How to use httpstan - 9 common examples

To help you get started, we’ve selected a few httpstan examples, based on popular ways it is used in public projects.

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github stan-dev / pystan-next / stan / fit.py View on Github external
# These names are gathered later in this function by inspecting the output from Stan.
        self.sample_and_sampler_param_names: Sequence[str]

        num_flat_params = sum(np.product(dims_ or 1) for dims_ in dims)  # if dims == [] then it is a scalar
        assert num_flat_params == len(constrained_param_names)
        num_samples_saved = (self.num_samples + self.num_warmup * self.save_warmup) // self.num_thin

        # self._draws holds all the draws. We cannot allocate it before looking at the draws
        # because we do not know how many sampler-specific parameters are present. Later in this
        # function we count them and only then allocate the array for `self._draws`.
        self._draws: np.ndarray

        for chain_index, stan_output in zip(range(self.num_chains), self.stan_outputs):
            draw_index = 0
            for msg in stan_output:
                if msg.topic == callbacks_writer_pb2.WriterMessage.Topic.Value("SAMPLE"):
                    # Ignore sample message which is mixed together with proper draws.
                    if msg.feature and msg.feature[0].name == "":
                        continue

                    draw_row = []  # a "row" of values from a single draw from Stan C++

                    # for the first draw: collect sample and sampler parameter names.
                    if not hasattr(self, "_draws"):
                        feature_names = tuple(fea.name for fea in msg.feature)
                        self.sample_and_sampler_param_names = tuple(
                            name for name in feature_names if name.endswith("__")
                        )
                        num_rows = len(self.sample_and_sampler_param_names) + num_flat_params
                        # column-major order ("F") aligns with how the draws are stored (in cols).
                        self._draws = np.empty((num_rows, num_samples_saved, num_chains), order="F")
                        # rudimentary check of parameter order (sample & sampler params must be first)
github stan-dev / pystan-next / stan / model.py View on Github external
for chain in range(1, num_chains + 1):
            payload = {"function": "stan::services::sample::hmc_nuts_diag_e_adapt"}
            payload.update(kwargs)
            payload["chain"] = chain
            payload["data"] = self.data
            payload["init"] = init.pop(0)
            if self.random_seed is not None:
                payload["random_seed"] = self.random_seed

            # fit needs to know num_samples, num_warmup, num_thin, save_warmup
            # progress bar needs to know some of these
            num_warmup = payload.get("num_warmup", arguments.lookup_default(arguments.Method["SAMPLE"], "num_warmup"))
            num_samples = payload.get(
                "num_samples", arguments.lookup_default(arguments.Method["SAMPLE"], "num_samples"),
            )
            num_thin = payload.get("num_thin", arguments.lookup_default(arguments.Method["SAMPLE"], "num_thin"))
            save_warmup = payload.get(
                "save_warmup", arguments.lookup_default(arguments.Method["SAMPLE"], "save_warmup"),
            )
            payloads.append(payload)

        def extract_protobuf_messages(fit_bytes):
            varint_decoder = google.protobuf.internal.decoder._DecodeVarint32
            next_pos, pos = 0, 0
            while pos < len(fit_bytes):
                msg = callbacks_writer_pb2.WriterMessage()
                next_pos, pos = varint_decoder(fit_bytes, pos)
                msg.ParseFromString(fit_bytes[pos : pos + next_pos])
                yield msg
                pos += next_pos

        async def go():
github stan-dev / pystan-next / stan / model.py View on Github external
if len(init) != num_chains:
            raise ValueError("Initial values must be provided for each chain.")

        payloads = []
        for chain in range(1, num_chains + 1):
            payload = {"function": "stan::services::sample::hmc_nuts_diag_e_adapt"}
            payload.update(kwargs)
            payload["chain"] = chain
            payload["data"] = self.data
            payload["init"] = init.pop(0)
            if self.random_seed is not None:
                payload["random_seed"] = self.random_seed

            # fit needs to know num_samples, num_warmup, num_thin, save_warmup
            # progress bar needs to know some of these
            num_warmup = payload.get("num_warmup", arguments.lookup_default(arguments.Method["SAMPLE"], "num_warmup"))
            num_samples = payload.get(
                "num_samples", arguments.lookup_default(arguments.Method["SAMPLE"], "num_samples"),
            )
            num_thin = payload.get("num_thin", arguments.lookup_default(arguments.Method["SAMPLE"], "num_thin"))
            save_warmup = payload.get(
                "save_warmup", arguments.lookup_default(arguments.Method["SAMPLE"], "save_warmup"),
            )
            payloads.append(payload)

        def extract_protobuf_messages(fit_bytes):
            varint_decoder = google.protobuf.internal.decoder._DecodeVarint32
            next_pos, pos = 0, 0
            while pos < len(fit_bytes):
                msg = callbacks_writer_pb2.WriterMessage()
                next_pos, pos = varint_decoder(fit_bytes, pos)
                msg.ParseFromString(fit_bytes[pos : pos + next_pos])
github stan-dev / pystan-next / stan / model.py View on Github external
payload.update(kwargs)
            payload["chain"] = chain
            payload["data"] = self.data
            payload["init"] = init.pop(0)
            if self.random_seed is not None:
                payload["random_seed"] = self.random_seed

            # fit needs to know num_samples, num_warmup, num_thin, save_warmup
            # progress bar needs to know some of these
            num_warmup = payload.get("num_warmup", arguments.lookup_default(arguments.Method["SAMPLE"], "num_warmup"))
            num_samples = payload.get(
                "num_samples", arguments.lookup_default(arguments.Method["SAMPLE"], "num_samples"),
            )
            num_thin = payload.get("num_thin", arguments.lookup_default(arguments.Method["SAMPLE"], "num_thin"))
            save_warmup = payload.get(
                "save_warmup", arguments.lookup_default(arguments.Method["SAMPLE"], "save_warmup"),
            )
            payloads.append(payload)

        def extract_protobuf_messages(fit_bytes):
            varint_decoder = google.protobuf.internal.decoder._DecodeVarint32
            next_pos, pos = 0, 0
            while pos < len(fit_bytes):
                msg = callbacks_writer_pb2.WriterMessage()
                next_pos, pos = varint_decoder(fit_bytes, pos)
                msg.ParseFromString(fit_bytes[pos : pos + next_pos])
                yield msg
                pos += next_pos

        async def go():
            progress_bar = ProgressBar(ConsoleIO())
            progress_bar.set_format("very_verbose")
github stan-dev / pystan-next / stan / model.py View on Github external
payloads = []
        for chain in range(1, num_chains + 1):
            payload = {"function": "stan::services::sample::hmc_nuts_diag_e_adapt"}
            payload.update(kwargs)
            payload["chain"] = chain
            payload["data"] = self.data
            payload["init"] = init.pop(0)
            if self.random_seed is not None:
                payload["random_seed"] = self.random_seed

            # fit needs to know num_samples, num_warmup, num_thin, save_warmup
            # progress bar needs to know some of these
            num_warmup = payload.get("num_warmup", arguments.lookup_default(arguments.Method["SAMPLE"], "num_warmup"))
            num_samples = payload.get(
                "num_samples", arguments.lookup_default(arguments.Method["SAMPLE"], "num_samples"),
            )
            num_thin = payload.get("num_thin", arguments.lookup_default(arguments.Method["SAMPLE"], "num_thin"))
            save_warmup = payload.get(
                "save_warmup", arguments.lookup_default(arguments.Method["SAMPLE"], "save_warmup"),
            )
            payloads.append(payload)

        def extract_protobuf_messages(fit_bytes):
            varint_decoder = google.protobuf.internal.decoder._DecodeVarint32
            next_pos, pos = 0, 0
            while pos < len(fit_bytes):
                msg = callbacks_writer_pb2.WriterMessage()
                next_pos, pos = varint_decoder(fit_bytes, pos)
                msg.ParseFromString(fit_bytes[pos : pos + next_pos])
                yield msg
                pos += next_pos
github stan-dev / pystan-next / stan / model.py View on Github external
def extract_protobuf_messages(fit_bytes):
            varint_decoder = google.protobuf.internal.decoder._DecodeVarint32
            next_pos, pos = 0, 0
            while pos < len(fit_bytes):
                msg = callbacks_writer_pb2.WriterMessage()
                next_pos, pos = varint_decoder(fit_bytes, pos)
                msg.ParseFromString(fit_bytes[pos : pos + next_pos])
                yield msg
                pos += next_pos
github stan-dev / pystan-next / stan / model.py View on Github external
async def go():
        io = ConsoleIO()
        io.error("Building...")
        async with stan.common.httpstan_server() as (host, port):
            # Check to see if model is in cache.
            model_name = httpstan.models.calculate_model_name(program_code)
            path, payload = f"/v1/{model_name}/params", {"data": data}
            async with aiohttp.request("POST", f"http://{host}:{port}{path}", json=payload) as resp:
                model_in_cache = resp.status != 404
            io.error_line(" Found model in cache." if model_in_cache else " This may take some time.")
            # Note: during compilation `httpstan` redirects stderr to /dev/null, making `print` impossible.
            path, payload = "/v1/models", {"program_code": program_code}
            async with aiohttp.request("POST", f"http://{host}:{port}{path}", json=payload) as resp:
                if resp.status != 201:
                    raise RuntimeError((await resp.json())["message"])
                assert model_name == (await resp.json())["name"]

            path, payload = f"/v1/{model_name}/params", {"data": data}
            async with aiohttp.request("POST", f"http://{host}:{port}{path}", json=payload) as resp:
                if resp.status != 200:
                    raise RuntimeError((await resp.json())["message"])
                params_list = (await resp.json())["params"]
github stan-dev / pystan-next / stan / model.py View on Github external
def __init__(
        self,
        model_name: str,
        program_code: str,
        data: dict,
        param_names: typing.Tuple[str],
        constrained_param_names: typing.Tuple[str],
        dims: typing.Tuple[typing.Tuple[int]],
        random_seed: typing.Optional[int],
    ) -> None:
        if model_name != httpstan.models.calculate_model_name(program_code):
            raise ValueError("`model_name` does not match `program_code`.")
        self.model_name = model_name
        self.program_code = program_code
        self.data = data or {}
        self.param_names = param_names
        self.constrained_param_names = constrained_param_names
        self.dims = dims
        self.random_seed = random_seed
github stan-dev / pystan-next / stan / common.py View on Github external
async def httpstan_server():
    """Manage starting and stopping the httpstan HTTP server."""
    host, port = "127.0.0.1", unused_tcp_port()
    app = httpstan.app.make_app()
    runner = aiohttp.web.AppRunner(app)
    await runner.setup()
    site = aiohttp.web.TCPSite(runner, host, port)
    await site.start()
    yield (host, port)
    await runner.cleanup()

httpstan

HTTP-based interface to Stan, a package for Bayesian inference.

ISC
Latest version published 4 months ago

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77 / 100
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