From 9ff9055a93b5bc5c402519bd0bc8c85bf97d6d84 Mon Sep 17 00:00:00 2001
From: Marc Glisse <marc.glisse@inria.fr>
Date: Sun, 24 May 2020 22:40:44 +0200
Subject: Move metric to params

---
 src/python/gudhi/clustering/tomato.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

(limited to 'src/python/gudhi/clustering/tomato.py')

diff --git a/src/python/gudhi/clustering/tomato.py b/src/python/gudhi/clustering/tomato.py
index 1c586f4f..29f30481 100644
--- a/src/python/gudhi/clustering/tomato.py
+++ b/src/python/gudhi/clustering/tomato.py
@@ -41,7 +41,6 @@ class Tomato:
         self,
         graph_type="knn",
         density_type="logDTM",
-        metric="minkowski",
         n_clusters=None,
         merge_threshold=None,
         #       eliminate_threshold=None,
@@ -52,9 +51,9 @@ class Tomato:
         Each parameter has a corresponding attribute, like self.merge_threshold_, that can be changed later.
 
         Args:
-            metric (str|Callable): Describes how to interpret the argument of `fit()`. Defaults to Minkowski of parameter p.
             graph_type (str): 'manual', 'knn' or 'radius'.
             density_type (str): 'manual', 'DTM', 'logDTM', 'KDE' or 'logKDE'.
+            metric (str|Callable): metric used when calculating the distance between instances in a feature array. Defaults to Minkowski of parameter p.
             kde_params (dict): if density_type is 'KDE' or 'logKDE', additional parameters passed directly to sklearn.neighbors.KernelDensity.
             k (int): number of neighbors for a knn graph (including the vertex itself). Defaults to 10.
             k_DTM (int): number of neighbors for the DTM density estimation (including the vertex itself). Defaults to k.
@@ -71,7 +70,6 @@ class Tomato:
         """
         # Should metric='precomputed' mean input_type='distance_matrix'?
         # Should we be able to pass metric='minkowski' (what None does currently)?
-        self.metric_ = metric
         self.graph_type_ = graph_type
         self.density_type_ = density_type
         self.params_ = params
@@ -102,7 +100,7 @@ class Tomato:
             # FIXME: uniformize "message 'option'" vs 'message "option"'
             assert density_type == "manual", 'If graph_type is "manual", density_type must be as well'
         else:
-            metric = self.metric_
+            metric = self.params_.get("metric", "minkowski")
             if metric != "precomputed":
                 self.points_ = X
 
@@ -163,6 +161,7 @@ class Tomato:
                 eps = self.params_.get("eps", 0)
                 self.neighbors_ = t.query_ball_tree(t, r=self.params_["r"], p=p, eps=eps)
 
+            # TODO: sklearn's NearestNeighbors can handle more metrics efficiently via its BallTree
             elif metric != "precomputed":
                 from sklearn.metrics import pairwise_distances
 
@@ -176,8 +175,9 @@ class Tomato:
                 self.neighbors_ = [numpy.flatnonzero(l <= r) for l in X]
 
         if self.density_type_ in {"KDE", "logKDE"}:
-            assert metric not in ["neighbors", "precomputed"], "Scikit-learn's KernelDensity requires point coordinates"
+            assert graph_type != "manual" and metric != "precomputed", "Scikit-learn's KernelDensity requires point coordinates"
             kde_params = self.params_.get("kde_params", dict())
+            kde_params.setdefault("metric", metric)
             from sklearn.neighbors import KernelDensity
             weights = KernelDensity(**kde_params).fit(self.points_).score_samples(self.points_)
             if self.density_type_ == "KDE":
-- 
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