def _maybe_convert_data(self, data, target,
*args, **kwargs):
"""
Internal function to instanciate data and target
Parameters
----------
data : instance converted to ``pandas.DataFrame``
target : instance converted to ``pandas.Series``
args : argument passed from ``__init__``
kwargs : argument passed from ``__init__``
"""
init_df = isinstance(data, pd.DataFrame)
init_target = isinstance(target, (pd.Series, pd.DataFrame))
def _maybe_convert_target(data, target, index=None):
if data is not None:
index = data.index
target = np.array(target)
if len(target.shape) == 1:
target = pd.Series(target, index=index)
else:
target = pd.DataFrame(target, index=index)
return target
if not init_df and not init_target:
if data is not None:
data = pd.DataFrame(data, *args, **kwargs)
if is_list_like(target):
target = _maybe_convert_target(data, target)
elif not init_df:
if data is not None:
index = kwargs.pop('index', target.index)
data = pd.DataFrame(data, index=index, *args, **kwargs)
elif not init_target:
if is_list_like(target):
target = _maybe_convert_target(data, target)
else:
# no conversion required
pass
if isinstance(target, pd.Series) and target.name is None:
target = pd.Series(target, name=self._TARGET_NAME)
return data, target
def _maybe_convert_data(self, data, target,
*args, **kwargs):
"""
Internal function to instanciate data and target
Parameters
----------
data : instance converted to ``pandas.DataFrame``
target : instance converted to ``pandas.Series``
args : argument passed from ``__init__``
kwargs : argument passed from ``__init__``
"""
init_df = isinstance(data, pd.DataFrame)
init_target = isinstance(target, (pd.Series, pd.DataFrame))
def _maybe_convert_target(data, target, index=None):
if data is not None:
index = data.index
target = np.array(target)
if len(target.shape) == 1:
target = pd.Series(target, index=index)
else:
target = pd.DataFrame(target, index=index)
return target
if not init_df and not init_target:
if data is not None:
data = pd.DataFrame(data, *args, **kwargs)
if is_list_like(target):
target = _maybe_convert_target(data, target)
elif not init_df:
if data is not None:
index = kwargs.pop('index', target.index)
data = pd.DataFrame(data, index=index, *args, **kwargs)
elif not init_target:
if is_list_like(target):
target = _maybe_convert_target(data, target)
else:
# no conversion required
pass
if isinstance(target, pd.Series) and target.name is None:
target = pd.Series(target, name=self._TARGET_NAME)
return data, target
def __init__(self, data, target=None,
*args, **kwargs):
if data is None and target is None:
msg = '{0} must have either data or target'
raise ValueError(msg.format(self.__class__.__name__))
elif data is None and not is_list_like(target):
msg = 'target must be list-like when data is None'
raise ValueError(msg)
data, target = skaccessors._maybe_sklearn_data(data, target)
data, target = smaccessors._maybe_statsmodels_data(data, target)
# retrieve target_name
if isinstance(data, ModelFrame):
target_name = data.target_name
data, target = self._maybe_convert_data(data, target, *args, **kwargs)
if target is not None and not is_list_like(target):
if target in data.columns:
target_name = target
df = data
else:
msg = "Specified target '{0}' is not included in data"
raise ValueError(msg.format(target))
self._target_name = target_name
else:
df, target = self._concat_target(data, target)
if isinstance(target, pd.Series):
self._target_name = target.name
elif isinstance(target, pd.DataFrame):
if len(target.columns) > 1:
self._target_name = target.columns
else:
self._target_name = target.columns[0]
else:
# target may be None
self._target_name = self._TARGET_NAME
pd.DataFrame.__init__(self, df)
def __init__(self, data, target=None,
*args, **kwargs):
if data is None and target is None:
msg = '{0} must have either data or target'
raise ValueError(msg.format(self.__class__.__name__))
elif data is None and not is_list_like(target):
msg = 'target must be list-like when data is None'
raise ValueError(msg)
data, target = skaccessors._maybe_sklearn_data(data, target)
data, target = smaccessors._maybe_statsmodels_data(data, target)
# retrieve target_name
if isinstance(data, ModelFrame):
target_name = data.target_name
data, target = self._maybe_convert_data(data, target, *args, **kwargs)
if target is not None and not is_list_like(target):
if target in data.columns:
target_name = target
df = data
else:
msg = "Specified target '{0}' is not included in data"
raise ValueError(msg.format(target))
self._target_name = target_name
else:
df, target = self._concat_target(data, target)
if isinstance(target, pd.Series):
self._target_name = target.name
elif isinstance(target, pd.DataFrame):
if len(target.columns) > 1:
self._target_name = target.columns
else:
self._target_name = target.columns[0]
else:
# target may be None
self._target_name = self._TARGET_NAME
pd.DataFrame.__init__(self, df)
def target(self, target):
if target is None:
del self.target
return
if not self.has_target():
# allow to update target_name only when target attibute doesn't exist
if isinstance(target, pd.Series):
# Series.name may be blank
if target.name is not None:
self.target_name = target.name
elif isinstance(target, pd.DataFrame):
# DataFrame.columns should have values
self.target_name = target.columns
if not is_list_like(target):
if target in self.columns:
self.target_name = target
else:
msg = "Specified target '{0}' is not included in data"
raise ValueError(msg.format(target))
return
if isinstance(target, pd.Series):
if target.name != self.target_name:
msg = "Passed data is being renamed to '{0}'".format(
self.target_name)
warnings.warn(msg)
target = pd.Series(target, name=self.target_name)
elif isinstance(target, pd.DataFrame):
if not target.columns.equals(self.target_name):
if len(target.columns) == len(self.target_name):
msg = "Passed data is being renamed to '{0}'".format(
self.target_name)
warnings.warn(msg)
target = target.copy()
target.columns = self.target_name
else:
msg = 'target and target_name are unmatched, target_name will be updated'
warnings.warn(msg)
data = self.data # hack
self.target_name = target.columns
self.data = data
else:
_, target = self._maybe_convert_data(self.data, target,
self.target_name)
df, _ = self._concat_target(self.data, target)
self._update_inplace(df)
def target(self, target):
if target is None:
del self.target
return
if not self.has_target():
# allow to update target_name only when target attibute doesn't exist
if isinstance(target, pd.Series):
# Series.name may be blank
if target.name is not None:
self.target_name = target.name
elif isinstance(target, pd.DataFrame):
# DataFrame.columns should have values
self.target_name = target.columns
if not is_list_like(target):
if target in self.columns:
self.target_name = target
else:
msg = "Specified target '{0}' is not included in data"
raise ValueError(msg.format(target))
return
if isinstance(target, pd.Series):
if target.name != self.target_name:
msg = "Passed data is being renamed to '{0}'".format(self.target_name)
warnings.warn(msg)
target = pd.Series(target, name=self.target_name)
elif isinstance(target, pd.DataFrame):
if not target.columns.equals(self.target_name):
if len(target.columns) == len(self.target_name):
msg = "Passed data is being renamed to '{0}'".format(self.target_name)
warnings.warn(msg)
target = target.copy()
target.columns = self.target_name
else:
msg = 'target and target_name are unmatched, target_name will be updated'
warnings.warn(msg)
data = self.data # hack
self.target_name = target.columns
self.data = data
else:
_, target = self._maybe_convert_data(self.data, target, self.target_name)
df, _ = self._concat_target(self.data, target)
self._update_inplace(df)
def _add_meta_columns(df, meta_name):
df = df.copy()
if not is_list_like(meta_name):
meta_name = [meta_name]
df.columns = pd.MultiIndex.from_product([meta_name, df.columns])
return df
def _add_meta_columns(df, meta_name):
df = df.copy()
if not is_list_like(meta_name):
meta_name = [meta_name]
df.columns = pd.MultiIndex.from_product([meta_name, df.columns])
return df
