103 lines
3.2 KiB
Python
103 lines
3.2 KiB
Python
|
# Sebastian Raschka 2014-2020
|
||
|
|
# mlxtend Machine Learning Library Extensions
|
||
|
|
#
|
||
|
|
# Base Clusteer (Clutering Parent Class)
|
||
|
|
# Author: Sebastian Raschka <sebastianraschka.com>
|
||
|
|
#
|
||
|
|
# License: BSD 3 clause
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
from time import time
|
||
|
|
|
||
|
|
|
||
|
|
class _Classifier(object):
|
||
|
|
|
||
|
|
def __init__(self):
|
||
|
|
pass
|
||
|
|
|
||
|
|
def _check_target_array(self, y, allowed=None):
|
||
|
|
if not isinstance(y[0], (int, np.integer)):
|
||
|
|
raise AttributeError('y must be an integer array.\nFound %s'
|
||
|
|
% y.dtype)
|
||
|
|
found_labels = np.unique(y)
|
||
|
|
if (found_labels < 0).any():
|
||
|
|
raise AttributeError('y array must not contain negative labels.'
|
||
|
|
'\nFound %s' % found_labels)
|
||
|
|
if allowed is not None:
|
||
|
|
found_labels = tuple(found_labels)
|
||
|
|
if found_labels not in allowed:
|
||
|
|
raise AttributeError('Labels not in %s.\nFound %s'
|
||
|
|
% (allowed, found_labels))
|
||
|
|
|
||
|
|
def score(self, X, y):
|
||
|
|
""" Compute the prediction accuracy
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
X : {array-like, sparse matrix}, shape = [n_samples, n_features]
|
||
|
|
Training vectors, where n_samples is the number of samples and
|
||
|
|
n_features is the number of features.
|
||
|
|
y : array-like, shape = [n_samples]
|
||
|
|
Target values (true class labels).
|
||
|
|
|
||
|
|
Returns
|
||
|
|
---------
|
||
|
|
acc : float
|
||
|
|
The prediction accuracy as a float
|
||
|
|
between 0.0 and 1.0 (perfect score).
|
||
|
|
|
||
|
|
"""
|
||
|
|
y_pred = self.predict(X)
|
||
|
|
acc = np.sum(y == y_pred, axis=0) / float(X.shape[0])
|
||
|
|
return acc
|
||
|
|
|
||
|
|
def fit(self, X, y, init_params=True):
|
||
|
|
"""Learn model from training data.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
X : {array-like, sparse matrix}, shape = [n_samples, n_features]
|
||
|
|
Training vectors, where n_samples is the number of samples and
|
||
|
|
n_features is the number of features.
|
||
|
|
y : array-like, shape = [n_samples]
|
||
|
|
Target values.
|
||
|
|
init_params : bool (default: True)
|
||
|
|
Re-initializes model parameters prior to fitting.
|
||
|
|
Set False to continue training with weights from
|
||
|
|
a previous model fitting.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
self : object
|
||
|
|
|
||
|
|
"""
|
||
|
|
self._is_fitted = False
|
||
|
|
self._check_arrays(X=X, y=y)
|
||
|
|
self._check_target_array(y)
|
||
|
|
if hasattr(self, 'self.random_seed') and self.random_seed:
|
||
|
|
self._rgen = np.random.RandomState(self.random_seed)
|
||
|
|
self._init_time = time()
|
||
|
|
self._fit(X=X, y=y, init_params=init_params)
|
||
|
|
self._is_fitted = True
|
||
|
|
return self
|
||
|
|
|
||
|
|
def predict(self, X):
|
||
|
|
"""Predict targets from X.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
X : {array-like, sparse matrix}, shape = [n_samples, n_features]
|
||
|
|
Training vectors, where n_samples is the number of samples and
|
||
|
|
n_features is the number of features.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
----------
|
||
|
|
target_values : array-like, shape = [n_samples]
|
||
|
|
Predicted target values.
|
||
|
|
|
||
|
|
"""
|
||
|
|
self._check_arrays(X=X)
|
||
|
|
if not self._is_fitted:
|
||
|
|
raise AttributeError('Model is not fitted, yet.')
|
||
|
|
return self._predict(X)
|