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56 lines
2.0 KiB
Python
56 lines
2.0 KiB
Python
import pandas as pd
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from azureml.core import Environment
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from azureml.train.estimator import Estimator
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from azureml.core.run import Run
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def run_inference(test_experiment, compute_target, script_folder, train_run,
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test_dataset, target_column_name, model_name):
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inference_env = train_run.get_environment()
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est = Estimator(source_directory=script_folder,
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entry_script='infer.py',
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script_params={
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'--target_column_name': target_column_name,
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'--model_name': model_name
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},
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inputs=[
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test_dataset.as_named_input('test_data')
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],
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compute_target=compute_target,
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environment_definition=inference_env)
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run = test_experiment.submit(
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est, tags={
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'training_run_id': train_run.id,
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'run_algorithm': train_run.properties['run_algorithm'],
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'valid_score': train_run.properties['score'],
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'primary_metric': train_run.properties['primary_metric']
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})
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run.log("run_algorithm", run.tags['run_algorithm'])
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return run
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def get_result_df(remote_run):
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children = list(remote_run.get_children(recursive=True))
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summary_df = pd.DataFrame(index=['run_id', 'run_algorithm',
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'primary_metric', 'Score'])
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goal_minimize = False
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for run in children:
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if('run_algorithm' in run.properties and 'score' in run.properties):
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summary_df[run.id] = [run.id, run.properties['run_algorithm'],
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run.properties['primary_metric'],
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float(run.properties['score'])]
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if('goal' in run.properties):
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goal_minimize = run.properties['goal'].split('_')[-1] == 'min'
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summary_df = summary_df.T.sort_values(
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'Score',
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ascending=goal_minimize).drop_duplicates(['run_algorithm'])
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summary_df = summary_df.set_index('run_algorithm')
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return summary_df
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