CompIL6#

Index#

Instantiate model class
Define clock metadata
Download clock dependencies
Load features
Load weights into base model
Load reference values
Load preprocess and postprocess objects
Check all clock parameters
Basic test
Save torch model
Clear directory

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.CompIL6)

class CompIL6(LinearReferenceClock):
    pass

[3]:

model = pya.models.CompIL6()

Define clock metadata#

[4]:

model.metadata["clock_name"] = 'compil6'
model.metadata["data_type"] = 'methylation'
model.metadata["species"] = 'Homo sapiens'
model.metadata["year"] = 2021
model.metadata["approved_by_author"] = '⌛'
model.metadata["citation"] = "Stevenson, Anna J., et al. \"Creating and validating a DNA methylation-based proxy for interleukin-6.\" The Journals of Gerontology: Series A 76.12 (2021): 2284-2292."
model.metadata["doi"] = "https://doi.org/10.1093/gerona/glab046"
model.metadata["research_only"] = None
model.metadata["notes"] = "Blood DNA-methylation surrogate for circulating interleukin-6 built by elastic-net regression over 35 CpGs, providing a stable epigenetic proxy that captures chronic inflammatory burden better than a single serum measurement."
model.metadata["tissue"] = 'whole blood'
model.metadata["predicts"] = 'serum interleukin-6 (IL-6) level (DNAm proxy)'
model.metadata["unit"] = 'score (arbitrary)'
model.metadata["model_type"] = 'Elastic net'
model.metadata["platform"] = 'Illumina 450K/EPIC'
model.metadata["population"] = 'adults (trained in older adults, mean age ~70)'
model.metadata["journal"] = 'The Journals of Gerontology Series A'
model.metadata["last_author"] = 'Riccardo E. Marioni'
model.metadata["n_features"] = 35
model.metadata["citations"] = 55
model.metadata["citations_date"] = '2026-07-05'

Download clock dependencies#

[5]:

supplementary_url = "https://raw.githubusercontent.com/Duzhaozhen/OmniAge/c10fbe8cb92957520fbff1d55ae1def0691252e5/OmniAgePy/src/omniage/data/CompIL6.csv"
supplementary_file_name = "coefficients.csv"
os.system(f"curl -sL -o {supplementary_file_name} {supplementary_url}")

[5]:

Load features#

[6]:

df = pd.read_csv('coefficients.csv')
if str(df.columns[0]).startswith('Unnamed'):
    df = df.iloc[:, 1:]
mask = df['probe'].astype(str).str.lower().isin(['intercept', '(intercept)'])
intercept_value = float(df.loc[mask, 'coef'].iloc[0]) if mask.any() else 0.0
coef_df = df.loc[~mask].reset_index(drop=True)
model.features = coef_df['probe'].tolist()

Load weights into base model#

[7]:

weights = torch.tensor(coef_df['coef'].tolist()).unsqueeze(0).float()
intercept = torch.tensor([intercept_value]).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': 'Stevenson, Anna J., et al. "Creating and validating a DNA '
             'methylation-based proxy for interleukin-6." The Journals of '
             'Gerontology: Series A 76.12 (2021): 2284-2292.',
 'clock_name': 'compil6',
 'data_type': 'methylation',
 'doi': 'https://doi.org/10.1093/gerona/glab046',
 'notes': None,
 'research_only': None,
 'species': 'Homo sapiens',
 'version': None,
 'year': 2021}
reference_values: None
preprocess_name: None
preprocess_dependencies: None
postprocess_name: None
postprocess_dependencies: None
features: ['cg26144437', 'cg03076319', 'cg05575921', 'cg21368161', 'cg04642923', 'cg21123519', 'cg04583842', 'cg16357224', 'cg24455236', 'cg25323809', 'cg03885055', 'cg17412005', 'cg19638572', 'cg04921989', 'cg14965639', 'cg04381957', 'cg20789595', 'cg24935598', 'cg26230601', 'cg16508480', 'cg12503394', 'cg18925601', 'cg19584649', 'cg03245734', 'cg04366687', 'cg14044707', 'cg25250132', 'cg04468741', 'cg23729763', 'cg10195814']... [Total elements: 35]
base_model_features: None

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

base_model: LinearModel(
  (linear): Linear(in_features=35, out_features=1, bias=True)
)

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

base_model.linear.weight: [0.05400000140070915, 0.3799999952316284, -0.14000000059604645, 0.11999999731779099, 0.25, 0.004999999888241291, 0.00019999999494757503, 0.003599999938160181, 0.06400000303983688, 0.1899999976158142, -0.44999998807907104, -0.08799999952316284, 0.36000001430511475, 0.0860000029206276, -0.10999999940395355, -0.01899999938905239, -0.3100000023841858, 0.09099999815225601, -0.10999999940395355, 0.028999999165534973, 0.0010999999940395355, -0.017999999225139618, 0.08100000023841858, 0.020999999716877937, 0.0820000022649765, 0.007499999832361937, 0.23999999463558197, 0.02500000037252903, 0.10000000149011612, 0.06300000101327896]... [Tensor of shape torch.Size([1, 35])]
base_model.linear.bias: tensor([0.])

%==================================== 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([[-1.3984],
        [ 0.5055],
        [-1.2893],
        [ 0.7687],
        [ 1.4874],
        [ 1.2169],
        [-0.0179],
        [ 0.4686],
        [ 1.8998],
        [-1.8617]], 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)

Deleted file: coefficients.csv