<jupyter_start><jupyter_text>Let's consider Italy.<jupyter_code>country = 'italy'<jupyter_output><empty_output><jupyter_text>getting all the datasets which we separated into the training and testing sets using test.py<jupyter_code>path = Path.cwd()
country = 'italy'
processed_data_path = path.parent / 'data' / 'processed'

x_train_file_path = processed_data_path / (
                    country + '_features.csv')
y_train_file_path = processed_data_path / (
                        country + '_labels.csv')
print(load(x_train_file_path).T.shape)
print(load(y_train_file_path, is_labels=True).shape)
x_train_file_path = path.parent / 'data' / 'test'/ country / 'X_train.csv'
x_train_file_path
y_train_file_path = path.parent / 'data' / 'test'/ country / 'y_train.csv'
features_train = sg.create_features(load(x_train_file_path).T)
labels_train = sg.create_labels(
                    load(y_train_file_path, is_labels=True))
mean_rule = sg.create_combination_rule("MeanRule")
rand_forest = sg.create_machine("RandomForest",
                                            labels=labels_train,
                                            num_bags=5, seed=1,
                                            combination_rule=mean_rule)
rand_forest.train(features_train)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.10240496508921644, 'fraction_numerical': 0.0015515903801396431, 'mean_word_length': 3.056603773584906, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, '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}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '303518', 'license': 'BSD-3-Clause', 'n_tokens_mistral': 416, 'n_tokens_neox': 397, 'n_words': 64, 'path': 'gsoc_application_projects/2020/influenza/model/notebooks/serialization.ipynb', 'repo_name': 'Hephaestus12/applications', 'size': '1289'}