Created
October 13, 2020 06:21
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Usage of DeepRenewal
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usage: deeprenewal [-h] [--use-cuda USE_CUDA] | |
[--datasource {retail_dataset}] | |
[--regenerate-datasource REGENERATE_DATASOURCE] | |
[--model-save-dir MODEL_SAVE_DIR] | |
[--point-forecast {median,mean}] | |
[--calculate-spec CALCULATE_SPEC] | |
[--batch_size BATCH_SIZE] | |
[--learning-rate LEARNING_RATE] | |
[--max-epochs MAX_EPOCHS] | |
[--number-of-batches-per-epoch NUMBER_OF_BATCHES_PER_EPOCH] | |
[--clip-gradient CLIP_GRADIENT] | |
[--weight-decay WEIGHT_DECAY] | |
[--context-length-multiplier CONTEXT_LENGTH_MULTIPLIER] | |
[--num-layers NUM_LAYERS] | |
[--num-cells NUM_CELLS] | |
[--cell-type CELL_TYPE] | |
[--dropout-rate DROPOUT_RATE] | |
[--use-feat-dynamic-real USE_FEAT_DYNAMIC_REAL] | |
[--use-feat-static-cat USE_FEAT_STATIC_CAT] | |
[--use-feat-static-real USE_FEAT_STATIC_REAL] | |
[--scaling SCALING] | |
[--num-parallel-samples NUM_PARALLEL_SAMPLES] | |
[--num-lags NUM_LAGS] | |
[--forecast-type FORECAST_TYPE] | |
GluonTS implementation of paper 'Intermittent Demand Forecasting with Deep | |
Renewal Processes' | |
optional arguments: | |
-h, --help show this help message and exit | |
--use-cuda USE_CUDA | |
--datasource {retail_dataset} | |
--regenerate-datasource REGENERATE_DATASOURCE | |
Whether to discard locally saved dataset and | |
regenerate from source | |
--model-save-dir MODEL_SAVE_DIR | |
Folder to save models | |
--point-forecast {median,mean} | |
How to estimate point forecast? Mean or Median | |
--calculate-spec CALCULATE_SPEC | |
Whether to calculate SPEC. It is computationally | |
expensive and therefore False by default | |
--batch_size BATCH_SIZE | |
--learning-rate LEARNING_RATE | |
--max-epochs MAX_EPOCHS | |
--number-of-batches-per-epoch NUMBER_OF_BATCHES_PER_EPOCH | |
--clip-gradient CLIP_GRADIENT | |
--weight-decay WEIGHT_DECAY | |
--context-length-multiplier CONTEXT_LENGTH_MULTIPLIER | |
If context multipler is 2, context available to hte | |
RNN is 2*prediction length | |
--num-layers NUM_LAYERS | |
--num-cells NUM_CELLS | |
--cell-type CELL_TYPE | |
--dropout-rate DROPOUT_RATE | |
--use-feat-dynamic-real USE_FEAT_DYNAMIC_REAL | |
--use-feat-static-cat USE_FEAT_STATIC_CAT | |
--use-feat-static-real USE_FEAT_STATIC_REAL | |
--scaling SCALING Whether to scale targets or not | |
--num-parallel-samples NUM_PARALLEL_SAMPLES | |
--num-lags NUM_LAGS Number of lags to be included as feature | |
--forecast-type FORECAST_TYPE | |
Defines how the forecast is decoded. For details look | |
at the documentation | |
An example of training process is as follows: | |
python3 deeprenewal --datasource retail_dataset --lr 0.001 --epochs 50 |
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