[jira] [Assigned] (SPARK-13969) Extend input format that feature hashing can handle
[ https://issues.apache.org/jira/browse/SPARK-13969?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Nick Pentreath reassigned SPARK-13969: -- Assignee: Nick Pentreath > Extend input format that feature hashing can handle > --- > > Key: SPARK-13969 > URL: https://issues.apache.org/jira/browse/SPARK-13969 > Project: Spark > Issue Type: Sub-task > Components: ML, MLlib >Reporter: Nick Pentreath >Assignee: Nick Pentreath >Priority: Minor > Fix For: 2.3.0 > > > Currently {{HashingTF}} works like {{CountVectorizer}} (the equivalent in > scikit-learn is {{HashingVectorizer}}). That is, it works on a sequence of > strings and computes term frequencies. > The use cases for feature hashing extend to arbitrary feature values (binary, > count or real-valued). For example, scikit-learn's {{FeatureHasher}} can > accept a sequence of (feature_name, value) pairs (e.g. a map, list). In this > way, feature hashing can operate as both "one-hot encoder" and "vector > assembler" at the same time. > Investigate adding a more generic feature hasher (that in turn can be used by > {{HashingTF}}). -- This message was sent by Atlassian JIRA (v6.4.14#64029) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Assigned] (SPARK-13969) Extend input format that feature hashing can handle
[ https://issues.apache.org/jira/browse/SPARK-13969?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Apache Spark reassigned SPARK-13969: Assignee: (was: Apache Spark) > Extend input format that feature hashing can handle > --- > > Key: SPARK-13969 > URL: https://issues.apache.org/jira/browse/SPARK-13969 > Project: Spark > Issue Type: Sub-task > Components: ML, MLlib >Reporter: Nick Pentreath >Priority: Minor > > Currently {{HashingTF}} works like {{CountVectorizer}} (the equivalent in > scikit-learn is {{HashingVectorizer}}). That is, it works on a sequence of > strings and computes term frequencies. > The use cases for feature hashing extend to arbitrary feature values (binary, > count or real-valued). For example, scikit-learn's {{FeatureHasher}} can > accept a sequence of (feature_name, value) pairs (e.g. a map, list). In this > way, feature hashing can operate as both "one-hot encoder" and "vector > assembler" at the same time. > Investigate adding a more generic feature hasher (that in turn can be used by > {{HashingTF}}). -- This message was sent by Atlassian JIRA (v6.4.14#64029) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Assigned] (SPARK-13969) Extend input format that feature hashing can handle
[ https://issues.apache.org/jira/browse/SPARK-13969?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Apache Spark reassigned SPARK-13969: Assignee: Apache Spark > Extend input format that feature hashing can handle > --- > > Key: SPARK-13969 > URL: https://issues.apache.org/jira/browse/SPARK-13969 > Project: Spark > Issue Type: Sub-task > Components: ML, MLlib >Reporter: Nick Pentreath >Assignee: Apache Spark >Priority: Minor > > Currently {{HashingTF}} works like {{CountVectorizer}} (the equivalent in > scikit-learn is {{HashingVectorizer}}). That is, it works on a sequence of > strings and computes term frequencies. > The use cases for feature hashing extend to arbitrary feature values (binary, > count or real-valued). For example, scikit-learn's {{FeatureHasher}} can > accept a sequence of (feature_name, value) pairs (e.g. a map, list). In this > way, feature hashing can operate as both "one-hot encoder" and "vector > assembler" at the same time. > Investigate adding a more generic feature hasher (that in turn can be used by > {{HashingTF}}). -- This message was sent by Atlassian JIRA (v6.4.14#64029) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org