134 lines
4.3 KiB
Python
134 lines
4.3 KiB
Python
# Sebastian Raschka 2014-2020
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# mlxtend Machine Learning Library Extensions
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#
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# Estimator for Linear Regression
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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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import numpy as np
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from .._base import _Cluster
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from .._base import _BaseModel
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from .._base import _IterativeModel
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# from scipy.spatial.distance import euclidean
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class Kmeans(_BaseModel, _Cluster, _IterativeModel):
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""" K-means clustering class.
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Added in 0.4.1dev
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Parameters
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------------
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k : int
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Number of clusters
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max_iter : int (default: 10)
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Number of iterations during cluster assignment.
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Cluster re-assignment stops automatically when the algorithm
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converged.
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convergence_tolerance : float (default: 1e-05)
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Compares current centroids with centroids of the previous iteration
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using the given tolerance (a small positive float)to determine
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if the algorithm converged early.
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random_seed : int (default: None)
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Set random state for the initial centroid assignment.
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print_progress : int (default: 0)
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Prints progress in fitting to stderr.
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0: No output
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1: Iterations elapsed
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2: 1 plus time elapsed
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3: 2 plus estimated time until completion
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Attributes
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-----------
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centroids_ : 2d-array, shape={k, n_features}
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Feature values of the k cluster centroids.
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custers_ : dictionary
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The cluster assignments stored as a Python dictionary;
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the dictionary keys denote the cluster indeces and the items are
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Python lists of the sample indices that were assigned to each
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cluster.
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iterations_ : int
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Number of iterations until convergence.
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Examples
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-----------
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For usage examples, please see
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http://rasbt.github.io/mlxtend/user_guide/classifier/Kmeans/
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"""
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def __init__(self, k, max_iter=10,
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convergence_tolerance=1e-05,
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random_seed=None, print_progress=0):
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_BaseModel.__init__(self)
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_Cluster.__init__(self)
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_IterativeModel.__init__(self)
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self.k = k
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self.max_iter = max_iter
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self.convergence_tolerance = convergence_tolerance
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self.random_seed = random_seed
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self.print_progress = print_progress
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self._is_fitted = False
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def _fit(self, X, init_params=True):
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"""Learn cluster centroids from training data.
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Called in self.fit
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"""
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n_samples = X.shape[0]
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if init_params:
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self.iterations_ = 0
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# initialize centroids
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rgen = np.random.RandomState(self.random_seed)
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idx = rgen.choice(n_samples, self.k, replace=False)
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self.centroids_ = X[idx]
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for _ in range(self.max_iter):
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# assign samples to cluster centroids
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self.clusters_ = {i: [] for i in range(self.k)}
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for sample_idx, cluster_idx in enumerate(
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self._get_cluster_idx(X=X, centroids=self.centroids_)):
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self.clusters_[cluster_idx].append(sample_idx)
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# recompute centroids
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new_centroids = np.array([np.mean(X[self.clusters_[k]], axis=0)
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for k in sorted(self.clusters_.keys())])
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# stop if cluster assignment doesn't change
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if np.allclose(self.centroids_, new_centroids,
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rtol=self.convergence_tolerance,
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atol=1e-08, equal_nan=False):
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break
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else:
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self.centroids_ = new_centroids
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self.iterations_ += 1
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if self.print_progress:
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self._print_progress(iteration=self.iterations_,
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n_iter=self.max_iter)
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return self
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def _get_cluster_idx(self, X, centroids):
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for sample_idx, sample in enumerate(X):
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# dist = [euclidean(sample, c) for c in self.centroids_]
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dist = np.sqrt(np.sum(np.square(sample - self.centroids_), axis=1))
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yield np.argmin(dist)
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def _predict(self, X):
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"""Predict cluster labels of X.
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Called in self.predict
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"""
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pred = np.array([idx for idx in self._get_cluster_idx(X=X,
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centroids=self.centroids_)])
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return pred
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