308 lines
13 KiB
Python
308 lines
13 KiB
Python
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"""
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Testing for export functions of decision trees (sklearn.tree.export).
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"""
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from re import finditer, search
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from numpy.random import RandomState
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from sklearn.base import is_classifier
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from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
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from sklearn.ensemble import GradientBoostingClassifier
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from sklearn.tree import export_graphviz
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from sklearn.externals.six import StringIO
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from sklearn.utils.testing import (assert_in, assert_equal, assert_raises,
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assert_less_equal, assert_raises_regex,
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assert_raise_message)
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from sklearn.exceptions import NotFittedError
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# toy sample
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X = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]]
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y = [-1, -1, -1, 1, 1, 1]
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y2 = [[-1, 1], [-1, 1], [-1, 1], [1, 2], [1, 2], [1, 3]]
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w = [1, 1, 1, .5, .5, .5]
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y_degraded = [1, 1, 1, 1, 1, 1]
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def test_graphviz_toy():
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# Check correctness of export_graphviz
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clf = DecisionTreeClassifier(max_depth=3,
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min_samples_split=2,
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criterion="gini",
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random_state=2)
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clf.fit(X, y)
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# Test export code
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contents1 = export_graphviz(clf, out_file=None)
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contents2 = 'digraph Tree {\n' \
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'node [shape=box] ;\n' \
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'0 [label="X[0] <= 0.0\\ngini = 0.5\\nsamples = 6\\n' \
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'value = [3, 3]"] ;\n' \
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'1 [label="gini = 0.0\\nsamples = 3\\nvalue = [3, 0]"] ;\n' \
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'0 -> 1 [labeldistance=2.5, labelangle=45, ' \
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'headlabel="True"] ;\n' \
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'2 [label="gini = 0.0\\nsamples = 3\\nvalue = [0, 3]"] ;\n' \
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'0 -> 2 [labeldistance=2.5, labelangle=-45, ' \
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'headlabel="False"] ;\n' \
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'}'
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assert_equal(contents1, contents2)
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# Test with feature_names
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contents1 = export_graphviz(clf, feature_names=["feature0", "feature1"],
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out_file=None)
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contents2 = 'digraph Tree {\n' \
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'node [shape=box] ;\n' \
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'0 [label="feature0 <= 0.0\\ngini = 0.5\\nsamples = 6\\n' \
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'value = [3, 3]"] ;\n' \
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'1 [label="gini = 0.0\\nsamples = 3\\nvalue = [3, 0]"] ;\n' \
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'0 -> 1 [labeldistance=2.5, labelangle=45, ' \
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'headlabel="True"] ;\n' \
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'2 [label="gini = 0.0\\nsamples = 3\\nvalue = [0, 3]"] ;\n' \
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'0 -> 2 [labeldistance=2.5, labelangle=-45, ' \
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'headlabel="False"] ;\n' \
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'}'
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assert_equal(contents1, contents2)
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# Test with class_names
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contents1 = export_graphviz(clf, class_names=["yes", "no"], out_file=None)
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contents2 = 'digraph Tree {\n' \
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'node [shape=box] ;\n' \
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'0 [label="X[0] <= 0.0\\ngini = 0.5\\nsamples = 6\\n' \
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'value = [3, 3]\\nclass = yes"] ;\n' \
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'1 [label="gini = 0.0\\nsamples = 3\\nvalue = [3, 0]\\n' \
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'class = yes"] ;\n' \
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'0 -> 1 [labeldistance=2.5, labelangle=45, ' \
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'headlabel="True"] ;\n' \
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'2 [label="gini = 0.0\\nsamples = 3\\nvalue = [0, 3]\\n' \
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'class = no"] ;\n' \
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'0 -> 2 [labeldistance=2.5, labelangle=-45, ' \
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'headlabel="False"] ;\n' \
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'}'
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assert_equal(contents1, contents2)
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# Test plot_options
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contents1 = export_graphviz(clf, filled=True, impurity=False,
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proportion=True, special_characters=True,
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rounded=True, out_file=None)
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contents2 = 'digraph Tree {\n' \
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'node [shape=box, style="filled, rounded", color="black", ' \
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'fontname=helvetica] ;\n' \
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'edge [fontname=helvetica] ;\n' \
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'0 [label=<X<SUB>0</SUB> ≤ 0.0<br/>samples = 100.0%<br/>' \
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'value = [0.5, 0.5]>, fillcolor="#e5813900"] ;\n' \
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'1 [label=<samples = 50.0%<br/>value = [1.0, 0.0]>, ' \
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'fillcolor="#e58139ff"] ;\n' \
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'0 -> 1 [labeldistance=2.5, labelangle=45, ' \
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'headlabel="True"] ;\n' \
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'2 [label=<samples = 50.0%<br/>value = [0.0, 1.0]>, ' \
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'fillcolor="#399de5ff"] ;\n' \
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'0 -> 2 [labeldistance=2.5, labelangle=-45, ' \
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'headlabel="False"] ;\n' \
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'}'
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assert_equal(contents1, contents2)
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# Test max_depth
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contents1 = export_graphviz(clf, max_depth=0,
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class_names=True, out_file=None)
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contents2 = 'digraph Tree {\n' \
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'node [shape=box] ;\n' \
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'0 [label="X[0] <= 0.0\\ngini = 0.5\\nsamples = 6\\n' \
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'value = [3, 3]\\nclass = y[0]"] ;\n' \
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'1 [label="(...)"] ;\n' \
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'0 -> 1 ;\n' \
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'2 [label="(...)"] ;\n' \
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'0 -> 2 ;\n' \
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'}'
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assert_equal(contents1, contents2)
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# Test max_depth with plot_options
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contents1 = export_graphviz(clf, max_depth=0, filled=True,
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out_file=None, node_ids=True)
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contents2 = 'digraph Tree {\n' \
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'node [shape=box, style="filled", color="black"] ;\n' \
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'0 [label="node #0\\nX[0] <= 0.0\\ngini = 0.5\\n' \
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'samples = 6\\nvalue = [3, 3]", fillcolor="#e5813900"] ;\n' \
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'1 [label="(...)", fillcolor="#C0C0C0"] ;\n' \
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'0 -> 1 ;\n' \
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'2 [label="(...)", fillcolor="#C0C0C0"] ;\n' \
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'0 -> 2 ;\n' \
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'}'
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assert_equal(contents1, contents2)
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# Test multi-output with weighted samples
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clf = DecisionTreeClassifier(max_depth=2,
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min_samples_split=2,
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criterion="gini",
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random_state=2)
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clf = clf.fit(X, y2, sample_weight=w)
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contents1 = export_graphviz(clf, filled=True,
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impurity=False, out_file=None)
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contents2 = 'digraph Tree {\n' \
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'node [shape=box, style="filled", color="black"] ;\n' \
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'0 [label="X[0] <= 0.0\\nsamples = 6\\n' \
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'value = [[3.0, 1.5, 0.0]\\n' \
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'[3.0, 1.0, 0.5]]", fillcolor="#e5813900"] ;\n' \
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'1 [label="samples = 3\\nvalue = [[3, 0, 0]\\n' \
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'[3, 0, 0]]", fillcolor="#e58139ff"] ;\n' \
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'0 -> 1 [labeldistance=2.5, labelangle=45, ' \
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'headlabel="True"] ;\n' \
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'2 [label="X[0] <= 1.5\\nsamples = 3\\n' \
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'value = [[0.0, 1.5, 0.0]\\n' \
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'[0.0, 1.0, 0.5]]", fillcolor="#e5813986"] ;\n' \
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'0 -> 2 [labeldistance=2.5, labelangle=-45, ' \
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'headlabel="False"] ;\n' \
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'3 [label="samples = 2\\nvalue = [[0, 1, 0]\\n' \
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'[0, 1, 0]]", fillcolor="#e58139ff"] ;\n' \
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'2 -> 3 ;\n' \
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'4 [label="samples = 1\\nvalue = [[0.0, 0.5, 0.0]\\n' \
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'[0.0, 0.0, 0.5]]", fillcolor="#e58139ff"] ;\n' \
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'2 -> 4 ;\n' \
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'}'
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assert_equal(contents1, contents2)
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# Test regression output with plot_options
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clf = DecisionTreeRegressor(max_depth=3,
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min_samples_split=2,
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criterion="mse",
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random_state=2)
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clf.fit(X, y)
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contents1 = export_graphviz(clf, filled=True, leaves_parallel=True,
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out_file=None, rotate=True, rounded=True)
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contents2 = 'digraph Tree {\n' \
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'node [shape=box, style="filled, rounded", color="black", ' \
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'fontname=helvetica] ;\n' \
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'graph [ranksep=equally, splines=polyline] ;\n' \
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'edge [fontname=helvetica] ;\n' \
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'rankdir=LR ;\n' \
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'0 [label="X[0] <= 0.0\\nmse = 1.0\\nsamples = 6\\n' \
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'value = 0.0", fillcolor="#e5813980"] ;\n' \
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'1 [label="mse = 0.0\\nsamples = 3\\nvalue = -1.0", ' \
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'fillcolor="#e5813900"] ;\n' \
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'0 -> 1 [labeldistance=2.5, labelangle=-45, ' \
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'headlabel="True"] ;\n' \
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'2 [label="mse = 0.0\\nsamples = 3\\nvalue = 1.0", ' \
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'fillcolor="#e58139ff"] ;\n' \
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'0 -> 2 [labeldistance=2.5, labelangle=45, ' \
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'headlabel="False"] ;\n' \
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'{rank=same ; 0} ;\n' \
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'{rank=same ; 1; 2} ;\n' \
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'}'
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assert_equal(contents1, contents2)
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# Test classifier with degraded learning set
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clf = DecisionTreeClassifier(max_depth=3)
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clf.fit(X, y_degraded)
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contents1 = export_graphviz(clf, filled=True, out_file=None)
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contents2 = 'digraph Tree {\n' \
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'node [shape=box, style="filled", color="black"] ;\n' \
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'0 [label="gini = 0.0\\nsamples = 6\\nvalue = 6.0", ' \
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'fillcolor="#e5813900"] ;\n' \
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'}'
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assert_equal(contents1, contents2)
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def test_graphviz_errors():
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# Check for errors of export_graphviz
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clf = DecisionTreeClassifier(max_depth=3, min_samples_split=2)
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# Check not-fitted decision tree error
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out = StringIO()
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assert_raises(NotFittedError, export_graphviz, clf, out)
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clf.fit(X, y)
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# Check if it errors when length of feature_names
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# mismatches with number of features
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message = ("Length of feature_names, "
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"1 does not match number of features, 2")
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assert_raise_message(ValueError, message, export_graphviz, clf, None,
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feature_names=["a"])
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message = ("Length of feature_names, "
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"3 does not match number of features, 2")
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assert_raise_message(ValueError, message, export_graphviz, clf, None,
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feature_names=["a", "b", "c"])
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# Check class_names error
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out = StringIO()
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assert_raises(IndexError, export_graphviz, clf, out, class_names=[])
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# Check precision error
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out = StringIO()
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assert_raises_regex(ValueError, "should be greater or equal",
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export_graphviz, clf, out, precision=-1)
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assert_raises_regex(ValueError, "should be an integer",
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export_graphviz, clf, out, precision="1")
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def test_friedman_mse_in_graphviz():
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clf = DecisionTreeRegressor(criterion="friedman_mse", random_state=0)
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clf.fit(X, y)
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dot_data = StringIO()
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export_graphviz(clf, out_file=dot_data)
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clf = GradientBoostingClassifier(n_estimators=2, random_state=0)
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clf.fit(X, y)
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for estimator in clf.estimators_:
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export_graphviz(estimator[0], out_file=dot_data)
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for finding in finditer("\[.*?samples.*?\]", dot_data.getvalue()):
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assert_in("friedman_mse", finding.group())
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def test_precision():
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rng_reg = RandomState(2)
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rng_clf = RandomState(8)
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for X, y, clf in zip(
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(rng_reg.random_sample((5, 2)),
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rng_clf.random_sample((1000, 4))),
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(rng_reg.random_sample((5, )),
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rng_clf.randint(2, size=(1000, ))),
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(DecisionTreeRegressor(criterion="friedman_mse", random_state=0,
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max_depth=1),
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DecisionTreeClassifier(max_depth=1, random_state=0))):
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clf.fit(X, y)
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for precision in (4, 3):
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dot_data = export_graphviz(clf, out_file=None, precision=precision,
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proportion=True)
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# With the current random state, the impurity and the threshold
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# will have the number of precision set in the export_graphviz
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# function. We will check the number of precision with a strict
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# equality. The value reported will have only 2 precision and
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# therefore, only a less equal comparison will be done.
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# check value
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for finding in finditer("value = \d+\.\d+", dot_data):
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assert_less_equal(
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len(search("\.\d+", finding.group()).group()),
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precision + 1)
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# check impurity
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if is_classifier(clf):
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pattern = "gini = \d+\.\d+"
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else:
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pattern = "friedman_mse = \d+\.\d+"
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# check impurity
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for finding in finditer(pattern, dot_data):
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assert_equal(len(search("\.\d+", finding.group()).group()),
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precision + 1)
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# check threshold
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for finding in finditer("<= \d+\.\d+", dot_data):
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assert_equal(len(search("\.\d+", finding.group()).group()),
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precision + 1)
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