513 lines
17 KiB
Python
513 lines
17 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 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, 0.5, 0.5, 0.5]
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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(
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max_depth=3, min_samples_split=2, criterion="gini", random_state=2
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)
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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 = (
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"digraph Tree {\n"
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'node [shape=box, fontname="helvetica"] ;\n'
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'edge [fontname="helvetica"] ;\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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)
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assert contents1 == contents2
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# Test with feature_names
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contents1 = export_graphviz(
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clf, feature_names=["feature0", "feature1"], out_file=None
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)
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contents2 = (
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"digraph Tree {\n"
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'node [shape=box, fontname="helvetica"] ;\n'
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'edge [fontname="helvetica"] ;\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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)
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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 = (
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"digraph Tree {\n"
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'node [shape=box, fontname="helvetica"] ;\n'
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'edge [fontname="helvetica"] ;\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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)
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assert contents1 == contents2
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# Test plot_options
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contents1 = export_graphviz(
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clf,
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filled=True,
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impurity=False,
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proportion=True,
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special_characters=True,
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rounded=True,
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out_file=None,
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fontname="sans",
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)
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contents2 = (
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"digraph Tree {\n"
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'node [shape=box, style="filled, rounded", color="black", '
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'fontname="sans"] ;\n'
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'edge [fontname="sans"] ;\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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)
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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, class_names=True, out_file=None)
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contents2 = (
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"digraph Tree {\n"
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'node [shape=box, fontname="helvetica"] ;\n'
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'edge [fontname="helvetica"] ;\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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)
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assert contents1 == contents2
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# Test max_depth with plot_options
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contents1 = export_graphviz(
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clf, max_depth=0, filled=True, out_file=None, node_ids=True
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)
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contents2 = (
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"digraph Tree {\n"
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'node [shape=box, style="filled", color="black", '
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'fontname="helvetica"] ;\n'
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'edge [fontname="helvetica"] ;\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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)
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assert contents1 == contents2
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# Test multi-output with weighted samples
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clf = DecisionTreeClassifier(
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max_depth=2, min_samples_split=2, criterion="gini", random_state=2
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)
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clf = clf.fit(X, y2, sample_weight=w)
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contents1 = export_graphviz(clf, filled=True, impurity=False, out_file=None)
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contents2 = (
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"digraph Tree {\n"
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'node [shape=box, style="filled", color="black", '
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'fontname="helvetica"] ;\n'
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'edge [fontname="helvetica"] ;\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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)
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assert contents1 == contents2
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# Test regression output with plot_options
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clf = DecisionTreeRegressor(
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max_depth=3, min_samples_split=2, criterion="squared_error", random_state=2
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)
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clf.fit(X, y)
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contents1 = export_graphviz(
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clf,
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filled=True,
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leaves_parallel=True,
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out_file=None,
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rotate=True,
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rounded=True,
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fontname="sans",
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)
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contents2 = (
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"digraph Tree {\n"
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'node [shape=box, style="filled, rounded", color="black", '
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'fontname="sans"] ;\n'
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"graph [ranksep=equally, splines=polyline] ;\n"
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'edge [fontname="sans"] ;\n'
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"rankdir=LR ;\n"
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'0 [label="x[0] <= 0.0\\nsquared_error = 1.0\\nsamples = 6\\n'
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'value = 0.0", fillcolor="#f2c09c"] ;\n'
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'1 [label="squared_error = 0.0\\nsamples = 3\\'
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'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="squared_error = 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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)
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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 = (
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"digraph Tree {\n"
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'node [shape=box, style="filled", color="black", '
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'fontname="helvetica"] ;\n'
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'edge [fontname="helvetica"] ;\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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)
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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, 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, 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)), rng_clf.random_sample((1000, 4))),
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(rng_reg.random_sample((5,)), rng_clf.randint(2, size=(1000,))),
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(
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DecisionTreeRegressor(
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criterion="friedman_mse", random_state=0, max_depth=1
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),
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DecisionTreeClassifier(max_depth=1, random_state=0),
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),
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):
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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(
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clf, out_file=None, precision=precision, proportion=True
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)
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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):
|
||
|
|
assert len(search(r"\.\d+", finding.group()).group()) <= precision + 1
|
||
|
|
# check impurity
|
||
|
|
if is_classifier(clf):
|
||
|
|
pattern = r"gini = \d+\.\d+"
|
||
|
|
else:
|
||
|
|
pattern = r"friedman_mse = \d+\.\d+"
|
||
|
|
|
||
|
|
# check impurity
|
||
|
|
for finding in finditer(pattern, dot_data):
|
||
|
|
assert len(search(r"\.\d+", finding.group()).group()) == precision + 1
|
||
|
|
# check threshold
|
||
|
|
for finding in finditer(r"<= \d+\.\d+", dot_data):
|
||
|
|
assert len(search(r"\.\d+", finding.group()).group()) == precision + 1
|
||
|
|
|
||
|
|
|
||
|
|
def test_export_text_errors():
|
||
|
|
clf = DecisionTreeClassifier(max_depth=2, random_state=0)
|
||
|
|
clf.fit(X, y)
|
||
|
|
|
||
|
|
err_msg = "max_depth bust be >= 0, given -1"
|
||
|
|
with pytest.raises(ValueError, match=err_msg):
|
||
|
|
export_text(clf, max_depth=-1)
|
||
|
|
err_msg = "feature_names must contain 2 elements, got 1"
|
||
|
|
with pytest.raises(ValueError, match=err_msg):
|
||
|
|
export_text(clf, feature_names=["a"])
|
||
|
|
err_msg = "decimals must be >= 0, given -1"
|
||
|
|
with pytest.raises(ValueError, match=err_msg):
|
||
|
|
export_text(clf, decimals=-1)
|
||
|
|
err_msg = "spacing must be > 0, given 0"
|
||
|
|
with pytest.raises(ValueError, match=err_msg):
|
||
|
|
export_text(clf, spacing=0)
|
||
|
|
|
||
|
|
|
||
|
|
def test_export_text():
|
||
|
|
clf = DecisionTreeClassifier(max_depth=2, random_state=0)
|
||
|
|
clf.fit(X, y)
|
||
|
|
|
||
|
|
expected_report = dedent(
|
||
|
|
"""
|
||
|
|
|--- feature_1 <= 0.00
|
||
|
|
| |--- class: -1
|
||
|
|
|--- feature_1 > 0.00
|
||
|
|
| |--- class: 1
|
||
|
|
"""
|
||
|
|
).lstrip()
|
||
|
|
|
||
|
|
assert export_text(clf) == expected_report
|
||
|
|
# testing that leaves at level 1 are not truncated
|
||
|
|
assert export_text(clf, max_depth=0) == expected_report
|
||
|
|
# testing that the rest of the tree is truncated
|
||
|
|
assert export_text(clf, max_depth=10) == expected_report
|
||
|
|
|
||
|
|
expected_report = dedent(
|
||
|
|
"""
|
||
|
|
|--- b <= 0.00
|
||
|
|
| |--- class: -1
|
||
|
|
|--- b > 0.00
|
||
|
|
| |--- class: 1
|
||
|
|
"""
|
||
|
|
).lstrip()
|
||
|
|
assert export_text(clf, feature_names=["a", "b"]) == expected_report
|
||
|
|
|
||
|
|
expected_report = dedent(
|
||
|
|
"""
|
||
|
|
|--- feature_1 <= 0.00
|
||
|
|
| |--- weights: [3.00, 0.00] class: -1
|
||
|
|
|--- feature_1 > 0.00
|
||
|
|
| |--- weights: [0.00, 3.00] class: 1
|
||
|
|
"""
|
||
|
|
).lstrip()
|
||
|
|
assert export_text(clf, show_weights=True) == expected_report
|
||
|
|
|
||
|
|
expected_report = dedent(
|
||
|
|
"""
|
||
|
|
|- feature_1 <= 0.00
|
||
|
|
| |- class: -1
|
||
|
|
|- feature_1 > 0.00
|
||
|
|
| |- class: 1
|
||
|
|
"""
|
||
|
|
).lstrip()
|
||
|
|
assert export_text(clf, spacing=1) == expected_report
|
||
|
|
|
||
|
|
X_l = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1], [-1, 1]]
|
||
|
|
y_l = [-1, -1, -1, 1, 1, 1, 2]
|
||
|
|
clf = DecisionTreeClassifier(max_depth=4, random_state=0)
|
||
|
|
clf.fit(X_l, y_l)
|
||
|
|
expected_report = dedent(
|
||
|
|
"""
|
||
|
|
|--- feature_1 <= 0.00
|
||
|
|
| |--- class: -1
|
||
|
|
|--- feature_1 > 0.00
|
||
|
|
| |--- truncated branch of depth 2
|
||
|
|
"""
|
||
|
|
).lstrip()
|
||
|
|
assert export_text(clf, max_depth=0) == expected_report
|
||
|
|
|
||
|
|
X_mo = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]]
|
||
|
|
y_mo = [[-1, -1], [-1, -1], [-1, -1], [1, 1], [1, 1], [1, 1]]
|
||
|
|
|
||
|
|
reg = DecisionTreeRegressor(max_depth=2, random_state=0)
|
||
|
|
reg.fit(X_mo, y_mo)
|
||
|
|
|
||
|
|
expected_report = dedent(
|
||
|
|
"""
|
||
|
|
|--- feature_1 <= 0.0
|
||
|
|
| |--- value: [-1.0, -1.0]
|
||
|
|
|--- feature_1 > 0.0
|
||
|
|
| |--- value: [1.0, 1.0]
|
||
|
|
"""
|
||
|
|
).lstrip()
|
||
|
|
assert export_text(reg, decimals=1) == expected_report
|
||
|
|
assert export_text(reg, decimals=1, show_weights=True) == expected_report
|
||
|
|
|
||
|
|
X_single = [[-2], [-1], [-1], [1], [1], [2]]
|
||
|
|
reg = DecisionTreeRegressor(max_depth=2, random_state=0)
|
||
|
|
reg.fit(X_single, y_mo)
|
||
|
|
|
||
|
|
expected_report = dedent(
|
||
|
|
"""
|
||
|
|
|--- first <= 0.0
|
||
|
|
| |--- value: [-1.0, -1.0]
|
||
|
|
|--- first > 0.0
|
||
|
|
| |--- value: [1.0, 1.0]
|
||
|
|
"""
|
||
|
|
).lstrip()
|
||
|
|
assert export_text(reg, decimals=1, feature_names=["first"]) == expected_report
|
||
|
|
assert (
|
||
|
|
export_text(reg, decimals=1, show_weights=True, feature_names=["first"])
|
||
|
|
== expected_report
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
def test_plot_tree_entropy(pyplot):
|
||
|
|
# mostly smoke tests
|
||
|
|
# Check correctness of export_graphviz for criterion = entropy
|
||
|
|
clf = DecisionTreeClassifier(
|
||
|
|
max_depth=3, min_samples_split=2, criterion="entropy", random_state=2
|
||
|
|
)
|
||
|
|
clf.fit(X, y)
|
||
|
|
|
||
|
|
# Test export code
|
||
|
|
feature_names = ["first feat", "sepal_width"]
|
||
|
|
nodes = plot_tree(clf, feature_names=feature_names)
|
||
|
|
assert len(nodes) == 3
|
||
|
|
assert (
|
||
|
|
nodes[0].get_text()
|
||
|
|
== "first feat <= 0.0\nentropy = 1.0\nsamples = 6\nvalue = [3, 3]"
|
||
|
|
)
|
||
|
|
assert nodes[1].get_text() == "entropy = 0.0\nsamples = 3\nvalue = [3, 0]"
|
||
|
|
assert nodes[2].get_text() == "entropy = 0.0\nsamples = 3\nvalue = [0, 3]"
|
||
|
|
|
||
|
|
|
||
|
|
def test_plot_tree_gini(pyplot):
|
||
|
|
# mostly smoke tests
|
||
|
|
# Check correctness of export_graphviz for criterion = gini
|
||
|
|
clf = DecisionTreeClassifier(
|
||
|
|
max_depth=3, min_samples_split=2, criterion="gini", random_state=2
|
||
|
|
)
|
||
|
|
clf.fit(X, y)
|
||
|
|
|
||
|
|
# Test export code
|
||
|
|
feature_names = ["first feat", "sepal_width"]
|
||
|
|
nodes = plot_tree(clf, feature_names=feature_names)
|
||
|
|
assert len(nodes) == 3
|
||
|
|
assert (
|
||
|
|
nodes[0].get_text()
|
||
|
|
== "first feat <= 0.0\ngini = 0.5\nsamples = 6\nvalue = [3, 3]"
|
||
|
|
)
|
||
|
|
assert nodes[1].get_text() == "gini = 0.0\nsamples = 3\nvalue = [3, 0]"
|
||
|
|
assert nodes[2].get_text() == "gini = 0.0\nsamples = 3\nvalue = [0, 3]"
|
||
|
|
|
||
|
|
|
||
|
|
def test_not_fitted_tree(pyplot):
|
||
|
|
|
||
|
|
# Testing if not fitted tree throws the correct error
|
||
|
|
clf = DecisionTreeRegressor()
|
||
|
|
with pytest.raises(NotFittedError):
|
||
|
|
plot_tree(clf)
|