Error stan.sample() RuntimeError: Initialization failed

Hi everyone,

I’m a new user of PyStan and I’m trying to replicate a model that a friend built. I’m using Stan v3.3.0 and I’m coding in notebooks (even in .py files the same error raises). My current issue is that I can build the model but I can’t sample it. The raised error is the following:

Sampling: 0% Sampling: Initialization failed.
RuntimeError: Initialization failed.

RuntimeError                              Traceback (most recent call last)
Input In [235], in <module>
----> 1 model_fit = model_reg.sample(num_chains=1, num_samples=2000, num_warmup=500)

File ~/Dropbox/Doctorado/Paper RIS - Sim Match Bayers/venv/lib/python3.10/site-packages/stan/, in Model.sample(self, num_chains, **kwargs)
     61 def sample(self, *, num_chains=4, **kwargs) ->
     62     """Draw samples from the model.
     64     Parameters in ``kwargs`` will be passed to the default sample function.
     88     """
---> 89     return self.hmc_nuts_diag_e_adapt(num_chains=num_chains, **kwargs)

File ~/Dropbox/Doctorado/Paper RIS - Sim Match Bayers/venv/lib/python3.10/site-packages/stan/, in Model.hmc_nuts_diag_e_adapt(self, num_chains, **kwargs)
     92 """Draw samples from the model using ``stan::services::sample::hmc_nuts_diag_e_adapt``.
     94 Parameters in ``kwargs`` will be passed to the (Python wrapper of)
    106 """
    107 function = "stan::services::sample::hmc_nuts_diag_e_adapt"
--> 108 return self._create_fit(function=function, num_chains=num_chains, **kwargs)

File ~/Dropbox/Doctorado/Paper RIS - Sim Match Bayers/venv/lib/python3.10/site-packages/stan/, in Model._create_fit(self, function, num_chains, **kwargs)
    308     return fit
    310 try:
--> 311     return
    312 except KeyboardInterrupt:
    313     return

File ~/Dropbox/Doctorado/Paper RIS - Sim Match Bayers/venv/lib/python3.10/site-packages/, in _patch_asyncio.<locals>.run(main, debug)
     36 task = asyncio.ensure_future(main)
     37 try:
---> 38     return loop.run_until_complete(task)
     39 finally:
     40     if not task.done():

File ~/Dropbox/Doctorado/Paper RIS - Sim Match Bayers/venv/lib/python3.10/site-packages/, in _patch_loop.<locals>.run_until_complete(self, future)
     78 if not f.done():
     79     raise RuntimeError(
     80         'Event loop stopped before Future completed.')
---> 81 return f.result()

File /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/asyncio/, in Future.result(self)
    199 self.__log_traceback = False
    200 if self._exception is not None:
--> 201     raise self._exception
    202 return self._result

File /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/asyncio/, in Task.__step(***failed resolving arguments***)
    228 try:
    229     if exc is None:
    230         # We use the `send` method directly, because coroutines
    231         # don't have `__iter__` and `__next__` methods.
--> 232         result = coro.send(None)
    233     else:
    234         result = coro.throw(exc)

File ~/Dropbox/Doctorado/Paper RIS - Sim Match Bayers/venv/lib/python3.10/site-packages/stan/, in Model._create_fit.<locals>.go()
    233         sampling_output.clear()
    234         sampling_output.write_line("<info>Sampling:</info> <error>Initialization failed.</error>")
--> 235         raise RuntimeError("Initialization failed.")
    236     raise RuntimeError(message)
    238 resp = await client.get(f"/{fit_name}")

RuntimeError: Initialization failed.

Here is my model:

stan_model = ””” 
data {
    int<lower=0> N_obs; // number of observations
    int N_players; // number of players
    int N_goalkeepers; // number of goalkeepers
    int N_zones; // number of field zones
    int N_times; // number of time frames
    int N_results; // number of types of results
    int N_locations; // number of types home event

    int<lower=1,upper=N_players> player_id[N_obs];
    int<lower=1,upper=N_goalkeepers> goalkeeper_id[N_obs];
    int<lower=1,upper=N_zones> zone_id[N_obs];
    int<lower=1,upper=N_times> time_id[N_obs];
    int<lower=1,upper=N_results> result_id[N_obs];
    int<lower=1,upper=N_locations> location_id[N_obs];

    int goal[N_obs]; // dependent variable
parameters {

    real alpha; // intercept

    vector<lower=0,upper=1>[N_players] beta_player; // coefficient associated with each player
    vector[N_goalkeepers] beta_goalkeeper; // coefficient associated with each goalkeeper
    vector[N_zones] beta_zone; // coefficient associated with each zone
    vector[N_times] beta_time; // coefficient associated with each time frame
    vector[N_results] beta_result; // coefficient associated with each result
    vector[N_locations] beta_location; // coefficient associated with each type of localia

model {
    // priors
    alpha ~ normal(0,1);
    beta_player ~ beta(3.44,7.34);
    beta_goalkeeper ~ normal(0,0.38);
    beta_zone ~ normal(-0.17,0.58);
    beta_time ~ normal(-0.23,0.37);
    beta_result ~ normal(-0.5,0.5);
    beta_location ~ normal(-0.78,0.6);

    goal ~ bernoulli_logit(alpha + beta_player[player_id] + beta_goalkeeper[goalkeeper_id] +
        beta_zone[zone_id] + beta_time[time_id] + beta_result[result_id] + beta_location[location_id]);

data = {"N_obs": len(df_model),
        "N_players": len(df_model.new_id.unique()),
        "N_goalkeepers": len(df_model.glk_id.unique()),
        "N_zones": len(df_model.cat_zone.unique()),
        "N_times": len(df_model.timeFrame.unique()),
        "N_results": len(df_model.cat_res.unique()),
        "N_locations": len(df_model.home_event.unique()),
        "player_id": df_model.new_id.to_list(),
        "goalkeeper_id": df_model.glk_id.to_list(),
        "zone_id": df_model.cat_zone.to_list(),
        "time_id": df_model.timeFrame.to_list(),
        "result_id": df_model.cat_res.to_list(),
        "location_id": df_model.home_event.to_list(),
        "goal": df_model.goal.to_list()

Here is the building of the model (which runs well, it raises Building: done without any warning)

model_reg =, data=data, random_seed=1)

Here is the sample of the model that I’m trying run but raises the error.

model_fit = model_reg.sample(num_chains=1, num_samples=2000, num_warmup=500)

Could you help me to understand what is the error and how can I solve it? Thanks in advance!

don’t understand the error as I’m not a PyStan user, but it’s quite possible that your input data doesn’t match the names/types/shapes of the variables declared in the model’s data block.

CmdStanPy is a newer Python interface. it can be installed via conda and will do a better job on error messages.

I agree that the error messages are not getting displayed particularly well. All you get is an “Initialization failed.” error.

If you try out CmdStanPy and get a better error message, please report back here. PyStan should display the same thing, at least in theory.