Refactor python tests (#3410)
* Add unit test utility * Refactor updater tests. Add coverage for histmaker.
This commit is contained in:
Rory Mitchell
GitHub
@@ -1,24 +1,17 @@
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from __future__ import print_function
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import itertools as it
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import numpy as np
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import sys
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import os
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import glob
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import testing as tm
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import unittest
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import xgboost as xgb
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try:
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from sklearn import metrics, datasets
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from sklearn.linear_model import ElasticNet
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from sklearn.preprocessing import scale
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from regression_test_utilities import run_suite, parameter_combinations
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except ImportError:
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None
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rng = np.random.RandomState(199)
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num_rounds = 1000
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def is_float(s):
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try:
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@@ -32,130 +25,53 @@ def xgb_get_weights(bst):
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return np.array([float(s) for s in bst.get_dump()[0].split() if is_float(s)])
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def check_ElasticNet(X, y, pred, tol, reg_alpha, reg_lambda, weights):
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enet = ElasticNet(alpha=reg_alpha + reg_lambda,
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l1_ratio=reg_alpha / (reg_alpha + reg_lambda))
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enet.fit(X, y)
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enet_pred = enet.predict(X)
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assert np.isclose(weights, enet.coef_, rtol=tol, atol=tol).all()
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assert np.isclose(enet_pred, pred, rtol=tol, atol=tol).all()
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def assert_regression_result(results, tol):
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regression_results = [r for r in results if r["param"]["objective"] == "reg:linear"]
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for res in regression_results:
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X = scale(res["dataset"].X, with_mean=isinstance(res["dataset"].X, np.ndarray))
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y = res["dataset"].y
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reg_alpha = res["param"]["alpha"]
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reg_lambda = res["param"]["lambda"]
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pred = res["bst"].predict(xgb.DMatrix(X))
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weights = xgb_get_weights(res["bst"])[1:]
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enet = ElasticNet(alpha=reg_alpha + reg_lambda,
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l1_ratio=reg_alpha / (reg_alpha + reg_lambda))
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enet.fit(X, y)
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enet_pred = enet.predict(X)
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assert np.isclose(weights, enet.coef_, rtol=tol, atol=tol).all(), (weights, enet.coef_)
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assert np.isclose(enet_pred, pred, rtol=tol, atol=tol).all(), (
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res["dataset"].name, enet_pred[:5], pred[:5])
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def train_diabetes(param_in):
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data = datasets.load_diabetes()
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X = scale(data.data)
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dtrain = xgb.DMatrix(X, label=data.target)
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param = {}
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param.update(param_in)
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bst = xgb.train(param, dtrain, num_rounds)
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xgb_pred = bst.predict(dtrain)
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check_ElasticNet(X, data.target, xgb_pred, 1e-2,
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param['alpha'], param['lambda'],
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xgb_get_weights(bst)[1:])
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def train_breast_cancer(param_in):
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data = datasets.load_breast_cancer()
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X = scale(data.data)
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dtrain = xgb.DMatrix(X, label=data.target)
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param = {'objective': 'binary:logistic'}
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param.update(param_in)
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bst = xgb.train(param, dtrain, num_rounds)
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xgb_pred = bst.predict(dtrain)
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xgb_score = metrics.accuracy_score(data.target, np.round(xgb_pred))
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assert xgb_score >= 0.8
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def train_classification(param_in):
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X, y = datasets.make_classification(random_state=rng)
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X = scale(X)
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dtrain = xgb.DMatrix(X, label=y)
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param = {'objective': 'binary:logistic'}
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param.update(param_in)
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bst = xgb.train(param, dtrain, num_rounds)
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xgb_pred = bst.predict(dtrain)
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xgb_score = metrics.accuracy_score(y, np.round(xgb_pred))
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assert xgb_score >= 0.8
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def train_classification_multi(param_in):
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num_class = 3
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X, y = datasets.make_classification(n_samples=100, random_state=rng,
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n_classes=num_class, n_informative=4,
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n_features=4, n_redundant=0)
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X = scale(X)
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dtrain = xgb.DMatrix(X, label=y)
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param = {'objective': 'multi:softmax', 'num_class': num_class}
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param.update(param_in)
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bst = xgb.train(param, dtrain, num_rounds)
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xgb_pred = bst.predict(dtrain)
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xgb_score = metrics.accuracy_score(y, np.round(xgb_pred))
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assert xgb_score >= 0.50
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def train_boston(param_in):
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data = datasets.load_boston()
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X = scale(data.data)
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dtrain = xgb.DMatrix(X, label=data.target)
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param = {}
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param.update(param_in)
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bst = xgb.train(param, dtrain, num_rounds)
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xgb_pred = bst.predict(dtrain)
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check_ElasticNet(X, data.target, xgb_pred, 1e-2,
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param['alpha'], param['lambda'],
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xgb_get_weights(bst)[1:])
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def train_external_mem(param_in):
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data = datasets.load_boston()
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X = scale(data.data)
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y = data.target
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param = {}
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param.update(param_in)
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dtrain = xgb.DMatrix(X, label=y)
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bst = xgb.train(param, dtrain, num_rounds)
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xgb_pred = bst.predict(dtrain)
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np.savetxt('tmptmp_1234.csv', np.hstack((y.reshape(len(y), 1), X)),
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delimiter=',', fmt='%10.9f')
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dtrain = xgb.DMatrix('tmptmp_1234.csv?format=csv&label_column=0#tmptmp_')
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bst = xgb.train(param, dtrain, num_rounds)
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xgb_pred_ext = bst.predict(dtrain)
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assert np.abs(xgb_pred_ext - xgb_pred).max() < 1e-3
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del dtrain, bst
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for f in glob.glob("tmptmp_*"):
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os.remove(f)
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# Enumerates all permutations of variable parameters
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def assert_updater_accuracy(linear_updater, variable_param):
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param = {'booster': 'gblinear', 'updater': linear_updater, 'eta': 1.,
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'top_k': 10, 'tolerance': 1e-5, 'nthread': 2}
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names = sorted(variable_param)
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combinations = it.product(*(variable_param[Name] for Name in names))
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for set in combinations:
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param_tmp = param.copy()
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for i, name in enumerate(names):
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param_tmp[name] = set[i]
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print(param_tmp, file=sys.stderr)
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train_boston(param_tmp)
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train_diabetes(param_tmp)
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train_classification(param_tmp)
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train_classification_multi(param_tmp)
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train_breast_cancer(param_tmp)
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if 'gpu' not in linear_updater:
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train_external_mem(param_tmp)
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# TODO: More robust classification tests
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def assert_classification_result(results):
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classification_results = [r for r in results if r["param"]["objective"] != "reg:linear"]
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for res in classification_results:
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# Check accuracy is reasonable
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assert res["eval"][-1] < 0.5, (res["dataset"].name, res["eval"][-1])
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class TestLinear(unittest.TestCase):
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def test_coordinate(self):
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tm._skip_if_no_sklearn()
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variable_param = {'alpha': [.005, .1], 'lambda': [.005],
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'feature_selector': ['cyclic', 'shuffle', 'greedy', 'thrifty']}
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assert_updater_accuracy('coord_descent', variable_param)
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variable_param = {'booster': ['gblinear'], 'updater': ['coord_descent'], 'eta': [0.5],
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'top_k': [10], 'tolerance': [1e-5], 'nthread': [2],
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'alpha': [.005, .1], 'lambda': [.005],
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'feature_selector': ['cyclic', 'shuffle', 'greedy', 'thrifty']
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}
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for param in parameter_combinations(variable_param):
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results = run_suite(param, 200, None, scale_features=True)
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assert_regression_result(results, 1e-2)
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assert_classification_result(results)
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def test_shotgun(self):
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tm._skip_if_no_sklearn()
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variable_param = {'alpha': [.005, .1], 'lambda': [.005, .1]}
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assert_updater_accuracy('shotgun', variable_param)
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variable_param = {'booster': ['gblinear'], 'updater': ['shotgun'], 'eta': [0.5],
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'top_k': [10], 'tolerance': [1e-5], 'nthread': [2],
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'alpha': [.005, .1], 'lambda': [.005],
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'feature_selector': ['cyclic', 'shuffle']
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}
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for param in parameter_combinations(variable_param):
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results = run_suite(param, 200, None, True)
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assert_regression_result(results, 1e-2)
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assert_classification_result(results)
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