Linear Inference Sessions

An overview and example of a linear inference model for performing tasks such as GWAS in HFL.

The built-in model package iai_linear_inference trains a bundle of linear models for the target of interest against a specified list of predictors. It obtains the coefficients and variance estimates, and also calculates the p-values from the corresponding hypothesis tests. Linear inference is particularly useful for genome-wide association studies (GWAS), to identify genomic variants that are statistically associated with a risk for a disease or a particular trait.

This is a horizontal federated learning (HFL) model package.

The integrateai_HFL.ipynb, available in the integrate.ai sample repository, demonstrates this model package using the sample data that is available for download here.

Follow the instructions in the Installing the SDK section to prepare a local test environment for this tutorial.

Overview of the iai_linear_inference package

There are two strategies available in the package:

• LogitRegInference - for use when the target of interest is binary

• LinearRegInference - for use when the target is continuous

# Example model_config for a binary target

model_config_logit = {
    "strategy": {"name": "LogitRegInference", "params": {}},
    "seed": 23,  # for reproducibility
    }

# Example data_config

data_config_logit = {
    "target": "y",
    "shared_predictors": ["x1", "x2"],
    "chunked_predictors": ["x0", "x3", "x10", "x11"]
    }

The data_config dictionary should include the following three fields.

• target: the target column of interest

• shared_predictors: predictor columns that should be included in all linear models. For example, the confounding factors like age, gender in GWAS.

• chunked_predictors: predictor columns that should be included in the linear model one at a time. For example, the gene expressions in GWAS.

Note: The columns in all the fields can be specified as either names/strings or indices/integers.

With this example data configuration, the session trains four logistic regression models with y as the target, and x1, x2 plus any one of x0, x3, x10, x11 as predictors.

Create a linear inference training session

train_path1 = f's3://{base_aws_bucket}/synthetic/train_silo0.parquet'
test_path1 = f's3://{base_aws_bucket}/synthetic/test.parquet'
train_path2 = f's3://{base_aws_bucket}/synthetic/train_silo1.parquet'
test_path2 = f's3://{base_aws_bucket}/synthetic/test.parquet'

Specify the file paths.

For this example, there are two (2) training clients and the model is trained over five (5) rounds.

# Example training session

training_session_logit = client.create_fl_session(
    name="Testing linear inference session",
    description="I am testing linear inference session creation using a task runner through a notebook",
    min_num_clients=2,
    num_rounds=5,
    package_name="iai_linear_inference",
    model_config=model_config_logit,
    data_config=data_config_logit
).start()

training_session_logit.id   //prints the sesssion ID, for reference

Argument (type)	Description
name (str)	Name to set for the session
description (str)	Description to set for the session
min_num_clients (int)	Number of clients required to connect before the session can begin
num_rounds (str)	Number of rounds of federated model training to perform
package_name (str)	Name of the model package to be used in the session
model_config (dict)	Contains the model configuration to be used for the session
data_config (dict)	Contains the data configuration to be used for the session

Start the linear inference training session

This example demonstrates starting a training session with two tasks (clients).

#Create a task group

task_group_context = (
    SessionTaskGroup(training_session_logit)
    .add_task(iai_tb_aws.hfl(train_path=train_path1, test_path=test_path1))
    .add_task(iai_tb_aws.hfl(train_path=train_path2, test_path=test_path2))
    
.start()
)

Poll for linear inference session status

Use a polling function, such as the example provided, to wait for the session to complete.

for i in task_group_context.contexts.values():
    print(json.dumps(i.status(), indent=4))
task_group_context.monitor_task_logs()

#Wait for result = True
task_group_context.wait(60*5, 2)

View training metrics and model details

Once the session is complete, you can view the training metrics and model details such as the model coefficients and p-values. In this example, since there are a bundle of models being trained, the metrics are the average values of all the models.

training_session_logit.metrics().as_dict()

# Example output

{'session_id': '3cdf4be992',
 'federated_metrics': [{'loss': 0.6931747794151306},
  {'loss': 0.6766608953475952},
  {'loss': 0.6766080856323242},
  {'loss': 0.6766077876091003},
  {'loss': 0.6766077876091003}],
 'client_metrics': [{'user@integrate.ai:dedbb7e9be2046e3a49b28b0131c4b97': {'test_loss': 0.6931748060977674,
    'test_accuracy': 0.4995,
    'test_roc_auc': 0.5,
    'test_num_examples': 4000},
   'user@integrate.ai:339d50e453f244ed9cb2662ab2d3bb66': {'test_loss': 0.6931748060977674,
    'test_accuracy': 0.4995,
    'test_roc_auc': 0.5,
    'test_num_examples': 4000}},
  {'user@integrate.ai:dedbb7e9be2046e3a49b28b0131c4b97': {'test_num_examples': 4000,
    'test_loss': 0.6766608866775886,
    'test_roc_auc': 0.5996664746664747,
    'test_accuracy': 0.57625},
   'user@integrate.ai:339d50e453f244ed9cb2662ab2d3bb66': {'test_num_examples': 4000,
    'test_loss': 0.6766608866775886,
    'test_accuracy': 0.57625,
    'test_roc_auc': 0.5996664746664747}},
  {'user@integrate.ai:339d50e453f244ed9cb2662ab2d3bb66': {'test_loss': 0.6766080602706078,
    'test_accuracy': 0.5761875,
    'test_num_examples': 4000,
...
   'user@integrate.ai:339d50e453f244ed9cb2662ab2d3bb66': {'test_accuracy': 0.5761875,
    'test_roc_auc': 0.5996632246632246,
    'test_num_examples': 4000,
    'test_loss': 0.6766078165060236}}],
 'latest_global_model_federated_loss': 0.6766077876091003}

Plot the metrics

training_session_logit.metrics().plot()

Example output:

Retrieve the trained model

# Example of retrieving p-values

pv = model_logit.p_values()
pv

#Example p-value output:

x0      112.350396
x3      82.436540
x10     0.999893
x11     27.525280
dtype: float64

The LinearInferenceModel object can be retrieved using the model’s as_pytorch method. The relevant information, such as p-values, can be accessed directly from the model object.

model_logit = training_session_logit.model().as_pytorch()

Retrieve the p-values

The .p_values() function returns the p-values of the chunked predictors.

Summary information

The .summary method fetches the coefficient, standard error, and p-value of the model corresponding to the specified predictor.

#Example of fetching summary

summary_x0 = model_logit.summary("x0")
summary_x0`

Example summary output:

Making predictions from a linear inference session

from torch.utils.data import DataLoader
from integrate_ai_sdk.packages.LinearInference.dataset import ChunkedTabularDataset

ds = ChunkedTabularDataset(path=f"{data_path}/test.parquet", **data_config_logit)
dl = DataLoader(ds, batch_size=len(ds), shuffle=False)
x, _ = next(iter(dl))
y_pred = model_logit(x)
y_pred

You can also make predictions with the resulting bundle of models when the data is loaded by the ChunkedTabularDataset from the iai_linear_inference package. The predictions will be of shape (n_samples, n_chunked_predictors) where each column corresponds to one model from the bundle.

#Example prediction output:

tensor([[0.3801, 0.3548, 0.4598, 0.4809],
        [0.4787, 0.3761, 0.4392, 0.3579],
        [0.5151, 0.3497, 0.4837, 0.5054],
        ...,
        [0.7062, 0.6533, 0.6516, 0.6717],
        [0.3114, 0.3322, 0.4257, 0.4461],
        [0.4358, 0.4912, 0.4897, 0.5110]], dtype=torch.float64)

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