pasi 0.3.0

Predictive Accuracy Subgroup Identification (PASI) trees and ensemble methods

pasi

Predictive Accuracy Subgroup Identification (PASI) — decision trees and ensemble methods for identifying subgroups with heterogeneous predictive model accuracy.

Installation

pip install pasi

For Numba-accelerated computation:

pip install pasi[numba]

Quick Start

import pandas as pd
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_auc_score
from pasi import pasiTree

# Load data
data = pd.read_csv("thyroid_dataset.csv")
X = data.drop(columns=["target"])
y = data["target"].values

# Fit a predictive model
model = LogisticRegression(max_iter=1000)
model.fit(X, y)
y_pred = model.predict_proba(X)[:, 1]

# Fit a PASI tree (AUC-based)
pasi_model = pasiTree(measure='auc', min_samples_leaf=100, max_depth=3)
pasi_model.fit(X, y=y, y_pred=y_pred)

# Visualize
dot_string = pasi_model.tree.export_graphviz(
    feature_names=list(X.columns),
    measure_name='auc'
)

Available Classes

Class	Description
pasiTree	PASI decision tree with support for indiv, auc, and auprc measures
pasiRF	Random forest ensemble of PASI trees
pasiGB	Gradient boosting with PASI tree weak learners
mvModelComb	Majority-vote model combiner
emModelComb	EM-based model combiner

Accuracy Measures

• indiv — Individual-level accuracy using pseudo-response mu
• auc — ROC AUC-based splitting via DeLong's method
• auprc — Area Under Precision-Recall Curve (bootstrap-based)

License

MIT