update the example

Author: Ziqun Ye

docs/source/user_guide/model_registration/frameworks/automlmodel.rst

@@ -83,7 +83,7 @@ Example
 
 .. code-block:: python3
 
-  import pandas as pd
+    import pandas as pd
     import numpy as np
     import tempfile
     from sklearn.metrics import roc_auc_score, confusion_matrix, make_scorer, f1_score

docs/source/user_guide/model_registration/quick_start.rst

@@ -257,6 +257,66 @@ Create a model, prepare it, verify that it works, save it to the model catalog,
     #Register TensorFlow model
     model_id = tf_model.save(display_name="TensorFlow Model")
 
+AutoMLx Frameworks
+------------------
+
+.. code-block:: python3
+
+    import pandas as pd
+    import numpy as np
+    import tempfile
+    from sklearn.metrics import roc_auc_score, confusion_matrix, make_scorer, f1_score
+    from sklearn.linear_model import LogisticRegression
+    from sklearn.compose import make_column_selector as selector
+    from sklearn.impute import SimpleImputer
+    from sklearn.preprocessing import StandardScaler, OneHotEncoder
+    from sklearn.compose import ColumnTransformer
+    from sklearn.pipeline import Pipeline
+    from sklearn.datasets import fetch_openml
+    from sklearn.model_selection import train_test_split
+
+    import ads
+    import automl
+    from automl import init
+    from ads.model import AutoMLModel
+    from ads.common.model_metadata import UseCaseType
+    from ads.model.framework.automl_model import AutoMLModel
+
+    dataset = fetch_openml(name='adult', as_frame=True)
+    df, y = dataset.data, dataset.target
+
+    # Several of the columns are incorrectly labeled as category type in the original dataset
+    numeric_columns = ['age', 'capitalgain', 'capitalloss', 'hoursperweek']
+    for col in df.columns:
+        if col in numeric_columns:
+            df[col] = df[col].astype(int)
+        
+
+    X_train, X_test, y_train, y_test = train_test_split(df,
+                                                        y.map({'>50K': 1, '<=50K': 0}).astype(int),
+                                                        train_size=0.7,
+                                                        random_state=0)
+
+    init(engine='local')
+    est = automl.Pipeline(task='classification')
+    est.fit(X_train, y_train)
+
+    ads.set_auth("resource_principal")
+    artifact_dir = tempfile.mkdtemp()
+    automl_model = AutoMLModel(estimator=model, artifact_dir=artifact_dir)
+    automl_model.prepare(inference_conda_env="automlx_p38_cpu_v1",
+                         training_conda_env="automlx_p38_cpu_v1",
+                         use_case_type=UseCaseType.BINARY_CLASSIFICATION,
+                         X_sample=X_test,
+                         force_overwrite=True)
+    automl_model.verify(X_test.iloc[:2])
+    model_id = automl_model.save(display_name='Demo AutoMLModel model')
+    deploy = automl_model.deploy(display_name='Demo AutoMLModel deployment')
+    automl_model.predict(X_test.iloc[:2])
+    automl_model.delete_deployment(wait_for_completion=True)
+    ModelCatalog(compartment_id=os.environ['NB_SESSION_COMPARTMENT_OCID']).delete_model(model_id)
+
+
 Other Frameworks
 ----------------
 

docs/source/user_guide/model_serialization/automlmodel.rst

@@ -183,7 +183,7 @@ Example
 
 .. code-block:: python3
 
-  import pandas as pd
+    import pandas as pd
     import numpy as np
     import tempfile
     from sklearn.metrics import roc_auc_score, confusion_matrix, make_scorer, f1_score