def test_main_empty(mock_save_results, mock_fetch_data):
mock_fetch_data.return_value = t.inputs_regression(include_nominal=True,
limit_to=0)
main()
output = json.loads(mock_save_results.call_args[0][0])
assert output == {}
def test_aggregate(mock_save_results, mock_get_results, mock_fetch_data):
# run partial jobs
inputs_1 = t.inputs_regression(limit_from=0,
limit_to=8,
include_nominal=True)
mock_fetch_data.return_value = inputs_1
intermediate()
output_1 = mock_save_results.call_args[0][0]
inputs_2 = t.inputs_regression(limit_from=8,
limit_to=20,
include_nominal=True)
mock_fetch_data.return_value = inputs_2
intermediate()
output_2 = mock_save_results.call_args[0][0]
mock_get_results.side_effect = [
mock.MagicMock(data=output_1, error=''),
mock.MagicMock(data=output_2, error=''),
]
# run computations
aggregate(['1', '2'])
output_agg = json.loads(mock_save_results.call_args[0][0])
beta_agg = {k: v['coef'] for k, v in output_agg.items()}
# calculate coefficients from single-node regression
mock_fetch_data.return_value = t.inputs_regression(limit_to=20,
include_nominal=True)
main()
output_single = json.loads(mock_save_results.call_args[0][0])
beta_single = {k: v['coef'] for k, v in output_single.items()}
assert t.round_dict(beta_agg) == t.round_dict(beta_single)
def test_main_logistic(mock_save_results, mock_fetch_data):
mock_fetch_data.return_value = t.inputs_classification(
limit_to=50, include_nominal=True)
main()
output = json.loads(mock_save_results.call_args[0][0])
assert t.round_dict(fx.output_classification()) == t.round_dict(output)
def test_main_logistic_single_category(mock_exit, mock_save_error,
mock_fetch_data):
data = t.inputs_classification(limit_to=5, include_nominal=True)
# single output
data['data']['dependent'][0]['series'] = len(
data['data']['dependent'][0]['series']) * ['AD']
mock_fetch_data.return_value = data
main()
assert mock_save_error.call_args[0] == (
'Not enough data to apply logistic regression.', )
def main():
'''
knn model
由于模型训练参数速度较慢，大约2个小时，这里可以选择是否选择重新跑参数，默认为加载原有的参数
predict_from_params = False 则表示重新跑参数
'''
# knn.main(predict_from_params=False)
linear_regression.main()
'''
sarimax 时间序列模型
需要三个小时跑模型
'''
sarimax_run.main()
'''
两个模型结果融合
'''
linear_sarimax_ensemble.linear_sarimax_ensemble()
def test_aggregate_single(mock_save_results, mock_get_results,
mock_fetch_data):
"""Aggregation on single node should give same results as ordinary linear regression."""
# run partial jobs
inputs = t.inputs_regression(limit_from=0, limit_to=20)
mock_fetch_data.return_value = inputs
intermediate()
output = mock_save_results.call_args[0][0]
mock_get_results.side_effect = [
mock.MagicMock(data=output, error=''),
]
# run computations
aggregate(['1'])
output_agg = json.loads(mock_save_results.call_args[0][0])
# calculate coefficients from single-node regression
mock_fetch_data.return_value = t.inputs_regression(limit_to=20)
main()
output_single = json.loads(mock_save_results.call_args[0][0])
assert t.round_dict(output_agg) == t.round_dict(output_single)
def test_linear_regression(self): linear_regression.main([None, "--train_steps=1"])
import logistic_regression as lo
import linear_regression as lg
import neural_network as nn
lg.main()
print("\n")
lo.main()
print("\n")
nn.main()
print("\n")
def train_linear_reg(X, y, tscv):
import linear_regression as lin_reg
lin_reg.main(X, y, tscv.split(X))
def test_main(mock_save_results, mock_fetch_data):
mock_fetch_data.return_value = t.inputs_regression(include_nominal=True)
main()
output = json.loads(mock_save_results.call_args[0][0])
assert t.round_dict(fx.output_regression()) == t.round_dict(output)
def test_main(mock_save_results, mock_fetch_data):
mock_fetch_data.return_value = fx.inputs
main()
output = json.loads(mock_save_results.call_args[0][0])
assert _round_dict(fx.outputs) == _round_dict(output)
def test_linear_regression(self): linear_regression.main([None, "--train_steps=1"])
def main(datafile="datafile.csv"):
wine_data, wine_features = import_data.import_data(datafile)
linear_regression.main(wine_data, wine_features)
knn_regression.main(wine_data)
polynomial_model.main(wine_data)
polynomial_model_bayesian.main(wine_data)