This package implements OWL, a robust approach for fitting probabilistic models with likelihood functions.
If you have conda installed, then you can install by running the following from the base directory.
conda env create -f env.yaml
conda activate owl
pip install -e .
Otherwise, you should install the packages listed in env.yaml before running pip install -e ..
To fit a probabilistic model using OWL, create a class that extends the OWLModel class. You must implement two functions: maximize_weighted_likelihood and log_likelihood. Below is a simple exponential distribution.
from owl.models import OWLModel
'''
Simple univariate exponential distribution.
'''
class Exponential(OWLModel):
def __init__(self,
X: np.ndarray, ## Input samples (1-dimensional)
w:np.ndarray = None, ## Weights over the samples (set to None for uniform)
**kwargs
):
self.X = X.copy()
n = len(X)
super().__init__(n=n, w=w, **kwargs)
self.lam = 1.0 ## Parameter of the exponential distribution
def maximize_weighted_likelihood(self, **kwargs):
self.lam = np.sum(self.w)/np.dot(self.w, self.X)
def log_likelihood(self):
return( np.log(self.lam) - (self.lam*self.X) )Once the class is created, then we need to choose the Ball class that we will fit it with. In all the experiments in the paper, the L1Ball class is used.
from owl.ball import L1Ball
## Generate data from an exponential distribution
n = 1000
lam = 5.0
x = np.random.exponential(scale=(1./lam), size=n)
## Randomly corrupt 5 percent of the data
epsilon = 0.05
corrupt_inds = np.random.choice(n, size=int(n*epsilon), replace=False)
for i in corrupt_inds:
x[i] = 5.0 + np.random.standard_normal()
## Fit an owl estimate to the data
owl = Exponential(X=x)
l1ball = L1Ball(n=n, r=epsilon)
owl.fit_owl(ball=l1ball, n_iters=10, verbose=True)More examples are in the examples/Simple OWL models.ipynb notebook.
If you use this code, please cite the preprint:
Robustifying likelihoods by optimistically re-weighting data
M. Dewaskar, C.Tosh, J. Knoblauch, and D. Dunson
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