Python plot_cluster_assignments Examples

Example #1

File: clusterers.py Project: keypointt/python-weka-wrapper-examples

def main():
    """
    Just runs some example code.
    """

    # load a dataset
    iris_file = helper.get_data_dir() + os.sep + "iris.arff"
    helper.print_info("Loading dataset: " + iris_file)
    loader = Loader("weka.core.converters.ArffLoader")
    data = loader.load_file(iris_file)

    # remove class attribute
    data.delete_last_attribute()

    # build a clusterer and output model
    helper.print_title("Training SimpleKMeans clusterer")
    clusterer = Clusterer(classname="weka.clusterers.SimpleKMeans", options=["-N", "3"])
    clusterer.build_clusterer(data)
    print(clusterer)
    helper.print_info("Evaluating on data")
    evaluation = ClusterEvaluation()
    evaluation.set_model(clusterer)
    evaluation.test_model(data)
    print("# clusters: " + str(evaluation.num_clusters))
    print("log likelihood: " + str(evaluation.log_likelihood))
    print("cluster assignments:\n" + str(evaluation.cluster_assignments))
    plc.plot_cluster_assignments(evaluation, data, inst_no=True)

    # using a filtered clusterer
    helper.print_title("Filtered clusterer")
    loader = Loader("weka.core.converters.ArffLoader")
    data = loader.load_file(iris_file)
    clusterer = Clusterer(classname="weka.clusterers.SimpleKMeans", options=["-N", "3"])
    remove = Filter(classname="weka.filters.unsupervised.attribute.Remove", options=["-R", "last"])
    fclusterer = FilteredClusterer()
    fclusterer.clusterer = clusterer
    fclusterer.filter = remove
    fclusterer.build_clusterer(data)
    print(fclusterer)

    # load a dataset incrementally and build clusterer incrementally
    helper.print_title("Incremental clusterer")
    loader = Loader("weka.core.converters.ArffLoader")
    iris_inc = loader.load_file(iris_file, incremental=True)
    clusterer = Clusterer("weka.clusterers.Cobweb")
    remove = Filter(classname="weka.filters.unsupervised.attribute.Remove", options=["-R", "last"])
    remove.inputformat(iris_inc)
    iris_filtered = remove.outputformat()
    clusterer.build_clusterer(iris_filtered)
    for inst in loader:
        remove.input(inst)
        inst_filtered = remove.output()
        clusterer.update_clusterer(inst_filtered)
    clusterer.update_finished()
    print(clusterer.to_commandline())
    print(clusterer)
    print(clusterer.graph)
    plg.plot_dot_graph(clusterer.graph)

Example #2

def main():
    """
    Just runs some example code.
    """

    # load a dataset
    iris_file = helper.get_data_dir() + os.sep + "iris.arff"
    helper.print_info("Loading dataset: " + iris_file)
    loader = Loader("weka.core.converters.ArffLoader")
    data = loader.load_file(iris_file)

    # remove class attribute
    data.delete_last_attribute()

    # build a clusterer and output model
    helper.print_title("Training SimpleKMeans clusterer")
    clusterer = Clusterer(classname="weka.clusterers.SimpleKMeans", options=["-N", "3"])
    clusterer.build_clusterer(data)
    print(clusterer)
    helper.print_info("Evaluating on data")
    evaluation = ClusterEvaluation()
    evaluation.set_model(clusterer)
    evaluation.test_model(data)
    print("# clusters: " + str(evaluation.num_clusters))
    print("log likelihood: " + str(evaluation.log_likelihood))
    print("cluster assignments:\n" + str(evaluation.cluster_assignments))
    plc.plot_cluster_assignments(evaluation, data, inst_no=True)

    # using a filtered clusterer
    helper.print_title("Filtered clusterer")
    loader = Loader("weka.core.converters.ArffLoader")
    data = loader.load_file(iris_file)
    clusterer = Clusterer(classname="weka.clusterers.SimpleKMeans", options=["-N", "3"])
    remove = Filter(classname="weka.filters.unsupervised.attribute.Remove", options=["-R", "last"])
    fclusterer = FilteredClusterer()
    fclusterer.clusterer = clusterer
    fclusterer.filter = remove
    fclusterer.build_clusterer(data)
    print(fclusterer)

    # load a dataset incrementally and build clusterer incrementally
    helper.print_title("Incremental clusterer")
    loader = Loader("weka.core.converters.ArffLoader")
    iris_inc = loader.load_file(iris_file, incremental=True)
    clusterer = Clusterer("weka.clusterers.Cobweb")
    remove = Filter(classname="weka.filters.unsupervised.attribute.Remove", options=["-R", "last"])
    remove.inputformat(iris_inc)
    iris_filtered = remove.outputformat()
    clusterer.build_clusterer(iris_filtered)
    for inst in loader:
        remove.input(inst)
        inst_filtered = remove.output()
        clusterer.update_clusterer(inst_filtered)
    clusterer.update_finished()
    print(clusterer.to_commandline())
    print(clusterer)
    print(clusterer.graph)
    plg.plot_dot_graph(clusterer.graph)

Example #3

File: clusterers.py Project: EinsteinUnicorn/python-weka-wrapper

    def test_plot_cluster_assignments(self):
        """
        Tests the plot_cluster_assignments method.
        """
        loader = converters.Loader("weka.core.converters.ArffLoader")
        data = loader.load_file(self.datafile("iris.arff"))
        data.delete_last_attribute()

        # build a clusterer and output model
        clusterer = clusterers.Clusterer(classname="weka.clusterers.SimpleKMeans", options=["-N", "3"])
        clusterer.build_clusterer(data)
        evaluation = clusterers.ClusterEvaluation()
        evaluation.set_model(clusterer)
        evaluation.test_model(data)
        plot.plot_cluster_assignments(evaluation, data, inst_no=True, wait=False)

Example #4

File: clusterers.py Project: zhuyaolin/python-weka-wrapper3

    def test_plot_cluster_assignments(self):
        """
        Tests the plot_cluster_assignments method.
        """
        loader = converters.Loader("weka.core.converters.ArffLoader")
        data = loader.load_file(self.datafile("iris.arff"))
        data.delete_last_attribute()

        # build a clusterer and output model
        clusterer = clusterers.Clusterer(
            classname="weka.clusterers.SimpleKMeans", options=["-N", "3"])
        clusterer.build_clusterer(data)
        evaluation = clusterers.ClusterEvaluation()
        evaluation.set_model(clusterer)
        evaluation.test_model(data)
        plot.plot_cluster_assignments(evaluation,
                                      data,
                                      inst_no=True,
                                      wait=False)

Example #5

File: class-3.6.py Project: echavarria/wekamooc

data = loader.load_file(fname)

# remove class attribute
flt = Filter(classname="weka.filters.unsupervised.attribute.Remove", options=["-R", "last"])
flt.set_inputformat(data)
filtered = flt.filter(data)

# build KMeans
print("\n--> SimpleKMeans\n")
cl = Clusterer(classname="weka.clusterers.SimpleKMeans", options=["-N", "3"])
cl.build_clusterer(filtered)
evl = ClusterEvaluation()
evl.set_model(cl)
evl.test_model(filtered)
print(evl.get_cluster_results())
plc.plot_cluster_assignments(evl, data, atts=[], inst_no=True, wait=True)

# use AddCluster filter
print("\n--> AddCluster filter\n")
flt = Filter(classname="weka.filters.unsupervised.attribute.AddCluster",
             options=["-W", "weka.clusterers.SimpleKMeans -N 3"])
flt.set_inputformat(filtered)
addcl = flt.filter(filtered)
print(addcl)

# classes-to-clusters evaluation
print("\n--> Classes to clusters\n")
data.set_class_index(data.num_attributes() - 1)
cl = Clusterer(classname="weka.clusterers.SimpleKMeans", options=["-N", "3"])
cl.build_clusterer(filtered)  # WITHOUT class attribute
evl = ClusterEvaluation()

Example #6

File: class-3.6.py Project: sudhakar2205/wekamooc

# remove class attribute
flt = Filter(classname="weka.filters.unsupervised.attribute.Remove",
             options=["-R", "last"])
flt.inputformat(data)
filtered = flt.filter(data)

# build KMeans
print("\n--> SimpleKMeans\n")
cl = Clusterer(classname="weka.clusterers.SimpleKMeans", options=["-N", "3"])
cl.build_clusterer(filtered)
evl = ClusterEvaluation()
evl.set_model(cl)
evl.test_model(filtered)
print(evl.cluster_results)
plc.plot_cluster_assignments(evl, data, atts=[], inst_no=True, wait=True)

# use AddCluster filter
print("\n--> AddCluster filter\n")
flt = Filter(classname="weka.filters.unsupervised.attribute.AddCluster",
             options=["-W", "weka.clusterers.SimpleKMeans -N 3"])
flt.inputformat(filtered)
addcl = flt.filter(filtered)
print(addcl)

# classes-to-clusters evaluation
print("\n--> Classes to clusters\n")
data.class_is_last()
cl = Clusterer(classname="weka.clusterers.SimpleKMeans", options=["-N", "3"])
cl.build_clusterer(filtered)  # WITHOUT class attribute
evl = ClusterEvaluation()