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230 lines
8.2 KiB
Python
230 lines
8.2 KiB
Python
# coding=utf-8
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# Copyright 2020 The Google Research Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Lint as: python3
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"""Custom formatting functions for Alpha158 dataset.
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Defines dataset specific column definitions and data transformations.
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"""
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import data_formatters.base
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import libs.utils as utils
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import sklearn.preprocessing
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GenericDataFormatter = data_formatters.base.GenericDataFormatter
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DataTypes = data_formatters.base.DataTypes
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InputTypes = data_formatters.base.InputTypes
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class Alpha158Formatter(GenericDataFormatter):
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"""Defines and formats data for the Alpha158 dataset.
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Attributes:
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column_definition: Defines input and data type of column used in the
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experiment.
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identifiers: Entity identifiers used in experiments.
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"""
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_column_definition = [
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("instrument", DataTypes.CATEGORICAL, InputTypes.ID),
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("LABEL0", DataTypes.REAL_VALUED, InputTypes.TARGET),
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("date", DataTypes.DATE, InputTypes.TIME),
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("month", DataTypes.CATEGORICAL, InputTypes.KNOWN_INPUT),
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("day_of_week", DataTypes.CATEGORICAL, InputTypes.KNOWN_INPUT),
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# Selected features
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("RESI5", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
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("WVMA5", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
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("RSQR5", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
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("KLEN", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
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("RSQR10", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
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("CORR5", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
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("CORD5", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
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("CORR10", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
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("ROC60", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
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("RESI10", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
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("VSTD5", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
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("RSQR60", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
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("CORR60", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
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("WVMA60", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
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("STD5", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
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("RSQR20", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
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("CORD60", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
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("CORD10", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
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("CORR20", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
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("KLOW", DataTypes.REAL_VALUED, InputTypes.OBSERVED_INPUT),
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("const", DataTypes.CATEGORICAL, InputTypes.STATIC_INPUT),
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]
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def __init__(self):
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"""Initialises formatter."""
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self.identifiers = None
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self._real_scalers = None
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self._cat_scalers = None
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self._target_scaler = None
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self._num_classes_per_cat_input = None
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def split_data(self, df, valid_boundary=2016, test_boundary=2018):
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"""Splits data frame into training-validation-test data frames.
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This also calibrates scaling object, and transforms data for each split.
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Args:
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df: Source data frame to split.
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valid_boundary: Starting year for validation data
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test_boundary: Starting year for test data
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Returns:
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Tuple of transformed (train, valid, test) data.
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"""
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print("Formatting train-valid-test splits.")
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index = df["year"]
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train = df.loc[index < valid_boundary]
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valid = df.loc[(index >= valid_boundary) & (index < test_boundary)]
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test = df.loc[index >= test_boundary]
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self.set_scalers(train)
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return (self.transform_inputs(data) for data in [train, valid, test])
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def set_scalers(self, df):
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"""Calibrates scalers using the data supplied.
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Args:
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df: Data to use to calibrate scalers.
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"""
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print("Setting scalers with training data...")
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column_definitions = self.get_column_definition()
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id_column = utils.get_single_col_by_input_type(InputTypes.ID, column_definitions)
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target_column = utils.get_single_col_by_input_type(InputTypes.TARGET, column_definitions)
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# Extract identifiers in case required
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self.identifiers = list(df[id_column].unique())
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# Format real scalers
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real_inputs = utils.extract_cols_from_data_type(
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DataTypes.REAL_VALUED, column_definitions, {InputTypes.ID, InputTypes.TIME}
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)
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data = df[real_inputs].values
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self._real_scalers = sklearn.preprocessing.StandardScaler().fit(data)
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self._target_scaler = sklearn.preprocessing.StandardScaler().fit(
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df[[target_column]].values
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) # used for predictions
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# Format categorical scalers
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categorical_inputs = utils.extract_cols_from_data_type(
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DataTypes.CATEGORICAL, column_definitions, {InputTypes.ID, InputTypes.TIME}
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)
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categorical_scalers = {}
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num_classes = []
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for col in categorical_inputs:
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# Set all to str so that we don't have mixed integer/string columns
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srs = df[col].apply(str)
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categorical_scalers[col] = sklearn.preprocessing.LabelEncoder().fit(srs.values)
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num_classes.append(srs.nunique())
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# Set categorical scaler outputs
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self._cat_scalers = categorical_scalers
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self._num_classes_per_cat_input = num_classes
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def transform_inputs(self, df):
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"""Performs feature transformations.
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This includes both feature engineering, preprocessing and normalisation.
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Args:
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df: Data frame to transform.
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Returns:
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Transformed data frame.
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"""
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output = df.copy()
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if self._real_scalers is None and self._cat_scalers is None:
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raise ValueError("Scalers have not been set!")
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column_definitions = self.get_column_definition()
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real_inputs = utils.extract_cols_from_data_type(
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DataTypes.REAL_VALUED, column_definitions, {InputTypes.ID, InputTypes.TIME}
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)
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categorical_inputs = utils.extract_cols_from_data_type(
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DataTypes.CATEGORICAL, column_definitions, {InputTypes.ID, InputTypes.TIME}
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)
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# Format real inputs
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output[real_inputs] = self._real_scalers.transform(df[real_inputs].values)
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# Format categorical inputs
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for col in categorical_inputs:
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string_df = df[col].apply(str)
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output[col] = self._cat_scalers[col].transform(string_df)
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return output
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def format_predictions(self, predictions):
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"""Reverts any normalisation to give predictions in original scale.
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Args:
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predictions: Dataframe of model predictions.
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Returns:
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Data frame of unnormalised predictions.
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"""
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output = predictions.copy()
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column_names = predictions.columns
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for col in column_names:
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if col not in {"forecast_time", "identifier"}:
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output[col] = self._target_scaler.inverse_transform(predictions[col])
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return output
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# Default params
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def get_fixed_params(self):
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"""Returns fixed model parameters for experiments."""
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fixed_params = {
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"total_time_steps": 6 + 6,
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"num_encoder_steps": 6,
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"num_epochs": 100,
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"early_stopping_patience": 10,
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"multiprocessing_workers": 5,
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}
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return fixed_params
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def get_default_model_params(self):
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"""Returns default optimised model parameters."""
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model_params = {
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"dropout_rate": 0.4,
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"hidden_layer_size": 160,
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"learning_rate": 0.0001,
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"minibatch_size": 128,
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"max_gradient_norm": 0.0135,
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"num_heads": 1,
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"stack_size": 1,
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}
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return model_params
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