Review

src/fes/datasets/synthetic_dataset.py

@@ -1,4 +1,5 @@
 from typing import Dict, Any
+from utils import calculate_snr
 
 import numpy as np
 from numpy import random
@@ -145,13 +146,4 @@ def generate_grouped_data(n, m, noise_std, redundancy_rate, features_fill, num_g
     y_true = X @ w
     y = y_true + np.random.standard_normal((n)) * noise_std
 
-    return y, X, w, y_true, features_mask, groups_labels
-
-
-"""
-Support utils
-"""
-
-
-def calculate_snr(y_true, noise_std):
-    return (20 * np.log10(abs(np.where(noise_std == 0, 0, y_true / noise_std)))).mean()
+    return y, X, w, y_true, features_mask, groups_labels

src/fes/datasets/utils.py

@@ -0,0 +1,11 @@
+import numpy as np
+
+
+
+"""
+Support utils
+"""
+
+
+def calculate_snr(y_true, noise_std):
+    return (20 * np.log10(abs(np.where(noise_std == 0, 0, y_true / noise_std)))).mean()

src/fes/pipelines/data_science/nodes.py

@@ -5,6 +5,19 @@
 from sklearn.metrics import mean_squared_error, r2_score
 
 
+# Tool to check dimensionality
+def assert_shapes(x, x_shape, y, y_shape):
+    assert_shape(x, x_shape)
+    assert_shape(y, y_shape)
+
+    shapes = defaultdict(set)
+    for arr, shape in [(x, x_shape), (y, y_shape)]:
+        for i, char in enumerate(shape):
+            if isinstance(char, str):
+                shapes[char].add(arr.shape[i])
+    for _, _set in shapes.items():
+        assert len(_set) == 1, (x, x_shape, y, y_shape)
+
 def fit_model(y, X):
     """
     Parameters
@@ -79,3 +92,4 @@ def evaluate_perm_importance(regressor, y, X, w, y_true, features_mask, paramete
 
     print(f"Approximation with {pi_parameters['explanation_rate']} explanation rate:")
     print(f"Number of proposed features: {len(features_hat_idx)}, {er_mse:.3f} MSE, {er_r2:.3f} R2", end='\n\n')
+

src/fes/pipelines/data_science/pipeline.py

@@ -8,13 +8,19 @@ def perm_importance_pipeline(**kwargs):
         [
             node(
                 func=fit_model,
-                inputs=["y", "X"],
+                inputs=[
+                "y", "X"
+                ],
                 outputs="regressor",
                 name="fit_model_node",
             ),
             node(
                 func=evaluate_perm_importance,
-                inputs=["regressor", "y", "X", "w", "y_true", "features_mask", "parameters"],
+                inputs=[
+                "regressor", 
+                "y", "X", "w", "y_true", 
+                "features_mask", "parameters"
+                ],
                 outputs=None,
                 name="evaluate_perm_importance_node",
             ),