pdstools.adm ============ .. py:module:: pdstools.adm Submodules ---------- .. toctree:: :maxdepth: 1 /autoapi/pdstools/adm/ADMDatamart/index /autoapi/pdstools/adm/ADMTrees/index /autoapi/pdstools/adm/Aggregates/index /autoapi/pdstools/adm/BinAggregator/index /autoapi/pdstools/adm/CDH_Guidelines/index /autoapi/pdstools/adm/Plots/index /autoapi/pdstools/adm/Reports/index /autoapi/pdstools/adm/Schema/index Classes ------- .. autoapisummary:: pdstools.adm.ADMDatamart Package Contents ---------------- .. py:class:: ADMDatamart(model_df: Optional[polars.LazyFrame] = None, predictor_df: Optional[polars.LazyFrame] = None, *, query: Optional[pdstools.utils.types.QUERY] = None, extract_pyname_keys: bool = True) Monitor and analyze ADM data from the Pega Datamart. To initialize this class, either 1. Initialize directly with the model_df and predictor_df polars LazyFrames 2. Use one of the class methods: `from_ds_export`, `from_s3`, `from_dataflow_export` etc. This class will read in the data from different sources, properly structure them from further analysis, and apply correct typing and useful renaming. There is also a few "namespaces" that you can call from this class: - `.plot` contains ready-made plots to analyze the data with - `.aggregates` contains mostly internal data aggregations queries - `.agb` contains analysis utilities for Adaptive Gradient Boosting models - `.generate` leads to some ready-made reports, such as the Health Check - `.bin_aggregator` allows you to compare the bins across various models :param model_df: The Polars LazyFrame representation of the model snapshot table. :type model_df: pl.LazyFrame, optional :param predictor_df: The Polars LazyFrame represenation of the predictor binning table. :type predictor_df: pl.LazyFrame, optional :param query: An optional query to apply to the input data. For details, see :meth:`pdstools.utils.cdh_utils._apply_query`. :type query: QUERY, optional :param extract_pyname_keys: Whether to extract extra keys from the `pyName` column. In older Pega versions, this contained pyTreatment among other (customizable) fields. By default True :type extract_pyname_keys: bool, default = True .. rubric:: Examples >>> from pdstools import ADMDatamart >>> from glob import glob >>> dm = ADMDatamart( model_df = pl.scan_parquet('models.parquet'), predictor_df = pl.scan_parquet('predictors.parquet') query = {"Configuration":["Web_Click_Through"]} ) >>> dm = ADMDatamart.from_ds_export(base_path='/my_export_folder') >>> dm = ADMDatamart.from_s3("pega_export") >>> dm = ADMDatamart.from_dataflow_export(glob("data/models*"), glob("data/preds*")) .. note:: This class depends on two datasets: - `pyModelSnapshots` corresponds to the `model_data` attribute - `pyADMPredictorSnapshots` corresponds to the `predictor_data` attribute For instructions on how to download these datasets, please refer to the following article: https://docs.pega.com/bundle/platform/page/platform/decision-management/exporting-monitoring-database.html .. seealso:: :obj:`pdstools.adm.Plots` The out of the box plots on the Datamart data :obj:`pdstools.adm.Reports` Methods to generate the Health Check and Model Report :obj:`pdstools.utils.cdh_utils._apply_query` How to query the ADMDatamart class and methods .. py:attribute:: model_data :type: Optional[polars.LazyFrame] .. py:attribute:: predictor_data :type: Optional[polars.LazyFrame] .. py:attribute:: combined_data :type: Optional[polars.LazyFrame] .. py:attribute:: plot :type: pdstools.adm.Plots.Plots .. py:attribute:: aggregates :type: pdstools.adm.Aggregates.Aggregates .. py:attribute:: agb :type: pdstools.adm.ADMTrees.AGB .. py:attribute:: generate :type: pdstools.adm.Reports.Reports .. py:attribute:: cdh_guidelines :type: pdstools.adm.CDH_Guidelines.CDHGuidelines .. py:attribute:: bin_aggregator :type: pdstools.adm.BinAggregator.BinAggregator .. py:attribute:: first_action_dates :type: Optional[polars.LazyFrame] .. py:attribute:: context_keys :type: List[str] :value: ['Channel', 'Direction', 'Issue', 'Group', 'Name'] .. py:method:: _get_first_action_dates(df: Optional[polars.LazyFrame]) -> polars.LazyFrame .. py:method:: from_ds_export(model_filename: Optional[str] = None, predictor_filename: Optional[str] = None, base_path: Union[os.PathLike, str] = '.', *, query: Optional[pdstools.utils.types.QUERY] = None, extract_pyname_keys: bool = True) :classmethod: Import the ADMDatamart class from a Pega Dataset Export :param model_filename: The full path or name (if base_path is given) to the model snapshot files, by default None :type model_filename: Optional[str], optional :param predictor_filename: The full path or name (if base_path is given) to the predictor binning snapshot files, by default None :type predictor_filename: Optional[str], optional :param base_path: A base path to provide so that we can automatically find the most recent files for both the model and predictor snapshots, if model_filename and predictor_filename are not given as full paths, by default "." :type base_path: Union[os.PathLike, str], optional :param query: An optional argument to filter out selected data, by default None :type query: Optional[QUERY], optional :param extract_pyname_keys: Whether to extract additional keys from the `pyName` column, by default True :type extract_pyname_keys: bool, optional :returns: The properly initialized ADMDatamart class :rtype: ADMDatamart .. rubric:: Examples >>> from pdstools import ADMDatamart >>> # To automatically find the most recent files in the 'my_export_folder' dir: >>> dm = ADMDatamart.from_ds_export(base_path='/my_export_folder') >>> # To specify individual files: >>> dm = ADMDatamart.from_ds_export( model_df='/Downloads/model_snapshots.parquet', predictor_df = '/Downloads/predictor_snapshots.parquet' ) .. note:: By default, the dataset export in Infinity returns a zip file per table. You do not need to open up this zip file! You can simply point to the zip, and this method will be able to read in the underlying data. .. seealso:: :obj:`pdstools.pega_io.File.read_ds_export` More information on file compatibility :obj:`pdstools.utils.cdh_utils._apply_query` How to query the ADMDatamart class and methods .. py:method:: from_s3() :classmethod: Not implemented yet. Please let us know if you would like this functionality! .. py:method:: from_dataflow_export(model_data_files: Union[Iterable[str], str], predictor_data_files: Union[Iterable[str], str], *, query: Optional[pdstools.utils.types.QUERY] = None, extract_pyname_keys: bool = True, cache_file_prefix: str = '', extension: Literal['json'] = 'json', compression: Literal['gzip'] = 'gzip', cache_directory: Union[os.PathLike, str] = 'cache') :classmethod: Read in data generated by a data flow, such as the Prediction Studio export. Dataflows are able to export data from and to various sources. As they are meant to be used in production, they are highly resiliant. For every partition and every node, a dataflow will output a small json file every few seconds. While this is great for production loads, it can be a bit more tricky to read in the data for smaller-scale and ad-hoc analyses. This method aims to make the ingestion of such highly partitioned data easier. It reads in every individual small json file that the dataflow has output, and caches them to a parquet file in the `cache_directory` folder. As such, if you re-run this method later with more data added since the last export, we will not read in from the (slow) dataflow files, but rather from the (much faster) cache. :param model_data_files: A list of files to read in as the model snapshots :type model_data_files: Union[Iterable[str], str] :param predictor_data_files: A list of files to read in as the predictor snapshots :type predictor_data_files: Union[Iterable[str], str] :param query: A, by default None :type query: Optional[QUERY], optional :param extract_pyname_keys: Whether to extract extra keys from the pyName column, by default True :type extract_pyname_keys: bool, optional :param cache_file_prefix: An optional prefix for the cache files, by default "" :type cache_file_prefix: str, optional :param extension: The extension of the source data, by default "json" :type extension: Literal["json"], optional :param compression: The compression of the source files, by default "gzip" :type compression: Literal["gzip"], optional :param cache_directory: Where to store the cached files, by default "cache" :type cache_directory: Union[os.PathLike, str], optional :returns: An initialized instance of the datamart class :rtype: ADMDatamart .. rubric:: Examples >>> from pdstools import ADMDatamart >>> import glob >>> dm = ADMDatamart.from_dataflow_export(glob("data/models*"), glob("data/preds*")) .. seealso:: :obj:`pdstools.utils.cdh_utils._apply_query` How to query the ADMDatamart class and methods :obj:`glob` Makes creating lists of files much easier .. py:method:: from_pdc(df: polars.LazyFrame, return_df=False) :classmethod: .. py:method:: _validate_model_data(df: Optional[polars.LazyFrame], extract_pyname_keys: bool = True) -> Optional[polars.LazyFrame] Internal method to validate model data .. py:method:: _validate_predictor_data(df: Optional[polars.LazyFrame]) -> Optional[polars.LazyFrame] Internal method to validate predictor data .. py:method:: apply_predictor_categorization(categorization: Union[polars.Expr, Callable[Ellipsis, polars.Expr], Dict[str, Union[str, List[str]]]] = cdh_utils.default_predictor_categorization, *, use_regexp: bool = False, df: Optional[polars.LazyFrame] = None) Apply a new predictor categorization to the datamart tables In certain plots, we use the predictor categorization to indicate what 'kind' a certain predictor is, such as IH, Customer, etc. Call this method with a custom Polars Expression (or a method that returns one) or a simple mapping and it will be applied to the predictor data (and the combined dataset too). When the categorization provides no match, the existing categories are kept as they are. For a reference implementation of a custom predictor categorization, refer to `pdstools.utils.cdh_utils.default_predictor_categorization`. :param categorization: A Polars Expression (or method that returns one) that returns the predictor categories. Should be based on Polars' when.then.otherwise syntax. Alternatively can be a dictionary of categories to (list of) string matches which can be either exact (the default) or regular expressions. By default, `pdstools.utils.cdh_utils.default_predictor_categorization` is used. :type categorization: Union[pl.Expr, Callable[..., pl.Expr], Dict[str, Union[str, List[str]]]] :param use_regexp: Treat the mapping patterns in the `categorization` dictionary as regular expressions rather than plain strings. When treated as regular expressions, they will be interpreted in non-strict mode, so invalid expressions will return in no match. See https://docs.pola.rs/api/python/stable/reference/series/api/polars.Series.str.contains.html for exact behavior of the regular expressions. By default, False :type use_regexp: bool, optional :param df: A Polars Lazyframe to apply the categorization to. If not provided, applies it over the predictor data and combined datasets. By default, None :type df: Optional[pl.LazyFrame], optional .. seealso:: :obj:`pdstools.utils.cdh_utils.default_predictor_categorization` The default method .. rubric:: Examples >>> dm = ADMDatamart(my_data) #uses the OOTB predictor categorization >>> # Uses a custom Polars expression to set the categories >>> dm.apply_predictor_categorization(categorization=pl.when( >>> pl.col("PredictorName").cast(pl.Utf8).str.contains("Propensity") >>> ).then(pl.lit("External Model") >>> ) >>> # Uses a simple dictionary to set the categories >>> dm.apply_predictor_categorization(categorization={ >>> "External Model" : ["Score", "Propensity"]} >>> ) .. py:method:: save_data(path: Union[os.PathLike, str] = '.', selected_model_ids: Optional[List[str]] = None) -> Tuple[Optional[pathlib.Path], Optional[pathlib.Path]] Caches model_data and predictor_data to files. :param path: Where to place the files :type path: str :param selected_model_ids: Optional list of model IDs to restrict to :type selected_model_ids: List[str] :returns: The paths to the model and predictor data files :rtype: (Optional[Path], Optional[Path]) .. py:property:: unique_channels A consistently ordered set of unique channels in the data Used for making the color schemes in different plots consistent .. py:property:: unique_configurations A consistently ordered set of unique configurations in the data Used for making the color schemes in different plots consistent .. py:property:: unique_channel_direction A consistently ordered set of unique channel+direction combos in the data Used for making the color schemes in different plots consistent .. py:property:: unique_configuration_channel_direction A consistently ordered set of unique configuration+channel+direction Used for making the color schemes in different plots consistent .. py:property:: unique_predictor_categories A consistently ordered set of unique predictor categories in the data Used for making the color schemes in different plots consistent .. py:method:: _minMaxScoresPerModel(bin_data: polars.LazyFrame) -> polars.LazyFrame :classmethod: .. py:method:: active_ranges(model_ids: Optional[Union[str, List[str]]] = None) -> polars.LazyFrame Calculate the active, reachable bins in classifiers. The classifiers exported by Pega contain (in certain product versions) more than the bins that can be reached given the current state of the predictors. This method first calculates the min and max score range from the predictor log odds, then maps that to the interval boundaries of the classifier(s) to find the min and max index. It returns a LazyFrame with the score min/max, the min/max index, as well as the AUC as reported in the datamart data, when calculated from the full range, and when calculated from the reachable bins only. This information can be used in the Health Check documents or when verifying the AUC numbers from the datamart. :param model_ids: An optional list of model id's, or just a single one, to report on. When not given, the information is returned for all models. :type model_ids: Optional[Union[str, List[str]]], optional :returns: A table with all the index and AUC information for all the models with the following fields: Model Identification: - ModelID - The unique identifier for the model AUC Metrics: - AUC_Datamart - The AUC value as reported in the datamart - AUC_FullRange - The AUC calculated from the full range of bins in the classifier - AUC_ActiveRange - The AUC calculated from only the active/reachable bins Classifier Information: - Bins - The total number of bins in the classifier - nActivePredictors - The number of active predictors in the model Log Odds Information (mostly for internal use): - classifierLogOffset - The log offset of the classifier (baseline log odds) - sumMinLogOdds - The sum of minimum log odds across all active predictors - sumMaxLogOdds - The sum of maximum log odds across all active predictors - score_min - The minimum score (normalized sum of log odds including classifier offset) - score_max - The maximum score (normalized sum of log odds including classifier offset) Active Range Information: - idx_min - The minimum bin index that can be reached given the current binning of all predictors - idx_max - The maximum bin index that can be reached given the current binning of all predictors :rtype: pl.LazyFrame