# cluster_selection_epsilons=[0,0.05,0.1,0.15],
# cluster_selection_methods=['eom','leaf'],
# lsi_dimensions='all')
- inpath = "/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors-tf_10k_LSI/"
+ inpath = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity/comment_authors_compex_LSI"
outpath = "test_hdbscan";
min_cluster_sizes=[2,3,4];
min_samples=[1,2,3];
cluster_selection_epsilons=[0,0.1,0.3,0.5];
- cluster_selection_methods=['eom'];
+ cluster_selection_methods=[1];
lsi_dimensions='all'
gs = hdbscan_lsi_grid_sweep(inpath, "all", outpath, min_cluster_sizes, min_samples, cluster_selection_epsilons, cluster_selection_methods)
gs.run(20)
map(int,min_cluster_sizes),
map(int,min_samples),
map(float,cluster_selection_epsilons),
- map(float,cluster_selection_methods))
+ cluster_selection_methods)
obj.run()
obj.save(savefile)
s += f"_lsi-{self.lsi_dim}"
return s
-def run_hdbscan_lsi_grid_sweep(savefile, inpath, outpath, min_cluster_sizes=[2], min_samples=[1], cluster_selection_epsilons=[0], cluster_selection_methods=['eom'],lsi_dimensions='all'):
+def run_hdbscan_lsi_grid_sweep(savefile, inpath, outpath, min_cluster_sizes=[2], min_samples=[1], cluster_selection_epsilons=[0], cluster_selection_methods=[1],lsi_dimensions='all'):
"""Run hdbscan clustering once or more with different parameters.
Usage:
list(map(int,min_cluster_sizes)),
list(map(int,min_samples)),
list(map(float,cluster_selection_epsilons)),
- cluster_selection_methods
- )
+ cluster_selection_methods)
+
obj.run(10)
obj.save(savefile)
self.subsweep = subsweep
inpath = Path(inpath)
if lsi_dimensions == 'all':
- lsi_paths = list(inpath.glob("*"))
+ lsi_paths = list(inpath.glob("*.feather"))
else:
lsi_paths = [inpath / (str(dim) + '.feather') for dim in lsi_dimensions]
+ print(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)]
+#!/usr/bin/env python3
import fire
import pandas as pd
from pathlib import Path
import shutil
-selection_data="/gscratch/comdata/output/reddit_clustering/subreddit_comment_authors-tf_10k_LSI/hdbscan/selection_data.csv"
+selection_data="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/clustering/comment_authors_compex_LSI/selection_data.csv"
outpath = 'test_best.feather'
-min_clusters=50; max_isolates=5000; min_cluster_size=2
+min_clusters=50; max_isolates=7500; min_cluster_size=2
# pick the best clustering according to silhouette score subject to contraints
def pick_best_clustering(selection_data, output, min_clusters, max_isolates, min_cluster_size):
df.loc[df.n_isolates_0,'n_isolates'] = 0
df.loc[~df.n_isolates_0,'n_isolates'] = df.loc[~df.n_isolates_0].n_isolates_str.apply(lambda l: int(l))
- best_cluster = df[(df.n_isolates <= max_isolates)&(df.n_clusters >= min_clusters)&(df.min_cluster_size==min_cluster_size)].iloc[df.shape[1]]
+ best_cluster = df[(df.n_isolates <= max_isolates)&(df.n_clusters >= min_clusters)&(df.min_cluster_size==min_cluster_size)]
+ best_cluster = best_cluster.iloc[0]
+
+ best_lsi_dimensions = best_cluster.lsi_dimensions
print(best_cluster.to_dict())
best_path = Path(best_cluster.outpath) / (str(best_cluster['name']) + ".feather")
shutil.copy(best_path,output)
-
+ print(f"lsi dimensions:{best_lsi_dimensions}")
+
if __name__ == "__main__":
fire.Fire(pick_best_clustering)
'relative_tf':ds.field('relative_tf').cast('float32'),
'tf_idf':ds.field('tf_idf').cast('float32')}
+ print(projection)
df = tfidf_ds.to_table(filter=ds_filter,columns=projection)
def lsi_column_similarities(tfidfmat,n_components=300,n_iter=10,random_state=1968,algorithm='randomized',lsi_model_save=None,lsi_model_load=None):
# first compute the lsi of the matrix
# then take the column similarities
- print("running LSI",flush=True)
if type(n_components) is int:
n_components = [n_components]
svd_components = n_components[0]
- if lsi_model_load is not None:
+ if lsi_model_load is not None and Path(lsi_model_load).exists():
+ print("loading LSI")
mod = pickle.load(open(lsi_model_load ,'rb'))
+ lsi_model_save = lsi_model_load
else:
+ print("running LSI",flush=True)
+
svd = TruncatedSVD(n_components=svd_components,random_state=random_state,algorithm=algorithm,n_iter=n_iter)
mod = svd.fit(tfidfmat.T)
from similarities_helper import tfidf_dataset, build_weekly_tfidf_dataset, select_topN_subreddits
def _tfidf_wrapper(func, inpath, outpath, topN, term_colname, exclude, included_subreddits):
- spark = SparkSession.builder.getOrCreate()
+ spark = SparkSession.builder.getOrCreate()y
df = spark.read.parquet(inpath)
def tfidf_weekly(inpath, outpath, topN, term_colname, exclude, included_subreddits):
return _tfidf_wrapper(build_weekly_tfidf_dataset, inpath, outpath, topN, term_colname, exclude, included_subreddits)
-def tfidf_authors(outpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet',
+def tfidf_authors(inpath="/gscratch/comdata/output/reddit_ngrams/comment_authors.parquet",
+ outpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet',
topN=None,
included_subreddits=None):
- return tfidf("/gscratch/comdata/output/reddit_ngrams/comment_authors.parquet",
+ return tfidf(inpath,
outpath,
topN,
'author',
included_subreddits=included_subreddits
)
-def tfidf_terms(outpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms.parquet',
+def tfidf_terms(inpath="/gscratch/comdata/output/reddit_ngrams/comment_terms.parquet",
+ outpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms.parquet',
topN=None,
included_subreddits=None):
- return tfidf("/gscratch/comdata/output/reddit_ngrams/comment_terms.parquet",
+ return tfidf(inpath,
outpath,
topN,
'term',
included_subreddits=included_subreddits
)
-def tfidf_authors_weekly(outpath='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors.parquet',
+def tfidf_authors_weekly(inpath="/gscratch/comdata/output/reddit_ngrams/comment_authors.parquet",
+ outpath='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors.parquet',
topN=None,
included_subreddits=None):
- return tfidf_weekly("/gscratch/comdata/output/reddit_ngrams/comment_authors.parquet",
+ return tfidf_weekly(inpath,
outpath,
topN,
'author',
included_subreddits=included_subreddits
)
-def tfidf_terms_weekly(outpath='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet',
+def tfidf_terms_weekly(inpath="/gscratch/comdata/output/reddit_ngrams/comment_terms.parquet",
+ outpath='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet',
topN=None,
included_subreddits=None):
- return tfidf_weekly("/gscratch/comdata/output/reddit_ngrams/comment_terms.parquet",
+ return tfidf_weekly(inpath,
outpath,
topN,
'term',
+#!/usr/bin/env python3
from pyspark.sql import functions as f
from pyspark.sql import SparkSession
from pyspark.sql import Window
import fire
from itertools import islice, chain
from pathlib import Path
-from similarities_helper import pull_tfidf, column_similarities, write_weekly_similarities
+from similarities_helper import pull_tfidf, column_similarities, write_weekly_similarities, lsi_column_similarities
from scipy.sparse import csr_matrix
from multiprocessing import Pool, cpu_count
from functools import partial
-# infile = "/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_test.parquet"
-# tfidf_path = infile
-# min_df=None
-# max_df = None
-# topN=100
-# term_colname='author'
+infile = "/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_10k.parquet"
+tfidf_path = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/tfidf/comment_authors_compex.parquet"
+min_df=None
+included_subreddits="/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/included_subreddits.txt"
+max_df = None
+topN=100
+term_colname='author'
# outfile = '/gscratch/comdata/output/reddit_similarity/weekly/comment_authors_test.parquet'
# included_subreddits=None
max_df=max_df,
included_subreddits=included_subreddits,
topN=topN,
- week=week.isoformat(),
+ week=week,
rescale_idf=False)
tfidf_colname='tf_idf'
mat = csr_matrix((entries[tfidf_colname],(entries[term_id_new]-1, entries.subreddit_id_new-1)),shape=(nterms,subreddit_names.shape[0]))
print('computing similarities')
- sims = simfunc(mat.T)
+ sims = simfunc(mat)
del mat
sims = pd.DataFrame(sims)
sims = sims.rename({i: sr for i, sr in enumerate(subreddit_names.subreddit.values)}, axis=1)
def pull_weeks(batch):
return set(batch.to_pandas()['week'])
+# This requires a prefit LSI model, since we shouldn't fit different LSI models for every week.
+def cosine_similarities_weekly_lsi(n_components=100, lsi_model=None, *args, **kwargs):
+ term_colname= kwargs.get('term_colname')
+ #lsi_model = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity/comment_terms_compex_LSI/1000_term_LSIMOD.pkl"
+
+ # simfunc = partial(lsi_column_similarities,n_components=n_components,n_iter=n_iter,random_state=random_state,algorithm='randomized',lsi_model_load=lsi_model)
+
+ simfunc = partial(lsi_column_similarities,n_components=n_components,n_iter=kwargs.get('n_iter'),random_state=kwargs.get('random_state'),algorithm=kwargs.get('algorithm'),lsi_model_load=lsi_model)
+
+ return cosine_similarities_weekly(*args, simfunc=simfunc, **kwargs)
+
#tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf_weekly.parquet')
-def cosine_similarities_weekly(tfidf_path, outfile, term_colname, min_df = None, max_df=None, included_subreddits = None, topN = 500):
+def cosine_similarities_weekly(tfidf_path, outfile, term_colname, min_df = None, max_df=None, included_subreddits = None, topN = 500, simfunc=column_similarities):
print(outfile)
# do this step in parallel if we have the memory for it.
# should be doable with pool.map
spark = SparkSession.builder.getOrCreate()
df = spark.read.parquet(tfidf_path)
+
+ # load subreddits + topN
+
subreddit_names = df.select(['subreddit','subreddit_id']).distinct().toPandas()
subreddit_names = subreddit_names.sort_values("subreddit_id")
nterms = df.select(f.max(f.col(term_colname + "_id")).alias('max')).collect()[0].max
spark.stop()
print(f"computing weekly similarities")
- week_similarities_helper = partial(_week_similarities,simfunc=column_similarities, tfidf_path=tfidf_path, term_colname=term_colname, outdir=outfile, min_df=min_df,max_df=max_df,included_subreddits=included_subreddits,topN=topN, subreddit_names=subreddit_names,nterms=nterms)
+ week_similarities_helper = partial(_week_similarities,simfunc=simfunc, tfidf_path=tfidf_path, term_colname=term_colname, outdir=outfile, min_df=min_df,max_df=max_df,included_subreddits=included_subreddits,topN=topN, subreddit_names=subreddit_names,nterms=nterms)
pool = Pool(cpu_count())
# with Pool(cpu_count()) as pool: # maybe it can be done with 40 cores on the huge machine?
-def author_cosine_similarities_weekly(outfile, min_df=2, max_df=None, included_subreddits=None, topN=500):
- return cosine_similarities_weekly('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_test.parquet',
+def author_cosine_similarities_weekly(outfile, infile='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_test.parquet', min_df=2, max_df=None, included_subreddits=None, topN=500):
+ return cosine_similarities_weekly(infile,
outfile,
'author',
min_df,
included_subreddits,
topN)
-def term_cosine_similarities_weekly(outfile, min_df=None, max_df=None, included_subreddits=None, topN=500):
- return cosine_similarities_weekly('/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet',
+def term_cosine_similarities_weekly(outfile, infile='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet', min_df=None, max_df=None, included_subreddits=None, topN=None):
+ return cosine_similarities_weekly(infile,
outfile,
'term',
min_df,
included_subreddits,
topN)
+
+def author_cosine_similarities_weekly_lsi(outfile, infile = '/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_test.parquet', min_df=2, max_df=None, included_subreddits=None, topN=None,n_components=100,lsi_model=None):
+ return cosine_similarities_weekly_lsi(infile,
+ outfile,
+ 'author',
+ min_df,
+ max_df,
+ included_subreddits,
+ topN,
+ n_components=n_components,
+ lsi_model=lsi_model)
+
+
+def term_cosine_similarities_weekly_lsi(outfile, infile = '/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet', min_df=None, max_df=None, included_subreddits=None, topN=500,n_components=100,lsi_model=None):
+ return cosine_similarities_weekly_lsi(infile,
+ outfile,
+ 'term',
+ min_df,
+ max_df,
+ included_subreddits,
+ topN,
+ n_components=n_components,
+ lsi_model=lsi_model)
+
if __name__ == "__main__":
fire.Fire({'authors':author_cosine_similarities_weekly,
- 'terms':term_cosine_similarities_weekly})
+ 'terms':term_cosine_similarities_weekly,
+ 'authors-lsi':author_cosine_similarities_weekly_lsi,
+ 'terms-lsi':term_cosine_similarities_weekly
+ })
projects / cdsc_reddit.git / commitdiff