From fcfa048e3122865ab2d990d81b5f099858d97807 Mon Sep 17 00:00:00 2001
From: Alexandre Gramfort <alexandre.gramfort@inria.fr>
Date: Wed, 14 Sep 2011 11:29:20 -0400
Subject: [PATCH] STY : pep8
---
sklearn/metrics/metrics.py | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/sklearn/metrics/metrics.py b/sklearn/metrics/metrics.py
index 930e3381d9..a85e8ce7e8 100644
--- a/sklearn/metrics/metrics.py
+++ b/sklearn/metrics/metrics.py
@@ -134,7 +134,7 @@ def roc_curve(y_true, y_score):
current_pos_count = current_neg_count = sum_pos = sum_neg = idx = 0
signal = np.c_[y_score, y_true]
- sorted_signal = signal[signal[:,0].argsort(),:][::-1]
+ sorted_signal = signal[signal[:, 0].argsort(), :][::-1]
last_score = sorted_signal[0][0]
for score, value in sorted_signal:
if score == last_score:
@@ -288,7 +288,7 @@ def fbeta_score(y_true, y_pred, beta, pos_label=1):
The F_beta score is the weighted harmonic mean of precision and recall,
reaching its optimal value at 1 and its worst value at 0.
-
+
The beta parameter determines the weight of precision in the combined
score. beta < 1 lends more weight to precision, while beta > 1 favors
precision (beta == 0 considers only precision, beta == inf only recall).
--
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