# # Licensed to the Apache Software Foundation (ASF) under one or more # contributor license agreements. See the NOTICE file distributed with # this work for additional information regarding copyright ownership. # The ASF licenses this file to You under the Apache License, Version 2.0 # (the "License"); you may not use this file except in compliance with # the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # import sys import array as pyarray from math import exp, log from collections import namedtuple from numpy import array, random, tile from pyspark import SparkContext, since from pyspark.rdd import RDD from pyspark.mllib.common import JavaModelWrapper, callMLlibFunc, callJavaFunc, _py2java, _java2py from pyspark.mllib.linalg import SparseVector, _convert_to_vector, DenseVector # noqa: F401 from pyspark.mllib.stat.distribution import MultivariateGaussian from pyspark.mllib.util import Saveable, Loader, inherit_doc, JavaLoader, JavaSaveable from pyspark.streaming import DStream __all__ = ['BisectingKMeansModel', 'BisectingKMeans', 'KMeansModel', 'KMeans', 'GaussianMixtureModel', 'GaussianMixture', 'PowerIterationClusteringModel', 'PowerIterationClustering', 'StreamingKMeans', 'StreamingKMeansModel', 'LDA', 'LDAModel'] [docs]@inherit_doc class BisectingKMeansModel(JavaModelWrapper): """ A clustering model derived from the bisecting k-means method. .. versionadded:: 2.0.0 Examples -------- >>> data = array([0.0,0.0, 1.0,1.0, 9.0,8.0, 8.0,9.0]).reshape(4, 2) >>> bskm = BisectingKMeans() >>> model = bskm.train(sc.parallelize(data, 2), k=4) >>> p = array([0.0, 0.0]) >>> model.predict(p) 0 >>> model.k 4 >>> model.computeCost(p) 0.0 """ def __init__(self, java_model): super(BisectingKMeansModel, self).__init__(java_model) self.centers = [c.toArray() for c in self.call("clusterCenters")] @property @since('2.0.0') def clusterCenters(self): """Get the cluster centers, represented as a list of NumPy arrays.""" return self.centers @property @since('2.0.0') def k(self): """Get the number of clusters""" return self.call("k") [docs] def predict(self, x): """ Find the cluster that each of the points belongs to in this model. .. versionadded:: 2.0.0 Parameters ---------- x : :py:class:`pyspark.mllib.linalg.Vector` or :py:class:`pyspark.RDD` A data point (or RDD of points) to determine cluster index. :py:class:`pyspark.mllib.linalg.Vector` can be replaced with equivalent objects (list, tuple, numpy.ndarray). Returns ------- int or :py:class:`pyspark.RDD` of int Predicted cluster index or an RDD of predicted cluster indices if the input is an RDD. """ if isinstance(x, RDD): vecs = x.map(_convert_to_vector) return self.call("predict", vecs) x = _convert_to_vector(x) return self.call("predict", x) [docs] def computeCost(self, x): """ Return the Bisecting K-means cost (sum of squared distances of points to their nearest center) for this model on the given data. If provided with an RDD of points returns the sum. .. versionadded:: 2.0.0 Parameters ---------- point : :py:class:`pyspark.mllib.linalg.Vector` or :py:class:`pyspark.RDD` A data point (or RDD of points) to compute the cost(s). :py:class:`pyspark.mllib.linalg.Vector` can be replaced with equivalent objects (list, tuple, numpy.ndarray). """ if isinstance(x, RDD): vecs = x.map(_convert_to_vector) return self.call("computeCost", vecs) return self.call("computeCost", _convert_to_vector(x)) [docs]class BisectingKMeans(object): """ A bisecting k-means algorithm based on the paper "A comparison of document clustering techniques" by Steinbach, Karypis, and Kumar, with modification to fit Spark. The algorithm starts from a single cluster that contains all points. Iteratively it finds divisible clusters on the bottom level and bisects each of them using k-means, until there are `k` leaf clusters in total or no leaf clusters are divisible. The bisecting steps of clusters on the same level are grouped together to increase parallelism. If bisecting all divisible clusters on the bottom level would result more than `k` leaf clusters, larger clusters get higher priority. .. versionadded:: 2.0.0 Notes ----- See the original paper [1]_ .. [1] Steinbach, M. et al. "A Comparison of Document Clustering Techniques." (2000). KDD Workshop on Text Mining, 2000 http://glaros.dtc.umn.edu/gkhome/fetch/papers/docclusterKDDTMW00.pdf """ [docs] @classmethod def train(self, rdd, k=4, maxIterations=20, minDivisibleClusterSize=1.0, seed=-1888008604): """ Runs the bisecting k-means algorithm return the model. .. versionadded:: 2.0.0 Parameters ---------- rdd : :py:class:`pyspark.RDD` Training points as an `RDD` of `Vector` or convertible sequence types. k : int, optional The desired number of leaf clusters. The actual number could be smaller if there are no divisible leaf clusters. (default: 4) maxIterations : int, optional Maximum number of iterations allowed to split clusters. (default: 20) minDivisibleClusterSize : float, optional Minimum number of points (if >= 1.0) or the minimum proportion of points (if < 1.0) of a divisible cluster. (default: 1) seed : int, optional Random seed value for cluster initialization. (default: -1888008604 from classOf[BisectingKMeans].getName.##) """ java_model = callMLlibFunc( "trainBisectingKMeans", rdd.map(_convert_to_vector), k, maxIterations, minDivisibleClusterSize, seed) return BisectingKMeansModel(java_model) [docs]@inherit_doc class KMeansModel(Saveable, Loader): """A clustering model derived from the k-means method. .. versionadded:: 0.9.0 Examples -------- >>> data = array([0.0,0.0, 1.0,1.0, 9.0,8.0, 8.0,9.0]).reshape(4, 2) >>> model = KMeans.train( ... sc.parallelize(data), 2, maxIterations=10, initializationMode="random", ... seed=50, initializationSteps=5, epsilon=1e-4) >>> model.predict(array([0.0, 0.0])) == model.predict(array([1.0, 1.0])) True >>> model.predict(array([8.0, 9.0])) == model.predict(array([9.0, 8.0])) True >>> model.k 2 >>> model.computeCost(sc.parallelize(data)) 2.0 >>> model = KMeans.train(sc.parallelize(data), 2) >>> sparse_data = [ ... SparseVector(3, {1: 1.0}), ... SparseVector(3, {1: 1.1}), ... SparseVector(3, {2: 1.0}), ... SparseVector(3, {2: 1.1}) ... ] >>> model = KMeans.train(sc.parallelize(sparse_data), 2, initializationMode="k-means||", ... seed=50, initializationSteps=5, epsilon=1e-4) >>> model.predict(array([0., 1., 0.])) == model.predict(array([0, 1.1, 0.])) True >>> model.predict(array([0., 0., 1.])) == model.predict(array([0, 0, 1.1])) True >>> model.predict(sparse_data[0]) == model.predict(sparse_data[1]) True >>> model.predict(sparse_data[2]) == model.predict(sparse_data[3]) True >>> isinstance(model.clusterCenters, list) True >>> import os, tempfile >>> path = tempfile.mkdtemp() >>> model.save(sc, path) >>> sameModel = KMeansModel.load(sc, path) >>> sameModel.predict(sparse_data[0]) == model.predict(sparse_data[0]) True >>> from shutil import rmtree >>> try: ... rmtree(path) ... except OSError: ... pass >>> data = array([-383.1,-382.9, 28.7,31.2, 366.2,367.3]).reshape(3, 2) >>> model = KMeans.train(sc.parallelize(data), 3, maxIterations=0, ... initialModel = KMeansModel([(-1000.0,-1000.0),(5.0,5.0),(1000.0,1000.0)])) >>> model.clusterCenters [array([-1000., -1000.]), array([ 5., 5.]), array([ 1000., 1000.])] """ def __init__(self, centers): self.centers = centers @property @since('1.0.0') def clusterCenters(self): """Get the cluster centers, represented as a list of NumPy arrays.""" return self.centers @property @since('1.4.0') def k(self): """Total number of clusters.""" return len(self.centers) [docs] def predict(self, x): """ Find the cluster that each of the points belongs to in this model. .. versionadded:: 0.9.0 Parameters ---------- x : :py:class:`pyspark.mllib.linalg.Vector` or :py:class:`pyspark.RDD` A data point (or RDD of points) to determine cluster index. :py:class:`pyspark.mllib.linalg.Vector` can be replaced with equivalent objects (list, tuple, numpy.ndarray). Returns ------- int or :py:class:`pyspark.RDD` of int Predicted cluster index or an RDD of predicted cluster indices if the input is an RDD. """ best = 0 best_distance = float("inf") if isinstance(x, RDD): return x.map(self.predict) x = _convert_to_vector(x) for i in range(len(self.centers)): distance = x.squared_distance(self.centers[i]) if distance < best_distance: best = i best_distance = distance return best [docs] def computeCost(self, rdd): """ Return the K-means cost (sum of squared distances of points to their nearest center) for this model on the given data. .. versionadded:: 1.4.0 Parameters ---------- rdd : ::py:class:`pyspark.RDD` The RDD of points to compute the cost on. """ cost = callMLlibFunc("computeCostKmeansModel", rdd.map(_convert_to_vector), [_convert_to_vector(c) for c in self.centers]) return cost [docs] @since('1.4.0') def save(self, sc, path): """ Save this model to the given path. """ java_centers = _py2java(sc, [_convert_to_vector(c) for c in self.centers]) java_model = sc._jvm.org.apache.spark.mllib.clustering.KMeansModel(java_centers) java_model.save(sc._jsc.sc(), path) [docs] @classmethod @since('1.4.0') def load(cls, sc, path): """ Load a model from the given path. """ java_model = sc._jvm.org.apache.spark.mllib.clustering.KMeansModel.load(sc._jsc.sc(), path) return KMeansModel(_java2py(sc, java_model.clusterCenters())) [docs]class KMeans(object): """ K-means clustering. .. versionadded:: 0.9.0 """ [docs] @classmethod def train(cls, rdd, k, maxIterations=100, initializationMode="k-means||", seed=None, initializationSteps=2, epsilon=1e-4, initialModel=None): """ Train a k-means clustering model. .. versionadded:: 0.9.0 Parameters ---------- rdd : ::py:class:`pyspark.RDD` Training points as an `RDD` of :py:class:`pyspark.mllib.linalg.Vector` or convertible sequence types. k : int Number of clusters to create. maxIterations : int, optional Maximum number of iterations allowed. (default: 100) initializationMode : str, optional The initialization algorithm. This can be either "random" or "k-means||". (default: "k-means||") seed : int, optional Random seed value for cluster initialization. Set as None to generate seed based on system time. (default: None) initializationSteps : Number of steps for the k-means|| initialization mode. This is an advanced setting -- the default of 2 is almost always enough. (default: 2) epsilon : float, optional Distance threshold within which a center will be considered to have converged. If all centers move less than this Euclidean distance, iterations are stopped. (default: 1e-4) initialModel : :py:class:`KMeansModel`, optional Initial cluster centers can be provided as a KMeansModel object rather than using the random or k-means|| initializationModel. (default: None) """ clusterInitialModel = [] if initialModel is not None: if not isinstance(initialModel, KMeansModel): raise TypeError("initialModel is of " + str(type(initialModel)) + ". It needs " "to be of <type 'KMeansModel'>") clusterInitialModel = [_convert_to_vector(c) for c in initialModel.clusterCenters] model = callMLlibFunc("trainKMeansModel", rdd.map(_convert_to_vector), k, maxIterations, initializationMode, seed, initializationSteps, epsilon, clusterInitialModel) centers = callJavaFunc(rdd.context, model.clusterCenters) return KMeansModel([c.toArray() for c in centers]) [docs]@inherit_doc class GaussianMixtureModel(JavaModelWrapper, JavaSaveable, JavaLoader): """ A clustering model derived from the Gaussian Mixture Model method. .. versionadded:: 1.3.0 Examples -------- >>> from pyspark.mllib.linalg import Vectors, DenseMatrix >>> from numpy.testing import assert_equal >>> from shutil import rmtree >>> import os, tempfile >>> clusterdata_1 = sc.parallelize(array([-0.1,-0.05,-0.01,-0.1, ... 0.9,0.8,0.75,0.935, ... -0.83,-0.68,-0.91,-0.76 ]).reshape(6, 2), 2) >>> model = GaussianMixture.train(clusterdata_1, 3, convergenceTol=0.0001, ... maxIterations=50, seed=10) >>> labels = model.predict(clusterdata_1).collect() >>> labels[0]==labels[1] False >>> labels[1]==labels[2] False >>> labels[4]==labels[5] True >>> model.predict([-0.1,-0.05]) 0 >>> softPredicted = model.predictSoft([-0.1,-0.05]) >>> abs(softPredicted[0] - 1.0) < 0.03 True >>> abs(softPredicted[1] - 0.0) < 0.03 True >>> abs(softPredicted[2] - 0.0) < 0.03 True >>> path = tempfile.mkdtemp() >>> model.save(sc, path) >>> sameModel = GaussianMixtureModel.load(sc, path) >>> assert_equal(model.weights, sameModel.weights) >>> mus, sigmas = list( ... zip(*[(g.mu, g.sigma) for g in model.gaussians])) >>> sameMus, sameSigmas = list( ... zip(*[(g.mu, g.sigma) for g in sameModel.gaussians])) >>> mus == sameMus True >>> sigmas == sameSigmas True >>> from shutil import rmtree >>> try: ... rmtree(path) ... except OSError: ... pass >>> data = array([-5.1971, -2.5359, -3.8220, ... -5.2211, -5.0602, 4.7118, ... 6.8989, 3.4592, 4.6322, ... 5.7048, 4.6567, 5.5026, ... 4.5605, 5.2043, 6.2734]) >>> clusterdata_2 = sc.parallelize(data.reshape(5,3)) >>> model = GaussianMixture.train(clusterdata_2, 2, convergenceTol=0.0001, ... maxIterations=150, seed=4) >>> labels = model.predict(clusterdata_2).collect() >>> labels[0]==labels[1] True >>> labels[2]==labels[3]==labels[4] True """ @property @since('1.4.0') def weights(self): """ Weights for each Gaussian distribution in the mixture, where weights[i] is the weight for Gaussian i, and weights.sum == 1. """ return array(self.call("weights")) @property @since('1.4.0') def gaussians(self): """ Array of MultivariateGaussian where gaussians[i] represents the Multivariate Gaussian (Normal) Distribution for Gaussian i. """ return [ MultivariateGaussian(gaussian[0], gaussian[1]) for gaussian in self.call("gaussians")] @property @since('1.4.0') def k(self): """Number of gaussians in mixture.""" return len(self.weights) [docs] def predict(self, x): """ Find the cluster to which the point 'x' or each point in RDD 'x' has maximum membership in this model. .. versionadded:: 1.3.0 Parameters ---------- x : :py:class:`pyspark.mllib.linalg.Vector` or :py:class:`pyspark.RDD` A feature vector or an RDD of vectors representing data points. Returns ------- numpy.float64 or :py:class:`pyspark.RDD` of int Predicted cluster label or an RDD of predicted cluster labels if the input is an RDD. """ if isinstance(x, RDD): cluster_labels = self.predictSoft(x).map(lambda z: z.index(max(z))) return cluster_labels else: z = self.predictSoft(x) return z.argmax() [docs] def predictSoft(self, x): """ Find the membership of point 'x' or each point in RDD 'x' to all mixture components. .. versionadded:: 1.3.0 Parameters ---------- x : :py:class:`pyspark.mllib.linalg.Vector` or :py:class:`pyspark.RDD` A feature vector or an RDD of vectors representing data points. Returns ------- numpy.ndarray or :py:class:`pyspark.RDD` The membership value to all mixture components for vector 'x' or each vector in RDD 'x'. """ if isinstance(x, RDD): means, sigmas = zip(*[(g.mu, g.sigma) for g in self.gaussians]) membership_matrix = callMLlibFunc("predictSoftGMM", x.map(_convert_to_vector), _convert_to_vector(self.weights), means, sigmas) return membership_matrix.map(lambda x: pyarray.array('d', x)) else: return self.call("predictSoft", _convert_to_vector(x)).toArray() [docs] @classmethod def load(cls, sc, path): """Load the GaussianMixtureModel from disk. .. versionadded:: 1.5.0 Parameters ---------- sc : :py:class:`SparkContext` path : str Path to where the model is stored. """ model = cls._load_java(sc, path) wrapper = sc._jvm.org.apache.spark.mllib.api.python.GaussianMixtureModelWrapper(model) return cls(wrapper) [docs]class GaussianMixture(object): """ Learning algorithm for Gaussian Mixtures using the expectation-maximization algorithm. .. versionadded:: 1.3.0 """ [docs] @classmethod def train(cls, rdd, k, convergenceTol=1e-3, maxIterations=100, seed=None, initialModel=None): """ Train a Gaussian Mixture clustering model. .. versionadded:: 1.3.0 Parameters ---------- rdd : ::py:class:`pyspark.RDD` Training points as an `RDD` of :py:class:`pyspark.mllib.linalg.Vector` or convertible sequence types. k : int Number of independent Gaussians in the mixture model. convergenceTol : float, optional Maximum change in log-likelihood at which convergence is considered to have occurred. (default: 1e-3) maxIterations : int, optional Maximum number of iterations allowed. (default: 100) seed : int, optional Random seed for initial Gaussian distribution. Set as None to generate seed based on system time. (default: None) initialModel : GaussianMixtureModel, optional Initial GMM starting point, bypassing the random initialization. (default: None) """ initialModelWeights = None initialModelMu = None initialModelSigma = None if initialModel is not None: if initialModel.k != k: raise ValueError("Mismatched cluster count, initialModel.k = %s, however k = %s" % (initialModel.k, k)) initialModelWeights = list(initialModel.weights) initialModelMu = [initialModel.gaussians[i].mu for i in range(initialModel.k)] initialModelSigma = [initialModel.gaussians[i].sigma for i in range(initialModel.k)] java_model = callMLlibFunc("trainGaussianMixtureModel", rdd.map(_convert_to_vector), k, convergenceTol, maxIterations, seed, initialModelWeights, initialModelMu, initialModelSigma) return GaussianMixtureModel(java_model) [docs]class PowerIterationClusteringModel(JavaModelWrapper, JavaSaveable, JavaLoader): """ Model produced by :py:class:`PowerIterationClustering`. .. versionadded:: 1.5.0 Examples -------- >>> import math >>> def genCircle(r, n): ... points = [] ... for i in range(0, n): ... theta = 2.0 * math.pi * i / n ... points.append((r * math.cos(theta), r * math.sin(theta))) ... return points >>> def sim(x, y): ... dist2 = (x[0] - y[0]) * (x[0] - y[0]) + (x[1] - y[1]) * (x[1] - y[1]) ... return math.exp(-dist2 / 2.0) >>> r1 = 1.0 >>> n1 = 10 >>> r2 = 4.0 >>> n2 = 40 >>> n = n1 + n2 >>> points = genCircle(r1, n1) + genCircle(r2, n2) >>> similarities = [(i, j, sim(points[i], points[j])) for i in range(1, n) for j in range(0, i)] >>> rdd = sc.parallelize(similarities, 2) >>> model = PowerIterationClustering.train(rdd, 2, 40) >>> model.k 2 >>> result = sorted(model.assignments().collect(), key=lambda x: x.id) >>> result[0].cluster == result[1].cluster == result[2].cluster == result[3].cluster True >>> result[4].cluster == result[5].cluster == result[6].cluster == result[7].cluster True >>> import os, tempfile >>> path = tempfile.mkdtemp() >>> model.save(sc, path) >>> sameModel = PowerIterationClusteringModel.load(sc, path) >>> sameModel.k 2 >>> result = sorted(model.assignments().collect(), key=lambda x: x.id) >>> result[0].cluster == result[1].cluster == result[2].cluster == result[3].cluster True >>> result[4].cluster == result[5].cluster == result[6].cluster == result[7].cluster True >>> from shutil import rmtree >>> try: ... rmtree(path) ... except OSError: ... pass """ @property @since('1.5.0') def k(self): """ Returns the number of clusters. """ return self.call("k") [docs] @since('1.5.0') def assignments(self): """ Returns the cluster assignments of this model. """ return self.call("getAssignments").map( lambda x: (PowerIterationClustering.Assignment(*x))) [docs] @classmethod @since('1.5.0') def load(cls, sc, path): """ Load a model from the given path. """ model = cls._load_java(sc, path) wrapper =\ sc._jvm.org.apache.spark.mllib.api.python.PowerIterationClusteringModelWrapper(model) return PowerIterationClusteringModel(wrapper) [docs]class PowerIterationClustering(object): """ Power Iteration Clustering (PIC), a scalable graph clustering algorithm. Developed by Lin and Cohen [1]_. From the abstract: "PIC finds a very low-dimensional embedding of a dataset using truncated power iteration on a normalized pair-wise similarity matrix of the data." .. versionadded:: 1.5.0 .. [1] Lin, Frank & Cohen, William. (2010). Power Iteration Clustering. http://www.cs.cmu.edu/~frank/papers/icml2010-pic-final.pdf """ [docs] @classmethod def train(cls, rdd, k, maxIterations=100, initMode="random"): r""" Train PowerIterationClusteringModel .. versionadded:: 1.5.0 Parameters ---------- rdd : :py:class:`pyspark.RDD` An RDD of (i, j, s\ :sub:`ij`\) tuples representing the affinity matrix, which is the matrix A in the PIC paper. The similarity s\ :sub:`ij`\ must be nonnegative. This is a symmetric matrix and hence s\ :sub:`ij`\ = s\ :sub:`ji`\ For any (i, j) with nonzero similarity, there should be either (i, j, s\ :sub:`ij`\) or (j, i, s\ :sub:`ji`\) in the input. Tuples with i = j are ignored, because it is assumed s\ :sub:`ij`\ = 0.0. k : int Number of clusters. maxIterations : int, optional Maximum number of iterations of the PIC algorithm. (default: 100) initMode : str, optional Initialization mode. This can be either "random" to use a random vector as vertex properties, or "degree" to use normalized sum similarities. (default: "random") """ model = callMLlibFunc("trainPowerIterationClusteringModel", rdd.map(_convert_to_vector), int(k), int(maxIterations), initMode) return PowerIterationClusteringModel(model) class Assignment(namedtuple("Assignment", ["id", "cluster"])): """ Represents an (id, cluster) tuple. .. versionadded:: 1.5.0 """ [docs]class StreamingKMeansModel(KMeansModel): """ Clustering model which can perform an online update of the centroids. The update formula for each centroid is given by - c_t+1 = ((c_t * n_t * a) + (x_t * m_t)) / (n_t + m_t) - n_t+1 = n_t * a + m_t where - c_t: Centroid at the n_th iteration. - n_t: Number of samples (or) weights associated with the centroid at the n_th iteration. - x_t: Centroid of the new data closest to c_t. - m_t: Number of samples (or) weights of the new data closest to c_t - c_t+1: New centroid. - n_t+1: New number of weights. - a: Decay Factor, which gives the forgetfulness. .. versionadded:: 1.5.0 Parameters ---------- clusterCenters : list of :py:class:`pyspark.mllib.linalg.Vector` or covertible Initial cluster centers. clusterWeights : :py:class:`pyspark.mllib.linalg.Vector` or covertible List of weights assigned to each cluster. Notes ----- If a is set to 1, it is the weighted mean of the previous and new data. If it set to zero, the old centroids are completely forgotten. Examples -------- >>> initCenters = [[0.0, 0.0], [1.0, 1.0]] >>> initWeights = [1.0, 1.0] >>> stkm = StreamingKMeansModel(initCenters, initWeights) >>> data = sc.parallelize([[-0.1, -0.1], [0.1, 0.1], ... [0.9, 0.9], [1.1, 1.1]]) >>> stkm = stkm.update(data, 1.0, "batches") >>> stkm.centers array([[ 0., 0.], [ 1., 1.]]) >>> stkm.predict([-0.1, -0.1]) 0 >>> stkm.predict([0.9, 0.9]) 1 >>> stkm.clusterWeights [3.0, 3.0] >>> decayFactor = 0.0 >>> data = sc.parallelize([DenseVector([1.5, 1.5]), DenseVector([0.2, 0.2])]) >>> stkm = stkm.update(data, 0.0, "batches") >>> stkm.centers array([[ 0.2, 0.2], [ 1.5, 1.5]]) >>> stkm.clusterWeights [1.0, 1.0] >>> stkm.predict([0.2, 0.2]) 0 >>> stkm.predict([1.5, 1.5]) 1 """ def __init__(self, clusterCenters, clusterWeights): super(StreamingKMeansModel, self).__init__(centers=clusterCenters) self._clusterWeights = list(clusterWeights) @property @since('1.5.0') def clusterWeights(self): """Return the cluster weights.""" return self._clusterWeights [docs] @since('1.5.0') def update(self, data, decayFactor, timeUnit): """Update the centroids, according to data .. versionadded:: 1.5.0 Parameters ---------- data : :py:class:`pyspark.RDD` RDD with new data for the model update. decayFactor : float Forgetfulness of the previous centroids. timeUnit : str Can be "batches" or "points". If points, then the decay factor is raised to the power of number of new points and if batches, then decay factor will be used as is. """ if not isinstance(data, RDD): raise TypeError("Data should be of an RDD, got %s." % type(data)) data = data.map(_convert_to_vector) decayFactor = float(decayFactor) if timeUnit not in ["batches", "points"]: raise ValueError( "timeUnit should be 'batches' or 'points', got %s." % timeUnit) vectorCenters = [_convert_to_vector(center) for center in self.centers] updatedModel = callMLlibFunc( "updateStreamingKMeansModel", vectorCenters, self._clusterWeights, data, decayFactor, timeUnit) self.centers = array(updatedModel[0]) self._clusterWeights = list(updatedModel[1]) return self [docs]class StreamingKMeans(object): """ Provides methods to set k, decayFactor, timeUnit to configure the KMeans algorithm for fitting and predicting on incoming dstreams. More details on how the centroids are updated are provided under the docs of StreamingKMeansModel. .. versionadded:: 1.5.0 Parameters ---------- k : int, optional Number of clusters. (default: 2) decayFactor : float, optional Forgetfulness of the previous centroids. (default: 1.0) timeUnit : str, optional Can be "batches" or "points". If points, then the decay factor is raised to the power of number of new points and if batches, then decay factor will be used as is. (default: "batches") """ def __init__(self, k=2, decayFactor=1.0, timeUnit="batches"): self._k = k self._decayFactor = decayFactor if timeUnit not in ["batches", "points"]: raise ValueError( "timeUnit should be 'batches' or 'points', got %s." % timeUnit) self._timeUnit = timeUnit self._model = None [docs] @since('1.5.0') def latestModel(self): """Return the latest model""" return self._model def _validate(self, dstream): if self._model is None: raise ValueError( "Initial centers should be set either by setInitialCenters " "or setRandomCenters.") if not isinstance(dstream, DStream): raise TypeError( "Expected dstream to be of type DStream, " "got type %s" % type(dstream)) [docs] @since('1.5.0') def setK(self, k): """Set number of clusters.""" self._k = k return self [docs] @since('1.5.0') def setDecayFactor(self, decayFactor): """Set decay factor.""" self._decayFactor = decayFactor return self [docs] @since('1.5.0') def setHalfLife(self, halfLife, timeUnit): """ Set number of batches after which the centroids of that particular batch has half the weightage. """ self._timeUnit = timeUnit self._decayFactor = exp(log(0.5) / halfLife) return self [docs] @since('1.5.0') def setInitialCenters(self, centers, weights): """ Set initial centers. Should be set before calling trainOn. """ self._model = StreamingKMeansModel(centers, weights) return self [docs] @since('1.5.0') def setRandomCenters(self, dim, weight, seed): """ Set the initial centers to be random samples from a gaussian population with constant weights. """ rng = random.RandomState(seed) clusterCenters = rng.randn(self._k, dim) clusterWeights = tile(weight, self._k) self._model = StreamingKMeansModel(clusterCenters, clusterWeights) return self [docs] @since('1.5.0') def trainOn(self, dstream): """Train the model on the incoming dstream.""" self._validate(dstream) def update(rdd): self._model.update(rdd, self._decayFactor, self._timeUnit) dstream.foreachRDD(update) [docs] @since('1.5.0') def predictOn(self, dstream): """ Make predictions on a dstream. Returns a transformed dstream object """ self._validate(dstream) return dstream.map(lambda x: self._model.predict(x)) [docs] @since('1.5.0') def predictOnValues(self, dstream): """ Make predictions on a keyed dstream. Returns a transformed dstream object. """ self._validate(dstream) return dstream.mapValues(lambda x: self._model.predict(x)) [docs]class LDAModel(JavaModelWrapper, JavaSaveable, Loader): """ A clustering model derived from the LDA method. Latent Dirichlet Allocation (LDA), a topic model designed for text documents. Terminology - "word" = "term": an element of the vocabulary - "token": instance of a term appearing in a document - "topic": multinomial distribution over words representing some concept .. versionadded:: 1.5.0 Notes ----- See the original LDA paper (journal version) [1]_ .. [1] Blei, D. et al. "Latent Dirichlet Allocation." J. Mach. Learn. Res. 3 (2003): 993-1022. https://www.jmlr.org/papers/v3/blei03a Examples -------- >>> from pyspark.mllib.linalg import Vectors >>> from numpy.testing import assert_almost_equal, assert_equal >>> data = [ ... [1, Vectors.dense([0.0, 1.0])], ... [2, SparseVector(2, {0: 1.0})], ... ] >>> rdd = sc.parallelize(data) >>> model = LDA.train(rdd, k=2, seed=1) >>> model.vocabSize() 2 >>> model.describeTopics() [([1, 0], [0.5..., 0.49...]), ([0, 1], [0.5..., 0.49...])] >>> model.describeTopics(1) [([1], [0.5...]), ([0], [0.5...])] >>> topics = model.topicsMatrix() >>> topics_expect = array([[0.5, 0.5], [0.5, 0.5]]) >>> assert_almost_equal(topics, topics_expect, 1) >>> import os, tempfile >>> from shutil import rmtree >>> path = tempfile.mkdtemp() >>> model.save(sc, path) >>> sameModel = LDAModel.load(sc, path) >>> assert_equal(sameModel.topicsMatrix(), model.topicsMatrix()) >>> sameModel.vocabSize() == model.vocabSize() True >>> try: ... rmtree(path) ... except OSError: ... pass """ [docs] @since('1.5.0') def topicsMatrix(self): """Inferred topics, where each topic is represented by a distribution over terms.""" return self.call("topicsMatrix").toArray() [docs] @since('1.5.0') def vocabSize(self): """Vocabulary size (number of terms or terms in the vocabulary)""" return self.call("vocabSize") [docs] def describeTopics(self, maxTermsPerTopic=None): """Return the topics described by weighted terms. .. versionadded:: 1.6.0 .. warning:: If vocabSize and k are large, this can return a large object! Parameters ---------- maxTermsPerTopic : int, optional Maximum number of terms to collect for each topic. (default: vocabulary size) Returns ------- list Array over topics. Each topic is represented as a pair of matching arrays: (term indices, term weights in topic). Each topic's terms are sorted in order of decreasing weight. """ if maxTermsPerTopic is None: topics = self.call("describeTopics") else: topics = self.call("describeTopics", maxTermsPerTopic) return topics [docs] @classmethod def load(cls, sc, path): """Load the LDAModel from disk. .. versionadded:: 1.5.0 Parameters ---------- sc : :py:class:`pyspark.SparkContext` path : str Path to where the model is stored. """ if not isinstance(sc, SparkContext): raise TypeError("sc should be a SparkContext, got type %s" % type(sc)) if not isinstance(path, str): raise TypeError("path should be a string, got type %s" % type(path)) model = callMLlibFunc("loadLDAModel", sc, path) return LDAModel(model) [docs]class LDA(object): """ Train Latent Dirichlet Allocation (LDA) model. .. versionadded:: 1.5.0 """ [docs] @classmethod def train(cls, rdd, k=10, maxIterations=20, docConcentration=-1.0, topicConcentration=-1.0, seed=None, checkpointInterval=10, optimizer="em"): """Train a LDA model. .. versionadded:: 1.5.0 Parameters ---------- rdd : :py:class:`pyspark.RDD` RDD of documents, which are tuples of document IDs and term (word) count vectors. The term count vectors are "bags of words" with a fixed-size vocabulary (where the vocabulary size is the length of the vector). Document IDs must be unique and >= 0. k : int, optional Number of topics to infer, i.e., the number of soft cluster centers. (default: 10) maxIterations : int, optional Maximum number of iterations allowed. (default: 20) docConcentration : float, optional Concentration parameter (commonly named "alpha") for the prior placed on documents' distributions over topics ("theta"). (default: -1.0) topicConcentration : float, optional Concentration parameter (commonly named "beta" or "eta") for the prior placed on topics' distributions over terms. (default: -1.0) seed : int, optional Random seed for cluster initialization. Set as None to generate seed based on system time. (default: None) checkpointInterval : int, optional Period (in iterations) between checkpoints. (default: 10) optimizer : str, optional LDAOptimizer used to perform the actual calculation. Currently "em", "online" are supported. (default: "em") """ model = callMLlibFunc("trainLDAModel", rdd, k, maxIterations, docConcentration, topicConcentration, seed, checkpointInterval, optimizer) return LDAModel(model) def _test(): import doctest import numpy import pyspark.mllib.clustering try: # Numpy 1.14+ changed it's string format. numpy.set_printoptions(legacy='1.13') except TypeError: pass globs = pyspark.mllib.clustering.__dict__.copy() globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2) (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS) globs['sc'].stop() if failure_count: sys.exit(-1) if __name__ == "__main__": _test()