def test_row_is_nan(self):
M = np.array(
[(0, 1,), (1, np.nan), (2, 0), (3, np.nan), (4, 1)],
dtype=[('id', int), ('label', float)])
arguments = [{'func': row_is_nan, 'col_name': 'label', 'vals': None}]
res = remove_rows_where(M, arguments)
ctrl = np.array(
[(0, 1), (2, 0), (4, 1)],
dtype=[('id', int), ('label', float)])
self.assertTrue(np.array_equal(res, ctrl))
def test_remove_rows_where(self):
M = [[1,2,3], [2,3,4], [3,4,5]]
col_names = ['heigh','weight', 'age']
lables= [0,0,1]
M = diogenes.utils.cast_list_of_list_to_sa(
M,
col_names=col_names)
arguments = [{'func': row_val_eq, 'col_name': 'heigh', 'vals': 1},
{'func': row_val_lt, 'col_name': 'weight', 'vals': 3},
{'func': row_val_between, 'col_name': 'age', 'vals':
(3, 4)}]
res = remove_rows_where(
M,
arguments)
ctrl = cast_list_of_list_to_sa([[2,3,4],[3,4,5]],col_names=['heigh','weight', 'age'])
self.assertTrue(np.array_equal(res, ctrl))
#new eval function
def rounds_to_val(M, col_name, boundary):
return (np.round(M[col_name]) == boundary)
M = choose_rows_where(M, [{
'func': rounds_to_val,
'col_name': 'f0',
'vals': 5
}], 'new_col')
from diogenes.modify import (fewer_then_n_nonzero_in_col,
remove_rows_where, remove_cols, val_eq)
#remove Useless row
M = fewer_then_n_nonzero_in_col(M, 1)
M = append_cols(M, labels, 'labels')
M = remove_rows_where(M, val_eq, 'labels', 2)
labels = M['labels']
M = remove_cols(M, 'labels')
from diogenes.operate import run_std_classifiers, run_alt_classifiers #run_alt_classifiers not working yet
exp = run_std_classifiers(M, labels)
exp.make_csv()
import pdb
pdb.set_trace()
####################Communicate#######################
#Pretend .1 is wrong so set all values of .1 in M[3] as .2
# make a new column where its a test if col,val, (3,.2), (2,1.4) is true.
return (np.round(M[col_name]) == boundary)
M = choose_rows_where(M,
[{'func': rounds_to_val,
'col_name': 'f0',
'vals': 5}],
'new_col')
from diogenes.modify import (fewer_then_n_nonzero_in_col,
remove_rows_where,
remove_cols,
val_eq)
#remove Useless row
M = fewer_then_n_nonzero_in_col(M,1)
M = append_cols(M, labels, 'labels')
M = remove_rows_where(M, val_eq, 'labels', 2)
labels=M['labels']
M = remove_cols(M, 'labels')
from diogenes.operate import run_std_classifiers, run_alt_classifiers #run_alt_classifiers not working yet
exp = run_std_classifiers(M,labels)
exp.make_csv()
import pdb; pdb.set_trace()
####################Communicate#######################
#Pretend .1 is wrong so set all values of .1 in M[3] as .2
