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Refactor Python tests. (#3897)

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* Deprecate nose tests.
* Format python tests.
This commit is contained in:
Jiaming Yuan
2018-11-15 13:56:33 +13:00
committed by GitHub
parent c76d993681
commit 2ea0f887c1
23 changed files with 302 additions and 225 deletions
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38
tests/python/test_linear.py
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@@ -3,6 +3,8 @@ from __future__ import print_function
import numpy as np
import testing as tm
import unittest
import pytest
import xgboost as xgb
try:
@@ -22,13 +24,16 @@ def is_float(s):
def xgb_get_weights(bst):
return np.array([float(s) for s in bst.get_dump()[0].split() if is_float(s)])
return np.array([float(s) for s in bst.get_dump()[0].split() if
is_float(s)])
def assert_regression_result(results, tol):
regression_results = [r for r in results if r["param"]["objective"] == "reg:linear"]
regression_results = [r for r in results if
r["param"]["objective"] == "reg:linear"]
for res in regression_results:
X = scale(res["dataset"].X, with_mean=isinstance(res["dataset"].X, np.ndarray))
X = scale(res["dataset"].X,
with_mean=isinstance(res["dataset"].X, np.ndarray))
y = res["dataset"].y
reg_alpha = res["param"]["alpha"]
reg_lambda = res["param"]["lambda"]
@@ -38,14 +43,16 @@ def assert_regression_result(results, tol):
l1_ratio=reg_alpha / (reg_alpha + reg_lambda))
enet.fit(X, y)
enet_pred = enet.predict(X)
assert np.isclose(weights, enet.coef_, rtol=tol, atol=tol).all(), (weights, enet.coef_)
assert np.isclose(weights, enet.coef_, rtol=tol,
atol=tol).all(), (weights, enet.coef_)
assert np.isclose(enet_pred, pred, rtol=tol, atol=tol).all(), (
res["dataset"].name, enet_pred[:5], pred[:5])
# TODO: More robust classification tests
def assert_classification_result(results):
classification_results = [r for r in results if r["param"]["objective"] != "reg:linear"]
classification_results = [r for r in results if
r["param"]["objective"] != "reg:linear"]
for res in classification_results:
# Check accuracy is reasonable
assert res["eval"][-1] < 0.5, (res["dataset"].name, res["eval"][-1])
@@ -56,25 +63,26 @@ class TestLinear(unittest.TestCase):
datasets = ["Boston", "Digits", "Cancer", "Sparse regression",
"Boston External Memory"]
@pytest.mark.skipif(**tm.no_sklearn())
def test_coordinate(self):
tm._skip_if_no_sklearn()
variable_param = {'booster': ['gblinear'], 'updater': ['coord_descent'], 'eta': [0.5],
'top_k': [10], 'tolerance': [1e-5], 'nthread': [2],
variable_param = {'booster': ['gblinear'], 'updater':
['coord_descent'], 'eta': [0.5], 'top_k':
[10], 'tolerance': [1e-5], 'nthread': [2],
'alpha': [.005, .1], 'lambda': [.005],
'feature_selector': ['cyclic', 'shuffle', 'greedy', 'thrifty']
}
'feature_selector': ['cyclic', 'shuffle',
'greedy', 'thrifty']}
for param in parameter_combinations(variable_param):
results = run_suite(param, 200, self.datasets, scale_features=True)
assert_regression_result(results, 1e-2)
assert_classification_result(results)
@pytest.mark.skipif(**tm.no_sklearn())
def test_shotgun(self):
tm._skip_if_no_sklearn()
variable_param = {'booster': ['gblinear'], 'updater': ['shotgun'], 'eta': [0.5],
'top_k': [10], 'tolerance': [1e-5], 'nthread': [2],
variable_param = {'booster': ['gblinear'], 'updater':
['shotgun'], 'eta': [0.5], 'top_k': [10],
'tolerance': [1e-5], 'nthread': [2],
'alpha': [.005, .1], 'lambda': [.005],
'feature_selector': ['cyclic', 'shuffle']
}
'feature_selector': ['cyclic', 'shuffle']}
for param in parameter_combinations(variable_param):
results = run_suite(param, 200, self.datasets, True)
assert_regression_result(results, 1e-2)