144 lines
4.9 KiB
Python
144 lines
4.9 KiB
Python
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# Sebastian Raschka 2014-2020
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# mlxtend Machine Learning Library Extensions
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#
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# Base Clusteer (Clutering Parent Class)
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# Author: Sebastian Raschka <sebastianraschka.com>
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#
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# License: BSD 3 clause
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from time import time
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from collections import defaultdict
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try:
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from inspect import signature
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except ImportError:
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from ..externals.signature_py27 import signature
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class _BaseModel(object):
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def __init__(self):
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self._init_time = time()
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def _check_arrays(self, X, y=None):
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if isinstance(X, list):
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raise ValueError('X must be a numpy array')
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if not len(X.shape) == 2:
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raise ValueError('X must be a 2D array. Try X[:, numpy.newaxis]')
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try:
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if y is None:
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return
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except(AttributeError):
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if not len(y.shape) == 1:
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raise ValueError('y must be a 1D array.')
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if not len(y) == X.shape[0]:
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raise ValueError('X and y must contain the same number of samples')
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@classmethod
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def _get_param_names(cls):
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"""Get parameter names for the estimator
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adapted from
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https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/base.py
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Author: Gael Varoquaux <gael.varoquaux@normalesup.org>
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License: BSD 4 clause
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"""
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# fetch the constructor or the original constructor before
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# deprecation wrapping if any
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init = getattr(cls.__init__, 'deprecated_original', cls.__init__)
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if init is object.__init__:
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# No explicit constructor to introspect
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return []
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# introspect the constructor arguments to find the model parameters
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# to represent
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init_signature = signature(init)
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# Consider the constructor parameters excluding 'self'
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parameters = [p for p in init_signature.parameters.values()
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if p.name != 'self' and p.kind != p.VAR_KEYWORD]
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for p in parameters:
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if p.kind == p.VAR_POSITIONAL:
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raise RuntimeError("scikit-learn estimators should always "
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"specify their parameters in the signature"
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" of their __init__ (no varargs)."
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" %s with constructor %s doesn't "
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" follow this convention."
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% (cls, init_signature))
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# Extract and sort argument names excluding 'self'
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return sorted([p.name for p in parameters])
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def get_params(self, deep=True):
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"""Get parameters for this estimator.
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Parameters
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----------
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deep : boolean, optional
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If True, will return the parameters for this estimator and
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contained subobjects that are estimators.
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Returns
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-------
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params : mapping of string to any
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Parameter names mapped to their values.'
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adapted from
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https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/base.py
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Author: Gael Varoquaux <gael.varoquaux@normalesup.org>
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License: BSD 3 clause
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"""
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out = dict()
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for key in self._get_param_names():
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value = getattr(self, key, None)
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if deep and hasattr(value, 'get_params'):
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deep_items = value.get_params().items()
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out.update((key + '__' + k, val) for k, val in deep_items)
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out[key] = value
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return out
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def set_params(self, **params):
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"""Set the parameters of this estimator.
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The method works on simple estimators as well as on nested objects
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(such as pipelines). The latter have parameters of the form
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``<component>__<parameter>`` so that it's possible to update each
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component of a nested object.
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Returns
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-------
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self
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adapted from
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https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/base.py
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Author: Gael Varoquaux <gael.varoquaux@normalesup.org>
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License: BSD 3 clause
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"""
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if not params:
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# Simple optimization to gain speed (inspect is slow)
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return self
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valid_params = self.get_params(deep=True)
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nested_params = defaultdict(dict) # grouped by prefix
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for key, value in params.items():
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key, delim, sub_key = key.partition('__')
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if key not in valid_params:
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raise ValueError('Invalid parameter %s for estimator %s. '
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'Check the list of available parameters '
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'with `estimator.get_params().keys()`.' %
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(key, self))
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if delim:
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nested_params[key][sub_key] = value
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else:
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setattr(self, key, value)
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valid_params[key] = value
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for key, sub_params in nested_params.items():
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valid_params[key].set_params(**sub_params)
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return self
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