Example #1
0
    def __new__(cls, data, address='localhost:9465', batch=100, max_iter=100):
        from orangecontrib.remote import aborted, save_state
        import Orange.data.sql.table

        cont = Continuize(multinomial_treatment=Continuize.Remove,
                          normalize_continuous=None)
        data = cont(data)
        pca = Orange.projection.IncrementalPCA()
        percent = batch / data.approx_len() * 100
        if percent < 100:
            data_sample = data.sample_percentage(percent, no_cache=True)
        else:
            data_sample = data
        data_sample.download_data(1000000)
        data_sample = Orange.data.Table.from_numpy(
            Orange.data.Domain(data_sample.domain.attributes), data_sample.X)
        model = pca(data_sample)
        save_state(model)
        for i in range(max_iter if percent < 100 else 0):
            data_sample = data.sample_percentage(percent, no_cache=True)
            data_sample.download_data(1000000)
            data_sample = Orange.data.Table.from_numpy(
                Orange.data.Domain(data_sample.domain.attributes),
                data_sample.X)
            model.partial_fit(data_sample)
            model.iteration = i
            save_state(model)
            if aborted():
                break
        return model
Example #2
0
    def __new__(cls, data, address='localhost:9465', batch=100, max_iter=100):
        from orangecontrib.remote import aborted, save_state
        import Orange.data.sql.table

        cont = Continuize(multinomial_treatment=Continuize.Remove)
        data = cont(data)
        pca = Orange.projection.IncrementalPCA()
        percent = batch / data.approx_len() * 100
        if percent < 100:
            data_sample = data.sample_percentage(percent, no_cache=True)
        else:
            data_sample = data
        data_sample.download_data(1000000)
        data_sample = Orange.data.Table.from_numpy(
            Orange.data.Domain(data_sample.domain.attributes),
            data_sample.X)
        model = pca(data_sample)
        save_state(model)
        for i in range(max_iter if percent < 100 else 0):
            data_sample = data.sample_percentage(percent, no_cache=True)
            data_sample.download_data(1000000)
            data_sample = Orange.data.Table.from_numpy(
                Orange.data.Domain(data_sample.domain.attributes),
                data_sample.X)
            model.partial_fit(data_sample)
            model.iteration = i
            save_state(model)
            if aborted():
                break
        return model
Example #3
0
 def __new__(cls, data, batch=100, max_iter=100):
     cont = Continuize(multinomial_treatment=Continuize.Remove)
     data = cont(data)
     model = Orange.projection.IncrementalPCA()
     percent = batch / data.approx_len() * 100
     for i in range(max_iter):
         data_sample = data.sample_percentage(percent, no_cache=True)
         if not data_sample:
             continue
         data_sample.download_data(1000000)
         data_sample = Orange.data.Table.from_numpy(
             Orange.data.Domain(data_sample.domain.attributes),
             data_sample.X)
         model = model.partial_fit(data_sample)
         model.iteration = i
         save_state(model)
         if aborted() or data_sample is data:
             break
     return model
Example #4
0
 def __new__(cls, data, batch=100, max_iter=100):
     cont = Continuize(multinomial_treatment=Continuize.Remove)
     data = cont(data)
     model = Orange.projection.IncrementalPCA()
     percent = batch / data.approx_len() * 100
     for i in range(max_iter):
         data_sample = data.sample_percentage(percent, no_cache=True)
         if not data_sample:
             continue
         data_sample.download_data(1000000)
         data_sample = Orange.data.Table.from_numpy(
             Orange.data.Domain(data_sample.domain.attributes),
             data_sample.X)
         model = model.partial_fit(data_sample)
         model.iteration = i
         save_state(model)
         if aborted() or data_sample is data:
             break
     return model