Update docstring for new api

Author: William de Vazelhes

metric_learn/itml.py

@@ -86,13 +86,17 @@ def fit(self, pairs, y, bounds=None):
 
     Parameters
     ----------
-    X : (n x d) data matrix
-        each row corresponds to a single instance
-    constraints : 4-tuple of arrays
-        (a,b,c,d) indices into X, with (a,b) specifying positive and (c,d)
-        negative pairs
+    pairs: array-like, shape=(n_constraints, 2, n_features)
+        Array of pairs. Each row corresponds to two points.
+    y: array-like, of shape (n_constraints,)
+        Labels of constraints. Should be 0 for dissimilar pair, 1 for similar.
     bounds : list (pos,neg) pairs, optional
         bounds on similarity, s.t. d(X[a],X[b]) < pos and d(X[c],X[d]) > neg
+
+    Returns
+    -------
+    self : object
+        Returns the instance.
     """
     pairs, y = self._process_pairs(pairs, y, bounds)
     gamma = self.gamma

metric_learn/lsml.py

@@ -65,12 +65,18 @@ def fit(self, quadruplets, weights=None):
 
     Parameters
     ----------
-    X : (n x d) data matrix
-        each row corresponds to a single instance
-    constraints : 4-tuple of arrays
-        (a,b,c,d) indices into X, such that d(X[a],X[b]) < d(X[c],X[d])
-    weights : (m,) array of floats, optional
+    quadruplets : array-like, shape=(n_constraints, 4, n_features)
+        Each row corresponds to 4 points. In order to supervise the
+        algorithm in the right way, we should have the four samples ordered
+        in a way such that: d(pairs[i, 0],X[i, 1]) < d(X[i, 2], X[i, 3])
+        for all 0 <= i < n_constraints.
+    weights : (n_constraints,) array of floats, optional
         scale factor for each constraint
+
+    Returns
+    -------
+    self : object
+        Returns the instance.
     """
     self._prepare_quadruplets(quadruplets, weights)
     step_sizes = np.logspace(-10, 0, 10)

metric_learn/mmc.py

@@ -64,11 +64,15 @@ def fit(self, pairs, y):
 
     Parameters
     ----------
-    X : (n x d) data matrix
-        each row corresponds to a single instance
-    constraints : 4-tuple of arrays
-        (a,b,c,d) indices into X, with (a,b) specifying similar and (c,d)
-        dissimilar pairs
+    pairs: array-like, shape=(n_constraints, 2, n_features)
+        Array of pairs. Each row corresponds to two points.
+    y: array-like, of shape (n_constraints,)
+        Labels of constraints. Should be 0 for dissimilar pair, 1 for similar.
+
+    Returns
+    -------
+    self : object
+        Returns the instance.
     """
     pairs, y = self._process_pairs(pairs, y)
     if self.diagonal:

metric_learn/sdml.py

@@ -62,10 +62,10 @@ def fit(self, pairs, y):
 
     Parameters
     ----------
-    X : array-like, shape (n, d)
-        data matrix, where each row corresponds to a single instance
-    W : array-like, shape (n, n)
-        connectivity graph, with +1 for positive pairs and -1 for negative
+    pairs: array-like, shape=(n_constraints, 2, n_features)
+        Array of pairs. Each row corresponds to two points.
+    y: array-like, of shape (n_constraints,)
+        Labels of constraints. Should be 0 for dissimilar pair, 1 for similar.
 
     Returns
     -------