def test_display_dimensions(self):
data_dir = 'datasets/'
data_set = 'glass/glass.data'
file = os.path.join(data_dir, data_set)
print('Displaying data set {%s} in the Rn' % file)
glass = Retriever(file, delimiter=',')
# Glass has the samples' ids in the first column.
glass.split_column(0)
# Additionally, its last column represents the target feature.
glass.split_target()
data, c = glass.retrieve()
reduced_data = algorithms.Isomap(data, e=20).run()
d = Displayer(title=data_set)
# Scatter all dimensions (3-by-3), using as many graphs as necessary.
for begin in range(0, glass.features_count, 3):
end = min(glass.features_count, begin + 3)
d.load(data[:, begin:end],
color=c,
title='Dimensions: d e [%i, %i]' % (begin + 1, end))
d \
.load('Reduced glass data-set', reduced_data, c) \
.show()
def _load_data(self):
r = Retriever('../../datasets/breast-cancer/wdbc.data', delimiter=',')
r.split_column(0) # Remove ids.
# Split target from data and retrieve both.
# Target feature is actually located in the 2nd column, but considering
# we had the ids removed, it's now in the 1st one.
self.data, self.target = r.split_target(0).retrieve()
self.data = self.data.astype(float)
self.feature_names = ['radius', 'texture', 'perimeter']
def _load_data(self):
r = Retriever(self.file, delimiter=',')
r.split_column(0) # Remove ids.
self.data, self.target = r.split_target().retrieve()
self.original_data = self.data