129 lines
6.4 KiB
Python
129 lines
6.4 KiB
Python
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
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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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from data_utils import InputTypes, DataTypes, FeatureSpec
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import datetime
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class ElectricityConfig():
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def __init__(self):
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self.features = [
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FeatureSpec('id', InputTypes.ID, DataTypes.CATEGORICAL),
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FeatureSpec('hours_from_start', InputTypes.TIME, DataTypes.CONTINUOUS),
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FeatureSpec('power_usage', InputTypes.TARGET, DataTypes.CONTINUOUS),
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FeatureSpec('hour', InputTypes.KNOWN, DataTypes.CONTINUOUS),
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FeatureSpec('day_of_week', InputTypes.KNOWN, DataTypes.CONTINUOUS),
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FeatureSpec('hours_from_start', InputTypes.KNOWN, DataTypes.CONTINUOUS),
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FeatureSpec('categorical_id', InputTypes.STATIC, DataTypes.CATEGORICAL),
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]
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# Dataset split boundaries
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self.time_ids = 'days_from_start' # This column contains time indices across which we split the data
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self.train_range = (1096, 1315)
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self.valid_range = (1308, 1339)
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self.test_range = (1332, 1346)
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self.dataset_stride = 1 #how many timesteps between examples
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self.scale_per_id = True
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self.missing_id_strategy = None
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self.missing_cat_data_strategy='encode_all'
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# Feature sizes
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self.static_categorical_inp_lens = [369]
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self.temporal_known_categorical_inp_lens = []
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self.temporal_observed_categorical_inp_lens = []
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self.quantiles = [0.1, 0.5, 0.9]
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self.example_length = 8 * 24
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self.encoder_length = 7 * 24
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self.n_head = 4
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self.hidden_size = 128
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self.dropout = 0.1
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self.attn_dropout = 0.0
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#### Derived variables ####
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self.temporal_known_continuous_inp_size = len([x for x in self.features
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if x.feature_type == InputTypes.KNOWN and x.feature_embed_type == DataTypes.CONTINUOUS])
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self.temporal_observed_continuous_inp_size = len([x for x in self.features
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if x.feature_type == InputTypes.OBSERVED and x.feature_embed_type == DataTypes.CONTINUOUS])
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self.temporal_target_size = len([x for x in self.features if x.feature_type == InputTypes.TARGET])
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self.static_continuous_inp_size = len([x for x in self.features
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if x.feature_type == InputTypes.STATIC and x.feature_embed_type == DataTypes.CONTINUOUS])
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self.num_static_vars = self.static_continuous_inp_size + len(self.static_categorical_inp_lens)
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self.num_future_vars = self.temporal_known_continuous_inp_size + len(self.temporal_known_categorical_inp_lens)
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self.num_historic_vars = sum([self.num_future_vars,
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self.temporal_observed_continuous_inp_size,
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self.temporal_target_size,
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len(self.temporal_observed_categorical_inp_lens),
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])
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class TrafficConfig():
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def __init__(self):
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self.features = [
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FeatureSpec('id', InputTypes.ID, DataTypes.CATEGORICAL),
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FeatureSpec('hours_from_start', InputTypes.TIME, DataTypes.CONTINUOUS),
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FeatureSpec('values', InputTypes.TARGET, DataTypes.CONTINUOUS),
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FeatureSpec('time_on_day', InputTypes.KNOWN, DataTypes.CONTINUOUS),
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FeatureSpec('day_of_week', InputTypes.KNOWN, DataTypes.CONTINUOUS),
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FeatureSpec('hours_from_start', InputTypes.KNOWN, DataTypes.CONTINUOUS),
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FeatureSpec('categorical_id', InputTypes.STATIC, DataTypes.CATEGORICAL),
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]
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# Dataset split boundaries
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self.time_ids = 'sensor_day' # This column contains time indices across which we split the data
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self.train_range = (0, 151)
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self.valid_range = (144, 166)
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self.test_range = (159, float('inf'))
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self.dataset_stride = 1 #how many timesteps between examples
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self.scale_per_id = False
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self.missing_id_strategy = None
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self.missing_cat_data_strategy='encode_all'
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# Feature sizes
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self.static_categorical_inp_lens = [963]
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self.temporal_known_categorical_inp_lens = []
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self.temporal_observed_categorical_inp_lens = []
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self.quantiles = [0.1, 0.5, 0.9]
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self.example_length = 8 * 24
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self.encoder_length = 7 * 24
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self.n_head = 4
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self.hidden_size = 128
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self.dropout = 0.3
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self.attn_dropout = 0.0
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#### Derived variables ####
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self.temporal_known_continuous_inp_size = len([x for x in self.features
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if x.feature_type == InputTypes.KNOWN and x.feature_embed_type == DataTypes.CONTINUOUS])
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self.temporal_observed_continuous_inp_size = len([x for x in self.features
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if x.feature_type == InputTypes.OBSERVED and x.feature_embed_type == DataTypes.CONTINUOUS])
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self.temporal_target_size = len([x for x in self.features if x.feature_type == InputTypes.TARGET])
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self.static_continuous_inp_size = len([x for x in self.features
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if x.feature_type == InputTypes.STATIC and x.feature_embed_type == DataTypes.CONTINUOUS])
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self.num_static_vars = self.static_continuous_inp_size + len(self.static_categorical_inp_lens)
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self.num_future_vars = self.temporal_known_continuous_inp_size + len(self.temporal_known_categorical_inp_lens)
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self.num_historic_vars = sum([self.num_future_vars,
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self.temporal_observed_continuous_inp_size,
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self.temporal_target_size,
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len(self.temporal_observed_categorical_inp_lens),
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])
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CONFIGS = {'electricity': ElectricityConfig,
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'traffic': TrafficConfig,
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}
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