plot_kernel_density,
plot_box_plot)
from diogenes.grid_search import Experiment
from diogenes.grid_search import std_clfs as std_clfs
from diogenes.utils import remove_cols
data = open_csv_url(
'http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv',
delimiter=';')
y = data['quality']
M = remove_cols(data, 'quality')
y = y < np.average(y)
if False:
for x in describe_cols(M):
print x
if False:
plot_correlation_scatter_plot(M)
plot_correlation_matrix(M)
plot_kernel_density(M['f0']) #no designation of col name
plot_box_plot(M['f0']) #no designation of col name
exp = Experiment(M, y, clfs=std_clfs)
exp.make_csv()
M = cast_np_nd_to_sa(M)
#M is multi class, we want to remove those rows.
keep_index = np.where(labels != 2)
labels = labels[keep_index]
M = M[keep_index]
if False:
for x in describe_cols(M):
print x
if False:
plot_correlation_scatter_plot(M)
plot_correlation_matrix(M)
plot_kernel_density(M['f0']) #no designation of col name
plot_box_plot(M['f0']) #no designation of col name
if False:
from diogenes.generate import val_between, choose_rows_where, append_cols #val_btwn, where
#generate a composite rule
M = choose_rows_where(M, [{
'func': val_between,
'col_name': 'f0',
'vals': (3.5, 5.0)
}, {
'func': val_between,
'col_name': 'f1',
'vals': (2.7, 3.1)
}], 'a new col_name')
def test_plot_kernel_density(self):
np.random.seed(0)
data = np.random.normal(size=(1000,))
fig = dsp.plot_kernel_density(data, verbose=False)
self.add_fig_to_report(fig, 'plot_kernel_density')
