OUTPUT_DIR = './outputs/'
os.makedirs(OUTPUT_DIR, exist_ok=True)
# Get workspace from the run context
run = Run.get_context()
ws = run.experiment.workspace
# Get the AutoML run object from the experiment name and the workspace
experiment = Experiment(ws, '<<experiment_name>>')
automl_run = Run(experiment=experiment, run_id='<<run_id>>')
# Check if this AutoML model is explainable
if not automl_check_model_if_explainable(automl_run):
raise Exception("Model explanations is currently not supported for " + automl_run.get_properties().get(
'run_algorithm'))
# Download the best model from the artifact store
automl_run.download_file(name=MODEL_PATH, output_file_path='model.pkl')
# Load the AutoML model into memory
fitted_model = joblib.load('model.pkl')
# Get the train dataset from the workspace
train_dataset = Dataset.get_by_name(workspace=ws, name='<<train_dataset_name>>')
# Drop the lablled column to get the training set.
X_train = train_dataset.drop_columns(columns=['<<target_column_name>>'])
y_train = train_dataset.keep_columns(columns=['<<target_column_name>>'], validate=True)
# Get the train dataset from the workspace
test_dataset = Dataset.get_by_name(workspace=ws, name='<<test_dataset_name>>')
OUTPUT_DIR = './outputs/'
os.makedirs(OUTPUT_DIR, exist_ok=True)
# Get workspace from the run context
run = Run.get_context()
ws = run.experiment.workspace
# Get the AutoML run object from the experiment name and the workspace
experiment = Experiment(ws, '<<experiment_name>>')
automl_run = Run(experiment=experiment, run_id='<<run_id>>')
# Check if this AutoML model is explainable
if not automl_check_model_if_explainable(automl_run):
raise Exception("Model explanations is currently not supported for " +
automl_run.get_properties().get('run_algorithm'))
# Download the best model from the artifact store
automl_run.download_file(name=MODEL_PATH, output_file_path='model.pkl')
# Load the AutoML model into memory
fitted_model = joblib.load('model.pkl')
# Get the train dataset from the workspace
train_dataset = Dataset.get_by_name(workspace=ws,
name='<<train_dataset_name>>')
# Drop the lablled column to get the training set.
X_train = train_dataset.drop_columns(columns=['<<target_column_name>>'])
y_train = train_dataset.keep_columns(columns=['<<target_column_name>>'],
validate=True)
OUTPUT_DIR = "./outputs/"
os.makedirs(OUTPUT_DIR, exist_ok=True)
# Get workspace from the run context
run = Run.get_context()
ws = run.experiment.workspace
# Get the AutoML run object from the experiment name and the workspace
experiment = Experiment(ws, "<<experiment_name>>")
automl_run = Run(experiment=experiment, run_id="<<run_id>>")
# Check if this AutoML model is explainable
if not automl_check_model_if_explainable(automl_run):
raise Exception("Model explanations are currently not supported for " +
automl_run.get_properties().get("run_algorithm"))
# Download the best model from the artifact store
automl_run.download_file(name=MODEL_PATH, output_file_path="model.pkl")
# Load the AutoML model into memory
fitted_model = joblib.load("model.pkl")
# Get the train dataset from the workspace
train_dataset = Dataset.get_by_name(workspace=ws,
name="<<train_dataset_name>>")
# Drop the labeled column to get the training set.
X_train = train_dataset.drop_columns(columns=["<<target_column_name>>"])
y_train = train_dataset.keep_columns(columns=["<<target_column_name>>"],
validate=True)
