Project

class datarobot.models.Project

A project built from a particular training dataset

Variables:

id (str) – the id of the project
project_name (str) – the name of the project
project_description (str) – an optional description for the project
mode (int) – The current autopilot mode. 0: Full Autopilot. 2: Manual Mode. 4: Comprehensive Autopilot. null: Mode not set.
target (str) – the name of the selected target features
target_type (str) – Indicating what kind of modeling is being done in this project Options are: ‘Regression’, ‘Binary’ (Binary classification), ‘Multiclass’ (Multiclass classification), ‘Multilabel’ (Multilabel classification)
holdout_unlocked (bool) – whether the holdout has been unlocked
metric (str) – the selected project metric (e.g. LogLoss)
stage (str) – the stage the project has reached - one of datarobot.enums.PROJECT_STAGE
partition (dict) – information about the selected partitioning options
positive_class (str) – for binary classification projects, the selected positive class; otherwise, None
created (datetime.datetime) – the time the project was created
advanced_options (AdvancedOptions) – information on the advanced options that were selected for the project settings, e.g. a weights column or a cap of the runtime of models that can advance autopilot stages
max_train_pct (float) – The maximum percentage of the project dataset that can be used without going into the validation data or being too large to submit any blueprint for training
max_train_rows (int) – the maximum number of rows that can be trained on without going into the validation data or being too large to submit any blueprint for training
file_name (str) – The name of the file uploaded for the project dataset
credentials (Optional[List]) – A list of credentials for the datasets used in relationship configuration (previously graphs). For Feature Discovery projects, the list must be formatted in dictionary record format. Provide the catalogVersionId and credentialId for each dataset that is to be used in the project that requires authentication.
feature_engineering_prediction_point (Optional[str]) – For time-aware Feature Engineering, this parameter specifies the column from the primary dataset to use as the prediction point.
unsupervised_mode (Optional[bool]) – (New in version v2.20) defaults to False, indicates whether this is an unsupervised project.
relationships_configuration_id (Optional[str]) – (New in version v2.21) id of the relationships configuration to use
query_generator_id (Optional[str]) – (New in version v2.27) id of the query generator applied for time series data prep
segmentation (dict, optional) – information on the segmentation options for segmented project
partitioning_method (PartitioningMethod, optional) – (New in version v3.0) The partitioning class for this project. This attribute should only be used with newly-created projects and before calling Project.analyze_and_model(). After the project has been aimed, see Project.partition for actual partitioning options.
catalog_id (str) – (New in version v3.0) ID of the dataset used during creation of the project.
catalog_version_id (str) – (New in version v3.0) The object ID of the catalog_version which the project’s dataset belongs to.
use_gpu (bool) – (New in version v3.2) Whether project allows usage of GPUs

set_options(options=None, **kwargs)

Update the advanced options of this project.

Either accepts an AdvancedOptions object or individual keyword arguments. This is an inplace update.

Raises:: ValueError – Raised if an object passed to the options parameter is not an AdvancedOptions instance, a valid keyword argument from the AdvancedOptions class, or a combination of an AdvancedOptions instance AND keyword arguments.
Return type:: None

get_options()

Return the stored advanced options for this project.

Return type:: AdvancedOptions

classmethod get(project_id)

Gets information about a project.

Parameters:: project_id (str) – The identifier of the project you want to load.
Returns:: project – The queried project
Return type:: Project

Examples

import datarobot as dr
p = dr.Project.get(project_id='54e639a18bd88f08078ca831')
p.id
>>>'54e639a18bd88f08078ca831'
p.project_name
>>>'Some project name'

classmethod create(cls, sourcedata, project_name='Untitled Project', max_wait=600, read_timeout=600, dataset_filename=None, *, use_case=None)

Creates a project with provided data.

Project creation is asynchronous process, which means that after initial request we will keep polling status of async process that is responsible for project creation until it’s finished. For SDK users this only means that this method might raise exceptions related to it’s async nature.

Parameters:

sourcedata (basestring, file, pathlib.Path or pandas.DataFrame) – Dataset to use for the project. If string can be either a path to a local file, url to publicly available file or raw file content. If using a file, the filename must consist of ASCII characters only.
project_name (str, unicode, optional) – The name to assign to the empty project.
max_wait (Optional[int]) – Time in seconds after which project creation is considered unsuccessful
read_timeout (int) – The maximum number of seconds to wait for the server to respond indicating that the initial upload is complete
dataset_filename (string or None, optional) – (New in version v2.14) File name to use for dataset. Ignored for url and file path sources.
use_case (UseCase | string, optional) – A single UseCase object or ID to add this new Project to. Must be a kwarg.

Returns:

project – Instance with initialized data.

Return type:

Project

Raises:

InputNotUnderstoodError – Raised if sourcedata isn’t one of supported types.
AsyncFailureError – Polling for status of async process resulted in response with unsupported status code. Beginning in version 2.1, this will be ProjectAsyncFailureError, a subclass of AsyncFailureError
AsyncProcessUnsuccessfulError – Raised if project creation was unsuccessful
AsyncTimeoutError – Raised if project creation took more time, than specified by max_wait parameter

Examples

p = Project.create('/home/datasets/somedataset.csv',
                   project_name="New API project")
p.id
>>> '5921731dkqshda8yd28h'
p.project_name
>>> 'New API project'

classmethod encrypted_string(plaintext)

Sends a string to DataRobot to be encrypted

This is used for passwords that DataRobot uses to access external data sources

Parameters:: plaintext (str) – The string to encrypt
Returns:: ciphertext – The encrypted string
Return type:: str

classmethod create_from_hdfs(cls, url, port=None, project_name=None, max_wait=600)

Create a project from a datasource on a WebHDFS server.

Parameters:

url (str) – The location of the WebHDFS file, both server and full path. Per the DataRobot specification, must begin with hdfs://, e.g. hdfs:///tmp/10kDiabetes.csv
port (Optional[int]) – The port to use. If not specified, will default to the server default (50070)
project_name (Optional[str]) – A name to give to the project
max_wait (int) – The maximum number of seconds to wait before giving up.

Return type:

Project

Examples

p = Project.create_from_hdfs('hdfs:///tmp/somedataset.csv',
                             project_name="New API project")
p.id
>>> '5921731dkqshda8yd28h'
p.project_name
>>> 'New API project'

classmethod create_from_data_source(cls, data_source_id, username=None, password=None, credential_id=None, use_kerberos=None, credential_data=None, project_name=None, max_wait=600, *, use_case=None)

Create a project from a data source. Either data_source or data_source_id should be specified.

Parameters:

data_source_id (str) – the identifier of the data source.
username (Optional[str]) – The username for database authentication. If supplied password must also be supplied.
password (Optional[str]) – The password for database authentication. The password is encrypted at server side and never saved / stored. If supplied username must also be supplied.
credential_id (Optional[str]) – The ID of the set of credentials to use instead of user and password. Note that with this change, username and password will become optional.
use_kerberos (Optional[bool]) – Server default is False. If true, use kerberos authentication for database authentication.
credential_data (dict, optional) – The credentials to authenticate with the database, to use instead of user/password or credential ID.
project_name (Optional[str]) – optional, a name to give to the project.
max_wait (int) – optional, the maximum number of seconds to wait before giving up.
use_case (UseCase | string, optional) – A single UseCase object or ID to add this new Project to. Must be a kwarg.

Raises:

InvalidUsageError – Raised if either username or password is passed without the other.

Return type:

Project

classmethod create_from_dataset(cls, dataset_id, dataset_version_id=None, project_name=None, user=None, password=None, credential_id=None, use_kerberos=None, use_sample_from_dataset=None, credential_data=None, max_wait=600, *, use_case=None)

Create a Project from a datarobot.models.Dataset

Parameters:

dataset_id (string) – The ID of the dataset entry to user for the project’s Dataset
dataset_version_id (string, optional) – The ID of the dataset version to use for the project dataset. If not specified - uses latest version associated with dataset_id
project_name (string, optional) – The name of the project to be created. If not specified, will be “Untitled Project” for database connections, otherwise the project name will be based on the file used.
user (string, optional) – The username for database authentication.
password (string, optional) – The password (in cleartext) for database authentication. The password will be encrypted on the server side in scope of HTTP request and never saved or stored
credential_id (string, optional) – The ID of the set of credentials to use instead of user and password.
use_kerberos (Optional[bool]) – Server default is False. If true, use kerberos authentication for database authentication.
use_sample_from_dataset (Optional[bool]) – Server default is False If true, use the EDA sample for the project instead of the full data. It is optional for datasets between 500 MB and 10 GB. For datasets over 10 GB, this is always set to True on the server side.
credential_data (dict, optional) – The credentials to authenticate with the database, to use instead of user/password or credential ID.
max_wait (int) – optional, the maximum number of seconds to wait before giving up.
use_case (UseCase | string, optional) – A single UseCase object or ID to add this new Project to. Must be a kwarg.

Return type:

Project

classmethod create_from_recipe(cls, recipe_id, *, use_case=None)

Create a project from a recipe

Parameters:: recipe_id (string) – The ID of the recipe entry to use to create the project’s dataset.
Return type:: Project

classmethod create_segmented_project_from_clustering_model(cls, clustering_project_id, clustering_model_id, target, max_wait=600, *, use_case=None)

Create a new segmented project from a clustering model

Parameters:

clustering_project_id (str) – The identifier of the clustering project you want to use as the base.
clustering_model_id (str) – The identifier of the clustering model you want to use as the segmentation method.
target (str) – The name of the target column that will be used from the clustering project.
max_wait (int) – optional, the maximum number of seconds to wait before giving up.
use_case (UseCase | string, optional) – A single UseCase object or ID to add this new Project to. Must be a kwarg.

Returns:

project – The created project

Return type:

Project

classmethod from_async(async_location, max_wait=600)

Given a temporary async status location poll for no more than max_wait seconds until the async process (project creation or setting the target, for example) finishes successfully, then return the ready project

Parameters:

async_location (str) – The URL for the temporary async status resource. This is returned as a header in the response to a request that initiates an async process
max_wait (int) – The maximum number of seconds to wait before giving up.

Returns:

project – The project, now ready

Return type:

Project

Raises:

ProjectAsyncFailureError – If the server returned an unexpected response while polling for the asynchronous operation to resolve
AsyncProcessUnsuccessfulError – If the final result of the asynchronous operation was a failure
AsyncTimeoutError – If the asynchronous operation did not resolve within the time specified

classmethod start(cls, sourcedata, target=None, project_name='Untitled Project', worker_count=None, metric=None, autopilot_on=True, blueprint_threshold=None, response_cap=None, partitioning_method=None, positive_class=None, target_type=None, unsupervised_mode=False, blend_best_models=None, prepare_model_for_deployment=None, consider_blenders_in_recommendation=None, scoring_code_only=None, min_secondary_validation_model_count=None, shap_only_mode=None, relationships_configuration_id=None, autopilot_with_feature_discovery=None, feature_discovery_supervised_feature_reduction=None, unsupervised_type=None, autopilot_cluster_list=None, bias_mitigation_feature_name=None, bias_mitigation_technique=None, include_bias_mitigation_feature_as_predictor_variable=None, incremental_learning_only_mode=None, incremental_learning_on_best_model=None, number_of_incremental_learning_iterations_before_best_model_selection=None, *, use_case=None)

Chain together project creation, file upload, and target selection.

Note

While this function provides a simple means to get started, it does not expose all possible parameters. For advanced usage, using create, set_advanced_options and analyze_and_model directly is recommended.

Parameters:

sourcedata (str or pandas.DataFrame) – The path to the file to upload. Can be either a path to a local file or a publicly accessible URL (starting with http://, https://, file://, or s3://). If the source is a DataFrame, it will be serialized to a temporary buffer. If using a file, the filename must consist of ASCII characters only.
target (Optional[str]) – The name of the target column in the uploaded file. Should not be provided if unsupervised_mode is True.
project_name (str) – The project name.
worker_count (Optional[int]) – The number of workers that you want to allocate to this project.
metric (Optional[str]) – The name of metric to use.
autopilot_on (boolean, default True) – Whether or not to begin modeling automatically.
blueprint_threshold (Optional[int]) – Number of hours the model is permitted to run. Minimum 1
response_cap (Optional[float]) – Quantile of the response distribution to use for response capping Must be in range 0.5 .. 1.0
partitioning_method (PartitioningMethod object, optional) – Instance of one of the Partition Classes defined in datarobot.helpers.partitioning_methods. As an alternative, use Project.set_partitioning_method or Project.set_datetime_partitioning to set the partitioning for the project.
positive_class (str, float, or int; optional) – Specifies a level of the target column that should be treated as the positive class for binary classification. May only be specified for binary classification targets.
target_type (Optional[str]) – Override the automatically selected target_type. An example usage would be setting the target_type=’Multiclass’ when you want to preform a multiclass classification task on a numeric column that has a low cardinality. You can use TARGET_TYPE enum.
unsupervised_mode (boolean, default False) – Specifies whether to create an unsupervised project.
blend_best_models (Optional[bool]) – blend best models during Autopilot run
scoring_code_only (Optional[bool]) – Keep only models that can be converted to scorable java code during Autopilot run.
shap_only_mode (Optional[bool]) – Keep only models that support SHAP values during Autopilot run. Use SHAP-based insights wherever possible. Defaults to False.
prepare_model_for_deployment (Optional[bool]) – Prepare model for deployment during Autopilot run. The preparation includes creating reduced feature list models, retraining best model on higher sample size, computing insights and assigning “RECOMMENDED FOR DEPLOYMENT” label.
consider_blenders_in_recommendation (Optional[bool]) – Include blenders when selecting a model to prepare for deployment in an Autopilot Run. Defaults to False.
min_secondary_validation_model_count (Optional[int]) – Compute “All backtest” scores (datetime models) or cross validation scores for the specified number of highest ranking models on the Leaderboard, if over the Autopilot default.
relationships_configuration_id (Optional[str]) – (New in version v2.23) id of the relationships configuration to use
autopilot_with_feature_discovery (Optional[bool].) – (New in version v2.23) If true, autopilot will run on a feature list that includes features found via search for interactions.
feature_discovery_supervised_feature_reduction (Optional[bool]) – (New in version v2.23) Run supervised feature reduction for feature discovery projects.
unsupervised_type (UnsupervisedTypeEnum, optional) – (New in version v2.27) Specifies whether an unsupervised project is anomaly detection or clustering.
autopilot_cluster_list (list(int), optional) – (New in version v2.27) Specifies the list of clusters to build for each model during Autopilot. Specifying multiple values in a list will build models with each number of clusters for the Leaderboard.
bias_mitigation_feature_name (Optional[str]) – The feature from protected features that will be used in a bias mitigation task to mitigate bias
bias_mitigation_technique (Optional[str]) – One of datarobot.enums.BiasMitigationTechnique Options: - ‘preprocessingReweighing’ - ‘postProcessingRejectionOptionBasedClassification’ The technique by which we’ll mitigate bias, which will inform which bias mitigation task we insert into blueprints
include_bias_mitigation_feature_as_predictor_variable (Optional[bool]) – Whether we should also use the mitigation feature as in input to the modeler just like any other categorical used for training, i.e. do we want the model to “train on” this feature in addition to using it for bias mitigation
use_case (UseCase | string, optional) – A single UseCase object or ID to add this new Project to. Must be a kwarg.

Returns:

project – The newly created and initialized project.

Return type:

Project

Raises:

AsyncFailureError – Polling for status of async process resulted in response with unsupported status code
AsyncProcessUnsuccessfulError – Raised if project creation or target setting was unsuccessful
AsyncTimeoutError – Raised if project creation or target setting timed out

Examples

Project.start("./tests/fixtures/file.csv",
              "a_target",
              project_name="test_name",
              worker_count=4,
              metric="a_metric")

This is an example of using a URL to specify the datasource:

Project.start("https://example.com/data/file.csv",
              "a_target",
              project_name="test_name",
              worker_count=4,
              metric="a_metric")

classmethod list(search_params=None, use_cases=None, offset=None, limit=None)

Returns the projects associated with this account.

Parameters:

search_params (dict, optional.) –
If not None, the returned projects are filtered by lookup. Currently you can query projects by:
- project_name
use_cases (Union[UseCase, List[UseCase], str, List[str]], optional.) – If not None, the returned projects are filtered to those associated with a specific Use Case or Use Cases. Accepts either the entity or the ID.
offset (Optional[int]) – If provided, specifies the number of results to skip.
limit (Optional[int]) – If provided, specifies the maximum number of results to return. If not provided, returns a maximum of 1000 results.

Returns:

projects – Contains a list of projects associated with this user account.

Return type:

list of Project instances

Raises:

TypeError – Raised if search_params parameter is provided, but is not of supported type.

Examples

List all projects .. code-block:: python

p_list = Project.list() p_list >>> [Project(‘Project One’), Project(‘Two’)]

Search for projects by name .. code-block:: python

Project.list(search_params={‘project_name’: ‘red’}) >>> [Project(‘Prediction Time’), Project(‘Fred Project’)]

List 2nd and 3rd projects .. code-block:: python

Project.list(offset=1, limit=2) >>> [Project(‘Project 2’), Project(‘Project 3’)]

refresh()

Fetches the latest state of the project, and updates this object with that information. This is an in place update, not a new object.

Return type:: None

delete()

Removes this project from your account.

Return type:: None

analyze_and_model(target=None, mode='quick', metric=None, worker_count=None, positive_class=None, partitioning_method=None, featurelist_id=None, advanced_options=None, max_wait=600, target_type=None, credentials=None, feature_engineering_prediction_point=None, unsupervised_mode=False, relationships_configuration_id=None, class_mapping_aggregation_settings=None, segmentation_task_id=None, unsupervised_type=None, autopilot_cluster_list=None, use_gpu=None)

Set target variable of an existing project and begin the autopilot process or send data to DataRobot for feature analysis only if manual mode is specified.

Any options saved using set_options will be used if nothing is passed to advanced_options. However, saved options will be ignored if advanced_options are passed.

Target setting is an asynchronous process, which means that after initial request we will keep polling status of async process that is responsible for target setting until it’s finished. For SDK users this only means that this method might raise exceptions related to it’s async nature.

When execution returns to the caller, the autopilot process will already have commenced (again, unless manual mode is specified).

Parameters:

target (Optional[str]) – The name of the target column in the uploaded file. Should not be provided if unsupervised_mode is True.
mode (Optional[str]) –
You can use AUTOPILOT_MODE enum to choose between
- AUTOPILOT_MODE.FULL_AUTO
- AUTOPILOT_MODE.MANUAL
- AUTOPILOT_MODE.QUICK
- AUTOPILOT_MODE.COMPREHENSIVE: Runs all blueprints in the repository (warning: this may be extremely slow).
If unspecified, QUICK is used. If the MANUAL value is used, the model creation process will need to be started by executing the start_autopilot function with the desired featurelist. It will start immediately otherwise.
metric (Optional[str]) – Name of the metric to use for evaluating models. You can query the metrics available for the target by way of Project.get_metrics. If none is specified, then the default recommended by DataRobot is used.
worker_count (Optional[int]) – The number of concurrent workers to request for this project. If None, then the default is used. (New in version v2.14) Setting this to -1 will request the maximum number available to your account.
partitioning_method (PartitioningMethod object, optional) – Instance of one of the Partition Classes defined in datarobot.helpers.partitioning_methods. As an alternative, use Project.set_partitioning_method or Project.set_datetime_partitioning to set the partitioning for the project.
positive_class (str, float, or int; optional) – Specifies a level of the target column that should be treated as the positive class for binary classification. May only be specified for binary classification targets.
featurelist_id (Optional[str]) – Specifies which feature list to use.
advanced_options (AdvancedOptions, optional) – Used to set advanced options of project creation. Will override any options saved using set_options.
max_wait (Optional[int]) – Time in seconds after which target setting is considered unsuccessful.
target_type (Optional[str]) – Override the automatically selected target_type. An example usage would be setting the target_type=’Multiclass’ when you want to preform a multiclass classification task on a numeric column that has a low cardinality. You can use TARGET_TYPE enum.
credentials (Optional[List]) – a list of credentials for the datasets used in relationship configuration (previously graphs).
feature_engineering_prediction_point (Optional[str]) – additional aim parameter.
unsupervised_mode (boolean, default False) – (New in version v2.20) Specifies whether to create an unsupervised project. If True, target may not be provided.
relationships_configuration_id (Optional[str]) – (New in version v2.21) ID of the relationships configuration to use.
segmentation_task_id (str or SegmentationTask, optional) – (New in version v2.28) The segmentation task that should be used to split the project for segmented modeling.
unsupervised_type (UnsupervisedTypeEnum, optional) – (New in version v2.27) Specifies whether an unsupervised project is anomaly detection or clustering.
autopilot_cluster_list (list(int), optional) – (New in version v2.27) Specifies the list of clusters to build for each model during Autopilot. Specifying multiple values in a list will build models with each number of clusters for the Leaderboard.
use_gpu (Optional[bool]) – (New in version v3.2) Specifies whether project should use GPUs

Returns:

project – The instance with updated attributes.

Return type:

Project

Raises:

AsyncFailureError – Polling for status of async process resulted in response with unsupported status code
AsyncProcessUnsuccessfulError – Raised if target setting was unsuccessful
AsyncTimeoutError – Raised if target setting took more time, than specified by max_wait parameter
TypeError – Raised if advanced_options, partitioning_method or target_type is provided, but is not of supported type

See also

datarobot.models.Project.start: Combines project creation, file upload, and target selection. Provides fewer options, but is useful for getting started quickly.
datarobot.models.Project.analyze_and_model: the method replacing set_target after it is removed.

get_model_records(sort_by_partition='validation', sort_by_metric=None, with_metric=None, search_term=None, featurelists=None, families=None, blueprints=None, labels=None, characteristics=None, training_filters=None, number_of_clusters=None, limit=100, offset=0)

Retrieve paginated model records, sorted by scores, with optional filtering.

Parameters:

sort_by_partition (str, one of validation, backtesting, crossValidation or holdout) – Set the partition to use for sorted (by score) list of models. validation is the default.
sort_by_metric (str) –

Set the project metric to use for model sorting. DataRobot-selected project optimization metric
is the default.
with_metric (str) – For a single-metric list of results, specify that project metric.
search_term (str) – If specified, only models containing the term in their name or processes are returned.
featurelists (List[str]) – If specified, only models trained on selected featurelists are returned.
families (List[str]) – If specified, only models belonging to selected families are returned.
blueprints (List[str]) – If specified, only models trained on specified blueprint IDs are returned.
labels (List[str], starred or prepared for deployment) – If specified, only models tagged with all listed labels are returned.
characteristics (List[str]) – If specified, only models matching all listed characteristics are returned. Possible values “frozen”,”trained on gpu”,”with exportable coefficients”,”with mono constraints”,”with rating table”, “with scoring code”,”new series optimized”
training_filters (List[str]) – If specified, only models matching at least one of the listed training conditions are returned. The following formats are supported for autoML and datetime partitioned projects: - number of rows in training subset For datetime partitioned projects: - <training duration>, example P6Y0M0D - <training_duration>-<time_window_sample_percent>-<sampling_method> Example: P6Y0M0D-78-Random, (returns models trained on 6 years of data, sampling rate 78%, random sampling). - Start/end date - Project settings
number_of_clusters (list of int) – Filter models by number of clusters. Applicable only in unsupervised clustering projects.
limit (int)
offset (int)

Returns:

generic_models

Return type:

list of GenericModel

get_models(order_by=None, search_params=None, with_metric=None, use_new_models_retrieval=False)

List all completed, successful models in the leaderboard for the given project.

Parameters:

order_by (str or list of strings, optional) –
If not None, the returned models are ordered by this attribute. If None, the default return is the order of default project metric.

Allowed attributes to sort by are:
- metric
- sample_pct
If the sort attribute is preceded by a hyphen, models will be sorted in descending order, otherwise in ascending order.

Multiple sort attributes can be included as a comma-delimited string or in a list e.g. order_by=`sample_pct,-metric` or order_by=[sample_pct, -metric]

Using metric to sort by will result in models being sorted according to their validation score by how well they did according to the project metric.
search_params (dict, optional.) –
If not None, the returned models are filtered by lookup. Currently you can query models by:
- name
- sample_pct
- is_starred
with_metric (Optional[str].) – If not None, the returned models will only have scores for this metric. Otherwise all the metrics are returned.
use_new_models_retrieval (bool, False by default) – If true, new retrieval route is used, which supports filtering and returns fewer attributes per individual model. Following attributes are absent and could be retrieved from the blueprint level: monotonic_increasing_featurelist_id, monotonic_decreasing_featurelist_id, supports_composable_ml and supports_monotonic_constraints. Following attributes are absent and could be retrieved from the individual model level: has_empty_clusters, is_n_clusters_dynamically_determined, prediction_threshold and prediction_threshold_read_only. Attribute n_clusters in Model is renamed to number_of_clusters in GenericModel and is returned for unsupervised clustering models.

Returns:

models – All models trained in the project.

Return type:

a list of Model or a list of GenericModel if `use_new_models_retrieval is True.`

Raises:

TypeError – Raised if order_by or search_params parameter is provided, but is not of supported type.

Examples

Project.get('pid').get_models(order_by=['-sample_pct',
                              'metric'])

# Getting models that contain "Ridge" in name
Project.get('pid').get_models(
    search_params={
        'name': "Ridge"
    })

# Filtering models based on 'starred' flag:
Project.get('pid').get_models(search_params={'is_starred': True})

# retrieve additional attributes for the model
model_records = project.get_models(use_new_models_retrieval=True)
model_record = model_records[0]
blueprint_id = model_record.blueprint_id
blueprint = dr.Blueprint.get(project.id, blueprint_id)
model_record.number_of_clusters
blueprint.supports_composable_ml
blueprint.supports_monotonic_constraints
blueprint.monotonic_decreasing_featurelist_id
blueprint.monotonic_increasing_featurelist_id
model = dr.Model.get(project.id, model_record.id)
model.prediction_threshold
model.prediction_threshold_read_only
model.has_empty_clusters
model.is_n_clusters_dynamically_determined

recommended_model()

Returns the default recommended model, or None if there is no default recommended model.

Returns:: recommended_model – The default recommended model.
Return type:: Model or None

get_top_model(metric=None)

Obtain the top ranked model for a given metric/ If no metric is passed in, it uses the project’s default metric. Models that display score of N/A in the UI are not included in the ranking (see https://docs.datarobot.com/en/docs/modeling/reference/model-detail/leaderboard-ref.html#na-scores).

Parameters:: metric (Optional[str]) – Metric to sort models
Returns:: model – The top model
Return type:: Model
Raises:: ValueError – Raised if the project is unsupervised. Raised if the project has no target set. Raised if no metric was passed or the project has no metric. Raised if the metric passed is not used by the models on the leaderboard.

Examples

from datarobot.models.project import Project

project = Project.get("<MY_PROJECT_ID>")
top_model = project.get_top_model()

get_datetime_models()

List all models in the project as DatetimeModels

Requires the project to be datetime partitioned. If it is not, a ClientError will occur.

Returns:: models – the datetime models
Return type:: list of DatetimeModel

get_prime_models()

List all DataRobot Prime models for the project Prime models were created to approximate a parent model, and have downloadable code.

Returns:: models
Return type:: list of PrimeModel

get_prime_files(parent_model_id=None, model_id=None)

List all downloadable code files from DataRobot Prime for the project

Parameters:

parent_model_id (Optional[str]) – Filter for only those prime files approximating this parent model
model_id (Optional[str]) – Filter for only those prime files with code for this prime model

Returns:

files

Return type:

list of PrimeFile

get_dataset()

Retrieve the dataset used to create a project.

Returns:: Dataset used for creation of project or None if no catalog_id present.
Return type:: Dataset

Examples

from datarobot.models.project import Project

project = Project.get("<MY_PROJECT_ID>")
dataset = project.get_dataset()

get_datasets()

List all the datasets that have been uploaded for predictions

Returns:: datasets
Return type:: list of PredictionDataset instances

upload_dataset(sourcedata, max_wait=600, read_timeout=600, forecast_point=None, predictions_start_date=None, predictions_end_date=None, dataset_filename=None, relax_known_in_advance_features_check=None, credentials=None, actual_value_column=None, secondary_datasets_config_id=None)

Upload a new dataset to make predictions against

Parameters:

sourcedata (str, file or pandas.DataFrame) – Data to be used for predictions. If string, can be either a path to a local file, a publicly accessible URL (starting with http://, https://, file://), or raw file content. If using a file on disk, the filename must consist of ASCII characters only.
max_wait (Optional[int]) – The maximum number of seconds to wait for the uploaded dataset to be processed before raising an error.
read_timeout (Optional[int]) – The maximum number of seconds to wait for the server to respond indicating that the initial upload is complete
forecast_point (datetime.datetime or None, optional) – (New in version v2.8) May only be specified for time series projects, otherwise the upload will be rejected. The time in the dataset relative to which predictions should be generated in a time series project. See the Time Series documentation for more information. If not provided, will default to using the latest forecast point in the dataset.
predictions_start_date (datetime.datetime or None, optional) – (New in version v2.11) May only be specified for time series projects. The start date for bulk predictions. Note that this parameter is for generating historical predictions using the training data. This parameter should be provided in conjunction with predictions_end_date. Cannot be provided with the forecast_point parameter.
predictions_end_date (datetime.datetime or None, optional) – (New in version v2.11) May only be specified for time series projects. The end date for bulk predictions, exclusive. Note that this parameter is for generating historical predictions using the training data. This parameter should be provided in conjunction with predictions_start_date. Cannot be provided with the forecast_point parameter.
actual_value_column (string, optional) – (New in version v2.21) Actual value column name, valid for the prediction files if the project is unsupervised and the dataset is considered as bulk predictions dataset. Cannot be provided with the forecast_point parameter.
dataset_filename (string or None, optional) – (New in version v2.14) File name to use for the dataset. Ignored for url and file path sources.
relax_known_in_advance_features_check (Optional[bool]) – (New in version v2.15) For time series projects only. If True, missing values in the known in advance features are allowed in the forecast window at the prediction time. If omitted or False, missing values are not allowed.
credentials (Optional[List] a list of credentials for the datasets used) – in Feature discovery project
secondary_datasets_config_id (string or None, optional) – (New in version v2.23) The Id of the alternative secondary dataset config to use during prediction for Feature discovery project.

Returns:

dataset – The newly uploaded dataset.

Return type:

PredictionDataset

Raises:

InputNotUnderstoodError – Raised if sourcedata isn’t one of supported types.
AsyncFailureError – Raised if polling for the status of an async process resulted in a response with an unsupported status code.
AsyncProcessUnsuccessfulError – Raised if project creation was unsuccessful (i.e. the server reported an error in uploading the dataset).
AsyncTimeoutError – Raised if processing the uploaded dataset took more time than specified by the max_wait parameter.
ValueError – Raised if forecast_point or predictions_start_date and predictions_end_date are provided, but are not of the supported type.

upload_dataset_from_data_source(data_source_id, username, password, max_wait=600, forecast_point=None, relax_known_in_advance_features_check=None, credentials=None, predictions_start_date=None, predictions_end_date=None, actual_value_column=None, secondary_datasets_config_id=None)

Upload a new dataset from a data source to make predictions against

Parameters:

data_source_id (str) – The identifier of the data source.
username (str) – The username for database authentication.
password (str) – The password for database authentication. The password is encrypted at server side and never saved / stored.
max_wait (Optional[int]) – Optional, the maximum number of seconds to wait before giving up.
forecast_point (datetime.datetime or None, optional) – (New in version v2.8) For time series projects only. This is the default point relative to which predictions will be generated, based on the forecast window of the project. See the time series prediction documentation for more information.
relax_known_in_advance_features_check (Optional[bool]) – (New in version v2.15) For time series projects only. If True, missing values in the known in advance features are allowed in the forecast window at the prediction time. If omitted or False, missing values are not allowed.
credentials (Optional[List] a list of credentials for the datasets used) – in Feature discovery project
predictions_start_date (datetime.datetime or None, optional) – (New in version v2.20) For time series projects only. The start date for bulk predictions. Note that this parameter is for generating historical predictions using the training data. This parameter should be provided in conjunction with predictions_end_date. Can’t be provided with the forecast_point parameter.
predictions_end_date (datetime.datetime or None, optional) – (New in version v2.20) For time series projects only. The end date for bulk predictions, exclusive. Note that this parameter is for generating historical predictions using the training data. This parameter should be provided in conjunction with predictions_start_date. Can’t be provided with the forecast_point parameter.
actual_value_column (string, optional) – (New in version v2.21) Actual value column name, valid for the prediction files if the project is unsupervised and the dataset is considered as bulk predictions dataset. Cannot be provided with the forecast_point parameter.
secondary_datasets_config_id (string or None, optional) – (New in version v2.23) The Id of the alternative secondary dataset config to use during prediction for Feature discovery project.

Returns:

dataset – the newly uploaded dataset

Return type:

PredictionDataset

upload_dataset_from_catalog(dataset_id, credential_id=None, credential_data=None, dataset_version_id=None, max_wait=600, forecast_point=None, relax_known_in_advance_features_check=None, credentials=None, predictions_start_date=None, predictions_end_date=None, actual_value_column=None, secondary_datasets_config_id=None)

Upload a new dataset from a catalog dataset to make predictions against

Parameters:

dataset_id (str) – The identifier of the dataset.
credential_id (Optional[str]) – The credential ID of the AI Catalog dataset to upload.
credential_data (BasicCredentialsDataDict | S3CredentialsDataDict | OAuthCredentialsDataDict, optional) –
Credential data of the catalog dataset to upload. credential_data can be in one of the following forms:
Basic Credentials:
- credentialType (str)
  The credential type. For basic credentials, this value must be CredentialTypes.BASIC.
- user (str)
  The username for database authentication.
- password (str)
  The password for database authentication. The password is encrypted at rest and never saved or stored.
S3 Credentials
- credentialType (str)
  The credential type. For S3 credentials, this value must be CredentialTypes.S3.
- awsAccessKeyId (Optional[str])
  The S3 AWS access key ID.
- awsSecretAccessKey (Optional[str])
  The S3 AWS secret access key.
- awsSessionToken (Optional[str])
  The S3 AWS session token.
- config_id (Optional[str])
  The ID of the saved shared secure configuration. If specified, cannot include awsAccessKeyId, awsSecretAccessKey or awsSessionToken.
OAuth Credentials
- credentialType (str)
  The credential type. For OAuth credentials, this value must be CredentialTypes.OAUTH.
- oauthRefreshToken (str)
  The oauth refresh token.
- oauthClientId (str)
  The oauth client ID.
- oauthClientSecret (str)
  The oauth client secret.
- oauthAccessToken (str)
  The oauth access token.
Snowflake Key Pair Credentials
- credentialType (str)
  The credential type. For Snowflake Key Pair, this value must be CredentialTypes.SNOWFLAKE_KEY_PAIR_AUTH.
- user (Optional[str])
  The Snowflake login name.
- privateKeyStr (Optional[str])
  The private key copied exactly from user private key file. Since it contains multiple lines, when assign to a variable, put the key string inside triple-quotes
- passphrase (Optional[str])
  The string used to encrypt the private key.
- configId (Optional[str])
  The ID of the saved shared secure configuration. If specified, cannot include user, privateKeyStr or passphrase.
Databricks Access Token Credentials
- credentialType (str)
  The credential type. For a Databricks access token, this value must be CredentialTypes.DATABRICKS_ACCESS_TOKEN.
- databricksAccessToken (str)
  The Databricks personal access token.
Databricks Service Principal Credentials
- credentialType (str)
  The credential type. For Databricks service principal, this value must be CredentialTypes.DATABRICKS_SERVICE_PRINCIPAL.
- clientId (Optional[str])
  The client ID for Databricks service principal.
- clientSecret (Optional[str])
  The client secret for Databricks service principal.
- configId (Optional[str])
  The ID of the saved shared secure configuration. If specified, cannot include clientId and clientSecret.
Azure Service Principal Credentials
- credentialType (str)
  The credential type. For Azure service principal, this value must be CredentialTypes.AZURE_SERVICE_PRINCIPAL.
- clientId (Optional[str])
  The client ID for Azure service principal.
- clientSecret (Optional[str])
  The client secret for Azure service principal.
- azureTenantId (Optional[str])
  The azure tenant ID for Azure service principal.
- configId (Optional[str])
  The ID of the saved shared secure configuration. If specified, cannot include clientId and clientSecret.
dataset_version_id (Optional[str]) – The version id of the dataset to use.
max_wait (Optional[int]) – Optional, the maximum number of seconds to wait before giving up.
forecast_point (datetime.datetime or None, optional) – For time series projects only. This is the default point relative to which predictions will be generated, based on the forecast window of the project. See the time series prediction documentation for more information.
relax_known_in_advance_features_check (Optional[bool]) – For time series projects only. If True, missing values in the known in advance features are allowed in the forecast window at the prediction time. If omitted or False, missing values are not allowed.
credentials (list[BasicCredentialsDict | CredentialIdCredentialsDict], optional) –
A list of credentials for the datasets used in Feature discovery project.

Items in credentials can have the following forms:
Basic Credentials
- user (str)
  The username for database authentication.
- password (str)
  The password (in cleartext) for database authentication. The password will be encrypted on the server side in scope of HTTP request and never saved or stored.
Credential ID
- credentialId (str)
  The ID of the set of credentials to use instead of user and password. Note that with this change, username and password will become optional.
predictions_start_date (datetime.datetime or None, optional) – For time series projects only. The start date for bulk predictions. Note that this parameter is for generating historical predictions using the training data. This parameter should be provided in conjunction with predictions_end_date. Can’t be provided with the forecast_point parameter.
predictions_end_date (datetime.datetime or None, optional) – For time series projects only. The end date for bulk predictions, exclusive. Note that this parameter is for generating historical predictions using the training data. This parameter should be provided in conjunction with predictions_start_date. Can’t be provided with the forecast_point parameter.
actual_value_column (string, optional) – Actual value column name, valid for the prediction files if the project is unsupervised and the dataset is considered as bulk predictions dataset. Cannot be provided with the forecast_point parameter.
secondary_datasets_config_id (string or None, optional) – The Id of the alternative secondary dataset config to use during prediction for Feature discovery project.

Returns:

dataset – the newly uploaded dataset

Return type:

PredictionDataset

get_blueprints()

List all blueprints recommended for a project.

Returns:: menu – All blueprints in a project’s repository.
Return type:: list of Blueprint instances

get_features()

List all features for this project

Returns:: all features for this project
Return type:: list of Feature

get_modeling_features(batch_size=None)

List all modeling features for this project

Only available once the target and partitioning settings have been set. For more information on the distinction between input and modeling features, see the time series documentation.

Parameters:: batch_size (Optional[int]) – The number of features to retrieve in a single API call. If specified, the client may make multiple calls to retrieve the full list of features. If not specified, an appropriate default will be chosen by the server.
Returns:: All modeling features in this project
Return type:: list of ModelingFeature

get_featurelists()

List all featurelists created for this project

Returns:: All featurelists created for this project
Return type:: list of Featurelist

get_associations(assoc_type, metric, featurelist_id=None)

Get the association statistics and metadata for a project’s informative features

Added in version v2.17.

Parameters:

assoc_type (string or None) – The type of association, must be either ‘association’ or ‘correlation’
metric (string or None) – The specified association metric, belongs under either association or correlation umbrella
featurelist_id (string or None) – The desired featurelist for which to get association statistics (New in version v2.19)

Returns:

association_data – Pairwise metric strength data, feature clustering data, and ordering data for Feature Association Matrix visualization

Return type:

dict

get_association_featurelists()

List featurelists and get feature association status for each

Added in version v2.19.

Returns:: feature_lists – Dict with ‘featurelists’ as key, with list of featurelists as values
Return type:: dict

get_association_matrix_details(feature1, feature2)

Get a sample of the actual values used to measure the association between a pair of features

Added in version v2.17.

Parameters:

feature1 (str) – Feature name for the first feature of interest
feature2 (str) – Feature name for the second feature of interest

Returns:

dict – This data has 3 keys: chart_type, features, values, and types
chart_type (str) – Type of plotting the pair of features gets in the UI. e.g. ‘HORIZONTAL_BOX’, ‘VERTICAL_BOX’, ‘SCATTER’ or ‘CONTINGENCY’
values (list) – A list of triplet lists e.g. {“values”: [[460.0, 428.5, 0.001], [1679.3, 259.0, 0.001], …] The first entry of each list is a value of feature1, the second entry of each list is a value of feature2, and the third is the relative frequency of the pair of datapoints in the sample.
features (List[str]) – A list of the passed features, [feature1, feature2]
types (List[str]) – A list of the passed features’ types inferred by DataRobot. e.g. [‘NUMERIC’, ‘CATEGORICAL’]

get_modeling_featurelists(batch_size=None)

List all modeling featurelists created for this project

Modeling featurelists can only be created after the target and partitioning options have been set for a project. In time series projects, these are the featurelists that can be used for modeling; in other projects, they behave the same as regular featurelists.

See the time series documentation for more information.

Parameters:: batch_size (Optional[int]) – The number of featurelists to retrieve in a single API call. If specified, the client may make multiple calls to retrieve the full list of features. If not specified, an appropriate default will be chosen by the server.
Returns:: all modeling featurelists in this project
Return type:: list of ModelingFeaturelist

get_discarded_features()

Retrieve discarded during feature generation features. Applicable for time series projects. Can be called at the modeling stage.

Returns:: discarded_features_info
Return type:: DiscardedFeaturesInfo

restore_discarded_features(features, max_wait=600)

Restore discarded during feature generation features. Applicable for time series projects. Can be called at the modeling stage.

Returns:: status – information about features requested to be restored.
Return type:: FeatureRestorationStatus

create_type_transform_feature(name, parent_name, variable_type, replacement=None, date_extraction=None, max_wait=600)

Create a new feature by transforming the type of an existing feature in the project

Note that only the following transformations are supported:

Text to categorical or numeric
Categorical to text or numeric
Numeric to categorical
Date to categorical or numeric

(type-transform-considerations)= .. note:: Special considerations when casting numeric to categorical

There are two parameters which can be used for variableType to convert numeric data to categorical levels. These differ in the assumptions they make about the input data, and are very important when considering the data that will be used to make predictions. The assumptions that each makes are:

categorical : The data in the column is all integral, and there are no missing values. If either of these conditions do not hold in the training set, the transformation will be rejected. During predictions, if any of the values in the parent column are missing, the predictions will error.

categoricalInt : New in v2.6 All of the data in the column should be considered categorical in its string form when cast to an int by truncation. For example the value 3 will be cast as the string 3 and the value 3.14 will also be cast as the string 3. Further, the value -3.6 will become the string -3. Missing values will still be recognized as missing.

For convenience these are represented in the enum VARIABLE_TYPE_TRANSFORM with the names CATEGORICAL and CATEGORICAL_INT.

Parameters:

name (str) – The name to give to the new feature
parent_name (str) – The name of the feature to transform
variable_type (str) – The type the new column should have. See the values within datarobot.enums.VARIABLE_TYPE_TRANSFORM.
replacement (str or Optional[float]) – The value that missing or unconvertable data should have
date_extraction (Optional[str]) – Must be specified when parent_name is a date column (and left None otherwise). Specifies which value from a date should be extracted. See the list of values in datarobot.enums.DATE_EXTRACTION
max_wait (Optional[int]) – The maximum amount of time to wait for DataRobot to finish processing the new column. This process can take more time with more data to process. If this operation times out, an AsyncTimeoutError will occur. DataRobot continues the processing and the new column may successfully be constructed.

Returns:

The data of the new Feature

Return type:

Feature

Raises:

AsyncFailureError – If any of the responses from the server are unexpected
AsyncProcessUnsuccessfulError – If the job being waited for has failed or has been cancelled
AsyncTimeoutError – If the resource did not resolve in time

get_featurelist_by_name(name)

Creates a new featurelist

Parameters:: name (Optional[str]) – The name of the Project’s featurelist to get.
Returns:: featurelist found by name, optional
Return type:: Featurelist

Examples

project = Project.get('5223deadbeefdeadbeef0101')
featurelist = project.get_featurelist_by_name("Raw Features")

create_featurelist(name=None, features=None, starting_featurelist=None, starting_featurelist_id=None, starting_featurelist_name=None, features_to_include=None, features_to_exclude=None)

Creates a new featurelist

Parameters:

name (Optional[str]) – The name to give to this new featurelist. Names must be unique, so an error will be returned from the server if this name has already been used in this project. We dynamically create a name if none is provided.
features (list of Optional[str]) – The names of the features. Each feature must exist in the project already.
starting_featurelist (Featurelist, optional) – The featurelist to use as the basis when creating a new featurelist. starting_featurelist.features will be read to get the list of features that we will manipulate.
starting_featurelist_id (Optional[str]) – The featurelist ID used instead of passing an object instance.
starting_featurelist_name (Optional[str]) – The featurelist name like “Informative Features” to find a featurelist via the API, and use to fetch features.
features_to_include (list of Optional[str]) – The list of the feature names to include in new featurelist. Throws an error if an item in this list is not in the featurelist that was passed, or that was retrieved from the API. If nothing is passed, all features are included from the starting featurelist.
features_to_exclude (list of Optional[str]) – The list of the feature names to exclude in the new featurelist. Throws an error if an item in this list is not in the featurelist that was passed, also throws an error if a feature is in this list as well as features_to_include. Method cannot use both at the same time.

Returns:

newly created featurelist

Return type:

Featurelist

Raises:

DuplicateFeaturesError – Raised if features variable contains duplicate features
InvalidUsageError – Raised method is called with incompatible arguments

Examples

project = Project.get('5223deadbeefdeadbeef0101')
flists = project.get_featurelists()

# Create a new featurelist using a subset of features from an
# existing featurelist
flist = flists[0]
features = flist.features[::2]  # Half of the features

new_flist = project.create_featurelist(
    name='Feature Subset',
    features=features,
)

project = Project.get('5223deadbeefdeadbeef0101')

# Create a new featurelist using a subset of features from an
# existing featurelist by using features_to_exclude param

new_flist = project.create_featurelist(
    name='Feature Subset of Existing Featurelist',
    starting_featurelist_name="Informative Features",
    features_to_exclude=["metformin", "weight", "age"],
)

create_modeling_featurelist(name, features, skip_datetime_partition_column=False)

Create a new modeling featurelist

Modeling featurelists can only be created after the target and partitioning options have been set for a project. In time series projects, these are the featurelists that can be used for modeling; in other projects, they behave the same as regular featurelists.

See the time series documentation for more information.

Parameters:

name (str) – the name of the modeling featurelist to create. Names must be unique within the project, or the server will return an error.
features (List[str]) – the names of the features to include in the modeling featurelist. Each feature must be a modeling feature.
skip_datetime_partition_column (boolean, optional) – False by default. If True, featurelist will not contain datetime partition column. Use to create monotonic feature lists in Time Series projects. Setting makes no difference for not Time Series projects. Monotonic featurelists can not be used for modeling.

Returns:

featurelist – the newly created featurelist

Return type:

ModelingFeaturelist

Examples

project = Project.get('1234deadbeeffeeddead4321')
modeling_features = project.get_modeling_features()
selected_features = [feat.name for feat in modeling_features][:5]  # select first five
new_flist = project.create_modeling_featurelist('Model This', selected_features)

get_metrics(feature_name)

Get the metrics recommended for modeling on the given feature.

Parameters:

feature_name (str) – The name of the feature to query regarding which metrics are recommended for modeling.

Returns:

feature_name (str) – The name of the feature that was looked up
available_metrics (List[str]) – An array of strings representing the appropriate metrics. If the feature cannot be selected as the target, then this array will be empty.
metric_details (list of dict) – The list of metricDetails objects

metric_name: str
Name of the metric

supports_timeseries: boolean
This metric is valid for timeseries

supports_multiclass: boolean
This metric is valid for multiclass classification

supports_binary: boolean
This metric is valid for binary classification

supports_regression: boolean
This metric is valid for regression

ascending: boolean
Should the metric be sorted in ascending order

get_status()

Query the server for project status.

Returns:

status – Contains:

autopilot_done : a boolean.
stage : a short string indicating which stage the project is in.
stage_description : a description of what stage means.

Return type:

dict

Examples

{"autopilot_done": False,
 "stage": "modeling",
 "stage_description": "Ready for modeling"}

pause_autopilot()

Pause autopilot, which stops processing the next jobs in the queue.

Returns:: paused – Whether the command was acknowledged
Return type:: boolean

unpause_autopilot()

Unpause autopilot, which restarts processing the next jobs in the queue.

Returns:: unpaused – Whether the command was acknowledged.
Return type:: boolean

start_autopilot(featurelist_id, mode='quick', blend_best_models=False, scoring_code_only=False, prepare_model_for_deployment=True, consider_blenders_in_recommendation=False, run_leakage_removed_feature_list=True, autopilot_cluster_list=None)

Start Autopilot on provided featurelist with the specified Autopilot settings, halting the current Autopilot run.

Only one autopilot can be running at the time. That’s why any ongoing autopilot on a different featurelist will be halted - modeling jobs in queue would not be affected but new jobs would not be added to queue by the halted autopilot.

Parameters:

featurelist_id (str) – Identifier of featurelist that should be used for autopilot
mode (Optional[str]) –
The Autopilot mode to run. You can use AUTOPILOT_MODE enum to choose between
- AUTOPILOT_MODE.FULL_AUTO
- AUTOPILOT_MODE.QUICK
- AUTOPILOT_MODE.COMPREHENSIVE
If unspecified, AUTOPILOT_MODE.QUICK is used.
blend_best_models (Optional[bool]) – Blend best models during Autopilot run. This option is not supported in SHAP-only ‘ ‘mode.
scoring_code_only (Optional[bool]) – Keep only models that can be converted to scorable java code during Autopilot run.
prepare_model_for_deployment (Optional[bool]) – Prepare model for deployment during Autopilot run. The preparation includes creating reduced feature list models, retraining best model on higher sample size, computing insights and assigning “RECOMMENDED FOR DEPLOYMENT” label.
consider_blenders_in_recommendation (Optional[bool]) – Include blenders when selecting a model to prepare for deployment in an Autopilot Run. This option is not supported in SHAP-only mode or for multilabel projects.
run_leakage_removed_feature_list (Optional[bool]) – Run Autopilot on Leakage Removed feature list (if exists).
autopilot_cluster_list (list of Optional[int]) – (New in v2.27) A list of integers, where each value will be used as the number of clusters in Autopilot model(s) for unsupervised clustering projects. Cannot be specified unless project unsupervisedMode is true and unsupervisedType is set to ‘clustering’.

Raises:

AppPlatformError – Raised project’s target was not selected or the settings for Autopilot are invalid for the project project.

Return type:

None

train(trainable, sample_pct=None, featurelist_id=None, source_project_id=None, scoring_type=None, training_row_count=None, monotonic_increasing_featurelist_id=<object object>, monotonic_decreasing_featurelist_id=<object object>, n_clusters=None)

Submit a job to the queue to train a model.

Either sample_pct or training_row_count can be used to specify the amount of data to use, but not both. If neither are specified, a default of the maximum amount of data that can safely be used to train any blueprint without going into the validation data will be selected.

In smart-sampled projects, sample_pct and training_row_count are assumed to be in terms of rows of the minority class.

Note

If the project uses datetime partitioning, use Project.train_datetime instead.

Parameters:

trainable (str or Blueprint) –
For str, this is assumed to be a blueprint_id. If no source_project_id is provided, the project_id will be assumed to be the project that this instance represents.

Otherwise, for a Blueprint, it contains the blueprint_id and source_project_id that we want to use. featurelist_id will assume the default for this project if not provided, and sample_pct will default to using the maximum training value allowed for this project’s partition setup. source_project_id will be ignored if a Blueprint instance is used for this parameter
sample_pct (Optional[float]) – The amount of data to use for training, as a percentage of the project dataset from 0 to 100.
featurelist_id (Optional[str]) – The identifier of the featurelist to use. If not defined, the default for this project is used.
source_project_id (Optional[str]) – Which project created this blueprint_id. If None, it defaults to looking in this project. Note that you must have read permissions in this project.
scoring_type (Optional[str]) – Either validation or crossValidation (also dr.SCORING_TYPE.validation or dr.SCORING_TYPE.cross_validation). validation is available for every partitioning type, and indicates that the default model validation should be used for the project. If the project uses a form of cross-validation partitioning, crossValidation can also be used to indicate that all of the available training/validation combinations should be used to evaluate the model.
training_row_count (Optional[int]) – The number of rows to use to train the requested model.
monotonic_increasing_featurelist_id (Optional[str]) – (new in version 2.11) the id of the featurelist that defines the set of features with a monotonically increasing relationship to the target. Passing None disables increasing monotonicity constraint. Default (dr.enums.MONOTONICITY_FEATURELIST_DEFAULT) is the one specified by the blueprint.
monotonic_decreasing_featurelist_id (Optional[str]) – (new in version 2.11) the id of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. Passing None disables decreasing monotonicity constraint. Default (dr.enums.MONOTONICITY_FEATURELIST_DEFAULT) is the one specified by the blueprint.
n_clusters (Optional[int]) – (new in version 2.27) Number of clusters to use in an unsupervised clustering model. This parameter is used only for unsupervised clustering models that don’t automatically determine the number of clusters.

Returns:

model_job_id – id of created job, can be used as parameter to ModelJob.get method or wait_for_async_model_creation function

Return type:

str

Examples

Use a Blueprint instance:

blueprint = project.get_blueprints()[0]
model_job_id = project.train(blueprint, training_row_count=project.max_train_rows)

Use a blueprint_id, which is a string. In the first case, it is assumed that the blueprint was created by this project. If you are using a blueprint used by another project, you will need to pass the id of that other project as well.

blueprint_id = 'e1c7fc29ba2e612a72272324b8a842af'
project.train(blueprint, training_row_count=project.max_train_rows)

another_project.train(blueprint, source_project_id=project.id)

You can also easily use this interface to train a new model using the data from an existing model:

model = project.get_models()[0]
model_job_id = project.train(model.blueprint.id,
                             sample_pct=100)

train_datetime(blueprint_id, featurelist_id=None, training_row_count=None, training_duration=None, source_project_id=None, monotonic_increasing_featurelist_id=<object object>, monotonic_decreasing_featurelist_id=<object object>, use_project_settings=False, sampling_method=None, n_clusters=None)

Create a new model in a datetime partitioned project

If the project is not datetime partitioned, an error will occur.

All durations should be specified with a duration string such as those returned by the partitioning_methods.construct_duration_string helper method. Please see datetime partitioned project documentation for more information on duration strings.

Parameters:

blueprint_id (str) – the blueprint to use to train the model
featurelist_id (Optional[str]) – the featurelist to use to train the model. If not specified, the project default will be used.
training_row_count (Optional[int]) – the number of rows of data that should be used to train the model. If specified, neither training_duration nor use_project_settings may be specified.
training_duration (Optional[str]) – a duration string specifying what time range the data used to train the model should span. If specified, neither training_row_count nor use_project_settings may be specified.
sampling_method (Optional[str]) – (New in version v2.23) defines the way training data is selected. Can be either random or latest. In combination with training_row_count defines how rows are selected from backtest (latest by default). When training data is defined using time range (training_duration or use_project_settings) this setting changes the way time_window_sample_pct is applied (random by default). Applicable to OTV projects only.
use_project_settings (Optional[bool]) – (New in version v2.20) defaults to False. If True, indicates that the custom backtest partitioning settings specified by the user will be used to train the model and evaluate backtest scores. If specified, neither training_row_count nor training_duration may be specified.
source_project_id (Optional[str]) – the id of the project this blueprint comes from, if not this project. If left unspecified, the blueprint must belong to this project.
monotonic_increasing_featurelist_id (Optional[str]) – (New in version v2.18) optional, the id of the featurelist that defines the set of features with a monotonically increasing relationship to the target. Passing None disables increasing monotonicity constraint. Default (dr.enums.MONOTONICITY_FEATURELIST_DEFAULT) is the one specified by the blueprint.
monotonic_decreasing_featurelist_id (Optional[str]) – (New in version v2.18) optional, the id of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. Passing None disables decreasing monotonicity constraint. Default (dr.enums.MONOTONICITY_FEATURELIST_DEFAULT) is the one specified by the blueprint.
n_clusters (Optional[int]) – The number of clusters to use in the specified unsupervised clustering model. ONLY VALID IN UNSUPERVISED CLUSTERING PROJECTS

Returns:

job – the created job to build the model

Return type:

ModelJob

blend(model_ids, blender_method)

Submit a job for creating blender model. Upon success, the new job will be added to the end of the queue.

Parameters:

model_ids (List[str]) – List of model ids that will be used to create blender. These models should have completed validation stage without errors, and can’t be blenders or DataRobot Prime
blender_method (str) – Chosen blend method, one from datarobot.enums.BLENDER_METHOD. If this is a time series project, only methods in datarobot.enums.TS_BLENDER_METHOD are allowed.

Returns:

model_job – New ModelJob instance for the blender creation job in queue.

Return type:

ModelJob

See also

datarobot.models.Project.check_blendable: to confirm if models can be blended

check_blendable(model_ids, blender_method)

Check if the specified models can be successfully blended

Parameters:

model_ids (List[str]) – List of model ids that will be used to create blender. These models should have completed validation stage without errors, and can’t be blenders or DataRobot Prime
blender_method (str) – Chosen blend method, one from datarobot.enums.BLENDER_METHOD. If this is a time series project, only methods in datarobot.enums.TS_BLENDER_METHOD are allowed.

Return type:

EligibilityResult

start_prepare_model_for_deployment(model_id)

Prepare a specific model for deployment.

The requested model will be trained on the maximum autopilot size then go through the recommendation stages. For datetime partitioned projects, this includes the feature impact stage, retraining on a reduced feature list, and retraining the best of the reduced feature list model and the max autopilot original model on recent data. For non-datetime partitioned projects, this includes the feature impact stage, retraining on a reduced feature list, retraining the best of the reduced feature list model and the max autopilot original model up to the holdout size, then retraining the up-to-the holdout model on the full dataset.

Parameters:: model_id (str) – The model to prepare for deployment.
Return type:: None

get_all_jobs(status=None)

Get a list of jobs

This will give Jobs representing any type of job, including modeling or predict jobs.

Parameters:

status (QUEUE_STATUS enum, optional) –

If called with QUEUE_STATUS.INPROGRESS, will return the jobs that are currently running.

If called with QUEUE_STATUS.QUEUE, will return the jobs that are waiting to be run.

If called with QUEUE_STATUS.ERROR, will return the jobs that have errored.

If no value is provided, will return all jobs currently running or waiting to be run.

Returns:

jobs – Each is an instance of Job

Return type:

list

get_blenders()

Get a list of blender models.

Returns:: list of all blender models in project.
Return type:: list of BlenderModel

get_frozen_models()

Get a list of frozen models

Returns:: list of all frozen models in project.
Return type:: list of FrozenModel

get_combined_models()

Get a list of models in segmented project.

Returns:: list of all combined models in segmented project.
Return type:: list of CombinedModel

get_active_combined_model()

Retrieve currently active combined model in segmented project.

Returns:: currently active combined model in segmented project.
Return type:: CombinedModel

get_segments_models(combined_model_id=None)

Retrieve a list of all models belonging to the segments/child projects of the segmented project.

Parameters:: combined_model_id (Optional[str]) – Id of the combined model to get segments for. If there is only a single combined model it can be retrieved automatically, but this must be specified when there are > 1 combined models.
Returns:: segments_models – A list of dictionaries containing all of the segments/child projects, each with a list of their models ordered by metric from best to worst.
Return type:: list(dict)

get_model_jobs(status=None)

Get a list of modeling jobs

Parameters:

status (QUEUE_STATUS enum, optional) –

If called with QUEUE_STATUS.INPROGRESS, will return the modeling jobs that are currently running.

If called with QUEUE_STATUS.QUEUE, will return the modeling jobs that are waiting to be run.

If called with QUEUE_STATUS.ERROR, will return the modeling jobs that have errored.

If no value is provided, will return all modeling jobs currently running or waiting to be run.

Returns:

jobs – Each is an instance of ModelJob

Return type:

list

get_predict_jobs(status=None)

Get a list of prediction jobs

Parameters:

status (QUEUE_STATUS enum, optional) –

If called with QUEUE_STATUS.INPROGRESS, will return the prediction jobs that are currently running.

If called with QUEUE_STATUS.QUEUE, will return the prediction jobs that are waiting to be run.

If called with QUEUE_STATUS.ERROR, will return the prediction jobs that have errored.

If called without a status, will return all prediction jobs currently running or waiting to be run.

Returns:

jobs – Each is an instance of PredictJob

Return type:

list

wait_for_autopilot(check_interval=20.0, timeout=86400, verbosity=1)

Blocks until autopilot is finished. This will raise an exception if the autopilot mode is changed from AUTOPILOT_MODE.FULL_AUTO.

It makes API calls to sync the project state with the server and to look at which jobs are enqueued.

Parameters:

check_interval (float or int) – The maximum time (in seconds) to wait between checks for whether autopilot is finished
timeout (float or int or None) – After this long (in seconds), we give up. If None, never timeout.
verbosity (Union[int, Enum]) – This should be VERBOSITY_LEVEL.SILENT or VERBOSITY_LEVEL.VERBOSE. For VERBOSITY_LEVEL.SILENT, nothing will be displayed about progress. For VERBOSITY_LEVEL.VERBOSE, the number of jobs in progress or queued is shown. Note that new jobs are added to the queue along the way.

Raises:

AsyncTimeoutError – If autopilot does not finished in the amount of time specified
RuntimeError – If a condition is detected that indicates that autopilot will not complete on its own

Return type:

None

rename(project_name)

Update the name of the project.

Parameters:: project_name (str) – The new name
Return type:: None

set_project_description(project_description)

Set or Update the project description.

Parameters:: project_description (str) – The new description for this project.
Return type:: None

unlock_holdout()

Unlock the holdout for this project.

This will cause subsequent queries of the models of this project to contain the metric values for the holdout set, if it exists.

Take care, as this cannot be undone. Remember that best practice is to select a model before analyzing the model performance on the holdout set

Return type:: None

set_worker_count(worker_count)

Sets the number of workers allocated to this project.

Note that this value is limited to the number allowed by your account. Lowering the number will not stop currently running jobs, but will cause the queue to wait for the appropriate number of jobs to finish before attempting to run more jobs.

Parameters:: worker_count (int) – The number of concurrent workers to request from the pool of workers. (New in version v2.14) Setting this to -1 will update the number of workers to the maximum available to your account.
Return type:: None

set_advanced_options(advanced_options=None, **kwargs)

Update the advanced options of this project.

Note

project options will not be stored at the database level, so the options set via this method will only be attached to a project instance for the lifetime of a client session (if you quit your session and reopen a new one before running autopilot, the advanced options will be lost).

Either accepts an AdvancedOptions object to replace all advanced options or individual keyword arguments. This is an inplace update, not a new object. The options set will only remain for the life of this project instance within a given session.

Parameters:

advanced_options (AdvancedOptions, optional) – AdvancedOptions instance as an alternative to passing individual parameters.
weights (string, optional) – The name of a column indicating the weight of each row
response_cap (float in [0.5, 1), optional) – Quantile of the response distribution to use for response capping.
blueprint_threshold (Optional[int]) – Number of hours models are permitted to run before being excluded from later autopilot stages Minimum 1
seed (Optional[int]) – a seed to use for randomization
smart_downsampled (Optional[bool]) – whether to use smart downsampling to throw away excess rows of the majority class. Only applicable to classification and zero-boosted regression projects.
majority_downsampling_rate (Optional[float]) – The percentage between 0 and 100 of the majority rows that should be kept. Specify only if using smart downsampling. May not cause the majority class to become smaller than the minority class.
offset (list of Optional[str]) – (New in version v2.6) the list of the names of the columns containing the offset of each row
exposure (string, optional) – (New in version v2.6) the name of a column containing the exposure of each row
accuracy_optimized_mb (Optional[bool]) – (New in version v2.6) Include additional, longer-running models that will be run by the autopilot and available to run manually.
events_count (string, optional) – (New in version v2.8) the name of a column specifying events count.
monotonic_increasing_featurelist_id (string, optional) – (new in version 2.11) the id of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If None, no such constraints are enforced. When specified, this will set a default for the project that can be overridden at model submission time if desired.
monotonic_decreasing_featurelist_id (string, optional) – (new in version 2.11) the id of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If None, no such constraints are enforced. When specified, this will set a default for the project that can be overridden at model submission time if desired.
only_include_monotonic_blueprints (Optional[bool]) – (new in version 2.11) when true, only blueprints that support enforcing monotonic constraints will be available in the project or selected for the autopilot.
allowed_pairwise_interaction_groups (list of tuple, optional) – (New in version v2.19) For GA2M models - specify groups of columns for which pairwise interactions will be allowed. E.g. if set to [(A, B, C), (C, D)] then GA2M models will allow interactions between columns A x B, B x C, A x C, C x D. All others (A x D, B x D) will not be considered.
blend_best_models (Optional[bool]) – (New in version v2.19) blend best models during Autopilot run
scoring_code_only (Optional[bool]) – (New in version v2.19) Keep only models that can be converted to scorable java code during Autopilot run
shap_only_mode (Optional[bool]) – (New in version v2.21) Keep only models that support SHAP values during Autopilot run. Use SHAP-based insights wherever possible. Defaults to False.
prepare_model_for_deployment (Optional[bool]) – (New in version v2.19) Prepare model for deployment during Autopilot run. The preparation includes creating reduced feature list models, retraining best model on higher sample size, computing insights and assigning “RECOMMENDED FOR DEPLOYMENT” label.
consider_blenders_in_recommendation (Optional[bool]) – (New in version 2.22.0) Include blenders when selecting a model to prepare for deployment in an Autopilot Run. Defaults to False.
min_secondary_validation_model_count (Optional[int]) – (New in version v2.19) Compute “All backtest” scores (datetime models) or cross validation scores for the specified number of highest ranking models on the Leaderboard, if over the Autopilot default.
autopilot_data_sampling_method (Optional[str]) – (New in version v2.23) one of datarobot.enums.DATETIME_AUTOPILOT_DATA_SAMPLING_METHOD. Applicable for OTV projects only, defines if autopilot uses “random” or “latest” sampling when iteratively building models on various training samples. Defaults to “random” for duration-based projects and to “latest” for row-based projects.
run_leakage_removed_feature_list (Optional[bool]) – (New in version v2.23) Run Autopilot on Leakage Removed feature list (if exists).
autopilot_with_feature_discovery (Optional[bool].) – (New in version v2.23) If true, autopilot will run on a feature list that includes features found via search for interactions.
feature_discovery_supervised_feature_reduction (Optional[bool]) – (New in version v2.23) Run supervised feature reduction for feature discovery projects.
exponentially_weighted_moving_alpha (Optional[float]) – (New in version v2.26) defaults to None, value between 0 and 1 (inclusive), indicates alpha parameter used in exponentially weighted moving average within feature derivation window.
external_time_series_baseline_dataset_id (Optional[str].) – (New in version v2.26) If provided, will generate metrics scaled by external model predictions metric for time series projects. The external predictions catalog must be validated before autopilot starts, see Project.validate_external_time_series_baseline and external baseline predictions documentation for further explanation.
use_supervised_feature_reduction (bool, default True optional) – Time Series only. When true, during feature generation DataRobot runs a supervised algorithm to retain only qualifying features. Setting to false can severely impact autopilot duration, especially for datasets with many features.
primary_location_column (Optional[str].) – The name of primary location column.
protected_features (list of Optional[str].) – (New in version v2.24) A list of project features to mark as protected for Bias and Fairness testing calculations. Max number of protected features allowed is 10.
preferable_target_value (Optional[str].) – (New in version v2.24) A target value that should be treated as a favorable outcome for the prediction. For example, if we want to check gender discrimination for giving a loan and our target is named is_bad, then the positive outcome for the prediction would be No, which means that the loan is good and that’s what we treat as a favorable result for the loaner.
fairness_metrics_set (Optional[str].) – (New in version v2.24) Metric to use for calculating fairness. Can be one of proportionalParity, equalParity, predictionBalance, trueFavorableAndUnfavorableRateParity or favorableAndUnfavorablePredictiveValueParity. Used and required only if Bias & Fairness in AutoML feature is enabled.
fairness_threshold (Optional[str].) – (New in version v2.24) Threshold value for the fairness metric. Can be in a range of [0.0, 1.0]. If the relative (i.e. normalized) fairness score is below the threshold, then the user will see a visual indication on the
bias_mitigation_feature_name (Optional[str]) – The feature from protected features that will be used in a bias mitigation task to mitigate bias
bias_mitigation_technique (Optional[str]) – One of datarobot.enums.BiasMitigationTechnique Options: - ‘preprocessingReweighing’ - ‘postProcessingRejectionOptionBasedClassification’ The technique by which we’ll mitigate bias, which will inform which bias mitigation task we insert into blueprints
include_bias_mitigation_feature_as_predictor_variable (Optional[bool]) – Whether we should also use the mitigation feature as in input to the modeler just like any other categorical used for training, i.e. do we want the model to “train on” this feature in addition to using it for bias mitigation
series_id (string, optional) – (New in version v3.6) The name of a column containing the series ID for each row.
forecast_distance (string, optional) – (New in version v3.6) The name of a column containing the forecast distance for each row.
forecast_offsets (list of Optional[str]) – (New in version v3.6) The list of the names of the columns containing the forecast offsets for each row.
incremental_learning_only_mode (Optional[bool]) – (New in version v3.4) Keep only models that support incremental learning during Autopilot run.
incremental_learning_on_best_model (Optional[bool]) – (New in version v3.4) Run incremental learning on the best model during Autopilot run.
chunk_definition_id (string, optional) – (New in version v3.4) Unique definition for chunks needed to run automated incremental learning.
incremental_learning_early_stopping_rounds (Optional[int]) – (New in version v3.4) Early stopping rounds used in the automated incremental learning service.
number_of_incremental_learning_iterations_before_best_model_selection (Optional[int] = None) – (New in version v3.6) Number of iterations top 5 models complete prior to best model selection. The minimum is 1, which means no additional iterations after the first iteration (initial model) will be run. The maximum is 10.

Return type:

None

list_advanced_options()

View the advanced options that have been set on a project instance. Includes those that haven’t been set (with value of None).

Return type:: dict of advanced options and their values

set_partitioning_method(cv_method=None, validation_type=None, seed=0, reps=None, user_partition_col=None, training_level=None, validation_level=None, holdout_level=None, cv_holdout_level=None, validation_pct=None, holdout_pct=None, partition_key_cols=None, partitioning_method=None)

Configures the partitioning method for this project.

If this project does not already have a partitioning method set, creates a new configuration based on provided args.

If the partitioning_method arg is set, that configuration will instead be used.

Note

This is an inplace update, not a new object. The options set will only remain for the life of this project instance within a given session. You must still call set_target to make this change permanent for the project. Calling refresh without first calling set_target will invalidate this configuration. Similarly, calling get to retrieve a second copy of the project will not include this configuration.

Added in version v3.0.

Parameters:

cv_method (str) – The partitioning method used. Supported values can be found in datarobot.enums.CV_METHOD.
validation_type (str) – May be “CV” (K-fold cross-validation) or “TVH” (Training, validation, and holdout).
seed (int) – A seed to use for randomization.
reps (int) – Number of cross validation folds to use.
user_partition_col (str) – The name of the column containing the partition assignments.
training_level (Union[str,int]) – The value of the partition column indicating a row is part of the training set.
validation_level (Union[str,int]) – The value of the partition column indicating a row is part of the validation set.
holdout_level (Union[str,int]) – The value of the partition column indicating a row is part of the holdout set (use None if you want no holdout set).
cv_holdout_level (Union[str,int]) – The value of the partition column indicating a row is part of the holdout set.
validation_pct (int) – The desired percentage of dataset to assign to validation set.
holdout_pct (int) – The desired percentage of dataset to assign to holdout set.
partition_key_cols (list) – A list containing a single string, where the string is the name of the column whose values should remain together in partitioning.
partitioning_method (PartitioningMethod, optional) – An instance of datarobot.helpers.partitioning_methods.PartitioningMethod that will be used instead of creating a new instance from the other args.

Raises:

TypeError – If cv_method or validation_type are not set and partitioning_method is not set.
InvalidUsageError – If invoked after project.set_target or project.start, or if invoked with the wrong combination of args for a given partitioning method.

Returns:

project – The instance with updated attributes.

Return type:

Project

get_uri()

Returns:: url – Permanent static hyperlink to a project leaderboard.
Return type:: str

get_rating_table_models()

Get a list of models with a rating table

Returns:: list of all models with a rating table in project.
Return type:: list of RatingTableModel

get_rating_tables()

Get a list of rating tables

Returns:: list of rating tables in project.
Return type:: list of RatingTable

get_access_list()

Retrieve users who have access to this project and their access levels

Added in version v2.15.

Return type:: list of SharingAccess

share(access_list, send_notification=None, include_feature_discovery_entities=None)

Modify the ability of users to access this project

Added in version v2.15.

Parameters:

access_list (list of SharingAccess) – the modifications to make.
send_notification (boolean, default None) – (New in version v2.21) optional, whether or not an email notification should be sent, default to None
include_feature_discovery_entities (boolean, default None) – (New in version v2.21) optional (default: None), whether or not to share all the related entities i.e., datasets for a project with Feature Discovery enabled

Raises:

datarobot.ClientError : – if you do not have permission to share this project, if the user you’re sharing with doesn’t exist, if the same user appears multiple times in the access_list, or if these changes would leave the project without an owner

Return type:

None

Examples

Transfer access to the project from old_user@datarobot.com to new_user@datarobot.com

import datarobot as dr

new_access = dr.SharingAccess(new_user@datarobot.com,
                              dr.enums.SHARING_ROLE.OWNER, can_share=True)
access_list = [dr.SharingAccess(old_user@datarobot.com, None), new_access]

dr.Project.get('my-project-id').share(access_list)

batch_features_type_transform(parent_names, variable_type, prefix=None, suffix=None, max_wait=600)

Create new features by transforming the type of existing ones.

Added in version v2.17.

Note

The following transformations are only supported in batch mode:

Text to categorical or numeric

Categorical to text or numeric

Numeric to categorical

See {ref}`here <type-transform-considerations>` for special considerations when casting numeric to categorical. Date to categorical or numeric transformations are not currently supported for batch mode but can be performed individually using create_type_transform_feature.

Parameters:

parent_names (list[str]) – The list of variable names to be transformed.
variable_type (str) – The type new columns should have. Can be one of ‘categorical’, ‘categoricalInt’, ‘numeric’, and ‘text’ - supported values can be found in datarobot.enums.VARIABLE_TYPE_TRANSFORM.
prefix (Optional[str]) –

Note

Either prefix, suffix, or both must be provided.

The string that will preface all feature names. At least one of prefix and suffix must be specified.
suffix (Optional[str]) –

Note

Either prefix, suffix, or both must be provided.

The string that will be appended at the end to all feature names. At least one of prefix and suffix must be specified.
max_wait (Optional[int]) – The maximum amount of time to wait for DataRobot to finish processing the new column. This process can take more time with more data to process. If this operation times out, an AsyncTimeoutError will occur. DataRobot continues the processing and the new column may successfully be constructed.

Returns:

all features for this project after transformation.

Return type:

list of Features

Raises:

TypeError: – If parent_names is not a list.
ValueError – If value of variable_type is not from datarobot.enums.VARIABLE_TYPE_TRANSFORM.
AsyncFailureError – If any of the responses from the server are unexpected.
AsyncProcessUnsuccessfulError – If the job being waited for has failed or has been cancelled.
AsyncTimeoutError – If the resource did not resolve in time.

clone_project(new_project_name=None, max_wait=600)

Create a fresh (post-EDA1) copy of this project that is ready for setting targets and modeling options.

Parameters:

new_project_name (Optional[str]) – The desired name of the new project. If omitted, the API will default to ‘Copy of <original project>’
max_wait (Optional[int]) – Time in seconds after which project creation is considered unsuccessful

Return type:

datarobot.models.Project

create_interaction_feature(name, features, separator, max_wait=600)

Create a new interaction feature by combining two categorical ones.

Added in version v2.21.

Parameters:

name (str) – The name of final Interaction Feature
features (list(str)) – List of two categorical feature names
separator (str) – The character used to join the two data values, one of these ` + - / | & . _ , `
max_wait (Optional[int]) – Time in seconds after which project creation is considered unsuccessful.

Returns:

The data of the new Interaction feature

Return type:

datarobot.models.InteractionFeature

Raises:

ClientError –
If requested Interaction feature can not be created. Possible reasons for example are:
- one of features either does not exist or is of unsupported type
- feature with requested name already exists
- invalid separator character submitted.
AsyncFailureError – If any of the responses from the server are unexpected
AsyncProcessUnsuccessfulError – If the job being waited for has failed or has been cancelled
AsyncTimeoutError – If the resource did not resolve in time

get_relationships_configuration()

Get the relationships configuration for a given project

Added in version v2.21.

Returns:: relationships_configuration – relationships configuration applied to project
Return type:: RelationshipsConfiguration

download_feature_discovery_dataset(file_name, pred_dataset_id=None)

Download Feature discovery training or prediction dataset

Parameters:

file_name (str) – File path where dataset will be saved.
pred_dataset_id (Optional[str]) – ID of the prediction dataset

Return type:

None

download_feature_discovery_recipe_sqls(file_name, model_id=None, max_wait=600)

Export and download Feature discovery recipe SQL statements .. versionadded:: v2.25

Parameters:

file_name (str) – File path where dataset will be saved.
model_id (Optional[str]) – ID of the model to export SQL for. If specified, QL to generate only features used by the model will be exported. If not specified, SQL to generate all features will be exported.
max_wait (Optional[int]) – Time in seconds after which export is considered unsuccessful.

Raises:

ClientError – If requested SQL cannot be exported. Possible reason is the feature is not available to user.
AsyncFailureError – If any of the responses from the server are unexpected.
AsyncProcessUnsuccessfulError – If the job being waited for has failed or has been cancelled.
AsyncTimeoutError – If the resource did not resolve in time.

Return type:

None

validate_external_time_series_baseline(catalog_version_id, target, datetime_partitioning, max_wait=600)

Validate external baseline prediction catalog.

The forecast windows settings, validation and holdout duration specified in the datetime specification must be consistent with project settings as these parameters are used to check whether the specified catalog version id has been validated or not. See external baseline predictions documentation for example usage.

Parameters:

catalog_version_id (str) – Id of the catalog version for validating external baseline predictions.
target (str) – The name of the target column.
datetime_partitioning (DatetimePartitioning object) –
Instance of the DatetimePartitioning defined in datarobot.helpers.partitioning_methods.

Attributes of the object used to check the validation are:
- datetime_partition_column
- forecast_window_start
- forecast_window_end
- holdout_start_date
- holdout_end_date
- backtests
- multiseries_id_columns
If the above attributes are different from the project settings, the catalog version will not pass the validation check in the autopilot.
max_wait (Optional[int]) – The maximum number of seconds to wait for the catalog version to be validated before raising an error.

Returns:

external_baseline_validation_info – Validation result of the specified catalog version.

Return type:

ExternalBaselineValidationInfo

Raises:

AsyncTimeoutError – Raised if the catalog version validation took more time than specified by the max_wait parameter.

download_multicategorical_data_format_errors(file_name)

Download multicategorical data format errors to the CSV file. If any format errors where detected in potentially multicategorical features the resulting file will contain at max 10 entries. CSV file content contains feature name, dataset index in which the error was detected, row value and type of error detected. In case that there were no errors or none of the features where potentially multicategorical the CSV file will be empty containing only the header.

Parameters:: file_name (str) – File path where CSV file will be saved.
Return type:: None

get_multiseries_names()

For a multiseries timeseries project it returns all distinct entries in the multiseries column. For a non timeseries project it will just return an empty list.

Returns:: multiseries_names – List of all distinct entries in the multiseries column
Return type:: List[str]

restart_segment(segment)

Restart single segment in a segmented project.

Added in version v2.28.

Segment restart is allowed only for segments that haven’t reached modeling phase. Restart will permanently remove previous project and trigger set up of a new one for particular segment.

Parameters:: segment (str) – Segment to restart

get_bias_mitigated_models(parent_model_id=None, offset=0, limit=100)

List the child models with bias mitigation applied

Added in version v2.29.

Parameters:

parent_model_id (Optional[str]) – Filter by parent models
offset (Optional[int]) – Number of items to skip.
limit (Optional[int]) – Number of items to return.

Returns:

models

Return type:

list of dict

apply_bias_mitigation(bias_mitigation_parent_leaderboard_id, bias_mitigation_feature_name, bias_mitigation_technique, include_bias_mitigation_feature_as_predictor_variable)

Apply bias mitigation to an existing model by training a version of that model but with bias mitigation applied. An error will be returned if the model does not support bias mitigation with the technique requested.

Added in version v2.29.

Parameters:

bias_mitigation_parent_leaderboard_id (str) – The leaderboard id of the model to apply bias mitigation to
bias_mitigation_feature_name (str) – The feature name of the protected features that will be used in a bias mitigation task to attempt to mitigate bias
bias_mitigation_technique (Optional[str]) – One of datarobot.enums.BiasMitigationTechnique Options: - ‘preprocessingReweighing’ - ‘postProcessingRejectionOptionBasedClassification’ The technique by which we’ll mitigate bias, which will inform which bias mitigation task we insert into blueprints
include_bias_mitigation_feature_as_predictor_variable (bool) – Whether we should also use the mitigation feature as in input to the modeler just like any other categorical used for training, i.e. do we want the model to “train on” this feature in addition to using it for bias mitigation

Returns:

the job of the model with bias mitigation applied that was just submitted for training

Return type:

ModelJob

request_bias_mitigation_feature_info(bias_mitigation_feature_name)

Request a compute job for bias mitigation feature info for a given feature, which will include - if there are any rare classes - if there are any combinations of the target values and the feature values that never occur in the same row - if the feature has a high number of missing values. Note that this feature check is dependent on the current target selected for the project.

Added in version v2.29.

Parameters:: bias_mitigation_feature_name (str) – The feature name of the protected features that will be used in a bias mitigation task to attempt to mitigate bias
Returns:: Bias mitigation feature info model for the requested feature
Return type:: BiasMitigationFeatureInfo

get_bias_mitigation_feature_info(bias_mitigation_feature_name)

Get the computed bias mitigation feature info for a given feature, which will include - if there are any rare classes - if there are any combinations of the target values and the feature values that never occur in the same row - if the feature has a high number of missing values. Note that this feature check is dependent on the current target selected for the project. If this info has not already been computed, this will raise a 404 error.

Added in version v2.29.

Parameters:: bias_mitigation_feature_name (str) – The feature name of the protected features that will be used in a bias mitigation task to attempt to mitigate bias
Returns:: Bias mitigation feature info model for the requested feature
Return type:: BiasMitigationFeatureInfo

classmethod from_data(data)

Instantiate an object of this class using a dict.

Parameters:: data (dict) – Correctly snake_cased keys and their values.
Return type:: TypeVar(T, bound= APIObject)

classmethod from_server_data(data, keep_attrs=None)

Instantiate an object of this class using the data directly from the server, meaning that the keys may have the wrong camel casing

Parameters:

data (dict) – The directly translated dict of JSON from the server. No casing fixes have taken place
keep_attrs (iterable) – List, set or tuple of the dotted namespace notations for attributes to keep within the object structure even if their values are None

Return type:

TypeVar(T, bound= APIObject)

open_in_browser()

Opens class’ relevant web browser location. If default browser is not available the URL is logged.

Note: If text-mode browsers are used, the calling process will block until the user exits the browser.

Return type:: None

set_datetime_partitioning(datetime_partition_spec=None, **kwargs)

Set the datetime partitioning method for a time series project by either passing in a DatetimePartitioningSpecification instance or any individual attributes of that class. Updates self.partitioning_method if already set previously (does not replace it).

This is an alternative to passing a specification to Project.analyze_and_model via the partitioning_method parameter. To see the full partitioning based on the project dataset, use DatetimePartitioning.generate.

Added in version v3.0.

Parameters:: datetime_partition_spec (DatetimePartitioningSpecification) – DatetimePartitioningSpecification, optional The customizable aspects of datetime partitioning for a time series project. An alternative to passing individual settings (attributes of the DatetimePartitioningSpecification class).
Returns:: Full partitioning including user-specified attributes as well as those determined by DR based on the dataset.
Return type:: DatetimePartitioning

list_datetime_partition_spec()

List datetime partitioning settings.

This method makes an API call to retrieve settings from the DB if project is in the modeling stage, i.e. if analyze_and_model (autopilot) has already been called.

If analyze_and_model has not yet been called, this method will instead simply print settings from project.partitioning_method.

Added in version v3.0.

Return type:: DatetimePartitioningSpecification or None

class datarobot.helpers.eligibility_result.EligibilityResult

Represents whether a particular operation is supported

For instance, a function to check whether a set of models can be blended can return an EligibilityResult specifying whether or not blending is supported and why it may not be supported.

Variables:

supported (bool) – whether the operation this result represents is supported
reason (str) – why the operation is or is not supported
context (str) – what operation isn’t supported