def __init__(self):
self.incremental_kmeans = cluster.KMeans(
n_clusters=5, halflife=0.4, sigma=3, seed=0)
self.metric_ssw = metrics.cluster.SSW()
self.metric_cohesion = metrics.cluster.Cohesion()
self.metric_separation = metrics.cluster.Separation()
self.metric_ssb = metrics.cluster.SSB()
self.metric_bic = metrics.cluster.BIC()
self.metric_silhouette = metrics.cluster.Silhouette()
self.metric_xieBeni = metrics.cluster.XieBeni()
def __init__(self, chunk_size=10, n_clusters=2, **kwargs):
super().__init__()
self.time_stamp = 0
self.n_clusters = n_clusters
self.chunk_size = chunk_size
self.kwargs = kwargs
self._kmeans = cluster.KMeans(n_clusters=self.n_clusters,
**self.kwargs)
self._temp_chunk = {}
self.centers = {}
def predict_one(self, x):
micro_cluster_centers = {
i: self._get_micro_clustering_result()[i].center
for i in range(len(self._get_micro_clustering_result()))
}
kmeans = cluster.KMeans(n_clusters=self.n_macro_clusters,
seed=self.seed,
**self.kwargs)
for center in micro_cluster_centers.values():
kmeans = kmeans.learn_one(center)
self.centers = kmeans.centers
index, _ = self._get_closest_micro_cluster(
x, self._get_micro_clustering_result())
y = kmeans.predict_one(micro_cluster_centers[index])
return y
def predict_one(self, x):
micro_cluster_centers = {
i: result.center
for i, result in self._get_micro_clustering_result().items()
}
kmeans = cluster.KMeans(n_clusters=self.n_macro_clusters,
seed=self.seed,
**self.kwargs)
for center in micro_cluster_centers.values():
kmeans = kmeans.learn_one(center)
self.centers = kmeans.centers
index, _ = self._get_closest_micro_cluster(
x, self._get_micro_clustering_result())
try:
return kmeans.predict_one(micro_cluster_centers[index])
except KeyError:
return 0
def learn_one(self, x, sample_weight=None):
self.time_stamp += 1
index = self.time_stamp % self.chunk_size
if index == 0:
self._temp_chunk[self.chunk_size - 1] = x
elif index == 1:
self._temp_chunk = {0: x}
else:
self._temp_chunk[index - 1] = x
if index == 0:
kmeans_i = cluster.KMeans(n_clusters=self.n_clusters,
**self.kwargs)
for point_j in self._temp_chunk.values():
kmeans_i = kmeans_i.learn_one(point_j)
for center_j in kmeans_i.centers.values():
self._kmeans = self._kmeans.learn_one(center_j)
self.centers = self._kmeans.centers
return self
"estimator, check",
[
pytest.param(estimator, check, id=f"{estimator}:{check.__name__}")
for estimator in list(get_all_estimators()) + [
feature_extraction.TFIDF(),
linear_model.LogisticRegression(),
preprocessing.StandardScaler() | linear_model.LinearRegression(),
preprocessing.StandardScaler() | linear_model.PAClassifier(),
(preprocessing.StandardScaler()
| multiclass.OneVsRestClassifier(
linear_model.LogisticRegression())),
(preprocessing.StandardScaler()
| multiclass.OneVsRestClassifier(linear_model.PAClassifier())),
naive_bayes.GaussianNB(),
preprocessing.StandardScaler(),
cluster.KMeans(n_clusters=5, seed=42),
preprocessing.MinMaxScaler(),
preprocessing.MinMaxScaler() + preprocessing.StandardScaler(),
feature_extraction.PolynomialExtender(),
(feature_extraction.PolynomialExtender()
| preprocessing.StandardScaler()
| linear_model.LinearRegression()),
feature_selection.VarianceThreshold(),
feature_selection.SelectKBest(similarity=stats.PearsonCorr()),
] for check in utils.estimator_checks.yield_checks(estimator)
if check.__name__ not in estimator._unit_test_skips()
],
)
def test_check_estimator(estimator, check):
check(copy.deepcopy(estimator))
# sns.relplot(x="monetary_value", y="recency", hue='cluster',
# sizes=(50, 500), alpha=.3,palette=sns.color_palette('hls', 5),
# height=5, data=rm_cluster5)
#-------------------------------------------------------------------------------------------------------------------------------------------------------------------------
#-------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# Incremental k-means
from river import cluster
from river import metrics
X = df_rm_normal.iloc[0:50]
incremental_kmeans = cluster.KMeans(n_clusters=5,
halflife=0.4,
sigma=3,
seed=0)
metric_ssw = metrics.cluster.SSW()
metric_cohesion = metrics.cluster.Cohesion()
metric_separation = metrics.cluster.Separation()
metric_ssb = metrics.cluster.SSB()
metric_bic = metrics.cluster.BIC()
metric_silhouette = metrics.cluster.Silhouette()
metric_xieBeni = metrics.cluster.XieBeni()
for row in X.to_dict('records'):
incremental_kmeans = incremental_kmeans.learn_one(row)
prediction = incremental_kmeans.predict_one(row)
metric_ssw = metric_ssw.update(row, prediction, incremental_kmeans.centers)
metric_cohesion = metric_cohesion.update(row, prediction,
from river import base
from river import cluster
from river import compat
from river import linear_model
from river import preprocessing
@pytest.mark.parametrize(
"estimator",
[
pytest.param(estimator, id=str(estimator)) for estimator in [
linear_model.LinearRegression(),
linear_model.LogisticRegression(),
preprocessing.StandardScaler(),
cluster.KMeans(seed=42),
]
],
)
@pytest.mark.filterwarnings(
"ignore::sklearn.utils.estimator_checks.SkipTestWarning")
def test_river_to_sklearn_check_estimator(estimator: base.Estimator):
skl_estimator = compat.convert_river_to_sklearn(estimator)
estimator_checks.check_estimator(skl_estimator)
@pytest.mark.filterwarnings(
"ignore::sklearn.utils.estimator_checks.SkipTestWarning")
def test_sklearn_check_twoway():
estimator = sk_linear_model.SGDRegressor()
river_estimator = compat.convert_sklearn_to_river(estimator)
import functools
import random
import time
import pandas as pd
from streamz import Stream
import hvplot.streamz
from streamz.river import RiverTrain
from river import cluster
import holoviews as hv
from panel.pane.holoviews import HoloViews
import panel as pn
hv.extension('bokeh')
model = cluster.KMeans(n_clusters=3, sigma=0.1, mu=0.5)
centres = [[random.random(), random.random()] for _ in range(3)]
count = [0]
def gen(move_chance=0.05):
centre = int(random.random() * 3) # 3x faster than random.randint(0, 2)
if random.random() < move_chance:
centres[centre][0] += random.random() / 5 - 0.1
centres[centre][1] += random.random() / 5 - 0.1
value = {
'x': random.random() / 20 + centres[centre][0],
'y': random.random() / 20 + centres[centre][1]
}
count[0] += 1
return value