new_ids = df.loc[:,['subreddit_id','subreddit_id_new']].drop_duplicates()
new_ids = new_ids.set_index('subreddit_id')
subreddit_names = subreddit_names.join(new_ids,on='subreddit_id').reset_index()
- subreddit_names = subreddit_names.drop("subreddit_id",axis=1)
+ subreddit_names = subreddit_names.drop("subreddit_id",1)
subreddit_names = subreddit_names.sort_values("subreddit_id_new")
return(df, subreddit_names)
df, _, _ = _pull_or_reindex_tfidf(*args, **kwargs, reindex=False)
return df
-def _pull_or_reindex_tfidf(infile, term_colname, min_df=None, max_df=None, included_subreddits=None, topN=None, week=None, from_date=None, to_date=None, rescale_idf=True, tf_family=tf_weight.MaxTF, reindex=True):
- print(f"loading tfidf {infile}, week {week}, min_df {min_df}, max_df {max_df}", flush=True)
-
+def _pull_or_reindex_tfidf(infile, term_colname, min_df=None, max_df=None, included_subreddits=None, topN=500, week=None, from_date=None, to_date=None, rescale_idf=True, tf_family=tf_weight.MaxTF, reindex=True):
+ print(f"loading tfidf {infile}", flush=True)
if week is not None:
tfidf_ds = ds.dataset(infile, partitioning='hive')
else:
projection = {
'subreddit_id':ds.field('subreddit_id'),
term_id:ds.field(term_id),
+ 'relative_tf':ds.field('relative_tf').cast('float32'),
'tf_idf':ds.field('tf_idf').cast('float32')}
- print(projection, flush=True)
- print(ds_filter, flush=True)
+ print(projection)
+
df = tfidf_ds.to_table(filter=ds_filter,columns=projection)
df = df.to_pandas(split_blocks=True,self_destruct=True)
-
+ print("assigning indexes",flush=True)
if reindex:
- print("assigning indexes",flush=True)
- df['subreddit_id_new'] = df.groupby("subreddit_id").ngroup() + 1
+ df['subreddit_id_new'] = df.groupby("subreddit_id").ngroup()
else:
df['subreddit_id_new'] = df['subreddit_id']
if reindex:
grouped = df.groupby(term_id)
- df[term_id_new] = grouped.ngroup() + 1
+ df[term_id_new] = grouped.ngroup()
else:
df[term_id_new] = df[term_id]
return (df, tfidf_ds, ds_filter)
- # with Pool(cpu_count()) as pool:
- # chunks = pool.imap_unordered(pull_names,batches)
- # subreddit_names = pd.concat(chunks,copy=False).drop_duplicates()
+ with Pool(cpu_count()) as pool:
+ chunks = pool.imap_unordered(pull_names,batches)
+ subreddit_names = pd.concat(chunks,copy=False).drop_duplicates()
- # subreddit_names = subreddit_names.set_index("subreddit_id")
- # new_ids = df.loc[:,['subreddit_id','subreddit_id_new']].drop_duplicates()
- # new_ids = new_ids.set_index('subreddit_id')
- # subreddit_names = subreddit_names.join(new_ids,on='subreddit_id').reset_index()
- # subreddit_names = subreddit_names.drop("subreddit_id",1)
- # subreddit_names = subreddit_names.sort_values("subreddit_id_new")
- # return(df, subreddit_names)
+ subreddit_names = subreddit_names.set_index("subreddit_id")
+ new_ids = df.loc[:,['subreddit_id','subreddit_id_new']].drop_duplicates()
+ new_ids = new_ids.set_index('subreddit_id')
+ subreddit_names = subreddit_names.join(new_ids,on='subreddit_id').reset_index()
+ subreddit_names = subreddit_names.drop("subreddit_id",1)
+ subreddit_names = subreddit_names.sort_values("subreddit_id_new")
+ return(df, subreddit_names)
def pull_names(batch):
return(batch.to_pandas().drop_duplicates())
term_id_new = term + '_id_new'
entries, subreddit_names = reindex_tfidf(inpath, term_colname=term_colname, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=topN,from_date=from_date,to_date=to_date)
- mat = csr_matrix((entries[tfidf_colname],(entries[term_id_new]-1, entries.subreddit_id_new-1)))
+ mat = csr_matrix((entries[tfidf_colname],(entries[term_id_new], entries.subreddit_id_new)))
print("loading matrix")
# if n_components is a list we'll return a list of similarities with different latent dimensionalities
# if algorithm is 'randomized' instead of 'arpack' then n_iter gives the number of iterations.
# this function takes the svd and then the column similarities of it
-# lsi_model_load = "/gscratch/comdata/users/nathante/competitive_exclusion_reddit/data/similarity/comment_terms_compex_LSI/1000_term_LSIMOD.pkl"
-def lsi_column_similarities(tfidfmat,n_components=300,n_iter=10,random_state=1968,algorithm='randomized',lsi_model_save=None,lsi_model=None):
+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
svd_components = n_components[0]
- if lsi_model is 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)
- else:
- mod = lsi_model
lsimat = mod.transform(tfidfmat.T)
if lsi_model_save is not None:
- Path(lsi_model_save).parent.mkdir(exist_ok=True,parents=True)
+ Path(lsi_model_save).parent.mkdir(exist_ok=True, parents=True)
pickle.dump(mod, open(lsi_model_save,'wb'))
- print(n_components)
+ sims_list = []
for n_dims in n_components:
- print("computing similarities")
sims = column_similarities(lsimat[:,np.arange(n_dims)])
- yield (sims, n_dims)
-
+ if len(n_components) > 1:
+ yield (sims, n_dims)
+ else:
+ return sims
def column_similarities(mat):
else: # tf_fam = tf_weight.Norm05
df = df.withColumn("tf_idf", (0.5 + 0.5 * df.relative_tf) * df.idf)
- df = df.repartition('week')
+ df = df.repartition(400,'subreddit','week')
dfwriter = df.write.partitionBy("week")
return dfwriter
-def _calc_tfidf(df, term_colname, tf_family, min_df=None, max_df=None):
+def _calc_tfidf(df, term_colname, tf_family):
term = term_colname
term_id = term + '_id'
idf = idf.withColumn('idf',f.log(N_docs/(1+f.col('count')))+1)
# collect the dictionary to make a pydict of terms to indexes
- terms = idf
- if min_df is not None:
- terms = terms.filter(f.col('count')>=min_df)
- if max_df is not None:
- terms = terms.filter(f.col('count')<=max_df)
-
- terms = terms.select(term).distinct() # terms are distinct
+ terms = idf.select(term).distinct() # terms are distinct
terms = terms.withColumn(term_id,f.row_number().over(Window.orderBy(term))) # term ids are distinct
# make subreddit ids
df = df.join(subreddits,on='subreddit')
# map terms to indexes in the tfs and the idfs
- df = df.join(terms,on=term,how='inner') # subreddit-term-id is unique
+ df = df.join(terms,on=term) # subreddit-term-id is unique
- idf = idf.join(terms,on=term,how='inner')
+ idf = idf.join(terms,on=term)
# join on subreddit/term to create tf/dfs indexed by term
- df = df.join(idf, on=[term_id, term],how='inner')
+ df = df.join(idf, on=[term_id, term])
# agg terms by subreddit to make sparse tf/df vectors
if tf_family == tf_weight.MaxTF:
return df
-def tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05, min_df=None, max_df=None):
+def tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05):
term = term_colname
term_id = term + '_id'
-
+ # aggregate counts by week. now subreddit-term is distinct
df = df.filter(df.subreddit.isin(include_subs))
df = df.groupBy(['subreddit',term]).agg(f.sum('tf').alias('tf'))
- df = _calc_tfidf(df, term_colname, tf_family, min_df, max_df)
+ df = _calc_tfidf(df, term_colname, tf_family)
df = df.repartition('subreddit')
dfwriter = df.write
return dfwriter
projects / cdsc_reddit.git / blobdiff