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LarsKueLarsKue
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turn all single-quoted strings into double-quoted strings
amend to trigger workflow
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‎examples/From_ABC_to_BayesFlow.ipynb‎

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@@ -479,7 +479,7 @@
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"\n",
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"# For BayesFlow devs: this ensures that the latest dev version can be found\n",
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"import sys\n",
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"sys.path.append('../')\n",
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"sys.path.append(\"../\")\n",
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"\n",
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"import bayesflow as bf"
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]
@@ -513,11 +513,11 @@
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"source": [
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"def prior_helper():\n",
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" \"\"\"The ABC prior returns a Parameter Object from pyabc which we convert to a dict.\"\"\"\n",
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" return dict(rate=prior.rvs()['rate'])\n",
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" return dict(rate=prior.rvs()[\"rate\"])\n",
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"\n",
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"def sim_helper(rate):\n",
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" \"\"\"The simulator returns a dict, we extract the output at the test times.\"\"\"\n",
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" temp = sim({'rate': rate})\n",
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" temp = sim({\"rate\": rate})\n",
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" xt_ind = np.searchsorted(temp[\"t\"], t_test_times) - 1\n",
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" obs = temp[\"X\"][:, 1][xt_ind]\n",
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" return dict(obs=obs)"
@@ -568,8 +568,8 @@
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],
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"source": [
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"adapter = bf.approximators.ContinuousApproximator.build_adapter(\n",
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" inference_variables='rate',\n",
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" inference_conditions='obs',\n",
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" inference_variables=\"rate\",\n",
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" inference_conditions=\"obs\",\n",
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" summary_variables=None\n",
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")\n",
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"adapter"
@@ -665,25 +665,25 @@
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"output_type": "stream",
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"text": [
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"Epoch 1/10\n",
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"\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 62ms/step - loss: 0.4428 - loss/inference_loss: 0.4428 - val_loss: 0.4605 - val_loss/inference_loss: 0.4605\n",
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"\u001B[1m100/100\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m6s\u001B[0m 62ms/step - loss: 0.4428 - loss/inference_loss: 0.4428 - val_loss: 0.4605 - val_loss/inference_loss: 0.4605\n",
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"Epoch 2/10\n",
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"\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 64ms/step - loss: 0.3700 - loss/inference_loss: 0.3700 - val_loss: 0.4467 - val_loss/inference_loss: 0.4467\n",
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"\u001B[1m100/100\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m6s\u001B[0m 64ms/step - loss: 0.3700 - loss/inference_loss: 0.3700 - val_loss: 0.4467 - val_loss/inference_loss: 0.4467\n",
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"Epoch 3/10\n",
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"\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 68ms/step - loss: 0.3458 - loss/inference_loss: 0.3458 - val_loss: 0.3627 - val_loss/inference_loss: 0.3627\n",
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"\u001B[1m100/100\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m7s\u001B[0m 68ms/step - loss: 0.3458 - loss/inference_loss: 0.3458 - val_loss: 0.3627 - val_loss/inference_loss: 0.3627\n",
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"Epoch 4/10\n",
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"\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 70ms/step - loss: 0.3771 - loss/inference_loss: 0.3771 - val_loss: 0.3637 - val_loss/inference_loss: 0.3637\n",
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"\u001B[1m100/100\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m7s\u001B[0m 70ms/step - loss: 0.3771 - loss/inference_loss: 0.3771 - val_loss: 0.3637 - val_loss/inference_loss: 0.3637\n",
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"Epoch 5/10\n",
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"\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 69ms/step - loss: 0.3729 - loss/inference_loss: 0.3729 - val_loss: 0.2138 - val_loss/inference_loss: 0.2138\n",
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"\u001B[1m100/100\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m7s\u001B[0m 69ms/step - loss: 0.3729 - loss/inference_loss: 0.3729 - val_loss: 0.2138 - val_loss/inference_loss: 0.2138\n",
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"Epoch 6/10\n",
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"\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 66ms/step - loss: 0.3567 - loss/inference_loss: 0.3567 - val_loss: 0.2888 - val_loss/inference_loss: 0.2888\n",
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"\u001B[1m100/100\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m7s\u001B[0m 66ms/step - loss: 0.3567 - loss/inference_loss: 0.3567 - val_loss: 0.2888 - val_loss/inference_loss: 0.2888\n",
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"Epoch 7/10\n",
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"\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 62ms/step - loss: 0.4077 - loss/inference_loss: 0.4077 - val_loss: 0.3235 - val_loss/inference_loss: 0.3235\n",
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"\u001B[1m100/100\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m6s\u001B[0m 62ms/step - loss: 0.4077 - loss/inference_loss: 0.4077 - val_loss: 0.3235 - val_loss/inference_loss: 0.3235\n",
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"Epoch 8/10\n",
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"\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 61ms/step - loss: 0.4124 - loss/inference_loss: 0.4124 - val_loss: 0.3256 - val_loss/inference_loss: 0.3256\n",
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"\u001B[1m100/100\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m6s\u001B[0m 61ms/step - loss: 0.4124 - loss/inference_loss: 0.4124 - val_loss: 0.3256 - val_loss/inference_loss: 0.3256\n",
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"Epoch 9/10\n",
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"\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 61ms/step - loss: 0.3960 - loss/inference_loss: 0.3960 - val_loss: 0.2767 - val_loss/inference_loss: 0.2767\n",
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"\u001B[1m100/100\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m6s\u001B[0m 61ms/step - loss: 0.3960 - loss/inference_loss: 0.3960 - val_loss: 0.2767 - val_loss/inference_loss: 0.2767\n",
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"Epoch 10/10\n",
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"\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 60ms/step - loss: 0.4217 - loss/inference_loss: 0.4217 - val_loss: 0.3482 - val_loss/inference_loss: 0.3482\n"
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"\u001B[1m100/100\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m6s\u001B[0m 60ms/step - loss: 0.4217 - loss/inference_loss: 0.4217 - val_loss: 0.3482 - val_loss/inference_loss: 0.3482\n"
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]
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}
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],
@@ -829,7 +829,7 @@
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"obs = observations[\"X\"][:, 1][xt_ind]\n",
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"\n",
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"# Obtain 1000 posterior samples\n",
832-
"samples = workflow.sample(conditions={'obs': [obs]}, num_samples=num_samples)"
832+
"samples = workflow.sample(conditions={\"obs\": [obs]}, num_samples=num_samples)"
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]
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},
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{
@@ -881,9 +881,9 @@
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"source": [
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"# abc gives us weighted samples, we resample them to get comparable samples\n",
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"df, w = abc_history.get_distribution()\n",
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"abc_samples = weighted_statistics.resample(df['rate'].values, w, 1000)\n",
884+
"abc_samples = weighted_statistics.resample(df[\"rate\"].values, w, 1000)\n",
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"\n",
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"f = bf.diagnostics.plots.pairs_posterior({'rate': abc_samples}, targets=np.array([true_rate]))"
886+
"f = bf.diagnostics.plots.pairs_posterior({\"rate\": abc_samples}, targets=np.array([true_rate]))"
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]
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},
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{

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