Modified score_samples to keep predictions and actual labels the same shape with unidimensional outputs.

Replace denominator of scoring function with np.var(y). This means samples with equal error will have equal score. It also allows the average value of the samples to equal the r2_score for the entire set in the case of unidimensional data.

Author: DRudel

pyearth/earth.py

@@ -1316,8 +1316,13 @@ def score_samples(self, X, y=None, missing=None):
         y_hat = self.predict(X, missing=missing)
         if y_hat.ndim == 1:
             y_hat = y_hat.reshape(-1, 1)
-        residual = 1 - (y - y_hat) ** 2 / np.var(y)
-        return residual
+        squared_errors = (y - y_hat) ** 2
+        variances = np.var(y, axis=0).reshape(1, -1)
+        nze = variances != 0 # non-zero variance
+        nze = nze.ravel()
+        output = np.ones(squared_errors.shape)
+        output[:, nze] = 1 - squared_errors[:, nze] / variances[:, nze]
+        return output
 
     def transform(self, X, missing=None):
         '''