"azureml-explain-model\": \"1.38.0\", \"azureml-defaults\": \"1.38.0\", \"azureml-dataset-runtime\": \"1.38.0\", \"azureml-dataprep\": \"2.26.0\", \"azureml-dataprep-rslex\": \"2.2.0\", \"azureml-dataprep-native\": \"38.0.0\", \"azureml-core\": \"1.38.0\", \"azureml-contrib-dataset\": \"1.38.0\", \"azureml-contrib-automl-pipeline-steps\": \"1.38.0\", \"azureml-automl-runtime\": \"1.38.0\", \"azureml-automl-core\": \"1.38.0\"}',\n",
              "  '_aml_system_scenario_identification': 'Local.Parent',\n",
              "  'ClientSdkVersion': '1.38.0',\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",
              "  '_azureml.ComputeTargetType': 'local',\n",
              "  'ProblemInfoJsonString': '{\"dataset_num_categorical\": 0, \"is_sparse\": true, \"subsampling\": false, \"has_extra_col\": true, \"dataset_classes\": 2, \"dataset_features\": 167, \"dataset_samples\": 60654, \"single_frequency_class_detected\": false}',\n",
              "  'model_output_path': ''},\n",
              " 'inputDatasets': [{'dataset': {'id': '873b7b15-bfdc-4c59-ba5b-1eb648fbf60d'}, 'consumptionDetails': {'type': 'RunInput', 'inputName': 'training_data', 'mechanism': 'Direct'}}],\n",
              " 'outputDatasets': [],\n",
              " 'logFiles': {},\n",
              " 'submittedBy': 'Maaz Mohammed'}"
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "remote_run.get_details()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "gather": {
          "logged": 1643808407733
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[('datatransformer',\n",
              "  DataTransformer(\n",
              "      task='classification',\n",
              "      is_onnx_compatible=False,\n",
              "      enable_feature_sweeping=True,\n",
              "      enable_dnn=False,\n",
              "      force_text_dnn=False,\n",
              "      feature_sweeping_timeout=86400,\n",
              "      featurization_config=None,\n",
              "      is_cross_validation=True,\n",
              "      feature_sweeping_config={}\n",
              "  )),\n",
              " ('MaxAbsScaler', MaxAbsScaler(copy=True)),\n",
              " ('LightGBMClassifier',\n",
              "  LightGBMClassifier(\n",
              "      boosting_type='gbdt',\n",
              "      colsample_bytree=0.7922222222222222,\n",
              "      learning_rate=0.04737368421052632,\n",
              "      max_bin=380,\n",
              "      max_depth=4,\n",
              "      min_child_weight=8,\n",
              "      min_data_in_leaf=0.03793724137931035,\n",
              "      min_split_gain=0.3684210526315789,\n",
              "      n_estimators=400,\n",
              "      num_leaves=224,\n",
              "      reg_alpha=0,\n",
              "      reg_lambda=0.6842105263157894,\n",
              "      subsample=0.14894736842105263,\n",
              "      random_state=None,\n",
              "      n_jobs=-1,\n",
              "      problem_info=ProblemInfo(\n",
              "          gpu_training_param_dict={'processing_unit_type': 'cpu'}\n",
              "      )\n",
              "  ))]"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "best_run , fitted_model = remote_run.get_output()\n",
        "fitted_model.steps"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "gather": {
          "logged": 1643808658789
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['Application Type_ModeCatImputer_LabelEncoder',\n",
              " 'Batch Enrolled_CharGramCountVectorizer_1104812',\n",
              " 'Batch Enrolled_CharGramCountVectorizer_1135695',\n",
              " 'Batch Enrolled_CharGramCountVectorizer_1184694',\n",
              " 'Batch Enrolled_CharGramCountVectorizer_1467036',\n",
              " 'Batch Enrolled_CharGramCountVectorizer_1586599',\n",
              " 'Batch Enrolled_CharGramCountVectorizer_1761981',\n",
              " 'Batch Enrolled_CharGramCountVectorizer_1766061',\n",
              " 'Batch Enrolled_CharGramCountVectorizer_1780517',\n",
              " 'Batch Enrolled_CharGramCountVectorizer_1930365',\n",
              " 'Batch Enrolled_CharGramCountVectorizer_2003848',\n",
              " 'Batch Enrolled_CharGramCountVectorizer_2078974',\n",
              " 'Batch Enrolled_CharGramCountVectorizer_2136391',\n",
              " 'Batch Enrolled_CharGramCountVectorizer_224923',\n",
              " 'Batch Enrolled_CharGramCountVectorizer_2252229',\n",
              " 'Batch Enrolled_CharGramCountVectorizer_2333412',\n",
              " 'Batch Enrolled_CharGramCountVectorizer_2428731',\n",
              " 'Batch Enrolled_CharGramCountVectorizer_2522922',\n",
              " 'Batch Enrolled_CharGramCountVectorizer_2558388',\n",
              " 'Batch Enrolled_CharGramCountVectorizer_2575549',\n",
              " 'Batch Enrolled_CharGramCountVectorizer_2803411',\n",
              " 'Batch Enrolled_CharGramCountVectorizer_2833642',\n",
              " 'Batch Enrolled_CharGramCountVectorizer_3193689',\n",
              " 'Batch Enrolled_CharGramCountVectorizer_3461431',\n",
              " 'Batch Enrolled_CharGramCountVectorizer_3726927',\n",
              " 'Batch Enrolled_CharGramCountVectorizer_3865626',\n",
              " 'Batch Enrolled_CharGramCountVectorizer_3873588',\n",
              " 'Batch Enrolled_CharGramCountVectorizer_4136152',\n",
              " 'Batch Enrolled_CharGramCountVectorizer_4271519',\n",
              " 'Batch Enrolled_CharGramCountVectorizer_4351734',\n",
              " 'Batch Enrolled_CharGramCountVectorizer_4694572',\n",
              " 'Batch Enrolled_CharGramCountVectorizer_4722912',\n",
              " 'Batch Enrolled_CharGramCountVectorizer_4808022',\n",
              " 'Batch Enrolled_CharGramCountVectorizer_5341619',\n",
              " 'Batch Enrolled_CharGramCountVectorizer_5489674',\n",
              " 'Batch Enrolled_CharGramCountVectorizer_5525466',\n",
              " 'Batch Enrolled_CharGramCountVectorizer_5547201',\n",
              " 'Batch Enrolled_CharGramCountVectorizer_5629144',\n",
              " 'Batch Enrolled_CharGramCountVectorizer_5714674',\n",
              " 'Batch Enrolled_CharGramCountVectorizer_5811547',\n",
              " 'Batch Enrolled_CharGramCountVectorizer_5849876',\n",
              " 'Batch Enrolled_CharGramCountVectorizer_5924421',\n",
              " 'Collection 12 months Medical_ModeCatImputer_LabelEncoder',\n",
              " 'Delinquency - two years_CharGramCountVectorizer_0',\n",
              " 'Delinquency - two years_CharGramCountVectorizer_1',\n",
              " 'Delinquency - two years_CharGramCountVectorizer_2',\n",
              " 'Delinquency - two years_CharGramCountVectorizer_3',\n",
              " 'Delinquency - two years_CharGramCountVectorizer_4',\n",
              " 'Delinquency - two years_CharGramCountVectorizer_5',\n",
              " 'Delinquency - two years_CharGramCountVectorizer_6',\n",
              " 'Delinquency - two years_CharGramCountVectorizer_7',\n",
              " 'Delinquency - two years_CharGramCountVectorizer_8',\n",
              " 'Employment Duration_CharGramCountVectorizer_0',\n",
              " 'Employment Duration_CharGramCountVectorizer_1',\n",
              " 'Employment Duration_CharGramCountVectorizer_2',\n",
              " 'Grade_CharGramCountVectorizer_1',\n",
              " 'Grade_CharGramCountVectorizer_2',\n",
              " 'Grade_CharGramCountVectorizer_3',\n",
              " 'Grade_CharGramCountVectorizer_4',\n",
              " 'Grade_CharGramCountVectorizer_5',\n",
              " 'Grade_CharGramCountVectorizer_6',\n",
              " 'Grade_CharGramCountVectorizer_7',\n",
              " 'Initial List Status_ModeCatImputer_LabelEncoder',\n",
              " 'Inquires - six months_CharGramCountVectorizer_0',\n",
              " 'Inquires - six months_CharGramCountVectorizer_1',\n",
              " 'Inquires - six months_CharGramCountVectorizer_2',\n",
              " 'Inquires - six months_CharGramCountVectorizer_3',\n",
              " 'Inquires - six months_CharGramCountVectorizer_4',\n",
              " 'Inquires - six months_CharGramCountVectorizer_5',\n",
              " 'Open Account_CharGramCountVectorizer_10',\n",
              " 'Open Account_CharGramCountVectorizer_11',\n",
              " 'Open Account_CharGramCountVectorizer_12',\n",
              " 'Open Account_CharGramCountVectorizer_13',\n",
              " 'Open Account_CharGramCountVectorizer_14',\n",
              " 'Open Account_CharGramCountVectorizer_15',\n",
              " 'Open Account_CharGramCountVectorizer_16',\n",
              " 'Open Account_CharGramCountVectorizer_17',\n",
              " 'Open Account_CharGramCountVectorizer_18',\n",
              " 'Open Account_CharGramCountVectorizer_19',\n",
              " 'Open Account_CharGramCountVectorizer_2',\n",
              " 'Open Account_CharGramCountVectorizer_20',\n",
              " 'Open Account_CharGramCountVectorizer_21',\n",
              " 'Open Account_CharGramCountVectorizer_22',\n",
              " 'Open Account_CharGramCountVectorizer_23',\n",
              " 'Open Account_CharGramCountVectorizer_24',\n",
              " 'Open Account_CharGramCountVectorizer_25',\n",
              " 'Open Account_CharGramCountVectorizer_26',\n",
              " 'Open Account_CharGramCountVectorizer_27',\n",
              " 'Open Account_CharGramCountVectorizer_28',\n",
              " 'Open Account_CharGramCountVectorizer_29',\n",
              " 'Open Account_CharGramCountVectorizer_3',\n",
              " 'Open Account_CharGramCountVectorizer_30',\n",
              " 'Open Account_CharGramCountVectorizer_31',\n",
              " 'Open Account_CharGramCountVectorizer_32',\n",
              " 'Open Account_CharGramCountVectorizer_33',\n",
              " 'Open Account_CharGramCountVectorizer_34',\n",
              " 'Open Account_CharGramCountVectorizer_35',\n",
              " 'Open Account_CharGramCountVectorizer_36',\n",
              " 'Open Account_CharGramCountVectorizer_37',\n",
              " 'Open Account_CharGramCountVectorizer_4',\n",
              " 'Open Account_CharGramCountVectorizer_5',\n",
              " 'Open Account_CharGramCountVectorizer_6',\n",
              " 'Open Account_CharGramCountVectorizer_7',\n",
              " 'Open Account_CharGramCountVectorizer_8',\n",
              " 'Open Account_CharGramCountVectorizer_9',\n",
              " 'Public Record_CharGramCountVectorizer_0',\n",
              " 'Public Record_CharGramCountVectorizer_1',\n",
              " 'Public Record_CharGramCountVectorizer_2',\n",
              " 'Public Record_CharGramCountVectorizer_3',\n",
              " 'Public Record_CharGramCountVectorizer_4',\n",
              " 'Sub Grade_CharGramCountVectorizer_11',\n",
              " 'Sub Grade_CharGramCountVectorizer_12',\n",
              " 'Sub Grade_CharGramCountVectorizer_13',\n",
              " 'Sub Grade_CharGramCountVectorizer_14',\n",
              " 'Sub Grade_CharGramCountVectorizer_15',\n",
              " 'Sub Grade_CharGramCountVectorizer_21',\n",
              " 'Sub Grade_CharGramCountVectorizer_22',\n",
              " 'Sub Grade_CharGramCountVectorizer_23',\n",
              " 'Sub Grade_CharGramCountVectorizer_24',\n",
              " 'Sub Grade_CharGramCountVectorizer_25',\n",
              " 'Sub Grade_CharGramCountVectorizer_31',\n",
              " 'Sub Grade_CharGramCountVectorizer_32',\n",
              " 'Sub Grade_CharGramCountVectorizer_33',\n",
              " 'Sub Grade_CharGramCountVectorizer_34',\n",
              " 'Sub Grade_CharGramCountVectorizer_35',\n",
              " 'Sub Grade_CharGramCountVectorizer_41',\n",
              " 'Sub Grade_CharGramCountVectorizer_42',\n",
              " 'Sub Grade_CharGramCountVectorizer_43',\n",
              " 'Sub Grade_CharGramCountVectorizer_44',\n",
              " 'Sub Grade_CharGramCountVectorizer_45',\n",
              " 'Sub Grade_CharGramCountVectorizer_51',\n",
              " 'Sub Grade_CharGramCountVectorizer_52',\n",
              " 'Sub Grade_CharGramCountVectorizer_53',\n",
              " 'Sub Grade_CharGramCountVectorizer_54',\n",
              " 'Sub Grade_CharGramCountVectorizer_55',\n",
              " 'Sub Grade_CharGramCountVectorizer_61',\n",
              " 'Sub Grade_CharGramCountVectorizer_62',\n",
              " 'Sub Grade_CharGramCountVectorizer_64',\n",
              " 'Sub Grade_CharGramCountVectorizer_65',\n",
              " 'Sub Grade_CharGramCountVectorizer_71',\n",
              " 'Sub Grade_CharGramCountVectorizer_72',\n",
              " 'Sub Grade_CharGramCountVectorizer_74',\n",
              " 'Sub Grade_CharGramCountVectorizer_75',\n",
              " 'Term_CharGramCountVectorizer_36',\n",
              " 'Term_CharGramCountVectorizer_58',\n",
              " 'Term_CharGramCountVectorizer_59',\n",
              " 'Verification Status_CharGramCountVectorizer_0',\n",
              " 'Verification Status_CharGramCountVectorizer_1',\n",
              " 'Verification Status_CharGramCountVectorizer_2',\n",
              " 'Collection Recovery Fee_MeanImputer',\n",
              " 'Debit to Income_MeanImputer',\n",
              " 'Funded Amount_MeanImputer',\n",
              " 'Funded Amount Investor_MeanImputer',\n",
              " 'Home Ownership_MeanImputer',\n",
              " 'Interest Rate_MeanImputer',\n",
              " 'Last week Pay_MeanImputer',\n",
              " 'Loan Amount_MeanImputer',\n",
              " 'Loan Title_MeanImputer',\n",
              " 'Recoveries_MeanImputer',\n",
              " 'Revolving Balance_MeanImputer',\n",
              " 'Revolving Utilities_MeanImputer',\n",
              " 'Total Accounts_MeanImputer',\n",
              " 'Total Collection Amount_MeanImputer',\n",
              " 'Total Current Balance_MeanImputer',\n",
              " 'Total Received Interest_MeanImputer',\n",
              " 'Total Received Late Fee_MeanImputer',\n",
              " 'Total Revolving Credit Limit_MeanImputer']"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "fitted_model.named_steps['datatransformer'].get_engineered_feature_names()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "gather": {
          "logged": 1643808912846
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      },
      "outputs": [
        {
          "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>RawFeatureName</th>\n",
              "      <th>TypeDetected</th>\n",
              "      <th>Dropped</th>\n",
              "      <th>EngineeredFeatureCount</th>\n",
              "      <th>Transformations</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>Application Type</td>\n",
              "      <td>Categorical</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
              "      <td>[ModeCatImputer-StringCast-LabelEncoder]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>Batch Enrolled</td>\n",
              "      <td>Categorical</td>\n",
              "      <td>No</td>\n",
              "      <td>41</td>\n",
              "      <td>[StringCast-CharGramCountVectorizer]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>Collection 12 months Medical</td>\n",
              "      <td>Categorical</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
              "      <td>[ModeCatImputer-StringCast-LabelEncoder]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>Delinquency - two years</td>\n",
              "      <td>Categorical</td>\n",
              "      <td>No</td>\n",
              "      <td>9</td>\n",
              "      <td>[StringCast-CharGramCountVectorizer]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>Employment Duration</td>\n",
              "      <td>Categorical</td>\n",
              "      <td>No</td>\n",
              "      <td>3</td>\n",
              "      <td>[StringCast-CharGramCountVectorizer]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>Grade</td>\n",
              "      <td>Categorical</td>\n",
              "      <td>No</td>\n",
              "      <td>7</td>\n",
              "      <td>[StringCast-CharGramCountVectorizer]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>Initial List Status</td>\n",
              "      <td>Categorical</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
              "      <td>[ModeCatImputer-StringCast-LabelEncoder]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>Inquires - six months</td>\n",
              "      <td>Categorical</td>\n",
              "      <td>No</td>\n",
              "      <td>6</td>\n",
              "      <td>[StringCast-CharGramCountVectorizer]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>Open Account</td>\n",
              "      <td>Categorical</td>\n",
              "      <td>No</td>\n",
              "      <td>36</td>\n",
              "      <td>[StringCast-CharGramCountVectorizer]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>Public Record</td>\n",
              "      <td>Categorical</td>\n",
              "      <td>No</td>\n",
              "      <td>5</td>\n",
              "      <td>[StringCast-CharGramCountVectorizer]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>Sub Grade</td>\n",
              "      <td>Categorical</td>\n",
              "      <td>No</td>\n",
              "      <td>33</td>\n",
              "      <td>[StringCast-CharGramCountVectorizer]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>Term</td>\n",
              "      <td>Categorical</td>\n",
              "      <td>No</td>\n",
              "      <td>3</td>\n",
              "      <td>[StringCast-CharGramCountVectorizer]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>Verification Status</td>\n",
              "      <td>Categorical</td>\n",
              "      <td>No</td>\n",
              "      <td>3</td>\n",
              "      <td>[StringCast-CharGramCountVectorizer]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>Collection Recovery Fee</td>\n",
              "      <td>Numeric</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
              "      <td>[MeanImputer]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>Debit to Income</td>\n",
              "      <td>Numeric</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
              "      <td>[MeanImputer]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>Funded Amount</td>\n",
              "      <td>Numeric</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
              "      <td>[MeanImputer]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>Funded Amount Investor</td>\n",
              "      <td>Numeric</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
              "      <td>[MeanImputer]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17</td>\n",
              "      <td>Home Ownership</td>\n",
              "      <td>Numeric</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
              "      <td>[MeanImputer]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18</td>\n",
              "      <td>Interest Rate</td>\n",
              "      <td>Numeric</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
              "      <td>[MeanImputer]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19</td>\n",
              "      <td>Last week Pay</td>\n",
              "      <td>Numeric</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
              "      <td>[MeanImputer]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>Loan Amount</td>\n",
              "      <td>Numeric</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
              "      <td>[MeanImputer]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>21</td>\n",
              "      <td>Loan Title</td>\n",
              "      <td>Numeric</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
              "      <td>[MeanImputer]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>22</td>\n",
              "      <td>Recoveries</td>\n",
              "      <td>Numeric</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
              "      <td>[MeanImputer]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>23</td>\n",
              "      <td>Revolving Balance</td>\n",
              "      <td>Numeric</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
              "      <td>[MeanImputer]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>24</td>\n",
              "      <td>Revolving Utilities</td>\n",
              "      <td>Numeric</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
              "      <td>[MeanImputer]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>25</td>\n",
              "      <td>Total Accounts</td>\n",
              "      <td>Numeric</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
              "      <td>[MeanImputer]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>26</td>\n",
              "      <td>Total Collection Amount</td>\n",
              "      <td>Numeric</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
              "      <td>[MeanImputer]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>27</td>\n",
              "      <td>Total Current Balance</td>\n",
              "      <td>Numeric</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
              "      <td>[MeanImputer]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>28</td>\n",
              "      <td>Total Received Interest</td>\n",
              "      <td>Numeric</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
              "      <td>[MeanImputer]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>29</td>\n",
              "      <td>Total Received Late Fee</td>\n",
              "      <td>Numeric</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
              "      <td>[MeanImputer]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>Total Revolving Credit Limit</td>\n",
              "      <td>Numeric</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
              "      <td>[MeanImputer]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  RawFeatureName TypeDetected Dropped  EngineeredFeatureCount  \\\n",
              "0               Application Type  Categorical      No                       1   \n",
              "1                 Batch Enrolled  Categorical      No                      41   \n",
              "2   Collection 12 months Medical  Categorical      No                       1   \n",
              "3        Delinquency - two years  Categorical      No                       9   \n",
              "4            Employment Duration  Categorical      No                       3   \n",
              "5                          Grade  Categorical      No                       7   \n",
              "6            Initial List Status  Categorical      No                       1   \n",
              "7          Inquires - six months  Categorical      No                       6   \n",
              "8                   Open Account  Categorical      No                      36   \n",
              "9                  Public Record  Categorical      No                       5   \n",
              "10                     Sub Grade  Categorical      No                      33   \n",
              "11                          Term  Categorical      No                       3   \n",
              "12           Verification Status  Categorical      No                       3   \n",
              "13       Collection Recovery Fee      Numeric      No                       1   \n",
              "14               Debit to Income      Numeric      No                       1   \n",
              "15                 Funded Amount      Numeric      No                       1   \n",
              "16        Funded Amount Investor      Numeric      No                       1   \n",
              "17                Home Ownership      Numeric      No                       1   \n",
              "18                 Interest Rate      Numeric      No                       1   \n",
              "19                 Last week Pay      Numeric      No                       1   \n",
              "20                   Loan Amount      Numeric      No                       1   \n",
              "21                    Loan Title      Numeric      No                       1   \n",
              "22                    Recoveries      Numeric      No                       1   \n",
              "23             Revolving Balance      Numeric      No                       1   \n",
              "24           Revolving Utilities      Numeric      No                       1   \n",
              "25                Total Accounts      Numeric      No                       1   \n",
              "26       Total Collection Amount      Numeric      No                       1   \n",
              "27         Total Current Balance      Numeric      No                       1   \n",
              "28       Total Received Interest      Numeric      No                       1   \n",
              "29       Total Received Late Fee      Numeric      No                       1   \n",
              "30  Total Revolving Credit Limit      Numeric      No                       1   \n",
              "\n",
              "                             Transformations  \n",
              "0   [ModeCatImputer-StringCast-LabelEncoder]  \n",
              "1       [StringCast-CharGramCountVectorizer]  \n",
              "2   [ModeCatImputer-StringCast-LabelEncoder]  \n",
              "3       [StringCast-CharGramCountVectorizer]  \n",
              "4       [StringCast-CharGramCountVectorizer]  \n",
              "5       [StringCast-CharGramCountVectorizer]  \n",
              "6   [ModeCatImputer-StringCast-LabelEncoder]  \n",
              "7       [StringCast-CharGramCountVectorizer]  \n",
              "8       [StringCast-CharGramCountVectorizer]  \n",
              "9       [StringCast-CharGramCountVectorizer]  \n",
              "10      [StringCast-CharGramCountVectorizer]  \n",
              "11      [StringCast-CharGramCountVectorizer]  \n",
              "12      [StringCast-CharGramCountVectorizer]  \n",
              "13                             [MeanImputer]  \n",
              "14                             [MeanImputer]  \n",
              "15                             [MeanImputer]  \n",
              "16                             [MeanImputer]  \n",
              "17                             [MeanImputer]  \n",
              "18                             [MeanImputer]  \n",
              "19                             [MeanImputer]  \n",
              "20                             [MeanImputer]  \n",
              "21                             [MeanImputer]  \n",
              "22                             [MeanImputer]  \n",
              "23                             [MeanImputer]  \n",
              "24                             [MeanImputer]  \n",
              "25                             [MeanImputer]  \n",
              "26                             [MeanImputer]  \n",
              "27                             [MeanImputer]  \n",
              "28                             [MeanImputer]  \n",
              "29                             [MeanImputer]  \n",
              "30                             [MeanImputer]  "
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "featurization_summay = fitted_model.named_steps['datatransformer'].get_featurization_summary()\n",
        "pd.DataFrame.from_records(featurization_summay)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      },
      "outputs": [
        {
          "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>automl_loan_approval_prediction</td><td>AutoML_9301b394-5cc4-49a7-beee-c3f879f52408</td><td>automl</td><td>Completed</td><td><a href=\"https://ml.azure.com/runs/AutoML_9301b394-5cc4-49a7-beee-c3f879f52408?wsid=/subscriptions/8c96f3ed-e53f-4400-a002-420605371b18/resourcegroups/Azure_Project/workspaces/FRT_Project&amp;tid=3e3dbbae-d08e-4f6e-9f10-b5cf8e8f3231\" 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": [
              "Run(Experiment: automl_loan_approval_prediction,\n",
              "Id: AutoML_9301b394-5cc4-49a7-beee-c3f879f52408,\n",
              "Type: automl,\n",
              "Status: Completed)"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "run_id = \"AutoML_9301b394-5cc4-49a7-beee-c3f879f52408\"\n",
        "training_run = AutoMLRun(experiment,run_id)\n",
        "training_run"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {},
      "outputs": [
        {
          "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>automl_loan_approval_prediction</td><td>AutoML_9301b394-5cc4-49a7-beee-c3f879f52408_16</td><td></td><td>Completed</td><td><a href=\"https://ml.azure.com/runs/AutoML_9301b394-5cc4-49a7-beee-c3f879f52408_16?wsid=/subscriptions/8c96f3ed-e53f-4400-a002-420605371b18/resourcegroups/Azure_Project/workspaces/FRT_Project&amp;tid=3e3dbbae-d08e-4f6e-9f10-b5cf8e8f3231\" target=\"_blank\" rel=\"noopener\">Link to Azure Machine Learning studio</a></td><td><a href=\"https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.run.Run?view=azure-ml-py\" target=\"_blank\" rel=\"noopener\">Link to Documentation</a></td></tr></table>"
            ],
            "text/plain": [
              "Run(Experiment: automl_loan_approval_prediction,\n",
              "Id: AutoML_9301b394-5cc4-49a7-beee-c3f879f52408_16,\n",
              "Type: None,\n",
              "Status: Completed)"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "best_run"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'AutoML9301b394516'"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model_name = best_run.properties[\"model_name\"]\n",
        "model_name"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {},
      "outputs": [],
      "source": [
        "script_file_name = \"inference/score.py\"\n",
        "best_run.download_file(\"outputs/scoring_file_v_1_0_0.py\", \"inference/score.py\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "AutoML9301b394516\n"
          ]
        }
      ],
      "source": [
        "description = \"AutoML Model trained on bank data to predict whether to give out loan or not to a perticular person or entity.\"\n",
        "model = remote_run.register_model(model_name=model_name, \n",
        "                                  description=description, \n",
        "                                  tags=None)\n",
        "print(remote_run.model_id)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Name: AutoML9301b394516\n",
            "Version: 18\n"
          ]
        }
      ],
      "source": [
        "from azureml.core import Model\n",
        "\n",
        "print('Name:', model.name)\n",
        "print('Version:', model.version)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found existing cluster, use it.\n"
          ]
        }
      ],
      "source": [
        "from azureml.core.compute import ComputeTarget\n",
        "from azureml.core.compute import AksCompute\n",
        "from azureml.core.compute_target import ComputeTargetException\n",
        "\n",
        "# Choose a name for your AKS cluster\n",
        "aks_name = 'automl-aks'\n",
        "\n",
        "# Verify that cluster does not exist already\n",
        "try:\n",
        "    aks_target = ComputeTarget(workspace=ws, name=aks_name)\n",
        "    print('Found existing cluster, use it.')\n",
        "except ComputeTargetException:\n",
        "    # Use the default configuration (can also provide parameters to customize)\n",
        "    prov_config = prov_config = AksCompute.provisioning_configuration(vm_size = \"Standard_D2_v2\",\n",
        "                                                                      location = \"Central US\")\n",
        "\n",
        "    # Create the cluster\n",
        "    aks_target = ComputeTarget.create(workspace = ws, \n",
        "                                    name = aks_name, \n",
        "                                    provisioning_configuration = prov_config)\n",
        "\n",
        "if aks_target.get_status() != \"Succeeded\":\n",
        "    aks_target.wait_for_completion(show_output=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "AksCompute(workspace=Workspace.create(name='FRT_Project', subscription_id='8c96f3ed-e53f-4400-a002-420605371b18', resource_group='Azure_Project'), name=automl-aks, id=/subscriptions/8c96f3ed-e53f-4400-a002-420605371b18/resourceGroups/Azure_Project/providers/Microsoft.MachineLearningServices/workspaces/FRT_Project/computes/automl-aks, type=AKS, provisioning_state=Succeeded, location=westus, tags={})"
            ]
          },
          "execution_count": 64,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "aks_target"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Succeeded\n",
            "None\n"
          ]
        }
      ],
      "source": [
        "# aks_target.wait_for_completion(show_output = True)\n",
        "print(aks_target.provisioning_state)\n",
        "print(aks_target.provisioning_errors)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.webservice import Webservice, AksWebservice\n",
        "# Set the web service configuration (using default here)\n",
        "aks_config = AksWebservice.deploy_configuration()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.model import InferenceConfig\n",
        "from azureml.core.environment import Environment\n",
        "from azureml.automl.core.shared import constants\n",
        "best_run.download_file(constants.CONDA_ENV_FILE_PATH, 'myenv.yml')\n",
        "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n",
        "inference_config = InferenceConfig(entry_script=\"inference/score.py\", environment=myenv)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Tips: You can try get_logs(): https://aka.ms/debugimage#dockerlog or local deployment: https://aka.ms/debugimage#debug-locally to debug if deployment takes longer than 10 minutes.\n",
            "Running\n",
            "2022-02-04 00:03:38+05:30 Creating Container Registry if not exists.\n",
            "2022-02-04 00:03:38+05:30 Registering the environment.\n",
            "2022-02-04 00:03:40+05:30 Use the existing image.\n",
            "2022-02-04 00:03:42+05:30 Creating resources in AKS.\n",
            "2022-02-04 00:03:43+05:30 Submitting deployment to compute.\n",
            "2022-02-04 00:03:43+05:30 Checking the status of deployment aks-service..\n",
            "2022-02-04 00:07:02+05:30 Checking the status of inference endpoint aks-service.\n",
            "Succeeded\n",
            "AKS service creation operation finished, operation \"Succeeded\"\n",
            "Healthy\n",
            "Wall time: 4min 3s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "aks_service_name ='aks-service'\n",
        "\n",
        "aks_service = Model.deploy(workspace=ws,\n",
        "                           name=aks_service_name,\n",
        "                           models=[model],\n",
        "                           inference_config=inference_config,\n",
        "                           deployment_config=aks_config,\n",
        "                           deployment_target=aks_target)\n",
        "\n",
        "aks_service.wait_for_deployment(show_output = True)\n",
        "print(aks_service.state)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'http://138.91.136.177:80/api/v1/service/aks-service/score'"
            ]
          },
          "execution_count": 71,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "aks_service.scoring_uri"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "interpreter": {
      "hash": "ecf5722fdaf1897a315d257d89d94520bfcaa453217d5becf09b39e73618b0de"
    },
    "kernel_info": {
      "name": "python3-azureml"
    },
    "kernelspec": {
      "display_name": "Python 3.6 - AzureML",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.11"
    },
    "nteract": {
      "version": "nteract-front-end@1.0.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
}