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Modification of simulator for extra control, extra verbose variable n…
…ames, option to pass varaibles without a config
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,8 +1,4 @@ | ||
|
|
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| from data.h5_data import H5Data | ||
| from data.pickle_data import PickleData | ||
|
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| DataModules = { | ||
| "H5Data": H5Data, | ||
| "PickleData": PickleData | ||
| } | ||
| DataModules = {"H5Data": H5Data, "PickleData": PickleData} |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,50 +1,126 @@ | ||
| import importlib.util | ||
| import sys | ||
| import os | ||
| import numpy as np | ||
|
|
||
| from utils.config import get_item | ||
| from utils.defaults import Defaults | ||
|
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||
| class Data: | ||
| def __init__(self, path:str, simulator_name: str): | ||
|
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| class Data: | ||
| def __init__( | ||
| self, | ||
| path: str, | ||
| simulator_name: str, | ||
| simulator_kwargs: dict = None, | ||
| prior: str = "data", | ||
| prior_kwargs: dict = None, | ||
| ): | ||
| self.rng = np.random.default_rng( | ||
| get_item("common", "random_seed", raise_exception=False) | ||
| ) | ||
| self.data = self._load(path) | ||
| self.simulator = self._load_simulator(simulator_name) | ||
| self.simulator = self._load_simulator(simulator_name, simulator_kwargs) | ||
| self.prior_dist = self.load_prior(prior, prior_kwargs) | ||
| self.n_dims = self.theta_true().shape[1] | ||
|
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| def _load_simulator(self, name): | ||
| try: | ||
| simulator_path = os.environ[f"{Defaults['common']['sim_location']}:{name}"] | ||
| except KeyError as e: | ||
| raise RuntimeError(f"Simulator cannot be found using env var {e}. Hint: have you registered your simulation with utils.register_simulator?") | ||
| def _load_simulator(self, name): | ||
| try: | ||
| sim_location = get_item("common", "sim_location", raise_exception=False) | ||
| simulator_path = os.environ[f"{sim_location}:{name}"] | ||
| except KeyError as e: | ||
| raise RuntimeError( | ||
| f"Simulator cannot be found using env var {e}. Hint: have you registered your simulation with utils.register_simulator?" | ||
| ) | ||
|
|
||
| new_class = os.path.dirname(simulator_path) | ||
| sys.path.insert(1, new_class) | ||
|
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| # TODO robust error checks | ||
| module_name = os.path.basename(simulator_path.rstrip('.py')) | ||
| # TODO robust error checks | ||
| module_name = os.path.basename(simulator_path.rstrip(".py")) | ||
| m = importlib.import_module(module_name) | ||
|
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| simulator = getattr(m, name) | ||
| return simulator() | ||
|
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| def _load(self, path:str): | ||
| raise NotImplementedError | ||
| simulator_kwargs = get_item("data", "simulator_kwargs", raise_exception=False) | ||
| simulator_kwargs = {} if simulator_kwargs is None else simulator_kwargs | ||
| simulator_instance = simulator(**simulator_kwargs) | ||
|
|
||
| if not hasattr(simulator_instance, "generate_context"): | ||
| raise RuntimeError( | ||
| "Simulator improperly formed - requires a generate_context method." | ||
| ) | ||
|
|
||
| if not hasattr(simulator_instance, "simulate"): | ||
| raise RuntimeError( | ||
| "Simulator improperly formed - requires a simulate method." | ||
| ) | ||
|
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| def x_true(self): | ||
| # From Data | ||
| return simulator_instance | ||
|
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| def _load(self, path: str): | ||
| raise NotImplementedError | ||
|
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| def y_true(self): | ||
| return self.simulator(self.theta_true(), self.x_true()) | ||
|
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| def prior(self): | ||
|
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| def true_context(self): | ||
| # From Data | ||
| raise NotImplementedError | ||
|
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| def theta_true(self): | ||
| return get_item("data", "theta_true") | ||
|
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| def sigma_true(self): | ||
| return get_item("data", "sigma_true") | ||
|
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| def save(self, data, path:str): | ||
| raise NotImplementedError | ||
|
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| def true_simulator_outcome(self): | ||
| return self.simulator(self.theta_true(), self.true_context()) | ||
|
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| def sample_prior(self, n_samples: int): | ||
| return self.prior_dist(size=(n_samples, self.n_dims)) | ||
|
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| def simulator_outcome(self, theta, condition_context=None, n_samples=None): | ||
| if condition_context is None: | ||
| if n_samples is None: | ||
| raise ValueError( | ||
| "Samples required if condition context is not specified" | ||
| ) | ||
| return self.simulator(theta, n_samples) | ||
| else: | ||
| return self.simulator.simulate(theta, condition_context) | ||
|
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| def simulated_context(self, n_samples): | ||
| return self.simulator.generate_context(n_samples) | ||
|
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| def theta_true(self): | ||
| if hasattr(self, "theta_true"): | ||
| return self.theta_true | ||
| else: | ||
| return get_item("data", "theta_true") | ||
|
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| def sigma_true(self): | ||
| if hasattr(self, "sigma_true"): | ||
| return self.sigma_true | ||
| else: | ||
| return get_item("data", "sigma_true") | ||
|
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| def save(self, data, path: str): | ||
| raise NotImplementedError | ||
|
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| def read_prior(self): | ||
| raise NotImplementedError | ||
|
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| def load_prior(self, prior, prior_kwargs): | ||
| try: | ||
| prior = self.read_prior() | ||
| except NotImplementedError: | ||
| choices = { | ||
| "normal": self.rng.normal, | ||
| "poisson": self.rng.poisson, | ||
| "uniform": self.rng.uniform, | ||
| "gamma": self.rng.gamma, | ||
| "beta": self.rng.beta, | ||
| "binominal": self.rng.binomial, | ||
| } | ||
|
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| if prior not in choices.keys(): | ||
| raise NotImplementedError( | ||
| f"{prior} is not an option for a prior, choose from {list(choices.keys())}" | ||
| ) | ||
| if prior_kwargs is None: | ||
| prior_kwargs = {} | ||
| return lambda size: choices[prior](**prior_kwargs, size=size) | ||
|
|
||
| except KeyError as e: | ||
| raise RuntimeError(f"Data missing a prior specification - {e}") |
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