Feature

class datarobot.models.Feature(id, project_id=None, name=None, feature_type=None, importance=None, low_information=None, unique_count=None, na_count=None, date_format=None, min=None, max=None, mean=None, median=None, std_dev=None, time_series_eligible=None, time_series_eligibility_reason=None, time_step=None, time_unit=None, target_leakage=None, feature_lineage_id=None, key_summary=None, multilabel_insights=None)

A feature from a project’s dataset

These are features either included in the originally uploaded dataset or added to it via feature transformations. In time series projects, these will be distinct from the ModelingFeature s created during partitioning; otherwise, they will correspond to the same features. For more information about input and modeling features, see the time series documentation.

The min, max, mean, median, and std_dev attributes provide information about the distribution of the feature in the EDA sample data. For non-numeric features or features created prior to these summary statistics becoming available, they will be None. For features where the summary statistics are available, they will be in a format compatible with the data type, i.e. date type features will have their summary statistics expressed as ISO-8601 formatted date strings.

Attributes:

idint

the id for the feature - note that name is used to reference the feature instead of id

project_idstr

the id of the project the feature belongs to

namestr

the name of the feature

feature_typestr

the type of the feature, e.g. ‘Categorical’, ‘Text’

importancefloat or None

numeric measure of the strength of relationship between the feature and target (independent of any model or other features); may be None for non-modeling features such as partition columns

low_informationbool

whether a feature is considered too uninformative for modeling (e.g. because it has too few values)

unique_countint

number of unique values

na_countint or None

number of missing values

date_formatstr or None

For Date features, the date format string for how this feature was interpreted, compatible with https://docs.python.org/2/library/time.html#time.strftime . For other feature types, None.

minstr, int, float, or None

The minimum value of the source data in the EDA sample

maxstr, int, float, or None

The maximum value of the source data in the EDA sample

meanstr, int, or, float

The arithmetic mean of the source data in the EDA sample

medianstr, int, float, or None

The median of the source data in the EDA sample

std_devstr, int, float, or None

The standard deviation of the source data in the EDA sample

time_series_eligiblebool

Whether this feature can be used as the datetime partition column in a time series project.

time_series_eligibility_reasonstr

Why the feature is ineligible for the datetime partition column in a time series project, or ‘suitable’ when it is eligible.

time_stepint or None

For time series eligible features, a positive integer determining the interval at which windows can be specified. If used as the datetime partition column on a time series project, the feature derivation and forecast windows must start and end at an integer multiple of this value. None for features that are not time series eligible.

time_unitstr or None

For time series eligible features, the time unit covered by a single time step, e.g. ‘HOUR’, or None for features that are not time series eligible.

target_leakagestr

Whether a feature is considered to have target leakage or not. A value of ‘SKIPPED_DETECTION’ indicates that target leakage detection was not run on the feature. ‘FALSE’ indicates no leakage, ‘MODERATE’ indicates a moderate risk of target leakage, and ‘HIGH_RISK’ indicates a high risk of target leakage

feature_lineage_idstr

id of a lineage for automatically discovered features or derived time series features.

key_summary: list of dict

Statistics for top 50 keys (truncated to 103 characters) of Summarized Categorical column example:

{‘key’:’DataRobot’, ‘summary’:{‘min’:0, ‘max’:29815.0, ‘stdDev’:6498.029, ‘mean’:1490.75, ‘median’:0.0, ‘pctRows’:5.0}}

where,

key: string or None

name of the key

summary: dict

statistics of the key

max: maximum value of the key. min: minimum value of the key. mean: mean value of the key. median: median value of the key. stdDev: standard deviation of the key. pctRows: percentage occurrence of key in the EDA sample of the feature.

multilabel_insights_keystr or None

For multicategorical columns this will contain a key for multilabel insights. The key is unique for a project, feature and EDA stage combination. This will be the key for the most recent, finished EDA stage.

classmethod get(project_id, feature_name)

Retrieve a single feature

Parameters:

project_idstr

The ID of the project the feature is associated with.

feature_namestr

The name of the feature to retrieve

Returns:

featureFeature

The queried instance

get_multiseries_properties(multiseries_id_columns, max_wait=600)

Retrieve time series properties for a potential multiseries datetime partition column

Multiseries time series projects use multiseries id columns to model multiple distinct series within a single project. This function returns the time series properties (time step and time unit) of this column if it were used as a datetime partition column with the specified multiseries id columns, running multiseries detection automatically if it had not previously been successfully ran.

Parameters:

multiseries_id_columnslist of str

the name(s) of the multiseries id columns to use with this datetime partition column. Currently only one multiseries id column is supported.

max_waitint, optional

if a multiseries detection task is run, the maximum amount of time to wait for it to complete before giving up

Returns:

propertiesdict

A dict with three keys:

• time_series_eligible : bool, whether the column can be used as a partition column

• time_unit : str or null, the inferred time unit if used as a partition column

• time_step : int or null, the inferred time step if used as a partition column

get_cross_series_properties(datetime_partition_column, cross_series_group_by_columns, max_wait=600)

Retrieve cross-series properties for multiseries ID column.

This function returns the cross-series properties (eligibility as group-by column) of this column if it were used with specified datetime partition column and with current multiseries id column, running cross-series group-by validation automatically if it had not previously been successfully ran.

Parameters:

datetime_partition_columndatetime partition column

cross_series_group_by_columnslist of str

the name(s) of the columns to use with this multiseries ID column. Currently only one cross-series group-by column is supported.

max_waitint, optional

if a multiseries detection task is run, the maximum amount of time to wait for it to complete before giving up

Returns:

propertiesdict

A dict with three keys:

• name : str, column name

• eligibility : str, reason for column eligibility

• isEligible : bool, is column eligible as cross-series group-by

get_multicategorical_histogram()

Retrieve multicategorical histogram for this feature

Added in version v2.24.

Returns:

datarobot.models.MulticategoricalHistogram

Raises:

datarobot.errors.InvalidUsageError

if this method is called on a unsuited feature

ValueError

if no multilabel_insights_key is present for this feature

get_pairwise_correlations()

Retrieve pairwise label correlation for multicategorical features

Added in version v2.24.

Returns:

datarobot.models.PairwiseCorrelations

Raises:

datarobot.errors.InvalidUsageError

if this method is called on a unsuited feature

ValueError

if no multilabel_insights_key is present for this feature

get_pairwise_joint_probabilities()

Retrieve pairwise label joint probabilities for multicategorical features

Added in version v2.24.

Returns:

datarobot.models.PairwiseJointProbabilities

Raises:

datarobot.errors.InvalidUsageError

if this method is called on a unsuited feature

ValueError

if no multilabel_insights_key is present for this feature

get_pairwise_conditional_probabilities()

Retrieve pairwise label conditional probabilities for multicategorical features

Added in version v2.24.

Returns:

datarobot.models.PairwiseConditionalProbabilities

Raises:

datarobot.errors.InvalidUsageError

if this method is called on a unsuited feature

ValueError

if no multilabel_insights_key is present for this feature

classmethod from_data(data)

Instantiate an object of this class using a dict.

Parameters:

datadict

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:

datadict

The directly translated dict of JSON from the server. No casing fixes have taken place

keep_attrsiterable

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)

get_histogram(bin_limit=None)

Retrieve a feature histogram

Parameters:

bin_limitint or None

Desired max number of histogram bins. If omitted, by default endpoint will use 60.

Returns:

featureHistogramFeatureHistogram

The requested histogram with desired number or bins

class datarobot.models.ModelingFeature(project_id=None, name=None, feature_type=None, importance=None, low_information=None, unique_count=None, na_count=None, date_format=None, min=None, max=None, mean=None, median=None, std_dev=None, parent_feature_names=None, key_summary=None, is_restored_after_reduction=None)

A feature used for modeling

In time series projects, a new set of modeling features is created after setting the partitioning options. These features are automatically derived from those in the project’s dataset and are the features used for modeling. Modeling features are only accessible once the target and partitioning options have been set. In projects that don’t use time series modeling, once the target has been set, ModelingFeatures and Features will behave the same.

For more information about input and modeling features, see the time series documentation.

As with the Feature object, the min, max, `mean, median, and std_dev attributes provide information about the distribution of the feature in the EDA sample data. For non-numeric features, they will be None. For features where the summary statistics are available, they will be in a format compatible with the data type, i.e. date type features will have their summary statistics expressed as ISO-8601 formatted date strings.

Attributes:

project_idstr

the id of the project the feature belongs to

namestr

the name of the feature

feature_typestr

the type of the feature, e.g. ‘Categorical’, ‘Text’

importancefloat or None

numeric measure of the strength of relationship between the feature and target (independent of any model or other features); may be None for non-modeling features such as partition columns

low_informationbool

whether a feature is considered too uninformative for modeling (e.g. because it has too few values)

unique_countint

number of unique values

na_countint or None

number of missing values

date_formatstr or None

For Date features, the date format string for how this feature was interpreted, compatible with https://docs.python.org/2/library/time.html#time.strftime . For other feature types, None.

minstr, int, float, or None

The minimum value of the source data in the EDA sample

maxstr, int, float, or None

The maximum value of the source data in the EDA sample

meanstr, int, or, float

The arithmetic mean of the source data in the EDA sample

medianstr, int, float, or None

The median of the source data in the EDA sample

std_devstr, int, float, or None

The standard deviation of the source data in the EDA sample

parent_feature_nameslist of str

A list of the names of input features used to derive this modeling feature. In cases where the input features and modeling features are the same, this will simply contain the feature’s name. Note that if a derived feature was used to create this modeling feature, the values here will not necessarily correspond to the features that must be supplied at prediction time.

key_summary: list of dict

Statistics for top 50 keys (truncated to 103 characters) of Summarized Categorical column example:

{‘key’:’DataRobot’, ‘summary’:{‘min’:0, ‘max’:29815.0, ‘stdDev’:6498.029, ‘mean’:1490.75, ‘median’:0.0, ‘pctRows’:5.0}}

where,

key: string or None

name of the key

summary: dict

statistics of the key

max: maximum value of the key. min: minimum value of the key. mean: mean value of the key. median: median value of the key. stdDev: standard deviation of the key. pctRows: percentage occurrence of key in the EDA sample of the feature.

classmethod get(project_id, feature_name)

Retrieve a single modeling feature

Parameters:

project_idstr

The ID of the project the feature is associated with.

feature_namestr

The name of the feature to retrieve

Returns:

featureModelingFeature

The requested feature

class datarobot.models.DatasetFeature(id_, dataset_id=None, dataset_version_id=None, name=None, feature_type=None, low_information=None, unique_count=None, na_count=None, date_format=None, min_=None, max_=None, mean=None, median=None, std_dev=None, time_series_eligible=None, time_series_eligibility_reason=None, time_step=None, time_unit=None, target_leakage=None, target_leakage_reason=None)

A feature from a project’s dataset

These are features either included in the originally uploaded dataset or added to it via feature transformations.

The min, max, mean, median, and std_dev attributes provide information about the distribution of the feature in the EDA sample data. For non-numeric features or features created prior to these summary statistics becoming available, they will be None. For features where the summary statistics are available, they will be in a format compatible with the data type, i.e. date type features will have their summary statistics expressed as ISO-8601 formatted date strings.

Attributes:

idint

the id for the feature - note that name is used to reference the feature instead of id

dataset_idstr

the id of the dataset the feature belongs to

dataset_version_idstr

the id of the dataset version the feature belongs to

namestr

the name of the feature

feature_typestr, optional

the type of the feature, e.g. ‘Categorical’, ‘Text’

low_informationbool, optional

whether a feature is considered too uninformative for modeling (e.g. because it has too few values)

unique_countint, optional

number of unique values

na_countint, optional

number of missing values

date_formatstr, optional

For Date features, the date format string for how this feature was interpreted, compatible with https://docs.python.org/2/library/time.html#time.strftime . For other feature types, None.

minstr, int, float, optional

The minimum value of the source data in the EDA sample

maxstr, int, float, optional

The maximum value of the source data in the EDA sample

meanstr, int, float, optional

The arithmetic mean of the source data in the EDA sample

medianstr, int, float, optional

The median of the source data in the EDA sample

std_devstr, int, float, optional

The standard deviation of the source data in the EDA sample

time_series_eligiblebool, optional

Whether this feature can be used as the datetime partition column in a time series project.

time_series_eligibility_reasonstr, optional

Why the feature is ineligible for the datetime partition column in a time series project, or ‘suitable’ when it is eligible.

time_stepint, optional

For time series eligible features, a positive integer determining the interval at which windows can be specified. If used as the datetime partition column on a time series project, the feature derivation and forecast windows must start and end at an integer multiple of this value. None for features that are not time series eligible.

time_unitstr, optional

For time series eligible features, the time unit covered by a single time step, e.g. ‘HOUR’, or None for features that are not time series eligible.

target_leakagestr, optional

Whether a feature is considered to have target leakage or not. A value of ‘SKIPPED_DETECTION’ indicates that target leakage detection was not run on the feature. ‘FALSE’ indicates no leakage, ‘MODERATE’ indicates a moderate risk of target leakage, and ‘HIGH_RISK’ indicates a high risk of target leakage

target_leakage_reason: string, optional

The descriptive text explaining the reason for target leakage, if any.

get_histogram(bin_limit=None)

Retrieve a feature histogram

Parameters:

bin_limitint or None

Desired max number of histogram bins. If omitted, by default endpoint will use 60.

Returns:

featureHistogramDatasetFeatureHistogram

The requested histogram with desired number or bins

class datarobot.models.DatasetFeatureHistogram(plot)

classmethod get(dataset_id, feature_name, bin_limit=None, key_name=None)

Retrieve a single feature histogram

Parameters:

dataset_idstr

The ID of the Dataset the feature is associated with.

feature_namestr

The name of the feature to retrieve

bin_limitint or None

Desired max number of histogram bins. If omitted, by default the endpoint will use 60.

key_name: string or None

(Only required for summarized categorical feature) Name of the top 50 keys for which plot to be retrieved

Returns:

featureHistogramFeatureHistogram

The queried instance with plot attribute in it.

class datarobot.models.FeatureHistogram(plot)

classmethod get(project_id, feature_name, bin_limit=None, key_name=None)

Retrieve a single feature histogram

Parameters:

project_idstr

The ID of the project the feature is associated with.

feature_namestr

The name of the feature to retrieve

bin_limitint or None

Desired max number of histogram bins. If omitted, by default endpoint will use 60.

key_name: string or None

(Only required for summarized categorical feature) Name of the top 50 keys for which plot to be retrieved

Returns:

featureHistogramFeatureHistogram

The queried instance with plot attribute in it.

class datarobot.models.InteractionFeature(rows, source_columns, bars, bubbles)

Interaction feature data

Added in version v2.21.

Attributes:

rows: int

Total number of rows

source_columns: list(str)

names of two categorical features which were combined into this one

bars: list(dict)

dictionaries representing frequencies of each independent value from the source columns

bubbles: list(dict)

dictionaries representing frequencies of each combined value in the interaction feature.

classmethod get(project_id, feature_name)

Retrieve a single Interaction feature

Parameters:

project_idstr

The id of the project the feature belongs to

feature_namestr

The name of the Interaction feature to retrieve

Returns:

featureInteractionFeature

The queried instance

class datarobot.models.MulticategoricalHistogram(feature_name, histogram)

Histogram for Multicategorical feature.

Added in version v2.24.

Notes

HistogramValues contains:

• values.[].label : string - Label name

• values.[].plot : list - Histogram for label

• values.[].plot.[].label_relevance : int - Label relevance value

• values.[].plot.[].row_count : int - Row count where label has given relevance

• values.[].plot.[].row_pct : float - Percentage of rows where label has given relevance

Attributes:

feature_namestr

Name of the feature

valueslist(dict)

List of Histogram values with a schema described as HistogramValues

classmethod get(multilabel_insights_key)

Retrieves multicategorical histogram

You might find it more convenient to use Feature.get_multicategorical_histogram instead.

Parameters:

multilabel_insights_key: string

Key for multilabel insights, unique for a project, feature and EDA stage combination. The multilabel_insights_key can be retrieved via Feature.multilabel_insights_key.

Returns:

MulticategoricalHistogram

The multicategorical histogram for multilabel_insights_key

to_dataframe()

Convenience method to get all the information from this multicategorical_histogram instance in form of a pandas.DataFrame.

Returns:

pandas.DataFrame

Histogram information as a multicategorical_histogram. The dataframe will contain these columns: feature_name, label, label_relevance, row_count and row_pct

class datarobot.models.PairwiseCorrelations(*args, **kwargs)

Correlation of label pairs for multicategorical feature.

Added in version v2.24.

Notes

CorrelationValues contain:

• values.[].label_configuration : list of length 2 - Configuration of the label pair

• values.[].label_configuration.[].label : str – Label name

• values.[].statistic_value : float – Statistic value

Attributes:

feature_namestr

Name of the feature

valueslist(dict)

List of correlation values with a schema described as CorrelationValues

statistic_dataframepandas.DataFrame

Correlation values for all label pairs as a DataFrame

classmethod get(multilabel_insights_key)

Retrieves pairwise correlations

You might find it more convenient to use Feature.get_pairwise_correlations instead.

Parameters:

multilabel_insights_key: string

Key for multilabel insights, unique for a project, feature and EDA stage combination. The multilabel_insights_key can be retrieved via Feature.multilabel_insights_key.

Returns:

PairwiseCorrelations

The pairwise label correlations

as_dataframe()

The pairwise label correlations as a (num_labels x num_labels) DataFrame.

Returns:

pandas.DataFrame

The pairwise label correlations. Index and column names allow the interpretation of the values.

class datarobot.models.PairwiseJointProbabilities(*args, **kwargs)

Joint probabilities of label pairs for multicategorical feature.

Added in version v2.24.

Notes

ProbabilityValues contain:

• values.[].label_configuration : list of length 2 - Configuration of the label pair

• values.[].label_configuration.[].relevance : int – 0 for absence of the labels, 1 for the presence of labels

• values.[].label_configuration.[].label : str – Label name

• values.[].statistic_value : float – Statistic value

Attributes:

feature_namestr

Name of the feature

valueslist(dict)

List of joint probability values with a schema described as ProbabilityValues

statistic_dataframesdict(pandas.DataFrame)

Joint Probability values as DataFrames for different relevance combinations.

E.g. The probability P(A=0,B=1) can be retrieved via: pairwise_joint_probabilities.statistic_dataframes[(0,1)].loc['A', 'B']

classmethod get(multilabel_insights_key)

Retrieves pairwise joint probabilities

You might find it more convenient to use Feature.get_pairwise_joint_probabilities instead.

Parameters:

multilabel_insights_key: string

Key for multilabel insights, unique for a project, feature and EDA stage combination. The multilabel_insights_key can be retrieved via Feature.multilabel_insights_key.

Returns:

PairwiseJointProbabilities

The pairwise joint probabilities

as_dataframe(relevance_configuration)

Joint probabilities of label pairs as a (num_labels x num_labels) DataFrame.

Parameters:

relevance_configuration: tuple of length 2

Valid options are (0, 0), (0, 1), (1, 0) and (1, 1). Values of 0 indicate absence of labels and 1 indicates presence of labels. The first value describes the presence for the labels in axis=0 and the second value describes the presence for the labels in axis=1.

For example the matrix values for a relevance configuration of (0, 1) describe the probabilities of absent labels in the index axis and present labels in the column axis.

E.g. The probability P(A=0,B=1) can be retrieved via: pairwise_joint_probabilities.as_dataframe((0,1)).loc['A', 'B']

Returns:

pandas.DataFrame

The joint probabilities for the requested relevance_configuration. Index and column names allow the interpretation of the values.

class datarobot.models.PairwiseConditionalProbabilities(*args, **kwargs)

Conditional probabilities of label pairs for multicategorical feature.

Added in version v2.24.

Notes

ProbabilityValues contain:

• values.[].label_configuration : list of length 2 - Configuration of the label pair

• values.[].label_configuration.[].relevance : int – 0 for absence of the labels, 1 for the presence of labels

• values.[].label_configuration.[].label : str – Label name

• values.[].statistic_value : float – Statistic value

Attributes:

feature_namestr

Name of the feature

valueslist(dict)

List of conditional probability values with a schema described as ProbabilityValues

statistic_dataframesdict(pandas.DataFrame)

Conditional Probability values as DataFrames for different relevance combinations. The label names in the columns are the events, on which we condition. The label names in the index are the events whose conditional probability given the indexes is in the dataframe.

E.g. The probability P(A=0|B=1) can be retrieved via: pairwise_conditional_probabilities.statistic_dataframes[(0,1)].loc['A', 'B']

classmethod get(multilabel_insights_key)

Retrieves pairwise conditional probabilities

You might find it more convenient to use Feature.get_pairwise_conditional_probabilities instead.

Parameters:

multilabel_insights_key: string

Key for multilabel insights, unique for a project, feature and EDA stage combination. The multilabel_insights_key can be retrieved via Feature.multilabel_insights_key.

Returns:

PairwiseConditionalProbabilities

The pairwise conditional probabilities

as_dataframe(relevance_configuration)

Conditional probabilities of label pairs as a (num_labels x num_labels) DataFrame. The label names in the columns are the events, on which we condition. The label names in the index are the events whose conditional probability given the indexes is in the dataframe.

E.g. The probability P(A=0|B=1) can be retrieved via: pairwise_conditional_probabilities.as_dataframe((0, 1)).loc['A', 'B']

Parameters:

relevance_configuration: tuple of length 2

Valid options are (0, 0), (0, 1), (1, 0) and (1, 1). Values of 0 indicate absence of labels and 1 indicates presence of labels. The first value describes the presence for the labels in axis=0 and the second value describes the presence for the labels in axis=1.

For example the matrix values for a relevance configuration of (0, 1) describe the probabilities of absent labels in the index axis given the presence of labels in the column axis.

Returns:

pandas.DataFrame

The conditional probabilities for the requested relevance_configuration. Index and column names allow the interpretation of the values.

Restoring Discarded Features

class datarobot.models.restore_discarded_features.DiscardedFeaturesInfo(total_restore_limit, remaining_restore_limit, count, features)

An object containing information about time series features which were reduced during time series feature generation process. These features can be restored back to the project. They will be included into All Time Series Features and can be used to create new feature lists.

Added in version v2.27.

Attributes:

total_restore_limitint

The total limit indicating how many features can be restored in this project.

remaining_restore_limitint

The remaining available number of the features which can be restored in this project.

featureslist of strings

Discarded features which can be restored.

countint

Discarded features count.

classmethod restore(project_id, features_to_restore, max_wait=600)

Restore discarded during time series feature generation process features back to the project. After restoration features will be included into All Time Series Features. :rtype: FeatureRestorationStatus

Added in version v2.27.

Parameters:

project_id: string

features_to_restore: list of strings

List of the feature names to restore

max_wait: int, optional

max time to wait for features to be restored. Defaults to 10 min

Returns:

status: FeatureRestorationStatus

information about features which were restored and which were not.

classmethod retrieve(project_id)

Retrieve the discarded features information for a given project. :rtype: DiscardedFeaturesInfo

Added in version v2.27.

Parameters:

project_id: string

Returns:

info: DiscardedFeaturesInfo

information about features which were discarded during feature generation process and limits how many features can be restored.

class datarobot.models.restore_discarded_features.FeatureRestorationStatus(warnings, features_to_restore)

Status of the feature restoration process.

Added in version v2.27.

Attributes:

warningslist of strings

Warnings generated for those features which failed to restore

remaining_restore_limitint

The remaining available number of the features which can be restored in this project.

restored_featureslist of strings

Features which were restored