42 lines
1.3 KiB
Python
42 lines
1.3 KiB
Python
from sklearn.cluster import KMeans
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import matplotlib.pyplot as plt
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##### Clustering ######
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## KMeans
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#eval_artworks = eval_art[eval_art.nettype == "alldata"].iloc[:,range(1,5)]
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eval_artworks = eval_art[eval_art.nettype == "subdata"].iloc[:,range(1,5)]
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kmeans = KMeans(n_clusters=4, max_iter=1000).fit(eval_artworks)
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#from sklearn.manifold import MDS
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#coord = pd.DataFrame(MDS(normalized_stress='auto').fit_transform(eval_artworks))
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coord = eval_artworks
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coord["clusters"] = kmeans.labels_
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for i in coord.clusters.unique():
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#plt.scatter(coord[coord.clusters == i].iloc[:,0], coord[coord.clusters == i].iloc[:,1],
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plt.scatter(coord[coord.clusters == i].iloc[:,1], coord[coord.clusters == i].iloc[:,2],
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#plt.scatter(coord[coord.clusters == i].iloc[:,2], coord[coord.clusters == i].iloc[:,4],
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label = i)
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plt.legend()
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plt.show()
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### Scree plot
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sse = {}
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for k in range(1, 10):
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kmeans = KMeans(n_clusters=k, max_iter=1000).fit(eval_artworks[["precision", "generalizability"]])
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#data["clusters"] = kmeans.labels_
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#print(data["clusters"])
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sse[k] = kmeans.inertia_ # Inertia: Sum of distances of samples to their closest cluster center
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plt.figure()
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plt.plot(list(sse.keys()), list(sse.values()))
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plt.xlabel("Number of clusters")
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plt.ylabel("SSE")
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plt.show()
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