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Use compile instead of compile_pymc to silence warning
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@jessegrabowski
jessegrabowski committed Feb 5, 2025
1 parent 0151dec commit aa97238
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104 changes: 52 additions & 52 deletions notebooks/Exponential Trend Smoothing.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -188,7 +188,7 @@
"\n",
" # For the forecasts we need a function that lets us take draws from the distribution. We'll get the mean\n",
" # and covariance from samples by calling it a lot of times.\n",
" f_forecast = pm.compile_pymc(pm.inputvars(obs_forecast), obs_forecast, mode=\"JAX\")\n",
" f_forecast = pm.compile(pm.inputvars(obs_forecast), obs_forecast, mode=\"JAX\")\n",
"\n",
" return f_ets, f_forecast\n",
"\n",
Expand Down Expand Up @@ -863,17 +863,17 @@
"</pre>\n"
],
"text/plain": [
"\u001b[3m Model Requirements \u001b[0m\n",
"\u001B[3m Model Requirements \u001B[0m\n",
" \n",
" \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",
" \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",
" ──────────────────────────────────────────────────── \n",
" initial_level \u001b[3;35mNone\u001b[0m \u001b[3;35mNone\u001b[0m \n",
" alpha \u001b[3;35mNone\u001b[0m \u001b[1;36m0\u001b[0m < alpha < \u001b[1;36m1\u001b[0m \u001b[3;35mNone\u001b[0m \n",
" sigma_state \u001b[3;35mNone\u001b[0m Positive \u001b[3;35mNone\u001b[0m \n",
" initial_level \u001B[3;35mNone\u001B[0m \u001B[3;35mNone\u001B[0m \n",
" alpha \u001B[3;35mNone\u001B[0m \u001B[1;36m0\u001B[0m < alpha < \u001B[1;36m1\u001B[0m \u001B[3;35mNone\u001B[0m \n",
" sigma_state \u001B[3;35mNone\u001B[0m Positive \u001B[3;35mNone\u001B[0m \n",
" \n",
"\u001b[2;3m These parameters should be assigned priors inside a \u001b[0m\n",
"\u001b[2;3m PyMC model block before calling the \u001b[0m\n",
"\u001b[2;3m build_statespace_graph method. \u001b[0m\n"
"\u001B[2;3m These parameters should be assigned priors inside a \u001B[0m\n",
"\u001B[2;3m PyMC model block before calling the \u001B[0m\n",
"\u001B[2;3m build_statespace_graph method. \u001B[0m\n"
]
},
"metadata": {},
Expand Down Expand Up @@ -1394,19 +1394,19 @@
"</pre>\n"
],
"text/plain": [
"\u001b[3m Model Requirements \u001b[0m\n",
"\u001B[3m Model Requirements \u001B[0m\n",
" \n",
" \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",
" \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",
" ──────────────────────────────────────────────────── \n",
" initial_level \u001b[3;35mNone\u001b[0m \u001b[3;35mNone\u001b[0m \n",
" initial_trend \u001b[3;35mNone\u001b[0m \u001b[3;35mNone\u001b[0m \n",
" alpha \u001b[3;35mNone\u001b[0m \u001b[1;36m0\u001b[0m < alpha < \u001b[1;36m1\u001b[0m \u001b[3;35mNone\u001b[0m \n",
" beta \u001b[3;35mNone\u001b[0m \u001b[1;36m0\u001b[0m < beta < \u001b[1;36m1\u001b[0m \u001b[3;35mNone\u001b[0m \n",
" sigma_state \u001b[3;35mNone\u001b[0m Positive \u001b[3;35mNone\u001b[0m \n",
" initial_level \u001B[3;35mNone\u001B[0m \u001B[3;35mNone\u001B[0m \n",
" initial_trend \u001B[3;35mNone\u001B[0m \u001B[3;35mNone\u001B[0m \n",
" alpha \u001B[3;35mNone\u001B[0m \u001B[1;36m0\u001B[0m < alpha < \u001B[1;36m1\u001B[0m \u001B[3;35mNone\u001B[0m \n",
" beta \u001B[3;35mNone\u001B[0m \u001B[1;36m0\u001B[0m < beta < \u001B[1;36m1\u001B[0m \u001B[3;35mNone\u001B[0m \n",
" sigma_state \u001B[3;35mNone\u001B[0m Positive \u001B[3;35mNone\u001B[0m \n",
" \n",
"\u001b[2;3m These parameters should be assigned priors inside a \u001b[0m\n",
"\u001b[2;3m PyMC model block before calling the \u001b[0m\n",
"\u001b[2;3m build_statespace_graph method. \u001b[0m\n"
"\u001B[2;3m These parameters should be assigned priors inside a \u001B[0m\n",
"\u001B[2;3m PyMC model block before calling the \u001B[0m\n",
"\u001B[2;3m build_statespace_graph method. \u001B[0m\n"
]
},
"metadata": {},
Expand Down Expand Up @@ -2044,20 +2044,20 @@
"</pre>\n"
],
"text/plain": [
"\u001b[3m Model Requirements \u001b[0m\n",
"\u001B[3m Model Requirements \u001B[0m\n",
" \n",
" \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",
" \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",
" ──────────────────────────────────────────────────── \n",
" initial_level \u001b[3;35mNone\u001b[0m \u001b[3;35mNone\u001b[0m \n",
" initial_trend \u001b[3;35mNone\u001b[0m \u001b[3;35mNone\u001b[0m \n",
" alpha \u001b[3;35mNone\u001b[0m \u001b[1;36m0\u001b[0m < alpha < \u001b[1;36m1\u001b[0m \u001b[3;35mNone\u001b[0m \n",
" beta \u001b[3;35mNone\u001b[0m \u001b[1;36m0\u001b[0m < beta < \u001b[1;36m1\u001b[0m \u001b[3;35mNone\u001b[0m \n",
" phi \u001b[3;35mNone\u001b[0m \u001b[1;36m0\u001b[0m < phi < \u001b[1;36m1\u001b[0m \u001b[3;35mNone\u001b[0m \n",
" sigma_state \u001b[3;35mNone\u001b[0m Positive \u001b[3;35mNone\u001b[0m \n",
" initial_level \u001B[3;35mNone\u001B[0m \u001B[3;35mNone\u001B[0m \n",
" initial_trend \u001B[3;35mNone\u001B[0m \u001B[3;35mNone\u001B[0m \n",
" alpha \u001B[3;35mNone\u001B[0m \u001B[1;36m0\u001B[0m < alpha < \u001B[1;36m1\u001B[0m \u001B[3;35mNone\u001B[0m \n",
" beta \u001B[3;35mNone\u001B[0m \u001B[1;36m0\u001B[0m < beta < \u001B[1;36m1\u001B[0m \u001B[3;35mNone\u001B[0m \n",
" phi \u001B[3;35mNone\u001B[0m \u001B[1;36m0\u001B[0m < phi < \u001B[1;36m1\u001B[0m \u001B[3;35mNone\u001B[0m \n",
" sigma_state \u001B[3;35mNone\u001B[0m Positive \u001B[3;35mNone\u001B[0m \n",
" \n",
"\u001b[2;3m These parameters should be assigned priors inside a \u001b[0m\n",
"\u001b[2;3m PyMC model block before calling the \u001b[0m\n",
"\u001b[2;3m build_statespace_graph method. \u001b[0m\n"
"\u001B[2;3m These parameters should be assigned priors inside a \u001B[0m\n",
"\u001B[2;3m PyMC model block before calling the \u001B[0m\n",
"\u001B[2;3m build_statespace_graph method. \u001B[0m\n"
]
},
"metadata": {},
Expand Down Expand Up @@ -2664,19 +2664,19 @@
"</pre>\n"
],
"text/plain": [
"\u001b[3m Model Requirements \u001b[0m\n",
"\u001B[3m Model Requirements \u001B[0m\n",
" \n",
" \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",
" \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",
" ──────────────────────────────────────────────────────────────────────────────────────────── \n",
" 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",
" 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",
" 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",
" 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",
" 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",
" 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",
" 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",
" 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",
" 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",
" 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",
" 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",
" 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",
" \n",
"\u001b[2;3m These parameters should be assigned priors inside a PyMC model block before calling the \u001b[0m\n",
"\u001b[2;3m build_statespace_graph method. \u001b[0m\n"
"\u001B[2;3m These parameters should be assigned priors inside a PyMC model block before calling the \u001B[0m\n",
"\u001B[2;3m build_statespace_graph method. \u001B[0m\n"
]
},
"metadata": {},
Expand Down Expand Up @@ -3633,21 +3633,21 @@
"</pre>\n"
],
"text/plain": [
"\u001b[3m Model Requirements \u001b[0m\n",
"\u001B[3m Model Requirements \u001B[0m\n",
" \n",
" \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",
" \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",
" ────────────────────────────────────────────────────────────── \n",
" initial_level \u001b[3;35mNone\u001b[0m \u001b[3;35mNone\u001b[0m \n",
" initial_trend \u001b[3;35mNone\u001b[0m \u001b[3;35mNone\u001b[0m \n",
" 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",
" alpha \u001b[3;35mNone\u001b[0m \u001b[1;36m0\u001b[0m < alpha < \u001b[1;36m1\u001b[0m \u001b[3;35mNone\u001b[0m \n",
" beta \u001b[3;35mNone\u001b[0m \u001b[1;36m0\u001b[0m < beta < \u001b[1;36m1\u001b[0m \u001b[3;35mNone\u001b[0m \n",
" gamma \u001b[3;35mNone\u001b[0m \u001b[1;36m0\u001b[0m < gamma< \u001b[1;36m1\u001b[0m \u001b[3;35mNone\u001b[0m \n",
" phi \u001b[3;35mNone\u001b[0m \u001b[1;36m0\u001b[0m < phi < \u001b[1;36m1\u001b[0m \u001b[3;35mNone\u001b[0m \n",
" sigma_state \u001b[3;35mNone\u001b[0m Positive \u001b[3;35mNone\u001b[0m \n",
" initial_level \u001B[3;35mNone\u001B[0m \u001B[3;35mNone\u001B[0m \n",
" initial_trend \u001B[3;35mNone\u001B[0m \u001B[3;35mNone\u001B[0m \n",
" 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",
" alpha \u001B[3;35mNone\u001B[0m \u001B[1;36m0\u001B[0m < alpha < \u001B[1;36m1\u001B[0m \u001B[3;35mNone\u001B[0m \n",
" beta \u001B[3;35mNone\u001B[0m \u001B[1;36m0\u001B[0m < beta < \u001B[1;36m1\u001B[0m \u001B[3;35mNone\u001B[0m \n",
" gamma \u001B[3;35mNone\u001B[0m \u001B[1;36m0\u001B[0m < gamma< \u001B[1;36m1\u001B[0m \u001B[3;35mNone\u001B[0m \n",
" phi \u001B[3;35mNone\u001B[0m \u001B[1;36m0\u001B[0m < phi < \u001B[1;36m1\u001B[0m \u001B[3;35mNone\u001B[0m \n",
" sigma_state \u001B[3;35mNone\u001B[0m Positive \u001B[3;35mNone\u001B[0m \n",
" \n",
"\u001b[2;3m These parameters should be assigned priors inside a PyMC model \u001b[0m\n",
"\u001b[2;3m block before calling the build_statespace_graph method. \u001b[0m\n"
"\u001B[2;3m These parameters should be assigned priors inside a PyMC model \u001B[0m\n",
"\u001B[2;3m block before calling the build_statespace_graph method. \u001B[0m\n"
]
},
"metadata": {},
Expand Down
3 changes: 2 additions & 1 deletion pymc_extras/model/marginal/marginal_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,7 +19,8 @@
model_free_rv,
model_from_fgraph,
)
from pymc.pytensorf import collect_default_updates, compile_pymc, constant_fold, toposort_replace
from pymc.pytensorf import collect_default_updates, constant_fold, toposort_replace
from pymc.pytensorf import compile as compile_pymc
from pymc.util import RandomState, _get_seeds_per_chain
from pytensor import In, Out
from pytensor.compile import SharedVariable
Expand Down
2 changes: 1 addition & 1 deletion pymc_extras/statespace/core/compile.py
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Original file line number Diff line number Diff line change
Expand Up @@ -30,7 +30,7 @@ def compile_statespace(

inputs = list(pytensor.graph.basic.explicit_graph_inputs(outputs))

_f = pm.compile_pymc(inputs, outputs, on_unused_input="ignore", **compile_kwargs)
_f = pm.compile(inputs, outputs, on_unused_input="ignore", **compile_kwargs)

def f(*, draws=1, **params):
if isinstance(steps, pt.Variable):
Expand Down

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