def generate_visualization(viz_params: dict): #pylint: disable=redefined-outer-name
"""Generates the visualization object.
Returns:
output_dict : output dict of vizualization obj.
"""
viz_obj = Visualization(
mlpipeline_ui_metadata=viz_params["mlpipeline_ui_metadata"],
mlpipeline_metrics=viz_params["mlpipeline_metrics"],
confusion_matrix_dict=viz_params["confusion_matrix_dict"],
test_accuracy=viz_params["test_accuracy"],
markdown=viz_params["markdown"],
)
return viz_obj.output_dict
run_code = subprocess.run(entry_point, stdout=subprocess.PIPE)
print(run_code.stdout)
visualization_arguments = {
"output": {
"mlpipeline_ui_metadata": args["mlpipeline_ui_metadata"],
"dataset_download_path": args["output_data"],
},
}
markdown_dict = {"storage": "inline", "source": visualization_arguments}
print("Visualization arguments: ", markdown_dict)
visualization = Visualization(
mlpipeline_ui_metadata=args["mlpipeline_ui_metadata"],
markdown=markdown_dict,
)
y_array = np.array(y)
label_names = [
"airplane",
"automobile",
"bird",
"cat",
"deer",
"dog",
"frog",
"horse",
"ship",
"truck",
fp.write(json.dumps(data))
visualization_arguments = {
"input": {
"tensorboard_root": TENSORBOARD_ROOT,
"checkpoint_dir": CHECKPOINT_DIR,
"dataset_path": DATASET_PATH,
"model_name": script_dict["model_name"],
"confusion_matrix_url": script_dict["confusion_matrix_url"],
},
"output": {
"mlpipeline_ui_metadata": args["mlpipeline_ui_metadata"],
"mlpipeline_metrics": args["mlpipeline_metrics"],
},
}
markdown_dict = {"storage": "inline", "source": visualization_arguments}
print("Visualization Arguments: ", markdown_dict)
visualization = Visualization(
test_accuracy=test_accuracy,
confusion_matrix_dict=confusion_matrix_dict,
mlpipeline_ui_metadata=args["mlpipeline_ui_metadata"],
mlpipeline_metrics=args["mlpipeline_metrics"],
markdown=markdown_dict,
)
checpoint_dir_contents = os.listdir(CHECKPOINT_DIR)
print(f"Checkpoint Directory Contents: {checpoint_dir_contents}")