10.18.0

Author: mathias-von-ottenbreit

.github/workflows/build_wheels.yml

@@ -6,11 +6,11 @@ jobs:
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        os: [ubuntu-latest, windows-latest, macos-13, macos-14]
+        os: [ubuntu-latest, ubuntu-24.04-arm, windows-latest, windows-11-arm, macos-15-intel, macos-14]
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v5
       - name: Build wheels
-        uses: pypa/cibuildwheel@v2.22.0
+        uses: pypa/cibuildwheel@v3.2.1
         env:
           CIBW_SKIP: "*musllinux* pp*"
           CIBW_ENVIRONMENT: MACOSX_DEPLOYMENT_TARGET=11.0

API_REFERENCE_FOR_APLR_TUNER.md

@@ -11,14 +11,14 @@ The parameters that you wish to tune.
 Whether you want to use APLRRegressor (True) or APLRClassifier (False).
 
 
-## Method: fit(X: FloatMatrix, y: FloatVector, **kwargs)
+## Method: fit(X: Union[pd.DataFrame, FloatMatrix], y: FloatVector, **kwargs)
 
 ***This method tunes the model to data.***
 
 ### Parameters
 
 #### X
-A numpy matrix with predictor values.
+A numpy matrix or pandas DataFrame with predictor values.
 
 #### y
 A numpy vector with response values.
@@ -27,40 +27,40 @@ A numpy vector with response values.
 Optional parameters sent to the fit methods in the underlying APLRRegressor or APLRClassifier models.
 
 
-## Method: predict(X: FloatMatrix, **kwargs)
+## Method: predict(X: Union[pd.DataFrame, FloatMatrix], **kwargs)
 
 ***Returns the predictions of the best tuned model as a numpy array if regression or as a list of strings if classification.***
 
 ### Parameters
 
 #### X
-A numpy matrix with predictor values.
+A numpy matrix or pandas DataFrame with predictor values.
 
 #### kwargs
 Optional parameters sent to the predict method in the best tuned model.
 
 
-## Method: predict_class_probabilities(X: FloatMatrix, **kwargs)
+## Method: predict_class_probabilities(X: Union[pd.DataFrame, FloatMatrix], **kwargs)
 
 ***This method returns predicted class probabilities of the best tuned model as a numpy matrix.***
 
 ### Parameters
 
 #### X
-A numpy matrix with predictor values.
+A numpy matrix or pandas DataFrame with predictor values.
 
 #### kwargs
 Optional parameters sent to the predict_class_probabilities method in the best tuned model.
 
 
-## Method: predict_proba(X: FloatMatrix, **kwargs)
+## Method: predict_proba(X: Union[pd.DataFrame, FloatMatrix], **kwargs)
 
 ***This method returns predicted class probabilities of the best tuned model as a numpy matrix. Similar to the predict_class_probabilities method but the name predict_proba is compatible with scikit-learn.***
 
 ### Parameters
 
 #### X
-A numpy matrix with predictor values.
+A numpy matrix or pandas DataFrame with predictor values.
 
 #### kwargs
 Optional parameters sent to the predict_class_probabilities method in the best tuned model.

API_REFERENCE_FOR_CLASSIFICATION.md

@@ -65,23 +65,23 @@ Restricts the maximum number of terms in any of the underlying models trained to
 Specifies the (weighted) ridge penalty applied to the model. Positive values can smooth model effects and help mitigate boundary problems, such as regression coefficients with excessively high magnitudes near the boundaries. To find the optimal value, consider using a grid search or similar. Negative values are treated as zero.
 
 
-## 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] = [], predictor_min_observations_in_split: List[int] = [])
+## Method: fit(X:Union[pd.DataFrame, FloatMatrix], y:Union[FloatVector, 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] = [], predictor_min_observations_in_split: List[int] = [])
 
 ***This method fits the model to data.***
 
 ### Parameters
 
 #### X
-A numpy matrix with predictor values.
+A numpy matrix or pandas DataFrame with predictor values. If a pandas DataFrame is provided, the model will automatically handle categorical features and missing values. Categorical features will be one-hot encoded. Missing values will be imputed with the median of the column, and a new binary feature will be added to indicate that the value was missing.
 
 #### y
-A list of strings with response values (class names).
+A numpy array or list of strings with response values (class names). Other data types will be converted to strings.
 
 #### sample_weight
 An optional numpy vector with sample weights. If not specified then the observations are weighted equally.
 
 #### X_names
-An optional list of strings containing names for each predictor in ***X***. Naming predictors may increase model readability because model terms get names based on ***X_names***.
+An optional list of strings containing names for each predictor in ***X***. Naming predictors may increase model readability because model terms get names based on ***X_names***. **Note:** This parameter is ignored if ***X*** is a pandas DataFrame; the DataFrame's column names will be used instead.
 
 #### cv_observations
 An optional integer matrix specifying how each training observation is used in cross validation. If this is specified then ***cv_folds*** is not used. Specifying ***cv_observations*** may be useful for example when modelling time series data (you can place more recent observations in the holdout folds). ***cv_observations*** must contain a column for each desired fold combination. For a given column, row values equalling 1 specify that these rows will be used for training, while row values equalling -1 specify that these rows will be used for validation. Row values equalling 0 will not be used.
@@ -108,35 +108,35 @@ An optional list of floats specifying interaction penalties for each predictor.
 An optional list of integers specifying the minimum effective number of observations in a split for each predictor. If provided then this supercedes ***min_observations_in_split***.
 
 
-## Method: predict_class_probabilities(X:FloatMatrix, cap_predictions_to_minmax_in_training:bool = False)
+## Method: predict_class_probabilities(X:Union[pd.DataFrame, FloatMatrix], cap_predictions_to_minmax_in_training:bool = False)
 
 ***Returns a numpy matrix containing predictions of the data in X. Requires that the model has been fitted with the fit method.***
 
 ### Parameters
 
 #### X
-A numpy matrix with predictor values.
+A numpy matrix or pandas DataFrame with predictor values.
 
 #### cap_predictions_to_minmax_in_training
 If ***True*** then for each underlying logit model the 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.
 
 
-## Method: predict(X:FloatMatrix, cap_predictions_to_minmax_in_training:bool = False)
+## Method: predict(X:Union[pd.DataFrame, FloatMatrix], cap_predictions_to_minmax_in_training:bool = False)
 
 ***Returns a list of strings containing predictions of the data in X. An observation is classified to the category with the highest predicted class probability. Requires that the model has been fitted with the fit method.***
 
 ### Parameters
 Parameters are the same as in ***predict_class_probabilities()***.
 
 
-## Method: calculate_local_feature_contribution(X:FloatMatrix)
+## Method: calculate_local_feature_contribution(X:Union[pd.DataFrame, FloatMatrix])
 
 ***Returns a numpy matrix containing feature contribution to the linear predictor in X for each predictor. For each prediction this method uses calculate_local_feature_contribution() in the logit APLRRegressor model for the category that corresponds to the prediction. Example: If a prediction is "myclass" then the method uses calculate_local_feature_contribution() in the logit model that predicts whether an observation belongs to class "myclass" or not.***
 
 ### Parameters
 
 #### X
-A numpy matrix with predictor values.
+A numpy matrix or pandas DataFrame with predictor values.
 
 
 ## Method: get_categories()

API_REFERENCE_FOR_REGRESSION.md

@@ -139,14 +139,14 @@ If true, then a mean bias correction is applied to the model's intercept term. T
 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] = [])
+## Method: fit(X:Union[pd.DataFrame, 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] = [])
 
 ***This method fits the model to data.***
 
 ### Parameters
 
 #### X
-A numpy matrix with predictor values.
+A numpy matrix or pandas DataFrame with predictor values. If a pandas DataFrame is provided, the model will automatically handle categorical features and missing values. Categorical features will be one-hot encoded. Missing values will be imputed with the median of the column, and a new binary feature will be added to indicate that the value was missing.
 
 #### y
 A numpy vector with response values.
@@ -155,7 +155,7 @@ A numpy vector with response values.
 An optional numpy vector with sample weights. If not specified then the observations are weighted equally.
 
 #### X_names
-An optional list of strings containing names for each predictor in ***X***. Naming predictors may increase model readability because model terms get names based on ***X_names***.
+An optional list of strings containing names for each predictor in ***X***. Naming predictors may increase model readability because model terms get names based on ***X_names***. **Note:** This parameter is ignored if ***X*** is a pandas DataFrame; the DataFrame's column names will be used instead.
 
 #### cv_observations
 An optional integer matrix specifying how each training observation is used in cross validation. If this is specified then ***cv_folds*** is not used. Specifying ***cv_observations*** may be useful for example when modelling time series data (you can place more recent observations in the holdout folds). ***cv_observations*** must contain a column for each desired fold combination. For a given column, row values equalling 1 specify that these rows will be used for training, while row values equalling -1 specify that these rows will be used for validation. Row values equalling 0 will not be used.
@@ -188,14 +188,14 @@ An optional list of floats specifying interaction penalties for each predictor.
 An optional list of integers specifying the minimum effective number of observations in a split for each predictor. If provided then this supercedes ***min_observations_in_split***.
 
 
-## Method: predict(X:FloatMatrix, cap_predictions_to_minmax_in_training:bool = True)
+## Method: predict(X:Union[pd.DataFrame, FloatMatrix], cap_predictions_to_minmax_in_training:bool = True)
 
 ***Returns a numpy vector containing predictions of the data in X. Requires that the model has been fitted with the fit method.***
 
 ### Parameters
 
 #### X
-A numpy matrix with predictor values.
+A numpy matrix or pandas DataFrame with predictor values.
 
 #### cap_predictions_to_minmax_in_training
 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***.
@@ -211,67 +211,67 @@ 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 = np.empty(0))
+## Method: calculate_feature_importance(X:Union[pd.DataFrame, FloatMatrix], sample_weight:FloatVector = np.empty(0))
 
 ***Returns a numpy matrix containing estimated feature importance in X for each predictor.***
 
 ### Parameters
 
 #### X
-A numpy matrix with predictor values.
+A numpy matrix or pandas DataFrame with predictor values.
 
 
-## Method: calculate_term_importance(X:FloatMatrix, sample_weight:FloatVector = np.empty(0))
+## Method: calculate_term_importance(X:Union[pd.DataFrame, FloatMatrix], sample_weight:FloatVector = np.empty(0))
 
 ***Returns a numpy matrix containing estimated term importance in X for each term in the model.***
 
 ### Parameters
 
 #### X
-A numpy matrix with predictor values.
+A numpy matrix or pandas DataFrame with predictor values.
 
 
-## Method: calculate_local_feature_contribution(X:FloatMatrix)
+## Method: calculate_local_feature_contribution(X:Union[pd.DataFrame, FloatMatrix])
 
 ***Returns a numpy matrix containing feature contribution to the linear predictor in X for each predictor.***
 
 ### Parameters
 
 #### X
-A numpy matrix with predictor values.
+A numpy matrix or pandas DataFrame with predictor values.
 
 
-## Method: calculate_local_term_contribution(X:FloatMatrix)
+## Method: calculate_local_term_contribution(X:Union[pd.DataFrame, FloatMatrix])
 
 ***Returns a numpy matrix containing term contribution to the linear predictor in X for each term in the model.***
 
 ### Parameters
 
 #### X
-A numpy matrix with predictor values.
+A numpy matrix or pandas DataFrame with predictor values.
 
 
-## Method: calculate_local_contribution_from_selected_terms(X:FloatMatrix, predictor_indexes:List[int])
+## Method: calculate_local_contribution_from_selected_terms(X:Union[pd.DataFrame, 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.***
 
 ### Parameters
 
 #### X
-A numpy matrix with predictor values.
+A numpy matrix or pandas DataFrame with predictor values.
 
 #### predictor_indexes
 A list of integers specifying the indexes of predictors in X to use. For example, [1, 3] means the second and fourth predictors in X.
 
 
-## Method: calculate_terms(X:FloatMatrix)
+## Method: calculate_terms(X:Union[pd.DataFrame, FloatMatrix])
 
 ***Returns a numpy matrix containing values of model terms calculated on X.***
 
 ### Parameters
 
 #### X
-A numpy matrix with predictor values.
+A numpy matrix or pandas DataFrame with predictor values.
 
 
 ## Method: get_term_names()