from pyspark.sql import SparkSession
from pyspark.sql import Window
from pyspark.sql import functions as f
from enum import Enum
from pyspark.mllib.linalg.distributed import CoordinateMatrix
from tempfile import TemporaryDirectory
import pyarrow
import pyarrow.dataset as ds
from sklearn.metrics import pairwise_distances
from scipy.sparse import csr_matrix, issparse
from sklearn.decomposition import TruncatedSVD
import pandas as pd
import numpy as np
import pathlib
from datetime import datetime
from pathlib import Path

class tf_weight(Enum):
    MaxTF = 1
    Norm05 = 2

infile = "/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_100k.parquet"
cache_file = "/gscratch/comdata/users/nathante/cdsc_reddit/similarities/term_tfidf_entries_bak.parquet"

def reindex_tfidf_time_interval(infile, term_colname, min_df=None, max_df=None, included_subreddits=None, topN=500, exclude_phrases=False, from_date=None, to_date=None):
    term = term_colname
    term_id = term + '_id'
    term_id_new = term + '_id_new'

    spark = SparkSession.builder.getOrCreate()
    conf = spark.sparkContext.getConf()
    print(exclude_phrases)
    tfidf_weekly = spark.read.parquet(infile)

    # create the time interval
    if from_date is not None:
        if type(from_date) is str:
            from_date = datetime.fromisoformat(from_date)

        tfidf_weekly = tfidf_weekly.filter(tfidf_weekly.week >= from_date)
        
    if to_date is not None:
        if type(to_date) is str:
            to_date = datetime.fromisoformat(to_date)
        tfidf_weekly = tfidf_weekly.filter(tfidf_weekly.week < to_date)

    tfidf = tfidf_weekly.groupBy(["subreddit","week", term_id, term]).agg(f.sum("tf").alias("tf"))
    tfidf = _calc_tfidf(tfidf, term_colname, tf_weight.Norm05)
    tempdir = prep_tfidf_entries(tfidf, term_colname, min_df, max_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
    return(tempdir, subreddit_names)

# subreddits missing after this step don't have any terms that have a high enough idf
def reindex_tfidf(infile, term_colname, min_df=None, max_df=None, included_subreddits=None, topN=500,  tf_family=tf_weight.MaxTF):
    spark = SparkSession.builder.getOrCreate()
    conf = spark.sparkContext.getConf()
    print(exclude_phrases)

    tfidf_ds = ds.dataset(infile)

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

    ds_filter = ds.field("subreddit").isin(included_subreddits)

    if min_df is not None:
        ds_filter &= ds.field("count") >= min_df

    if max_df is not None:
        ds_filter &= ds.field("count") <= max_df

    term = term_colname
    term_id = term + '_id'
    term_id_new = term + '_id_new'

    df = tfidf_ds.to_table(filter=ds_filter,columns=['subreddit','subreddit_id',term_id,'relative_tf']).to_pandas()

    sub_ids = df.subreddit_id.drop_duplicates()
    new_sub_ids = pd.DataFrame({'subreddit_id':old,'subreddit_id_new':new} for new, old in enumerate(sorted(sub_ids)))
    df = df.merge(new_sub_ids,on='subreddit_id',how='inner',validate='many_to_one')

    new_count = df.groupby(term_id)[term_id].aggregate(new_count='count').reset_index()
    df = df.merge(new_count,on=term_id,how='inner',validate='many_to_one')

    term_ids = df[term_id].drop_duplicates()
    new_term_ids = pd.DataFrame({term_id:old,term_id_new:new} for new, old in enumerate(sorted(term_ids)))

    df = df.merge(new_term_ids, on=term_id, validate='many_to_one')
    N_docs = sub_ids.shape[0]

    df['idf'] = np.log(N_docs/(1+df.new_count)) + 1

    # agg terms by subreddit to make sparse tf/df vectors
    if tf_family == tf_weight.MaxTF:
        df["tf_idf"] = df.relative_tf * df.idf
    else: # tf_fam = tf_weight.Norm05
        df["tf_idf"] = (0.5 + 0.5 * df.relative_tf) * df.idf

    subreddit_names = df.loc[:,['subreddit','subreddit_id_new']].drop_duplicates()
    subreddit_names = subreddit_names.sort_values("subreddit_id_new")
    return(df, subreddit_names)


def similarities(infile, simfunc, term_colname, outfile, min_df=None, max_df=None, included_subreddits=None, topN=500, exclude_phrases=False, from_date=None, to_date=None, tfidf_colname='tf_idf'):
    '''
    tfidf_colname: set to 'relative_tf' to use normalized term frequency instead of tf-idf, which can be useful for author-based similarities.
    '''
    if from_date is not None or to_date is not None:
        tempdir, subreddit_names = reindex_tfidf_time_interval(infile, term_colname=term_colname, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=topN, exclude_phrases=False, from_date=from_date, to_date=to_date)
        mat = read_tfidf_matrix(tempdir.name, term_colname, tfidf_colname)        
    else:
        entries, subreddit_names = reindex_tfidf(infile, term_colname=term_colname, min_df=min_df, max_df=max_df, included_subreddits=included_subreddits, topN=topN, exclude_phrases=False)
        mat = csr_matrix((entries[tfidf_colname],(entries[term_id_new]-1, entries.subreddit_id_new-1)))

    print("loading matrix")        

    #    mat = read_tfidf_matrix("term_tfidf_entries7ejhvnvl.parquet", term_colname)

    print(f'computing similarities on mat. mat.shape:{mat.shape}')
    print(f"size of mat is:{mat.data.nbytes}")
    sims = simfunc(mat)
    del mat

    if issparse(sims):
        sims = sims.todense()

    print(f"shape of sims:{sims.shape}")
    print(f"len(subreddit_names.subreddit.values):{len(subreddit_names.subreddit.values)}")
    sims = pd.DataFrame(sims)
    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 read_tfidf_matrix_weekly(path, term_colname, week, tfidf_colname='tf_idf'):
    term = term_colname
    term_id = term + '_id'
    term_id_new = term + '_id_new'

    dataset = ds.dataset(path,format='parquet')
    entries = dataset.to_table(columns=[tfidf_colname,'subreddit_id_new', term_id_new],filter=ds.field('week')==week).to_pandas()
    return(csr_matrix((entries[tfidf_colname], (entries[term_id_new]-1, entries.subreddit_id_new-1))))

def read_tfidf_matrix(path, term_colname, tfidf_colname='tf_idf'):
    term = term_colname
    term_id = term + '_id'
    term_id_new = term + '_id_new'
    dataset = ds.dataset(path,format='parquet')
    print(f"tfidf_colname:{tfidf_colname}")
    entries = dataset.to_table(columns=[tfidf_colname, 'subreddit_id_new',term_id_new]).to_pandas()
    return(csr_matrix((entries[tfidf_colname],(entries[term_id_new]-1, entries.subreddit_id_new-1))))
    

def write_weekly_similarities(path, sims, week, names):
    sims['week'] = week
    p = pathlib.Path(path)
    if not p.is_dir():
        p.mkdir(exist_ok=True,parents=True)
        
    # reformat as a pairwise list
    sims = sims.melt(id_vars=['_subreddit','week'],value_vars=names.subreddit.values)
    sims.to_parquet(p / week.isoformat())

def column_overlaps(mat):
    non_zeros = (mat != 0).astype('double')
    
    intersection = non_zeros.T @ non_zeros
    card1 = non_zeros.sum(axis=0)
    den = np.add.outer(card1,card1) - intersection

    return intersection / den
    
# n_components is the latent dimensionality. sklearn recommends 100. More might be better
# if algorithm is 'random' instead of 'arpack' then n_iter gives the number of iterations.
# this function takes the svd and then the column similarities of it
def lsi_column_similarities(tfidfmat,n_components=300,n_iter=5,random_state=1968,algorithm='arpack'):
    # first compute the lsi of the matrix
    # then take the column similarities
    svd = TruncatedSVD(n_components=n_components,random_state=random_state,algorithm='arpack')
    mod = svd.fit(tfidfmat.T)
    lsimat = mod.transform(tfidfmat.T)
    sims = column_similarities(lsimat)
    return sims
    

def column_similarities(mat):
    return 1 - pairwise_distances(mat,metric='cosine')
    # if issparse(mat):
    #     norm = np.matrix(np.power(mat.power(2).sum(axis=0),0.5,dtype=np.float32))
    #     mat = mat.multiply(1/norm)
    # else:
    #     norm = np.matrix(np.power(np.power(mat,2).sum(axis=0),0.5,dtype=np.float32))
    #     mat = np.multiply(mat,1/norm)
    # sims = mat.T @ mat
    # return(sims)


def prep_tfidf_entries_weekly(tfidf, term_colname, min_df, max_df, included_subreddits):
    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('count') >= min_df)
    if max_df is not None:
        tfidf = tfidf.filter(f.col('count') <= max_df)

    tfidf = tfidf.filter(f.col("subreddit").isin(included_subreddits))

    # we might not have the same terms or subreddits each week, so we need to make unique ids for each week.
    sub_ids = tfidf.select(['subreddit_id','week']).distinct()
    sub_ids = sub_ids.withColumn("subreddit_id_new",f.row_number().over(Window.partitionBy('week').orderBy("subreddit_id")))
    tfidf = tfidf.join(sub_ids,['subreddit_id','week'])

    # only use terms in at least min_df included subreddits in a given week
    new_count = tfidf.groupBy([term_id,'week']).agg(f.count(term_id).alias('new_count'))
    tfidf = tfidf.join(new_count,[term_id,'week'],how='inner')

    # reset the term ids
    term_ids = tfidf.select([term_id,'week']).distinct()
    term_ids = term_ids.withColumn(term_id_new,f.row_number().over(Window.partitionBy('week').orderBy(term_id)))
    tfidf = tfidf.join(term_ids,[term_id,'week'])

    tfidf = tfidf.withColumnRenamed("tf_idf","tf_idf_old")
    tfidf = tfidf.withColumn("tf_idf", (tfidf.relative_tf * tfidf.idf).cast('float'))

    tempdir =TemporaryDirectory(suffix='.parquet',prefix='term_tfidf_entries',dir='.')

    tfidf = tfidf.repartition('week')

    tfidf.write.parquet(tempdir.name,mode='overwrite',compression='snappy')
    return(tempdir)
    

def prep_tfidf_entries(tfidf, term_colname, min_df, max_df, included_subreddits):
    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('count') >= min_df)
    if max_df is not None:
        tfidf = tfidf.filter(f.col('count') <= max_df)

    tfidf = tfidf.filter(f.col("subreddit").isin(included_subreddits))

    # 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'))
    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.withColumn("tf_idf", (tfidf.relative_tf * tfidf.idf).cast('float'))
    
    tempdir =TemporaryDirectory(suffix='.parquet',prefix='term_tfidf_entries',dir='.')
    
    tfidf.write.parquet(tempdir.name,mode='overwrite',compression='snappy')
    return tempdir


# try computing cosine similarities using spark
def spark_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'))
    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.withColumn("tf_idf", tfidf.relative_tf * tfidf.idf)

    # step 1 make an rdd of entires
    # sorted by (dense) spark subreddit id
    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_weekly_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,'week']).agg(f.sum('tf').alias('tf'))

    max_subreddit_terms = df.groupby(['subreddit','week']).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','week'])
    df = df.withColumn("relative_tf", df.tf / df.sr_max_tf)

    # group by term. term is unique
    idf = df.groupby([term,'week']).count()

    N_docs = df.select(['subreddit','week']).distinct().groupby(['week']).agg(f.count("subreddit").alias("subreddits_in_week"))

    idf = idf.join(N_docs, on=['week'])

    # add a little smoothing to the idf
    idf = idf.withColumn('idf',f.log(idf.subreddits_in_week) / (1+f.col('count'))+1)

    # collect the dictionary to make a pydict of terms to indexes
    terms = idf.select([term,'week']).distinct() # terms are distinct

    terms = terms.withColumn(term_id,f.row_number().over(Window.partitionBy('week').orderBy(term))) # term ids are distinct

    # make subreddit ids
    subreddits = df.select(['subreddit','week']).distinct()
    subreddits = subreddits.withColumn('subreddit_id',f.row_number().over(Window.partitionBy("week").orderBy("subreddit")))

    df = df.join(subreddits,on=['subreddit','week'])

    # map terms to indexes in the tfs and the idfs
    df = df.join(terms,on=[term,'week']) # subreddit-term-id is unique

    idf = idf.join(terms,on=[term,'week'])

    # join on subreddit/term to create tf/dfs indexed by term
    df = df.join(idf, on=[term_id, term,'week'])

    # 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

def _calc_tfidf(df, term_colname, tf_family):
    term = term_colname
    term_id = term + '_id'

    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
    

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'))

    df = _calc_tfidf(df, term_colname, tf_family)

    return df

def select_topN_subreddits(topN, path="/gscratch/comdata/output/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