Weidner

Index

1. Instantiate model class

2. Define clock metadata

3. Download clock dependencies

4. Load features

5. Load weights into base model

6. Load reference values

7. Load preprocess and postprocess objects

8. Check all clock parameters

9. Basic test

10. Save torch model

11. Clear directory

Let’s first import some packages:

import os
import inspect
import shutil
import json
import torch
import pandas as pd
import pyaging as pya

Instantiate model class

def print_entire_class(cls):
    source = inspect.getsource(cls)
    print(source)

print_entire_class(pya.models.Weidner)

class Weidner(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

model = pya.models.Weidner()

Define clock metadata

model.metadata["clock_name"] = 'weidner'
model.metadata["data_type"] = 'methylation'
model.metadata["species"] = 'Homo sapiens'
model.metadata["year"] = 2014
model.metadata["approved_by_author"] = '⌛'
model.metadata["citation"] = "Weidner, Carola I., et al. \"Aging of blood can be tracked by DNA methylation changes at just three CpG sites.\" Genome biology 15.2 (2014): R24."
model.metadata["doi"] = "https://doi.org/10.1186/gb-2014-15-2-r24"
model.metadata["research_only"] = None
model.metadata["notes"] = "Whole-blood chronological age predictor based on multivariate linear regression over just three age-associated CpG sites (in ITGA2B, ASPA and PDE4C), measurable by targeted bisulfite pyrosequencing."
model.metadata["tissue"] = 'whole blood'
model.metadata["predicts"] = 'chronological age'
model.metadata["unit"] = 'years'
model.metadata["model_type"] = 'Linear regression'
model.metadata["platform"] = 'Illumina 27K'
model.metadata["population"] = 'adults'
model.metadata["journal"] = 'Genome biology'
model.metadata["last_author"] = 'Wolfgang Wagner'
model.metadata["n_features"] = 3
model.metadata["citations"] = 973
model.metadata["citations_date"] = '2026-07-05'

Download clock dependencies

supplementary_url = "https://raw.githubusercontent.com/bio-learn/biolearn/180852e2bab473303cb85da627178b1695ee9d86/biolearn/data/Weidner.csv"
supplementary_file_name = "Weidner.csv"
os.system(f"curl -sL -o {supplementary_file_name} {supplementary_url}")

0

Load features

df = pd.read_csv('Weidner.csv')
model.features = df['CpGmarker'].tolist()

Load weights into base model

weights = torch.tensor(df['CoefficientTraining'].tolist()).unsqueeze(0).float()
intercept = torch.tensor([38.0]).float()

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

model.reference_values = None

Load preprocess and postprocess objects

model.preprocess_name = None
model.preprocess_dependencies = None

model.postprocess_name = None
model.postprocess_dependencies = None

Check all clock parameters

pya.utils.print_model_details(model)

%==================================== Model Details ====================================%
Model Attributes:

training: True
metadata: {'approved_by_author': '⌛',
 'citation': 'Weidner, Carola I., et al. "Aging of blood can be tracked by DNA '
             'methylation changes at just three CpG sites." Genome biology '
             '15.2 (2014): R24.',
 'clock_name': 'weidner',
 'data_type': 'methylation',
 'doi': 'https://doi.org/10.1186/gb-2014-15-2-r24',
 'notes': None,
 'research_only': None,
 'species': 'Homo sapiens',
 'version': None,
 'year': 2014}
reference_values: None
preprocess_name: None
preprocess_dependencies: None
postprocess_name: None
postprocess_dependencies: None
features: ['cg02228185', 'cg25809905', 'cg17861230']
base_model_features: None

%==================================== Model Details ====================================%
Model Structure:

base_model: LinearModel(
  (linear): Linear(in_features=3, out_features=1, bias=True)
)

%==================================== Model Details ====================================%
Model Parameters and Weights:

base_model.linear.weight: tensor([[-26.4000, -23.7000, 164.7000]])
base_model.linear.bias: tensor([38.])

%==================================== Model Details ====================================%

Basic test

torch.manual_seed(42)
input = torch.randn(10, len(model.features), dtype=float)
model.eval()
model.to(float)
pred = model(input)
pred

tensor([[ 70.9916],
        [157.0004],
        [155.2728],
        [ 58.9885],
        [ 38.9402],
        [143.5184],
        [396.4215],
        [ 66.8971],
        [130.9565],
        [-26.1560]], dtype=torch.float64, grad_fn=<AddmmBackward0>)

Save torch model

torch.save(model, f"../weights/{model.metadata['clock_name']}.pt")

Clear directory

# 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)

Deleted file: Weidner.csv