FEAT add ssc function

doc/index.rst

@@ -12,12 +12,13 @@
 .. currentmodule:: fastcan
 
 
-FastCan Class
+API Reference
 ~~~~~~~~~~~~~
 .. autosummary::
    :toctree: generated/
 
    FastCan
+   ssc
 
 
 ...................

fastcan/__init__.py

@@ -3,7 +3,9 @@
 """
 
 from ._fastcan import FastCan
+from ._ssc import ssc
 
 __all__ = [
     "FastCan",
+    "ssc",
 ]

fastcan/_ssc.py

@@ -0,0 +1,47 @@
+"""Sum squared of correlation."""
+
+import numpy as np
+from sklearn.cross_decomposition import CCA
+from sklearn.utils import check_X_y
+from sklearn.utils._param_validation import validate_params
+
+
+@validate_params(
+    {
+        "X": ["array-like"],
+        "y": ["array-like"],
+    },
+    prefer_skip_nested_validation=True,
+)
+def ssc(X, y):
+    """Sum of the squared canonical correlation coefficients.
+
+    Parameters
+    ----------
+    X : array-like of shape (n_samples, n_features)
+        Feature matrix.
+
+    y : array-like of shape (n_samples, n_outputs)
+        Target matrix.
+
+    Returns
+    -------
+    ssc : float
+        Sum of the squared canonical correlation coefficients.
+
+    Examples
+    --------
+    >>> from fastcan import ssc
+    >>> X = [[1], [-1], [0]]
+    >>> y = [[0], [1], [-1]]
+    >>> ssc(X, y)
+    np.float64(0.25)
+    """
+    X, y = check_X_y(
+        X, y, dtype=float, ensure_2d=True, multi_output=True, ensure_min_samples=2
+    )
+    n_components = min(X.shape[1], y.shape[1])
+    cca = CCA(n_components=n_components)
+    X_c, y_c = cca.fit_transform(X, y)
+    corrcoef = np.diagonal(np.corrcoef(X_c, y_c, rowvar=False), offset=n_components)
+    return sum(corrcoef**2)

tests/test_correlation.py → tests/test_fastcan.py

@@ -1,4 +1,3 @@
-# pylint: skip-file
 """Test FastCan"""
 
 import numpy as np

tests/test_ssc.py

@@ -0,0 +1,32 @@
+"Test ssc"
+
+import numpy as np
+from numpy.testing import assert_almost_equal
+from sklearn.linear_model import LinearRegression
+
+from fastcan import ssc
+
+
+def test_pearson_r():
+    """Test Pearson's correlation."""
+    rng = np.random.default_rng(12345)
+    X = rng.random(100)
+    y = rng.random(100)
+    r2 = ssc(X.reshape(-1, 1), y.reshape(-1, 1))
+    gtruth_r2 = np.corrcoef(X, y)[0, 1]**2
+    assert_almost_equal(actual=r2, desired=gtruth_r2)
+
+def test_multi_r():
+    """Test multiple correlation."""
+    rng = np.random.default_rng(12345)
+    X = rng.random((100, 10))
+    y = rng.random(100)
+    r2 = ssc(X, y.reshape(-1, 1))
+    gtruth_r2 = LinearRegression().fit(X, y).score(X, y)
+    assert_almost_equal(actual=r2, desired=gtruth_r2)
+
+    X = rng.random(100)
+    y = rng.random((100, 10))
+    r2 = ssc(X.reshape(-1, 1), y)
+    gtruth_r2 = LinearRegression().fit(y, X).score(y, X)
+    assert_almost_equal(actual=r2, desired=gtruth_r2)