make pass keyword arg to dataframe.drop

[cdsc_reddit.git] / similarities / similarities_helper.py

diff --git a/similarities/similarities_helper.py b/similarities/similarities_helper.py
index a4983b38ef4ca6d3bb248631ce6e3d8cb7340276..6925a15d55582589a080f1c6351eb823a4a37f2f 100644 (file)
--- 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()
     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.drop("subreddit_id",axis=1)

     subreddit_names = subreddit_names.sort_values("subreddit_id_new")
     return(df, subreddit_names)
 
     subreddit_names = subreddit_names.sort_values("subreddit_id_new")
     return(df, subreddit_names)
 


@@ -51,8 +51,9 @@ def pull_tfidf(*args, **kwargs):
     df, _, _ =  _pull_or_reindex_tfidf(*args, **kwargs, reindex=False)
     return df
 
     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=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)
+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: 
     if week is not None:
         tfidf_ds = ds.dataset(infile, partitioning='hive')
     else: 


@@ -97,18 +98,21 @@ def _pull_or_reindex_tfidf(infile, term_colname, min_df=None, max_df=None, inclu
             'relative_tf':ds.field('relative_tf').cast('float32'),
             'tf_idf':ds.field('tf_idf').cast('float32')}
 
             '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)

     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:
     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:

-        df['subreddit_id_new'] = df.groupby("subreddit_id").ngroup()
+        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)
     else:
         df['subreddit_id_new'] = df['subreddit_id']
 
     if reindex:
         grouped = df.groupby(term_id)

-        df[term_id_new] = grouped.ngroup()
+        df[term_id_new] = grouped.ngroup() + 1 

     else:
         df[term_id_new] = df[term_id]
 
     else:
         df[term_id_new] = df[term_id]
 


@@ -157,7 +161,7 @@ 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)
     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], entries.subreddit_id_new)))
+    mat = csr_matrix((entries[tfidf_colname],(entries[term_id_new]-1, entries.subreddit_id_new-1)))

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


@@ -165,7 +169,6 @@ def similarities(inpath, simfunc, term_colname, outfile, min_df=None, max_df=Non
 
     print(f'computing similarities on mat. mat.shape:{mat.shape}')
     print(f"size of mat is:{mat.data.nbytes}",flush=True)
 
     print(f'computing similarities on mat. mat.shape:{mat.shape}')
     print(f"size of mat is:{mat.data.nbytes}",flush=True)

-    # transform this to debug term tfidf

     sims = simfunc(mat)
     del mat
 
     sims = simfunc(mat)
     del mat
 


@@ -229,7 +232,6 @@ def test_lsi_sims():
 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
 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]
 
     if type(n_components) is int:
         n_components = [n_components]


@@ -238,31 +240,33 @@ def lsi_column_similarities(tfidfmat,n_components=300,n_iter=10,random_state=196
     
     svd_components = n_components[0]
     
     
     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'))
         mod = pickle.load(open(lsi_model_load ,'rb'))

+        lsi_model_save = lsi_model_load

 
     else:
 
     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)
 
         svd = TruncatedSVD(n_components=svd_components,random_state=random_state,algorithm=algorithm,n_iter=n_iter)
         mod = svd.fit(tfidfmat.T)
 

-    lsimat = mod.transform(tfidfmat.T)

     if lsi_model_save is not None:
     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'))
 
         pickle.dump(mod, open(lsi_model_save,'wb'))
 

-    sims_list = []
+    print(n_components, flush=True)
+    lsimat = mod.transform(tfidfmat.T)

     for n_dims in n_components:
     for n_dims in n_components:

+        print("computing similarities", flush=True)

         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):
     return 1 - pairwise_distances(mat,metric='cosine')
 
 
 def column_similarities(mat):
     return 1 - pairwise_distances(mat,metric='cosine')
 

-# need to rewrite this so that subreddit ids and term ids are fixed over the whole thing.
-# this affords taking the LSI similarities.
-# fill all 0s if we don't have it.
+

 def build_weekly_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05):
     term = term_colname
     term_id = term + '_id'
 def build_weekly_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm05):
     term = term_colname
     term_id = term + '_id'


@@ -295,7 +299,6 @@ def build_weekly_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weig
     subreddits = df.select(['subreddit']).distinct()
     subreddits = subreddits.withColumn('subreddit_id',f.row_number().over(Window.orderBy("subreddit")))
 
     subreddits = df.select(['subreddit']).distinct()
     subreddits = subreddits.withColumn('subreddit_id',f.row_number().over(Window.orderBy("subreddit")))
 

-    # df = df.cache()

     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


@@ -313,11 +316,11 @@ 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'
 


@@ -335,7 +338,13 @@ 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


@@ -345,12 +354,12 @@ 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:


@@ -361,19 +370,19 @@ def _calc_tfidf(df, term_colname, tf_family):
     return df
     
 
     return df
     
 

-def 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'
     # 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'))
 
     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)
+    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
 

-def select_topN_subreddits(topN, path="/gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments_nonsfw.csv"):
+def select_topN_subreddits(topN, path="../../data/reddit_similarity/subreddits_by_num_comments_nonsfw.csv"):

     rankdf = pd.read_csv(path)
     included_subreddits = set(rankdf.loc[rankdf.comments_rank <= topN,'subreddit'].values)
     return included_subreddits
     rankdf = pd.read_csv(path)
     included_subreddits = set(rankdf.loc[rankdf.comments_rank <= topN,'subreddit'].values)
     return included_subreddits