pdstools.adm.ADMTrees¶

Classes¶

`AGB`
`ADMTrees`
`ADMTreesModel`	Functions for ADM Gradient boosting
`MultiTrees`

Module Contents¶

class AGB(datamart: pdstools.adm.ADMDatamart.ADMDatamart)¶

Parameters:: datamart (pdstools.adm.ADMDatamart.ADMDatamart)

datamart¶

discover_model_types(df: polars.LazyFrame, by: str = 'Configuration') → dict¶

Discovers the type of model embedded in the pyModelData column.

By default, we do a group_by Configuration, because a model rule can only contain one type of model. Then, for each configuration, we look into the pyModelData blob and find the _serialClass, returning it in a dict.

Parameters:

df (pl.LazyFrame) – The dataframe to search for model types
by (str) – The column to look for types in. Configuration is recommended.
allow_collect (bool, default = False) – set to True to allow discovering modelTypes, even if in lazy strategy. It will fetch one modelData string per configuration.

Return type:

dict

get_agb_models(last: bool = False, by: str = 'Configuration', n_threads: int = 1, query: pdstools.utils.types.QUERY | None = None, verbose: bool = True, **kwargs) → ADMTrees¶

Method to automatically extract AGB models.

Recommended to subset using the querying functionality to cut down on execution time, because it checks for each model ID. If you only have AGB models remaining after the query, it will only return proper AGB models.

Parameters:

last (bool, default = False) – Whether to only look at the last snapshot for each model
by (str, default = 'Configuration') – Which column to determine unique models with
n_threads (int, default = 6) – The number of threads to use for extracting the models. Since we use multithreading, setting this to a reasonable value helps speed up the import.
query (Optional[Union[pl.Expr, list[pl.Expr], str, dict[str, list]]]) – Please refer to _apply_query()
verbose (bool, default = False) – Whether to print out information while importing

Return type:

ADMTrees

class ADMTrees¶

static get_multi_trees(file: polars.DataFrame, n_threads=1, verbose=True, **kwargs)¶

Parameters:: file (polars.DataFrame)

class ADMTreesModel(file: str, **kwargs)¶

Functions for ADM Gradient boosting

ADM Gradient boosting models consist of multiple trees, which build upon each other in a ‘boosting’ fashion. This class provides some functions to extract data from these trees, such as the features on which the trees split, important values for these features, statistics about the trees, or visualising each individual tree.

Parameters:: file (str) – The input file as a json (see notes)

trees¶

Type:: dict

properties¶

Type:: dict

learning_rate¶

Type:: float

model¶

Type:: dict

treeStats¶

Type:: dict

splitsPerTree¶

Type:: dict

gainsPerTree¶

Type:: dict

gainsPerSplit¶

Type:: pl.DataFrame

groupedGainsPerSplit¶

Type:: dict

predictors¶

Type:: set

allValuesPerSplit¶

Type:: dict

Notes

The input file is the extracted json file of the ‘save model’ action in Prediction Studio. The Datamart column ‘pyModelData’ also contains this information, but it is compressed and the values for each split is encoded. Using the ‘save model’ button, only that data is decompressed and decoded.

nospaces = True¶

property metrics: dict[str, Any]¶

Compute CDH_ADM005-style diagnostic metrics for this model.

Returns a flat dictionary of key/value pairs aligned with the CDH_ADM005 telemetry event specification. Metrics that cannot be computed from an exported model (e.g. saturation counts that require bin-level data) are omitted.

See also

Pega

Returns:: Metric name → value mapping.
Return type:: dict[str, Any]

static metric_descriptions() → dict[str, str]¶

Return a dictionary mapping metric names to human-readable descriptions.

These descriptions document every metric returned by the metrics property. They can be used programmatically to annotate reports or plots.

Returns:: Metric name → one-line description.
Return type:: dict[str, str]

property predictors¶

property tree_stats¶

property splits_per_tree¶

property gains_per_tree¶

property gains_per_split¶

property grouped_gains_per_split¶

property all_values_per_split¶

property splits_per_variable_type¶

parse_split_values(value) → tuple[str, str, str]¶

Parses the raw ‘split’ string into its three components.

Once the split is parsed, Python can use it to evaluate.

Parameters:

value (str) – The raw ‘split’ string

Returns:

tuple[str, str, str]
The variable on which the split is done,
The direction of the split (< or ‘in’)
The value on which to split

Return type:

tuple[str, str, str]

static parse_split_values_with_spaces(value) → tuple[str, str, str]¶

Return type:: tuple[str, str, str]

get_predictors() → dict | None¶

Extract predictor names and types from model metadata.

Tries to find predictor metadata from the configuration section of the JSON. Models exported via the Prediction Studio “Save Model” button include a configuration key with an explicit predictor list. However, models exported in the newer format (e.g. via automated pipelines or newer Pega versions) may omit the configuration section entirely, containing only type, modelVersion, algorithm, trainingStats, auc, etc. at the top level. In that case, predictor names and types are inferred from the tree split nodes instead.

Return type:: dict | None

get_gains_per_split() → tuple[dict, dict, polars.DataFrame]¶

Function to compute the gains of each split in each tree.

Return type:: tuple[dict, dict, polars.DataFrame]

get_grouped_gains_per_split() → polars.DataFrame¶

Function to get the gains per split, grouped by split.

It adds some additional information, such as the possible values, the mean gains, and the number of times the split is performed.

Return type:: polars.DataFrame

get_splits_recursively(tree: dict, splits: list, gains: list) → tuple[list, list]¶

Recursively finds splits and their gains for each node.

By Python’s mutatable list mechanic, the easiest way to achieve this is to explicitly supply the function with empty lists. Therefore, the ‘splits’ and ‘gains’ parameter expect empty lists when initially called.

Parameters:

tree (dict)
splits (list)
gains (list)

Returns:

tuple[list, list]
Each split, and its corresponding gain

Return type:

tuple[list, list]

plot_splits_per_variable(subset: set | None = None, show=True)¶

Plots the splits for each variable in the tree.

Parameters:

subset (Optional[set]) – Optional parameter to subset the variables to plot
show (bool) – Whether to display each plot

Return type:

plt.figure

get_tree_stats() → polars.DataFrame¶

Generate a dataframe with useful stats for each tree

Return type:: polars.DataFrame

get_all_values_per_split() → dict¶

Generate a dictionary with the possible values for each split

Return type:: dict

get_nodes_recursively(tree: dict, nodelist: dict, counter: list, childs: dict) → tuple[dict, dict]¶

Recursively walks through each node, used for tree representation.

Again, nodelist, counter and childs expects empty dict, dict and list parameters.

Parameters:

tree (dict)
nodelist (dict)
counter (dict)
childs (list)

Returns:

tuple[dict, list]
The dictionary of nodes and the list of child nodes

Return type:

tuple[dict, dict]

get_tree_representation(tree_number: int) → dict¶

Generates a more usable tree representation.

In this tree representation, each node has an ID, and its attributes are the attributes, with parent and child nodes added as well.

Parameters:

tree_number (int) – The number of the tree, in order of the original json
returns (dict)

Return type:

dict

plot_tree(tree_number: int, highlighted: dict | list | None = None, show=True) → pydot.Graph¶

Plots the chosen decision tree.

Parameters:

tree_number (int) – The number of the tree to visualise
highlighted (Optional[dict, list]) – Optional parameter to highlight nodes in green If a dictionary, it expects an ‘x’: i.e., features with their corresponding values. If a list, expects a list of node IDs for that tree.

Return type:

pydot.Graph

get_visited_nodes(treeID: int, x: dict, save_all: bool = False) → tuple[list, float, list]¶

Finds all visited nodes for a given tree, given an x

Parameters:

treeID (int) – The ID of the tree
x (dict) – Features to split on, with their values
save_all (bool, default = False) – Whether to save all gains for each individual split

Returns:

The list of visited nodes, The score of the final leaf node, The gains for each split in the visited nodes

Return type:

list, float, list

get_all_visited_nodes(x: dict) → polars.DataFrame¶

Loops through each tree, and records the scoring info

Parameters:: x (dict) – Features to split on, with their values
Return type:: pl.DataFrame

score(x: dict) → float¶

Computes the score for a given x

Parameters:: x (dict)
Return type:: float

plot_contribution_per_tree(x: dict, show=True)¶

Plots the contribution of each tree towards the final propensity.

Parameters:: x (dict)

predictor_categorization(x: str, context_keys=None)¶

Parameters:: x (str)

compute_categorization_over_time(predictorCategorization=None, context_keys=None)¶

plot_splits_per_variable_type(predictor_categorization=None, **kwargs)¶

class MultiTrees¶

trees: dict¶

model_name: str | None = None¶

context_keys: list | None = None¶

__repr__()¶

__getitem__(index)¶

__len__()¶

__add__(other)¶

property first¶

property last¶

compute_over_time(predictor_categorization=None)¶