Tear me to shreds. The class RandomForestRetrainer will be used to retrain a machine learning algorithm. It has functionality for taking in a directory containing malware or benignware files and splitting them into training and testing sets, creating statistics from these files, creating concatenated mw/bw training/testing stat files, balancing the mw count with bw count via a reduction algorithm, and finally sending them to the ML classifier for training.p.s. let me know if you want to see code from the other classes import osimport datetimefrom retraining.StatsFile import StatsFilefrom retraining.Dataset import Datasetfrom retraining.Partitioner import Partitionerfrom retraining.GridBasedBalancingRandom import GridBasedBalancingRandomfrom retraining.Classifier import Classifierimport configclass RandomForestRetrainer(object):def __init__(self, previous_mw_dataset=None,                   previous_bw_dataset=None):    self.mw_datasets = []    self.bw_datasets = []    self.malware = None    self.benignware = None    self.data_folder = self._initialize_data_folder()    self.balancer = GridBasedBalancingRandom()    self.classifier = Classifier(self.data_folder)    if previous_mw_dataset is not None:        if type(previous_mw_dataset) is not Dataset:            raise TypeError(one or more arguments not of type Dataset)        self.mw_datasets.append(previous_mw_dataset)    if previous_bw_dataset is not None:        if type(previous_bw_dataset) is not Dataset:            raise TypeError(one or more arguments not of type Dataset)        self.bw_datasets.append(previous_bw_dataset)def add_malware_dataset(self, path):    if type(path) is not str:        raise TypeError(path must be a string)    dataset = self._build_dataset_from_path(path, is_malware=True)    self.mw_datasets.append(dataset)def add_benignware_dataset(self, path):    if type(path) is not str:        raise TypeError(path must be a string)    dataset = self._build_dataset_from_path(path, is_malware=False)    self.bw_datasets.append(dataset)def malware_count(self):    return self._count_files_in_datasets(self.mw_datasets)def benignware_count(self):    return self._count_files_in_datasets(self.bw_datasets)def concatenate_stat_files(self):    if not self.mw_datasets and not self.bw_datasets:        raise RuntimeError(No datasets have been added)    mw_train, mw_test, bw_train, bw_test = self._create_concatenated_stat_files()    self.malware = Dataset(mw_train, mw_test, is_malware=True)    self.benignware = Dataset(bw_train, bw_test, is_malware=False)         def balance_datasets(self):    if not self.malware and not self.benignware:        raise RuntimeError(Concatenated stat files have not been created)    self.balancer = GridBasedBalancingRandom()    self.balancer.set_malware_dataset(self.malware)    self.balancer.set_benignware_dataset(self.benignware)    self.balancer.balance()def train(self):    if not self.malware and not self.benignware:        raise RuntimeError(Concatenated stat files have not been created)    self.classifier.add_training_data(self._get_malware_training_stats_path())    self.classifier.add_training_data(self._get_benignware_training_stats_path())    return self.classifier.train()def run_test_metrics(self):    if not self.malware and not self.benignware:        raise RuntimeError(Concatenated stat files have not been created)    self.classifier.add_testing_data(self._get_malware_testing_stats_path())    self.classifier.add_testing_data(self._get_benignware_testing_stats_path())    return self.classifier.test()'''Private'''def _initialize_data_folder(self):    data_folder = os.path.join(config.BASE_DATA_FOLDER,                                    datetime.datetime.now().isoformat())    if not os.path.isdir(config.BASE_DATA_FOLDER):        os.makedirs(config.BASE_DATA_FOLDER)    os.makedirs(data_folder)    return data_folderdef _build_dataset_from_path(self, path, is_malware):    partitioner = Partitioner(path, self.data_folder)    partitioner.process()    training = StatsFile(partitioner.training_stats_file)    testing = StatsFile(partitioner.testing_stats_file)    return Dataset(training, testing, is_malware)def _count_files_in_datasets(self, datasets):    training = 0    testing = 0    for dataset in datasets:        training += dataset.training_stats.get_count()        testing += dataset.testing_stats.get_count()    return training, testingdef _create_concatenated_stat_files(self):    mw_train_file = open(self._get_malware_training_stats_path(), 'w')    mw_test_file = open(self._get_malware_testing_stats_path(), 'w')    bw_train_file = open(self._get_benignware_training_stats_path(), 'w')    bw_test_file = open(self._get_benignware_testing_stats_path(), 'w')    for dataset in self.mw_datasets:        self._write_stats_to_file(dataset, mw_train_file, mw_test_file)    for dataset in self.bw_datasets:        self._write_stats_to_file(dataset, bw_train_file, bw_test_file)    mw_train_file.close()    mw_test_file.close()    bw_train_file.close()    bw_test_file.close()    mw_train = StatsFile(self._get_malware_training_stats_path())    mw_test = StatsFile(self._get_malware_testing_stats_path())    bw_train = StatsFile(self._get_benignware_training_stats_path())    bw_test = StatsFile(self._get_benignware_testing_stats_path())    return mw_train, mw_test, bw_train, bw_test def _write_stats_to_file(self, dataset, training_file, testing_file):    training_file.writelines(dataset.training_stats.get_stats())    testing_file.writelines(dataset.testing_stats.get_stats())def _get_malware_training_stats_path(self):    return os.path.join(self.data_folder, config.CONCATENATED_MALWARE_TRAINING_STATS)def _get_benignware_training_stats_path(self):    return os.path.join(self.data_folder, config.CONCATENATED_BENIGNWARE_TRAINING_STATS)def _get_malware_testing_stats_path(self):    return os.path.join(self.data_folder, config.CONCATENATED_MALWARE_TESTING_STATS)def _get_benignware_testing_stats_path(self):    return os.path.join(self.data_folder, config.CONCATENATED_BENIGNWARE_TESTING_STATS) 
ML Retraining project
python;machine learning
My knowledge of machine learning is a rounding error, so I cant assess the accuracy of the code. I can provide some feedback on the general programming quality:There are no comments or documentation. None, nada, zilch. This program would be significantly improved if there were some comments or docstrings, so that I could tell what the code was supposed to do. Explaining the motivation behind the code will make it much easier to read, review and maintain.You have a lot of checks of the form:if type(foo) is not bar:    raise TypeError(foo is not of type bar)One potential risk here is that you dont cater for inheritance. Suppose I have a subclass of bar called girder. If I pass in a variable of type girder, it will raise a TypeError, even though it probably supports the same interface as bar and is probably fine.The alternative is to useif not isinstance(foo, bar):    raise TypeError(foo is not of type bar or of a subclass of bar)This answer on Stack Overflow explains the difference between type() and isinstance() quite well. Im not saying you should definitely use one or the other, but unless I had a good reason Id usually use isinstance().You add the balancer attribute to the class in the balance_datasets() method. This is sometimes frowned upon  even if you initialise it to None, its good to declare all your attributes up front in the constructor. It makes it easier to find out what sort of attributes your class might have.The one docstring in the file (private) is incorrect. Strictly speaking, these are protected methods, not private.Python doesnt have access control for methods and attributes. The rules are enforced by convention, and everybody is expected to behave sensibly (were all consenting adults). https://stackoverflow.com/a/797814The _initialize_data_folder() method is subject to a race condition. If the base data folder doesnt exist, but is created between your if statement and the os.makedirs() call, youll throw an OSError.A better approach is to pass the exist_ok method to os.makedirs(), which will suppress the error if the folder already exists.You have a bunch of methods that start with the word get. It would be more Pythonic to decorate these with @property and use them as attributes.In the _create_concatenated_state_files() method, you should really use with when opening files:with open(self.malware_training_stats_path, 'w') as mw_train_file,     open(self.malware_testing_stats_path, 'w') as mw_test_file:    for dataset in self.mw_datasets:        self._write_stats_to_file(dataset, mw_train_file, mw_test_file)This ensures that the file is closed correctly, even if the body throws an exception, and ensures the close() call cannot be forgotten or omitted.