Alex Blank
168 lines
4.9 KiB
Python
168 lines
4.9 KiB
Python
import os
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from functools import partial
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import math
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import torch
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from torch import nn
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import numpy as np
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from sklearn.preprocessing import StandardScaler, MinMaxScaler, RobustScaler
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from torch.optim.lr_scheduler import OneCycleLR
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from torch.optim import AdamW
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from data_analysis.models.ov_detection.config import model_config
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from utils.feature_functions import *
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from utils.loss_functions import weighted_bce_loss_fn
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from utils.training_utils import collate
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from vsm_datascience_common import constants
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feature_config = {
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"feature_set_name": "full_feature_set",
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"filter_criteria": {
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"measurements.length": {
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"$gt": constants.MEASUREMENTS_PER_DAY * 10,
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"$lt": constants.MEASUREMENTS_PER_DAY * 150
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},
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"ends_at": {
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"$exists": True,
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"$ne": None
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},
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"$and": [
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{"measurements.values": {"$not": {"$elemMatch": {"$lt": 35}}}},
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{"measurements.values": {"$not": {"$elemMatch": {"$gt": 43}}}}
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]
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},
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"augmentation": {
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"use_augmentation": True,
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"max_lookback": 4,
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},
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"feature_sets": [
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"static_categorical_features",
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"static_continuous_features",
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"known_categorical_features",
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"known_continuous_features",
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"observed_categorical_features",
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"observed_continuous_features",
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"target_features"
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],
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"static_categorical_features": [
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],
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"static_continuous_features": [
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{
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"name": "average_cycle_length",
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"fn": get_cycle_length_stats,
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"scaler": RobustScaler
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},
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{
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"name": "average_ovulation_day",
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"fn": get_average_ovulation_day,
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"scaler": RobustScaler
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},
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{
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"name": "ovulation_std",
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"fn": get_ovulation_std,
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"scaler": StandardScaler
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},
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{
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"name": "biphasic_fraction",
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"fn": get_biphasic_fraction,
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"scaler": MinMaxScaler
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},
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{
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"name": "num_cycles",
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"fn": get_num_cycles,
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"scaler": RobustScaler
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},
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{
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"name": "temperature_averages",
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"fn": get_average_temperatures,
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"scaler": StandardScaler
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}
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],
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"known_categorical_features": [
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],
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"known_continuous_features": [
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{
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"name": "hours_from_start",
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"fn": partial(get_hours_from_start, shift=0),
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"scaler": MinMaxScaler,
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"accumulation_fn": np.max
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},
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{
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"name": "hour_of_day",
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"fn": partial(get_hour_of_day_encoded, shift=0),
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"scaler": None,
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"accumulation_fn": np.max
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},
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{
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"name": "day_of_week",
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"fn": partial(get_day_of_week_encoded, shift=0),
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"scaler": None,
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"accumulation_fn": np.max
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},
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{
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"name": "month_of_year",
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"fn": partial(get_month_of_year_encoded, shift=0),
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"scaler": None,
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"accumulation_fn": np.max
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}
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],
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"observed_categorical_features": [
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],
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"observed_continuous_features": [
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{
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"name": "temperature",
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"fn": partial(get_temperature, shift=0),
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"scaler": StandardScaler
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},
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{
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"name": "rolling_average_temperature",
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"fn": partial(get_rolling_average_with_padding, shift=0),
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"scaler": StandardScaler
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},
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{
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"name": "rolling_window_temperature_min",
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"fn": partial(get_window_fn, window_size=constants.MEASUREMENTS_PER_DAY, fn=partial(np.min, axis=1)),
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"scaler": StandardScaler
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},
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{
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"name": "rolling_window_temperature_max",
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"fn": partial(get_window_fn, window_size=constants.MEASUREMENTS_PER_DAY, fn=partial(np.max, axis=1)),
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"scaler": StandardScaler
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},
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],
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"target_features": [
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# {
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# "name": "fertility_probability",
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# "fn": partial(get_fertility_probability, shift=0),
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# "scaler": MinMaxScaler,
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# "accumulation_fn": np.max
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# },
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# {
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# "name": "ov_over_probability",
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# "fn": partial(get_ov_over_probability, shift=0),
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# "scaler": MinMaxScaler,
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# "accumulation_fn": np.max
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# },
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{
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"name": "days_relative_to_ov",
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"fn": get_days_relative_to_ov,
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"scaler": RobustScaler,
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"accumulation_fn": np.max
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},
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{
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"name": "ov_day",
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"fn": get_ov_day,
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"scaler": RobustScaler,
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"accumulation_fn": np.max
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},
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{
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"name": "is_biphasic",
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"fn": get_is_biphasic,
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"scaler": MinMaxScaler,
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"accumulation_fn": np.max
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}
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]
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}
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