<commit_before>import pandas as pd
import numpy as np
import pickle
from sklearn.preprocessing import MultiLabelBinarizer
from sklearn import datasets
from sklearn.multiclass import OneVsRestClassifier
from sklearn.svm import LinearSVC

xtrain = pd.read_pickle(open('Desktop/X_train.pickle', 'rb'))
ytrain = pd.read_pickle(open('Desktop/y_train.pickle', 'rb'))

xtrain = xtrain.iloc[:,-100:]
xtrain = xtrain.values

model = OneVsRestClassifier(LinearSVC(random_state=1), n_jobs=2)
model.fit(xtrain, ytrain)

filename = 'OvRmodel.sav'
pickle.dump(model, open(filename, 'wb'))<commit_msg>tweaked script<commit_after>import pandas as pd
import numpy as np
import pickle
from sklearn.preprocessing import MultiLabelBinarizer
from sklearn import datasets
from sklearn.multiclass import OneVsRestClassifier
from sklearn.svm import LinearSVC

xtrain = pd.read_pickle(open('X_train.pickle', 'rb'))
ytrain = pd.read_pickle(open('y_train.pickle', 'rb'))

xtrain = xtrain.iloc[:,-100:]
xtrain = xtrain.values

#model.fit(xtrain, ytrain)

def main():
	model = OneVsRestClassifier(LinearSVC(random_state=1), n_jobs=-1)

	filename = 'OvRmodel.sav'
	pickle.dump(model, open(filename, 'wb'))

if __name__ == "__main__":
	main()
{'fraction_non_alphanumeric': 0.11890999174236168, 'fraction_numerical': 0.008257638315441783, 'mean_word_length': 6.87012987012987, 'pattern_counts': {'":': 1, '<': 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': '4000917', 'n_tokens_mistral': 448, 'n_tokens_neox': 431, 'n_words': 98}