How to use JohnSnowLabs NLP Spell correction module NorvigSweetingModel?












0














I was going through the JohnSnowLabs SpellChecker here.



I found the Norvig's algorithm implementation there, and the example section has just the following two lines:



import com.johnsnowlabs.nlp.annotator.NorvigSweetingModel
NorvigSweetingModel.pretrained()


Can anyone please help me on how to apply this pretrained model on my dataframe (df)below for spell correcting the "names" column.



+----------------+---+------------+
| names|age| color|
+----------------+---+------------+
| [abc, cde]| 19| red, abc|
|[eefg, efa, efb]|192|efg, efz efz|
+----------------+---+------------+


I have tried to do it as follows:



val schk = NorvigSweetingModel.pretrained().setInputCols("names").setOutputCol("Corrected")

val cdf = schk.transform(df)


But the above code gave me the following error:



java.lang.IllegalArgumentException: requirement failed: Wrong or missing inputCols annotators in SPELL_a1f11bacb851. Received inputCols: names. Make sure such columns have following annotator types: token
at scala.Predef$.require(Predef.scala:224)
at com.johnsnowlabs.nlp.AnnotatorModel.transform(AnnotatorModel.scala:51)
... 49 elided


Thanks.










share|improve this question





























    0














    I was going through the JohnSnowLabs SpellChecker here.



    I found the Norvig's algorithm implementation there, and the example section has just the following two lines:



    import com.johnsnowlabs.nlp.annotator.NorvigSweetingModel
    NorvigSweetingModel.pretrained()


    Can anyone please help me on how to apply this pretrained model on my dataframe (df)below for spell correcting the "names" column.



    +----------------+---+------------+
    | names|age| color|
    +----------------+---+------------+
    | [abc, cde]| 19| red, abc|
    |[eefg, efa, efb]|192|efg, efz efz|
    +----------------+---+------------+


    I have tried to do it as follows:



    val schk = NorvigSweetingModel.pretrained().setInputCols("names").setOutputCol("Corrected")

    val cdf = schk.transform(df)


    But the above code gave me the following error:



    java.lang.IllegalArgumentException: requirement failed: Wrong or missing inputCols annotators in SPELL_a1f11bacb851. Received inputCols: names. Make sure such columns have following annotator types: token
    at scala.Predef$.require(Predef.scala:224)
    at com.johnsnowlabs.nlp.AnnotatorModel.transform(AnnotatorModel.scala:51)
    ... 49 elided


    Thanks.










    share|improve this question



























      0












      0








      0







      I was going through the JohnSnowLabs SpellChecker here.



      I found the Norvig's algorithm implementation there, and the example section has just the following two lines:



      import com.johnsnowlabs.nlp.annotator.NorvigSweetingModel
      NorvigSweetingModel.pretrained()


      Can anyone please help me on how to apply this pretrained model on my dataframe (df)below for spell correcting the "names" column.



      +----------------+---+------------+
      | names|age| color|
      +----------------+---+------------+
      | [abc, cde]| 19| red, abc|
      |[eefg, efa, efb]|192|efg, efz efz|
      +----------------+---+------------+


      I have tried to do it as follows:



      val schk = NorvigSweetingModel.pretrained().setInputCols("names").setOutputCol("Corrected")

      val cdf = schk.transform(df)


      But the above code gave me the following error:



      java.lang.IllegalArgumentException: requirement failed: Wrong or missing inputCols annotators in SPELL_a1f11bacb851. Received inputCols: names. Make sure such columns have following annotator types: token
      at scala.Predef$.require(Predef.scala:224)
      at com.johnsnowlabs.nlp.AnnotatorModel.transform(AnnotatorModel.scala:51)
      ... 49 elided


      Thanks.










      share|improve this question















      I was going through the JohnSnowLabs SpellChecker here.



      I found the Norvig's algorithm implementation there, and the example section has just the following two lines:



      import com.johnsnowlabs.nlp.annotator.NorvigSweetingModel
      NorvigSweetingModel.pretrained()


      Can anyone please help me on how to apply this pretrained model on my dataframe (df)below for spell correcting the "names" column.



      +----------------+---+------------+
      | names|age| color|
      +----------------+---+------------+
      | [abc, cde]| 19| red, abc|
      |[eefg, efa, efb]|192|efg, efz efz|
      +----------------+---+------------+


      I have tried to do it as follows:



      val schk = NorvigSweetingModel.pretrained().setInputCols("names").setOutputCol("Corrected")

      val cdf = schk.transform(df)


      But the above code gave me the following error:



      java.lang.IllegalArgumentException: requirement failed: Wrong or missing inputCols annotators in SPELL_a1f11bacb851. Received inputCols: names. Make sure such columns have following annotator types: token
      at scala.Predef$.require(Predef.scala:224)
      at com.johnsnowlabs.nlp.AnnotatorModel.transform(AnnotatorModel.scala:51)
      ... 49 elided


      Thanks.







      scala apache-spark nlp apache-spark-ml johnsnowlabs-spark-nlp






      share|improve this question















      share|improve this question













      share|improve this question




      share|improve this question








      edited Nov 28 '18 at 5:13









      Community

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      asked Nov 21 '18 at 18:15









      user3243499user3243499

      74611126




      74611126
























          1 Answer
          1






          active

          oldest

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          1














          spark-nlp are designed to be used in its own specific pipelines and input columns for different transformers have to include special metadata.



          The exception already tells you that input to the NorvigSweetingModel should be tokenized:




          Make sure such columns have following annotator types: token




          If I am not mistaken, at minimum you'll have assemble documents and tokenized here.



          import com.johnsnowlabs.nlp.DocumentAssembler
          import com.johnsnowlabs.nlp.annotator.NorvigSweetingModel
          import com.johnsnowlabs.nlp.annotators.Tokenizer
          import org.apache.spark.ml.Pipeline

          val df = Seq(Seq("abc", "cde"), Seq("eefg", "efa", "efb")).toDF("names")

          val nlpPipeline = new Pipeline().setStages(Array(
          new DocumentAssembler().setInputCol("names").setOutputCol("document"),
          new Tokenizer().setInputCols("document").setOutputCol("tokens"),
          NorvigSweetingModel.pretrained().setInputCols("tokens").setOutputCol("corrected")
          ))


          A Pipeline like this, can be applied on your data with small adjustment - input data has to be string not array<string>*:



          val result = df
          .transform(_.withColumn("names", concat_ws(" ", $"names")))
          .transform(df => nlpPipeline.fit(df).transform(df))
          result.show()




          +------------+--------------------+--------------------+--------------------+
          | names| document| tokens| corrected|
          +------------+--------------------+--------------------+--------------------+
          | abc cde|[[document, 0, 6,...|[[token, 0, 2, ab...|[[token, 0, 2, ab...|
          |eefg efa efb|[[document, 0, 11...|[[token, 0, 3, ee...|[[token, 0, 3, ee...|
          +------------+--------------------+--------------------+--------------------+


          If you want an output that can be exported you should extend your Pipeline with Finisher.



          import com.johnsnowlabs.nlp.Finisher

          new Finisher().setInputCols("corrected").transform(result).show




           +------------+------------------+
          | names|finished_corrected|
          +------------+------------------+
          | abc cde| [abc, cde]|
          |eefg efa efb| [eefg, efa, efb]|
          +------------+------------------+




          * According to the docs DocumentAssembler




          can read either a String column or an Array[String]




          but it doesn't look like it works in practice in 1.7.3:



          df.transform(df => nlpPipeline.fit(df).transform(df)).show()




          org.apache.spark.sql.AnalysisException: cannot resolve 'UDF(names)' due to data type mismatch: argument 1 requires string type, however, '`names`' is of array<string> type.;;
          'Project [names#62, UDF(names#62) AS document#343]
          +- AnalysisBarrier
          +- Project [value#60 AS names#62]
          +- LocalRelation [value#60]





          share|improve this answer























          • How to get the spell corrected values. Values under "corrected" comes as [[token, 0, 3, eefg, [sentence -> 1]], [token, 5, 7, efa, [sentence -> 1]], [token, 9, 11, efb, [sentence -> 1]]]
            – user3243499
            Nov 21 '18 at 19:21










          • Does each of this list items, like [sentence ->1], have any standard structure/meaning?
            – user3243499
            Nov 21 '18 at 19:23










          • @user3243499 How to get the spell corrected values - Please check the Finisher part.
            – user10465355
            Nov 21 '18 at 20:18










          • Does each of this list items, like [sentence ->1], have any standard structure/meaning? - it is metadata. It is map<string,string> so structure is not fixed, but in this case it contains information about the sentence form the document.
            – user10465355
            Nov 21 '18 at 20:21











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          1 Answer
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          1 Answer
          1






          active

          oldest

          votes









          active

          oldest

          votes






          active

          oldest

          votes









          1














          spark-nlp are designed to be used in its own specific pipelines and input columns for different transformers have to include special metadata.



          The exception already tells you that input to the NorvigSweetingModel should be tokenized:




          Make sure such columns have following annotator types: token




          If I am not mistaken, at minimum you'll have assemble documents and tokenized here.



          import com.johnsnowlabs.nlp.DocumentAssembler
          import com.johnsnowlabs.nlp.annotator.NorvigSweetingModel
          import com.johnsnowlabs.nlp.annotators.Tokenizer
          import org.apache.spark.ml.Pipeline

          val df = Seq(Seq("abc", "cde"), Seq("eefg", "efa", "efb")).toDF("names")

          val nlpPipeline = new Pipeline().setStages(Array(
          new DocumentAssembler().setInputCol("names").setOutputCol("document"),
          new Tokenizer().setInputCols("document").setOutputCol("tokens"),
          NorvigSweetingModel.pretrained().setInputCols("tokens").setOutputCol("corrected")
          ))


          A Pipeline like this, can be applied on your data with small adjustment - input data has to be string not array<string>*:



          val result = df
          .transform(_.withColumn("names", concat_ws(" ", $"names")))
          .transform(df => nlpPipeline.fit(df).transform(df))
          result.show()




          +------------+--------------------+--------------------+--------------------+
          | names| document| tokens| corrected|
          +------------+--------------------+--------------------+--------------------+
          | abc cde|[[document, 0, 6,...|[[token, 0, 2, ab...|[[token, 0, 2, ab...|
          |eefg efa efb|[[document, 0, 11...|[[token, 0, 3, ee...|[[token, 0, 3, ee...|
          +------------+--------------------+--------------------+--------------------+


          If you want an output that can be exported you should extend your Pipeline with Finisher.



          import com.johnsnowlabs.nlp.Finisher

          new Finisher().setInputCols("corrected").transform(result).show




           +------------+------------------+
          | names|finished_corrected|
          +------------+------------------+
          | abc cde| [abc, cde]|
          |eefg efa efb| [eefg, efa, efb]|
          +------------+------------------+




          * According to the docs DocumentAssembler




          can read either a String column or an Array[String]




          but it doesn't look like it works in practice in 1.7.3:



          df.transform(df => nlpPipeline.fit(df).transform(df)).show()




          org.apache.spark.sql.AnalysisException: cannot resolve 'UDF(names)' due to data type mismatch: argument 1 requires string type, however, '`names`' is of array<string> type.;;
          'Project [names#62, UDF(names#62) AS document#343]
          +- AnalysisBarrier
          +- Project [value#60 AS names#62]
          +- LocalRelation [value#60]





          share|improve this answer























          • How to get the spell corrected values. Values under "corrected" comes as [[token, 0, 3, eefg, [sentence -> 1]], [token, 5, 7, efa, [sentence -> 1]], [token, 9, 11, efb, [sentence -> 1]]]
            – user3243499
            Nov 21 '18 at 19:21










          • Does each of this list items, like [sentence ->1], have any standard structure/meaning?
            – user3243499
            Nov 21 '18 at 19:23










          • @user3243499 How to get the spell corrected values - Please check the Finisher part.
            – user10465355
            Nov 21 '18 at 20:18










          • Does each of this list items, like [sentence ->1], have any standard structure/meaning? - it is metadata. It is map<string,string> so structure is not fixed, but in this case it contains information about the sentence form the document.
            – user10465355
            Nov 21 '18 at 20:21
















          1














          spark-nlp are designed to be used in its own specific pipelines and input columns for different transformers have to include special metadata.



          The exception already tells you that input to the NorvigSweetingModel should be tokenized:




          Make sure such columns have following annotator types: token




          If I am not mistaken, at minimum you'll have assemble documents and tokenized here.



          import com.johnsnowlabs.nlp.DocumentAssembler
          import com.johnsnowlabs.nlp.annotator.NorvigSweetingModel
          import com.johnsnowlabs.nlp.annotators.Tokenizer
          import org.apache.spark.ml.Pipeline

          val df = Seq(Seq("abc", "cde"), Seq("eefg", "efa", "efb")).toDF("names")

          val nlpPipeline = new Pipeline().setStages(Array(
          new DocumentAssembler().setInputCol("names").setOutputCol("document"),
          new Tokenizer().setInputCols("document").setOutputCol("tokens"),
          NorvigSweetingModel.pretrained().setInputCols("tokens").setOutputCol("corrected")
          ))


          A Pipeline like this, can be applied on your data with small adjustment - input data has to be string not array<string>*:



          val result = df
          .transform(_.withColumn("names", concat_ws(" ", $"names")))
          .transform(df => nlpPipeline.fit(df).transform(df))
          result.show()




          +------------+--------------------+--------------------+--------------------+
          | names| document| tokens| corrected|
          +------------+--------------------+--------------------+--------------------+
          | abc cde|[[document, 0, 6,...|[[token, 0, 2, ab...|[[token, 0, 2, ab...|
          |eefg efa efb|[[document, 0, 11...|[[token, 0, 3, ee...|[[token, 0, 3, ee...|
          +------------+--------------------+--------------------+--------------------+


          If you want an output that can be exported you should extend your Pipeline with Finisher.



          import com.johnsnowlabs.nlp.Finisher

          new Finisher().setInputCols("corrected").transform(result).show




           +------------+------------------+
          | names|finished_corrected|
          +------------+------------------+
          | abc cde| [abc, cde]|
          |eefg efa efb| [eefg, efa, efb]|
          +------------+------------------+




          * According to the docs DocumentAssembler




          can read either a String column or an Array[String]




          but it doesn't look like it works in practice in 1.7.3:



          df.transform(df => nlpPipeline.fit(df).transform(df)).show()




          org.apache.spark.sql.AnalysisException: cannot resolve 'UDF(names)' due to data type mismatch: argument 1 requires string type, however, '`names`' is of array<string> type.;;
          'Project [names#62, UDF(names#62) AS document#343]
          +- AnalysisBarrier
          +- Project [value#60 AS names#62]
          +- LocalRelation [value#60]





          share|improve this answer























          • How to get the spell corrected values. Values under "corrected" comes as [[token, 0, 3, eefg, [sentence -> 1]], [token, 5, 7, efa, [sentence -> 1]], [token, 9, 11, efb, [sentence -> 1]]]
            – user3243499
            Nov 21 '18 at 19:21










          • Does each of this list items, like [sentence ->1], have any standard structure/meaning?
            – user3243499
            Nov 21 '18 at 19:23










          • @user3243499 How to get the spell corrected values - Please check the Finisher part.
            – user10465355
            Nov 21 '18 at 20:18










          • Does each of this list items, like [sentence ->1], have any standard structure/meaning? - it is metadata. It is map<string,string> so structure is not fixed, but in this case it contains information about the sentence form the document.
            – user10465355
            Nov 21 '18 at 20:21














          1












          1








          1






          spark-nlp are designed to be used in its own specific pipelines and input columns for different transformers have to include special metadata.



          The exception already tells you that input to the NorvigSweetingModel should be tokenized:




          Make sure such columns have following annotator types: token




          If I am not mistaken, at minimum you'll have assemble documents and tokenized here.



          import com.johnsnowlabs.nlp.DocumentAssembler
          import com.johnsnowlabs.nlp.annotator.NorvigSweetingModel
          import com.johnsnowlabs.nlp.annotators.Tokenizer
          import org.apache.spark.ml.Pipeline

          val df = Seq(Seq("abc", "cde"), Seq("eefg", "efa", "efb")).toDF("names")

          val nlpPipeline = new Pipeline().setStages(Array(
          new DocumentAssembler().setInputCol("names").setOutputCol("document"),
          new Tokenizer().setInputCols("document").setOutputCol("tokens"),
          NorvigSweetingModel.pretrained().setInputCols("tokens").setOutputCol("corrected")
          ))


          A Pipeline like this, can be applied on your data with small adjustment - input data has to be string not array<string>*:



          val result = df
          .transform(_.withColumn("names", concat_ws(" ", $"names")))
          .transform(df => nlpPipeline.fit(df).transform(df))
          result.show()




          +------------+--------------------+--------------------+--------------------+
          | names| document| tokens| corrected|
          +------------+--------------------+--------------------+--------------------+
          | abc cde|[[document, 0, 6,...|[[token, 0, 2, ab...|[[token, 0, 2, ab...|
          |eefg efa efb|[[document, 0, 11...|[[token, 0, 3, ee...|[[token, 0, 3, ee...|
          +------------+--------------------+--------------------+--------------------+


          If you want an output that can be exported you should extend your Pipeline with Finisher.



          import com.johnsnowlabs.nlp.Finisher

          new Finisher().setInputCols("corrected").transform(result).show




           +------------+------------------+
          | names|finished_corrected|
          +------------+------------------+
          | abc cde| [abc, cde]|
          |eefg efa efb| [eefg, efa, efb]|
          +------------+------------------+




          * According to the docs DocumentAssembler




          can read either a String column or an Array[String]




          but it doesn't look like it works in practice in 1.7.3:



          df.transform(df => nlpPipeline.fit(df).transform(df)).show()




          org.apache.spark.sql.AnalysisException: cannot resolve 'UDF(names)' due to data type mismatch: argument 1 requires string type, however, '`names`' is of array<string> type.;;
          'Project [names#62, UDF(names#62) AS document#343]
          +- AnalysisBarrier
          +- Project [value#60 AS names#62]
          +- LocalRelation [value#60]





          share|improve this answer














          spark-nlp are designed to be used in its own specific pipelines and input columns for different transformers have to include special metadata.



          The exception already tells you that input to the NorvigSweetingModel should be tokenized:




          Make sure such columns have following annotator types: token




          If I am not mistaken, at minimum you'll have assemble documents and tokenized here.



          import com.johnsnowlabs.nlp.DocumentAssembler
          import com.johnsnowlabs.nlp.annotator.NorvigSweetingModel
          import com.johnsnowlabs.nlp.annotators.Tokenizer
          import org.apache.spark.ml.Pipeline

          val df = Seq(Seq("abc", "cde"), Seq("eefg", "efa", "efb")).toDF("names")

          val nlpPipeline = new Pipeline().setStages(Array(
          new DocumentAssembler().setInputCol("names").setOutputCol("document"),
          new Tokenizer().setInputCols("document").setOutputCol("tokens"),
          NorvigSweetingModel.pretrained().setInputCols("tokens").setOutputCol("corrected")
          ))


          A Pipeline like this, can be applied on your data with small adjustment - input data has to be string not array<string>*:



          val result = df
          .transform(_.withColumn("names", concat_ws(" ", $"names")))
          .transform(df => nlpPipeline.fit(df).transform(df))
          result.show()




          +------------+--------------------+--------------------+--------------------+
          | names| document| tokens| corrected|
          +------------+--------------------+--------------------+--------------------+
          | abc cde|[[document, 0, 6,...|[[token, 0, 2, ab...|[[token, 0, 2, ab...|
          |eefg efa efb|[[document, 0, 11...|[[token, 0, 3, ee...|[[token, 0, 3, ee...|
          +------------+--------------------+--------------------+--------------------+


          If you want an output that can be exported you should extend your Pipeline with Finisher.



          import com.johnsnowlabs.nlp.Finisher

          new Finisher().setInputCols("corrected").transform(result).show




           +------------+------------------+
          | names|finished_corrected|
          +------------+------------------+
          | abc cde| [abc, cde]|
          |eefg efa efb| [eefg, efa, efb]|
          +------------+------------------+




          * According to the docs DocumentAssembler




          can read either a String column or an Array[String]




          but it doesn't look like it works in practice in 1.7.3:



          df.transform(df => nlpPipeline.fit(df).transform(df)).show()




          org.apache.spark.sql.AnalysisException: cannot resolve 'UDF(names)' due to data type mismatch: argument 1 requires string type, however, '`names`' is of array<string> type.;;
          'Project [names#62, UDF(names#62) AS document#343]
          +- AnalysisBarrier
          +- Project [value#60 AS names#62]
          +- LocalRelation [value#60]






          share|improve this answer














          share|improve this answer



          share|improve this answer








          edited Nov 21 '18 at 23:26

























          answered Nov 21 '18 at 19:04









          user10465355user10465355

          1,3781413




          1,3781413












          • How to get the spell corrected values. Values under "corrected" comes as [[token, 0, 3, eefg, [sentence -> 1]], [token, 5, 7, efa, [sentence -> 1]], [token, 9, 11, efb, [sentence -> 1]]]
            – user3243499
            Nov 21 '18 at 19:21










          • Does each of this list items, like [sentence ->1], have any standard structure/meaning?
            – user3243499
            Nov 21 '18 at 19:23










          • @user3243499 How to get the spell corrected values - Please check the Finisher part.
            – user10465355
            Nov 21 '18 at 20:18










          • Does each of this list items, like [sentence ->1], have any standard structure/meaning? - it is metadata. It is map<string,string> so structure is not fixed, but in this case it contains information about the sentence form the document.
            – user10465355
            Nov 21 '18 at 20:21


















          • How to get the spell corrected values. Values under "corrected" comes as [[token, 0, 3, eefg, [sentence -> 1]], [token, 5, 7, efa, [sentence -> 1]], [token, 9, 11, efb, [sentence -> 1]]]
            – user3243499
            Nov 21 '18 at 19:21










          • Does each of this list items, like [sentence ->1], have any standard structure/meaning?
            – user3243499
            Nov 21 '18 at 19:23










          • @user3243499 How to get the spell corrected values - Please check the Finisher part.
            – user10465355
            Nov 21 '18 at 20:18










          • Does each of this list items, like [sentence ->1], have any standard structure/meaning? - it is metadata. It is map<string,string> so structure is not fixed, but in this case it contains information about the sentence form the document.
            – user10465355
            Nov 21 '18 at 20:21
















          How to get the spell corrected values. Values under "corrected" comes as [[token, 0, 3, eefg, [sentence -> 1]], [token, 5, 7, efa, [sentence -> 1]], [token, 9, 11, efb, [sentence -> 1]]]
          – user3243499
          Nov 21 '18 at 19:21




          How to get the spell corrected values. Values under "corrected" comes as [[token, 0, 3, eefg, [sentence -> 1]], [token, 5, 7, efa, [sentence -> 1]], [token, 9, 11, efb, [sentence -> 1]]]
          – user3243499
          Nov 21 '18 at 19:21












          Does each of this list items, like [sentence ->1], have any standard structure/meaning?
          – user3243499
          Nov 21 '18 at 19:23




          Does each of this list items, like [sentence ->1], have any standard structure/meaning?
          – user3243499
          Nov 21 '18 at 19:23












          @user3243499 How to get the spell corrected values - Please check the Finisher part.
          – user10465355
          Nov 21 '18 at 20:18




          @user3243499 How to get the spell corrected values - Please check the Finisher part.
          – user10465355
          Nov 21 '18 at 20:18












          Does each of this list items, like [sentence ->1], have any standard structure/meaning? - it is metadata. It is map<string,string> so structure is not fixed, but in this case it contains information about the sentence form the document.
          – user10465355
          Nov 21 '18 at 20:21




          Does each of this list items, like [sentence ->1], have any standard structure/meaning? - it is metadata. It is map<string,string> so structure is not fixed, but in this case it contains information about the sentence form the document.
          – user10465355
          Nov 21 '18 at 20:21


















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