🐛 Add target_column to predict_proba_arff and include the parameter i…

…n unit test.

docs/source/releases.rst

@@ -1,6 +1,11 @@
 Release Notes
 =============
 
+Version 19.11.2
+---------------
+Bugfixes:
+ - `predict_proba_arff` now also accepts a `target_column` as expected from the previous update.
+
 Version 19.11.1
 ---------------
 Features:

gama/GamaClassifier.py

@@ -1,5 +1,5 @@
 import inspect
-from typing import Union
+from typing import Union, Optional
 
 import numpy as np
 import pandas as pd
@@ -88,17 +88,25 @@ def predict_proba(self, x: Union[pd.DataFrame, np.ndarray]):
                 x[col] = x[col].astype(self._X[col].dtype)
         return self._predict_proba(x)
 
-    def predict_proba_arff(self, arff_file_path: str):
+    def predict_proba_arff(self, arff_file_path: str, target_column: Optional[str] = None):
         """ Predict the class probabilities for input in the arff_file, must have empty target column.
 
-        Predict target for X, using the best found pipeline(s) during the `fit` call.
-
-        :param arff_file_path: str
+        Parameters
+        ----------
+        arff_file_path: str
+            An ARFF file with the same columns as the one that used in fit.
+            Target column must be present in file, but its values are ignored (can be '?').
+        target_column: str, optional (default=None)
+            Specifies which column the model should predict.
+            If left None, the last column is taken to be the target.
 
-        :return: a numpy array with class probabilities. The array is of shape (N, K) where N is the length of the
+        Returns
+        -------
+        numpy.ndarray
+            Numpy array with class probabilities. The array is of shape (N, K) where N is the length of the
             first dimension of X, and K is the number of class labels found in `y` of `fit`.
         """
-        X, _ = X_y_from_arff(arff_file_path)
+        X, _ = X_y_from_arff(arff_file_path, target_column)
         return self._predict_proba(X)
 
     def fit(self, x, y, *args, **kwargs):

tests/system/test_gamaclassifier.py

@@ -35,6 +35,7 @@
 breast_cancer_missing = dict(
     name='breast_cancer_missing',
     load=load_breast_cancer,
+    target='status',
     test_size=143,
     n_classes=2,
     base_accuracy=0.62937,
@@ -98,9 +99,9 @@ def _test_dataset_problem(
         y_test = [str(val) for val in y_test]
 
         with Stopwatch() as sw:
-            gama.fit_arff(train_path)
-        class_predictions = gama.predict_arff(test_path)
-        class_probabilities = gama.predict_proba_arff(test_path)
+            gama.fit_arff(train_path, target_column=data['target'])
+        class_predictions = gama.predict_arff(test_path, target_column=data['target'])
+        class_probabilities = gama.predict_proba_arff(test_path, target_column=data['target'])
         gama_score = gama.score_arff(test_path)
     else:
         X, y = data['load'](return_X_y=True)