10.17.1

Author: mathias-von-ottenbreit

API_REFERENCE_FOR_REGRESSION.md

@@ -136,7 +136,7 @@ Specifies the (weighted) ridge penalty applied to the model. Positive values can
 If true, then a mean bias correction is applied to the model's intercept term. This can be useful for some loss functions, such as "huber", that can otherwise produce biased predictions. The correction is only applied for the "identity" and "log" link functions.
 
 #### faster_convergence (default = False)
-If true, then a scaling is applied to the negative gradient to speed up convergence. This should primarily be used when the algorithm otherwise converges too slowly. This is only applied for the "identity" and "log" link functions.
+If true, then a scaling is applied to the negative gradient to speed up convergence. This should primarily be used when the algorithm otherwise converges too slowly or prematurely. This is only applied for the "identity" and "log" link functions.
 This will not speed up the combination of "mse" loss with an "identity" link, as this combination is already optimized for speed within the algorithm. Furthermore, this option is not effective for all loss functions, such as "mae" and "quantile".
 
 ## 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] = [], predictor_min_observations_in_split: List[int] = [])

cpp/APLRRegressor.h

@@ -196,6 +196,7 @@ class APLRRegressor
     void throw_error_if_m_is_invalid();
     bool model_has_not_been_trained();
     void throw_error_if_quantile_is_invalid();
+    void throw_error_if_validation_tuning_metric_is_invalid();
     std::vector<size_t> compute_relevant_term_indexes(const std::string &unique_term_affiliation);
     std::vector<double> compute_split_points(size_t predictor_index, const std::vector<size_t> &relevant_term_indexes);
     VectorXd compute_contribution_to_linear_predictor_from_specific_terms(const MatrixXd &X, const std::vector<size_t> &term_indexes,
@@ -471,6 +472,7 @@ void APLRRegressor::fit(const MatrixXd &X, const VectorXd &y, const VectorXd &sa
     throw_error_if_dispersion_parameter_is_invalid();
     throw_error_if_quantile_is_invalid();
     throw_error_if_m_is_invalid();
+    throw_error_if_validation_tuning_metric_is_invalid();
     validate_input_to_fit(X, y, sample_weight, X_names, cv_observations, prioritized_predictors_indexes, monotonic_constraints, group,
                           interaction_constraints, other_data, predictor_learning_rates, predictor_penalties_for_non_linearity,
                           predictor_penalties_for_interactions);
@@ -707,6 +709,34 @@ void APLRRegressor::throw_error_if_quantile_is_invalid()
     }
 }
 
+void APLRRegressor::throw_error_if_validation_tuning_metric_is_invalid()
+{
+    bool metric_exists{false};
+    if (validation_tuning_metric == "default")
+        metric_exists = true;
+    else if (validation_tuning_metric == "mse")
+        metric_exists = true;
+    else if (validation_tuning_metric == "mae")
+        metric_exists = true;
+    else if (validation_tuning_metric == "huber")
+        metric_exists = true;
+    else if (validation_tuning_metric == "negative_gini")
+        metric_exists = true;
+    else if (validation_tuning_metric == "group_mse")
+        metric_exists = true;
+    else if (validation_tuning_metric == "group_mse_by_prediction")
+        metric_exists = true;
+    else if (validation_tuning_metric == "neg_top_quantile_mean_response")
+        metric_exists = true;
+    else if (validation_tuning_metric == "bottom_quantile_mean_response")
+        metric_exists = true;
+    else if (validation_tuning_metric == "custom_function")
+        metric_exists = true;
+
+    if (!metric_exists)
+        throw std::runtime_error("validation_tuning_metric " + validation_tuning_metric + " is not available in APLR.");
+}
+
 void APLRRegressor::validate_input_to_fit(const MatrixXd &X, const VectorXd &y, const VectorXd &sample_weight,
                                           const std::vector<std::string> &X_names, const MatrixXi &cv_observations,
                                           const std::vector<size_t> &prioritized_predictors_indexes, const std::vector<int> &monotonic_constraints, const VectorXi &group,

documentation/APLR 10.17.0.pdf renamed to documentation/APLR 10.17.1.pdf

@@ -28,7 +28,7 @@
 
 setuptools.setup(
     name="aplr",
-    version="10.17.0",
+    version="10.17.1",
     description="Automatic Piecewise Linear Regression",
     ext_modules=[sfc_module],
     author="Mathias von Ottenbreit",