From cae96f2081f5b6f97151066161355149e867f191 Mon Sep 17 00:00:00 2001 From: Samuel Hinton Date: Mon, 16 Oct 2023 09:24:20 +1000 Subject: [PATCH] Updating MR --- .pre-commit-config.yaml | 2 +- .../plot_4_misc_chain_visuals.py | 21 +- docs/examples/plot_0_contours.py | 6 +- docs/examples/plot_5_emcee_arviz_numpyro.py | 124 + poetry.lock | 2016 ++--------------- pyproject.toml | 7 +- src/chainconsumer/chain.py | 46 +- tests/test_translators.py | 10 +- 8 files changed, 317 insertions(+), 1915 deletions(-) create mode 100644 docs/examples/plot_5_emcee_arviz_numpyro.py diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index fe8aa0b1..61b9bdfc 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,6 +1,6 @@ repos: - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.4.0 + rev: v4.5.0 hooks: - id: check-added-large-files args: ["--maxkb=5000"] diff --git a/docs/examples/advanced_examples/plot_4_misc_chain_visuals.py b/docs/examples/advanced_examples/plot_4_misc_chain_visuals.py index bdf859bf..c4a7d17a 100644 --- a/docs/examples/advanced_examples/plot_4_misc_chain_visuals.py +++ b/docs/examples/advanced_examples/plot_4_misc_chain_visuals.py @@ -4,7 +4,8 @@ Rather than having one example for each option, let's condense things. """ # %% -# # Shade Gradient +# Shade Gradient +# -------------- # # Pretty simple - it controls how much visual difference there is in your contours. import numpy as np @@ -22,7 +23,8 @@ fig = c.plotter.plot() # %% -# # Shade Alpha +# Shade Alpha +# ----------- # # Controls how opaque the contours are. Like everything else, you # can specify this when making the chain, or apply a single override @@ -31,7 +33,8 @@ fig = c.plotter.plot() # %% -# # Contour Labels +# Contour Labels +# -------------- # # Add labels to contours. I used to have this configurable to be either # sigma levels or percentages, but there was confusion over the 1D vs 2D sigma levels, @@ -41,7 +44,8 @@ fig = c.plotter.plot() # %% -# # Linestyles and widths +# Linestyles and widths +# --------------------- # # Fairly simple to do. To show different ones, I'll remake the chains, # rather than having a single override. Note you *could* try something @@ -56,7 +60,8 @@ fig = c2.plotter.plot() # %% -# # Marker styles and sizes +# Marker styles and sizes +# ----------------------- # # Provided you have a posterior column, you can plot the maximum probability point. @@ -64,7 +69,8 @@ fig = c.plotter.plot() # %% -# # Cloud and Sigma Levels +# Cloud and Sigma Levels +# ---------------------- # # Choose custom sigma levels and display point cloud. c.set_override( @@ -78,7 +84,8 @@ fig = c.plotter.plot() # %% -# # Smoothing (or not) +# Smoothing (or not) +# ------------------ # # The histograms behind the scene in ChainConsumer are smoothed. But you can turn this off. # The higher the smoothing vaule, the more subidivisions of your bins there will be. diff --git a/docs/examples/plot_0_contours.py b/docs/examples/plot_0_contours.py index d464ce2a..85995424 100644 --- a/docs/examples/plot_0_contours.py +++ b/docs/examples/plot_0_contours.py @@ -35,7 +35,8 @@ fig = c.plotter.plot() # %% Third cell -# # Customising Chains +# Customising Chains +# ------------------ # # There's a lot you can configure using chains, and to make it easy, Chains are defined as pydantic # base models so you can easily see the default and values you can pass in. Don't worry, there will be @@ -48,7 +49,8 @@ # %% Fourth cell -# # Weights and Posteriors +# Weights and Posteriors +# ---------------------- # # If you provide the log posteriors in the chain, you can ask for the maximum probability point # to be plotted as well. Similarly, if you have samples with non-uniform weights, you can diff --git a/docs/examples/plot_5_emcee_arviz_numpyro.py b/docs/examples/plot_5_emcee_arviz_numpyro.py new file mode 100644 index 00000000..0e70d3be --- /dev/null +++ b/docs/examples/plot_5_emcee_arviz_numpyro.py @@ -0,0 +1,124 @@ +""" +# Using external samples easily + +`emcee`, `arviz`, and `numpyro` are all popular MCMC packages. ChainConsumer +provides class methods to turn results from these packages into chains efficiently. + +If you want to request support for another type of chain, please open a +[discussion](https://github.com/Samreay/ChainConsumer/discussions) with a code +example, and we can add it in. The brave may even provide a PR! +""" + +import arviz as az +import emcee +import numpy as np +import numpyro +import numpyro.distributions as dist +from jax import random +from numpyro.infer import MCMC, NUTS +from scipy.stats import norm + +from chainconsumer import Chain, ChainConsumer + +# %% +# Emcee +# ----- +# +# Let's make a dummy model here. + + +# Of course, your code is probably a bit more complex +def run_emcee_mcmc(n_steps, n_walkers): + rng = np.random.default_rng(42) + observed_data = rng.normal(loc=1, scale=1, size=100) + + def log_likelihood(theta, data): + mu, log_sigma = theta + return np.sum(norm.logpdf(data, mu, np.exp(log_sigma))) + + def log_prior(theta): + mu, log_sigma = theta + if -10 < mu < 10 and -10 < log_sigma < 10: + return 0.0 + return -np.inf + + def log_probability(theta, data): + lp = log_prior(theta) + if not np.isfinite(lp): + return -np.inf + return lp + log_likelihood(theta, data) + + ndim = 2 + p0 = rng.uniform(low=0, high=1, size=(n_walkers, ndim)) + sampler = emcee.EnsembleSampler(n_walkers, ndim, log_probability, args=(observed_data,)) + sampler.run_mcmc(p0, n_steps, progress=False) + + return sampler + + +sampler = run_emcee_mcmc(8000, 16) +params = [r"$\mu$", r"$\log(\sigma)$"] +chain = Chain.from_emcee(sampler, params, "an emcee chain", discard=200, thin=2, color="indigo") +consumer = ChainConsumer().add_chain(chain) + +# %% +# Let's plot the walks to make sure we've discard enough burn-in +fig = consumer.plotter.plot_walks() + +# %% +# And then show the contours themselves +fig = consumer.plotter.plot() + + +# %% +# Numpyro +# ------- +# +# Let's start with numpyro. Again, let's make a dummy model we can sample from. + + +def run_numpyro_mcmc(n_steps, n_chains): + rng = np.random.default_rng(42) + observed_data = rng.normal(loc=0, scale=1, size=100) + + def model(data): + # Prior + mu = numpyro.sample("mu", dist.Normal(0, 10)) + sigma = numpyro.sample("sigma", dist.HalfNormal(10)) + + # Likelihood + with numpyro.plate("data", size=len(data)): + numpyro.sample("obs", dist.Normal(mu, sigma), obs=data) # type: ignore + + # Running MCMC + kernel = NUTS(model) + mcmc = MCMC(kernel, num_warmup=500, num_samples=n_steps, num_chains=n_chains, progress_bar=False) + rng_key = random.PRNGKey(0) + mcmc.run(rng_key, data=observed_data) + + return mcmc + + +mcmc = run_numpyro_mcmc(8000, 1) +chain = Chain.from_numpyro(mcmc, "numpyro chain", color="teal") +consumer = ChainConsumer().add_chain(chain) + +# %% +# Let's plot the walks to make sure we've discard enough burn-in +fig = consumer.plotter.plot_walks() + +# %% +# And then show the contours themselves +fig = consumer.plotter.plot() + +# %% +# Arviz +# ----- +# +# To simplify the process, we're going to make our arviz sample from +# the numpyro one. + +# %% +arviz_id = az.from_numpyro(mcmc) +chain = Chain.from_arviz(arviz_id, "arviz chain", color="amber") +fig = ChainConsumer().add_chain(chain).plotter.plot() diff --git a/poetry.lock b/poetry.lock index 8466a2aa..b147b7d4 100644 --- a/poetry.lock +++ b/poetry.lock @@ -11,114 +11,6 @@ files = [ {file = "annotated_types-0.6.0.tar.gz", hash = "sha256:563339e807e53ffd9c267e99fc6d9ea23eb8443c08f112651963e24e22f84a5d"}, ] -[[package]] -name = "anyio" -version = "4.0.0" -description = "High level compatibility layer for multiple asynchronous event loop implementations" -optional = false -python-versions = ">=3.8" -files = [ - 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["websockets"] - -[[package]] -name = "widgetsnbextension" -version = "4.0.9" -description = "Jupyter interactive widgets for Jupyter Notebook" -optional = false -python-versions = ">=3.7" -files = [ - {file = "widgetsnbextension-4.0.9-py3-none-any.whl", hash = "sha256:91452ca8445beb805792f206e560c1769284267a30ceb1cec9f5bcc887d15175"}, - {file = "widgetsnbextension-4.0.9.tar.gz", hash = "sha256:3c1f5e46dc1166dfd40a42d685e6a51396fd34ff878742a3e47c6f0cc4a2a385"}, -] - [[package]] name = "xarray" version = "2023.9.0" @@ -4042,4 +2304,4 @@ test = ["hypothesis", "packaging", "pytest", "pytest-cov"] [metadata] lock-version = "2.0" python-versions = ">=3.10,<3.13" -content-hash = "40b662a12a08db90262e8c485106041a0c11ab8b40eb02007a443eead04aa071" +content-hash = "591bf56448db713e19b5256f065ab49a122bb0727129974a3b2d4bf372b117d8" diff --git a/pyproject.toml b/pyproject.toml index 72b9f301..30ef641e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -18,7 +18,7 @@ python = ">=3.10,<3.13" numpy = "^1.23.0" scipy = "^1.8.0" matplotlib = "^3.6.0" -statsmodels = "^0.14.0" +statsmodels = ">=0.14.0, <1" pandas = "^2.1.1" pillow = "^10.0.1" pydantic = "^2.2.0" @@ -26,9 +26,9 @@ pydantic = "^2.2.0" [tool.poetry.group.test.dependencies] pytest = "^7.4.0" -numpyro = "^0.13.2" +numpyro = ">=0.13.2, <1" emcee = "^3.1.4" -arviz = "^0.16.1" +arviz = ">=0.16.1, <1" [tool.poetry.group.dev.dependencies] black = ">=23.3.0" @@ -39,7 +39,6 @@ pandas-stubs = "^2.1.1.230928" [tool.poetry.group.docs.dependencies] -jupyter = "^1.0.0" Markdown = "^3.3.7" mkdocs = "^1.1.2" mkdocs-material = "^9.1.19" diff --git a/src/chainconsumer/chain.py b/src/chainconsumer/chain.py index ad24d721..8933f414 100644 --- a/src/chainconsumer/chain.py +++ b/src/chainconsumer/chain.py @@ -358,7 +358,7 @@ def get_correlation(self, columns: list[str] | None = None) -> Named2DMatrix: """ cov = self.get_covariance(columns) diag = np.sqrt(np.diag(cov.matrix)) - divisor = diag[None, :] * diag[:, None] + divisor = diag[None, :] * diag[:, None] # type: ignore correlations = cov.matrix / divisor return Named2DMatrix(columns=cov.columns, matrix=correlations) @@ -367,20 +367,26 @@ def from_emcee( cls, sampler: emcee.EnsembleSampler, columns: list[str], - name: str = "Chain", + name: str, + thin: int = 1, + discard: int = 0, **kwargs: Any, ) -> Chain: - """ - Constructor from an emcee sampler + """Constructor from an emcee sampler - Args + Args: sampler: The emcee sampler columns: The names of the parameters name: The name of the chain + thin: The thinning to apply to the chain + discard: The number of steps to discard from the start of the chain kwargs: Any other arguments to pass to the Chain constructor. - """ - df = pd.DataFrame.from_dict({col: val for col, val in zip(columns, sampler.get_chain(flat=True).T)}) + Returns: + A ChainConsumer Chain made from the emcee samples + """ + chain: np.ndarray = sampler.get_chain(flat=True, thin=thin, discard=discard) # type: ignore + df = pd.DataFrame.from_dict({col: val for col, val in zip(columns, chain.T)}) return cls(samples=df, name=name, **kwargs) @@ -388,36 +394,38 @@ def from_emcee( def from_numpyro( cls, mcmc: numpyro.infer.MCMC, - name: str = "Chain", + name: str, **kwargs: Any, ) -> Chain: - """ - Constructor from an emcee sampler + """Constructor from numpyro samples - Args - sampler: The emcee sampler + Args: + mcmc: The numpyro sampler name: The name of the chain kwargs: Any other arguments to pass to the Chain constructor. - """ + Returns: + A ChainConsumer Chain made from numpyro samples + """ df = pd.DataFrame.from_dict({key: np.ravel(value) for key, value in mcmc.get_samples().items()}) - return cls(samples=df, name=name, **kwargs) @classmethod def from_arviz( cls, arviz_id: arviz.InferenceData, - name: str = "Chain", + name: str, **kwargs: Any, ) -> Chain: - """ - Constructor from an arviz InferenceData object + """Constructor from an arviz InferenceData object - Args - arviz_id: The arviz sampler + Args: + arviz_id: The arviz inference data name: The name of the chain kwargs: Any other arguments to pass to the Chain constructor. + + Returns: + A ChainConsumer Chain made from the arviz chain """ df = arviz_id.to_dataframe(groups="posterior").drop(columns=["chain", "draw"]) diff --git a/tests/test_translators.py b/tests/test_translators.py index a8e15e02..7439ada8 100644 --- a/tests/test_translators.py +++ b/tests/test_translators.py @@ -18,14 +18,14 @@ def run_numpyro_mcmc(n_steps, n_chains): rng = np.random.default_rng(42) observed_data = rng.normal(loc=0, scale=1, size=100) - def model(data=None): + def model(data): # Prior mu = numpyro.sample("mu", dist.Normal(0, 10)) sigma = numpyro.sample("sigma", dist.HalfNormal(10)) # Likelihood with numpyro.plate("data", size=len(data)): - numpyro.sample("obs", dist.Normal(mu, sigma), obs=data) + numpyro.sample("obs", dist.Normal(mu, sigma), obs=data) # type: ignore # Running MCMC kernel = NUTS(model) @@ -79,18 +79,18 @@ class TestTranslators: def test_arviz_translator(self): numpyro_mcmc = run_numpyro_mcmc(self.n_steps, self.n_chains) arviz_id = az.from_numpyro(numpyro_mcmc) - chain = Chain.from_arviz(arviz_id) + chain = Chain.from_arviz(arviz_id, "Arviz") assert chain.samples.shape == (self.n_steps * self.n_chains, self.n_params + 1) # +1 for weight column def test_numpyro_translator(self): numpyro_mcmc = run_numpyro_mcmc(self.n_steps, self.n_chains) - chain = Chain.from_numpyro(numpyro_mcmc) + chain = Chain.from_numpyro(numpyro_mcmc, "numpyro") assert chain.samples.shape == (self.n_steps * self.n_chains, self.n_params + 1) def test_emcee_translator(self): emcee_sampler = run_emcee_mcmc(self.n_steps, self.n_chains) - chain = Chain.from_emcee(emcee_sampler, ["mu", "sigma"]) + chain = Chain.from_emcee(emcee_sampler, ["mu", "sigma"], "emcee") assert chain.samples.shape == (self.n_steps * self.n_chains, self.n_params + 1)