50 lines
1.4 KiB
Python
50 lines
1.4 KiB
Python
from pathlib import Path
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import numpy as np
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import pandas as pd
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from dataclasses import dataclass
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def sim_to_dist(mat):
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dist = 1-mat
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dist[dist < 0] = 0
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np.fill_diagonal(dist,0)
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return dist
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def process_clustering_result(clustering, subreddits):
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if hasattr(clustering,'n_iter_'):
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print(f"clustering took {clustering.n_iter_} iterations")
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clusters = clustering.labels_
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print(f"found {len(set(clusters))} clusters")
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cluster_data = pd.DataFrame({'subreddit': subreddits,'cluster':clustering.labels_})
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cluster_sizes = cluster_data.groupby("cluster").count().reset_index()
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print(f"the largest cluster has {cluster_sizes.loc[cluster_sizes.cluster!=-1].subreddit.max()} members")
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print(f"the median cluster has {cluster_sizes.subreddit.median()} members")
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print(f"{(cluster_sizes.subreddit==1).sum()} clusters have 1 member")
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print(f"{(cluster_sizes.loc[cluster_sizes.cluster==-1,['subreddit']])} subreddits are in cluster -1",flush=True)
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return cluster_data
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@dataclass
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class clustering_result:
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outpath:Path
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max_iter:int
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silhouette_score:float
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alt_silhouette_score:float
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name:str
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n_clusters:int
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def read_similarity_mat(similarities, use_threads=True):
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df = pd.read_feather(similarities, use_threads=use_threads)
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mat = np.array(df.drop('_subreddit',1))
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n = mat.shape[0]
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mat[range(n),range(n)] = 1
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return (df._subreddit,mat)
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