FIX move docstrings from _fit to fit

Author: William de Vazelhes

metric_learn/itml.py

@@ -80,22 +80,6 @@ def _process_pairs(self, pairs, y, bounds):
     return pairs, y
 
   def _fit(self, pairs, y, bounds=None):
-    """Learn the ITML model.
-
-    Parameters
-    ----------
-    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 -1 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
     pos_pairs, neg_pairs = pairs[y == 1], pairs[y == -1]
@@ -154,6 +138,22 @@ def metric(self):
 class ITML(_BaseITML, _PairsClassifierMixin):
 
   def fit(self, pairs, y, bounds=None):
+    """Learn the ITML model.
+
+    Parameters
+    ----------
+    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 -1 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.
+    """
     return self._fit(pairs, y, bounds=bounds)
 
 

metric_learn/lsml.py

@@ -62,23 +62,6 @@ def metric(self):
     return self.M_
 
   def _fit(self, quadruplets, weights=None):
-    """Learn the LSML model.
-
-    Parameters
-    ----------
-    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)
     # Keep track of the best step size and the loss at that step.
@@ -144,6 +127,23 @@ def _gradient(self, metric):
 class LSML(_BaseLSML, _QuadrupletsClassifierMixin):
 
   def fit(self, quadruplets, weights=None):
+    """Learn the LSML model.
+
+    Parameters
+    ----------
+    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.
+    """
     return self._fit(quadruplets, weights=weights)
 
 

metric_learn/mmc.py

@@ -58,20 +58,6 @@ def __init__(self, max_iter=100, max_proj=10000, convergence_threshold=1e-3,
     self.verbose = verbose
 
   def _fit(self, pairs, y):
-    """Learn the MMC model.
-
-    Parameters
-    ----------
-    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 -1 for dissimilar pair, 1 for similar.
-
-    Returns
-    -------
-    self : object
-        Returns the instance.
-    """
     pairs, y = self._process_pairs(pairs, y)
     if self.diagonal:
       return self._fit_diag(pairs, y)
@@ -389,6 +375,20 @@ def transformer(self):
 class MMC(_BaseMMC, _PairsClassifierMixin):
 
   def fit(self, pairs, y):
+    """Learn the MMC model.
+
+    Parameters
+    ----------
+    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 -1 for dissimilar pair, 1 for similar.
+
+    Returns
+    -------
+    self : object
+        Returns the instance.
+    """
     return self._fit(pairs, y)
 
 

metric_learn/sdml.py

@@ -59,6 +59,18 @@ def metric(self):
     return self.M_
 
   def _fit(self, pairs, y):
+    loss_matrix = self._prepare_pairs(pairs, y)
+    P = self.M_ + self.balance_param * loss_matrix
+    emp_cov = pinvh(P)
+    # hack: ensure positive semidefinite
+    emp_cov = emp_cov.T.dot(emp_cov)
+    _, self.M_ = graph_lasso(emp_cov, self.sparsity_param, verbose=self.verbose)
+    return self
+
+
+class SDML(_BaseSDML, _PairsClassifierMixin):
+
+  def fit(self, pairs, y):
     """Learn the SDML model.
 
     Parameters
@@ -73,18 +85,6 @@ def _fit(self, pairs, y):
     self : object
         Returns the instance.
     """
-    loss_matrix = self._prepare_pairs(pairs, y)
-    P = self.M_ + self.balance_param * loss_matrix
-    emp_cov = pinvh(P)
-    # hack: ensure positive semidefinite
-    emp_cov = emp_cov.T.dot(emp_cov)
-    _, self.M_ = graph_lasso(emp_cov, self.sparsity_param, verbose=self.verbose)
-    return self
-
-
-class SDML(_BaseSDML, _PairsClassifierMixin):
-
-  def fit(self, pairs, y):
     return self._fit(pairs, y)