tods/primitive_tests/build_MatrixProfile_pipeline.py

from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep
from d3m.metadata import hyperparams

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
#                                             extract_columns_by_semantic_types(targets)    ->            ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# # Step 2: test primitive
primitive_2 = index.get_primitive('d3m.primitives.tods.detection_algorithm.matrix_profile')

step_2 = PrimitiveStep(primitive=primitive_2)
step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,)) # There is sth wrong with multi-dimensional
step_2.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=3) # There is sth wrong with multi-dimensional
step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
pipeline_description.add_step(step_2)

# # Final Output
pipeline_description.add_output(name='output predictions', data_reference='steps.2.produce')

# # Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
    f.write(yaml)
print(yaml)

# Or you can output json
#data = pipline_description.to_json()