From 564134ed898e0104c7dccb68d933de658c8ab01c Mon Sep 17 00:00:00 2001
From: Olivier Hervieu <olivier.hervieu@tinyclues.com>
Date: Tue, 30 Aug 2011 16:05:57 +0200
Subject: [PATCH] Refactor roc_curve method. The new implementation, even if it
looks very naive, reduces the computation time of fpr/tpr vectors. roc_curve
computation time depends now only on the length of y_score. For comparison,
here are the results between the old and the new implementation for the
following vectors:
- 10^6 length vector (y_score has 1000 unique values):
- old impl.: 28.29 seconds
- new impl.: 3.14 seconds
- 10^6 length vector (y_score has 10000 unique values):
- old impl.: 267.61 seconds
- new impl.: 3.64 seconds
---
scikits/learn/metrics/metrics.py | 36 ++++++++++++++++++++++++++------
1 file changed, 30 insertions(+), 6 deletions(-)
diff --git a/scikits/learn/metrics/metrics.py b/scikits/learn/metrics/metrics.py
index e5d2b8bf99..dd653c245e 100644
--- a/scikits/learn/metrics/metrics.py
+++ b/scikits/learn/metrics/metrics.py
@@ -118,16 +118,39 @@ def roc_curve(y_true, y_score):
y_score = y_score.ravel()
thresholds = np.sort(np.unique(y_score))[::-1]
- n_thresholds = thresholds.size
- tpr = np.empty(n_thresholds) # True positive rate
- fpr = np.empty(n_thresholds) # False positive rate
n_pos = float(np.sum(y_true == classes[1])) # nb of true positive
n_neg = float(np.sum(y_true == classes[0])) # nb of true negative
- for i, t in enumerate(thresholds):
- tpr[i] = np.sum(y_true[y_score >= t] == classes[1]) / n_pos
- fpr[i] = np.sum(y_true[y_score >= t] == classes[0]) / n_neg
+ thresholds = np.unique(y_score)
+ neg_value, pos_value = classes[0], classes[1]
+
+ tpr = np.empty(thresholds.size) # True positive rate
+ fpr = np.empty(thresholds.size) # False positive rate
+
+ # Buid tpr/fpr vector
+ dpos = dneg = sum_pos = sum_neg = idx = 0
+
+ sorted_signal = sorted(zip(y_score, y_true), reverse=True)
+ last_input = sorted_signal[0][0]
+ for each, value in sorted_signal:
+ if each == last_input:
+ if value == pos_value:
+ dpos += 1
+ else:
+ dneg += 1
+ else:
+ tpr[idx] = (sum_pos + dpos) / n_pos
+ fpr[idx] = (sum_neg + dneg) / n_neg
+ sum_pos += dpos
+ sum_neg += dneg
+ dpos = 1 if value == pos_value else 0
+ dneg = 1 if value == neg_value else 0
+ idx += 1
+ last_input = each
+ else:
+ tpr[-1] = (sum_pos + dpos) / n_pos
+ fpr[-1] = (sum_neg + dneg) / n_neg
# hard decisions, add (0,0)
if fpr.shape[0] == 2:
@@ -137,6 +160,7 @@ def roc_curve(y_true, y_score):
elif fpr.shape[0] == 1:
fpr = np.array([0.0, fpr[0], 1.0])
tpr = np.array([0.0, tpr[0], 1.0])
+
return fpr, tpr, thresholds
--
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