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bug fix in affinity clustering

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This commit is contained in:
Nate E TeBlunthuis 2021-05-13 22:26:03 -07:00
parent 4cb7eeec80
commit 582cf263ea
6 changed files with 18 additions and 14 deletions
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2
clustering/Makefile
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@ -4,7 +4,7 @@ similarity_data=/gscratch/comdata/output/reddit_similarity
clustering_data=/gscratch/comdata/output/reddit_clustering
kmeans_selection_grid="--max_iters=[3000] --n_inits=[10] --n_clusters=[100,500,1000,1250,1500,1750,2000]"
hdbscan_selection_grid="--min_cluster_sizes=[2,3,4,5] --min_samples=[2,3,4,5] --cluster_selection_epsilons=[0,0.01,0.05,0.1,0.15,0.2] --cluster_selection_methods=eom,leaf"
affinity_selection_grid="--dampings=[0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.95,0.97,0.99] --preference_quantiles=[0.1,0.3,0.5,0.7,0.9] --convergence_iters=[30]"
affinity_selection_grid="--dampings=[0.5,0.6,0.7,0.8,0.95,0.97,0.99] --preference_quantiles=[0.1,0.3,0.5,0.7,0.9] --convergence_iters=[15]"
authors_10k_input=$(similarity_data)/subreddit_comment_authors_10k.feather
authors_10k_input_lsi=$(similarity_data)/subreddit_comment_authors_10k_LSI

4
clustering/affinity_clustering.py
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@ -81,7 +81,7 @@ class affinity_grid_sweep(grid_sweep):
return f"damp-{damping}_maxit-{max_iter}_convit-{convergence_iter}_prefq-{preference_quantile}"
def run_affinity_grid_sweep(savefile, inpath, outpath, dampings=[0.8], max_iters=[3000], convergence_iters=[30], preference_quantiles=[0.5]):
def run_affinity_grid_sweep(savefile, inpath, outpath, dampings=[0.8], max_iters=[3000], convergence_iters=[30], preference_quantiles=[0.5],n_cores=10):
"""Run affinity clustering once or more with different parameters.
Usage:
@ -102,7 +102,7 @@ def run_affinity_grid_sweep(savefile, inpath, outpath, dampings=[0.8], max_iters
map(int,max_iters),
map(int,convergence_iters),
map(float,preference_quantiles))
obj.run(1)
obj.run(n_cores)
obj.save(savefile)
def test_select_affinity_clustering():

6
clustering/affinity_clustering_lsi.py
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@ -58,7 +58,7 @@ class _affinity_lsi_grid_sweep(grid_sweep):
inpath,
outpath,
self.namer,
self.lsi_dim,
[self.lsi_dim],
*args,
**kwargs)
@ -67,7 +67,7 @@ class _affinity_lsi_grid_sweep(grid_sweep):
s += f"_lsi-{self.lsi_dim}"
return s
def run_affinity_lsi_grid_sweep(savefile, inpath, outpath, dampings=[0.8], max_iters=[3000], convergence_iters=[30], preference_quantiles=[0.5], lsi_dimensions='all'):
def run_affinity_lsi_grid_sweep(savefile, inpath, outpath, dampings=[0.8], max_iters=[3000], convergence_iters=[30], preference_quantiles=[0.5], lsi_dimensions='all',n_cores=30):
"""Run affinity clustering once or more with different parameters.
Usage:
@ -92,7 +92,7 @@ def run_affinity_lsi_grid_sweep(savefile, inpath, outpath, dampings=[0.8], max_i
map(int,convergence_iters),
map(float,preference_quantiles))
obj.run(1)
obj.run(n_cores)
obj.save(savefile)
if __name__ == "__main__":

12
clustering/clustering_base.py
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@ -3,6 +3,7 @@ import numpy as np
import pandas as pd
from dataclasses import dataclass
from sklearn.metrics import silhouette_score, silhouette_samples
from collections import Counter
# this is meant to be an interface, not created directly
class clustering_job:
@ -38,9 +39,11 @@ class clustering_job:
return self.result
def silhouette(self):
isolates = self.clustering.labels_ == -1
counts = Counter(self.clustering.labels_)
singletons = [key for key, value in counts.items() if value == 1]
isolates = (self.clustering.labels_ == -1) | (np.isin(self.clustering.labels_,np.array(singletons)))
scoremat = self.mat[~isolates][:,~isolates]
if scoremat.shape[0] > 0:
if self.n_clusters > 1:
score = silhouette_score(scoremat, self.clustering.labels_[~isolates], metric='precomputed')
silhouette_samp = silhouette_samples(self.mat, self.clustering.labels_, metric='precomputed')
silhouette_samp = pd.DataFrame({'subreddit':self.subreddits,'score':silhouette_samp})
@ -80,8 +83,9 @@ class clustering_job:
print(f"{n_isolates1} clusters have 1 member")
n_isolates2 = (cluster_sizes.loc[cluster_sizes.cluster==-1,['subreddit']])
n_isolates2 = cluster_sizes.loc[cluster_sizes.cluster==-1,:]['subreddit'].to_list()
if len(n_isolates2) > 0:
n_isloates2 = n_isolates2[0]
print(f"{n_isolates2} subreddits are in cluster -1",flush=True)
if n_isolates1 == 0:

4
clustering/fit_tsne.py
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@ -17,7 +17,7 @@ def fit_tsne(similarities, output, learning_rate=750, perplexity=50, n_iter=1000
df = pd.read_feather(similarities)
n = df.shape[0]
mat = np.array(df.drop('subreddit',1),dtype=np.float64)
mat = np.array(df.drop('_subreddit',1),dtype=np.float64)
mat[range(n),range(n)] = 1
mat[mat > 1] = 1
dist = 2*np.arccos(mat)/np.pi
@ -26,7 +26,7 @@ def fit_tsne(similarities, output, learning_rate=750, perplexity=50, n_iter=1000
tsne_fit_whole = tsne_fit_model.fit_transform(dist)
plot_data = pd.DataFrame({'x':tsne_fit_whole[:,0],'y':tsne_fit_whole[:,1], 'subreddit':df.subreddit})
plot_data = pd.DataFrame({'x':tsne_fit_whole[:,0],'y':tsne_fit_whole[:,1], '_subreddit':df['_subreddit']})
plot_data.to_feather(output)

4
clustering/lsi_base.py
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@ -20,9 +20,9 @@ class lsi_grid_sweep(grid_sweep):
if lsi_dimensions == 'all':
lsi_paths = list(inpath.glob("*"))
else:
lsi_paths = [inpath / (dim + '.feather') for dim in lsi_dimensions]
lsi_paths = [inpath / (str(dim) + '.feather') for dim in lsi_dimensions]
lsi_nums = [p.stem for p in lsi_paths]
lsi_nums = [int(p.stem) for p in lsi_paths]
self.hasrun = False
self.subgrids = [self.subsweep(lsi_path, outpath, lsi_dim, *args, **kwargs) for lsi_dim, lsi_path in zip(lsi_nums, lsi_paths)]
self.jobs = list(chain(*map(lambda gs: gs.jobs, self.subgrids)))