Changing default and adding warnings

Author: Christian Jorgensen

src/skmatter/decomposition/_kernel_pcovc.py

@@ -1,3 +1,4 @@
+import warnings
 import numpy as np
 
 from sklearn import clone
@@ -119,6 +120,14 @@ class KernelPCovC(LinearClassifierMixin, _BaseKPCov):
         and for matrix inversions.
         Must be of range [0.0, infinity).
 
+    z_mean_tol: float, default=1e-12
+    Tolerance for the column means of Z.
+    Must be of range [0.0, infinity).
+
+    z_var_tol: float, default=1.5
+        Tolerance for the column variances of Z.
+        Must be of range [0.0, infinity).
+
     n_jobs : int, default=None
         The number of parallel jobs to run.
         :obj:`None` means 1 unless in a :obj:`joblib.parallel_backend` context.
@@ -203,7 +212,7 @@ def __init__(
         n_components=None,
         svd_solver="auto",
         classifier=None,
-        scale_z=True,
+        scale_z=False,
         kernel="linear",
         gamma=None,
         degree=3,
@@ -212,6 +221,8 @@ def __init__(
         center=False,
         fit_inverse_transform=False,
         tol=1e-12,
+        z_mean_tol=1e-12,
+        z_var_tol=1.5,
         n_jobs=None,
         iterated_power="auto",
         random_state=None,
@@ -234,6 +245,8 @@ def __init__(
         )
         self.classifier = classifier
         self.scale_z = scale_z
+        self.z_mean_tol = z_mean_tol
+        self.z_var_tol = z_var_tol
 
     def fit(self, X, Y, W=None):
         r"""Fit the model with X and Y.
@@ -331,6 +344,23 @@ def fit(self, X, Y, W=None):
         if self.scale_z:
             Z = StandardFlexibleScaler().fit_transform(Z)
 
+        self.z_means_ = np.mean(Z, axis=0)
+        self.z_vars_ = np.var(Z, axis=0)
+
+        if np.max(np.abs(self.z_means_)) > self.z_mean_tol:
+            warnings.warn(
+                "This class does not automatically center Z, and the column means "
+                "of Z are greater than the supplied tolerance. We recommend scaling "
+                "Z (and the weights) by setting `scale_z=True`."
+            )
+
+        if np.max(self.z_vars_) > self.z_var_tol:
+            warnings.warn(
+                "This class does not automatically scale Z, and the column variances "
+                "of Z are greater than the supplied tolerance. We recommend scaling "
+                "Z (and the weights) by setting `scale_z=True`. "
+            )
+
         self._fit(K, Z, W)
 
         self.ptk_ = self.pt__ @ K

src/skmatter/decomposition/_pcovc.py

@@ -17,6 +17,7 @@
 from skmatter.decomposition import _BasePCov
 from skmatter.utils import check_cl_fit
 from skmatter.preprocessing import StandardFlexibleScaler
+import warnings
 
 
 class PCovC(LinearClassifierMixin, _BasePCov):
@@ -124,7 +125,7 @@ class PCovC(LinearClassifierMixin, _BasePCov):
         If None, ``sklearn.linear_model.LogisticRegression()``
         is used as the classifier.
 
-    scale_z: bool, default=True
+    scale_z: bool, default=False
         Whether to scale Z prior to eigendecomposition.
 
     iterated_power : int or 'auto', default='auto'
@@ -147,6 +148,14 @@ class PCovC(LinearClassifierMixin, _BasePCov):
         Tolerance for singular values computed by svd_solver == 'arpack'.
         Must be of range [0.0, infinity).
 
+    z_mean_tol: float, default=1e-12
+        Tolerance for the column means of Z.
+        Must be of range [0.0, infinity).
+
+    z_var_tol: float, default=1.5
+        Tolerance for the column variances of Z.
+        Must be of range [0.0, infinity).
+
     space: {'feature', 'sample', 'auto'}, default='auto'
         whether to compute the PCovC in ``sample`` or ``feature`` space.
         The default is equal to ``sample`` when :math:`{n_{samples} < n_{features}}`
@@ -212,9 +221,11 @@ def __init__(
         n_components=None,
         svd_solver="auto",
         tol=1e-12,
+        z_mean_tol=1e-12,
+        z_var_tol=1.5,
         space="auto",
         classifier=None,
-        scale_z=True,
+        scale_z=False,
         iterated_power="auto",
         random_state=None,
         whiten=False,
@@ -231,6 +242,8 @@ def __init__(
         )
         self.classifier = classifier
         self.scale_z = scale_z
+        self.z_mean_tol = z_mean_tol
+        self.z_var_tol = z_var_tol
 
     def fit(self, X, Y, W=None):
         r"""Fit the model with X and Y.
@@ -312,6 +325,23 @@ def fit(self, X, Y, W=None):
             Z = z_scaler.transform(Z)
             W /= z_scaler.scale_.reshape(1, -1)
 
+        self.z_means_ = np.mean(Z, axis=0)
+        self.z_vars_ = np.var(Z, axis=0)
+
+        if np.max(np.abs(self.z_means_)) > self.z_mean_tol:
+            warnings.warn(
+                "This class does not automatically center Z, and the column means "
+                "of Z are greater than the supplied tolerance. We recommend scaling "
+                "Z (and the weights) by setting `scale_z=True`."
+            )
+
+        if np.max(self.z_vars_) > self.z_var_tol:
+            warnings.warn(
+                "This class does not automatically scale Z, and the column variances "
+                "of Z are greater than the supplied tolerance. We recommend scaling "
+                "Z (and the weights) by setting `scale_z=True`."
+            )
+
         if self.space_ == "feature":
             self._fit_feature_space(X, Y, Z)
         else: