def test_linear_regression_persistence(self):
model = LinearRegression(['a', 'b', 'c'], fit_intercept=False)
model.fit(self.train_x, self.train_y)
desc = model.save()
new_model = LinearRegression.load(desc)
calculated_y = new_model.predict(self.predict_x)
expected_y = model.predict(self.predict_x)
np.testing.assert_array_almost_equal(calculated_y, expected_y)
def test_load_model(self):
model = LinearRegression(['a', 'b', 'c'])
model.fit(self.trained_x, self.trained_y)
model_desc = model.save()
new_model = load_model(model_desc)
np.testing.assert_array_almost_equal(model.predict(self.predict_x),
new_model.predict(self.predict_x))
self.assertEqual(model.features, new_model.features)
self.assertEqual(model.trained_time, new_model.trained_time)
import numpy as np
import pandas as pd
from alphamind.api import *
from alphamind.data.dbmodel.models import Models
from alphamind.model.linearmodel import LinearRegression
engine = SqlEngine(
'postgresql+psycopg2://postgres:[email protected]/alpha')
x = np.random.randn(1000, 3)
y = np.random.randn(1000)
model = LinearRegression(['a', 'b', 'c'])
model.fit(x, y)
model_desc = model.save()
df = pd.DataFrame()
new_row = dict(trade_date='2017-09-05',
portfolio_name='test',
model_type='LinearRegression',
version=1,
model_desc=model_desc,
update_time=arrow.now().format())
df = df.append([new_row])
df.to_sql(Models.__table__.name,
engine.engine,
if_exists='append',