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[SPARK-29867][ML][PYTHON] Add __repr__ in Python ML Models
### What changes were proposed in this pull request? Add ```__repr__``` in Python ML Models ### Why are the changes needed? In Python ML Models, some of them have ```__repr__```, others don't. In the doctest, when calling Model.setXXX, some of the Models print out the xxxModel... correctly, some of them can't because of lacking the ```__repr__``` method. For example: ``` >>> gm = GaussianMixture(k=3, tol=0.0001, seed=10) >>> model = gm.fit(df) >>> model.setPredictionCol("newPrediction") GaussianMixture... ``` After the change, the above code will become the following: ``` >>> gm = GaussianMixture(k=3, tol=0.0001, seed=10) >>> model = gm.fit(df) >>> model.setPredictionCol("newPrediction") GaussianMixtureModel... ``` ### Does this PR introduce any user-facing change? Yes. ### How was this patch tested? doctest Closes apache#26489 from huaxingao/spark-29876. Authored-by: Huaxin Gao <[email protected]> Signed-off-by: Dongjoon Hyun <[email protected]>
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‎python/pyspark/ml/classification.py

Lines changed: 4 additions & 7 deletions
Original file line numberDiff line numberDiff line change
@@ -192,11 +192,11 @@ class LinearSVC(JavaClassifier, _LinearSVCParams, JavaMLWritable, JavaMLReadable
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0.01
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>>> model = svm.fit(df)
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>>> model.setPredictionCol("newPrediction")
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LinearSVC...
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LinearSVCModel...
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>>> model.getPredictionCol()
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'newPrediction'
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>>> model.setThreshold(0.5)
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LinearSVC...
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LinearSVCModel...
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>>> model.getThreshold()
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0.5
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>>> model.coefficients
@@ -812,9 +812,6 @@ def evaluate(self, dataset):
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java_blr_summary = self._call_java("evaluate", dataset)
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return BinaryLogisticRegressionSummary(java_blr_summary)
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815-
def __repr__(self):
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return self._call_java("toString")
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class LogisticRegressionSummary(JavaWrapper):
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"""
@@ -1921,7 +1918,7 @@ class NaiveBayes(JavaProbabilisticClassifier, _NaiveBayesParams, HasThresholds,
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>>> nb = NaiveBayes(smoothing=1.0, modelType="multinomial", weightCol="weight")
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>>> model = nb.fit(df)
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>>> model.setFeaturesCol("features")
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NaiveBayes_...
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NaiveBayesModel...
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>>> model.getSmoothing()
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1.0
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>>> model.pi
@@ -2114,7 +2111,7 @@ class MultilayerPerceptronClassifier(JavaProbabilisticClassifier, _MultilayerPer
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100
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>>> model = mlp.fit(df)
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>>> model.setFeaturesCol("features")
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MultilayerPerceptronClassifier...
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MultilayerPerceptronClassificationModel...
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>>> model.layers
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[2, 2, 2]
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>>> model.weights.size

‎python/pyspark/ml/clustering.py

Lines changed: 5 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -234,7 +234,7 @@ class GaussianMixture(JavaEstimator, _GaussianMixtureParams, JavaMLWritable, Jav
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>>> model.getFeaturesCol()
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'features'
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>>> model.setPredictionCol("newPrediction")
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GaussianMixture...
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GaussianMixtureModel...
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>>> model.predict(df.head().features)
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2
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>>> model.predictProbability(df.head().features)
@@ -532,7 +532,7 @@ class KMeans(JavaEstimator, _KMeansParams, JavaMLWritable, JavaMLReadable):
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>>> model.getDistanceMeasure()
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'euclidean'
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>>> model.setPredictionCol("newPrediction")
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KMeans...
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KMeansModel...
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>>> model.predict(df.head().features)
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0
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>>> centers = model.clusterCenters()
@@ -794,7 +794,7 @@ class BisectingKMeans(JavaEstimator, _BisectingKMeansParams, JavaMLWritable, Jav
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>>> model.getMaxIter()
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20
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>>> model.setPredictionCol("newPrediction")
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BisectingKMeans...
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BisectingKMeansModel...
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>>> model.predict(df.head().features)
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0
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>>> centers = model.clusterCenters()
@@ -1265,6 +1265,8 @@ class LDA(JavaEstimator, _LDAParams, JavaMLReadable, JavaMLWritable):
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10
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>>> lda.clear(lda.maxIter)
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>>> model = lda.fit(df)
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>>> model.setSeed(1)
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DistributedLDAModel...
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>>> model.getTopicDistributionCol()
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'topicDistribution'
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>>> model.isDistributed()

‎python/pyspark/ml/feature.py

Lines changed: 24 additions & 6 deletions
Original file line numberDiff line numberDiff line change
@@ -337,6 +337,8 @@ class BucketedRandomProjectionLSH(_LSH, _BucketedRandomProjectionLSHParams,
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>>> model = brp.fit(df)
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>>> model.getBucketLength()
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1.0
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>>> model.setOutputCol("hashes")
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BucketedRandomProjectionLSHModel...
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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 ]),),
@@ -733,6 +735,8 @@ class CountVectorizer(JavaEstimator, _CountVectorizerParams, JavaMLReadable, Jav
733735
>>> cv.setOutputCol("vectors")
734736
CountVectorizer...
735737
>>> model = cv.fit(df)
738+
>>> model.setInputCol("raw")
739+
CountVectorizerModel...
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>>> model.transform(df).show(truncate=False)
737741
+-----+---------------+-------------------------+
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|label|raw |vectors |
@@ -1345,6 +1349,8 @@ class IDF(JavaEstimator, _IDFParams, JavaMLReadable, JavaMLWritable):
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>>> idf.setOutputCol("idf")
13461350
IDF...
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>>> model = idf.fit(df)
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>>> model.setOutputCol("idf")
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IDFModel...
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>>> model.getMinDocFreq()
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3
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>>> model.idf
@@ -1519,6 +1525,8 @@ class Imputer(JavaEstimator, _ImputerParams, JavaMLReadable, JavaMLWritable):
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>>> imputer.getRelativeError()
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0.001
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>>> model = imputer.fit(df)
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>>> model.setInputCols(["a", "b"])
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ImputerModel...
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>>> model.getStrategy()
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'mean'
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>>> model.surrogateDF.show()
@@ -1810,7 +1818,7 @@ class MaxAbsScaler(JavaEstimator, _MaxAbsScalerParams, JavaMLReadable, JavaMLWri
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MaxAbsScaler...
18111819
>>> model = maScaler.fit(df)
18121820
>>> model.setOutputCol("scaledOutput")
1813-
MaxAbsScaler...
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MaxAbsScalerModel...
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>>> model.transform(df).show()
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+-----+------------+
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| a|scaledOutput|
@@ -1928,6 +1936,8 @@ class MinHashLSH(_LSH, HasInputCol, HasOutputCol, HasSeed, JavaMLReadable, JavaM
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>>> mh.setSeed(12345)
19291937
MinHashLSH...
19301938
>>> model = mh.fit(df)
1939+
>>> model.setInputCol("features")
1940+
MinHashLSHModel...
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>>> model.transform(df).head()
19321942
Row(id=0, features=SparseVector(6, {0: 1.0, 1: 1.0, 2: 1.0}), hashes=[DenseVector([6179668...
19331943
>>> data2 = [(3, Vectors.sparse(6, [1, 3, 5], [1.0, 1.0, 1.0]),),
@@ -2056,7 +2066,7 @@ class MinMaxScaler(JavaEstimator, _MinMaxScalerParams, JavaMLReadable, JavaMLWri
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MinMaxScaler...
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>>> model = mmScaler.fit(df)
20582068
>>> model.setOutputCol("scaledOutput")
2059-
MinMaxScaler...
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MinMaxScalerModel...
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>>> model.originalMin
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DenseVector([0.0])
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>>> model.originalMax
@@ -2421,6 +2431,8 @@ class OneHotEncoder(JavaEstimator, _OneHotEncoderParams, JavaMLReadable, JavaMLW
24212431
>>> ohe.setOutputCols(["output"])
24222432
OneHotEncoder...
24232433
>>> model = ohe.fit(df)
2434+
>>> model.setOutputCols(["output"])
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OneHotEncoderModel...
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>>> model.getHandleInvalid()
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'error'
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>>> model.transform(df).head().output
@@ -2935,7 +2947,7 @@ class RobustScaler(JavaEstimator, _RobustScalerParams, JavaMLReadable, JavaMLWri
29352947
RobustScaler...
29362948
>>> model = scaler.fit(df)
29372949
>>> model.setOutputCol("output")
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RobustScaler...
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RobustScalerModel...
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>>> model.median
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DenseVector([2.0, -2.0])
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>>> model.range
@@ -3330,7 +3342,7 @@ class StandardScaler(JavaEstimator, _StandardScalerParams, JavaMLReadable, JavaM
33303342
>>> model.getInputCol()
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'a'
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>>> model.setOutputCol("output")
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StandardScaler...
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StandardScalerModel...
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>>> model.mean
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DenseVector([1.0])
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>>> model.std
@@ -3490,6 +3502,8 @@ class StringIndexer(JavaEstimator, _StringIndexerParams, JavaMLReadable, JavaMLW
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>>> stringIndexer.setHandleInvalid("error")
34913503
StringIndexer...
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>>> model = stringIndexer.fit(stringIndDf)
3505+
>>> model.setHandleInvalid("error")
3506+
StringIndexerModel...
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>>> td = model.transform(stringIndDf)
34943508
>>> sorted(set([(i[0], i[1]) for i in td.select(td.id, td.indexed).collect()]),
34953509
... key=lambda x: x[0])
@@ -4166,7 +4180,7 @@ class VectorIndexer(JavaEstimator, _VectorIndexerParams, JavaMLReadable, JavaMLW
41664180
>>> indexer.getHandleInvalid()
41674181
'error'
41684182
>>> model.setOutputCol("output")
4169-
VectorIndexer...
4183+
VectorIndexerModel...
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>>> model.transform(df).head().output
41714185
DenseVector([1.0, 0.0])
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>>> model.numFeatures
@@ -4487,6 +4501,8 @@ class Word2Vec(JavaEstimator, _Word2VecParams, JavaMLReadable, JavaMLWritable):
44874501
>>> model = word2Vec.fit(doc)
44884502
>>> model.getMinCount()
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5
4504+
>>> model.setInputCol("sentence")
4505+
Word2VecModel...
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>>> model.getVectors().show()
44914507
+----+--------------------+
44924508
|word| vector|
@@ -4714,7 +4730,7 @@ class PCA(JavaEstimator, _PCAParams, JavaMLReadable, JavaMLWritable):
47144730
>>> model.getK()
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2
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>>> model.setOutputCol("output")
4717-
PCA...
4733+
PCAModel...
47184734
>>> model.transform(df).collect()[0].output
47194735
DenseVector([1.648..., -4.013...])
47204736
>>> model.explainedVariance
@@ -5139,6 +5155,8 @@ class ChiSqSelector(JavaEstimator, _ChiSqSelectorParams, JavaMLReadable, JavaMLW
51395155
>>> model = selector.fit(df)
51405156
>>> model.getFeaturesCol()
51415157
'features'
5158+
>>> model.setFeaturesCol("features")
5159+
ChiSqSelectorModel...
51425160
>>> model.transform(df).head().selectedFeatures
51435161
DenseVector([18.0])
51445162
>>> model.selectedFeatures

‎python/pyspark/ml/fpm.py

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -166,7 +166,7 @@ class FPGrowth(JavaEstimator, _FPGrowthParams, JavaMLWritable, JavaMLReadable):
166166
>>> fp = FPGrowth(minSupport=0.2, minConfidence=0.7)
167167
>>> fpm = fp.fit(data)
168168
>>> fpm.setPredictionCol("newPrediction")
169-
FPGrowth...
169+
FPGrowthModel...
170170
>>> fpm.freqItemsets.show(5)
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+---------+----+
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| items|freq|

‎python/pyspark/ml/recommendation.py

Lines changed: 2 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -225,6 +225,8 @@ class ALS(JavaEstimator, _ALSParams, JavaMLWritable, JavaMLReadable):
225225
>>> model = als.fit(df)
226226
>>> model.getUserCol()
227227
'user'
228+
>>> model.setUserCol("user")
229+
ALSModel...
228230
>>> model.getItemCol()
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'item'
230232
>>> model.setPredictionCol("newPrediction")

‎python/pyspark/ml/regression.py

Lines changed: 5 additions & 5 deletions
Original file line numberDiff line numberDiff line change
@@ -105,9 +105,9 @@ class LinearRegression(JavaPredictor, _LinearRegressionParams, JavaMLWritable, J
105105
LinearRegression...
106106
>>> model = lr.fit(df)
107107
>>> model.setFeaturesCol("features")
108-
LinearRegression...
108+
LinearRegressionModel...
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>>> model.setPredictionCol("newPrediction")
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LinearRegression...
110+
LinearRegressionModel...
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>>> model.getMaxIter()
112112
5
113113
>>> test0 = spark.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
@@ -591,7 +591,7 @@ class IsotonicRegression(JavaEstimator, _IsotonicRegressionParams, HasWeightCol,
591591
>>> ir = IsotonicRegression()
592592
>>> model = ir.fit(df)
593593
>>> model.setFeaturesCol("features")
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IsotonicRegression...
594+
IsotonicRegressionModel...
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>>> test0 = spark.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
596596
>>> model.transform(test0).head().prediction
597597
0.0
@@ -1546,7 +1546,7 @@ class AFTSurvivalRegression(JavaEstimator, _AFTSurvivalRegressionParams,
15461546
>>> aftsr.clear(aftsr.maxIter)
15471547
>>> model = aftsr.fit(df)
15481548
>>> model.setFeaturesCol("features")
1549-
AFTSurvivalRegression...
1549+
AFTSurvivalRegressionModel...
15501550
>>> model.predict(Vectors.dense(6.3))
15511551
1.0
15521552
>>> model.predictQuantiles(Vectors.dense(6.3))
@@ -1881,7 +1881,7 @@ class GeneralizedLinearRegression(JavaPredictor, _GeneralizedLinearRegressionPar
18811881
>>> glr.clear(glr.maxIter)
18821882
>>> model = glr.fit(df)
18831883
>>> model.setFeaturesCol("features")
1884-
GeneralizedLinearRegression...
1884+
GeneralizedLinearRegressionModel...
18851885
>>> model.getMaxIter()
18861886
25
18871887
>>> model.getAggregationDepth()

‎python/pyspark/ml/tree.py

Lines changed: 0 additions & 6 deletions
Original file line numberDiff line numberDiff line change
@@ -56,9 +56,6 @@ def predictLeaf(self, value):
5656
"""
5757
return self._call_java("predictLeaf", value)
5858

59-
def __repr__(self):
60-
return self._call_java("toString")
61-
6259

6360
class _DecisionTreeParams(HasCheckpointInterval, HasSeed, HasWeightCol):
6461
"""
@@ -208,9 +205,6 @@ def predictLeaf(self, value):
208205
"""
209206
return self._call_java("predictLeaf", value)
210207

211-
def __repr__(self):
212-
return self._call_java("toString")
213-
214208

215209
class _TreeEnsembleParams(_DecisionTreeParams):
216210
"""

‎python/pyspark/ml/wrapper.py

Lines changed: 3 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -372,6 +372,9 @@ def __init__(self, java_model=None):
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373373
self._resetUid(java_model.uid())
374374

375+
def __repr__(self):
376+
return self._call_java("toString")
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375378

376379
@inherit_doc
377380
class _JavaPredictorParams(HasLabelCol, HasFeaturesCol, HasPredictionCol):

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