Created
July 7, 2024 12:48
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from tslearn.clustering import TimeSeriesKMeans | |
from tslearn.utils import to_time_series_dataset | |
import pandas as pd | |
data = [ | |
[0, 1, 2, 3, 4], | |
[1, 2, 3, 4, 0], | |
[0, 1, 2, 3, 4], | |
[0, 1, 2, 3, 4], | |
[5, 6, 3, 4, 0], | |
[5, 6, 3, 4, 0, 5, 6, 3, 4, 0], | |
] | |
X = to_time_series_dataset(data) | |
km = TimeSeriesKMeans(n_clusters=4, metric='dtw', verbose=False, random_state=0, max_iter=5, n_init=3) | |
labels = km.fit_predict(X) | |
# Using pandas DataFrame | |
df = pd.DataFrame([ | |
{'id': i, 'timeseries': x} | |
for i, x in enumerate(data) | |
]).set_index('turnaround_id') | |
X = to_time_series_dataset(df['timeseries'].to_list()) | |
km = TimeSeriesKMeans(n_clusters=4, metric='dtw', verbose=False, random_state=0, max_iter=5, n_init=3) | |
df['labels'] = km.fit_predict(X) | |
# Silhouette | |
from tslearn.clustering import silhouette_score | |
silhouette_score(X, labels) |
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