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Improve DBSCAN epsilon identification

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elintornquist 2022-08-21 17:24:57 +02:00
parent 085f81ec9e
commit 6855727f5c
1 changed files with 26 additions and 4 deletions
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30
freqtrade/freqai/data_kitchen.py
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@ -601,6 +601,8 @@ class FreqaiDataKitchen:
is an outlier.
"""
from math import sin, cos
if predict:
train_ft_df = self.data_dictionary['train_features']
pred_ft_df = self.data_dictionary['prediction_features']
@ -619,16 +621,35 @@ class FreqaiDataKitchen:
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
# measure pairwise distances to train_features.shape[1]*2 nearest neighbours
neighbors = NearestNeighbors(
n_neighbors=MinPts, n_jobs=self.thread_count)
neighbors_fit = neighbors.fit(self.data_dictionary['train_features'])
distances, _ = neighbors_fit.kneighbors(self.data_dictionary['train_features'])
distances = np.sort(distances, axis=0)
index_ten_pct = int(len(distances[:, 1]) * 0.1)
distances = distances[index_ten_pct:, 1]
epsilon = distances[-1]
distances = np.sort(distances, axis=0).mean(axis=1)
normalised_distances = normalise_distances(distances)
x_range = np.linspace(0, 1, len(distances))
line = np.linspace(normalise_distances[0], normalise_distances[-1], len(normalise_distances))
deflection = np.abs(normalise_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,
n_jobs=int(self.thread_count)).fit(
@ -654,6 +675,7 @@ class FreqaiDataKitchen:
logger.info(
f"DBSCAN tossed {dropped_points.sum()}"
f" train points from {len(clustering.labels_)}"
f" ({np.round(dropped_points.sum()/len(clustering.labels_),0)}%)"
)
return