Feature Discovery¶
Feature Discovery allows you to generate features automatically from secondary datasets connected to a primary dataset (training data). You can create this type of connection using DataRobot’s Relationships Configuration.
Register a primary dataset to start a project¶
To start a Feature Discovery Project, upload the primary (training) dataset: Projects
import datarobot as dr
primary_dataset = dr.Dataset.create_from_file(file_path='your-training_file.csv')
project = dr.Project.create_from_dataset(primary_dataset.id, project_name='Lending Club')
Next, register all the secondary datasets which you want to connect with primary dataset.
Register secondary datasets in the AI Catalog¶
You can register the dataset using
Dataset.create_from_file, which can take either
a path to a local file or any streamable file object.
profile_dataset = dr.Dataset.create_from_file(file_path='your_profile_file.csv')
transaction_dataset = dr.Dataset.create_from_file(file_path='your_transaction_file.csv')
Create dataset definitions and relationships using helper functions¶
Create the DatasetDefinition and Relationship for the profile and transaction datasets created above using helper functions.
profile_catalog_id = profile_dataset.id
profile_catalog_version_id = profile_dataset.version_id
transac_catalog_id = transaction_dataset.id
transac_catalog_version_id = transaction_dataset.version_id
profile_dataset_definition = dr.DatasetDefinition(
identifier='profile',
catalog_id=profile_catalog_id,
catalog_version_id=profile_catalog_version_id
)
transaction_dataset_definition = dr.DatasetDefinition(
identifier='transaction',
catalog_id=transac_catalog_id,
catalog_version_id=transac_catalog_version_id,
primary_temporal_key='Date'
)
profile_transaction_relationship = dr.Relationship(
dataset1_identifier='profile',
dataset2_identifier='transaction',
dataset1_keys=['CustomerID'],
dataset2_keys=['CustomerID']
)
primary_profile_relationship = dr.Relationship(
dataset2_identifier='profile',
dataset1_keys=['CustomerID'],
dataset2_keys=['CustomerID'],
feature_derivation_window_start=-14,
feature_derivation_window_end=-1,
feature_derivation_window_time_unit='DAY',
prediction_point_rounding=1,
prediction_point_rounding_time_unit='DAY'
)
dataset_definitions = [profile_dataset_definition, transaction_dataset_definition]
relationships = [primary_profile_relationship, profile_transaction_relationship]
Create a relationship configuration¶
Create a relationship configuration using the dataset definitions and relationships created above.
# Create the relationships configuration to define connection between the datasets
relationship_config = dr.RelationshipsConfiguration.create(dataset_definitions=dataset_definitions, relationships=relationships)
Create a Feature Discovery project¶
Once you have configured relationships for your datasets, you can start a Feature Discovery project.
# Set the datetime partitionining column (`date` in this example)
partitioning_spec = dr.DatetimePartitioningSpecification('date')
# As of v3.0, use ``Project.set_datetime_partitioning`` instead of passing the spec to ``Project.analyze_and_model`` via ``partitioning_method``.
project.set_datetime_partitioning(datetime_partition_spec=partitioning_spec)
# Set the target for the project and start Feature discovery (if ``Project.set_datetime_partitioning`` was used there is no need to pass ``partitioning_method``)
project.analyze_and_model(target='BadLoan', relationships_configuration_id=relationship_config.id, mode='manual', partitioning_method=partitioning_spec)
Project(train.csv)
To start training a model, reference the ref:modeling <model> documentation.
Create secondary dataset configuration for predictions¶
Create configurations for your secondary datasets with Secondary Dataset:
new_secondary_dataset_config = dr.SecondaryDatasetConfigurations.create(
project_id=project.id,
name='My config',
secondary_datasets=secondary_datasets
)
For more details, reference the Secondary Dataset configuration documentation.
Make predictions with a trained model¶
To make predictions with a trained model, reference the Predictions documentation.
dataset_from_path = project.upload_dataset(
'./data_to_predict.csv',
secondary_datasets_config_id=new_secondary_dataset_config.id
)
predict_job_1 = model.request_predictions(dataset_from_path.id)
Common Errors¶
Dataset registration Failed¶
datasetdr.Dataset.create_from_file(file_path='file.csv')
datarobot.errors.AsyncProcessUnsuccessfulError: The job did not complete successfully.
Solution
Check the internet connectivity sometimes network flakiness cause upload error
Is the dataset file too big then you might want to upload using URL rather than file
Relationship configuration errors¶
datarobot.errors.ClientError: 422 client error: {u'message': u'Invalid field data',
u'errors': {u'datasetDefinitions': {u'1': {u'identifier': u'value cannot contain characters: $ - " . { } / \\'},
u'0': {u'identifier': u'value cannot contain characters: $ - " . { } / \\'}}}}
Solution:
Check the identifier name passed in datasets_definitions and relationships.
Tip: Do not use the name of the dataset if you did not specify it when registering the dataset to the AI Catalog.
datarobot.errors.ClientError: 422 client error: {u'message': u'Invalid field data',
u'errors': {u'datasetDefinitions': {u'1': {u'primaryTemporalKey': u'date column doesnt exist'},
}}}
Solution:
Check if the name of the column passed as primaryTemporalKey is correct, as it is case-sensitive.
Configure relationships¶
A relationship’s configuration specifies additional datasets to be included to a project, how these datasets are related to each other, and the primary dataset. When a relationships configuration is specified for a project, Feature Discovery will create features automatically from these datasets.
You can create a relationship configuration from uploaded AI Catalog items. After uploading all the secondary datasets in the AI Catalog:
Create the dataset’s definition to specify which datasets to be used as secondary datasets along with its details
Configure relationships among the above datasets.
relationship_config = dr.RelationshipsConfiguration.create(dataset_definitions=dataset_definitions, relationships=relationships)
>>> relationship_config.id
u'5506fcd38bd88f5953219da0'
Dataset definitions and relationships using helper functions¶
Create the DatasetDefinition and Relationship for the profile and transaction dataset using helper functions.
profile_catalog_id = '5ec4aec1f072bc028e3471ae'
profile_catalog_version_id = '5ec4aec2f072bc028e3471b1'
transac_catalog_id = '5ec4aec268f0f30289a03901'
transac_catalog_version_id = '5ec4aec268f0f30289a03900'
profile_dataset_definition = dr.DatasetDefinition(
identifier='profile',
catalog_id=profile_catalog_id,
catalog_version_id=profile_catalog_version_id
)
transaction_dataset_definition = dr.DatasetDefinition(
identifier='transaction',
catalog_id=transac_catalog_id,
catalog_version_id=transac_catalog_version_id,
primary_temporal_key='Date'
)
profile_transaction_relationship = dr.Relationship(
dataset1_identifier='profile',
dataset2_identifier='transaction',
dataset1_keys=['CustomerID'],
dataset2_keys=['CustomerID']
)
primary_profile_relationship = dr.Relationship(
dataset2_identifier='profile',
dataset1_keys=['CustomerID'],
dataset2_keys=['CustomerID'],
feature_derivation_window_start=-14,
feature_derivation_window_end=-1,
feature_derivation_window_time_unit='DAY',
prediction_point_rounding=1,
prediction_point_rounding_time_unit='DAY'
)
dataset_definitions = [profile_dataset_definition, transaction_dataset_definition]
relationships = [primary_profile_relationship, profile_transaction_relationship]
Dataset definition and relationship using a dictionary¶
Create the dataset definitions and relationships for the profile and transaction dataset using dict directly.
profile_catalog_id = profile_dataset.id
profile_catalog_version_id = profile_dataset.version_id
transac_catalog_id = transaction_dataset.id
transac_catalog_version_id = transaction_dataset.version_id
dataset_definitions = [
{
'identifier': 'transaction',
'catalogVersionId': transac_catalog_version_id,
'catalogId': transac_catalog_id,
'primaryTemporalKey': 'Date',
'snapshotPolicy': 'latest',
},
{
'identifier': 'profile',
'catalogId': profile_catalog_id,
'catalogVersionId': profile_catalog_version_id,
'snapshotPolicy': 'latest',
},
]
relationships = [
{
'dataset2Identifier': 'profile',
'dataset1Keys': ['CustomerID'],
'dataset2Keys': ['CustomerID'],
'featureDerivationWindowStart': -14,
'featureDerivationWindowEnd': -1,
'featureDerivationWindowTimeUnit': 'DAY',
'predictionPointRounding': 1,
'predictionPointRoundingTimeUnit': 'DAY',
},
{
'dataset1Identifier': 'profile',
'dataset2Identifier': 'transaction',
'dataset1Keys': ['CustomerID'],
'dataset2Keys': ['CustomerID'],
},
]
Retrieving relationship configuration¶
You can retrieve a specific relationship’s configuration using the ID of the relationship configuration.
relationship_config_id = '5506fcd38bd88f5953219da0'
relationship_config = dr.RelationshipsConfiguration(id=relationship_config_id).get()
>>> relationship_config.id == relationship_config_id
True
# Get all the datasets used in this relationship's configuration
>> len(relationship_config.dataset_definitions) == 2
True
>> relationship_config.dataset_definitions[0]
{
'feature_list_id': '5ec4af93603f596525d382d3',
'snapshot_policy': 'latest',
'catalog_id': '5ec4aec268f0f30289a03900',
'catalog_version_id': '5ec4aec268f0f30289a03901',
'primary_temporal_key': 'Date',
'is_deleted': False,
'identifier': 'transaction',
'feature_lists':
[
{
'name': 'Raw Features',
'description': 'System created featurelist',
'created_by': 'User1',
'creation_date': datetime.datetime(2020, 5, 20, 4, 18, 27, 150000, tzinfo=tzutc()),
'user_created': False,
'dataset_id': '5ec4aec268f0f30289a03900',
'id': '5ec4af93603f596525d382d1',
'features': [u'CustomerID', u'AccountID', u'Date', u'Amount', u'Description']
},
{
'name': 'universe',
'description': 'System created featurelist',
'created_by': 'User1',
'creation_date': datetime.datetime(2020, 5, 20, 4, 18, 27, 172000, tzinfo=tzutc()),
'user_created': False,
'dataset_id': '5ec4aec268f0f30289a03900',
'id': '5ec4af93603f596525d382d2',
'features': [u'CustomerID', u'AccountID', u'Date', u'Amount', u'Description']
},
{
'features': [u'CustomerID', u'AccountID', u'Date', u'Amount', u'Description'],
'description': 'System created featurelist',
'created_by': u'Garvit Bansal',
'creation_date': datetime.datetime(2020, 5, 20, 4, 18, 27, 179000, tzinfo=tzutc()),
'dataset_version_id': '5ec4aec268f0f30289a03901',
'user_created': False,
'dataset_id': '5ec4aec268f0f30289a03900',
'id': u'5ec4af93603f596525d382d3',
'name': 'Informative Features'
}
]
}
# Get information regarding how the datasets are connected among themselves as well as theprimary dataset
>> relationship_config.relationships
[
{
'dataset2Identifier': 'profile',
'dataset1Keys': ['CustomerID'],
'dataset2Keys': ['CustomerID'],
'featureDerivationWindowStart': -14,
'featureDerivationWindowEnd': -1,
'featureDerivationWindowTimeUnit': 'DAY',
'predictionPointRounding': 1,
'predictionPointRoundingTimeUnit': 'DAY',
},
{
'dataset1Identifier': 'profile',
'dataset2Identifier': 'transaction',
'dataset1Keys': ['CustomerID'],
'dataset2Keys': ['CustomerID'],
},
]
Update details of a relationship configuration¶
Use the snippet below as an example of how to update the details of the existing relationship configuration.
relationship_config_id = '5506fcd38bd88f5953219da0'
relationship_config = dr.RelationshipsConfiguration(id=relationship_config_id)
# Remove obsolete dataset definitions and its relationships
new_datasets_definiton =
[
{
'identifier': 'user',
'catalogVersionId': '5c88a37770fc42a2fcc62759',
'catalogId': '5c88a37770fc42a2fcc62759',
'snapshotPolicy': 'latest',
},
]
# Get information regarding how the datasets are connected among themselves as well as the primary dataset
new_relationships =
[
{
'dataset2Identifier': 'user',
'dataset1Keys': ['user_id', 'dept_id'],
'dataset2Keys': ['user_id', 'dept_id'],
},
]
new_config = relationship_config.replace(new_datasets_definiton, new_relationships)
>>> new_config.id == relationship_config_id
True
>>> new_config.datasets_definition
[
{
'identifier': 'user',
'catalogVersionId': '5c88a37770fc42a2fcc62759',
'catalogId': '5c88a37770fc42a2fcc62759',
'snapshotPolicy': 'latest',
},
]
>>> new_config.relationships
[
{
'dataset2Identifier': 'user',
'dataset1Keys': ['user_id', 'dept_id'],
'dataset2Keys': ['user_id', 'dept_id'],
},
]
Delete relationships configuration¶
You can delete a relationship configuration that is not used by any project.
relationship_config_id = '5506fcd38bd88f5953219da0'
relationship_config = dr.RelationshipsConfiguration(id=relationship_config_id)
result = relationship_config.get()
>>> result.id == relationship_config_id
True
# Delete the relationships configuration
>>> relationship_config.delete()
>>> relationship_config.get()
ClientError: Relationships Configuration 5506fcd38bd88f5953219da0 not found
Secondary dataset configuration¶
Secondary dataset configuration allows you to use the different secondary datasets for a Feature Discovery project when making predictions.
Secondary datasets using helper functions¶
Create the Secondary Dataset using helper functions.
>>> profile_catalog_id = '5ec4aec1f072bc028e3471ae'
>>> profile_catalog_version_id = '5ec4aec2f072bc028e3471b1'
>>> transac_catalog_id = '5ec4aec268f0f30289a03901'
>>> transac_catalog_version_id = '5ec4aec268f0f30289a03900'
profile_secondary_dataset = dr.SecondaryDataset(
identifier='profile',
catalog_id=profile_catalog_id,
catalog_version_id=profile_catalog_version_id,
snapshot_policy='latest'
)
transaction_secondary_dataset = dr.SecondaryDataset(
identifier='transaction',
catalog_id=transac_catalog_id,
catalog_version_id=transac_catalog_version_id,
snapshot_policy='latest'
)
secondary_datasets = [profile_secondary_dataset, transaction_secondary_dataset]
Create secondary datasets with dict¶
You can create secondary datasets using raw dict structure.
secondary_datasets = [
{
'snapshot_policy': u'latest',
'identifier': u'profile',
'catalog_version_id': u'5fd06b4af24c641b68e4d88f',
'catalog_id': u'5fd06b4af24c641b68e4d88e'
},
{
'snapshot_policy': u'dynamic',
'identifier': u'transaction',
'catalog_version_id': u'5fd1e86c589238a4e635e98e',
'catalog_id': u'5fd1e86c589238a4e635e98d'
}
]
Create a secondary dataset configuration¶
Create a secondary dataset configuration for a Feature Discovery Project which uses two secondary datasets: profile and transaction.
import datarobot as dr
project = dr.Project.get(project_id='54e639a18bd88f08078ca831')
new_secondary_dataset_config = dr.SecondaryDatasetConfigurations.create(
project_id=project.id,
name='My config',
secondary_datasets=secondary_datasets
)
>>> new_secondary_dataset_config.id
'5fd1e86c589238a4e635e93d'
Retrieve a secondary dataset configuration¶
You can retrieve specific secondary dataset configurations using the configuration ID.
>>> config_id = '5fd1e86c589238a4e635e93d'
secondary_dataset_config = dr.SecondaryDatasetConfigurations(id=config_id).get()
>>> secondary_dataset_config.id == config_id
True
>>> secondary_dataset_config
{
'created': datetime.datetime(2020, 12, 9, 6, 16, 22, tzinfo=tzutc()),
'creator_full_name': u'[email protected]',
'creator_user_id': u'asdf4af1gf4bdsd2fba1de0a',
'credential_ids': None,
'featurelist_id': None,
'id': u'5fd1e86c589238a4e635e93d',
'is_default': True,
'name': u'My config',
'project_id': u'5fd06afce2456ec1e9d20457',
'project_version': None,
'secondary_datasets': [
{
'snapshot_policy': u'latest',
'identifier': u'profile',
'catalog_version_id': u'5fd06b4af24c641b68e4d88f',
'catalog_id': u'5fd06b4af24c641b68e4d88e'
},
{
'snapshot_policy': u'dynamic',
'identifier': u'transaction',
'catalog_version_id': u'5fd1e86c589238a4e635e98e',
'catalog_id': u'5fd1e86c589238a4e635e98d'
}
]
}
List all secondary dataset configurations¶
You can list all secondary dataset configurations created in the project.
>>> secondary_dataset_configs = dr.SecondaryDatasetConfigurations.list(project.id)
>>> secondary_dataset_configs[0]
{
'created': datetime.datetime(2020, 12, 9, 6, 16, 22, tzinfo=tzutc()),
'creator_full_name': u'[email protected]',
'creator_user_id': u'asdf4af1gf4bdsd2fba1de0a',
'credential_ids': None,
'featurelist_id': None,
'id': u'5fd1e86c589238a4e635e93d',
'is_default': True,
'name': u'My config',
'project_id': u'5fd06afce2456ec1e9d20457',
'project_version': None,
'secondary_datasets': [
{
'snapshot_policy': u'latest',
'identifier': u'profile',
'catalog_version_id': u'5fd06b4af24c641b68e4d88f',
'catalog_id': u'5fd06b4af24c641b68e4d88e'
},
{
'snapshot_policy': u'dynamic',
'identifier': u'transaction',
'catalog_version_id': u'5fd1e86c589238a4e635e98e',
'catalog_id': u'5fd1e86c589238a4e635e98d'
}
]
}