Run black on core notebooks

Author: ricardoV94

.pre-commit-config.yaml

@@ -45,6 +45,7 @@ repos:
   rev: 22.3.0
   hooks:
     - id: black
+    - id: black-jupyter
 - repo: https://github.com/PyCQA/pylint
   rev: v2.14.0
   hooks:

docs/source/learn/core_notebooks/GLM_linear.ipynb

@@ -402,7 +402,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "idata.posterior[\"y_model\"] = idata.posterior[\"Intercept\"] + idata.posterior[\"x\"]*xr.DataArray(x)"
+    "idata.posterior[\"y_model\"] = idata.posterior[\"Intercept\"] + idata.posterior[\"x\"] * xr.DataArray(x)"
    ]
   },
   {

docs/source/learn/core_notebooks/dimensionality.ipynb

@@ -160,7 +160,7 @@
     }
    ],
    "source": [
-    "random_sample = pm.Normal.dist(mu=[1,10,100], sigma=.0001).eval()\n",
+    "random_sample = pm.Normal.dist(mu=[1, 10, 100], sigma=0.0001).eval()\n",
     "random_sample, random_sample.shape"
    ]
   },
@@ -310,11 +310,11 @@
    ],
    "source": [
     "with pm.Model() as pmodel:\n",
-    "    pm.Normal(\"scalar\") # shape=()\n",
-    "    pm.Normal(\"vector (implied)\", mu=[1,2,3])\n",
+    "    pm.Normal(\"scalar\")  # shape=()\n",
+    "    pm.Normal(\"vector (implied)\", mu=[1, 2, 3])\n",
     "    pm.Normal(\"vector (from shape)\", shape=(4,))\n",
     "    pm.Normal(\"vector (from size)\", size=(5,))\n",
-    "    \n",
+    "\n",
     "pm.model_to_graphviz(pmodel)"
    ]
   },
@@ -401,7 +401,7 @@
     "with pm.Model() as pmodel:\n",
     "    pm.Normal(\"red\", size=2, dims=\"B\")\n",
     "\n",
-    "    pm.Normal(\"one\", [1,2,3,4], dims=\"Dim_A\")  # (4,)\n",
+    "    pm.Normal(\"one\", [1, 2, 3, 4], dims=\"Dim_A\")  # (4,)\n",
     "    pm.Normal(\"two\", dims=\"Dim_A\")\n",
     "\n",
     "\n",
@@ -421,10 +421,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "with pm.Model(coords={\n",
-    "    \"year\": [2020, 2021, 2022],\n",
-    "}) as pmodel:\n",
-    "    \n",
+    "with pm.Model(\n",
+    "    coords={\n",
+    "        \"year\": [2020, 2021, 2022],\n",
+    "    }\n",
+    ") as pmodel:\n",
+    "\n",
     "    pm.Normal(\"Normal_RV\", dims=\"year\")\n",
     "\n",
     "    pm.model_to_graphviz(pmodel)"
@@ -483,7 +485,7 @@
     }
    ],
    "source": [
-    "pm.MvNormal.dist(mu=[[1,2,3], [4,5,6]], cov=np.eye(3)*.0001).eval()"
+    "pm.MvNormal.dist(mu=[[1, 2, 3], [4, 5, 6]], cov=np.eye(3) * 0.0001).eval()"
    ]
   },
   {
@@ -587,9 +589,11 @@
     }
    ],
    "source": [
-    "with pm.Model(coords={\n",
-    "    \"year\": [2020, 2021, 2022],\n",
-    "}) as pmodel:\n",
+    "with pm.Model(\n",
+    "    coords={\n",
+    "        \"year\": [2020, 2021, 2022],\n",
+    "    }\n",
+    ") as pmodel:\n",
     "    mv = pm.MvNormal(\"implied\", mu=[0, 0, 0], cov=np.eye(3))\n",
     "    print(mv.shape.eval())\n",
     "\n",
@@ -598,11 +602,11 @@
     "\n",
     "    mv = pm.MvNormal(\"with size\", mu=[0, 0], cov=np.eye(2), size=3, dims=(\"repeats\", \"implied\"))\n",
     "    print(mv.shape.eval())\n",
-    "    \n",
+    "\n",
     "    # ⚠ Size dims are always __prepended__\n",
     "    mv = pm.MvNormal(\"with shape\", mu=[0, 0], cov=np.eye(2), shape=(3, ...), dims=(\"repeats\", ...))\n",
     "    print(mv.shape.eval())\n",
-    "    \n",
+    "\n",
     "    mv = pm.MvNormal(\"with coords\", mu=[0, 0], cov=np.eye(2), dims=(\"year\", ...))\n",
     "    print(mv.shape.eval())\n",
     "\n",

docs/source/learn/core_notebooks/posterior_predictive.ipynb

@@ -56,6 +56,7 @@
     "RANDOM_SEED = 58\n",
     "rng = np.random.default_rng(RANDOM_SEED)\n",
     "\n",
+    "\n",
     "def standardize(series):\n",
     "    \"\"\"Standardize a pandas series\"\"\"\n",
     "    return (series - series.mean()) / series.std()"
@@ -939,17 +940,20 @@
    "source": [
     "_, ax = plt.subplots()\n",
     "\n",
-    "ax.plot(predictor_scaled, mu_pp.mean((\"chain\", \"draw\")), label=\"Mean outcome\", color=\"C1\", alpha=0.6);\n",
+    "ax.plot(\n",
+    "    predictor_scaled, mu_pp.mean((\"chain\", \"draw\")), label=\"Mean outcome\", color=\"C1\", alpha=0.6\n",
+    ")\n",
     "az.plot_lm(\n",
-    "    idata=idata, \n",
-    "    y=\"obs\", \n",
-    "    x=predictor_scaled, \n",
+    "    idata=idata,\n",
+    "    y=\"obs\",\n",
+    "    x=predictor_scaled,\n",
     "    kind_pp=\"hdi\",\n",
     "    y_kwargs={\"color\": \"C0\", \"marker\": \"o\", \"ms\": 4, \"alpha\": 0.4},\n",
     "    y_hat_fill_kwargs=dict(fill_kwargs={\"alpha\": 0.8}, color=\"xkcd:jade\"),\n",
     "    axes=ax,\n",
     ")\n",
-    "ax.set_xlabel(\"Predictor (stdz)\"); ax.set_ylabel(\"Outcome (stdz)\");"
+    "ax.set_xlabel(\"Predictor (stdz)\")\n",
+    "ax.set_ylabel(\"Outcome (stdz)\");"
    ]
   },
   {
@@ -1221,7 +1225,12 @@
     "    pm.set_data({\"pred\": predictors_out_of_sample})\n",
     "    # use the updated values and predict outcomes and probabilities:\n",
     "    idata_2 = pm.sample_posterior_predictive(\n",
-    "        idata_2, var_names=[\"p\"], return_inferencedata=True, predictions=True, extend_inferencedata=True, random_seed=rng,\n",
+    "        idata_2,\n",
+    "        var_names=[\"p\"],\n",
+    "        return_inferencedata=True,\n",
+    "        predictions=True,\n",
+    "        extend_inferencedata=True,\n",
+    "        random_seed=rng,\n",
     "    )"
    ]
   },

docs/source/learn/core_notebooks/pymc_aesara.ipynb

@@ -61,10 +61,12 @@
     "import scipy.stats\n",
     "\n",
     "\n",
-    "print(f\"\"\"\n",
+    "print(\n",
+    "    f\"\"\"\n",
     "# Aesara version: {aesara.__version__}\n",
     "# PyMC version: {pm.__version__}\n",
-    "\"\"\")"
+    "\"\"\"\n",
+    ")"
    ]
   },
   {
@@ -133,13 +135,15 @@
     "x = at.scalar(name=\"x\")\n",
     "y = at.vector(name=\"y\")\n",
     "\n",
-    "print(f\"\"\"\n",
+    "print(\n",
+    "    f\"\"\"\n",
     "x type: {x.type}\n",
     "x name = {x.name}\n",
     "---\n",
     "y type: {y.type}\n",
     "y name = {y.name}\n",
-    "\"\"\")"
+    "\"\"\"\n",
+    ")"
    ]
   },
   {
@@ -331,7 +335,7 @@
     }
    ],
    "source": [
-    "w.eval({x: 0, y:[1, np.e]})"
+    "w.eval({x: 0, y: [1, np.e]})"
    ]
   },
   {
@@ -566,14 +570,16 @@
     }
    ],
    "source": [
-    "print(f\"\"\"\n",
+    "print(\n",
+    "    f\"\"\"\n",
     "z type: {z.type}\n",
     "z name = {z.name}\n",
     "z owner = {z.owner}\n",
     "z owner inputs = {z.owner.inputs}\n",
     "z owner op = {z.owner.op}\n",
     "z owner output = {z.owner.outputs}\n",
-    "\"\"\")"
+    "\"\"\"\n",
+    ")"
    ]
   },
   {
@@ -746,8 +752,8 @@
    },
    "outputs": [],
    "source": [
-    "parent_of_w = w.owner.inputs[0] # get z tensor\n",
-    "new_parent_of_w = at.exp(parent_of_w) # modify the parent of w\n",
+    "parent_of_w = w.owner.inputs[0]  # get z tensor\n",
+    "new_parent_of_w = at.exp(parent_of_w)  # modify the parent of w\n",
     "new_parent_of_w.name = \"exp(x + y)\""
    ]
   },
@@ -878,7 +884,7 @@
     }
    ],
    "source": [
-    "new_w.eval({x: 0, y:[1, np.e]})"
+    "new_w.eval({x: 0, y: [1, np.e]})"
    ]
   },
   {
@@ -2050,7 +2056,7 @@
     }
    ],
    "source": [
-    " # Equivalent to rv_draw = pm.draw(rv, 3)\n",
+    "# Equivalent to rv_draw = pm.draw(rv, 3)\n",
     "rv.rvs(3)"
    ]
   },
@@ -2215,7 +2221,7 @@
     "# element-wise log-probability of the model (we do not take te sum)\n",
     "logp_graph = at.stack(model_2.logp(sum=False))\n",
     "# evaluate by passing concrete values\n",
-    "logp_graph.eval({mu_value: 0, sigma_log_value: -10, x_value:0})"
+    "logp_graph.eval({mu_value: 0, sigma_log_value: -10, x_value: 0})"
    ]
   },
   {
@@ -2251,11 +2257,13 @@
     }
    ],
    "source": [
-    "print(f\"\"\"\n",
+    "print(\n",
+    "    f\"\"\"\n",
     "mu_value -> {scipy.stats.norm.logpdf(x=0, loc=0, scale=2)}\n",
     "sigma_log_value -> {- 10 + scipy.stats.halfnorm.logpdf(x=np.exp(-10), loc=0, scale=3)} \n",
     "x_value -> {scipy.stats.norm.logpdf(x=0, loc=0, scale=np.exp(-10))}\n",
-    "\"\"\")\n"
+    "\"\"\"\n",
+    ")"
    ]
   },
   {

docs/source/learn/core_notebooks/pymc_overview.ipynb

@@ -3218,7 +3218,7 @@
     }
    ],
    "source": [
-    "test_scores = pd.read_csv(pm.get_data('test_scores.csv'), index_col=0)\n",
+    "test_scores = pd.read_csv(pm.get_data(\"test_scores.csv\"), index_col=0)\n",
     "test_scores.head()"
    ]
   },
@@ -3295,7 +3295,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "D0 = int(D/2)"
+    "D0 = int(D / 2)"
    ]
   },
   {
@@ -3347,7 +3347,7 @@
    "source": [
     "import aesara.tensor as at\n",
     "\n",
-    "with pm.Model(coords={\"predictors\":X.columns.values}) as test_score_model:\n",
+    "with pm.Model(coords={\"predictors\": X.columns.values}) as test_score_model:\n",
     "\n",
     "    # Prior on error SD\n",
     "    sigma = pm.HalfNormal(\"sigma\", 25)\n",
@@ -3357,12 +3357,14 @@
     "    # Local shrinkage prior\n",
     "    lam = pm.HalfStudentT(\"lam\", 2, dims=\"predictors\")\n",
     "    c2 = pm.InverseGamma(\"c2\", 1, 0.1)\n",
-    "    z = pm.Normal(\"z\", 0., 1., dims=\"predictors\")\n",
+    "    z = pm.Normal(\"z\", 0.0, 1.0, dims=\"predictors\")\n",
     "    # Shrunken coefficients\n",
-    "    beta = pm.Deterministic(\"beta\", z * tau * lam * at.sqrt(c2 / (c2 + tau**2 * lam**2)), dims=\"predictors\")\n",
+    "    beta = pm.Deterministic(\n",
+    "        \"beta\", z * tau * lam * at.sqrt(c2 / (c2 + tau**2 * lam**2)), dims=\"predictors\"\n",
+    "    )\n",
     "    # No shrinkage on intercept\n",
-    "    beta0 = pm.Normal(\"beta0\", 100, 25.)\n",
-    "    \n",
+    "    beta0 = pm.Normal(\"beta0\", 100, 25.0)\n",
+    "\n",
     "    scores = pm.Normal(\"scores\", beta0 + at.dot(X.values, beta), sigma, observed=y.values)"
    ]
   },
@@ -3579,8 +3581,19 @@
     }
    ],
    "source": [
-    "az.plot_dist(test_scores[\"score\"].values, kind=\"hist\", color=\"C1\", hist_kwargs=dict(alpha=0.6), label=\"observed\")\n",
-    "az.plot_dist(prior_samples.prior_predictive[\"scores\"], kind=\"hist\", hist_kwargs=dict(alpha=0.6), label=\"simulated\");\n",
+    "az.plot_dist(\n",
+    "    test_scores[\"score\"].values,\n",
+    "    kind=\"hist\",\n",
+    "    color=\"C1\",\n",
+    "    hist_kwargs=dict(alpha=0.6),\n",
+    "    label=\"observed\",\n",
+    ")\n",
+    "az.plot_dist(\n",
+    "    prior_samples.prior_predictive[\"scores\"],\n",
+    "    kind=\"hist\",\n",
+    "    hist_kwargs=dict(alpha=0.6),\n",
+    "    label=\"simulated\",\n",
+    ")\n",
     "plt.xticks(rotation=45);"
    ]
   },
@@ -3941,9 +3954,7 @@
    "source": [
     "with pm.Model() as disaster_model:\n",
     "\n",
-    "    switchpoint = pm.DiscreteUniform(\n",
-    "        \"switchpoint\", lower=years.min(), upper=years.max()\n",
-    "    )\n",
+    "    switchpoint = pm.DiscreteUniform(\"switchpoint\", lower=years.min(), upper=years.max())\n",
     "\n",
     "    # Priors for pre- and post-switch rates number of disasters\n",
     "    early_rate = pm.Exponential(\"early_rate\", 1.0)\n",
@@ -4071,7 +4082,7 @@
     "        labels = [label.get_text() for label in ax.get_xticklabels()]\n",
     "        ax.set_xticklabels(labels, rotation=45, ha=\"right\")\n",
     "        break\n",
-    "plt.draw()\n"
+    "plt.draw()"
    ]
   },
   {
@@ -4146,9 +4157,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
     "from aesara.compile.ops import as_op\n",
     "\n",
+    "\n",
     "@as_op(itypes=[at.lscalar], otypes=[at.lscalar])\n",
     "def crazy_modulo3(value):\n",
     "    if value > 0:\n",
@@ -4218,7 +4229,7 @@
     "        raise NotImplementedError(\"Cannot sample from beta variable\")\n",
     "\n",
     "\n",
-    "beta = BetaRV()\n"
+    "beta = BetaRV()"
    ]
   },
   {
@@ -4232,11 +4243,10 @@
     "    rv_op = beta\n",
     "\n",
     "    @classmethod\n",
-    "    def dist(cls, mu=0, **kwargs):        \n",
+    "    def dist(cls, mu=0, **kwargs):\n",
     "        mu = at.as_tensor_variable(mu)\n",
     "        return super().dist([mu], **kwargs)\n",
     "\n",
-    "\n",
     "    def logp(self, value):\n",
     "        mu = self.mu\n",
     "        return beta_logp(value - mu)\n",