bugfix

Author: mathias-von-ottenbreit

API_REFERENCE_FOR_CLASSIFICATION.md

@@ -62,7 +62,7 @@ Specifies a penalty in the range [0.0, 1.0] on interaction terms. A higher value
 Restricts the maximum number of terms in any of the underlying models trained to ***max_terms***. The default value of 0 means no limit. After the limit is reached, the remaining boosting steps are used to further update the coefficients of already included terms. An optional tuning objective could be to find the lowest positive value of ***max_terms*** that does not increase the prediction error significantly. Low positive values can speed up the training process significantly. Setting a limit with ***max_terms*** may require a higher learning rate for best results.
 
 
-## Method: fit(X:FloatMatrix, y:StrVector, sample_weight:FloatVector = [], X_names:StrVector = [], cv_observations:IntMatrix = [], prioritized_predictors_indexes:IntVector = [], monotonic_constraints:IntVector = [], interaction_constraints:List[List[int]] = [], predictor_learning_rates:FloatVector = [], predictor_penalties_for_non_linearity:FloatVector = [], predictor_penalties_for_interactions:FloatVector = [])
+## Method: fit(X:FloatMatrix, y:List[str], sample_weight:FloatVector = np.empty(0), X_names:List[str] = [], cv_observations:IntMatrix = np.empty([0, 0]), prioritized_predictors_indexes:List[int] = [], monotonic_constraints:List[int] = [], interaction_constraints:List[List[int]] = [], predictor_learning_rates:List[float] = [], predictor_penalties_for_non_linearity:List[float] = [], predictor_penalties_for_interactions:List[float] = [])
 
 ***This method fits the model to data.***
 
@@ -72,7 +72,7 @@ Restricts the maximum number of terms in any of the underlying models trained to
 A numpy matrix with predictor values.
 
 #### y
-A numpy vector with response values.
+A list of strings with response values (class names).
 
 #### sample_weight
 An optional numpy vector with sample weights. If not specified then the observations are weighted equally.

API_REFERENCE_FOR_REGRESSION.md

@@ -130,7 +130,7 @@ Specifies a penalty in the range [0.0, 1.0] on interaction terms. A higher value
 Restricts the maximum number of terms in any of the underlying models trained to ***max_terms***. The default value of 0 means no limit. After the limit is reached, the remaining boosting steps are used to further update the coefficients of already included terms. An optional tuning objective could be to find the lowest positive value of ***max_terms*** that does not increase the prediction error significantly. Low positive values can speed up the training process significantly. Setting a limit with ***max_terms*** may require a higher learning rate for best results.
 
 
-## Method: fit(X:FloatMatrix, y:FloatVector, sample_weight:FloatVector = [], X_names:StrVector = [], cv_observations:IntMatrix = [], prioritized_predictors_indexes:IntVector = [], monotonic_constraints:IntVector = [], group:FloatVector = [], interaction_constraints:List[List[int]] = [], other_data:FloatMatrix = [], predictor_learning_rates:FloatVector = [], predictor_penalties_for_non_linearity:FloatVector = [], predictor_penalties_for_interactions:FloatVector = [])
+## Method: fit(X:FloatMatrix, y:FloatVector, sample_weight:FloatVector = np.empty(0), X_names:List[str] = [], cv_observations:IntMatrix = np.empty([0, 0]), prioritized_predictors_indexes:List[int] = [], monotonic_constraints:List[int] = [], group:FloatVector = np.empty(0), interaction_constraints:List[List[int]] = [], other_data:FloatMatrix = np.empty([0, 0]), predictor_learning_rates:List[float] = [], predictor_penalties_for_non_linearity:List[float] = [], predictor_penalties_for_interactions:List[float] = [])
 
 ***This method fits the model to data.***
 
@@ -189,7 +189,7 @@ A numpy matrix with predictor values.
 If ***True*** then predictions are capped so that they are not less than the minimum and not greater than the maximum prediction or response in the training dataset. This is recommended especially if ***max_interaction_level*** is high. However, if you need the model to extrapolate then set this parameter to ***False***.
 
 
-## Method: set_term_names(X_names:StrVector)
+## Method: set_term_names(X_names:List[str])
 
 ***This method sets the names of terms based on X_names.***
 
@@ -199,7 +199,7 @@ If ***True*** then predictions are capped so that they are not less than the min
 A list of strings containing names for each predictor in the ***X*** matrix that the model was trained on.
 
 
-## Method: calculate_feature_importance(X:FloatMatrix, sample_weight:FloatVector = [])
+## Method: calculate_feature_importance(X:FloatMatrix, sample_weight:FloatVector = np.empty(0))
 
 ***Returns a numpy matrix containing estimated feature importance in X for each predictor.***
 
@@ -209,7 +209,7 @@ A list of strings containing names for each predictor in the ***X*** matrix that
 A numpy matrix with predictor values.
 
 
-## Method: calculate_term_importance(X:FloatMatrix, sample_weight:FloatVector = [])
+## Method: calculate_term_importance(X:FloatMatrix, sample_weight:FloatVector = np.empty(0))
 
 ***Returns a numpy matrix containing estimated term importance in X for each term in the model.***
 
@@ -239,7 +239,7 @@ A numpy matrix with predictor values.
 A numpy matrix with predictor values.
 
 
-## Method: calculate_local_contribution_from_selected_terms(X:FloatMatrix, predictor_indexes:IntVector)
+## Method: calculate_local_contribution_from_selected_terms(X:FloatMatrix, predictor_indexes:List[int])
 
 ***Returns a numpy vector containing the contribution to the linear predictor from an user specified combination of interacting predictors for each observation in X. This makes it easier to interpret interactions (or main effects if just one predictor is specified), for example by plotting predictor values against the term contribution.***
 

aplr/aplr.py

@@ -1,11 +1,11 @@
 from typing import List, Callable, Optional, Dict
+import numpy as np
 import aplr_cpp
 
-FloatVector = List[float]
-FloatMatrix = List[List[float]]
-IntVector = List[int]
-IntMatrix = List[List[int]]
-StrVector = List[str]
+FloatVector = np.ndarray
+FloatMatrix = np.ndarray
+IntVector = np.ndarray
+IntMatrix = np.ndarray
 
 
 class APLRRegressor:
@@ -184,17 +184,17 @@ def fit(
         self,
         X: FloatMatrix,
         y: FloatVector,
-        sample_weight: FloatVector = [],
-        X_names: StrVector = [],
-        cv_observations: IntMatrix = [],
-        prioritized_predictors_indexes: IntVector = [],
-        monotonic_constraints: IntVector = [],
-        group: FloatVector = [],
+        sample_weight: FloatVector = np.empty(0),
+        X_names: List[str] = [],
+        cv_observations: IntMatrix = np.empty([0, 0]),
+        prioritized_predictors_indexes: List[int] = [],
+        monotonic_constraints: List[int] = [],
+        group: FloatVector = np.empty(0),
         interaction_constraints: List[List[int]] = [],
-        other_data: FloatMatrix = [],
-        predictor_learning_rates: FloatVector = [],
-        predictor_penalties_for_non_linearity: FloatVector = [],
-        predictor_penalties_for_interactions: FloatVector = [],
+        other_data: FloatMatrix = np.empty([0, 0]),
+        predictor_learning_rates: List[float] = [],
+        predictor_penalties_for_non_linearity: List[float] = [],
+        predictor_penalties_for_interactions: List[float] = [],
     ):
         self.__set_params_cpp()
         self.APLRRegressor.fit(
@@ -222,16 +222,16 @@ def predict(
             )
         return self.APLRRegressor.predict(X, cap_predictions_to_minmax_in_training)
 
-    def set_term_names(self, X_names: StrVector):
+    def set_term_names(self, X_names: List[str]):
         self.APLRRegressor.set_term_names(X_names)
 
     def calculate_feature_importance(
-        self, X: FloatMatrix, sample_weight: FloatVector = []
+        self, X: FloatMatrix, sample_weight: FloatVector = np.empty(0)
     ) -> FloatVector:
         return self.APLRRegressor.calculate_feature_importance(X, sample_weight)
 
     def calculate_term_importance(
-        self, X: FloatMatrix, sample_weight: FloatVector = []
+        self, X: FloatMatrix, sample_weight: FloatVector = np.empty(0)
     ) -> FloatVector:
         return self.APLRRegressor.calculate_term_importance(X, sample_weight)
 
@@ -242,7 +242,7 @@ def calculate_local_term_contribution(self, X: FloatMatrix) -> FloatMatrix:
         return self.APLRRegressor.calculate_local_term_contribution(X)
 
     def calculate_local_contribution_from_selected_terms(
-        self, X: FloatMatrix, predictor_indexes: IntVector
+        self, X: FloatMatrix, predictor_indexes: List[int]
     ) -> FloatVector:
         return self.APLRRegressor.calculate_local_contribution_from_selected_terms(
             X, predictor_indexes
@@ -251,13 +251,13 @@ def calculate_local_contribution_from_selected_terms(
     def calculate_terms(self, X: FloatMatrix) -> FloatMatrix:
         return self.APLRRegressor.calculate_terms(X)
 
-    def get_term_names(self) -> StrVector:
+    def get_term_names(self) -> List[str]:
         return self.APLRRegressor.get_term_names()
 
-    def get_term_affiliations(self) -> StrVector:
+    def get_term_affiliations(self) -> List[str]:
         return self.APLRRegressor.get_term_affiliations()
 
-    def get_unique_term_affiliations(self) -> StrVector:
+    def get_unique_term_affiliations(self) -> List[str]:
         return self.APLRRegressor.get_unique_term_affiliations()
 
     def get_base_predictors_in_each_unique_term_affiliation(self) -> List[List[int]]:
@@ -433,16 +433,16 @@ def __set_params_cpp(self):
     def fit(
         self,
         X: FloatMatrix,
-        y: StrVector,
-        sample_weight: FloatVector = [],
-        X_names: StrVector = [],
-        cv_observations: IntMatrix = [],
-        prioritized_predictors_indexes: IntVector = [],
-        monotonic_constraints: IntVector = [],
+        y: List[str],
+        sample_weight: FloatVector = np.empty(0),
+        X_names: List[str] = [],
+        cv_observations: IntMatrix = np.empty([0, 0]),
+        prioritized_predictors_indexes: List[int] = [],
+        monotonic_constraints: List[int] = [],
         interaction_constraints: List[List[int]] = [],
-        predictor_learning_rates: FloatVector = [],
-        predictor_penalties_for_non_linearity: FloatVector = [],
-        predictor_penalties_for_interactions: FloatVector = [],
+        predictor_learning_rates: List[float] = [],
+        predictor_penalties_for_non_linearity: List[float] = [],
+        predictor_penalties_for_interactions: List[float] = [],
     ):
         self.__set_params_cpp()
         self.APLRClassifier.fit(
@@ -468,13 +468,13 @@ def predict_class_probabilities(
 
     def predict(
         self, X: FloatMatrix, cap_predictions_to_minmax_in_training: bool = False
-    ) -> StrVector:
+    ) -> List[str]:
         return self.APLRClassifier.predict(X, cap_predictions_to_minmax_in_training)
 
     def calculate_local_feature_contribution(self, X: FloatMatrix) -> FloatMatrix:
         return self.APLRClassifier.calculate_local_feature_contribution(X)
 
-    def get_categories(self) -> StrVector:
+    def get_categories(self) -> List[str]:
         return self.APLRClassifier.get_categories()
 
     def get_logit_model(self, category: str) -> APLRRegressor:
@@ -489,7 +489,7 @@ def get_cv_error(self) -> float:
     def get_feature_importance(self) -> FloatVector:
         return self.APLRClassifier.get_feature_importance()
 
-    def get_unique_term_affiliations(self) -> StrVector:
+    def get_unique_term_affiliations(self) -> List[str]:
         return self.APLRClassifier.get_unique_term_affiliations()
 
     def get_base_predictors_in_each_unique_term_affiliation(self) -> List[List[int]]:

dependencies/pybind11/__init__.py

@@ -1,6 +1,6 @@
 import sys
 
-if sys.version_info < (3, 6):
+if sys.version_info < (3, 6):  # noqa: UP036
     msg = "pybind11 does not support Python < 3.6. 2.9 was the last release supporting Python 2.7 and 3.5."
     raise ImportError(msg)
 

dependencies/pybind11/_version.py

@@ -8,5 +8,5 @@ def _to_int(s: str) -> Union[int, str]:
         return s
 
 
-__version__ = "2.10.4"
+__version__ = "2.12.0"
 version_info = tuple(_to_int(s) for s in __version__.split("."))

dependencies/pybind11/commands.py

@@ -3,7 +3,7 @@
 DIR = os.path.abspath(os.path.dirname(__file__))
 
 
-def get_include(user: bool = False) -> str:  # pylint: disable=unused-argument
+def get_include(user: bool = False) -> str:  # noqa: ARG001
     """
     Return the path to the pybind11 include directory. The historical "user"
     argument is unused, and may be removed.

dependencies/pybind11/include/pybind11/attr.h

@@ -26,6 +26,9 @@ struct is_method {
     explicit is_method(const handle &c) : class_(c) {}
 };
 
+/// Annotation for setters
+struct is_setter {};
+
 /// Annotation for operators
 struct is_operator {};
 
@@ -188,8 +191,8 @@ struct argument_record {
 struct function_record {
     function_record()
         : is_constructor(false), is_new_style_constructor(false), is_stateless(false),
-          is_operator(false), is_method(false), has_args(false), has_kwargs(false),
-          prepend(false) {}
+          is_operator(false), is_method(false), is_setter(false), has_args(false),
+          has_kwargs(false), prepend(false) {}
 
     /// Function name
     char *name = nullptr; /* why no C++ strings? They generate heavier code.. */
@@ -230,6 +233,9 @@ struct function_record {
     /// True if this is a method
     bool is_method : 1;
 
+    /// True if this is a setter
+    bool is_setter : 1;
+
     /// True if the function has a '*args' argument
     bool has_args : 1;
 
@@ -426,6 +432,12 @@ struct process_attribute<is_method> : process_attribute_default<is_method> {
     }
 };
 
+/// Process an attribute which indicates that this function is a setter
+template <>
+struct process_attribute<is_setter> : process_attribute_default<is_setter> {
+    static void init(const is_setter &, function_record *r) { r->is_setter = true; }
+};
+
 /// Process an attribute which indicates the parent scope of a method
 template <>
 struct process_attribute<scope> : process_attribute_default<scope> {

dependencies/pybind11/include/pybind11/buffer_info.h

@@ -37,6 +37,9 @@ inline std::vector<ssize_t> f_strides(const std::vector<ssize_t> &shape, ssize_t
     return strides;
 }
 
+template <typename T, typename SFINAE = void>
+struct compare_buffer_info;
+
 PYBIND11_NAMESPACE_END(detail)
 
 /// Information record describing a Python buffer object
@@ -150,6 +153,17 @@ struct buffer_info {
     Py_buffer *view() const { return m_view; }
     Py_buffer *&view() { return m_view; }
 
+    /* True if the buffer item type is equivalent to `T`. */
+    // To define "equivalent" by example:
+    // `buffer_info::item_type_is_equivalent_to<int>(b)` and
+    // `buffer_info::item_type_is_equivalent_to<long>(b)` may both be true
+    // on some platforms, but `int` and `unsigned` will never be equivalent.
+    // For the ground truth, please inspect `detail::compare_buffer_info<>`.
+    template <typename T>
+    bool item_type_is_equivalent_to() const {
+        return detail::compare_buffer_info<T>::compare(*this);
+    }
+
 private:
     struct private_ctr_tag {};
 
@@ -170,9 +184,10 @@ struct buffer_info {
 
 PYBIND11_NAMESPACE_BEGIN(detail)
 
-template <typename T, typename SFINAE = void>
+template <typename T, typename SFINAE>
 struct compare_buffer_info {
     static bool compare(const buffer_info &b) {
+        // NOLINTNEXTLINE(bugprone-sizeof-expression) Needed for `PyObject *`
         return b.format == format_descriptor<T>::format() && b.itemsize == (ssize_t) sizeof(T);
     }
 };