<commit_before>import sys
import pandas as pd
import numpy as np
import itertools
from sklearn.preprocessing import StandardScaler
from sklearn.svm import LinearSVC
from sklearn.cross_validation import cross_val_score
from sklearn.pipeline import make_pipeline
import itertools

dataset = sys.argv[1]

# Read the data set into memory
input_data = pd.read_csv(dataset, compression='gzip', sep='\t')

for (C, loss, penalty, dual, tol, fit_intercept) in itertools.product(np.concatenate(np.arange(0., 1.0, 0.1), np.arange(1., 10.01, 1.)),
                                                                      ['hinge', 'squared_hinge'],
                                                                      ['l1', 'l2'],
                                                                      [True, False],
                                                                      [1e-4],
                                                                      [True]):
    features = input_data.drop('class', axis=1).values.astype(float)
    labels = input_data['class'].values

    try:
        # Create the pipeline for the model
        clf = make_pipeline(StandardScaler(),
                            LinearSVC(C=C,
                                      loss=loss,<commit_msg>Update LinearSVC.py<commit_after>import sys
import pandas as pd
import numpy as np
import itertools
from sklearn.preprocessing import StandardScaler
from sklearn.svm import LinearSVC
from sklearn.cross_validation import cross_val_score
from sklearn.pipeline import make_pipeline
import itertools

dataset = sys.argv[1]

# Read the data set into memory
input_data = pd.read_csv(dataset, compression='gzip', sep='\t')

for (C, loss, penalty, dual, tol, fit_intercept) in itertools.product(np.concatenate((np.arange(0., 1.0, 0.1),
                                                                                      np.arange(1., 10.01, 1.))),
                                                                      ['hinge', 'squared_hinge'],
                                                                      ['l1', 'l2'],
                                                                      [True, False],
                                                                      [1e-4],
                                                                      [True]):
    features = input_data.drop('class', axis=1).values.astype(float)
    labels = input_data['class'].values

    try:
        # Create the pipeline for the model
        clf = make_pipeline(StandardScaler(),
                            LinearSVC(C=C,
                                      loss=loss,
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{'dir': 'git-commits-cleaned', 'id': '7218035', 'n_tokens_mistral': 621, 'n_tokens_neox': 603, 'n_words': 160}