Alex Blank
134 lines
4.4 KiB
Python
134 lines
4.4 KiB
Python
from typing import Callable
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import configs
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from configs.feature_config import feature_config
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from configs.cnn_run_config import get_cnn_run_config
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from configs.transformer_run_config import get_transformer_run_config
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take_every_nth = int(288 / 12)
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shift_in_hours = 12
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input_window_length = (288 // take_every_nth) * 80
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# output_window_length = (288 // take_every_nth) * 1
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output_window_length = 1
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output_window_offset = input_window_length + (288 // take_every_nth) * 0
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window_shift = int((288 // take_every_nth) / 24 * shift_in_hours)
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min_input_length_fraction_for_padding = ((288 // take_every_nth) * 4) / input_window_length
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def config_generator(config_gen_fn: Callable,
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fixed_params: dict,
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variable_param_configs: list,
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) -> list:
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configs = list()
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for variable_config in variable_param_configs:
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config = fixed_params.copy()
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for param_name, param_value in variable_config.items():
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# update the config with the variable parameter, values can be None, if default should be used
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if param_value is not None:
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config[param_name] = param_value
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configs.append(config_gen_fn(**config))
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return configs
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run_configuration = {
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"name": "ovulation_regression",
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"runs": config_generator(
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get_cnn_run_config,
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fixed_params={
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"run_name": "cnn_ovulation_regression",
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"run_description": "CNN model for ovulation regression",
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"input_window_length": input_window_length,
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"output_window_length": output_window_length,
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"take_every_nth": take_every_nth,
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"shift_in_hours": shift_in_hours,
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"output_window_offset": output_window_offset,
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"batch_size": 128,
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"max_lr": 1e-5,
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"num_epochs": 10,
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"patience": 3,
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"feature_config": feature_config,
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},
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variable_param_configs=[
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{
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"model_parameters": {
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"cnn_channels": 32,
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"kernel_size": 3,
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"embed_dim": 32,
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"num_enc_layers": 2,
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"num_heads": 2,
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},
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},
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{
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"batch_size": 64,
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"model_parameters": {
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"cnn_channels": 64,
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"kernel_size": 3,
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"embed_dim": 64,
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"num_enc_layers": 2,
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"num_heads": 2,
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},
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},
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{
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"batch_size": 64,
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"model_parameters": {
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"cnn_channels": 128,
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"kernel_size": 5,
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"embed_dim": 128,
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"num_enc_layers": 4,
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"num_heads": 4,
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},
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},
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]
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) + config_generator(
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get_transformer_run_config,
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fixed_params={
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"run_name": "transformer_ovulation_regression",
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"run_description": "Transformer model for ovulation regression",
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"input_window_length": input_window_length,
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"output_window_length": output_window_length,
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"take_every_nth": take_every_nth,
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"shift_in_hours": shift_in_hours,
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"output_window_offset": output_window_offset,
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"batch_size": 128,
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"max_lr": 1e-5,
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"num_epochs": 10,
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"patience": 3,
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"feature_config": feature_config,
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},
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variable_param_configs=[
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{
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"model_parameters": {
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"embed_dim": 64,
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"num_enc_layers": 2,
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"num_heads": 2,
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},
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},
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{
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"batch_size": 64,
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"model_parameters": {
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"embed_dim": 128,
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"num_enc_layers": 4,
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"num_heads": 4,
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},
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},
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{
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"batch_size": 64,
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"model_parameters": {
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"embed_dim": 256,
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"num_enc_layers": 4,
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"num_heads": 4,
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},
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},
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{
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"batch_size": 32,
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"model_parameters": {
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"embed_dim": 512,
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"num_enc_layers": 4,
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"num_heads": 4,
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},
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},
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]
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)
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}
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