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[WIP] Attempt to clean up and package code to improve reproduciblity #2

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[WIP] Attempt to clean up and package code to improve reproduciblity #2

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9dd8c2a
wip: turn codebase into package
CompRhys Apr 4, 2024
4cde378
wip: no linting issues
CompRhys Apr 4, 2024
283c9d1
clean: update git ignore
CompRhys Apr 4, 2024
874cd16
clean: remove unused imports
CompRhys Apr 4, 2024
6ab7e6f
maint: add fixme note to tests
CompRhys Apr 4, 2024
c674362
wip: attempt to fix the example calibration notebooks
CompRhys Apr 4, 2024
5aa8fd7
merge
CompRhys Apr 5, 2024
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7 changes: 6 additions & 1 deletion .gitignore
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Original file line number Diff line number Diff line change
@@ -1,9 +1,14 @@
# cache & build
__pycache__/
*.egg-info
.mypy_cache/
build/

*.pth
*.swp
*.log
*.png
wandb/
.ipynb_checkpoints
checkpoints/
*.pt
*.pt
39 changes: 39 additions & 0 deletions .pre-commit-config.yaml
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@@ -0,0 +1,39 @@
# First install `pre-commit` with `pip install pre-commit`.
# Next install these hooks with `pre-commit install`.
# A good overview of many of the pre-commit tools added below is
# available here: https://scikit-hep.org/developer/style

ci:
autoupdate_schedule: quarterly

repos:
- repo: https://github.com/astral-sh/ruff-pre-commit
rev: v0.1.9
hooks:
- id: ruff
args: [--fix]

- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v4.5.0
hooks:
- id: check-case-conflict
- id: check-symlinks
- id: check-yaml
args: [--unsafe]
- id: destroyed-symlinks
- id: end-of-file-fixer
- id: mixed-line-ending
- id: trailing-whitespace

- repo: https://github.com/codespell-project/codespell
rev: v2.2.6
hooks:
- id: codespell
exclude_types: [json]
args: [--ignore-words-list, 'datas,calender', --builtin, 'clear']

# - repo: https://github.com/pre-commit/mirrors-mypy
# rev: v0.942
# hooks:
# - id: mypy
# exclude: (tests|examples)
20 changes: 6 additions & 14 deletions dirac_phi.py → dcsurvival/dirac_phi.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,4 @@
"""
Contains the generator for ACNet (in the class DiracPhi).
"""Contains the generator for ACNet (in the class DiracPhi).
This is named as such since the mixing variable is a convex combination of dirac delta functions.
"""

Expand All @@ -8,12 +7,10 @@


class DiracPhi(nn.Module):
'''
TODO: streamline 3 cases in forward pass.
'''
"""TODO: streamline 3 cases in forward pass."""

def __init__(self, depth, widths, lc_w_range, shift_w_range, device, tol):
super(DiracPhi, self).__init__()
def __init__(self, depth, widths, lc_w_range, shift_w_range, device, tol) -> None:
super().__init__()

# Depth is the number of hidden layers.
self.depth = depth
Expand Down Expand Up @@ -52,19 +49,14 @@ def get_sizes_w_(self):
lc_sizes, shift_sizes = [], []

# Shift weights
prev_width = 1
for pos in range(depth):
width = widths[pos]
shift_sizes.append((width,))
prev_width = width

# Linear combination weights
for pos in range(depth):
width = widths[pos]
if pos < depth-1:
next_width = widths[pos+1]
else:
next_width = 1
next_width = widths[pos + 1] if pos < depth - 1 else 1
lc_sizes.append((next_width, width))

return shift_sizes + lc_sizes
Expand Down Expand Up @@ -122,6 +114,6 @@ def pf(x): return torch.exp(x)

output = states[-1]
assert (output >= 0.).all() and \
(output <= 1.+ self.tol).all(), "t %s, output %s, tol %s, max %s, min %s" % (t, output, self.tol, torch.max(output), torch.min(output))
(output <= 1.+ self.tol).all(), f"t {t}, output {output}, tol {self.tol}, max {torch.max(output)}, min {torch.min(output)}"

return output.reshape(t_raw.size())
83 changes: 41 additions & 42 deletions metrics/metric.py → dcsurvival/metrics/metric.py
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Original file line number Diff line number Diff line change
@@ -1,13 +1,12 @@
import torch
import copy

import numpy as np
from tqdm import tqdm
import torch
import copy


def Survival(truth_model, estimate, x, time_steps):
"""
model: the learned survival model
truth: the true survival model
"""model: the learned survival model
truth: the true survival model.
"""
device = torch.device("cpu")
estimate = copy.deepcopy(estimate).to(device)
Expand All @@ -23,7 +22,7 @@ def Survival(truth_model, estimate, x, time_steps):


def surv_diff(truth_model, estimate, x, steps):
device = torch.device("cpu")
torch.device("cpu")
surv1, surv2, time_steps, t_m = Survival(truth_model, estimate, x, steps)
# integ = torch.abs(surv1-surv2).sum()
integ = torch.sum( torch.diff(torch.cat([torch.zeros(1), time_steps])) * torch.abs(surv1-surv2) )
Expand Down Expand Up @@ -65,7 +64,7 @@ def BS_censored(t, event_t, x, model,e, km_h, km_p):
t_ind2 = (t < event_t).type(torch.float32)
G_t = KM_evaluater(t, km_h, km_p).clamp(0.01,1)
G_event = KM_evaluater(event_t, km_h, km_p).clamp(0.01,100)

#print(torch.sum(t_ind1/(G_event+1e-9*(G_event==0))), torch.sum(t_ind2/(G_t+1e-9*(G_t==0))))
return torch.mean((tmp *(t_ind1/(G_event+1e-9*(G_event==0)))) + (tmp * (t_ind2/(G_t+1e-9*(G_t==0)))))

Expand All @@ -83,49 +82,49 @@ def IBS_plain(event_t, x, model, t_max, n_bins=100):
ibs = 0
for t_ in torch.linspace(0, t_max, n_bins):
tmp = BS(torch.ones_like(event_t, device=event_t.device)*t_, event_t, x, model)

ibs += tmp * len_bin
return ibs/t_max



def evaluate_c_index(dep_model, indep_model, dgp, test_dict, E_reverse = False):
E = test_dict['E']
E = test_dict["E"]
if E_reverse:
E = 1-test_dict['E']
dgp_obs = C_index(test_dict['T'], test_dict['X'], E, dgp).cpu().detach().numpy().item()
dep_obs = C_index(test_dict['T'], test_dict['X'], E, dep_model).cpu().detach().numpy().item()
indep_obs = C_index(test_dict['T'], test_dict['X'], E, indep_model).cpu().detach().numpy().item()
E = 1-test_dict["E"]
dgp_obs = C_index(test_dict["T"], test_dict["X"], E, dgp).cpu().detach().numpy().item()
dep_obs = C_index(test_dict["T"], test_dict["X"], E, dep_model).cpu().detach().numpy().item()
indep_obs = C_index(test_dict["T"], test_dict["X"], E, indep_model).cpu().detach().numpy().item()
aux_e = torch.ones_like(E, device = E.device)
t = test_dict['t1']
t = test_dict["t1"]
if E_reverse:
t = test_dict['t2']
dgp_tot = C_index(t, test_dict['X'], aux_e, dgp).cpu().numpy().item()
dep_tot = C_index(t, test_dict['X'], aux_e, dep_model).cpu().numpy().item()
indep_tot = C_index(t, test_dict['X'], aux_e, indep_model).cpu().numpy().item()
t = test_dict["t2"]
dgp_tot = C_index(t, test_dict["X"], aux_e, dgp).cpu().numpy().item()
dep_tot = C_index(t, test_dict["X"], aux_e, dep_model).cpu().numpy().item()
indep_tot = C_index(t, test_dict["X"], aux_e, indep_model).cpu().numpy().item()
return [[dgp_obs, dep_obs, indep_obs], [dgp_tot, dep_tot, indep_tot]]



def evaluate_IBS(dep_model, indep_model, dgp, test_dict,km_h, km_p, E_reverse):
t = test_dict['t1']
t = test_dict["t1"]
if E_reverse:
t = test_dict['t2']
dgp_tot = IBS_plain(t, test_dict['X'], dgp, torch.max(t), n_bins=100).cpu().numpy().item()
dep_tot = IBS_plain(t, test_dict['X'], dep_model, torch.max(t), n_bins=100).cpu().numpy().item()
indep_tot = IBS_plain(t, test_dict['X'], indep_model, torch.max(t), n_bins=100).cpu().numpy().item()
E = test_dict['E']
t = test_dict["t2"]
dgp_tot = IBS_plain(t, test_dict["X"], dgp, torch.max(t), n_bins=100).cpu().numpy().item()
dep_tot = IBS_plain(t, test_dict["X"], dep_model, torch.max(t), n_bins=100).cpu().numpy().item()
indep_tot = IBS_plain(t, test_dict["X"], indep_model, torch.max(t), n_bins=100).cpu().numpy().item()
E = test_dict["E"]
if E_reverse:
E = 1-test_dict['E']
dgp_obs = IBS(test_dict['T'], test_dict['X'], dgp, torch.max(test_dict['T']), E, km_h, km_p).cpu().numpy().item()
dep_obs = IBS(test_dict['T'], test_dict['X'], dep_model, torch.max(test_dict['T']), E, km_h, km_p).cpu().numpy().item()
indep_obs = IBS(test_dict['T'], test_dict['X'], indep_model, torch.max(test_dict['T']), E, km_h, km_p).cpu().numpy().item()
E = 1-test_dict["E"]
dgp_obs = IBS(test_dict["T"], test_dict["X"], dgp, torch.max(test_dict["T"]), E, km_h, km_p).cpu().numpy().item()
dep_obs = IBS(test_dict["T"], test_dict["X"], dep_model, torch.max(test_dict["T"]), E, km_h, km_p).cpu().numpy().item()
indep_obs = IBS(test_dict["T"], test_dict["X"], indep_model, torch.max(test_dict["T"]), E, km_h, km_p).cpu().numpy().item()
return [[dgp_obs, dep_obs, indep_obs], [dgp_tot, dep_tot, indep_tot]]

def KM(t, e):
device= t.device
t = t.cpu().numpy().reshape(-1,)
e = e.cpu().numpy().reshape(-1,)
t = t.cpu().numpy().reshape(-1)
e = e.cpu().numpy().reshape(-1)
indices = np.argsort(t)
t_sorted = t[indices]
e_sorted = e[indices]
Expand All @@ -142,7 +141,7 @@ def KM(t, e):
event_times_end = np.zeros(event_times.shape[0]+1)
event_times_end[1:] = event_times
return torch.from_numpy(event_times_end).to(device), torch.from_numpy(prob_end).to(device)

def KM_evaluater(t, h, p):
device = t.device
h = h.cpu().numpy()
Expand All @@ -151,10 +150,10 @@ def KM_evaluater(t, h, p):
if len(t.shape) == 1:
idx = np.digitize(t, h, False)
return torch.from_numpy(p[idx-1]).to(device)
else:
idx = np.digitize(t, h, False)
prob = np.zeros_like(t)
for i in range(idx.shape[0]):
for j in range(idx.shape[1]):
prob[i, j] = p[idx[i,j]-1]
return torch.from_numpy(prob).to(device)

idx = np.digitize(t, h, False)
prob = np.zeros_like(t)
for i in range(idx.shape[0]):
for j in range(idx.shape[1]):
prob[i, j] = p[idx[i,j]-1]
return torch.from_numpy(prob).to(device)
79 changes: 38 additions & 41 deletions metrics/metric_pycox.py → dcsurvival/metrics/metric_pycox.py
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Original file line number Diff line number Diff line change
@@ -1,13 +1,10 @@
import torch
import numpy as np
from tqdm import tqdm
import torch


def Survival(truth_model, estimate, x, time_steps):
"""
model: the learned survival model
truth: the true survival model
"""model: the learned survival model
truth: the true survival model.
"""
device = torch.device("cpu")
# surv1 = torch.zeros((x.shape[0], time_steps.shape[0]),device=device)
Expand All @@ -23,7 +20,7 @@ def Survival(truth_model, estimate, x, time_steps):


def surv_diff(truth_model, estimate, x, steps):
device = torch.device("cpu")
torch.device("cpu")
surv1, surv2, time_steps, t_m = Survival(truth_model, estimate, x, steps)
# integ = torch.abs(surv1-surv2).sum()
integ = torch.sum( torch.diff(torch.cat([torch.zeros(1), time_steps])) * torch.abs(surv1-surv2) )
Expand Down Expand Up @@ -64,7 +61,7 @@ def BS_censored(t, event_t, x, model,e, km_h, km_p):
t_ind2 = (t < event_t).type(torch.float32)
G_t = KM_evaluater(t, km_h, km_p).clamp(0.01,1)
G_event = KM_evaluater(event_t, km_h, km_p).clamp(0.01,100)

#print(torch.sum(t_ind1/(G_event+1e-9*(G_event==0))), torch.sum(t_ind2/(G_t+1e-9*(G_t==0))))
return torch.mean((tmp *(t_ind1/(G_event+1e-9*(G_event==0)))) + (tmp * (t_ind2/(G_t+1e-9*(G_t==0)))))

Expand All @@ -82,49 +79,49 @@ def IBS_plain(event_t, x, model, t_max, n_bins=100):
ibs = 0
for t_ in torch.linspace(0, t_max, n_bins):
tmp = BS(torch.ones_like(event_t, device=event_t.device)*t_, event_t, x, model)

ibs += tmp * len_bin
return ibs/t_max



def evaluate_c_index(dep_model, indep_model, dgp, test_dict, E_reverse = False):
E = test_dict['E']
E = test_dict["E"]
if E_reverse:
E = 1-test_dict['E']
dgp_obs = C_index(test_dict['T'], test_dict['X'], E, dgp).cpu().detach().numpy().item()
dep_obs = C_index(test_dict['T'], test_dict['X'], E, dep_model).cpu().detach().numpy().item()
indep_obs = C_index(test_dict['T'], test_dict['X'], E, indep_model).cpu().detach().numpy().item()
E = 1-test_dict["E"]
dgp_obs = C_index(test_dict["T"], test_dict["X"], E, dgp).cpu().detach().numpy().item()
dep_obs = C_index(test_dict["T"], test_dict["X"], E, dep_model).cpu().detach().numpy().item()
indep_obs = C_index(test_dict["T"], test_dict["X"], E, indep_model).cpu().detach().numpy().item()
aux_e = torch.ones_like(E, device = E.device)
t = test_dict['t1']
t = test_dict["t1"]
if E_reverse:
t = test_dict['t2']
dgp_tot = C_index(t, test_dict['X'], aux_e, dgp).cpu().numpy().item()
dep_tot = C_index(t, test_dict['X'], aux_e, dep_model).cpu().numpy().item()
indep_tot = C_index(t, test_dict['X'], aux_e, indep_model).cpu().numpy().item()
t = test_dict["t2"]
dgp_tot = C_index(t, test_dict["X"], aux_e, dgp).cpu().numpy().item()
dep_tot = C_index(t, test_dict["X"], aux_e, dep_model).cpu().numpy().item()
indep_tot = C_index(t, test_dict["X"], aux_e, indep_model).cpu().numpy().item()
return [[dgp_obs, dep_obs, indep_obs], [dgp_tot, dep_tot, indep_tot]]



def evaluate_IBS(dep_model, indep_model, dgp, test_dict,km_h, km_p, E_reverse):
t = test_dict['t1']
t = test_dict["t1"]
if E_reverse:
t = test_dict['t2']
dgp_tot = IBS_plain(t, test_dict['X'], dgp, torch.max(t), n_bins=100).cpu().numpy().item()
dep_tot = IBS_plain(t, test_dict['X'], dep_model, torch.max(t), n_bins=100).cpu().numpy().item()
indep_tot = IBS_plain(t, test_dict['X'], indep_model, torch.max(t), n_bins=100).cpu().numpy().item()
E = test_dict['E']
t = test_dict["t2"]
dgp_tot = IBS_plain(t, test_dict["X"], dgp, torch.max(t), n_bins=100).cpu().numpy().item()
dep_tot = IBS_plain(t, test_dict["X"], dep_model, torch.max(t), n_bins=100).cpu().numpy().item()
indep_tot = IBS_plain(t, test_dict["X"], indep_model, torch.max(t), n_bins=100).cpu().numpy().item()
E = test_dict["E"]
if E_reverse:
E = 1-test_dict['E']
dgp_obs = IBS(test_dict['T'], test_dict['X'], dgp, torch.max(test_dict['T']), E, km_h, km_p).cpu().numpy().item()
dep_obs = IBS(test_dict['T'], test_dict['X'], dep_model, torch.max(test_dict['T']), E, km_h, km_p).cpu().numpy().item()
indep_obs = IBS(test_dict['T'], test_dict['X'], indep_model, torch.max(test_dict['T']), E, km_h, km_p).cpu().numpy().item()
E = 1-test_dict["E"]
dgp_obs = IBS(test_dict["T"], test_dict["X"], dgp, torch.max(test_dict["T"]), E, km_h, km_p).cpu().numpy().item()
dep_obs = IBS(test_dict["T"], test_dict["X"], dep_model, torch.max(test_dict["T"]), E, km_h, km_p).cpu().numpy().item()
indep_obs = IBS(test_dict["T"], test_dict["X"], indep_model, torch.max(test_dict["T"]), E, km_h, km_p).cpu().numpy().item()
return [[dgp_obs, dep_obs, indep_obs], [dgp_tot, dep_tot, indep_tot]]

def KM(t, e):
device= t.device
t = t.cpu().numpy().reshape(-1,)
e = e.cpu().numpy().reshape(-1,)
t = t.cpu().numpy().reshape(-1)
e = e.cpu().numpy().reshape(-1)
indices = np.argsort(t)
t_sorted = t[indices]
e_sorted = e[indices]
Expand All @@ -141,7 +138,7 @@ def KM(t, e):
event_times_end = np.zeros(event_times.shape[0]+1)
event_times_end[1:] = event_times
return torch.from_numpy(event_times_end).to(device), torch.from_numpy(prob_end).to(device)

def KM_evaluater(t, h, p):
device = t.device
h = h.cpu().numpy()
Expand All @@ -150,10 +147,10 @@ def KM_evaluater(t, h, p):
if len(t.shape) == 1:
idx = np.digitize(t, h, False)
return torch.from_numpy(p[idx-1]).to(device)
else:
idx = np.digitize(t, h, False)
prob = np.zeros_like(t)
for i in range(idx.shape[0]):
for j in range(idx.shape[1]):
prob[i, j] = p[idx[i,j]-1]
return torch.from_numpy(prob).to(device)

idx = np.digitize(t, h, False)
prob = np.zeros_like(t)
for i in range(idx.shape[0]):
for j in range(idx.shape[1]):
prob[i, j] = p[idx[i,j]-1]
return torch.from_numpy(prob).to(device)
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