def enumerate(row: ps.Row, enumerator: FragmentReactionSliceEnumerator, max_cuts: int) -> List[ps.Row]:
attachments = AttachmentPoints()
fields = row.split("\t")
smiles = fields[0]
mol = uc.to_mol(smiles)
out_rows = []
if mol:
for sliced_mol in enumerator.enumerate(mol, cuts=max_cuts):
row_dict = {
DataframeColumnsEnum.SCAFFOLDS:
attachments.remove_attachment_point_numbers(sliced_mol.scaffold_smiles),
DataframeColumnsEnum.DECORATIONS: sliced_mol.decorations_smiles,
DataframeColumnsEnum.ORIGINAL: sliced_mol.original_smiles,
DataframeColumnsEnum.MAX_CUTS: max_cuts}
out_rows.append(ps.Row(**row_dict))
return out_rows
def collect_failures(
self, row: ps.Row,
enumerator: FailingReactionsEnumerator) -> List[ps.Row]:
fields = row.split("\t")
smiles = fields[0]
mol = uc.to_mol(smiles)
out_rows = []
if mol:
for failed_reaction in enumerator.enumerate(
mol, failures_limit=self.configuration.failures_limit):
row_dict = {
self._columns.REACTION: failed_reaction.reaction_smirks,
self._columns.ORIGINAL: failed_reaction.molecule_smiles
}
print("found failed reaction")
out_rows.append(ps.Row(**row_dict))
if self.configuration.failures_limit <= len(out_rows):
break
return out_rows
return previous_row[-1]
movie_data = sys.argv[1]
user_data = sys.argv[2]
output = sys.argv[3]
userId = 112132212
movie = Row("id", "movieName")
movie_table = sc.textFile(movie_data + str("/movies.dat"))
rating_table = sc.textFile(movie_data + str("/ratings.dat"))
user_data_table = sc.textFile(movie_data + str("/users.dat"))
new_user = sc.textFile(user_data)
movieRDD = movie_table.map(lambda movie: movie.split("::"))
ratingDF = (rating_table.map(lambda rating: rating.split("::")).map(
lambda rate: (int(rate[0]), int(rate[1]), float(rate[2]))).map(
lambda (uid, mid, rate): Rating(uid, mid, rate))).toDF()
newUserRDD = new_user.map(lambda movie: movie.split(" ", 1))
joinRDD = movieRDD.cartesian(newUserRDD)
joinRDD = (joinRDD.map(lambda (movie, umovie): (movie[0], movie[1], umovie[
0], umovie[1])).map(lambda (id, movie, urate, umovie): (umovie, (
id, urate, levenshtein(movie, umovie)))).reduceByKey(
lambda x1, x2: min(x1, x2, key=lambda x: x[-1])))
userMovie = (
joinRDD.map(lambda (key, value): (userId, value[0], value[1])).map(
