from pyspark.sql import functions as f
from pyspark.sql import SparkSession
import pandas as pd
import fire
from pathlib import Path
from similarities_helper import prep_tfidf_entries, read_tfidf_matrix, select_topN_subreddits


def cosine_similarities(infile, term_colname, outfile, min_df=None, included_subreddits=None, topN=500, exclude_phrases=False):
    spark = SparkSession.builder.getOrCreate()
    conf = spark.sparkContext.getConf()
    print(outfile)
    print(exclude_phrases)

    tfidf = spark.read.parquet(infile)

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

    if exclude_phrases == True:
        tfidf = tfidf.filter(~f.col(term_colname).contains("_"))

    print("creating temporary parquet with matrix indicies")
    tempdir = prep_tfidf_entries(tfidf, term_colname, min_df, included_subreddits)
    tfidf = spark.read.parquet(tempdir.name)
    subreddit_names = tfidf.select(['subreddit','subreddit_id_new']).distinct().toPandas()
    subreddit_names = subreddit_names.sort_values("subreddit_id_new")
    subreddit_names['subreddit_id_new'] = subreddit_names['subreddit_id_new'] - 1
    spark.stop()

    print("loading matrix")
    mat = read_tfidf_matrix(tempdir.name, term_colname)
    print('computing similarities')
    sims = column_similarities(mat)
    del mat
    
    sims = pd.DataFrame(sims.todense())
    sims = sims.rename({i:sr for i, sr in enumerate(subreddit_names.subreddit.values)}, axis=1)
    sims['subreddit'] = subreddit_names.subreddit.values

    p = Path(outfile)

    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"))

    sims.to_feather(outfile)
    tempdir.cleanup()

def term_cosine_similarities(outfile, min_df=None, included_subreddits=None, topN=500, exclude_phrases=False):
    return cosine_similarities('/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms.parquet',
                               'term',
                               outfile,
                               min_df,
                               included_subreddits,
                               topN,
                               exclude_phrases)

def author_cosine_similarities(outfile, min_df=2, included_subreddits=None, topN=10000):
    return cosine_similarities('/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet',
                               'author',
                               outfile,
                               min_df,
                               included_subreddits,
                               topN,
                               exclude_phrases=False)

if __name__ == "__main__":
    fire.Fire({'term':term_cosine_similarities,
               'author':author_cosine_similarities})