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base.py
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"""
Base class template for transformers.
class name: BaseTransformer
Covers all types of transformers.
Type and behaviour of transformer is determined by the following tags:
"input_data_type" tag with values "Primitives" or "Series"
this determines expected type of input of transform
if "Primitives", expected inputs X are pd.DataFrame
if "Series", expected inputs X are Series or Panel
Note: placeholder tag for upwards compatibility currently only "Series" is
supported
"output_data_type" tag with values "Primitives", or "Series"
this determines type of output of transform
if "Primitives", output is pd.DataFrame with as many rows as X has instances
i-th instance of X is transformed into i-th row of output
if "Series", output is a Series or Panel, with as many instances as X i-th
instance of X is transformed into i-th instance of output
Series are treated as one-instance-Panels
if Series is input, output is a 1-row pd.DataFrame or a Series
"instancewise" tag which is boolean
if True, fit/transform is statistically independent by instance
Class defining methods:
fitting - fit(self, X, y=None)
transform - transform(self, X, y=None)
fit&transform - fit_transform(self, X, y=None)
updating - update(self, X, y=None)
Inspection methods:
hyper-parameter inspection - get_params()
fitted parameter inspection - get_fitted_params()
State:
fitted model/strategy - by convention, any attributes ending in "_"
fitted state flag - is_fitted (property)
fitted state inspection - check_is_fitted()
"""
__author__ = ["mloning", "fkiraly", "miraep8"]
__all__ = [
"BaseTransformer",
]
from itertools import product
from typing import Union
import numpy as np
import pandas as pd
from aeon.base import BaseEstimator
from aeon.datatypes import (
VectorizedDF,
check_is_mtype,
check_is_scitype,
convert_to,
mtype_to_scitype,
update_data,
)
from aeon.datatypes._series_as_panel import convert_to_scitype
from aeon.utils.sklearn import (
is_sklearn_classifier,
is_sklearn_regressor,
is_sklearn_transformer,
)
from aeon.utils.validation._dependencies import _check_estimator_deps
# single/multiple primitives
Primitive = Union[np.integer, int, float, str]
Primitives = np.ndarray
# tabular/cross-sectional data
Tabular = Union[pd.DataFrame, np.ndarray] # 2d arrays
# univariate/multivariate series
UnivariateSeries = Union[pd.Series, np.ndarray]
MultivariateSeries = Union[pd.DataFrame, np.ndarray]
Series = Union[UnivariateSeries, MultivariateSeries]
# panel/longitudinal/series-as-features data
Panel = Union[pd.DataFrame, np.ndarray] # 3d or nested array
def _coerce_to_list(obj):
"""Return [obj] if obj is not a list, otherwise obj."""
if not isinstance(obj, list):
return [obj]
else:
return obj
class BaseTransformer(BaseEstimator):
"""Transformer base class."""
# default tag values - these typically make the "safest" assumption
_tags = {
"input_data_type": "Series",
"output_data_type": "Series",
"transform_labels": "None",
"instancewise": True,
"univariate-only": False, # can the transformer handle multivariate X?
"X_inner_type": "pd.DataFrame", # which mtypes do _fit/_predict support for X?
# this can be a Panel mtype even if transform-input is Series, vectorized
"y_inner_type": "None", # which mtypes do _fit/_predict support for y?
"requires_y": False, # does y need to be passed in fit?
"enforce_index_type": None, # index type that needs to be enforced in X/y
"fit_is_empty": True, # is fit empty and can be skipped? Yes = True
"X-y-must-have-same-index": False, # can estimator handle different X/y index?
"transform-returns-same-time-index": False,
# does transform return have the same time index as input X
"skip-inverse-transform": False, # is inverse-transform skipped when called?
"capability:inverse_transform": False, # can the transformer inverse transform?
"capability:unequal_length": True,
"capability:unequal_length:removes": False,
"capability:missing_values": False, # can estimator handle missing data?
"capability:missing_values:removes": False,
# is transform result always guaranteed to contain no missing values?
"python_version": None, # PEP 440 python version specifier to limit versions
"remember_data": False, # whether all data seen is remembered as self._X
}
# allowed types for transformers - Series and Panel
ALLOWED_INPUT_TYPES = [
"pd.Series",
"pd.DataFrame",
"np.ndarray",
"nested_univ",
"numpy3D",
# "numpy2D",
"pd-multiindex",
# "pd-wide",
# "pd-long",
"df-list",
"np-list",
"pd_multiindex_hier",
]
def __init__(self, _output_convert="auto"):
self._converter_store_X = dict() # storage dictionary for in/output conversion
self._output_convert = _output_convert
super(BaseTransformer, self).__init__()
_check_estimator_deps(self)
def __mul__(self, other):
"""Magic * method, return (right) concatenated TransformerPipeline.
Implemented for `other` being a transformer, otherwise returns `NotImplemented`.
Parameters
----------
other: `aeon` transformer, must inherit from BaseTransformer
otherwise, `NotImplemented` is returned
Returns
-------
TransformerPipeline object, concatenation of `self` (first) with `other` (last).
not nested, contains only non-TransformerPipeline `aeon` transformers
"""
from aeon.transformations.compose import TransformerPipeline
# we wrap self in a pipeline, and concatenate with the other
# the TransformerPipeline does the rest, e.g., case distinctions on other
if (
isinstance(other, BaseTransformer)
or is_sklearn_classifier(other)
or is_sklearn_regressor(other)
or is_sklearn_transformer(other)
):
self_as_pipeline = TransformerPipeline(steps=[self])
return self_as_pipeline * other
else:
return NotImplemented
def __rmul__(self, other):
"""Magic * method, return (left) concatenated TransformerPipeline.
Implemented for `other` being a transformer, otherwise returns `NotImplemented`.
Parameters
----------
other: `aeon` transformer, must inherit from BaseTransformer
otherwise, `NotImplemented` is returned
Returns
-------
TransformerPipeline object, concatenation of `other` (first) with `self` (last).
not nested, contains only non-TransformerPipeline `aeon` transformers
"""
from aeon.transformations.compose import TransformerPipeline
# we wrap self in a pipeline, and concatenate with the other
# the TransformerPipeline does the rest, e.g., case distinctions on other
if isinstance(other, BaseTransformer) or is_sklearn_transformer(other):
self_as_pipeline = TransformerPipeline(steps=[self])
return other * self_as_pipeline
else:
return NotImplemented
def __or__(self, other):
"""Magic | method, return MultiplexTranformer.
Implemented for `other` being either a MultiplexTransformer or a transformer.
Parameters
----------
other: `aeon` transformer or aeon MultiplexTransformer
Returns
-------
MultiplexTransformer object
"""
from aeon.transformations.compose import MultiplexTransformer
if isinstance(other, BaseTransformer):
multiplex_self = MultiplexTransformer([self])
return multiplex_self | other
else:
return NotImplemented
def __add__(self, other):
"""Magic + method, return (right) concatenated FeatureUnion.
Implemented for `other` being a transformer, otherwise returns `NotImplemented`.
Parameters
----------
other: `aeon` transformer, must inherit from BaseTransformer
otherwise, `NotImplemented` is returned
Returns
-------
FeatureUnion object, concatenation of `self` (first) with `other` (last).
not nested, contains only non-TransformerPipeline `aeon` transformers
"""
from aeon.transformations.compose import FeatureUnion
# we wrap self in a pipeline, and concatenate with the other
# the FeatureUnion does the rest, e.g., case distinctions on other
if isinstance(other, BaseTransformer):
self_as_pipeline = FeatureUnion(transformer_list=[self])
return self_as_pipeline + other
else:
return NotImplemented
def __radd__(self, other):
"""Magic + method, return (left) concatenated FeatureUnion.
Implemented for `other` being a transformer, otherwise returns `NotImplemented`.
Parameters
----------
other: `aeon` transformer, must inherit from BaseTransformer
otherwise, `NotImplemented` is returned
Returns
-------
FeatureUnion object, concatenation of `other` (first) with `self` (last).
not nested, contains only non-FeatureUnion `aeon` transformers
"""
from aeon.transformations.compose import FeatureUnion
# we wrap self in a pipeline, and concatenate with the other
# the TransformerPipeline does the rest, e.g., case distinctions on other
if isinstance(other, BaseTransformer):
self_as_pipeline = FeatureUnion(transformer_list=[self])
return other + self_as_pipeline
else:
return NotImplemented
def __invert__(self):
"""Magic unary ~ (inversion) method, return InvertTransform of self.
Returns
-------
`InvertTransform` object, containing `self`.
"""
from aeon.transformations.compose import InvertTransform
return InvertTransform(self)
def __neg__(self):
"""Magic unary - (negation) method, return OptionalPassthrough of self.
Intuition: `OptionalPassthrough` is "not having transformer", as an option.
Returns
-------
`OptionalPassthrough` object, containing `self`, with `passthrough=False`.
The `passthrough` parameter can be set via `set_params`.
"""
from aeon.transformations.compose import OptionalPassthrough
return OptionalPassthrough(self, passthrough=False)
def __getitem__(self, key):
"""Magic [...] method, return column subsetted transformer.
First index does intput subsetting, second index does output subsetting.
Keys must be valid inputs for `columns` in `ColumnSubset`.
Parameters
----------
key: valid input for `columns` in `ColumnSubset`, or pair thereof
keys can also be a :-slice, in which case it is considered as not passed
Returns
-------
the following TransformerPipeline object:
ColumnSubset(columns1) * self * ColumnSubset(columns2)
where `columns1` is first or only item in `key`, and `columns2` is the last
if only one item is passed in `key`, only `columns1` is applied to input
"""
from aeon.transformations.subset import ColumnSelect
def is_noneslice(obj):
res = isinstance(obj, slice)
res = res and obj.start is None and obj.stop is None and obj.step is None
return res
if isinstance(key, tuple):
if not len(key) == 2:
raise ValueError(
"there should be one or two keys when calling [] or getitem, "
"e.g., mytrafo[key], or mytrafo[key1, key2]"
)
columns1 = key[0]
columns2 = key[1]
if is_noneslice(columns1) and is_noneslice(columns2):
return self
elif is_noneslice(columns2):
return ColumnSelect(columns1) * self
elif is_noneslice(columns1):
return self * ColumnSelect(columns2)
else:
return ColumnSelect(columns1) * self * ColumnSelect(columns2)
else:
return ColumnSelect(key) * self
def fit(self, X, y=None):
"""Fit transformer to X, optionally to y.
State change:
Changes state to "fitted".
Writes to self:
_is_fitted : flag is set to True.
_X : X, coerced copy of X, if remember_data tag is True
possibly coerced to inner type or update_data compatible type
by reference, when possible
model attributes (ending in "_") : dependent on estimator
Parameters
----------
X : Series or Panel, any supported mtype
Data to fit transform to, of python type as follows:
Series: pd.Series, pd.DataFrame, or np.ndarray (1D or 2D)
Panel: pd.DataFrame with 2-level MultiIndex, list of pd.DataFrame,
nested pd.DataFrame, or pd.DataFrame in long/wide format.
y : Series or Panel, default=None
Additional data, e.g., labels for transformation.
Returns
-------
self : a fitted instance of the estimator
"""
# input checks and datatype conversion
X_inner, y_inner = self._fit_checks(X, y)
# skip the rest if fit_is_empty is True
if self.get_tag("fit_is_empty"):
self._is_fitted = True
return self
# checks and conversions complete, pass to inner fit
#####################################################
vectorization_needed = isinstance(X_inner, VectorizedDF)
self._is_vectorized = vectorization_needed
# we call the ordinary _fit if no looping/vectorization needed
if not vectorization_needed:
self._fit(X=X_inner, y=y_inner)
else:
# otherwise we call the vectorized version of fit
self._vectorize("fit", X=X_inner, y=y_inner)
# this should happen last: fitted state is set to True
self._is_fitted = True
return self
def transform(self, X, y=None):
"""Transform X and return a transformed version.
State required:
Requires state to be "fitted".
Accesses in self:
_is_fitted : must be True
_X : optionally accessed, only available if remember_data tag is True
fitted model attributes (ending in "_") : must be set, accessed by _transform
Parameters
----------
X : Series or Panel, any supported mtype
Data to be transformed, of python type as follows:
Series: pd.Series, pd.DataFrame, or np.ndarray (1D or 2D)
Panel: pd.DataFrame with 2-level MultiIndex, list of pd.DataFrame,
nested pd.DataFrame, or pd.DataFrame in long/wide format
y : Series or Panel, default=None
Additional data, e.g., labels for transformation
Returns
-------
transformed version of X
type depends on type of X and output_data_type tag:
| | `transform` | |
| `X` | `-output` | type of return |
|__________|______________|________________________|
| `Series` | `Primitives` | `pd.DataFrame` (1-row) |
| `Panel` | `Primitives` | `pd.DataFrame` |
| `Series` | `Series` | `Series` |
| `Panel` | `Series` | `Panel` |
| `Series` | `Panel` | `Panel` |
instances in return correspond to instances in `X`
combinations not in the table are currently not supported
Explicitly, with examples:
if `X` is `Series` (e.g., `pd.DataFrame`) and `transform-output` is `Series`
then the return is a single `Series` of the same mtype
Example: detrending a single series
if `X` is `Panel` (e.g., `pd-multiindex`) and `transform-output` is `Series`
then the return is `Panel` with same number of instances as `X`
(the transformer is applied to each input Series instance)
Example: all series in the panel are detrended individually
if `X` is `Series` or `Panel` and `transform-output` is `Primitives`
then the return is `pd.DataFrame` with as many rows as instances in `X`
Example: i-th row of the return has mean and variance of the i-th series
if `X` is `Series` and `transform-output` is `Panel`
then the return is a `Panel` object of type `pd-multiindex`
Example: i-th instance of the output is the i-th window running over `X`
"""
# check whether is fitted
self.check_is_fitted()
# input check and conversion for X/y
X_inner, y_inner, metadata = self._check_X_y(X=X, y=y, return_metadata=True)
if not isinstance(X_inner, VectorizedDF):
Xt = self._transform(X=X_inner, y=y_inner)
else:
# otherwise we call the vectorized version of predict
Xt = self._vectorize("transform", X=X_inner, y=y_inner)
# convert to output mtype
if not hasattr(self, "_output_convert") or self._output_convert == "auto":
Xt = self._convert_output(Xt, metadata=metadata)
return Xt
def fit_transform(self, X, y=None):
"""Fit to data, then transform it.
Fits the transformer to X and y and returns a transformed version of X.
State change: changes state to "fitted".
Writes to self:
_is_fitted : flag is set to True.
_X : X, coerced copy of X, if remember_data tag is True
possibly coerced to inner type or update_data compatible type
by reference, when possible
model attributes (ending in "_") : dependent on estimator
Parameters
----------
X : Series or Panel, any supported mtype
Data to be transformed, of python type as follows:
Series: pd.Series, pd.DataFrame, or np.ndarray (1D or 2D)
Panel: pd.DataFrame with 2-level MultiIndex, list of pd.DataFrame,
nested pd.DataFrame, or pd.DataFrame in long/wide format
y : Series or Panel, default=None
Additional data, e.g., labels for transformation
Returns
-------
transformed version of X
type depends on type of X and output_data_type tag:
| `X` | `tf-output` | type of return |
|__________|______________|________________________|
| `Series` | `Primitives` | `pd.DataFrame` (1-row) |
| `Panel` | `Primitives` | `pd.DataFrame` |
| `Series` | `Series` | `Series` |
| `Panel` | `Series` | `Panel` |
| `Series` | `Panel` | `Panel` |
instances in return correspond to instances in `X`
combinations not in the table are currently not supported
Explicitly, with examples:
if `X` is `Series` (e.g., `pd.DataFrame`) and `transform-output` is `Series`
then the return is a single `Series` of the same mtype
Example: detrending a single series
if `X` is `Panel` (e.g., `pd-multiindex`) and `transform-output` is `Series`
then the return is `Panel` with same number of instances as `X`
(the transformer is applied to each input Series instance)
Example: all series in the panel are detrended individually
if `X` is `Series` or `Panel` and `transform-output` is `Primitives`
then the return is `pd.DataFrame` with as many rows as instances in `X`
Example: i-th row of the return has mean and variance of the i-th series
if `X` is `Series` and `transform-output` is `Panel`
then the return is a `Panel` object of type `pd-multiindex`
Example: i-th instance of the output is the i-th window running over `X`
"""
# input checks and datatype conversion
X_inner, y_inner, metadata = self._fit_checks(X, y, False, True)
# checks and conversions complete, pass to inner fit_transform
####################################################
vectorization_needed = isinstance(X_inner, VectorizedDF)
self._is_vectorized = vectorization_needed
# we call the ordinary _fit_transform if no looping/vectorization needed
if not vectorization_needed:
Xt = self._fit_transform(X=X_inner, y=y_inner)
else:
# otherwise we call the vectorized version of fit_transform
Xt = self._vectorize("fit_transform", X=X_inner, y=y_inner)
self._is_fitted = True
# convert to output mtype
if not hasattr(self, "_output_convert") or self._output_convert == "auto":
Xt = self._convert_output(Xt, metadata=metadata)
return Xt
def inverse_transform(self, X, y=None):
"""Inverse transform X and return an inverse transformed version.
Currently it is assumed that only transformers with tags
"input_data_type"="Series", "output_data_type"="Series",
have an inverse_transform.
State required:
Requires state to be "fitted".
Accesses in self:
_is_fitted : must be True
_X : optionally accessed, only available if remember_data tag is True
fitted model attributes (ending in "_") : accessed by _inverse_transform
Parameters
----------
X : Series or Panel, any supported mtype
Data to be inverse transformed, of python type as follows:
Series: pd.Series, pd.DataFrame, or np.ndarray (1D or 2D)
Panel: pd.DataFrame with 2-level MultiIndex, list of pd.DataFrame,
nested pd.DataFrame, or pd.DataFrame in long/wide format
y : Series or Panel, default=None
Additional data, e.g., labels for transformation
Returns
-------
inverse transformed version of X
of the same type as X, and conforming to mtype format specifications
"""
if self.get_tag("skip-inverse-transform"):
return X
if not self.get_tag("capability:inverse_transform"):
raise NotImplementedError(
f"{type(self)} does not implement inverse_transform"
)
# check whether is fitted
self.check_is_fitted()
# input check and conversion for X/y
X_inner, y_inner, metadata = self._check_X_y(X=X, y=y, return_metadata=True)
if not isinstance(X_inner, VectorizedDF):
Xt = self._inverse_transform(X=X_inner, y=y_inner)
else:
# otherwise we call the vectorized version of predict
Xt = self._vectorize("inverse_transform", X=X_inner, y=y_inner)
# convert to output mtype
if not hasattr(self, "_output_convert") or self._output_convert == "auto":
Xt = self._convert_output(Xt, metadata=metadata, inverse=True)
return Xt
def update(self, X, y=None, update_params=True):
"""Update transformer with X, optionally y.
State required:
Requires state to be "fitted".
Accesses in self:
_is_fitted : must be True
_X : accessed by _update and by update_data, if remember_data tag is True
fitted model attributes (ending in "_") : must be set, accessed by _update
Writes to self:
_X : updated by values in X, via update_data, if remember_data tag is True
fitted model attributes (ending in "_") : only if update_params=True
type and nature of update are dependent on estimator
Parameters
----------
X : Series or Panel, any supported mtype
Data to fit transform to, of python type as follows:
Series: pd.Series, pd.DataFrame, or np.ndarray (1D or 2D)
Panel: pd.DataFrame with 2-level MultiIndex, list of pd.DataFrame,
nested pd.DataFrame, or pd.DataFrame in long/wide format
y : Series or Panel, default=None
Additional data, e.g., labels for transformation
update_params : bool, default=True
whether the model is updated. Yes if true, if false, simply skips call.
argument exists for compatibility with forecasting module.
Returns
-------
self : a fitted instance of the estimator
"""
# check whether is fitted
self.check_is_fitted()
# if requires_y is set, y is required in fit and update
if self.get_tag("requires_y") and y is None:
raise ValueError(f"{self.__class__.__name__} requires `y` in `update`.")
# check and convert X/y
X_inner, y_inner = self._check_X_y(X=X, y=y)
# update memory of X, if remember_data tag is set to True
if self.get_tag("remember_data", False):
self._X = update_data(None, X_new=X_inner)
# skip everything if update_params is False
# skip everything if fit_is_empty is True
if not update_params or self.get_tag("fit_is_empty", False):
return self
# checks and conversions complete, pass to inner fit
#####################################################
vectorization_needed = isinstance(X_inner, VectorizedDF)
# we call the ordinary _fit if no looping/vectorization needed
if not vectorization_needed:
self._update(X=X_inner, y=y_inner)
else:
# otherwise we call the vectorized version of fit
self._vectorize("update", X=X_inner, y=y_inner)
return self
def get_fitted_params(self, deep=True):
"""Get fitted parameters.
State required:
Requires state to be "fitted".
Parameters
----------
deep : bool, default=True
Whether to return fitted parameters of components.
* If True, will return a dict of parameter name : value for this object,
including fitted parameters of fittable components
(= BaseEstimator-valued parameters).
* If False, will return a dict of parameter name : value for this object,
but not include fitted parameters of components.
Returns
-------
fitted_params : dict with str-valued keys
Dictionary of fitted parameters, paramname : paramvalue
keys-value pairs include:
* always: all fitted parameters of this object, as via `get_param_names`
values are fitted parameter value for that key, of this object
* if `deep=True`, also contains keys/value pairs of component parameters
parameters of components are indexed as `[componentname]__[paramname]`
all parameters of `componentname` appear as `paramname` with its value
* if `deep=True`, also contains arbitrary levels of component recursion,
e.g., `[componentname]__[componentcomponentname]__[paramname]`, etc
"""
# if self is not vectorized, run the default get_fitted_params
if not getattr(self, "_is_vectorized", False):
return super(BaseTransformer, self).get_fitted_params(deep=deep)
# otherwise, we delegate to the instances' get_fitted_params
# instances' parameters are returned at dataframe-slice-like keys
fitted_params = {}
# transformers contains a pd.DataFrame with the individual transformers
transformers = self.transformers_
# return transformers in the "transformers" param
fitted_params["transformers"] = transformers
def _to_str(x):
if isinstance(x, str):
x = f"'{x}'"
return str(x)
# populate fitted_params with transformers and their parameters
for ix, col in product(transformers.index, transformers.columns):
trafo = transformers.loc[ix, col]
trafo_key = f"transformers.loc[{_to_str(ix)},{_to_str(col)}]"
fitted_params[trafo_key] = trafo
trafo_params = trafo.get_fitted_params(deep=deep)
for key, val in trafo_params.items():
fitted_params[f"{trafo_key}__{key}"] = val
return fitted_params
def _check_X_y(self, X=None, y=None, return_metadata=False):
"""Check and coerce X/y for fit/transform functions.
Parameters
----------
X : object of aeon compatible time series type
can be Series, Panel, Hierarchical
y : None (default), or object of aeon compatible time series type
can be Series, Panel, Hierarchical
return_metadata : bool, optional, default=False
whether to return the metadata return object
Returns
-------
X_inner : Series, Panel, or Hierarchical object, or VectorizedDF
compatible with self.get_tag("X_inner_type") format
Case 1: self.get_tag("X_inner_type") supports scitype of X, then
converted/coerced version of X, mtype determined by "X_inner_type" tag
Case 2: self.get_tag("X_inner_type") supports *higher* scitype than X
then X converted to "one-Series" or "one-Panel" sub-case of that scitype
always pd-multiindex (Panel) or pd_multiindex_hier (Hierarchical)
Case 3: self.get_tag("X_inner_type") supports only *simpler* scitype than X
then VectorizedDF of X, iterated as the most complex supported scitype
y_inner : Series, Panel, or Hierarchical object, or VectorizedDF
compatible with self.get_tag("y_inner_type") format
Case 1: self.get_tag("y_inner_type") supports scitype of y, then
converted/coerced version of y, mtype determined by "y_inner_type" tag
Case 2: self.get_tag("y_inner_type") supports *higher* scitype than y
then X converted to "one-Series" or "one-Panel" sub-case of that scitype
always pd-multiindex (Panel) or pd_multiindex_hier (Hierarchical)
Case 3: self.get_tag("y_inner_type") supports only *simpler* scitype than y
then VectorizedDF of X, iterated as the most complex supported scitype
Case 4: None if y was None, or self.get_tag("y_inner_type") is "None"
Complexity order above: Hierarchical > Panel > Series
metadata : dict, returned only if return_metadata=True
dictionary with str keys, contents as follows
_converter_store_X : dict, metadata from X conversion, for back-conversion
_X_mtype_last_seen : str, mtype of X seen last
_X_input_scitype : str, scitype of X seen last
_convert_case : str, coversion case (see above), one of
"case 1: scitype supported"
"case 2: higher scitype supported"
"case 3: requires vectorization"
Raises
------
TypeError if X is None
TypeError if X or y is not one of the permissible Series mtypes
TypeError if X is not compatible with self.get_tag("univariate_only")
if tag value is "True", X must be univariate
ValueError if self.get_tag("requires_y")=True but y is None
"""
if X is None:
raise TypeError("X cannot be None, but found None")
metadata = dict()
metadata["_converter_store_X"] = dict()
def _most_complex_scitype(scitypes, smaller_equal_than=None):
"""Return most complex scitype in a list of str."""
if "Hierarchical" in scitypes and smaller_equal_than == "Hierarchical":
return "Hierarchical"
elif "Panel" in scitypes and smaller_equal_than != "Series":
return "Panel"
elif "Series" in scitypes:
return "Series"
elif smaller_equal_than is not None:
return _most_complex_scitype(scitypes)
else:
raise ValueError("no series scitypes supported, bug in estimator")
def _scitype_A_higher_B(scitypeA, scitypeB):
"""Compare two scitypes regarding complexity."""
if scitypeA == "Series":
return False
if scitypeA == "Panel" and scitypeB == "Series":
return True
if scitypeA == "Hierarchical" and scitypeB != "Hierarchical":
return True
return False
# retrieve supported mtypes
X_inner_type = _coerce_to_list(self.get_tag("X_inner_type"))
y_inner_type = _coerce_to_list(self.get_tag("y_inner_type"))
X_inner_scitype = mtype_to_scitype(X_inner_type, return_unique=True)
y_inner_scitype = mtype_to_scitype(y_inner_type, return_unique=True)
ALLOWED_SCITYPES = ["Series", "Panel", "Hierarchical"]
ALLOWED_MTYPES = self.ALLOWED_INPUT_TYPES
# checking X
X_valid, msg, X_metadata = check_is_scitype(
X,
scitype=ALLOWED_SCITYPES,
return_metadata=True,
var_name="X",
)
msg_invalid_input = (
f"must be in an aeon compatible format, "
f"of scitype Series, Panel or Hierarchical, "
f"for instance a pandas.DataFrame with aeon compatible time indices, "
f"or with MultiIndex and last(-1) level an aeon compatible time index. "
f"Allowed compatible mtype format specifications are: {ALLOWED_MTYPES} ."
# f"See the transformers tutorial examples/05_transformers.ipynb."
f"If you think the data is already in an aeon supported input format, "
f"run aeon.datatypes.check_raise(data, mtype) to diagnose the error, "
f"where mtype is the string of the type specification you want. "
f"Error message for checked mtypes, in format [mtype: message], as follows:"
)
if not X_valid:
for mtype, err in msg.items():
msg_invalid_input += f" [{mtype}: {err}] "
raise TypeError("X " + msg_invalid_input)
X_scitype = X_metadata["scitype"]
X_mtype = X_metadata["mtype"]
# remember these for potential back-conversion (in transform etc)
metadata["_X_mtype_last_seen"] = X_mtype
metadata["_X_input_scitype"] = X_scitype
if X_mtype not in ALLOWED_MTYPES:
raise TypeError("X " + msg_invalid_input)
if X_scitype in X_inner_scitype:
case = "case 1: scitype supported"
req_vec_because_rows = False
elif any(_scitype_A_higher_B(x, X_scitype) for x in X_inner_scitype):
case = "case 2: higher scitype supported"
req_vec_because_rows = False
else:
case = "case 3: requires vectorization"
req_vec_because_rows = True
metadata["_convert_case"] = case
# checking X vs tags
inner_univariate = self.get_tag("univariate-only")
# we remember whether we need to vectorize over columns, and at all
req_vec_because_cols = inner_univariate and not X_metadata["is_univariate"]
requires_vectorization = req_vec_because_rows or req_vec_because_cols
# end checking X
if y_inner_type != ["None"] and y is not None:
if "Table" in y_inner_scitype:
y_possible_scitypes = "Table"
elif X_scitype == "Series":
y_possible_scitypes = "Series"
elif X_scitype == "Panel":
y_possible_scitypes = "Panel"
elif X_scitype == "Hierarchical":
y_possible_scitypes = ["Panel", "Hierarchical"]
y_valid, _, y_metadata = check_is_scitype(
y, scitype=y_possible_scitypes, return_metadata=True, var_name="y"
)
if not y_valid:
raise TypeError("y " + msg_invalid_input)
y_scitype = y_metadata["scitype"]
else:
# y_scitype is used below - set to None if y is None
y_scitype = None
# end checking y
# no compabitility checks between X and y
# end compatibility checking X and y
# convert X & y to supported inner type, if necessary
#####################################################
# convert X and y to a supported internal mtype
# it X/y mtype is already supported, no conversion takes place
# if X/y is None, then no conversion takes place (returns None)
# if vectorization is required, we wrap in VectorizedDF
# case 2. internal only has higher scitype, e.g., inner is Panel and X Series
# or inner is Hierarchical and X is Panel or Series
# then, consider X as one-instance Panel or Hierarchical
if case == "case 2: higher scitype supported":
if X_scitype == "Series" and "Panel" in X_inner_scitype:
as_scitype = "Panel"
else:
as_scitype = "Hierarchical"
X = convert_to_scitype(X, to_scitype=as_scitype, from_scitype=X_scitype)
X_scitype = as_scitype
# then pass to case 1, which we've reduced to, X now has inner scitype
# case 1. scitype of X is supported internally
# case in ["case 1: scitype supported", "case 2: higher scitype supported"]
# and does not require vectorization because of cols (multivariate)
if not requires_vectorization:
# converts X
X_inner = convert_to(
X,
to_type=X_inner_type,
store=metadata["_converter_store_X"],
store_behaviour="reset",
)
# converts y, returns None if y is None
if y_inner_type != ["None"] and y is not None:
y_inner = convert_to(
y,
to_type=y_inner_type,
as_scitype=y_scitype,
)
else:
y_inner = None
# case 3. scitype of X is not supported, only lower complexity one is
# then apply vectorization, loop method execution over series/panels
# elif case == "case 3: requires vectorization":
else: # if requires_vectorization
iterate_X = _most_complex_scitype(X_inner_scitype, X_scitype)
X_inner = VectorizedDF(
X=X,
iterate_as=iterate_X,
is_scitype=X_scitype,
iterate_cols=req_vec_because_cols,
)
# we also assume that y must be vectorized in this case
if y_inner_type != ["None"] and y is not None:
# raise ValueError(
# f"{type(self).__name__} does not support Panel X if y is not "
# f"None, since {type(self).__name__} supports only Series. "
# "Auto-vectorization to extend Series X to Panel X can only be "
# 'carried out if y is None, or "y_inner_type" tag is "None". '
# "Consider extending _fit and _transform to handle the following "
# "input types natively: Panel X and non-None y."
# )
iterate_y = _most_complex_scitype(y_inner_scitype, y_scitype)
y_inner = VectorizedDF(X=y, iterate_as=iterate_y, is_scitype=y_scitype)
else:
y_inner = None
if return_metadata:
return X_inner, y_inner, metadata
else:
return X_inner, y_inner
def _check_X(self, X=None):
"""Shorthand for _check_X_y with one argument X, see _check_X_y."""
return self._check_X_y(X=X)[0]
def _convert_output(self, X, metadata, inverse=False):
"""Convert transform or inverse_transform output to expected format.
Parameters
----------
X : output of _transform or _vectorize("transform"), or inverse variants
metadata : dict, output of _check_X_y
inverse : bool, optional, default = False
whether conversion is for transform (False) or inverse_transform (True)
Returns
-------
Xt : final output of transform or inverse_transform
"""
Xt = X
X_input_mtype = metadata["_X_mtype_last_seen"]
X_input_scitype = metadata["_X_input_scitype"]
case = metadata["_convert_case"]
_converter_store_X = metadata["_converter_store_X"]
if inverse:
# the output of inverse transform is equal to input of transform
output_scitype = self.get_tag("input_data_type")
else:
output_scitype = self.get_tag("output_data_type")
# if we converted Series to "one-instance-Panel/Hierarchical",
# or Panel to "one-instance-Hierarchical", then revert that
# remainder is as in case 1
# skipped for output_scitype = "Primitives"
# since then the output always is a pd.DataFrame
if case == "case 2: higher scitype supported" and output_scitype == "Series":
Xt = convert_to(
Xt,
to_type=["pd-multiindex", "numpy3D", "df-list", "pd_multiindex_hier"],
)