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"""
This is a module to be used as a reference for building other modules
"""
import numpy as np
from sklearn.base import BaseEstimator, ClassifierMixin, TransformerMixin
from sklearn.utils.validation import check_X_y, check_array, check_is_fitted
from sklearn.utils.multiclass import unique_labels
from sklearn.metrics import euclidean_distances
class TemplateEstimator(BaseEstimator):
""" A template estimator to be used as a reference implementation.
For more information regarding how to build your own estimator, read more
in the :ref:`User Guide <user_guide>`.
Parameters
----------
demo_param : str, default='demo_param'
A parameter used for demonstation of how to pass and store paramters.
"""
def __init__(self, demo_param='demo_param'):
self.demo_param = demo_param
def fit(self, X, y):
"""A reference implementation of a fitting function.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
The training input samples.
y : array-like, shape (n_samples,) or (n_samples, n_outputs)
The target values (class labels in classification, real numbers in
regression).
Returns
-------
self : object
Returns self.
"""
X, y = check_X_y(X, y, accept_sparse=True)
self.is_fitted_ = True
# `fit` should always return `self`
return self
def predict(self, X):
""" A reference implementation of a predicting function.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
The training input samples.
Returns
-------
y : ndarray, shape (n_samples,)
Returns an array of ones.
"""
X = check_array(X, accept_sparse=True)
check_is_fitted(self, 'is_fitted_')
return np.ones(X.shape[0], dtype=np.int64)
class TemplateClassifier(BaseEstimator, ClassifierMixin):
""" An example classifier which implements a 1-NN algorithm.
For more information regarding how to build your own classifier, read more
in the :ref:`User Guide <user_guide>`.
Parameters
----------
demo_param : str, default='demo'
A parameter used for demonstation of how to pass and store paramters.
Attributes
----------
X_ : ndarray, shape (n_samples, n_features)
The input passed during :meth:`fit`.
y_ : ndarray, shape (n_samples,)
The labels passed during :meth:`fit`.
classes_ : ndarray, shape (n_classes,)
The classes seen at :meth:`fit`.
"""
def __init__(self, demo_param='demo'):
self.demo_param = demo_param
def fit(self, X, y):
"""A reference implementation of a fitting function for a classifier.
Parameters
----------
X : array-like, shape (n_samples, n_features)
The training input samples.
y : array-like, shape (n_samples,)
The target values. An array of int.
Returns
-------
self : object
Returns self.
"""
# Check that X and y have correct shape
X, y = check_X_y(X, y)
# Store the classes seen during fit
self.classes_ = unique_labels(y)
self.X_ = X
self.y_ = y
# Return the classifier
return self
def predict(self, X):
""" A reference implementation of a prediction for a classifier.
Parameters
----------
X : array-like, shape (n_samples, n_features)
The input samples.
Returns
-------
y : ndarray, shape (n_samples,)
The label for each sample is the label of the closest sample
seen udring fit.
"""
# Check is fit had been called
check_is_fitted(self, ['X_', 'y_'])
# Input validation
X = check_array(X)
closest = np.argmin(euclidean_distances(X, self.X_), axis=1)
return self.y_[closest]
class TemplateTransformer(BaseEstimator, TransformerMixin):
""" An example transformer that returns the element-wise square root.
For more information regarding how to build your own transformer, read more
in the :ref:`User Guide <user_guide>`.
Parameters
----------
demo_param : str, default='demo'
A parameter used for demonstation of how to pass and store paramters.
Attributes
----------
n_features_ : int
The number of features of the data passed to :meth:`fit`.
"""
def __init__(self, demo_param='demo'):
self.demo_param = demo_param
def fit(self, X, y=None):
"""A reference implementation of a fitting function for a transformer.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
The training input samples.
y : None
There is no need of a target in a transformer, yet the pipeline API
requires this parameter.
Returns
-------
self : object
Returns self.
"""
X = check_array(X, accept_sparse=True)
self.n_features_ = X.shape[1]
# Return the transformer
return self
def transform(self, X):
""" A reference implementation of a transform function.
Parameters
----------
X : {array-like, sparse-matrix}, shape (n_samples, n_features)
The input samples.
Returns
-------
X_transformed : array, shape (n_samples, n_features)
The array containing the element-wise square roots of the values
in ``X``.
"""
# Check is fit had been called
check_is_fitted(self, 'n_features_')
# Input validation
X = check_array(X, accept_sparse=True)
# Check that the input is of the same shape as the one passed
# during fit.
if X.shape[1] != self.n_features_:
raise ValueError('Shape of input is different from what was seen'
'in `fit`')
return np.sqrt(X)