bump nbqa and pyupgrade

Author: MarcoGorelli

.pre-commit-config.yaml

@@ -1,13 +1,13 @@
 repos:
 - repo: https://github.com/nbQA-dev/nbQA
-  rev: 0.6.0
+  rev: 0.10.0
   hooks:
     - id: nbqa-black
       additional_dependencies: [black==20.8b1]
     - id: nbqa-isort
       additional_dependencies: [isort==5.6.4]
     - id: nbqa-pyupgrade
-      additional_dependencies: [pyupgrade==2.7.4]
+      additional_dependencies: [pyupgrade==2.19.0]
       args: [--py37-plus]
 - repo: https://github.com/MarcoGorelli/check-execution-order
   rev: v0.1.1

examples/case_studies/blackbox_external_likelihood.ipynb

@@ -688,7 +688,7 @@
     "for i, samples in enumerate([samples_pymc3, samples_pymc3_2, samples_pymc3_3]):\n",
     "    # get maximum chain autocorrelartion length\n",
     "    autocorrlen = int(np.max(emcee.autocorr.integrated_time(samples, c=3)))\n",
-    "    print(\"Auto-correlation length ({}): {}\".format(labels[i], autocorrlen))\n",
+    "    print(f\"Auto-correlation length ({labels[i]}): {autocorrlen}\")\n",
     "\n",
     "    if i == 0:\n",
     "        fig = corner.corner(\n",

examples/case_studies/conditional-autoregressive-model.ipynb

@@ -215,7 +215,7 @@
     "    dtype=theano.config.floatX,\n",
     ")\n",
     "\n",
-    "maxwz = max([sum(w) for w in weights])\n",
+    "maxwz = max(sum(w) for w in weights)\n",
     "N = len(weights)\n",
     "wmat = np.zeros((N, maxwz))\n",
     "amat = np.zeros((N, maxwz), dtype=\"int32\")\n",
@@ -1556,7 +1556,7 @@
     }
    ],
    "source": [
-    "maxwz = max([sum(w) for w in weights])\n",
+    "maxwz = max(sum(w) for w in weights)\n",
     "N = len(weights)\n",
     "wmat2 = np.zeros((N, N))\n",
     "amat2 = np.zeros((N, N), dtype=\"int32\")\n",

examples/case_studies/factor_analysis.ipynb

@@ -256,7 +256,7 @@
    "source": [
     "for i in trace.posterior.chain.values:\n",
     "    samples = trace.posterior[\"W\"].sel(chain=i, observed_columns=3, latent_columns=1)\n",
-    "    plt.plot(samples, label=\"Chain {}\".format(i + 1))\n",
+    "    plt.plot(samples, label=f\"Chain {i + 1}\")\n",
     "    plt.axhline(samples.mean(), color=f\"C{i}\")\n",
     "plt.legend(ncol=4, loc=\"upper center\", fontsize=12, frameon=True), plt.xlabel(\"Sample\");"
    ]
@@ -417,7 +417,7 @@
     "\n",
     "for i in range(4):\n",
     "    samples = trace.posterior[\"W\"].sel(chain=i, observed_columns=3, latent_columns=1)\n",
-    "    plt.plot(samples, label=\"Chain {}\".format(i + 1))\n",
+    "    plt.plot(samples, label=f\"Chain {i + 1}\")\n",
     "\n",
     "plt.legend(ncol=4, loc=\"lower center\", fontsize=8), plt.xlabel(\"Sample\");"
    ]
@@ -532,7 +532,7 @@
     "    mcmc_samples = trace.posterior[\"W\"].sel(chain=i, observed_columns=3, latent_columns=1)\n",
     "    az.plot_kde(\n",
     "        mcmc_samples,\n",
-    "        label=\"MCMC posterior for chain {}\".format(i + 1),\n",
+    "        label=f\"MCMC posterior for chain {i + 1}\",\n",
     "        plot_kwargs={\"color\": f\"C{i}\"},\n",
     "    )\n",
     "az.plot_kde(\n",

examples/case_studies/probabilistic_matrix_factorization.ipynb

@@ -1078,7 +1078,7 @@
     "    Method = baseline_methods[name]\n",
     "    method = Method(train)\n",
     "    baselines[name] = method.rmse(test)\n",
-    "    print(\"{} RMSE:\\t{:.5f}\".format(method, baselines[name]))"
+    "    print(f\"{method} RMSE:\\t{baselines[name]:.5f}\")"
    ]
   },
   {

examples/gaussian_processes/GP-MaunaLoa.ipynb

@@ -1137,7 +1137,7 @@
    ],
    "source": [
     "# display the results, dont show transformed parameter values\n",
-    "sorted([name + \":\" + str(mp[name]) for name in mp.keys() if not name.endswith(\"_\")])"
+    "sorted(name + \":\" + str(mp[name]) for name in mp.keys() if not name.endswith(\"_\"))"
    ]
   },
   {

examples/generalized_linear_models/GLM-hierarchical-binominal-model.ipynb

@@ -214,7 +214,7 @@
     "ix_z, ix_x = np.unravel_index(np.argmax(surface, axis=None), surface.shape)\n",
     "ax.scatter([X[0, ix_x]], [Z[ix_z, 0]], color=\"red\")\n",
     "\n",
-    "text = r\"$({a},{b})$\".format(a=np.round(X[0, ix_x], 2), b=np.round(Z[ix_z, 0], 2))\n",
+    "text = fr\"$({np.round(X[0, ix_x], 2)},{np.round(Z[ix_z, 0], 2)})$\"\n",
     "\n",
     "ax.annotate(\n",
     "    text,\n",

examples/generalized_linear_models/GLM-logistic.ipynb

@@ -107,7 +107,7 @@
     "    with model:\n",
     "        ax = az.plot_trace(\n",
     "            traces[-retain:],\n",
-    "            lines=tuple([(k, {}, v[\"mean\"]) for k, v in az.summary(traces[-retain:]).iterrows()]),\n",
+    "            lines=tuple((k, {}, v[\"mean\"]) for k, v in az.summary(traces[-retain:]).iterrows()),\n",
     "        )\n",
     "\n",
     "        for i, mn in enumerate(az.summary(traces[-retain:])[\"mean\"]):\n",
@@ -130,7 +130,7 @@
     "    Create a polynomial modelspec string for patsy\n",
     "    \"\"\"\n",
     "    return (\n",
-    "        \"income ~ educ + hours + age \" + \" \".join([f\"+ np.power(age,{j})\" for j in range(2, k + 1)])\n",
+    "        \"income ~ educ + hours + age \" + \" \".join(f\"+ np.power(age,{j})\" for j in range(2, k + 1))\n",
     "    ).strip()"
    ]
   },
@@ -1088,7 +1088,7 @@
    "source": [
     "lb, ub = np.percentile(b, 2.5), np.percentile(b, 97.5)\n",
     "\n",
-    "print(\"P({:.3f} < O.R. < {:.3f}) = 0.95\".format(np.exp(lb), np.exp(ub)))"
+    "print(f\"P({np.exp(lb):.3f} < O.R. < {np.exp(ub):.3f}) = 0.95\")"
    ]
   },
   {

examples/generalized_linear_models/GLM-model-selection.ipynb

@@ -192,7 +192,7 @@
     "\n",
     "    ax = az.plot_trace(\n",
     "        traces[-retain:],\n",
-    "        lines=tuple([(k, {}, v[\"mean\"]) for k, v in az.summary(traces[-retain:]).iterrows()]),\n",
+    "        lines=tuple((k, {}, v[\"mean\"]) for k, v in az.summary(traces[-retain:]).iterrows()),\n",
     "    )\n",
     "\n",
     "    for i, mn in enumerate(az.summary(traces[-retain:])[\"mean\"]):\n",
@@ -214,7 +214,7 @@
     "    Convenience function:\n",
     "    Create a polynomial modelspec string for patsy\n",
     "    \"\"\"\n",
-    "    return (\"y ~ 1 + x \" + \" \".join([f\"+ np.power(x,{j})\" for j in range(2, k + 1)])).strip()\n",
+    "    return (\"y ~ 1 + x \" + \" \".join(f\"+ np.power(x,{j})\" for j in range(2, k + 1))).strip()\n",
     "\n",
     "\n",
     "def run_models(df, upper_order=5):\n",

examples/generalized_linear_models/GLM-robust-with-outlier-detection.ipynb

@@ -297,7 +297,7 @@
     "    columns=[\"id\", \"x\", \"y\", \"sigma_y\", \"sigma_x\", \"rho_xy\"],\n",
     ")\n",
     "\n",
-    "dfhogg[\"id\"] = dfhogg[\"id\"].apply(lambda x: \"p{}\".format(int(x)))\n",
+    "dfhogg[\"id\"] = dfhogg[\"id\"].apply(lambda x: f\"p{int(x)}\")\n",
     "dfhogg.set_index(\"id\", inplace=True)\n",
     "dfhogg.head()"
    ]

examples/ode_models/ODE_with_manual_gradients.ipynb

@@ -100,7 +100,7 @@
     "        self._y0 = y0\n",
     "\n",
     "    def simulate(self, parameters, times):\n",
-    "        alpha, beta, gamma, delta, Xt0, Yt0 = [x for x in parameters]\n",
+    "        alpha, beta, gamma, delta, Xt0, Yt0 = (x for x in parameters)\n",
     "\n",
     "        def rhs(y, t, p):\n",
     "            X, Y = y\n",
@@ -165,7 +165,7 @@
     "        return self._simulate(parameters, times, True)\n",
     "\n",
     "    def _simulate(self, parameters, times, sensitivities):\n",
-    "        alpha, beta, gamma, delta, Xt0, Yt0 = [x for x in parameters]\n",
+    "        alpha, beta, gamma, delta, Xt0, Yt0 = (x for x in parameters)\n",
     "\n",
     "        def r(y, t, p):\n",
     "            X, Y = y\n",
@@ -954,7 +954,7 @@
     "        self._times = times\n",
     "\n",
     "    def _simulate(self, parameters, times):\n",
-    "        a, b, c = [float(x) for x in parameters]\n",
+    "        a, b, c = (float(x) for x in parameters)\n",
     "\n",
     "        def rhs(y, t, p):\n",
     "            V, R = y\n",

examples/samplers/MLDA_gravity_surveying.ipynb

@@ -2369,7 +2369,7 @@
     "        y_offset = trace.get_values(\"theta\").max() + 0.25 * (\n",
     "            trace.get_values(\"theta\").max() - trace.get_values(\"theta\").min()\n",
     "        )\n",
-    "        plt.text(x_offset, y_offset, \"{} Sampler\".format(method_names[i]))"
+    "        plt.text(x_offset, y_offset, f\"{method_names[i]} Sampler\")"
    ]
   },
   {

examples/variational_inference/bayesian_neural_network_advi.ipynb

@@ -533,7 +533,7 @@
     }
    ],
    "source": [
-    "print(\"Accuracy = {}%\".format((Y_test == pred).mean() * 100))"
+    "print(f\"Accuracy = {(Y_test == pred).mean() * 100}%\")"
    ]
   },
   {

examples/variational_inference/lda-advi-aevb.ipynb

@@ -154,14 +154,12 @@
     "n_samples_te = tf.shape[0] - n_samples_tr\n",
     "docs_tr = tf[:n_samples_tr, :]\n",
     "docs_te = tf[n_samples_tr:, :]\n",
-    "print(\"Number of docs for training = {}\".format(docs_tr.shape[0]))\n",
-    "print(\"Number of docs for test = {}\".format(docs_te.shape[0]))\n",
+    "print(f\"Number of docs for training = {docs_tr.shape[0]}\")\n",
+    "print(f\"Number of docs for test = {docs_te.shape[0]}\")\n",
     "\n",
     "n_tokens = np.sum(docs_tr[docs_tr.nonzero()])\n",
     "print(f\"Number of tokens in training set = {n_tokens}\")\n",
-    "print(\n",
-    "    \"Sparsity = {}\".format(len(docs_tr.nonzero()[0]) / float(docs_tr.shape[0] * docs_tr.shape[1]))\n",
-    ")"
+    "print(f\"Sparsity = {len(docs_tr.nonzero()[0]) / float(docs_tr.shape[0] * docs_tr.shape[1])}\")"
    ]
   },
   {
@@ -546,7 +544,7 @@
     "    for i in range(len(beta)):\n",
     "        print(\n",
     "            (\"Topic #%d: \" % i)\n",
-    "            + \" \".join([feature_names[j] for j in beta[i].argsort()[: -n_top_words - 1 : -1]])\n",
+    "            + \" \".join(feature_names[j] for j in beta[i].argsort()[: -n_top_words - 1 : -1])\n",
     "        )\n",
     "\n",
     "\n",