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author | Yanbo Liang <ybliang8@gmail.com> | 2015-08-12 13:24:18 -0700 |
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committer | Joseph K. Bradley <joseph@databricks.com> | 2015-08-12 13:24:18 -0700 |
commit | 762bacc16ac5e74c8b05a7c1e3e367d1d1633cef (patch) | |
tree | da72f2717842672fcdbe092947284c0b4f009cf2 | |
parent | 60103ecd3d9c92709a5878be7ebd57012813ab48 (diff) | |
download | spark-762bacc16ac5e74c8b05a7c1e3e367d1d1633cef.tar.gz spark-762bacc16ac5e74c8b05a7c1e3e367d1d1633cef.tar.bz2 spark-762bacc16ac5e74c8b05a7c1e3e367d1d1633cef.zip |
[SPARK-9766] [ML] [PySpark] check and add miss docs for PySpark ML
Check and add miss docs for PySpark ML (this issue only check miss docs for o.a.s.ml not o.a.s.mllib).
Author: Yanbo Liang <ybliang8@gmail.com>
Closes #8059 from yanboliang/SPARK-9766.
-rw-r--r-- | python/pyspark/ml/classification.py | 12 | ||||
-rw-r--r-- | python/pyspark/ml/clustering.py | 4 | ||||
-rw-r--r-- | python/pyspark/ml/evaluation.py | 3 | ||||
-rw-r--r-- | python/pyspark/ml/feature.py | 9 |
4 files changed, 20 insertions, 8 deletions
diff --git a/python/pyspark/ml/classification.py b/python/pyspark/ml/classification.py index 5978d8f4d3..6702dce554 100644 --- a/python/pyspark/ml/classification.py +++ b/python/pyspark/ml/classification.py @@ -34,6 +34,7 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti HasRegParam, HasTol, HasProbabilityCol, HasRawPredictionCol): """ Logistic regression. + Currently, this class only supports binary classification. >>> from pyspark.sql import Row >>> from pyspark.mllib.linalg import Vectors @@ -96,8 +97,8 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti # is an L2 penalty. For alpha = 1, it is an L1 penalty. self.elasticNetParam = \ Param(self, "elasticNetParam", - "the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty " + - "is an L2 penalty. For alpha = 1, it is an L1 penalty.") + "the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, " + + "the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.") #: param for whether to fit an intercept term. self.fitIntercept = Param(self, "fitIntercept", "whether to fit an intercept term.") #: param for threshold in binary classification prediction, in range [0, 1]. @@ -656,6 +657,13 @@ class NaiveBayes(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, H HasRawPredictionCol): """ Naive Bayes Classifiers. + It supports both Multinomial and Bernoulli NB. Multinomial NB + (`http://nlp.stanford.edu/IR-book/html/htmledition/naive-bayes-text-classification-1.html`) + can handle finitely supported discrete data. For example, by converting documents into + TF-IDF vectors, it can be used for document classification. By making every vector a + binary (0/1) data, it can also be used as Bernoulli NB + (`http://nlp.stanford.edu/IR-book/html/htmledition/the-bernoulli-model-1.html`). + The input feature values must be nonnegative. >>> from pyspark.sql import Row >>> from pyspark.mllib.linalg import Vectors diff --git a/python/pyspark/ml/clustering.py b/python/pyspark/ml/clustering.py index b5e9b6549d..48338713a2 100644 --- a/python/pyspark/ml/clustering.py +++ b/python/pyspark/ml/clustering.py @@ -37,7 +37,9 @@ class KMeansModel(JavaModel): @inherit_doc class KMeans(JavaEstimator, HasFeaturesCol, HasMaxIter, HasSeed): """ - K-means Clustering + K-means clustering with support for multiple parallel runs and a k-means++ like initialization + mode (the k-means|| algorithm by Bahmani et al). When multiple concurrent runs are requested, + they are executed together with joint passes over the data for efficiency. >>> from pyspark.mllib.linalg import Vectors >>> data = [(Vectors.dense([0.0, 0.0]),), (Vectors.dense([1.0, 1.0]),), diff --git a/python/pyspark/ml/evaluation.py b/python/pyspark/ml/evaluation.py index 06e8093522..2734092575 100644 --- a/python/pyspark/ml/evaluation.py +++ b/python/pyspark/ml/evaluation.py @@ -23,7 +23,8 @@ from pyspark.ml.param.shared import HasLabelCol, HasPredictionCol, HasRawPredict from pyspark.ml.util import keyword_only from pyspark.mllib.common import inherit_doc -__all__ = ['Evaluator', 'BinaryClassificationEvaluator', 'RegressionEvaluator'] +__all__ = ['Evaluator', 'BinaryClassificationEvaluator', 'RegressionEvaluator', + 'MulticlassClassificationEvaluator'] @inherit_doc diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py index cb4dfa2129..535d553266 100644 --- a/python/pyspark/ml/feature.py +++ b/python/pyspark/ml/feature.py @@ -26,10 +26,11 @@ from pyspark.ml.wrapper import JavaEstimator, JavaModel, JavaTransformer from pyspark.mllib.common import inherit_doc from pyspark.mllib.linalg import _convert_to_vector -__all__ = ['Binarizer', 'HashingTF', 'IDF', 'IDFModel', 'NGram', 'Normalizer', 'OneHotEncoder', - 'PolynomialExpansion', 'RegexTokenizer', 'StandardScaler', 'StandardScalerModel', - 'StringIndexer', 'StringIndexerModel', 'Tokenizer', 'VectorAssembler', 'VectorIndexer', - 'Word2Vec', 'Word2VecModel', 'PCA', 'PCAModel', 'RFormula', 'RFormulaModel'] +__all__ = ['Binarizer', 'Bucketizer', 'HashingTF', 'IDF', 'IDFModel', 'NGram', 'Normalizer', + 'OneHotEncoder', 'PolynomialExpansion', 'RegexTokenizer', 'StandardScaler', + 'StandardScalerModel', 'StringIndexer', 'StringIndexerModel', 'Tokenizer', + 'VectorAssembler', 'VectorIndexer', 'Word2Vec', 'Word2VecModel', 'PCA', + 'PCAModel', 'RFormula', 'RFormulaModel'] @inherit_doc |