Reuse 'FastICA' class validations in 'fastica' funcation

kianelbo · kianelbo · commit 3bdfdeb62cd7 · 2022-11-15T23:46:37.000+01:00
diff --git a/sklearn/decomposition/_fastica.py b/sklearn/decomposition/_fastica.py
@@ -156,21 +156,6 @@ def _cube(x, fun_args):
 
 @validate_params(
     {
-        "X": ["array-like"],
-        "n_components": [Interval(Integral, 1, None, closed="left"), None],
-        "algorithm": [StrOptions({"parallel", "deflation"})],
-        "whiten": [
-            Hidden(StrOptions({"warn"})),
-            StrOptions({"arbitrary-variance", "unit-variance"}),
-            "boolean",
-        ],
-        "fun": [StrOptions({"logcosh", "exp", "cube"}), callable],
-        "fun_args": [dict, None],
-        "max_iter": [Interval(Integral, 1, None, closed="left")],
-        "tol": [Interval(Real, 0.0, None, closed="left")],
-        "w_init": ["array-like", None],
-        "whiten_solver": [StrOptions({"eigh", "svd"})],
-        "random_state": ["random_state"],
         "return_X_mean": ["boolean"],
         "compute_sources": ["boolean"],
         "return_n_iter": ["boolean"],
@@ -341,7 +326,11 @@ def my_g(x):
         whiten_solver=whiten_solver,
         random_state=random_state,
     )
-    S = est._fit_transform(X, compute_sources=compute_sources)
+    if compute_sources:
+        S = est.fit_transform(X)
+    else:
+        est.fit(X)
+        S = None
 
     if est._whiten in ["unit-variance", "arbitrary-variance"]:
         K = est.whitening_
