<commit_before>from pyspark.mllib.recommendation import ALS
from numpy import array

# Load and parse the data
data = sc.textFile("mllib/data/als/test.data")
ratings = data.map(lambda line: array([float(x) for x in line.split(',')]))

# Build the recommendation model using Alternating Least Squares<commit_msg>minor change<commit_after>from pyspark.mllib.recommendation import ALS
from numpy import array

# Load and parse the data
# data = sc.textFile("mllib/data/als/test.data")
data = sc.textFile("data.txt")
ratings = data.map(lambda line: array([float(x) for x in line.split('\t')]))

# Build the recommendation model using Alternating Least Squares
{'fraction_non_alphanumeric': 0.11603053435114503, 'fraction_numerical': 0.0, 'mean_word_length': 5.626262626262626, 'pattern_counts': {'":': 0, '<': 3, '<?xml version=': 0, '>': 3, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '7184580', 'n_tokens_mistral': 214, 'n_tokens_neox': 207, 'n_words': 64}