1f9a57d17b
Previously, we use `libsvm` as default when format is not specified. However, the dmlc data parser is not particularly robust against errors, and the most common type of error is undefined format. Along with which, we will recommend users to use other data loader instead. We will continue the maintenance of the parsers as it's currently used for many internal tests including federated learning.
97 lines
3.3 KiB
Python
97 lines
3.3 KiB
Python
import json
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import numpy as np
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import pytest
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import xgboost as xgb
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from xgboost import testing as tm
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try:
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import matplotlib
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matplotlib.use('Agg')
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from graphviz import Source
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from matplotlib.axes import Axes
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except ImportError:
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pass
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pytestmark = pytest.mark.skipif(**tm.no_multiple(tm.no_matplotlib(),
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tm.no_graphviz()))
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class TestPlotting:
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def test_plotting(self):
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m, _ = tm.load_agaricus(__file__)
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booster = xgb.train({'max_depth': 2, 'eta': 1,
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'objective': 'binary:logistic'}, m,
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num_boost_round=2)
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ax = xgb.plot_importance(booster)
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assert isinstance(ax, Axes)
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assert ax.get_title() == 'Feature importance'
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assert ax.get_xlabel() == 'F score'
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assert ax.get_ylabel() == 'Features'
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assert len(ax.patches) == 4
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ax = xgb.plot_importance(booster, color='r',
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title='t', xlabel='x', ylabel='y')
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assert isinstance(ax, Axes)
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assert ax.get_title() == 't'
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assert ax.get_xlabel() == 'x'
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assert ax.get_ylabel() == 'y'
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assert len(ax.patches) == 4
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for p in ax.patches:
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assert p.get_facecolor() == (1.0, 0, 0, 1.0) # red
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ax = xgb.plot_importance(booster, color=['r', 'r', 'b', 'b'],
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title=None, xlabel=None, ylabel=None)
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assert isinstance(ax, Axes)
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assert ax.get_title() == ''
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assert ax.get_xlabel() == ''
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assert ax.get_ylabel() == ''
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assert len(ax.patches) == 4
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assert ax.patches[0].get_facecolor() == (1.0, 0, 0, 1.0) # red
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assert ax.patches[1].get_facecolor() == (1.0, 0, 0, 1.0) # red
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assert ax.patches[2].get_facecolor() == (0, 0, 1.0, 1.0) # blue
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assert ax.patches[3].get_facecolor() == (0, 0, 1.0, 1.0) # blue
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g = xgb.to_graphviz(booster, num_trees=0)
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assert isinstance(g, Source)
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ax = xgb.plot_tree(booster, num_trees=0)
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assert isinstance(ax, Axes)
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def test_importance_plot_lim(self):
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np.random.seed(1)
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dm = xgb.DMatrix(np.random.randn(100, 100), label=[0, 1] * 50)
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bst = xgb.train({}, dm)
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assert len(bst.get_fscore()) == 71
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ax = xgb.plot_importance(bst)
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assert ax.get_xlim() == (0., 11.)
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assert ax.get_ylim() == (-1., 71.)
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ax = xgb.plot_importance(bst, xlim=(0, 5), ylim=(10, 71))
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assert ax.get_xlim() == (0., 5.)
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assert ax.get_ylim() == (10., 71.)
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def run_categorical(self, tree_method: str) -> None:
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X, y = tm.make_categorical(1000, 31, 19, onehot=False)
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reg = xgb.XGBRegressor(
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enable_categorical=True, n_estimators=10, tree_method=tree_method
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)
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reg.fit(X, y)
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trees = reg.get_booster().get_dump(dump_format="json")
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for tree in trees:
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j_tree = json.loads(tree)
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assert "leaf" in j_tree.keys() or isinstance(
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j_tree["split_condition"], list
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)
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graph = xgb.to_graphviz(reg, num_trees=len(j_tree) - 1)
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assert isinstance(graph, Source)
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ax = xgb.plot_tree(reg, num_trees=len(j_tree) - 1)
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assert isinstance(ax, Axes)
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@pytest.mark.skipif(**tm.no_pandas())
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def test_categorical(self) -> None:
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self.run_categorical("approx")
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