Source: scikit-learn Version: 1.4.1.post1+dfsg-1 Severity: serious User: debian...@lists.debian.org Usertags: regression
Hi Maintainer scikit-learn's autopkgtest regresses on i386 when tested with numpy 1.26 [1]. I've copied what I hope is the relevant part of the log below. Regards Graham [1] https://ci.debian.net/packages/s/scikit-learn/testing/i386/ 1386s =================================== FAILURES =================================== 35104 1386s ______________________________ test_graphviz_toy _______________________________ 35105 1386s 35106 1386s def test_graphviz_toy(): 35107 1386s # Check correctness of export_graphviz 35108 1386s clf = DecisionTreeClassifier( 35109 1386s max_depth=3, min_samples_split=2, criterion="gini", random_state=2 35110 1386s ) 35111 1386s clf.fit(X, y) 35112 1386s 35113 1386s # Test export code 35114 1386s contents1 = export_graphviz(clf, out_file=None) 35115 1386s contents2 = ( 35116 1386s "digraph Tree {\n" 35117 1386s 'node [shape=box, fontname="helvetica"] ;\n' 35118 1386s 'edge [fontname="helvetica"] ;\n' 35119 1386s '0 [label="x[0] <= 0.0\\ngini = 0.5\\nsamples = 6\\n' 35120 1386s 'value = [3, 3]"] ;\n' 35121 1386s '1 [label="gini = 0.0\\nsamples = 3\\nvalue = [3, 0]"] ;\n' 35122 1386s "0 -> 1 [labeldistance=2.5, labelangle=45, " 35123 1386s 'headlabel="True"] ;\n' 35124 1386s '2 [label="gini = 0.0\\nsamples = 3\\nvalue = [0, 3]"] ;\n' 35125 1386s "0 -> 2 [labeldistance=2.5, labelangle=-45, " 35126 1386s 'headlabel="False"] ;\n' 35127 1386s "}" 35128 1386s ) 35129 1386s 35130 1386s assert contents1 == contents2 35131 1386s 35132 1386s # Test plot_options 35133 1386s contents1 = export_graphviz( 35134 1386s clf, 35135 1386s filled=True, 35136 1386s impurity=False, 35137 1386s proportion=True, 35138 1386s special_characters=True, 35139 1386s rounded=True, 35140 1386s out_file=None, 35141 1386s fontname="sans", 35142 1386s ) 35143 1386s contents2 = ( 35144 1386s "digraph Tree {\n" 35145 1386s 'node [shape=box, style="filled, rounded", color="black", ' 35146 1386s 'fontname="sans"] ;\n' 35147 1386s 'edge [fontname="sans"] ;\n' 35148 1386s "0 [label=<x<SUB>0</SUB> ≤ 0.0<br/>samples = 100.0%<br/>" 35149 1386s 'value = [0.5, 0.5]>, fillcolor="#ffffff"] ;\n' 35150 1386s "1 [label=<samples = 50.0%<br/>value = [1.0, 0.0]>, " 35151 1386s 'fillcolor="#e58139"] ;\n' 35152 1386s "0 -> 1 [labeldistance=2.5, labelangle=45, " 35153 1386s 'headlabel="True"] ;\n' 35154 1386s "2 [label=<samples = 50.0%<br/>value = [0.0, 1.0]>, " 35155 1386s 'fillcolor="#399de5"] ;\n' 35156 1386s "0 -> 2 [labeldistance=2.5, labelangle=-45, " 35157 1386s 'headlabel="False"] ;\n' 35158 1386s "}" 35159 1386s ) 35160 1386s 35161 1386s assert contents1 == contents2 35162 1386s 35163 1386s # Test max_depth 35164 1386s contents1 = export_graphviz(clf, max_depth=0, class_names=True, out_file=None) 35165 1386s contents2 = ( 35166 1386s "digraph Tree {\n" 35167 1386s 'node [shape=box, fontname="helvetica"] ;\n' 35168 1386s 'edge [fontname="helvetica"] ;\n' 35169 1386s '0 [label="x[0] <= 0.0\\ngini = 0.5\\nsamples = 6\\n' 35170 1386s 'value = [3, 3]\\nclass = y[0]"] ;\n' 35171 1386s '1 [label="(...)"] ;\n' 35172 1386s "0 -> 1 ;\n" 35173 1386s '2 [label="(...)"] ;\n' 35174 1386s "0 -> 2 ;\n" 35175 1386s "}" 35176 1386s ) 35177 1386s 35178 1386s assert contents1 == contents2 35179 1386s 35180 1386s # Test max_depth with plot_options 35181 1386s contents1 = export_graphviz( 35182 1386s clf, max_depth=0, filled=True, out_file=None, node_ids=True 35183 1386s ) 35184 1386s contents2 = ( 35185 1386s "digraph Tree {\n" 35186 1386s 'node [shape=box, style="filled", color="black", ' 35187 1386s 'fontname="helvetica"] ;\n' 35188 1386s 'edge [fontname="helvetica"] ;\n' 35189 1386s '0 [label="node #0\\nx[0] <= 0.0\\ngini = 0.5\\n' 35190 1386s 'samples = 6\\nvalue = [3, 3]", fillcolor="#ffffff"] ;\n' 35191 1386s '1 [label="(...)", fillcolor="#C0C0C0"] ;\n' 35192 1386s "0 -> 1 ;\n" 35193 1386s '2 [label="(...)", fillcolor="#C0C0C0"] ;\n' 35194 1386s "0 -> 2 ;\n" 35195 1386s "}" 35196 1386s ) 35197 1386s 35198 1386s assert contents1 == contents2 35199 1386s 35200 1386s # Test multi-output with weighted samples 35201 1386s clf = DecisionTreeClassifier( 35202 1386s max_depth=2, min_samples_split=2, criterion="gini", random_state=2 35203 1386s ) 35204 1386s clf = clf.fit(X, y2, sample_weight=w) 35205 1386s 35206 1386s > contents1 = export_graphviz(clf, filled=True, impurity=False, out_file=None) 35207 1386s 35208 1386s /usr/lib/python3/dist-packages/sklearn/tree/tests/test_export.py:131: 35209 1386s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 35210 1386s /usr/lib/python3/dist-packages/sklearn/utils/_param_validation.py:213: in wrapper 35211 1386s return func(*args, **kwargs) 35212 1386s /usr/lib/python3/dist-packages/sklearn/tree/_export.py:906: in export_graphviz 35213 1386s exporter.export(decision_tree) 35214 1386s /usr/lib/python3/dist-packages/sklearn/tree/_export.py:466: in export 35215 1386s self.recurse(decision_tree.tree_, 0, criterion=decision_tree.criterion) 35216 1386s /usr/lib/python3/dist-packages/sklearn/tree/_export.py:529: in recurse 35217 1386s ', fillcolor="%s"' % self.get_fill_color(tree, node_id) 35218 1386s /usr/lib/python3/dist-packages/sklearn/tree/_export.py:286: in get_fill_color 35219 1386s return self.get_color(node_val) 35220 1386s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 35221 1386s 35222 1386s self = <sklearn.tree._export._DOTTreeExporter object at 0xe643bcf0> 35223 1386s value = -0.4691358024691358 35224 1386s 35225 1386s def get_color(self, value): 35226 1386s # Find the appropriate color & intensity for a node 35227 1386s if self.colors["bounds"] is None: 35228 1386s # Classification tree 35229 1386s color = list(self.colors["rgb"][np.argmax(value)]) 35230 1386s sorted_values = sorted(value, reverse=True) 35231 1386s if len(sorted_values) == 1: 35232 1386s alpha = 0.0 35233 1386s else: 35234 1386s alpha = (sorted_values[0] - sorted_values[1]) / (1 - sorted_values[1]) 35235 1386s else: 35236 1386s # Regression tree or multi-output 35237 1386s color = list(self.colors["rgb"][0]) 35238 1386s alpha = (value - self.colors["bounds"][0]) / ( 35239 1386s self.colors["bounds"][1] - self.colors["bounds"][0] 35240 1386s ) 35241 1386s # compute the color as alpha against white 35242 1386s > color = [int(round(alpha * c + (1 - alpha) * 255, 0)) for c in color] 35243 1386s E ValueError: cannot convert float NaN to integer 35244 1386s 35245 1386s /usr/lib/python3/dist-packages/sklearn/tree/_export.py:258: ValueError
