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(>2.9,<2.16)", "tensorflow-probability (<2.16)", "tensorflow-text (<2.16)", "tf2onnx"] +tf-cpu = ["keras (>2.9,<2.16)", "keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>2.9,<2.16)", "tensorflow-probability (<0.24)", "tensorflow-text (<2.16)", "tf2onnx"] tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"] -timm = ["timm"] +timm = ["timm (<=0.9.16)"] tokenizers = ["tokenizers (>=0.19,<0.20)"] torch = ["accelerate (>=0.21.0)", "torch"] torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"] torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"] -torchhub = ["filelock", "huggingface-hub (>=0.23.0,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.19,<0.20)", "torch", "tqdm (>=4.27)"] +torchhub = ["filelock", "huggingface-hub (>=0.23.2,<1.0)", "importlib-metadata", "numpy (>=1.17,<2.0)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.19,<0.20)", "torch", "tqdm (>=4.27)"] video = ["av (==9.2.0)", "decord (==0.6.0)"] vision = ["Pillow (>=10.0.1,<=15.0)"] @@ -5762,13 +5761,13 @@ zstd = ["zstandard (>=0.18.0)"] [[package]] name = "utilsforecast" -version = "0.1.10" +version = "0.2.0" description = "Forecasting utilities" optional = false python-versions = ">=3.8" files = [ - {file = "utilsforecast-0.1.10-py3-none-any.whl", hash = "sha256:186cad81be70466a883a18c284ac1697118af6d896af1c0ab32fb4b124df7194"}, - {file = "utilsforecast-0.1.10.tar.gz", hash = "sha256:0f19ba507dcc642af190968268ea5407d31b7cfa7e4b9d81f9e9344c96069834"}, + {file = "utilsforecast-0.2.0-py3-none-any.whl", hash = "sha256:a4825bf8da547e3dc552f9b9a7a8159341a118c3a5d122191f09bc3683cba433"}, + {file = "utilsforecast-0.2.0.tar.gz", hash = "sha256:3db4245da4e361f26c8eaeef216c2d1206b20defbb033bf11d3e66ce2b1d6ef8"}, ] [package.dependencies] @@ -5777,10 +5776,9 @@ packaging = "*" pandas = ">=1.1.1" [package.extras] -dev = ["datasetsforecast (==0.0.8)", "nbdev", "numba", "pandas[plot]", "plotly", "plotly-resampler", "polars", "pyarrow", "scipy"] +dev = ["datasetsforecast (==0.0.8)", "nbdev", "pandas[plot]", "plotly", "plotly-resampler", "polars[numpy]", "pyarrow", "scipy"] plotting = ["pandas[plot]", "plotly", "plotly-resampler"] -polars = ["polars"] -scalers = ["numba", "scipy"] +polars = ["polars[numpy]"] [[package]] name = "wcwidth" @@ -5974,4 +5972,4 @@ test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", [metadata] lock-version = "2.0" python-versions = ">=3.10,<3.11" -content-hash = "640ff49d7d249bebced6f89477c72046ec94763c34cc6c1fdecd369c99b57e91" +content-hash = "734b625d8c483c4cdced33cc30e90a5199fa1b27724677de68b0429013365853" diff --git a/pyproject.toml b/pyproject.toml index f8e10df4..2a56af80 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -30,15 +30,15 @@ include = [ [tool.poetry.dependencies] python = ">=3.10,<3.11" -einshape = "1.0.0" -numpy = "1.26.4" -pandas = "2.1.4" -paxml = "1.4.0" -utilsforecast = "0.1.10" -jax = {version = "0.4.26", extras = ["cuda12"]} -jaxlib = "0.4.26" -huggingface_hub = {version = "0.23.0", extras = ["cli"]} -scikit-learn = "1.0.2" +einshape = ">=1.0.0" +numpy = ">=1.26.4" +pandas = ">=2.1.4" +paxml = ">=1.4.0" +utilsforecast = ">=0.1.10" +jax = {version = ">=0.4.26", extras = ["cuda12"]} +jaxlib = ">=0.4.26" +huggingface_hub = {version = ">=0.23.0", extras = ["cli"]} +scikit-learn = ">=1.2.2" [build-system] requires = ["poetry-core"] diff --git a/src/timesfm/xreg_lib.py b/src/timesfm/xreg_lib.py index c083bcdc..0062a22a 100644 --- a/src/timesfm/xreg_lib.py +++ b/src/timesfm/xreg_lib.py @@ -11,7 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - """Helper functions for in-context covariates and regression.""" import itertools @@ -36,20 +35,19 @@ def _unnest(nested: Sequence[Sequence[Any]]) -> np.ndarray: def _repeat(elements: Iterable[Any], counts: Iterable[int]) -> np.ndarray: return np.array( list( - itertools.chain.from_iterable(map(itertools.repeat, elements, counts)) - ) - ) + itertools.chain.from_iterable(map(itertools.repeat, elements, + counts)))) def _to_padded_jax_array(x: np.ndarray) -> jax.Array: if x.ndim == 1: (i,) = x.shape - di = 2 ** math.ceil(math.log2(i)) - i + di = 2**math.ceil(math.log2(i)) - i return jnp.pad(x, ((0, di),), mode="constant", constant_values=0.0) elif x.ndim == 2: i, j = x.shape - di = 2 ** math.ceil(math.log2(i)) - i - dj = 2 ** math.ceil(math.log2(j)) - j + di = 2**math.ceil(math.log2(i)) - i + dj = 2**math.ceil(math.log2(j)) - j return jnp.pad(x, ((0, di), (0, dj)), mode="constant", constant_values=0.0) else: raise ValueError(f"Unsupported array shape: {x.shape}") @@ -86,21 +84,16 @@ def __init__( train_lens: Sequence[int], test_lens: Sequence[int], train_dynamic_numerical_covariates: ( - Mapping[str, Sequence[Sequence[float]]] | None - ) = None, + Mapping[str, Sequence[Sequence[float]]] | None) = None, train_dynamic_categorical_covariates: ( - Mapping[str, Sequence[Sequence[Category]]] | None - ) = None, + Mapping[str, Sequence[Sequence[Category]]] | None) = None, test_dynamic_numerical_covariates: ( - Mapping[str, Sequence[Sequence[float]]] | None - ) = None, + Mapping[str, Sequence[Sequence[float]]] | None) = None, test_dynamic_categorical_covariates: ( - Mapping[str, Sequence[Sequence[Category]]] | None - ) = None, + Mapping[str, Sequence[Sequence[Category]]] | None) = None, static_numerical_covariates: Mapping[str, Sequence[float]] | None = None, - static_categorical_covariates: ( - Mapping[str, Sequence[Category]] | None - ) = None, + static_categorical_covariates: (Mapping[str, Sequence[Category]] | + None) = None, ) -> None: """Initializes with the exogenous covariate inputs. @@ -187,17 +180,13 @@ def __init__( self.train_lens = train_lens self.test_lens = test_lens self.train_dynamic_numerical_covariates = ( - train_dynamic_numerical_covariates or {} - ) + train_dynamic_numerical_covariates or {}) self.train_dynamic_categorical_covariates = ( - train_dynamic_categorical_covariates or {} - ) - self.test_dynamic_numerical_covariates = ( - test_dynamic_numerical_covariates or {} - ) + train_dynamic_categorical_covariates or {}) + self.test_dynamic_numerical_covariates = (test_dynamic_numerical_covariates + or {}) self.test_dynamic_categorical_covariates = ( - test_dynamic_categorical_covariates or {} - ) + test_dynamic_categorical_covariates or {}) self.static_numerical_covariates = static_numerical_covariates or {} self.static_categorical_covariates = static_categorical_covariates or {} @@ -205,31 +194,23 @@ def _assert_covariates(self, assert_covariate_shapes: bool = False) -> None: """Verifies the validity of the covariate inputs.""" # Check presence. - if ( - self.train_dynamic_numerical_covariates - and not self.test_dynamic_numerical_covariates - ) or ( - not self.train_dynamic_numerical_covariates - and self.test_dynamic_numerical_covariates - ): + if (self.train_dynamic_numerical_covariates and + not self.test_dynamic_numerical_covariates) or ( + not self.train_dynamic_numerical_covariates and + self.test_dynamic_numerical_covariates): raise ValueError( "train_dynamic_numerical_covariates and" " test_dynamic_numerical_covariates must be both present or both" - " absent." - ) - - if ( - self.train_dynamic_categorical_covariates - and not self.test_dynamic_categorical_covariates - ) or ( - not self.train_dynamic_categorical_covariates - and self.test_dynamic_categorical_covariates - ): + " absent.") + + if (self.train_dynamic_categorical_covariates and + not self.test_dynamic_categorical_covariates) or ( + not self.train_dynamic_categorical_covariates and + self.test_dynamic_categorical_covariates): raise ValueError( "train_dynamic_categorical_covariates and" " test_dynamic_categorical_covariates must be both present or both" - " absent." - ) + " absent.") # Check keys. for dict_a, dict_b, dict_a_name, dict_b_name in ( @@ -248,46 +229,38 @@ def _assert_covariates(self, assert_covariate_shapes: bool = False) -> None: ): if w := set(dict_a.keys()) - set(dict_b.keys()): raise ValueError( - f"{dict_a_name} has keys not present in {dict_b_name}: {w}" - ) + f"{dict_a_name} has keys not present in {dict_b_name}: {w}") if w := set(dict_b.keys()) - set(dict_a.keys()): raise ValueError( - f"{dict_b_name} has keys not present in {dict_a_name}: {w}" - ) + f"{dict_b_name} has keys not present in {dict_a_name}: {w}") # Check shapes. if assert_covariate_shapes: if len(self.targets) != len(self.train_lens): raise ValueError( - "targets and train_lens must have the same number of elements." - ) + "targets and train_lens must have the same number of elements.") if len(self.train_lens) != len(self.test_lens): raise ValueError( - "train_lens and test_lens must have the same number of elements." - ) + "train_lens and test_lens must have the same number of elements.") - for i, (target, train_len) in enumerate( - zip(self.targets, self.train_lens) - ): + for i, (target, train_len) in enumerate(zip(self.targets, + self.train_lens)): if len(target) != train_len: raise ValueError( - f"targets[{i}] has length {len(target)} != expected {train_len}." - ) + f"targets[{i}] has length {len(target)} != expected {train_len}.") for key, values in self.static_numerical_covariates.items(): if len(values) != len(self.train_lens): raise ValueError( f"static_numerical_covariates has key {key} with number of" - f" examples {len(values)} != expected {len(self.train_lens)}." - ) + f" examples {len(values)} != expected {len(self.train_lens)}.") for key, values in self.static_categorical_covariates.items(): if len(values) != len(self.train_lens): raise ValueError( f"static_categorical_covariates has key {key} with number of" - f" examples {len(values)} != expected {len(self.train_lens)}." - ) + f" examples {len(values)} != expected {len(self.train_lens)}.") for lens, dict_cov, dict_cov_name in ( ( @@ -315,14 +288,12 @@ def _assert_covariates(self, assert_covariate_shapes: bool = False) -> None: if len(cov_values) != len(lens): raise ValueError( f"{dict_cov_name} has key {key} with number of examples" - f" {len(cov_values)} != expected {len(lens)}." - ) + f" {len(cov_values)} != expected {len(lens)}.") for i, cov_value in enumerate(cov_values): if len(cov_value) != lens[i]: raise ValueError( f"{dict_cov_name} has key {key} with its {i}-th example" - f" length {len(cov_value)} != expected {lens[i]}." - ) + f" length {len(cov_value)} != expected {lens[i]}.") def create_covariate_matrix( self, @@ -356,11 +327,9 @@ def create_covariate_matrix( # Numerical features. for name in sorted(self.train_dynamic_numerical_covariates): x_train.append( - _unnest(self.train_dynamic_numerical_covariates[name])[:, np.newaxis] - ) + _unnest(self.train_dynamic_numerical_covariates[name])[:, np.newaxis]) x_test.append( - _unnest(self.test_dynamic_numerical_covariates[name])[:, np.newaxis] - ) + _unnest(self.test_dynamic_numerical_covariates[name])[:, np.newaxis]) for covs in self.static_numerical_covariates.values(): x_train.append(_repeat(covs, self.train_lens)[:, np.newaxis]) @@ -372,25 +341,22 @@ def create_covariate_matrix( # Normalize for robustness. x_mean = np.mean(x_train, axis=0, keepdims=True) - x_std = np.where( - (w := np.std(x_train, axis=0, keepdims=True)) > _TOL, w, 1.0 - ) + x_std = np.where((w := np.std(x_train, axis=0, keepdims=True)) > _TOL, w, + 1.0) x_train = [(x_train - x_mean) / x_std] x_test = [(x_test - x_mean) / x_std] # Categorical features. Encode one by one. one_hot_encoder = preprocessing.OneHotEncoder( drop=one_hot_encoder_drop, - sparse=False, + sparse_output=False, handle_unknown="ignore", ) for name in sorted(self.train_dynamic_categorical_covariates.keys()): - ohe_train = _unnest(self.train_dynamic_categorical_covariates[name])[ - :, np.newaxis - ] - ohe_test = _unnest(self.test_dynamic_categorical_covariates[name])[ - :, np.newaxis - ] + ohe_train = _unnest( + self.train_dynamic_categorical_covariates[name])[:, np.newaxis] + ohe_test = _unnest( + self.test_dynamic_categorical_covariates[name])[:, np.newaxis] x_train.append(np.array(one_hot_encoder.fit_transform(ohe_train))) x_test.append(np.array(one_hot_encoder.transform(ohe_test))) @@ -426,12 +392,8 @@ def fit( debug_info: bool = False, assert_covariates: bool = False, assert_covariate_shapes: bool = False, - ) -> ( - list[np.ndarray] - | tuple[ - list[np.ndarray], list[np.ndarray], jax.Array, jax.Array, jax.Array - ] - ): + ) -> (list[np.ndarray] | tuple[list[np.ndarray], list[np.ndarray], jax.Array, + jax.Array, jax.Array]): """Fits a linear model for in-context regression. Args: @@ -495,14 +457,10 @@ def fit( x_train = _to_padded_jax_array(x_train) flat_targets = _to_padded_jax_array(flat_targets) x_test = _to_padded_jax_array(x_test) - beta_hat = ( - jnp.linalg.pinv( - x_train.T @ x_train + ridge * jnp.eye(x_train.shape[1]), - hermitian=True, - ) - @ x_train.T - @ flat_targets - ) + beta_hat = (jnp.linalg.pinv( + x_train.T @ x_train + ridge * jnp.eye(x_train.shape[1]), + hermitian=True, + ) @ x_train.T @ flat_targets) y_hat = x_test @ beta_hat y_hat_context = x_train_raw @ beta_hat if debug_info else None @@ -511,18 +469,14 @@ def fit( # Reconstruct the ragged 2-dim batched forecasts from flattened linear fits. train_index, test_index = 0, 0 - for train_index_delta, test_index_delta in zip( - self.train_lens, self.test_lens - ): - outputs.append( - np.array(y_hat[test_index : (test_index + test_index_delta)]) - ) + for train_index_delta, test_index_delta in zip(self.train_lens, + self.test_lens): + outputs.append(np.array(y_hat[test_index:(test_index + + test_index_delta)])) if debug_info: outputs_context.append( - np.array( - y_hat_context[train_index : (train_index + train_index_delta)] - ) - ) + np.array(y_hat_context[train_index:(train_index + + train_index_delta)])) train_index += train_index_delta test_index += test_index_delta