def test_plot_correlation_matrix(self):
col1 = range(10)
col2 = [cell * 3 + 1 for cell in col1]
col3 = [1, 5, 8, 4, 1, 8, 5, 9, 0, 1]
sa = utils.convert_to_sa(
zip(col1, col2, col3),
col_names=['base', 'linear_trans', 'no_correlation'])
fig = comm.plot_correlation_matrix(sa, verbose=False)
self.add_fig_to_report(fig, 'plot_correlation_matrix')
def test_plot_correlation_matrix(self):
col1 = range(10)
col2 = [cell * 3 + 1 for cell in col1]
col3 = [1, 5, 8, 4, 1, 8, 5, 9, 0, 1]
sa = utils.convert_to_sa(
zip(col1, col2, col3),
col_names=['base', 'linear_trans', 'no_correlation'])
fig = comm.plot_correlation_matrix(sa, verbose=False)
self.add_fig_to_report(fig, 'plot_correlation_matrix')
# 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 eights.generate import val_between, where_all_are_true, append_cols # val_btwn, where
# generate a composite rule
M = where_all_are_true(
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",
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 eights.generate import val_between, where_all_are_true, append_cols #val_btwn, where
#generate a composite rule
M = where_all_are_true(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')