DEBIAS-M - Domain adaptation with phenotype Estimation and Batch Integration Across Studies of the Microbiome
DEBIAS-M can be installed with pip through the following command:
pip install git+https://github.com/korem-lab/DEBIAS-M.git
As DEBIAS-M is a light package, the install time requires less than a minute on a standard machine.
All of our analyses tested DEBIAS-M in python 3.6, and per our setup.py file, numpy, pandas, torch==1.10.2, pytorch-lightning==1.5.10, and lightning-bolts==0.4.0. All of our analyses have been run on a local unix machine, and on an ubuntu CodeOcean capsule. No non-standard hardware is required for DEBIAS-M.
To begin, we recommend running the example demo we provide, which runs DEBIAS-M on a randomly generated dataset.
See the Example.ipynb notebook to see how to use the package. This notebook demonstrates the inputs and outputs of DEBIAS-M implementation (whichmimicks the standard scikit-learn structure), and fits a DEBIAS-M model on the synthetic data. For the generated example of 480 samples and 100 features, the DEBIAS-M example completes in less than a minute. We provide a copy of the walkthrough code below. For further details on reproducing all results on our main analyses, please refer to our CodeOcean capsule, or our github repository containing the analysis code. Refer to the Multitask-example.ipynb notebook for an example running DEBIAS-M on multiple phenotypes at once.
import numpy as np
from debiasm import DebiasMClassifierThe single-task DEBIAS-M reuiqres two inputs: 1) an X matrix, of dimension (n_samples) x (1 + n_features), whose first column denotes the batch the sample originates from, and whose remaining columns describe the read counts observed for the corresponding sample-taxon pair; and 2) a binary y vector, of length (n_features), describing a corresponding sample's classification label.
np.random.seed(123)
n_samples = 96*5
n_batches = 5
n_features = 100
## the read count matrix
X = ( np.random.rand(n_samples, n_features) * 1000 ).astype(int)
## the labels
y = np.random.rand(n_samples)>0.5
## the batches
batches = ( np.random.rand(n_samples) * n_batches ).astype(int)## we assume the batches are numbered ints starting at '0',
## and they are in the first column of the input X matrices
X_with_batch = np.hstack((batches[:, np.newaxis], X))
X_with_batch[:5, :5]array([[ 4, 696, 286, 226, 551],
[ 4, 513, 666, 105, 130],
[ 3, 542, 66, 653, 996],
[ 3, 16, 721, 7, 84],
[ 1, 456, 279, 932, 314]])
y[:5]array([ True, False, False, True, True])
## set the valdiation batch to '4'
val_inds = batches==4
X_train, X_val = X_with_batch[~val_inds], X_with_batch[val_inds]
y_train, y_val = y[~val_inds], y[val_inds]y_train.shape(374,)
X_train.shape(374, 101)
dmc = DebiasMClassifier(x_val=X_val) ## give it the held-out inputs to account for
## those domains shifts while training
dmc.fit(X_train, y_train)
print('finished training!')GPU available: False, used: False
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
/Users/george/opt/anaconda3/envs/pl2/lib/python3.6/site-packages/pytorch_lightning/trainer/data_loading.py:133: UserWarning: The dataloader, val_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.
f"The dataloader, {name}, does not have many workers which may be a bottleneck."
/Users/george/Desktop/sandbox/sandbox2/package_dev/DEBIAS-M/debiasm/torch_functions.py:75: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.
y_hat = softmax(x)
/Users/george/opt/anaconda3/envs/pl2/lib/python3.6/site-packages/pytorch_lightning/trainer/data_loading.py:133: UserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.
f"The dataloader, {name}, does not have many workers which may be a bottleneck."
/Users/george/opt/anaconda3/envs/pl2/lib/python3.6/site-packages/pytorch_lightning/trainer/data_loading.py:433: UserWarning: The number of training samples (22) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.
f"The number of training samples ({self.num_training_batches}) is smaller than the logging interval"
Trainer was signaled to stop but required minimum epochs (25) or minimum steps (None) has not been met. Training will continue...
Trainer was signaled to stop but required minimum epochs (25) or minimum steps (None) has not been met. Training will continue...
Trainer was signaled to stop but required minimum epochs (25) or minimum steps (None) has not been met. Training will continue...
Trainer was signaled to stop but required minimum epochs (25) or minimum steps (None) has not been met. Training will continue...
Trainer was signaled to stop but required minimum epochs (25) or minimum steps (None) has not been met. Training will continue...
Trainer was signaled to stop but required minimum epochs (25) or minimum steps (None) has not been met. Training will continue...
Trainer was signaled to stop but required minimum epochs (25) or minimum steps (None) has not been met. Training will continue...
Trainer was signaled to stop but required minimum epochs (25) or minimum steps (None) has not been met. Training will continue...
Trainer was signaled to stop but required minimum epochs (25) or minimum steps (None) has not been met. Training will continue...
Trainer was signaled to stop but required minimum epochs (25) or minimum steps (None) has not been met. Training will continue...
finished training!
from sklearn.metrics import roc_auc_score
roc_auc_score(y_val, dmc.predict_proba(X_val)[:, 1])
## should be ~~0.5 in this example , since the data is all random0.4784313725490196
X_debiassed = dmc.transform(X_with_batch)
X_debiassed[:5, :5]array([[0.01457723, 0.00534167, 0.00494237, 0.01204327, 0.01374663],
[0.01068423, 0.01236932, 0.00228337, 0.00282551, 0.00610285],
[0.01086351, 0.00140875, 0.01448351, 0.02095727, 0.01629219],
[0.00031793, 0.01525674, 0.00015392, 0.00175223, 0.00472576],
[0.00966502, 0.00629951, 0.01727931, 0.00599989, 0.01835283]])