lib/xlearn/model.rb in xlearn-0.1.0 vs lib/xlearn/model.rb in xlearn-0.1.1

@@ -18,31 +18,34 @@
 
       set_params(options)
     end
 
     def fit(x, y = nil, eval_set: nil)
-      if x.is_a?(String)
-        check_call FFI.XLearnSetTrain(@handle, x)
-        check_call FFI.XLearnSetBool(@handle, "from_file", true)
-      else
-        train_set = DMatrix.new(x, label: y)
-        check_call FFI.XLearnSetDMatrix(@handle, "train", train_set)
-        check_call FFI.XLearnSetBool(@handle, "from_file", false)
-      end
+      @model_path = nil
+      partial_fit(x, y, eval_set: eval_set)
+    end
 
+    def partial_fit(x, y = nil, eval_set: nil)
+      check_call FFI.XLearnSetPreModel(@handle, @model_path || "")
+
+      set_train_set(x, y)
+
       if eval_set
         if eval_set.is_a?(String)
           check_call FFI.XLearnSetValidate(@handle, eval_set)
         else
           valid_set = DMatrix.new(x, label: y)
           check_call FFI.XLearnSetDMatrix(@handle, "validate", valid_set)
         end
       end
 
-      # TODO unlink in finalizer
-      @model_file = Tempfile.new("xlearn")
+      @txt_file ||= create_tempfile
+      check_call FFI.XLearnSetTXTModel(@handle, @txt_file.path)
+
+      @model_file ||= create_tempfile
       check_call FFI.XLearnFit(@handle, @model_file.path)
+      @model_path = @model_file.path
     end
 
     def predict(x, out_path: nil)
       if x.is_a?(String)
         check_call FFI.XLearnSetTest(@handle, x)
@@ -61,28 +64,76 @@
         check_call FFI.XLearnPredictForMat(@handle, @model_file.path, length, out_arr)
         out_arr.read_pointer.read_array_of_float(length.read_uint64)
       end
     end
 
+    def cv(x, y = nil, folds: nil)
+      set_params(fold: folds) if folds
+      set_train_set(x, y)
+      check_call FFI.XLearnCV(@handle)
+    end
+
     def save_model(path)
       raise Error, "Not trained" unless @model_file
       FileUtils.cp(@model_file.path, path)
     end
 
+    def save_txt(path)
+      raise Error, "Not trained" unless @txt_file
+      FileUtils.cp(@txt_file.path, path)
+    end
+
     def load_model(path)
-      @model_file ||= Tempfile.new("xlearn")
+      @model_file ||= create_tempfile
       # TODO ensure tempfile is still cleaned up
       FileUtils.cp(path, @model_file.path)
     end
 
+    def bias_term
+      read_txt do |line|
+        return line.split(":").last.to_f if line.start_with?("bias:")
+      end
+    end
+
+    def linear_term
+      term = []
+      read_txt do |line|
+        if line.start_with?("i_")
+          term << line.split(":").last.to_f
+        elsif line.start_with?("v_")
+          break
+        end
+      end
+      term
+    end
+
     def self.finalize(pointer)
       # must use proc instead of stabby lambda
       proc { FFI.XLearnHandleFree(pointer) }
     end
 
+    def self.finalize_file(file)
+      # must use proc instead of stabby lambda
+      proc do
+        file.close
+        file.unlink
+      end
+    end
+
     private
 
+    def set_train_set(x, y)
+      if x.is_a?(String)
+        check_call FFI.XLearnSetTrain(@handle, x)
+        check_call FFI.XLearnSetBool(@handle, "from_file", true)
+      else
+        train_set = DMatrix.new(x, label: y)
+        check_call FFI.XLearnSetDMatrix(@handle, "train", train_set)
+        check_call FFI.XLearnSetBool(@handle, "from_file", false)
+      end
+    end
+
     def set_params(params)
       params.each do |k, v|
         k = k.to_s
         ret =
           case k
@@ -97,8 +148,22 @@
           else
             raise ArgumentError, "Invalid parameter: #{k}"
           end
         check_call ret
       end
+    end
+
+    def read_txt
+      if @txt_file
+        File.foreach(@txt_file.path) do |line|
+          yield line
+        end
+      end
+    end
+
+    def create_tempfile
+      file = Tempfile.new("xlearn")
+      ObjectSpace.define_finalizer(self, self.class.finalize_file(file))
+      file
     end
   end
 end