remore · October 4, 2016 14:10
diff --git a/svm_classification_using_cross_validation.rb b/svm_classification_using_cross_validation.rb
 skl = VirtualModule.new(:lang=>:python, :pkgs=>["sklearn"=>["datasets", "svm", "grid_search", "cross_validation"]])
 include skl
 iris = datasets.load_iris(:_)
 clf = grid_search.GridSearchCV(svm.LinearSVC(:_), {'C':[1, 3, 5],'loss':['hinge', 'squared_hinge']}, verbose:0)
 clf.fit(iris.data, iris.target)
 p "Best Parameters: #{best_params = clf.best_params_}" #"Best Parameters: {\"loss\"=>\"squared_hinge\", \"C\"=>1}"
 score = cross_validation.cross_val_score(svm.LinearSVC(loss:'squared_hinge', C:1), iris.data, iris.target, cv:5)
 p "Scores: #{[:mean,:min,:max,:std].map{|e| e.to_s + '=' + score.send(e, :_).to_s }.join(',')}" # "Scores: mean=0.9666666666666668,min=0.9,max=1.0,std=0.04216370213557838"
	skl = VirtualModule.new(:lang=>:python, :pkgs=>["sklearn"=>["datasets", "svm", "grid_search", "cross_validation"]])
	include skl
	iris = datasets.load_iris(:_)
	clf = grid_search.GridSearchCV(svm.LinearSVC(:_), {'C':[1, 3, 5],'loss':['hinge', 'squared_hinge']}, verbose:0)
	clf.fit(iris.data, iris.target)
	p "Best Parameters: #{best_params = clf.best_params_}" #"Best Parameters: {\"loss\"=>\"squared_hinge\", \"C\"=>1}"
	score = cross_validation.cross_val_score(svm.LinearSVC(loss:'squared_hinge', C:1), iris.data, iris.target, cv:5)
	p "Scores: #{[:mean,:min,:max,:std].map{\|e\| e.to_s + '=' + score.send(e, :_).to_s }.join(',')}" # "Scores: mean=0.9666666666666668,min=0.9,max=1.0,std=0.04216370213557838"