Use float64 instead of float32 in tfd.Beta and tfd.Binomial tests to …

…work around a breakage in OSS following scipy 1.7.2 release. Unpin the scipy version.

PiperOrigin-RevId: 408643791

tensorflow_probability/python/distributions/beta_test.py

@@ -282,9 +282,10 @@ def testBetaSampleMultidimensional(self):
     sample_values = self.evaluate(samples)
     self.assertEqual(sample_values.shape, (100000, 3, 2, 2))
     self.assertFalse(np.any(sample_values < 0.0))
+    # Pass f64 values to avoid errors in scipy.
     self.assertAllClose(
         sample_values[:, 1, :].mean(axis=0),
-        sp_stats.beta.mean(a, b)[1, :],
+        sp_stats.beta.mean(a.astype(np.float64), b.astype(np.float64))[1, :],
         atol=1e-1)
 
   @parameterized.parameters((np.float32, 5e-3), (np.float64, 1e-4))

tensorflow_probability/python/distributions/binomial_test.py

@@ -282,11 +282,14 @@ def testSampleUnbiasedVectorBatch(self):
           sample_variance,
       ])
       self.assertAllEqual([4], sample_mean.shape)
+      # Pass f64 values to avoid errors in scipy.
       self.assertAllClose(
-          stats.binom.mean(counts, probs), sample_mean_, atol=0., rtol=0.10)
+          stats.binom.mean(counts.astype(np.float64), probs.astype(np.float64)),
+          sample_mean_, atol=0., rtol=0.10)
       self.assertAllEqual([4], sample_variance.shape)
       self.assertAllClose(
-          stats.binom.var(counts, probs), sample_variance_, atol=0., rtol=0.20)
+          stats.binom.var(counts.astype(np.float64), probs.astype(np.float64)),
+          sample_variance_, atol=0., rtol=0.20)
 
   def testSampleExtremeValues(self):
     total_count = tf.constant(17., dtype=tf.float32)

testing/dependency_install_lib.sh

@@ -80,8 +80,7 @@ install_common_packages() {
 install_test_only_packages() {
   # The following unofficial dependencies are used only by tests.
   PIP_FLAGS=${1-}
-  # TODO(b/205627112): Unpin scipy version.
-  python -m pip install $PIP_FLAGS hypothesis matplotlib mock mpmath scipy==1.7.1 pandas
+  python -m pip install $PIP_FLAGS hypothesis matplotlib mock mpmath scipy pandas
 }
 
 dump_versions() {