from pyspark.sql import functions as f
from pyspark.sql import SparkSession
from pyspark.sql import Window
import numpy as np
import pyarrow
import pandas as pd
import fire
from itertools import islice
from pathlib import Path
from similarities_helper import cosine_similarities

spark = SparkSession.builder.getOrCreate()
conf = spark.sparkContext.getConf()

# outfile = '/gscratch/comdata/users/nathante/test_similarities_500.feather'; min_df = None; included_subreddits=None; similarity_threshold=0;
def author_cosine_similarities(outfile, min_df = None, included_subreddits=None, similarity_threshold=0, topN=500):
    '''
    Compute similarities between subreddits based on tfi-idf vectors of author comments
    
    included_subreddits : string
        Text file containing a list of subreddits to include (one per line) if included_subreddits is None then do the top 500 subreddits

    similarity_threshold : double (default = 0)
        set > 0 for large numbers of subreddits to get an approximate solution using the DIMSUM algorithm
https://stanford.edu/~rezab/papers/dimsum.pdf. If similarity_threshold=0 we get an exact solution using an O(N^2) algorithm.

    min_df : int (default = 0.1 * (number of included_subreddits)
         exclude terms that appear in fewer than this number of documents.

    outfile: string
         where to output csv and feather outputs
'''

    print(outfile)

    tfidf = spark.read.parquet('/gscratch/comdata/users/nathante/subreddit_tfidf_authors.parquet')

    if included_subreddits is None:
        included_subreddits = list(islice(open("/gscratch/comdata/users/nathante/cdsc-reddit/top_25000_subs_by_comments.txt"),topN))
        included_subreddits = {s.strip('\n') for s in included_subreddits}

    else:
        included_subreddits = set(open(included_subreddits))

    sim_dist, tfidf = cosine_similarities(tfidf, 'author', min_df, included_subreddits, similarity_threshold)

    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"))
    sim_dist = sim_dist.entries.toDF()

    sim_dist = sim_dist.repartition(1)
    sim_dist.write.parquet(str(output_parquet),mode='overwrite',compression='snappy')
    


    #instead of toLocalMatrix() why not read as entries and put strait into numpy
    sim_entries = pd.read_parquet(output_parquet)

    df = tfidf.select('subreddit','subreddit_id_new').distinct().toPandas()

    spark.stop()
    df['subreddit_id_new'] = df['subreddit_id_new'] - 1
    df = df.sort_values('subreddit_id_new').reset_index(drop=True)
    df = df.set_index('subreddit_id_new')

    similarities = sim_entries.join(df, on='i')
    similarities = similarities.rename(columns={'subreddit':"subreddit_i"})
    similarities = similarities.join(df, on='j')
    similarities = similarities.rename(columns={'subreddit':"subreddit_j"})

    similarities.to_feather(output_feather)
    similarities.to_csv(output_csv)
    return similarities
    
if __name__ == '__main__':
    fire.Fire(author_cosine_similarities)