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@@ -254,9 +254,9 @@ class DistributedHyperparamOptManager(HyperparamOptManager):
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param_ranges: Discrete hyperparameter range for random search.
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fixed_params: Fixed model parameters per experiment.
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root_model_folder: Folder to store optimisation artifacts.
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worker_number: Worker index definining which set of hyperparameters to
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worker_number: Worker index defining which set of hyperparameters to
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test.
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search_iterations: Maximum numer of random search iterations.
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search_iterations: Maximum number of random search iterations.
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num_iterations_per_worker: How many iterations are handled per worker.
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clear_serialised_params: Whether to regenerate hyperparameter
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combinations.
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@@ -330,7 +330,7 @@ class DistributedHyperparamOptManager(HyperparamOptManager):
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if os.path.exists(self.serialised_ranges_folder):
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df = pd.read_csv(self.serialised_ranges_path, index_col=0)
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else:
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print("Unable to load - regenerating serach ranges instead")
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print("Unable to load - regenerating search ranges instead")
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df = self.update_serialised_hyperparam_df()
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return df
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@@ -342,7 +342,7 @@ class TFTDataCache:
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@classmethod
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def contains(cls, key):
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"""Retuns boolean indicating whether key is present in cache."""
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"""Returns boolean indicating whether key is present in cache."""
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return key in cls._data_cache
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@@ -1120,10 +1120,10 @@ class TemporalFusionTransformer:
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Args:
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df: Input dataframe
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return_targets: Whether to also return outputs aligned with predictions to
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faciliate evaluation
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facilitate evaluation
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Returns:
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Input dataframe or tuple of (input dataframe, algined output dataframe).
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Input dataframe or tuple of (input dataframe, aligned output dataframe).
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"""
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data = self._batch_data(df)
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