Merge branch 'main' of https://github.com/cmu-phil/causal-learn

Author: kunwuz

causallearn/search/FCMBased/lingam/hsic.py

@@ -1,7 +1,9 @@
 """
 Python implementation of the LiNGAM algorithms.
+ * some slight modification for speedup, 04/26/2022
 The LiNGAM Project: https://sites.google.com/site/sshimizu06/lingam
 """
+import time
 
 import numpy as np
 from scipy.stats import gamma
@@ -34,16 +36,12 @@ def get_kernel_width(X):
         X_med = X
 
     G = np.sum(X_med * X_med, 1).reshape(n_samples, 1)
-    Q = np.tile(G, (1, n_samples))
-    R = np.tile(G.T, (n_samples, 1))
-
-    dists = Q + R - 2 * np.dot(X_med, X_med.T)
+    dists = G + G.T - 2 * np.dot(X_med, X_med.T)
     dists = dists - np.tril(dists)
     dists = dists.reshape(n_samples ** 2, 1)
 
     return np.sqrt(0.5 * np.median(dists[dists > 0]))
 
-
 def _rbf_dot(X, Y, width):
     """Compute the inner product of radial basis functions."""
     n_samples_X = X.shape[0]
@@ -57,6 +55,11 @@ def _rbf_dot(X, Y, width):
 
     return np.exp(-H / 2 / (width ** 2))
 
+def _rbf_dot_XX(X, width):
+    """rbf dot, in special case with X dot X"""
+    G = np.sum(X * X, axis=1)
+    H = G[None, :] + G[:, None] - 2 * np.dot(X, X.T)
+    return np.exp(-H / 2 / (width ** 2))
 
 def get_gram_matrix(X, width):
     """Get the centered gram matrices.
@@ -76,11 +79,13 @@ def get_gram_matrix(X, width):
         the centered gram matrices.
     """
     n = X.shape[0]
-    H = np.eye(n) - 1 / n * np.ones((n, n))
-
-    K = _rbf_dot(X, X, width)
-    Kc = np.dot(np.dot(H, K), H)
 
+    K = _rbf_dot_XX(X, width)
+    K_colsums = K.sum(axis=0)
+    K_rowsums = K.sum(axis=1)
+    K_allsum = K_rowsums.sum()
+    Kc = K - (K_colsums[None, :] + K_rowsums[:, None]) / n + np.ones((n, n)) * (K_allsum / n ** 2)
+    # equivalent to H @ K @ H, where H = np.eye(n) - 1 / n * np.ones((n, n)).
     return K, Kc
 
 
@@ -101,7 +106,7 @@ def hsic_teststat(Kc, Lc, n):
         the HSIC statistic.
     """
     # test statistic m*HSICb under H1
-    return 1 / n * np.sum(np.sum(Kc.T * Lc))
+    return 1 / n * np.sum(Kc.T * Lc)
 
 
 def hsic_test_gamma(X, Y, bw_method='mdbs'):
@@ -148,25 +153,47 @@ def hsic_test_gamma(X, Y, bw_method='mdbs'):
 
     # test statistic m*HSICb under H1
     n = X.shape[0]
-    bone = np.ones((n, 1))
     test_stat = hsic_teststat(Kc, Lc, n)
 
     var = (1 / 6 * Kc * Lc) ** 2
     # second subtracted term is bias correction
-    var = 1 / n / (n - 1) * (np.sum(np.sum(var)) - np.sum(np.diag(var)))
+    var = 1 / n / (n - 1) * (np.sum(var) - np.trace(var))
     # variance under H0
     var = 72 * (n - 4) * (n - 5) / n / (n - 1) / (n - 2) / (n - 3) * var
 
-    K = K - np.diag(np.diag(K))
-    L = L - np.diag(np.diag(L))
-    mu_X = 1 / n / (n - 1) * np.dot(bone.T, np.dot(K, bone))
-    mu_Y = 1 / n / (n - 1) * np.dot(bone.T, np.dot(L, bone))
+    K[np.diag_indices(n)] = 0
+    L[np.diag_indices(n)] = 0
+    mu_X = 1 / n / (n - 1) * K.sum()
+    mu_Y = 1 / n / (n - 1) * L.sum()
     # mean under H0
     mean = 1 / n * (1 + mu_X * mu_Y - mu_X - mu_Y)
 
     alpha = mean ** 2 / var
     # threshold for hsicArr*m
-    beta = np.dot(var, n) / mean
-    p = 1 - gamma.cdf(test_stat, alpha, scale=beta)[0][0]
+    beta = var * n / mean
+    p = 1 - gamma.cdf(test_stat, alpha, scale=beta)
 
     return test_stat, p
+
+
+if __name__ == '__main__':
+    X = np.random.uniform(0, 1, (15000,))
+    Y = X ** 2 + np.random.uniform(0, 1, (15000,))
+    tic = time.time()
+    test_stat, p = hsic_test_gamma(X, Y)
+    print(f'now used: {time.time() - tic: .5f}s')
+
+    from causallearn.search.FCMBased.lingam.hsic import hsic_test_gamma as hsic_test_gamma_old
+    tic = time.time()
+    test_stat_old, p_old = hsic_test_gamma_old(X, Y)
+    print(f'originally used: {time.time() - tic: .5f}s')
+
+    assert np.isclose(test_stat, test_stat_old)
+    assert np.isclose(p, p_old)
+    print('equivalent test passed.')
+
+    '''
+    now used:  6.78904s
+    originally used:  65.28648s
+    equivalent test passed.
+    '''

causallearn/search/HiddenCausal/GIN/GIN.py

@@ -23,9 +23,8 @@
 
 def fisher_test(pvals):
     pvals = [pval if pval >= 1e-5 else 1e-5 for pval in pvals]
-    return min(pvals)
-    # fisher_stat = -2.0 * np.sum(np.log(pvals))
-    # return 1 - chi2.cdf(fisher_stat, 2 * len(pvals))
+    fisher_stat = -2.0 * np.sum(np.log(pvals))
+    return 1 - chi2.cdf(fisher_stat, 2 * len(pvals))
 
 
 def GIN(data, indep_test=kci, alpha=0.05):