Run the pre-commit

Author: Muhammad-Rebaal

pymc/distributions/multivariate.py

@@ -2158,7 +2158,7 @@ def make_node(self, rng, size, mu, W, alpha, tau, W_is_valid):
     @classmethod
     def rng_fn(cls, rng, mu, W, alpha, tau, W_is_valid, size=None):
         """Sample from the CAR distribution.
-        
+
         Parameters
         ----------
         rng : numpy.random.Generator
@@ -2175,7 +2175,7 @@ def rng_fn(cls, rng, mu, W, alpha, tau, W_is_valid, size=None):
             Flag indicating whether W is a valid adjacency matrix
         size : tuple, optional
             Size of the samples to generate
-        
+
         Returns
         -------
         ndarray
@@ -2186,26 +2186,21 @@ def rng_fn(cls, rng, mu, W, alpha, tau, W_is_valid, size=None):
 
         if np.any(alpha >= 1) or np.any(alpha <= -1):
             raise ValueError("the domain of alpha is: -1 < alpha < 1")
-            
+
         W = np.asarray(W)
         N = W.shape[0]
-        
+
         # Construct the precision matrix
         D = np.diag(W.sum(axis=1))
         Q = tau * (D - alpha * W)
-        
+
         # Convert precision to covariance matrix
         cov = np.linalg.inv(Q)
-        
+
         # Generate samples using multivariate_normal with covariance matrix
         mean = np.zeros(N) if mu is None else np.asarray(mu)
-        
-        return stats.multivariate_normal.rvs(
-            mean=mean, 
-            cov=cov,
-            size=size, 
-            random_state=rng
-        )
+
+        return stats.multivariate_normal.rvs(mean=mean, cov=cov, size=size, random_state=rng)
 
 
 car = CARRV()
@@ -2342,8 +2337,6 @@ def logp(value, mu, W, alpha, tau, W_is_valid):
             W_is_valid,
             msg="-1 < alpha < 1, tau > 0, W is a symmetric adjacency matrix.",
         )
-    
-
 
 
 class ICARRV(RandomVariable):
@@ -2358,7 +2351,7 @@ def __call__(self, W, sigma, zero_sum_stdev, size=None, **kwargs):
     @classmethod
     def rng_fn(cls, rng, W, sigma, zero_sum_stdev, size=None):
         """Sample from the ICAR distribution.
-        
+
         Parameters
         ----------
         rng : numpy.random.Generator
@@ -2371,34 +2364,31 @@ def rng_fn(cls, rng, W, sigma, zero_sum_stdev, size=None):
             Controls how strongly to enforce the zero-sum constraint
         size : tuple, optional
             Size of the samples to generate
-        
+
         Returns
         -------
         ndarray
             Samples from the ICAR distribution
         """
         W = np.asarray(W)
         N = W.shape[0]
-        
+
         # Construct the precision matrix (graph Laplacian)
         D = np.diag(W.sum(axis=1))
         Q = D - W
-        
+
         # Add regularization for the zero eigenvalue based on zero_sum_stdev
-        zero_sum_precision = 1.0 / (zero_sum_stdev * N)**2
+        zero_sum_precision = 1.0 / (zero_sum_stdev * N) ** 2
         Q_reg = Q + zero_sum_precision * np.ones((N, N)) / N
-        
+
         # Convert precision to covariance matrix
         cov = np.linalg.inv(Q_reg)
-        
+
         # Generate samples using multivariate_normal with covariance matrix
         mean = np.zeros(N)
-        
+
         return sigma * stats.multivariate_normal.rvs(
-            mean=mean, 
-            cov=cov,
-            size=size, 
-            random_state=rng
+            mean=mean, cov=cov, size=size, random_state=rng
         )
 
 

tests/distributions/test_multivariate.py

@@ -2253,28 +2253,25 @@ class TestCAR:
     def test_car_rng_fn(self):
         """Test the random number generator for the CAR distribution."""
         # Create a simple adjacency matrix for a grid
-        W = np.array([
-            [0, 1, 0, 1],
-            [1, 0, 1, 0],
-            [0, 1, 0, 1],
-            [1, 0, 1, 0]
-        ], dtype=np.int32)  # Explicitly set dtype
-        
+        W = np.array(
+            [[0, 1, 0, 1], [1, 0, 1, 0], [0, 1, 0, 1], [1, 0, 1, 0]], dtype=np.int32
+        )  # Explicitly set dtype
+
         rng = np.random.default_rng(42)
         mu = np.array([1.0, 2.0, 3.0, 4.0])
         alpha = 0.7
         tau = 0.5
-        
+
         # Generate samples - use W directly instead of tensor
         car_dist = pm.CAR.dist(mu=mu, W=W, alpha=alpha, tau=tau)
         car_samples = np.array([draw(car_dist, random_seed=rng) for _ in range(1000)])
-        
+
         # Test shape
         assert car_samples.shape == (1000, 4)
-        
+
         # Test mean
         assert np.allclose(car_samples.mean(axis=0), mu, atol=0.1)
-        
+
         # Test covariance structure - neighbors should be more correlated
         sample_corr = np.corrcoef(car_samples.T)
         for i in range(4):
@@ -2316,34 +2313,31 @@ def test_icar_logp(self):
     def test_icar_rng_fn(self):
         """Test the random number generator for the ICAR distribution."""
         # Create a simple adjacency matrix for a grid
-        W = np.array([
-            [0, 1, 0, 1],
-            [1, 0, 1, 0],
-            [0, 1, 0, 1],
-            [1, 0, 1, 0]
-        ], dtype=np.int32)  # Explicitly set dtype
-        
-        rng = np.random.default_rng(42)        
+        W = np.array(
+            [[0, 1, 0, 1], [1, 0, 1, 0], [0, 1, 0, 1], [1, 0, 1, 0]], dtype=np.int32
+        )  # Explicitly set dtype
+
+        rng = np.random.default_rng(42)
         sigma = 2.0
         zero_sum_stdev = 0.001
-        
+
         # Use W directly instead of converting to tensor
         icar_dist = pm.ICAR.dist(W=W, sigma=sigma, zero_sum_stdev=zero_sum_stdev)
         icar_samples = np.array([draw(icar_dist, random_seed=rng) for _ in range(1000)])
-        
+
         # Test shape
         assert icar_samples.shape == (1000, 4)
-        
+
         # Test approximate zero-sum constraint
         assert np.abs(icar_samples.sum(axis=1).mean()) < 0.1
-        
+
         # Test variance scale - expect variance ≈ sigma^2 * (N-1)/N due to constraint
         var_scale = (W.shape[0] - 1) / W.shape[0]  # Degrees of freedom adjustment
         expected_var = sigma**2 * var_scale
         observed_var = np.var(icar_samples, axis=1).mean()
-        
+
         # Use a more generous tolerance to account for the zero sum constraint's impact on variance
-        assert np.abs(observed_var - expected_var) < 2.0  
+        assert np.abs(observed_var - expected_var) < 2.0
 
     @pytest.mark.parametrize(
         "W,msg",