Commits

Aleš Erjavec committed 42dac8d

Added new style widget meta descriptions.

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Files changed (8)

orangecontrib/multitarget/widgets/OWBinaryRelevance.py

 from OWWidget import *
 import OWGUI
 
+NAME = "Binary Relevance"
+DESCRIPTION = "Binary relevance learner wrapper"
+LONG_DESCRIPTION = """
+Wrap an ordinary input learner instance in a Binary Relevance wrapper.
+"""
+PRIORITY = 1000
+KEYWORDS = ["multitarget", "binary", "relevance", "wrapper"]
+ICON = "icons/BinaryRelevance.svg"
+
+INPUTS = [("Data", Orange.data.Table, "set_data"),
+          ("Base Learner", Orange.classification.Learner, "set_learner")]
+
+OUTPUTS = [("Learner", Orange.classification.Learner),
+           ("Classifier", Orange.classification.Classifier)]
+
+REPLACES = ["_multitarget.widgets.OWBinaryRelevance.OWBinaryRelevance"]
+
 
 class OWBinaryRelevance(OWWidget):
     settingsList = ["name"]

orangecontrib/multitarget/widgets/OWClassifierChain.py

 from OWWidget import *
 import OWGUI
 
+NAME = "Classifier Chain"
+DESCRIPTION = """
+Chain normal single target classifiers to support multi-target problems.
+"""
+CATEGORY = "Multitarget"
+PRIORITY = 1100
+KEYWORDS = ["wrapper", "multitarget", "chain"]
+ICON = "icons/ClassifierChain.svg"
+
+INPUTS = [("Data", Orange.data.Table, "set_data"),
+          ("Base Learner", Orange.classification.Learner, "set_learner")]
+
+OUTPUTS = [("Learner", Orange.classification.Learner),
+           ("Classifier", Orange.classification.Classifier)]
+
+REPLACES = ["_multitarget.widgets.OWClassifierChain.OWClassifierChain"]
+
 
 class OWClassifierChain(OWWidget):
     settingsList = ["name", "actual_values"]

orangecontrib/multitarget/widgets/OWClusteringRandomForest.py

 from OWWidget import *
 import OWGUI
 
+
+NAME = "Clustering Random Forest"
+DESCRIPTION = """
+Clustering random forest learner/classifier for multi-target classification.
+"""
+CATEGORY = "Multitarget"
+PRIORITY = 110
+ICON = "icons/ClusteringRandomForest.png"
+
+INPUTS = [("Data", Orange.data.Table, "set_data"),
+          ("Preprocess", PreprocessedLearner, "set_preprocessor")]
+
+OUTPUTS = [("Learner", Orange.classification.Learner),
+           ("Classifier", Orange.classification.Classifier)]
+
+REPLACES = ["_multitarget.widgets.OWClusteringRandomForest.OWClusteringRandomForest"]
+
 class OWClusteringRandomForest(OWWidget):
     settingsList = ["name", "trees", "min_instances", "min_majority",
                     "max_depth", "min_MSE", "method"]

orangecontrib/multitarget/widgets/OWClusteringTree.py

 from OWWidget import *
 import OWGUI
 
+
+NAME = "Clustering Tree"
+DESCRIPTION = """
+Classification tree learner/classifier for multi-target classification.
+"""
+CATEGORY = "Multitarget"
+PRIORITY = 100
+ICON = "icons/ClusteringTree.svg"
+
+KEYWORDS = ["tree", "multitarget"]
+
+INPUTS = [("Data", Orange.data.Table, "set_data"),
+          ("Preprocess", PreprocessedLearner, "set_preprocessor")]
+
+OUTPUTS = [("Learner", Orange.classification.Learner),
+           ("Classifier", Orange.classification.Classifier)]
+
+REPLACES = ["_multitarget.widgets.OWClusteringTree.OWClusteringTree"]
+
+
 class OWClusteringTree(OWWidget):
     settingsList = ["name", "min_instances", "min_majority",
                     "max_depth", "min_MSE", "method"]

orangecontrib/multitarget/widgets/OWEnsembleClassifierChain.py

 import OWGUI
 
 
+NAME = "Ensemble Classifier Chain"
+DESCRIPTION = "Train an ensemble of chain classifiers"
+CATEGORY = "Multitarget"
+PRIORITY = 1200
+ICON = "icons/EnsembleClassifierChain.svg"
+KEYWORDS = ["wrapper", "multitarget", "chain", "ensemble"]
+
+INPUTS = [("Data", Orange.data.Table, "set_data"),
+          ("Base Learner", Orange.classification.Learner, "set_learner")]
+
+OUTPUTS = [("Learner", Orange.classification.Learner),
+           ("Classifier", Orange.classification.Classifier)]
+
+REPLACES = ["_multitarget.widgets.OWEnsembleClassifierChain.OWEnsembleClassifierChain"]
+
+
 class OWEnsembleClassifierChain(OWWidget):
     settingsList = ["name", "n_chains", "sample_size", "actual_values"]
 

orangecontrib/multitarget/widgets/OWNeuralNetwork.py

 from OWWidget import *
 import OWGUI
 
+
+NAME = "Neural Network"
+DESCRIPTION = """
+Neural network learner/classifier supporting multi-target problems.
+"""
+CATEGORY = "Multitarget"
+PRIORITY = 20
+KEYWORDS = ["neural", "network", "multitarget"]
+ICON = "icons/NeuralNetwork.svg"
+
+INPUTS = [("Data", Orange.data.Table, "set_data"),
+          ("Preprocess", PreprocessedLearner, "set_preprocessor")]
+
+OUTPUTS = [("Learner", Orange.classification.Learner),
+           ("Classifier", Orange.classification.Classifier)]
+
+REPLACES = ["_multitarget.widgets.OWNeuralNetwork.OWNeuralNetwork"]
+
+
 class OWNeuralNetwork(OWWidget):
     settingsList = ["name", "n_mid", "reg_fact", "max_iter"]
 

orangecontrib/multitarget/widgets/OWPLSClassifier.py

 from orngWrap import PreprocessedLearner
 
 
+NAME = "PLS Classification"
+DESCRIPTION = "Partial Least Squares Classification"
+CATEGORY = "Multitarget"
+PRIORITY = 160
+ICON = "icons/PLSClassification.png"
+
+INPUTS = [("Data", Orange.data.Table, "set_data"),
+          ("Preprocess", PreprocessedLearner, "set_preprocessor")]
+
+OUTPUTS = [("Learner", Orange.classification.Learner),
+           ("Classifier", Orange.classification.Classifier)]
+
+REPLACES = ["_multitarget.widgets.OWPLSClassifier.OWPLSClassifier"]
+
+
 class OWPLSClassifier(OWWidget):
     settings = ["name", "n_comp"]
 

orangecontrib/multitarget/widgets/OWTestMultitargetLearners.py

 """
 <icon>icons/TestMTLearners.png</icon>
 <name>Test Multitarget Learners</name>
-</description>A widget for scoring the performance of learning algorithms
+<description>A widget for scoring the performance of learning algorithms
 on multitarget domains</description>
 <category>Multitarget</category>
 <priority>2000</priority>
 from OWTestLearners import OWTestLearners, Score, Learner
 
 
+NAME = "Test Multitarget Learners"
+DESCRIPTION = """
+A widget for scoring the performance of learning algorithms on
+multi-target domains.
+"""
+CATEGORTY = "Multitarget"
+PRIORITY = 2000
+ICON = "icons/TestMTLearners.png"
+
+INPUTS = [("Data", Orange.data.Table, "setData", Default),
+          ("Separate Test Data", Orange.data.Table, "setTestData"),
+          ("Learner", Orange.core.Learner, "setLearner", Default + Multiple),
+          ("Preprocess", PreprocessedLearner, "setPreprocessor")]
+
+OUTPUTS = [("Evaluation Results", Orange.evaluation.testing.ExperimentResults)]
+
+REPLACES = ["_multitarget.widgets.OWTestMultitargetLearners.OWTestMultitargetLearners"]
+
+
 def avg_logloss(res):
     return Orange.multitarget.scoring.mt_average_score(
                 res, Orange.evaluation.scoring.logloss)