Rename verbose to display_progress

Author: okiner-3

dte_adj/base.py

@@ -28,7 +28,7 @@ def predict_dte(
         alpha: float = 0.05,
         variance_type="moment",
         n_bootstrap=500,
-        verbose: bool = True,
+        display_progress: bool = True,
     ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
         """
         Compute Distribution Treatment Effects (DTE) based on the estimator for the distribution function.
@@ -45,7 +45,7 @@ def predict_dte(
             variance_type (str, optional): Variance type to be used to compute confidence intervals.
                 Available values are "moment", "simple", and "uniform". Defaults to "moment".
             n_bootstrap (int, optional): Number of bootstrap samples. Defaults to 500.
-            verbose (bool, optional): Whether to display a progress bar. Defaults to True.
+            display_progress (bool, optional): Whether to display a progress bar. Defaults to True.
 
         Returns:
             Tuple[np.ndarray, np.ndarray, np.ndarray]: A tuple containing:
@@ -87,7 +87,7 @@ def predict_dte(
             alpha,
             variance_type,
             n_bootstrap,
-            verbose,
+            display_progress,
         )
 
     def predict_pte(
@@ -98,7 +98,7 @@ def predict_pte(
         alpha: float = 0.05,
         variance_type="moment",
         n_bootstrap=500,
-        verbose: bool = True,
+        display_progress: bool = True,
     ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
         """
         Compute Probability Treatment Effects (PTE) based on the estimator for the distribution function.
@@ -116,7 +116,7 @@ def predict_pte(
             variance_type (str, optional): Variance type to be used to compute confidence intervals.
                 Available values are "moment", "simple", and "uniform". Defaults to "moment".
             n_bootstrap (int, optional): Number of bootstrap samples. Defaults to 500.
-            verbose (bool, optional): Whether to display a progress bar. Defaults to True.
+            display_progress (bool, optional): Whether to display a progress bar. Defaults to True.
 
         Returns:
             Tuple[np.ndarray, np.ndarray, np.ndarray]: A tuple containing:
@@ -161,7 +161,7 @@ def predict_pte(
             alpha,
             variance_type,
             n_bootstrap,
-            verbose,
+            display_progress,
         )
 
     def predict_qte(
@@ -171,7 +171,7 @@ def predict_qte(
         quantiles: Optional[np.ndarray] = None,
         alpha: float = 0.05,
         n_bootstrap=500,
-        verbose: bool = True,
+        display_progress: bool = True,
     ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
         """
         Compute Quantile Treatment Effects (QTE) based on the estimator for the distribution function.
@@ -186,7 +186,7 @@ def predict_qte(
             quantiles (np.ndarray, optional): Quantiles used for QTE. Defaults to [0.1, 0.2, ..., 0.9].
             alpha (float, optional): Significance level of the confidence bound. Defaults to 0.05.
             n_bootstrap (int, optional): Number of bootstrap samples. Defaults to 500.
-            verbose (bool, optional): Whether to display a progress bar. Defaults to True.
+            display_progress (bool, optional): Whether to display a progress bar. Defaults to True.
 
         Returns:
             Tuple[np.ndarray, np.ndarray, np.ndarray]: A tuple containing:
@@ -236,7 +236,7 @@ def predict_qte(
 
         qtes = np.zeros((n_bootstrap, qte.shape[0]))
         bootstrap_iter = range(n_bootstrap)
-        if verbose:
+        if display_progress:
             bootstrap_iter = tqdm(bootstrap_iter, desc="Bootstrap QTE")
         for b in bootstrap_iter:
             bootstrap_indexes = np.random.choice(indexes, size=n_obs, replace=True)
@@ -266,7 +266,7 @@ def _compute_dtes(
         alpha: float,
         variance_type: str,
         n_bootstrap: int,
-        verbose: bool = False,
+        display_progress: bool = False,
     ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
         """Compute expected DTEs."""
         treatment_cdf, treatment_cdf_mat, _ = self._compute_cumulative_distribution(
@@ -275,15 +275,15 @@ def _compute_dtes(
             self.covariates,
             self.treatment_arms,
             self.outcomes,
-            verbose=verbose,
+            display_progress=display_progress,
         )
         control_cdf, control_cdf_mat, _ = self._compute_cumulative_distribution(
             control_treatment_arm,
             locations,
             self.covariates,
             self.treatment_arms,
             self.outcomes,
-            verbose=verbose,
+            display_progress=display_progress,
         )
 
         dte = treatment_cdf - control_cdf
@@ -320,7 +320,7 @@ def _compute_ptes(
         alpha: float,
         variance_type: str,
         n_bootstrap: int,
-        verbose: bool = False,
+        display_progress: bool = False,
     ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
         """Compute expected PTEs."""
         treatment_pdf, treatment_pdf_mat, _ = self._compute_interval_probability(
@@ -329,15 +329,15 @@ def _compute_ptes(
             self.covariates,
             self.treatment_arms,
             self.outcomes,
-            verbose=verbose,
+            display_progress=display_progress,
         )
         control_pdf, control_pdf_mat, _ = self._compute_interval_probability(
             control_treatment_arm,
             locations,
             self.covariates,
             self.treatment_arms,
             self.outcomes,
-            verbose=verbose,
+            display_progress=display_progress,
         )
 
         pte = treatment_pdf - control_pdf
@@ -417,15 +417,15 @@ def find_quantile(quantile, arm):
         return result
 
     def predict(
-        self, treatment_arm: int, locations: np.ndarray, verbose: bool = True
+        self, treatment_arm: int, locations: np.ndarray, display_progress: bool = True
     ) -> np.ndarray:
         """
         Compute cumulative distribution values.
 
         Args:
             treatment_arm (int): The index of the treatment arm.
             outcomes (np.ndarray): Scalar values to be used for computing the cumulative distribution.
-            verbose (bool, optional): Whether to display a progress bar. Defaults to True.
+            display_progress (bool, optional): Whether to display a progress bar. Defaults to True.
 
         Returns:
             np.ndarray: Estimated cumulative distribution values for the input.
@@ -446,7 +446,7 @@ def predict(
             self.covariates,
             self.treatment_arms,
             self.outcomes,
-            verbose=verbose,
+            display_progress=display_progress,
         )[0]
 
     def _compute_cumulative_distribution(
@@ -456,7 +456,7 @@ def _compute_cumulative_distribution(
         covariates: np.ndarray,
         treatment_arms: np.ndarray,
         outcomes: np.array,
-        verbose: bool = False,
+        display_progress: bool = False,
     ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
         """
         Compute the cumulative distribution values.
@@ -467,7 +467,7 @@ def _compute_cumulative_distribution(
             covariates: (np.ndarray): An array of covariates variables in the observed data.
             treatment_arms (np.ndarray): An array of treatment arms in the observed data.
             outcomes (np.ndarray): An array of outcomes in the observed data.
-            verbose (bool): Whether to display a progress bar.
+            display_progress (bool): Whether to display a progress bar.
 
         Returns:
             Tuple[np.ndarray, np.ndarray, np.ndarray]: Estimated cumulative distribution values, prediction for each observation, and superset prediction for each observation.

dte_adj/local.py

@@ -61,7 +61,7 @@ def predict_ldte(
         control_treatment_arm: int,
         locations: np.ndarray,
         alpha: float = 0.05,
-        verbose: bool = True,
+        display_progress: bool = True,
     ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
         """
         Compute Local Distribution Treatment Effects (LDTE).
@@ -75,7 +75,7 @@ def predict_ldte(
             control_treatment_arm (int): The index of the treatment arm of the control group.
             locations (np.ndarray): Scalar values to be used for computing the cumulative distribution.
             alpha (float, optional): Significance level of the confidence bound. Defaults to 0.05.
-            verbose (bool, optional): Whether to display a progress bar. Defaults to True.
+            display_progress (bool, optional): Whether to display a progress bar. Defaults to True.
 
         Returns:
             Tuple[np.ndarray, np.ndarray, np.ndarray]: A tuple containing:
@@ -119,7 +119,7 @@ def predict_ldte(
             control_treatment_arm,
             locations,
             alpha,
-            verbose,
+            display_progress,
         )
 
     def predict_lpte(
@@ -128,7 +128,7 @@ def predict_lpte(
         control_treatment_arm: int,
         locations: np.ndarray,
         alpha: float = 0.05,
-        verbose: bool = True,
+        display_progress: bool = True,
     ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
         """
         Compute Local Probability Treatment Effects (LPTE).
@@ -143,7 +143,7 @@ def predict_lpte(
             locations (np.ndarray): Scalar values defining interval boundaries for probability computation.
                 For each interval (locations[i], locations[i+1]], the LPTE is computed.
             alpha (float, optional): Significance level of the confidence bound. Defaults to 0.05.
-            verbose (bool, optional): Whether to display a progress bar. Defaults to True.
+            display_progress (bool, optional): Whether to display a progress bar. Defaults to True.
 
         Returns:
             Tuple[np.ndarray, np.ndarray, np.ndarray]: A tuple containing:
@@ -189,7 +189,7 @@ def predict_lpte(
             control_treatment_arm,
             locations,
             alpha,
-            verbose,
+            display_progress,
         )
 
 
@@ -236,7 +236,7 @@ def predict_ldte(
         control_treatment_arm: int,
         locations: np.ndarray,
         alpha: float = 0.05,
-        verbose: bool = True,
+        display_progress: bool = True,
     ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
         """
         Compute Local Distribution Treatment Effects (LDTE) using ML adjustment.
@@ -249,7 +249,7 @@ def predict_ldte(
             control_treatment_arm (int): The index of the treatment arm of the control group.
             locations (np.ndarray): Scalar values to be used for computing the cumulative distribution.
             alpha (float, optional): Significance level of the confidence bound. Defaults to 0.05.
-            verbose (bool, optional): Whether to display a progress bar. Defaults to True.
+            display_progress (bool, optional): Whether to display a progress bar. Defaults to True.
 
         Returns:
             Tuple[np.ndarray, np.ndarray, np.ndarray]: A tuple containing:
@@ -295,7 +295,7 @@ def predict_ldte(
             control_treatment_arm,
             locations,
             alpha,
-            verbose,
+            display_progress,
         )
 
     def predict_lpte(
@@ -304,7 +304,7 @@ def predict_lpte(
         control_treatment_arm: int,
         locations: np.ndarray,
         alpha: float = 0.05,
-        verbose: bool = True,
+        display_progress: bool = True,
     ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
         """
         Compute Local Probability Treatment Effects (LPTE) using ML adjustment.
@@ -318,7 +318,7 @@ def predict_lpte(
             locations (np.ndarray): Scalar values defining interval boundaries for probability computation.
                 For each interval (locations[i], locations[i+1]], the LPTE is computed.
             alpha (float, optional): Significance level of the confidence bound. Defaults to 0.05.
-            verbose (bool, optional): Whether to display a progress bar. Defaults to True.
+            display_progress (bool, optional): Whether to display a progress bar. Defaults to True.
 
         Returns:
             Tuple[np.ndarray, np.ndarray, np.ndarray]: A tuple containing:
@@ -367,5 +367,5 @@ def predict_lpte(
             control_treatment_arm,
             locations,
             alpha,
-            verbose,
+            display_progress,
         )

dte_adj/stratified.py

@@ -55,7 +55,7 @@ def _compute_cumulative_distribution(
         covariates: np.ndarray,
         treatment_arms: np.ndarray,
         outcomes: np.array,
-        verbose: bool = False,
+        display_progress: bool = False,
     ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
         """
         Compute the cumulative distribution values.
@@ -66,7 +66,7 @@ def _compute_cumulative_distribution(
             covariates: (np.ndarray): An array of covariates variables in the observed data.
             treatment_arm (np.ndarray): An array of treatment arms in the observed data.
             outcomes (np.ndarray): An array of outcomes in the observed data
-            verbose (bool): Whether to display a progress bar.
+            display_progress (bool): Whether to display a progress bar.
 
         Returns:
             Tuple of numpy arrays:
@@ -105,7 +105,7 @@ def _compute_interval_probability(
         covariates: np.ndarray,
         treatment_arms: np.ndarray,
         outcomes: np.array,
-        verbose: bool = False,
+        display_progress: bool = False,
     ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
         """Compute the interval probabilities.
 
@@ -115,7 +115,7 @@ def _compute_interval_probability(
             covariates: (np.ndarray): An array of covariates variables in the observed data.
             treatment_arm (np.ndarray): An array of treatment arms in the observed data.
             outcomes (np.ndarray): An array of outcomes in the observed data
-            verbose (bool): Whether to display a progress bar.
+            display_progress (bool): Whether to display a progress bar.
 
         Returns:
             Tuple of numpy arrays:
@@ -224,7 +224,7 @@ def _compute_cumulative_distribution(
         covariates: np.ndarray,
         treatment_arms: np.ndarray,
         outcomes: np.array,
-        verbose: bool = False,
+        display_progress: bool = False,
     ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
         """
         Compute the cumulative distribution values.
@@ -235,7 +235,7 @@ def _compute_cumulative_distribution(
             covariates: (np.ndarray): An array of covariates variables in the observed data.
             treatment_arm (np.ndarray): An array of treatment arms in the observed data.
             outcomes (np.ndarray): An array of outcomes in the observed data
-            verbose (bool): Whether to display a progress bar.
+            display_progress (bool): Whether to display a progress bar.
 
         Returns:
             Tuple of numpy arrays:
@@ -254,7 +254,7 @@ def _compute_cumulative_distribution(
         if self.is_multi_task:
             binomial = (outcomes.reshape(-1, 1) <= locations) * 1  # (n_records, n_loc)
             fold_iter = range(self.folds)
-            if verbose:
+            if display_progress:
                 fold_iter = tqdm(fold_iter, desc="Cross-fitting (multi-task)")
             for fold in fold_iter:
                 fold_mask = (folds != fold) & treatment_mask
@@ -281,7 +281,7 @@ def _compute_cumulative_distribution(
                     superset_prediction[superset_mask] = pred
         else:
             loc_iter = enumerate(locations)
-            if verbose:
+            if display_progress:
                 loc_iter = tqdm(loc_iter, total=len(locations), desc="Computing CDF")
             for i, location in loc_iter:
                 binomial = (outcomes <= location) * 1  # (n_records)
@@ -335,7 +335,7 @@ def _compute_interval_probability(
         covariates: np.ndarray,
         treatment_arms: np.ndarray,
         outcomes: np.array,
-        verbose: bool = False,
+        display_progress: bool = False,
     ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
         """
         Compute the interval probabilities.
@@ -346,7 +346,7 @@ def _compute_interval_probability(
             covariates: (np.ndarray): An array of covariates variables in the observed data.
             treatment_arm (np.ndarray): An array of treatment arms in the observed data.
             outcomes (np.ndarray): An array of outcomes in the observed data
-            verbose (bool): Whether to display a progress bar.
+            display_progress (bool): Whether to display a progress bar.
 
         Returns:
             Tuple of numpy arrays:
@@ -364,7 +364,7 @@ def _compute_interval_probability(
         s_list = np.unique(strata)
         binominals = (outcomes[:, np.newaxis] <= locations) * 1  # (n_records, n_loc)
         interval_iter = range(len(locations) - 1)
-        if verbose:
+        if display_progress:
             interval_iter = tqdm(interval_iter, desc="Computing interval prob.")
         for i in interval_iter:
             binomial = binominals[:, i + 1] - binominals[:, i]