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Improve DBSCAN epsilon identification (#7269)

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* Improve DBSCAN epsilon identification
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th0rntwig 2022-08-22 19:57:20 +02:00 committed by GitHub
parent 60ba921f56
commit 5ce1c69803
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1 changed files with 32 additions and 6 deletions
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38
freqtrade/freqai/data_kitchen.py
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@ -601,6 +601,8 @@ class FreqaiDataKitchen:
is an outlier. is an outlier.
""" """
from math import cos, sin
if predict: if predict:
train_ft_df = self.data_dictionary['train_features'] train_ft_df = self.data_dictionary['train_features']
pred_ft_df = self.data_dictionary['prediction_features'] pred_ft_df = self.data_dictionary['prediction_features']
@ -619,23 +621,47 @@ class FreqaiDataKitchen:
else: else:
def normalise_distances(distances):
normalised_distances = (distances - distances.min()) / \
(distances.max() - distances.min())
return normalised_distances
def rotate_point(origin, point, angle):
# rotate a point counterclockwise by a given angle (in radians)
# around a given origin
x = origin[0] + cos(angle) * (point[0] - origin[0]) - \
sin(angle) * (point[1] - origin[1])
y = origin[1] + sin(angle) * (point[0] - origin[0]) + \
cos(angle) * (point[1] - origin[1])
return (x, y)
MinPts = len(self.data_dictionary['train_features'].columns) * 2 MinPts = len(self.data_dictionary['train_features'].columns) * 2
# measure pairwise distances to train_features.shape[1]*2 nearest neighbours # measure pairwise distances to train_features.shape[1]*2 nearest neighbours
neighbors = NearestNeighbors( neighbors = NearestNeighbors(
n_neighbors=MinPts, n_jobs=self.thread_count) n_neighbors=MinPts, n_jobs=self.thread_count)
neighbors_fit = neighbors.fit(self.data_dictionary['train_features']) neighbors_fit = neighbors.fit(self.data_dictionary['train_features'])
distances, _ = neighbors_fit.kneighbors(self.data_dictionary['train_features']) distances, _ = neighbors_fit.kneighbors(self.data_dictionary['train_features'])
distances = np.sort(distances, axis=0) distances = np.sort(distances, axis=0).mean(axis=1)
index_ten_pct = int(len(distances[:, 1]) * 0.1)
distances = distances[index_ten_pct:, 1] normalised_distances = normalise_distances(distances)
epsilon = distances[-1] x_range = np.linspace(0, 1, len(distances))
line = np.linspace(normalised_distances[0],
normalised_distances[-1], len(normalised_distances))
deflection = np.abs(normalised_distances - line)
max_deflection_loc = np.where(deflection == deflection.max())[0][0]
origin = x_range[max_deflection_loc], line[max_deflection_loc]
point = x_range[max_deflection_loc], normalised_distances[max_deflection_loc]
rot_angle = np.pi / 4
elbow_loc = rotate_point(origin, point, rot_angle)
epsilon = elbow_loc[1] * (distances[-1] - distances[0]) + distances[0]
clustering = DBSCAN(eps=epsilon, min_samples=MinPts, clustering = DBSCAN(eps=epsilon, min_samples=MinPts,
n_jobs=int(self.thread_count)).fit( n_jobs=int(self.thread_count)).fit(
self.data_dictionary['train_features'] self.data_dictionary['train_features']
) )
logger.info(f'DBSCAN found eps of {epsilon}.') logger.info(f'DBSCAN found eps of {epsilon:.2f}.')
self.data['DBSCAN_eps'] = epsilon self.data['DBSCAN_eps'] = epsilon
self.data['DBSCAN_min_samples'] = MinPts self.data['DBSCAN_min_samples'] = MinPts
@ -698,7 +724,7 @@ class FreqaiDataKitchen:
if (len(do_predict) - do_predict.sum()) > 0: if (len(do_predict) - do_predict.sum()) > 0:
logger.info( logger.info(
f"DI tossed {len(do_predict) - do_predict.sum():.2f} predictions for " f"DI tossed {len(do_predict) - do_predict.sum()} predictions for "
"being too far from training data" "being too far from training data"
) )