280 lines
8.9 KiB
Python
280 lines
8.9 KiB
Python
from functools import partial
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from configs.config_utils import *
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from configs.feature_config import feature_config
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from experiment_setup import get_fertility_based_eval_functions
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from utils.data_utils import *
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from utils.training import *
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from models.cnn_lstm import *
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from models.utils import *
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from models.collation import *
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cnn_lstm_base_config = {
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"model_name": "cnn_lstm",
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"version": "1.0.0",
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"model_class": CNNLSTM,
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"feature_config": feature_config | {"ignored_features":
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[
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"fertility_probability",
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"ov_over_probability",
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# "days_relative_to_ov",
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# "is_biphasic",
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"ov_day",
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]},
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"batch_fn": produce_window_batches,
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"collate_fn": simple_x_y_collate,
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"model_creation_fn": simple_model_creation,
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"model_save_fn": simple_model_save,
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"model_load_fn": partial(simple_model_load, model_creation_fn=simple_model_creation),
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"batch_loss_fn": get_model_loss,
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"actual_fn": simple_get_y,
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"predict_fn": simple_x_y_predict,
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}
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base_cnn_lstm_training_config = {
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"batch_size": 128,
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"learning_parameters": {
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"max_lr": 1e-5,
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"epochs": 30,
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"patience": 5,
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},
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"loss_functions": [
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nn.MSELoss(),
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nn.BCEWithLogitsLoss(),
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],
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"max_grad_norm": 1.0,
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"train_size": 0.7,
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"val_size": 0.15,
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"test_size": 0.15,
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}
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fertility_complexity_run = get_run_config(
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run_name="cnn_lstm_complexity_run",
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base_model_configuration=cnn_lstm_base_config | get_data_config_set(
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values_per_day=288,
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shift_in_hours=12,
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input_window_length_in_days=40,
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output_window_length=1,
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output_window_offset=0
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),
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base_training_configuration=base_cnn_lstm_training_config,
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model_config_kwargs_list=[
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{
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"model_parameters": {
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"embed_dim": 32,
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"lstm_hidden_size": 32,
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"num_layers": 2,
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},
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},
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{
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"model_parameters": {
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"embed_dim": 64,
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"lstm_hidden_size": 64,
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"num_layers": 2,
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},
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},
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{
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"model_parameters": {
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"embed_dim": 128,
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"lstm_hidden_size": 128,
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"num_layers": 4,
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},
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},
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]
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)
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model_input_run = get_run_config(
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run_name="cnn_lstm_input_size_run",
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base_model_configuration=cnn_lstm_base_config |
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{
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"model_parameters": {
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"embed_dim": 128,
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"lstm_hidden_size": 128,
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"num_layers": 4,
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},
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},
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base_training_configuration=base_cnn_lstm_training_config,
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model_config_kwargs_list=input_run_kwargs_list,
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)
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fertility_input_run = get_run_config(
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run_name="cnn_lstm_fertility_input_run",
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base_model_configuration=cnn_lstm_base_config |
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{
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"feature_config": feature_config | {"ignored_features":
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[
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# "fertility_probability",
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# "ov_over_probability",
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"days_relative_to_ov",
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"is_biphasic",
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"ov_day",
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]},
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"model_parameters": {
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"embed_dim": 128,
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"lstm_hidden_size": 128,
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"num_layers": 4,
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},
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},
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base_training_configuration=base_cnn_lstm_training_config,
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model_config_kwargs_list=[
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get_data_config_set(
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values_per_day=288,
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shift_in_hours=12,
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input_window_length_in_days=10,
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output_window_length=1,
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output_window_offset=0
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),
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get_data_config_set(
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values_per_day=288,
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shift_in_hours=12,
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input_window_length_in_days=20,
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output_window_length=1,
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output_window_offset=0
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),
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get_data_config_set(
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values_per_day=288,
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shift_in_hours=12,
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input_window_length_in_days=40,
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output_window_length=1,
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output_window_offset=0
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),
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get_data_config_set(
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values_per_day=288,
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shift_in_hours=12,
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input_window_length_in_days=80,
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output_window_length=1,
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output_window_offset=0
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),
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get_data_config_set(
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values_per_day=288,
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shift_in_hours=12,
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input_window_length_in_days=160,
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output_window_length=1,
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output_window_offset=0
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)
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],
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evaluation_configuration={
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"eval_functions_getter": get_fertility_based_eval_functions
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},
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)
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fertility_complexity_run = get_run_config(
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run_name="cnn_lstm_complexity_run",
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base_model_configuration=cnn_lstm_base_config | get_data_config_set(
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values_per_day=288,
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shift_in_hours=12,
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input_window_length_in_days=40,
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output_window_length=1,
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output_window_offset=0
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) | {
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"feature_config": feature_config | {"ignored_features":
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[
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# "fertility_probability",
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# "ov_over_probability",
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"days_relative_to_ov",
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"is_biphasic",
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"ov_day",
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]},
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},
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base_training_configuration=base_cnn_lstm_training_config,
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evaluation_configuration={
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"eval_functions_getter": get_fertility_based_eval_functions
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},
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model_config_kwargs_list=[
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{
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"model_parameters": {
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"embed_dim": 16,
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"lstm_hidden_size": 16,
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"num_layers": 1,
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},
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},
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{
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"model_parameters": {
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"embed_dim": 32,
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"lstm_hidden_size": 32,
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"num_layers": 1,
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},
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},
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{
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"model_parameters": {
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"embed_dim": 32,
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"lstm_hidden_size": 32,
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"num_layers": 2,
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},
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},
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{
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"model_parameters": {
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"embed_dim": 64,
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"lstm_hidden_size": 64,
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"num_layers": 2,
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},
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},
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{
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"model_parameters": {
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"embed_dim": 128,
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"lstm_hidden_size": 128,
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"num_layers": 2,
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},
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},
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{
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"model_parameters": {
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"embed_dim": 128,
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"lstm_hidden_size": 128,
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"num_layers": 4,
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},
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},
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{
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"model_parameters": {
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"embed_dim": 256,
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"lstm_hidden_size": 256,
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"num_layers": 4,
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},
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},
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{
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"model_parameters": {
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"embed_dim": 512,
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"lstm_hidden_size": 512,
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"num_layers": 4,
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},
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},
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]
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)
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best_config_run = get_run_config(
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run_name="cnn_lstm_best_config_run",
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base_model_configuration=cnn_lstm_base_config | get_data_config_set(
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values_per_day=288,
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shift_in_hours=12,
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input_window_length_in_days=40,
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output_window_length=1,
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output_window_offset=0
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) | {
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"feature_config": feature_config | {"ignored_features":
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[
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# "fertility_probability",
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# "ov_over_probability",
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"days_relative_to_ov",
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"is_biphasic",
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"ov_day",
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]},
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"model_parameters": {
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"embed_dim": 512,
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"lstm_hidden_size": 512,
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"num_layers": 4,
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},
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},
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base_training_configuration=base_cnn_lstm_training_config | {
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"learning_parameters": {
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"max_lr": 1e-4,
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# "learning_rate": base_lr * (batch_size / 4),
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"epochs": 30,
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"patience": 5,
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},
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},
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model_config_kwargs_list=[],
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evaluation_configuration={
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"eval_functions_getter": get_fertility_based_eval_functions
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},
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)
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