Clustering

class datarobot.models.ClusteringModel(id=None, processes=None, featurelist_name=None, featurelist_id=None, project_id=None, sample_pct=None, model_type=None, model_category=None, is_frozen=None, is_n_clusters_dynamically_determined=None, blueprint_id=None, metrics=None, monotonic_increasing_featurelist_id=None, monotonic_decreasing_featurelist_id=None, n_clusters=None, has_empty_clusters=None, supports_monotonic_constraints=None, is_starred=None, prediction_threshold=None, prediction_threshold_read_only=None, model_number=None, parent_model_id=None, supports_composable_ml=None, training_row_count=None, training_duration=None, training_start_date=None, training_end_date=None, data_selection_method=None, time_window_sample_pct=None, sampling_method=None, model_family_full_name=None, is_trained_into_validation=None, is_trained_into_holdout=None)

ClusteringModel extends Model class. It provides provides properties and methods specific to clustering projects.

compute_insights(max_wait=600)

Compute and retrieve cluster insights for model. This method awaits completion of job computing cluster insights and returns results after it is finished. If computation takes longer than specified max_wait exception will be raised.

Parameters:

project_id: str: Project to start creation in.
model_id: str: Project’s model to start creation in.
max_wait: int: Maximum number of seconds to wait before giving up

Returns:

List of ClusterInsight

Raises:

ClientError: Server rejected creation due to client error. Most likely cause is bad project_id or model_id.
AsyncFailureError: If any of the responses from the server are unexpected
AsyncProcessUnsuccessfulError: If the cluster insights computation has failed or was cancelled.
AsyncTimeoutError: If the cluster insights computation did not resolve in time

Return type:

List[ClusterInsight]

property insights: List[ClusterInsight]

Return actual list of cluster insights if already computed.

Returns:

List of ClusterInsight

property clusters: List[Cluster]

Return actual list of Clusters.

Returns:

List of Cluster

update_cluster_names(cluster_name_mappings)

Change many cluster names at once based on list of name mappings.

Parameters:

cluster_name_mappings: List of tuples

Cluster names mapping consisting of current cluster name and old cluster name. Example:

cluster_name_mappings = [
    ("current cluster name 1", "new cluster name 1"),
    ("current cluster name 2", "new cluster name 2")]

Returns:

List of Cluster

Raises:

datarobot.errors.ClientError: Server rejected update of cluster names. Possible reasons include: incorrect format of mapping, mapping introduces duplicates.

Return type:

List[Cluster]

update_cluster_name(current_name, new_name)

Change cluster name from current_name to new_name.

Parameters:

current_name: str: Current cluster name.
new_name: str: New cluster name.

Returns:

List of Cluster

Raises:

datarobot.errors.ClientError: Server rejected update of cluster names.

Return type:

List[Cluster]

class datarobot.models.cluster.Cluster(**kwargs)

Representation of a single cluster.

Attributes:

name: str: Current cluster name
percent: float: Percent of data contained in the cluster. This value is reported after cluster insights are computed for the model.

classmethod list(project_id, model_id)

Retrieve a list of clusters in the model.

Parameters:

project_id: str: ID of the project that the model is part of.
model_id: str: ID of the model.

Returns:

List of clusters

Return type:

List[Cluster]

classmethod update_multiple_names(project_id, model_id, cluster_name_mappings)

Update many clusters at once based on list of name mappings.

Parameters:

project_id: str

ID of the project that the model is part of.

model_id: str

ID of the model.

cluster_name_mappings: List of tuples

Cluster name mappings, consisting of current and previous names for each cluster. Example:

cluster_name_mappings = [
    ("current cluster name 1", "new cluster name 1"),
    ("current cluster name 2", "new cluster name 2")]

Returns:

List of clusters

Raises:

datarobot.errors.ClientError: Server rejected update of cluster names.
ValueError: Invalid cluster name mapping provided.

Return type:

List[Cluster]

classmethod update_name(project_id, model_id, current_name, new_name)

Change cluster name from current_name to new_name

Parameters:

project_id: str: ID of the project that the model is part of.
model_id: str: ID of the model.
current_name: str: Current cluster name
new_name: str: New cluster name

Returns:

List of Cluster

Return type:

List[Cluster]

class datarobot.models.cluster_insight.ClusterInsight(**kwargs)

Holds data on all insights related to feature as well as breakdown per cluster.

Parameters:

feature_name: str: Name of a feature from the dataset.
feature_type: str: Type of feature.
insightsList of classes (ClusterInsight): List provides information regarding the importance of a specific feature in relation to each cluster. Results help understand how the model is grouping data and what each cluster represents.
feature_impact: float: Impact of a feature ranging from 0 to 1.

classmethod compute(project_id, model_id, max_wait=600)

Starts creation of cluster insights for the model and if successful, returns computed ClusterInsights. This method allows calculation to continue for a specified time and if not complete, cancels the request.

Parameters:

project_id: str: ID of the project to begin creation of cluster insights for.
model_id: str: ID of the project model to begin creation of cluster insights for.
max_wait: int: Maximum number of seconds to wait canceling the request.

Returns:

List[ClusterInsight]

Raises:

ClientError: Server rejected creation due to client error. Most likely cause is bad project_id or model_id.
AsyncFailureError: Indicates whether any of the responses from the server are unexpected.
AsyncProcessUnsuccessfulError: Indicates whether the cluster insights computation failed or was cancelled.
AsyncTimeoutError: Indicates whether the cluster insights computation did not resolve within the specified time limit (max_wait).

Return type:

List[ClusterInsight]