VidalBralo#
Index#
Let’s first import some packages:
[1]:
import os
import inspect
import shutil
import json
import torch
import pandas as pd
import pyaging as pya
Instantiate model class#
[2]:
def print_entire_class(cls):
source = inspect.getsource(cls)
print(source)
print_entire_class(pya.models.VidalBralo)
class VidalBralo(pyagingModel):
def __init__(self):
super().__init__()
def preprocess(self, x):
if self.reference_values is None:
return x
if isinstance(self.reference_values, torch.Tensor):
reference = self.reference_values.to(device=x.device, dtype=x.dtype)
else:
reference = torch.tensor(self.reference_values, device=x.device, dtype=x.dtype)
return torch.where(torch.isnan(x), reference, x)
def postprocess(self, x):
return x
[3]:
model = pya.models.VidalBralo()
Define clock metadata#
[4]:
model.metadata["clock_name"] = 'vidalbralo'
model.metadata["data_type"] = 'methylation'
model.metadata["species"] = 'Homo sapiens'
model.metadata["year"] = 2016
model.metadata["approved_by_author"] = '⌛'
model.metadata["citation"] = "Vidal-Bralo, Laura, Yolanda Lopez-Golan, and Antonio Gonzalez. \"Simplified assay for epigenetic age estimation in whole blood of adults.\" Frontiers in genetics 7 (2016): 126."
model.metadata["doi"] = "https://doi.org/10.3389/fgene.2016.00126"
model.metadata["research_only"] = None
model.metadata["notes"] = "Whole-blood epigenetic age estimator for adults built by stepwise multiple linear regression over eight CpG sites, designed to run as a single low-cost MS-SNuPE multiplex assay rather than a genome-wide methylation microarray."
model.metadata["tissue"] = 'whole blood'
model.metadata["predicts"] = 'chronological age'
model.metadata["unit"] = 'years'
model.metadata["model_type"] = 'Bayesian regression'
model.metadata["platform"] = 'Illumina 27K/450K'
model.metadata["population"] = 'adults (>20 years)'
model.metadata["journal"] = 'Frontiers in Genetics'
model.metadata["last_author"] = 'Antonio González'
model.metadata["n_features"] = 8
model.metadata["citations"] = 145
model.metadata["citations_date"] = '2026-07-05'
Download clock dependencies#
[5]:
# The 8-CpG DmAM coefficients are given directly in Table 2 of
# Vidal-Bralo et al. 2016 (Front. Genet. 7:126); they are defined inline below,
# so no external download is required.
Load features#
[6]:
# Multiple linear regression parameters of the 8 CpG DmAM (Vidal-Bralo et al. 2016, Table 2)
coef_df = pd.DataFrame({
'CpG': ['cg16386080', 'cg24768561', 'cg19761273', 'cg25809905',
'cg09809672', 'cg02228185', 'cg17471102', 'cg10917602'],
'coefficient': [59.5, 33.9, -44.0, -19.7, -22.8, -16.8, -17.7, -11.4],
})
model.features = coef_df['CpG'].tolist()
Load weights into base model#
[7]:
weights = torch.tensor(coef_df['coefficient'].tolist()).unsqueeze(0).float()
intercept = torch.tensor([84.7]).float()
[8]:
base_model = pya.models.LinearModel(input_dim=len(model.features))
base_model.linear.weight.data = weights.float()
base_model.linear.bias.data = intercept.float()
model.base_model = base_model
Load reference values#
[9]:
model.reference_values = None
Load preprocess and postprocess objects#
[10]:
model.preprocess_name = None
model.preprocess_dependencies = None
[11]:
model.postprocess_name = None
model.postprocess_dependencies = None
Check all clock parameters#
[12]:
pya.utils.print_model_details(model)
%==================================== Model Details ====================================%
Model Attributes:
training: True
metadata: {'approved_by_author': '⌛',
'citation': 'Vidal-Bralo, Laura, Yolanda Lopez-Golan, and Antonio Gonzalez. '
'"Simplified assay for epigenetic age estimation in whole blood '
'of adults." Frontiers in genetics 7 (2016): 126.',
'clock_name': 'vidalbralo',
'data_type': 'methylation',
'doi': 'https://doi.org/10.3389/fgene.2016.00126',
'notes': None,
'research_only': None,
'species': 'Homo sapiens',
'version': None,
'year': 2016}
reference_values: None
preprocess_name: None
preprocess_dependencies: None
postprocess_name: None
postprocess_dependencies: None
features: ['cg16386080',
'cg24768561',
'cg19761273',
'cg25809905',
'cg09809672',
'cg02228185',
'cg17471102',
'cg10917602']
base_model_features: None
%==================================== Model Details ====================================%
Model Structure:
base_model: LinearModel(
(linear): Linear(in_features=8, out_features=1, bias=True)
)
%==================================== Model Details ====================================%
Model Parameters and Weights:
base_model.linear.weight: tensor([[ 59.5000, 33.9000, -44.0000, -19.7000, -22.8000, -16.8000, -17.7000,
-11.4000]])
base_model.linear.bias: tensor([84.7000])
%==================================== Model Details ====================================%
Basic test#
[13]:
torch.manual_seed(42)
input = torch.randn(10, len(model.features), dtype=float)
model.eval()
model.to(float)
pred = model(input)
pred
[13]:
tensor([[126.0523],
[104.1466],
[ 75.2432],
[ 58.5473],
[160.9427],
[ 27.2660],
[137.7398],
[262.1763],
[125.6747],
[171.3440]], dtype=torch.float64, grad_fn=<AddmmBackward0>)
Save torch model#
[14]:
torch.save(model, f"../weights/{model.metadata['clock_name']}.pt")
Clear directory#
[15]:
# Function to remove a folder and all its contents
def remove_folder(path):
try:
shutil.rmtree(path)
print(f"Deleted folder: {path}")
except Exception as e:
print(f"Error deleting folder {path}: {e}")
# Get a list of all files and folders in the current directory
all_items = os.listdir('.')
# Loop through the items
for item in all_items:
# Check if it's a file and does not end with .ipynb
if os.path.isfile(item) and not item.endswith('.ipynb'):
os.remove(item)
print(f"Deleted file: {item}")
# Check if it's a folder
elif os.path.isdir(item):
remove_folder(item)