Factorie 0.10.1 released

» by Sameer Singh on 09 Feb 2012

After a long delay, the latest version of Factorie has been released. Numerous major features have been added, and significant renames and refactoring has been performed.

• Executable Jar: factorie-0.10.1.jar
• Source files: factorie-0.10.1-src.tar.gz

Here’s an incomplete changelog:

New in version 0.10.1:

• Many renames, new features and refactors; the list below is partially complete.

• Initial support for sparse value iteration in factor/families

• Data representation for app.nlp like Tokens, ParseTrees, Spans, Sentences, etc.

• Initial version for POS, NER, within-doc coref for app.nlp

• Additional vectors that mix sparse and dense representations (SparseOuterVector) in factorie.la

• Added Families that represent sets of factors. Templates are a type of Family now.

• Initial support for MaxLikelihood and Piecewise Training using the new BP framework

• Added a more flexible, modular BP framework

• DiscreteVector and CategoricalVector

  The old names “DiscretesValue”, “DiscretesVariable”, etc were deemed too easily misread (easy to miss the little “s” in the middle) and have been renamed “DiscreteVectorValue”, “DiscreteVectorVariable”, etc.

• Factors independent of Templates

• Models independent of Templates

• Redesigned cc.factorie.generative package

New in version 0.10.0:

• Variable ‘value’ methods:

  All variables must now have a ‘value’ method with return type ‘this.Value’. By default this type is Any. If you want to override use the VarAndValueType trait, which sets the covariant types ‘VariableType’ and ‘ValueType’. ‘Value’ is magically defined from these to be psuedo-invariant.

  The canonical representation of DiscreteVariable (and CategoricalVariable) values used to be an Int. Now it is a DiscreteValue (or CategoricalValue) object, which is a wrapper around an integer (and its corresponding categorical value). These objects are created automatically in the DiscreteDomain (or CategoricalDomain), and are guaranteed to be unique for each integer value, and thus can be compared by pointer equality.

  For example, if ‘label’ is a CategoricalVariable[String] label.value is a CategoricalValue. label.intValue == label.value.index, is an integer label.categoryValue == label.value.category, is a String

• Discrete variables and vectors

  DiscreteValues has been renamed DiscretesValue. Similarily there are now classes DiscretesVariable, CategoricalsValue and CategoricalsVariable. These plural names refer to vector values and their variables. For example, CategoricalsVariable is a superclass of the BinaryFeatureVectorVariable.

  The singular DiscreteValue, DiscreteVariable, CategoricalValue and CategoricalVariable hold single values (i.e. which could be mapped to single integers), but are subclasses their plural counterparts, with values that are singleton vectors.

  The domain of the plural types (i.e. vectors, not necessarily singleton vectors) are DiscretesDomain and CategoricalsDomain. The length of these vectors are determined by an inner DiscreteDomain or CategoricalDomain. Hence to create a domain for vectors of length 10:

    new DiscretesDomain {
      val dimensionDomain = new DiscreteDomain { def count = 10 }
    }
  

• TrueSetting renamed to TargetValue

  Now that all variables have a ‘value’, the name ‘setting’ is deprecated. Also, “true” and “truth” were deemed confusable with boolean values, and are now deprecated. The preferred alternative is “target”. Hence, the “TrueSetting” trait has been renamed “TargetValue”, and various methods renamed: setToTruth => setToTarget valueIsTruth => valueIsTarget trueIntValue => targetIntValue

• Domains:

  Previously there was a one-to-one correspondence between variable classes and domains; the variable looked up its domain in a global hashtable whose keys were the variable classes. Furthermore Domain objects were often created for the user auto-magically. This scheme lacked flexibility and was sometimes confusing. The one-to-one correspondence has now been removed. The ‘domain’ method in Variable is now abstract. Some subclasses of Variable define this method, such as RealVariable; others still leave it abstract. For example, in subclasses of DiscreteVariable and CategoricalVariable you must define the ‘domain’ method. In these cases you must also create your domain objects explicitly. Thus we have sacrificed a little brevity for clarity and flexibility. Here is an example of typical code for creating class labels:

    object MyLabelDomain extends CategoricalDomain[String]
    class MyLabel(theValue:String) extends CategoricalVariable(theValue) {
      def domain = MyLabelDomain
    }
  

  or

    class MyLabel(theValue:String, val domain = MyLabelDomain) extends CategoricalVariable(theValue)
  

  The type argument for domains used to be the variable class; now it is the ‘ValueType’ type of the domain (and its variables).

  Templates now automatically gather the domains of the neighbor variables. VectorTemplates also gather the domains of their statistics values. [TODO: Discuss the dangers of this automatic mechanism and consider others mechanisms.]

• Template statistics:

  Previously the constructor arguments of Stat objects were Variables. They have now been changed to Variable values instead. Furthermore, whereas the old Template.statistics method took as arguments a list of variables, the new Template.statistics method takes a “Values” object, which is a simple Tuple-style case class containing variable values.

  For example, old code:

    new Template2[Label,Label] extends DotStatistics1[BooleanVariable] {
      def statistics(y1:Label, y2:Label) =
        Stat(new BooleanVariable(y1.intValue == y2.intValue)
    }
  

  might be re-written as:

    new Template2[Label,Label] extends DotStatistics1[BooleanValue] {
      def statistics(values:Values) = Stat(values._1 == values._2)
    }
  

• VectorTemplate

  VectorStatistics1, VectorStatistics2, VectorStatistics3 used to take VectorVar type arguments. They now take DiscretesValue type arguments. The method ‘statsize’ has been renmed ‘statisticsVectorLength’ for clarity.

• Generative modeling package

  The probability calculations and sampling routines are no longer implemented in the variable, but in templates instead. Each GeneratedVar must have a value “generativeTemplate” and a method “generativeFactor”. Many changes have been made to the generative modeling package, but they are not yet finished or usable. The code is being checked in now in order to facilitate others’ work on the undirected models.

New in Version 0.9.0:

Rudimentary optimize package includes ConjugateGradient and LimitedMemoryBFGS.

LogLinearMaximumLikelihood sets parameters by BFGS on likelihood gradient calculated by belief propagation on trees. Additional inference methods to come soon.

Belief propagation now works.

Variables no longer use their own “set” method to initialize their values. This means that if you are relying on “override def set” to do some coordination during object initialization, you must separately set up this coordination in your own constructors.

Rename Factor neighbor variables from “n1” to “_1” to better match Scala’s Tuples.

Support for generative models has been completely overhauled, and is now in its own separate package: cc.factorie.generative.

Many variables have been renamed to better match standard names in statistics, including EnumVariable => CategoricalVariable.