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236 lines
7.3 KiB
Python
236 lines
7.3 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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"""Default data formatting functions for experiments.
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For new datasets, inherit form GenericDataFormatter and implement
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all abstract functions.
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These dataset-specific methods:
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1) Define the column and input types for tabular dataframes used by model
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2) Perform the necessary input feature engineering & normalisation steps
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3) Reverts the normalisation for predictions
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4) Are responsible for train, validation and test splits
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"""
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import abc
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import enum
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# Type defintions
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class DataTypes(enum.IntEnum):
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"""Defines numerical types of each column."""
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REAL_VALUED = 0
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CATEGORICAL = 1
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DATE = 2
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class InputTypes(enum.IntEnum):
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"""Defines input types of each column."""
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TARGET = 0
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OBSERVED_INPUT = 1
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KNOWN_INPUT = 2
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STATIC_INPUT = 3
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ID = 4 # Single column used as an entity identifier
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TIME = 5 # Single column exclusively used as a time index
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class GenericDataFormatter(abc.ABC):
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"""Abstract base class for all data formatters.
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User can implement the abstract methods below to perform dataset-specific
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manipulations.
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"""
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@abc.abstractmethod
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def set_scalers(self, df):
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"""Calibrates scalers using the data supplied."""
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raise NotImplementedError()
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@abc.abstractmethod
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def transform_inputs(self, df):
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"""Performs feature transformation."""
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raise NotImplementedError()
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@abc.abstractmethod
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def format_predictions(self, df):
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"""Reverts any normalisation to give predictions in original scale."""
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raise NotImplementedError()
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@abc.abstractmethod
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def split_data(self, df):
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"""Performs the default train, validation and test splits."""
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raise NotImplementedError()
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@property
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@abc.abstractmethod
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def _column_definition(self):
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"""Defines order, input type and data type of each column."""
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raise NotImplementedError()
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@abc.abstractmethod
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def get_fixed_params(self):
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"""Defines the fixed parameters used by the model for training.
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Requires the following keys:
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'total_time_steps': Defines the total number of time steps used by TFT
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'num_encoder_steps': Determines length of LSTM encoder (i.e. history)
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'num_epochs': Maximum number of epochs for training
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'early_stopping_patience': Early stopping param for keras
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'multiprocessing_workers': # of cpus for data processing
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Returns:
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A dictionary of fixed parameters, e.g.:
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fixed_params = {
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'total_time_steps': 252 + 5,
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'num_encoder_steps': 252,
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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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"""
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raise NotImplementedError
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# Shared functions across data-formatters
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@property
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def num_classes_per_cat_input(self):
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"""Returns number of categories per relevant input.
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This is seqeuently required for keras embedding layers.
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"""
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return self._num_classes_per_cat_input
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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 -1, -1
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def get_column_definition(self):
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""""Returns formatted column definition in order expected by the TFT."""
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column_definition = self._column_definition
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# Sanity checks first.
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# Ensure only one ID and time column exist
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def _check_single_column(input_type):
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length = len([tup for tup in column_definition if tup[2] == input_type])
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if length != 1:
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raise ValueError('Illegal number of inputs ({}) of type {}'.format(
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length, input_type))
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_check_single_column(InputTypes.ID)
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_check_single_column(InputTypes.TIME)
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identifier = [tup for tup in column_definition if tup[2] == InputTypes.ID]
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time = [tup for tup in column_definition if tup[2] == InputTypes.TIME]
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real_inputs = [
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tup for tup in column_definition if tup[1] == DataTypes.REAL_VALUED and
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tup[2] not in {InputTypes.ID, InputTypes.TIME}
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]
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categorical_inputs = [
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tup for tup in column_definition if tup[1] == DataTypes.CATEGORICAL and
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tup[2] not in {InputTypes.ID, InputTypes.TIME}
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]
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return identifier + time + real_inputs + categorical_inputs
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def _get_input_columns(self):
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"""Returns names of all input columns."""
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return [
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tup[0]
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for tup in self.get_column_definition()
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if tup[2] not in {InputTypes.ID, InputTypes.TIME}
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]
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def _get_tft_input_indices(self):
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"""Returns the relevant indexes and input sizes required by TFT."""
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# Functions
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def _extract_tuples_from_data_type(data_type, defn):
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return [
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tup for tup in defn if tup[1] == data_type and
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tup[2] not in {InputTypes.ID, InputTypes.TIME}
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]
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def _get_locations(input_types, defn):
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return [i for i, tup in enumerate(defn) if tup[2] in input_types]
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# Start extraction
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column_definition = [
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tup for tup in self.get_column_definition()
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if tup[2] not in {InputTypes.ID, InputTypes.TIME}
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]
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categorical_inputs = _extract_tuples_from_data_type(DataTypes.CATEGORICAL,
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column_definition)
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real_inputs = _extract_tuples_from_data_type(DataTypes.REAL_VALUED,
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column_definition)
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locations = {
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'input_size':
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len(self._get_input_columns()),
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'output_size':
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len(_get_locations({InputTypes.TARGET}, column_definition)),
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'category_counts':
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self.num_classes_per_cat_input,
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'input_obs_loc':
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_get_locations({InputTypes.TARGET}, column_definition),
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'static_input_loc':
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_get_locations({InputTypes.STATIC_INPUT}, column_definition),
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'known_regular_inputs':
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_get_locations({InputTypes.STATIC_INPUT, InputTypes.KNOWN_INPUT},
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real_inputs),
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'known_categorical_inputs':
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_get_locations({InputTypes.STATIC_INPUT, InputTypes.KNOWN_INPUT},
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categorical_inputs),
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}
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return locations
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def get_experiment_params(self):
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"""Returns fixed model parameters for experiments."""
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required_keys = [
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'total_time_steps', 'num_encoder_steps', 'num_epochs',
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'early_stopping_patience', 'multiprocessing_workers'
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]
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fixed_params = self.get_fixed_params()
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for k in required_keys:
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if k not in fixed_params:
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raise ValueError('Field {}'.format(k) +
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' missing from fixed parameter definitions!')
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fixed_params['column_definition'] = self.get_column_definition()
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fixed_params.update(self._get_tft_input_indices())
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return fixed_params
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