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Update pm.compile_pymc -> pm.compile 
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‎notebooks/Exponential Trend Smoothing.ipynb

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@@ -188,7 +188,7 @@
188188
"\n",
189189
" # For the forecasts we need a function that lets us take draws from the distribution. We'll get the mean\n",
190190
" # and covariance from samples by calling it a lot of times.\n",
191-
" f_forecast = pm.compile_pymc(pm.inputvars(obs_forecast), obs_forecast, mode=\"JAX\")\n",
191+
" f_forecast = pm.compile(pm.inputvars(obs_forecast), obs_forecast, mode=\"JAX\")\n",
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"\n",
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" return f_ets, f_forecast\n",
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"\n",
@@ -863,17 +863,17 @@
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"</pre>\n"
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],
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"text/plain": [
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"\u001b[3m Model Requirements \u001b[0m\n",
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"\u001B[3m Model Requirements \u001B[0m\n",
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" \n",
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" \u001b[1m \u001b[0m\u001b[1mVariable \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mShape\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mConstraints \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mDimensions\u001b[0m\u001b[1m \u001b[0m \n",
868+
" \u001B[1m \u001B[0m\u001B[1mVariable \u001B[0m\u001B[1m \u001B[0m \u001B[1m \u001B[0m\u001B[1mShape\u001B[0m\u001B[1m \u001B[0m \u001B[1m \u001B[0m\u001B[1mConstraints \u001B[0m\u001B[1m \u001B[0m \u001B[1m \u001B[0m\u001B[1mDimensions\u001B[0m\u001B[1m \u001B[0m \n",
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" ──────────────────────────────────────────────────── \n",
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" initial_level \u001b[3;35mNone\u001b[0m \u001b[3;35mNone\u001b[0m \n",
871-
" alpha \u001b[3;35mNone\u001b[0m \u001b[1;36m0\u001b[0m < alpha < \u001b[1;36m1\u001b[0m \u001b[3;35mNone\u001b[0m \n",
872-
" sigma_state \u001b[3;35mNone\u001b[0m Positive \u001b[3;35mNone\u001b[0m \n",
870+
" initial_level \u001B[3;35mNone\u001B[0m \u001B[3;35mNone\u001B[0m \n",
871+
" alpha \u001B[3;35mNone\u001B[0m \u001B[1;36m0\u001B[0m < alpha < \u001B[1;36m1\u001B[0m \u001B[3;35mNone\u001B[0m \n",
872+
" sigma_state \u001B[3;35mNone\u001B[0m Positive \u001B[3;35mNone\u001B[0m \n",
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" \n",
874-
"\u001b[2;3m These parameters should be assigned priors inside a \u001b[0m\n",
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"\u001b[2;3m PyMC model block before calling the \u001b[0m\n",
876-
"\u001b[2;3m build_statespace_graph method. \u001b[0m\n"
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"\u001B[2;3m These parameters should be assigned priors inside a \u001B[0m\n",
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"\u001B[2;3m PyMC model block before calling the \u001B[0m\n",
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"\u001B[2;3m build_statespace_graph method. \u001B[0m\n"
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]
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},
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"metadata": {},
@@ -1394,19 +1394,19 @@
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"</pre>\n"
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],
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"text/plain": [
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"\u001b[3m Model Requirements \u001b[0m\n",
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"\u001B[3m Model Requirements \u001B[0m\n",
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" \n",
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" \u001b[1m \u001b[0m\u001b[1mVariable \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mShape\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mConstraints \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mDimensions\u001b[0m\u001b[1m \u001b[0m \n",
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" \u001B[1m \u001B[0m\u001B[1mVariable \u001B[0m\u001B[1m \u001B[0m \u001B[1m \u001B[0m\u001B[1mShape\u001B[0m\u001B[1m \u001B[0m \u001B[1m \u001B[0m\u001B[1mConstraints \u001B[0m\u001B[1m \u001B[0m \u001B[1m \u001B[0m\u001B[1mDimensions\u001B[0m\u001B[1m \u001B[0m \n",
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" ──────────────────────────────────────────────────── \n",
1401-
" initial_level \u001b[3;35mNone\u001b[0m \u001b[3;35mNone\u001b[0m \n",
1402-
" initial_trend \u001b[3;35mNone\u001b[0m \u001b[3;35mNone\u001b[0m \n",
1403-
" alpha \u001b[3;35mNone\u001b[0m \u001b[1;36m0\u001b[0m < alpha < \u001b[1;36m1\u001b[0m \u001b[3;35mNone\u001b[0m \n",
1404-
" beta \u001b[3;35mNone\u001b[0m \u001b[1;36m0\u001b[0m < beta < \u001b[1;36m1\u001b[0m \u001b[3;35mNone\u001b[0m \n",
1405-
" sigma_state \u001b[3;35mNone\u001b[0m Positive \u001b[3;35mNone\u001b[0m \n",
1401+
" initial_level \u001B[3;35mNone\u001B[0m \u001B[3;35mNone\u001B[0m \n",
1402+
" initial_trend \u001B[3;35mNone\u001B[0m \u001B[3;35mNone\u001B[0m \n",
1403+
" alpha \u001B[3;35mNone\u001B[0m \u001B[1;36m0\u001B[0m < alpha < \u001B[1;36m1\u001B[0m \u001B[3;35mNone\u001B[0m \n",
1404+
" beta \u001B[3;35mNone\u001B[0m \u001B[1;36m0\u001B[0m < beta < \u001B[1;36m1\u001B[0m \u001B[3;35mNone\u001B[0m \n",
1405+
" sigma_state \u001B[3;35mNone\u001B[0m Positive \u001B[3;35mNone\u001B[0m \n",
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" \n",
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"\u001b[2;3m These parameters should be assigned priors inside a \u001b[0m\n",
1408-
"\u001b[2;3m PyMC model block before calling the \u001b[0m\n",
1409-
"\u001b[2;3m build_statespace_graph method. \u001b[0m\n"
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"\u001B[2;3m These parameters should be assigned priors inside a \u001B[0m\n",
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"\u001B[2;3m PyMC model block before calling the \u001B[0m\n",
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"\u001B[2;3m build_statespace_graph method. \u001B[0m\n"
14101410
]
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},
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"metadata": {},
@@ -2044,20 +2044,20 @@
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"</pre>\n"
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],
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"text/plain": [
2047-
"\u001b[3m Model Requirements \u001b[0m\n",
2047+
"\u001B[3m Model Requirements \u001B[0m\n",
20482048
" \n",
2049-
" \u001b[1m \u001b[0m\u001b[1mVariable \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mShape\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mConstraints \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mDimensions\u001b[0m\u001b[1m \u001b[0m \n",
2049+
" \u001B[1m \u001B[0m\u001B[1mVariable \u001B[0m\u001B[1m \u001B[0m \u001B[1m \u001B[0m\u001B[1mShape\u001B[0m\u001B[1m \u001B[0m \u001B[1m \u001B[0m\u001B[1mConstraints \u001B[0m\u001B[1m \u001B[0m \u001B[1m \u001B[0m\u001B[1mDimensions\u001B[0m\u001B[1m \u001B[0m \n",
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" ──────────────────────────────────────────────────── \n",
2051-
" initial_level \u001b[3;35mNone\u001b[0m \u001b[3;35mNone\u001b[0m \n",
2052-
" initial_trend \u001b[3;35mNone\u001b[0m \u001b[3;35mNone\u001b[0m \n",
2053-
" alpha \u001b[3;35mNone\u001b[0m \u001b[1;36m0\u001b[0m < alpha < \u001b[1;36m1\u001b[0m \u001b[3;35mNone\u001b[0m \n",
2054-
" beta \u001b[3;35mNone\u001b[0m \u001b[1;36m0\u001b[0m < beta < \u001b[1;36m1\u001b[0m \u001b[3;35mNone\u001b[0m \n",
2055-
" phi \u001b[3;35mNone\u001b[0m \u001b[1;36m0\u001b[0m < phi < \u001b[1;36m1\u001b[0m \u001b[3;35mNone\u001b[0m \n",
2056-
" sigma_state \u001b[3;35mNone\u001b[0m Positive \u001b[3;35mNone\u001b[0m \n",
2051+
" initial_level \u001B[3;35mNone\u001B[0m \u001B[3;35mNone\u001B[0m \n",
2052+
" initial_trend \u001B[3;35mNone\u001B[0m \u001B[3;35mNone\u001B[0m \n",
2053+
" alpha \u001B[3;35mNone\u001B[0m \u001B[1;36m0\u001B[0m < alpha < \u001B[1;36m1\u001B[0m \u001B[3;35mNone\u001B[0m \n",
2054+
" beta \u001B[3;35mNone\u001B[0m \u001B[1;36m0\u001B[0m < beta < \u001B[1;36m1\u001B[0m \u001B[3;35mNone\u001B[0m \n",
2055+
" phi \u001B[3;35mNone\u001B[0m \u001B[1;36m0\u001B[0m < phi < \u001B[1;36m1\u001B[0m \u001B[3;35mNone\u001B[0m \n",
2056+
" sigma_state \u001B[3;35mNone\u001B[0m Positive \u001B[3;35mNone\u001B[0m \n",
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" \n",
2058-
"\u001b[2;3m These parameters should be assigned priors inside a \u001b[0m\n",
2059-
"\u001b[2;3m PyMC model block before calling the \u001b[0m\n",
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"\u001b[2;3m build_statespace_graph method. \u001b[0m\n"
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"\u001B[2;3m These parameters should be assigned priors inside a \u001B[0m\n",
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"\u001B[2;3m PyMC model block before calling the \u001B[0m\n",
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"\u001B[2;3m build_statespace_graph method. \u001B[0m\n"
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]
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},
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"metadata": {},
@@ -2664,19 +2664,19 @@
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"</pre>\n"
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],
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"text/plain": [
2667-
"\u001b[3m Model Requirements \u001b[0m\n",
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"\u001B[3m Model Requirements \u001B[0m\n",
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" \n",
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" \u001b[1m \u001b[0m\u001b[1mVariable \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mShape \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mConstraints \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1m Dimensions\u001b[0m\u001b[1m \u001b[0m \n",
2669+
" \u001B[1m \u001B[0m\u001B[1mVariable \u001B[0m\u001B[1m \u001B[0m \u001B[1m \u001B[0m\u001B[1mShape \u001B[0m\u001B[1m \u001B[0m \u001B[1m \u001B[0m\u001B[1mConstraints \u001B[0m\u001B[1m \u001B[0m \u001B[1m \u001B[0m\u001B[1m Dimensions\u001B[0m\u001B[1m \u001B[0m \n",
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" ──────────────────────────────────────────────────────────────────────────────────────────── \n",
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" initial_level \u001b[1m(\u001b[0m\u001b[1;36m2\u001b[0m,\u001b[1m)\u001b[0m \u001b[1m(\u001b[0m\u001b[32m'observed_state'\u001b[0m,\u001b[1m)\u001b[0m \n",
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" initial_trend \u001b[1m(\u001b[0m\u001b[1;36m2\u001b[0m,\u001b[1m)\u001b[0m \u001b[1m(\u001b[0m\u001b[32m'observed_state'\u001b[0m,\u001b[1m)\u001b[0m \n",
2673-
" alpha \u001b[1m(\u001b[0m\u001b[1;36m2\u001b[0m,\u001b[1m)\u001b[0m \u001b[1;36m0\u001b[0m < alpha < \u001b[1;36m1\u001b[0m \u001b[1m(\u001b[0m\u001b[32m'observed_state'\u001b[0m,\u001b[1m)\u001b[0m \n",
2674-
" beta \u001b[1m(\u001b[0m\u001b[1;36m2\u001b[0m,\u001b[1m)\u001b[0m \u001b[1;36m0\u001b[0m < beta < \u001b[1;36m1\u001b[0m \u001b[1m(\u001b[0m\u001b[32m'observed_state'\u001b[0m,\u001b[1m)\u001b[0m \n",
2675-
" phi \u001b[1m(\u001b[0m\u001b[1;36m2\u001b[0m,\u001b[1m)\u001b[0m \u001b[1;36m0\u001b[0m < phi < \u001b[1;36m1\u001b[0m \u001b[1m(\u001b[0m\u001b[32m'observed_state'\u001b[0m,\u001b[1m)\u001b[0m \n",
2676-
" state_cov \u001b[1m(\u001b[0m\u001b[1;36m2\u001b[0m, \u001b[1;36m2\u001b[0m\u001b[1m)\u001b[0m Positive Semi-definite \u001b[1m(\u001b[0m\u001b[32m'observed_state'\u001b[0m, \u001b[32m'observed_state_aux'\u001b[0m\u001b[1m)\u001b[0m \n",
2671+
" initial_level \u001B[1m(\u001B[0m\u001B[1;36m2\u001B[0m,\u001B[1m)\u001B[0m \u001B[1m(\u001B[0m\u001B[32m'observed_state'\u001B[0m,\u001B[1m)\u001B[0m \n",
2672+
" initial_trend \u001B[1m(\u001B[0m\u001B[1;36m2\u001B[0m,\u001B[1m)\u001B[0m \u001B[1m(\u001B[0m\u001B[32m'observed_state'\u001B[0m,\u001B[1m)\u001B[0m \n",
2673+
" alpha \u001B[1m(\u001B[0m\u001B[1;36m2\u001B[0m,\u001B[1m)\u001B[0m \u001B[1;36m0\u001B[0m < alpha < \u001B[1;36m1\u001B[0m \u001B[1m(\u001B[0m\u001B[32m'observed_state'\u001B[0m,\u001B[1m)\u001B[0m \n",
2674+
" beta \u001B[1m(\u001B[0m\u001B[1;36m2\u001B[0m,\u001B[1m)\u001B[0m \u001B[1;36m0\u001B[0m < beta < \u001B[1;36m1\u001B[0m \u001B[1m(\u001B[0m\u001B[32m'observed_state'\u001B[0m,\u001B[1m)\u001B[0m \n",
2675+
" phi \u001B[1m(\u001B[0m\u001B[1;36m2\u001B[0m,\u001B[1m)\u001B[0m \u001B[1;36m0\u001B[0m < phi < \u001B[1;36m1\u001B[0m \u001B[1m(\u001B[0m\u001B[32m'observed_state'\u001B[0m,\u001B[1m)\u001B[0m \n",
2676+
" state_cov \u001B[1m(\u001B[0m\u001B[1;36m2\u001B[0m, \u001B[1;36m2\u001B[0m\u001B[1m)\u001B[0m Positive Semi-definite \u001B[1m(\u001B[0m\u001B[32m'observed_state'\u001B[0m, \u001B[32m'observed_state_aux'\u001B[0m\u001B[1m)\u001B[0m \n",
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" \n",
2678-
"\u001b[2;3m These parameters should be assigned priors inside a PyMC model block before calling the \u001b[0m\n",
2679-
"\u001b[2;3m build_statespace_graph method. \u001b[0m\n"
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"\u001B[2;3m These parameters should be assigned priors inside a PyMC model block before calling the \u001B[0m\n",
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"\u001B[2;3m build_statespace_graph method. \u001B[0m\n"
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]
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"metadata": {},
@@ -3633,21 +3633,21 @@
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"</pre>\n"
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"text/plain": [
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"\u001b[3m Model Requirements \u001b[0m\n",
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"\u001B[3m Model Requirements \u001B[0m\n",
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" \n",
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" \u001b[1m \u001b[0m\u001b[1mVariable \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mShape\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mConstraints \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1m Dimensions\u001b[0m\u001b[1m \u001b[0m \n",
3638+
" \u001B[1m \u001B[0m\u001B[1mVariable \u001B[0m\u001B[1m \u001B[0m \u001B[1m \u001B[0m\u001B[1mShape\u001B[0m\u001B[1m \u001B[0m \u001B[1m \u001B[0m\u001B[1mConstraints \u001B[0m\u001B[1m \u001B[0m \u001B[1m \u001B[0m\u001B[1m Dimensions\u001B[0m\u001B[1m \u001B[0m \n",
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" ────────────────────────────────────────────────────────────── \n",
3640-
" initial_level \u001b[3;35mNone\u001b[0m \u001b[3;35mNone\u001b[0m \n",
3641-
" initial_trend \u001b[3;35mNone\u001b[0m \u001b[3;35mNone\u001b[0m \n",
3642-
" initial_seasonal \u001b[1m(\u001b[0m\u001b[1;36m12\u001b[0m,\u001b[1m)\u001b[0m \u001b[1m(\u001b[0m\u001b[32m'seasonal_lag'\u001b[0m,\u001b[1m)\u001b[0m \n",
3643-
" alpha \u001b[3;35mNone\u001b[0m \u001b[1;36m0\u001b[0m < alpha < \u001b[1;36m1\u001b[0m \u001b[3;35mNone\u001b[0m \n",
3644-
" beta \u001b[3;35mNone\u001b[0m \u001b[1;36m0\u001b[0m < beta < \u001b[1;36m1\u001b[0m \u001b[3;35mNone\u001b[0m \n",
3645-
" gamma \u001b[3;35mNone\u001b[0m \u001b[1;36m0\u001b[0m < gamma< \u001b[1;36m1\u001b[0m \u001b[3;35mNone\u001b[0m \n",
3646-
" phi \u001b[3;35mNone\u001b[0m \u001b[1;36m0\u001b[0m < phi < \u001b[1;36m1\u001b[0m \u001b[3;35mNone\u001b[0m \n",
3647-
" sigma_state \u001b[3;35mNone\u001b[0m Positive \u001b[3;35mNone\u001b[0m \n",
3640+
" initial_level \u001B[3;35mNone\u001B[0m \u001B[3;35mNone\u001B[0m \n",
3641+
" initial_trend \u001B[3;35mNone\u001B[0m \u001B[3;35mNone\u001B[0m \n",
3642+
" initial_seasonal \u001B[1m(\u001B[0m\u001B[1;36m12\u001B[0m,\u001B[1m)\u001B[0m \u001B[1m(\u001B[0m\u001B[32m'seasonal_lag'\u001B[0m,\u001B[1m)\u001B[0m \n",
3643+
" alpha \u001B[3;35mNone\u001B[0m \u001B[1;36m0\u001B[0m < alpha < \u001B[1;36m1\u001B[0m \u001B[3;35mNone\u001B[0m \n",
3644+
" beta \u001B[3;35mNone\u001B[0m \u001B[1;36m0\u001B[0m < beta < \u001B[1;36m1\u001B[0m \u001B[3;35mNone\u001B[0m \n",
3645+
" gamma \u001B[3;35mNone\u001B[0m \u001B[1;36m0\u001B[0m < gamma< \u001B[1;36m1\u001B[0m \u001B[3;35mNone\u001B[0m \n",
3646+
" phi \u001B[3;35mNone\u001B[0m \u001B[1;36m0\u001B[0m < phi < \u001B[1;36m1\u001B[0m \u001B[3;35mNone\u001B[0m \n",
3647+
" sigma_state \u001B[3;35mNone\u001B[0m Positive \u001B[3;35mNone\u001B[0m \n",
36483648
" \n",
3649-
"\u001b[2;3m These parameters should be assigned priors inside a PyMC model \u001b[0m\n",
3650-
"\u001b[2;3m block before calling the build_statespace_graph method. \u001b[0m\n"
3649+
"\u001B[2;3m These parameters should be assigned priors inside a PyMC model \u001B[0m\n",
3650+
"\u001B[2;3m block before calling the build_statespace_graph method. \u001B[0m\n"
36513651
]
36523652
},
36533653
"metadata": {},

‎pymc_extras/inference/find_map.py

Lines changed: 6 additions & 6 deletions
Original file line numberDiff line numberDiff line change
@@ -177,7 +177,7 @@ def _compile_functions(
177177
compute_hessp: bool
178178
Whether to compile a function that computes the Hessian-vector product of the loss function.
179179
compile_kwargs: dict, optional
180-
Additional keyword arguments to pass to the ``pm.compile_pymc`` function.
180+
Additional keyword arguments to pass to the ``pm.compile`` function.
181181
182182
Returns
183183
-------
@@ -193,19 +193,19 @@ def _compile_functions(
193193
if compute_grad:
194194
grads = pytensor.gradient.grad(loss, inputs)
195195
grad = pt.concatenate([grad.ravel() for grad in grads])
196-
f_loss_and_grad = pm.compile_pymc(inputs, [loss, grad], **compile_kwargs)
196+
f_loss_and_grad = pm.compile(inputs, [loss, grad], **compile_kwargs)
197197
else:
198-
f_loss = pm.compile_pymc(inputs, loss, **compile_kwargs)
198+
f_loss = pm.compile(inputs, loss, **compile_kwargs)
199199
return [f_loss]
200200

201201
if compute_hess:
202202
hess = pytensor.gradient.jacobian(grad, inputs)[0]
203-
f_hess = pm.compile_pymc(inputs, hess, **compile_kwargs)
203+
f_hess = pm.compile(inputs, hess, **compile_kwargs)
204204

205205
if compute_hessp:
206206
p = pt.tensor("p", shape=inputs[0].type.shape)
207207
hessp = pytensor.gradient.hessian_vector_product(loss, inputs, p)
208-
f_hessp = pm.compile_pymc([*inputs, p], hessp[0], **compile_kwargs)
208+
f_hessp = pm.compile([*inputs, p], hessp[0], **compile_kwargs)
209209

210210
return [f_loss_and_grad, f_hess, f_hessp]
211211

@@ -240,7 +240,7 @@ def scipy_optimize_funcs_from_loss(
240240
gradient_backend: str, default "pytensor"
241241
Which backend to use to compute gradients. Must be one of "jax" or "pytensor"
242242
compile_kwargs:
243-
Additional keyword arguments to pass to the ``pm.compile_pymc`` function.
243+
Additional keyword arguments to pass to the ``pm.compile`` function.
244244
245245
Returns
246246
-------

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