def get_best(self):
""" Produce a best instance of this strategy.
Args:
void.
Returns:
self: Euler instance. With the params and model having the
highest score among all combinations.
"""
# Log enter.
logger.info("Euler: Selecting best for %s.", self.instrument)
# Initialize the scores array and strategy parameters.
scores = []
strategy_params = util.get_euler_params()
# TODO: Do more than 1 run.
# Run for all predictive models.
counter = 0
for model in self.all_models:
scores_row = []
model_params = util.get_model_params(model)
# Try different model parameters.
for model_param in model_params:
scores_col = []
model = self.learner.build_model(model, 0.9, **model_param)
pred, _ = self.learner.test_model(model)
# And different strategy parameters, e.g. threshold.
for strategy_param in strategy_params:
self.set_params(**strategy_param)
# Do the dry run.
plot_name = '{0}_{1}.png'.format(self.instrument, counter)
balance = self.dry_run(pred, export_plot=plot_name)
# Determine the quality of the params via score.
scores_col.append(util.get_strategy_score(balance))
counter += 1
scores_row.append(scores_col)
scores.append(scores_row)
# Get the best and set the parameters to the best.
best = np.unravel_index(np.argmax(scores), np.array(scores).shape)
model = self.all_models[best[0]]
model_param = util.get_model_params(model)[best[1]]
model = self.learner.build_model(model, 1, **model_param)
logger.info("Best score is: %s.", str(np.array(scores)[best]))
self.set_params(**strategy_params[best[2]])
self.model = model
return self
def test_params(self):
""" Make sure the parameter spaces are correct."""
# First up, a tree model.
tree_model = tree.DecisionTreeClassifier()
model_params = util.get_model_params(tree_model)
self.assertEqual(len(model_params), 20)
self.assertEqual(model_params[0]['max_depth'], 4)
self.assertEqual(model_params[-3]['min_samples_split'], 10)
# Now params for strategy Euler.
strategy_params = util.get_euler_params()
self.assertEqual(len(strategy_params), 16)
self.assertEqual(strategy_params[3]['unit_shape'], common.UNIT_LOG)
self.assertEqual(strategy_params[-2]['threshold'], 100.0)
return