pdstools.prediction

Submodules

Classes

Prediction

Monitor Pega Prediction Studio Predictions

Package Contents

class Prediction(df: polars.LazyFrame, *, query: pdstools.utils.types.QUERY | None = None)

Monitor Pega Prediction Studio Predictions

Parameters:

  • df (polars.LazyFrame)

  • query (Optional[pdstools.utils.types.QUERY])

predictions: polars.LazyFrame
plot: PredictionPlots
prediction_validity_expr
cdh_guidelines
static from_mock_data(days=70)
property is_available: bool
Return type:

bool

property is_valid: bool
Return type:

bool

summary_by_channel(custom_predictions: List[List] | None = None, by_period: str = None) → polars.LazyFrame

Summarize prediction per channel

Parameters:

  • custom_predictions (Optional[List[CDH_Guidelines.NBAD_Prediction]], optional) – Optional list with custom prediction name to channel mappings. Defaults to None.

  • by_period (str, optional) – Optional grouping by time period. Format string as in polars.Expr.dt.truncate (https://docs.pola.rs/api/python/stable/reference/expressions/api/polars.Expr.dt.truncate.html), for example “1mo”, “1w”, “1d” for calendar month, week day. If provided, creates a new Period column with the truncated date/time. Defaults to None. NOTE: this argument is going to be deprecated in favor of explicit start/end dates in the near future.

  • start_date (Optional[datetime], optional) – Optional start date for the period to report on. Either use by_period or both start_date and end_date. Not implemented yet.

  • end_date (Optional[datetime], optional) – Optional end date for the period to report on. Either use by_period or both start_date and end_date. Not implemented yet.

Returns:

Dataframe with prediction summary with the following columns: - Prediction: The prediction name - Channel: The channel name - Direction: The direction (e.g., Inbound, Outbound) - isStandardNBADPrediction: Boolean indicating if this is a standard NBAD prediction - isMultiChannelPrediction: Boolean indicating if this is a multi-channel prediction - Period: (if by_period is specified) The time period for the summary - Performance: Weighted performance metric - Positives: Sum of positive responses - Negatives: Sum of negative responses - Responses: Sum of all responses - Positives_Test: Sum of positive responses in test group - Positives_Control: Sum of positive responses in control group - Positives_NBA: Sum of positive responses in NBA group - Negatives_Test: Sum of negative responses in test group - Negatives_Control: Sum of negative responses in control group - Negatives_NBA: Sum of negative responses in NBA group - usesImpactAnalyzer: Boolean indicating if Impact Analyzer is used - ControlPercentage: Percentage of responses in control group - TestPercentage: Percentage of responses in test group - CTR: Click-through rate (Positives / (Positives + Negatives)) - CTR_Test: Click-through rate for test group - CTR_Control: Click-through rate for control group - CTR_NBA: Click-through rate for NBA group - ChannelDirectionGroup: Combined Channel/Direction identifier - isValid: Boolean indicating if the prediction data is valid - Lift: Lift value ((CTR_Test - CTR_Control) / CTR_Control)

Return type:

pl.LazyFrame

overall_summary(custom_predictions: List[List] | None = None, by_period: str = None) → polars.LazyFrame

Overall prediction summary. Only valid prediction data is included.

Parameters:

  • custom_predictions (Optional[List[CDH_Guidelines.NBAD_Prediction]], optional) – Optional list with custom prediction name to channel mappings. Defaults to None.

  • by_period (str, optional) – Optional grouping by time period. Format string as in polars.Expr.dt.truncate (https://docs.pola.rs/api/python/stable/reference/expressions/api/polars.Expr.dt.truncate.html), for example “1mo”, “1w”, “1d” for calendar month, week day. If provided, creates a new Period column with the truncated date/time. Defaults to None. NOTE: this argument is going to be deprecated in favor of explicit start/end dates in the near future.

Returns:

Summary across all valid predictions as a dataframe with the following columns: - Period: (if by_period is specified) The time period for the summary - Number of Valid Channels: Count of unique valid channel/direction combinations - Overall Lift: Weighted average lift across all valid channels - Performance: Weighted average performance across all valid channels - Positives Inbound: Sum of positive responses across all valid inbound channels - Positives Outbound: Sum of positive responses across all valid outbound channels - Responses Inbound: Sum of all responses across all valid inbound channels - Responses Outbound: Sum of all responses across all valid outbound channels - Channel with Minimum Negative Lift: Channel with the lowest negative lift value - Minimum Negative Lift: The lowest negative lift value found - usesImpactAnalyzer: Boolean indicating if any channel uses Impact Analyzer - ControlPercentage: Weighted average percentage of control group responses - TestPercentage: Weighted average percentage of test group responses

Return type:

pl.LazyFrame