[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]>

python/pyspark/ml/classification.py

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

python/pyspark/ml/clustering.py

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

python/pyspark/ml/feature.py

@@ -337,6 +337,8 @@ class BucketedRandomProjectionLSH(_LSH, _BucketedRandomProjectionLSHParams,
     >>> model = brp.fit(df)
     >>> model.getBucketLength()
     1.0
+    >>> model.setOutputCol("hashes")
+    BucketedRandomProjectionLSHModel...
     >>> model.transform(df).head()
     Row(id=0, features=DenseVector([-1.0, -1.0]), hashes=[DenseVector([-1.0])])
     >>> data2 = [(4, Vectors.dense([2.0, 2.0 ]),),
@@ -733,6 +735,8 @@ class CountVectorizer(JavaEstimator, _CountVectorizerParams, JavaMLReadable, Jav
     >>> cv.setOutputCol("vectors")
     CountVectorizer...
     >>> model = cv.fit(df)
+    >>> model.setInputCol("raw")
+    CountVectorizerModel...
     >>> model.transform(df).show(truncate=False)
     +-----+---------------+-------------------------+
     |label|raw            |vectors                  |
@@ -1345,6 +1349,8 @@ class IDF(JavaEstimator, _IDFParams, JavaMLReadable, JavaMLWritable):
     >>> idf.setOutputCol("idf")
     IDF...
     >>> model = idf.fit(df)
+    >>> model.setOutputCol("idf")
+    IDFModel...
     >>> model.getMinDocFreq()
     3
     >>> model.idf
@@ -1519,6 +1525,8 @@ class Imputer(JavaEstimator, _ImputerParams, JavaMLReadable, JavaMLWritable):
     >>> imputer.getRelativeError()
     0.001
     >>> model = imputer.fit(df)
+    >>> model.setInputCols(["a", "b"])
+    ImputerModel...
     >>> model.getStrategy()
     'mean'
     >>> model.surrogateDF.show()
@@ -1810,7 +1818,7 @@ class MaxAbsScaler(JavaEstimator, _MaxAbsScalerParams, JavaMLReadable, JavaMLWri
     MaxAbsScaler...
     >>> model = maScaler.fit(df)
     >>> model.setOutputCol("scaledOutput")
-    MaxAbsScaler...
+    MaxAbsScalerModel...
     >>> model.transform(df).show()
     +-----+------------+
     |    a|scaledOutput|
@@ -1928,6 +1936,8 @@ class MinHashLSH(_LSH, HasInputCol, HasOutputCol, HasSeed, JavaMLReadable, JavaM
     >>> mh.setSeed(12345)
     MinHashLSH...
     >>> model = mh.fit(df)
+    >>> model.setInputCol("features")
+    MinHashLSHModel...
     >>> 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]),),
@@ -2056,7 +2066,7 @@ class MinMaxScaler(JavaEstimator, _MinMaxScalerParams, JavaMLReadable, JavaMLWri
     MinMaxScaler...
     >>> model = mmScaler.fit(df)
     >>> model.setOutputCol("scaledOutput")
-    MinMaxScaler...
+    MinMaxScalerModel...
     >>> model.originalMin
     DenseVector([0.0])
     >>> model.originalMax
@@ -2421,6 +2431,8 @@ class OneHotEncoder(JavaEstimator, _OneHotEncoderParams, JavaMLReadable, JavaMLW
     >>> ohe.setOutputCols(["output"])
     OneHotEncoder...
     >>> model = ohe.fit(df)
+    >>> model.setOutputCols(["output"])
+    OneHotEncoderModel...
     >>> model.getHandleInvalid()
     'error'
     >>> model.transform(df).head().output
@@ -2935,7 +2947,7 @@ class RobustScaler(JavaEstimator, _RobustScalerParams, JavaMLReadable, JavaMLWri
     RobustScaler...
     >>> model = scaler.fit(df)
     >>> model.setOutputCol("output")
-    RobustScaler...
+    RobustScalerModel...
     >>> model.median
     DenseVector([2.0, -2.0])
     >>> model.range
@@ -3330,7 +3342,7 @@ class StandardScaler(JavaEstimator, _StandardScalerParams, JavaMLReadable, JavaM
     >>> model.getInputCol()
     'a'
     >>> model.setOutputCol("output")
-    StandardScaler...
+    StandardScalerModel...
     >>> model.mean
     DenseVector([1.0])
     >>> model.std
@@ -3490,6 +3502,8 @@ class StringIndexer(JavaEstimator, _StringIndexerParams, JavaMLReadable, JavaMLW
     >>> stringIndexer.setHandleInvalid("error")
     StringIndexer...
     >>> model = stringIndexer.fit(stringIndDf)
+    >>> model.setHandleInvalid("error")
+    StringIndexerModel...
     >>> 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])
@@ -4166,7 +4180,7 @@ class VectorIndexer(JavaEstimator, _VectorIndexerParams, JavaMLReadable, JavaMLW
     >>> indexer.getHandleInvalid()
     'error'
     >>> model.setOutputCol("output")
-    VectorIndexer...
+    VectorIndexerModel...
     >>> model.transform(df).head().output
     DenseVector([1.0, 0.0])
     >>> model.numFeatures
@@ -4487,6 +4501,8 @@ class Word2Vec(JavaEstimator, _Word2VecParams, JavaMLReadable, JavaMLWritable):
     >>> model = word2Vec.fit(doc)
     >>> model.getMinCount()
     5
+    >>> model.setInputCol("sentence")
+    Word2VecModel...
     >>> model.getVectors().show()
     +----+--------------------+
     |word|              vector|
@@ -4714,7 +4730,7 @@ class PCA(JavaEstimator, _PCAParams, JavaMLReadable, JavaMLWritable):
     >>> model.getK()
     2
     >>> model.setOutputCol("output")
-    PCA...
+    PCAModel...
     >>> model.transform(df).collect()[0].output
     DenseVector([1.648..., -4.013...])
     >>> model.explainedVariance
@@ -5139,6 +5155,8 @@ class ChiSqSelector(JavaEstimator, _ChiSqSelectorParams, JavaMLReadable, JavaMLW
     >>> model = selector.fit(df)
     >>> model.getFeaturesCol()
     'features'
+    >>> model.setFeaturesCol("features")
+    ChiSqSelectorModel...
     >>> model.transform(df).head().selectedFeatures
     DenseVector([18.0])
     >>> model.selectedFeatures

python/pyspark/ml/fpm.py

@@ -166,7 +166,7 @@ class FPGrowth(JavaEstimator, _FPGrowthParams, JavaMLWritable, JavaMLReadable):
     >>> fp = FPGrowth(minSupport=0.2, minConfidence=0.7)
     >>> fpm = fp.fit(data)
     >>> fpm.setPredictionCol("newPrediction")
-    FPGrowth...
+    FPGrowthModel...
     >>> fpm.freqItemsets.show(5)
     +---------+----+
     |    items|freq|

python/pyspark/ml/recommendation.py

@@ -225,6 +225,8 @@ class ALS(JavaEstimator, _ALSParams, JavaMLWritable, JavaMLReadable):
     >>> model = als.fit(df)
     >>> model.getUserCol()
     'user'
+    >>> model.setUserCol("user")
+    ALSModel...
     >>> model.getItemCol()
     'item'
     >>> model.setPredictionCol("newPrediction")

python/pyspark/ml/regression.py

@@ -105,9 +105,9 @@ class LinearRegression(JavaPredictor, _LinearRegressionParams, JavaMLWritable, J
     LinearRegression...
     >>> model = lr.fit(df)
     >>> model.setFeaturesCol("features")
-    LinearRegression...
+    LinearRegressionModel...
     >>> model.setPredictionCol("newPrediction")
-    LinearRegression...
+    LinearRegressionModel...
     >>> model.getMaxIter()
     5
     >>> test0 = spark.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
@@ -591,7 +591,7 @@ class IsotonicRegression(JavaEstimator, _IsotonicRegressionParams, HasWeightCol,
     >>> ir = IsotonicRegression()
     >>> model = ir.fit(df)
     >>> model.setFeaturesCol("features")
-    IsotonicRegression...
+    IsotonicRegressionModel...
     >>> test0 = spark.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
     >>> model.transform(test0).head().prediction
     0.0
@@ -1546,7 +1546,7 @@ class AFTSurvivalRegression(JavaEstimator, _AFTSurvivalRegressionParams,
     >>> aftsr.clear(aftsr.maxIter)
     >>> model = aftsr.fit(df)
     >>> model.setFeaturesCol("features")
-    AFTSurvivalRegression...
+    AFTSurvivalRegressionModel...
     >>> model.predict(Vectors.dense(6.3))
     1.0
     >>> model.predictQuantiles(Vectors.dense(6.3))
@@ -1881,7 +1881,7 @@ class GeneralizedLinearRegression(JavaPredictor, _GeneralizedLinearRegressionPar
     >>> glr.clear(glr.maxIter)
     >>> model = glr.fit(df)
     >>> model.setFeaturesCol("features")
-    GeneralizedLinearRegression...
+    GeneralizedLinearRegressionModel...
     >>> model.getMaxIter()
     25
     >>> model.getAggregationDepth()

python/pyspark/ml/tree.py

@@ -56,9 +56,6 @@ def predictLeaf(self, value):
         """
         return self._call_java("predictLeaf", value)
 
-    def __repr__(self):
-        return self._call_java("toString")
-
 
 class _DecisionTreeParams(HasCheckpointInterval, HasSeed, HasWeightCol):
     """
@@ -208,9 +205,6 @@ def predictLeaf(self, value):
         """
         return self._call_java("predictLeaf", value)
 
-    def __repr__(self):
-        return self._call_java("toString")
-
 
 class _TreeEnsembleParams(_DecisionTreeParams):
     """

python/pyspark/ml/wrapper.py

@@ -372,6 +372,9 @@ def __init__(self, java_model=None):
 
             self._resetUid(java_model.uid())
 
+    def __repr__(self):
+        return self._call_java("toString")
+
 
 @inherit_doc
 class _JavaPredictorParams(HasLabelCol, HasFeaturesCol, HasPredictionCol):