def helper_display_dataset(bunch):
"""Helper for display_dataset visualization
:param bunch:
:return: a dict
"""
data = bunch["data"]
target = bunch["target"]
csv = []
nrows, ncols = data.shape
for i in range(0, nrows):
csv.append([])
# Features:
for j in range(0, ncols):
if dataset.is_feature_nominal(bunch, j):
#nominal feat
for i in range(0, nrows):
if np.isnan(data[i][j]):
csv[i].append(np.nan)
else:
val = int(data[i][j])
csv[i].append(bunch.feature_value_names[j][val] +
(" [%d]" % val))
else:
for i in range(0, nrows):
csv[i].append(data[i][j])
# Target:
if dataset.is_target_nominal(bunch):
#nominal target
for i in range(0, nrows):
if np.isnan(target[i]):
csv[i].append(np.nan)
else:
val = int(target[i])
csv[i].append(bunch.target_names[val] + (" [%d]" % val))
else:
for i in range(0, nrows):
csv[i].append(target[i])
if bunch.has_key("feature_names"):
attrs = bunch.feature_names
else:
attrs = ["attribute" + str(i)
for i in range(len(data[0]))] #name of attributes
# attrs.append('class')
metas = ''
data_new = csv #fill table with data
return {
'attrs': attrs,
'metas': metas,
'data_new': data_new,
'class_var': 'class'
}
def helper_display_dataset(bunch):
"""Helper for display_dataset visualization
:param bunch:
:return: a dict
"""
data = bunch["data"]
target = bunch["target"]
csv=[]
nrows, ncols = data.shape
for i in range(0,nrows):
csv.append([])
# Features:
for j in range(0,ncols):
if dataset.is_feature_nominal(bunch, j):
#nominal feat
for i in range(0,nrows):
if np.isnan(data[i][j]):
csv[i].append( np.nan )
else:
val = int(data[i][j])
csv[i].append( bunch.feature_value_names[j][val]+(" [%d]" % val) )
else:
for i in range(0,nrows):
csv[i].append( data[i][j] )
# Target:
if dataset.is_target_nominal(bunch):
#nominal target
for i in range(0,nrows):
if np.isnan(target[i]):
csv[i].append( np.nan )
else:
val = int(target[i])
csv[i].append( bunch.target_names[val]+(" [%d]" % val) )
else:
for i in range(0,nrows):
csv[i].append( target[i] )
if bunch.has_key("feature_names"):
attrs = bunch.feature_names
else:
attrs = ["attribute"+str(i) for i in range(len(data[0]))] #name of attributes
# attrs.append('class')
metas = ''
data_new = csv #fill table with data
return {'attrs':attrs, 'metas':metas, 'data_new':data_new, 'class_var':'class'}
def split_dataset_randomly(input_dict):
""" Randomly splits a given dataset into a train and test dataset."""
inst = input_dict['data']
test_size = 1 - float(input_dict["p"])
# train test split
from sklearn.cross_validation import train_test_split
data_train, data_test, target_train, target_test = train_test_split(
inst['data'],
inst['target'],
test_size=test_size,
random_state=1)
from sklearn.datasets import base as ds
if dataset.is_target_nominal(inst):
a_train = ds.Bunch(data=data_train,
target=target_train,
feature_names=inst.feature_names,
DESCR=inst.DESCR,
target_names=inst.target_names)
a_test = ds.Bunch(data=data_test,
target=target_test,
feature_names=inst.feature_names,
DESCR=inst.DESCR,
target_names=inst.target_names)
else:
a_train = ds.Bunch(data=data_train,
target=target_train,
feature_names=inst.feature_names,
DESCR=inst.DESCR)
a_test = ds.Bunch(data=data_test,
target=target_test,
feature_names=inst.feature_names,
DESCR=inst.DESCR)
if inst.has_key("feature_value_names"):
a_train["feature_value_names"] = inst.feature_value_names
a_test["feature_value_names"] = inst.feature_value_names
return {'train_data': a_train, 'test_data': a_test}
def select_data(request, input_dict, output_dict, widget):
bunch = input_dict["data"]
attrs = {}
if bunch.has_key("feature_names"):
for i, f in enumerate(bunch.feature_names):
if bunch.has_key("feature_value_names") and len(bunch.feature_value_names[i]) > 0:
vals = [str(v) for v in bunch.feature_value_names[i]]
attrs[f] = {"values": vals, "type": "Discrete", "feature": 1}
else:
attrs[f] = {"values": [], "type": "Continuous", "feature": 1}
# Target:
if dataset.is_target_nominal(bunch):
# nominal target
attrs["class"] = {"values": bunch.target_names, "type": "Discrete", "feature": 0}
else:
attrs["class"] = {"values": [], "type": "Continuous", "feature": 0}
attrs_as_list = attrs.items() # do not sort the features
return render(request, "interactions/select_data.html", {"widget": widget, "attrs": attrs_as_list})
def select_data(request, input_dict, output_dict, widget):
bunch = input_dict['data']
attrs = {}
if bunch.has_key('feature_names'):
for i,f in enumerate(bunch.feature_names):
if bunch.has_key('feature_value_names') and len(bunch.feature_value_names[i])>0:
vals = [str(v) for v in bunch.feature_value_names[i]]
attrs[f] = {'values': vals, 'type': 'Discrete', 'feature': 1}
else:
attrs[f] = {'values': [], 'type': 'Continuous', 'feature': 1}
# Target:
if dataset.is_target_nominal(bunch):
#nominal target
attrs['class'] = {'values': bunch.target_names, 'type': 'Discrete', 'feature': 0}
else:
attrs['class'] = {'values': [], 'type': 'Continuous', 'feature': 0}
attrs_as_list = attrs.items() # do not sort the features
return render(request, 'interactions/select_data.html',
{'widget' : widget, 'attrs' : attrs_as_list})