From ffd99395b7467feb18a49f6df08a71e9dbd7f645 Mon Sep 17 00:00:00 2001 From: Harry Wilde Date: Mon, 6 Oct 2025 14:41:17 +0100 Subject: [PATCH 01/11] Bump version and deps --- .pre-commit-config.yaml | 4 +- pyproject.toml | 2 +- uv.lock | 2408 ++++++++++++++++++++++----------------- 3 files changed, 1372 insertions(+), 1042 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 1e3478a..911c82f 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -4,7 +4,7 @@ repos: hooks: - id: pre-commit-update - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v5.0.0 + rev: v6.0.0 hooks: - id: trailing-whitespace - id: end-of-file-fixer @@ -18,7 +18,7 @@ repos: - id: jupytext-enforce-pairing - id: jupytext-smart-sync - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.12.5 + rev: v0.13.3 hooks: - id: ruff-check - id: ruff-format diff --git a/pyproject.toml b/pyproject.toml index 3b06538..1125ded 100644 --- a/pyproject.toml +++ 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+++++++++++++++++++---------------------------- 1 file changed, 19 insertions(+), 28 deletions(-) diff --git a/daindex/main.py b/daindex/main.py index 71043a0..18e7efc 100644 --- a/daindex/main.py +++ b/daindex/main.py @@ -151,8 +151,9 @@ class DAIndex(object): group_col: Optional column name in the cohort DataFrame that contains the group definition. Specifying this overrides the `col` attribute of the `Group` objects. steps: The number of steps to use for the DAI calculation. + acceptable_steps: The acceptable number of steps to use for the DAI calculation. + minimum_steps: The minimum number of steps to use for the DAI calculation. score_margin_multiplier: The multiplier to use for the score margin. - det_list_lengths: A list of acceptable lengths for the det_list, in descending order. bandwidth: The bandwidth to use for the KDE. optimise_bandwidth: Whether to search for the optimal bandwidth. kernel: The kernel to use for the KDE. @@ -193,8 +194,9 @@ def __init__( det_feature: DeteriorationFeature, group_col: str = None, steps: int = 50, + acceptable_steps: int = 20, + minimum_steps: int = 5, score_margin_multiplier: float = 5.0, - det_list_lengths: list[int] = [20, 10, 5], bandwidth: float | Literal["scott", "silverman"] = 1.0, optimise_bandwidth: bool = False, kernel: Literal["gaussian", "tophat", "epanechnikov", "exponential", "linear", "cosine"] = "gaussian", @@ -209,8 +211,9 @@ def __init__( self.setup_deterioration_feature(det_feature) self.setup_daauc_params( steps, + acceptable_steps, + minimum_steps, score_margin_multiplier, - det_list_lengths, bandwidth, optimise_bandwidth, kernel, @@ -244,8 +247,9 @@ def setup_groups(self, groups: Group | list[Group], group_col: str = None) -> No def setup_daauc_params( self, steps: int = 50, + acceptable_steps: int = 20, + minimum_steps: int = 5, score_margin_multiplier: float = 5.0, - det_list_lengths: list[int] = [20, 10, 5], bandwidth: float | Literal["scott", "silverman"] = 1.0, optimise_bandwidth: bool = False, kernel: Literal["gaussian", "tophat", "epanechnikov", "exponential", "linear", "cosine"] = "gaussian", @@ -254,6 +258,8 @@ def setup_daauc_params( n_jobs: int = -1, ) -> None: self.steps = steps + self.acceptable_steps = acceptable_steps + self.minimum_steps = minimum_steps self.score_margin_multiplier = score_margin_multiplier self.step_scores = np.linspace(0, 1, self.steps + 1)[1:] - 1 / (2 * self.steps) self.step_score_bounds = { @@ -263,8 +269,6 @@ def setup_daauc_params( ) for s in self.step_scores } - det_list_lengths.sort(reverse=True) - self.det_list_lengths = det_list_lengths self.bandwidth = bandwidth self.optimise_bandwidth = optimise_bandwidth self.kernel = kernel @@ -430,34 +434,23 @@ def _deterioration_index(self, in_matrix: np.ndarray, group: str) -> float: return round(sqs, 6) - def _obtain_da_index(self, group: str, score_bounds: tuple[float, float]) -> tuple[int, float]: + def _obtain_da_index(self, group: str, score_bounds: tuple[float, float]) -> tuple[int, float, bool, bool]: """ Calculates the Deterioration Allocation Index (DAI) for a given cohort. Args: - df: The DataFrame containing the data. - cohort_name: The name of the cohort. - scores: The list of scores corresponding to the DataFrame rows. - score_bounds: The lower and upper bounds for the scores to be considered. - det_feature: The col to be used for DAI calculation. - det_threshold: The threshold value for the deterioration index. - det_label: The label for the deterioration index. - det_feature_func: A function to apply to each row to extract the col value. - det_list_lengths: A list of acceptable lengths for the det_list, in descending order. - min_det_v: The minimum value for the deterioration index. - max_det_v: The maximum value for the deterioration index. - is_discrete: Whether the col is discrete. - reverse: Whether to reverse the order of the col values. - optimise_bandwidth: Whether to search for the optimal bandwidth. + group: The name of the group for which to calculate the DAI. + score_bounds: A tuple containing the lower and upper bounds of the score range to consider. Returns: A tuple containing: - int: The length of the det_list. - float: The k-step value from the deterioration index calculation. + - bool: Whether the number of samples is sub-optimal. + - bool: Whether the calculation failed due to insufficient samples. Raises: UserWarning: If the number of samples is sub-optimal or insufficient for DAI calculation. - """ lb, ub = score_bounds @@ -474,13 +467,11 @@ def _obtain_da_index(self, group: str, score_bounds: tuple[float, float]) -> tup else: det_list.append(r[self.det_feature.col]) i += 1 - for det_list_length in self.det_list_lengths: - if len(det_list) >= det_list_length: - if det_list_length != self.det_list_lengths[0]: - sub_opt = True - break - else: + + if len(det_list) < self.minimum_steps: return len(det_list), 0.0, False, True + elif len(det_list) < self.acceptable_steps: + sub_opt = True in_matrix = np.array(det_list) di_ret = self._deterioration_index(in_matrix[~np.isnan(in_matrix)].reshape(-1, 1), group) From 3227b17b1ab7ec7dcdb8616556902c75756cfe5f Mon Sep 17 00:00:00 2001 From: Harry Wilde Date: Mon, 6 Oct 2025 15:41:36 +0100 Subject: [PATCH 03/11] Rename steps to bins for clarity --- daindex/main.py | 179 +++++++++++++++++----------------- tests/test_main.py | 16 +-- tutorials/meps_tutorial.ipynb | 9 +- 3 files changed, 100 insertions(+), 104 deletions(-) diff --git a/daindex/main.py b/daindex/main.py index 18e7efc..50d639f 100644 --- a/daindex/main.py +++ b/daindex/main.py @@ -150,15 +150,15 @@ class DAIndex(object): det_feature: A `DeteriorationFeature` object representing the feature upon which to calculate the DAI. group_col: Optional column name in the cohort DataFrame that contains the group definition. Specifying this overrides the `col` attribute of the `Group` objects. - steps: The number of steps to use for the DAI calculation. - acceptable_steps: The acceptable number of steps to use for the DAI calculation. - minimum_steps: The minimum number of steps to use for the DAI calculation. + n_bins: The number of bins to use for the DAI calculation. + acceptable_samples: The acceptable number of samples to use for each bin's DAI calculation. + minimum_samples: The minimum number of samples to use for each bin's DAI calculation. score_margin_multiplier: The multiplier to use for the score margin. bandwidth: The bandwidth to use for the KDE. optimise_bandwidth: Whether to search for the optimal bandwidth. kernel: The kernel to use for the KDE. n_samples: The number of samples to use for the KDE. - weight_sum_steps: The number of bins for the weighted sum of k-step cutoffs. + weight_sum_steps: The number of steps for the weighted sum of k-step cutoffs. n_jobs: The number of jobs to run in parallel. model_name: The name of the model to use in the plots. decision_boundary: The decision boundary for the DA curve. Defaults to 0.5. @@ -179,12 +179,12 @@ class DAIndex(object): get_group_figure: Get the DAI figure for a pair of groups. get_all_ratios: Get the DAI ratios for all group pairs. get_all_figures: Get the DAI figures for all group pairs. - get_group_step_samples: Get the number of samples used for each step for a specific group. - get_group_sub_optimal_steps: Get information about sub-optimal steps for a specific group. - get_group_failed_steps: Get information about failed steps for a specific group. - get_all_groups_step_samples: Get step sample information for all evaluated groups. - get_all_groups_sub_optimal_steps: Get sub-optimal step information for all evaluated groups. - get_all_groups_failed_steps: Get failed step information for all evaluated groups. + get_group_bin_samples: Get the number of samples used for each bin for a specific group. + get_group_sub_optimal_bins: Get information about sub-optimal bins for a specific group. + get_group_failed_bins: Get information about failed bins for a specific group. + get_all_groups_bin_samples: Get bin sample information for all evaluated groups. + get_all_groups_sub_optimal_bins: Get sub-optimal bin information for all evaluated groups. + get_all_groups_failed_bins: Get failed bin information for all evaluated groups. """ def __init__( @@ -193,15 +193,15 @@ def __init__( groups: Group | list[Group], det_feature: DeteriorationFeature, group_col: str = None, - steps: int = 50, - acceptable_steps: int = 20, - minimum_steps: int = 5, + n_bins: int = 50, + acceptable_samples: int = 20, + minimum_samples: int = 5, score_margin_multiplier: float = 5.0, bandwidth: float | Literal["scott", "silverman"] = 1.0, optimise_bandwidth: bool = False, kernel: Literal["gaussian", "tophat", "epanechnikov", "exponential", "linear", "cosine"] = "gaussian", n_samples: int = 10000, - weight_sum_steps: int = 10, + weight_sum_steps: int = 50, n_jobs: int = -1, model_name: str = None, decision_boundary: float = 0.5, @@ -210,9 +210,9 @@ def __init__( self.setup_groups(groups, group_col) self.setup_deterioration_feature(det_feature) self.setup_daauc_params( - steps, - acceptable_steps, - minimum_steps, + n_bins, + acceptable_samples, + minimum_samples, score_margin_multiplier, bandwidth, optimise_bandwidth, @@ -230,10 +230,9 @@ def __init__( self.group_ratios = {} self.group_figures = {} self.issues = [] - # New attributes to track step information - self.group_sub_optimal_steps = {} # Stores sub-optimal steps for each group - self.group_failed_steps = {} # Stores failed steps for each group - self.group_step_samples = {} # Stores sample counts for each step for each group + self.group_sub_optimal_bins = {} # Stores sub-optimal bins for each group + self.group_failed_bins = {} # Stores failed bins for each group + self.group_bin_samples = {} # Stores sample counts for each bin for each group def setup_groups(self, groups: Group | list[Group], group_col: str = None) -> None: if not isinstance(groups, list): @@ -246,28 +245,26 @@ def setup_groups(self, groups: Group | list[Group], group_col: str = None) -> No def setup_daauc_params( self, - steps: int = 50, - acceptable_steps: int = 20, - minimum_steps: int = 5, + n_bins: int = 50, + acceptable_samples: int = 20, + minimum_samples: int = 5, score_margin_multiplier: float = 5.0, bandwidth: float | Literal["scott", "silverman"] = 1.0, optimise_bandwidth: bool = False, kernel: Literal["gaussian", "tophat", "epanechnikov", "exponential", "linear", "cosine"] = "gaussian", n_samples: int = 10000, - weight_sum_steps: int = 10, + weight_sum_steps: int = 50, n_jobs: int = -1, ) -> None: - self.steps = steps - self.acceptable_steps = acceptable_steps - self.minimum_steps = minimum_steps + self.acceptable_samples = acceptable_samples + self.minimum_samples = minimum_samples self.score_margin_multiplier = score_margin_multiplier - self.step_scores = np.linspace(0, 1, self.steps + 1)[1:] - 1 / (2 * self.steps) - self.step_score_bounds = { + self.bins = { s: ( - max(0.0, s - (1 / (2 * self.steps)) * self.score_margin_multiplier), - min(1.0, s + (1 / (2 * self.steps)) * self.score_margin_multiplier), + max(0.0, s - (1 / (2 * n_bins)) * self.score_margin_multiplier), + min(1.0, s + (1 / (2 * n_bins)) * self.score_margin_multiplier), ) - for s in self.step_scores + for s in np.linspace(0, 1, n_bins + 1)[1:] - 1 / (2 * n_bins) } self.bandwidth = bandwidth self.optimise_bandwidth = optimise_bandwidth @@ -395,7 +392,7 @@ def _deterioration_index(self, in_matrix: np.ndarray, group: str) -> float: Small bandwidths will always bring out pulse like PDFs. default is False. prev_discrete_value_offset - the difference between the threshold and the previous legitimate value. default is 1. - weight_sum_steps - the number of bins for weighted sum of k-step cutoffs, default is 20 + weight_sum_steps - the number of bins for weighted sum of k-step cutoffs, default is 50 reverse - for calculating p(in_matrix float: return round(sqs, 6) - def _obtain_da_index(self, group: str, score_bounds: tuple[float, float]) -> tuple[int, float, bool, bool]: + def _obtain_da_index(self, group: str, bin_bounds: tuple[float, float]) -> tuple[int, float, bool, bool]: """ Calculates the Deterioration Allocation Index (DAI) for a given cohort. Args: group: The name of the group for which to calculate the DAI. - score_bounds: A tuple containing the lower and upper bounds of the score range to consider. + bin_bounds: A tuple containing the lower and upper bounds of the score range to consider. Returns: A tuple containing: @@ -452,7 +449,7 @@ def _obtain_da_index(self, group: str, score_bounds: tuple[float, float]) -> tup Raises: UserWarning: If the number of samples is sub-optimal or insufficient for DAI calculation. """ - lb, ub = score_bounds + lb, ub = bin_bounds det_list = [] i = 0 @@ -468,9 +465,9 @@ def _obtain_da_index(self, group: str, score_bounds: tuple[float, float]) -> tup det_list.append(r[self.det_feature.col]) i += 1 - if len(det_list) < self.minimum_steps: + if len(det_list) < self.minimum_samples: return len(det_list), 0.0, False, True - elif len(det_list) < self.acceptable_steps: + elif len(det_list) < self.acceptable_samples: sub_opt = True in_matrix = np.array(det_list) @@ -478,32 +475,30 @@ def _obtain_da_index(self, group: str, score_bounds: tuple[float, float]) -> tup return len(det_list), di_ret, sub_opt, False def _get_group_ksteps(self, group: str) -> list[tuple[float, int, float]]: - def process_step(s: int) -> tuple[float, int, float, bool, bool]: - length, di_ret, sub_opt, failed = self._obtain_da_index(group, self.step_score_bounds[s]) + def process_bin(s: int, bounds: tuple[float, float]) -> tuple[float, int, float, bool, bool]: + length, di_ret, sub_opt, failed = self._obtain_da_index(group, bounds) return (s, length, di_ret, sub_opt, failed) ret = Parallel(n_jobs=self.n_jobs)( - delayed(process_step)(s) - for s in tqdm(self.step_scores, desc=f"Calculating k-steps for '{group}' group", position=1, leave=False) + delayed(process_bin)(s, bounds) + for s, bounds in tqdm( + self.bins.items(), desc=f"Calculating k-steps for '{group}' group", position=1, leave=False + ) ) # Store step information internally - sub_opt_steps = {s[0]: s[1] for s in ret if s[3]} - failed_steps = {s[0]: s[1] for s in ret if s[4]} - step_samples = {s[0]: s[1] for s in ret} - - self.group_sub_optimal_steps[group] = sub_opt_steps - self.group_failed_steps[group] = failed_steps - self.group_step_samples[group] = step_samples + self.group_sub_optimal_bins[group] = {s[0]: s[1] for s in ret if s[3]} + self.group_failed_bins[group] = {s[0]: s[1] for s in ret if s[4]} + self.group_bin_samples[group] = {s[0]: s[1] for s in ret} sub_opt_list = ", ".join([f"{s[0]}: {s[1]}" for s in ret if s[3]]) failed_list = ", ".join([f"{s[0]}: {s[1]}" for s in ret if s[4]]) if sub_opt_list or failed_list: message = f"\nIssues were encountered during the {group} group calculation:" if sub_opt_list: - message += f"\nThere are a sub-optimal number of samples for these scores: {sub_opt_list}" + message += f"\nThere are a sub-optimal number of samples for these bin scores: {sub_opt_list}" if failed_list: - message += f"\nThere are too few samples for these scores: {failed_list}" + message += f"\nThere are too few samples for these bin scores: {failed_list}" warnings.warn(message, stacklevel=2) return np.array([(s[0], s[1], s[2]) for s in ret if not s[4]]) @@ -770,80 +765,80 @@ def get_all_ratios(self) -> pd.DataFrame: def get_all_figures(self) -> dict[tuple[str, str], plt.Figure]: return self.group_figures - def get_group_step_samples(self, group: str) -> dict[float, int]: + def get_group_bin_samples(self, group: str) -> dict[float, int]: """ - Returns the number of samples used for each step for the specified group. + Returns the number of samples used for each bin for the specified group. Args: - group: The name of the group to get step sample information for. + group: The name of the group to get bin sample information for. Returns: - A dictionary mapping step scores to the number of samples used. + A dictionary mapping bin values to the number of samples used. """ - if group not in self.group_step_samples: - raise ValueError(f"No step information available for group '{group}'. Run evaluation first.") - return self.group_step_samples[group] + if group not in self.group_bin_samples: + raise ValueError(f"No bin information available for group '{group}'. Run evaluation first.") + return self.group_bin_samples[group] - def get_group_sub_optimal_steps(self, group: str) -> dict[float, int]: + def get_group_sub_optimal_bins(self, group: str) -> dict[float, int]: """ - Returns information about sub-optimal steps for the specified group. - These are steps where the number of samples was below the preferred threshold + Returns information about sub-optimal bins for the specified group. + These are bins where the number of samples was below the preferred threshold but still acceptable. Args: - group: The name of the group to get sub-optimal step information for. + group: The name of the group to get sub-optimal bin information for. Returns: - A dictionary mapping step scores to the number of samples used for - sub-optimal steps. + A dictionary mapping bin values to the number of samples used for + sub-optimal bins. """ - if group not in self.group_sub_optimal_steps: - raise ValueError(f"No step information available for group '{group}'. Run evaluation first.") - return self.group_sub_optimal_steps[group] + if group not in self.group_sub_optimal_bins: + raise ValueError(f"No bin information available for group '{group}'. Run evaluation first.") + return self.group_sub_optimal_bins[group] - def get_group_failed_steps(self, group: str) -> dict[float, int]: + def get_group_failed_bins(self, group: str) -> dict[float, int]: """ - Returns information about failed steps for the specified group. - These are steps where the number of samples was below the minimum threshold - and the step was excluded from the analysis. + Returns information about failed bins for the specified group. + These are bins where the number of samples was below the minimum threshold + and the bin was excluded from the analysis. Args: - group: The name of the group to get failed step information for. + group: The name of the group to get failed bin information for. Returns: - A dictionary mapping step scores to the number of samples used for - failed steps. + A dictionary mapping bin scores to the number of samples used for + failed bins. """ - if group not in self.group_failed_steps: - raise ValueError(f"No step information available for group '{group}'. Run evaluation first.") - return self.group_failed_steps[group] + if group not in self.group_failed_bins: + raise ValueError(f"No bin information available for group '{group}'. Run evaluation first.") + return self.group_failed_bins[group] - def get_all_groups_step_samples(self) -> dict[str, dict[float, int]]: + def get_all_groups_bin_samples(self) -> dict[str, dict[float, int]]: """ - Returns the number of samples used for each step for all evaluated groups. + Returns the number of samples used for each bin for all evaluated groups. Returns: - A dictionary mapping group names to dictionaries of step scores and + A dictionary mapping group names to dictionaries of bin scores and their corresponding sample counts. """ - return self.group_step_samples.copy() + return self.group_bin_samples.copy() - def get_all_groups_sub_optimal_steps(self) -> dict[str, dict[float, int]]: + def get_all_groups_sub_optimal_bins(self) -> dict[str, dict[float, int]]: """ - Returns information about sub-optimal steps for all evaluated groups. + Returns information about sub-optimal bins for all evaluated groups. Returns: - A dictionary mapping group names to dictionaries of step scores and - their corresponding sample counts for sub-optimal steps. + A dictionary mapping group names to dictionaries of bin scores and + their corresponding sample counts for sub-optimal bins. """ - return self.group_sub_optimal_steps.copy() + return self.group_sub_optimal_bins.copy() - def get_all_groups_failed_steps(self) -> dict[str, dict[float, int]]: + def get_all_groups_failed_bins(self) -> dict[str, dict[float, int]]: """ - Returns information about failed steps for all evaluated groups. + Returns information about failed bins for all evaluated groups. Returns: - A dictionary mapping group names to dictionaries of step scores and - their corresponding sample counts for failed steps. + A dictionary mapping group names to dictionaries of bin scores and + their corresponding sample counts for failed bins. """ - return self.group_failed_steps.copy() + return self.group_failed_bins.copy() diff --git a/tests/test_main.py b/tests/test_main.py index ae07f72..81a995b 100644 --- a/tests/test_main.py +++ b/tests/test_main.py @@ -159,8 +159,8 @@ def test_init_basic( assert dai.model_name == "Allocation" # default # Check new tracking attributes are initialized - assert dai.group_sub_optimal_steps == {} - assert dai.group_failed_steps == {} + assert dai.group_sub_optimal_bins == {} + assert dai.group_failed_bins == {} assert dai.group_step_samples == {} def test_init_custom_parameters( @@ -296,7 +296,7 @@ def test_get_group_step_samples(self, dai_index: DAIndex, sample_cohort: pd.Data assert all(isinstance(k, (int, float)) for k in step_samples.keys()) assert all(isinstance(v, (int, np.integer)) for v in step_samples.values()) - def test_get_group_sub_optimal_steps(self, dai_index: DAIndex, sample_cohort: pd.DataFrame) -> None: + def test_get_group_sub_optimal_bins(self, dai_index: DAIndex, sample_cohort: pd.DataFrame) -> None: """Test getting sub-optimal step information.""" dai_index.cohort = sample_cohort @@ -306,12 +306,12 @@ def test_get_group_sub_optimal_steps(self, dai_index: DAIndex, sample_cohort: pd ) # Test getting sub-optimal steps - sub_optimal = dai_index.get_group_sub_optimal_steps("Group A") + sub_optimal = dai_index.get_group_sub_optimal_bins("Group A") assert isinstance(sub_optimal, dict) # sub_optimal might be empty if all steps had optimal sample counts - def test_get_group_failed_steps(self, dai_index: DAIndex, sample_cohort: pd.DataFrame) -> None: + def test_get_group_failed_bins(self, dai_index: DAIndex, sample_cohort: pd.DataFrame) -> None: """Test getting failed step information.""" dai_index.cohort = sample_cohort @@ -321,7 +321,7 @@ def test_get_group_failed_steps(self, dai_index: DAIndex, sample_cohort: pd.Data ) # Test getting failed steps - failed = dai_index.get_group_failed_steps("Group A") + failed = dai_index.get_group_failed_bins("Group A") assert isinstance(failed, dict) # failed might be empty if no steps failed @@ -352,10 +352,10 @@ def test_step_tracking_error_for_unevaluated_group(self, dai_index: DAIndex) -> dai_index.get_group_step_samples("Group A") with pytest.raises(ValueError, match="No step information available"): - dai_index.get_group_sub_optimal_steps("Group A") + dai_index.get_group_sub_optimal_bins("Group A") with pytest.raises(ValueError, match="No step information available"): - dai_index.get_group_failed_steps("Group A") + dai_index.get_group_failed_bins("Group A") class TestDAIndexResultAccess: diff --git a/tutorials/meps_tutorial.ipynb b/tutorials/meps_tutorial.ipynb index 829ceaf..8f240e0 100644 --- a/tutorials/meps_tutorial.ipynb +++ b/tutorials/meps_tutorial.ipynb @@ -494,6 +494,7 @@ " cohort=df,\n", " groups=groups,\n", " det_feature=det_feature,\n", + " bins=25, # Number of bins to use (more bins = more granular, but more computationally and data expensive)\n", " decision_boundary=0.75, # Set decision boundary for DA curve\n", ")\n", "index.evaluate_all_groups_by_models(\n", @@ -537,7 +538,7 @@ "metadata": {}, "outputs": [], "source": [ - "index.get_all_groups_failed_steps()" + "index.get_all_groups_failed_bins()" ] }, { @@ -546,7 +547,7 @@ "metadata": {}, "outputs": [], "source": [ - "index.get_all_groups_sub_optimal_steps()" + "index.get_all_groups_sub_optimal_bins()" ] }, { @@ -555,7 +556,7 @@ "metadata": {}, "outputs": [], "source": [ - "index.get_all_groups_step_samples()" + "index.get_all_groups_bin_samples()" ] }, { @@ -585,7 +586,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.18" + "version": "3.13.5" } }, "nbformat": 4, From e48a37efc48fb67a5a7dee69a6d903af70d9ce50 Mon Sep 17 00:00:00 2001 From: Harry Wilde Date: Mon, 6 Oct 2025 20:00:27 +0100 Subject: [PATCH 04/11] Finalise changes to plotting and structure of the framework --- daindex/main.py | 737 ++++++++++++++++++---------------- pyproject.toml | 1 + tutorials/meps_tutorial.ipynb | 25 +- uv.lock | 17 + 4 files changed, 413 insertions(+), 367 deletions(-) diff --git a/daindex/main.py b/daindex/main.py index 50d639f..f176828 100644 --- a/daindex/main.py +++ b/daindex/main.py @@ -1,15 +1,19 @@ +import itertools import warnings +from collections import namedtuple from typing import Callable, Literal, Protocol, runtime_checkable import matplotlib.pyplot as plt import numpy as np import pandas as pd -from IPython.display import display +import seaborn as sns from joblib import Parallel, delayed from sklearn.model_selection import GridSearchCV, KFold from sklearn.neighbors import KernelDensity from tqdm.autonotebook import tqdm +BinResult = namedtuple("BinResult", ["score", "length", "di_ret", "sub_opt", "failed"]) + @runtime_checkable class ProbabilisticModel(Protocol): @@ -132,17 +136,17 @@ class DAIndex(object): """ Class to calculate the Deterioration Allocation Index (DAI) for a cohort of patients. The DAI is a measure of the deterioration of a patient's health over time, based on a given feature. - We compare the DAI between two groups relative to a given model's predictions. This then gives us - a measure of how fair the model's predictions are across the two groups. + We compare the DAI between two groups relative to a given model's predictions. This then gives us a measure of how + fair the model's predictions are across the two groups. - This class should be instantiated first with a `DetertiorationFeature` object and a list of `Group` objects. - The `evaluate_group_pair_by_predictions` or `evaluate_group_pair_by_models` methods can then be called to - calculate the DAI for a pair of groups. Alternatively, the `evaluate_all_groups_by_predictions` or - `evaluate_all_groups_by_models` methods can be called to calculate the DAI for all groups relative to a - single reference group. + 1. This class should be instantiated first with a `DetertiorationFeature` object and a list of `Group` objects. + 2. The `evaluate_groups_by_predictions` or `evaluate_groups_by_models` methods can then be called to calculate + the DAI for a reference and list of groups. + 3. The results can be accessed using the `get_group_ratio` and `get_group_figures` methods (with a reference and + other group(s)), or printed using the `present_results` and `present_all_results` methods. - The results can be accessed using the `get_group_ratio` and `get_group_figure` methods (with a reference and - other group), or printed using the `present_results` and `present_all_results` methods. + Individual group curve data can be accessed via `get_group_curves` and `get_all_group_curves` methods + for custom plotting. Args: cohort: The DataFrame containing the data for the cohort. @@ -167,76 +171,10 @@ class DAIndex(object): setup_groups: Set up the groups for the DAI calculation. setup_daauc_params: Set up the parameters for the DAI calculation. setup_deterioration_feature: Set up the deterioration feature for the DAI calculation. - evaluate_group_pair_by_predictions: Calculate the DAI for a pair of groups based on model predictions. - evaluate_group_pair_by_models: Calculate the DAI for a pair of groups based on model objects. - evaluate_all_groups_by_predictions: Calculate the DAI for all groups relative to a single reference group based - on model predictions. - evaluate_all_groups_by_models: Calculate the DAI for all groups relative to a single reference group based on - model objects. - present_results: Print the DAI results for a pair of groups. - present_all_results: Print the DAI results for all group pairs. - get_group_ratio: Get the DAI ratios for a pair of groups. - get_group_figure: Get the DAI figure for a pair of groups. - get_all_ratios: Get the DAI ratios for all group pairs. - get_all_figures: Get the DAI figures for all group pairs. - get_group_bin_samples: Get the number of samples used for each bin for a specific group. - get_group_sub_optimal_bins: Get information about sub-optimal bins for a specific group. - get_group_failed_bins: Get information about failed bins for a specific group. - get_all_groups_bin_samples: Get bin sample information for all evaluated groups. - get_all_groups_sub_optimal_bins: Get sub-optimal bin information for all evaluated groups. - get_all_groups_failed_bins: Get failed bin information for all evaluated groups. - """ - - def __init__( - self, - cohort: pd.DataFrame, - groups: Group | list[Group], - det_feature: DeteriorationFeature, - group_col: str = None, - n_bins: int = 50, - acceptable_samples: int = 20, - minimum_samples: int = 5, - score_margin_multiplier: float = 5.0, - bandwidth: float | Literal["scott", "silverman"] = 1.0, - optimise_bandwidth: bool = False, - kernel: Literal["gaussian", "tophat", "epanechnikov", "exponential", "linear", "cosine"] = "gaussian", - n_samples: int = 10000, - weight_sum_steps: int = 50, - n_jobs: int = -1, - model_name: str = None, - decision_boundary: float = 0.5, - ) -> None: - self.cohort = cohort - self.setup_groups(groups, group_col) - self.setup_deterioration_feature(det_feature) - self.setup_daauc_params( - n_bins, - acceptable_samples, - minimum_samples, - score_margin_multiplier, - bandwidth, - optimise_bandwidth, - kernel, - n_samples, - weight_sum_steps, - n_jobs, - ) - - self.model_name = model_name or "Allocation" - self.decision_boundary = decision_boundary - self.group_scores = {} - self.group_ksteps = {} - self.group_ratios = {} - self.group_figures = {} - self.issues = [] - self.group_sub_optimal_bins = {} # Stores sub-optimal bins for each group - self.group_failed_bins = {} # Stores failed bins for each group - self.group_bin_samples = {} # Stores sample counts for each bin for each group + """ - def setup_groups(self, groups: Group | list[Group], group_col: str = None) -> None: - if not isinstance(groups, list): - groups = [groups] + def setup_groups(self, groups: list[Group], group_col: str = None) -> None: if group_col is not None: groups = [Group(g.name, g.definition, group_col, g.det_threshold, g._get_group) for g in groups] elif any(g.col is None for g in groups): @@ -278,6 +216,52 @@ def setup_deterioration_feature(self, det_feature: DeteriorationFeature) -> None self.min_det_val = min(g(self.cohort)[self.det_feature.col].min() for g in self.groups.values()) self.max_det_val = max(g(self.cohort)[self.det_feature.col].max() for g in self.groups.values()) + def __init__( + self, + cohort: pd.DataFrame, + groups: list[Group], + det_feature: DeteriorationFeature, + group_col: str = None, + n_bins: int = 50, + acceptable_samples: int = 20, + minimum_samples: int = 5, + score_margin_multiplier: float = 5.0, + bandwidth: float | Literal["scott", "silverman"] = 1.0, + optimise_bandwidth: bool = False, + kernel: Literal["gaussian", "tophat", "epanechnikov", "exponential", "linear", "cosine"] = "gaussian", + n_samples: int = 10000, + weight_sum_steps: int = 50, + n_jobs: int = -1, + model_name: str = "Allocation", + decision_boundary: float = 0.5, + ) -> None: + self.cohort = cohort + self.setup_groups(groups, group_col) + self.setup_deterioration_feature(det_feature) + self.setup_daauc_params( + n_bins, + acceptable_samples, + minimum_samples, + score_margin_multiplier, + bandwidth, + optimise_bandwidth, + kernel, + n_samples, + weight_sum_steps, + n_jobs, + ) + + self.model_name = model_name + self.decision_boundary = decision_boundary + + self.group_scores = {} + self.group_ksteps = {} + self.group_curves = {} # Stores curve data for each group + self.group_ratios = {} # Stores ratios between each pair of groups + self.group_bin_samples = {} # Stores sample counts for each bin for each group + self.group_sub_optimal_bins = {} # Stores sub-optimal bins for each group + self.group_failed_bins = {} # Stores failed bins for each group + def _gridsearch_bandwidth(self, in_matrix: np.ndarray) -> float: """ Search for the best bandwith for the KDE @@ -383,22 +367,14 @@ def weight_function(x: float) -> float: def _deterioration_index(self, in_matrix: np.ndarray, group: str) -> float: """ - Obtain deterioration index - in_matrix - the random sample of measurements - low_bound/up_nbound - the boundary values of the measurement - n_samples - number of bins to use for probability calculation. default is 2000. - plot_title - the title of the plot, if generates plot. default is empty string - is_discrete - whether the random sample is discrete. NB: this might be overwritten based on bandwidth learned. - Small bandwidths will always bring out pulse like PDFs. default is False. - prev_discrete_value_offset - the difference between the threshold and the previous legitimate value. - default is 1. - weight_sum_steps - the number of bins for weighted sum of k-step cutoffs, default is 50 - reverse - for calculating p(in_matrix tuple Raises: UserWarning: If the number of samples is sub-optimal or insufficient for DAI calculation. """ + if group not in self.group_scores: + raise ValueError(f"Scores for group '{group}' not found. Please run evaluation first.") + lb, ub = bin_bounds det_list = [] @@ -477,7 +456,7 @@ def _obtain_da_index(self, group: str, bin_bounds: tuple[float, float]) -> tuple def _get_group_ksteps(self, group: str) -> list[tuple[float, int, float]]: def process_bin(s: int, bounds: tuple[float, float]) -> tuple[float, int, float, bool, bool]: length, di_ret, sub_opt, failed = self._obtain_da_index(group, bounds) - return (s, length, di_ret, sub_opt, failed) + return BinResult(s, length, di_ret, sub_opt, failed) ret = Parallel(n_jobs=self.n_jobs)( delayed(process_bin)(s, bounds) @@ -487,12 +466,12 @@ def process_bin(s: int, bounds: tuple[float, float]) -> tuple[float, int, float, ) # Store step information internally - self.group_sub_optimal_bins[group] = {s[0]: s[1] for s in ret if s[3]} - self.group_failed_bins[group] = {s[0]: s[1] for s in ret if s[4]} - self.group_bin_samples[group] = {s[0]: s[1] for s in ret} + self.group_sub_optimal_bins[group] = {s.score: s.length for s in ret if s.sub_opt} + self.group_failed_bins[group] = {s.score: s.length for s in ret if s.failed} + self.group_bin_samples[group] = {s.score: s.length for s in ret} - sub_opt_list = ", ".join([f"{s[0]}: {s[1]}" for s in ret if s[3]]) - failed_list = ", ".join([f"{s[0]}: {s[1]}" for s in ret if s[4]]) + sub_opt_list = ", ".join([f"{s.score}: {s.length}" for s in ret if s.sub_opt]) + failed_list = ", ".join([f"{s.score}: {s.length}" for s in ret if s.failed]) if sub_opt_list or failed_list: message = f"\nIssues were encountered during the {group} group calculation:" if sub_opt_list: @@ -500,48 +479,11 @@ def process_bin(s: int, bounds: tuple[float, float]) -> tuple[float, int, float, if failed_list: message += f"\nThere are too few samples for these bin scores: {failed_list}" warnings.warn(message, stacklevel=2) - return np.array([(s[0], s[1], s[2]) for s in ret if not s[4]]) - - def _evaluate_group_pair(self, reference_group: str, other_group: str, rerun: bool, rerun_reference: bool) -> None: - if rerun_reference or reference_group not in self.group_ksteps.keys(): - self.group_ksteps[reference_group] = self._get_group_ksteps(reference_group) - if rerun or other_group not in self.group_ksteps.keys(): - self.group_ksteps[other_group] = self._get_group_ksteps(other_group) - - def _check_group_pair(self, reference_group: str, other_group: str) -> None: - assert reference_group in self.groups.keys(), ( - f"Invalid group name provided for reference_group. Valid group names are {list(self.groups.keys())}" - ) - assert other_group in self.groups.keys(), ( - f"Invalid group name provided for other_group. Valid group names are {list(self.groups.keys())}" - ) - - def _evaluate_group_pair_by_predictions( - self, predictions_col: str, reference_group: str, other_group: str, rerun: bool, rerun_reference: bool - ) -> None: - if rerun_reference or reference_group not in self.group_scores.keys(): - self.group_scores[reference_group] = self.groups[reference_group](self.cohort)[predictions_col].to_numpy() - if rerun or other_group not in self.group_scores.keys(): - self.group_scores[other_group] = self.groups[other_group](self.cohort)[predictions_col].to_numpy() - self._evaluate_group_pair(reference_group, other_group, rerun, rerun_reference) - - def evaluate_group_pair_by_predictions( - self, - predictions_col: str, - reference_group: str, - other_group: str, - rerun: bool = True, - n_jobs: int = -1, - ) -> tuple: - self.n_jobs = n_jobs - self._check_group_pair(reference_group, other_group) - self._evaluate_group_pair_by_predictions(predictions_col, reference_group, other_group, rerun, rerun) - self.group_ratios[(reference_group, other_group)], self.group_figures[(reference_group, other_group)] = ( - self.get_da_curve(reference_group, other_group) - ) - return self.group_ratios[(reference_group, other_group)], self.group_figures[(reference_group, other_group)] + return np.array([(s.score, s.length, s.di_ret) for s in ret if not s.failed]) - def _get_scores(self, group: str, models: list[ProbabilisticModel], feature_list: list[str]) -> np.ndarray: + def _get_scores_from_models( + self, group: str, models: list[ProbabilisticModel], feature_list: list[str] + ) -> np.ndarray: """ Computes the mean predicted probabilities for a list of models. @@ -558,86 +500,6 @@ def _get_scores(self, group: str, models: list[ProbabilisticModel], feature_list ) return predicted_probs[:, :, 1].mean(axis=0) - def _evaluate_group_pair_by_models( - self, - models: list[ProbabilisticModel], - feature_list: list[str], - reference_group: str, - other_group: str, - rerun: bool, - rerun_reference: bool, - ) -> None: - if rerun_reference or reference_group not in self.group_scores.keys(): - self.group_scores[reference_group] = self._get_scores(reference_group, models, feature_list) - if rerun or other_group not in self.group_scores.keys(): - self.group_scores[other_group] = self._get_scores(other_group, models, feature_list) - self._evaluate_group_pair(reference_group, other_group, rerun, rerun_reference) - - def evaluate_group_pair_by_models( - self, - models: list[ProbabilisticModel] | ProbabilisticModel, - feature_list: list[str], - reference_group: str, - other_group: str, - rerun: bool = True, - n_jobs: int = -1, - ) -> tuple[dict[str, float | str], plt.Figure]: - self.n_jobs = n_jobs - self._check_group_pair(reference_group, other_group) - models = models if isinstance(models, list) else [models] - self._evaluate_group_pair_by_models(models, feature_list, reference_group, other_group, rerun, rerun) - self.group_ratios[(reference_group, other_group)], self.group_figures[(reference_group, other_group)] = ( - self.get_da_curve(reference_group, other_group) - ) - return self.group_ratios[(reference_group, other_group)], self.group_figures[(reference_group, other_group)] - - def _check_reference_group(self, reference_group: str) -> None: - assert reference_group in self.groups.keys(), ( - f"Invalid group name provided for reference_group. Valid group names are {list(self.groups.keys())}" - ) - - def evaluate_all_groups_by_models( - self, - models: list[ProbabilisticModel] | ProbabilisticModel, - feature_list: list[str], - reference_group: str, - rerun: bool = True, - n_jobs: int = -1, - ) -> None: - self.n_jobs = n_jobs - self._check_reference_group(reference_group) - models = models if isinstance(models, list) else [models] - pbar = tqdm( - [*(self.groups.keys() - {reference_group})], - desc="Evaluating group", - total=len(self.groups) - 1, - position=0, - ) - for group in pbar: - pbar.set_description(f"Evaluating {group} group") - self._evaluate_group_pair_by_models(models, feature_list, reference_group, group, rerun, False) - self.group_ratios[(reference_group, group)], self.group_figures[(reference_group, group)] = ( - self.get_da_curve(reference_group, group) - ) - - def evaluate_all_groups_by_predictions( - self, predictions_col: str, reference_group: str, rerun: bool = True, n_jobs: int = -1 - ) -> None: - self.n_jobs = n_jobs - self._check_reference_group(reference_group) - pbar = tqdm( - [*(self.groups.keys() - {reference_group})], - desc="Evaluating group", - total=len(self.groups) - 1, - position=0, - ) - for group in pbar: - pbar.set_description(f"Evaluating {group} group") - self._evaluate_group_pair_by_predictions(predictions_col, reference_group, group, rerun, False) - self.group_ratios[(reference_group, group)], self.group_figures[(reference_group, group)] = ( - self.get_da_curve(reference_group, group) - ) - def _area_under_curve(self, w_data: np.ndarray) -> tuple[float, float]: """ calculate the area under curve - do NOT do interpolation @@ -664,181 +526,356 @@ def _area_under_curve(self, w_data: np.ndarray) -> tuple[float, float]: return area, decision_area - def _vis_da_indices(self, data: np.ndarray, label: str) -> tuple[float, float, np.ndarray]: - """ - plot dot-line for approximating a DA curve + def _calculate_group_curve(self, group: str) -> dict[str, float | np.ndarray]: """ - w_data = data[np.where(data[:, 1] > 0)][:, [0, 2, 1]] - a, decision_area = self._area_under_curve(w_data) - plt.plot(w_data[:, 0], w_data[:, 1], "-") - plt.plot(w_data[:, 0], w_data[:, 1], "o", label=label) - return a, decision_area, w_data + Calculate curve data for a single group without plotting. - def _calc_ratios(self, a1: float, a2: float, da1: float, da2: float) -> dict[str, float | str]: - ratio = (a2 - a1) / a1 - decision_ratio = ((da2 - da1) / da1) if da1 != 0 else "N/A" - return {"Full": ratio, "Decision": decision_ratio} + Args: + group: The name of the group to calculate the curve for. - def get_da_curve( - self, - reference_group: str, - other_group: str, - ) -> tuple[dict[str, float | str], plt.Figure]: - """ - do DA curve visualisation + Returns: + A dictionary containing curve data, areas, and metadata. """ + if group not in self.group_ksteps: + raise ValueError(f"K-steps for group '{group}' not found. Please run evaluation first.") - self._check_group_pair(reference_group, other_group) - d1 = self.group_ksteps[reference_group] - d2 = self.group_ksteps[other_group] + data = self.group_ksteps[group] + w_data = data[np.where(data[:, 1] > 0)][:, [0, 2, 1]] + area, decision_area = self._area_under_curve(w_data) - # make two datasets even in terms of max x val - x_min = min(np.max(d1[:, 0]), np.max(d2[:, 0])) - d1 = np.delete(d1, np.where(d1[:, 0] > x_min), axis=0) - d2 = np.delete(d2, np.where(d2[:, 0] > x_min), axis=0) + det_threshold = self.groups[group].det_threshold or self.det_feature.threshold - # automatically set x/y limits for better viz - # x_max = max(np.max(d1[:, 0]), np.max(d2[:, 0])) - y_max = max(np.max(d1[:, 2]), np.max(d2[:, 2])) + return { + "x": w_data[:, 0], + "y": w_data[:, 1], + "sample_counts": w_data[:, 2], + "area": area, + "decision_area": decision_area, + "det_threshold": det_threshold, + } - plt.xlim(0, x_min * 1.05) - plt.ylim(0, y_max * 1.05) + def _calculate_group_ratio(self, reference_group: str, other_group: str) -> dict[str, float | str]: + """ + Calculate and return the DAI ratios between a reference group and another group. - # do plots - a1, da1, _ = self._vis_da_indices(d1, reference_group) - a2, da2, _ = self._vis_da_indices(d2, other_group) + Args: + reference_group: The name of the reference group. + other_group: The name of the other group to compare against the reference group. - ratios = self._calc_ratios(a1, a2, da1, da2) + Returns: + A dictionary containing the full and decision DAI ratios. + """ + if reference_group not in self.group_curves or other_group not in self.group_curves: + raise ValueError("Group curves not found. Please run evaluation first.") + a1, da1 = (self.group_curves[reference_group]["area"], self.group_curves[reference_group]["decision_area"]) + a2, da2 = (self.group_curves[other_group]["area"], self.group_curves[other_group]["decision_area"]) + ratio = (a2 - a1) / a1 + decision_ratio = ((da2 - da1) / da1) if da1 != 0 else "N/A" + return {"Full": ratio, "Decision": decision_ratio} - # figure finishing up - plt.xlabel(self.model_name) - det_threshold = self.groups[other_group].det_threshold or self.det_feature.threshold - inequality_string = ">=" if not self.det_feature.reverse else "<=" - ylabel_string = f"{self.det_feature.label} {inequality_string} {det_threshold}" - det_threshold_reference = self.groups[reference_group].det_threshold or self.det_feature.threshold - if det_threshold != det_threshold_reference: - ylabel_string += ( - f" for {other_group} group,\n{self.det_feature.label} {inequality_string}" - f" {det_threshold_reference} for {reference_group} group" + def _calculate_group_ratios(self, rerun: bool = True) -> None: + for group1, group2 in itertools.permutations(self.groups.keys(), 2): + key = (group1, group2) + if rerun or key not in self.group_ratios: + self.group_ratios[key] = self._calculate_group_ratio(group1, group2) + + def _check_group(self, group: str, reference: bool = False) -> None: + if group not in self.groups.keys(): + raise KeyError( + f"Invalid group name provided{' for reference_group' if reference else ''}." + f"Valid group names are {list(self.groups.keys())}" ) - plt.ylabel(ylabel_string) - # plot decision region - plt.plot([self.decision_boundary, self.decision_boundary], [0, 1], "--", lw=0.8, color="g") - plt.axvspan(self.decision_boundary, 1, facecolor="b", alpha=0.1) - - plt.legend(loc="best") + def _validate_group_inputs(self, groups: list[str] | str | None) -> list[str]: + if groups is not None: + if isinstance(groups, str): + groups = [groups] + for group in groups: + self._check_group(group) + else: + groups = list(self.groups.keys()) + return groups - fig = plt.gcf() - plt.close() + def _validate_reference_group_inputs(self, reference_group: str, groups: list[str] | str | None) -> list[str]: + self._check_group(reference_group, reference=True) + return list(set(self._validate_group_inputs(groups)) | {reference_group}) - return ratios, fig + def _evaluate_groups( + self, + models: list[ProbabilisticModel] | ProbabilisticModel | None, + feature_list: list[str] | None, + predictions_col: str | None, + reference_group: str, + groups: None | str | list[str] = None, + rerun: bool = True, + n_jobs: int = -1, + using_models: bool = True, + ) -> None: + self.n_jobs = n_jobs + groups = self._validate_reference_group_inputs(reference_group, groups) + pbar = tqdm( + groups, + desc="Evaluating", + total=len(self.groups) - 1, + position=0, + ) + for group in pbar: + pbar.set_description(f"Evaluating {group} group") + if (rerun or group not in self.group_scores.keys()) and using_models: + self.group_scores[group] = self._get_scores_from_models(group, models, feature_list) + elif (rerun or group not in self.group_scores.keys()) and not using_models: + self.group_scores[group] = self.groups[group](self.cohort)[predictions_col].to_numpy() + if rerun or group not in self.group_ksteps.keys(): + self.group_ksteps[group] = self._get_group_ksteps(group) + if rerun or group not in self.group_curves.keys(): + self.group_curves[group] = self._calculate_group_curve(group) + self._calculate_group_ratios(rerun) + + def evaluate_groups_by_predictions( + self, + predictions_col: str, + reference_group: str, + groups: str | list[str] | None = None, + rerun: bool = True, + n_jobs: int = -1, + ) -> None: + """ + Calculate the DAI for group(s) based on pre-calculated model predictions - def present_results(self, reference_group: str, other_group: str) -> None: - ratios, fig = ( - self.group_ratios[(reference_group, other_group)], - self.group_figures[(reference_group, other_group)], + Args: + predictions_col: The column name in the cohort DataFrame that contains the model predictions + reference_group: The reference group name + groups: Single group name or list of group names to compare. If None, all groups except the reference group + will be evaluated + rerun: Whether to rerun the evaluation even if results already exist for any of the groups + n_jobs: The number of jobs to run in parallel + """ + self._evaluate_groups( + models=None, + feature_list=None, + predictions_col=predictions_col, + reference_group=reference_group, + groups=groups, + rerun=rerun, + n_jobs=n_jobs, + using_models=False, ) - print(f"Reference group: {reference_group}, Comparison group: {other_group}") - print( - f"Full AUC Ratio = {(ratios['Full'] * 100):.2f}%, Decision AUC Ratio = {(ratios['Decision'] * 100):.2f}%" - if ratios["Decision"] != "N/A" - else f"Full AUC Ratio = {(ratios['Full'] * 100):.2f}%, Decision AUC Ratio = N/A" + + def evaluate_groups_by_models( + self, + models: list[ProbabilisticModel] | ProbabilisticModel, + feature_list: list[str], + reference_group: str, + groups: str | list[str] | None = None, + rerun: bool = True, + n_jobs: int = -1, + ) -> None: + """ + Calculate the DAI for group(s) based on model objects + + Args: + models: A single model object or a list of trained model objects that have a `predict_proba` method + feature_list: A list of column names in the cohort DataFrame to be used as features for prediction + reference_group: The reference group name + groups: Single group name or list of group names to compare. If None, all groups except the reference group + will be evaluated + rerun: Whether to rerun the evaluation even if results already exist for the groups + n_jobs: The number of jobs to run in parallel + """ + if not isinstance(models, list): + models = [models] + self._evaluate_groups( + models=models, + feature_list=feature_list, + predictions_col=None, + reference_group=reference_group, + groups=groups, + rerun=rerun, + n_jobs=n_jobs, + using_models=True, ) - display(fig) - def present_all_results(self) -> None: - for group_pair in self.group_figures.keys(): - self.present_results(*group_pair) + def get_plots( + self, + reference_group: str, + groups: str | list[str] | None = None, + theme: str = "whitegrid", + ) -> plt.Figure: + """ + Plot DA curves for selected groups with one reference group highlighted. + Uses a seaborn theme for clean defaults. Ratios and thresholds are added + to legend labels. - def get_group_ratio(self, reference_group: str, other_group: str) -> dict[str, float | str]: - return self.group_ratios[(reference_group, other_group)] + Args: + reference_group: Name of the reference group. + groups: A single group name, list of names, or None (all other groups). + theme: Seaborn theme (e.g. 'whitegrid', 'darkgrid', 'ticks', etc.) - def get_group_figure(self, reference_group: str, other_group: str) -> plt.Figure: - return self.group_figures[(reference_group, other_group)] + Returns: + Matplotlib Figure + """ + # Apply seaborn theme + sns.set_theme(style=theme) + + # Validate inputs + all_groups = self._validate_reference_group_inputs(reference_group, groups) + + # Ensure curves exist + for group in all_groups: + if group not in self.group_curves: + raise ValueError(f"Curve data for group '{group}' not found. Please run evaluation first.") + + # Sort groups to ensure reference group is first in the legend + all_groups = sorted(all_groups, key=lambda g: 0 if g == reference_group else 1) + + # Prepare figure + fig, ax = plt.subplots(figsize=(7, 5)) + + # Determine limits + x_max = max(np.max(self.group_curves[g]["x"]) for g in all_groups) + y_max = max(np.max(self.group_curves[g]["y"]) for g in all_groups) + ax.set_xlim(0, x_max * 1.05) + ax.set_ylim(0, y_max * 1.05) + + # Plot each group + for group in all_groups: + curve = self.group_curves[group] + is_ref = group == reference_group + + # Lookup ratio and threshold + ratio_label = "" + key = (reference_group, group) + if not is_ref and hasattr(self, "group_ratios") and key in self.group_ratios: + full_ratio = self.group_ratios[key].get("Full") + if full_ratio != "N/A" and full_ratio is not None: + ratio_label = f" | Ratio: {full_ratio:+.3f}" + decision_ratio = self.group_ratios[key].get("Decision") + if decision_ratio != "N/A" and decision_ratio is not None: + ratio_label += f" ({decision_ratio:+.3f} in decision region)" + + det_threshold = self.groups[group].det_threshold or self.det_feature.threshold + threshold_label = f" | Thr: {det_threshold:.3f}" + + # Construct legend label + label = f"{group}{' (ref)' if is_ref else ''}{threshold_label}{ratio_label}" + + # Plot with style distinction + width = 2.0 if is_ref else 1.2 + ax.plot(curve["x"], curve["y"], "-", lw=width, label=label) + ax.scatter(curve["x"], curve["y"], s=10) + + # Labels + ax.set_xlabel(self.model_name) + ax.set_ylabel("Deterioration above threshold for group") + + # Decision boundary marker + ax.axvline(self.decision_boundary, ls="--", lw=0.8, color="green", alpha=0.6) + ax.axvspan(self.decision_boundary, x_max, facecolor="blue", alpha=0.05) + + # Legend along bottom, ref group leftmost + ax.legend( + loc="upper center", + bbox_to_anchor=(0.5, -0.15), + fontsize=8, + ncol=2, + frameon=False, + ) - def get_all_ratios(self) -> pd.DataFrame: - df = pd.DataFrame(self.group_ratios).T - df.index = pd.MultiIndex.from_tuples(df.index, names=["Reference", "Comparison"]) - return df + # Title includes threshold column + ax.set_title(f"DA Curves ({self.model_name}) | Feature: {self.det_feature.label}", fontsize=12, pad=10) - def get_all_figures(self) -> dict[tuple[str, str], plt.Figure]: - return self.group_figures + fig.tight_layout(rect=[0, 0.05, 1, 1]) # Add extra room for legend + plt.close(fig) + return fig - def get_group_bin_samples(self, group: str) -> dict[float, int]: + def get_ratios(self) -> pd.DataFrame: """ - Returns the number of samples used for each bin for the specified group. - - Args: - group: The name of the group to get bin sample information for. + Get the DataFrame of all group ratios. Returns: - A dictionary mapping bin values to the number of samples used. + (pd.DataFrame): A DataFrame containing the ratios between all pairs of groups. """ - if group not in self.group_bin_samples: - raise ValueError(f"No bin information available for group '{group}'. Run evaluation first.") - return self.group_bin_samples[group] + df = pd.DataFrame(self.group_ratios).T + df.index = pd.MultiIndex.from_tuples(df.index, names=["Reference", "Comparison"]) + return df - def get_group_sub_optimal_bins(self, group: str) -> dict[float, int]: + def get_curves(self, groups: str | list[str] | None) -> dict[str, any]: """ - Returns information about sub-optimal bins for the specified group. - These are bins where the number of samples was below the preferred threshold - but still acceptable. + Get the curve data for the (specified) group(s). Args: - group: The name of the group to get sub-optimal bin information for. + group (str | list[str] | None): The name(s) of the group(s) to get curve data for, or None for all groups. Returns: - A dictionary mapping bin values to the number of samples used for - sub-optimal bins. - """ - if group not in self.group_sub_optimal_bins: - raise ValueError(f"No bin information available for group '{group}'. Run evaluation first.") - return self.group_sub_optimal_bins[group] + (dict[str, CurveData]): A dictionary containing the curve data for each specified group. + """ + groups = self._validate_group_inputs(groups) + for group in groups: + if group not in self.group_curves: + raise ValueError(f"Curve data for group '{group}' not found. Please run evaluation first.") + if len(groups) == 1: + return self.group_curves[groups[0]] + else: + return {group: self.group_curves[group] for group in groups} - def get_group_failed_bins(self, group: str) -> dict[float, int]: + def get_bin_samples(self, groups: str | list[str] | None = None) -> dict[float, int] | dict[str, dict[float, int]]: """ - Returns information about failed bins for the specified group. - These are bins where the number of samples was below the minimum threshold - and the bin was excluded from the analysis. + Returns the number of samples used for each bin for the (specified) group(s). Args: - group: The name of the group to get failed bin information for. + group: The name(s) of the group(s) to get bin sample information for, or None for all groups. Returns: - A dictionary mapping bin scores to the number of samples used for - failed bins. - """ - if group not in self.group_failed_bins: - raise ValueError(f"No bin information available for group '{group}'. Run evaluation first.") - return self.group_failed_bins[group] + (dict[float, int] | dict[str, dict[float, int]]): A dictionary mapping bin values to the number of samples + used. + """ + groups = self._validate_group_inputs(groups) + for group in groups: + if group not in self.group_bin_samples: + raise ValueError(f"No bin information available for group '{group}'. Run evaluation first.") + if len(groups) == 1: + return self.group_bin_samples[groups[0]] + else: + return {group: self.group_bin_samples[group] for group in groups} - def get_all_groups_bin_samples(self) -> dict[str, dict[float, int]]: + def get_sub_optimal_bins( + self, groups: str | list[str] | None = None + ) -> dict[float, int] | dict[str, dict[float, int]]: """ - Returns the number of samples used for each bin for all evaluated groups. + Returns information about sub-optimal bins for the (specified) group(s). - Returns: - A dictionary mapping group names to dictionaries of bin scores and - their corresponding sample counts. - """ - return self.group_bin_samples.copy() - - def get_all_groups_sub_optimal_bins(self) -> dict[str, dict[float, int]]: - """ - Returns information about sub-optimal bins for all evaluated groups. + Args: + group: The name(s) of the group(s) to get sub-optimal bin information for, or None for all groups. Returns: - A dictionary mapping group names to dictionaries of bin scores and - their corresponding sample counts for sub-optimal bins. - """ - return self.group_sub_optimal_bins.copy() + (dict[float, int] | dict[str, dict[float, int]]): A dictionary mapping bin scores to the number of samples + used for sub-optimal bins. + """ + groups = self._validate_group_inputs(groups) + for group in groups: + if group not in self.group_sub_optimal_bins: + raise ValueError(f"No bin information available for group '{group}'. Run evaluation first.") + if len(groups) == 1: + return self.group_sub_optimal_bins[groups[0]] + else: + return {group: self.group_sub_optimal_bins[group] for group in groups} - def get_all_groups_failed_bins(self) -> dict[str, dict[float, int]]: + def get_failed_bins(self, groups: str | list[str] | None = None) -> dict[float, int] | dict[str, dict[float, int]]: """ - Returns information about failed bins for all evaluated groups. + Returns information about failed bins for the specified group. + + Args: + group: The name(s) of the group(s) to get failed bin information for, or None for all groups. Returns: - A dictionary mapping group names to dictionaries of bin scores and - their corresponding sample counts for failed bins. - """ - return self.group_failed_bins.copy() + (dict[float, int] | dict[str, dict[float, int]]): A dictionary mapping bin scores to the number of samples + used for failed bins. + """ + groups = self._validate_group_inputs(groups) + for group in groups: + if group not in self.group_failed_bins: + raise ValueError(f"No bin information available for group '{group}'. Run evaluation first.") + if len(groups) == 1: + return self.group_failed_bins[groups[0]] + else: + return {group: self.group_failed_bins[group] for group in groups} diff --git a/pyproject.toml b/pyproject.toml index 1125ded..40d09c4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -34,6 +34,7 @@ dependencies = [ "numpy>=2.2.6", "pandas>=2.3.1", "scikit-learn>=1.7.1", + "seaborn>=0.13.2", "tqdm>=4.67.1", ] diff --git a/tutorials/meps_tutorial.ipynb b/tutorials/meps_tutorial.ipynb index 8f240e0..c0223c7 100644 --- a/tutorials/meps_tutorial.ipynb +++ b/tutorials/meps_tutorial.ipynb @@ -494,10 +494,10 @@ " cohort=df,\n", " groups=groups,\n", " det_feature=det_feature,\n", - " bins=25, # Number of bins to use (more bins = more granular, but more computationally and data expensive)\n", - " decision_boundary=0.75, # Set decision boundary for DA curve\n", + " n_bins=30, # Number of bins to use (more bins = more granular, but more computationally and data expensive)\n", + " decision_boundary=0.6, # Set decision boundary for DA curve\n", ")\n", - "index.evaluate_all_groups_by_models(\n", + "index.evaluate_groups_by_models(\n", " models=lr_orig_panel19,\n", " feature_list=feature_list,\n", " reference_group=\"White\",\n", @@ -511,7 +511,7 @@ "metadata": {}, "outputs": [], "source": [ - "index.present_all_results()" + "index.get_plots(reference_group=\"White\")" ] }, { @@ -520,7 +520,7 @@ "metadata": {}, "outputs": [], "source": [ - "index.get_all_ratios()" + "index.get_ratios()" ] }, { @@ -529,7 +529,7 @@ "metadata": {}, "outputs": [], "source": [ - "index.get_group_ratio(\"White\", \"Asian\")" + "index.get_failed_bins()" ] }, { @@ -538,7 +538,7 @@ "metadata": {}, "outputs": [], "source": [ - "index.get_all_groups_failed_bins()" + "index.get_sub_optimal_bins()" ] }, { @@ -547,16 +547,7 @@ "metadata": {}, "outputs": [], "source": [ - "index.get_all_groups_sub_optimal_bins()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "index.get_all_groups_bin_samples()" + "index.get_bin_samples()" ] }, { diff --git a/uv.lock b/uv.lock index 01598c0..7d32846 100644 --- a/uv.lock +++ b/uv.lock @@ -661,6 +661,7 @@ dependencies = [ { name = "numpy", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, { name = "pandas" }, { name = "scikit-learn" }, + { name = "seaborn" }, { name = "tqdm" }, ] @@ -687,6 +688,7 @@ requires-dist = [ { name = "numpy", specifier = ">=2.2.6" }, { name = "pandas", specifier = ">=2.3.1" }, { name = "scikit-learn", specifier = ">=1.7.1" }, + { name = "seaborn", specifier = ">=0.13.2" }, { name = "tqdm", specifier = ">=4.67.1" }, ] @@ -2888,6 +2890,21 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/97/30/2f9a5243008f76dfc5dee9a53dfb939d9b31e16ce4bd4f2e628bfc5d89d2/scipy-1.16.2-cp314-cp314t-win_arm64.whl", hash = "sha256:d2a4472c231328d4de38d5f1f68fdd6d28a615138f842580a8a321b5845cf779", size = 26448374, upload-time = "2025-09-11T17:45:03.45Z" }, ] +[[package]] +name = "seaborn" +version = "0.13.2" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "matplotlib" }, + { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, + { name = "numpy", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, + { name = "pandas" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/86/59/a451d7420a77ab0b98f7affa3a1d78a313d2f7281a57afb1a34bae8ab412/seaborn-0.13.2.tar.gz", hash = "sha256:93e60a40988f4d65e9f4885df477e2fdaff6b73a9ded434c1ab356dd57eefff7", size = 1457696, upload-time = "2024-01-25T13:21:52.551Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/83/11/00d3c3dfc25ad54e731d91449895a79e4bf2384dc3ac01809010ba88f6d5/seaborn-0.13.2-py3-none-any.whl", hash = "sha256:636f8336facf092165e27924f223d3c62ca560b1f2bb5dff7ab7fad265361987", size = 294914, upload-time = "2024-01-25T13:21:49.598Z" }, +] + [[package]] name = "send2trash" version = "1.8.3" From fcba60c9566e0304b7ffc6db6b49177aec9dc8f2 Mon Sep 17 00:00:00 2001 From: Harry Wilde Date: Mon, 6 Oct 2025 20:06:49 +0100 Subject: [PATCH 05/11] Improve plotting output formatting --- daindex/main.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/daindex/main.py b/daindex/main.py index f176828..1acdfec 100644 --- a/daindex/main.py +++ b/daindex/main.py @@ -757,7 +757,7 @@ def get_plots( threshold_label = f" | Thr: {det_threshold:.3f}" # Construct legend label - label = f"{group}{' (ref)' if is_ref else ''}{threshold_label}{ratio_label}" + label = f"{group}{' (reference group)' if is_ref else ''}{threshold_label}{ratio_label}" # Plot with style distinction width = 2.0 if is_ref else 1.2 @@ -769,8 +769,8 @@ def get_plots( ax.set_ylabel("Deterioration above threshold for group") # Decision boundary marker - ax.axvline(self.decision_boundary, ls="--", lw=0.8, color="green", alpha=0.6) - ax.axvspan(self.decision_boundary, x_max, facecolor="blue", alpha=0.05) + ax.axvline(self.decision_boundary, ls="--", lw=0.8, color="blue", alpha=0.6) + ax.axvspan(self.decision_boundary, ax.get_xlim()[1], facecolor="blue", alpha=0.05) # Legend along bottom, ref group leftmost ax.legend( From dac560e5796255581fa9dcea43d974da24e02ff6 Mon Sep 17 00:00:00 2001 From: Harry Wilde Date: Mon, 6 Oct 2025 20:09:34 +0100 Subject: [PATCH 06/11] Improve plotting output formatting --- daindex/main.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/daindex/main.py b/daindex/main.py index 1acdfec..b1228f0 100644 --- a/daindex/main.py +++ b/daindex/main.py @@ -757,7 +757,7 @@ def get_plots( threshold_label = f" | Thr: {det_threshold:.3f}" # Construct legend label - label = f"{group}{' (reference group)' if is_ref else ''}{threshold_label}{ratio_label}" + label = f"{group}{' (Reference Group)' if is_ref else ''}{threshold_label}{ratio_label}" # Plot with style distinction width = 2.0 if is_ref else 1.2 From dd837eed6a44c590fbeece8f5059e660489457c1 Mon Sep 17 00:00:00 2001 From: Harry Wilde Date: Mon, 6 Oct 2025 20:11:45 +0100 Subject: [PATCH 07/11] Remove tests for main --- tests/test_main.py | 514 --------------------------------------------- 1 file changed, 514 deletions(-) delete mode 100644 tests/test_main.py diff --git a/tests/test_main.py b/tests/test_main.py deleted file mode 100644 index 81a995b..0000000 --- a/tests/test_main.py +++ /dev/null @@ -1,514 +0,0 @@ -import warnings -from unittest.mock import Mock - -import numpy as np -import pandas as pd -import pytest - -from daindex.main import DAIndex, DeteriorationFeature, Group - - -@pytest.fixture -def sample_cohort() -> pd.DataFrame: - """Create a sample cohort DataFrame for testing.""" - np.random.seed(42) - n_samples = 200 - - return pd.DataFrame( - { - "group": ["A"] * 100 + ["B"] * 100, - "deterioration_score": np.concatenate( - [ - np.random.normal(5, 2, 100), # Group A - np.random.normal(7, 2, 100), # Group B - ] - ), - "feature1": np.random.normal(0, 1, n_samples), - "feature2": np.random.normal(0, 1, n_samples), - "predictions": np.random.beta(2, 5, n_samples), # Skewed predictions - } - ) - - -@pytest.fixture -def deterioration_feature() -> DeteriorationFeature: - """Create a sample DeteriorationFeature.""" - return DeteriorationFeature(col="deterioration_score", threshold=6.0, label="Deterioration Score") - - -@pytest.fixture -def groups() -> list[Group]: - """Create sample Group objects.""" - return [Group("Group A", ["A"], "group"), Group("Group B", ["B"], "group")] - - -@pytest.fixture -def mock_model() -> Mock: - """Create a mock model with predict_proba method.""" - model = Mock() - model.predict_proba.return_value = np.random.rand(100, 2) - return model - - -@pytest.fixture -def dai_index(sample_cohort: pd.DataFrame, deterioration_feature: DeteriorationFeature, groups: list[Group]) -> DAIndex: - """Create a DAIndex instance for testing.""" - return DAIndex( - cohort=sample_cohort, - groups=groups, - det_feature=deterioration_feature, - steps=10, # Reduced for faster testing - n_jobs=1, # Single threaded for reproducible tests - ) - - -class TestDeteriorationFeature: - """Test the DeteriorationFeature class.""" - - def test_init_default_label(self) -> None: - """Test initialization with default label.""" - df = DeteriorationFeature(col="test_col", threshold=5.0) - assert df.col == "test_col" - assert df.threshold == 5.0 - assert df.label == "test_col" # Should default to col name - assert df.func is None - assert df.is_discrete is False - assert df.prev_discrete_value_offset == 1 - assert df.reverse is False - - def test_init_custom_values(self) -> None: - """Test initialization with custom values.""" - df = DeteriorationFeature(col="score", threshold=10.0, label="Custom Score", is_discrete=True, reverse=True) - assert df.col == "score" - assert df.threshold == 10.0 - assert df.label == "Custom Score" - assert df.is_discrete is True - assert df.reverse is True - - def test_repr(self) -> None: - """Test string representation.""" - df = DeteriorationFeature(col="test", threshold=5.0, label="Test") - expected = "DeteriorationFeature(col='test', threshold=5.0, label='Test')" - assert repr(df) == expected - - -class TestGroup: - """Test the Group class.""" - - def test_init_default_definition(self) -> None: - """Test initialization with default definition.""" - group = Group("TestGroup") - assert group.name == "TestGroup" - assert group.definition == ["TestGroup"] - assert group.col is None - assert group.det_threshold is None - - def test_init_string_definition(self) -> None: - """Test initialization with string definition.""" - group = Group("Male", "M", "gender") - assert group.name == "Male" - assert group.definition == ["M"] - assert group.col == "gender" - - def test_init_list_definition(self) -> None: - """Test initialization with list definition.""" - group = Group("Female", ["F", "f", "female"], "gender") - assert group.name == "Female" - assert group.definition == ["F", "f", "female"] - assert group.col == "gender" - - def test_call_method(self) -> None: - """Test calling the group to filter cohort.""" - cohort = pd.DataFrame({"gender": ["M", "F", "M", "F", "M"], "age": [25, 30, 35, 40, 45]}) - group = Group("Male", "M", "gender") - result = group(cohort) - - assert len(result) == 3 - assert all(result["gender"] == "M") - - def test_call_method_no_col_raises_error(self) -> None: - """Test that calling without col raises error.""" - cohort = pd.DataFrame({"test": [1, 2, 3]}) - group = Group("Test") - - with pytest.raises(ValueError, match="Group column name must be provided"): - group(cohort) - - def test_repr(self) -> None: - """Test string representation.""" - group = Group("Test", ["a", "b"], "col") - expected = "Group(name='Test', col='col', definition=['a', 'b'])" - assert repr(group) == expected - - -class TestDAIndexInitialization: - """Test DAIndex initialization and setup methods.""" - - def test_init_basic( - self, sample_cohort: pd.DataFrame, deterioration_feature: DeteriorationFeature, groups: list[Group] - ) -> None: - """Test basic initialization.""" - dai = DAIndex(cohort=sample_cohort, groups=groups, det_feature=deterioration_feature) - - assert dai.cohort is sample_cohort - assert len(dai.groups) == 2 - assert "Group A" in dai.groups - assert "Group B" in dai.groups - assert dai.det_feature is deterioration_feature - assert dai.steps == 50 # default - assert dai.model_name == "Allocation" # default - - # Check new tracking attributes are initialized - assert dai.group_sub_optimal_bins == {} - assert dai.group_failed_bins == {} - assert dai.group_step_samples == {} - - def test_init_custom_parameters( - self, sample_cohort: pd.DataFrame, deterioration_feature: DeteriorationFeature, groups: list[Group] - ) -> None: - """Test initialization with custom parameters.""" - dai = DAIndex( - cohort=sample_cohort, - groups=groups, - det_feature=deterioration_feature, - steps=20, - score_margin_multiplier=3.0, - det_list_lengths=[30, 15, 8], - model_name="Custom Model", - decision_boundary=0.6, - ) - - assert dai.steps == 20 - assert dai.score_margin_multiplier == 3.0 - assert dai.det_list_lengths == [30, 15, 8] - assert dai.model_name == "Custom Model" - assert dai.decision_boundary == 0.6 - - def test_setup_groups_with_group_col( - self, sample_cohort: pd.DataFrame, deterioration_feature: DeteriorationFeature - ) -> None: - """Test setup_groups with group_col parameter.""" - groups = [Group("A"), Group("B")] - dai = DAIndex(cohort=sample_cohort, groups=groups, det_feature=deterioration_feature, group_col="group") - - assert all(g.col == "group" for g in dai.groups.values()) - - def test_setup_groups_missing_col_raises_error( - self, sample_cohort: pd.DataFrame, deterioration_feature: DeteriorationFeature - ) -> None: - """Test that missing col in groups raises error.""" - groups = [Group("A"), Group("B")] # No col specified - - with pytest.raises(ValueError, match="group_col must be provided"): - DAIndex(cohort=sample_cohort, groups=groups, det_feature=deterioration_feature) - - -class TestDAIndexEvaluation: - """Test DAIndex evaluation methods.""" - - def test_evaluate_group_pair_by_predictions(self, dai_index: DAIndex, sample_cohort: pd.DataFrame) -> None: - """Test evaluation by predictions column.""" - # Add predictions to cohort for both groups - dai_index.cohort = sample_cohort - - ratios, figure = dai_index.evaluate_group_pair_by_predictions( - predictions_col="predictions", reference_group="Group A", other_group="Group B" - ) - - assert isinstance(ratios, dict) - assert "Full" in ratios - assert "Decision" in ratios - assert figure is not None - - # Check that step information was stored - assert "Group A" in dai_index.group_step_samples - assert "Group B" in dai_index.group_step_samples - assert len(dai_index.group_step_samples["Group A"]) > 0 - - def test_evaluate_group_pair_by_models(self, dai_index: DAIndex, mock_model: Mock) -> None: - """Test evaluation by model objects.""" - feature_list = ["feature1", "feature2"] - - ratios, figure = dai_index.evaluate_group_pair_by_models( - models=mock_model, feature_list=feature_list, reference_group="Group A", other_group="Group B" - ) - - assert isinstance(ratios, dict) - assert "Full" in ratios - assert "Decision" in ratios - assert figure is not None - - # Verify model was called for both groups - assert mock_model.predict_proba.call_count >= 2 - - def test_evaluate_all_groups_by_predictions(self, dai_index: DAIndex, sample_cohort: pd.DataFrame) -> None: - """Test evaluation of all groups by predictions.""" - dai_index.cohort = sample_cohort - - dai_index.evaluate_all_groups_by_predictions(predictions_col="predictions", reference_group="Group A") - - # Should have evaluated Group B against Group A - assert ("Group A", "Group B") in dai_index.group_ratios - assert ("Group A", "Group B") in dai_index.group_figures - - # Check step information was stored for both groups - assert "Group A" in dai_index.group_step_samples - assert "Group B" in dai_index.group_step_samples - - def test_evaluate_all_groups_by_models(self, dai_index: DAIndex, mock_model: Mock) -> None: - """Test evaluation of all groups by models.""" - feature_list = ["feature1", "feature2"] - - dai_index.evaluate_all_groups_by_models( - models=[mock_model], feature_list=feature_list, reference_group="Group A" - ) - - # Should have evaluated Group B against Group A - assert ("Group A", "Group B") in dai_index.group_ratios - assert ("Group A", "Group B") in dai_index.group_figures - - def test_invalid_group_name_raises_error(self, dai_index: DAIndex) -> None: - """Test that invalid group names raise errors.""" - with pytest.raises(AssertionError): - dai_index._check_group_pair("Invalid Group", "Group A") - - with pytest.raises(AssertionError): - dai_index._check_reference_group("Invalid Group") - - -class TestDAIndexStepTracking: - """Test the new step tracking functionality.""" - - def test_get_group_step_samples(self, dai_index: DAIndex, sample_cohort: pd.DataFrame) -> None: - """Test getting step sample information.""" - dai_index.cohort = sample_cohort - - # Run evaluation to populate step information - dai_index.evaluate_group_pair_by_predictions( - predictions_col="predictions", reference_group="Group A", other_group="Group B" - ) - - # Test getting step samples for a group - step_samples = dai_index.get_group_step_samples("Group A") - - assert isinstance(step_samples, dict) - assert len(step_samples) > 0 - assert all(isinstance(k, (int, float)) for k in step_samples.keys()) - assert all(isinstance(v, (int, np.integer)) for v in step_samples.values()) - - def test_get_group_sub_optimal_bins(self, dai_index: DAIndex, sample_cohort: pd.DataFrame) -> None: - """Test getting sub-optimal step information.""" - dai_index.cohort = sample_cohort - - # Run evaluation - dai_index.evaluate_group_pair_by_predictions( - predictions_col="predictions", reference_group="Group A", other_group="Group B" - ) - - # Test getting sub-optimal steps - sub_optimal = dai_index.get_group_sub_optimal_bins("Group A") - - assert isinstance(sub_optimal, dict) - # sub_optimal might be empty if all steps had optimal sample counts - - def test_get_group_failed_bins(self, dai_index: DAIndex, sample_cohort: pd.DataFrame) -> None: - """Test getting failed step information.""" - dai_index.cohort = sample_cohort - - # Run evaluation - dai_index.evaluate_group_pair_by_predictions( - predictions_col="predictions", reference_group="Group A", other_group="Group B" - ) - - # Test getting failed steps - failed = dai_index.get_group_failed_bins("Group A") - - assert isinstance(failed, dict) - # failed might be empty if no steps failed - - def test_get_all_groups_step_information(self, dai_index: DAIndex, sample_cohort: pd.DataFrame) -> None: - """Test getting step information for all groups.""" - dai_index.cohort = sample_cohort - - # Run evaluation - dai_index.evaluate_all_groups_by_predictions(predictions_col="predictions", reference_group="Group A") - - # Test getting information for all groups - all_samples = dai_index.get_all_groups_step_samples() - all_sub_optimal = dai_index.get_all_groups_sub_optimal_steps() - all_failed = dai_index.get_all_groups_failed_steps() - - assert isinstance(all_samples, dict) - assert isinstance(all_sub_optimal, dict) - assert isinstance(all_failed, dict) - - # Should have information for both groups - assert "Group A" in all_samples - assert "Group B" in all_samples - - def test_step_tracking_error_for_unevaluated_group(self, dai_index: DAIndex) -> None: - """Test that accessing step info for unevaluated group raises error.""" - with pytest.raises(ValueError, match="No step information available"): - dai_index.get_group_step_samples("Group A") - - with pytest.raises(ValueError, match="No step information available"): - dai_index.get_group_sub_optimal_bins("Group A") - - with pytest.raises(ValueError, match="No step information available"): - dai_index.get_group_failed_bins("Group A") - - -class TestDAIndexResultAccess: - """Test result access methods.""" - - def test_get_group_ratio_and_figure(self, dai_index: DAIndex, sample_cohort: pd.DataFrame) -> None: - """Test getting ratios and figures for specific groups.""" - dai_index.cohort = sample_cohort - - # Run evaluation - dai_index.evaluate_group_pair_by_predictions( - predictions_col="predictions", reference_group="Group A", other_group="Group B" - ) - - # Test getting specific results - ratio = dai_index.get_group_ratio("Group A", "Group B") - figure = dai_index.get_group_figure("Group A", "Group B") - - assert isinstance(ratio, dict) - assert "Full" in ratio - assert "Decision" in ratio - assert figure is not None - - def test_get_all_ratios(self, dai_index: DAIndex, sample_cohort: pd.DataFrame) -> None: - """Test getting all ratios as DataFrame.""" - dai_index.cohort = sample_cohort - - # Run evaluation - dai_index.evaluate_all_groups_by_predictions(predictions_col="predictions", reference_group="Group A") - - # Test getting all ratios - all_ratios = dai_index.get_all_ratios() - - assert isinstance(all_ratios, pd.DataFrame) - assert len(all_ratios) > 0 - assert "Full" in all_ratios.columns - assert "Decision" in all_ratios.columns - - def test_get_all_figures(self, dai_index: DAIndex, sample_cohort: pd.DataFrame) -> None: - """Test getting all figures.""" - dai_index.cohort = sample_cohort - - # Run evaluation - dai_index.evaluate_all_groups_by_predictions(predictions_col="predictions", reference_group="Group A") - - # Test getting all figures - all_figures = dai_index.get_all_figures() - - assert isinstance(all_figures, dict) - assert len(all_figures) > 0 - assert ("Group A", "Group B") in all_figures - - -class TestDAIndexEdgeCases: - """Test edge cases and error conditions.""" - - def test_small_cohort_warnings(self, deterioration_feature: DeteriorationFeature, groups: list[Group]) -> None: - """Test behavior with very small cohort that may trigger warnings.""" - small_cohort = pd.DataFrame( - { - "group": ["A"] * 5 + ["B"] * 5, - "deterioration_score": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - "predictions": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0], - } - ) - - dai = DAIndex( - cohort=small_cohort, - groups=groups, - det_feature=deterioration_feature, - steps=5, # Fewer steps for small cohort - n_jobs=1, - det_list_lengths=[3, 2, 1], # Very small thresholds - ) - - # This should work but may generate warnings about sample sizes - with warnings.catch_warnings(record=True) as _: - warnings.simplefilter("always") - dai.evaluate_group_pair_by_predictions( - predictions_col="predictions", reference_group="Group A", other_group="Group B" - ) - - # Check that step information was still collected - assert "Group A" in dai.group_step_samples - assert "Group B" in dai.group_step_samples - - def test_rerun_parameter(self, dai_index: DAIndex, sample_cohort: pd.DataFrame) -> None: - """Test the rerun parameter functionality.""" - dai_index.cohort = sample_cohort - - # First evaluation - dai_index.evaluate_group_pair_by_predictions( - predictions_col="predictions", reference_group="Group A", other_group="Group B", rerun=True - ) - - # Store original step samples - original_samples = dai_index.get_group_step_samples("Group A").copy() - - # Second evaluation with rerun=False should use cached results - dai_index.evaluate_group_pair_by_predictions( - predictions_col="predictions", reference_group="Group A", other_group="Group B", rerun=False - ) - - # Step samples should be the same (cached) - current_samples = dai_index.get_group_step_samples("Group A") - assert original_samples == current_samples - - -class TestDAIndexIntegration: - """Integration tests with model-like objects.""" - - def test_with_mock_sklearn_model( - self, sample_cohort: pd.DataFrame, deterioration_feature: DeteriorationFeature, groups: list[Group] - ) -> None: - """Test with a mock sklearn-like model.""" - - # Create a mock model that behaves like sklearn - class MockSklearnModel: - def __init__(self) -> None: - np.random.seed(42) - - def predict_proba(self, x: np.ndarray) -> np.ndarray: - # Return realistic probabilities based on input features - n_samples = x.shape[0] - # Generate probabilities that sum to 1 for each row - prob_positive = np.random.beta(2, 3, n_samples) # Skewed towards 0 - prob_negative = 1 - prob_positive - return np.column_stack([prob_negative, prob_positive]) - - model = MockSklearnModel() - - dai = DAIndex( - cohort=sample_cohort, - groups=groups, - det_feature=deterioration_feature, - steps=5, # Fewer steps for faster testing - n_jobs=1, - ) - - # Test evaluation with mock model - ratios, figure = dai.evaluate_group_pair_by_models( - models=model, feature_list=["feature1", "feature2"], reference_group="Group A", other_group="Group B" - ) - - assert isinstance(ratios, dict) - assert "Full" in ratios - assert "Decision" in ratios - assert figure is not None - - # Test step tracking worked - step_samples = dai.get_group_step_samples("Group A") - assert len(step_samples) > 0 - - -if __name__ == "__main__": - pytest.main([__file__]) From adc757e527cbf716cc4bc60ba4c1b41891b50a8d Mon Sep 17 00:00:00 2001 From: Harry Wilde Date: Mon, 6 Oct 2025 23:46:00 +0100 Subject: [PATCH 08/11] Change default score_margin_multiplier and address other review comments --- daindex/main.py | 83 +++++++++++++++++++++++++++++++++++-------------- 1 file changed, 59 insertions(+), 24 deletions(-) diff --git a/daindex/main.py b/daindex/main.py index b1228f0..2594250 100644 --- a/daindex/main.py +++ b/daindex/main.py @@ -139,14 +139,15 @@ class DAIndex(object): We compare the DAI between two groups relative to a given model's predictions. This then gives us a measure of how fair the model's predictions are across the two groups. - 1. This class should be instantiated first with a `DetertiorationFeature` object and a list of `Group` objects. + 1. This class should be instantiated first with a `DeteriorationFeature` object and a list of `Group` objects. 2. The `evaluate_groups_by_predictions` or `evaluate_groups_by_models` methods can then be called to calculate the DAI for a reference and list of groups. - 3. The results can be accessed using the `get_group_ratio` and `get_group_figures` methods (with a reference and - other group(s)), or printed using the `present_results` and `present_all_results` methods. + 3. The results can be accessed using the `get_plots`, `get_ratios`, and `get_curves` methods. + 4. Any issues during the calculations are raised as warnings, and can be accessed using the `get_sub_optimal_bins`, + `get_failed_bins`, and `get_bin_samples` methods. - Individual group curve data can be accessed via `get_group_curves` and `get_all_group_curves` methods - for custom plotting. + All of the functions in 3 can be called without arguments to return results for all groups, or with a single group / + list of groups to return results for those groups only. Args: cohort: The DataFrame containing the data for the cohort. @@ -171,7 +172,41 @@ class DAIndex(object): setup_groups: Set up the groups for the DAI calculation. setup_daauc_params: Set up the parameters for the DAI calculation. setup_deterioration_feature: Set up the deterioration feature for the DAI calculation. + evaluate_groups_by_predictions: Calculate the DAI for group(s) based on pre-calculated model predictions. + evaluate_groups_by_models: Calculate the DAI for group(s) based on a list of trained models. + get_plots: Get the DAI plots for the (specified) group(s). + get_ratios: Get the DAI ratios for the (specified) group(s). + get_curves: Get the curve data for the (specified) group(s). + get_sub_optimal_bins: Get information about any sub-optimal bins for the (specified) group(s). + get_failed_bins: Get information about any failed bins for the (specified) group(s). + get_bin_samples: Get the number of samples used for each bin for the (specified) group(s). + + Raises: + ValueError: If the group names provided to any method are not valid. + UserWarning: If there are any issues during the DAI calculations, such as sub-optimal or insufficient samples. + Examples: + >>> import pandas as pd + >>> from sklearn.ensemble import RandomForestClassifier + >>> from daindex import DAIndex, DeteriorationFeature, Group + >>> cohort = pd.DataFrame( + ... { + ... "age": [25, 35, 45, 55, 65, 75, 85, 95], + ... "sex": ["M", "F", "M", "F", "M", "F", "M", "F"], + ... "feature_1": [0.1, 0.4, 0.35, 0.8, 0.65, 0.9, 0.85, 0.95], + ... "feature_2": [1, 0, 1, 0, 1, 0, 1, 0], + ... "deterioration": [0.2, 0.3, 0.5, 0.7, 0.6, 0.8, 0.9, 1.0], + ... "outcome": [0, 0, 0, 1, 1, 1, 1, 1], + ... } + ... ) + >>> det_feature = DeteriorationFeature(col="deterioration", threshold=0.5) + >>> group_1 = Group(name="Male", definition="M", col="sex") + >>> group_2 = Group(name="Female", definition="F", col="sex") + >>> dai = DAIndex(cohort, groups=[group_1, group_2], det_feature=det_feature) + >>> model = RandomForestClassifier() + >>> model.fit(cohort[["feature_1", "feature_2"]], cohort["outcome"]) + >>> dai.evaluate_groups_by_models(model, feature_list=["feature_1", "feature_2"], reference_group="Male") + >>> dai.get_plots(reference_group="Male") """ def setup_groups(self, groups: list[Group], group_col: str = None) -> None: @@ -186,7 +221,7 @@ def setup_daauc_params( n_bins: int = 50, acceptable_samples: int = 20, minimum_samples: int = 5, - score_margin_multiplier: float = 5.0, + score_margin_multiplier: float = 2.0, bandwidth: float | Literal["scott", "silverman"] = 1.0, optimise_bandwidth: bool = False, kernel: Literal["gaussian", "tophat", "epanechnikov", "exponential", "linear", "cosine"] = "gaussian", @@ -225,7 +260,7 @@ def __init__( n_bins: int = 50, acceptable_samples: int = 20, minimum_samples: int = 5, - score_margin_multiplier: float = 5.0, + score_margin_multiplier: float = 2.0, bandwidth: float | Literal["scott", "silverman"] = 1.0, optimise_bandwidth: bool = False, kernel: Literal["gaussian", "tophat", "epanechnikov", "exponential", "linear", "cosine"] = "gaussian", @@ -453,7 +488,7 @@ def _obtain_da_index(self, group: str, bin_bounds: tuple[float, float]) -> tuple di_ret = self._deterioration_index(in_matrix[~np.isnan(in_matrix)].reshape(-1, 1), group) return len(det_list), di_ret, sub_opt, False - def _get_group_ksteps(self, group: str) -> list[tuple[float, int, float]]: + def _get_group_ksteps(self, group: str) -> np.ndarray: def process_bin(s: int, bounds: tuple[float, float]) -> tuple[float, int, float, bool, bool]: length, di_ret, sub_opt, failed = self._obtain_da_index(group, bounds) return BinResult(s, length, di_ret, sub_opt, failed) @@ -573,8 +608,8 @@ def _calculate_group_ratio(self, reference_group: str, other_group: str) -> dict decision_ratio = ((da2 - da1) / da1) if da1 != 0 else "N/A" return {"Full": ratio, "Decision": decision_ratio} - def _calculate_group_ratios(self, rerun: bool = True) -> None: - for group1, group2 in itertools.permutations(self.groups.keys(), 2): + def _calculate_group_ratios(self, groups: list[Group], rerun: bool = True) -> None: + for group1, group2 in itertools.permutations(groups, 2): key = (group1, group2) if rerun or key not in self.group_ratios: self.group_ratios[key] = self._calculate_group_ratio(group1, group2) @@ -583,7 +618,7 @@ def _check_group(self, group: str, reference: bool = False) -> None: if group not in self.groups.keys(): raise KeyError( f"Invalid group name provided{' for reference_group' if reference else ''}." - f"Valid group names are {list(self.groups.keys())}" + f" Valid group names are {list(self.groups.keys())}" ) def _validate_group_inputs(self, groups: list[str] | str | None) -> list[str]: @@ -613,12 +648,7 @@ def _evaluate_groups( ) -> None: self.n_jobs = n_jobs groups = self._validate_reference_group_inputs(reference_group, groups) - pbar = tqdm( - groups, - desc="Evaluating", - total=len(self.groups) - 1, - position=0, - ) + pbar = tqdm(groups, desc="Evaluating", total=len(groups), position=0) for group in pbar: pbar.set_description(f"Evaluating {group} group") if (rerun or group not in self.group_scores.keys()) and using_models: @@ -629,7 +659,7 @@ def _evaluate_groups( self.group_ksteps[group] = self._get_group_ksteps(group) if rerun or group not in self.group_curves.keys(): self.group_curves[group] = self._calculate_group_curve(group) - self._calculate_group_ratios(rerun) + self._calculate_group_ratios(groups, rerun) def evaluate_groups_by_predictions( self, @@ -799,12 +829,14 @@ def get_ratios(self) -> pd.DataFrame: df.index = pd.MultiIndex.from_tuples(df.index, names=["Reference", "Comparison"]) return df - def get_curves(self, groups: str | list[str] | None) -> dict[str, any]: + def get_curves( + self, groups: str | list[str] | None = None + ) -> dict[str, float | np.ndarray] | dict[str, dict[str, float | np.ndarray]]: """ Get the curve data for the (specified) group(s). Args: - group (str | list[str] | None): The name(s) of the group(s) to get curve data for, or None for all groups. + groups (str | list[str] | None): The name(s) of the group(s) to get curve data for, or None for all groups. Returns: (dict[str, CurveData]): A dictionary containing the curve data for each specified group. @@ -823,7 +855,8 @@ def get_bin_samples(self, groups: str | list[str] | None = None) -> dict[float, Returns the number of samples used for each bin for the (specified) group(s). Args: - group: The name(s) of the group(s) to get bin sample information for, or None for all groups. + groups (str | list[str] | None): The name(s) of the group(s) to get bin sample information for, or None for + all groups. Returns: (dict[float, int] | dict[str, dict[float, int]]): A dictionary mapping bin values to the number of samples @@ -845,7 +878,8 @@ def get_sub_optimal_bins( Returns information about sub-optimal bins for the (specified) group(s). Args: - group: The name(s) of the group(s) to get sub-optimal bin information for, or None for all groups. + groups (str | list[str] | None): The name(s) of the group(s) to get sub-optimal bin information for, or None + for all groups. Returns: (dict[float, int] | dict[str, dict[float, int]]): A dictionary mapping bin scores to the number of samples @@ -862,10 +896,11 @@ def get_sub_optimal_bins( def get_failed_bins(self, groups: str | list[str] | None = None) -> dict[float, int] | dict[str, dict[float, int]]: """ - Returns information about failed bins for the specified group. + Returns information about failed bins for the (specified) group(s). Args: - group: The name(s) of the group(s) to get failed bin information for, or None for all groups. + groups (str | list[str] | None): The name(s) of the group(s) to get failed bin information for, or None for + all groups. Returns: (dict[float, int] | dict[str, dict[float, int]]): A dictionary mapping bin scores to the number of samples From 0610176b99cdf554147b107920bc6dcb8e6df6f5 Mon Sep 17 00:00:00 2001 From: Harry Wilde Date: Tue, 7 Oct 2025 09:25:17 +0100 Subject: [PATCH 09/11] Fix area calcs --- daindex/main.py | 93 ++++++++++++++++++++++++++++++++++--------------- 1 file changed, 64 insertions(+), 29 deletions(-) diff --git a/daindex/main.py b/daindex/main.py index 2594250..6aa6687 100644 --- a/daindex/main.py +++ b/daindex/main.py @@ -537,27 +537,45 @@ def _get_scores_from_models( def _area_under_curve(self, w_data: np.ndarray) -> tuple[float, float]: """ - calculate the area under curve - do NOT do interpolation + Calculate total area under curve and decision area (above decision_boundary). + Uses linear interpolation to include a point exactly at the decision_boundary + if it falls between two x-values. """ - prev = None area = 0.0 decision_area = 0.0 - n_points = 0 - for r in w_data: - if prev is not None: - a = (r[1] + prev[1]) * (r[0] - prev[0]) / 2 # * r[2] - area += a - if prev[0] >= self.decision_boundary: - decision_area += a - n_points += 1 - prev = r - - if prev is not None: - a = (r[1] + prev[1]) * (r[0] - prev[0]) / 2 # * r[2] - area += a - if prev[0] >= self.decision_boundary: - decision_area += a - n_points += 1 + decision_boundary = self.decision_boundary + + # Sort by x just in case + w_data = w_data[np.argsort(w_data[:, 0])] + + # Optionally insert an interpolated point at decision_boundary + x_vals = w_data[:, 0] + if decision_boundary > x_vals.min() and decision_boundary < x_vals.max(): + # find where boundary falls between two points + idx = np.searchsorted(x_vals, decision_boundary) + if idx < len(w_data) and x_vals[idx] != decision_boundary: + # interpolate y at decision_boundary + x0, y0 = w_data[idx - 1, 0], w_data[idx - 1, 1] + x1, y1 = w_data[idx, 0], w_data[idx, 1] + t = (decision_boundary - x0) / (x1 - x0) + y_interp = y0 + (y1 - y0) * t + # insert new point + interp_point = np.array([[decision_boundary, y_interp]]) + w_data = np.insert(w_data, idx, interp_point, axis=0) + + # Integrate trapezoids + for i in range(1, len(w_data)): + x0, y0 = w_data[i - 1] + x1, y1 = w_data[i] + trap_area = (y0 + y1) * (x1 - x0) / 2 # * w_data[i, 2] # weight by sample count + area += trap_area + if x1 > decision_boundary: + # Handle partial trapezoid if it starts before boundary + if x0 < decision_boundary: + # area from boundary to x1 + # Already have interpolated point, so this is exact + pass # covered by insertion + decision_area += trap_area return area, decision_area @@ -576,22 +594,18 @@ def _calculate_group_curve(self, group: str) -> dict[str, float | np.ndarray]: data = self.group_ksteps[group] w_data = data[np.where(data[:, 1] > 0)][:, [0, 2, 1]] - area, decision_area = self._area_under_curve(w_data) - - det_threshold = self.groups[group].det_threshold or self.det_feature.threshold return { "x": w_data[:, 0], "y": w_data[:, 1], "sample_counts": w_data[:, 2], - "area": area, - "decision_area": decision_area, - "det_threshold": det_threshold, + "det_threshold": self.groups[group].det_threshold or self.det_feature.threshold, } def _calculate_group_ratio(self, reference_group: str, other_group: str) -> dict[str, float | str]: """ Calculate and return the DAI ratios between a reference group and another group. + Ensures both curves are evaluated over the same x-range. Args: reference_group: The name of the reference group. @@ -602,10 +616,31 @@ def _calculate_group_ratio(self, reference_group: str, other_group: str) -> dict """ if reference_group not in self.group_curves or other_group not in self.group_curves: raise ValueError("Group curves not found. Please run evaluation first.") - a1, da1 = (self.group_curves[reference_group]["area"], self.group_curves[reference_group]["decision_area"]) - a2, da2 = (self.group_curves[other_group]["area"], self.group_curves[other_group]["decision_area"]) - ratio = (a2 - a1) / a1 - decision_ratio = ((da2 - da1) / da1) if da1 != 0 else "N/A" + + ref_curve = self.group_curves[reference_group] + oth_curve = self.group_curves[other_group] + + x_ref, y_ref = ref_curve["x"], ref_curve["y"] + x_oth, y_oth = oth_curve["x"], oth_curve["y"] + + # Determine common x-range + x_min = max(x_ref.min(), x_oth.min()) + x_max = min(x_ref.max(), x_oth.max()) + + # Trim both curves to common range + ref_mask = (x_ref >= x_min) & (x_ref <= x_max) + oth_mask = (x_oth >= x_min) & (x_oth <= x_max) + + ref_trimmed = np.column_stack([x_ref[ref_mask], y_ref[ref_mask]]) + oth_trimmed = np.column_stack([x_oth[oth_mask], y_oth[oth_mask]]) + + # Compute areas and ratios + a1, da1 = self._area_under_curve(ref_trimmed) + a2, da2 = self._area_under_curve(oth_trimmed) + + ratio = (a2 - a1) / a1 if a1 != 0 else "N/A" + decision_ratio = (da2 - da1) / da1 if da1 != 0 else "N/A" + return {"Full": ratio, "Decision": decision_ratio} def _calculate_group_ratios(self, groups: list[Group], rerun: bool = True) -> None: @@ -796,7 +831,7 @@ def get_plots( # Labels ax.set_xlabel(self.model_name) - ax.set_ylabel("Deterioration above threshold for group") + ax.set_ylabel("Proportion above deterioration threshold") # Decision boundary marker ax.axvline(self.decision_boundary, ls="--", lw=0.8, color="blue", alpha=0.6) From 275a6e5f4bcfaba27938011c9527bc2c038e46fe Mon Sep 17 00:00:00 2001 From: Harry Wilde Date: Tue, 7 Oct 2025 09:46:14 +0100 Subject: [PATCH 10/11] Add tests and fix det_feature ref --- daindex/main.py | 6 +- tests/test_main.py | 391 +++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 394 insertions(+), 3 deletions(-) create mode 100644 tests/test_main.py diff --git a/daindex/main.py b/daindex/main.py index 6aa6687..4e94ff3 100644 --- a/daindex/main.py +++ b/daindex/main.py @@ -320,10 +320,10 @@ def _kde_estimate(self, in_matrix: np.ndarray) -> KernelDensity: # detect pulse like PDF if bandwidth < 0.1: # force is_discrete because the bestfitted_bandwith_bw would lead to one - self.is_discrete = True + self.det_feature.is_discrete = True elif bandwidth > 0.7: # force is_discrete to be false because the fitted_bandwith would lead to one - self.is_discrete = False + self.det_feature.is_discrete = False return kde @@ -349,7 +349,7 @@ def _get_threshold_index( threshold_index = int((threshold - low_bound) / step) if threshold == low_bound + boundary_offset: threshold_index = int((threshold - low_bound - boundary_offset) / step) - elif self.is_discrete: + elif self.det_feature.is_discrete: # discrete values will lead to PDF shape like pulses, # it's important to start with the valley between the pulse you want to include and the one before that # the following is to find the valley index diff --git a/tests/test_main.py b/tests/test_main.py new file mode 100644 index 0000000..a6a0b58 --- /dev/null +++ b/tests/test_main.py @@ -0,0 +1,391 @@ +""" +Test suite for the main daindex module. + +This test file provides comprehensive coverage for the main classes and functions +in daindex/main.py while maintaining minimal setup requirements. +""" + +import warnings +from unittest.mock import MagicMock, patch + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import pytest +from sklearn.ensemble import RandomForestClassifier +from sklearn.neighbors import KernelDensity + +from daindex.main import DAIndex, DeteriorationFeature, Group, ProbabilisticModel + + +class TestDeteriorationFeature: + """Test cases for DeteriorationFeature class.""" + + def test_init_with_defaults(self) -> None: + """Test initialization with default parameters.""" + feature = DeteriorationFeature(col="test_col", threshold=0.5) + assert feature.col == "test_col" + assert feature.threshold == 0.5 + assert feature.label == "test_col" # defaults to col + assert feature.func is None + assert feature.is_discrete is False + assert feature.reverse is False + + def test_init_with_custom_params(self) -> None: + """Test initialization with custom parameters.""" + + def func(x: float) -> float: + return x * 2 + + feature = DeteriorationFeature( + col="custom_col", + threshold=0.8, + label="Custom Label", + func=func, + is_discrete=True, + reverse=True, + ) + assert feature.col == "custom_col" + assert feature.threshold == 0.8 + assert feature.label == "Custom Label" + assert feature.func == func + assert feature.is_discrete is True + assert feature.reverse is True + + +class TestGroup: + """Test cases for Group class.""" + + def test_init_with_defaults(self) -> None: + """Test initialization with default parameters.""" + group = Group("Test Group") + assert group.name == "Test Group" + assert group.definition == ["Test Group"] # defaults to [name] + assert group.col is None + + def test_init_single_definition_to_list(self) -> None: + """Test that single definition value is converted to list.""" + group = Group("Test", definition="single_value") + assert group.definition == ["single_value"] + + def test_call_basic_filtering(self) -> None: + """Test basic group filtering functionality.""" + cohort = pd.DataFrame({"group_col": ["A", "B", "A", "C"], "value": [1, 2, 3, 4]}) + group = Group("Group A", definition="A", col="group_col") + result = group(cohort) + expected = pd.DataFrame({"group_col": ["A", "A"], "value": [1, 3]}, index=[0, 2]) + pd.testing.assert_frame_equal(result, expected) + + def test_call_multiple_definitions(self) -> None: + """Test filtering with multiple definition values.""" + cohort = pd.DataFrame({"sex": ["M", "F", "m", "F", "f", "female"], "value": [1, 2, 3, 4, 5, 6]}) + group = Group("Female", definition=["F", "f", "female"], col="sex") + result = group(cohort) + expected = pd.DataFrame({"sex": ["F", "F", "f", "female"], "value": [2, 4, 5, 6]}, index=[1, 3, 4, 5]) + pd.testing.assert_frame_equal(result, expected) + + def test_call_without_col_raises_error(self) -> None: + """Test that calling without col raises ValueError.""" + cohort = pd.DataFrame({"value": [1, 2, 3]}) + group = Group("Test") # no col specified + with pytest.raises(ValueError, match="Group column name must be provided"): + group(cohort) + + +class TestDAIndex: + """Test cases for DAIndex class.""" + + @pytest.fixture + def sample_cohort(self) -> pd.DataFrame: + """Create a sample cohort DataFrame for testing.""" + np.random.seed(42) + return pd.DataFrame( + { + "age": [25, 35, 45, 55, 65, 75, 85, 95], + "sex": ["M", "F", "M", "F", "M", "F", "M", "F"], + "feature_1": [0.1, 0.4, 0.35, 0.8, 0.65, 0.9, 0.85, 0.95], + "feature_2": [1, 0, 1, 0, 1, 0, 1, 0], + "deterioration": [0.2, 0.3, 0.5, 0.7, 0.6, 0.8, 0.9, 1.0], + "outcome": [0, 0, 0, 1, 1, 1, 1, 1], + "predictions": [0.1, 0.2, 0.4, 0.6, 0.7, 0.8, 0.9, 0.95], + } + ) + + @pytest.fixture + def sample_groups(self) -> list[Group]: + """Create sample groups for testing.""" + return [ + Group(name="Male", definition="M", col="sex"), + Group(name="Female", definition="F", col="sex"), + ] + + @pytest.fixture + def sample_det_feature(self) -> DeteriorationFeature: + """Create sample deterioration feature for testing.""" + return DeteriorationFeature(col="deterioration", threshold=0.5) + + @pytest.fixture + def dai_instance( + self, sample_cohort: pd.DataFrame, sample_groups: list[Group], sample_det_feature: DeteriorationFeature + ) -> DAIndex: + """Create a DAIndex instance for testing.""" + return DAIndex( + cohort=sample_cohort, + groups=sample_groups, + det_feature=sample_det_feature, + n_bins=10, # smaller for faster tests + ) + + def test_init( + self, sample_cohort: pd.DataFrame, sample_groups: list[Group], sample_det_feature: DeteriorationFeature + ) -> None: + """Test DAIndex initialization.""" + dai = DAIndex( + cohort=sample_cohort, + groups=sample_groups, + det_feature=sample_det_feature, + ) + assert dai.cohort is sample_cohort + assert len(dai.groups) == 2 + assert "Male" in dai.groups + assert "Female" in dai.groups + assert dai.det_feature is sample_det_feature + assert dai.model_name == "Allocation" + assert dai.decision_boundary == 0.5 + + def test_setup_groups_missing_col_raises_error( + self, sample_cohort: pd.DataFrame, sample_det_feature: DeteriorationFeature + ) -> None: + """Test that missing col in groups raises ValueError.""" + groups = [Group("Group1", "A")] # no col specified + dai = DAIndex.__new__(DAIndex) + dai.cohort = sample_cohort + with pytest.raises(ValueError, match="group_col must be provided"): + dai.setup_groups(groups) + + def test_kde_estimate(self, dai_instance: DAIndex) -> None: + """Test KDE estimation.""" + data = np.array([[0.1], [0.2], [0.3], [0.4], [0.5]]) + kde = dai_instance._kde_estimate(data) + assert isinstance(kde, KernelDensity) + + def test_stepped_severity(self, dai_instance: DAIndex) -> None: + """Test stepped severity calculation.""" + probs = np.array([0.1, 0.2, 0.3, 0.2, 0.1]) + result = dai_instance._stepped_severity(probs, 0.4, 0.6, 0.0, 0.1, 0.0) + assert isinstance(result, float) + assert 0 <= result <= 1 + + def test_obtain_da_index_insufficient_samples(self, dai_instance: DAIndex) -> None: + """Test DA index calculation with insufficient samples.""" + # Mock group scores with very few matching samples + dai_instance.group_scores = {"Male": [0.1, 0.9, 0.9, 0.9]} # only first matches bin + length, di_ret, sub_opt, failed = dai_instance._obtain_da_index("Male", (0.05, 0.15)) + assert failed is True + assert di_ret == 0.0 + + def test_get_scores_from_models(self, dai_instance: DAIndex, sample_cohort: pd.DataFrame) -> None: + """Test score calculation from models.""" + # Create a mock model + mock_model = MagicMock(spec=ProbabilisticModel) + mock_model.predict_proba.return_value = np.array([[0.7, 0.3], [0.4, 0.6], [0.2, 0.8], [0.1, 0.9]]) + + scores = dai_instance._get_scores_from_models("Male", [mock_model], ["feature_1", "feature_2"]) + assert len(scores) == 4 # 4 males in sample data + assert all(0 <= score <= 1 for score in scores) + + def test_area_under_curve(self, dai_instance: DAIndex) -> None: + """Test area under curve calculation.""" + # Simple test data + w_data = np.array([[0.0, 0.1], [0.25, 0.3], [0.5, 0.6], [0.75, 0.4], [1.0, 0.2]]) + area, decision_area = dai_instance._area_under_curve(w_data) + assert isinstance(area, float) + assert isinstance(decision_area, float) + assert area > 0 + assert decision_area >= 0 + + def test_calculate_group_curve(self, dai_instance: DAIndex) -> None: + """Test group curve calculation.""" + # Mock k-steps data + dai_instance.group_ksteps = {"Male": np.array([[0.1, 5, 0.3], [0.5, 3, 0.7], [0.9, 2, 0.9]])} + curve = dai_instance._calculate_group_curve("Male") + assert "x" in curve + assert "y" in curve + assert "sample_counts" in curve + assert "det_threshold" in curve + + def test_calculate_group_ratio(self, dai_instance: DAIndex) -> None: + """Test group ratio calculation.""" + # Mock curve data + dai_instance.group_curves = { + "Male": {"x": np.array([0.1, 0.5, 0.9]), "y": np.array([0.3, 0.6, 0.9])}, + "Female": {"x": np.array([0.2, 0.6, 0.8]), "y": np.array([0.4, 0.7, 0.8])}, + } + ratio = dai_instance._calculate_group_ratio("Male", "Female") + assert "Full" in ratio + assert "Decision" in ratio + + def test_check_group_invalid(self, dai_instance: DAIndex) -> None: + """Test group validation with invalid group.""" + with pytest.raises(KeyError, match="Invalid group name"): + dai_instance._check_group("InvalidGroup") + + def test_validate_group_inputs_none(self, dai_instance: DAIndex) -> None: + """Test group input validation with None (all groups).""" + result = dai_instance._validate_group_inputs(None) + assert set(result) == {"Male", "Female"} + + def test_evaluate_groups_by_predictions(self, dai_instance: DAIndex) -> None: + """Test evaluation using pre-calculated predictions.""" + with ( + patch.object(dai_instance, "_get_group_ksteps") as mock_ksteps, + patch.object(dai_instance, "_calculate_group_curve") as mock_curve, + ): + mock_ksteps.return_value = np.array([[0.1, 5, 0.3]]) + mock_curve.return_value = {"x": np.array([0.1]), "y": np.array([0.3])} + + dai_instance.evaluate_groups_by_predictions(predictions_col="predictions", reference_group="Male") + + assert "Male" in dai_instance.group_scores + assert "Female" in dai_instance.group_scores + + def test_evaluate_groups_by_models(self, dai_instance: DAIndex) -> None: + """Test evaluation using model objects.""" + mock_model = MagicMock(spec=ProbabilisticModel) + mock_model.predict_proba.return_value = np.array([[0.3, 0.7], [0.4, 0.6], [0.2, 0.8], [0.1, 0.9]]) + + with ( + patch.object(dai_instance, "_get_group_ksteps") as mock_ksteps, + patch.object(dai_instance, "_calculate_group_curve") as mock_curve, + ): + mock_ksteps.return_value = np.array([[0.1, 5, 0.3]]) + mock_curve.return_value = {"x": np.array([0.1]), "y": np.array([0.3])} + + dai_instance.evaluate_groups_by_models( + models=mock_model, feature_list=["feature_1", "feature_2"], reference_group="Male" + ) + + def test_get_plots(self, dai_instance: DAIndex) -> None: + """Test plot generation.""" + # Mock the required data + dai_instance.group_curves = { + "Male": {"x": np.array([0.1, 0.5, 0.9]), "y": np.array([0.3, 0.6, 0.9])}, + "Female": {"x": np.array([0.2, 0.6, 0.8]), "y": np.array([0.4, 0.7, 0.8])}, + } + dai_instance.group_ratios = {("Male", "Female"): {"Full": 0.1, "Decision": 0.05}} + + fig = dai_instance.get_plots(reference_group="Male") + assert isinstance(fig, plt.Figure) + + def test_get_ratios(self, dai_instance: DAIndex) -> None: + """Test getting ratios DataFrame.""" + dai_instance.group_ratios = { + ("Male", "Female"): {"Full": 0.1, "Decision": 0.05}, + ("Female", "Male"): {"Full": -0.09, "Decision": -0.047}, + } + df = dai_instance.get_ratios() + assert isinstance(df, pd.DataFrame) + assert len(df) == 2 + + def test_get_curves_single_group(self, dai_instance: DAIndex) -> None: + """Test getting curve data for single group.""" + dai_instance.group_curves = { + "Male": {"x": np.array([0.1]), "y": np.array([0.3])}, + } + result = dai_instance.get_curves("Male") + assert "x" in result + assert "y" in result + + def test_warning_on_sub_optimal_bins(self, dai_instance: DAIndex) -> None: + """Test that warnings are raised for sub-optimal bins.""" + dai_instance.group_scores = {"Male": [0.1, 0.2, 0.3]} # minimal data + + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter("always") + # This should trigger sub-optimal sample warnings + with patch.object(dai_instance, "_obtain_da_index", return_value=(15, 0.5, True, False)): + dai_instance._get_group_ksteps("Male") + + # Check that a warning was issued + assert len(w) > 0 + assert "sub-optimal" in str(w[0].message) + + +class TestIntegration: + """Integration tests combining multiple components.""" + + def test_end_to_end_workflow_with_predictions(self) -> None: + """Test complete workflow using predictions.""" + # Create test data + cohort = pd.DataFrame( + { + "group": ["A", "B"] * 20, + "feature1": np.random.random(40), + "feature2": np.random.random(40), + "deterioration": np.random.random(40), + "outcome": np.random.choice([0, 1], 40), + "predictions": np.random.random(40), + } + ) + + groups = [ + Group("Group A", "A", "group"), + Group("Group B", "B", "group"), + ] + + det_feature = DeteriorationFeature("deterioration", 0.5) + + dai = DAIndex(cohort, groups, det_feature, n_bins=5) + + # This should complete without errors + with warnings.catch_warnings(): + warnings.simplefilter("ignore") # Ignore sample size warnings for test data + dai.evaluate_groups_by_predictions("predictions", "Group A") + + # Verify results exist + assert "Group A" in dai.group_scores + assert "Group B" in dai.group_scores + + def test_end_to_end_workflow_with_models(self) -> None: + """Test complete workflow using model objects.""" + # Create test data + np.random.seed(42) + cohort = pd.DataFrame( + { + "group": ["A", "B"] * 20, + "feature1": np.random.random(40), + "feature2": np.random.random(40), + "deterioration": np.random.random(40), + "outcome": np.random.choice([0, 1], 40), + } + ) + + groups = [ + Group("Group A", "A", "group"), + Group("Group B", "B", "group"), + ] + + det_feature = DeteriorationFeature("deterioration", 0.5) + + # Train a simple model + model = RandomForestClassifier(n_estimators=5, random_state=42) + model.fit(cohort[["feature1", "feature2"]], cohort["outcome"]) + + dai = DAIndex(cohort, groups, det_feature, n_bins=5) + + # This should complete without errors + with warnings.catch_warnings(): + warnings.simplefilter("ignore") # Ignore sample size warnings + dai.evaluate_groups_by_models(model, ["feature1", "feature2"], "Group A") + + # Verify results exist + assert "Group A" in dai.group_scores + assert "Group B" in dai.group_scores + + # Test getting results + ratios_df = dai.get_ratios() + assert isinstance(ratios_df, pd.DataFrame) + + curves = dai.get_curves() + assert "Group A" in curves + assert "Group B" in curves From 67c0c5261f2e18e222636e5dee9304a905edaea4 Mon Sep 17 00:00:00 2001 From: Harry Wilde Date: Tue, 7 Oct 2025 09:58:00 +0100 Subject: [PATCH 11/11] Final adjustments --- daindex/main.py | 57 ++++++++++++++++++++++++------------------------- 1 file changed, 28 insertions(+), 29 deletions(-) diff --git a/daindex/main.py b/daindex/main.py index 4e94ff3..d2d1d1e 100644 --- a/daindex/main.py +++ b/daindex/main.py @@ -142,7 +142,7 @@ class DAIndex(object): 1. This class should be instantiated first with a `DeteriorationFeature` object and a list of `Group` objects. 2. The `evaluate_groups_by_predictions` or `evaluate_groups_by_models` methods can then be called to calculate the DAI for a reference and list of groups. - 3. The results can be accessed using the `get_plots`, `get_ratios`, and `get_curves` methods. + 3. The results can be accessed using the `get_plot`, `get_ratios`, and `get_curves` methods. 4. Any issues during the calculations are raised as warnings, and can be accessed using the `get_sub_optimal_bins`, `get_failed_bins`, and `get_bin_samples` methods. @@ -174,7 +174,7 @@ class DAIndex(object): setup_deterioration_feature: Set up the deterioration feature for the DAI calculation. evaluate_groups_by_predictions: Calculate the DAI for group(s) based on pre-calculated model predictions. evaluate_groups_by_models: Calculate the DAI for group(s) based on a list of trained models. - get_plots: Get the DAI plots for the (specified) group(s). + get_plot: Get the DAI plots for the (specified) group(s). get_ratios: Get the DAI ratios for the (specified) group(s). get_curves: Get the curve data for the (specified) group(s). get_sub_optimal_bins: Get information about any sub-optimal bins for the (specified) group(s). @@ -206,7 +206,7 @@ class DAIndex(object): >>> model = RandomForestClassifier() >>> model.fit(cohort[["feature_1", "feature_2"]], cohort["outcome"]) >>> dai.evaluate_groups_by_models(model, feature_list=["feature_1", "feature_2"], reference_group="Male") - >>> dai.get_plots(reference_group="Male") + >>> dai.get_plot(reference_group="Male") """ def setup_groups(self, groups: list[Group], group_col: str = None) -> None: @@ -489,7 +489,7 @@ def _obtain_da_index(self, group: str, bin_bounds: tuple[float, float]) -> tuple return len(det_list), di_ret, sub_opt, False def _get_group_ksteps(self, group: str) -> np.ndarray: - def process_bin(s: int, bounds: tuple[float, float]) -> tuple[float, int, float, bool, bool]: + def process_bin(s: float, bounds: tuple[float, float]) -> tuple[float, int, float, bool, bool]: length, di_ret, sub_opt, failed = self._obtain_da_index(group, bounds) return BinResult(s, length, di_ret, sub_opt, failed) @@ -643,7 +643,7 @@ def _calculate_group_ratio(self, reference_group: str, other_group: str) -> dict return {"Full": ratio, "Decision": decision_ratio} - def _calculate_group_ratios(self, groups: list[Group], rerun: bool = True) -> None: + def _calculate_group_ratios(self, groups: list[str], rerun: bool = True) -> None: for group1, group2 in itertools.permutations(groups, 2): key = (group1, group2) if rerun or key not in self.group_ratios: @@ -700,7 +700,7 @@ def evaluate_groups_by_predictions( self, predictions_col: str, reference_group: str, - groups: str | list[str] | None = None, + groups: list[str] | str | None = None, rerun: bool = True, n_jobs: int = -1, ) -> None: @@ -710,8 +710,7 @@ def evaluate_groups_by_predictions( Args: predictions_col: The column name in the cohort DataFrame that contains the model predictions reference_group: The reference group name - groups: Single group name or list of group names to compare. If None, all groups except the reference group - will be evaluated + groups: Single group name or list of group names to compare. If None, all groups will be evaluated rerun: Whether to rerun the evaluation even if results already exist for any of the groups n_jobs: The number of jobs to run in parallel """ @@ -731,7 +730,7 @@ def evaluate_groups_by_models( models: list[ProbabilisticModel] | ProbabilisticModel, feature_list: list[str], reference_group: str, - groups: str | list[str] | None = None, + groups: list[str] | str | None = None, rerun: bool = True, n_jobs: int = -1, ) -> None: @@ -742,8 +741,7 @@ def evaluate_groups_by_models( models: A single model object or a list of trained model objects that have a `predict_proba` method feature_list: A list of column names in the cohort DataFrame to be used as features for prediction reference_group: The reference group name - groups: Single group name or list of group names to compare. If None, all groups except the reference group - will be evaluated + groups: Single group name or list of group names to compare. If None, all groups will be evaluated rerun: Whether to rerun the evaluation even if results already exist for the groups n_jobs: The number of jobs to run in parallel """ @@ -760,11 +758,12 @@ def evaluate_groups_by_models( using_models=True, ) - def get_plots( + def get_plot( self, reference_group: str, - groups: str | list[str] | None = None, - theme: str = "whitegrid", + groups: list[str] | str | None = None, + style: str = "darkgrid", + **kwargs: dict, ) -> plt.Figure: """ Plot DA curves for selected groups with one reference group highlighted. @@ -772,15 +771,15 @@ def get_plots( to legend labels. Args: - reference_group: Name of the reference group. - groups: A single group name, list of names, or None (all other groups). - theme: Seaborn theme (e.g. 'whitegrid', 'darkgrid', 'ticks', etc.) + reference_group (str): Name of the reference group. + groups (list[str] | str | None): A single group name, list of names, or None (all other groups). + style (str): Seaborn theme (e.g. 'whitegrid', 'darkgrid', 'ticks', etc.) + **kwargs: Additional keyword arguments passed to `sns.set_theme()` Returns: - Matplotlib Figure + (plt.Figure): The final figure object. """ - # Apply seaborn theme - sns.set_theme(style=theme) + sns.set_theme(style=style, **kwargs) # Validate inputs all_groups = self._validate_reference_group_inputs(reference_group, groups) @@ -816,7 +815,7 @@ def get_plots( ratio_label = f" | Ratio: {full_ratio:+.3f}" decision_ratio = self.group_ratios[key].get("Decision") if decision_ratio != "N/A" and decision_ratio is not None: - ratio_label += f" ({decision_ratio:+.3f} in decision region)" + ratio_label += f" ({decision_ratio:+.3f} decision)" det_threshold = self.groups[group].det_threshold or self.det_feature.threshold threshold_label = f" | Thr: {det_threshold:.3f}" @@ -865,13 +864,13 @@ def get_ratios(self) -> pd.DataFrame: return df def get_curves( - self, groups: str | list[str] | None = None + self, groups: list[str] | str | None = None ) -> dict[str, float | np.ndarray] | dict[str, dict[str, float | np.ndarray]]: """ Get the curve data for the (specified) group(s). Args: - groups (str | list[str] | None): The name(s) of the group(s) to get curve data for, or None for all groups. + groups (list[str] | str | None): The name(s) of the group(s) to get curve data for, or None for all groups. Returns: (dict[str, CurveData]): A dictionary containing the curve data for each specified group. @@ -885,12 +884,12 @@ def get_curves( else: return {group: self.group_curves[group] for group in groups} - def get_bin_samples(self, groups: str | list[str] | None = None) -> dict[float, int] | dict[str, dict[float, int]]: + def get_bin_samples(self, groups: list[str] | str | None = None) -> dict[float, int] | dict[str, dict[float, int]]: """ Returns the number of samples used for each bin for the (specified) group(s). Args: - groups (str | list[str] | None): The name(s) of the group(s) to get bin sample information for, or None for + groups (list[str] | str | None): The name(s) of the group(s) to get bin sample information for, or None for all groups. Returns: @@ -907,13 +906,13 @@ def get_bin_samples(self, groups: str | list[str] | None = None) -> dict[float, return {group: self.group_bin_samples[group] for group in groups} def get_sub_optimal_bins( - self, groups: str | list[str] | None = None + self, groups: list[str] | str | None = None ) -> dict[float, int] | dict[str, dict[float, int]]: """ Returns information about sub-optimal bins for the (specified) group(s). Args: - groups (str | list[str] | None): The name(s) of the group(s) to get sub-optimal bin information for, or None + groups (list[str] | str | None): The name(s) of the group(s) to get sub-optimal bin information for, or None for all groups. Returns: @@ -929,12 +928,12 @@ def get_sub_optimal_bins( else: return {group: self.group_sub_optimal_bins[group] for group in groups} - def get_failed_bins(self, groups: str | list[str] | None = None) -> dict[float, int] | dict[str, dict[float, int]]: + def get_failed_bins(self, groups: list[str] | str | None = None) -> dict[float, int] | dict[str, dict[float, int]]: """ Returns information about failed bins for the (specified) group(s). Args: - groups (str | list[str] | None): The name(s) of the group(s) to get failed bin information for, or None for + groups (list[str] | str | None): The name(s) of the group(s) to get failed bin information for, or None for all groups. Returns: