critical_parameter_ranking

critical_parameter_ranking ¤

Outcome-driven ranking of candidate critical process parameters (CPPs).

During process development the question is rarely "is this signal in spec?" -- it is "which of these signals drives the outcome we care about?" This detector ranks candidate input parameters by their statistical association with a per-run quality outcome (yield, pass/fail, KPI), so the process development engineer can shortlist a small set of true CPPs for tighter control.

Three association measures are supported, all from scipy.stats:

pearson -- linear correlation (best when both inputs and outcome are continuous and approximately linear).
spearman -- rank correlation (robust to non-linear monotonic relationships and outliers).
anova_f -- one-way ANOVA F-statistic between groups defined by discrete levels of the parameter (the right test when factors were swept at discrete levels in a DOE).

CriticalParameterRankingEvents ¤

CriticalParameterRankingEvents(
    dataframe: DataFrame | None = None,
    *,
    event_uuid: str = "dev:cpp_ranking",
    time_column: str = "systime"
)

Bases: Base

Rank input parameters by their statistical link to a quality outcome.

The detector operates on a per-run table where each row is a completed run (batch, DOE point, shift), each candidate column is the aggregated value of an input parameter during that run, and an outcome column holds the quality / yield measurement of interest.

:meth:rank takes the wide per-run table directly, because aggregating "the right number" for each parameter is a development-engineer judgment call (mean of the hold phase? settled value? peak?) that varies by parameter. The constructor's long-format dataframe is optional and not used by :meth:rank: CriticalParameterRankingEvents().rank(runs, ...) works without a time column.

Example

import pandas as pd runs = pd.DataFrame({"temp": [180, 185, 190, 195, 200], ... "speed": [5, 3, 4, 2, 1], ... "yield_pct": [90, 92, 94, 96, 98]}) ranked = CriticalParameterRankingEvents().rank( ... runs, ["temp", "speed"], "yield_pct") ranked["parameter"].tolist() ['temp', 'speed']

Parameters:

Name	Type	Description	Default
`dataframe`	`DataFrame \| None`	Optional long-format data; :meth:`rank` does not use it.	`None`
`event_uuid`	`str`	`uuid` of the emitted rows.	`'dev:cpp_ranking'`
`time_column`	`str`	Timestamp column of `dataframe`.	`'systime'`

rank ¤

rank(
    per_run_df: DataFrame,
    candidate_columns: list[str],
    outcome_column: str,
    *,
    method: str = "spearman",
    anova_bins: int = 3
) -> pd.DataFrame

Rank candidate parameters by statistical association with the outcome.

Parameters:

Name	Type	Description	Default
`per_run_df`	`DataFrame`	One row per run, one column per candidate parameter, plus an outcome column.	required
`candidate_columns`	`list[str]`	Columns to evaluate as candidate CPPs.	required
`outcome_column`	`str`	Outcome (response) column.	required
`method`	`str`	`"pearson"`, `"spearman"`, or `"anova_f"`.	`'spearman'`
`anova_bins`	`int`	Only used for `method="anova_f"`. Each candidate column is quantile-binned into this many groups before the one-way ANOVA is run.	`3`

Returns:

Type	Description
`DataFrame`	Summary DataFrame: `parameter`, `method`, `statistic`,
`DataFrame`	`p_value`, `abs_effect_size`, `rank`. Sorted by
`DataFrame`	`abs_effect_size` descending so the top driver is the first
`DataFrame`	row.

Raises:

Type	Description
`ColumnNotFoundError`	If `outcome_column` is not in `per_run_df` (a `ValueError` subclass).

top_drivers ¤

top_drivers(
    per_run_df: DataFrame,
    candidate_columns: list[str],
    outcome_column: str,
    *,
    method: str = "spearman",
    k: int = 5,
    alpha: float = 0.05,
    anova_bins: int = 3
) -> pd.DataFrame

Return the top-k candidates with p_value <= alpha.

Wraps :meth:rank and filters by significance. The result keeps the same column schema as :meth:rank.

Parameters:

Name	Type	Description	Default
`per_run_df`	`DataFrame`	One row per run, one column per candidate parameter, plus an outcome column.	required
`candidate_columns`	`list[str]`	Columns to evaluate as candidate CPPs.	required
`outcome_column`	`str`	Outcome (response) column.	required
`method`	`str`	`"pearson"`, `"spearman"`, or `"anova_f"`. Default `"spearman"`.	`'spearman'`
`k`	`int`	Maximum number of significant parameters to return.	`5`
`alpha`	`float`	Significance level; only parameters with `p_value <= alpha` are kept.	`0.05`
`anova_bins`	`int`	Only used for `method="anova_f"`; number of quantile bins per candidate column.	`3`

Returns:

Type	Description
`DataFrame`	Same columns as :meth:`rank` (`parameter`, `method`,
`DataFrame`	`statistic`, `p_value`, `abs_effect_size`, `rank`),
`DataFrame`	limited to at most `k` significant rows.