Merge pull request #82 from titanscouting/improve-clustering

Added new clustering tools and reorganize existing ones
This commit is contained in:
Arthur Lu 2021-09-27 15:32:12 -07:00 committed by GitHub
commit d57745547f
4 changed files with 80 additions and 3 deletions

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@ -5,6 +5,7 @@ from sklearn import metrics
from tra_analysis import Analysis as an
from tra_analysis import Array
from tra_analysis import ClassificationMetric
from tra_analysis import Clustering
from tra_analysis import CorrelationTest
from tra_analysis import Fit
from tra_analysis import KNN
@ -230,4 +231,18 @@ def test_equation():
"-(sgn(cos(PI/4)))": -1,
}
for key in list(correctParse.keys()):
assert parser.eval(key) == correctParse[key]
assert parser.eval(key) == correctParse[key]
def test_clustering():
normalizer = sklearn.preprocessing.Normalizer()
data = X = np.array([[1, 2], [2, 2], [2, 3], [8, 7], [8, 8], [25, 80]])
assert Clustering.dbscan(data, eps=3, min_samples=2).tolist() == [0, 0, 0, 1, 1, -1]
assert Clustering.dbscan(data, normalizer=normalizer, eps=3, min_samples=2).tolist() == [0, 0, 0, 0, 0, 0]
data = np.array([[1, 1], [2, 1], [1, 0], [4, 7], [3, 5], [3, 6]])
assert Clustering.spectral(data, n_clusters=2, assign_labels='discretize', random_state=0).tolist() == [1, 1, 1, 0, 0, 0]
assert Clustering.spectral(data, normalizer=normalizer, n_clusters=2, assign_labels='discretize', random_state=0).tolist() == [0, 1, 1, 0, 0, 0]

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@ -599,7 +599,7 @@ def npmin(data):
def npmax(data):
return np.amax(data)
""" need to decide what to do with this function
def kmeans(data, n_clusters=8, init="k-means++", n_init=10, max_iter=300, tol=0.0001, precompute_distances="auto", verbose=0, random_state=None, copy_x=True, n_jobs=None, algorithm="auto"):
kernel = sklearn.cluster.KMeans(n_clusters = n_clusters, init = init, n_init = n_init, max_iter = max_iter, tol = tol, precompute_distances = precompute_distances, verbose = verbose, random_state = random_state, copy_x = copy_x, n_jobs = n_jobs, algorithm = algorithm)
@ -608,7 +608,7 @@ def kmeans(data, n_clusters=8, init="k-means++", n_init=10, max_iter=300, tol=0.
centers = kernel.cluster_centers_
return centers, predictions
"""
def pca(data, n_components = None, copy = True, whiten = False, svd_solver = "auto", tol = 0.0, iterated_power = "auto", random_state = None):
kernel = sklearn.decomposition.PCA(n_components = n_components, copy = copy, whiten = whiten, svd_solver = svd_solver, tol = tol, iterated_power = iterated_power, random_state = random_state)

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@ -0,0 +1,61 @@
# Titan Robotics Team 2022: Clustering submodule
# Written by Arthur Lu
# Notes:
# this should be imported as a python module using 'from tra_analysis import Clustering'
# setup:
__version__ = "2.0.1"
# changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog:
2.0.1:
- added normalization preprocessing to clustering, expects instance of sklearn.preprocessing.Normalizer()
2.0.0:
- added dbscan clustering algo
- added spectral clustering algo
1.0.0:
- created this submodule
- copied kmeans clustering from Analysis
"""
__author__ = (
"Arthur Lu <learthurgo@gmail.com>",
)
__all__ = [
"kmeans",
"dbscan",
"spectral",
]
import sklearn
def kmeans(data, normalizer = None, n_clusters=8, init="k-means++", n_init=10, max_iter=300, tol=0.0001, precompute_distances="auto", verbose=0, random_state=None, copy_x=True, n_jobs=None, algorithm="auto"):
if normalizer != None:
data = normalizer.transform(data)
kernel = sklearn.cluster.KMeans(n_clusters = n_clusters, init = init, n_init = n_init, max_iter = max_iter, tol = tol, precompute_distances = precompute_distances, verbose = verbose, random_state = random_state, copy_x = copy_x, n_jobs = n_jobs, algorithm = algorithm)
kernel.fit(data)
predictions = kernel.predict(data)
centers = kernel.cluster_centers_
return centers, predictions
def dbscan(data, normalizer=None, eps=0.5, min_samples=5, metric='euclidean', metric_params=None, algorithm='auto', leaf_size=30, p=None, n_jobs=None):
if normalizer != None:
data = normalizer.transform(data)
model = sklearn.cluster.DBSCAN(eps = eps, min_samples = min_samples, metric = metric, metric_params = metric_params, algorithm = algorithm, leaf_size = leaf_size, p = p, n_jobs = n_jobs).fit(data)
return model.labels_
def spectral(data, normalizer=None, n_clusters=8, eigen_solver=None, n_components=None, random_state=None, n_init=10, gamma=1.0, affinity='rbf', n_neighbors=10, eigen_tol=0.0, assign_labels='kmeans', degree=3, coef0=1, kernel_params=None, n_jobs=None, verbose=False):
if normalizer != None:
data = normalizer.transform(data)
model = sklearn.cluster.SpectralClustering(n_clusters = n_clusters, eigen_solver = eigen_solver, n_components = n_components, random_state = random_state, n_init = n_init, gamma = gamma, affinity = affinity, n_neighbors = n_neighbors, eigen_tol = eigen_tol, assign_labels = assign_labels, degree = degree, coef0 = coef0, kernel_params = kernel_params, n_jobs = n_jobs).fit(data)
return model.labels_

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@ -60,6 +60,7 @@ __all__ = [
from . import Analysis as Analysis
from .Array import Array
from .ClassificationMetric import ClassificationMetric
from . import Clustering
from . import CorrelationTest
from .equation import Expression
from . import Fit