Refactor tfidf code to for code resuse.

[cdsc_reddit.git] / similarities_helper.py

diff --git a/similarities_helper.py b/similarities_helper.py
new file mode 100644 (file)
index 0000000..c69983f
--- /dev/null
+++ b/similarities_helper.py
@@ -0,0 +1,116 @@
+from pyspark.sql import Window
+from pyspark.sql import functions as f
+from enum import Enum
+from pyspark.mllib.linalg.distributed import CoordinateMatrix
+
+class tf_weight(Enum):
+    MaxTF = 1
+    Norm05 = 2
+
+
+def cosine_similarities(tfidf, term_colname, min_df, included_subreddits, similarity_threshold):
+    term = term_colname
+    term_id = term + '_id'
+    term_id_new = term + '_id_new'
+
+    if min_df is None:
+        min_df = 0.1 * len(included_subreddits)
+
+    tfidf = tfidf.filter(f.col("subreddit").isin(included_subreddits))
+    tfidf = tfidf.cache()
+
+    # reset the subreddit ids
+    sub_ids = tfidf.select('subreddit_id').distinct()
+    sub_ids = sub_ids.withColumn("subreddit_id_new",f.row_number().over(Window.orderBy("subreddit_id")))
+    tfidf = tfidf.join(sub_ids,'subreddit_id')
+
+    # only use terms in at least min_df included subreddits
+    new_count = tfidf.groupBy(term_id).agg(f.count(term_id).alias('new_count'))
+#    new_count = new_count.filter(f.col('new_count') >= min_df)
+    tfidf = tfidf.join(new_count,term_id,how='inner')
+    
+    # reset the term ids
+    term_ids = tfidf.select([term_id]).distinct()
+    term_ids = term_ids.withColumn(term_id_new,f.row_number().over(Window.orderBy(term_id)))
+    tfidf = tfidf.join(term_ids,term_id)
+
+    tfidf = tfidf.withColumnRenamed("tf_idf","tf_idf_old")
+    # tfidf = tfidf.withColumnRenamed("idf","idf_old")
+    # tfidf = tfidf.withColumn("idf",f.log(25000/f.col("count")))
+    tfidf = tfidf.withColumn("tf_idf", tfidf.relative_tf * tfidf.idf)
+
+    # step 1 make an rdd of entires
+    # sorted by (dense) spark subreddit id
+    #    entries = tfidf.filter((f.col('subreddit') == 'asoiaf') | (f.col('subreddit') == 'gameofthrones') | (f.col('subreddit') == 'christianity')).select(f.col("term_id_new")-1,f.col("subreddit_id_new")-1,"tf_idf").rdd
+ 
+    n_partitions = int(len(included_subreddits)*2 / 5)
+
+    entries = tfidf.select(f.col(term_id_new)-1,f.col("subreddit_id_new")-1,"tf_idf").rdd.repartition(n_partitions)
+
+    # put like 10 subredis in each partition
+
+    # step 2 make it into a distributed.RowMatrix
+    coordMat = CoordinateMatrix(entries)
+
+    coordMat = CoordinateMatrix(coordMat.entries.repartition(n_partitions))
+
+    # this needs to be an IndexedRowMatrix()
+    mat = coordMat.toRowMatrix()
+
+    #goal: build a matrix of subreddit columns and tf-idfs rows
+    sim_dist = mat.columnSimilarities(threshold=similarity_threshold)
+
+    return (sim_dist, tfidf)
+
+
+def build_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'))
+
+    max_subreddit_terms = df.groupby(['subreddit']).max('tf') # subreddits are unique
+    max_subreddit_terms = max_subreddit_terms.withColumnRenamed('max(tf)','sr_max_tf')
+
+    df = df.join(max_subreddit_terms, on='subreddit')
+
+    df = df.withColumn("relative_tf", df.tf / df.sr_max_tf)
+
+    # group by term. term is unique
+    idf = df.groupby([term]).count()
+
+    N_docs = df.select('subreddit').distinct().count()
+
+    # add a little smoothing to the idf
+    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 = terms.withColumn(term_id,f.row_number().over(Window.orderBy(term))) # term ids are distinct
+
+    # make subreddit ids
+    subreddits = df.select(['subreddit']).distinct()
+    subreddits = subreddits.withColumn('subreddit_id',f.row_number().over(Window.orderBy("subreddit")))
+
+    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
+
+    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])
+
+    # agg terms by subreddit to make sparse tf/df vectors
+    
+    if tf_family == tf_weight.MaxTF:
+        df = df.withColumn("tf_idf",  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)
+
+    return df
+
+