Merge pull request #56 from umnil/develop

Develop

.github/workflows/build-and-test.yml

@@ -21,7 +21,7 @@ jobs:
     - name: Set up Python
       uses: actions/setup-python@v2
       with:
-        python-version: 3.8
+        python-version: "3.10"
 
     - name: Install dependencies
       run: pip install -r requirements.txt

pipeline/feature_union.py → preprocessingpipeline/feature_union.py

@@ -1,6 +1,11 @@
 import numpy as np
 
 from sklearn.pipeline import FeatureUnion  # type: ignore
+from sklearn.utils import Bunch  # type: ignore
+from sklearn.utils.metadata_routing import (  # type: ignore
+    _routing_enabled,
+    process_routing,
+)
 from .transform_pipeline import _transform_one, _fit_transform_one
 
 try:
@@ -10,7 +15,7 @@
 
 
 class TransformFeatureUnion(FeatureUnion):
-    def fit_transform(self, x, y=None, **fit_params):
+    def fit_transform(self, x, y=None, **params):
         """Fit all transformers, transform the data and concatenate results.
 
         Parameters
@@ -21,7 +26,7 @@ def fit_transform(self, x, y=None, **fit_params):
         y : array-like of shape (n_samples, n_outputs), default=None
             Targets for supervised learning.
 
-        **fit_params : dict, default=None
+        **params : dict, default=None
             Parameters to pass to the fit method of the estimator.
 
         Returns
@@ -31,7 +36,20 @@ def fit_transform(self, x, y=None, **fit_params):
             The `hstack` of results of transformers. `sum_n_components` is the
             sum of `n_components` (output dimension) over transformers.
         """
-        results = self._parallel_func(x, y, fit_params, _fit_transform_one)
+        if _routing_enabled():
+            routed_params = process_routing(self, "fit_transform", **params)
+        else:
+            routed_params = Bunch()
+            for name, obj in self.transformer_list:
+                if hasattr(obj, "fit_transformer"):
+                    routed_params[name] = Bunch(fit_transform={})
+                    routed_params[name].fit_transform = params
+                else:
+                    routed_params[name] = Bunch(fit={})
+                    routed_params[name] = Bunch(transform={})
+                    routed_params[name].fit = params
+
+        results = self._parallel_func(x, y, _fit_transform_one, routed_params)
         if not results:
             # All transformers are None
             self._y_hat = y

pipeline/funcs.py → preprocessingpipeline/funcs.py

@@ -117,8 +117,8 @@ def good_channels(x: List[mne.io.Raw]) -> List[mne.io.Raw]:
     ]
 
 
-def pwr(x: np.ndarray) -> np.ndarray:
-    """Compute the power scale of a spectral density
+def db(x: np.ndarray) -> np.ndarray:
+    """Compute the decibel scale of a spectral density
 
     Parameters
     ----------
@@ -130,4 +130,4 @@ def pwr(x: np.ndarray) -> np.ndarray:
     np.ndarray
         The same ax x transformed
     """
-    return -20 * np.log10(x + 1e-15)
+    return 10 * np.log10(x + 1e-15)