add black formatter to pre-commit (#4163)

* add black formatter to pre-commit

* fmt off benchmarks

Co-authored-by: Marco Gorelli <[email protected]>

Author: MarcoGorelli

.pre-commit-config.yaml

@@ -5,7 +5,7 @@ repos:
     -   id: end-of-file-fixer
     -   id: check-toml
 - repo: https://github.com/nbQA-dev/nbQA
-  rev: 0.2.1
+  rev: 0.2.3
   hooks:
     - id: nbqa
       args: ['isort']
@@ -22,3 +22,7 @@ repos:
   hooks:
     - id: pyupgrade
       args: ['--py36-plus']
+- repo: https://github.com/psf/black
+  rev: 20.8b1
+  hooks:
+    - id: black

benchmarks/benchmarks/benchmarks.py

@@ -25,23 +25,23 @@
 def glm_hierarchical_model(random_seed=123):
     """Sample glm hierarchical model to use in benchmarks"""
     np.random.seed(random_seed)
-    data = pd.read_csv(pm.get_data('radon.csv'))
-    data['log_radon'] = data['log_radon'].astype(theano.config.floatX)
+    data = pd.read_csv(pm.get_data("radon.csv"))
+    data["log_radon"] = data["log_radon"].astype(theano.config.floatX)
     county_idx = data.county_code.values
 
     n_counties = len(data.county.unique())
     with pm.Model() as model:
-        mu_a = pm.Normal('mu_a', mu=0., sd=100**2)
-        sigma_a = pm.HalfCauchy('sigma_a', 5)
-        mu_b = pm.Normal('mu_b', mu=0., sd=100**2)
-        sigma_b = pm.HalfCauchy('sigma_b', 5)
-        a = pm.Normal('a', mu=0, sd=1, shape=n_counties)
-        b = pm.Normal('b', mu=0, sd=1, shape=n_counties)
+        mu_a = pm.Normal("mu_a", mu=0.0, sd=100 ** 2)
+        sigma_a = pm.HalfCauchy("sigma_a", 5)
+        mu_b = pm.Normal("mu_b", mu=0.0, sd=100 ** 2)
+        sigma_b = pm.HalfCauchy("sigma_b", 5)
+        a = pm.Normal("a", mu=0, sd=1, shape=n_counties)
+        b = pm.Normal("b", mu=0, sd=1, shape=n_counties)
         a = mu_a + sigma_a * a
         b = mu_b + sigma_b * b
-        eps = pm.HalfCauchy('eps', 5)
+        eps = pm.HalfCauchy("eps", 5)
         radon_est = a[county_idx] + b[county_idx] * data.floor.values
-        pm.Normal('radon_like', mu=radon_est, sd=eps, observed=data.log_radon)
+        pm.Normal("radon_like", mu=radon_est, sd=eps, observed=data.log_radon)
     return model
 
 
@@ -50,24 +50,27 @@ def mixture_model(random_seed=1234):
     np.random.seed(1234)
     size = 1000
     w_true = np.array([0.35, 0.4, 0.25])
-    mu_true = np.array([0., 2., 5.])
-    sigma = np.array([0.5, 0.5, 1.])
+    mu_true = np.array([0.0, 2.0, 5.0])
+    sigma = np.array([0.5, 0.5, 1.0])
     component = np.random.choice(mu_true.size, size=size, p=w_true)
     x = np.random.normal(mu_true[component], sigma[component], size=size)
 
     with pm.Model() as model:
-        w = pm.Dirichlet('w', a=np.ones_like(w_true))
-        mu = pm.Normal('mu', mu=0., sd=10., shape=w_true.shape)
-        enforce_order = pm.Potential('enforce_order', tt.switch(mu[0] - mu[1] <= 0, 0., -np.inf) +
-                                                      tt.switch(mu[1] - mu[2] <= 0, 0., -np.inf))
-        tau = pm.Gamma('tau', alpha=1., beta=1., shape=w_true.shape)
-        pm.NormalMixture('x_obs', w=w, mu=mu, tau=tau, observed=x)
+        w = pm.Dirichlet("w", a=np.ones_like(w_true))
+        mu = pm.Normal("mu", mu=0.0, sd=10.0, shape=w_true.shape)
+        enforce_order = pm.Potential(
+            "enforce_order",
+            tt.switch(mu[0] - mu[1] <= 0, 0.0, -np.inf)
+            + tt.switch(mu[1] - mu[2] <= 0, 0.0, -np.inf),
+        )
+        tau = pm.Gamma("tau", alpha=1.0, beta=1.0, shape=w_true.shape)
+        pm.NormalMixture("x_obs", w=w, mu=mu, tau=tau, observed=x)
 
     # Initialization can be poorly specified, this is a hack to make it work
     start = {
-        'mu': mu_true.copy(),
-        'tau_log__': np.log(1. / sigma**2),
-        'w_stickbreaking__': np.array([-0.03,  0.44])
+        "mu": mu_true.copy(),
+        "tau_log__": np.log(1.0 / sigma ** 2),
+        "w_stickbreaking__": np.array([-0.03, 0.44]),
     }
     return model, start
 
@@ -77,26 +80,34 @@ class OverheadSuite:
     Just tests how long sampling from a normal distribution takes for various
     samplers
     """
+
     params = [pm.NUTS, pm.HamiltonianMC, pm.Metropolis, pm.Slice]
     timer = timeit.default_timer
 
     def setup(self, step):
         self.n_steps = 10000
         with pm.Model() as self.model:
-            pm.Normal('x', mu=0, sd=1)
+            pm.Normal("x", mu=0, sd=1)
 
     def time_overhead_sample(self, step):
         with self.model:
-            pm.sample(self.n_steps, step=step(), random_seed=1,
-                      progressbar=False, compute_convergence_checks=False)
+            pm.sample(
+                self.n_steps,
+                step=step(),
+                random_seed=1,
+                progressbar=False,
+                compute_convergence_checks=False,
+            )
 
 
 class ExampleSuite:
     """Implements examples to keep up with benchmarking them."""
+
     timeout = 360.0  # give it a few minutes
     timer = timeit.default_timer
 
     def time_drug_evaluation(self):
+        # fmt: off
         drug = np.array([101, 100, 102, 104, 102, 97, 105, 105, 98, 101,
                          100, 123, 105, 103, 100, 95, 102, 106, 109, 102, 82,
                          102, 100, 102, 102, 101, 102, 102, 103, 103, 97, 97,
@@ -107,46 +118,52 @@ def time_drug_evaluation(self):
                             100, 101, 102, 103, 97, 101, 101, 100, 101, 99,
                             101, 100, 100, 101, 100, 99, 101, 100, 102, 99,
                             100, 99])
-
-        y = pd.DataFrame({
-            'value': np.r_[drug, placebo],
-            'group': np.r_[['drug']*len(drug), ['placebo']*len(placebo)]
-            })
+        # fmt: on
+
+        y = pd.DataFrame(
+            {
+                "value": np.r_[drug, placebo],
+                "group": np.r_[["drug"] * len(drug), ["placebo"] * len(placebo)],
+            }
+        )
         y_mean = y.value.mean()
         y_std = y.value.std() * 2
 
         sigma_low = 1
         sigma_high = 10
         with pm.Model():
-            group1_mean = pm.Normal('group1_mean', y_mean, sd=y_std)
-            group2_mean = pm.Normal('group2_mean', y_mean, sd=y_std)
-            group1_std = pm.Uniform('group1_std', lower=sigma_low, upper=sigma_high)
-            group2_std = pm.Uniform('group2_std', lower=sigma_low, upper=sigma_high)
-            lambda_1 = group1_std**-2
-            lambda_2 = group2_std**-2
-
-            nu = pm.Exponential('ν_minus_one', 1/29.) + 1
-
-            pm.StudentT('drug', nu=nu, mu=group1_mean, lam=lambda_1, observed=drug)
-            pm.StudentT('placebo', nu=nu, mu=group2_mean, lam=lambda_2, observed=placebo)
-            diff_of_means = pm.Deterministic('difference of means', group1_mean - group2_mean)
-            pm.Deterministic('difference of stds', group1_std - group2_std)
+            group1_mean = pm.Normal("group1_mean", y_mean, sd=y_std)
+            group2_mean = pm.Normal("group2_mean", y_mean, sd=y_std)
+            group1_std = pm.Uniform("group1_std", lower=sigma_low, upper=sigma_high)
+            group2_std = pm.Uniform("group2_std", lower=sigma_low, upper=sigma_high)
+            lambda_1 = group1_std ** -2
+            lambda_2 = group2_std ** -2
+
+            nu = pm.Exponential("ν_minus_one", 1 / 29.0) + 1
+
+            pm.StudentT("drug", nu=nu, mu=group1_mean, lam=lambda_1, observed=drug)
+            pm.StudentT("placebo", nu=nu, mu=group2_mean, lam=lambda_2, observed=placebo)
+            diff_of_means = pm.Deterministic("difference of means", group1_mean - group2_mean)
+            pm.Deterministic("difference of stds", group1_std - group2_std)
             pm.Deterministic(
-                'effect size', diff_of_means / np.sqrt((group1_std**2 + group2_std**2) / 2))
-            pm.sample(draws=20000, cores=4, chains=4,
-                      progressbar=False, compute_convergence_checks=False)
+                "effect size", diff_of_means / np.sqrt((group1_std ** 2 + group2_std ** 2) / 2)
+            )
+            pm.sample(
+                draws=20000, cores=4, chains=4, progressbar=False, compute_convergence_checks=False
+            )
 
     def time_glm_hierarchical(self):
         with glm_hierarchical_model():
-            pm.sample(draws=20000, cores=4, chains=4,
-                      progressbar=False, compute_convergence_checks=False)
+            pm.sample(
+                draws=20000, cores=4, chains=4, progressbar=False, compute_convergence_checks=False
+            )
 
 
 class NUTSInitSuite:
-    """Tests initializations for NUTS sampler on models
-    """
+    """Tests initializations for NUTS sampler on models"""
+
     timeout = 360.0
-    params = ('adapt_diag', 'jitter+adapt_diag', 'jitter+adapt_full', 'adapt_full')
+    params = ("adapt_diag", "jitter+adapt_diag", "jitter+adapt_full", "adapt_full")
     number = 1
     repeat = 1
     draws = 10000
@@ -159,32 +176,49 @@ def time_glm_hierarchical_init(self, init):
 
     def track_glm_hierarchical_ess(self, init):
         with glm_hierarchical_model():
-            start, step = pm.init_nuts(init=init, chains=self.chains, progressbar=False, random_seed=123)
+            start, step = pm.init_nuts(
+                init=init, chains=self.chains, progressbar=False, random_seed=123
+            )
             t0 = time.time()
-            trace = pm.sample(draws=self.draws, step=step, cores=4, chains=self.chains,
-                              start=start, random_seed=100, progressbar=False,
-                              compute_convergence_checks=False)
+            trace = pm.sample(
+                draws=self.draws,
+                step=step,
+                cores=4,
+                chains=self.chains,
+                start=start,
+                random_seed=100,
+                progressbar=False,
+                compute_convergence_checks=False,
+            )
             tot = time.time() - t0
-        ess = float(pm.ess(trace, var_names=['mu_a'])['mu_a'].values)
+        ess = float(pm.ess(trace, var_names=["mu_a"])["mu_a"].values)
         return ess / tot
 
     def track_marginal_mixture_model_ess(self, init):
         model, start = mixture_model()
         with model:
-            _, step = pm.init_nuts(init=init, chains=self.chains,
-                                   progressbar=False, random_seed=123)
+            _, step = pm.init_nuts(
+                init=init, chains=self.chains, progressbar=False, random_seed=123
+            )
             start = [{k: v for k, v in start.items()} for _ in range(self.chains)]
             t0 = time.time()
-            trace = pm.sample(draws=self.draws, step=step, cores=4, chains=self.chains,
-                              start=start, random_seed=100, progressbar=False,
-                              compute_convergence_checks=False)
+            trace = pm.sample(
+                draws=self.draws,
+                step=step,
+                cores=4,
+                chains=self.chains,
+                start=start,
+                random_seed=100,
+                progressbar=False,
+                compute_convergence_checks=False,
+            )
             tot = time.time() - t0
-        ess = pm.ess(trace, var_names=['mu'])['mu'].values.min()  # worst case
+        ess = pm.ess(trace, var_names=["mu"])["mu"].values.min()  # worst case
         return ess / tot
 
 
-NUTSInitSuite.track_glm_hierarchical_ess.unit = 'Effective samples per second'
-NUTSInitSuite.track_marginal_mixture_model_ess.unit = 'Effective samples per second'
+NUTSInitSuite.track_glm_hierarchical_ess.unit = "Effective samples per second"
+NUTSInitSuite.track_marginal_mixture_model_ess.unit = "Effective samples per second"
 
 
 class CompareMetropolisNUTSSuite:
@@ -200,15 +234,21 @@ def track_glm_hierarchical_ess(self, step):
             if step is not None:
                 step = step()
             t0 = time.time()
-            trace = pm.sample(draws=self.draws, step=step, cores=4, chains=4,
-                              random_seed=100, progressbar=False,
-                              compute_convergence_checks=False)
+            trace = pm.sample(
+                draws=self.draws,
+                step=step,
+                cores=4,
+                chains=4,
+                random_seed=100,
+                progressbar=False,
+                compute_convergence_checks=False,
+            )
             tot = time.time() - t0
-        ess = float(pm.ess(trace, var_names=['mu_a'])['mu_a'].values)
+        ess = float(pm.ess(trace, var_names=["mu_a"])["mu_a"].values)
         return ess / tot
 
 
-CompareMetropolisNUTSSuite.track_glm_hierarchical_ess.unit = 'Effective samples per second'
+CompareMetropolisNUTSSuite.track_glm_hierarchical_ess.unit = "Effective samples per second"
 
 
 class DifferentialEquationSuite:
@@ -223,30 +263,34 @@ def freefall(y, t, p):
 
         # Times for observation
         times = np.arange(0, 10, 0.5)
-        y = np.array([
-            -2.01,
-            9.49,
-            15.58,
-            16.57,
-            27.58,
-            32.26,
-            35.13,
-            38.07,
-            37.36,
-            38.83,
-            44.86,
-            43.58,
-            44.59,
-            42.75,
-            46.9,
-            49.32,
-            44.06,
-            49.86,
-            46.48,
-            48.18
-        ]).reshape(-1, 1)
-
-        ode_model = pm.ode.DifferentialEquation(func=freefall, times=times, n_states=1, n_theta=2, t0=0)
+        y = np.array(
+            [
+                -2.01,
+                9.49,
+                15.58,
+                16.57,
+                27.58,
+                32.26,
+                35.13,
+                38.07,
+                37.36,
+                38.83,
+                44.86,
+                43.58,
+                44.59,
+                42.75,
+                46.9,
+                49.32,
+                44.06,
+                49.86,
+                46.48,
+                48.18,
+            ]
+        ).reshape(-1, 1)
+
+        ode_model = pm.ode.DifferentialEquation(
+            func=freefall, times=times, n_states=1, n_theta=2, t0=0
+        )
         with pm.Model() as model:
             # Specify prior distributions for some of our model parameters
             sigma = pm.HalfCauchy("sigma", 1)
@@ -263,4 +307,4 @@ def freefall(y, t, p):
         return np.mean([ess.sigma, ess.gamma]) / tot
 
 
-DifferentialEquationSuite.track_1var_2par_ode_ess.unit = 'Effective samples per second'
+DifferentialEquationSuite.track_1var_2par_ode_ess.unit = "Effective samples per second"

docs/source/conf.py

@@ -155,7 +155,7 @@
         ("Books + Videos", "learn"),
         ("API", "api"),
         ("Developer Guide", "developer_guide"),
-        ("About PyMC3", "about")
+        ("About PyMC3", "about"),
     ],
     #     "fixed_sidebar": "false",
     #     "description": "Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Theano"
@@ -264,9 +264,7 @@
 # Grouping the document tree into LaTeX files. List of tuples
 # (source start file, target name, title,
 #  author, documentclass [howto, manual, or own class]).
-latex_documents = [
-    (master_doc, "pymc3.tex", "PyMC3 Documentation", "PyMC developers", "manual")
-]
+latex_documents = [(master_doc, "pymc3.tex", "PyMC3 Documentation", "PyMC developers", "manual")]
 
 # The name of an image file (relative to this directory) to place at the top of
 # the title page.
@@ -330,7 +328,5 @@
 
 
 def setup(app):
-    app.add_css_file(
-        "https://cdn.jsdelivr.net/npm/[email protected]/dist/semantic.min.css"
-    )
+    app.add_css_file("https://cdn.jsdelivr.net/npm/[email protected]/dist/semantic.min.css")
     app.add_css_file("default.css")