Move norm out of fit to init for deprecated OTDA

Author: toto6

ot/da.py

@@ -650,15 +650,16 @@ class OTDA(object):
 
     """
 
-    def __init__(self, metric='sqeuclidean'):
+    def __init__(self, metric='sqeuclidean', norm=None):
         """ Class initialization"""
         self.xs = 0
         self.xt = 0
         self.G = 0
         self.metric = metric
+        self.norm = norm
         self.computed = False
 
-    def fit(self, xs, xt, ws=None, wt=None, norm=None, max_iter=100000):
+    def fit(self, xs, xt, ws=None, wt=None, max_iter=100000):
         """Fit domain adaptation between samples is xs and xt
         (with optional weights)"""
         self.xs = xs
@@ -673,7 +674,7 @@ def fit(self, xs, xt, ws=None, wt=None, norm=None, max_iter=100000):
         self.wt = wt
 
         self.M = dist(xs, xt, metric=self.metric)
-        self.M = cost_normalization(self.M, norm)
+        self.M = cost_normalization(self.M, self.norm)
         self.G = emd(ws, wt, self.M, max_iter)
         self.computed = True
 
@@ -752,7 +753,7 @@ class OTDA_sinkhorn(OTDA):
 
     """
 
-    def fit(self, xs, xt, reg=1, ws=None, wt=None, norm=None, **kwargs):
+    def fit(self, xs, xt, reg=1, ws=None, wt=None, **kwargs):
         """Fit regularized domain adaptation between samples is xs and xt
         (with optional weights)"""
         self.xs = xs
@@ -767,7 +768,7 @@ def fit(self, xs, xt, reg=1, ws=None, wt=None, norm=None, **kwargs):
         self.wt = wt
 
         self.M = dist(xs, xt, metric=self.metric)
-        self.M = cost_normalization(self.M, norm)
+        self.M = cost_normalization(self.M, self.norm)
         self.G = sinkhorn(ws, wt, self.M, reg, **kwargs)
         self.computed = True
 
@@ -779,8 +780,7 @@ class OTDA_lpl1(OTDA):
     """Class for domain adaptation with optimal transport with entropic and
     group regularization"""
 
-    def fit(self, xs, ys, xt, reg=1, eta=1, ws=None, wt=None, norm=None,
-            **kwargs):
+    def fit(self, xs, ys, xt, reg=1, eta=1, ws=None, wt=None, **kwargs):
         """Fit regularized domain adaptation between samples is xs and xt
         (with optional weights),  See ot.da.sinkhorn_lpl1_mm for fit
         parameters"""
@@ -796,7 +796,7 @@ def fit(self, xs, ys, xt, reg=1, eta=1, ws=None, wt=None, norm=None,
         self.wt = wt
 
         self.M = dist(xs, xt, metric=self.metric)
-        self.M = cost_normalization(self.M, norm)
+        self.M = cost_normalization(self.M, self.norm)
         self.G = sinkhorn_lpl1_mm(ws, ys, wt, self.M, reg, eta, **kwargs)
         self.computed = True
 
@@ -808,8 +808,7 @@ class OTDA_l1l2(OTDA):
     """Class for domain adaptation with optimal transport with entropic
     and group lasso regularization"""
 
-    def fit(self, xs, ys, xt, reg=1, eta=1, ws=None, wt=None, norm=None,
-            **kwargs):
+    def fit(self, xs, ys, xt, reg=1, eta=1, ws=None, wt=None, **kwargs):
         """Fit regularized domain adaptation between samples is xs and xt
            (with optional weights),  See ot.da.sinkhorn_lpl1_gl for fit
            parameters"""
@@ -825,7 +824,7 @@ def fit(self, xs, ys, xt, reg=1, eta=1, ws=None, wt=None, norm=None,
         self.wt = wt
 
         self.M = dist(xs, xt, metric=self.metric)
-        self.M = cost_normalization(self.M, norm)
+        self.M = cost_normalization(self.M, self.norm)
         self.G = sinkhorn_l1l2_gl(ws, ys, wt, self.M, reg, eta, **kwargs)
         self.computed = True