Repository: bigmlcom/bigmler (language: python)

Issue:
Changing to ntpath support both Windows and Unix style paths in the XML



Discussion / hints:
I wonder if using pathlib would make more sense

Fix patch:
diff --git a/HISTORY.rst b/HISTORY.rst
index 83c03602..ff0648a6 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -3,6 +3,11 @@
 History
 -------
 
+5.8.1 (2024-05-31)
+~~~~~~~~~~~~~~~~~~
+
+- Fixing Pascal VOC to BigML-COCO translator.
+
 5.8.0 (2024-04-05)
 ~~~~~~~~~~~~~~~~~~
 
diff --git a/bigmler/__init__.py b/bigmler/__init__.py
index 5411dd58..18a66c5c 100644
--- a/bigmler/__init__.py
+++ b/bigmler/__init__.py
@@ -1,2 +1,2 @@
 # -*- coding: utf-8 -*-
-__version__ = '5.8.0'
+__version__ = '5.8.1'
diff --git a/bigmler/processing/annotations.py b/bigmler/processing/annotations.py
index a00b3195..fa1ddc1a 100644
--- a/bigmler/processing/annotations.py
+++ b/bigmler/processing/annotations.py
@@ -173,10 +173,9 @@ def bigml_coco_file(args, session_file):
 
 def get_image_info(annotation_root):
     """Returns the basic image descriptors"""
+    filename = annotation_root.findtext('filename')
     path = annotation_root.findtext('path')
-    if path is None:
-        filename = annotation_root.findtext('filename')
-    else:
+    if filename is None and path is not None:
         filename = os.path.basename(path)
     img_name = os.path.basename(filename)
     img_id = os.path.splitext(img_name)[0]
@@ -530,6 +529,7 @@ def voc_to_cocojson(voc_dir, args, session_file):
             if args.images_dir is not None:
                 base_dir = os.path.join(base_dir, args.images_dir)
             image_filename_base = os.path.join(folder, img_info['filename'])
+            filename = image_filename_base
             if args.images_dir is not None:
                 filename = os.path.join(base_dir, image_filename_base)
 
diff --git a/check_files/evaluation_iris_dn.json b/check_files/evaluation_iris_dn.json
index 647c7d22..b228fc4d 100644
--- a/check_files/evaluation_iris_dn.json
+++ b/check_files/evaluation_iris_dn.json
@@ -1,1 +1,1 @@
-{"class_names": ["Iris-setosa", "Iris-versicolor", "Iris-virginica"], "mode": {"accuracy": 0.43333, "average_area_under_pr_curve": 0, "average_area_under_roc_curve": 0, "average_balanced_accuracy": 0.5, "average_f_measure": 0.20155, "average_kendalls_tau_b": 0, "average_ks_statistic": 0, "average_max_phi": 0, "average_phi": 0, "average_precision": 0.14444, "average_recall": 0.33333, "average_spearmans_rho": 0, "confusion_matrix": [[13, 0, 0], [8, 0, 0], [9, 0, 0]], "per_class_statistics": [{"accuracy": 0.43333, "balanced_accuracy": 0.5, "class_name": "Iris-setosa", "f_measure": 0.60465, "phi_coefficient": 0, "precision": 0.43333, "present_in_test_data": true, "recall": 1}, {"accuracy": 0.73333, "balanced_accuracy": 0.5, "class_name": "Iris-versicolor", "f_measure": 0, "phi_coefficient": 0, "precision": 0, "present_in_test_data": true, "recall": 0}, {"accuracy": 0.7, "balanced_accuracy": 0.5, "class_name": "Iris-virginica", "f_measure": 0, "phi_coefficient": 0, "precision": 0, "present_in_test_data": true, "recall": 0}]}, "model": {"accuracy": 0.93333, "average_area_under_pr_curve": 0.99117, "average_area_under_roc_curve": 0.99634, "average_balanced_accuracy": 0.94781, "average_f_measure": 0.9213, "average_kendalls_tau_b": 0.76919, "average_ks_statistic": 0.96898, "average_max_phi": 0.94898, "average_phi": 0.8985, "average_precision": 0.93333, "average_recall": 0.92593, "average_spearmans_rho": 0.85377, "confusion_matrix": [[13, 0, 0], [0, 8, 0], [0, 2, 7]], "per_class_statistics": [{"accuracy": 1, "area_under_pr_curve": 1, "area_under_roc_curve": 1, "balanced_accuracy": 1, "class_name": "Iris-setosa", "f_measure": 1, "gain_curve": [[0, 0, 1], [0.13333, 0.30769, 0.99999], [0.33333, 0.76923, 0.99998], [0.4, 0.92308, 0.99996], [0.43333, 1, 0.00021], [0.46667, 1, 1e-05], [0.5, 1, 0], [1, 1, null]], "kendalls_tau_b": 0.84707, "ks_statistic": [1, 0.00021], "lift_curve": [[0, 0, 1], [0.13333, 2.30769, 0.99999], [0.33333, 2.30769, 0.99998], [0.4, 2.30769, 0.99996], [0.43333, 2.30769, 0.00021], [0.46667, 2.14286, 1e-05], [0.5, 2, 0], [1, 1, null]], "max_phi": [1, 0.00021], "negative_cdf": [[0, 0, 1], [0.13333, 0, 0.99999], [0.33333, 0, 0.99998], [0.4, 0, 0.99996], [0.43333, 0, 0.00021], [0.46667, 0.05882, 1e-05], [0.5, 0.11765, 0], [1, 1, null]], "per_threshold_confusion_matrices": [[[13, 17, 0, 0], null], [[13, 2, 15, 0], 0], [[13, 1, 16, 0], 1e-05], [[13, 0, 17, 0], 0.00021], [[12, 0, 17, 1], 0.99996], [[10, 0, 17, 3], 0.99998], [[4, 0, 17, 9], 0.99999], [[0, 0, 17, 13], 1]], "phi_coefficient": 1, "pr_curve": [[0, 1, 1], [0.30769, 1, 0.99999], [0.76923, 1, 0.99998], [0.92308, 1, 0.99996], [1, 1, 0.00021], [1, 0.92857, 1e-05], [1, 0.86667, 0], [1, 0.43333, null]], "precision": 1, "present_in_test_data": true, "recall": 1, "roc_curve": [[0, 0, 1], [0, 0.30769, 0.99999], [0, 0.76923, 0.99998], [0, 0.92308, 0.99996], [0, 1, 0.00021], [0.05882, 1, 1e-05], [0.11765, 1, 0], [1, 1, null]], "spearmans_rho": 0.92326}, {"accuracy": 0.93333, "area_under_pr_curve": 0.98524, "area_under_roc_curve": 0.99432, "balanced_accuracy": 0.95455, "class_name": "Iris-versicolor", "f_measure": 0.88889, "gain_curve": [[0, 0, 1], [0.1, 0.375, 0.99997], [0.13333, 0.5, 0.99996], [0.16667, 0.625, 0.99983], [0.2, 0.75, 0.99979], [0.23333, 0.875, 0.87021], [0.26667, 0.875, 0.80792], [0.3, 1, 0.71352], [0.33333, 1, 0.01229], [0.36667, 1, 0.00363], [0.4, 1, 0.00017], [0.43333, 1, 4e-05], [0.46667, 1, 2e-05], [0.53333, 1, 1e-05], [0.73333, 1, 0], [1, 1, null]], "kendalls_tau_b": 0.66585, "ks_statistic": [0.95455, 0.71352], "lift_curve": [[0, 0, 1], [0.1, 3.75, 0.99997], [0.13333, 3.75, 0.99996], [0.16667, 3.75, 0.99983], [0.2, 3.75, 0.99979], [0.23333, 3.75, 0.87021], [0.26667, 3.28125, 0.80792], [0.3, 3.33333, 0.71352], [0.33333, 3, 0.01229], [0.36667, 2.72727, 0.00363], [0.4, 2.5, 0.00017], [0.43333, 2.30769, 4e-05], [0.46667, 2.14286, 2e-05], [0.53333, 1.875, 1e-05], [0.73333, 1.36364, 0], [1, 1, null]], "max_phi": [0.92113, 0.71352], "negative_cdf": [[0, 0, 1], [0.1, 0, 0.99997], [0.13333, 0, 0.99996], [0.16667, 0, 0.99983], [0.2, 0, 0.99979], [0.23333, 0, 0.87021], [0.26667, 0.04545, 0.80792], [0.3, 0.04545, 0.71352], [0.33333, 0.09091, 0.01229], [0.36667, 0.13636, 0.00363], [0.4, 0.18182, 0.00017], [0.43333, 0.22727, 4e-05], [0.46667, 0.27273, 2e-05], [0.53333, 0.36364, 1e-05], [0.73333, 0.63636, 0], [1, 1, null]], "per_threshold_confusion_matrices": [[[8, 22, 0, 0], null], [[8, 14, 8, 0], 0], [[8, 8, 14, 0], 1e-05], [[8, 6, 16, 0], 2e-05], [[8, 5, 17, 0], 4e-05], [[8, 4, 18, 0], 0.00017], [[8, 3, 19, 0], 0.00363], [[8, 2, 20, 0], 0.01229], [[8, 1, 21, 0], 0.71352], [[7, 1, 21, 1], 0.80792], [[7, 0, 22, 1], 0.87021], [[6, 0, 22, 2], 0.99979], [[5, 0, 22, 3], 0.99983], [[4, 0, 22, 4], 0.99996], [[3, 0, 22, 5], 0.99997], [[0, 0, 22, 8], 1]], "phi_coefficient": 0.8528, "pr_curve": [[0, 1, 1], [0.375, 1, 0.99997], [0.5, 1, 0.99996], [0.625, 1, 0.99983], [0.75, 1, 0.99979], [0.875, 1, 0.87021], [0.875, 0.875, 0.80792], [1, 0.88889, 0.71352], [1, 0.8, 0.01229], [1, 0.72727, 0.00363], [1, 0.66667, 0.00017], [1, 0.61538, 4e-05], [1, 0.57143, 2e-05], [1, 0.5, 1e-05], [1, 0.36364, 0], [1, 0.26667, null]], "precision": 0.8, "present_in_test_data": true, "recall": 1, "roc_curve": [[0, 0, 1], [0, 0.375, 0.99997], [0, 0.5, 0.99996], [0, 0.625, 0.99983], [0, 0.75, 0.99979], [0, 0.875, 0.87021], [0.04545, 0.875, 0.80792], [0.04545, 1, 0.71352], [0.09091, 1, 0.01229], [0.13636, 1, 0.00363], [0.18182, 1, 0.00017], [0.22727, 1, 4e-05], [0.27273, 1, 2e-05], [0.36364, 1, 1e-05], [0.63636, 1, 0], [1, 1, null]], "spearmans_rho": 0.76833}, {"accuracy": 0.93333, "area_under_pr_curve": 0.98827, "area_under_roc_curve": 0.99471, "balanced_accuracy": 0.88889, "class_name": "Iris-virginica", "f_measure": 0.875, "gain_curve": [[0, 0, 1], [0.13333, 0.44444, 0.99983], [0.16667, 0.55556, 0.99637], [0.2, 0.66667, 0.98771], [0.23333, 0.77778, 0.28648], [0.26667, 0.88889, 0.19208], [0.3, 0.88889, 0.12979], [0.33333, 1, 0.00017], [0.36667, 1, 4e-05], [0.4, 1, 2e-05], [0.43333, 1, 0], [1, 1, null]], "kendalls_tau_b": 0.79465, "ks_statistic": [0.95238, 0.00017], "lift_curve": [[0, 0, 1], [0.13333, 3.33333, 0.99983], [0.16667, 3.33333, 0.99637], [0.2, 3.33333, 0.98771], [0.23333, 3.33333, 0.28648], [0.26667, 3.33333, 0.19208], [0.3, 2.96296, 0.12979], [0.33333, 3, 0.00017], [0.36667, 2.72727, 4e-05], [0.4, 2.5, 2e-05], [0.43333, 2.30769, 0], [1, 1, null]], "max_phi": [0.92582, 0.00017], "negative_cdf": [[0, 0, 1], [0.13333, 0, 0.99983], [0.16667, 0, 0.99637], [0.2, 0, 0.98771], [0.23333, 0, 0.28648], [0.26667, 0, 0.19208], [0.3, 0.04762, 0.12979], [0.33333, 0.04762, 0.00017], [0.36667, 0.09524, 4e-05], [0.4, 0.14286, 2e-05], [0.43333, 0.19048, 0], [1, 1, null]], "per_threshold_confusion_matrices": [[[9, 21, 0, 0], null], [[9, 4, 17, 0], 0], [[9, 3, 18, 0], 2e-05], [[9, 2, 19, 0], 4e-05], [[9, 1, 20, 0], 0.00017], [[8, 1, 20, 1], 0.12979], [[8, 0, 21, 1], 0.19208], [[7, 0, 21, 2], 0.28648], [[6, 0, 21, 3], 0.98771], [[5, 0, 21, 4], 0.99637], [[4, 0, 21, 5], 0.99983], [[0, 0, 21, 9], 1]], "phi_coefficient": 0.8427, "pr_curve": [[0, 1, 1], [0.44444, 1, 0.99983], [0.55556, 1, 0.99637], [0.66667, 1, 0.98771], [0.77778, 1, 0.28648], [0.88889, 1, 0.19208], [0.88889, 0.88889, 0.12979], [1, 0.9, 0.00017], [1, 0.81818, 4e-05], [1, 0.75, 2e-05], [1, 0.69231, 0], [1, 0.3, null]], "precision": 1, "present_in_test_data": true, "recall": 0.77778, "roc_curve": [[0, 0, 1], [0, 0.44444, 0.99983], [0, 0.55556, 0.99637], [0, 0.66667, 0.98771], [0, 0.77778, 0.28648], [0, 0.88889, 0.19208], [0.04762, 0.88889, 0.12979], [0.04762, 1, 0.00017], [0.09524, 1, 4e-05], [0.14286, 1, 2e-05], [0.19048, 1, 0], [1, 1, null]], "spearmans_rho": 0.86973}]}, "random": {"accuracy": 0.36667, "average_area_under_pr_curve": 0, "average_area_under_roc_curve": 0, "average_balanced_accuracy": 0.52447, "average_f_measure": 0.35892, "average_kendalls_tau_b": 0, "average_ks_statistic": 0, "average_max_phi": 0, "average_phi": 0.05351, "average_precision": 0.37306, "average_recall": 0.35969, "average_spearmans_rho": 0, "confusion_matrix": [[5, 4, 4], [3, 2, 3], [1, 4, 4]], "per_class_statistics": [{"accuracy": 0.6, "balanced_accuracy": 0.57466, "class_name": "Iris-setosa", "f_measure": 0.45455, "phi_coefficient": 0.16147, "precision": 0.55556, "present_in_test_data": true, "recall": 0.38462}, {"accuracy": 0.53333, "balanced_accuracy": 0.44318, "class_name": "Iris-versicolor", "f_measure": 0.22222, "phi_coefficient": -0.1066, "precision": 0.2, "present_in_test_data": true, "recall": 0.25}, {"accuracy": 0.6, "balanced_accuracy": 0.55556, "class_name": "Iris-virginica", "f_measure": 0.4, "phi_coefficient": 0.10566, "precision": 0.36364, "present_in_test_data": true, "recall": 0.44444}]}}
+{"class_names": ["Iris-setosa", "Iris-versicolor", "Iris-virginica"], "mode": {"accuracy": 0.43333, "average_area_under_pr_curve": 0, "average_area_under_roc_curve": 0, "average_balanced_accuracy": 0.5, "average_f_measure": 0.20155, "average_kendalls_tau_b": 0, "average_ks_statistic": 0, "average_max_phi": 0, "average_phi": 0, "average_precision": 0.14444, "average_recall": 0.33333, "average_spearmans_rho": 0, "confusion_matrix": [[13, 0, 0], [8, 0, 0], [9, 0, 0]], "per_class_statistics": [{"accuracy": 0.43333, "balanced_accuracy": 0.5, "class_name": "Iris-setosa", "f_measure": 0.60465, "phi_coefficient": 0, "precision": 0.43333, "present_in_test_data": true, "recall": 1}, {"accuracy": 0.73333, "balanced_accuracy": 0.5, "class_name": "Iris-versicolor", "f_measure": 0, "phi_coefficient": 0, "precision": 0, "present_in_test_data": true, "recall": 0}, {"accuracy": 0.7, "balanced_accuracy": 0.5, "class_name": "Iris-virginica", "f_measure": 0, "phi_coefficient": 0, "precision": 0, "present_in_test_data": true, "recall": 0}]}, "model": {"accuracy": 1, "average_area_under_pr_curve": 1, "average_area_under_roc_curve": 1, "average_balanced_accuracy": 1, "average_f_measure": 1, "average_kendalls_tau_b": 0.79062, "average_ks_statistic": 1, "average_max_phi": 1, "average_phi": 1, "average_precision": 1, "average_recall": 1, "average_spearmans_rho": 0.86887, "confusion_matrix": [[13, 0, 0], [0, 8, 0], [0, 0, 9]], "per_class_statistics": [{"accuracy": 1, "area_under_pr_curve": 1, "area_under_roc_curve": 1, "balanced_accuracy": 1, "class_name": "Iris-setosa", "f_measure": 1, "gain_curve": [[0, 0, 1], [0.43333, 1, 0.00021], [0.46667, 1, 0.00015], [0.5, 1, 5e-05], [0.53333, 1, 4e-05], [0.56667, 1, 1e-05], [0.63333, 1, 0], [1, 1, null]], "kendalls_tau_b": 0.85687, "ks_statistic": [1, 0.00021], "lift_curve": [[0, 0, 1], [0.43333, 2.30769, 0.00021], [0.46667, 2.14286, 0.00015], [0.5, 2, 5e-05], [0.53333, 1.875, 4e-05], [0.56667, 1.76471, 1e-05], [0.63333, 1.57895, 0], [1, 1, null]], "max_phi": [1, 0.00021], "negative_cdf": [[0, 0, 1], [0.43333, 0, 0.00021], [0.46667, 0.05882, 0.00015], [0.5, 0.11765, 5e-05], [0.53333, 0.17647, 4e-05], [0.56667, 0.23529, 1e-05], [0.63333, 0.35294, 0], [1, 1, null]], "per_threshold_confusion_matrices": [[[13, 17, 0, 0], null], [[13, 6, 11, 0], 0], [[13, 4, 13, 0], 1e-05], [[13, 3, 14, 0], 4e-05], [[13, 2, 15, 0], 5e-05], [[13, 1, 16, 0], 0.00015], [[13, 0, 17, 0], 0.00021], [[0, 0, 17, 13], 1]], "phi_coefficient": 1, "pr_curve": [[0, 1, 1], [1, 1, 0.00021], [1, 0.92857, 0.00015], [1, 0.86667, 5e-05], [1, 0.8125, 4e-05], [1, 0.76471, 1e-05], [1, 0.68421, 0], [1, 0.43333, null]], "precision": 1, "present_in_test_data": true, "recall": 1, "roc_curve": [[0, 0, 1], [0, 1, 0.00021], [0.05882, 1, 0.00015], [0.11765, 1, 5e-05], [0.17647, 1, 4e-05], [0.23529, 1, 1e-05], [0.35294, 1, 0], [1, 1, null]], "spearmans_rho": 0.92077}, {"accuracy": 1, "area_under_pr_curve": 1, "area_under_roc_curve": 1, "balanced_accuracy": 1, "class_name": "Iris-versicolor", "f_measure": 1, "gain_curve": [[0, 0, 0.99997], [0.03333, 0.125, 0.99996], [0.06667, 0.25, 0.99989], [0.1, 0.375, 0.99979], [0.13333, 0.5, 0.99972], [0.16667, 0.625, 0.9992], [0.2, 0.75, 0.99878], [0.23333, 0.875, 0.69706], [0.26667, 1, 0.17107], [0.3, 1, 0.04435], [0.33333, 1, 0.02242], [0.36667, 1, 0.00553], [0.4, 1, 0.00082], [0.43333, 1, 0], [1, 1, null]], "kendalls_tau_b": 0.76722, "ks_statistic": [1, 0.17107], "lift_curve": [[0, 0, 0.99997], [0.03333, 3.75, 0.99996], [0.06667, 3.75, 0.99989], [0.1, 3.75, 0.99979], [0.13333, 3.75, 0.99972], [0.16667, 3.75, 0.9992], [0.2, 3.75, 0.99878], [0.23333, 3.75, 0.69706], [0.26667, 3.75, 0.17107], [0.3, 3.33333, 0.04435], [0.33333, 3, 0.02242], [0.36667, 2.72727, 0.00553], [0.4, 2.5, 0.00082], [0.43333, 2.30769, 0], [1, 1, null]], "max_phi": [1, 0.17107], "negative_cdf": [[0, 0, 0.99997], [0.03333, 0, 0.99996], [0.06667, 0, 0.99989], [0.1, 0, 0.99979], [0.13333, 0, 0.99972], [0.16667, 0, 0.9992], [0.2, 0, 0.99878], [0.23333, 0, 0.69706], [0.26667, 0, 0.17107], [0.3, 0.04545, 0.04435], [0.33333, 0.09091, 0.02242], [0.36667, 0.13636, 0.00553], [0.4, 0.18182, 0.00082], [0.43333, 0.22727, 0], [1, 1, null]], "per_threshold_confusion_matrices": [[[8, 22, 0, 0], null], [[8, 5, 17, 0], 0], [[8, 4, 18, 0], 0.00082], [[8, 3, 19, 0], 0.00553], [[8, 2, 20, 0], 0.02242], [[8, 1, 21, 0], 0.04435], [[8, 0, 22, 0], 0.17107], [[7, 0, 22, 1], 0.69706], [[6, 0, 22, 2], 0.99878], [[5, 0, 22, 3], 0.9992], [[4, 0, 22, 4], 0.99972], [[3, 0, 22, 5], 0.99979], [[2, 0, 22, 6], 0.99989], [[1, 0, 22, 7], 0.99996], [[0, 0, 22, 8], 0.99997]], "phi_coefficient": 1, "pr_curve": [[0, 1, 0.99997], [0.125, 1, 0.99996], [0.25, 1, 0.99989], [0.375, 1, 0.99979], [0.5, 1, 0.99972], [0.625, 1, 0.9992], [0.75, 1, 0.99878], [0.875, 1, 0.69706], [1, 1, 0.17107], [1, 0.88889, 0.04435], [1, 0.8, 0.02242], [1, 0.72727, 0.00553], [1, 0.66667, 0.00082], [1, 0.61538, 0], [1, 0.26667, null]], "precision": 1, "present_in_test_data": true, "recall": 1, "roc_curve": [[0, 0, 0.99997], [0, 0.125, 0.99996], [0, 0.25, 0.99989], [0, 0.375, 0.99979], [0, 0.5, 0.99972], [0, 0.625, 0.9992], [0, 0.75, 0.99878], [0, 0.875, 0.69706], [0, 1, 0.17107], [0.04545, 1, 0.04435], [0.09091, 1, 0.02242], [0.13636, 1, 0.00553], [0.18182, 1, 0.00082], [0.22727, 1, 0], [1, 1, null]], "spearmans_rho": 0.8471}, {"accuracy": 1, "area_under_pr_curve": 1, "area_under_roc_curve": 1, "balanced_accuracy": 1, "class_name": "Iris-virginica", "f_measure": 1, "gain_curve": [[0, 0, 1], [0.13333, 0.44444, 0.99918], [0.16667, 0.55556, 0.99447], [0.2, 0.66667, 0.97758], [0.23333, 0.77778, 0.95565], [0.26667, 0.88889, 0.82893], [0.3, 1, 0.30294], [0.33333, 1, 0.00117], [0.36667, 1, 0.00079], [0.4, 1, 0.00013], [0.43333, 1, 7e-05], [0.46667, 1, 4e-05], [0.5, 1, 2e-05], [0.53333, 1, 0], [1, 1, null]], "kendalls_tau_b": 0.74778, "ks_statistic": [1, 0.30294], "lift_curve": [[0, 0, 1], [0.13333, 3.33333, 0.99918], [0.16667, 3.33333, 0.99447], [0.2, 3.33333, 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\ No newline at end of file
diff --git a/check_files/evaluation_iris_fs.json b/check_files/evaluation_iris_fs.json
index 0e77e055..3b28d016 100644
--- a/check_files/evaluation_iris_fs.json
+++ b/check_files/evaluation_iris_fs.json
@@ -1,1 +1,1 @@
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[0.33333, 2.30769, 0.99345], [0.43333, 2.30769, 0.04167], [0.46667, 2.14286, 0.00432], [0.5, 2, 0.00379], [0.76667, 1.30435, 0.00356], [0.8, 1.25, 0.0035], [0.83333, 1.2, 0.00349], [0.86667, 1.15385, 0.00348], [0.96667, 1.03448, 0.00347], [1, 1, null]], "max_phi": [1, 0.04167], "negative_cdf": [[0, 0, 0.99346], [0.13333, 0, 0.99346], [0.33333, 0, 0.99345], [0.43333, 0, 0.04167], [0.46667, 0.05882, 0.00432], [0.5, 0.11765, 0.00379], [0.76667, 0.58824, 0.00356], [0.8, 0.64706, 0.0035], [0.83333, 0.70588, 0.00349], [0.86667, 0.76471, 0.00348], [0.96667, 0.94118, 0.00347], [1, 1, null]], "per_threshold_confusion_matrices": [[[13, 17, 0, 0], null], [[13, 16, 1, 0], 0.00347], [[13, 13, 4, 0], 0.00348], [[13, 12, 5, 0], 0.00349], [[13, 11, 6, 0], 0.0035], [[13, 10, 7, 0], 0.00356], [[13, 2, 15, 0], 0.00379], [[13, 1, 16, 0], 0.00432], [[13, 0, 17, 0], 0.04167], [[10, 0, 17, 3], 0.99345], [[4, 0, 17, 9], 0.99346], [[0, 0, 17, 13], 0.99346]], "phi_coefficient": 1, "pr_curve": [[0, 1, 0.99346], [0.30769, 1, 0.99346], [0.76923, 1, 0.99345], [1, 1, 0.04167], [1, 0.92857, 0.00432], [1, 0.86667, 0.00379], [1, 0.56522, 0.00356], [1, 0.54167, 0.0035], [1, 0.52, 0.00349], [1, 0.5, 0.00348], [1, 0.44828, 0.00347], [1, 0.43333, null]], "precision": 1, "present_in_test_data": true, "recall": 1, "roc_curve": [[0, 0, 0.99346], [0, 0.30769, 0.99346], [0, 0.76923, 0.99345], [0, 1, 0.04167], [0.05882, 1, 0.00432], [0.11765, 1, 0.00379], [0.58824, 1, 0.00356], [0.64706, 1, 0.0035], [0.70588, 1, 0.00349], [0.76471, 1, 0.00348], [0.94118, 1, 0.00347], [1, 1, null]], "spearmans_rho": 0.87216}, {"accuracy": 1, "area_under_pr_curve": 1, "area_under_roc_curve": 1, "balanced_accuracy": 1, "class_name": "Iris-versicolor", "f_measure": 1, "gain_curve": [[0, 0, 0.99305], [0.03333, 0.125, 0.99303], [0.06667, 0.25, 0.99295], [0.1, 0.375, 0.9928], [0.13333, 0.5, 0.99244], [0.16667, 0.625, 0.99207], [0.2, 0.75, 0.9915], [0.23333, 0.875, 0.94401], [0.26667, 1, 0.06915], [0.3, 1, 0.01385], [0.33333, 1, 0.01097], [0.36667, 1, 0.00617], [0.4, 1, 0.00413], [0.43333, 1, 0.00379], [0.56667, 1, 0.00328], [0.66667, 1, 0.00328], [0.86667, 1, 0.00327], [1, 1, null]], "kendalls_tau_b": 0.65922, "ks_statistic": [1, 0.06915], "lift_curve": [[0, 0, 0.99305], [0.03333, 3.75, 0.99303], [0.06667, 3.75, 0.99295], [0.1, 3.75, 0.9928], [0.13333, 3.75, 0.99244], [0.16667, 3.75, 0.99207], [0.2, 3.75, 0.9915], [0.23333, 3.75, 0.94401], [0.26667, 3.75, 0.06915], [0.3, 3.33333, 0.01385], [0.33333, 3, 0.01097], [0.36667, 2.72727, 0.00617], [0.4, 2.5, 0.00413], [0.43333, 2.30769, 0.00379], [0.56667, 1.76471, 0.00328], [0.66667, 1.5, 0.00328], [0.86667, 1.15385, 0.00327], [1, 1, null]], "max_phi": [1, 0.06915], "negative_cdf": [[0, 0, 0.99305], [0.03333, 0, 0.99303], [0.06667, 0, 0.99295], [0.1, 0, 0.9928], [0.13333, 0, 0.99244], [0.16667, 0, 0.99207], [0.2, 0, 0.9915], [0.23333, 0, 0.94401], [0.26667, 0, 0.06915], [0.3, 0.04545, 0.01385], [0.33333, 0.09091, 0.01097], [0.36667, 0.13636, 0.00617], [0.4, 0.18182, 0.00413], [0.43333, 0.22727, 0.00379], [0.56667, 0.40909, 0.00328], [0.66667, 0.54545, 0.00328], [0.86667, 0.81818, 0.00327], [1, 1, null]], "per_threshold_confusion_matrices": [[[8, 22, 0, 0], null], [[8, 18, 4, 0], 0.00327], [[8, 12, 10, 0], 0.00328], [[8, 9, 13, 0], 0.00328], [[8, 5, 17, 0], 0.00379], [[8, 4, 18, 0], 0.00413], [[8, 3, 19, 0], 0.00617], [[8, 2, 20, 0], 0.01097], [[8, 1, 21, 0], 0.01385], [[8, 0, 22, 0], 0.06915], [[7, 0, 22, 1], 0.94401], [[6, 0, 22, 2], 0.9915], [[5, 0, 22, 3], 0.99207], [[4, 0, 22, 4], 0.99244], [[3, 0, 22, 5], 0.9928], [[2, 0, 22, 6], 0.99295], [[1, 0, 22, 7], 0.99303], [[0, 0, 22, 8], 0.99305]], "phi_coefficient": 1, "pr_curve": [[0, 1, 0.99305], [0.125, 1, 0.99303], [0.25, 1, 0.99295], [0.375, 1, 0.9928], [0.5, 1, 0.99244], [0.625, 1, 0.99207], [0.75, 1, 0.9915], [0.875, 1, 0.94401], [1, 1, 0.06915], [1, 0.88889, 0.01385], [1, 0.8, 0.01097], [1, 0.72727, 0.00617], [1, 0.66667, 0.00413], [1, 0.61538, 0.00379], [1, 0.47059, 0.00328], [1, 0.4, 0.00328], [1, 0.30769, 0.00327], [1, 0.26667, null]], "precision": 1, "present_in_test_data": true, "recall": 1, "roc_curve": [[0, 0, 0.99305], [0, 0.125, 0.99303], [0, 0.25, 0.99295], [0, 0.375, 0.9928], [0, 0.5, 0.99244], [0, 0.625, 0.99207], [0, 0.75, 0.9915], [0, 0.875, 0.94401], [0, 1, 0.06915], [0.04545, 1, 0.01385], [0.09091, 1, 0.01097], [0.13636, 1, 0.00617], [0.18182, 1, 0.00413], [0.22727, 1, 0.00379], [0.40909, 1, 0.00328], [0.54545, 1, 0.00328], [0.81818, 1, 0.00327], [1, 1, null]], "spearmans_rho": 0.77145}, {"accuracy": 1, "area_under_pr_curve": 1, "area_under_roc_curve": 1, "balanced_accuracy": 1, "class_name": "Iris-virginica", "f_measure": 1, "gain_curve": [[0, 0, 0.99242], [0.13333, 0.44444, 0.99208], [0.16667, 0.55556, 0.99004], [0.2, 0.66667, 0.98524], [0.23333, 0.77778, 0.98236], [0.26667, 0.88889, 0.88919], [0.3, 1, 0.05252], [0.33333, 1, 0.00502], [0.36667, 1, 0.00409], [0.4, 1, 0.0037], [0.43333, 1, 0.00361], [0.46667, 1, 0.0035], [0.5, 1, 0.00348], [0.56667, 1, 0.00327], [1, 1, null]], "kendalls_tau_b": 0.73485, "ks_statistic": [1, 0.05252], "lift_curve": [[0, 0, 0.99242], [0.13333, 3.33333, 0.99208], [0.16667, 3.33333, 0.99004], [0.2, 3.33333, 0.98524], [0.23333, 3.33333, 0.98236], [0.26667, 3.33333, 0.88919], [0.3, 3.33333, 0.05252], [0.33333, 3, 0.00502], [0.36667, 2.72727, 0.00409], [0.4, 2.5, 0.0037], [0.43333, 2.30769, 0.00361], [0.46667, 2.14286, 0.0035], [0.5, 2, 0.00348], [0.56667, 1.76471, 0.00327], [1, 1, null]], "max_phi": [1, 0.05252], "negative_cdf": [[0, 0, 0.99242], [0.13333, 0, 0.99208], [0.16667, 0, 0.99004], [0.2, 0, 0.98524], [0.23333, 0, 0.98236], [0.26667, 0, 0.88919], [0.3, 0, 0.05252], [0.33333, 0.04762, 0.00502], [0.36667, 0.09524, 0.00409], [0.4, 0.14286, 0.0037], [0.43333, 0.19048, 0.00361], [0.46667, 0.2381, 0.0035], [0.5, 0.28571, 0.00348], [0.56667, 0.38095, 0.00327], [1, 1, null]], "per_threshold_confusion_matrices": [[[9, 21, 0, 0], null], [[9, 8, 13, 0], 0.00327], [[9, 6, 15, 0], 0.00348], [[9, 5, 16, 0], 0.0035], [[9, 4, 17, 0], 0.00361], [[9, 3, 18, 0], 0.0037], [[9, 2, 19, 0], 0.00409], [[9, 1, 20, 0], 0.00502], [[9, 0, 21, 0], 0.05252], [[8, 0, 21, 1], 0.88919], [[7, 0, 21, 2], 0.98236], [[6, 0, 21, 3], 0.98524], [[5, 0, 21, 4], 0.99004], [[4, 0, 21, 5], 0.99208], [[0, 0, 21, 9], 0.99242]], "phi_coefficient": 1, "pr_curve": [[0, 1, 0.99242], [0.44444, 1, 0.99208], [0.55556, 1, 0.99004], [0.66667, 1, 0.98524], [0.77778, 1, 0.98236], [0.88889, 1, 0.88919], [1, 1, 0.05252], [1, 0.9, 0.00502], [1, 0.81818, 0.00409], [1, 0.75, 0.0037], [1, 0.69231, 0.00361], [1, 0.64286, 0.0035], [1, 0.6, 0.00348], [1, 0.52941, 0.00327], [1, 0.3, null]], "precision": 1, "present_in_test_data": true, "recall": 1, "roc_curve": [[0, 0, 0.99242], [0, 0.44444, 0.99208], [0, 0.55556, 0.99004], [0, 0.66667, 0.98524], [0, 0.77778, 0.98236], [0, 0.88889, 0.88919], [0, 1, 0.05252], [0.04762, 1, 0.00502], [0.09524, 1, 0.00409], [0.14286, 1, 0.0037], [0.19048, 1, 0.00361], [0.2381, 1, 0.0035], [0.28571, 1, 0.00348], [0.38095, 1, 0.00327], [1, 1, null]], "spearmans_rho": 0.82952}]}, "random": {"accuracy": 0.26667, "average_area_under_pr_curve": 0, "average_area_under_roc_curve": 0, "average_balanced_accuracy": 0.43104, "average_f_measure": 0.23018, "average_kendalls_tau_b": 0, "average_ks_statistic": 0, "average_max_phi": 0, "average_phi": -0.13603, "average_precision": 0.23333, "average_recall": 0.22792, "average_spearmans_rho": 0, "confusion_matrix": [[6, 2, 5], [5, 0, 3], [1, 6, 2]], "per_class_statistics": [{"accuracy": 0.56667, "balanced_accuracy": 0.5543, "class_name": "Iris-setosa", "f_measure": 0.48, "phi_coefficient": 0.10985, "precision": 0.5, "present_in_test_data": true, "recall": 0.46154}, {"accuracy": 0.46667, "balanced_accuracy": 0.31818, "class_name": "Iris-versicolor", "f_measure": 0, "phi_coefficient": -0.36364, "precision": 0, "present_in_test_data": true, "recall": 0}, {"accuracy": 0.5, "balanced_accuracy": 0.42063, "class_name": "Iris-virginica", "f_measure": 0.21053, "phi_coefficient": -0.1543, "precision": 0.2, "present_in_test_data": true, "recall": 0.22222}]}}
\ No newline at end of file
diff --git a/check_files/predictions_iris_fs.csv b/check_files/predictions_iris_fs.csv
index 796ac2bf..d1c04868 100644
--- a/check_files/predictions_iris_fs.csv
+++ b/check_files/predictions_iris_fs.csv
@@ -1,30 +1,30 @@
-Iris-versicolor,0.6641
+Iris-versicolor,0.43695
 Iris-setosa,0.99346
-Iris-setosa,0.66653
+Iris-setosa,0.66624
 Iris-setosa,0.99346
+Iris-setosa,0.99345
 Iris-setosa,0.99346
+Iris-setosa,0.99345
 Iris-setosa,0.99346
 Iris-setosa,0.99346
 Iris-setosa,0.99346
-Iris-setosa,0.99346
-Iris-setosa,0.99346
-Iris-versicolor,0.55547
-Iris-versicolor,0.99296
-Iris-versicolor,0.99124
-Iris-versicolor,0.99294
-Iris-versicolor,0.9891
-Iris-virginica,0.85968
-Iris-versicolor,0.99302
+Iris-versicolor,0.57099
+Iris-versicolor,0.99303
+Iris-versicolor,0.9922
 Iris-versicolor,0.9929
+Iris-versicolor,0.97285
+Iris-virginica,0.86632
+Iris-versicolor,0.99304
+Iris-versicolor,0.99286
 Iris-versicolor,0.99296
-Iris-versicolor,0.99246
+Iris-versicolor,0.99267
 Iris-virginica,0.99242
-Iris-virginica,0.99238
+Iris-virginica,0.9924
 Iris-virginica,0.99242
-Iris-virginica,0.99229
+Iris-virginica,0.99237
 Iris-virginica,0.99242
 Iris-virginica,0.99242
-Iris-virginica,0.82592
-Iris-versicolor,0.4819
-Iris-versicolor,0.51524
+Iris-virginica,0.83058
+Iris-virginica,0.59938
+Iris-virginica,0.54864
 Iris-virginica,0.99242
diff --git a/check_files/predictions_iris_fs2.csv b/check_files/predictions_iris_fs2.csv
new file mode 100644
index 00000000..298b0d0e
--- /dev/null
+++ b/check_files/predictions_iris_fs2.csv
@@ -0,0 +1,30 @@
+Iris-versicolor,0.43695
+Iris-setosa,0.99346
+Iris-setosa,0.66624
+Iris-setosa,0.993455
+Iris-setosa,0.99345
+Iris-setosa,0.993455
+Iris-setosa,0.99345
+Iris-setosa,0.99346
+Iris-setosa,0.99346
+Iris-setosa,0.993455
+Iris-versicolor,0.57099
+Iris-versicolor,0.99303
+Iris-versicolor,0.9922
+Iris-versicolor,0.9929
+Iris-versicolor,0.97285
+Iris-virginica,0.86633
+Iris-versicolor,0.99304
+Iris-versicolor,0.99286
+Iris-versicolor,0.99296
+Iris-versicolor,0.99267
+Iris-virginica,0.99242
+Iris-virginica,0.9924
+Iris-virginica,0.99242
+Iris-virginica,0.99237
+Iris-virginica,0.99242
+Iris-virginica,0.99242
+Iris-virginica,0.83058
+Iris-virginica,0.59938
+Iris-virginica,0.54864
+Iris-virginica,0.99242
diff --git a/check_files/predictions_iris_fs_prob.csv b/check_files/predictions_iris_fs_prob.csv
index fade423e..494b8726 100644
--- a/check_files/predictions_iris_fs_prob.csv
+++ b/check_files/predictions_iris_fs_prob.csv
@@ -1,30 +1,30 @@
-4.1,2.4,,,Iris-versicolor,0.6641,0.16698,0.6641,0.16892
+4.1,2.4,,,Iris-versicolor,0.43695,0.16805,0.43695,0.39501
 5.0,3.7,1.3,0.2,Iris-setosa,0.99346,0.99346,0.00327,0.00327
-4.5,,,0.2,Iris-setosa,0.66653,0.66653,0.1668,0.16667
-4.9,3.2,1.3,0.2,Iris-setosa,0.99346,0.99346,0.00327,0.00327
-5.0,3.5,1.6,0.6,Iris-setosa,0.99346,0.99346,0.00327,0.00327
-5.1,3.8,1.9,0.4,Iris-setosa,0.99346,0.99346,0.00327,0.00327
-4.8,3.0,1.4,0.2,Iris-setosa,0.99346,0.99346,0.00327,0.00327
+4.5,,,0.2,Iris-setosa,0.66624,0.66624,0.16709,0.16667
+4.9,3.2,1.3,0.2,Iris-setosa,0.993455,0.993455,0.003275,0.00327
+5.0,3.5,1.6,0.6,Iris-setosa,0.99345,0.99345,0.00328,0.00327
+5.1,3.8,1.9,0.4,Iris-setosa,0.993455,0.993455,0.003275,0.00327
+4.8,3.0,1.4,0.2,Iris-setosa,0.99345,0.99345,0.00328,0.00327
 5.1,3.8,1.6,0.2,Iris-setosa,0.99346,0.99346,0.00327,0.00327
 4.6,3.2,1.4,0.2,Iris-setosa,0.99346,0.99346,0.00327,0.00327
-5.3,3.7,1.5,0.2,Iris-setosa,0.99346,0.99346,0.00327,0.00327
-6.7,3.1,4.7,1.7,Iris-versicolor,0.55547,0.08334,0.55547,0.36119
-6.3,2.3,4.4,1.2,Iris-versicolor,0.99296,0.00347,0.99296,0.00357
-5.6,3.0,4.1,1.2,Iris-versicolor,0.99124,0.00528,0.99124,0.00349
-5.5,2.5,4.0,1.2,Iris-versicolor,0.99294,0.00348,0.99294,0.00359
-5.5,2.6,4.9,1.2,Iris-versicolor,0.9891,0.00347,0.9891,0.00743
-6.1,3.0,4.6,1.9,Iris-virginica,0.85968,0.05556,0.08477,0.85968
-5.8,2.6,4.0,1.2,Iris-versicolor,0.99302,0.00348,0.99302,0.00351
-5.0,2.3,3.3,1.0,Iris-versicolor,0.9929,0.00362,0.9929,0.00349
-5.6,2.7,4.2,1.2,Iris-versicolor,0.99296,0.00349,0.99296,0.00356
-5.7,3.0,4.2,1.2,Iris-versicolor,0.99246,0.00405,0.99246,0.00349
+5.3,3.7,1.5,0.2,Iris-setosa,0.993455,0.993455,0.003275,0.00327
+6.7,3.1,4.7,1.7,Iris-versicolor,0.57099,0.08333,0.57099,0.34568
+6.3,2.3,4.4,1.2,Iris-versicolor,0.99303,0.00347,0.99303,0.0035
+5.6,3.0,4.1,1.2,Iris-versicolor,0.9922,0.00433,0.9922,0.00347
+5.5,2.5,4.0,1.2,Iris-versicolor,0.9929,0.00348,0.9929,0.00362
+5.5,2.6,4.9,1.2,Iris-versicolor,0.97285,0.00348,0.97285,0.02367
+6.1,3.0,4.6,1.9,Iris-virginica,0.86633,0.05556,0.07812,0.86633
+5.8,2.6,4.0,1.2,Iris-versicolor,0.99304,0.00348,0.99304,0.00349
+5.0,2.3,3.3,1.0,Iris-versicolor,0.99286,0.00356,0.99286,0.00358
+5.6,2.7,4.2,1.2,Iris-versicolor,0.99296,0.0035,0.99296,0.00355
+5.7,3.0,4.2,1.2,Iris-versicolor,0.99267,0.00386,0.99267,0.00348
 6.3,3.3,6.0,2.7,Iris-virginica,0.99242,0.00379,0.00379,0.99242
-5.1,2.7,5.1,1.9,Iris-virginica,0.99238,0.00379,0.00383,0.99238
+5.1,2.7,5.1,1.9,Iris-virginica,0.9924,0.00379,0.00381,0.9924
 7.1,3.0,5.9,2.1,Iris-virginica,0.99242,0.00379,0.00379,0.99242
-6.3,2.9,5.6,1.8,Iris-virginica,0.99229,0.00379,0.00392,0.99229
+6.3,2.9,5.6,1.8,Iris-virginica,0.99237,0.00379,0.00384,0.99237
 6.5,3.0,5.8,2.2,Iris-virginica,0.99242,0.00379,0.00379,0.99242
 7.6,3.0,6.6,2.1,Iris-virginica,0.99242,0.00379,0.00379,0.99242
-4.9,2.7,4.7,1.7,Iris-virginica,0.82592,0.08333,0.09075,0.82592
-7.3,2.9,6.3,1.1,Iris-versicolor,0.4819,0.04167,0.4819,0.47644
-6.7,2.5,5.8,1.1,Iris-versicolor,0.51524,0.04167,0.51524,0.4431
+4.9,2.7,4.7,1.7,Iris-virginica,0.83058,0.08333,0.08609,0.83058
+7.3,2.9,6.3,1.1,Iris-virginica,0.59938,0.04167,0.35896,0.59938
+6.7,2.5,5.8,1.1,Iris-virginica,0.54864,0.04167,0.4097,0.54864
 7.2,3.6,6.1,2.5,Iris-virginica,0.99242,0.00379,0.00379,0.99242
diff --git a/data/images/VOC_annotations/000004.xml b/data/images/VOC_annotations/000004.xml
index ee0047f7..5bf30011 100644
--- a/data/images/VOC_annotations/000004.xml
+++ b/data/images/VOC_annotations/000004.xml
@@ -1,6 +1,7 @@
 <annotation>
 	<folder>f1</folder>
 	<filename>fruits1b.png</filename>
+	<path>C:\fruits1b.png</path>
 	<source>
 		<database>The VOC2007 Database</database>
 		<annotation>PASCAL VOC2007</annotation>


Test patch:
diff --git a/bigmler/tests/test_40_fusion.py b/bigmler/tests/test_40_fusion.py
index bcc8007b..5383c325 100644
--- a/bigmler/tests/test_40_fusion.py
+++ b/bigmler/tests/test_40_fusion.py
@@ -116,7 +116,7 @@ def test_scenario02(self):
         headers = ["test", "output", "predictions_file"]
         examples = [
             ['data/test_iris.csv', 'scenario2_fs/predictions.csv',
-             'check_files/predictions_iris_fs.csv']]
+             'check_files/predictions_iris_fs2.csv']]
         for example in examples:
             example = dict(zip(headers, example))
             show_method(self, self.bigml["method"], example)