- from d3m import index
- from d3m.metadata.base import ArgumentType
- from d3m.metadata.pipeline import Pipeline, PrimitiveStep
-
- # -> 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
- step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_parser'))
- 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: extract_columns_by_semantic_types(attributes)
- step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types'))
- step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
- step_2.add_output('produce')
- step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE,
- data=['https://metadata.datadrivendiscovery.org/types/Attribute'])
- pipeline_description.add_step(step_2)
-
- # Step 3: extract_columns_by_semantic_types(targets)
- step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types'))
- step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
- step_3.add_output('produce')
- step_3.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE,
- data=['https://metadata.datadrivendiscovery.org/types/TrueTarget'])
- pipeline_description.add_step(step_3)
-
- attributes = 'steps.2.produce'
- targets = 'steps.3.produce'
-
- # Step 4: imputer
- step_4 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.impute_missing'))
- step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference=attributes)
- step_4.add_output('produce')
- pipeline_description.add_step(step_4)
-
- # Step 5: holt winters exponential smoothing
- step_5 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.holt_winters_exponential_smoothing'))
- step_5.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference=attributes)
- step_5.add_hyperparameter(name="use_columns", argument_type=ArgumentType.VALUE, data = (2, 3))
- step_5.add_hyperparameter(name="use_semantic_types", argument_type=ArgumentType.VALUE, data = True)
- step_5.add_output('produce')
- pipeline_description.add_step(step_5)
-
- # Step 6: isolation forest
- #step_6 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.anomaly_detection.isolation_forest.Algorithm'))
- #step_6.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.5.produce')
- #step_6.add_argument(name='outputs', argument_type=ArgumentType.CONTAINER, data_reference=targets)
- #step_6.add_output('produce')
- #pipeline_description.add_step(step_6)
-
- # Final Output
- pipeline_description.add_output(name='output predictions', data_reference='steps.5.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()
-
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