470 lines
18 KiB
Python
470 lines
18 KiB
Python
"""
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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 textwrap import dedent
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from numpy.random import RandomState
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import pytest
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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, plot_tree, export_text
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from io import StringIO
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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 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 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 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="#ffffff"] ;\n' \
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'1 [label=<samples = 50.0%<br/>value = [1.0, 0.0]>, ' \
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'fillcolor="#e58139"] ;\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="#399de5"] ;\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 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 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="#ffffff"] ;\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 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="#ffffff"] ;\n' \
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'1 [label="samples = 3\\nvalue = [[3, 0, 0]\\n' \
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'[3, 0, 0]]", fillcolor="#e58139"] ;\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="#f1bd97"] ;\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="#e58139"] ;\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="#e58139"] ;\n' \
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'2 -> 4 ;\n' \
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'}'
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assert 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="#f2c09c"] ;\n' \
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'1 [label="mse = 0.0\\nsamples = 3\\nvalue = -1.0", ' \
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'fillcolor="#ffffff"] ;\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="#e58139"] ;\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 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="#ffffff"] ;\n' \
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'}'
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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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with pytest.raises(NotFittedError):
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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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with pytest.raises(ValueError, match=message):
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export_graphviz(clf, None, 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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with pytest.raises(ValueError, match=message):
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export_graphviz(clf, None, feature_names=["a", "b", "c"])
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# Check error when argument is not an estimator
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message = "is not an estimator instance"
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with pytest.raises(TypeError, match=message):
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export_graphviz(clf.fit(X, y).tree_)
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# Check class_names error
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out = StringIO()
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with pytest.raises(IndexError):
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export_graphviz(clf, out, class_names=[])
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# Check precision error
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out = StringIO()
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with pytest.raises(ValueError, match="should be greater or equal"):
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export_graphviz(clf, out, precision=-1)
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with pytest.raises(ValueError, match="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(r"\[.*?samples.*?\]", dot_data.getvalue()):
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assert "friedman_mse" in 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(r"value = \d+\.\d+", dot_data):
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assert (
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len(search(r"\.\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 = r"gini = \d+\.\d+"
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else:
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pattern = r"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 (len(search(r"\.\d+", finding.group()).group()) ==
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precision + 1)
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# check threshold
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for finding in finditer(r"<= \d+\.\d+", dot_data):
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assert (len(search(r"\.\d+", finding.group()).group()) ==
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precision + 1)
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def test_export_text_errors():
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clf = DecisionTreeClassifier(max_depth=2, random_state=0)
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clf.fit(X, y)
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err_msg = "max_depth bust be >= 0, given -1"
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with pytest.raises(ValueError, match=err_msg):
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export_text(clf, max_depth=-1)
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err_msg = "feature_names must contain 2 elements, got 1"
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with pytest.raises(ValueError, match=err_msg):
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export_text(clf, feature_names=['a'])
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err_msg = "decimals must be >= 0, given -1"
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with pytest.raises(ValueError, match=err_msg):
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export_text(clf, decimals=-1)
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err_msg = "spacing must be > 0, given 0"
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with pytest.raises(ValueError, match=err_msg):
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export_text(clf, spacing=0)
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def test_export_text():
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clf = DecisionTreeClassifier(max_depth=2, random_state=0)
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clf.fit(X, y)
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expected_report = dedent("""
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|--- feature_1 <= 0.00
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| |--- class: -1
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|--- feature_1 > 0.00
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| |--- class: 1
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""").lstrip()
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assert export_text(clf) == expected_report
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# testing that leaves at level 1 are not truncated
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assert export_text(clf, max_depth=0) == expected_report
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# testing that the rest of the tree is truncated
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assert export_text(clf, max_depth=10) == expected_report
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expected_report = dedent("""
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|--- b <= 0.00
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| |--- class: -1
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|--- b > 0.00
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| |--- class: 1
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""").lstrip()
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assert export_text(clf, feature_names=['a', 'b']) == expected_report
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expected_report = dedent("""
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|--- feature_1 <= 0.00
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| |--- weights: [3.00, 0.00] class: -1
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|--- feature_1 > 0.00
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| |--- weights: [0.00, 3.00] class: 1
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""").lstrip()
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assert export_text(clf, show_weights=True) == expected_report
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expected_report = dedent("""
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|- feature_1 <= 0.00
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| |- class: -1
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|- feature_1 > 0.00
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| |- class: 1
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""").lstrip()
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assert export_text(clf, spacing=1) == expected_report
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X_l = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1], [-1, 1]]
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y_l = [-1, -1, -1, 1, 1, 1, 2]
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clf = DecisionTreeClassifier(max_depth=4, random_state=0)
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clf.fit(X_l, y_l)
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expected_report = dedent("""
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|--- feature_1 <= 0.00
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| |--- class: -1
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|--- feature_1 > 0.00
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| |--- truncated branch of depth 2
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""").lstrip()
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assert export_text(clf, max_depth=0) == expected_report
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X_mo = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]]
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y_mo = [[-1, -1], [-1, -1], [-1, -1], [1, 1], [1, 1], [1, 1]]
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reg = DecisionTreeRegressor(max_depth=2, random_state=0)
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reg.fit(X_mo, y_mo)
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expected_report = dedent("""
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|--- feature_1 <= 0.0
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| |--- value: [-1.0, -1.0]
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|--- feature_1 > 0.0
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| |--- value: [1.0, 1.0]
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""").lstrip()
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assert export_text(reg, decimals=1) == expected_report
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assert export_text(reg, decimals=1, show_weights=True) == expected_report
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X_single = [[-2], [-1], [-1], [1], [1], [2]]
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reg = DecisionTreeRegressor(max_depth=2, random_state=0)
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reg.fit(X_single, y_mo)
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expected_report = dedent("""
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|--- first <= 0.0
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| |--- value: [-1.0, -1.0]
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|--- first > 0.0
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| |--- value: [1.0, 1.0]
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""").lstrip()
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assert export_text(reg, decimals=1,
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feature_names=['first']) == expected_report
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assert export_text(reg, decimals=1, show_weights=True,
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feature_names=['first']) == expected_report
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def test_plot_tree_entropy(pyplot):
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# mostly smoke tests
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# Check correctness of export_graphviz for criterion = entropy
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clf = DecisionTreeClassifier(max_depth=3,
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min_samples_split=2,
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criterion="entropy",
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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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feature_names = ['first feat', 'sepal_width']
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nodes = plot_tree(clf, feature_names=feature_names)
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assert len(nodes) == 3
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assert nodes[0].get_text() == ("first feat <= 0.0\nentropy = 1.0\n"
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"samples = 6\nvalue = [3, 3]")
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assert nodes[1].get_text() == "entropy = 0.0\nsamples = 3\nvalue = [3, 0]"
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assert nodes[2].get_text() == "entropy = 0.0\nsamples = 3\nvalue = [0, 3]"
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def test_plot_tree_gini(pyplot):
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# mostly smoke tests
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# Check correctness of export_graphviz for criterion = gini
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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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feature_names = ['first feat', 'sepal_width']
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nodes = plot_tree(clf, feature_names=feature_names)
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assert len(nodes) == 3
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assert nodes[0].get_text() == ("first feat <= 0.0\ngini = 0.5\n"
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"samples = 6\nvalue = [3, 3]")
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assert nodes[1].get_text() == "gini = 0.0\nsamples = 3\nvalue = [3, 0]"
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assert nodes[2].get_text() == "gini = 0.0\nsamples = 3\nvalue = [0, 3]"
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# FIXME: to be removed in 1.0
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def test_plot_tree_rotate_deprecation(pyplot):
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tree = DecisionTreeClassifier()
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tree.fit(X, y)
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# test that a warning is raised when rotate is used.
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match = (r"'rotate' has no effect and is deprecated in 0.23. "
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r"It will be removed in 1.0 \(renaming of 0.25\).")
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with pytest.warns(FutureWarning, match=match):
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plot_tree(tree, rotate=True)
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def test_not_fitted_tree(pyplot):
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# Testing if not fitted tree throws the correct error
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clf = DecisionTreeRegressor()
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with pytest.raises(NotFittedError):
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plot_tree(clf)
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