#Required Import
import numpy as np
import eights.investigate as inv
#Investigate
M,labels = inv.open_cvs(file_loc)
#choose to Numpy Structures Arrays
#Descriptive statistics
inv.describe_cols(data)
inv.cross_tabs
inv.plot_correlation_matrix
inv.plot_correlation_scatter_plot
inv.plot_box_plot
# Decontaminate Data
import eights.investigate as dec
replace_with_n_bins
replace_missing_vals
#generate features
def is_this_word_in(a_text, word):
return word in a_text
# 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
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,
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
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)
