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import string
from time import perf_counter
from typing import Callable
from lightning.fabric.wrappers import _FabricModule
import numpy as np
import torch
import torch.nn as nn
def _test_condition(condition: bool, text: str):
if not condition:
raise ValueError(text)
# SAMPLE WEIGHTS
""" def cal_weights_model_1_forward(dataset):
#predicting 'diseased'
labels = []
for data in dataset:
labels += data.diseased.tolist()
labels_tensor = torch.tensor(labels).squeeze()
n_positive = labels_tensor.nonzero().size(0)
n_negative = labels_tensor.size(0) - n_positive
n_full = labels_tensor.size(0)
return torch.tensor([n_full / (2 * n_negative), n_full / (2 * n_positive)])
def cal_weights_model_1_backward(dataset):
#predicting 'treated'
labels = []
for data in dataset:
labels += data.treated.tolist()
labels_tensor = torch.tensor(labels).squeeze()
n_positive = labels_tensor.nonzero().size(0)
n_negative = labels_tensor.size(0) - n_positive
n_full = labels_tensor.size(0)
return torch.tensor([n_full / (2 * n_negative), n_full / (2 * n_positive)])
def cal_weights_model_2_backward(dataset):
#predicting 'intervention'
labels = []
for data in dataset:
labels += data.intervention.tolist()
labels_tensor = torch.tensor(labels).squeeze()
n_positive = labels_tensor.nonzero().size(0)
n_negative = labels_tensor.size(0) - n_positive
n_full = labels_tensor.size(0)
return torch.tensor([n_full / (2 * n_negative), n_full / (2 * n_positive)]) """
def calculate_loss_sample_weights(dataset, kind: str) -> torch.Tensor:
_test_condition(kind in {"diseased", "treated", "intervention"}, "`kind` should be one of (diseased, treated, intervention)")
labels = []
for data in dataset:
labels += getattr(data, kind).tolist()
labels_tensor = torch.tensor(labels).squeeze()
n_positive = labels_tensor.nonzero().size(0)
n_negative = labels_tensor.size(0) - n_positive
n_full = labels_tensor.size(0)
return torch.tensor([n_full/(2*n_negative), n_full/(2*n_positive)])
""" def get_threshold_healthy(dataset):
all_healthy_values = []
for data in dataset:
all_healthy_values.append(data.healthy.cpu())
percentiles = torch.Tensor(np.percentile(torch.stack(all_healthy_values).flatten(), [e for e in np.arange(0,100,0.2)] + [100]))
return percentiles
def get_threshold_diseased(dataset):
all_diseased_values = []
for data in dataset:
all_diseased_values.append(data.diseased.cpu())
percentiles = torch.Tensor(np.percentile(torch.stack(all_diseased_values).flatten(), [e for e in np.arange(0,100,0.2)] + [100]))
return percentiles
def get_threshold_treated(dataset):
all_treated_values = []
for data in dataset:
all_treated_values.append(data.treated.cpu())
percentiles = torch.Tensor(np.percentile(torch.stack(all_treated_values).flatten(), np.arange(0, 100.2, 0.2)))
return percentiles """
def _get_thresholds(dataset, kind: str):
_test_condition(kind in {"healthy", "diseased", "treated"}, "`kind` should be one of (diseased, treated, healthy)")
all_values = [getattr(data, kind).cpu() for data in dataset]
percentiles = torch.tensor(np.percentile(torch.stack(all_values).flatten(), [e for e in np.arange(0, 100, 0.2)] + [100]))
return percentiles
def get_thresholds(dataset):
return {
'healthy': _get_thresholds(dataset.train_dataset_forward, "healthy") if hasattr(dataset, 'train_dataset_forward') else None,
'diseased': _get_thresholds(dataset.train_dataset_backward, "diseased"),
'treated': _get_thresholds(dataset.train_dataset_backward, "treated")
}
class EarlyStopping:
def __init__(self, patience: int = 15, skip: int = 0, minmax: str = "min", rope: float = 1e-5,
model: _FabricModule = None, save_path: str = None):
self.skip = skip
self.patience = patience
self.rope = abs(rope)
self.minmax = minmax
self.comparison_f = (lambda x, y: x < y-self.rope) if self.minmax == "min" else (lambda x, y: x > y+self.rope)
self.reset()
self.model = model
self.save_path = save_path
self.successful_comparison = (self._save_model if (self.save_path and self.model) else lambda: None)
self.load_model = (self._load_model if (self.save_path and self.model) else lambda: None)
def _save_model(self):
tmp_model = deepcopy(self.model.module)
torch.save({"epoch": self.skip_counter, "model_state_dict": tmp_model.cpu().state_dict()}, self.save_path)
def _load_model(self) -> nn.Module:
checkpoint = torch.load(self.save_path)
tmp_model = deepcopy(self.model.module)
tmp_model.load_state_dict(checkpoint["model_state_dict"])
return tmp_model
def reset(self):
self.counter = 0
self.skip_counter = 0
self.is_stopped = False
self.value = float("inf") if self.minmax == "min" else -float("inf")
def __call__(self, value):
self.skip_counter += 1
if self.skip_counter < self.skip:
if self.comparison_f(value, self.value): # even when skipping, save best value
self.value = value
self.successful_comparison()
return False
if self.comparison_f(value, self.value):
self.value = value
self.counter = 0
self.successful_comparison()
else:
self.counter += 1
if self.counter >= self.patience:
self.is_stopped = True
return True
return False
class DummyEarlyStopping(EarlyStopping):
def __init__(self, patience: int = 15, skip: int = 0, minmax: str = "min", rope: float = 1e-5,
model: _FabricModule = None, save_path: str = None):
super().__init__(patience, skip, minmax, rope, model, save_path)
self.successful_comparison = lambda: None
self.load_model = lambda: None
def __call__(self, value):
return False
def tictoc(*args):
# https://stackoverflow.com/questions/3931627/how-to-build-a-decorator-with-optional-parameters
def wrap(function: Callable):
def wrapped_f(*args, **kwargs):
tic = perf_counter()
result = function(*args, **kwargs)
toc = perf_counter()
print(text_to_format.format(toc-tic))
return result
return wrapped_f
if len(args) >= 1 and callable(args[0]):
text_to_format: str = args[1] if len(args) >= 2 else "{}secs"
to_return = wrap(args[0])
else:
text_to_format = args[0] if args else "{}secs"
to_return = wrap
matches = [tup[1] for tup in string.Formatter().parse(text_to_format) if tup[1] is not None]
if len(matches) != 1:
raise ValueError(r"tictoc decorator requires string with one {}!")
return to_return
class DummyWriter:
def __init__(self, *args, **kwargs):
pass
def add_scalar(self, *args, **kwargs):
pass
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