From ca08b788af38a076f45f000003eb0e2f227d7fd5 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Sep 2018 11:09:48 +0200 Subject: deprecate ot.gpu and remove OTDA classes from it --- ot/gpu/__init__.py | 5 ++++ ot/gpu/da.py | 69 ------------------------------------------------------ 2 files changed, 5 insertions(+), 69 deletions(-) diff --git a/ot/gpu/__init__.py b/ot/gpu/__init__.py index a2fdd3d..ed6dcc4 100644 --- a/ot/gpu/__init__.py +++ b/ot/gpu/__init__.py @@ -9,4 +9,9 @@ from .bregman import sinkhorn # # License: MIT License +import warnings + +warnings.warn("the ot.gpu module is deprecated because cudamat in no longer maintained", DeprecationWarning, + stacklevel=2) + __all__ = ["bregman", "da", "sinkhorn"] diff --git a/ot/gpu/da.py b/ot/gpu/da.py index 71a485a..85d43e6 100644 --- a/ot/gpu/da.py +++ b/ot/gpu/da.py @@ -13,7 +13,6 @@ Domain adaptation with optimal transport with GPU implementation import numpy as np from ..utils import unif -from ..da import OTDA from .bregman import sinkhorn import cudamat @@ -185,71 +184,3 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M_GPU, reg, eta=0.1, numItermax=10, W_GPU = W_GPU.transpose() return transp_GPU.asarray() - - -class OTDA_GPU(OTDA): - - def normalizeM(self, norm): - if norm == "median": - self.M_GPU.divide(float(np.median(self.M_GPU.asarray()))) - elif norm == "max": - self.M_GPU.divide(float(np.max(self.M_GPU.asarray()))) - elif norm == "log": - self.M_GPU.add(1) - cudamat.log(self.M_GPU) - elif norm == "loglog": - self.M_GPU.add(1) - cudamat.log(self.M_GPU) - self.M_GPU.add(1) - cudamat.log(self.M_GPU) - - -class OTDA_sinkhorn(OTDA_GPU): - - def fit(self, xs, xt, reg=1, ws=None, wt=None, norm=None, **kwargs): - cudamat.init() - xs = np.asarray(xs, dtype=np.float64) - xt = np.asarray(xt, dtype=np.float64) - - self.xs = xs - self.xt = xt - - if wt is None: - wt = unif(xt.shape[0]) - if ws is None: - ws = unif(xs.shape[0]) - - self.ws = ws - self.wt = wt - - self.M_GPU = pairwiseEuclideanGPU(xs, xt, returnAsGPU=True, - squared=True) - self.normalizeM(norm) - self.G = sinkhorn(ws, wt, self.M_GPU, reg, **kwargs) - self.computed = True - - -class OTDA_lpl1(OTDA_GPU): - - def fit(self, xs, ys, xt, reg=1, eta=1, ws=None, wt=None, norm=None, - **kwargs): - cudamat.init() - xs = np.asarray(xs, dtype=np.float64) - xt = np.asarray(xt, dtype=np.float64) - - self.xs = xs - self.xt = xt - - if wt is None: - wt = unif(xt.shape[0]) - if ws is None: - ws = unif(xs.shape[0]) - - self.ws = ws - self.wt = wt - - self.M_GPU = pairwiseEuclideanGPU(xs, xt, returnAsGPU=True, - squared=True) - self.normalizeM(norm) - self.G = sinkhorn_lpl1_mm(ws, ys, wt, self.M_GPU, reg, eta, **kwargs) - self.computed = True -- cgit v1.2.3