Getting Started

In this document, we provide some toy examples for getting started. All the examples in this document and even more examples are available in examples.

Outlier Detection with Autoencoder on NAB Dataset

To perform the point-wise outlier detection on NAB dataset. We provide an example to construct such pipeline description:

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

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

# Step 0: dataset_to_dataframe
step_0 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.data_transformation.dataset_to_dataframe.Common'))
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.data_transformation.column_parser.Common'))
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.data_transformation.extract_columns_by_semantic_types.Common'))
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.data_transformation.extract_columns_by_semantic_types.Common'))
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: processing
step_4 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.axiswise_scaler'))
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: algorithm
step_5 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.detection_algorithm.pyod_ae'))
step_5.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.4.produce')
step_5.add_output('produce')
pipeline_description.add_step(step_5)

 # Step 6: Predictions
step_6 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.data_transformation.construct_predictions.Common'))
step_6.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.5.produce')
step_6.add_argument(name='reference', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_6.add_output('produce')
pipeline_description.add_step(step_6)

# Final Output
pipeline_description.add_output(name='output predictions', data_reference='steps.6.produce')

# Output to json
data = pipeline_description.to_json()
with open('example_pipeline.json', 'w') as f:
    f.write(data)
    print(data)

Note that, in order to call each primitive during pipeline construction, one may find the index (python_path) of primitives available in entry_points.ini.

The output description json file (example_pipeline.json) should look like something as follows:

{
"id": "e39bf406-06cf-4c76-88f0-8c8b4447e311",
"schema": "https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json",
"created": "2020-09-15T07:26:48.365447Z",
"inputs": [{"name": "inputs"}],
"outputs": [{"data": "steps.6.produce", "name": "output predictions"}],
"steps": [
    {"type": "PRIMITIVE", "primitive": {"id": "4b42ce1e-9b98-4a25-b68e-fad13311eb65", "version": "0.3.0", "python_path": "d3m.primitives.data_transformation.dataset_to_dataframe.Common", "name": "Extract a DataFrame from a Dataset", "digest": "a7f5a8f8b276f474c3b40b025d157541de898e4e02555cd8ef76fdeecfbed256"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "inputs.0"}}, "outputs": [{"id": "produce"}]},
    {"type": "PRIMITIVE", "primitive": {"id": "d510cb7a-1782-4f51-b44c-58f0236e47c7", "version": "0.6.0", "python_path": "d3m.primitives.data_transformation.column_parser.Common", "name": "Parses strings into their types", "digest": "eccfd70ed359901a625dbde6de40d6bbb4e69d9796ee0ca3a302fd95195451ed"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.0.produce"}}, "outputs": [{"id": "produce"}]},
    {"type": "PRIMITIVE", "primitive": {"id": "4503a4c6-42f7-45a1-a1d4-ed69699cf5e1", "version": "0.4.0", "python_path": "d3m.primitives.data_transformation.extract_columns_by_semantic_types.Common", "name": "Extracts columns by semantic type", "digest": "9f0303c354df6cec4df7bda0ebb46fb4f101c36ad9a4d1143b9b9c88004629aa"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.1.produce"}}, "outputs": [{"id": "produce"}], "hyperparams": {"semantic_types": {"type": "VALUE", "data": ["https://metadata.datadrivendiscovery.org/types/Attribute"]}}},
    {"type": "PRIMITIVE", "primitive": {"id": "4503a4c6-42f7-45a1-a1d4-ed69699cf5e1", "version": "0.4.0", "python_path": "d3m.primitives.data_transformation.extract_columns_by_semantic_types.Common", "name": "Extracts columns by semantic type", "digest": "9f0303c354df6cec4df7bda0ebb46fb4f101c36ad9a4d1143b9b9c88004629aa"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.0.produce"}}, "outputs": [{"id": "produce"}], "hyperparams": {"semantic_types": {"type": "VALUE", "data": ["https://metadata.datadrivendiscovery.org/types/TrueTarget"]}}},
    {"type": "PRIMITIVE", "primitive": {"id": "642de2e7-5590-3cab-9266-2a53c326c461", "version": "0.0.1", "python_path": "d3m.primitives.tods.timeseries_processing.transformation.axiswise_scaler", "name": "Axis_wise_scale"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.2.produce"}}, "outputs": [{"id": "produce"}]},
    {"type": "PRIMITIVE", "primitive": {"id": "67e7fcdf-d645-3417-9aa4-85cd369487d9", "version": "0.0.1", "python_path": "d3m.primitives.tods.detection_algorithm.pyod_ae", "name": "TODS.anomaly_detection_primitives.AutoEncoder"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.4.produce"}}, "outputs": [{"id": "produce"}]},
    {"type": "PRIMITIVE", "primitive": {"id": "8d38b340-f83f-4877-baaa-162f8e551736", "version": "0.3.0", "python_path": "d3m.primitives.data_transformation.construct_predictions.Common", "name": "Construct pipeline predictions output", "digest": "6de56912a3f84bbbcc0d1f7ffe646044209120e45bbb21a137236d00fed948e9"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.5.produce"}, "reference": {"type": "CONTAINER", "data": "steps.1.produce"}}, "outputs": [{"id": "produce"}]}],
"digest": "8c6a37e7ac9ef1b302810e56dffa43c3415826ab756ef6917d76dd8ee63d38fc"
}

With the pre-built pipeline description file, we can then feed the NAB data (twitter_IBM) and specify the desired evaluation metric with the path of pipeline description file with run_pipeline.py.

python examples/run_pipeline.py --pipeline_path example_pipeline.json --table_path datasets/NAB/realTweets/labeled_Twitter_volume_IBM.csv --metric F1_MACRO --target_index 2