From 51381c2a4cdf3c8cdd29fc45bc44a552dde7fd02 Mon Sep 17 00:00:00 2001 From: Simon Dirmeier Date: Sat, 28 Dec 2024 19:10:13 +0100 Subject: [PATCH 1/3] Remove nonfunctioning GPJax --- .github/workflows/ci.yaml | 3 - .github/workflows/release.yaml | 6 +- examples/reconciliation.py | 126 ++-- pyproject.toml | 44 +- reconcile/__init__.py | 2 +- reconcile/conftest.py | 8 +- reconcile/forecast.py | 6 +- reconcile/grouping.py | 4 +- reconcile/probabilistic_reconciliation.py | 13 +- uv.lock | 808 ++++++++++++++++++++++ 10 files changed, 908 insertions(+), 112 deletions(-) create mode 100644 uv.lock diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index fe48ad8..c3e9894 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -71,9 +71,6 @@ jobs: - name: Install dependencies run: | pip install hatch - - name: Build package - run: | - pip install jaxlib jax - name: Run tests run: | hatch run test:test diff --git a/.github/workflows/release.yaml b/.github/workflows/release.yaml index 3c918b9..d1b137c 100644 --- a/.github/workflows/release.yaml +++ b/.github/workflows/release.yaml @@ -10,11 +10,11 @@ jobs: runs-on: ubuntu-latest strategy: matrix: - python-version: [3.9] + python-version: [3.11] steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v2 + uses: actions/setup-python@v3 with: python-version: ${{ matrix.python-version }} - name: Install pypa/build diff --git a/examples/reconciliation.py b/examples/reconciliation.py index e1c2ab1..94f48ea 100644 --- a/examples/reconciliation.py +++ b/examples/reconciliation.py @@ -1,40 +1,37 @@ import chex -import distrax -import gpjax as gpx import jax import numpy as np -import optax import pandas as pd +from einops import rearrange +from jax import Array from jax import numpy as jnp from jax import random as jr +from ramsey import NP, train_neural_process +from ramsey.nn import MLP from statsmodels.tsa.arima_process import arma_generate_sample +from tensorflow_probability.substrates.jax import distributions as tfd from reconcile.forecast import Forecaster from reconcile.grouping import Grouping from reconcile.probabilistic_reconciliation import ProbabilisticReconciliation -jax.config.update("jax_enable_x64", True) - - -class GPForecaster(Forecaster): - """Example implementation of a forecaster""" +class NeuralProcessForecaster(Forecaster): + """Example implementation of a forecaster.""" def __init__(self): super().__init__() self._models: list = [] - self._xs: jax.Array = None - self._ys: jax.Array = None + self._xs: jax.Array + self._ys: jax.Array @property - def data(self): - """Returns the data""" - return self._ys, self._xs + def data(self) -> tuple[Array, Array]: + return self._xs, self._ys def fit( self, rng_key: jr.PRNGKey, ys: jax.Array, xs: jax.Array, niter=2000 ): - """Fit a model to each of the time series""" - + """Fit a model to each of the time series.""" self._xs = xs self._ys = ys chex.assert_rank([ys, xs], [3, 3]) @@ -43,44 +40,39 @@ def fit( p = xs.shape[1] self._models = [None] * p for i in np.arange(p): - x, y = xs[:, [i], :], ys[:, [i], :] + x, y = xs[..., i, :], ys[..., i, :] # fit a model for each time series - opt_posterior, _, D = self._fit_one(rng_key, x, y, niter) + model, params = self._fit_one(rng_key, x, y, niter) # save the learned parameters and the original data - self._models[i] = opt_posterior, D + self._models[i] = model, params def _fit_one(self, rng_key, x, y, niter): - # here we use GPs to model the time series - D = gpx.Dataset(X=x.reshape(-1, 1), y=y.reshape(-1, 1)) - elbo, q, likelihood = self._model(rng_key, D.n) - - negative_elbo = jax.jit(elbo) - optimiser = optax.adam(learning_rate=5e-3) - opt_posterior, history = gpx.fit( - model=q, - objective=negative_elbo, - train_data=D, - optim=optimiser, - num_iters=niter, - key=rng_key, + # here we use neural processes to model the time series + model = self._model() + n_context, n_target = 10, 20 + params, _ = train_neural_process( + rng_key, + model, + x=x.reshape(1, -1, 1), + y=y.reshape(1, -1, 1), + n_context=n_context, + n_target=n_target, + n_iter=1000, + batch_size=1, ) - return opt_posterior, history, D + return model, params @staticmethod - def _model(rng_key, n): - z = jr.uniform(rng_key, (20, 1)) - prior = gpx.gps.Prior( - mean_function=gpx.mean_functions.Constant(), - kernel=gpx.kernels.RBF(), - ) - likelihood = gpx.likelihoods.Gaussian(num_datapoints=n) - posterior = prior * likelihood - q = gpx.variational_families.CollapsedVariationalGaussian( - posterior=posterior, - inducing_inputs=z, - ) - elbo = gpx.objectives.CollapsedELBO(negative=True) - return elbo, q, likelihood + def _model(): + def get_neural_process(): + dim = 128 + np = NP( + decoder=MLP([dim] * 3 + [2]), + latent_encoder=(MLP([dim] * 3), MLP([dim, dim * 2])), + ) + return np + neural_process = get_neural_process() + return neural_process def posterior_predictive(self, rng_key, xs_test: jax.Array): """Compute the joint posterior predictive distribution at xs_test.""" @@ -88,25 +80,27 @@ def posterior_predictive(self, rng_key, xs_test: jax.Array): q = xs_test.shape[1] means = [None] * q - covs = [None] * q + scales = [None] * q for i in np.arange(q): - x_test = xs_test[:, [i], :].reshape(-1, 1) - opt_posterior, D = self._models[i] - _, q, likelihood = self._model(rng_key, D.n) - latent_dist = opt_posterior(x_test, train_data=D) - predictive_dist = opt_posterior.posterior.likelihood(latent_dist) - means[i] = predictive_dist.mean() - cov = predictive_dist.scale_tril - covs[i] = cov.reshape((1, *cov.shape)) - - # here we stack the means and covariance functions of all - # GP models we used - means = jnp.vstack(means) - covs = jnp.vstack(covs) - - # here we use a single distrax distribution to model the predictive + x_context = self._xs[..., i, :] + y_context = self._ys[..., i, :] + x_test = xs_test[..., i, :] + + model, params = self._models[i] + predictive_dist = model.apply( + variables=params, + rngs={"sample": rng_key}, + x_context=x_context.reshape(1, -1, 1), + y_context=y_context.reshape(1, -1, 1), + x_target=x_test.reshape(1, -1, 1), + ) + means[i] = predictive_dist.mean + scales[i] = predictive_dist.scale + + means = rearrange(jnp.vstack(means), "b t ... -> ... b t") + scales = rearrange(jnp.vstack(scales), "b t ... -> ... b t") # posterior of _all_ models - posterior_predictive = distrax.MultivariateNormalTri(means, covs) + posterior_predictive = tfd.MultivariateNormalDiag(means, scales) return posterior_predictive def predictive_posterior_probability( @@ -147,12 +141,12 @@ def sample_hierarchical_timeseries(): and the second one is a pd.DataFrame of groups """ - def _group_names(): + def _hierarchy(): hierarchy = ["A:10", "A:20", "B:10", "B:20", "B:30"] return pd.DataFrame.from_dict({"h1": hierarchy}) - return _sample_timeseries(100, 5), _group_names() + return _sample_timeseries(100, 5), _hierarchy() def run(): @@ -161,7 +155,7 @@ def run(): all_timeseries = grouping.all_timeseries(b) all_features = jnp.tile(x, [1, all_timeseries.shape[1], 1]) - forecaster = GPForecaster() + forecaster = NeuralProcessForecaster() forecaster.fit( jr.PRNGKey(1), all_timeseries[:, :, :90], diff --git a/pyproject.toml b/pyproject.toml index 92a277c..35011b2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,42 +7,40 @@ name = "probabilistic-reconciliation" description = "Probabilistic reconciliation of time series forecasts" authors = [{name = "Simon Dirmeier", email = "sfyrbnd@pm.me"}] readme = "README.md" -license = "Apache-2.0" -homepage = "https://github.com/dirmeier/reconcile" +license = {file = "LICENSE"} keywords = ["probabilistic reconciliation", "forecasting", "timeseries", "hierarchical time series"] -classifiers=[ +classifiers= [ "Development Status :: 3 - Alpha", "Intended Audience :: Science/Research", "License :: OSI Approved :: Apache Software License", "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.9", - "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", ] -requires-python = ">=3.9" +requires-python = ">=3.11" dependencies = [ - "blackjax-nightly>=0.9.6.post127", - "distrax>=0.1.2", - "chex>=0.1.5", - "jaxlib>=0.4.18", - "jax>=0.4.18", - "flax>=0.7.3", - "gpjax>=0.6.9", - "optax>=0.1.3", - "pandas>=1.5.1" + "blackjax>=1.2.4", + "chex>=0.1.8", + "einops>=0.8.0", + "flax>=0.10.2", + "jax>=0.4.38", + "optax>=0.2.4", + "pandas>=1.5.1", + "ramsey>=0.2.1", + "tfp-nightly[jax]>=0.26.0.dev20241227", ] dynamic = ["version"] [project.urls] homepage = "https://github.com/dirmeier/reconcile" -[tool.hatch.build.targets.wheel] -packages = ["reconcile"] - [tool.hatch.version] path = "reconcile/__init__.py" +[tool.hatch.build.targets.wheel] +packages = ["reconcile"] + [tool.hatch.build.targets.sdist] exclude = [ "/.github", @@ -55,20 +53,18 @@ dependencies = [ "ruff>=0.3.0", "pytest>=7.2.0", "pytest-cov>=4.0.0", - "gpjax>=0.5.0", "statsmodels>=0.13.2" ] -[tool.hatch.envs.test.scripts] -lint = 'ruff check reconcile examples' -test = 'pytest -v --doctest-modules --cov=./reconcile --cov-report=xml reconcile' - [tool.hatch.envs.examples] dependencies = [ - "gpjax>=0.5.0", "statsmodels>=0.13.2" ] +[tool.hatch.envs.test.scripts] +lint = 'ruff check reconcile examples' +test = 'pytest -v --doctest-modules --cov=./reconcile --cov-report=xml reconcile' + [tool.hatch.envs.examples.scripts] reconciliation = 'python examples/reconciliation.py' diff --git a/reconcile/__init__.py b/reconcile/__init__.py index a355066..a80d426 100644 --- a/reconcile/__init__.py +++ b/reconcile/__init__.py @@ -1,6 +1,6 @@ """reconcile: Probabilistic reconciliation of time series forecasts.""" -__version__ = "0.1.0" +__version__ = "0.2.0" from reconcile.forecast import Forecaster from reconcile.grouping import Grouping diff --git a/reconcile/conftest.py b/reconcile/conftest.py index a542b82..493a30f 100644 --- a/reconcile/conftest.py +++ b/reconcile/conftest.py @@ -6,7 +6,7 @@ from jax import numpy as jnp from jax import random -from examples.reconciliation import GPForecaster +from examples.reconciliation import NeuralProcessForecaster from reconcile import ProbabilisticReconciliation from reconcile.grouping import Grouping @@ -40,11 +40,11 @@ def reconciliator(): all_timeseries = grouping.all_timeseries(b) all_features = jnp.tile(x, [1, all_timeseries.shape[1], 1]) - forecaster = GPForecaster() + forecaster = NeuralProcessForecaster() forecaster.fit( random.PRNGKey(1), - all_timeseries[:, :90, :], - all_features[:, :90, :], + all_timeseries[:, :, :90], + all_features[:, :, :90], 100, ) diff --git a/reconcile/forecast.py b/reconcile/forecast.py index 879f719..ee699f7 100644 --- a/reconcile/forecast.py +++ b/reconcile/forecast.py @@ -2,9 +2,9 @@ import abc -import distrax from jax import Array from jax import random as jr +from tensorflow_probability.substrates.jax import distributions as tfp class Forecaster(metaclass=abc.ABCMeta): @@ -49,7 +49,7 @@ def fit(self, rng_key: jr.PRNGKey, ys: Array, xs: Array) -> None: @abc.abstractmethod def posterior_predictive( self, rng_key: jr.PRNGKey, xs_test: Array - ) -> distrax.Distribution: + ) -> tfp.Distribution: """Computes the posterior predictive distribution at some input points. Args: @@ -61,7 +61,7 @@ def posterior_predictive( elements as the original training data Return: - returns a distrax Distribution with batch shape (,P) and event + returns a TFP Distribution with batch shape (,P) and event shape (,M), such that a single sample has shape (P, M) and multiple samples have shape (S, P, M) """ diff --git a/reconcile/grouping.py b/reconcile/grouping.py index f88cd0b..737aade 100644 --- a/reconcile/grouping.py +++ b/reconcile/grouping.py @@ -79,12 +79,12 @@ def summing_matrix(self): def extract_bottom_timeseries(self, y): """Getter for the bottom time series.""" - return y[:, self.n_upper_timeseries :, :] + return y[..., self.n_upper_timeseries:, :] def upper_time_series(self, b): """Getter for upper time series.""" y = self.all_timeseries(b) - return y[:, : self.n_upper_timeseries, :] + return y[..., :self.n_upper_timeseries, :] @staticmethod def _paste0(a, b): diff --git a/reconcile/probabilistic_reconciliation.py b/reconcile/probabilistic_reconciliation.py index 6b7823c..1d4bf30 100644 --- a/reconcile/probabilistic_reconciliation.py +++ b/reconcile/probabilistic_reconciliation.py @@ -1,11 +1,12 @@ """Probabilistic reconciliation module.""" import logging -from typing import Callable +from collections.abc import Callable import blackjax import jax import optax +from einops import rearrange from flax import linen as nn from flax.training.early_stopping import EarlyStopping from flax.training.train_state import TrainState @@ -80,7 +81,7 @@ def lp(x): initial_positions = self._forecaster.posterior_predictive( curr_key, xs_test, - ).sample(seed=rng_key, sample_shape=(n_chains,)) + ).sample(seed=rng_key, sample_shape=(n_chains, )) initial_positions = { "b": self._grouping.extract_bottom_timeseries(initial_positions) } @@ -108,7 +109,7 @@ def _step(states, rng_key): states = _inference_loop(rng_key, kernel, initial_states, n_iter) b_samples = states.position["b"].block_until_ready() b_samples = b_samples[n_warmup:, ...] - + b_samples = rearrange(b_samples, "... f b t-> ... (f b) t") return b_samples def fit_reconciled_posterior_predictive( @@ -208,7 +209,7 @@ def loss_fn(params): state = TrainState.create(apply_fn=model.apply, params=params, tx=tx) batch_size = 64 - early_stop = EarlyStopping(min_delta=0.1, patience=5) + early_stop = EarlyStopping(min_delta=0.1, patience=10) itr = 0 while True: sample_key, rng_key = jr.split(rng_key) @@ -218,7 +219,7 @@ def loss_fn(params): ) state, loss = _step(state, ys, y_predictive_batch) logger.info("Loss after batch update %d", loss) - _, early_stop = early_stop.update(loss) + early_stop = early_stop.update(loss) if early_stop.should_stop and n_iter is None: logger.info("Met early stopping criteria, breaking...") break @@ -231,5 +232,5 @@ def loss_fn(params): seed=rng_key, sample_shape=(n_samples,) ) b_reconciled = state.apply_fn(state.params, y_predictive) - + b_reconciled = rearrange(b_reconciled, "... f b t-> ... 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-18,6 +18,7 @@ class NeuralProcessForecaster(Forecaster): """Example implementation of a forecaster.""" + def __init__(self): super().__init__() self._models: list = [] @@ -71,6 +72,7 @@ def get_neural_process(): latent_encoder=(MLP([dim] * 3), MLP([dim, dim * 2])), ) return np + neural_process = get_neural_process() return neural_process diff --git a/reconcile/grouping.py b/reconcile/grouping.py index 737aade..eb5a582 100644 --- a/reconcile/grouping.py +++ b/reconcile/grouping.py @@ -79,12 +79,12 @@ def summing_matrix(self): def extract_bottom_timeseries(self, y): """Getter for the bottom time series.""" - return y[..., self.n_upper_timeseries:, :] + return y[..., self.n_upper_timeseries :, :] def upper_time_series(self, b): """Getter for upper time series.""" y = self.all_timeseries(b) - return y[..., :self.n_upper_timeseries, :] + return y[..., : self.n_upper_timeseries, :] @staticmethod def _paste0(a, b): diff --git a/reconcile/probabilistic_reconciliation.py 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