diff --git a/Code/MonoMutliViewClassifiers/ResultAnalysis.py b/Code/MonoMutliViewClassifiers/ResultAnalysis.py
index 883e7e0ece1e453556b2b455d97f97d36d613350..720071c1fb5b535f1be0d2ce2e8d48ac3837456e 100644
--- a/Code/MonoMutliViewClassifiers/ResultAnalysis.py
+++ b/Code/MonoMutliViewClassifiers/ResultAnalysis.py
@@ -42,7 +42,7 @@ def genNamesFromRes(mono, multi):
return names
-def resultAnalysis(benchmark, results, name, times, metrics, directory):
+def resultAnalysis(benchmark, results, name, times, metrics, directory, minSize=10):
mono, multi = results
for metric in metrics:
names = genNamesFromRes(mono, multi)
@@ -55,30 +55,27 @@ def resultAnalysis(benchmark, results, name, times, metrics, directory):
validationScores = np.array(validationScores)
trainScores = np.array(trainScores)
names = np.array(names)
-
- f = pylab.figure(figsize=(40, 30))
- width = 0.35 # the width of the bars
- fig = plt.gcf()
- fig.subplots_adjust(bottom=105.0, top=105.01)
- ax = f.add_axes([0.1, 0.1, 0.8, 0.8])
- if metric[1] is not None:
- metricKWARGS = dict((index, metricConfig) for index, metricConfig in enumerate(metric[1]))
- else:
- metricKWARGS = {}
+ size = nbResults
+ if nbResults < minSize:
+ size = minSize
+ figKW = {"figsize" : (size, 3.0/4*size+2.0)}
+ f, ax = plt.subplots(nrows=1, ncols=1, **figKW)
+ barWidth= 0.35
sorted_indices = np.argsort(validationScores)
validationScores = validationScores[sorted_indices]
trainScores = trainScores[sorted_indices]
names = names[sorted_indices]
- ax.set_title(getattr(Metrics, metric[0]).getConfig(**metricKWARGS) + " on validation set for each classifier")
- rects = ax.bar(range(nbResults), validationScores, width, color="r", )
- rect2 = ax.bar(np.arange(nbResults) + width, trainScores, width, color="0.7", )
+ ax.set_title(metric[0] + "\n on validation set for each classifier")
+ rects = ax.bar(range(nbResults), validationScores, barWidth, color="r", )
+ rect2 = ax.bar(np.arange(nbResults) + barWidth, trainScores, barWidth, color="0.7", )
autolabel(rects, ax)
autolabel(rect2, ax)
ax.legend((rects[0], rect2[0]), ('Test', 'Train'))
- ax.set_xticks(np.arange(nbResults) + width)
+ ax.set_ylim(-0.1, 1.1)
+ ax.set_xticks(np.arange(nbResults) + barWidth)
ax.set_xticklabels(names, rotation="vertical")
-
+ plt.tight_layout()
f.savefig(directory + time.strftime("%Y%m%d-%H%M%S") + "-" + name + "-" + metric[0] + ".png")
plt.close()
@@ -94,7 +91,8 @@ def analyzeLabels(labelsArrays, realLabels, results, directory):
for classifierIndex in range(nbClassifiers):
for iterIndex in range(nbIter):
data[:, classifierIndex * nbIter + iterIndex] = tempData[classifierIndex, :]
- fig = pylab.figure(figsize=(10, 20))
+ figKW = {"figsize":(nbClassifiers/2, nbExamples/20)}
+ fig, ax = plt.subplots(nrows=1, ncols=1, **figKW)
cmap = mpl.colors.ListedColormap(['red', 'green'])
bounds = [-0.5, 0.5, 1.5]
norm = mpl.colors.BoundaryNorm(bounds, cmap.N)
@@ -106,11 +104,12 @@ def analyzeLabels(labelsArrays, realLabels, results, directory):
plt.xticks(ticks, labels, rotation="vertical")
cbar = fig.colorbar(cax, ticks=[0, 1])
cbar.ax.set_yticklabels(['Wrong', ' Right'])
+ fig.tight_layout()
fig.savefig(directory + time.strftime("%Y%m%d-%H%M%S") + "-error_analysis.png")
plt.close()
-def genScoresNames(iterResults, metric, nbResults, names, nbMono):
+def genScoresNames(iterResults, metric, nbResults, names, nbMono, minSize=10):
nbIter = len(iterResults)
validationScores = np.zeros((nbIter, nbResults))
trainScores = np.zeros((nbIter, nbResults))
@@ -121,21 +120,16 @@ def genScoresNames(iterResults, metric, nbResults, names, nbMono):
trainScores[iterIndex, :nbMono] = np.array([float(res[1][2][metric[0]][0]) for res in mono])
trainScores[iterIndex, nbMono:] = np.array([float(scores[metric[0]][0]) for a, b, scores, c in multi])
- # import pdb;pdb.set_trace()
validationSTDs = np.std(validationScores, axis=0)
trainSTDs = np.std(trainScores, axis=0)
validationMeans = np.mean(validationScores, axis=0)
trainMeans = np.mean(trainScores, axis=0)
-
- f = pylab.figure(figsize=(40, 30))
- width = 0.35 # the width of the bars
- fig = plt.gcf()
- fig.subplots_adjust(bottom=105.0, top=105.01)
- ax = f.add_axes([0.1, 0.1, 0.8, 0.8])
- if metric[1] is not None:
- metricKWARGS = dict((index, metricConfig) for index, metricConfig in enumerate(metric[1]))
- else:
- metricKWARGS = {}
+ size=nbResults
+ if nbResults<minSize:
+ size=minSize
+ figKW = {"figsize" : (size, 3.0/4*size+2.0)}
+ f, ax = plt.subplots(nrows=1, ncols=1, **figKW)
+ barWidth = 0.35 # the width of the bars
sorted_indices = np.argsort(validationMeans)
validationMeans = validationMeans[sorted_indices]
validationSTDs = validationSTDs[sorted_indices]
@@ -143,14 +137,16 @@ def genScoresNames(iterResults, metric, nbResults, names, nbMono):
trainMeans = trainMeans[sorted_indices]
names = np.array(names)[sorted_indices]
- ax.set_title(getattr(Metrics, metric[0]).getConfig(**metricKWARGS) + " for each classifier")
- rects = ax.bar(range(nbResults), validationMeans, width, color="r", yerr=validationSTDs)
- rect2 = ax.bar(np.arange(nbResults) + width, trainMeans, width, color="0.7", yerr=trainSTDs)
+ ax.set_title(metric[0] + " for each classifier")
+ rects = ax.bar(range(nbResults), validationMeans, barWidth, color="r", yerr=validationSTDs)
+ rect2 = ax.bar(np.arange(nbResults) + barWidth, trainMeans, barWidth, color="0.7", yerr=trainSTDs)
autolabel(rects, ax)
autolabel(rect2, ax)
+ ax.set_ylim(-0.1, 1.1)
ax.legend((rects[0], rect2[0]), ('Test', 'Train'))
- ax.set_xticks(np.arange(nbResults) + width)
+ ax.set_xticks(np.arange(nbResults) + barWidth)
ax.set_xticklabels(names, rotation="vertical")
+ f.tight_layout()
return f