def create_pipeline_json(self, prim_dict):
"""
Generate pipeline.json
"""
name = "Pipeline for evaluation"
pipeline_id = self.id #+ "_" + str(self.rank)
pipeline_description = Pipeline(pipeline_id=pipeline_id, context=Context.EVALUATION, name=name)
for ip in self.inputs:
pipeline_description.add_input(name=ip['name'])
num = self.num_steps()
for i in range(num):
p = prim_dict[self.primitives[i]]
pdesc = {}
pdesc['id'] = p.id
pdesc['version'] = p.primitive_class.version
pdesc['python_path'] = p.primitive_class.python_path
pdesc['name'] = p.primitive_class.name
pdesc['digest'] = p.primitive_class.digest
step = PrimitiveStep(primitive_description=pdesc)
for name, value in self.primitives_arguments[i].items():
origin = value['origin']
argument_type = ArgumentType.CONTAINER
step.add_argument(name=name, argument_type=argument_type, data_reference=value['data'])
step.add_output(output_id=p.primitive_class.produce_methods[0])
if self.hyperparams[i] is not None:
for name, value in self.hyperparams[i].items():
step.add_hyperparameter(name=name, argument_type=ArgumentType.VALUE, data=value)
pipeline_description.add_step(step)
for op in self.outputs:
pipeline_description.add_output(data_reference=op[2], name=op[3])
self.pipeline_description = pipeline_description
def create_pipeline(metric: str) -> Pipeline:
# create the basic pipeline
kanine_pipeline = Pipeline(context=PipelineContext.TESTING)
kanine_pipeline.add_input(name='inputs')
# Denormalize so that we have a single dataframe in the dataset
step = PrimitiveStep(
primitive_description=DenormalizePrimitive.metadata.query())
step.add_argument(name='inputs',
argument_type=ArgumentType.CONTAINER,
data_reference='inputs.0')
step.add_output('produce')
kanine_pipeline.add_step(step)
# step 1 - kanine classification
step = PrimitiveStep(primitive_description=Kanine.metadata.query())
step.add_argument(name='inputs',
argument_type=ArgumentType.CONTAINER,
data_reference='steps.0.produce')
step.add_argument(name='outputs',
argument_type=ArgumentType.CONTAINER,
data_reference='steps.0.produce')
step.add_output('produce')
kanine_pipeline.add_step(step)
# Adding output step to the pipeline
kanine_pipeline.add_output(name='output', data_reference='steps.1.produce')
return kanine_pipeline
def create_pipeline(metric: str) -> Pipeline:
# create the basic pipeline
vertex_classification_pipeline = Pipeline(context=PipelineContext.TESTING)
vertex_classification_pipeline.add_input(name='inputs')
# step 0 - extract the graphs
step = PrimitiveStep(
primitive_description=VertexClassificationParser.metadata.query())
step.add_argument(name='inputs',
argument_type=ArgumentType.CONTAINER,
data_reference='inputs.0')
step.add_output('produce')
vertex_classification_pipeline.add_step(step)
# step 1 - classify
step = PrimitiveStep(
primitive_description=VertexClassification.metadata.query())
step.add_argument(name='inputs',
argument_type=ArgumentType.CONTAINER,
data_reference='steps.0.produce')
step.add_hyperparameter('jvm_memory', ArgumentType.VALUE, 0.6)
step.add_output('produce')
vertex_classification_pipeline.add_step(step)
# Adding output step to the pipeline
vertex_classification_pipeline.add_output(name='output',
data_reference='steps.1.produce')
return vertex_classification_pipeline
def _add_primitive_to_pipeline(pipeline_description,
primitive,
resolver,
attributes=None,
targets=None,
dataframe_step=None):
step_model = PrimitiveStep(primitive=primitive, resolver=resolver)
if dataframe_step is None:
step_model.add_argument(name='inputs',
argument_type=ArgumentType.CONTAINER,
data_reference=attributes)
step_model.add_argument(name='outputs',
argument_type=ArgumentType.CONTAINER,
data_reference=targets)
else:
last_step_idx = len(pipeline_description.steps) - 1
step_model.add_argument(
name='inputs',
argument_type=ArgumentType.CONTAINER,
data_reference=pipeline_utils.int_to_step(last_step_idx))
step_model.add_argument(name='reference',
argument_type=ArgumentType.CONTAINER,
data_reference=dataframe_step)
step_model.add_output('produce')
pipeline_description.add_step(step_model)
def add_primitive_to_pipeline(self,
primitive,
attributes,
hyperparameters=[],
targets=None,
produce_collection=False):
inputs_ref = attributes if isinstance(
attributes, str) else self.get_output_str(attributes)
step = PrimitiveStep(primitive=primitive, resolver=self.resolver)
step.add_argument(name='inputs',
argument_type=ArgumentType.CONTAINER,
data_reference=inputs_ref)
for hyperparam in hyperparameters:
name, argument_type, data = hyperparam
step.add_hyperparameter(name=name,
argument_type=argument_type,
data=data)
if targets:
outputs_ref = targets if isinstance(
targets, str) else self.get_output_str(targets)
step.add_argument(name='outputs',
argument_type=ArgumentType.CONTAINER,
data_reference=outputs_ref)
step.add_output('produce')
if produce_collection:
step.add_output('produce_collection')
self.pipeline.add_step(step)
return step
def set_prediction(pipeline_description):
pred = PrimitiveStep(
primitive_description=d3m.primitives.data_transformation.
construct_predictions.Common.metadata.query())
pred.add_argument(
name='inputs',
argument_type=ArgumentType.CONTAINER,
data_reference=f"steps.{len(pipeline_description.steps) - 1}.produce")
pred.add_argument(name='reference',
argument_type=ArgumentType.CONTAINER,
data_reference='steps.{}.produce'.format(IP_STEP))
pred.add_output('produce')
pipeline_description.add_step(pred)
def __init__(self):
pipeline_description = Pipeline()
pipeline_description.add_input(name="inputs")
# Ts formatter
step = PrimitiveStep(primitive=index.get_primitive(
"d3m.primitives.data_transformation.time_series_formatter.DistilTimeSeriesFormatter"
))
step.add_argument(
name="inputs",
argument_type=ArgumentType.CONTAINER,
data_reference="inputs.0",
)
step.add_output("produce")
pipeline_description.add_step(step)
# DS to DF on formatted ts DS
step = PrimitiveStep(primitive=index.get_primitive(
"d3m.primitives.data_transformation.dataset_to_dataframe.Common"))
step.add_argument(
name="inputs",
argument_type=ArgumentType.CONTAINER,
data_reference="steps.0.produce",
)
step.add_output("produce")
pipeline_description.add_step(step)
# Grouping Field Compose
step = PrimitiveStep(primitive=index.get_primitive(
"d3m.primitives.data_transformation.grouping_field_compose.Common")
)
step.add_argument(
name="inputs",
argument_type=ArgumentType.CONTAINER,
data_reference="steps.1.produce",
)
step.add_output("produce")
pipeline_description.add_step(step)
# Storc primitive -> KMeans
step = PrimitiveStep(primitive=index.get_primitive(
"d3m.primitives.clustering.k_means.Sloth"))
step.add_argument(
name="inputs",
argument_type=ArgumentType.CONTAINER,
data_reference="steps.2.produce",
)
step.add_hyperparameter(name="nclusters",
argument_type=ArgumentType.VALUE,
data=3)
step.add_output("produce")
pipeline_description.add_step(step)
# Final Output
pipeline_description.add_output(name="output predictions",
data_reference="steps.3.produce")
self.pipeline = pipeline_description
def set_learner(pipeline_description, batch_size=BATCH_SIZE):
learner_idx = len(pipeline_description.steps)
step = PrimitiveStep(primitive_description=d3m.primitives.learner.model.
KerasWrap.metadata.query())
step.add_hyperparameter(name='loss',
argument_type=ArgumentType.PRIMITIVE,
data=LOSS_SETUP_IDX)
step.add_hyperparameter(name='model_type',
argument_type=ArgumentType.VALUE,
data='classification')
step.add_hyperparameter(name='network_last_layer',
argument_type=ArgumentType.PRIMITIVE,
data=learner_idx - 1)
step.add_hyperparameter(name='return_result',
argument_type=ArgumentType.VALUE,
data='replace')
lr = 0.0001
adam_hypers = d3m.primitives.learner.model.KerasWrap.metadata.get_hyperparams(
).defaults(path='optimizer.Adam')
adam_hypers = adam_hypers.replace({'lr': lr})
step.add_hyperparameter(name='optimizer',
argument_type=ArgumentType.VALUE,
data=adam_hypers)
pipeline_description.add_step(step)
bz_loader = PrimitiveStep(primitive_description=d3m.primitives.
data_wrangling.batching.TAMU.metadata.query())
bz_loader.add_argument(name='inputs',
argument_type=ArgumentType.CONTAINER,
data_reference=f'steps.{IP_STEP}.produce')
bz_loader.add_argument(name='outputs',
argument_type=ArgumentType.CONTAINER,
data_reference='steps.{}.produce'.format(OP_STEP))
bz_loader.add_hyperparameter(name='primitive_reader',
argument_type=ArgumentType.PRIMITIVE,
data=READER_STEP)
bz_loader.add_hyperparameter(name='primitive_learner',
argument_type=ArgumentType.PRIMITIVE,
data=learner_idx)
bz_loader.add_hyperparameter(name='batch_size',
argument_type=ArgumentType.VALUE,
data=batch_size)
bz_loader.add_hyperparameter(name='sampling_method',
argument_type=ArgumentType.VALUE,
data='random')
bz_loader.add_output('produce')
pipeline_description.add_step(bz_loader)
def build_demo_pipeline():
# Creating pipeline
pipeline = Pipeline(context=Context.TESTING)
pipeline.add_input(name='inputs')
# Step 0: DFS
step_0 = PrimitiveStep(primitive_description=Featuretools.metadata.query())
step_0.add_argument(name='inputs',
argument_type=ArgumentType.CONTAINER,
data_reference='inputs.0')
step_0.add_output('produce')
pipeline.add_step(step_0)
# Step 1: SKlearnImputer
step_1 = PrimitiveStep(
primitive_description=SKlearnImputer.metadata.query())
step_1.add_argument(name='inputs',
argument_type=ArgumentType.CONTAINER,
data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline.add_step(step_1)
# Step 2: SKlearnRFC
step_2 = PrimitiveStep(primitive_description=SKlearnRFC.metadata.query())
step_2.add_hyperparameter(name='use_semantic_types',
argument_type=ArgumentType.VALUE,
data=True)
step_2.add_hyperparameter(name='add_index_columns',
argument_type=ArgumentType.VALUE,
data=True)
step_2.add_argument(name='inputs',
argument_type=ArgumentType.CONTAINER,
data_reference='steps.1.produce')
step_2.add_argument(name='outputs',
argument_type=ArgumentType.CONTAINER,
data_reference='steps.1.produce')
step_2.add_output('produce')
pipeline.add_step(step_2)
# Step 3: ConstructPredictions
step_3 = PrimitiveStep(
primitive_description=DataFrameCommon.metadata.query())
step_3.add_argument(name='inputs',
argument_type=ArgumentType.CONTAINER,
data_reference='steps.2.produce')
step_3.add_argument(name='reference',
argument_type=ArgumentType.CONTAINER,
data_reference='steps.2.produce')
step_3.add_output('produce')
pipeline.add_step(step_3)
# Final Output
pipeline.add_output(name='output predictions',
data_reference='steps.3.produce')
return pipeline
def _new_pipeline(pipeline, hyperparams=None):
hyperparams = to_dicts(hyperparams) if hyperparams else dict()
new_pipeline = Pipeline(context=Context.TESTING)
for input_ in pipeline.inputs:
new_pipeline.add_input(name=input_['name'])
for step_id, old_step in enumerate(pipeline.steps):
new_step = PrimitiveStep(primitive=old_step.primitive)
for name, argument in old_step.arguments.items():
new_step.add_argument(
name=name,
argument_type=argument['type'],
data_reference=argument['data']
)
for output in old_step.outputs:
new_step.add_output(output)
new_hyperparams = hyperparams.get(str(step_id), dict())
for name, hyperparam in old_step.hyperparams.items():
if name not in new_hyperparams:
new_step.add_hyperparameter(
name=name,
argument_type=ArgumentType.VALUE,
data=hyperparam['data']
)
for name, value in new_hyperparams.items():
new_step.add_hyperparameter(
name=name,
argument_type=ArgumentType.VALUE,
data=value
)
new_pipeline.add_step(new_step)
for output in pipeline.outputs:
new_pipeline.add_output(
name=output['name'],
data_reference=output['data']
)
new_pipeline.cv_scores = list()
new_pipeline.score = None
return new_pipeline
def load_pipeline_architecture(self, pipeline_architecture_dict):
"""
Loads pipeline architecture dictionary and returns a d3m Pipeline object.
Return pipeline
"""
pipeline_description = Pipeline(context=Context.TESTING)
pipeline_description.add_input(name='inputs')
# For each corresponding stage in the dictionary create a step
steps = []
stage_name_to_reference_name = {}
for stage_dict in pipeline_architecture_dict:
# Extract stage attributes
primitive = stage_dict["primitive"]
if type(primitive) == str:
primitive = get_primitive_with_name(primitive)
cur_stage_name = stage_dict["stage_name"]
input_stage = stage_dict["input"]
# Create primitive step
step = PrimitiveStep(primitive_description=primitive.metadata.query())
data_reference = "inputs.0" if input_stage == PipelineWrapper.PIPELINE_INPUT else stage_name_to_reference_name[input_stage]
step.add_argument(name="inputs", argument_type=ArgumentType.CONTAINER, data_reference=data_reference)
if "hyperparameters" in stage_dict:
for k,v in stage_dict["hyperparameters"].items():
step.add_hyperparameter(name=k, argument_type=ArgumentType.VALUE, data=v)
if "arguments" in stage_dict:
for k,v in stage_dict["arguments"].items():
step.add_argument(name=k, argument_type=ArgumentType.CONTAINER, data_reference=stage_name_to_reference_name[v])
step.add_output("produce")
pipeline_description.add_step(step)
reference_name = next(iter(step.get_output_data_references()))
# Update accounting
stage_name_to_reference_name[cur_stage_name] = reference_name
steps.append(step)
# Output is output of the last step
last_output_reference = next(iter(steps[-1].get_output_data_references()))
pipeline_description.add_output(name="output", data_reference=last_output_reference)
return pipeline_description
def add_classifier(pipeline_description, dataset_to_dataframe_step, attributes, targets):
lr = PrimitiveStep(primitive=SKLogisticRegression)
lr.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER,
data_reference=attributes)
lr.add_argument(name='outputs', argument_type=ArgumentType.CONTAINER,
data_reference=targets)
lr.add_output('produce')
pipeline_description.add_step(lr)
construct_pred = PrimitiveStep(
primitive=index.get_primitive('d3m.primitives.data_transformation.construct_predictions.Common'))
construct_pred.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER,
data_reference=pipeline_utils.int_to_step(lr.index))
construct_pred.add_argument(name='reference', argument_type=ArgumentType.CONTAINER,
data_reference=dataset_to_dataframe_step)
construct_pred.add_output('produce')
pipeline_description.add_step(construct_pred)
# Final Output
pipeline_description.add_output(name='output predictions',
data_reference=pipeline_utils.int_to_step(construct_pred.index))
def create_pipeline(metric: str) -> Pipeline:
previous_step = 0
input_val = 'steps.{}.produce'
# create the basic pipeline
tsf_pipeline = Pipeline(context=PipelineContext.TESTING)
tsf_pipeline.add_input(name='inputs')
# step 0 - Extract dataframe from dataset
step = PrimitiveStep(
primitive_description=DatasetToDataFramePrimitive.metadata.query())
step.add_argument(name='inputs',
argument_type=ArgumentType.CONTAINER,
data_reference='inputs.0')
step.add_output('produce')
tsf_pipeline.add_step(step)
# step 1 - Parse columns.
step = PrimitiveStep(
primitive_description=ColumnParserPrimitive.metadata.query())
step.add_argument(name='inputs',
argument_type=ArgumentType.CONTAINER,
data_reference='steps.0.produce')
step.add_output('produce')
semantic_types = (
'http://schema.org/Boolean', 'http://schema.org/Integer',
'http://schema.org/Float',
'https://metadata.datadrivendiscovery.org/types/FloatVector')
step.add_hyperparameter('parse_semantic_types', ArgumentType.VALUE,
semantic_types)
tsf_pipeline.add_step(step)
# step 2 - Parrot ARIMA
step = PrimitiveStep(primitive_description=Parrot.metadata.query())
step.add_argument(name='inputs',
argument_type=ArgumentType.CONTAINER,
data_reference='steps.1.produce')
step.add_argument(name='outputs',
argument_type=ArgumentType.CONTAINER,
data_reference='steps.1.produce')
step.add_hyperparameter(name='seasonal_differencing',
argument_type=ArgumentType.VALUE,
data=11)
step.add_hyperparameter(name='n_periods',
argument_type=ArgumentType.VALUE,
data=21)
step.add_output('produce')
tsf_pipeline.add_step(step)
# step 3 - convert predictions to expected format
# step = PrimitiveStep(primitive_description=ConstructPredictionsPrimitive.metadata.query())
# step.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce')
# step.add_argument(name='reference', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
# step.add_output('produce')
# tsf_pipeline.add_step(step)
# Adding output step to the pipeline
tsf_pipeline.add_output(name='output', data_reference='steps.2.produce')
return tsf_pipeline
def create_pipeline(metric: str) -> Pipeline:
# create the basic pipeline
vertex_nomination_pipeline = Pipeline(context=PipelineContext.TESTING)
vertex_nomination_pipeline.add_input(name='inputs')
# step 0 - extract the graphs
step = PrimitiveStep(primitive_description=DistilSingleGraphLoaderPrimitive.metadata.query())
step.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step.add_output('produce')
step.add_output('produce_target')
vertex_nomination_pipeline.add_step(step)
# step 1 - predict links
step = PrimitiveStep(primitive_description=DistilLinkPredictionPrimitive.metadata.query())
step.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step.add_argument(name='outputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce_target')
step.add_hyperparameter('metric', ArgumentType.VALUE, metric)
step.add_output('produce')
vertex_nomination_pipeline.add_step(step)
# Adding output step to the pipeline
vertex_nomination_pipeline.add_output(name='output', data_reference='steps.1.produce')
return vertex_nomination_pipeline
def __init__(self, epochs: int = 10, n_steps: int = 20):
pipeline_description = Pipeline()
pipeline_description.add_input(name="inputs")
# Denormalize primitive
step = PrimitiveStep(primitive=index.get_primitive(
"d3m.primitives.data_transformation.denormalize.Common"))
step.add_argument(
name="inputs",
argument_type=ArgumentType.CONTAINER,
data_reference="inputs.0",
)
step.add_output("produce")
pipeline_description.add_step(step)
# DS to DF on input DS
step = PrimitiveStep(primitive=index.get_primitive(
"d3m.primitives.data_transformation.dataset_to_dataframe.Common"))
step.add_argument(
name="inputs",
argument_type=ArgumentType.CONTAINER,
data_reference="steps.0.produce",
)
step.add_output("produce")
pipeline_description.add_step(step)
# RetinaNet primitive
step = PrimitiveStep(primitive=index.get_primitive(
"d3m.primitives.object_detection.retina_net.ObjectDetectionRN"))
step.add_argument(
name="inputs",
argument_type=ArgumentType.CONTAINER,
data_reference="steps.1.produce",
)
step.add_argument(
name="outputs",
argument_type=ArgumentType.CONTAINER,
data_reference="steps.1.produce",
)
step.add_hyperparameter(name="n_epochs",
argument_type=ArgumentType.VALUE,
data=epochs)
step.add_hyperparameter(name="n_steps",
argument_type=ArgumentType.VALUE,
data=n_steps)
step.add_hyperparameter(name="weights_path",
argument_type=ArgumentType.VALUE,
data="/scratch_dir/")
step.add_output("produce")
pipeline_description.add_step(step)
# Final Output
pipeline_description.add_output(name="output predictions",
data_reference="steps.2.produce")
self.pipeline = pipeline_description
def _gen_pipeline(self):
pipeline = meta_pipeline.Pipeline()
pipeline.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.add_step(step_0)
# Step 1: dataset_to_dataframe
step_1 = PrimitiveStep(primitive=index.get_primitive(
'd3m.primitives.data_transformation.dataset_to_dataframe.Common'))
step_1.add_argument(name='inputs',
argument_type=ArgumentType.CONTAINER,
data_reference='inputs.0')
step_1.add_hyperparameter(name='dataframe_resource',
argument_type=ArgumentType.VALUE,
data='1')
step_1.add_output('produce')
pipeline.add_step(step_1)
# Step 2: dataset_to_dataframe
step_2 = PrimitiveStep(primitive=index.get_primitive(
'd3m.primitives.data_transformation.dataset_to_dataframe.Common'))
step_2.add_argument(name='inputs',
argument_type=ArgumentType.CONTAINER,
data_reference='inputs.0')
step_2.add_hyperparameter(name='dataframe_resource',
argument_type=ArgumentType.VALUE,
data='2')
step_2.add_output('produce')
pipeline.add_step(step_2)
step_3 = meta_pipeline.PrimitiveStep(
primitive_description=EuclideanNomination.metadata.query())
step_3.add_argument(name='inputs_1',
argument_type=ArgumentType.CONTAINER,
data_reference='steps.1.produce')
step_3.add_argument(name='inputs_2',
argument_type=ArgumentType.CONTAINER,
data_reference='steps.2.produce')
step_3.add_argument(name='reference',
argument_type=ArgumentType.CONTAINER,
data_reference='steps.0.produce')
step_3.add_output('produce')
pipeline.add_step(step_3)
# Adding output step to the pipeline
pipeline.add_output(name='Predictions',
data_reference='steps.3.produce')
return pipeline
def community_detection(resolver=None):
if resolver is None:
resolver = custom_resolver.BlackListResolver()
# Creating Pipeline
pipeline_description = Pipeline(context=PipelineContext.TESTING)
pipeline_description.add_input(name='inputs')
start_step = "inputs.0"
# Step 0
step_0 = PrimitiveStep(primitive_description=d3m.primitives.sri.graph.
CommunityDetectionParser.metadata.query(),
resolver=resolver)
step_0.add_argument(name='inputs',
argument_type=ArgumentType.CONTAINER,
data_reference=start_step)
step_0.add_output('produce')
pipeline_description.add_step(step_0)
# Step 1
step_1 = PrimitiveStep(primitive_description=d3m.primitives.sri.psl.
CommunityDetection.metadata.query(),
resolver=resolver)
step_1.add_argument(name='inputs',
argument_type=ArgumentType.CONTAINER,
data_reference='steps.0.produce')
step_1.add_hyperparameter(name='jvm_memory',
argument_type=ArgumentType.VALUE,
data=0.5)
step_1.add_output('produce')
pipeline_description.add_step(step_1)
# Step 2: RemoveColumns
step_2 = PrimitiveStep(primitive_description=d3m.primitives.data.
RemoveColumns.metadata.query(),
resolver=resolver)
step_2.add_argument(name='inputs',
argument_type=ArgumentType.CONTAINER,
data_reference='steps.1.produce')
step_2.add_hyperparameter(name='columns',
argument_type=ArgumentType.VALUE,
data=[0])
step_2.add_output('produce')
pipeline_description.add_step(step_2)
pipeline_description.add_output(name='Result',
data_reference='steps.2.produce')
last_step = len(pipeline_description.steps) - 1
attributes = pipelines.int_to_step(last_step - 1)
targets = pipelines.int_to_step(last_step)
return pipeline_description
def create_pipeline(metric: str) -> Pipeline:
previous_step = 0
input_val = 'steps.{}.produce'
# create the basic pipeline
var_pipeline = Pipeline(context=PipelineContext.TESTING)
var_pipeline.add_input(name='inputs')
# step 0 - Extract dataframe from dataset
step = PrimitiveStep(
primitive_description=DatasetToDataFramePrimitive.metadata.query())
step.add_argument(name='inputs',
argument_type=ArgumentType.CONTAINER,
data_reference='inputs.0')
step.add_output('produce')
var_pipeline.add_step(step)
# step 1 - Parse columns.
step = PrimitiveStep(
primitive_description=ColumnParserPrimitive.metadata.query())
step.add_argument(name='inputs',
argument_type=ArgumentType.CONTAINER,
data_reference='steps.0.produce')
step.add_output('produce')
semantic_types = (
'http://schema.org/Boolean', 'http://schema.org/Integer',
'http://schema.org/Float',
'https://metadata.datadrivendiscovery.org/types/FloatVector')
step.add_hyperparameter('parse_semantic_types', ArgumentType.VALUE,
semantic_types)
var_pipeline.add_step(step)
# step 2 - Vector Auto Regression
step = PrimitiveStep(primitive_description=VAR.metadata.query())
step.add_argument(name='inputs',
argument_type=ArgumentType.CONTAINER,
data_reference='steps.1.produce')
step.add_argument(name='outputs',
argument_type=ArgumentType.CONTAINER,
data_reference='steps.1.produce')
step.add_output('produce')
var_pipeline.add_step(step)
# Adding output step to the pipeline
var_pipeline.add_output(name='output', data_reference='steps.2.produce')
return var_pipeline
def create_pipeline(metric: str) -> Pipeline:
# create the basic pipeline
graph_matching_pipeline = Pipeline(context=PipelineContext.TESTING)
graph_matching_pipeline.add_input(name='inputs')
# step 0 - extract the graphs
step = PrimitiveStep(
primitive_description=DistilGraphLoaderPrimitive.metadata.query())
step.add_argument(name='inputs',
argument_type=ArgumentType.CONTAINER,
data_reference='inputs.0')
step.add_output('produce')
step.add_output('produce_target')
graph_matching_pipeline.add_step(step)
# step 1 - match the graphs that have been seeded
step = PrimitiveStep(
primitive_description=DistilSeededGraphMatchingPrimitive.metadata.
query())
step.add_argument(name='inputs',
argument_type=ArgumentType.CONTAINER,
data_reference='steps.0.produce')
step.add_argument(name='outputs',
argument_type=ArgumentType.CONTAINER,
data_reference='steps.0.produce_target')
step.add_hyperparameter('metric', ArgumentType.VALUE, metric)
step.add_output('produce')
graph_matching_pipeline.add_step(step)
# convert predictions to expected format
#step = PrimitiveStep(primitive_description=ConstructPredictionsPrimitive.metadata.query())
#step.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
#step.add_argument(name='reference', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce_target')
#step.add_output('produce')
#step.add_hyperparameter('use_columns', ArgumentType.VALUE, [0, 1])
#graph_matching_pipeline.add_step(step)
# Adding output step to the pipeline
graph_matching_pipeline.add_output(name='output',
data_reference='steps.1.produce')
return graph_matching_pipeline
def __init__(self, ):
pipeline_description = Pipeline()
pipeline_description.add_input(name="inputs")
# DS to DF on input DS
step = PrimitiveStep(primitive=index.get_primitive(
"d3m.primitives.data_transformation.dataset_to_dataframe.Common"))
step.add_argument(
name="inputs",
argument_type=ArgumentType.CONTAINER,
data_reference="inputs.0",
)
step.add_output("produce")
pipeline_description.add_step(step)
# column parser
step = PrimitiveStep(primitive=index.get_primitive(
"d3m.primitives.data_transformation.column_parser.Common"))
step.add_argument(
name="inputs",
argument_type=ArgumentType.CONTAINER,
data_reference="steps.0.produce",
)
step.add_output("produce")
pipeline_description.add_step(step)
# Duke primitive
step = PrimitiveStep(primitive=index.get_primitive(
"d3m.primitives.data_cleaning.text_summarization.Duke"))
step.add_argument(
name="inputs",
argument_type=ArgumentType.CONTAINER,
data_reference="steps.1.produce",
)
step.add_output("produce")
pipeline_description.add_step(step)
# Final Output
pipeline_description.add_output(name="output predictions",
data_reference="steps.2.produce")
self.pipeline = pipeline_description
from d3m import index
from d3m.metadata.base import ArgumentType, Context
from d3m.metadata.pipeline import Pipeline, PrimitiveStep
import sys
# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')
# Step 0: Denormalize primitive
step_0 = PrimitiveStep(primitive=index.get_primitive(
'd3m.primitives.data_transformation.denormalize.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: DISTIL/NK Storc primitive
step_1 = PrimitiveStep(
primitive=index.get_primitive('d3m.primitives.clustering.k_means.Sloth'))
step_1.add_argument(name='inputs',
argument_type=ArgumentType.CONTAINER,
data_reference='steps.0.produce')
step_1.add_hyperparameter(name='nclusters',
argument_type=ArgumentType.VALUE,
data=10)
step_1.add_hyperparameter(name='long_format',
argument_type=ArgumentType.VALUE,
data=True)
step_1.add_output('produce')
def generate_only():
# 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.schema_discovery.profiler.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: column_parser
step_2 = PrimitiveStep(primitive=index.get_primitive(
'd3m.primitives.data_transformation.column_parser.Common'))
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)
# Step 3: DFS Single Table
step_3 = PrimitiveStep(primitive=index.get_primitive(
'd3m.primitives.feature_construction.deep_feature_synthesis.SingleTableFeaturization'
))
step_3.add_argument(name='inputs',
argument_type=ArgumentType.CONTAINER,
data_reference='steps.2.produce')
step_3.add_output('produce')
pipeline_description.add_step(step_3)
# Step 4: learn model
step_4 = PrimitiveStep(primitive=index.get_primitive(
'd3m.primitives.regression.xgboost_gbtree.Common'))
step_4.add_argument(name='inputs',
argument_type=ArgumentType.CONTAINER,
data_reference='steps.3.produce')
step_4.add_argument(name='outputs',
argument_type=ArgumentType.CONTAINER,
data_reference='steps.2.produce')
step_4.add_output('produce')
pipeline_description.add_step(step_4)
# Step 5: construct output
step_5 = PrimitiveStep(primitive=index.get_primitive(
'd3m.primitives.data_transformation.construct_predictions.Common'))
step_5.add_argument(name='inputs',
argument_type=ArgumentType.CONTAINER,
data_reference='steps.4.produce')
step_5.add_argument(name='reference',
argument_type=ArgumentType.CONTAINER,
data_reference='steps.2.produce')
step_5.add_output('produce')
pipeline_description.add_step(step_5)
# Final Output
pipeline_description.add_output(name='output predictions',
data_reference='steps.5.produce')
# Generate .yml file for the pipeline
import featuretools_ta1
from pipeline_tests.utils import generate_pipeline
dataset_name = 'LL1_retail_sales_total_MIN_METADATA'
dataset_path = '/featuretools_ta1/datasets/seed_datasets_current'
primitive_name = 'd3m.primitives.feature_construction.deep_feature_synthesis.SingleTableFeaturization'
version = featuretools_ta1.__version__
test_name = os.path.splitext(os.path.basename(__file__))[0][5:]
yml, pipeline_run_file = generate_pipeline(
primitive_name=primitive_name,
pipeline_description=pipeline_description,
dataset_name=dataset_name,
test_name=test_name)
# fit-score command
fs_cmd = 'python3 -m d3m runtime -d /featuretools_ta1/datasets/ fit-score -p {}'.format(
yml)
fs_cmd += ' -r {}/{}/{}_problem/problemDoc.json'.format(
dataset_path, dataset_name, dataset_name)
fs_cmd += ' -i {}/{}/TRAIN/dataset_TRAIN/datasetDoc.json'.format(
dataset_path, dataset_name)
fs_cmd += ' -t {}/{}/TEST/dataset_TEST/datasetDoc.json'.format(
dataset_path, dataset_name)
fs_cmd += ' -a {}/{}/SCORE/dataset_SCORE/datasetDoc.json'.format(
dataset_path, dataset_name)
fs_cmd += ' -O {}'.format(pipeline_run_file)
# Run pipeline to save pipeline_run file
os.system(fs_cmd)
# Create and return command for running from pipeline_run file:
pipeline_run_cmd = 'python3 -m d3m --pipelines-path /featuretools_ta1/MIT_FeatureLabs/{}/{}/pipelines/'.format(
primitive_name, version)
pipeline_run_cmd += ' runtime -d /featuretools_ta1/datasets/ fit-score'
pipeline_run_cmd += ' -u {}'.format(pipeline_run_file)
return pipeline_run_cmd
def __init__(self):
pipeline_description = Pipeline()
pipeline_description.add_input(name="inputs")
# DS to DF on input DS
step = PrimitiveStep(primitive=index.get_primitive(
"d3m.primitives.data_transformation.dataset_to_dataframe.Common"))
step.add_argument(name="inputs",
argument_type=ArgumentType.CONTAINER,
data_reference="inputs.0")
step.add_output("produce")
pipeline_description.add_step(step)
# Simon
step = PrimitiveStep(primitive=index.get_primitive(
"d3m.primitives.data_cleaning.column_type_profiler.Simon"))
step.add_argument(
name="inputs",
argument_type=ArgumentType.CONTAINER,
data_reference="steps.0.produce",
)
step.add_output("produce")
pipeline_description.add_step(step)
# column parser on input DF
step = PrimitiveStep(primitive=index.get_primitive(
"d3m.primitives.data_transformation.column_parser.Common"))
step.add_argument(
name="inputs",
argument_type=ArgumentType.CONTAINER,
data_reference="steps.1.produce",
)
step.add_output("produce")
pipeline_description.add_step(step)
# XG Boost
step = PrimitiveStep(primitive=index.get_primitive(
'd3m.primitives.classification.xgboost_gbtree.Common'))
step.add_argument(name='inputs',
argument_type=ArgumentType.CONTAINER,
data_reference='steps.2.produce')
step.add_argument(name='outputs',
argument_type=ArgumentType.CONTAINER,
data_reference='steps.2.produce')
step.add_output('produce')
step.add_hyperparameter(name='add_index_columns',
argument_type=ArgumentType.VALUE,
data=True)
pipeline_description.add_step(step)
# construct predictions
step = PrimitiveStep(primitive=index.get_primitive(
"d3m.primitives.data_transformation.construct_predictions.Common"))
step.add_argument(
name="inputs",
argument_type=ArgumentType.CONTAINER,
data_reference="steps.3.produce",
)
step.add_argument(
name="reference",
argument_type=ArgumentType.CONTAINER,
data_reference="steps.0.produce",
)
step.add_output("produce")
pipeline_description.add_step(step)
# Final Output
pipeline_description.add_output(name="output predictions",
data_reference="steps.4.produce")
self.pipeline = pipeline_description
def build_pipeline(pipepline_info, pipepline_mapping, stdout=None):
default_stdout = sys.stdout
if stdout is not None:
sys.stdout = stdout
# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')
for primitive_info in pipepline_info:
print(primitive_info.python_path)
print(primitive_info.hyperparameter)
print(primitive_info.ancestors)
if primitive_info.python_path == 'HEAD':
dataset_fullname = primitive_info.hyperparameter['dataset_folder']
print(dataset_fullname)
continue
elif primitive_info.python_path == 'ENDING':
ancestors = primitive_info.ancestors
end_step_num = pipepline_mapping[ancestors['inputs']] - 1
pipeline_description.add_output(name='output predictions', data_reference='steps.' + str(end_step_num) + '.produce')
else:
# print(primitive_info.python_path)
primitive = index.get_primitive(primitive_info.python_path)
step = PrimitiveStep(primitive=primitive)
hyperparameters = primitive_info.hyperparameter
ancestors = primitive_info.ancestors
# add add_inputs
# print(ancestors)
if ancestors['inputs'] != 0:
for ances_key in ancestors.keys():
print(ances_key, ancestors[ances_key], pipepline_mapping[ancestors[ances_key]] - 1)
step_num = pipepline_mapping[ancestors[ances_key]] - 1
step.add_argument(name=ances_key, argument_type=ArgumentType.CONTAINER, data_reference='steps.' + str(step_num) + '.produce')
else:
step.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
# add add_hyperparameter
for hyper in hyperparameters.keys():
# print(hyper, hyperparameters[hyper], type(hyperparameters[hyper]))
hyper_value = hyperparameters[hyper]
step.add_hyperparameter(name=hyper, argument_type=ArgumentType.VALUE, data=hyper_value)
step.add_output('produce')
pipeline_description.add_step(step)
# print('\n')
# Output to json
data = pipeline_description.to_json()
with open('example_pipeline.json', 'w') as f:
f.write(data)
print(data)
# yaml = pipeline_description.to_yaml()
# with open('example_pipeline.yml', 'w') as f:
# f.write(yaml)
# print(yaml)
sys.stdout.flush()
sys.stdout = default_stdout
def __init__(
self,
epochs: int = 5000,
attention_lstm: bool = True,
):
pipeline_description = Pipeline()
pipeline_description.add_input(name="inputs")
# Ts formatter
step = PrimitiveStep(
primitive=index.get_primitive(
"d3m.primitives.data_transformation.time_series_formatter.DistilTimeSeriesFormatter"
)
)
step.add_argument(
name="inputs",
argument_type=ArgumentType.CONTAINER,
data_reference="inputs.0",
)
step.add_output("produce")
pipeline_description.add_step(step)
# DS to DF on formatted ts DS
step = PrimitiveStep(
primitive=index.get_primitive(
"d3m.primitives.data_transformation.dataset_to_dataframe.Common"
)
)
step.add_argument(
name="inputs",
argument_type=ArgumentType.CONTAINER,
data_reference="steps.0.produce",
)
step.add_output("produce")
pipeline_description.add_step(step)
# DS to DF on input DS
step = PrimitiveStep(
primitive=index.get_primitive(
"d3m.primitives.data_transformation.dataset_to_dataframe.Common"
)
)
step.add_argument(
name="inputs",
argument_type=ArgumentType.CONTAINER,
data_reference="inputs.0",
)
step.add_output("produce")
pipeline_description.add_step(step)
# column parser on input DF
step = PrimitiveStep(
primitive=index.get_primitive(
"d3m.primitives.data_transformation.column_parser.Common"
)
)
step.add_argument(
name="inputs",
argument_type=ArgumentType.CONTAINER,
data_reference="steps.2.produce",
)
step.add_output("produce")
step.add_hyperparameter(
name="parse_semantic_types",
argument_type=ArgumentType.VALUE,
data=[
"http://schema.org/Boolean",
"http://schema.org/Integer",
"http://schema.org/Float",
"https://metadata.datadrivendiscovery.org/types/FloatVector",
],
)
pipeline_description.add_step(step)
# parse target semantic types
step = PrimitiveStep(
primitive=index.get_primitive(
"d3m.primitives.data_transformation.extract_columns_by_semantic_types.Common"
)
)
step.add_argument(
name="inputs",
argument_type=ArgumentType.CONTAINER,
data_reference="steps.3.produce",
)
step.add_hyperparameter(
name="semantic_types",
argument_type=ArgumentType.VALUE,
data=[
"https://metadata.datadrivendiscovery.org/types/Target",
],
)
step.add_output("produce")
pipeline_description.add_step(step)
# LSTM FCN
step = PrimitiveStep(
primitive=index.get_primitive(
"d3m.primitives.time_series_classification.convolutional_neural_net.LSTM_FCN"
)
)
step.add_argument(
name="inputs",
argument_type=ArgumentType.CONTAINER,
data_reference="steps.1.produce",
)
step.add_argument(
name="outputs",
argument_type=ArgumentType.CONTAINER,
data_reference="steps.4.produce",
)
step.add_hyperparameter(
name="epochs", argument_type=ArgumentType.VALUE, data=epochs
)
step.add_hyperparameter(
name="attention_lstm", argument_type=ArgumentType.VALUE, data=attention_lstm
)
step.add_output("produce")
pipeline_description.add_step(step)
# construct predictions
step = PrimitiveStep(
primitive=index.get_primitive(
"d3m.primitives.data_transformation.construct_predictions.Common"
)
)
step.add_argument(
name="inputs",
argument_type=ArgumentType.CONTAINER,
data_reference="steps.5.produce",
)
step.add_argument(
name="reference",
argument_type=ArgumentType.CONTAINER,
data_reference="steps.2.produce",
)
step.add_output("produce")
pipeline_description.add_step(step)
# Final Output
pipeline_description.add_output(
name="output predictions", data_reference="steps.6.produce"
)
self.pipeline = 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: DS to DF on input DS
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: Simple Profiler Column Role Annotation
step_1 = PrimitiveStep(primitive=index.get_primitive(
"d3m.primitives.schema_discovery.profiler.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: column parser on input DF
step_2 = PrimitiveStep(primitive=index.get_primitive(
"d3m.primitives.data_transformation.column_parser.Common"))
# -> 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.data_transformation.column_parser.Common')
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: Discrete Cosine Transform
primitive_2 = index.get_primitive(
'd3m.primitives.tods.feature_analysis.discrete_cosine_transform')
def create_pipeline(metric: str) -> Pipeline:
previous_step = 0
input_val = 'steps.{}.produce'
# create the basic pipeline
qa_pipeline = Pipeline(context=PipelineContext.TESTING)
qa_pipeline.add_input(name='inputs')
# Denormalize so that we have a single dataframe in the dataset
step = PrimitiveStep(
primitive_description=DenormalizePrimitive.metadata.query())
step.add_argument(name='inputs',
argument_type=ArgumentType.CONTAINER,
data_reference='inputs.0')
step.add_output('produce')
qa_pipeline.add_step(step)
# Extract dataframe from dataset
step = PrimitiveStep(
primitive_description=DatasetToDataFramePrimitive.metadata.query())
step.add_argument(name='inputs',
argument_type=ArgumentType.CONTAINER,
data_reference=input_val.format(previous_step))
step.add_output('produce')
qa_pipeline.add_step(step)
previous_step += 1
# Parse columns.
step = PrimitiveStep(
primitive_description=ColumnParserPrimitive.metadata.query())
step.add_argument(name='inputs',
argument_type=ArgumentType.CONTAINER,
data_reference=input_val.format(previous_step))
step.add_output('produce')
semantic_types = (
'http://schema.org/Boolean', 'http://schema.org/Integer',
'http://schema.org/Float',
'https://metadata.datadrivendiscovery.org/types/FloatVector')
step.add_hyperparameter('parse_semantic_types', ArgumentType.VALUE,
semantic_types)
qa_pipeline.add_step(step)
previous_step += 1
parse_step = previous_step
# Extract attributes
step = PrimitiveStep(
primitive_description=ExtractColumnsBySemanticTypesPrimitive.metadata.
query())
step.add_argument(name='inputs',
argument_type=ArgumentType.CONTAINER,
data_reference=input_val.format(parse_step))
step.add_output('produce')
step.add_hyperparameter(
'semantic_types', ArgumentType.VALUE,
('https://metadata.datadrivendiscovery.org/types/Attribute', ))
qa_pipeline.add_step(step)
previous_step += 1
attributes_step = previous_step
# Extract targets
step = PrimitiveStep(
primitive_description=ExtractColumnsBySemanticTypesPrimitive.metadata.
query())
step.add_argument(name='inputs',
argument_type=ArgumentType.CONTAINER,
data_reference=input_val.format(parse_step))
step.add_output('produce')
target_types = (
'https://metadata.datadrivendiscovery.org/types/Target',
'https://metadata.datadrivendiscovery.org/types/TrueTarget')
step.add_hyperparameter('semantic_types', ArgumentType.VALUE, target_types)
qa_pipeline.add_step(step)
previous_step += 1
target_step = previous_step
# Generates a bert pair classification model.
step = PrimitiveStep(
primitive_description=BertPairClassificationPrimitive.metadata.query())
step.add_argument(name='inputs',
argument_type=ArgumentType.CONTAINER,
data_reference=input_val.format(attributes_step))
step.add_argument(name='outputs',
argument_type=ArgumentType.CONTAINER,
data_reference=input_val.format(target_step))
step.add_output('produce')
step.add_hyperparameter('metric', ArgumentType.VALUE, metric)
step.add_hyperparameter('doc_col_0', ArgumentType.VALUE, 1)
step.add_hyperparameter('doc_col_1', ArgumentType.VALUE, 3)
qa_pipeline.add_step(step)
previous_step += 1
# convert predictions to expected format
step = PrimitiveStep(
primitive_description=ConstructPredictionsPrimitive.metadata.query())
step.add_argument(name='inputs',
argument_type=ArgumentType.CONTAINER,
data_reference=input_val.format(previous_step))
step.add_argument(name='reference',
argument_type=ArgumentType.CONTAINER,
data_reference=input_val.format(parse_step))
step.add_output('produce')
qa_pipeline.add_step(step)
previous_step += 1
# Adding output step to the pipeline
qa_pipeline.add_output(name='output',
data_reference=input_val.format(previous_step))
return qa_pipeline
def __init__(self):
pipeline_description = Pipeline()
pipeline_description.add_input(name="inputs")
# Denormalize primitive
step = PrimitiveStep(primitive=index.get_primitive(
"d3m.primitives.data_transformation.denormalize.Common"))
step.add_argument(
name="inputs",
argument_type=ArgumentType.CONTAINER,
data_reference="inputs.0",
)
step.add_output("produce")
pipeline_description.add_step(step)
# DS to DF on input DS
step = PrimitiveStep(primitive=index.get_primitive(
"d3m.primitives.data_transformation.dataset_to_dataframe.Common"))
step.add_argument(
name="inputs",
argument_type=ArgumentType.CONTAINER,
data_reference="inputs.0",
)
step.add_output("produce")
pipeline_description.add_step(step)
# Goat forward
step = PrimitiveStep(primitive=index.get_primitive(
"d3m.primitives.data_cleaning.geocoding.Goat_forward"))
step.add_argument(
name="inputs",
argument_type=ArgumentType.CONTAINER,
data_reference="steps.1.produce",
)
step.add_hyperparameter(name="target_columns",
argument_type=ArgumentType.VALUE,
data=[1])
step.add_hyperparameter(name="cache_size",
argument_type=ArgumentType.VALUE,
data=2000)
step.add_output("produce")
pipeline_description.add_step(step)
# column parser on input DF
step = PrimitiveStep(primitive=index.get_primitive(
"d3m.primitives.data_transformation.column_parser.Common"))
step.add_argument(
name="inputs",
argument_type=ArgumentType.CONTAINER,
data_reference="steps.2.produce",
)
step.add_output("produce")
pipeline_description.add_step(step)
# XG Boost
step = PrimitiveStep(primitive=index.get_primitive(
"d3m.primitives.classification.xgboost_gbtree.Common"))
step.add_argument(
name="inputs",
argument_type=ArgumentType.CONTAINER,
data_reference="steps.3.produce",
)
step.add_argument(
name="outputs",
argument_type=ArgumentType.CONTAINER,
data_reference="steps.3.produce",
)
step.add_output("produce")
step.add_hyperparameter(name="return_result",
argument_type=ArgumentType.VALUE,
data="replace")
pipeline_description.add_step(step)
# construct predictions
step = PrimitiveStep(primitive=index.get_primitive(
"d3m.primitives.data_transformation.construct_predictions.Common"))
step.add_argument(
name="inputs",
argument_type=ArgumentType.CONTAINER,
data_reference="steps.4.produce",
)
step.add_argument(
name="reference",
argument_type=ArgumentType.CONTAINER,
data_reference="steps.1.produce",
)
step.add_output("produce")
pipeline_description.add_step(step)
# Final Output
pipeline_description.add_output(name="output predictions",
data_reference="steps.5.produce")
self.pipeline = pipeline_description
from d3m.metadata.pipeline import Pipeline, PrimitiveStep
import sys
# 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: Simple Profiler Column Role Annotation
step_1 = PrimitiveStep(primitive=index.get_primitive(
"d3m.primitives.schema_discovery.profiler.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 column parser -> labeled semantic types to data types
step_2 = PrimitiveStep(primitive=index.get_primitive(
