commit changes from smap project.

[cdsc_reddit.git] / similarities / similarities_helper.py

diff --git a/similarities/similarities_helper.py b/similarities/similarities_helper.py
index e59563e396bc0988cf645dc80a6cba27997a512e..202220c389653de068bc52320c50c249bd18d280 100644 (file)
--- a/similarities/similarities_helper.py
+++ b/similarities/similarities_helper.py
@@ -15,24 +15,54 @@ import numpy as np
 import pathlib
 from datetime import datetime
 from pathlib import Path
 import pathlib
 from datetime import datetime
 from pathlib import Path

+import pickle

 
 class tf_weight(Enum):
     MaxTF = 1
     Norm05 = 2
 
 
 class tf_weight(Enum):
     MaxTF = 1
     Norm05 = 2
 

-infile = "/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet"
-cache_file = "/gscratch/comdata/users/nathante/cdsc_reddit/similarities/term_tfidf_entries_bak.parquet"
+# infile = "/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet"
+# cache_file = "/gscratch/comdata/users/nathante/cdsc_reddit/similarities/term_tfidf_entries_bak.parquet"

 
 # subreddits missing after this step don't have any terms that have a high enough idf
 # try rewriting without merges
 
 # subreddits missing after this step don't have any terms that have a high enough idf
 # try rewriting without merges

-def 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):
-    print("loading tfidf", flush=True)
-    tfidf_ds = ds.dataset(infile)
+
+# does reindex_tfidf, but without reindexing.
+def reindex_tfidf(*args, **kwargs):
+    df, tfidf_ds, ds_filter = _pull_or_reindex_tfidf(*args, **kwargs, reindex=True)
+
+    print("assigning names")
+    subreddit_names = tfidf_ds.to_table(filter=ds_filter,columns=['subreddit','subreddit_id'])
+    batches = subreddit_names.to_batches()
+    
+    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",axis=1)
+    subreddit_names = subreddit_names.sort_values("subreddit_id_new")
+    return(df, subreddit_names)
+
+def pull_tfidf(*args, **kwargs):
+    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)
+
+    if week is not None:
+        tfidf_ds = ds.dataset(infile, partitioning='hive')
+    else: 
+        tfidf_ds = ds.dataset(infile)

 
     if included_subreddits is None:
         included_subreddits = select_topN_subreddits(topN)
     else:
 
     if included_subreddits is None:
         included_subreddits = select_topN_subreddits(topN)
     else:

-        included_subreddits = set(open(included_subreddits))
+        included_subreddits = set(map(str.strip,open(included_subreddits)))

 
     ds_filter = ds.field("subreddit").isin(included_subreddits)
 
 
     ds_filter = ds.field("subreddit").isin(included_subreddits)
 


@@ -65,18 +95,25 @@ def reindex_tfidf(infile, term_colname, min_df=None, max_df=None, included_subre
         projection = {
             'subreddit_id':ds.field('subreddit_id'),
             term_id:ds.field(term_id),
         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')}
 
             'tf_idf':ds.field('tf_idf').cast('float32')}
 

-    tfidf_ds = ds.dataset(infile)
-
+    print(projection, flush=True)
+    print(ds_filter, flush=True)

     df = tfidf_ds.to_table(filter=ds_filter,columns=projection)
 
     df = df.to_pandas(split_blocks=True,self_destruct=True)
     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)
-    df['subreddit_id_new'] = df.groupby("subreddit_id").ngroup()
-    grouped = df.groupby(term_id)
-    df[term_id_new] = grouped.ngroup()
+
+    if reindex:
+        print("assigning indexes",flush=True)
+        df['subreddit_id_new'] = df.groupby("subreddit_id").ngroup() + 1
+    else:
+        df['subreddit_id_new'] = df['subreddit_id']
+
+    if reindex:
+        grouped = df.groupby(term_id)
+        df[term_id_new] = grouped.ngroup() + 1 
+    else:
+        df[term_id_new] = df[term_id]

 
     if rescale_idf:
         print("computing idf", flush=True)
 
     if rescale_idf:
         print("computing idf", flush=True)


@@ -88,26 +125,24 @@ def reindex_tfidf(infile, term_colname, min_df=None, max_df=None, included_subre
         else: # tf_fam = tf_weight.Norm05
             df["tf_idf"] = (0.5 + 0.5 * df.relative_tf) * df.idf
 
         else: # tf_fam = tf_weight.Norm05
             df["tf_idf"] = (0.5 + 0.5 * df.relative_tf) * df.idf
 

-    print("assigning names")
-    subreddit_names = tfidf_ds.to_table(filter=ds_filter,columns=['subreddit','subreddit_id'])
-    batches = subreddit_names.to_batches()
+    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())
 
 
 def pull_names(batch):
     return(batch.to_pandas().drop_duplicates())
 

-def similarities(infile, simfunc, term_colname, outfile, min_df=None, max_df=None, included_subreddits=None, topN=500, from_date=None, to_date=None, tfidf_colname='tf_idf'):
+def similarities(inpath, simfunc, term_colname, outfile, min_df=None, max_df=None, included_subreddits=None, topN=500, from_date=None, to_date=None, tfidf_colname='tf_idf'):

     '''
     tfidf_colname: set to 'relative_tf' to use normalized term frequency instead of tf-idf, which can be useful for author-based similarities.
     '''
     '''
     tfidf_colname: set to 'relative_tf' to use normalized term frequency instead of tf-idf, which can be useful for author-based similarities.
     '''


@@ -127,7 +162,7 @@ def similarities(infile, simfunc, term_colname, outfile, min_df=None, max_df=Non
         output_feather =  Path(str(p).replace("".join(p.suffixes), ".feather"))
         output_csv =  Path(str(p).replace("".join(p.suffixes), ".csv"))
         output_parquet =  Path(str(p).replace("".join(p.suffixes), ".parquet"))
         output_feather =  Path(str(p).replace("".join(p.suffixes), ".feather"))
         output_csv =  Path(str(p).replace("".join(p.suffixes), ".csv"))
         output_parquet =  Path(str(p).replace("".join(p.suffixes), ".parquet"))

-        outfile.parent.mkdir(exist_ok=True, parents=True)
+        p.parent.mkdir(exist_ok=True, parents=True)

 
         sims.to_feather(outfile)
 
 
         sims.to_feather(outfile)
 


@@ -135,8 +170,8 @@ def similarities(infile, simfunc, term_colname, outfile, min_df=None, max_df=Non
     term_id = term + '_id'
     term_id_new = term + '_id_new'
 
     term_id = term + '_id'
     term_id_new = term + '_id_new'
 

-    entries, subreddit_names = reindex_tfidf(infile, 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], entries.subreddit_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)))

 
     print("loading matrix")        
 
 
     print("loading matrix")        
 


@@ -151,7 +186,7 @@ def similarities(infile, simfunc, term_colname, outfile, min_df=None, max_df=Non
         for simmat, name in sims:
             proc_sims(simmat, Path(outfile)/(str(name) + ".feather"))
     else:
         for simmat, name in sims:
             proc_sims(simmat, Path(outfile)/(str(name) + ".feather"))
     else:

-        proc_sims(simmat, outfile)
+        proc_sims(sims, outfile)

 
 def write_weekly_similarities(path, sims, week, names):
     sims['week'] = week
 
 def write_weekly_similarities(path, sims, week, names):
     sims['week'] = week


@@ -204,10 +239,10 @@ def test_lsi_sims():
 # 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
 # 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

-def lsi_column_similarities(tfidfmat,n_components=300,n_iter=10,random_state=1968,algorithm='randomized'):
+# 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):

     # first compute the lsi of the matrix
     # then take the column similarities
     # 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]
 
     if type(n_components) is int:
         n_components = [n_components]


@@ -215,15 +250,25 @@ def lsi_column_similarities(tfidfmat,n_components=300,n_iter=10,random_state=196
     n_components = sorted(n_components,reverse=True)
     
     svd_components = n_components[0]
     n_components = sorted(n_components,reverse=True)
     
     svd_components = n_components[0]

-    svd = TruncatedSVD(n_components=svd_components,random_state=random_state,algorithm=algorithm,n_iter=n_iter)
-    mod = svd.fit(tfidfmat.T)
+    
+    if lsi_model is None:
+        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)
     lsimat = mod.transform(tfidfmat.T)

+    if lsi_model_save is not None:
+        Path(lsi_model_save).parent.mkdir(exist_ok=True,parents=True)
+        pickle.dump(mod, open(lsi_model_save,'wb'))
+
+    print(n_components)

     for n_dims in n_components:
     for n_dims in n_components:

+        print("computing similarities")

         sims = column_similarities(lsimat[:,np.arange(n_dims)])
         sims = column_similarities(lsimat[:,np.arange(n_dims)])

-        if len(n_components) > 1:
-            yield (sims, n_dims)
-        else:
-            return sims
+        yield (sims, n_dims)
+

     
 
 def column_similarities(mat):
     
 
 def column_similarities(mat):


@@ -254,20 +299,20 @@ def build_weekly_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weig
     idf = idf.withColumn('idf',f.log(idf.subreddits_in_week) / (1+f.col('count'))+1)
 
     # collect the dictionary to make a pydict of terms to indexes
     idf = idf.withColumn('idf',f.log(idf.subreddits_in_week) / (1+f.col('count'))+1)
 
     # collect the dictionary to make a pydict of terms to indexes

-    terms = idf.select([term,'week']).distinct() # terms are distinct
+    terms = idf.select([term]).distinct() # terms are distinct

 
 

-    terms = terms.withColumn(term_id,f.row_number().over(Window.partitionBy('week').orderBy(term))) # term ids are distinct
+    terms = terms.withColumn(term_id,f.row_number().over(Window.orderBy(term))) # term ids are distinct

 
     # make subreddit ids
 
     # make subreddit ids

-    subreddits = df.select(['subreddit','week']).distinct()
-    subreddits = subreddits.withColumn('subreddit_id',f.row_number().over(Window.partitionBy("week").orderBy("subreddit")))
+    subreddits = df.select(['subreddit']).distinct()
+    subreddits = subreddits.withColumn('subreddit_id',f.row_number().over(Window.orderBy("subreddit")))

 
 

-    df = df.join(subreddits,on=['subreddit','week'])
+    df = df.join(subreddits,on=['subreddit'])

 
     # map terms to indexes in the tfs and the idfs
 
     # map terms to indexes in the tfs and the idfs

-    df = df.join(terms,on=[term,'week']) # subreddit-term-id is unique
+    df = df.join(terms,on=[term]) # subreddit-term-id is unique

 
 

-    idf = idf.join(terms,on=[term,'week'])
+    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,'week'])
 
     # join on subreddit/term to create tf/dfs indexed by term
     df = df.join(idf, on=[term_id, term,'week'])


@@ -279,11 +324,11 @@ def build_weekly_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weig
     else: # tf_fam = tf_weight.Norm05
         df = df.withColumn("tf_idf",  (0.5 + 0.5 * df.relative_tf) * df.idf)
 
     else: # tf_fam = tf_weight.Norm05
         df = df.withColumn("tf_idf",  (0.5 + 0.5 * df.relative_tf) * df.idf)
 

-    df = df.repartition(400,'subreddit','week')
+    df = df.repartition('week')

     dfwriter = df.write.partitionBy("week")
     return dfwriter
 
     dfwriter = df.write.partitionBy("week")
     return dfwriter
 

-def _calc_tfidf(df, term_colname, tf_family):
+def _calc_tfidf(df, term_colname, tf_family, min_df=None, max_df=None):

     term = term_colname
     term_id = term + '_id'
 
     term = term_colname
     term_id = term + '_id'
 


@@ -301,7 +346,13 @@ def _calc_tfidf(df, term_colname, tf_family):
     idf = idf.withColumn('idf',f.log(N_docs/(1+f.col('count')))+1)
 
     # collect the dictionary to make a pydict of terms to indexes
     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.select(term).distinct() # terms are distinct
+    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 = terms.withColumn(term_id,f.row_number().over(Window.orderBy(term))) # term ids are distinct
 
     # make subreddit ids
     terms = terms.withColumn(term_id,f.row_number().over(Window.orderBy(term))) # term ids are distinct
 
     # make subreddit ids


@@ -311,12 +362,12 @@ def _calc_tfidf(df, term_colname, tf_family):
     df = df.join(subreddits,on='subreddit')
 
     # map terms to indexes in the tfs and the idfs
     df = df.join(subreddits,on='subreddit')
 
     # map terms to indexes in the tfs and the idfs

-    df = df.join(terms,on=term) # subreddit-term-id is unique
+    df = df.join(terms,on=term,how='inner') # subreddit-term-id is unique

 
 

-    idf = idf.join(terms,on=term)
+    idf = idf.join(terms,on=term,how='inner')

 
     # join on subreddit/term to create tf/dfs indexed by term
 
     # join on subreddit/term to create tf/dfs indexed by term

-    df = df.join(idf, on=[term_id, term])
+    df = df.join(idf, on=[term_id, term],how='inner')

 
     # agg terms by subreddit to make sparse tf/df vectors
     if tf_family == tf_weight.MaxTF:
 
     # agg terms by subreddit to make sparse tf/df vectors
     if tf_family == tf_weight.MaxTF:


@@ -327,14 +378,14 @@ def _calc_tfidf(df, term_colname, tf_family):
     return df
     
 
     return df
     
 

-def build_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05):
+def tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05, min_df=None, max_df=None):

     term = term_colname
     term_id = term + '_id'
     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 = 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)
+    df = _calc_tfidf(df, term_colname, tf_family, min_df, max_df)

     df = df.repartition('subreddit')
     dfwriter = df.write
     return dfwriter
     df = df.repartition('subreddit')
     dfwriter = df.write
     return dfwriter