eb037a8180
### What changes were proposed in this pull request? The Experimental and Evolving annotations are both (like Unstable) used to express that a an API may change. However there are many things in the code that have been marked that way since even Spark 1.x. Per the dev thread, anything introduced at or before Spark 2.3.0 is pretty much 'stable' in that it would not change without a deprecation cycle. Therefore I'd like to remove most of these annotations. And, remove the `:: Experimental ::` scaladoc tag too. And likewise for Python, R. The changes below can be summarized as: - Generally, anything introduced at or before Spark 2.3.0 has been unmarked as neither Evolving nor Experimental - Obviously experimental items like DSv2, Barrier mode, ExperimentalMethods are untouched - I _did_ unmark a few MLlib classes introduced in 2.4, as I am quite confident they're not going to change (e.g. KolmogorovSmirnovTest, PowerIterationClustering) It's a big change to review, so I'd suggest scanning the list of _files_ changed to see if any area seems like it should remain partly experimental and examine those. ### Why are the changes needed? Many of these annotations are incorrect; the APIs are de facto stable. Leaving them also makes legitimate usages of the annotations less meaningful. ### Does this PR introduce any user-facing change? No. ### How was this patch tested? Existing tests. Closes #25558 from srowen/SPARK-28855. Authored-by: Sean Owen <sean.owen@databricks.com> Signed-off-by: Sean Owen <sean.owen@databricks.com>
4087 lines
151 KiB
Python
Executable file
4087 lines
151 KiB
Python
Executable file
#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import sys
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if sys.version > '3':
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basestring = str
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from pyspark import since, keyword_only, SparkContext
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from pyspark.rdd import ignore_unicode_prefix
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from pyspark.ml.linalg import _convert_to_vector
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from pyspark.ml.param.shared import *
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from pyspark.ml.util import JavaMLReadable, JavaMLWritable
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from pyspark.ml.wrapper import JavaEstimator, JavaModel, JavaParams, JavaTransformer, _jvm
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from pyspark.ml.common import inherit_doc
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__all__ = ['Binarizer',
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'BucketedRandomProjectionLSH', 'BucketedRandomProjectionLSHModel',
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'Bucketizer',
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'ChiSqSelector', 'ChiSqSelectorModel',
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'CountVectorizer', 'CountVectorizerModel',
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'DCT',
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'ElementwiseProduct',
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'FeatureHasher',
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'HashingTF',
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'IDF', 'IDFModel',
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'Imputer', 'ImputerModel',
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'IndexToString',
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'Interaction',
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'MaxAbsScaler', 'MaxAbsScalerModel',
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'MinHashLSH', 'MinHashLSHModel',
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'MinMaxScaler', 'MinMaxScalerModel',
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'NGram',
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'Normalizer',
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'OneHotEncoder', 'OneHotEncoderModel',
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'PCA', 'PCAModel',
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'PolynomialExpansion',
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'QuantileDiscretizer',
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'RobustScaler', 'RobustScalerModel',
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'RegexTokenizer',
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'RFormula', 'RFormulaModel',
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'SQLTransformer',
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'StandardScaler', 'StandardScalerModel',
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'StopWordsRemover',
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'StringIndexer', 'StringIndexerModel',
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'Tokenizer',
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'VectorAssembler',
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'VectorIndexer', 'VectorIndexerModel',
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'VectorSizeHint',
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'VectorSlicer',
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'Word2Vec', 'Word2VecModel']
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@inherit_doc
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class Binarizer(JavaTransformer, HasInputCol, HasOutputCol, JavaMLReadable, JavaMLWritable):
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"""
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Binarize a column of continuous features given a threshold.
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>>> df = spark.createDataFrame([(0.5,)], ["values"])
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>>> binarizer = Binarizer(threshold=1.0, inputCol="values", outputCol="features")
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>>> binarizer.transform(df).head().features
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0.0
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>>> binarizer.setParams(outputCol="freqs").transform(df).head().freqs
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0.0
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>>> params = {binarizer.threshold: -0.5, binarizer.outputCol: "vector"}
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>>> binarizer.transform(df, params).head().vector
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1.0
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>>> binarizerPath = temp_path + "/binarizer"
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>>> binarizer.save(binarizerPath)
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>>> loadedBinarizer = Binarizer.load(binarizerPath)
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>>> loadedBinarizer.getThreshold() == binarizer.getThreshold()
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True
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.. versionadded:: 1.4.0
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"""
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threshold = Param(Params._dummy(), "threshold",
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"Param for threshold used to binarize continuous features. " +
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"The features greater than the threshold will be binarized to 1.0. " +
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"The features equal to or less than the threshold will be binarized to 0.0",
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typeConverter=TypeConverters.toFloat)
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@keyword_only
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def __init__(self, threshold=0.0, inputCol=None, outputCol=None):
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"""
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__init__(self, threshold=0.0, inputCol=None, outputCol=None)
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"""
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super(Binarizer, self).__init__()
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self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.Binarizer", self.uid)
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self._setDefault(threshold=0.0)
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kwargs = self._input_kwargs
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self.setParams(**kwargs)
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@keyword_only
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@since("1.4.0")
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def setParams(self, threshold=0.0, inputCol=None, outputCol=None):
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"""
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setParams(self, threshold=0.0, inputCol=None, outputCol=None)
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Sets params for this Binarizer.
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"""
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kwargs = self._input_kwargs
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return self._set(**kwargs)
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@since("1.4.0")
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def setThreshold(self, value):
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"""
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Sets the value of :py:attr:`threshold`.
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"""
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return self._set(threshold=value)
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@since("1.4.0")
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def getThreshold(self):
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"""
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Gets the value of threshold or its default value.
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"""
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return self.getOrDefault(self.threshold)
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class LSHParams(Params):
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"""
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Mixin for Locality Sensitive Hashing (LSH) algorithm parameters.
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"""
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numHashTables = Param(Params._dummy(), "numHashTables", "number of hash tables, where " +
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"increasing number of hash tables lowers the false negative rate, " +
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"and decreasing it improves the running performance.",
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typeConverter=TypeConverters.toInt)
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def __init__(self):
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super(LSHParams, self).__init__()
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def setNumHashTables(self, value):
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"""
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Sets the value of :py:attr:`numHashTables`.
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"""
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return self._set(numHashTables=value)
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def getNumHashTables(self):
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"""
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Gets the value of numHashTables or its default value.
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"""
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return self.getOrDefault(self.numHashTables)
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class LSHModel(JavaModel):
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"""
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Mixin for Locality Sensitive Hashing (LSH) models.
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"""
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def approxNearestNeighbors(self, dataset, key, numNearestNeighbors, distCol="distCol"):
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"""
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Given a large dataset and an item, approximately find at most k items which have the
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closest distance to the item. If the :py:attr:`outputCol` is missing, the method will
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transform the data; if the :py:attr:`outputCol` exists, it will use that. This allows
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caching of the transformed data when necessary.
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.. note:: This method is experimental and will likely change behavior in the next release.
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:param dataset: The dataset to search for nearest neighbors of the key.
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:param key: Feature vector representing the item to search for.
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:param numNearestNeighbors: The maximum number of nearest neighbors.
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:param distCol: Output column for storing the distance between each result row and the key.
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Use "distCol" as default value if it's not specified.
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:return: A dataset containing at most k items closest to the key. A column "distCol" is
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added to show the distance between each row and the key.
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"""
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return self._call_java("approxNearestNeighbors", dataset, key, numNearestNeighbors,
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distCol)
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def approxSimilarityJoin(self, datasetA, datasetB, threshold, distCol="distCol"):
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"""
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Join two datasets to approximately find all pairs of rows whose distance are smaller than
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the threshold. If the :py:attr:`outputCol` is missing, the method will transform the data;
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if the :py:attr:`outputCol` exists, it will use that. This allows caching of the
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transformed data when necessary.
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:param datasetA: One of the datasets to join.
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:param datasetB: Another dataset to join.
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:param threshold: The threshold for the distance of row pairs.
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:param distCol: Output column for storing the distance between each pair of rows. Use
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"distCol" as default value if it's not specified.
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:return: A joined dataset containing pairs of rows. The original rows are in columns
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"datasetA" and "datasetB", and a column "distCol" is added to show the distance
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between each pair.
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"""
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threshold = TypeConverters.toFloat(threshold)
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return self._call_java("approxSimilarityJoin", datasetA, datasetB, threshold, distCol)
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@inherit_doc
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class BucketedRandomProjectionLSH(JavaEstimator, LSHParams, HasInputCol, HasOutputCol, HasSeed,
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JavaMLReadable, JavaMLWritable):
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"""
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LSH class for Euclidean distance metrics.
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The input is dense or sparse vectors, each of which represents a point in the Euclidean
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distance space. The output will be vectors of configurable dimension. Hash values in the same
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dimension are calculated by the same hash function.
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.. seealso:: `Stable Distributions
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<https://en.wikipedia.org/wiki/Locality-sensitive_hashing#Stable_distributions>`_
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.. seealso:: `Hashing for Similarity Search: A Survey <https://arxiv.org/abs/1408.2927>`_
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>>> from pyspark.ml.linalg import Vectors
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>>> from pyspark.sql.functions import col
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>>> data = [(0, Vectors.dense([-1.0, -1.0 ]),),
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... (1, Vectors.dense([-1.0, 1.0 ]),),
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... (2, Vectors.dense([1.0, -1.0 ]),),
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... (3, Vectors.dense([1.0, 1.0]),)]
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>>> df = spark.createDataFrame(data, ["id", "features"])
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>>> brp = BucketedRandomProjectionLSH(inputCol="features", outputCol="hashes",
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... seed=12345, bucketLength=1.0)
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>>> model = brp.fit(df)
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>>> model.transform(df).head()
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Row(id=0, features=DenseVector([-1.0, -1.0]), hashes=[DenseVector([-1.0])])
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>>> data2 = [(4, Vectors.dense([2.0, 2.0 ]),),
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... (5, Vectors.dense([2.0, 3.0 ]),),
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... (6, Vectors.dense([3.0, 2.0 ]),),
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... (7, Vectors.dense([3.0, 3.0]),)]
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>>> df2 = spark.createDataFrame(data2, ["id", "features"])
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>>> model.approxNearestNeighbors(df2, Vectors.dense([1.0, 2.0]), 1).collect()
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[Row(id=4, features=DenseVector([2.0, 2.0]), hashes=[DenseVector([1.0])], distCol=1.0)]
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>>> model.approxSimilarityJoin(df, df2, 3.0, distCol="EuclideanDistance").select(
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... col("datasetA.id").alias("idA"),
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... col("datasetB.id").alias("idB"),
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... col("EuclideanDistance")).show()
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+---+---+-----------------+
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|idA|idB|EuclideanDistance|
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+---+---+-----------------+
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| 3| 6| 2.23606797749979|
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+---+---+-----------------+
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...
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>>> model.approxSimilarityJoin(df, df2, 3, distCol="EuclideanDistance").select(
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... col("datasetA.id").alias("idA"),
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... col("datasetB.id").alias("idB"),
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... col("EuclideanDistance")).show()
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+---+---+-----------------+
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|idA|idB|EuclideanDistance|
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+---+---+-----------------+
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| 3| 6| 2.23606797749979|
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+---+---+-----------------+
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...
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>>> brpPath = temp_path + "/brp"
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>>> brp.save(brpPath)
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>>> brp2 = BucketedRandomProjectionLSH.load(brpPath)
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>>> brp2.getBucketLength() == brp.getBucketLength()
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True
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>>> modelPath = temp_path + "/brp-model"
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>>> model.save(modelPath)
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>>> model2 = BucketedRandomProjectionLSHModel.load(modelPath)
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>>> model.transform(df).head().hashes == model2.transform(df).head().hashes
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True
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.. versionadded:: 2.2.0
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"""
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bucketLength = Param(Params._dummy(), "bucketLength", "the length of each hash bucket, " +
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"a larger bucket lowers the false negative rate.",
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typeConverter=TypeConverters.toFloat)
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@keyword_only
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def __init__(self, inputCol=None, outputCol=None, seed=None, numHashTables=1,
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bucketLength=None):
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"""
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__init__(self, inputCol=None, outputCol=None, seed=None, numHashTables=1, \
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bucketLength=None)
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"""
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super(BucketedRandomProjectionLSH, self).__init__()
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self._java_obj = \
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self._new_java_obj("org.apache.spark.ml.feature.BucketedRandomProjectionLSH", self.uid)
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self._setDefault(numHashTables=1)
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kwargs = self._input_kwargs
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self.setParams(**kwargs)
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@keyword_only
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@since("2.2.0")
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def setParams(self, inputCol=None, outputCol=None, seed=None, numHashTables=1,
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bucketLength=None):
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"""
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setParams(self, inputCol=None, outputCol=None, seed=None, numHashTables=1, \
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bucketLength=None)
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Sets params for this BucketedRandomProjectionLSH.
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"""
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kwargs = self._input_kwargs
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return self._set(**kwargs)
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@since("2.2.0")
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def setBucketLength(self, value):
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"""
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Sets the value of :py:attr:`bucketLength`.
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"""
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return self._set(bucketLength=value)
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@since("2.2.0")
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def getBucketLength(self):
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"""
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Gets the value of bucketLength or its default value.
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"""
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return self.getOrDefault(self.bucketLength)
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def _create_model(self, java_model):
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return BucketedRandomProjectionLSHModel(java_model)
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class BucketedRandomProjectionLSHModel(LSHModel, JavaMLReadable, JavaMLWritable):
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r"""
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Model fitted by :py:class:`BucketedRandomProjectionLSH`, where multiple random vectors are
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stored. The vectors are normalized to be unit vectors and each vector is used in a hash
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function: :math:`h_i(x) = floor(r_i \cdot x / bucketLength)` where :math:`r_i` is the
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i-th random unit vector. The number of buckets will be `(max L2 norm of input vectors) /
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bucketLength`.
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.. versionadded:: 2.2.0
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"""
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@inherit_doc
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class Bucketizer(JavaTransformer, HasInputCol, HasOutputCol, HasHandleInvalid,
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JavaMLReadable, JavaMLWritable):
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"""
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Maps a column of continuous features to a column of feature buckets.
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>>> values = [(0.1,), (0.4,), (1.2,), (1.5,), (float("nan"),), (float("nan"),)]
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>>> df = spark.createDataFrame(values, ["values"])
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>>> bucketizer = Bucketizer(splits=[-float("inf"), 0.5, 1.4, float("inf")],
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... inputCol="values", outputCol="buckets")
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>>> bucketed = bucketizer.setHandleInvalid("keep").transform(df).collect()
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>>> len(bucketed)
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6
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>>> bucketed[0].buckets
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0.0
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>>> bucketed[1].buckets
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0.0
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>>> bucketed[2].buckets
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1.0
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>>> bucketed[3].buckets
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2.0
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>>> bucketizer.setParams(outputCol="b").transform(df).head().b
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0.0
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>>> bucketizerPath = temp_path + "/bucketizer"
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>>> bucketizer.save(bucketizerPath)
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>>> loadedBucketizer = Bucketizer.load(bucketizerPath)
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>>> loadedBucketizer.getSplits() == bucketizer.getSplits()
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True
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>>> bucketed = bucketizer.setHandleInvalid("skip").transform(df).collect()
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>>> len(bucketed)
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4
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.. versionadded:: 1.4.0
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"""
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splits = \
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Param(Params._dummy(), "splits",
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"Split points for mapping continuous features into buckets. With n+1 splits, " +
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"there are n buckets. A bucket defined by splits x,y holds values in the " +
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"range [x,y) except the last bucket, which also includes y. The splits " +
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"should be of length >= 3 and strictly increasing. Values at -inf, inf must be " +
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"explicitly provided to cover all Double values; otherwise, values outside the " +
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"splits specified will be treated as errors.",
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typeConverter=TypeConverters.toListFloat)
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handleInvalid = Param(Params._dummy(), "handleInvalid", "how to handle invalid entries "
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"containing NaN values. Values outside the splits will always be treated "
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"as errors. Options are 'skip' (filter out rows with invalid values), " +
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"'error' (throw an error), or 'keep' (keep invalid values in a special " +
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"additional bucket).",
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typeConverter=TypeConverters.toString)
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@keyword_only
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def __init__(self, splits=None, inputCol=None, outputCol=None, handleInvalid="error"):
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"""
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__init__(self, splits=None, inputCol=None, outputCol=None, handleInvalid="error")
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"""
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super(Bucketizer, self).__init__()
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self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.Bucketizer", self.uid)
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self._setDefault(handleInvalid="error")
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kwargs = self._input_kwargs
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self.setParams(**kwargs)
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@keyword_only
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@since("1.4.0")
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def setParams(self, splits=None, inputCol=None, outputCol=None, handleInvalid="error"):
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"""
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setParams(self, splits=None, inputCol=None, outputCol=None, handleInvalid="error")
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Sets params for this Bucketizer.
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"""
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kwargs = self._input_kwargs
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return self._set(**kwargs)
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@since("1.4.0")
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def setSplits(self, value):
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"""
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Sets the value of :py:attr:`splits`.
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"""
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return self._set(splits=value)
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@since("1.4.0")
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def getSplits(self):
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"""
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Gets the value of threshold or its default value.
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"""
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return self.getOrDefault(self.splits)
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|
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class _CountVectorizerParams(JavaParams, HasInputCol, HasOutputCol):
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"""
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Params for :py:attr:`CountVectorizer` and :py:attr:`CountVectorizerModel`.
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"""
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minTF = Param(
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Params._dummy(), "minTF", "Filter to ignore rare words in" +
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" a document. For each document, terms with frequency/count less than the given" +
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" threshold are ignored. If this is an integer >= 1, then this specifies a count (of" +
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" times the term must appear in the document); if this is a double in [0,1), then this " +
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"specifies a fraction (out of the document's token count). Note that the parameter is " +
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"only used in transform of CountVectorizerModel and does not affect fitting. Default 1.0",
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typeConverter=TypeConverters.toFloat)
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minDF = Param(
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Params._dummy(), "minDF", "Specifies the minimum number of" +
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" different documents a term must appear in to be included in the vocabulary." +
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" If this is an integer >= 1, this specifies the number of documents the term must" +
|
|
" appear in; if this is a double in [0,1), then this specifies the fraction of documents." +
|
|
" Default 1.0", typeConverter=TypeConverters.toFloat)
|
|
maxDF = Param(
|
|
Params._dummy(), "maxDF", "Specifies the maximum number of" +
|
|
" different documents a term could appear in to be included in the vocabulary." +
|
|
" A term that appears more than the threshold will be ignored. If this is an" +
|
|
" integer >= 1, this specifies the maximum number of documents the term could appear in;" +
|
|
" if this is a double in [0,1), then this specifies the maximum" +
|
|
" fraction of documents the term could appear in." +
|
|
" Default (2^63) - 1", typeConverter=TypeConverters.toFloat)
|
|
vocabSize = Param(
|
|
Params._dummy(), "vocabSize", "max size of the vocabulary. Default 1 << 18.",
|
|
typeConverter=TypeConverters.toInt)
|
|
binary = Param(
|
|
Params._dummy(), "binary", "Binary toggle to control the output vector values." +
|
|
" If True, all nonzero counts (after minTF filter applied) are set to 1. This is useful" +
|
|
" for discrete probabilistic models that model binary events rather than integer counts." +
|
|
" Default False", typeConverter=TypeConverters.toBoolean)
|
|
|
|
def __init__(self, *args):
|
|
super(_CountVectorizerParams, self).__init__(*args)
|
|
self._setDefault(minTF=1.0, minDF=1.0, maxDF=2 ** 63 - 1, vocabSize=1 << 18, binary=False)
|
|
|
|
@since("1.6.0")
|
|
def getMinTF(self):
|
|
"""
|
|
Gets the value of minTF or its default value.
|
|
"""
|
|
return self.getOrDefault(self.minTF)
|
|
|
|
@since("1.6.0")
|
|
def getMinDF(self):
|
|
"""
|
|
Gets the value of minDF or its default value.
|
|
"""
|
|
return self.getOrDefault(self.minDF)
|
|
|
|
@since("2.4.0")
|
|
def getMaxDF(self):
|
|
"""
|
|
Gets the value of maxDF or its default value.
|
|
"""
|
|
return self.getOrDefault(self.maxDF)
|
|
|
|
@since("1.6.0")
|
|
def getVocabSize(self):
|
|
"""
|
|
Gets the value of vocabSize or its default value.
|
|
"""
|
|
return self.getOrDefault(self.vocabSize)
|
|
|
|
@since("2.0.0")
|
|
def getBinary(self):
|
|
"""
|
|
Gets the value of binary or its default value.
|
|
"""
|
|
return self.getOrDefault(self.binary)
|
|
|
|
|
|
@inherit_doc
|
|
class CountVectorizer(JavaEstimator, _CountVectorizerParams, JavaMLReadable, JavaMLWritable):
|
|
"""
|
|
Extracts a vocabulary from document collections and generates a :py:attr:`CountVectorizerModel`.
|
|
|
|
>>> df = spark.createDataFrame(
|
|
... [(0, ["a", "b", "c"]), (1, ["a", "b", "b", "c", "a"])],
|
|
... ["label", "raw"])
|
|
>>> cv = CountVectorizer(inputCol="raw", outputCol="vectors")
|
|
>>> model = cv.fit(df)
|
|
>>> model.transform(df).show(truncate=False)
|
|
+-----+---------------+-------------------------+
|
|
|label|raw |vectors |
|
|
+-----+---------------+-------------------------+
|
|
|0 |[a, b, c] |(3,[0,1,2],[1.0,1.0,1.0])|
|
|
|1 |[a, b, b, c, a]|(3,[0,1,2],[2.0,2.0,1.0])|
|
|
+-----+---------------+-------------------------+
|
|
...
|
|
>>> sorted(model.vocabulary) == ['a', 'b', 'c']
|
|
True
|
|
>>> countVectorizerPath = temp_path + "/count-vectorizer"
|
|
>>> cv.save(countVectorizerPath)
|
|
>>> loadedCv = CountVectorizer.load(countVectorizerPath)
|
|
>>> loadedCv.getMinDF() == cv.getMinDF()
|
|
True
|
|
>>> loadedCv.getMinTF() == cv.getMinTF()
|
|
True
|
|
>>> loadedCv.getVocabSize() == cv.getVocabSize()
|
|
True
|
|
>>> modelPath = temp_path + "/count-vectorizer-model"
|
|
>>> model.save(modelPath)
|
|
>>> loadedModel = CountVectorizerModel.load(modelPath)
|
|
>>> loadedModel.vocabulary == model.vocabulary
|
|
True
|
|
>>> fromVocabModel = CountVectorizerModel.from_vocabulary(["a", "b", "c"],
|
|
... inputCol="raw", outputCol="vectors")
|
|
>>> fromVocabModel.transform(df).show(truncate=False)
|
|
+-----+---------------+-------------------------+
|
|
|label|raw |vectors |
|
|
+-----+---------------+-------------------------+
|
|
|0 |[a, b, c] |(3,[0,1,2],[1.0,1.0,1.0])|
|
|
|1 |[a, b, b, c, a]|(3,[0,1,2],[2.0,2.0,1.0])|
|
|
+-----+---------------+-------------------------+
|
|
...
|
|
|
|
.. versionadded:: 1.6.0
|
|
"""
|
|
|
|
@keyword_only
|
|
def __init__(self, minTF=1.0, minDF=1.0, maxDF=2 ** 63 - 1, vocabSize=1 << 18, binary=False,
|
|
inputCol=None, outputCol=None):
|
|
"""
|
|
__init__(self, minTF=1.0, minDF=1.0, maxDF=2 ** 63 - 1, vocabSize=1 << 18, binary=False,\
|
|
inputCol=None,outputCol=None)
|
|
"""
|
|
super(CountVectorizer, self).__init__()
|
|
self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.CountVectorizer",
|
|
self.uid)
|
|
kwargs = self._input_kwargs
|
|
self.setParams(**kwargs)
|
|
|
|
@keyword_only
|
|
@since("1.6.0")
|
|
def setParams(self, minTF=1.0, minDF=1.0, maxDF=2 ** 63 - 1, vocabSize=1 << 18, binary=False,
|
|
inputCol=None, outputCol=None):
|
|
"""
|
|
setParams(self, minTF=1.0, minDF=1.0, maxDF=2 ** 63 - 1, vocabSize=1 << 18, binary=False,\
|
|
inputCol=None, outputCol=None)
|
|
Set the params for the CountVectorizer
|
|
"""
|
|
kwargs = self._input_kwargs
|
|
return self._set(**kwargs)
|
|
|
|
@since("1.6.0")
|
|
def setMinTF(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`minTF`.
|
|
"""
|
|
return self._set(minTF=value)
|
|
|
|
@since("1.6.0")
|
|
def setMinDF(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`minDF`.
|
|
"""
|
|
return self._set(minDF=value)
|
|
|
|
@since("2.4.0")
|
|
def setMaxDF(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`maxDF`.
|
|
"""
|
|
return self._set(maxDF=value)
|
|
|
|
@since("1.6.0")
|
|
def setVocabSize(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`vocabSize`.
|
|
"""
|
|
return self._set(vocabSize=value)
|
|
|
|
@since("2.0.0")
|
|
def setBinary(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`binary`.
|
|
"""
|
|
return self._set(binary=value)
|
|
|
|
def _create_model(self, java_model):
|
|
return CountVectorizerModel(java_model)
|
|
|
|
|
|
@inherit_doc
|
|
class CountVectorizerModel(JavaModel, _CountVectorizerParams, JavaMLReadable, JavaMLWritable):
|
|
"""
|
|
Model fitted by :py:class:`CountVectorizer`.
|
|
|
|
.. versionadded:: 1.6.0
|
|
"""
|
|
|
|
@classmethod
|
|
@since("2.4.0")
|
|
def from_vocabulary(cls, vocabulary, inputCol, outputCol=None, minTF=None, binary=None):
|
|
"""
|
|
Construct the model directly from a vocabulary list of strings,
|
|
requires an active SparkContext.
|
|
"""
|
|
sc = SparkContext._active_spark_context
|
|
java_class = sc._gateway.jvm.java.lang.String
|
|
jvocab = CountVectorizerModel._new_java_array(vocabulary, java_class)
|
|
model = CountVectorizerModel._create_from_java_class(
|
|
"org.apache.spark.ml.feature.CountVectorizerModel", jvocab)
|
|
model.setInputCol(inputCol)
|
|
if outputCol is not None:
|
|
model.setOutputCol(outputCol)
|
|
if minTF is not None:
|
|
model.setMinTF(minTF)
|
|
if binary is not None:
|
|
model.setBinary(binary)
|
|
model._set(vocabSize=len(vocabulary))
|
|
return model
|
|
|
|
@property
|
|
@since("1.6.0")
|
|
def vocabulary(self):
|
|
"""
|
|
An array of terms in the vocabulary.
|
|
"""
|
|
return self._call_java("vocabulary")
|
|
|
|
@since("2.4.0")
|
|
def setMinTF(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`minTF`.
|
|
"""
|
|
return self._set(minTF=value)
|
|
|
|
@since("2.4.0")
|
|
def setBinary(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`binary`.
|
|
"""
|
|
return self._set(binary=value)
|
|
|
|
|
|
@inherit_doc
|
|
class DCT(JavaTransformer, HasInputCol, HasOutputCol, JavaMLReadable, JavaMLWritable):
|
|
"""
|
|
A feature transformer that takes the 1D discrete cosine transform
|
|
of a real vector. No zero padding is performed on the input vector.
|
|
It returns a real vector of the same length representing the DCT.
|
|
The return vector is scaled such that the transform matrix is
|
|
unitary (aka scaled DCT-II).
|
|
|
|
.. seealso:: `More information on Wikipedia
|
|
<https://en.wikipedia.org/wiki/Discrete_cosine_transform#DCT-II Wikipedia>`_.
|
|
|
|
>>> from pyspark.ml.linalg import Vectors
|
|
>>> df1 = spark.createDataFrame([(Vectors.dense([5.0, 8.0, 6.0]),)], ["vec"])
|
|
>>> dct = DCT(inverse=False, inputCol="vec", outputCol="resultVec")
|
|
>>> df2 = dct.transform(df1)
|
|
>>> df2.head().resultVec
|
|
DenseVector([10.969..., -0.707..., -2.041...])
|
|
>>> df3 = DCT(inverse=True, inputCol="resultVec", outputCol="origVec").transform(df2)
|
|
>>> df3.head().origVec
|
|
DenseVector([5.0, 8.0, 6.0])
|
|
>>> dctPath = temp_path + "/dct"
|
|
>>> dct.save(dctPath)
|
|
>>> loadedDtc = DCT.load(dctPath)
|
|
>>> loadedDtc.getInverse()
|
|
False
|
|
|
|
.. versionadded:: 1.6.0
|
|
"""
|
|
|
|
inverse = Param(Params._dummy(), "inverse", "Set transformer to perform inverse DCT, " +
|
|
"default False.", typeConverter=TypeConverters.toBoolean)
|
|
|
|
@keyword_only
|
|
def __init__(self, inverse=False, inputCol=None, outputCol=None):
|
|
"""
|
|
__init__(self, inverse=False, inputCol=None, outputCol=None)
|
|
"""
|
|
super(DCT, self).__init__()
|
|
self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.DCT", self.uid)
|
|
self._setDefault(inverse=False)
|
|
kwargs = self._input_kwargs
|
|
self.setParams(**kwargs)
|
|
|
|
@keyword_only
|
|
@since("1.6.0")
|
|
def setParams(self, inverse=False, inputCol=None, outputCol=None):
|
|
"""
|
|
setParams(self, inverse=False, inputCol=None, outputCol=None)
|
|
Sets params for this DCT.
|
|
"""
|
|
kwargs = self._input_kwargs
|
|
return self._set(**kwargs)
|
|
|
|
@since("1.6.0")
|
|
def setInverse(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`inverse`.
|
|
"""
|
|
return self._set(inverse=value)
|
|
|
|
@since("1.6.0")
|
|
def getInverse(self):
|
|
"""
|
|
Gets the value of inverse or its default value.
|
|
"""
|
|
return self.getOrDefault(self.inverse)
|
|
|
|
|
|
@inherit_doc
|
|
class ElementwiseProduct(JavaTransformer, HasInputCol, HasOutputCol, JavaMLReadable,
|
|
JavaMLWritable):
|
|
"""
|
|
Outputs the Hadamard product (i.e., the element-wise product) of each input vector
|
|
with a provided "weight" vector. In other words, it scales each column of the dataset
|
|
by a scalar multiplier.
|
|
|
|
>>> from pyspark.ml.linalg import Vectors
|
|
>>> df = spark.createDataFrame([(Vectors.dense([2.0, 1.0, 3.0]),)], ["values"])
|
|
>>> ep = ElementwiseProduct(scalingVec=Vectors.dense([1.0, 2.0, 3.0]),
|
|
... inputCol="values", outputCol="eprod")
|
|
>>> ep.transform(df).head().eprod
|
|
DenseVector([2.0, 2.0, 9.0])
|
|
>>> ep.setParams(scalingVec=Vectors.dense([2.0, 3.0, 5.0])).transform(df).head().eprod
|
|
DenseVector([4.0, 3.0, 15.0])
|
|
>>> elementwiseProductPath = temp_path + "/elementwise-product"
|
|
>>> ep.save(elementwiseProductPath)
|
|
>>> loadedEp = ElementwiseProduct.load(elementwiseProductPath)
|
|
>>> loadedEp.getScalingVec() == ep.getScalingVec()
|
|
True
|
|
|
|
.. versionadded:: 1.5.0
|
|
"""
|
|
|
|
scalingVec = Param(Params._dummy(), "scalingVec", "Vector for hadamard product.",
|
|
typeConverter=TypeConverters.toVector)
|
|
|
|
@keyword_only
|
|
def __init__(self, scalingVec=None, inputCol=None, outputCol=None):
|
|
"""
|
|
__init__(self, scalingVec=None, inputCol=None, outputCol=None)
|
|
"""
|
|
super(ElementwiseProduct, self).__init__()
|
|
self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.ElementwiseProduct",
|
|
self.uid)
|
|
kwargs = self._input_kwargs
|
|
self.setParams(**kwargs)
|
|
|
|
@keyword_only
|
|
@since("1.5.0")
|
|
def setParams(self, scalingVec=None, inputCol=None, outputCol=None):
|
|
"""
|
|
setParams(self, scalingVec=None, inputCol=None, outputCol=None)
|
|
Sets params for this ElementwiseProduct.
|
|
"""
|
|
kwargs = self._input_kwargs
|
|
return self._set(**kwargs)
|
|
|
|
@since("2.0.0")
|
|
def setScalingVec(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`scalingVec`.
|
|
"""
|
|
return self._set(scalingVec=value)
|
|
|
|
@since("2.0.0")
|
|
def getScalingVec(self):
|
|
"""
|
|
Gets the value of scalingVec or its default value.
|
|
"""
|
|
return self.getOrDefault(self.scalingVec)
|
|
|
|
|
|
@inherit_doc
|
|
class FeatureHasher(JavaTransformer, HasInputCols, HasOutputCol, HasNumFeatures, JavaMLReadable,
|
|
JavaMLWritable):
|
|
"""
|
|
Feature hashing projects a set of categorical or numerical features into a feature vector of
|
|
specified dimension (typically substantially smaller than that of the original feature
|
|
space). This is done using the hashing trick (https://en.wikipedia.org/wiki/Feature_hashing)
|
|
to map features to indices in the feature vector.
|
|
|
|
The FeatureHasher transformer operates on multiple columns. Each column may contain either
|
|
numeric or categorical features. Behavior and handling of column data types is as follows:
|
|
|
|
* Numeric columns:
|
|
For numeric features, the hash value of the column name is used to map the
|
|
feature value to its index in the feature vector. By default, numeric features
|
|
are not treated as categorical (even when they are integers). To treat them
|
|
as categorical, specify the relevant columns in `categoricalCols`.
|
|
|
|
* String columns:
|
|
For categorical features, the hash value of the string "column_name=value"
|
|
is used to map to the vector index, with an indicator value of `1.0`.
|
|
Thus, categorical features are "one-hot" encoded
|
|
(similarly to using :py:class:`OneHotEncoder` with `dropLast=false`).
|
|
|
|
* Boolean columns:
|
|
Boolean values are treated in the same way as string columns. That is,
|
|
boolean features are represented as "column_name=true" or "column_name=false",
|
|
with an indicator value of `1.0`.
|
|
|
|
Null (missing) values are ignored (implicitly zero in the resulting feature vector).
|
|
|
|
Since a simple modulo is used to transform the hash function to a vector index,
|
|
it is advisable to use a power of two as the `numFeatures` parameter;
|
|
otherwise the features will not be mapped evenly to the vector indices.
|
|
|
|
>>> data = [(2.0, True, "1", "foo"), (3.0, False, "2", "bar")]
|
|
>>> cols = ["real", "bool", "stringNum", "string"]
|
|
>>> df = spark.createDataFrame(data, cols)
|
|
>>> hasher = FeatureHasher(inputCols=cols, outputCol="features")
|
|
>>> hasher.transform(df).head().features
|
|
SparseVector(262144, {174475: 2.0, 247670: 1.0, 257907: 1.0, 262126: 1.0})
|
|
>>> hasher.setCategoricalCols(["real"]).transform(df).head().features
|
|
SparseVector(262144, {171257: 1.0, 247670: 1.0, 257907: 1.0, 262126: 1.0})
|
|
>>> hasherPath = temp_path + "/hasher"
|
|
>>> hasher.save(hasherPath)
|
|
>>> loadedHasher = FeatureHasher.load(hasherPath)
|
|
>>> loadedHasher.getNumFeatures() == hasher.getNumFeatures()
|
|
True
|
|
>>> loadedHasher.transform(df).head().features == hasher.transform(df).head().features
|
|
True
|
|
|
|
.. versionadded:: 2.3.0
|
|
"""
|
|
|
|
categoricalCols = Param(Params._dummy(), "categoricalCols",
|
|
"numeric columns to treat as categorical",
|
|
typeConverter=TypeConverters.toListString)
|
|
|
|
@keyword_only
|
|
def __init__(self, numFeatures=1 << 18, inputCols=None, outputCol=None, categoricalCols=None):
|
|
"""
|
|
__init__(self, numFeatures=1 << 18, inputCols=None, outputCol=None, categoricalCols=None)
|
|
"""
|
|
super(FeatureHasher, self).__init__()
|
|
self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.FeatureHasher", self.uid)
|
|
self._setDefault(numFeatures=1 << 18)
|
|
kwargs = self._input_kwargs
|
|
self.setParams(**kwargs)
|
|
|
|
@keyword_only
|
|
@since("2.3.0")
|
|
def setParams(self, numFeatures=1 << 18, inputCols=None, outputCol=None, categoricalCols=None):
|
|
"""
|
|
setParams(self, numFeatures=1 << 18, inputCols=None, outputCol=None, categoricalCols=None)
|
|
Sets params for this FeatureHasher.
|
|
"""
|
|
kwargs = self._input_kwargs
|
|
return self._set(**kwargs)
|
|
|
|
@since("2.3.0")
|
|
def setCategoricalCols(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`categoricalCols`.
|
|
"""
|
|
return self._set(categoricalCols=value)
|
|
|
|
@since("2.3.0")
|
|
def getCategoricalCols(self):
|
|
"""
|
|
Gets the value of binary or its default value.
|
|
"""
|
|
return self.getOrDefault(self.categoricalCols)
|
|
|
|
|
|
@inherit_doc
|
|
class HashingTF(JavaTransformer, HasInputCol, HasOutputCol, HasNumFeatures, JavaMLReadable,
|
|
JavaMLWritable):
|
|
"""
|
|
Maps a sequence of terms to their term frequencies using the hashing trick.
|
|
Currently we use Austin Appleby's MurmurHash 3 algorithm (MurmurHash3_x86_32)
|
|
to calculate the hash code value for the term object.
|
|
Since a simple modulo is used to transform the hash function to a column index,
|
|
it is advisable to use a power of two as the numFeatures parameter;
|
|
otherwise the features will not be mapped evenly to the columns.
|
|
|
|
>>> df = spark.createDataFrame([(["a", "b", "c"],)], ["words"])
|
|
>>> hashingTF = HashingTF(numFeatures=10, inputCol="words", outputCol="features")
|
|
>>> hashingTF.transform(df).head().features
|
|
SparseVector(10, {5: 1.0, 7: 1.0, 8: 1.0})
|
|
>>> hashingTF.setParams(outputCol="freqs").transform(df).head().freqs
|
|
SparseVector(10, {5: 1.0, 7: 1.0, 8: 1.0})
|
|
>>> params = {hashingTF.numFeatures: 5, hashingTF.outputCol: "vector"}
|
|
>>> hashingTF.transform(df, params).head().vector
|
|
SparseVector(5, {0: 1.0, 2: 1.0, 3: 1.0})
|
|
>>> hashingTFPath = temp_path + "/hashing-tf"
|
|
>>> hashingTF.save(hashingTFPath)
|
|
>>> loadedHashingTF = HashingTF.load(hashingTFPath)
|
|
>>> loadedHashingTF.getNumFeatures() == hashingTF.getNumFeatures()
|
|
True
|
|
>>> hashingTF.indexOf("b")
|
|
5
|
|
|
|
.. versionadded:: 1.3.0
|
|
"""
|
|
|
|
binary = Param(Params._dummy(), "binary", "If True, all non zero counts are set to 1. " +
|
|
"This is useful for discrete probabilistic models that model binary events " +
|
|
"rather than integer counts. Default False.",
|
|
typeConverter=TypeConverters.toBoolean)
|
|
|
|
@keyword_only
|
|
def __init__(self, numFeatures=1 << 18, binary=False, inputCol=None, outputCol=None):
|
|
"""
|
|
__init__(self, numFeatures=1 << 18, binary=False, inputCol=None, outputCol=None)
|
|
"""
|
|
super(HashingTF, self).__init__()
|
|
self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.HashingTF", self.uid)
|
|
self._setDefault(numFeatures=1 << 18, binary=False)
|
|
kwargs = self._input_kwargs
|
|
self.setParams(**kwargs)
|
|
|
|
@keyword_only
|
|
@since("1.3.0")
|
|
def setParams(self, numFeatures=1 << 18, binary=False, inputCol=None, outputCol=None):
|
|
"""
|
|
setParams(self, numFeatures=1 << 18, binary=False, inputCol=None, outputCol=None)
|
|
Sets params for this HashingTF.
|
|
"""
|
|
kwargs = self._input_kwargs
|
|
return self._set(**kwargs)
|
|
|
|
@since("2.0.0")
|
|
def setBinary(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`binary`.
|
|
"""
|
|
return self._set(binary=value)
|
|
|
|
@since("2.0.0")
|
|
def getBinary(self):
|
|
"""
|
|
Gets the value of binary or its default value.
|
|
"""
|
|
return self.getOrDefault(self.binary)
|
|
|
|
@since("3.0.0")
|
|
def indexOf(self, term):
|
|
"""
|
|
Returns the index of the input term.
|
|
"""
|
|
self._transfer_params_to_java()
|
|
return self._java_obj.indexOf(term)
|
|
|
|
|
|
@inherit_doc
|
|
class IDF(JavaEstimator, HasInputCol, HasOutputCol, JavaMLReadable, JavaMLWritable):
|
|
"""
|
|
Compute the Inverse Document Frequency (IDF) given a collection of documents.
|
|
|
|
>>> from pyspark.ml.linalg import DenseVector
|
|
>>> df = spark.createDataFrame([(DenseVector([1.0, 2.0]),),
|
|
... (DenseVector([0.0, 1.0]),), (DenseVector([3.0, 0.2]),)], ["tf"])
|
|
>>> idf = IDF(minDocFreq=3, inputCol="tf", outputCol="idf")
|
|
>>> model = idf.fit(df)
|
|
>>> model.idf
|
|
DenseVector([0.0, 0.0])
|
|
>>> model.docFreq
|
|
[0, 3]
|
|
>>> model.numDocs == df.count()
|
|
True
|
|
>>> model.transform(df).head().idf
|
|
DenseVector([0.0, 0.0])
|
|
>>> idf.setParams(outputCol="freqs").fit(df).transform(df).collect()[1].freqs
|
|
DenseVector([0.0, 0.0])
|
|
>>> params = {idf.minDocFreq: 1, idf.outputCol: "vector"}
|
|
>>> idf.fit(df, params).transform(df).head().vector
|
|
DenseVector([0.2877, 0.0])
|
|
>>> idfPath = temp_path + "/idf"
|
|
>>> idf.save(idfPath)
|
|
>>> loadedIdf = IDF.load(idfPath)
|
|
>>> loadedIdf.getMinDocFreq() == idf.getMinDocFreq()
|
|
True
|
|
>>> modelPath = temp_path + "/idf-model"
|
|
>>> model.save(modelPath)
|
|
>>> loadedModel = IDFModel.load(modelPath)
|
|
>>> loadedModel.transform(df).head().idf == model.transform(df).head().idf
|
|
True
|
|
|
|
.. versionadded:: 1.4.0
|
|
"""
|
|
|
|
minDocFreq = Param(Params._dummy(), "minDocFreq",
|
|
"minimum number of documents in which a term should appear for filtering",
|
|
typeConverter=TypeConverters.toInt)
|
|
|
|
@keyword_only
|
|
def __init__(self, minDocFreq=0, inputCol=None, outputCol=None):
|
|
"""
|
|
__init__(self, minDocFreq=0, inputCol=None, outputCol=None)
|
|
"""
|
|
super(IDF, self).__init__()
|
|
self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.IDF", self.uid)
|
|
self._setDefault(minDocFreq=0)
|
|
kwargs = self._input_kwargs
|
|
self.setParams(**kwargs)
|
|
|
|
@keyword_only
|
|
@since("1.4.0")
|
|
def setParams(self, minDocFreq=0, inputCol=None, outputCol=None):
|
|
"""
|
|
setParams(self, minDocFreq=0, inputCol=None, outputCol=None)
|
|
Sets params for this IDF.
|
|
"""
|
|
kwargs = self._input_kwargs
|
|
return self._set(**kwargs)
|
|
|
|
@since("1.4.0")
|
|
def setMinDocFreq(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`minDocFreq`.
|
|
"""
|
|
return self._set(minDocFreq=value)
|
|
|
|
@since("1.4.0")
|
|
def getMinDocFreq(self):
|
|
"""
|
|
Gets the value of minDocFreq or its default value.
|
|
"""
|
|
return self.getOrDefault(self.minDocFreq)
|
|
|
|
def _create_model(self, java_model):
|
|
return IDFModel(java_model)
|
|
|
|
|
|
class IDFModel(JavaModel, JavaMLReadable, JavaMLWritable):
|
|
"""
|
|
Model fitted by :py:class:`IDF`.
|
|
|
|
.. versionadded:: 1.4.0
|
|
"""
|
|
|
|
@property
|
|
@since("2.0.0")
|
|
def idf(self):
|
|
"""
|
|
Returns the IDF vector.
|
|
"""
|
|
return self._call_java("idf")
|
|
|
|
@property
|
|
@since("3.0.0")
|
|
def docFreq(self):
|
|
"""
|
|
Returns the document frequency.
|
|
"""
|
|
return self._call_java("docFreq")
|
|
|
|
@property
|
|
@since("3.0.0")
|
|
def numDocs(self):
|
|
"""
|
|
Returns number of documents evaluated to compute idf
|
|
"""
|
|
return self._call_java("numDocs")
|
|
|
|
|
|
@inherit_doc
|
|
class Imputer(JavaEstimator, HasInputCols, JavaMLReadable, JavaMLWritable):
|
|
"""
|
|
Imputation estimator for completing missing values, either using the mean or the median
|
|
of the columns in which the missing values are located. The input columns should be of
|
|
DoubleType or FloatType. Currently Imputer does not support categorical features and
|
|
possibly creates incorrect values for a categorical feature.
|
|
|
|
Note that the mean/median value is computed after filtering out missing values.
|
|
All Null values in the input columns are treated as missing, and so are also imputed. For
|
|
computing median, :py:meth:`pyspark.sql.DataFrame.approxQuantile` is used with a
|
|
relative error of `0.001`.
|
|
|
|
>>> df = spark.createDataFrame([(1.0, float("nan")), (2.0, float("nan")), (float("nan"), 3.0),
|
|
... (4.0, 4.0), (5.0, 5.0)], ["a", "b"])
|
|
>>> imputer = Imputer(inputCols=["a", "b"], outputCols=["out_a", "out_b"])
|
|
>>> model = imputer.fit(df)
|
|
>>> model.surrogateDF.show()
|
|
+---+---+
|
|
| a| b|
|
|
+---+---+
|
|
|3.0|4.0|
|
|
+---+---+
|
|
...
|
|
>>> model.transform(df).show()
|
|
+---+---+-----+-----+
|
|
| a| b|out_a|out_b|
|
|
+---+---+-----+-----+
|
|
|1.0|NaN| 1.0| 4.0|
|
|
|2.0|NaN| 2.0| 4.0|
|
|
|NaN|3.0| 3.0| 3.0|
|
|
...
|
|
>>> imputer.setStrategy("median").setMissingValue(1.0).fit(df).transform(df).show()
|
|
+---+---+-----+-----+
|
|
| a| b|out_a|out_b|
|
|
+---+---+-----+-----+
|
|
|1.0|NaN| 4.0| NaN|
|
|
...
|
|
>>> imputerPath = temp_path + "/imputer"
|
|
>>> imputer.save(imputerPath)
|
|
>>> loadedImputer = Imputer.load(imputerPath)
|
|
>>> loadedImputer.getStrategy() == imputer.getStrategy()
|
|
True
|
|
>>> loadedImputer.getMissingValue()
|
|
1.0
|
|
>>> modelPath = temp_path + "/imputer-model"
|
|
>>> model.save(modelPath)
|
|
>>> loadedModel = ImputerModel.load(modelPath)
|
|
>>> loadedModel.transform(df).head().out_a == model.transform(df).head().out_a
|
|
True
|
|
|
|
.. versionadded:: 2.2.0
|
|
"""
|
|
|
|
outputCols = Param(Params._dummy(), "outputCols",
|
|
"output column names.", typeConverter=TypeConverters.toListString)
|
|
|
|
strategy = Param(Params._dummy(), "strategy",
|
|
"strategy for imputation. If mean, then replace missing values using the mean "
|
|
"value of the feature. If median, then replace missing values using the "
|
|
"median value of the feature.",
|
|
typeConverter=TypeConverters.toString)
|
|
|
|
missingValue = Param(Params._dummy(), "missingValue",
|
|
"The placeholder for the missing values. All occurrences of missingValue "
|
|
"will be imputed.", typeConverter=TypeConverters.toFloat)
|
|
|
|
@keyword_only
|
|
def __init__(self, strategy="mean", missingValue=float("nan"), inputCols=None,
|
|
outputCols=None):
|
|
"""
|
|
__init__(self, strategy="mean", missingValue=float("nan"), inputCols=None, \
|
|
outputCols=None):
|
|
"""
|
|
super(Imputer, self).__init__()
|
|
self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.Imputer", self.uid)
|
|
self._setDefault(strategy="mean", missingValue=float("nan"))
|
|
kwargs = self._input_kwargs
|
|
self.setParams(**kwargs)
|
|
|
|
@keyword_only
|
|
@since("2.2.0")
|
|
def setParams(self, strategy="mean", missingValue=float("nan"), inputCols=None,
|
|
outputCols=None):
|
|
"""
|
|
setParams(self, strategy="mean", missingValue=float("nan"), inputCols=None, \
|
|
outputCols=None)
|
|
Sets params for this Imputer.
|
|
"""
|
|
kwargs = self._input_kwargs
|
|
return self._set(**kwargs)
|
|
|
|
@since("2.2.0")
|
|
def setOutputCols(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`outputCols`.
|
|
"""
|
|
return self._set(outputCols=value)
|
|
|
|
@since("2.2.0")
|
|
def getOutputCols(self):
|
|
"""
|
|
Gets the value of :py:attr:`outputCols` or its default value.
|
|
"""
|
|
return self.getOrDefault(self.outputCols)
|
|
|
|
@since("2.2.0")
|
|
def setStrategy(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`strategy`.
|
|
"""
|
|
return self._set(strategy=value)
|
|
|
|
@since("2.2.0")
|
|
def getStrategy(self):
|
|
"""
|
|
Gets the value of :py:attr:`strategy` or its default value.
|
|
"""
|
|
return self.getOrDefault(self.strategy)
|
|
|
|
@since("2.2.0")
|
|
def setMissingValue(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`missingValue`.
|
|
"""
|
|
return self._set(missingValue=value)
|
|
|
|
@since("2.2.0")
|
|
def getMissingValue(self):
|
|
"""
|
|
Gets the value of :py:attr:`missingValue` or its default value.
|
|
"""
|
|
return self.getOrDefault(self.missingValue)
|
|
|
|
def _create_model(self, java_model):
|
|
return ImputerModel(java_model)
|
|
|
|
|
|
class ImputerModel(JavaModel, JavaMLReadable, JavaMLWritable):
|
|
"""
|
|
Model fitted by :py:class:`Imputer`.
|
|
|
|
.. versionadded:: 2.2.0
|
|
"""
|
|
|
|
@property
|
|
@since("2.2.0")
|
|
def surrogateDF(self):
|
|
"""
|
|
Returns a DataFrame containing inputCols and their corresponding surrogates,
|
|
which are used to replace the missing values in the input DataFrame.
|
|
"""
|
|
return self._call_java("surrogateDF")
|
|
|
|
|
|
@inherit_doc
|
|
class Interaction(JavaTransformer, HasInputCols, HasOutputCol, JavaMLReadable, JavaMLWritable):
|
|
"""
|
|
Implements the feature interaction transform. This transformer takes in Double and Vector type
|
|
columns and outputs a flattened vector of their feature interactions. To handle interaction,
|
|
we first one-hot encode any nominal features. Then, a vector of the feature cross-products is
|
|
produced.
|
|
|
|
For example, given the input feature values `Double(2)` and `Vector(3, 4)`, the output would be
|
|
`Vector(6, 8)` if all input features were numeric. If the first feature was instead nominal
|
|
with four categories, the output would then be `Vector(0, 0, 0, 0, 3, 4, 0, 0)`.
|
|
|
|
>>> df = spark.createDataFrame([(0.0, 1.0), (2.0, 3.0)], ["a", "b"])
|
|
>>> interaction = Interaction(inputCols=["a", "b"], outputCol="ab")
|
|
>>> interaction.transform(df).show()
|
|
+---+---+-----+
|
|
| a| b| ab|
|
|
+---+---+-----+
|
|
|0.0|1.0|[0.0]|
|
|
|2.0|3.0|[6.0]|
|
|
+---+---+-----+
|
|
...
|
|
>>> interactionPath = temp_path + "/interaction"
|
|
>>> interaction.save(interactionPath)
|
|
>>> loadedInteraction = Interaction.load(interactionPath)
|
|
>>> loadedInteraction.transform(df).head().ab == interaction.transform(df).head().ab
|
|
True
|
|
|
|
.. versionadded:: 3.0.0
|
|
"""
|
|
|
|
@keyword_only
|
|
def __init__(self, inputCols=None, outputCol=None):
|
|
"""
|
|
__init__(self, inputCols=None, outputCol=None):
|
|
"""
|
|
super(Interaction, self).__init__()
|
|
self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.Interaction", self.uid)
|
|
self._setDefault()
|
|
kwargs = self._input_kwargs
|
|
self.setParams(**kwargs)
|
|
|
|
@keyword_only
|
|
@since("3.0.0")
|
|
def setParams(self, inputCols=None, outputCol=None):
|
|
"""
|
|
setParams(self, inputCols=None, outputCol=None)
|
|
Sets params for this Interaction.
|
|
"""
|
|
kwargs = self._input_kwargs
|
|
return self._set(**kwargs)
|
|
|
|
|
|
@inherit_doc
|
|
class MaxAbsScaler(JavaEstimator, HasInputCol, HasOutputCol, JavaMLReadable, JavaMLWritable):
|
|
"""
|
|
Rescale each feature individually to range [-1, 1] by dividing through the largest maximum
|
|
absolute value in each feature. It does not shift/center the data, and thus does not destroy
|
|
any sparsity.
|
|
|
|
>>> from pyspark.ml.linalg import Vectors
|
|
>>> df = spark.createDataFrame([(Vectors.dense([1.0]),), (Vectors.dense([2.0]),)], ["a"])
|
|
>>> maScaler = MaxAbsScaler(inputCol="a", outputCol="scaled")
|
|
>>> model = maScaler.fit(df)
|
|
>>> model.transform(df).show()
|
|
+-----+------+
|
|
| a|scaled|
|
|
+-----+------+
|
|
|[1.0]| [0.5]|
|
|
|[2.0]| [1.0]|
|
|
+-----+------+
|
|
...
|
|
>>> scalerPath = temp_path + "/max-abs-scaler"
|
|
>>> maScaler.save(scalerPath)
|
|
>>> loadedMAScaler = MaxAbsScaler.load(scalerPath)
|
|
>>> loadedMAScaler.getInputCol() == maScaler.getInputCol()
|
|
True
|
|
>>> loadedMAScaler.getOutputCol() == maScaler.getOutputCol()
|
|
True
|
|
>>> modelPath = temp_path + "/max-abs-scaler-model"
|
|
>>> model.save(modelPath)
|
|
>>> loadedModel = MaxAbsScalerModel.load(modelPath)
|
|
>>> loadedModel.maxAbs == model.maxAbs
|
|
True
|
|
|
|
.. versionadded:: 2.0.0
|
|
"""
|
|
|
|
@keyword_only
|
|
def __init__(self, inputCol=None, outputCol=None):
|
|
"""
|
|
__init__(self, inputCol=None, outputCol=None)
|
|
"""
|
|
super(MaxAbsScaler, self).__init__()
|
|
self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.MaxAbsScaler", self.uid)
|
|
self._setDefault()
|
|
kwargs = self._input_kwargs
|
|
self.setParams(**kwargs)
|
|
|
|
@keyword_only
|
|
@since("2.0.0")
|
|
def setParams(self, inputCol=None, outputCol=None):
|
|
"""
|
|
setParams(self, inputCol=None, outputCol=None)
|
|
Sets params for this MaxAbsScaler.
|
|
"""
|
|
kwargs = self._input_kwargs
|
|
return self._set(**kwargs)
|
|
|
|
def _create_model(self, java_model):
|
|
return MaxAbsScalerModel(java_model)
|
|
|
|
|
|
class MaxAbsScalerModel(JavaModel, JavaMLReadable, JavaMLWritable):
|
|
"""
|
|
Model fitted by :py:class:`MaxAbsScaler`.
|
|
|
|
.. versionadded:: 2.0.0
|
|
"""
|
|
|
|
@property
|
|
@since("2.0.0")
|
|
def maxAbs(self):
|
|
"""
|
|
Max Abs vector.
|
|
"""
|
|
return self._call_java("maxAbs")
|
|
|
|
|
|
@inherit_doc
|
|
class MinHashLSH(JavaEstimator, LSHParams, HasInputCol, HasOutputCol, HasSeed,
|
|
JavaMLReadable, JavaMLWritable):
|
|
|
|
"""
|
|
LSH class for Jaccard distance.
|
|
The input can be dense or sparse vectors, but it is more efficient if it is sparse.
|
|
For example, `Vectors.sparse(10, [(2, 1.0), (3, 1.0), (5, 1.0)])` means there are 10 elements
|
|
in the space. This set contains elements 2, 3, and 5. Also, any input vector must have at
|
|
least 1 non-zero index, and all non-zero values are treated as binary "1" values.
|
|
|
|
.. seealso:: `Wikipedia on MinHash <https://en.wikipedia.org/wiki/MinHash>`_
|
|
|
|
>>> from pyspark.ml.linalg import Vectors
|
|
>>> from pyspark.sql.functions import col
|
|
>>> data = [(0, Vectors.sparse(6, [0, 1, 2], [1.0, 1.0, 1.0]),),
|
|
... (1, Vectors.sparse(6, [2, 3, 4], [1.0, 1.0, 1.0]),),
|
|
... (2, Vectors.sparse(6, [0, 2, 4], [1.0, 1.0, 1.0]),)]
|
|
>>> df = spark.createDataFrame(data, ["id", "features"])
|
|
>>> mh = MinHashLSH(inputCol="features", outputCol="hashes", seed=12345)
|
|
>>> model = mh.fit(df)
|
|
>>> model.transform(df).head()
|
|
Row(id=0, features=SparseVector(6, {0: 1.0, 1: 1.0, 2: 1.0}), hashes=[DenseVector([6179668...
|
|
>>> data2 = [(3, Vectors.sparse(6, [1, 3, 5], [1.0, 1.0, 1.0]),),
|
|
... (4, Vectors.sparse(6, [2, 3, 5], [1.0, 1.0, 1.0]),),
|
|
... (5, Vectors.sparse(6, [1, 2, 4], [1.0, 1.0, 1.0]),)]
|
|
>>> df2 = spark.createDataFrame(data2, ["id", "features"])
|
|
>>> key = Vectors.sparse(6, [1, 2], [1.0, 1.0])
|
|
>>> model.approxNearestNeighbors(df2, key, 1).collect()
|
|
[Row(id=5, features=SparseVector(6, {1: 1.0, 2: 1.0, 4: 1.0}), hashes=[DenseVector([6179668...
|
|
>>> model.approxSimilarityJoin(df, df2, 0.6, distCol="JaccardDistance").select(
|
|
... col("datasetA.id").alias("idA"),
|
|
... col("datasetB.id").alias("idB"),
|
|
... col("JaccardDistance")).show()
|
|
+---+---+---------------+
|
|
|idA|idB|JaccardDistance|
|
|
+---+---+---------------+
|
|
| 0| 5| 0.5|
|
|
| 1| 4| 0.5|
|
|
+---+---+---------------+
|
|
...
|
|
>>> mhPath = temp_path + "/mh"
|
|
>>> mh.save(mhPath)
|
|
>>> mh2 = MinHashLSH.load(mhPath)
|
|
>>> mh2.getOutputCol() == mh.getOutputCol()
|
|
True
|
|
>>> modelPath = temp_path + "/mh-model"
|
|
>>> model.save(modelPath)
|
|
>>> model2 = MinHashLSHModel.load(modelPath)
|
|
>>> model.transform(df).head().hashes == model2.transform(df).head().hashes
|
|
True
|
|
|
|
.. versionadded:: 2.2.0
|
|
"""
|
|
|
|
@keyword_only
|
|
def __init__(self, inputCol=None, outputCol=None, seed=None, numHashTables=1):
|
|
"""
|
|
__init__(self, inputCol=None, outputCol=None, seed=None, numHashTables=1)
|
|
"""
|
|
super(MinHashLSH, self).__init__()
|
|
self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.MinHashLSH", self.uid)
|
|
self._setDefault(numHashTables=1)
|
|
kwargs = self._input_kwargs
|
|
self.setParams(**kwargs)
|
|
|
|
@keyword_only
|
|
@since("2.2.0")
|
|
def setParams(self, inputCol=None, outputCol=None, seed=None, numHashTables=1):
|
|
"""
|
|
setParams(self, inputCol=None, outputCol=None, seed=None, numHashTables=1)
|
|
Sets params for this MinHashLSH.
|
|
"""
|
|
kwargs = self._input_kwargs
|
|
return self._set(**kwargs)
|
|
|
|
def _create_model(self, java_model):
|
|
return MinHashLSHModel(java_model)
|
|
|
|
|
|
class MinHashLSHModel(LSHModel, JavaMLReadable, JavaMLWritable):
|
|
r"""
|
|
Model produced by :py:class:`MinHashLSH`, where where multiple hash functions are stored. Each
|
|
hash function is picked from the following family of hash functions, where :math:`a_i` and
|
|
:math:`b_i` are randomly chosen integers less than prime:
|
|
:math:`h_i(x) = ((x \cdot a_i + b_i) \mod prime)` This hash family is approximately min-wise
|
|
independent according to the reference.
|
|
|
|
.. seealso:: Tom Bohman, Colin Cooper, and Alan Frieze. "Min-wise independent linear
|
|
permutations." Electronic Journal of Combinatorics 7 (2000): R26.
|
|
|
|
.. versionadded:: 2.2.0
|
|
"""
|
|
|
|
|
|
@inherit_doc
|
|
class MinMaxScaler(JavaEstimator, HasInputCol, HasOutputCol, JavaMLReadable, JavaMLWritable):
|
|
"""
|
|
Rescale each feature individually to a common range [min, max] linearly using column summary
|
|
statistics, which is also known as min-max normalization or Rescaling. The rescaled value for
|
|
feature E is calculated as,
|
|
|
|
Rescaled(e_i) = (e_i - E_min) / (E_max - E_min) * (max - min) + min
|
|
|
|
For the case E_max == E_min, Rescaled(e_i) = 0.5 * (max + min)
|
|
|
|
.. note:: Since zero values will probably be transformed to non-zero values, output of the
|
|
transformer will be DenseVector even for sparse input.
|
|
|
|
>>> from pyspark.ml.linalg import Vectors
|
|
>>> df = spark.createDataFrame([(Vectors.dense([0.0]),), (Vectors.dense([2.0]),)], ["a"])
|
|
>>> mmScaler = MinMaxScaler(inputCol="a", outputCol="scaled")
|
|
>>> model = mmScaler.fit(df)
|
|
>>> model.originalMin
|
|
DenseVector([0.0])
|
|
>>> model.originalMax
|
|
DenseVector([2.0])
|
|
>>> model.transform(df).show()
|
|
+-----+------+
|
|
| a|scaled|
|
|
+-----+------+
|
|
|[0.0]| [0.0]|
|
|
|[2.0]| [1.0]|
|
|
+-----+------+
|
|
...
|
|
>>> minMaxScalerPath = temp_path + "/min-max-scaler"
|
|
>>> mmScaler.save(minMaxScalerPath)
|
|
>>> loadedMMScaler = MinMaxScaler.load(minMaxScalerPath)
|
|
>>> loadedMMScaler.getMin() == mmScaler.getMin()
|
|
True
|
|
>>> loadedMMScaler.getMax() == mmScaler.getMax()
|
|
True
|
|
>>> modelPath = temp_path + "/min-max-scaler-model"
|
|
>>> model.save(modelPath)
|
|
>>> loadedModel = MinMaxScalerModel.load(modelPath)
|
|
>>> loadedModel.originalMin == model.originalMin
|
|
True
|
|
>>> loadedModel.originalMax == model.originalMax
|
|
True
|
|
|
|
.. versionadded:: 1.6.0
|
|
"""
|
|
|
|
min = Param(Params._dummy(), "min", "Lower bound of the output feature range",
|
|
typeConverter=TypeConverters.toFloat)
|
|
max = Param(Params._dummy(), "max", "Upper bound of the output feature range",
|
|
typeConverter=TypeConverters.toFloat)
|
|
|
|
@keyword_only
|
|
def __init__(self, min=0.0, max=1.0, inputCol=None, outputCol=None):
|
|
"""
|
|
__init__(self, min=0.0, max=1.0, inputCol=None, outputCol=None)
|
|
"""
|
|
super(MinMaxScaler, self).__init__()
|
|
self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.MinMaxScaler", self.uid)
|
|
self._setDefault(min=0.0, max=1.0)
|
|
kwargs = self._input_kwargs
|
|
self.setParams(**kwargs)
|
|
|
|
@keyword_only
|
|
@since("1.6.0")
|
|
def setParams(self, min=0.0, max=1.0, inputCol=None, outputCol=None):
|
|
"""
|
|
setParams(self, min=0.0, max=1.0, inputCol=None, outputCol=None)
|
|
Sets params for this MinMaxScaler.
|
|
"""
|
|
kwargs = self._input_kwargs
|
|
return self._set(**kwargs)
|
|
|
|
@since("1.6.0")
|
|
def setMin(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`min`.
|
|
"""
|
|
return self._set(min=value)
|
|
|
|
@since("1.6.0")
|
|
def getMin(self):
|
|
"""
|
|
Gets the value of min or its default value.
|
|
"""
|
|
return self.getOrDefault(self.min)
|
|
|
|
@since("1.6.0")
|
|
def setMax(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`max`.
|
|
"""
|
|
return self._set(max=value)
|
|
|
|
@since("1.6.0")
|
|
def getMax(self):
|
|
"""
|
|
Gets the value of max or its default value.
|
|
"""
|
|
return self.getOrDefault(self.max)
|
|
|
|
def _create_model(self, java_model):
|
|
return MinMaxScalerModel(java_model)
|
|
|
|
|
|
class MinMaxScalerModel(JavaModel, JavaMLReadable, JavaMLWritable):
|
|
"""
|
|
Model fitted by :py:class:`MinMaxScaler`.
|
|
|
|
.. versionadded:: 1.6.0
|
|
"""
|
|
|
|
@property
|
|
@since("2.0.0")
|
|
def originalMin(self):
|
|
"""
|
|
Min value for each original column during fitting.
|
|
"""
|
|
return self._call_java("originalMin")
|
|
|
|
@property
|
|
@since("2.0.0")
|
|
def originalMax(self):
|
|
"""
|
|
Max value for each original column during fitting.
|
|
"""
|
|
return self._call_java("originalMax")
|
|
|
|
|
|
@inherit_doc
|
|
@ignore_unicode_prefix
|
|
class NGram(JavaTransformer, HasInputCol, HasOutputCol, JavaMLReadable, JavaMLWritable):
|
|
"""
|
|
A feature transformer that converts the input array of strings into an array of n-grams. Null
|
|
values in the input array are ignored.
|
|
It returns an array of n-grams where each n-gram is represented by a space-separated string of
|
|
words.
|
|
When the input is empty, an empty array is returned.
|
|
When the input array length is less than n (number of elements per n-gram), no n-grams are
|
|
returned.
|
|
|
|
>>> df = spark.createDataFrame([Row(inputTokens=["a", "b", "c", "d", "e"])])
|
|
>>> ngram = NGram(n=2, inputCol="inputTokens", outputCol="nGrams")
|
|
>>> ngram.transform(df).head()
|
|
Row(inputTokens=[u'a', u'b', u'c', u'd', u'e'], nGrams=[u'a b', u'b c', u'c d', u'd e'])
|
|
>>> # Change n-gram length
|
|
>>> ngram.setParams(n=4).transform(df).head()
|
|
Row(inputTokens=[u'a', u'b', u'c', u'd', u'e'], nGrams=[u'a b c d', u'b c d e'])
|
|
>>> # Temporarily modify output column.
|
|
>>> ngram.transform(df, {ngram.outputCol: "output"}).head()
|
|
Row(inputTokens=[u'a', u'b', u'c', u'd', u'e'], output=[u'a b c d', u'b c d e'])
|
|
>>> ngram.transform(df).head()
|
|
Row(inputTokens=[u'a', u'b', u'c', u'd', u'e'], nGrams=[u'a b c d', u'b c d e'])
|
|
>>> # Must use keyword arguments to specify params.
|
|
>>> ngram.setParams("text")
|
|
Traceback (most recent call last):
|
|
...
|
|
TypeError: Method setParams forces keyword arguments.
|
|
>>> ngramPath = temp_path + "/ngram"
|
|
>>> ngram.save(ngramPath)
|
|
>>> loadedNGram = NGram.load(ngramPath)
|
|
>>> loadedNGram.getN() == ngram.getN()
|
|
True
|
|
|
|
.. versionadded:: 1.5.0
|
|
"""
|
|
|
|
n = Param(Params._dummy(), "n", "number of elements per n-gram (>=1)",
|
|
typeConverter=TypeConverters.toInt)
|
|
|
|
@keyword_only
|
|
def __init__(self, n=2, inputCol=None, outputCol=None):
|
|
"""
|
|
__init__(self, n=2, inputCol=None, outputCol=None)
|
|
"""
|
|
super(NGram, self).__init__()
|
|
self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.NGram", self.uid)
|
|
self._setDefault(n=2)
|
|
kwargs = self._input_kwargs
|
|
self.setParams(**kwargs)
|
|
|
|
@keyword_only
|
|
@since("1.5.0")
|
|
def setParams(self, n=2, inputCol=None, outputCol=None):
|
|
"""
|
|
setParams(self, n=2, inputCol=None, outputCol=None)
|
|
Sets params for this NGram.
|
|
"""
|
|
kwargs = self._input_kwargs
|
|
return self._set(**kwargs)
|
|
|
|
@since("1.5.0")
|
|
def setN(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`n`.
|
|
"""
|
|
return self._set(n=value)
|
|
|
|
@since("1.5.0")
|
|
def getN(self):
|
|
"""
|
|
Gets the value of n or its default value.
|
|
"""
|
|
return self.getOrDefault(self.n)
|
|
|
|
|
|
@inherit_doc
|
|
class Normalizer(JavaTransformer, HasInputCol, HasOutputCol, JavaMLReadable, JavaMLWritable):
|
|
"""
|
|
Normalize a vector to have unit norm using the given p-norm.
|
|
|
|
>>> from pyspark.ml.linalg import Vectors
|
|
>>> svec = Vectors.sparse(4, {1: 4.0, 3: 3.0})
|
|
>>> df = spark.createDataFrame([(Vectors.dense([3.0, -4.0]), svec)], ["dense", "sparse"])
|
|
>>> normalizer = Normalizer(p=2.0, inputCol="dense", outputCol="features")
|
|
>>> normalizer.transform(df).head().features
|
|
DenseVector([0.6, -0.8])
|
|
>>> normalizer.setParams(inputCol="sparse", outputCol="freqs").transform(df).head().freqs
|
|
SparseVector(4, {1: 0.8, 3: 0.6})
|
|
>>> params = {normalizer.p: 1.0, normalizer.inputCol: "dense", normalizer.outputCol: "vector"}
|
|
>>> normalizer.transform(df, params).head().vector
|
|
DenseVector([0.4286, -0.5714])
|
|
>>> normalizerPath = temp_path + "/normalizer"
|
|
>>> normalizer.save(normalizerPath)
|
|
>>> loadedNormalizer = Normalizer.load(normalizerPath)
|
|
>>> loadedNormalizer.getP() == normalizer.getP()
|
|
True
|
|
|
|
.. versionadded:: 1.4.0
|
|
"""
|
|
|
|
p = Param(Params._dummy(), "p", "the p norm value.",
|
|
typeConverter=TypeConverters.toFloat)
|
|
|
|
@keyword_only
|
|
def __init__(self, p=2.0, inputCol=None, outputCol=None):
|
|
"""
|
|
__init__(self, p=2.0, inputCol=None, outputCol=None)
|
|
"""
|
|
super(Normalizer, self).__init__()
|
|
self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.Normalizer", self.uid)
|
|
self._setDefault(p=2.0)
|
|
kwargs = self._input_kwargs
|
|
self.setParams(**kwargs)
|
|
|
|
@keyword_only
|
|
@since("1.4.0")
|
|
def setParams(self, p=2.0, inputCol=None, outputCol=None):
|
|
"""
|
|
setParams(self, p=2.0, inputCol=None, outputCol=None)
|
|
Sets params for this Normalizer.
|
|
"""
|
|
kwargs = self._input_kwargs
|
|
return self._set(**kwargs)
|
|
|
|
@since("1.4.0")
|
|
def setP(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`p`.
|
|
"""
|
|
return self._set(p=value)
|
|
|
|
@since("1.4.0")
|
|
def getP(self):
|
|
"""
|
|
Gets the value of p or its default value.
|
|
"""
|
|
return self.getOrDefault(self.p)
|
|
|
|
|
|
@inherit_doc
|
|
class OneHotEncoder(JavaEstimator, HasInputCols, HasOutputCols, HasHandleInvalid,
|
|
JavaMLReadable, JavaMLWritable):
|
|
"""
|
|
A one-hot encoder that maps a column of category indices to a column of binary vectors, with
|
|
at most a single one-value per row that indicates the input category index.
|
|
For example with 5 categories, an input value of 2.0 would map to an output vector of
|
|
`[0.0, 0.0, 1.0, 0.0]`.
|
|
The last category is not included by default (configurable via :py:attr:`dropLast`),
|
|
because it makes the vector entries sum up to one, and hence linearly dependent.
|
|
So an input value of 4.0 maps to `[0.0, 0.0, 0.0, 0.0]`.
|
|
|
|
.. note:: This is different from scikit-learn's OneHotEncoder, which keeps all categories.
|
|
The output vectors are sparse.
|
|
|
|
When :py:attr:`handleInvalid` is configured to 'keep', an extra "category" indicating invalid
|
|
values is added as last category. So when :py:attr:`dropLast` is true, invalid values are
|
|
encoded as all-zeros vector.
|
|
|
|
.. note:: When encoding multi-column by using :py:attr:`inputCols` and
|
|
:py:attr:`outputCols` params, input/output cols come in pairs, specified by the order in
|
|
the arrays, and each pair is treated independently.
|
|
|
|
.. seealso:: :py:class:`StringIndexer` for converting categorical values into category indices
|
|
|
|
>>> from pyspark.ml.linalg import Vectors
|
|
>>> df = spark.createDataFrame([(0.0,), (1.0,), (2.0,)], ["input"])
|
|
>>> ohe = OneHotEncoder(inputCols=["input"], outputCols=["output"])
|
|
>>> model = ohe.fit(df)
|
|
>>> model.transform(df).head().output
|
|
SparseVector(2, {0: 1.0})
|
|
>>> ohePath = temp_path + "/ohe"
|
|
>>> ohe.save(ohePath)
|
|
>>> loadedOHE = OneHotEncoder.load(ohePath)
|
|
>>> loadedOHE.getInputCols() == ohe.getInputCols()
|
|
True
|
|
>>> modelPath = temp_path + "/ohe-model"
|
|
>>> model.save(modelPath)
|
|
>>> loadedModel = OneHotEncoderModel.load(modelPath)
|
|
>>> loadedModel.categorySizes == model.categorySizes
|
|
True
|
|
|
|
.. versionadded:: 2.3.0
|
|
"""
|
|
|
|
handleInvalid = Param(Params._dummy(), "handleInvalid", "How to handle invalid data during " +
|
|
"transform(). Options are 'keep' (invalid data presented as an extra " +
|
|
"categorical feature) or error (throw an error). Note that this Param " +
|
|
"is only used during transform; during fitting, invalid data will " +
|
|
"result in an error.",
|
|
typeConverter=TypeConverters.toString)
|
|
|
|
dropLast = Param(Params._dummy(), "dropLast", "whether to drop the last category",
|
|
typeConverter=TypeConverters.toBoolean)
|
|
|
|
@keyword_only
|
|
def __init__(self, inputCols=None, outputCols=None, handleInvalid="error", dropLast=True):
|
|
"""
|
|
__init__(self, inputCols=None, outputCols=None, handleInvalid="error", dropLast=True)
|
|
"""
|
|
super(OneHotEncoder, self).__init__()
|
|
self._java_obj = self._new_java_obj(
|
|
"org.apache.spark.ml.feature.OneHotEncoder", self.uid)
|
|
self._setDefault(handleInvalid="error", dropLast=True)
|
|
kwargs = self._input_kwargs
|
|
self.setParams(**kwargs)
|
|
|
|
@keyword_only
|
|
@since("2.3.0")
|
|
def setParams(self, inputCols=None, outputCols=None, handleInvalid="error", dropLast=True):
|
|
"""
|
|
setParams(self, inputCols=None, outputCols=None, handleInvalid="error", dropLast=True)
|
|
Sets params for this OneHotEncoder.
|
|
"""
|
|
kwargs = self._input_kwargs
|
|
return self._set(**kwargs)
|
|
|
|
@since("2.3.0")
|
|
def setDropLast(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`dropLast`.
|
|
"""
|
|
return self._set(dropLast=value)
|
|
|
|
@since("2.3.0")
|
|
def getDropLast(self):
|
|
"""
|
|
Gets the value of dropLast or its default value.
|
|
"""
|
|
return self.getOrDefault(self.dropLast)
|
|
|
|
def _create_model(self, java_model):
|
|
return OneHotEncoderModel(java_model)
|
|
|
|
|
|
class OneHotEncoderModel(JavaModel, JavaMLReadable, JavaMLWritable):
|
|
"""
|
|
Model fitted by :py:class:`OneHotEncoder`.
|
|
|
|
.. versionadded:: 2.3.0
|
|
"""
|
|
|
|
@property
|
|
@since("2.3.0")
|
|
def categorySizes(self):
|
|
"""
|
|
Original number of categories for each feature being encoded.
|
|
The array contains one value for each input column, in order.
|
|
"""
|
|
return self._call_java("categorySizes")
|
|
|
|
|
|
@inherit_doc
|
|
class PolynomialExpansion(JavaTransformer, HasInputCol, HasOutputCol, JavaMLReadable,
|
|
JavaMLWritable):
|
|
"""
|
|
Perform feature expansion in a polynomial space. As said in `wikipedia of Polynomial Expansion
|
|
<http://en.wikipedia.org/wiki/Polynomial_expansion>`_, "In mathematics, an
|
|
expansion of a product of sums expresses it as a sum of products by using the fact that
|
|
multiplication distributes over addition". Take a 2-variable feature vector as an example:
|
|
`(x, y)`, if we want to expand it with degree 2, then we get `(x, x * x, y, x * y, y * y)`.
|
|
|
|
>>> from pyspark.ml.linalg import Vectors
|
|
>>> df = spark.createDataFrame([(Vectors.dense([0.5, 2.0]),)], ["dense"])
|
|
>>> px = PolynomialExpansion(degree=2, inputCol="dense", outputCol="expanded")
|
|
>>> px.transform(df).head().expanded
|
|
DenseVector([0.5, 0.25, 2.0, 1.0, 4.0])
|
|
>>> px.setParams(outputCol="test").transform(df).head().test
|
|
DenseVector([0.5, 0.25, 2.0, 1.0, 4.0])
|
|
>>> polyExpansionPath = temp_path + "/poly-expansion"
|
|
>>> px.save(polyExpansionPath)
|
|
>>> loadedPx = PolynomialExpansion.load(polyExpansionPath)
|
|
>>> loadedPx.getDegree() == px.getDegree()
|
|
True
|
|
|
|
.. versionadded:: 1.4.0
|
|
"""
|
|
|
|
degree = Param(Params._dummy(), "degree", "the polynomial degree to expand (>= 1)",
|
|
typeConverter=TypeConverters.toInt)
|
|
|
|
@keyword_only
|
|
def __init__(self, degree=2, inputCol=None, outputCol=None):
|
|
"""
|
|
__init__(self, degree=2, inputCol=None, outputCol=None)
|
|
"""
|
|
super(PolynomialExpansion, self).__init__()
|
|
self._java_obj = self._new_java_obj(
|
|
"org.apache.spark.ml.feature.PolynomialExpansion", self.uid)
|
|
self._setDefault(degree=2)
|
|
kwargs = self._input_kwargs
|
|
self.setParams(**kwargs)
|
|
|
|
@keyword_only
|
|
@since("1.4.0")
|
|
def setParams(self, degree=2, inputCol=None, outputCol=None):
|
|
"""
|
|
setParams(self, degree=2, inputCol=None, outputCol=None)
|
|
Sets params for this PolynomialExpansion.
|
|
"""
|
|
kwargs = self._input_kwargs
|
|
return self._set(**kwargs)
|
|
|
|
@since("1.4.0")
|
|
def setDegree(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`degree`.
|
|
"""
|
|
return self._set(degree=value)
|
|
|
|
@since("1.4.0")
|
|
def getDegree(self):
|
|
"""
|
|
Gets the value of degree or its default value.
|
|
"""
|
|
return self.getOrDefault(self.degree)
|
|
|
|
|
|
@inherit_doc
|
|
class QuantileDiscretizer(JavaEstimator, HasInputCol, HasOutputCol, HasHandleInvalid,
|
|
JavaMLReadable, JavaMLWritable):
|
|
"""
|
|
`QuantileDiscretizer` takes a column with continuous features and outputs a column with binned
|
|
categorical features. The number of bins can be set using the :py:attr:`numBuckets` parameter.
|
|
It is possible that the number of buckets used will be less than this value, for example, if
|
|
there are too few distinct values of the input to create enough distinct quantiles.
|
|
|
|
NaN handling: Note also that
|
|
QuantileDiscretizer will raise an error when it finds NaN values in the dataset, but the user
|
|
can also choose to either keep or remove NaN values within the dataset by setting
|
|
:py:attr:`handleInvalid` parameter. If the user chooses to keep NaN values, they will be
|
|
handled specially and placed into their own bucket, for example, if 4 buckets are used, then
|
|
non-NaN data will be put into buckets[0-3], but NaNs will be counted in a special bucket[4].
|
|
|
|
Algorithm: The bin ranges are chosen using an approximate algorithm (see the documentation for
|
|
:py:meth:`~.DataFrameStatFunctions.approxQuantile` for a detailed description).
|
|
The precision of the approximation can be controlled with the
|
|
:py:attr:`relativeError` parameter.
|
|
The lower and upper bin bounds will be `-Infinity` and `+Infinity`, covering all real values.
|
|
|
|
>>> values = [(0.1,), (0.4,), (1.2,), (1.5,), (float("nan"),), (float("nan"),)]
|
|
>>> df = spark.createDataFrame(values, ["values"])
|
|
>>> qds = QuantileDiscretizer(numBuckets=2,
|
|
... inputCol="values", outputCol="buckets", relativeError=0.01, handleInvalid="error")
|
|
>>> qds.getRelativeError()
|
|
0.01
|
|
>>> bucketizer = qds.fit(df)
|
|
>>> qds.setHandleInvalid("keep").fit(df).transform(df).count()
|
|
6
|
|
>>> qds.setHandleInvalid("skip").fit(df).transform(df).count()
|
|
4
|
|
>>> splits = bucketizer.getSplits()
|
|
>>> splits[0]
|
|
-inf
|
|
>>> print("%2.1f" % round(splits[1], 1))
|
|
0.4
|
|
>>> bucketed = bucketizer.transform(df).head()
|
|
>>> bucketed.buckets
|
|
0.0
|
|
>>> quantileDiscretizerPath = temp_path + "/quantile-discretizer"
|
|
>>> qds.save(quantileDiscretizerPath)
|
|
>>> loadedQds = QuantileDiscretizer.load(quantileDiscretizerPath)
|
|
>>> loadedQds.getNumBuckets() == qds.getNumBuckets()
|
|
True
|
|
|
|
.. versionadded:: 2.0.0
|
|
"""
|
|
|
|
numBuckets = Param(Params._dummy(), "numBuckets",
|
|
"Maximum number of buckets (quantiles, or " +
|
|
"categories) into which data points are grouped. Must be >= 2.",
|
|
typeConverter=TypeConverters.toInt)
|
|
|
|
relativeError = Param(Params._dummy(), "relativeError", "The relative target precision for " +
|
|
"the approximate quantile algorithm used to generate buckets. " +
|
|
"Must be in the range [0, 1].",
|
|
typeConverter=TypeConverters.toFloat)
|
|
|
|
handleInvalid = Param(Params._dummy(), "handleInvalid", "how to handle invalid entries. " +
|
|
"Options are skip (filter out rows with invalid values), " +
|
|
"error (throw an error), or keep (keep invalid values in a special " +
|
|
"additional bucket).",
|
|
typeConverter=TypeConverters.toString)
|
|
|
|
@keyword_only
|
|
def __init__(self, numBuckets=2, inputCol=None, outputCol=None, relativeError=0.001,
|
|
handleInvalid="error"):
|
|
"""
|
|
__init__(self, numBuckets=2, inputCol=None, outputCol=None, relativeError=0.001, \
|
|
handleInvalid="error")
|
|
"""
|
|
super(QuantileDiscretizer, self).__init__()
|
|
self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.QuantileDiscretizer",
|
|
self.uid)
|
|
self._setDefault(numBuckets=2, relativeError=0.001, handleInvalid="error")
|
|
kwargs = self._input_kwargs
|
|
self.setParams(**kwargs)
|
|
|
|
@keyword_only
|
|
@since("2.0.0")
|
|
def setParams(self, numBuckets=2, inputCol=None, outputCol=None, relativeError=0.001,
|
|
handleInvalid="error"):
|
|
"""
|
|
setParams(self, numBuckets=2, inputCol=None, outputCol=None, relativeError=0.001, \
|
|
handleInvalid="error")
|
|
Set the params for the QuantileDiscretizer
|
|
"""
|
|
kwargs = self._input_kwargs
|
|
return self._set(**kwargs)
|
|
|
|
@since("2.0.0")
|
|
def setNumBuckets(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`numBuckets`.
|
|
"""
|
|
return self._set(numBuckets=value)
|
|
|
|
@since("2.0.0")
|
|
def getNumBuckets(self):
|
|
"""
|
|
Gets the value of numBuckets or its default value.
|
|
"""
|
|
return self.getOrDefault(self.numBuckets)
|
|
|
|
@since("2.0.0")
|
|
def setRelativeError(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`relativeError`.
|
|
"""
|
|
return self._set(relativeError=value)
|
|
|
|
@since("2.0.0")
|
|
def getRelativeError(self):
|
|
"""
|
|
Gets the value of relativeError or its default value.
|
|
"""
|
|
return self.getOrDefault(self.relativeError)
|
|
|
|
def _create_model(self, java_model):
|
|
"""
|
|
Private method to convert the java_model to a Python model.
|
|
"""
|
|
return Bucketizer(splits=list(java_model.getSplits()),
|
|
inputCol=self.getInputCol(),
|
|
outputCol=self.getOutputCol(),
|
|
handleInvalid=self.getHandleInvalid())
|
|
|
|
|
|
@inherit_doc
|
|
class RobustScaler(JavaEstimator, HasInputCol, HasOutputCol, JavaMLReadable, JavaMLWritable):
|
|
"""
|
|
RobustScaler removes the median and scales the data according to the quantile range.
|
|
The quantile range is by default IQR (Interquartile Range, quantile range between the
|
|
1st quartile = 25th quantile and the 3rd quartile = 75th quantile) but can be configured.
|
|
Centering and scaling happen independently on each feature by computing the relevant
|
|
statistics on the samples in the training set. Median and quantile range are then
|
|
stored to be used on later data using the transform method.
|
|
|
|
>>> from pyspark.ml.linalg import Vectors
|
|
>>> data = [(0, Vectors.dense([0.0, 0.0]),),
|
|
... (1, Vectors.dense([1.0, -1.0]),),
|
|
... (2, Vectors.dense([2.0, -2.0]),),
|
|
... (3, Vectors.dense([3.0, -3.0]),),
|
|
... (4, Vectors.dense([4.0, -4.0]),),]
|
|
>>> df = spark.createDataFrame(data, ["id", "features"])
|
|
>>> scaler = RobustScaler(inputCol="features", outputCol="scaled")
|
|
>>> model = scaler.fit(df)
|
|
>>> model.median
|
|
DenseVector([2.0, -2.0])
|
|
>>> model.range
|
|
DenseVector([2.0, 2.0])
|
|
>>> model.transform(df).collect()[1].scaled
|
|
DenseVector([0.5, -0.5])
|
|
>>> scalerPath = temp_path + "/robust-scaler"
|
|
>>> scaler.save(scalerPath)
|
|
>>> loadedScaler = RobustScaler.load(scalerPath)
|
|
>>> loadedScaler.getWithCentering() == scaler.getWithCentering()
|
|
True
|
|
>>> loadedScaler.getWithScaling() == scaler.getWithScaling()
|
|
True
|
|
>>> modelPath = temp_path + "/robust-scaler-model"
|
|
>>> model.save(modelPath)
|
|
>>> loadedModel = RobustScalerModel.load(modelPath)
|
|
>>> loadedModel.median == model.median
|
|
True
|
|
>>> loadedModel.range == model.range
|
|
True
|
|
|
|
.. versionadded:: 3.0.0
|
|
"""
|
|
|
|
lower = Param(Params._dummy(), "lower", "Lower quantile to calculate quantile range",
|
|
typeConverter=TypeConverters.toFloat)
|
|
upper = Param(Params._dummy(), "upper", "Upper quantile to calculate quantile range",
|
|
typeConverter=TypeConverters.toFloat)
|
|
withCentering = Param(Params._dummy(), "withCentering", "Whether to center data with median",
|
|
typeConverter=TypeConverters.toBoolean)
|
|
withScaling = Param(Params._dummy(), "withScaling", "Whether to scale the data to "
|
|
"quantile range", typeConverter=TypeConverters.toBoolean)
|
|
|
|
@keyword_only
|
|
def __init__(self, lower=0.25, upper=0.75, withCentering=False, withScaling=True,
|
|
inputCol=None, outputCol=None):
|
|
"""
|
|
__init__(self, lower=0.25, upper=0.75, withCentering=False, withScaling=True, \
|
|
inputCol=None, outputCol=None)
|
|
"""
|
|
super(RobustScaler, self).__init__()
|
|
self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.RobustScaler", self.uid)
|
|
self._setDefault(lower=0.25, upper=0.75, withCentering=False, withScaling=True)
|
|
kwargs = self._input_kwargs
|
|
self.setParams(**kwargs)
|
|
|
|
@keyword_only
|
|
@since("3.0.0")
|
|
def setParams(self, lower=0.25, upper=0.75, withCentering=False, withScaling=True,
|
|
inputCol=None, outputCol=None):
|
|
"""
|
|
setParams(self, lower=0.25, upper=0.75, withCentering=False, withScaling=True, \
|
|
inputCol=None, outputCol=None)
|
|
Sets params for this RobustScaler.
|
|
"""
|
|
kwargs = self._input_kwargs
|
|
return self._set(**kwargs)
|
|
|
|
@since("3.0.0")
|
|
def setLower(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`lower`.
|
|
"""
|
|
return self._set(lower=value)
|
|
|
|
@since("3.0.0")
|
|
def getLower(self):
|
|
"""
|
|
Gets the value of lower or its default value.
|
|
"""
|
|
return self.getOrDefault(self.lower)
|
|
|
|
@since("3.0.0")
|
|
def setUpper(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`upper`.
|
|
"""
|
|
return self._set(upper=value)
|
|
|
|
@since("3.0.0")
|
|
def getUpper(self):
|
|
"""
|
|
Gets the value of upper or its default value.
|
|
"""
|
|
return self.getOrDefault(self.upper)
|
|
|
|
@since("3.0.0")
|
|
def setWithCentering(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`withCentering`.
|
|
"""
|
|
return self._set(withCentering=value)
|
|
|
|
@since("3.0.0")
|
|
def getWithCentering(self):
|
|
"""
|
|
Gets the value of withCentering or its default value.
|
|
"""
|
|
return self.getOrDefault(self.withCentering)
|
|
|
|
@since("3.0.0")
|
|
def setWithScaling(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`withScaling`.
|
|
"""
|
|
return self._set(withScaling=value)
|
|
|
|
@since("3.0.0")
|
|
def getWithScaling(self):
|
|
"""
|
|
Gets the value of withScaling or its default value.
|
|
"""
|
|
return self.getOrDefault(self.withScaling)
|
|
|
|
def _create_model(self, java_model):
|
|
return RobustScalerModel(java_model)
|
|
|
|
|
|
class RobustScalerModel(JavaModel, JavaMLReadable, JavaMLWritable):
|
|
"""
|
|
Model fitted by :py:class:`RobustScaler`.
|
|
|
|
.. versionadded:: 3.0.0
|
|
"""
|
|
|
|
@property
|
|
@since("3.0.0")
|
|
def median(self):
|
|
"""
|
|
Median of the RobustScalerModel.
|
|
"""
|
|
return self._call_java("median")
|
|
|
|
@property
|
|
@since("3.0.0")
|
|
def range(self):
|
|
"""
|
|
Quantile range of the RobustScalerModel.
|
|
"""
|
|
return self._call_java("range")
|
|
|
|
|
|
@inherit_doc
|
|
@ignore_unicode_prefix
|
|
class RegexTokenizer(JavaTransformer, HasInputCol, HasOutputCol, JavaMLReadable, JavaMLWritable):
|
|
"""
|
|
A regex based tokenizer that extracts tokens either by using the
|
|
provided regex pattern (in Java dialect) to split the text
|
|
(default) or repeatedly matching the regex (if gaps is false).
|
|
Optional parameters also allow filtering tokens using a minimal
|
|
length.
|
|
It returns an array of strings that can be empty.
|
|
|
|
>>> df = spark.createDataFrame([("A B c",)], ["text"])
|
|
>>> reTokenizer = RegexTokenizer(inputCol="text", outputCol="words")
|
|
>>> reTokenizer.transform(df).head()
|
|
Row(text=u'A B c', words=[u'a', u'b', u'c'])
|
|
>>> # Change a parameter.
|
|
>>> reTokenizer.setParams(outputCol="tokens").transform(df).head()
|
|
Row(text=u'A B c', tokens=[u'a', u'b', u'c'])
|
|
>>> # Temporarily modify a parameter.
|
|
>>> reTokenizer.transform(df, {reTokenizer.outputCol: "words"}).head()
|
|
Row(text=u'A B c', words=[u'a', u'b', u'c'])
|
|
>>> reTokenizer.transform(df).head()
|
|
Row(text=u'A B c', tokens=[u'a', u'b', u'c'])
|
|
>>> # Must use keyword arguments to specify params.
|
|
>>> reTokenizer.setParams("text")
|
|
Traceback (most recent call last):
|
|
...
|
|
TypeError: Method setParams forces keyword arguments.
|
|
>>> regexTokenizerPath = temp_path + "/regex-tokenizer"
|
|
>>> reTokenizer.save(regexTokenizerPath)
|
|
>>> loadedReTokenizer = RegexTokenizer.load(regexTokenizerPath)
|
|
>>> loadedReTokenizer.getMinTokenLength() == reTokenizer.getMinTokenLength()
|
|
True
|
|
>>> loadedReTokenizer.getGaps() == reTokenizer.getGaps()
|
|
True
|
|
|
|
.. versionadded:: 1.4.0
|
|
"""
|
|
|
|
minTokenLength = Param(Params._dummy(), "minTokenLength", "minimum token length (>= 0)",
|
|
typeConverter=TypeConverters.toInt)
|
|
gaps = Param(Params._dummy(), "gaps", "whether regex splits on gaps (True) or matches tokens " +
|
|
"(False)")
|
|
pattern = Param(Params._dummy(), "pattern", "regex pattern (Java dialect) used for tokenizing",
|
|
typeConverter=TypeConverters.toString)
|
|
toLowercase = Param(Params._dummy(), "toLowercase", "whether to convert all characters to " +
|
|
"lowercase before tokenizing", typeConverter=TypeConverters.toBoolean)
|
|
|
|
@keyword_only
|
|
def __init__(self, minTokenLength=1, gaps=True, pattern="\\s+", inputCol=None,
|
|
outputCol=None, toLowercase=True):
|
|
"""
|
|
__init__(self, minTokenLength=1, gaps=True, pattern="\\s+", inputCol=None, \
|
|
outputCol=None, toLowercase=True)
|
|
"""
|
|
super(RegexTokenizer, self).__init__()
|
|
self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.RegexTokenizer", self.uid)
|
|
self._setDefault(minTokenLength=1, gaps=True, pattern="\\s+", toLowercase=True)
|
|
kwargs = self._input_kwargs
|
|
self.setParams(**kwargs)
|
|
|
|
@keyword_only
|
|
@since("1.4.0")
|
|
def setParams(self, minTokenLength=1, gaps=True, pattern="\\s+", inputCol=None,
|
|
outputCol=None, toLowercase=True):
|
|
"""
|
|
setParams(self, minTokenLength=1, gaps=True, pattern="\\s+", inputCol=None, \
|
|
outputCol=None, toLowercase=True)
|
|
Sets params for this RegexTokenizer.
|
|
"""
|
|
kwargs = self._input_kwargs
|
|
return self._set(**kwargs)
|
|
|
|
@since("1.4.0")
|
|
def setMinTokenLength(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`minTokenLength`.
|
|
"""
|
|
return self._set(minTokenLength=value)
|
|
|
|
@since("1.4.0")
|
|
def getMinTokenLength(self):
|
|
"""
|
|
Gets the value of minTokenLength or its default value.
|
|
"""
|
|
return self.getOrDefault(self.minTokenLength)
|
|
|
|
@since("1.4.0")
|
|
def setGaps(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`gaps`.
|
|
"""
|
|
return self._set(gaps=value)
|
|
|
|
@since("1.4.0")
|
|
def getGaps(self):
|
|
"""
|
|
Gets the value of gaps or its default value.
|
|
"""
|
|
return self.getOrDefault(self.gaps)
|
|
|
|
@since("1.4.0")
|
|
def setPattern(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`pattern`.
|
|
"""
|
|
return self._set(pattern=value)
|
|
|
|
@since("1.4.0")
|
|
def getPattern(self):
|
|
"""
|
|
Gets the value of pattern or its default value.
|
|
"""
|
|
return self.getOrDefault(self.pattern)
|
|
|
|
@since("2.0.0")
|
|
def setToLowercase(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`toLowercase`.
|
|
"""
|
|
return self._set(toLowercase=value)
|
|
|
|
@since("2.0.0")
|
|
def getToLowercase(self):
|
|
"""
|
|
Gets the value of toLowercase or its default value.
|
|
"""
|
|
return self.getOrDefault(self.toLowercase)
|
|
|
|
|
|
@inherit_doc
|
|
class SQLTransformer(JavaTransformer, JavaMLReadable, JavaMLWritable):
|
|
"""
|
|
Implements the transforms which are defined by SQL statement.
|
|
Currently we only support SQL syntax like 'SELECT ... FROM __THIS__'
|
|
where '__THIS__' represents the underlying table of the input dataset.
|
|
|
|
>>> df = spark.createDataFrame([(0, 1.0, 3.0), (2, 2.0, 5.0)], ["id", "v1", "v2"])
|
|
>>> sqlTrans = SQLTransformer(
|
|
... statement="SELECT *, (v1 + v2) AS v3, (v1 * v2) AS v4 FROM __THIS__")
|
|
>>> sqlTrans.transform(df).head()
|
|
Row(id=0, v1=1.0, v2=3.0, v3=4.0, v4=3.0)
|
|
>>> sqlTransformerPath = temp_path + "/sql-transformer"
|
|
>>> sqlTrans.save(sqlTransformerPath)
|
|
>>> loadedSqlTrans = SQLTransformer.load(sqlTransformerPath)
|
|
>>> loadedSqlTrans.getStatement() == sqlTrans.getStatement()
|
|
True
|
|
|
|
.. versionadded:: 1.6.0
|
|
"""
|
|
|
|
statement = Param(Params._dummy(), "statement", "SQL statement",
|
|
typeConverter=TypeConverters.toString)
|
|
|
|
@keyword_only
|
|
def __init__(self, statement=None):
|
|
"""
|
|
__init__(self, statement=None)
|
|
"""
|
|
super(SQLTransformer, self).__init__()
|
|
self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.SQLTransformer", self.uid)
|
|
kwargs = self._input_kwargs
|
|
self.setParams(**kwargs)
|
|
|
|
@keyword_only
|
|
@since("1.6.0")
|
|
def setParams(self, statement=None):
|
|
"""
|
|
setParams(self, statement=None)
|
|
Sets params for this SQLTransformer.
|
|
"""
|
|
kwargs = self._input_kwargs
|
|
return self._set(**kwargs)
|
|
|
|
@since("1.6.0")
|
|
def setStatement(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`statement`.
|
|
"""
|
|
return self._set(statement=value)
|
|
|
|
@since("1.6.0")
|
|
def getStatement(self):
|
|
"""
|
|
Gets the value of statement or its default value.
|
|
"""
|
|
return self.getOrDefault(self.statement)
|
|
|
|
|
|
@inherit_doc
|
|
class StandardScaler(JavaEstimator, HasInputCol, HasOutputCol, JavaMLReadable, JavaMLWritable):
|
|
"""
|
|
Standardizes features by removing the mean and scaling to unit variance using column summary
|
|
statistics on the samples in the training set.
|
|
|
|
The "unit std" is computed using the `corrected sample standard deviation \
|
|
<https://en.wikipedia.org/wiki/Standard_deviation#Corrected_sample_standard_deviation>`_,
|
|
which is computed as the square root of the unbiased sample variance.
|
|
|
|
>>> from pyspark.ml.linalg import Vectors
|
|
>>> df = spark.createDataFrame([(Vectors.dense([0.0]),), (Vectors.dense([2.0]),)], ["a"])
|
|
>>> standardScaler = StandardScaler(inputCol="a", outputCol="scaled")
|
|
>>> model = standardScaler.fit(df)
|
|
>>> model.mean
|
|
DenseVector([1.0])
|
|
>>> model.std
|
|
DenseVector([1.4142])
|
|
>>> model.transform(df).collect()[1].scaled
|
|
DenseVector([1.4142])
|
|
>>> standardScalerPath = temp_path + "/standard-scaler"
|
|
>>> standardScaler.save(standardScalerPath)
|
|
>>> loadedStandardScaler = StandardScaler.load(standardScalerPath)
|
|
>>> loadedStandardScaler.getWithMean() == standardScaler.getWithMean()
|
|
True
|
|
>>> loadedStandardScaler.getWithStd() == standardScaler.getWithStd()
|
|
True
|
|
>>> modelPath = temp_path + "/standard-scaler-model"
|
|
>>> model.save(modelPath)
|
|
>>> loadedModel = StandardScalerModel.load(modelPath)
|
|
>>> loadedModel.std == model.std
|
|
True
|
|
>>> loadedModel.mean == model.mean
|
|
True
|
|
|
|
.. versionadded:: 1.4.0
|
|
"""
|
|
|
|
withMean = Param(Params._dummy(), "withMean", "Center data with mean",
|
|
typeConverter=TypeConverters.toBoolean)
|
|
withStd = Param(Params._dummy(), "withStd", "Scale to unit standard deviation",
|
|
typeConverter=TypeConverters.toBoolean)
|
|
|
|
@keyword_only
|
|
def __init__(self, withMean=False, withStd=True, inputCol=None, outputCol=None):
|
|
"""
|
|
__init__(self, withMean=False, withStd=True, inputCol=None, outputCol=None)
|
|
"""
|
|
super(StandardScaler, self).__init__()
|
|
self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.StandardScaler", self.uid)
|
|
self._setDefault(withMean=False, withStd=True)
|
|
kwargs = self._input_kwargs
|
|
self.setParams(**kwargs)
|
|
|
|
@keyword_only
|
|
@since("1.4.0")
|
|
def setParams(self, withMean=False, withStd=True, inputCol=None, outputCol=None):
|
|
"""
|
|
setParams(self, withMean=False, withStd=True, inputCol=None, outputCol=None)
|
|
Sets params for this StandardScaler.
|
|
"""
|
|
kwargs = self._input_kwargs
|
|
return self._set(**kwargs)
|
|
|
|
@since("1.4.0")
|
|
def setWithMean(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`withMean`.
|
|
"""
|
|
return self._set(withMean=value)
|
|
|
|
@since("1.4.0")
|
|
def getWithMean(self):
|
|
"""
|
|
Gets the value of withMean or its default value.
|
|
"""
|
|
return self.getOrDefault(self.withMean)
|
|
|
|
@since("1.4.0")
|
|
def setWithStd(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`withStd`.
|
|
"""
|
|
return self._set(withStd=value)
|
|
|
|
@since("1.4.0")
|
|
def getWithStd(self):
|
|
"""
|
|
Gets the value of withStd or its default value.
|
|
"""
|
|
return self.getOrDefault(self.withStd)
|
|
|
|
def _create_model(self, java_model):
|
|
return StandardScalerModel(java_model)
|
|
|
|
|
|
class StandardScalerModel(JavaModel, JavaMLReadable, JavaMLWritable):
|
|
"""
|
|
Model fitted by :py:class:`StandardScaler`.
|
|
|
|
.. versionadded:: 1.4.0
|
|
"""
|
|
|
|
@property
|
|
@since("2.0.0")
|
|
def std(self):
|
|
"""
|
|
Standard deviation of the StandardScalerModel.
|
|
"""
|
|
return self._call_java("std")
|
|
|
|
@property
|
|
@since("2.0.0")
|
|
def mean(self):
|
|
"""
|
|
Mean of the StandardScalerModel.
|
|
"""
|
|
return self._call_java("mean")
|
|
|
|
|
|
class _StringIndexerParams(JavaParams, HasHandleInvalid, HasInputCol, HasOutputCol,
|
|
HasInputCols, HasOutputCols):
|
|
"""
|
|
Params for :py:attr:`StringIndexer` and :py:attr:`StringIndexerModel`.
|
|
"""
|
|
|
|
stringOrderType = Param(Params._dummy(), "stringOrderType",
|
|
"How to order labels of string column. The first label after " +
|
|
"ordering is assigned an index of 0. Supported options: " +
|
|
"frequencyDesc, frequencyAsc, alphabetDesc, alphabetAsc. " +
|
|
"Default is frequencyDesc. In case of equal frequency when " +
|
|
"under frequencyDesc/Asc, the strings are further sorted " +
|
|
"alphabetically",
|
|
typeConverter=TypeConverters.toString)
|
|
|
|
handleInvalid = Param(Params._dummy(), "handleInvalid", "how to handle invalid data (unseen " +
|
|
"or NULL values) in features and label column of string type. " +
|
|
"Options are 'skip' (filter out rows with invalid data), " +
|
|
"error (throw an error), or 'keep' (put invalid data " +
|
|
"in a special additional bucket, at index numLabels).",
|
|
typeConverter=TypeConverters.toString)
|
|
|
|
def __init__(self, *args):
|
|
super(_StringIndexerParams, self).__init__(*args)
|
|
self._setDefault(handleInvalid="error", stringOrderType="frequencyDesc")
|
|
|
|
@since("2.3.0")
|
|
def getStringOrderType(self):
|
|
"""
|
|
Gets the value of :py:attr:`stringOrderType` or its default value 'frequencyDesc'.
|
|
"""
|
|
return self.getOrDefault(self.stringOrderType)
|
|
|
|
|
|
@inherit_doc
|
|
class StringIndexer(JavaEstimator, _StringIndexerParams, JavaMLReadable, JavaMLWritable):
|
|
"""
|
|
A label indexer that maps a string column of labels to an ML column of label indices.
|
|
If the input column is numeric, we cast it to string and index the string values.
|
|
The indices are in [0, numLabels). By default, this is ordered by label frequencies
|
|
so the most frequent label gets index 0. The ordering behavior is controlled by
|
|
setting :py:attr:`stringOrderType`. Its default value is 'frequencyDesc'.
|
|
|
|
>>> stringIndexer = StringIndexer(inputCol="label", outputCol="indexed", handleInvalid="error",
|
|
... stringOrderType="frequencyDesc")
|
|
>>> model = stringIndexer.fit(stringIndDf)
|
|
>>> td = model.transform(stringIndDf)
|
|
>>> sorted(set([(i[0], i[1]) for i in td.select(td.id, td.indexed).collect()]),
|
|
... key=lambda x: x[0])
|
|
[(0, 0.0), (1, 2.0), (2, 1.0), (3, 0.0), (4, 0.0), (5, 1.0)]
|
|
>>> inverter = IndexToString(inputCol="indexed", outputCol="label2", labels=model.labels)
|
|
>>> itd = inverter.transform(td)
|
|
>>> sorted(set([(i[0], str(i[1])) for i in itd.select(itd.id, itd.label2).collect()]),
|
|
... key=lambda x: x[0])
|
|
[(0, 'a'), (1, 'b'), (2, 'c'), (3, 'a'), (4, 'a'), (5, 'c')]
|
|
>>> stringIndexerPath = temp_path + "/string-indexer"
|
|
>>> stringIndexer.save(stringIndexerPath)
|
|
>>> loadedIndexer = StringIndexer.load(stringIndexerPath)
|
|
>>> loadedIndexer.getHandleInvalid() == stringIndexer.getHandleInvalid()
|
|
True
|
|
>>> modelPath = temp_path + "/string-indexer-model"
|
|
>>> model.save(modelPath)
|
|
>>> loadedModel = StringIndexerModel.load(modelPath)
|
|
>>> loadedModel.labels == model.labels
|
|
True
|
|
>>> indexToStringPath = temp_path + "/index-to-string"
|
|
>>> inverter.save(indexToStringPath)
|
|
>>> loadedInverter = IndexToString.load(indexToStringPath)
|
|
>>> loadedInverter.getLabels() == inverter.getLabels()
|
|
True
|
|
>>> stringIndexer.getStringOrderType()
|
|
'frequencyDesc'
|
|
>>> stringIndexer = StringIndexer(inputCol="label", outputCol="indexed", handleInvalid="error",
|
|
... stringOrderType="alphabetDesc")
|
|
>>> model = stringIndexer.fit(stringIndDf)
|
|
>>> td = model.transform(stringIndDf)
|
|
>>> sorted(set([(i[0], i[1]) for i in td.select(td.id, td.indexed).collect()]),
|
|
... key=lambda x: x[0])
|
|
[(0, 2.0), (1, 1.0), (2, 0.0), (3, 2.0), (4, 2.0), (5, 0.0)]
|
|
>>> fromlabelsModel = StringIndexerModel.from_labels(["a", "b", "c"],
|
|
... inputCol="label", outputCol="indexed", handleInvalid="error")
|
|
>>> result = fromlabelsModel.transform(stringIndDf)
|
|
>>> sorted(set([(i[0], i[1]) for i in result.select(result.id, result.indexed).collect()]),
|
|
... key=lambda x: x[0])
|
|
[(0, 0.0), (1, 1.0), (2, 2.0), (3, 0.0), (4, 0.0), (5, 2.0)]
|
|
>>> testData = sc.parallelize([Row(id=0, label1="a", label2="e"),
|
|
... Row(id=1, label1="b", label2="f"),
|
|
... Row(id=2, label1="c", label2="e"),
|
|
... Row(id=3, label1="a", label2="f"),
|
|
... Row(id=4, label1="a", label2="f"),
|
|
... Row(id=5, label1="c", label2="f")], 3)
|
|
>>> multiRowDf = spark.createDataFrame(testData)
|
|
>>> inputs = ["label1", "label2"]
|
|
>>> outputs = ["index1", "index2"]
|
|
>>> stringIndexer = StringIndexer(inputCols=inputs, outputCols=outputs)
|
|
>>> model = stringIndexer.fit(multiRowDf)
|
|
>>> result = model.transform(multiRowDf)
|
|
>>> sorted(set([(i[0], i[1], i[2]) for i in result.select(result.id, result.index1,
|
|
... result.index2).collect()]), key=lambda x: x[0])
|
|
[(0, 0.0, 1.0), (1, 2.0, 0.0), (2, 1.0, 1.0), (3, 0.0, 0.0), (4, 0.0, 0.0), (5, 1.0, 0.0)]
|
|
>>> fromlabelsModel = StringIndexerModel.from_arrays_of_labels([["a", "b", "c"], ["e", "f"]],
|
|
... inputCols=inputs, outputCols=outputs)
|
|
>>> result = fromlabelsModel.transform(multiRowDf)
|
|
>>> sorted(set([(i[0], i[1], i[2]) for i in result.select(result.id, result.index1,
|
|
... result.index2).collect()]), key=lambda x: x[0])
|
|
[(0, 0.0, 0.0), (1, 1.0, 1.0), (2, 2.0, 0.0), (3, 0.0, 1.0), (4, 0.0, 1.0), (5, 2.0, 1.0)]
|
|
|
|
.. versionadded:: 1.4.0
|
|
"""
|
|
|
|
@keyword_only
|
|
def __init__(self, inputCol=None, outputCol=None, inputCols=None, outputCols=None,
|
|
handleInvalid="error", stringOrderType="frequencyDesc"):
|
|
"""
|
|
__init__(self, inputCol=None, outputCol=None, inputCols=None, outputCols=None, \
|
|
handleInvalid="error", stringOrderType="frequencyDesc")
|
|
"""
|
|
super(StringIndexer, self).__init__()
|
|
self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.StringIndexer", self.uid)
|
|
kwargs = self._input_kwargs
|
|
self.setParams(**kwargs)
|
|
|
|
@keyword_only
|
|
@since("1.4.0")
|
|
def setParams(self, inputCol=None, outputCol=None, inputCols=None, outputCols=None,
|
|
handleInvalid="error", stringOrderType="frequencyDesc"):
|
|
"""
|
|
setParams(self, inputCol=None, outputCol=None, inputCols=None, outputCols=None, \
|
|
handleInvalid="error", stringOrderType="frequencyDesc")
|
|
Sets params for this StringIndexer.
|
|
"""
|
|
kwargs = self._input_kwargs
|
|
return self._set(**kwargs)
|
|
|
|
def _create_model(self, java_model):
|
|
return StringIndexerModel(java_model)
|
|
|
|
@since("2.3.0")
|
|
def setStringOrderType(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`stringOrderType`.
|
|
"""
|
|
return self._set(stringOrderType=value)
|
|
|
|
|
|
class StringIndexerModel(JavaModel, _StringIndexerParams, JavaMLReadable, JavaMLWritable):
|
|
"""
|
|
Model fitted by :py:class:`StringIndexer`.
|
|
|
|
.. versionadded:: 1.4.0
|
|
"""
|
|
|
|
@classmethod
|
|
@since("2.4.0")
|
|
def from_labels(cls, labels, inputCol, outputCol=None, handleInvalid=None):
|
|
"""
|
|
Construct the model directly from an array of label strings,
|
|
requires an active SparkContext.
|
|
"""
|
|
sc = SparkContext._active_spark_context
|
|
java_class = sc._gateway.jvm.java.lang.String
|
|
jlabels = StringIndexerModel._new_java_array(labels, java_class)
|
|
model = StringIndexerModel._create_from_java_class(
|
|
"org.apache.spark.ml.feature.StringIndexerModel", jlabels)
|
|
model.setInputCol(inputCol)
|
|
if outputCol is not None:
|
|
model.setOutputCol(outputCol)
|
|
if handleInvalid is not None:
|
|
model.setHandleInvalid(handleInvalid)
|
|
return model
|
|
|
|
@classmethod
|
|
@since("3.0.0")
|
|
def from_arrays_of_labels(cls, arrayOfLabels, inputCols, outputCols=None,
|
|
handleInvalid=None):
|
|
"""
|
|
Construct the model directly from an array of array of label strings,
|
|
requires an active SparkContext.
|
|
"""
|
|
sc = SparkContext._active_spark_context
|
|
java_class = sc._gateway.jvm.java.lang.String
|
|
jlabels = StringIndexerModel._new_java_array(arrayOfLabels, java_class)
|
|
model = StringIndexerModel._create_from_java_class(
|
|
"org.apache.spark.ml.feature.StringIndexerModel", jlabels)
|
|
model.setInputCols(inputCols)
|
|
if outputCols is not None:
|
|
model.setOutputCols(outputCols)
|
|
if handleInvalid is not None:
|
|
model.setHandleInvalid(handleInvalid)
|
|
return model
|
|
|
|
@property
|
|
@since("1.5.0")
|
|
def labels(self):
|
|
"""
|
|
Ordered list of labels, corresponding to indices to be assigned.
|
|
"""
|
|
return self._call_java("labels")
|
|
|
|
@since("2.4.0")
|
|
def setHandleInvalid(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`handleInvalid`.
|
|
"""
|
|
return self._set(handleInvalid=value)
|
|
|
|
|
|
@inherit_doc
|
|
class IndexToString(JavaTransformer, HasInputCol, HasOutputCol, JavaMLReadable, JavaMLWritable):
|
|
"""
|
|
A :py:class:`Transformer` that maps a column of indices back to a new column of
|
|
corresponding string values.
|
|
The index-string mapping is either from the ML attributes of the input column,
|
|
or from user-supplied labels (which take precedence over ML attributes).
|
|
See :class:`StringIndexer` for converting strings into indices.
|
|
|
|
.. versionadded:: 1.6.0
|
|
"""
|
|
|
|
labels = Param(Params._dummy(), "labels",
|
|
"Optional array of labels specifying index-string mapping." +
|
|
" If not provided or if empty, then metadata from inputCol is used instead.",
|
|
typeConverter=TypeConverters.toListString)
|
|
|
|
@keyword_only
|
|
def __init__(self, inputCol=None, outputCol=None, labels=None):
|
|
"""
|
|
__init__(self, inputCol=None, outputCol=None, labels=None)
|
|
"""
|
|
super(IndexToString, self).__init__()
|
|
self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.IndexToString",
|
|
self.uid)
|
|
kwargs = self._input_kwargs
|
|
self.setParams(**kwargs)
|
|
|
|
@keyword_only
|
|
@since("1.6.0")
|
|
def setParams(self, inputCol=None, outputCol=None, labels=None):
|
|
"""
|
|
setParams(self, inputCol=None, outputCol=None, labels=None)
|
|
Sets params for this IndexToString.
|
|
"""
|
|
kwargs = self._input_kwargs
|
|
return self._set(**kwargs)
|
|
|
|
@since("1.6.0")
|
|
def setLabels(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`labels`.
|
|
"""
|
|
return self._set(labels=value)
|
|
|
|
@since("1.6.0")
|
|
def getLabels(self):
|
|
"""
|
|
Gets the value of :py:attr:`labels` or its default value.
|
|
"""
|
|
return self.getOrDefault(self.labels)
|
|
|
|
|
|
class StopWordsRemover(JavaTransformer, HasInputCol, HasOutputCol, JavaMLReadable, JavaMLWritable):
|
|
"""
|
|
A feature transformer that filters out stop words from input.
|
|
|
|
.. note:: null values from input array are preserved unless adding null to stopWords explicitly.
|
|
|
|
>>> df = spark.createDataFrame([(["a", "b", "c"],)], ["text"])
|
|
>>> remover = StopWordsRemover(inputCol="text", outputCol="words", stopWords=["b"])
|
|
>>> remover.transform(df).head().words == ['a', 'c']
|
|
True
|
|
>>> stopWordsRemoverPath = temp_path + "/stopwords-remover"
|
|
>>> remover.save(stopWordsRemoverPath)
|
|
>>> loadedRemover = StopWordsRemover.load(stopWordsRemoverPath)
|
|
>>> loadedRemover.getStopWords() == remover.getStopWords()
|
|
True
|
|
>>> loadedRemover.getCaseSensitive() == remover.getCaseSensitive()
|
|
True
|
|
|
|
.. versionadded:: 1.6.0
|
|
"""
|
|
|
|
stopWords = Param(Params._dummy(), "stopWords", "The words to be filtered out",
|
|
typeConverter=TypeConverters.toListString)
|
|
caseSensitive = Param(Params._dummy(), "caseSensitive", "whether to do a case sensitive " +
|
|
"comparison over the stop words", typeConverter=TypeConverters.toBoolean)
|
|
locale = Param(Params._dummy(), "locale", "locale of the input. ignored when case sensitive " +
|
|
"is true", typeConverter=TypeConverters.toString)
|
|
|
|
@keyword_only
|
|
def __init__(self, inputCol=None, outputCol=None, stopWords=None, caseSensitive=False,
|
|
locale=None):
|
|
"""
|
|
__init__(self, inputCol=None, outputCol=None, stopWords=None, caseSensitive=false, \
|
|
locale=None)
|
|
"""
|
|
super(StopWordsRemover, self).__init__()
|
|
self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.StopWordsRemover",
|
|
self.uid)
|
|
self._setDefault(stopWords=StopWordsRemover.loadDefaultStopWords("english"),
|
|
caseSensitive=False, locale=self._java_obj.getLocale())
|
|
kwargs = self._input_kwargs
|
|
self.setParams(**kwargs)
|
|
|
|
@keyword_only
|
|
@since("1.6.0")
|
|
def setParams(self, inputCol=None, outputCol=None, stopWords=None, caseSensitive=False,
|
|
locale=None):
|
|
"""
|
|
setParams(self, inputCol=None, outputCol=None, stopWords=None, caseSensitive=false, \
|
|
locale=None)
|
|
Sets params for this StopWordRemover.
|
|
"""
|
|
kwargs = self._input_kwargs
|
|
return self._set(**kwargs)
|
|
|
|
@since("1.6.0")
|
|
def setStopWords(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`stopWords`.
|
|
"""
|
|
return self._set(stopWords=value)
|
|
|
|
@since("1.6.0")
|
|
def getStopWords(self):
|
|
"""
|
|
Gets the value of :py:attr:`stopWords` or its default value.
|
|
"""
|
|
return self.getOrDefault(self.stopWords)
|
|
|
|
@since("1.6.0")
|
|
def setCaseSensitive(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`caseSensitive`.
|
|
"""
|
|
return self._set(caseSensitive=value)
|
|
|
|
@since("1.6.0")
|
|
def getCaseSensitive(self):
|
|
"""
|
|
Gets the value of :py:attr:`caseSensitive` or its default value.
|
|
"""
|
|
return self.getOrDefault(self.caseSensitive)
|
|
|
|
@since("2.4.0")
|
|
def setLocale(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`locale`.
|
|
"""
|
|
return self._set(locale=value)
|
|
|
|
@since("2.4.0")
|
|
def getLocale(self):
|
|
"""
|
|
Gets the value of :py:attr:`locale`.
|
|
"""
|
|
return self.getOrDefault(self.locale)
|
|
|
|
@staticmethod
|
|
@since("2.0.0")
|
|
def loadDefaultStopWords(language):
|
|
"""
|
|
Loads the default stop words for the given language.
|
|
Supported languages: danish, dutch, english, finnish, french, german, hungarian,
|
|
italian, norwegian, portuguese, russian, spanish, swedish, turkish
|
|
"""
|
|
stopWordsObj = _jvm().org.apache.spark.ml.feature.StopWordsRemover
|
|
return list(stopWordsObj.loadDefaultStopWords(language))
|
|
|
|
|
|
@inherit_doc
|
|
@ignore_unicode_prefix
|
|
class Tokenizer(JavaTransformer, HasInputCol, HasOutputCol, JavaMLReadable, JavaMLWritable):
|
|
"""
|
|
A tokenizer that converts the input string to lowercase and then
|
|
splits it by white spaces.
|
|
|
|
>>> df = spark.createDataFrame([("a b c",)], ["text"])
|
|
>>> tokenizer = Tokenizer(inputCol="text", outputCol="words")
|
|
>>> tokenizer.transform(df).head()
|
|
Row(text=u'a b c', words=[u'a', u'b', u'c'])
|
|
>>> # Change a parameter.
|
|
>>> tokenizer.setParams(outputCol="tokens").transform(df).head()
|
|
Row(text=u'a b c', tokens=[u'a', u'b', u'c'])
|
|
>>> # Temporarily modify a parameter.
|
|
>>> tokenizer.transform(df, {tokenizer.outputCol: "words"}).head()
|
|
Row(text=u'a b c', words=[u'a', u'b', u'c'])
|
|
>>> tokenizer.transform(df).head()
|
|
Row(text=u'a b c', tokens=[u'a', u'b', u'c'])
|
|
>>> # Must use keyword arguments to specify params.
|
|
>>> tokenizer.setParams("text")
|
|
Traceback (most recent call last):
|
|
...
|
|
TypeError: Method setParams forces keyword arguments.
|
|
>>> tokenizerPath = temp_path + "/tokenizer"
|
|
>>> tokenizer.save(tokenizerPath)
|
|
>>> loadedTokenizer = Tokenizer.load(tokenizerPath)
|
|
>>> loadedTokenizer.transform(df).head().tokens == tokenizer.transform(df).head().tokens
|
|
True
|
|
|
|
.. versionadded:: 1.3.0
|
|
"""
|
|
|
|
@keyword_only
|
|
def __init__(self, inputCol=None, outputCol=None):
|
|
"""
|
|
__init__(self, inputCol=None, outputCol=None)
|
|
"""
|
|
super(Tokenizer, self).__init__()
|
|
self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.Tokenizer", self.uid)
|
|
kwargs = self._input_kwargs
|
|
self.setParams(**kwargs)
|
|
|
|
@keyword_only
|
|
@since("1.3.0")
|
|
def setParams(self, inputCol=None, outputCol=None):
|
|
"""
|
|
setParams(self, inputCol=None, outputCol=None)
|
|
Sets params for this Tokenizer.
|
|
"""
|
|
kwargs = self._input_kwargs
|
|
return self._set(**kwargs)
|
|
|
|
|
|
@inherit_doc
|
|
class VectorAssembler(JavaTransformer, HasInputCols, HasOutputCol, HasHandleInvalid, JavaMLReadable,
|
|
JavaMLWritable):
|
|
"""
|
|
A feature transformer that merges multiple columns into a vector column.
|
|
|
|
>>> df = spark.createDataFrame([(1, 0, 3)], ["a", "b", "c"])
|
|
>>> vecAssembler = VectorAssembler(inputCols=["a", "b", "c"], outputCol="features")
|
|
>>> vecAssembler.transform(df).head().features
|
|
DenseVector([1.0, 0.0, 3.0])
|
|
>>> vecAssembler.setParams(outputCol="freqs").transform(df).head().freqs
|
|
DenseVector([1.0, 0.0, 3.0])
|
|
>>> params = {vecAssembler.inputCols: ["b", "a"], vecAssembler.outputCol: "vector"}
|
|
>>> vecAssembler.transform(df, params).head().vector
|
|
DenseVector([0.0, 1.0])
|
|
>>> vectorAssemblerPath = temp_path + "/vector-assembler"
|
|
>>> vecAssembler.save(vectorAssemblerPath)
|
|
>>> loadedAssembler = VectorAssembler.load(vectorAssemblerPath)
|
|
>>> loadedAssembler.transform(df).head().freqs == vecAssembler.transform(df).head().freqs
|
|
True
|
|
>>> dfWithNullsAndNaNs = spark.createDataFrame(
|
|
... [(1.0, 2.0, None), (3.0, float("nan"), 4.0), (5.0, 6.0, 7.0)], ["a", "b", "c"])
|
|
>>> vecAssembler2 = VectorAssembler(inputCols=["a", "b", "c"], outputCol="features",
|
|
... handleInvalid="keep")
|
|
>>> vecAssembler2.transform(dfWithNullsAndNaNs).show()
|
|
+---+---+----+-------------+
|
|
| a| b| c| features|
|
|
+---+---+----+-------------+
|
|
|1.0|2.0|null|[1.0,2.0,NaN]|
|
|
|3.0|NaN| 4.0|[3.0,NaN,4.0]|
|
|
|5.0|6.0| 7.0|[5.0,6.0,7.0]|
|
|
+---+---+----+-------------+
|
|
...
|
|
>>> vecAssembler2.setParams(handleInvalid="skip").transform(dfWithNullsAndNaNs).show()
|
|
+---+---+---+-------------+
|
|
| a| b| c| features|
|
|
+---+---+---+-------------+
|
|
|5.0|6.0|7.0|[5.0,6.0,7.0]|
|
|
+---+---+---+-------------+
|
|
...
|
|
|
|
.. versionadded:: 1.4.0
|
|
"""
|
|
|
|
handleInvalid = Param(Params._dummy(), "handleInvalid", "How to handle invalid data (NULL " +
|
|
"and NaN values). Options are 'skip' (filter out rows with invalid " +
|
|
"data), 'error' (throw an error), or 'keep' (return relevant number " +
|
|
"of NaN in the output). Column lengths are taken from the size of ML " +
|
|
"Attribute Group, which can be set using `VectorSizeHint` in a " +
|
|
"pipeline before `VectorAssembler`. Column lengths can also be " +
|
|
"inferred from first rows of the data since it is safe to do so but " +
|
|
"only in case of 'error' or 'skip').",
|
|
typeConverter=TypeConverters.toString)
|
|
|
|
@keyword_only
|
|
def __init__(self, inputCols=None, outputCol=None, handleInvalid="error"):
|
|
"""
|
|
__init__(self, inputCols=None, outputCol=None, handleInvalid="error")
|
|
"""
|
|
super(VectorAssembler, self).__init__()
|
|
self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.VectorAssembler", self.uid)
|
|
self._setDefault(handleInvalid="error")
|
|
kwargs = self._input_kwargs
|
|
self.setParams(**kwargs)
|
|
|
|
@keyword_only
|
|
@since("1.4.0")
|
|
def setParams(self, inputCols=None, outputCol=None, handleInvalid="error"):
|
|
"""
|
|
setParams(self, inputCols=None, outputCol=None, handleInvalid="error")
|
|
Sets params for this VectorAssembler.
|
|
"""
|
|
kwargs = self._input_kwargs
|
|
return self._set(**kwargs)
|
|
|
|
|
|
@inherit_doc
|
|
class VectorIndexer(JavaEstimator, HasInputCol, HasOutputCol, HasHandleInvalid, JavaMLReadable,
|
|
JavaMLWritable):
|
|
"""
|
|
Class for indexing categorical feature columns in a dataset of `Vector`.
|
|
|
|
This has 2 usage modes:
|
|
- Automatically identify categorical features (default behavior)
|
|
- This helps process a dataset of unknown vectors into a dataset with some continuous
|
|
features and some categorical features. The choice between continuous and categorical
|
|
is based upon a maxCategories parameter.
|
|
- Set maxCategories to the maximum number of categorical any categorical feature should
|
|
have.
|
|
- E.g.: Feature 0 has unique values {-1.0, 0.0}, and feature 1 values {1.0, 3.0, 5.0}.
|
|
If maxCategories = 2, then feature 0 will be declared categorical and use indices {0, 1},
|
|
and feature 1 will be declared continuous.
|
|
- Index all features, if all features are categorical
|
|
- If maxCategories is set to be very large, then this will build an index of unique
|
|
values for all features.
|
|
- Warning: This can cause problems if features are continuous since this will collect ALL
|
|
unique values to the driver.
|
|
- E.g.: Feature 0 has unique values {-1.0, 0.0}, and feature 1 values {1.0, 3.0, 5.0}.
|
|
If maxCategories >= 3, then both features will be declared categorical.
|
|
|
|
This returns a model which can transform categorical features to use 0-based indices.
|
|
|
|
Index stability:
|
|
- This is not guaranteed to choose the same category index across multiple runs.
|
|
- If a categorical feature includes value 0, then this is guaranteed to map value 0 to
|
|
index 0. This maintains vector sparsity.
|
|
- More stability may be added in the future.
|
|
|
|
TODO: Future extensions: The following functionality is planned for the future:
|
|
- Preserve metadata in transform; if a feature's metadata is already present,
|
|
do not recompute.
|
|
- Specify certain features to not index, either via a parameter or via existing metadata.
|
|
- Add warning if a categorical feature has only 1 category.
|
|
|
|
>>> from pyspark.ml.linalg import Vectors
|
|
>>> df = spark.createDataFrame([(Vectors.dense([-1.0, 0.0]),),
|
|
... (Vectors.dense([0.0, 1.0]),), (Vectors.dense([0.0, 2.0]),)], ["a"])
|
|
>>> indexer = VectorIndexer(maxCategories=2, inputCol="a", outputCol="indexed")
|
|
>>> model = indexer.fit(df)
|
|
>>> model.transform(df).head().indexed
|
|
DenseVector([1.0, 0.0])
|
|
>>> model.numFeatures
|
|
2
|
|
>>> model.categoryMaps
|
|
{0: {0.0: 0, -1.0: 1}}
|
|
>>> indexer.setParams(outputCol="test").fit(df).transform(df).collect()[1].test
|
|
DenseVector([0.0, 1.0])
|
|
>>> params = {indexer.maxCategories: 3, indexer.outputCol: "vector"}
|
|
>>> model2 = indexer.fit(df, params)
|
|
>>> model2.transform(df).head().vector
|
|
DenseVector([1.0, 0.0])
|
|
>>> vectorIndexerPath = temp_path + "/vector-indexer"
|
|
>>> indexer.save(vectorIndexerPath)
|
|
>>> loadedIndexer = VectorIndexer.load(vectorIndexerPath)
|
|
>>> loadedIndexer.getMaxCategories() == indexer.getMaxCategories()
|
|
True
|
|
>>> modelPath = temp_path + "/vector-indexer-model"
|
|
>>> model.save(modelPath)
|
|
>>> loadedModel = VectorIndexerModel.load(modelPath)
|
|
>>> loadedModel.numFeatures == model.numFeatures
|
|
True
|
|
>>> loadedModel.categoryMaps == model.categoryMaps
|
|
True
|
|
>>> dfWithInvalid = spark.createDataFrame([(Vectors.dense([3.0, 1.0]),)], ["a"])
|
|
>>> indexer.getHandleInvalid()
|
|
'error'
|
|
>>> model3 = indexer.setHandleInvalid("skip").fit(df)
|
|
>>> model3.transform(dfWithInvalid).count()
|
|
0
|
|
>>> model4 = indexer.setParams(handleInvalid="keep", outputCol="indexed").fit(df)
|
|
>>> model4.transform(dfWithInvalid).head().indexed
|
|
DenseVector([2.0, 1.0])
|
|
|
|
.. versionadded:: 1.4.0
|
|
"""
|
|
|
|
maxCategories = Param(Params._dummy(), "maxCategories",
|
|
"Threshold for the number of values a categorical feature can take " +
|
|
"(>= 2). If a feature is found to have > maxCategories values, then " +
|
|
"it is declared continuous.", typeConverter=TypeConverters.toInt)
|
|
|
|
handleInvalid = Param(Params._dummy(), "handleInvalid", "How to handle invalid data " +
|
|
"(unseen labels or NULL values). Options are 'skip' (filter out " +
|
|
"rows with invalid data), 'error' (throw an error), or 'keep' (put " +
|
|
"invalid data in a special additional bucket, at index of the number " +
|
|
"of categories of the feature).",
|
|
typeConverter=TypeConverters.toString)
|
|
|
|
@keyword_only
|
|
def __init__(self, maxCategories=20, inputCol=None, outputCol=None, handleInvalid="error"):
|
|
"""
|
|
__init__(self, maxCategories=20, inputCol=None, outputCol=None, handleInvalid="error")
|
|
"""
|
|
super(VectorIndexer, self).__init__()
|
|
self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.VectorIndexer", self.uid)
|
|
self._setDefault(maxCategories=20, handleInvalid="error")
|
|
kwargs = self._input_kwargs
|
|
self.setParams(**kwargs)
|
|
|
|
@keyword_only
|
|
@since("1.4.0")
|
|
def setParams(self, maxCategories=20, inputCol=None, outputCol=None, handleInvalid="error"):
|
|
"""
|
|
setParams(self, maxCategories=20, inputCol=None, outputCol=None, handleInvalid="error")
|
|
Sets params for this VectorIndexer.
|
|
"""
|
|
kwargs = self._input_kwargs
|
|
return self._set(**kwargs)
|
|
|
|
@since("1.4.0")
|
|
def setMaxCategories(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`maxCategories`.
|
|
"""
|
|
return self._set(maxCategories=value)
|
|
|
|
@since("1.4.0")
|
|
def getMaxCategories(self):
|
|
"""
|
|
Gets the value of maxCategories or its default value.
|
|
"""
|
|
return self.getOrDefault(self.maxCategories)
|
|
|
|
def _create_model(self, java_model):
|
|
return VectorIndexerModel(java_model)
|
|
|
|
|
|
class VectorIndexerModel(JavaModel, JavaMLReadable, JavaMLWritable):
|
|
"""
|
|
Model fitted by :py:class:`VectorIndexer`.
|
|
|
|
Transform categorical features to use 0-based indices instead of their original values.
|
|
- Categorical features are mapped to indices.
|
|
- Continuous features (columns) are left unchanged.
|
|
|
|
This also appends metadata to the output column, marking features as Numeric (continuous),
|
|
Nominal (categorical), or Binary (either continuous or categorical).
|
|
Non-ML metadata is not carried over from the input to the output column.
|
|
|
|
This maintains vector sparsity.
|
|
|
|
.. versionadded:: 1.4.0
|
|
"""
|
|
|
|
@property
|
|
@since("1.4.0")
|
|
def numFeatures(self):
|
|
"""
|
|
Number of features, i.e., length of Vectors which this transforms.
|
|
"""
|
|
return self._call_java("numFeatures")
|
|
|
|
@property
|
|
@since("1.4.0")
|
|
def categoryMaps(self):
|
|
"""
|
|
Feature value index. Keys are categorical feature indices (column indices).
|
|
Values are maps from original features values to 0-based category indices.
|
|
If a feature is not in this map, it is treated as continuous.
|
|
"""
|
|
return self._call_java("javaCategoryMaps")
|
|
|
|
|
|
@inherit_doc
|
|
class VectorSlicer(JavaTransformer, HasInputCol, HasOutputCol, JavaMLReadable, JavaMLWritable):
|
|
"""
|
|
This class takes a feature vector and outputs a new feature vector with a subarray
|
|
of the original features.
|
|
|
|
The subset of features can be specified with either indices (`setIndices()`)
|
|
or names (`setNames()`). At least one feature must be selected. Duplicate features
|
|
are not allowed, so there can be no overlap between selected indices and names.
|
|
|
|
The output vector will order features with the selected indices first (in the order given),
|
|
followed by the selected names (in the order given).
|
|
|
|
>>> from pyspark.ml.linalg import Vectors
|
|
>>> df = spark.createDataFrame([
|
|
... (Vectors.dense([-2.0, 2.3, 0.0, 0.0, 1.0]),),
|
|
... (Vectors.dense([0.0, 0.0, 0.0, 0.0, 0.0]),),
|
|
... (Vectors.dense([0.6, -1.1, -3.0, 4.5, 3.3]),)], ["features"])
|
|
>>> vs = VectorSlicer(inputCol="features", outputCol="sliced", indices=[1, 4])
|
|
>>> vs.transform(df).head().sliced
|
|
DenseVector([2.3, 1.0])
|
|
>>> vectorSlicerPath = temp_path + "/vector-slicer"
|
|
>>> vs.save(vectorSlicerPath)
|
|
>>> loadedVs = VectorSlicer.load(vectorSlicerPath)
|
|
>>> loadedVs.getIndices() == vs.getIndices()
|
|
True
|
|
>>> loadedVs.getNames() == vs.getNames()
|
|
True
|
|
|
|
.. versionadded:: 1.6.0
|
|
"""
|
|
|
|
indices = Param(Params._dummy(), "indices", "An array of indices to select features from " +
|
|
"a vector column. There can be no overlap with names.",
|
|
typeConverter=TypeConverters.toListInt)
|
|
names = Param(Params._dummy(), "names", "An array of feature names to select features from " +
|
|
"a vector column. These names must be specified by ML " +
|
|
"org.apache.spark.ml.attribute.Attribute. There can be no overlap with " +
|
|
"indices.", typeConverter=TypeConverters.toListString)
|
|
|
|
@keyword_only
|
|
def __init__(self, inputCol=None, outputCol=None, indices=None, names=None):
|
|
"""
|
|
__init__(self, inputCol=None, outputCol=None, indices=None, names=None)
|
|
"""
|
|
super(VectorSlicer, self).__init__()
|
|
self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.VectorSlicer", self.uid)
|
|
self._setDefault(indices=[], names=[])
|
|
kwargs = self._input_kwargs
|
|
self.setParams(**kwargs)
|
|
|
|
@keyword_only
|
|
@since("1.6.0")
|
|
def setParams(self, inputCol=None, outputCol=None, indices=None, names=None):
|
|
"""
|
|
setParams(self, inputCol=None, outputCol=None, indices=None, names=None):
|
|
Sets params for this VectorSlicer.
|
|
"""
|
|
kwargs = self._input_kwargs
|
|
return self._set(**kwargs)
|
|
|
|
@since("1.6.0")
|
|
def setIndices(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`indices`.
|
|
"""
|
|
return self._set(indices=value)
|
|
|
|
@since("1.6.0")
|
|
def getIndices(self):
|
|
"""
|
|
Gets the value of indices or its default value.
|
|
"""
|
|
return self.getOrDefault(self.indices)
|
|
|
|
@since("1.6.0")
|
|
def setNames(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`names`.
|
|
"""
|
|
return self._set(names=value)
|
|
|
|
@since("1.6.0")
|
|
def getNames(self):
|
|
"""
|
|
Gets the value of names or its default value.
|
|
"""
|
|
return self.getOrDefault(self.names)
|
|
|
|
|
|
@inherit_doc
|
|
@ignore_unicode_prefix
|
|
class Word2Vec(JavaEstimator, HasStepSize, HasMaxIter, HasSeed, HasInputCol, HasOutputCol,
|
|
JavaMLReadable, JavaMLWritable):
|
|
"""
|
|
Word2Vec trains a model of `Map(String, Vector)`, i.e. transforms a word into a code for further
|
|
natural language processing or machine learning process.
|
|
|
|
>>> sent = ("a b " * 100 + "a c " * 10).split(" ")
|
|
>>> doc = spark.createDataFrame([(sent,), (sent,)], ["sentence"])
|
|
>>> word2Vec = Word2Vec(vectorSize=5, seed=42, inputCol="sentence", outputCol="model")
|
|
>>> model = word2Vec.fit(doc)
|
|
>>> model.getVectors().show()
|
|
+----+--------------------+
|
|
|word| vector|
|
|
+----+--------------------+
|
|
| a|[0.09511678665876...|
|
|
| b|[-1.2028766870498...|
|
|
| c|[0.30153277516365...|
|
|
+----+--------------------+
|
|
...
|
|
>>> model.findSynonymsArray("a", 2)
|
|
[(u'b', 0.015859870240092278), (u'c', -0.5680795907974243)]
|
|
>>> from pyspark.sql.functions import format_number as fmt
|
|
>>> model.findSynonyms("a", 2).select("word", fmt("similarity", 5).alias("similarity")).show()
|
|
+----+----------+
|
|
|word|similarity|
|
|
+----+----------+
|
|
| b| 0.01586|
|
|
| c| -0.56808|
|
|
+----+----------+
|
|
...
|
|
>>> model.transform(doc).head().model
|
|
DenseVector([-0.4833, 0.1855, -0.273, -0.0509, -0.4769])
|
|
>>> word2vecPath = temp_path + "/word2vec"
|
|
>>> word2Vec.save(word2vecPath)
|
|
>>> loadedWord2Vec = Word2Vec.load(word2vecPath)
|
|
>>> loadedWord2Vec.getVectorSize() == word2Vec.getVectorSize()
|
|
True
|
|
>>> loadedWord2Vec.getNumPartitions() == word2Vec.getNumPartitions()
|
|
True
|
|
>>> loadedWord2Vec.getMinCount() == word2Vec.getMinCount()
|
|
True
|
|
>>> modelPath = temp_path + "/word2vec-model"
|
|
>>> model.save(modelPath)
|
|
>>> loadedModel = Word2VecModel.load(modelPath)
|
|
>>> loadedModel.getVectors().first().word == model.getVectors().first().word
|
|
True
|
|
>>> loadedModel.getVectors().first().vector == model.getVectors().first().vector
|
|
True
|
|
|
|
.. versionadded:: 1.4.0
|
|
"""
|
|
|
|
vectorSize = Param(Params._dummy(), "vectorSize",
|
|
"the dimension of codes after transforming from words",
|
|
typeConverter=TypeConverters.toInt)
|
|
numPartitions = Param(Params._dummy(), "numPartitions",
|
|
"number of partitions for sentences of words",
|
|
typeConverter=TypeConverters.toInt)
|
|
minCount = Param(Params._dummy(), "minCount",
|
|
"the minimum number of times a token must appear to be included in the " +
|
|
"word2vec model's vocabulary", typeConverter=TypeConverters.toInt)
|
|
windowSize = Param(Params._dummy(), "windowSize",
|
|
"the window size (context words from [-window, window]). Default value is 5",
|
|
typeConverter=TypeConverters.toInt)
|
|
maxSentenceLength = Param(Params._dummy(), "maxSentenceLength",
|
|
"Maximum length (in words) of each sentence in the input data. " +
|
|
"Any sentence longer than this threshold will " +
|
|
"be divided into chunks up to the size.",
|
|
typeConverter=TypeConverters.toInt)
|
|
|
|
@keyword_only
|
|
def __init__(self, vectorSize=100, minCount=5, numPartitions=1, stepSize=0.025, maxIter=1,
|
|
seed=None, inputCol=None, outputCol=None, windowSize=5, maxSentenceLength=1000):
|
|
"""
|
|
__init__(self, vectorSize=100, minCount=5, numPartitions=1, stepSize=0.025, maxIter=1, \
|
|
seed=None, inputCol=None, outputCol=None, windowSize=5, maxSentenceLength=1000)
|
|
"""
|
|
super(Word2Vec, self).__init__()
|
|
self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.Word2Vec", self.uid)
|
|
self._setDefault(vectorSize=100, minCount=5, numPartitions=1, stepSize=0.025, maxIter=1,
|
|
windowSize=5, maxSentenceLength=1000)
|
|
kwargs = self._input_kwargs
|
|
self.setParams(**kwargs)
|
|
|
|
@keyword_only
|
|
@since("1.4.0")
|
|
def setParams(self, vectorSize=100, minCount=5, numPartitions=1, stepSize=0.025, maxIter=1,
|
|
seed=None, inputCol=None, outputCol=None, windowSize=5, maxSentenceLength=1000):
|
|
"""
|
|
setParams(self, minCount=5, numPartitions=1, stepSize=0.025, maxIter=1, seed=None, \
|
|
inputCol=None, outputCol=None, windowSize=5, maxSentenceLength=1000)
|
|
Sets params for this Word2Vec.
|
|
"""
|
|
kwargs = self._input_kwargs
|
|
return self._set(**kwargs)
|
|
|
|
@since("1.4.0")
|
|
def setVectorSize(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`vectorSize`.
|
|
"""
|
|
return self._set(vectorSize=value)
|
|
|
|
@since("1.4.0")
|
|
def getVectorSize(self):
|
|
"""
|
|
Gets the value of vectorSize or its default value.
|
|
"""
|
|
return self.getOrDefault(self.vectorSize)
|
|
|
|
@since("1.4.0")
|
|
def setNumPartitions(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`numPartitions`.
|
|
"""
|
|
return self._set(numPartitions=value)
|
|
|
|
@since("1.4.0")
|
|
def getNumPartitions(self):
|
|
"""
|
|
Gets the value of numPartitions or its default value.
|
|
"""
|
|
return self.getOrDefault(self.numPartitions)
|
|
|
|
@since("1.4.0")
|
|
def setMinCount(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`minCount`.
|
|
"""
|
|
return self._set(minCount=value)
|
|
|
|
@since("1.4.0")
|
|
def getMinCount(self):
|
|
"""
|
|
Gets the value of minCount or its default value.
|
|
"""
|
|
return self.getOrDefault(self.minCount)
|
|
|
|
@since("2.0.0")
|
|
def setWindowSize(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`windowSize`.
|
|
"""
|
|
return self._set(windowSize=value)
|
|
|
|
@since("2.0.0")
|
|
def getWindowSize(self):
|
|
"""
|
|
Gets the value of windowSize or its default value.
|
|
"""
|
|
return self.getOrDefault(self.windowSize)
|
|
|
|
@since("2.0.0")
|
|
def setMaxSentenceLength(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`maxSentenceLength`.
|
|
"""
|
|
return self._set(maxSentenceLength=value)
|
|
|
|
@since("2.0.0")
|
|
def getMaxSentenceLength(self):
|
|
"""
|
|
Gets the value of maxSentenceLength or its default value.
|
|
"""
|
|
return self.getOrDefault(self.maxSentenceLength)
|
|
|
|
def _create_model(self, java_model):
|
|
return Word2VecModel(java_model)
|
|
|
|
|
|
class Word2VecModel(JavaModel, JavaMLReadable, JavaMLWritable):
|
|
"""
|
|
Model fitted by :py:class:`Word2Vec`.
|
|
|
|
.. versionadded:: 1.4.0
|
|
"""
|
|
|
|
@since("1.5.0")
|
|
def getVectors(self):
|
|
"""
|
|
Returns the vector representation of the words as a dataframe
|
|
with two fields, word and vector.
|
|
"""
|
|
return self._call_java("getVectors")
|
|
|
|
@since("1.5.0")
|
|
def findSynonyms(self, word, num):
|
|
"""
|
|
Find "num" number of words closest in similarity to "word".
|
|
word can be a string or vector representation.
|
|
Returns a dataframe with two fields word and similarity (which
|
|
gives the cosine similarity).
|
|
"""
|
|
if not isinstance(word, basestring):
|
|
word = _convert_to_vector(word)
|
|
return self._call_java("findSynonyms", word, num)
|
|
|
|
@since("2.3.0")
|
|
def findSynonymsArray(self, word, num):
|
|
"""
|
|
Find "num" number of words closest in similarity to "word".
|
|
word can be a string or vector representation.
|
|
Returns an array with two fields word and similarity (which
|
|
gives the cosine similarity).
|
|
"""
|
|
if not isinstance(word, basestring):
|
|
word = _convert_to_vector(word)
|
|
tuples = self._java_obj.findSynonymsArray(word, num)
|
|
return list(map(lambda st: (st._1(), st._2()), list(tuples)))
|
|
|
|
|
|
@inherit_doc
|
|
class PCA(JavaEstimator, HasInputCol, HasOutputCol, JavaMLReadable, JavaMLWritable):
|
|
"""
|
|
PCA trains a model to project vectors to a lower dimensional space of the
|
|
top :py:attr:`k` principal components.
|
|
|
|
>>> from pyspark.ml.linalg import Vectors
|
|
>>> data = [(Vectors.sparse(5, [(1, 1.0), (3, 7.0)]),),
|
|
... (Vectors.dense([2.0, 0.0, 3.0, 4.0, 5.0]),),
|
|
... (Vectors.dense([4.0, 0.0, 0.0, 6.0, 7.0]),)]
|
|
>>> df = spark.createDataFrame(data,["features"])
|
|
>>> pca = PCA(k=2, inputCol="features", outputCol="pca_features")
|
|
>>> model = pca.fit(df)
|
|
>>> model.transform(df).collect()[0].pca_features
|
|
DenseVector([1.648..., -4.013...])
|
|
>>> model.explainedVariance
|
|
DenseVector([0.794..., 0.205...])
|
|
>>> pcaPath = temp_path + "/pca"
|
|
>>> pca.save(pcaPath)
|
|
>>> loadedPca = PCA.load(pcaPath)
|
|
>>> loadedPca.getK() == pca.getK()
|
|
True
|
|
>>> modelPath = temp_path + "/pca-model"
|
|
>>> model.save(modelPath)
|
|
>>> loadedModel = PCAModel.load(modelPath)
|
|
>>> loadedModel.pc == model.pc
|
|
True
|
|
>>> loadedModel.explainedVariance == model.explainedVariance
|
|
True
|
|
|
|
.. versionadded:: 1.5.0
|
|
"""
|
|
|
|
k = Param(Params._dummy(), "k", "the number of principal components",
|
|
typeConverter=TypeConverters.toInt)
|
|
|
|
@keyword_only
|
|
def __init__(self, k=None, inputCol=None, outputCol=None):
|
|
"""
|
|
__init__(self, k=None, inputCol=None, outputCol=None)
|
|
"""
|
|
super(PCA, self).__init__()
|
|
self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.PCA", self.uid)
|
|
kwargs = self._input_kwargs
|
|
self.setParams(**kwargs)
|
|
|
|
@keyword_only
|
|
@since("1.5.0")
|
|
def setParams(self, k=None, inputCol=None, outputCol=None):
|
|
"""
|
|
setParams(self, k=None, inputCol=None, outputCol=None)
|
|
Set params for this PCA.
|
|
"""
|
|
kwargs = self._input_kwargs
|
|
return self._set(**kwargs)
|
|
|
|
@since("1.5.0")
|
|
def setK(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`k`.
|
|
"""
|
|
return self._set(k=value)
|
|
|
|
@since("1.5.0")
|
|
def getK(self):
|
|
"""
|
|
Gets the value of k or its default value.
|
|
"""
|
|
return self.getOrDefault(self.k)
|
|
|
|
def _create_model(self, java_model):
|
|
return PCAModel(java_model)
|
|
|
|
|
|
class PCAModel(JavaModel, JavaMLReadable, JavaMLWritable):
|
|
"""
|
|
Model fitted by :py:class:`PCA`. Transforms vectors to a lower dimensional space.
|
|
|
|
.. versionadded:: 1.5.0
|
|
"""
|
|
|
|
@property
|
|
@since("2.0.0")
|
|
def pc(self):
|
|
"""
|
|
Returns a principal components Matrix.
|
|
Each column is one principal component.
|
|
"""
|
|
return self._call_java("pc")
|
|
|
|
@property
|
|
@since("2.0.0")
|
|
def explainedVariance(self):
|
|
"""
|
|
Returns a vector of proportions of variance
|
|
explained by each principal component.
|
|
"""
|
|
return self._call_java("explainedVariance")
|
|
|
|
|
|
@inherit_doc
|
|
class RFormula(JavaEstimator, HasFeaturesCol, HasLabelCol, HasHandleInvalid,
|
|
JavaMLReadable, JavaMLWritable):
|
|
"""
|
|
Implements the transforms required for fitting a dataset against an
|
|
R model formula. Currently we support a limited subset of the R
|
|
operators, including '~', '.', ':', '+', '-', '*', and '^'.
|
|
Also see the `R formula docs
|
|
<http://stat.ethz.ch/R-manual/R-patched/library/stats/html/formula.html>`_.
|
|
|
|
>>> df = spark.createDataFrame([
|
|
... (1.0, 1.0, "a"),
|
|
... (0.0, 2.0, "b"),
|
|
... (0.0, 0.0, "a")
|
|
... ], ["y", "x", "s"])
|
|
>>> rf = RFormula(formula="y ~ x + s")
|
|
>>> model = rf.fit(df)
|
|
>>> model.transform(df).show()
|
|
+---+---+---+---------+-----+
|
|
| y| x| s| features|label|
|
|
+---+---+---+---------+-----+
|
|
|1.0|1.0| a|[1.0,1.0]| 1.0|
|
|
|0.0|2.0| b|[2.0,0.0]| 0.0|
|
|
|0.0|0.0| a|[0.0,1.0]| 0.0|
|
|
+---+---+---+---------+-----+
|
|
...
|
|
>>> rf.fit(df, {rf.formula: "y ~ . - s"}).transform(df).show()
|
|
+---+---+---+--------+-----+
|
|
| y| x| s|features|label|
|
|
+---+---+---+--------+-----+
|
|
|1.0|1.0| a| [1.0]| 1.0|
|
|
|0.0|2.0| b| [2.0]| 0.0|
|
|
|0.0|0.0| a| [0.0]| 0.0|
|
|
+---+---+---+--------+-----+
|
|
...
|
|
>>> rFormulaPath = temp_path + "/rFormula"
|
|
>>> rf.save(rFormulaPath)
|
|
>>> loadedRF = RFormula.load(rFormulaPath)
|
|
>>> loadedRF.getFormula() == rf.getFormula()
|
|
True
|
|
>>> loadedRF.getFeaturesCol() == rf.getFeaturesCol()
|
|
True
|
|
>>> loadedRF.getLabelCol() == rf.getLabelCol()
|
|
True
|
|
>>> loadedRF.getHandleInvalid() == rf.getHandleInvalid()
|
|
True
|
|
>>> str(loadedRF)
|
|
'RFormula(y ~ x + s) (uid=...)'
|
|
>>> modelPath = temp_path + "/rFormulaModel"
|
|
>>> model.save(modelPath)
|
|
>>> loadedModel = RFormulaModel.load(modelPath)
|
|
>>> loadedModel.uid == model.uid
|
|
True
|
|
>>> loadedModel.transform(df).show()
|
|
+---+---+---+---------+-----+
|
|
| y| x| s| features|label|
|
|
+---+---+---+---------+-----+
|
|
|1.0|1.0| a|[1.0,1.0]| 1.0|
|
|
|0.0|2.0| b|[2.0,0.0]| 0.0|
|
|
|0.0|0.0| a|[0.0,1.0]| 0.0|
|
|
+---+---+---+---------+-----+
|
|
...
|
|
>>> str(loadedModel)
|
|
'RFormulaModel(ResolvedRFormula(label=y, terms=[x,s], hasIntercept=true)) (uid=...)'
|
|
|
|
.. versionadded:: 1.5.0
|
|
"""
|
|
|
|
formula = Param(Params._dummy(), "formula", "R model formula",
|
|
typeConverter=TypeConverters.toString)
|
|
|
|
forceIndexLabel = Param(Params._dummy(), "forceIndexLabel",
|
|
"Force to index label whether it is numeric or string",
|
|
typeConverter=TypeConverters.toBoolean)
|
|
|
|
stringIndexerOrderType = Param(Params._dummy(), "stringIndexerOrderType",
|
|
"How to order categories of a string feature column used by " +
|
|
"StringIndexer. The last category after ordering is dropped " +
|
|
"when encoding strings. Supported options: frequencyDesc, " +
|
|
"frequencyAsc, alphabetDesc, alphabetAsc. The default value " +
|
|
"is frequencyDesc. When the ordering is set to alphabetDesc, " +
|
|
"RFormula drops the same category as R when encoding strings.",
|
|
typeConverter=TypeConverters.toString)
|
|
|
|
handleInvalid = Param(Params._dummy(), "handleInvalid", "how to handle invalid entries. " +
|
|
"Options are 'skip' (filter out rows with invalid values), " +
|
|
"'error' (throw an error), or 'keep' (put invalid data in a special " +
|
|
"additional bucket, at index numLabels).",
|
|
typeConverter=TypeConverters.toString)
|
|
|
|
@keyword_only
|
|
def __init__(self, formula=None, featuresCol="features", labelCol="label",
|
|
forceIndexLabel=False, stringIndexerOrderType="frequencyDesc",
|
|
handleInvalid="error"):
|
|
"""
|
|
__init__(self, formula=None, featuresCol="features", labelCol="label", \
|
|
forceIndexLabel=False, stringIndexerOrderType="frequencyDesc", \
|
|
handleInvalid="error")
|
|
"""
|
|
super(RFormula, self).__init__()
|
|
self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.RFormula", self.uid)
|
|
self._setDefault(forceIndexLabel=False, stringIndexerOrderType="frequencyDesc",
|
|
handleInvalid="error")
|
|
kwargs = self._input_kwargs
|
|
self.setParams(**kwargs)
|
|
|
|
@keyword_only
|
|
@since("1.5.0")
|
|
def setParams(self, formula=None, featuresCol="features", labelCol="label",
|
|
forceIndexLabel=False, stringIndexerOrderType="frequencyDesc",
|
|
handleInvalid="error"):
|
|
"""
|
|
setParams(self, formula=None, featuresCol="features", labelCol="label", \
|
|
forceIndexLabel=False, stringIndexerOrderType="frequencyDesc", \
|
|
handleInvalid="error")
|
|
Sets params for RFormula.
|
|
"""
|
|
kwargs = self._input_kwargs
|
|
return self._set(**kwargs)
|
|
|
|
@since("1.5.0")
|
|
def setFormula(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`formula`.
|
|
"""
|
|
return self._set(formula=value)
|
|
|
|
@since("1.5.0")
|
|
def getFormula(self):
|
|
"""
|
|
Gets the value of :py:attr:`formula`.
|
|
"""
|
|
return self.getOrDefault(self.formula)
|
|
|
|
@since("2.1.0")
|
|
def setForceIndexLabel(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`forceIndexLabel`.
|
|
"""
|
|
return self._set(forceIndexLabel=value)
|
|
|
|
@since("2.1.0")
|
|
def getForceIndexLabel(self):
|
|
"""
|
|
Gets the value of :py:attr:`forceIndexLabel`.
|
|
"""
|
|
return self.getOrDefault(self.forceIndexLabel)
|
|
|
|
@since("2.3.0")
|
|
def setStringIndexerOrderType(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`stringIndexerOrderType`.
|
|
"""
|
|
return self._set(stringIndexerOrderType=value)
|
|
|
|
@since("2.3.0")
|
|
def getStringIndexerOrderType(self):
|
|
"""
|
|
Gets the value of :py:attr:`stringIndexerOrderType` or its default value 'frequencyDesc'.
|
|
"""
|
|
return self.getOrDefault(self.stringIndexerOrderType)
|
|
|
|
def _create_model(self, java_model):
|
|
return RFormulaModel(java_model)
|
|
|
|
def __str__(self):
|
|
formulaStr = self.getFormula() if self.isDefined(self.formula) else ""
|
|
return "RFormula(%s) (uid=%s)" % (formulaStr, self.uid)
|
|
|
|
|
|
class RFormulaModel(JavaModel, JavaMLReadable, JavaMLWritable):
|
|
"""
|
|
Model fitted by :py:class:`RFormula`. Fitting is required to determine the
|
|
factor levels of formula terms.
|
|
|
|
.. versionadded:: 1.5.0
|
|
"""
|
|
|
|
def __str__(self):
|
|
resolvedFormula = self._call_java("resolvedFormula")
|
|
return "RFormulaModel(%s) (uid=%s)" % (resolvedFormula, self.uid)
|
|
|
|
|
|
@inherit_doc
|
|
class ChiSqSelector(JavaEstimator, HasFeaturesCol, HasOutputCol, HasLabelCol, JavaMLReadable,
|
|
JavaMLWritable):
|
|
"""
|
|
Chi-Squared feature selection, which selects categorical features to use for predicting a
|
|
categorical label.
|
|
The selector supports different selection methods: `numTopFeatures`, `percentile`, `fpr`,
|
|
`fdr`, `fwe`.
|
|
|
|
* `numTopFeatures` chooses a fixed number of top features according to a chi-squared test.
|
|
|
|
* `percentile` is similar but chooses a fraction of all features
|
|
instead of a fixed number.
|
|
|
|
* `fpr` chooses all features whose p-values are below a threshold,
|
|
thus controlling the false positive rate of selection.
|
|
|
|
* `fdr` uses the `Benjamini-Hochberg procedure <https://en.wikipedia.org/wiki/
|
|
False_discovery_rate#Benjamini.E2.80.93Hochberg_procedure>`_
|
|
to choose all features whose false discovery rate is below a threshold.
|
|
|
|
* `fwe` chooses all features whose p-values are below a threshold. The threshold is scaled by
|
|
1/numFeatures, thus controlling the family-wise error rate of selection.
|
|
|
|
By default, the selection method is `numTopFeatures`, with the default number of top features
|
|
set to 50.
|
|
|
|
|
|
>>> from pyspark.ml.linalg import Vectors
|
|
>>> df = spark.createDataFrame(
|
|
... [(Vectors.dense([0.0, 0.0, 18.0, 1.0]), 1.0),
|
|
... (Vectors.dense([0.0, 1.0, 12.0, 0.0]), 0.0),
|
|
... (Vectors.dense([1.0, 0.0, 15.0, 0.1]), 0.0)],
|
|
... ["features", "label"])
|
|
>>> selector = ChiSqSelector(numTopFeatures=1, outputCol="selectedFeatures")
|
|
>>> model = selector.fit(df)
|
|
>>> model.transform(df).head().selectedFeatures
|
|
DenseVector([18.0])
|
|
>>> model.selectedFeatures
|
|
[2]
|
|
>>> chiSqSelectorPath = temp_path + "/chi-sq-selector"
|
|
>>> selector.save(chiSqSelectorPath)
|
|
>>> loadedSelector = ChiSqSelector.load(chiSqSelectorPath)
|
|
>>> loadedSelector.getNumTopFeatures() == selector.getNumTopFeatures()
|
|
True
|
|
>>> modelPath = temp_path + "/chi-sq-selector-model"
|
|
>>> model.save(modelPath)
|
|
>>> loadedModel = ChiSqSelectorModel.load(modelPath)
|
|
>>> loadedModel.selectedFeatures == model.selectedFeatures
|
|
True
|
|
|
|
.. versionadded:: 2.0.0
|
|
"""
|
|
|
|
selectorType = Param(Params._dummy(), "selectorType",
|
|
"The selector type of the ChisqSelector. " +
|
|
"Supported options: numTopFeatures (default), percentile, fpr, fdr, fwe.",
|
|
typeConverter=TypeConverters.toString)
|
|
|
|
numTopFeatures = \
|
|
Param(Params._dummy(), "numTopFeatures",
|
|
"Number of features that selector will select, ordered by ascending p-value. " +
|
|
"If the number of features is < numTopFeatures, then this will select " +
|
|
"all features.", typeConverter=TypeConverters.toInt)
|
|
|
|
percentile = Param(Params._dummy(), "percentile", "Percentile of features that selector " +
|
|
"will select, ordered by ascending p-value.",
|
|
typeConverter=TypeConverters.toFloat)
|
|
|
|
fpr = Param(Params._dummy(), "fpr", "The highest p-value for features to be kept.",
|
|
typeConverter=TypeConverters.toFloat)
|
|
|
|
fdr = Param(Params._dummy(), "fdr", "The upper bound of the expected false discovery rate.",
|
|
typeConverter=TypeConverters.toFloat)
|
|
|
|
fwe = Param(Params._dummy(), "fwe", "The upper bound of the expected family-wise error rate.",
|
|
typeConverter=TypeConverters.toFloat)
|
|
|
|
@keyword_only
|
|
def __init__(self, numTopFeatures=50, featuresCol="features", outputCol=None,
|
|
labelCol="label", selectorType="numTopFeatures", percentile=0.1, fpr=0.05,
|
|
fdr=0.05, fwe=0.05):
|
|
"""
|
|
__init__(self, numTopFeatures=50, featuresCol="features", outputCol=None, \
|
|
labelCol="label", selectorType="numTopFeatures", percentile=0.1, fpr=0.05, \
|
|
fdr=0.05, fwe=0.05)
|
|
"""
|
|
super(ChiSqSelector, self).__init__()
|
|
self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.ChiSqSelector", self.uid)
|
|
self._setDefault(numTopFeatures=50, selectorType="numTopFeatures", percentile=0.1,
|
|
fpr=0.05, fdr=0.05, fwe=0.05)
|
|
kwargs = self._input_kwargs
|
|
self.setParams(**kwargs)
|
|
|
|
@keyword_only
|
|
@since("2.0.0")
|
|
def setParams(self, numTopFeatures=50, featuresCol="features", outputCol=None,
|
|
labelCol="labels", selectorType="numTopFeatures", percentile=0.1, fpr=0.05,
|
|
fdr=0.05, fwe=0.05):
|
|
"""
|
|
setParams(self, numTopFeatures=50, featuresCol="features", outputCol=None, \
|
|
labelCol="labels", selectorType="numTopFeatures", percentile=0.1, fpr=0.05, \
|
|
fdr=0.05, fwe=0.05)
|
|
Sets params for this ChiSqSelector.
|
|
"""
|
|
kwargs = self._input_kwargs
|
|
return self._set(**kwargs)
|
|
|
|
@since("2.1.0")
|
|
def setSelectorType(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`selectorType`.
|
|
"""
|
|
return self._set(selectorType=value)
|
|
|
|
@since("2.1.0")
|
|
def getSelectorType(self):
|
|
"""
|
|
Gets the value of selectorType or its default value.
|
|
"""
|
|
return self.getOrDefault(self.selectorType)
|
|
|
|
@since("2.0.0")
|
|
def setNumTopFeatures(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`numTopFeatures`.
|
|
Only applicable when selectorType = "numTopFeatures".
|
|
"""
|
|
return self._set(numTopFeatures=value)
|
|
|
|
@since("2.0.0")
|
|
def getNumTopFeatures(self):
|
|
"""
|
|
Gets the value of numTopFeatures or its default value.
|
|
"""
|
|
return self.getOrDefault(self.numTopFeatures)
|
|
|
|
@since("2.1.0")
|
|
def setPercentile(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`percentile`.
|
|
Only applicable when selectorType = "percentile".
|
|
"""
|
|
return self._set(percentile=value)
|
|
|
|
@since("2.1.0")
|
|
def getPercentile(self):
|
|
"""
|
|
Gets the value of percentile or its default value.
|
|
"""
|
|
return self.getOrDefault(self.percentile)
|
|
|
|
@since("2.1.0")
|
|
def setFpr(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`fpr`.
|
|
Only applicable when selectorType = "fpr".
|
|
"""
|
|
return self._set(fpr=value)
|
|
|
|
@since("2.1.0")
|
|
def getFpr(self):
|
|
"""
|
|
Gets the value of fpr or its default value.
|
|
"""
|
|
return self.getOrDefault(self.fpr)
|
|
|
|
@since("2.2.0")
|
|
def setFdr(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`fdr`.
|
|
Only applicable when selectorType = "fdr".
|
|
"""
|
|
return self._set(fdr=value)
|
|
|
|
@since("2.2.0")
|
|
def getFdr(self):
|
|
"""
|
|
Gets the value of fdr or its default value.
|
|
"""
|
|
return self.getOrDefault(self.fdr)
|
|
|
|
@since("2.2.0")
|
|
def setFwe(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`fwe`.
|
|
Only applicable when selectorType = "fwe".
|
|
"""
|
|
return self._set(fwe=value)
|
|
|
|
@since("2.2.0")
|
|
def getFwe(self):
|
|
"""
|
|
Gets the value of fwe or its default value.
|
|
"""
|
|
return self.getOrDefault(self.fwe)
|
|
|
|
def _create_model(self, java_model):
|
|
return ChiSqSelectorModel(java_model)
|
|
|
|
|
|
class ChiSqSelectorModel(JavaModel, JavaMLReadable, JavaMLWritable):
|
|
"""
|
|
Model fitted by :py:class:`ChiSqSelector`.
|
|
|
|
.. versionadded:: 2.0.0
|
|
"""
|
|
|
|
@property
|
|
@since("2.0.0")
|
|
def selectedFeatures(self):
|
|
"""
|
|
List of indices to select (filter).
|
|
"""
|
|
return self._call_java("selectedFeatures")
|
|
|
|
|
|
@inherit_doc
|
|
class VectorSizeHint(JavaTransformer, HasInputCol, HasHandleInvalid, JavaMLReadable,
|
|
JavaMLWritable):
|
|
"""
|
|
A feature transformer that adds size information to the metadata of a vector column.
|
|
VectorAssembler needs size information for its input columns and cannot be used on streaming
|
|
dataframes without this metadata.
|
|
|
|
.. note:: VectorSizeHint modifies `inputCol` to include size metadata and does not have an
|
|
outputCol.
|
|
|
|
>>> from pyspark.ml.linalg import Vectors
|
|
>>> from pyspark.ml import Pipeline, PipelineModel
|
|
>>> data = [(Vectors.dense([1., 2., 3.]), 4.)]
|
|
>>> df = spark.createDataFrame(data, ["vector", "float"])
|
|
>>>
|
|
>>> sizeHint = VectorSizeHint(inputCol="vector", size=3, handleInvalid="skip")
|
|
>>> vecAssembler = VectorAssembler(inputCols=["vector", "float"], outputCol="assembled")
|
|
>>> pipeline = Pipeline(stages=[sizeHint, vecAssembler])
|
|
>>>
|
|
>>> pipelineModel = pipeline.fit(df)
|
|
>>> pipelineModel.transform(df).head().assembled
|
|
DenseVector([1.0, 2.0, 3.0, 4.0])
|
|
>>> vectorSizeHintPath = temp_path + "/vector-size-hint-pipeline"
|
|
>>> pipelineModel.save(vectorSizeHintPath)
|
|
>>> loadedPipeline = PipelineModel.load(vectorSizeHintPath)
|
|
>>> loaded = loadedPipeline.transform(df).head().assembled
|
|
>>> expected = pipelineModel.transform(df).head().assembled
|
|
>>> loaded == expected
|
|
True
|
|
|
|
.. versionadded:: 2.3.0
|
|
"""
|
|
|
|
size = Param(Params._dummy(), "size", "Size of vectors in column.",
|
|
typeConverter=TypeConverters.toInt)
|
|
|
|
handleInvalid = Param(Params._dummy(), "handleInvalid",
|
|
"How to handle invalid vectors in inputCol. Invalid vectors include "
|
|
"nulls and vectors with the wrong size. The options are `skip` (filter "
|
|
"out rows with invalid vectors), `error` (throw an error) and "
|
|
"`optimistic` (do not check the vector size, and keep all rows). "
|
|
"`error` by default.",
|
|
TypeConverters.toString)
|
|
|
|
@keyword_only
|
|
def __init__(self, inputCol=None, size=None, handleInvalid="error"):
|
|
"""
|
|
__init__(self, inputCol=None, size=None, handleInvalid="error")
|
|
"""
|
|
super(VectorSizeHint, self).__init__()
|
|
self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.VectorSizeHint", self.uid)
|
|
self._setDefault(handleInvalid="error")
|
|
self.setParams(**self._input_kwargs)
|
|
|
|
@keyword_only
|
|
@since("2.3.0")
|
|
def setParams(self, inputCol=None, size=None, handleInvalid="error"):
|
|
"""
|
|
setParams(self, inputCol=None, size=None, handleInvalid="error")
|
|
Sets params for this VectorSizeHint.
|
|
"""
|
|
kwargs = self._input_kwargs
|
|
return self._set(**kwargs)
|
|
|
|
@since("2.3.0")
|
|
def getSize(self):
|
|
""" Gets size param, the size of vectors in `inputCol`."""
|
|
return self.getOrDefault(self.size)
|
|
|
|
@since("2.3.0")
|
|
def setSize(self, value):
|
|
""" Sets size param, the size of vectors in `inputCol`."""
|
|
return self._set(size=value)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
import doctest
|
|
import tempfile
|
|
|
|
import pyspark.ml.feature
|
|
from pyspark.sql import Row, SparkSession
|
|
|
|
globs = globals().copy()
|
|
features = pyspark.ml.feature.__dict__.copy()
|
|
globs.update(features)
|
|
|
|
# The small batch size here ensures that we see multiple batches,
|
|
# even in these small test examples:
|
|
spark = SparkSession.builder\
|
|
.master("local[2]")\
|
|
.appName("ml.feature tests")\
|
|
.getOrCreate()
|
|
sc = spark.sparkContext
|
|
globs['sc'] = sc
|
|
globs['spark'] = spark
|
|
testData = sc.parallelize([Row(id=0, label="a"), Row(id=1, label="b"),
|
|
Row(id=2, label="c"), Row(id=3, label="a"),
|
|
Row(id=4, label="a"), Row(id=5, label="c")], 2)
|
|
globs['stringIndDf'] = spark.createDataFrame(testData)
|
|
temp_path = tempfile.mkdtemp()
|
|
globs['temp_path'] = temp_path
|
|
try:
|
|
(failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
|
|
spark.stop()
|
|
finally:
|
|
from shutil import rmtree
|
|
try:
|
|
rmtree(temp_path)
|
|
except OSError:
|
|
pass
|
|
if failure_count:
|
|
sys.exit(-1)
|