model-auditor 0.1.12

A library for evaluating ML model performance across subgroups with stratified metrics and bootstrap confidence intervals

Model Auditor

A Python library for evaluating machine learning model performance across subgroups with support for stratified metrics, bootstrap confidence intervals, and hierarchical visualizations.

Installation

pip install model-auditor

Features

• Stratified Evaluation: Evaluate model metrics across different subgroups (e.g., by age, gender, region)
• Bootstrap Confidence Intervals: Calculate 95% confidence intervals for all supported metrics
• Comprehensive Metrics: Built-in support for classification metrics including:
  • Sensitivity, Specificity, Precision, Recall, F1 Score
  • AUROC, AUPRC
  • Matthews Correlation Coefficient (MCC)
  • F-beta Score (configurable beta)
  • TPR, TNR, FPR, FNR
  • Count metrics (N, TP, TN, FP, FN, Positive, Negative)
• Threshold Optimization: Automatic threshold selection using the Youden index
• Hierarchical Visualization: Generate data structures for sunburst/treemap plots
• Extensible Design: Protocol-based architecture for custom metrics

Quick Start

from model_auditor import Auditor
from model_auditor.metrics import Sensitivity, Specificity, AUROC, F1Score

# Initialize the auditor
auditor = Auditor()

# Add your data
auditor.add_data(df)

# Define stratification features
auditor.add_feature(name="age_group", label="Age Group")
auditor.add_feature(name="gender", label="Gender")

# Define the score column and threshold
auditor.add_score(name="risk_score", label="Risk Score", threshold=0.5)

# Define the outcome column
auditor.add_outcome(name="diagnosis", mapping={"positive": 1, "negative": 0})

# Set metrics to evaluate
auditor.set_metrics([
    Sensitivity(),
    Specificity(),
    AUROC(),
    F1Score()
])

# Run evaluation with bootstrap confidence intervals
results = auditor.evaluate_metrics(score_name="risk_score", n_bootstraps=1000)

# Convert results to a DataFrame
results_df = results.to_dataframe()
print(results_df)

Threshold Optimization

Find the optimal decision threshold using the Youden index:

auditor = Auditor()
auditor.add_data(df)
auditor.add_score(name="risk_score")
auditor.add_outcome(name="label")

# Find optimal threshold
optimal_threshold = auditor.optimize_score_threshold(score_name="risk_score")
# Output: Optimal threshold for 'risk_score' found at: 0.423

Available Metrics

Classification Metrics

Metric	Class	Description
Sensitivity	Sensitivity()	TP / (TP + FN)
Specificity	Specificity()	TN / (TN + FP)
Precision	Precision()	TP / (TP + FP)
Recall	Recall()	TP / (TP + FN)
F1 Score	F1Score()	Harmonic mean of precision and recall
F-beta	FBetaScore(beta=2.0)	Weighted harmonic mean
MCC	MatthewsCorrelationCoefficient()	Matthews Correlation Coefficient

Ranking Metrics

Metric	Class	Description
AUROC	AUROC()	Area Under ROC Curve
AUPRC	AUPRC()	Area Under Precision-Recall Curve

Rate Metrics

Metric	Class	Description
TPR	TPR()	True Positive Rate
TNR	TNR()	True Negative Rate
FPR	FPR()	False Positive Rate
FNR	FNR()	False Negative Rate

Count Metrics

Metric	Class	Description
N	nData()	Sample size
TP	nTP()	True positive count
TN	nTN()	True negative count
FP	nFP()	False positive count
FN	nFN()	False negative count
Positive	nPositive()	Positive class count
Negative	nNegative()	Negative class count

Custom Metrics

Create custom metrics by implementing the AuditorMetric protocol:

from model_auditor.metrics import AuditorMetric
import pandas as pd

class AccuracyMetric(AuditorMetric):
    name = "accuracy"
    label = "Accuracy"
    inputs = ["tp", "tn", "fp", "fn"]
    ci_eligible = True

    def data_call(self, data: pd.DataFrame) -> float:
        tp = data["tp"].sum()
        tn = data["tn"].sum()
        fp = data["fp"].sum()
        fn = data["fn"].sum()
        return (tp + tn) / (tp + tn + fp + fn)

# Use with the auditor
auditor.set_metrics([AccuracyMetric(), Sensitivity()])

Hierarchical Visualization

Generate data for hierarchical plots (sunburst, treemap):

from model_auditor.plotting import HierarchyPlotter

plotter = HierarchyPlotter()
plotter.set_data(df)
plotter.set_features(["region", "age_group", "gender"])
plotter.set_score(name="risk_score")
plotter.set_aggregator("median")  # or "mean", or a custom function

# Compile plot data
plot_data = plotter.compile(container="All Patients")

# Use with Plotly
import plotly.graph_objects as go

fig = go.Figure(go.Sunburst(
    labels=plot_data.labels,
    ids=plot_data.ids,
    parents=plot_data.parents,
    values=plot_data.values,
    marker=dict(colors=plot_data.colors)
))
fig.show()

Custom Hierarchies

Define complex hierarchies with conditional features:

from model_auditor.plotting.schemas import Hierarchy, HLevel, HItem

hierarchy = Hierarchy(levels=[
    HLevel([HItem(name="region")]),
    HLevel([
        HItem(name="urban_category", query="region == 'Urban'"),
        HItem(name="rural_category", query="region == 'Rural'")
    ]),
    HLevel([HItem(name="age_group")])
])

plotter.set_features(hierarchy)

Disabling Confidence Intervals

For faster evaluation without confidence intervals:

results = auditor.evaluate_metrics(score_name="risk_score", n_bootstraps=None)

Output Format

Results are returned as nested dataclass objects that can be converted to DataFrames:

# Get results as DataFrame
df = results.to_dataframe(n_decimals=3, metric_labels=True)

# Access specific feature results
gender_results = results.features["gender"].to_dataframe()

# Access specific level results
male_results = results.features["gender"].levels["Male"].to_dataframe()

Score Interval Plots

Visualise bootstrap confidence intervals for any metric across feature levels using plot_metric_intervals. The method requires matplotlib and returns one (Figure, Axes) tuple per feature:

# Run evaluation with bootstrap CIs first.
results = auditor.evaluate_metrics(score_name="risk_score", n_bootstraps=1000)

# Plot every feature (one figure per feature).
plots = results.plot_metric_intervals(metric="sensitivity")

# Select a subset of features.
plots = results.plot_metric_intervals(
    metric="sensitivity",
    feature_names=["gender", "age_group"],
)

# Metric can be supplied by name or by label.
plots = results.plot_metric_intervals(metric="Sensitivity")   # label also works

# Display in a Jupyter notebook.
import matplotlib.pyplot as plt

for feature_name, (fig, ax) in plots.items():
    plt.show()

Each figure shows levels on the y-axis (in evaluation order) and the metric value on the x-axis. Horizontal whiskers span the 95% CI bounds. Levels with no CI data (e.g. unobserved categorical placeholders, count metrics) are automatically excluded from the plot.

# Access the Axes object to customise the figure further.
fig, ax = plots["gender"]
ax.set_xlim(0.0, 1.0)
ax.axvline(0.8, linestyle="--", color="grey", label="Target")
ax.legend()
plt.tight_layout()
plt.show()

Score Distribution Plots

Visualize raw score distributions stratified by feature levels — no evaluate_metrics() call required:

import matplotlib
matplotlib.use("Agg")  # use a non-interactive backend if needed

# Plot score distributions for all registered features
plots = auditor.plot_score_distributions(score_name="risk_score")

# Each entry: (matplotlib.figure.Figure, numpy.ndarray of Axes)
fig, axes = plots["age_group"]
fig.savefig("age_group_distributions.png", dpi=150)

# Limit to a specific subset of features
plots = auditor.plot_score_distributions(
    score_name="risk_score",
    feature_names=["gender", "age_group"],
)

# Raw counts instead of density
plots = auditor.plot_score_distributions(
    score_name="risk_score",
    density=False,
)

# Custom binning
plots = auditor.plot_score_distributions(
    score_name="risk_score",
    bins=50,
)

Each figure contains one histogram subplot per feature level. All subplots share the same x-axis and use identical bin edges, enabling direct visual comparison of score distributions across subgroups. Bins are computed from the entire feature slice rather than per-level, so relative spread and overlap are preserved.

Controlling Feature Level Order

By default, feature levels appear in the order they were encountered in the data. To control the row order in exported DataFrames, assign the feature column a pd.Categorical dtype with an explicit categories list before passing the data to the auditor:

import pandas as pd
from model_auditor import Auditor
from model_auditor.metrics import Sensitivity, Specificity

# Declare the desired display order for the 'age_group' column.
# Categories not present in the data still appear as rows (with NaN values).
df["age_group"] = pd.Categorical(
    df["age_group"],
    categories=["<30", "30-50", "50-70", ">70"],
    ordered=True,
)

auditor = Auditor()
auditor.add_data(df)
auditor.add_feature(name="age_group")
auditor.add_score(name="risk_score", threshold=0.5)
auditor.add_outcome(name="outcome")
auditor.set_metrics([Sensitivity(), Specificity()])

results = auditor.evaluate_metrics(score_name="risk_score", n_bootstraps=None)

# Rows appear in the declared order: <30, 30-50, 50-70, >70.
# If no rows belong to a declared category (e.g. '>70' is absent from the
# data), that category still appears as a row with NaN metric values.
df_out = results.features["age_group"].to_dataframe()

The same order is preserved in style_dataframe() and in the score-level ScoreEvaluation.to_dataframe() / ScoreEvaluation.style_dataframe() exports. Non-categorical feature columns are unaffected.

Error Analysis

Use evaluate_errors() to understand which subgroups are over- or under-represented within each confusion-matrix group (TP, TN, FP, FN). For every feature level the canonical 2×2 odds ratio (OR) is computed:

OR(level, group) = (a × d) / (b × c)

Where a = count(level ∩ group), b = count(level ∩ not-group), c = count(not-level ∩ group), d = count(not-level ∩ not-group).

OR = 1 means the level has the same odds of appearing in that confusion group as all other levels combined. OR > 1 indicates over-representation; OR < 1 under-representation.

# No additional metric setup required — evaluate_errors() uses OddsRatio by default.
error_results = auditor.evaluate_errors(score_name="risk_score", n_bootstraps=1000)

# Convert to a wide analysis-ready DataFrame.
# Rows: MultiIndex(feature, level)
# Columns: MultiIndex(section, metric)
#   Overall section: N, % overall, N_pos, N_neg, Pos %
#   Per group (TP/TN/FP/FN): N, % overall, % group, odds_ratio,
#                             odds_ratio_ci_lower, odds_ratio_ci_upper
df = error_results.to_dataframe()
print(df)

# Use metric_labels=True for human-readable column names:
df_labels = error_results.to_dataframe(metric_labels=True)

# Per-group deep inspection is still available:
tp_age = error_results.groups["tp"].features["age_group"]
print(tp_age.to_dataframe())

# Styled wide view: OR cells include inline CI when bootstraps were used,
# and FP/FN tier colouring is inverted (higher OR = worse in error groups).
display(error_results.style_dataframe(n_decimals=3, metric_labels=True))

License

Notebook Styling

For Jupyter notebooks, style_dataframe(...) returns a pandas Styler that colours cells by relative performance tier within each metric column.

# Colour all levels in a feature by relative tier (default: performance metrics only)
display(results.features['age_group'].style_dataframe(n_decimals=3, metric_labels=True))

# Also colour count columns (N, TP, TN, …)
display(results.features['gender'].style_dataframe(include_count_metrics=True))

# Opt into custom colours
display(results.style_dataframe(
    low_color="#ffd6d6",
    medium_color="#fff9c4",
    high_color="#d0f0d0",
))

Tier assignment

Tier	Default colour	Meaning
High	#d4edda (green)	Top third of values in the column
Medium	#fff3cd (yellow)	Middle third
Low	#f8d7da (red)	Bottom third

Tiers are computed per metric column across all rows in the table. Lower-is-better metrics (fpr, fnr) are inverted: a lower value receives the high (green) tier.

Parameters

Parameter	Default	Description
n_decimals	3	Decimal places for numeric display
metric_labels	False	Use metric labels as column headers instead of names
include_count_metrics	False	Also style count columns (N, TP, TN, FP, FN, Pos., Neg.)
low_color	"#f8d7da"	Background colour for low-tier cells
medium_color	"#fff3cd"	Background colour for medium-tier cells
high_color	"#d4edda"	Background colour for high-tier cells

MIT License

Author

Beatrice BM