import numpy as np
import sklearn.datasets
from eights.investigate import (
cast_np_nd_to_sa,
describe_cols,
)
from eights.communicate import (plot_correlation_scatter_plot,
plot_correlation_matrix, plot_kernel_density,
plot_box_plot)
#import numpy array
M = sklearn.datasets.load_iris().data
labels = sklearn.datasets.load_iris().target
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
import numpy as np
import sklearn.datasets
from eights.investigate import cast_np_nd_to_sa, describe_cols
from eights.communicate import (
plot_correlation_scatter_plot,
plot_correlation_matrix,
plot_kernel_density,
plot_box_plot,
)
# import numpy array
M = sklearn.datasets.load_iris().data
labels = sklearn.datasets.load_iris().target
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_kernel_density,
plot_box_plot)
from eights.operate import run_std_classifiers
data = open_csv_url_as_list(
'http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv',
delimiter=';')
col_names = data[0][:-1]
labels = np.array([int(x[-1]) for x in data[1:]])
#make this problem binary
labels = np.array([0 if x < np.average(labels) else 1 for x in labels])
dtype = np.dtype({'names': col_names,'formats': [float] * (len(col_names)+1)})
M = cast_np_nd_to_sa(np.array([x[:-1] for x in data[1:]],dtype='float'), dtype)
import pdb; pdb.set_trace()
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
