X-Git-Url: https://code.communitydata.science/cdsc_reddit.git/blobdiff_plain/65deba5e4e4ad9e3f23e82573491f7d6b190e644..55b75ea6fcf421e95f4fe6b180dcec6e64676619:/similarities/similarities_helper.py?ds=sidebyside

diff --git a/similarities/similarities_helper.py b/similarities/similarities_helper.py
index 202220c..03c10b2 100644
--- a/similarities/similarities_helper.py
+++ b/similarities/similarities_helper.py
@@ -43,7 +43,7 @@ def reindex_tfidf(*args, **kwargs):
     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)
 
@@ -51,9 +51,8 @@ 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)
-
+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: 
@@ -95,23 +94,23 @@ def _pull_or_reindex_tfidf(infile, term_colname, min_df=None, max_df=None, inclu
         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]
 
@@ -127,17 +126,17 @@ def _pull_or_reindex_tfidf(infile, term_colname, min_df=None, max_df=None, inclu
 
     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())
@@ -171,7 +170,7 @@ def similarities(inpath, simfunc, term_colname, outfile, min_df=None, max_df=Non
     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")        
 
@@ -239,8 +238,7 @@ 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
-# 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
 
@@ -251,24 +249,29 @@ def lsi_column_similarities(tfidfmat,n_components=300,n_iter=10,random_state=196
     
     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):
@@ -324,11 +327,11 @@ 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)
 
-    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'
 
@@ -346,13 +349,7 @@ def _calc_tfidf(df, term_colname, tf_family, min_df=None, max_df=None):
     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
@@ -362,12 +359,12 @@ def _calc_tfidf(df, term_colname, tf_family, min_df=None, max_df=None):
     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:
@@ -378,14 +375,14 @@ def _calc_tfidf(df, term_colname, tf_family, min_df=None, max_df=None):
     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