Fixing np.bool Depreciation warnings: 3 files

pymc3/step_methods/hmc/hmc.py

@@ -39,14 +39,14 @@ class HamiltonianMC(BaseHMC):
         {
             "step_size": np.float64,
             "n_steps": np.int64,
-            "tune": np.bool,
+            "tune": bool,
             "step_size_bar": np.float64,
             "accept": np.float64,
-            "diverging": np.bool,
+            "diverging": bool,
             "energy_error": np.float64,
             "energy": np.float64,
             "path_length": np.float64,
-            "accepted": np.bool,
+            "accepted": bool,
             "model_logp": np.float64,
             "process_time_diff": np.float64,
             "perf_counter_diff": np.float64,

pymc3/step_methods/hmc/nuts.py

@@ -94,11 +94,11 @@ class NUTS(BaseHMC):
         {
             "depth": np.int64,
             "step_size": np.float64,
-            "tune": np.bool,
+            "tune": bool,
             "mean_tree_accept": np.float64,
             "step_size_bar": np.float64,
             "tree_size": np.float64,
-            "diverging": np.bool,
+            "diverging": bool,
             "energy_error": np.float64,
             "energy": np.float64,
             "max_energy_error": np.float64,

pymc3/step_methods/metropolis.py

@@ -108,8 +108,8 @@ class Metropolis(ArrayStepShared):
     stats_dtypes = [
         {
             "accept": np.float64,
-            "accepted": np.bool,
-            "tune": np.bool,
+            "accepted": bool,
+            "tune": bool,
             "scaling": np.float64,
         }
     ]
@@ -307,7 +307,7 @@ class BinaryMetropolis(ArrayStep):
     stats_dtypes = [
         {
             "accept": np.float64,
-            "tune": np.bool,
+            "tune": bool,
             "p_jump": np.float64,
         }
     ]
@@ -669,8 +669,8 @@ class DEMetropolis(PopulationArrayStepShared):
     stats_dtypes = [
         {
             "accept": np.float64,
-            "accepted": np.bool,
-            "tune": np.bool,
+            "accepted": bool,
+            "tune": bool,
             "scaling": np.float64,
             "lambda": np.float64,
         }
@@ -818,8 +818,8 @@ class DEMetropolisZ(ArrayStepShared):
     stats_dtypes = [
         {
             "accept": np.float64,
-            "accepted": np.bool,
-            "tune": np.bool,
+            "accepted": bool,
+            "tune": bool,
             "scaling": np.float64,
             "lambda": np.float64,
         }

pymc3/step_methods/mlda.py

@@ -679,7 +679,7 @@ def __init__(
 
         else:
             # otherwise, set it up from scratch.
-            self.stats_dtypes = [{"accept": np.float64, "accepted": np.bool, "tune": np.bool}]
+            self.stats_dtypes = [{"accept": np.float64, "accepted": bool, "tune": bool}]
 
             if isinstance(self.step_method_below, MetropolisMLDA):
                 self.stats_dtypes.append({"base_scaling": np.float64})

pymc3/tests/backend_fixtures.py

@@ -61,11 +61,11 @@ def test_append_invalid(self):
             with pytest.raises(ValueError):
                 self.strace.setup(self.draws, self.chain)
             with pytest.raises(ValueError):
-                vars = self.sampler_vars + [{"a": np.bool}]
+                vars = self.sampler_vars + [{"a": bool}]
                 self.strace.setup(self.draws, self.chain, vars)
         else:
             with pytest.raises((ValueError, TypeError)):
-                self.strace.setup(self.draws, self.chain, [{"a": np.bool}])
+                self.strace.setup(self.draws, self.chain, [{"a": bool}])
 
     def test_append(self):
         if self.sampler_vars is None:
@@ -103,7 +103,7 @@ def setup_method(self):
         self.draws, self.chain = 3, 0
 
     def test_bad_dtype(self):
-        bad_vars = [{"a": np.float64}, {"a": np.bool}]
+        bad_vars = [{"a": np.float64}, {"a": bool}]
         good_vars = [{"a": np.float64}, {"a": np.float64}]
         with self.model:
             strace = self.backend(self.name)
@@ -185,7 +185,7 @@ def setup_class(cls):
                 cls.expected_stats[0].append(stats)
                 cls.expected_stats[1].append(stats)
                 for key, dtype in vars.items():
-                    if dtype == np.bool:
+                    if dtype == bool:
                         stats[key] = np.zeros(cls.draws, dtype=dtype)
                     else:
                         stats[key] = np.arange(cls.draws, dtype=dtype)

pymc3/tests/test_ndarray_backend.py

@@ -21,7 +21,7 @@
 from pymc3.backends import base, ndarray
 from pymc3.tests import backend_fixtures as bf
 
-STATS1 = [{"a": np.float64, "b": np.bool}]
+STATS1 = [{"a": np.float64, "b": bool}]
 
 STATS2 = [
     {"a": np.float64},