From c54ae4379075707206319ca88d4e526f68fec707 Mon Sep 17 00:00:00 2001 From: geetu040 Date: Tue, 16 Jul 2024 15:10:52 +0200 Subject: [PATCH 1/6] use link instead of block --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 0191b86..e56e0b0 100644 --- a/README.md +++ b/README.md @@ -202,7 +202,7 @@ forecast_df = tfm.forecast_on_df( ## Covariates Support -We now have an external regressors library on top of TimesFM that can support static covariates as well as dynamic covariates available in the future. We have an usage example in `notebooks/covariates.ipynb`. +We now have an external regressors library on top of TimesFM that can support static covariates as well as dynamic covariates available in the future. We have an usage example in [notebooks/covariates.ipynb](notebooks/covariates.ipynb). Let's take a toy example of forecasting sales for a grocery store: @@ -241,11 +241,11 @@ In this example, besides the `Daily_sales`, we also have covariates `Category`, **Notice:** Here we make it mandatory that the dynamic covariates need to cover both the forecasting context and horizon. For example, all dynamic covariates in the example have 14 values: the first 7 correspond to the observed 7 days, and the last 7 correspond to the next 7 days. -We can now provide the past data of the two products along with static and dynamic covariates as a batch input to TimesFM and produce forecasts that take into the account the covariates. To learn more, check out the example in `notebooks/covariates.ipynb`. +We can now provide the past data of the two products along with static and dynamic covariates as a batch input to TimesFM and produce forecasts that take into the account the covariates. To learn more, check out the example in [notebooks/covariates.ipynb](notebooks/covariates.ipynb). ## Finetuning -We have provided an example of finetuning the model on a new dataset in `notebooks/finetuning.ipynb`. +We have provided an example of finetuning the model on a new dataset in [notebooks/finetuning.ipynb](notebooks/finetuning.ipynb). ## Contribution Style guide From 1311ad38681e768ed8d23b7a67301719c1390c9d Mon Sep 17 00:00:00 2001 From: geetu040 Date: Wed, 17 Jul 2024 09:11:12 +0200 Subject: [PATCH 2/6] use github links instead --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index e56e0b0..f34c228 100644 --- a/README.md +++ b/README.md @@ -202,7 +202,7 @@ forecast_df = tfm.forecast_on_df( ## Covariates Support -We now have an external regressors library on top of TimesFM that can support static covariates as well as dynamic covariates available in the future. We have an usage example in [notebooks/covariates.ipynb](notebooks/covariates.ipynb). +We now have an external regressors library on top of TimesFM that can support static covariates as well as dynamic covariates available in the future. We have an usage example in [notebooks/covariates.ipynb](https://github.com/google-research/timesfm/blob/master/notebooks/covariates.ipynb). Let's take a toy example of forecasting sales for a grocery store: @@ -241,11 +241,11 @@ In this example, besides the `Daily_sales`, we also have covariates `Category`, **Notice:** Here we make it mandatory that the dynamic covariates need to cover both the forecasting context and horizon. For example, all dynamic covariates in the example have 14 values: the first 7 correspond to the observed 7 days, and the last 7 correspond to the next 7 days. -We can now provide the past data of the two products along with static and dynamic covariates as a batch input to TimesFM and produce forecasts that take into the account the covariates. To learn more, check out the example in [notebooks/covariates.ipynb](notebooks/covariates.ipynb). +We can now provide the past data of the two products along with static and dynamic covariates as a batch input to TimesFM and produce forecasts that take into the account the covariates. To learn more, check out the example in [notebooks/covariates.ipynb](https://github.com/google-research/timesfm/blob/master/notebooks/covariates.ipynb). ## Finetuning -We have provided an example of finetuning the model on a new dataset in [notebooks/finetuning.ipynb](notebooks/finetuning.ipynb). +We have provided an example of finetuning the model on a new dataset in [notebooks/finetuning.ipynb](https://github.com/google-research/timesfm/blob/master/notebooks/finetuning.ipynb). ## Contribution Style guide From b9af57aed908f464f3dc46fc7c817adf2d821e40 Mon Sep 17 00:00:00 2001 From: Yichen Zhou Date: Wed, 17 Jul 2024 09:47:33 +0200 Subject: [PATCH 3/6] Update README.md --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 0191b86..a6c9c8a 100644 --- a/README.md +++ b/README.md @@ -18,6 +18,7 @@ We recommend at least 16GB RAM to load TimesFM dependencies. ## Update - July 15, 2024 +- To install TimesFM, you can now simply do: `pip install timesfm`. - Launched [finetuning support](https://github.com/google-research/timesfm/blob/master/notebooks/finetuning.ipynb) that lets you finetune the weights of the pretrained TimesFM model on your own data. - Launched [~zero-shot covariate support](https://github.com/google-research/timesfm/blob/master/notebooks/covariates.ipynb) with external regressors. More details [here](https://github.com/google-research/timesfm?tab=readme-ov-file#covariates-support). 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(>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.4.4)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm (<=0.9.16)", "tokenizers (>=0.19,<0.20)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"] flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)", "scipy (<1.13.0)"] flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"] ftfy = ["ftfy"] @@ -5659,25 +5657,26 @@ natten = ["natten (>=0.14.6,<0.15.0)"] onnx = ["onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "tf2onnx"] onnxruntime = ["onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)"] optuna = ["optuna"] -quality = ["GitPython (<3.1.19)", "datasets (!=2.5.0)", "isort (>=5.5.4)", "ruff (==0.1.5)", "urllib3 (<2.0.0)"] +quality = ["GitPython (<3.1.19)", "datasets (!=2.5.0)", "isort (>=5.5.4)", "ruff (==0.4.4)", "urllib3 (<2.0.0)"] ray = ["ray[tune] (>=2.7.0)"] retrieval = ["datasets (!=2.5.0)", "faiss-cpu"] +ruff = ["ruff (==0.4.4)"] sagemaker = ["sagemaker (>=2.31.0)"] sentencepiece = ["protobuf", "sentencepiece (>=0.1.91,!=0.1.92)"] serving = ["fastapi", "pydantic", "starlette", "uvicorn"] sigopt = ["sigopt"] sklearn = ["scikit-learn"] speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"] -testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk", "parameterized", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"] +testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk", "parameterized", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.4.4)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"] tf = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow (>2.9,<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 (<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 f8e10df..209c71d 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 = ">=1.0.2" [build-system] requires = ["poetry-core"] diff --git a/src/timesfm/xreg_lib.py b/src/timesfm/xreg_lib.py index c083bcd..0062a22 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 @@ class BatchedInContextXRegBase: 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 @@ class BatchedInContextXRegBase: 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 @@ class BatchedInContextXRegBase: """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." - ) + " 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 - ): + 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 @@ class BatchedInContextXRegBase: ): 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 @@ class BatchedInContextXRegBase: 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 @@ class BatchedInContextXRegBase: # 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 @@ class BatchedInContextXRegBase: # 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 @@ class BatchedInContextXRegLinear(BatchedInContextXRegBase): 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 @@ class BatchedInContextXRegLinear(BatchedInContextXRegBase): 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 @@ class BatchedInContextXRegLinear(BatchedInContextXRegBase): # 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 From dbf3d28131260d145060e073910be836a8c30419 Mon Sep 17 00:00:00 2001 From: Rajat Sen Date: Tue, 23 Jul 2024 12:45:23 +0000 Subject: [PATCH 6/6] scikit version update --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 209c71d..2a56af8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -38,7 +38,7 @@ 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" +scikit-learn = ">=1.2.2" [build-system] requires = ["poetry-core"]