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()
from diogenes.read import open_csv_url
from diogenes.display import (plot_correlation_scatter_plot,
plot_correlation_matrix, 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()
