LeeRobust#

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:

[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.LeeRobust)

class LeeRobust(pyagingModel):
    def __init__(self):
        super().__init__()

    def preprocess(self, x):
        return x

    def postprocess(self, x):
        return x


[3]:

model = pya.models.LeeRobust()

Define clock metadata#

[4]:

model.metadata["clock_name"] = "leerobust"
model.metadata["data_type"] = "DNA methylation"  # Paper: The clocks estimate gestational age from placental DNA methylation levels.
model.metadata["species"] = "Homo sapiens"  # Paper: The study assembled human placental methylation datasets.
model.metadata["year"] = 2019
model.metadata["approved_by_author"] = "✅"
model.metadata["citation"] = "Lee, Y., et al. “Placental epigenetic clocks: estimating gestational age using placental DNA methylation levels.” Aging 11(12): 4238–4253 (2019)."
model.metadata["doi"] = "https://doi.org/10.18632/aging.102049"
model.metadata["notes"] = "Robust placental clock trained across placentas with and without pregnancy complications and congenital abnormalities."
model.metadata["research_only"] = None
model.metadata["tissue"] = ["placenta"]  # Paper: Training datasets predominantly sampled the fetal side, including chorionic villi and near-cord-insertion placenta.
model.metadata["predicts"] = ["gestational age"]  # Paper: The placental clocks are estimators of gestational age based on placental tissue.
model.metadata["training_target"] = ["gestational age"]  # Paper: Gestational age was regressed as the dependent variable on CpG methylation levels.
model.metadata["unit"] = ["weeks"]  # Paper: Gestational-age ranges and prediction errors are reported in weeks.
model.metadata["model_type"] = "elastic net regression"  # Paper: Gestational age was regressed on CpG levels using elastic net regression.
model.metadata["platform"] = ["Illumina 450K", "Illumina EPIC"]  # Paper: Eighteen datasets used 450K and one used EPIC; models used autosomal probes shared by both.
model.metadata["population"] = "pregnancies"  # Paper: The RPC was trained in 1,102 samples and selected 558 CpGs.
model.metadata["journal"] = "Aging"
model.metadata["last_author"] = "Steve Horvath"
model.metadata["n_features"] = 558
model.metadata["citations"] = 170
model.metadata["citations_date"] = "2026-07-05"

Download clock dependencies#

Download directly with curl#

[5]:

supplementary_url = "https://www.aging-us.com/article/102049/supplementary/SD2/0/aging-v11i12-102049-supplementary-material-SD2.csv"
supplementary_file_name = "coefficients.csv"
os.system(f"curl -o {supplementary_file_name} {supplementary_url}")

[5]:

0

Load features#

From CSV file#

[6]:

df = pd.read_csv('coefficients.csv')
df['feature'] = df['CpGs']
df['coefficient'] = df['Coefficient_RPC']
df = df[df.coefficient != 0]

model.features = features = df['feature'][1:].tolist()

Load weights into base model#

[7]:

weights = torch.tensor(df['coefficient'][1:].tolist()).unsqueeze(0)
intercept = torch.tensor([df['coefficient'][0]])

Linear model#

[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': 'Lee, Yunsung, et al. "Placental epigenetic clocks: estimating '
             'gestational age using placental DNA methylation levels." Aging '
             '(Albany NY) 11.12 (2019): 4238.',
 'clock_name': 'leerobust',
 'data_type': 'methylation',
 'doi': 'https://doi.org/10.18632/aging.102049',
 'notes': None,
 'research_only': None,
 'species': 'Homo sapiens',
 'version': None,
 'year': 2019}
reference_values: None
preprocess_name: None
preprocess_dependencies: None
postprocess_name: None
postprocess_dependencies: None
features: ['cg00009871', 'cg00035630', 'cg00056066', 'cg00057476', 'cg00063979', 'cg00073090', 'cg00091483', 'cg00173659', 'cg00192031', 'cg00239899', 'cg00307685', 'cg00398130', 'cg00400547', 'cg00419702', 'cg00466827', 'cg00469856', 'cg00501482', 'cg00521434', 'cg00595223', 'cg00639010', 'cg00674365', 'cg00675037', 'cg00705661', 'cg00721170', 'cg00766497', 'cg00896578', 'cg01075918', 'cg01093285', 'cg01118711', 'cg01152073']... [Total elements: 558]
base_model_features: None

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

base_model: LinearModel(
  (linear): Linear(in_features=558, out_features=1, bias=True)
)

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

base_model.linear.weight: [-0.12465698271989822, -0.00647827098146081, 0.8594600558280945, 0.3723226487636566, -0.5090286135673523, -0.1893170177936554, 0.7658786773681641, -0.17820455133914948, 0.7639460563659668, -0.22348295152187347, 0.22304490208625793, 0.2099320888519287, 0.9854989051818848, -0.08535578846931458, 0.009702717885375023, -0.07904969155788422, -0.37377262115478516, 1.6936191320419312, -0.004757361952215433, -1.933017611503601, 0.007646538782864809, -0.18009760975837708, -2.810021162033081, 0.04612048342823982, 0.25931477546691895, -0.006801355164498091, -0.9294152855873108, 0.2601521909236908, 0.5462681651115417, -3.372469425201416]... [Tensor of shape torch.Size([1, 558])]
base_model.linear.bias: tensor([24.9977])

%==================================== 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([[42.0170],
        [55.4254],
        [49.9402],
        [36.4192],
        [45.8815],
        [10.6421],
        [32.3821],
        [38.4876],
        [24.5012],
        [70.2651]], 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