mob-ai commented on a change in pull request #26124: [SPARK-29224][ML]Implement
Factorization Machines as a ml-pipeline component
URL: https://github.com/apache/spark/pull/26124#discussion_r356380671
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File path: python/pyspark/ml/regression.py
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@@ -2126,6 +2127,172 @@ def __repr__(self):
return self._call_java("toString")
+@inherit_doc
+class FactorizationMachines(JavaPredictor, HasMaxIter, HasStepSize, HasTol,
HasSolver, HasLoss,
+ JavaMLWritable, JavaMLReadable):
+ """
+ Factorization Machines.
+
+ loss Supports:
+
+ * logisticLoss (default)
+ * squaredError
+
+ solver Supports:
+
+ * gd (normal mini-batch gradient descent)
+ * adamW (default)
+
+ >>> from pyspark.ml.linalg import Vectors
+ >>> from pyspark.ml.regression import FactorizationMachines
+ >>> df = spark.createDataFrame([
+ ... (1.0, Vectors.dense(1.0)),
+ ... (0.0, Vectors.sparse(1, [], []))], ["label", "features"])
+ >>> fm = FactorizationMachines(numFactors=2, )
+ >>> model = fm.fit(df)
+ >>> test0 = spark.createDataFrame([
+ ... (Vectors.dense(-1.0),),
+ ... (Vectors.dense(0.5),),
+ ... (Vectors.dense(1.0),),
+ ... (Vectors.dense(2.0),)], ["features"])
+ >>> model.transform(test0).show(10, False)
+ +--------+----------------------+
+ |features|prediction |
+ +--------+----------------------+
+ |[-1.0] |1.7219239347644947E-10|
+ |[0.5] |0.612429917023823 |
+ |[1.0] |0.99969782036162 |
+ |[2.0] |0.9999999999310394 |
+ +--------+----------------------+
+
+ .. versionadded:: 3.0.0
+ """
+
+ numFactors = Param(Params._dummy(), "numFactors", "dimensionality of the
factor vectors, " +
+ "which are used to get pairwise interactions between
variables",
Review comment:
It is resolved. The doc strings should start with a Capital (dimensionality
of .. -> Dimensionality of ...).
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