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[SPARK-11938][PySpark] Expose numFeatures in all ML PredictionModel for PySpark #13922

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[SPARK-11938][PySpark] Expose numFeatures in all ML PredictionModel for PySpark #13922

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8f2e5c6
[SPARK-11938] Expose numFeatures in all ML PredictionModel for PySpark
Lewuathe Apr 1, 2016
461b7c6
[SPARK-11938] Fix style
Lewuathe Apr 2, 2016
872d384
export numFeatures in ML PredictionModel
vectorijk Jun 27, 2016
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72 changes: 72 additions & 0 deletions python/pyspark/ml/classification.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -65,6 +65,8 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti
DenseVector([5.5...])
>>> model.intercept
-2.68...
>>> model.numFeatures
1
>>> test0 = sc.parallelize([Row(features=Vectors.dense(-1.0))]).toDF()
>>> result = model.transform(test0).head()
>>> result.prediction
Expand Down Expand Up @@ -92,6 +94,8 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti
True
>>> model.intercept == model2.intercept
True
>>> model.numFeatures == model2.numFeatures
True

.. versionadded:: 1.3.0
"""
Expand Down Expand Up @@ -239,6 +243,14 @@ def intercept(self):
"""
return self._call_java("intercept")

@property
@since("2.0.0")
def numFeatures(self):
"""
Number of features the model was trained on.
"""
return self._call_java("numFeatures")

@property
@since("2.0.0")
def summary(self):
Expand Down Expand Up @@ -524,6 +536,8 @@ class DecisionTreeClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred
1
>>> model.featureImportances
SparseVector(1, {0: 1.0})
>>> model.numFeatures
1
>>> print(model.toDebugString)
DecisionTreeClassificationModel (uid=...) of depth 1 with 3 nodes...
>>> test0 = spark.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
Expand All @@ -548,6 +562,8 @@ class DecisionTreeClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred
>>> model2 = DecisionTreeClassificationModel.load(model_path)
>>> model.featureImportances == model2.featureImportances
True
>>> model.numFeatures == model2.numFeatures
True

.. versionadded:: 1.4.0
"""
Expand Down Expand Up @@ -627,6 +643,14 @@ def featureImportances(self):
"""
return self._call_java("featureImportances")

@property
@since("2.0.0")
def numFeatures(self):
"""
Number of features the model was trained on.
"""
return self._call_java("numFeatures")


@inherit_doc
class RandomForestClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasSeed,
Expand Down Expand Up @@ -668,6 +692,8 @@ class RandomForestClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred
>>> test1 = spark.createDataFrame([(Vectors.sparse(1, [0], [1.0]),)], ["features"])
>>> model.transform(test1).head().prediction
1.0
>>> model.numFeatures
1
>>> model.trees
[DecisionTreeClassificationModel (uid=...) of depth..., DecisionTreeClassificationModel...]
>>> rfc_path = temp_path + "/rfc"
Expand All @@ -680,6 +706,8 @@ class RandomForestClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred
>>> model2 = RandomForestClassificationModel.load(model_path)
>>> model.featureImportances == model2.featureImportances
True
>>> model.numFeatures == model2.numFeatures
True

.. versionadded:: 1.4.0
"""
Expand Down Expand Up @@ -752,6 +780,14 @@ def featureImportances(self):
"""
return self._call_java("featureImportances")

@property
@since("2.0.0")
def numFeatures(self):
"""
Number of features the model was trained on.
"""
return self._call_java("numFeatures")

@property
@since("2.0.0")
def trees(self):
Expand Down Expand Up @@ -804,6 +840,8 @@ class GBTClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol
1.0
>>> model.totalNumNodes
15
>>> model.numFeatures
1
>>> print(model.toDebugString)
GBTClassificationModel (uid=...)...with 5 trees...
>>> gbtc_path = temp_path + "gbtc"
Expand All @@ -820,6 +858,8 @@ class GBTClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol
True
>>> model.trees
[DecisionTreeRegressionModel (uid=...) of depth..., DecisionTreeRegressionModel...]
>>> model.numFeatures == model2.numFeatures
True

.. versionadded:: 1.4.0
"""
Expand Down Expand Up @@ -907,6 +947,14 @@ def featureImportances(self):
"""
return self._call_java("featureImportances")

@property
@since("2.0.0")
def numFeatures(self):
"""
Number of features the model was trained on.
"""
return self._call_java("numFeatures")

@property
@since("2.0.0")
def trees(self):
Expand Down Expand Up @@ -952,6 +1000,8 @@ class NaiveBayes(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, H
>>> test1 = sc.parallelize([Row(features=Vectors.sparse(2, [0], [1.0]))]).toDF()
>>> model.transform(test1).head().prediction
1.0
>>> model.numFeatures
2
>>> nb_path = temp_path + "/nb"
>>> nb.save(nb_path)
>>> nb2 = NaiveBayes.load(nb_path)
Expand All @@ -969,6 +1019,8 @@ class NaiveBayes(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, H
>>> result = model3.transform(test0).head()
>>> result.prediction
0.0
>>> model.numFeatures == model2.numFeatures
True

.. versionadded:: 1.5.0
"""
Expand Down Expand Up @@ -1066,6 +1118,14 @@ def theta(self):
"""
return self._call_java("theta")

@property
@since("2.0.0")
def numFeatures(self):
"""
Number of features the model was trained on.
"""
return self._call_java("numFeatures")


@inherit_doc
class MultilayerPerceptronClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol,
Expand All @@ -1091,6 +1151,8 @@ class MultilayerPerceptronClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol,
[2, 2, 2]
>>> model.weights.size
12
>>> model.numFeatures
2
>>> testDF = spark.createDataFrame([
... (Vectors.dense([1.0, 0.0]),),
... (Vectors.dense([0.0, 0.0]),)], ["features"])
Expand Down Expand Up @@ -1120,6 +1182,8 @@ class MultilayerPerceptronClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol,
True
>>> model3.layers == model.layers
True
>>> model.numFeatures == model2.numFeatures
True

.. versionadded:: 1.6.0
"""
Expand Down Expand Up @@ -1267,6 +1331,14 @@ def weights(self):
"""
return self._call_java("weights")

@property
@since("2.0.0")
def numFeatures(self):
"""
Number of features the model was trained on.
"""
return self._call_java("numFeatures")


class OneVsRestParams(HasFeaturesCol, HasLabelCol, HasPredictionCol):
"""
Expand Down
48 changes: 48 additions & 0 deletions python/pyspark/ml/regression.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -71,6 +71,8 @@ class LinearRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPrediction
True
>>> abs(model.intercept - 0.0) < 0.001
True
>>> model.numFeatures
1
>>> test1 = spark.createDataFrame([(Vectors.sparse(1, [0], [1.0]),)], ["features"])
>>> abs(model.transform(test1).head().prediction - 1.0) < 0.001
True
Expand All @@ -90,6 +92,8 @@ class LinearRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPrediction
True
>>> model.intercept == model2.intercept
True
>>> model.numFeatures == model2.numFeatures
True

.. versionadded:: 1.4.0
"""
Expand Down Expand Up @@ -153,6 +157,14 @@ def intercept(self):
"""
return self._call_java("intercept")

@property
@since("2.0.0")
def numFeatures(self):
"""
Number of features the model was trained on.
"""
return self._call_java("numFeatures")

@property
@since("2.0.0")
def summary(self):
Expand Down Expand Up @@ -658,6 +670,8 @@ class DecisionTreeRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredi
3
>>> model.featureImportances
SparseVector(1, {0: 1.0})
>>> model.numFeatures
1
>>> test0 = spark.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
>>> model.transform(test0).head().prediction
0.0
Expand All @@ -678,6 +692,8 @@ class DecisionTreeRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredi
True
>>> model.transform(test1).head().variance
0.0
>>> model.numFeatures == model2.numFeatures
True

.. versionadded:: 1.4.0
"""
Expand Down Expand Up @@ -829,6 +845,14 @@ def featureImportances(self):
"""
return self._call_java("featureImportances")

@property
@since("2.0.0")
def numFeatures(self):
"""
Number of features the model was trained on.
"""
return self._call_java("numFeatures")


@inherit_doc
class RandomForestRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasSeed,
Expand All @@ -850,6 +874,8 @@ class RandomForestRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredi
>>> model = rf.fit(df)
>>> model.featureImportances
SparseVector(1, {0: 1.0})
>>> model.numFeatures
1
>>> allclose(model.treeWeights, [1.0, 1.0])
True
>>> test0 = spark.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
Expand All @@ -872,6 +898,8 @@ class RandomForestRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredi
>>> model2 = RandomForestRegressionModel.load(model_path)
>>> model.featureImportances == model2.featureImportances
True
>>> model.numFeatures == model2.numFeatures
True

.. versionadded:: 1.4.0
"""
Expand Down Expand Up @@ -951,6 +979,14 @@ def featureImportances(self):
"""
return self._call_java("featureImportances")

@property
@since("2.0.0")
def numFeatures(self):
"""
Number of features the model was trained on.
"""
return self._call_java("numFeatures")


@inherit_doc
class GBTRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter,
Expand All @@ -974,6 +1010,8 @@ class GBTRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol,
>>> model = gbt.fit(df)
>>> model.featureImportances
SparseVector(1, {0: 1.0})
>>> model.numFeatures
1
>>> allclose(model.treeWeights, [1.0, 0.1, 0.1, 0.1, 0.1])
True
>>> test0 = spark.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
Expand All @@ -996,6 +1034,8 @@ class GBTRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol,
True
>>> model.trees
[DecisionTreeRegressionModel (uid=...) of depth..., DecisionTreeRegressionModel...]
>>> model.numFeatures == model2.numFeatures
True

.. versionadded:: 1.4.0
"""
Expand Down Expand Up @@ -1087,6 +1127,14 @@ def featureImportances(self):
"""
return self._call_java("featureImportances")

@property
@since("2.0.0")
def numFeatures(self):
"""
Number of features the model was trained on.
"""
return self._call_java("numFeatures")

@property
@since("2.0.0")
def trees(self):
Expand Down