<commit_before>import numpy as np
import pandas as pd
from mock import MagicMock


def prepare_x(shape):
    return np.random.rand(*shape)


def prepare_y(shape):
    return np.random.randint(0, 2, shape)


def load_data():
    return prepare_x((10, 10)), prepare_y(10), prepare_x((5, 5)), prepare_y(5)


def create_models(n, predicted_value):
    models = []
    for i in range(n):
        new_model = MagicMock()
        new_model.predict.return_value = predicted_value
        new_model.predict_proba.return_value = np.stack([predicted_value,
                                                         1 - predicted_value]).T
        models.append(new_model)
    return models<commit_msg>Cleaning file<commit_after>import numpy as np
from mock import MagicMock


def prepare_x(shape):
    return np.random.rand(*shape)


def prepare_y(shape):
    return np.random.randint(0, 2, shape)


def load_data():
    return prepare_x((10, 10)), prepare_y(10), prepare_x((5, 5)), prepare_y(5)


def create_models(n, predicted_value):
    models = []
    for i in range(n):
        new_model = MagicMock()
        new_model.predict.return_value = predicted_value
        new_model.predict_proba.return_value = np.stack([predicted_value,
                                                         1 - predicted_value],
                                                        axis=1)
        models.append(new_model)
    return models
{'fraction_non_alphanumeric': 0.09929577464788733, 'fraction_numerical': 0.017605633802816902, 'mean_word_length': 2.3046511627906976, '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': 6, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '4461863', 'n_tokens_mistral': 431, 'n_tokens_neox': 407, 'n_words': 97}