From 18e140d7a1de12549579ad4cc315f589d85e17ac Mon Sep 17 00:00:00 2001
From: Patrick Bloebaum <bloebp@amazon.com>
Date: Thu, 9 Nov 2023 16:07:05 -0800
Subject: [PATCH] Remove deprecated feature.py module from GCM

These methods are now available in the feature_relevance.py module.

Signed-off-by: Patrick Bloebaum <bloebp@amazon.com>
---
 dowhy/gcm/feature.py | 90 --------------------------------------------
 1 file changed, 90 deletions(-)
 delete mode 100644 dowhy/gcm/feature.py

diff --git a/dowhy/gcm/feature.py b/dowhy/gcm/feature.py
deleted file mode 100644
index 651bff5261..0000000000
--- a/dowhy/gcm/feature.py
+++ /dev/null
@@ -1,90 +0,0 @@
-"""This module is deprecated! All functions are moved into the feature_relevance.py module. """
-import warnings
-from typing import Any, Callable, Dict, Optional, Tuple, Union
-
-import numpy as np
-import pandas as pd
-
-from dowhy.gcm import feature_relevance
-from dowhy.gcm.causal_models import StructuralCausalModel
-from dowhy.gcm.shapley import ShapleyConfig
-
-
-def parent_relevance(
-    causal_model: StructuralCausalModel,
-    target_node: Any,
-    parent_samples: Optional[pd.DataFrame] = None,
-    subset_scoring_func: Optional[Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]]] = None,
-    num_samples_randomization: int = 5000,
-    num_samples_baseline: int = 500,
-    max_batch_size: int = 100,
-    shapley_config: Optional[ShapleyConfig] = None,
-) -> Tuple[Dict[Any, Any], np.ndarray]:
-    """Deprecated, please use parent_relevance from the feature_relevance.py module instead."""
-    warnings.warn(
-        "This module is deprecated. All feature.py functions are moved into parent_relevance.py.", DeprecationWarning
-    )
-    return feature_relevance.parent_relevance(
-        causal_model=causal_model,
-        target_node=target_node,
-        parent_samples=parent_samples,
-        subset_scoring_func=subset_scoring_func,
-        num_samples_randomization=num_samples_randomization,
-        num_samples_baseline=num_samples_baseline,
-        max_batch_size=max_batch_size,
-        shapley_config=shapley_config,
-    )
-
-
-def feature_relevance_distribution(
-    prediction_method: Callable[[np.ndarray], np.ndarray],
-    feature_samples: np.ndarray,
-    subset_scoring_func: Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]],
-    max_num_samples_randomization: int = 5000,
-    max_num_baseline_samples: int = 500,
-    max_batch_size: int = 100,
-    randomize_features_jointly: bool = True,
-    shapley_config: Optional[ShapleyConfig] = None,
-) -> np.ndarray:
-    """Deprecated, please use feature_relevance_distribution from the feature_relevance.py module instead."""
-    warnings.warn(
-        "This module is deprecated. All feature.py functions are moved into parent_relevance.py.", DeprecationWarning
-    )
-    return feature_relevance.feature_relevance_distribution(
-        prediction_method=prediction_method,
-        feature_samples=feature_samples,
-        subset_scoring_func=subset_scoring_func,
-        max_num_samples_randomization=max_num_samples_randomization,
-        max_num_baseline_samples=max_num_baseline_samples,
-        max_batch_size=max_batch_size,
-        randomize_features_jointly=randomize_features_jointly,
-        shapley_config=shapley_config,
-    )
-
-
-def feature_relevance_sample(
-    prediction_method: Callable[[np.ndarray], np.ndarray],
-    feature_samples: np.ndarray,
-    baseline_samples: np.ndarray,
-    subset_scoring_func: Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]],
-    baseline_target_values: Optional[np.ndarray] = None,
-    average_set_function: bool = False,
-    max_batch_size: int = 100,
-    randomize_features_jointly: bool = True,
-    shapley_config: Optional[ShapleyConfig] = None,
-) -> np.ndarray:
-    """Deprecated, please use feature_relevance_sample from the feature_relevance.py module instead."""
-    warnings.warn(
-        "This module is deprecated. All feature.py functions are moved into parent_relevance.py.", DeprecationWarning
-    )
-    return feature_relevance.feature_relevance_sample(
-        prediction_method=prediction_method,
-        feature_samples=feature_samples,
-        baseline_samples=baseline_samples,
-        subset_scoring_func=subset_scoring_func,
-        baseline_target_values=baseline_target_values,
-        average_set_function=average_set_function,
-        max_batch_size=max_batch_size,
-        randomize_features_jointly=randomize_features_jointly,
-        shapley_config=shapley_config,
-    )