Feature/classifier metrics #33
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Trying to fix travis build https://travis-ci.org/tue-robotics/image_recognition/builds/306472861
reinzor
suggested changes
Dec 5, 2017
image_recognition_tools/setup.py
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| from catkin_pkg.python_setup import generate_distutils_setup | ||
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| d = generate_distutils_setup( | ||
| packages=['image_recognition_tools'], |
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Not required since it does not contain any code in the specified module
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Also the 'src' dir can be fully removed
| if args.output: | ||
| plt.savefig(args.output) | ||
| else: | ||
| plt.show() |
I thought I needed this to get the build working
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@reinzor reviews processed |
Rayman
previously requested changes
Dec 6, 2017
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| find_package(catkin REQUIRED COMPONENTS) | ||
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| catkin_python_setup() |
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Can be removed because it's not a python module (only a script)
image_recognition_tools/setup.py
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| @@ -0,0 +1,6 @@ | |||
| from distutils.core import setup | |||
reinzor
approved these changes
Dec 9, 2017
LoyVanBeek
commented
Dec 9, 2017
| print ">> Labels: {}".format(csm.labels) | ||
| print ">> Accuracy: {acc:.3f}".format(acc=accuracy) | ||
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| plot_false_positive_true_positive_rates(csm.labels, |
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Creating a figure on the side of the user allows to arrange the plots and subplots/figures by the user.
This is also the place to put a title etc.
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I agree, but this code is way more clean than it was. The user can still
set titles etc but indeed the subplot features are gone now. Would you
suggest to move the figure() call to the user side?
…-Rein
On Sat, Dec 9, 2017 at 9:57 PM, Loy ***@***.***> wrote:
***@***.**** commented on this pull request.
------------------------------
In image_recognition_analysis/scripts/classifier_metrics
<#33 (comment)>
:
> +parser = argparse.ArgumentParser(description='Calculate various metrics of a classifier that assigns a score per class')
+parser.add_argument("input", type=str, help='Input csv file. First column is ground truth label, other columns are the '
+ 'per-class scores, as output by `evaluate_classifier`',
+ default="result.csv")
+args = parser.parse_args()
+
+# Construct the classification score matrix from the input csv in order to perform all calculations
+csm = ClassificationScoreMatrix.from_file(args.input)
+
+accuracy = accuracy_score(csm.classifications_ground_truth, csm.classifications_predicted_label)
+
+print ">> # Classifications: {}".format(len(csm.classifications_ground_truth))
+print ">> Labels: {}".format(csm.labels)
+print ">> Accuracy: {acc:.3f}".format(acc=accuracy)
+
+plot_false_positive_true_positive_rates(csm.labels,
Creating a figure on the side of the user allows to arrange the plots and
subplots/figures by the user.
This is also the place to put a title etc.
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Yes I would. Low prio though |
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This PR re-adds the classifier metrics that were removed from #30 to make the PR cleaner.