ppboot updates

ppi_py/baselines.py

@@ -5,7 +5,7 @@
 from statsmodels.stats.weightstats import _zconfint_generic, _zstat_generic
 from sklearn.linear_model import LogisticRegression, LinearRegression
 from sklearn.isotonic import IsotonicRegression
-from .utils import dataframe_decorator
+from .utils import dataframe_decorator, bootstrap
 from .ppi import _ols, _wls
 import pdb
 
@@ -309,3 +309,100 @@ def classical_logistic_ci(X, Y, alpha=0.1, alternative="two-sided"):
  return _zconfint_generic(
  pointest, np.sqrt(np.diag(cov_mat) / n), alpha, alternative
  )
+
+"""
+ BOOTSTRAP CI
+
+"""
+
+def classical_bootstrap_ci(
+ estimator,
+ Y,
+ X=None,
+ n_resamples=1000,
+ alpha=0.1,
+ alternative="two-sided",
+ method="percentile",
+):
+ """Classical bootstrap confidence interval for the estimator.
+
+ Args:
+ estimator (callable): Estimator function. Takes in (X,Y) or (Y) and returns a point estimate.
+ Y (ndarray): Gold-standard labels.
+ X (ndarray, optional): Covariates corresponding to the gold-standard labels. Defaults to `None`. If `None`, the estimator is assumed to only take in `Y`.
+ n_resamples (int, optional): Number of bootstrap resamples. Defaults to `1000`.
+ alpha (float, optional): Error level; the confidence interval will target a coverage of 1 - alpha. Must be in (0, 1). Defaults to `0.1`.
+ alternative (str, optional): Alternative hypothesis, either 'two-sided', 'larger' or 'smaller'. Defaults to `'two-sided'`.
+ method (str, optional): Method to compute the confidence interval, either 'percentile' or 'basic'. Defaults to `'percentile'`.
+
+ Returns:
+ float or ndarray: Lower and upper bounds of the bootstrap confidence interval for the estimator.
+ """
+
+ if X is None:
+
+ pointest = estimator(Y)
+
+ bootstrap_distribution = np.array(
+ bootstrap(
+ [Y],
+ estimator,
+ n_resamples=n_resamples
+ )
+ )
+
+ else:
+
+ pointest = estimator(X, Y)
+
+ bootstrap_distribution = np.array(
+ bootstrap(
+ [X, Y],
+ estimator,
+ n_resamples=n_resamples,
+ paired=[[0, 1]]
+ )
+ )
+
+ # Deal with the different types of alternative hypotheses
+ if alternative == "two-sided":
+ alpha_lower = alpha / 2
+ alpha_upper = alpha / 2
+ elif alternative == "larger":
+ alpha_lower = alpha
+ alpha_upper = 0
+ elif alternative == "smaller":
+ alpha_lower = 0
+ alpha_upper = alpha
+
+ # Compute the lower and upper bounds depending on the method
+ if method == "percentile":
+ lower_bound = np.quantile(
+ bootstrap_distribution, alpha_lower, axis=0
+ )
+ upper_bound = np.quantile(
+ bootstrap_distribution, 1 - alpha_upper, axis=0
+ )
+ elif method == "basic":
+ lower_bound = 2 * pointest - np.quantile(
+ bootstrap_distribution, 1 - alpha_lower, axis=0
+ )
+ upper_bound = 2 * pointest - np.quantile(
+ bootstrap_distribution, alpha_upper, axis=0
+ )
+ else:
+ raise ValueError(
+ "Method must be either 'percentile' or 'basic'. The others are not implemented yet... want to contribute? ;)"
+ )
+
+ if alternative == "two-sided":
+ return lower_bound, upper_bound
+ elif alternative == "larger":
+ return -np.inf, upper_bound
+ elif alternative == "smaller":
+ return lower_bound, np.inf
+ else:
+ raise ValueError(
+ "Alternative must be either 'two-sided', 'larger' or 'smaller'."
+ )
+

ppi_py/ppi.py

@@ -1071,6 +1071,12 @@ def ppi_logistic_ci(
  )
 
 
+"""
+ PPBOOT
+
+"""
+
+
 def ppboot(
  estimator,
  Y,