def __init__(self, q = 10, variables = None): if not isinstance(q, int): raise ValueError('q must be an integer') self.q = q self.variables = _define_variables(variables)
def __init__(self, imputation_method='median', variables = None): if imputation_method not in ['median', 'mean']: raise ValueError("Imputation method takes only values 'median' or 'mean'") self.imputation_method = imputation_method self.variables = _define_variables(variables)
def __init__(self, exp = 0.5, variables = None): if not isinstance(exp, float) and not isinstance(exp, int): raise ValueError('exp must be a float or an int') self.exp = exp self.variables = _define_variables(variables)
def __init__(self, variables=None, random_state=None, seed='general', seeding_method='add'): if seed not in ['general', 'observation']: raise ValueError( "seed takes only values 'general' or 'observation'") if seeding_method not in ['add', 'multiply']: raise ValueError( "seeding_method takes only values 'add' or 'multiply'") if seed == 'general' and random_state: if not isinstance(random_state, int): raise ValueError( "if the seed == 'general' the random state must take an integer" ) if isinstance(random_state, str): random_state = list(random_state) self.variables = _define_variables(variables) self.random_state = random_state self.seed = seed self.seeding_method = seeding_method
def __init__(self, arbitrary_number = -999, variables = None): if isinstance(arbitrary_number, int) or isinstance(arbitrary_number, float): self.arbitrary_number = arbitrary_number else: raise ValueError('Arbitrary number must be numeric of type int or float') self.variables = _define_variables(variables)
def __init__(self, encoding_method='count', variables=None): if encoding_method not in ['count', 'frequency']: raise ValueError( "encoding_method takes only values 'count' and 'frequency'") self.encoding_method = encoding_method self.variables = _define_variables(variables)
def __init__(self, encoding_method='woe', variables=None): if encoding_method not in ['woe', 'ratio']: raise ValueError( "encoding_method takes only values 'woe' and 'ratio'") self.encoding_method = encoding_method self.variables = _define_variables(variables)
def __init__(self, encoding_method='ordered', variables=None): if encoding_method not in ['ordered', 'arbitrary']: raise ValueError( "encoding_method takes only values 'ordered' and 'arbitrary'") self.encoding_method = encoding_method self.variables = _define_variables(variables)
def __init__(self, bins = 10, variables = None, return_object=False): if not isinstance(bins, int): raise ValueError('q must be an integer') self.bins = bins self.variables = _define_variables(variables) self.return_object = return_object
def __init__(self, cv = 3, scoring='neg_mean_squared_error', variables = None, regression=True): if not isinstance(cv, int) or cv < 0: raise ValueError('cv can only take only positive integers') if not isinstance(regression, bool): raise ValueError('regression can only True or False') self.cv = cv self.scoring = scoring self.regression = regression self.variables = _define_variables(variables)
def __init__(self, tol=0.05, n_categories=10, variables=None): if tol < 0 or tol > 1: raise ValueError("tol takes values between 0 and 1") if n_categories < 0 or not isinstance(n_categories, int): raise ValueError( "n_categories takes only positive integer numbers") self.tol = tol self.n_categories = n_categories self.variables = _define_variables(variables)
def __init__(self, top_categories=None, variables=None, drop_last=False): if top_categories: if not isinstance(top_categories, int): raise ValueError( "top_categories takes only integer numbers, 1, 2, 3, etc.") self.top_categories = top_categories if drop_last not in [True, False]: raise ValueError("drop_last takes only True or False") self.drop_last = drop_last self.variables = _define_variables(variables)
def __init__(self, distribution='gaussian', tail='right', fold=3, variables = None): if distribution not in ['gaussian', 'skewed']: raise ValueError("distribution takes only values 'gaussian' or 'skewed'") if tail not in ['right', 'left']: raise ValueError("tail takes only values 'right' or 'left'") if fold <=0 : raise ValueError("fold takes only positive numbers") self.distribution = distribution self.tail = tail self.fold = fold self.variables = _define_variables(variables)
def __init__(self, cv = 3, scoring='neg_mean_squared_error', variables = None, param_grid = {'max_depth': [1,2,3,4]}, regression=True, random_state=None): if not isinstance(cv, int) or cv < 0: raise ValueError('cv can only take only positive integers') if not isinstance(regression, bool): raise ValueError('regression can only take True or False') self.cv = cv self.scoring = scoring self.regression = regression self.variables = _define_variables(variables) self.param_grid = param_grid self.random_state = random_state
def __init__(self, variables=None): self.variables = _define_variables(variables)
def __init__(self, variables=None, random_state=0): self.variables = _define_variables(variables) self.random_state = random_state