本文整理汇总了Python中numpy.testing.assert_approx_equal方法的典型用法代码示例。如果您正苦于以下问题：Python testing.assert_approx_equal方法的具体用法？Python testing.assert_approx_equal怎么用？Python testing.assert_approx_equal使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类numpy.testing的用法示例。
在下文中一共展示了testing.assert_approx_equal方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_non_convex_big_sigma
# 需要导入模块: from numpy import testing [as 别名]
# 或者: from numpy.testing import assert_approx_equal [as 别名]
def test_non_convex_big_sigma(self):
# Setup workspace with new sigma
opts = {'verbose': False, 'sigma': 5}
self.model.setup(P=self.P, q=self.q, A=self.A, l=self.l, u=self.u, **opts)
# Solve problem
res = self.model.solve()
# Assert close
self.assertEqual(res.info.status_val, constant('OSQP_NON_CVX'))
nptest.assert_approx_equal(res.info.obj_val, np.nan) 示例2: test_nan
# 需要导入模块: from numpy import testing [as 别名]
# 或者: from numpy.testing import assert_approx_equal [as 别名]
def test_nan(self):
nptest.assert_approx_equal(constant('OSQP_NAN'), np.nan) 示例3: test_algorithms
# 需要导入模块: from numpy import testing [as 别名]
# 或者: from numpy.testing import assert_approx_equal [as 别名]
def test_algorithms(self):
# Test different algorithms hyperoptimization and fitting results
# Hyperparameter optimization is based on randomized grid search, so pass criteria is not stringent
np.random.seed(42)
# Specify expected mean power, R2 and RMSE from the fits
required_metrics = {'etr': (0.999852, 130.0),
'gbm': (0.999999, 30.0),
'gam': (0.983174, 1330.0)}
# Loop through algorithms
for a in required_metrics.keys():
ml = MachineLearningSetup(a) # Setup ML object
# Perform randomized grid search only once for efficiency
ml.hyper_optimize(self.X, self.y, n_iter_search = 1, report = False, cv = KFold(n_splits = 2))
# Predict power based on model results
y_pred = ml.random_search.predict(self.X)
# Compute performance metrics which we'll test
corr = np.corrcoef(self.y, y_pred)[0,1] # Correlation between predicted and actual power
rmse = np.sqrt(mean_squared_error(self.y, y_pred)) # RMSE between predicted and actual power
# Mean power in GW is within 3 decimal places
nptest.assert_approx_equal(self.y.sum()/1e6, y_pred.sum()/1e6, significant = 3,
err_msg="Sum of predicted and actual power for {} not close enough".format(a))
# Test correlation of model fit
nptest.assert_approx_equal(corr, required_metrics[a][0], significant = 4,
err_msg="Correlation between {} features and response is wrong".format(a))
# Test RMSE of model fit
self.assertLess(rmse, required_metrics[a][1], "RMSE of {} fit is too high".format(a))