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MarcoGorelliMarcoGorelli
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CLN blacken first 15 notebooks (#4148)
* blacken first 15 files * ignore black for data block * turn off black for block in GLM-hierarchical-binominal-model
1 parent 985099d commit bc44cb1

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-704
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‎docs/source/notebooks/AR.ipynb

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@@ -53,7 +53,7 @@
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"%config InlineBackend.figure_format = 'retina'\n",
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"RANDOM_SEED = 8927\n",
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"np.random.seed(RANDOM_SEED)\n",
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"az.style.use('arviz-darkgrid')"
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"az.style.use(\"arviz-darkgrid\")"
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]
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},
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{

‎docs/source/notebooks/BEST.ipynb

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@@ -27,7 +27,7 @@
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"import pandas as pd\n",
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"import pymc3 as pm\n",
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"\n",
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"print(f'Running on PyMC3 v{pm.__version__}')"
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"print(f\"Running on PyMC3 v{pm.__version__}\")"
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]
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},
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{
@@ -37,7 +37,7 @@
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"outputs": [],
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"source": [
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"%config InlineBackend.figure_format = 'retina'\n",
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"az.style.use('arviz-darkgrid')"
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"az.style.use(\"arviz-darkgrid\")"
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]
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},
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{
@@ -94,18 +94,22 @@
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}
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],
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"source": [
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"# fmt: off\n",
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"drug = (101,100,102,104,102,97,105,105,98,101,100,123,105,103,100,95,102,106,\n",
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" 109,102,82,102,100,102,102,101,102,102,103,103,97,97,103,101,97,104,\n",
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" 96,103,124,101,101,100,101,101,104,100,101)\n",
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"placebo = (99,101,100,101,102,100,97,101,104,101,102,102,100,105,88,101,100,\n",
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" 104,100,100,100,101,102,103,97,101,101,100,101,99,101,100,100,\n",
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" 101,100,99,101,100,102,99,100,99)\n",
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"# fmt: on\n",
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"\n",
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"y1 = np.array(drug)\n",
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"y2 = np.array(placebo)\n",
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"y = pd.DataFrame(dict(value=np.r_[y1, y2], group=np.r_[['drug']*len(drug), ['placebo']*len(placebo)]))\n",
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"y = pd.DataFrame(\n",
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" dict(value=np.r_[y1, y2], group=np.r_[[\"drug\"] * len(drug), [\"placebo\"] * len(placebo)])\n",
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")\n",
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"\n",
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"y.hist('value', by='group', figsize=(12, 4));"
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"y.hist(\"value\", by=\"group\", figsize=(12, 4));"
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]
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},
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{
@@ -146,8 +150,8 @@
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"μ_s = y.value.std() * 2\n",
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"\n",
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"with pm.Model() as model:\n",
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" group1_mean = pm.Normal('group1_mean', mu=μ_m, sd=μ_s)\n",
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" group2_mean = pm.Normal('group2_mean', mu=μ_m, sd=μ_s)"
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" group1_mean = pm.Normal(\"group1_mean\", mu=μ_m, sd=μ_s)\n",
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" group2_mean = pm.Normal(\"group2_mean\", mu=μ_m, sd=μ_s)"
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]
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},
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{
@@ -176,8 +180,8 @@
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"σ_high = 10\n",
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"\n",
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"with model:\n",
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" group1_std = pm.Uniform('group1_std', lower=σ_low, upper=σ_high)\n",
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" group2_std = pm.Uniform('group2_std', lower=σ_low, upper=σ_high)"
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" group1_std = pm.Uniform(\"group1_std\", lower=σ_low, upper=σ_high)\n",
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" group2_std = pm.Uniform(\"group2_std\", lower=σ_low, upper=σ_high)"
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]
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},
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{
@@ -210,9 +214,9 @@
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],
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"source": [
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"with model:\n",
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" ν = pm.Exponential('ν_minus_one', 1/29.) + 1\n",
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" ν = pm.Exponential(\"ν_minus_one\", 1 / 29.0) + 1\n",
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"\n",
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"pm.kdeplot(np.random.exponential(30, size=10000), fill_kwargs={'alpha': 0.5});"
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"pm.kdeplot(np.random.exponential(30, size=10000), fill_kwargs={\"alpha\": 0.5});"
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]
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},
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{
@@ -234,11 +238,11 @@
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"outputs": [],
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"source": [
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"with model:\n",
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" λ1 = group1_std**-2\n",
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" λ2 = group2_std**-2\n",
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" λ1 = group1_std ** -2\n",
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" λ2 = group2_std ** -2\n",
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"\n",
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" group1 = pm.StudentT('drug', nu=ν, mu=group1_mean, lam=λ1, observed=y1)\n",
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" group2 = pm.StudentT('placebo', nu=ν, mu=group2_mean, lam=λ2, observed=y2)"
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" group1 = pm.StudentT(\"drug\", nu=ν, mu=group1_mean, lam=λ1, observed=y1)\n",
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" group2 = pm.StudentT(\"placebo\", nu=ν, mu=group2_mean, lam=λ2, observed=y2)"
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]
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},
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{
@@ -262,10 +266,11 @@
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"outputs": [],
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"source": [
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"with model:\n",
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" diff_of_means = pm.Deterministic('difference of means', group1_mean - group2_mean)\n",
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" diff_of_stds = pm.Deterministic('difference of stds', group1_std - group2_std)\n",
267-
" effect_size = pm.Deterministic('effect size', \n",
268-
" diff_of_means / np.sqrt((group1_std**2 + group2_std**2) / 2))"
269+
" diff_of_means = pm.Deterministic(\"difference of means\", group1_mean - group2_mean)\n",
270+
" diff_of_stds = pm.Deterministic(\"difference of stds\", group1_std - group2_std)\n",
271+
" effect_size = pm.Deterministic(\n",
272+
" \"effect size\", diff_of_means / np.sqrt((group1_std ** 2 + group2_std ** 2) / 2)\n",
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" )"
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]
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},
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{
@@ -369,8 +374,11 @@
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}
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],
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"source": [
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"pm.plot_posterior(trace, var_names=['group1_mean','group2_mean', 'group1_std', 'group2_std', 'ν_minus_one'],\n",
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" color='#87ceeb');"
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"pm.plot_posterior(\n",
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" trace,\n",
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" var_names=[\"group1_mean\", \"group2_mean\", \"group1_std\", \"group2_std\", \"ν_minus_one\"],\n",
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" color=\"#87ceeb\",\n",
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");"
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]
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},
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{
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}
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],
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"source": [
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"pm.plot_posterior(trace, var_names=['difference of means','difference of stds', 'effect size'],\n",
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" ref_val=0,\n",
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" color='#87ceeb');"
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"pm.plot_posterior(\n",
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" trace,\n",
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" var_names=[\"difference of means\", \"difference of stds\", \"effect size\"],\n",
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" ref_val=0,\n",
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" color=\"#87ceeb\",\n",
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");"
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]
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},
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{
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}
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],
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"source": [
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"pm.forestplot(trace, var_names=['group1_mean',\n",
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" 'group2_mean']);"
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"pm.forestplot(trace, var_names=[\"group1_mean\", \"group2_mean\"]);"
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]
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},
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{
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}
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],
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"source": [
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"pm.forestplot(trace, var_names=['group1_std',\n",
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" 'group2_std',\n",
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" 'ν_minus_one']);"
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"pm.forestplot(trace, var_names=[\"group1_std\", \"group2_std\", \"ν_minus_one\"]);"
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]
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{
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}
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],
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"source": [
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"pm.summary(trace, varnames=['difference of means', 'difference of stds', 'effect size'])"
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"pm.summary(trace, varnames=[\"difference of means\", \"difference of stds\", \"effect size\"])"
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]
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},
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{

‎docs/source/notebooks/Bayes_factor.ipynb

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@@ -340,9 +340,7 @@
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}
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],
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"source": [
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"BF_smc = np.exp(\n",
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" traces[1].report.log_marginal_likelihood - traces[0].report.log_marginal_likelihood\n",
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")\n",
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"BF_smc = np.exp(traces[1].report.log_marginal_likelihood - traces[0].report.log_marginal_likelihood)\n",
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"np.round(BF_smc)"
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]
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},

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