Fix pre-commit errors: Replace .format() with f-strings and list comprehensions with generators

Author: fonnesbeck

examples/case_studies/factor_analysis.ipynb

@@ -307,7 +307,7 @@
    "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\");"
    ]
@@ -484,7 +484,7 @@
     "\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\");"
    ]
@@ -704,7 +704,7 @@
     "\n",
     "ax = az.plot_kde(\n",
     "    trace.posterior[\"W\"].sel(**col_selection).values,\n",
-    "    label=\"MCMC posterior for the explicit model\".format(0),\n",
+    "    label=f\"MCMC posterior for the explicit model\",\n",
     "    plot_kwargs={\"color\": f\"C{1}\"},\n",
     ")\n",
     "\n",

examples/case_studies/factor_analysis.myst.md

@@ -130,7 +130,7 @@ At this point, there are already several warnings regarding failed convergence c
 ```{code-cell} ipython3
 for i in trace.posterior.chain.values:
     samples = trace.posterior["W"].sel(chain=i, observed_columns=3, latent_columns=1)
-    plt.plot(samples, label="Chain {}".format(i + 1))
+    plt.plot(samples, label=f"Chain {i + 1}")
     plt.axhline(samples.mean(), color=f"C{i}")
 plt.legend(ncol=4, loc="upper center", fontsize=12, frameon=True), plt.xlabel("Sample");
 ```
@@ -198,7 +198,7 @@ with pm.Model(coords=coords) as PPCA_identified:
 
 for i in range(4):
     samples = trace.posterior["W"].sel(chain=i, observed_columns=3, latent_columns=1)
-    plt.plot(samples, label="Chain {}".format(i + 1))
+    plt.plot(samples, label=f"Chain {i + 1}")
 
 plt.legend(ncol=4, loc="lower center", fontsize=8), plt.xlabel("Sample");
 ```
@@ -253,7 +253,7 @@ col_selection = dict(observed_columns=3, latent_columns=1)
 
 ax = az.plot_kde(
     trace.posterior["W"].sel(**col_selection).values,
-    label="MCMC posterior for the explicit model".format(0),
+    label=f"MCMC posterior for the explicit model",
     plot_kwargs={"color": f"C{1}"},
 )
 

examples/case_studies/probabilistic_matrix_factorization.ipynb

@@ -1135,7 +1135,7 @@
     "    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/case_studies/probabilistic_matrix_factorization.myst.md

@@ -537,7 +537,7 @@ for name in baseline_methods:
     Method = baseline_methods[name]
     method = Method(train)
     baselines[name] = method.rmse(test)
-    print("{} RMSE:\t{:.5f}".format(method, baselines[name]))
+    print(f"{method} RMSE:\t{baselines[name]:.5f}")
 ```
 
 As expected: the uniform random baseline is the worst by far, the global mean baseline is next best, and the mean of means method is our best baseline. Now let's see how PMF stacks up.

examples/gaussian_processes/GP-Heteroskedastic.ipynb

@@ -147,7 +147,7 @@
     "    return ℓ_μ, ℓ_σ\n",
     "\n",
     "\n",
-    "ℓ_μ, ℓ_σ = [stat for stat in get_ℓ_prior(X_)]"
+    "ℓ_μ, ℓ_σ = (stat for stat in get_ℓ_prior(X_))"
    ]
   },
   {
@@ -1036,7 +1036,7 @@
     "    return np.hstack([np.tile(x, (2, 1)), np.vstack([np.zeros(x.shape), np.ones(x.shape)])])\n",
     "\n",
     "\n",
-    "Xu_c, X_obs_c, Xnew_c = [add_coreg_idx(x) for x in [Xu, X_obs, Xnew]]\n",
+    "Xu_c, X_obs_c, Xnew_c = (add_coreg_idx(x) for x in [Xu, X_obs, Xnew])\n",
     "\n",
     "with pm.Model() as model_htsc:\n",
     "    ℓ = pm.InverseGamma(\"ℓ\", mu=ℓ_μ, sigma=ℓ_σ)\n",

examples/gaussian_processes/GP-Heteroskedastic.myst.md

@@ -87,7 +87,7 @@ def get_ℓ_prior(points):
     return ℓ_μ, ℓ_σ
 
 
-ℓ_μ, ℓ_σ = [stat for stat in get_ℓ_prior(X_)]
+ℓ_μ, ℓ_σ = (stat for stat in get_ℓ_prior(X_))
 ```
 
 ```{code-cell} ipython3
@@ -388,7 +388,7 @@ def add_coreg_idx(x):
     return np.hstack([np.tile(x, (2, 1)), np.vstack([np.zeros(x.shape), np.ones(x.shape)])])
 
 
-Xu_c, X_obs_c, Xnew_c = [add_coreg_idx(x) for x in [Xu, X_obs, Xnew]]
+Xu_c, X_obs_c, Xnew_c = (add_coreg_idx(x) for x in [Xu, X_obs, Xnew])
 
 with pm.Model() as model_htsc:
     ℓ = pm.InverseGamma("ℓ", mu=ℓ_μ, sigma=ℓ_σ)

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

@@ -203,7 +203,7 @@
     "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-hierarchical-binomial-model.myst.md

@@ -159,7 +159,7 @@ ax.set_ylabel(r"$\log(\alpha+\beta)$", fontsize=16)
 ix_z, ix_x = np.unravel_index(np.argmax(surface, axis=None), surface.shape)
 ax.scatter([X[0, ix_x]], [Z[ix_z, 0]], color="red")
 
-text = r"$({a},{b})$".format(a=np.round(X[0, ix_x], 2), b=np.round(Z[ix_z, 0], 2))
+text = fr"$({np.round(X[0, ix_x], 2)},{np.round(Z[ix_z, 0], 2)})$"
 
 ax.annotate(
     text,

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

@@ -245,7 +245,7 @@
     "    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/generalized_linear_models/GLM-robust-with-outlier-detection.myst.md

@@ -113,7 +113,7 @@ dfhogg = pd.DataFrame(
     columns=["id", "x", "y", "sigma_y", "sigma_x", "rho_xy"],
 )
 
-dfhogg["id"] = dfhogg["id"].apply(lambda x: "p{}".format(int(x)))
+dfhogg["id"] = dfhogg["id"].apply(lambda x: f"p{int(x)}")
 dfhogg.set_index("id", inplace=True)
 dfhogg.head()
 ```