pdstools.impactanalyzer¶
Submodules¶
Classes¶
Package Contents¶
- class ImpactAnalyzer(raw_data: polars.LazyFrame)¶
- Parameters:
raw_data (polars.LazyFrame)
- ia_data: polars.LazyFrame¶
- default_ia_experiments¶
- default_ia_controlgroups¶
- plot¶
- classmethod from_pdc(pdc_source: os.PathLike | str | dict, *, query: pdstools.utils.types.QUERY | None = None, return_input_df: bool | None = False, return_df: bool | None = False)¶
Create an ImpactAnalyzer instance from a PDC file
- Parameters:
pdc_filename (Union[os.PathLike, str]) – The full path to the PDC file
query (Optional[QUERY], optional) – An optional argument to filter out selected data, by default None
return_input_df (Optional[QUERY], optional) – Debugging option to return the wide data from the raw JSON file as a DataFrame, by default False
return_df (Optional[QUERY], optional) – Returns the processed input data as a DataFrame. Multiple of these can be stacked up and used to initialize the ImpactAnalyzer class, by default False
pdc_source (Union[os.PathLike, str, dict])
- Returns:
The properly initialized ImpactAnalyzer object
- Return type:
- classmethod _from_pdc_json(json_data: dict, *, query: pdstools.utils.types.QUERY | None = None, return_input_df: bool | None = False, return_df: bool | None = False)¶
Internal method to create an ImpactAnalyzer instance from PDC JSON data
The PDC data is really structured as a list of expriments: control group A vs control group B. There is no explicit indicator whether the B’s are really the same customers or not. The PDC data also contains a lot of UI related information that is not necessary.
We turn this data into a series of control groups with just counts of impressions and accepts. This does need to assume a few implicit assumptions.
- summary_by_channel() polars.LazyFrame¶
Summarization of the experiments in Impact Analyzer split by Channel.
- Returns:
Summary across all running Impact Analyzer experiments as a dataframe with the following fields:
Channel Identification: - Channel: The channel name
Performance Metrics: - CTR_Lift Adaptive Models vs Random Propensity: Lift in Engagement when testing prioritization with just Adaptive Models vs just Random Propensity - CTR_Lift NBA vs No Levers: Lift in Engagement for the full NBA Framework as configured vs prioritization without levers (only p, V and C) - CTR_Lift NBA vs Only Eligibility Rules: Lift in Engagement for the full NBA Framework as configured vs Only Eligibility policies applied (no Applicability or Suitability, and prioritized with pVCL) - CTR_Lift NBA vs Propensity Only: Lift in Engagement for the full NBA Framework as configured vs prioritization with model propensity only (no V, C or L) - CTR_Lift NBA vs Random: Lift in Engagement for the full NBA Framework as configured vs a Random eligible action (all engagement policies but randomly prioritized) - Value_Lift Adaptive Models vs Random Propensity: Lift in Expected Value when testing prioritization with just Adaptive Models vs just Random Propensity - Value_Lift NBA vs No Levers: Lift in Expected Value for the full NBA Framework as configured vs prioritization without levers (only p, V and C) - Value_Lift NBA vs Only Eligibility Rules: Lift in Expected Value for the full NBA Framework as configured vs Only Eligibility policies applied (no Applicability or Suitability, and prioritized with pVCL) - Value_Lift NBA vs Propensity Only: Lift in Expected Value for the full NBA Framework as configured vs prioritization with model propensity only (no V, C or L) - Value_Lift NBA vs Random: Lift in Expected Value for the full NBA Framework as configured vs a Random eligible action (all engagement policies but randomly prioritized)
- Return type:
pl.LazyFrame
- overall_summary() polars.LazyFrame¶
Summarization of the experiments in Impact Analyzer.
- Returns:
Summary across all running Impact Analyzer experiments as a dataframe with the following fields:
Performance Metrics: - CTR_Lift Adaptive Models vs Random Propensity: Lift in Engagement when testing prioritization with just Adaptive Models vs just Random Propensity - CTR_Lift NBA vs No Levers: Lift in Engagement for the full NBA Framework as configured vs prioritization without levers (only p, V and C) - CTR_Lift NBA vs Only Eligibility Rules: Lift in Engagement for the full NBA Framework as configured vs Only Eligibility policies applied (no Applicability or Suitability, and prioritized with pVCL) - CTR_Lift NBA vs Propensity Only: Lift in Engagement for the full NBA Framework as configured vs prioritization with model propensity only (no V, C or L) - CTR_Lift NBA vs Random: Lift in Engagement for the full NBA Framework as configured vs a Random eligible action (all engagement policies but randomly prioritized) - Value_Lift Adaptive Models vs Random Propensity: Lift in Expected Value when testing prioritization with just Adaptive Models vs just Random Propensity - Value_Lift NBA vs No Levers: Lift in Expected Value for the full NBA Framework as configured vs prioritization without levers (only p, V and C) - Value_Lift NBA vs Only Eligibility Rules: Lift in Expected Value for the full NBA Framework as configured vs Only Eligibility policies applied (no Applicability or Suitability, and prioritized with pVCL) - Value_Lift NBA vs Propensity Only: Lift in Expected Value for the full NBA Framework as configured vs prioritization with model propensity only (no V, C or L) - Value_Lift NBA vs Random: Lift in Expected Value for the full NBA Framework as configured vs a Random eligible action (all engagement policies but randomly prioritized)
- Return type:
pl.LazyFrame