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262 lines
8.1 KiB
Python
262 lines
8.1 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 Electricity dataset.
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Defines dataset specific column definitions and data transformations. Uses
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entity specific z-score normalization.
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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 pandas as pd
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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 ElectricityFormatter(GenericDataFormatter):
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"""Defines and formats data for the electricity dataset.
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Note that per-entity z-score normalization is used here, and is implemented
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across functions.
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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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('id', DataTypes.REAL_VALUED, InputTypes.ID),
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('hours_from_start', DataTypes.REAL_VALUED, InputTypes.TIME),
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('power_usage', DataTypes.REAL_VALUED, InputTypes.TARGET),
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('hour', DataTypes.REAL_VALUED, InputTypes.KNOWN_INPUT),
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('day_of_week', DataTypes.REAL_VALUED, InputTypes.KNOWN_INPUT),
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('hours_from_start', DataTypes.REAL_VALUED, InputTypes.KNOWN_INPUT),
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('categorical_id', 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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self._time_steps = self.get_fixed_params()['total_time_steps']
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def split_data(self, df, valid_boundary=1315, test_boundary=1339):
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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['days_from_start']
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train = df.loc[index < valid_boundary]
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valid = df.loc[(index >= valid_boundary - 7) & (index < test_boundary)]
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test = df.loc[index >= test_boundary - 7]
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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,
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column_definitions)
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target_column = utils.get_single_col_by_input_type(InputTypes.TARGET,
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column_definitions)
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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,
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{InputTypes.ID, InputTypes.TIME})
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# Initialise scaler caches
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self._real_scalers = {}
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self._target_scaler = {}
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identifiers = []
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for identifier, sliced in df.groupby(id_column):
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if len(sliced) >= self._time_steps:
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data = sliced[real_inputs].values
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targets = sliced[[target_column]].values
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self._real_scalers[identifier] \
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= sklearn.preprocessing.StandardScaler().fit(data)
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self._target_scaler[identifier] \
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= sklearn.preprocessing.StandardScaler().fit(targets)
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identifiers.append(identifier)
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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,
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{InputTypes.ID, InputTypes.TIME})
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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(
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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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# Extract identifiers in case required
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self.identifiers = identifiers
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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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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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# Extract relevant columns
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column_definitions = self.get_column_definition()
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id_col = utils.get_single_col_by_input_type(InputTypes.ID,
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column_definitions)
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real_inputs = utils.extract_cols_from_data_type(
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DataTypes.REAL_VALUED, column_definitions,
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{InputTypes.ID, InputTypes.TIME})
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categorical_inputs = utils.extract_cols_from_data_type(
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DataTypes.CATEGORICAL, column_definitions,
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{InputTypes.ID, InputTypes.TIME})
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# Transform real inputs per entity
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df_list = []
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for identifier, sliced in df.groupby(id_col):
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# Filter out any trajectories that are too short
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if len(sliced) >= self._time_steps:
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sliced_copy = sliced.copy()
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sliced_copy[real_inputs] = self._real_scalers[identifier].transform(
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sliced_copy[real_inputs].values)
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df_list.append(sliced_copy)
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output = pd.concat(df_list, axis=0)
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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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if self._target_scaler is None:
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raise ValueError('Scalers have not been set!')
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column_names = predictions.columns
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df_list = []
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for identifier, sliced in predictions.groupby('identifier'):
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sliced_copy = sliced.copy()
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target_scaler = self._target_scaler[identifier]
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for col in column_names:
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if col not in {'forecast_time', 'identifier'}:
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sliced_copy[col] = target_scaler.inverse_transform(sliced_copy[col])
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df_list.append(sliced_copy)
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output = pd.concat(df_list, axis=0)
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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': 8 * 24,
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'num_encoder_steps': 7 * 24,
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'num_epochs': 100,
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'early_stopping_patience': 5,
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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': 0.1,
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'hidden_layer_size': 160,
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'learning_rate': 0.001,
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'minibatch_size': 64,
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'max_gradient_norm': 0.01,
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'num_heads': 4,
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'stack_size': 1
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}
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return model_params
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def get_num_samples_for_calibration(self):
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"""Gets the default number of training and validation samples.
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Use to sub-sample the data for network calibration and a value of -1 uses
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all available samples.
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Returns:
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Tuple of (training samples, validation samples)
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"""
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return 450000, 50000
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