def get_size_and_dexsize(path_to_predictions, path_to_arff):
# read predictions
f = open(path_to_predictions, 'r')
content = f.readlines()
f.close()
error_index = []
for line in content:
if '+' in line:
error_index.append(int(line.split()[0]))
# generate error list
f = open(path_to_arff, 'r')
file = f.read()
f.close()
d = arff.loads(file)
error_list = []
i = 0
for index in error_index:
obj['data'].append(d['data'][index])
error_list.append({
'size': d['data'][index][0],
'dex_size': d['data'][index][1]
})
# write error vectors to arff
f = open('incorrectly_classified.arff', 'w')
arff.dump(obj, f)
f.close()
return error_list
def load_sparse_arff(path, label_n):
rows = []
labels = []
for i, r in enumerate(loads(codecs.open(path, 'r', 'utf8').read())):
print i
m = len(r._values)
rows.append(r._values[:m-label_n])
labels.append(r._values[m-label_n:])
# convert to sparse matrix
row_n = len(rows)
# X = csr_matrix((len(rows), m-label_n), dtype=np.bool_)
X = np.zeros((len(rows), m-label_n), dtype=np.bool_)
for i, r in enumerate(rows):
print "%d / %d" % (i, row_n)
for j, v in enumerate(r):
if v != None:
X[i, j] = int(v)
# y = csr_matrix((len(rows), label_n), dtype=np.bool_)
y = np.zeros((len(rows), label_n), dtype=np.bool_)
for i, r in enumerate(labels):
print "%d / %d" % (i, row_n)
for j, v in enumerate(r):
if v != None:
y[i, j] = int(v)
return csr_matrix(X), csr_matrix(y)
def replace_unknown(arff_path):
"""
Function to pull openSMILE output csv into a pandas series
Parameters
----------
arff_path : string
absolute path to csv file
Returns
-------
oS_data : string
arff formatted data string
"""
temp_oS = open(arff_path, 'r')
temp_oS_lines = temp_oS.readlines()
temp_oS_string = ""
for temp_oS_line in temp_oS_lines:
words = temp_oS_line.split()
if (len(words) == 3):
if ((words[0] == "@attribute") and (words[2] == "unknown")):
temp_oS_string = "".join([
temp_oS_string, " ".join([words[0], words[1], "string\n"])
])
else:
temp_oS_string = "".join([temp_oS_string, temp_oS_line])
else:
temp_oS_string = "".join([temp_oS_string, temp_oS_line])
tempcsv = "temp.csv"
tof = open(tempcsv, "w")
tof.write(temp_oS_string)
tof.close()
oS_data = arff.loads(open(tempcsv))
subprocess.run("rm temp.csv", shell=True)
return (oS_data)
def loads(s):
"""
load str to pandas DataFrame
:param str s:
:rtype: DataFrame
:return: pandas DataFrame
"""
data = liacarff.loads(s)
return __load(data)
def test_encode_destiny(self):
src = ARFF_DESTINY
count = 0
while count < 10:
count += 1
obj = arff.loads(src)
src = arff.dumps(obj)
self.assertEqual(src, ARFF_DESTINY)
def read(path):
common = {
'sonar': 'datasets/sonar.arff',
'spambase': 'datasets/spambase-460.arff',
'wdbc': 'datasets/wdbc.arff'
}
f = open(common.get(path, path))
data = arff.loads(f)
return Dataset(data['data'])
def test_simple(self):
dumps = self.get_dumps()
s = dumps(OBJ)
self.assertEqual(s, ARFF)
count = 0
while count < 10:
count += 1
obj = arff.loads(s)
src = arff.dumps(obj)
self.assertEqual(src, ARFF)
def parse_arff() -> dict:
"""
Function that gains data through file input and uses the arff library to parse this into a dictionary.
:return: Returns a dictionary containing the dataset from the arff file.
"""
lines = []
for line in fileinput.input():
lines.append(line)
data = arff.loads("\n".join(lines))
dataset: Dataset = data
return dataset
def test_create_dataset_row_id_attribute_inference(self):
# meta-information
name = '%s-pandas_testing_dataset' % self._get_sentinel()
description = 'Synthetic dataset created from a Pandas DataFrame'
creator = 'OpenML tester'
collection_date = '01-01-2018'
language = 'English'
licence = 'MIT'
default_target_attribute = 'target'
citation = 'None'
original_data_url = 'http://openml.github.io/openml-python'
paper_url = 'http://openml.github.io/openml-python'
# Check that the index name is well inferred.
data = [['a', 1, 0],
['b', 2, 1],
['c', 3, 0],
['d', 4, 1],
['e', 5, 0]]
column_names = ['rnd_str', 'integer', 'target']
df = pd.DataFrame(data, columns=column_names)
row_id_attr = [None, 'integer']
df_index_name = [None, 'index_name']
expected_row_id = [None, 'index_name', 'integer', 'integer']
for output_row_id, (row_id, index_name) in zip(expected_row_id,
product(row_id_attr,
df_index_name)):
df.index.name = index_name
dataset = openml.datasets.functions.create_dataset(
name=name,
description=description,
creator=creator,
contributor=None,
collection_date=collection_date,
language=language,
licence=licence,
default_target_attribute=default_target_attribute,
ignore_attribute=None,
citation=citation,
attributes='auto',
data=df,
row_id_attribute=row_id,
version_label='test',
original_data_url=original_data_url,
paper_url=paper_url
)
self.assertEqual(dataset.row_id_attribute, output_row_id)
upload_did = dataset.publish()
arff_dataset = arff.loads(_get_online_dataset_arff(upload_did))
arff_data = np.array(arff_dataset['data'], dtype=object)
# if we set the name of the index then the index will be added to
# the data
expected_shape = (5, 3) if index_name is None else (5, 4)
self.assertEqual(arff_data.shape, expected_shape)
def weka_get_attr_list(input_dict):
'''
Returns attribute values for a single attribute from the dataset. Defaults to the last attribute.
E.g., useful for calculating classification statistics.
'''
arff_file = input_dict['arff_file']
attr_name = input_dict.get('attr_name', None)
attr_list = []
dataset = arff.loads(arff_file)
attr_idx = -1
if attr_name:
attr_idx = map(lambda x: x[0], dataset['attributes'].index(attr_name))
for row in dataset['data']:
attr_list.append(row[attr_idx])
return {'attr_list': attr_list}
def weka_local_get_attr_list(input_dict):
'''
Returns attribute values for a single attribute from the dataset. Defaults to the last attribute.
E.g., useful for calculating classification statistics.
'''
arff_file = input_dict['arff_file']
attr_name = input_dict.get('attr_name', None)
attr_list = []
dataset = arff.loads(arff_file)
attr_idx = -1
if attr_name:
attr_idx = map(lambda x: x[0], dataset['attributes'].index(attr_name))
for row in dataset['data']:
attr_list.append(row[attr_idx])
return {'attr_list': attr_list}
def test_files(self):
fname = os.path.join(SRC_DIR, 'example.arff')
data = [
['blonde', 17.2, 1],
['blue', 27.2, 2],
['blue', 18.2, 3],
]
arff.dump(fname, data, relation='diabetics_data', names=('hair_color', 'age', 'patno'))
data = list(arff.load(os.path.join(SRC_DIR, fname)))
arff_rows = arff.dumps(data)
reparsed_data = list(arff.loads(arff_rows))
data = [list(row) for row in data]
reparsed_data = [list(row) for row in reparsed_data]
self.assertEqual(data, reparsed_data)
def loads(s):
"""
Convert a string instance containing the arff document into an arff object.
:param s: string with the arff document.
:return: arff object.
"""
load_obj = arff.loads(s)
# extract all of the description lines (i.e. all before @DATA, instead of the default behaviour with just
# the lines before @RELATION being considered description)
load_obj['description'] = ArffHelper._extract_description(
s.split('\n'))
ArffHelper._load_metadata(load_obj)
load_obj = ArffHelper.convert_data_to_structured_array(load_obj)
return load_obj
def __load__file(self, file_path):
file_object = open(file_path)
file_content = file_object.read()
dataset = arff.loads(file_content,
encode_nominal=True,
return_type=arff.DENSE)
#print(dataset['description'])
#print(dataset['relation'])
#print(dataset['attributes'])
#print(dataset['data'][0])
#print("number of imported lines: " + str(dataset['data'].__len__()))
#label data frame columns
#https://pandas.pydata.org/pandas-docs/stable/10min.html
df = pd.DataFrame(dataset['data'])
df_labels = pd.DataFrame(dataset['attributes'])
df.columns = df_labels[0]
return df
def arff_to_orange_table(arff_data):
'''
Constructs Orange.data.Table from ARFF data stored in a string
Parameters
----------
arff_data : str
ARFF file stored in a string.
Returns
-------
table : Orange.data.Table
Orange data table with the given domain and data. String attributes are stored as meta attributes.
'''
arff_description = arff.loads(arff_data)
domain = arffheader2domain(arff_description['attributes'])
table = Orange.data.Table.from_list(domain, arff_description['data'])
table.name = arff_description['relation']
return table
def _check_serialized_optimized_run(self, run_id):
run = openml.runs.get_run(run_id)
task = openml.tasks.get_task(run.task_id)
# TODO: assert holdout task
# downloads the predictions of the old task
predictions_url = openml._api_calls._file_id_to_url(
run.output_files['predictions'])
predictions = arff.loads(openml._api_calls._read_url(predictions_url))
# downloads the best model based on the optimization trace
# suboptimal (slow), and not guaranteed to work if evaluation
# engine is behind. TODO: mock this? We have the arff already on the server
self._wait_for_processed_run(run_id, 200)
try:
model_prime = openml.runs.initialize_model_from_trace(run_id, 0, 0)
except openml.exceptions.OpenMLServerException as e:
e.additional = str(e.additional) + '; run_id: ' + str(run_id)
raise e
run_prime = openml.runs.run_model_on_task(task,
model_prime,
avoid_duplicate_runs=False,
seed=1)
predictions_prime = run_prime._generate_arff_dict()
self.assertEquals(len(predictions_prime['data']),
len(predictions['data']))
# The original search model does not submit confidence bounds,
# so we can not compare the arff line
compare_slice = [0, 1, 2, -1, -2]
for idx in range(len(predictions['data'])):
# depends on the assumption "predictions are in same order"
# that does not necessarily hold.
# But with the current code base, it holds.
for col_idx in compare_slice:
self.assertEquals(predictions['data'][idx][col_idx],
predictions_prime['data'][idx][col_idx])
return True
def _load_E2_files(self):
'''
load complexity metrics file into a dataframe
Load task outcomes file into a dataframe
'''
#TASKS
dataset_tasks_E2 = arf.loads(open(self.file_tasks_E2, 'rt'))
array_tasks_E2 = np.array(dataset_tasks_E2['data'])
self.df_tasks_E2 = pd.DataFrame(array_tasks_E2)
#print(dataset_tasks_E2['attributes'])
#need to extract the header, because the arf.loads brings only data....
tasks_E2_header = np.take(dataset_tasks_E2['attributes'], [
0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34,
36, 38, 40, 42, 44, 46, 48, 50, 52, 54, 56, 58, 60, 62
])
self.df_tasks_E2.columns = tasks_E2_header
#COMPLEXITY METRICS
self.df_complexity_E2 = pd.read_csv(self.file_complexity_E2)
def openFile(self):
filename = QFileDialog.getOpenFileName(
self.m_tab, '打开文件', '/',
'Arff data files(*.arff);;CSV data files(*.csv)')
file = open(filename[0], 'rb')
self.m_FileName = os.path.basename(file.name).split('.')[0]
# 解析arff
with file:
s = file.read().decode('utf-8')
try:
data = arff.loads(s)
except arff.BadLayout:
Utils.DiglogWarning(self.m_Explor, "Syntax Errors in Data Sets")
return
inst = Instances(data)
print(data)
self.setInstances(inst)
self.m_tabWidget.setTabEnabled(1, True)
self.m_tabWidget.setTabEnabled(2, True)
def __init__(self,
arff_file_path,
learning_rate=0.5,
training_set_portion=1.0,
max_iterations=3):
"""Class contructor
Args:
arrf_file_path: String of the file path.
learning_rate: A float which is the learning rate.
training_set_portion: A float which identifies the portion of training set data.
max_iterations: The maximun number of iterations during training.
"""
self._validates_training_set_portion(training_set_portion)
self.bias = -1
self.learning_rate = learning_rate
self.training_set_portion = training_set_portion
self.max_iterations = max_iterations
self.arff_file = arff.loads(open(arff_file_path, "r"))
self.is_trained = False
self._initialize_weights()
def _check_serialized_optimized_run(self, run_id):
run = openml.runs.get_run(run_id)
task = openml.tasks.get_task(run.task_id)
# TODO: assert holdout task
# downloads the predictions of the old task
predictions_url = openml._api_calls._file_id_to_url(run.output_files['predictions'])
predictions = arff.loads(openml._api_calls._read_url(predictions_url))
# downloads the best model based on the optimization trace
# suboptimal (slow), and not guaranteed to work if evaluation
# engine is behind. TODO: mock this? We have the arff already on the server
self._wait_for_processed_run(run_id, 200)
try:
model_prime = openml.runs.initialize_model_from_trace(run_id, 0, 0)
except openml.exceptions.OpenMLServerException as e:
e.additional = str(e.additional) + '; run_id: ' + str(run_id)
raise e
run_prime = openml.runs.run_model_on_task(task, model_prime,
avoid_duplicate_runs=False,
seed=1)
predictions_prime = run_prime._generate_arff_dict()
self.assertEquals(len(predictions_prime['data']), len(predictions['data']))
# The original search model does not submit confidence bounds,
# so we can not compare the arff line
compare_slice = [0, 1, 2, -1, -2]
for idx in range(len(predictions['data'])):
# depends on the assumption "predictions are in same order"
# that does not necessarily hold.
# But with the current code base, it holds.
for col_idx in compare_slice:
self.assertEquals(predictions['data'][idx][col_idx], predictions_prime['data'][idx][col_idx])
return True
def test_read(self):
text = u('''@relation diabetics_data
@attribute hair_color {blonde, black, blue}
@attribute age real
@attribute patno integer
@data
blonde, 17.2, 1
blue, 27.2, 2
blue, 18.2, 3
''')
expected = [
['blonde', 17.2, 1],
['blue', 27.2, 2],
['blue', 18.2, 3],
]
result = list(arff.loads(text))
list_result = [list(row) for row in result]
self.assertEqual(list_result, expected)
self.assertEqual(result[0].hair_color, 'blonde')
self.assertEqual(result[0]['hair_color'], 'blonde')
def clus_display_tree_and_examples(request, input_dict, output_dict, widget):
"""Visualization displaying a decision tree and the examples in the tree"""
nodes, edges, index = clus_tree_to_node_edge(input_dict['classifier'], 0)
data = arff.loads(input_dict['arff'])
datanodes = []
for instance in data['data']:
instance_nodes = get_instance_nodes(input_dict['classifier'], instance,
data['attributes'])
datanodes.append({'data': instance, 'nodes': instance_nodes})
return render(
request, 'visualizations/cf_clus_display_tree_and_examples.html', {
'widget': widget,
'input_dict': input_dict,
'nodes': nodes,
'edges': edges,
'data': data,
'datanodes': datanodes,
'random': int(random() * 10000000),
})
def get_metric_fn(self, sklearn_fn, kwargs=None):
"""Calculates metric scores based on predicted values. Assumes the
run has been executed locally (and contains run_data). Furthermore,
it assumes that the 'correct' or 'truth' attribute is specified in
the arff (which is an optional field, but always the case for
openml-python runs)
Parameters
----------
sklearn_fn : function
a function pointer to a sklearn function that
accepts ``y_true``, ``y_pred`` and ``**kwargs``
Returns
-------
scores : list
a list of floats, of length num_folds * num_repeats
"""
kwargs = kwargs if kwargs else dict()
if self.data_content is not None and self.task_id is not None:
predictions_arff = self._generate_arff_dict()
elif 'predictions' in self.output_files:
predictions_file_url = openml._api_calls._file_id_to_url(
self.output_files['predictions'],
'predictions.arff',
)
response = openml._api_calls._read_url(predictions_file_url,
request_method='get')
predictions_arff = arff.loads(response)
# TODO: make this a stream reader
else:
raise ValueError('Run should have been locally executed or '
'contain outputfile reference.')
# Need to know more about the task to compute scores correctly
task = get_task(self.task_id)
attribute_names = [att[0] for att in predictions_arff['attributes']]
if (task.task_type_id in [
TaskTypeEnum.SUPERVISED_CLASSIFICATION,
TaskTypeEnum.LEARNING_CURVE
] and 'correct' not in attribute_names):
raise ValueError('Attribute "correct" should be set for '
'classification task runs')
if (task.task_type_id == TaskTypeEnum.SUPERVISED_REGRESSION
and 'truth' not in attribute_names):
raise ValueError('Attribute "truth" should be set for '
'regression task runs')
if (task.task_type_id != TaskTypeEnum.CLUSTERING
and 'prediction' not in attribute_names):
raise ValueError('Attribute "predict" should be set for '
'supervised task runs')
def _attribute_list_to_dict(attribute_list):
# convenience function: Creates a mapping to map from the name of
# attributes present in the arff prediction file to their index.
# This is necessary because the number of classes can be different
# for different tasks.
res = OrderedDict()
for idx in range(len(attribute_list)):
res[attribute_list[idx][0]] = idx
return res
attribute_dict = \
_attribute_list_to_dict(predictions_arff['attributes'])
repeat_idx = attribute_dict['repeat']
fold_idx = attribute_dict['fold']
predicted_idx = attribute_dict['prediction'] # Assume supervised task
if task.task_type_id == TaskTypeEnum.SUPERVISED_CLASSIFICATION or \
task.task_type_id == TaskTypeEnum.LEARNING_CURVE:
correct_idx = attribute_dict['correct']
elif task.task_type_id == TaskTypeEnum.SUPERVISED_REGRESSION:
correct_idx = attribute_dict['truth']
has_samples = False
if 'sample' in attribute_dict:
sample_idx = attribute_dict['sample']
has_samples = True
if predictions_arff['attributes'][predicted_idx][1] != \
predictions_arff['attributes'][correct_idx][1]:
pred = predictions_arff['attributes'][predicted_idx][1]
corr = predictions_arff['attributes'][correct_idx][1]
raise ValueError('Predicted and Correct do not have equal values:'
' %s Vs. %s' % (str(pred), str(corr)))
# TODO: these could be cached
values_predict = {}
values_correct = {}
for line_idx, line in enumerate(predictions_arff['data']):
rep = line[repeat_idx]
fold = line[fold_idx]
if has_samples:
samp = line[sample_idx]
else:
samp = 0 # No learning curve sample, always 0
if task.task_type_id in [
TaskTypeEnum.SUPERVISED_CLASSIFICATION,
TaskTypeEnum.LEARNING_CURVE
]:
prediction = predictions_arff['attributes'][predicted_idx][
1].index(line[predicted_idx])
correct = predictions_arff['attributes'][predicted_idx][1]. \
index(line[correct_idx])
elif task.task_type_id == TaskTypeEnum.SUPERVISED_REGRESSION:
prediction = line[predicted_idx]
correct = line[correct_idx]
if rep not in values_predict:
values_predict[rep] = OrderedDict()
values_correct[rep] = OrderedDict()
if fold not in values_predict[rep]:
values_predict[rep][fold] = OrderedDict()
values_correct[rep][fold] = OrderedDict()
if samp not in values_predict[rep][fold]:
values_predict[rep][fold][samp] = []
values_correct[rep][fold][samp] = []
values_predict[rep][fold][samp].append(prediction)
values_correct[rep][fold][samp].append(correct)
scores = []
for rep in values_predict.keys():
for fold in values_predict[rep].keys():
last_sample = len(values_predict[rep][fold]) - 1
y_pred = values_predict[rep][fold][last_sample]
y_true = values_correct[rep][fold][last_sample]
scores.append(sklearn_fn(y_true, y_pred, **kwargs))
return np.array(scores)
def load_arff(filename):
with open(filename, 'r') as f:
return arff.loads(f.read())
def main():
f = open('../weka_arff/benign_53422_all.arff', 'r')
file = f.read()
f.close()
dataset = arff.loads(file)
count = 0
d = dict()
less_than = [0,1,2,3,4,5,10,15,20,25,30,35,40,45,50,55,60,65,70,75,80,85,90,95,100,
105,110,115,120,125,130,135,140,145,150,155,160,165,170,175,180,185,190,195,200,
205,210,215,220,225,230,235,240,245,250,255,260,265,270,275,280,285,290,295,300,]
# less_than = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,
# 21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,
# 41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60]
# less_than = [2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40,
# 42,44,46,48,50,52,54,56,58,60,62,64,66,68,70,72,74,76,78,80,
# 82,84,86,88,90,92,94,96,98,100,102,104,106,108,110,112,114,116,118,120]
# Widgets
d['buttonCount'] = 0 # 25
button = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,]
d['TextViewCount'] = 0 # 26
text = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,]
d['EditViewCount'] = 0 # 27
edit = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,]
d['ImageButtonCount'] = 0 # 28
ibutton = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,]
d['CheckBoxCount'] = 0 # 29
checkbox = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,]
# 30
radiogroup = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,]
# 31
radiobutton = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,]
d['ToastCount'] = 0 # 32
toast = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,]
# 33
spinner = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,]
# 34
listview = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,]
d['hPictureCount'] = 0 # 64
h_pic = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,]
d['mPictureCount'] = 0 # 65
m_pic = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,]
d['lPictureCount'] = 0 # 66
l_pic = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,]
d['xPictureCount'] = 0 # 67
x_pic = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,]
d['totalCount'] = 0 # 68
t_pic = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,]
for data in dataset['data']:
count += 1
# d['buttonCount'] += data[24] # 25
# d['TextViewCount'] += data[25] # 26
# d['EditViewCount'] += data[26] # 27
# d['ImageButtonCount'] += data[27] # 28
# d['CheckBoxCount'] += data[28] # 29
# d['RadioGroupCount'] += data[29] # 30
# d['RadioButtonCount'] += data[30] # 31
# d['ToastCount'] += data[31] # 32
# d['SpinnerCount'] += data[32] # 33
# d['ListViewCount'] += data[33] # 34
# for i in range(0,len(less_than)):
# if data[24] <= less_than[i]:
# button[i] += 1
# if data[25] <= less_than[i]:
# text[i] += 1
# if data[26] <= less_than[i]:
# edit[i] += 1
# if data[27] <= less_than[i]:
# ibutton[i] += 1
# if data[28] <= less_than[i]:
# checkbox[i] += 1
# if data[29] <= less_than[i]:
# radiogroup[i] += 1
# if data[30] <= less_than[i]:
# radiobutton[i] += 1
# if data[31] <= less_than[i]:
# toast[i] += 1
# if data[32] <= less_than[i]:
# spinner[i] += 1
# if data[33] <= less_than[i]:
# listview[i] += 1
# d['hPictureCount'] += data[63] # 64
# d['mPictureCount'] += data[64] # 65
# d['lPictureCount'] += data[65] # 66
# d['xPictureCount'] += data[66] # 67
for i in range(0,len(less_than)):
if data[63] < less_than[i]:
h_pic[i] += 1
if data[64] < less_than[i]:
m_pic[i] += 1
if data[65] < less_than[i]:
l_pic[i] += 1
if data[66] < less_than[i]:
x_pic[i] += 1
if data[67] < less_than[i]:
t_pic[i] += 1
print 'Total instances', count
# print 'button:'
# coordinates = ''
# for i in range(0, len(button)):
# coordinates += '('
# coordinates += str(less_than[i])
# coordinates += ','
# coordinates += str(button[i] * 100 / count)
# coordinates += ')'
# print coordinates
# print 'text:'
# coordinates = ''
# for i in range(0, len(text)):
# coordinates += '('
# coordinates += str(less_than[i])
# coordinates += ','
# coordinates += str(text[i] * 100 / count)
# coordinates += ')'
# print coordinates
# print 'edit:'
# coordinates = ''
# for i in range(0, len(edit)):
# coordinates += '('
# coordinates += str(less_than[i])
# coordinates += ','
# coordinates += str(edit[i] * 100 / count)
# coordinates += ')'
# print coordinates
# print 'ibutton:'
# coordinates = ''
# for i in range(0, len(ibutton)):
# coordinates += '('
# coordinates += str(less_than[i])
# coordinates += ','
# coordinates += str(ibutton[i] * 100 / count)
# coordinates += ')'
# print coordinates
# print 'checkbox:'
# coordinates = ''
# for i in range(0, len(checkbox)):
# coordinates += '('
# coordinates += str(less_than[i])
# coordinates += ','
# coordinates += str(checkbox[i] * 100 / count)
# coordinates += ')'
# print coordinates
# print 'radiogroup:'
# coordinates = ''
# for i in range(0, len(radiogroup)):
# coordinates += '('
# coordinates += str(less_than[i])
# coordinates += ','
# coordinates += str(radiogroup[i] * 100 / count)
# coordinates += ')'
# print coordinates
# print 'radiobutton:'
# coordinates = ''
# for i in range(0, len(radiobutton)):
# coordinates += '('
# coordinates += str(less_than[i])
# coordinates += ','
# coordinates += str(radiobutton[i] * 100 / count)
# coordinates += ')'
# print coordinates
# print 'toast:'
# coordinates = ''
# for i in range(0, len(toast)):
# coordinates += '('
# coordinates += str(less_than[i])
# coordinates += ','
# coordinates += str(toast[i] * 100 / count)
# coordinates += ')'
# print coordinates
# print 'spinner:'
# coordinates = ''
# for i in range(0, len(spinner)):
# coordinates += '('
# coordinates += str(less_than[i])
# coordinates += ','
# coordinates += str(spinner[i] * 100 / count)
# coordinates += ')'
# print coordinates
# print 'listview:'
# coordinates = ''
# for i in range(0, len(listview)):
# coordinates += '('
# coordinates += str(less_than[i])
# coordinates += ','
# coordinates += str(listview[i] * 100 / count)
# coordinates += ')'
# print coordinates
print 'h_pic:'
coordinates = ''
for i in range(0, len(h_pic)):
coordinates += '('
coordinates += str(less_than[i])
coordinates += ','
coordinates += str(h_pic[i] * 100 / count)
coordinates += ')'
print coordinates
print 'm_pic:'
coordinates = ''
for i in range(0, len(m_pic)):
coordinates += '('
coordinates += str(less_than[i])
coordinates += ','
coordinates += str(m_pic[i] * 100 / count)
coordinates += ')'
print coordinates
print 'l_pic:'
coordinates = ''
for i in range(0, len(l_pic)):
coordinates += '('
coordinates += str(less_than[i])
coordinates += ','
coordinates += str(l_pic[i] * 100 / count)
coordinates += ')'
print coordinates
print 'x_pic:'
coordinates = ''
for i in range(0, len(x_pic)):
coordinates += '('
coordinates += str(less_than[i])
coordinates += ','
coordinates += str(x_pic[i] * 100 / count)
coordinates += ')'
print coordinates
print 't_pic:'
coordinates = ''
for i in range(0, len(t_pic)):
coordinates += '('
coordinates += str(less_than[i])
coordinates += ','
coordinates += str(t_pic[i] * 100 / count)
coordinates += ')'
print coordinates
def get_metric_fn(self, sklearn_fn, kwargs={}):
"""Calculates metric scores based on predicted values. Assumes the
run has been executed locally (and contains run_data). Furthermore,
it assumes that the 'correct' attribute is specified in the arff
(which is an optional field, but always the case for openml-python
runs)
Parameters
----------
sklearn_fn : function
a function pointer to a sklearn function that
accepts ``y_true``, ``y_pred`` and ``**kwargs``
Returns
-------
scores : list
a list of floats, of length num_folds * num_repeats
"""
if self.data_content is not None and self.task_id is not None:
predictions_arff = self._generate_arff_dict()
elif 'predictions' in self.output_files:
predictions_file_url = openml._api_calls._file_id_to_url(
self.output_files['predictions'], 'predictions.arff',
)
predictions_arff = arff.loads(openml._api_calls._read_url(predictions_file_url))
# TODO: make this a stream reader
else:
raise ValueError('Run should have been locally executed or contain outputfile reference.')
attribute_names = [att[0] for att in predictions_arff['attributes']]
if 'correct' not in attribute_names:
raise ValueError('Attribute "correct" should be set')
if 'prediction' not in attribute_names:
raise ValueError('Attribute "predict" should be set')
def _attribute_list_to_dict(attribute_list):
# convenience function: Creates a mapping to map from the name of attributes
# present in the arff prediction file to their index. This is necessary
# because the number of classes can be different for different tasks.
res = OrderedDict()
for idx in range(len(attribute_list)):
res[attribute_list[idx][0]] = idx
return res
attribute_dict = _attribute_list_to_dict(predictions_arff['attributes'])
# might throw KeyError!
predicted_idx = attribute_dict['prediction']
correct_idx = attribute_dict['correct']
repeat_idx = attribute_dict['repeat']
fold_idx = attribute_dict['fold']
sample_idx = attribute_dict['sample'] # TODO: this one might be zero
if predictions_arff['attributes'][predicted_idx][1] != predictions_arff['attributes'][correct_idx][1]:
pred = predictions_arff['attributes'][predicted_idx][1]
corr = predictions_arff['attributes'][correct_idx][1]
raise ValueError('Predicted and Correct do not have equal values: %s Vs. %s' %(str(pred), str(corr)))
# TODO: these could be cached
values_predict = {}
values_correct = {}
for line_idx, line in enumerate(predictions_arff['data']):
rep = line[repeat_idx]
fold = line[fold_idx]
samp = line[sample_idx]
# TODO: can be sped up bt preprocessing index, but OK for now.
prediction = predictions_arff['attributes'][predicted_idx][1].index(line[predicted_idx])
correct = predictions_arff['attributes'][predicted_idx][1].index(line[correct_idx])
if rep not in values_predict:
values_predict[rep] = OrderedDict()
values_correct[rep] = OrderedDict()
if fold not in values_predict[rep]:
values_predict[rep][fold] = OrderedDict()
values_correct[rep][fold] = OrderedDict()
if samp not in values_predict[rep][fold]:
values_predict[rep][fold][samp] = []
values_correct[rep][fold][samp] = []
values_predict[line[repeat_idx]][line[fold_idx]][line[sample_idx]].append(prediction)
values_correct[line[repeat_idx]][line[fold_idx]][line[sample_idx]].append(correct)
scores = []
for rep in values_predict.keys():
for fold in values_predict[rep].keys():
last_sample = len(values_predict[rep][fold]) - 1
y_pred = values_predict[rep][fold][last_sample]
y_true = values_correct[rep][fold][last_sample]
scores.append(sklearn_fn(y_true, y_pred, **kwargs))
return np.array(scores)
def transform(algorithm,
context,
target_att_value,
seed,
result_file,
transformations,
fold_nums=10):
fold_num = 0
for train_context, test_context in cv_split(context,
folds=fold_nums,
random_seed=seed):
fold_num += 1
print("FOLD", fold_num)
with open(result_file, 'a') as f:
f.write("FOLD {}\n".format(fold_num))
#ALEPH
if algorithm == "aleph":
start = time.time()
conv = AlephConverter(train_context,
target_att_val=target_att_value)
aleph = Aleph()
train_arff, features = aleph.induce('induce_features',
conv.positive_examples(),
conv.negative_examples(),
conv.background_knowledge(),
printOutput=False)
data = arff.loads(str(train_arff))
entries = []
targets = []
for entry in data['data']:
en = list(entry)
features_target = en[-1]
features_train = en[0:len(en) - 1]
features_train = [1 if x == "+" else 0 for x in features_train]
entries.append(features_train)
targets.append(features_target)
tmp_learner = 'aleph'
test_arff = mapper.domain_map(features,
tmp_learner,
train_context,
test_context,
format="csv",
positive_class=target_att_value)
test_ins = test_arff.split("\n")
entries_test = []
targets_test = []
for entry in test_ins:
en = entry.strip().split(",")
if en[-1] != '':
features_target = en[-1]
features_train = en[0:len(en) - 1]
features_train = [
1 if x == "+" else 0 for x in features_train
]
entries_test.append(features_train)
targets_test.append(features_target)
targets_test = [
'positive' if x == target_att_value else 'negative'
for x in targets_test
]
train_features = pd.DataFrame(entries).to_numpy()
train_targets = pd.DataFrame(targets).to_numpy()
test_features = pd.DataFrame(entries_test).to_numpy()
test_targets = pd.DataFrame(targets_test).to_numpy()
le = preprocessing.LabelEncoder()
le.fit(train_targets)
targets_train_encoded = le.transform(train_targets)
targets_test_encoded = le.transform(test_targets)
end = time.time()
run_time = end - start
train_data = (train_features, targets_train_encoded)
test_data = (test_features, targets_test_encoded)
pickle.dump(
train_data,
open("{}_{}_train.p".format(transformations, fold_num), "wb"))
pickle.dump(
test_data,
open("{}_{}_test.p".format(transformations, fold_num), "wb"))
print(algorithm, " TIME:", run_time)
with open(result_file, 'a') as f:
f.write("{} TIME: {}\n".format(algorithm, run_time))
#RSD
elif algorithm == "rsd":
start = time.time()
conv = RSDConverter(train_context)
rsd = RSD()
features, train_arff, _ = rsd.induce(conv.background_knowledge(),
examples=conv.all_examples(),
cn2sd=False)
data = arff.loads(str(train_arff))
entries = []
targets = []
for entry in data['data']:
en = list(entry)
features_target = en[-1]
features_train = en[0:len(en) - 1]
features_train = [1 if x == "+" else 0 for x in features_train]
entries.append(features_train)
targets.append(features_target)
tmp_learner = 'rsd'
test_arff = mapper.domain_map(features,
tmp_learner,
train_context,
test_context,
format="csv")
test_ins = test_arff.split("\n")
entries_test = []
targets_test = []
for entry in test_ins:
en = entry.strip().split(",")
if en[-1] != '':
features_target = en[-1]
features_train = en[0:len(en) - 1]
features_train = [
1 if x == "+" else 0 for x in features_train
]
entries_test.append(features_train)
targets_test.append(features_target)
train_features = pd.DataFrame(entries).to_numpy()
train_targets = pd.DataFrame(targets).to_numpy()
test_features = pd.DataFrame(entries_test).to_numpy()
test_targets = pd.DataFrame(targets_test).to_numpy()
le = preprocessing.LabelEncoder()
le.fit(train_targets)
targets_train_encoded = le.transform(train_targets)
targets_test_encoded = le.transform(test_targets)
end = time.time()
run_time = end - start
train_data = (train_features, targets_train_encoded)
test_data = (test_features, targets_test_encoded)
pickle.dump(
train_data,
open("{}_{}_train.p".format(transformations, fold_num), "wb"))
pickle.dump(
test_data,
open("{}_{}_test.p".format(transformations, fold_num), "wb"))
print(algorithm, " TIME:", run_time)
with open(result_file, 'a') as f:
f.write("{} TIME: {}\n".format(algorithm, run_time))
#Treeliker
elif algorithm == "treeliker":
start = time.time()
conv = TreeLikerConverter(train_context)
conv2 = TreeLikerConverter(test_context)
treeliker = TreeLiker(conv.dataset(), conv.default_template(),
conv2.dataset())
train_arff, test_arff = treeliker.run()
wtag = False
entries = []
targets = []
entries_test = []
targets_test = []
for entry in train_arff.split("\n"):
if wtag:
en = entry.split(",")
if len(en) > 1:
en = [x.replace(" ", "") for x in en]
targets.append(en[-1])
en = [1 if "+" in x else 0 for x in en]
entries.append(en[0:len(en) - 1])
if "@data" in entry:
wtag = True
wtag = False
for entry in test_arff.split("\n"):
if wtag:
en = entry.split(",")
if len(en) > 1:
en = [x.replace(" ", "") for x in en]
targets_test.append(en[-1])
en = [1 if "+" in x else 0 for x in en]
entries_test.append(en[0:len(en) - 1])
if "@data" in entry:
wtag = True
train_features = pd.DataFrame(entries).to_numpy()
train_targets = pd.DataFrame(targets).to_numpy()
test_features = pd.DataFrame(entries_test).to_numpy()
test_targets = pd.DataFrame(targets_test).to_numpy()
le = preprocessing.LabelEncoder()
le.fit(train_targets)
targets_train_encoded = le.transform(train_targets)
targets_test_encoded = le.transform(test_targets)
end = time.time()
run_time = end - start
train_data = (train_features, targets_train_encoded)
test_data = (test_features, targets_test_encoded)
pickle.dump(
train_data,
open("{}_{}_train.p".format(transformations, fold_num), "wb"))
pickle.dump(
test_data,
open("{}_{}_test.p".format(transformations, fold_num), "wb"))
print(algorithm, " TIME:", run_time)
with open(result_file, 'a') as f:
f.write("{} TIME: {}\n".format(algorithm, run_time))
#Wordification
elif algorithm == "wordification":
start = time.time()
corange = OrangeConverter(train_context)
torange = OrangeConverter(test_context)
wordification = Wordification(corange.target_Orange_table(),
corange.other_Orange_tables(),
train_context)
wordification.run(1)
wordification.calculate_weights()
train_arff = wordification.to_arff()
wordification_test = Wordification(torange.target_Orange_table(),
torange.other_Orange_tables(),
test_context)
wordification_test.run(1)
wordification_test.calculate_weights()
idfs = wordification.idf
docs = wordification_test.resulting_documents
classes = [str(a) for a in wordification_test.resulting_classes]
feature_names = wordification.word_features
feature_vectors = []
for doc in docs:
doc_vec = []
for feature in feature_names:
cnt = 0
for x in doc:
if x == feature:
cnt += 1
idf = cnt * idfs[feature]
doc_vec.append(idf)
feature_vectors.append(doc_vec)
print(feature_vectors, classes)
test_arff = wordification_test.to_arff()
entries = []
targets = []
entries_test = []
targets_test = []
wtag = False
for entry in train_arff.split("\n"):
if wtag:
en = entry.split(",")
if len(en) > 1:
en = [x.replace(" ", "") for x in en]
targets.append(en[-1])
entries.append([float(x) for x in en[0:len(en) - 1]])
if "@DATA" in entry:
wtag = True
wtag = False
targets_test = classes
entries_test = feature_vectors
train_features = pd.DataFrame(entries).to_numpy()
train_targets = pd.DataFrame(targets).to_numpy()
test_features = pd.DataFrame(entries_test).to_numpy()
test_targets = pd.DataFrame(targets_test).to_numpy()
le = preprocessing.LabelEncoder()
le.fit(np.concatenate([train_targets, test_targets]))
targets_train_encoded = le.transform(train_targets)
targets_test_encoded = le.transform(test_targets)
end = time.time()
run_time = end - start
train_data = (train_features, targets_train_encoded)
test_data = (test_features, targets_test_encoded)
pickle.dump(
train_data,
open("{}_{}_train.p".format(transformations, fold_num), "wb"))
pickle.dump(
test_data,
open("{}_{}_test.p".format(transformations, fold_num), "wb"))
print(algorithm, " TIME:", run_time)
with open(result_file, 'a') as f:
f.write("{} TIME: {}\n".format(algorithm, run_time))
#relaggs/nrelaggs
else:
converter = context_converter(train_context,
test_context,
verbose=0)
train_data = converter.get_train()
test_data = converter.get_test()
plan = converter.get_plan()
pickle.dump(
train_data,
open("{}_{}_train.p".format(transformations, fold_num), "wb"))
pickle.dump(
test_data,
open("{}_{}_test.p".format(transformations, fold_num), "wb"))
pickle.dump(
plan,
open("{}_{}_plan.p".format(transformations, fold_num), "wb"))
run_time = converter.get_time()
print(algorithm, " TIME:", run_time)
with open(result_file, 'a') as f:
f.write("{} TIME: {}\n".format(algorithm, run_time))
import pprint import os from sklearn import datasets from sklearn.cross_validation import train_test_split from sklearn.grid_search import GridSearchCV from sklearn.metrics import classification_report from sklearn.svm import SVC rootdir = os.getcwd() + '/' with open(rootdir + 'myfeatures_988_train.arff') as f: data = f.read() d = arff.loads(data) main_data_list = d['data'] mdata = [] labels = [] for lists in main_data_list: mdata.append(lists[1:-1]) labels.append(lists[-1]) # To apply an classifier on this data, we need to flatten the image, to # turn the data in a (samples, feature) matrix: n_samples = len(labels) X = mdata y = labels
def get_metric_fn(self, sklearn_fn, kwargs={}):
"""Calculates metric scores based on predicted values. Assumes the
run has been executed locally (and contains run_data). Furthermore,
it assumes that the 'correct' attribute is specified in the arff
(which is an optional field, but always the case for openml-python
runs)
Parameters
----------
sklearn_fn : function
a function pointer to a sklearn function that
accepts ``y_true``, ``y_pred`` and ``**kwargs``
Returns
-------
scores : list
a list of floats, of length num_folds * num_repeats
"""
if self.data_content is not None and self.task_id is not None:
predictions_arff = self._generate_arff_dict()
elif 'predictions' in self.output_files:
predictions_file_url = _file_id_to_url(self.output_files['predictions'], 'predictions.arff')
predictions_arff = arff.loads(openml._api_calls._read_url(predictions_file_url))
# TODO: make this a stream reader
else:
raise ValueError('Run should have been locally executed or contain outputfile reference.')
attribute_names = [att[0] for att in predictions_arff['attributes']]
if 'correct' not in attribute_names:
raise ValueError('Attribute "correct" should be set')
if 'prediction' not in attribute_names:
raise ValueError('Attribute "predict" should be set')
def _attribute_list_to_dict(attribute_list):
# convenience function: Creates a mapping to map from the name of attributes
# present in the arff prediction file to their index. This is necessary
# because the number of classes can be different for different tasks.
res = dict()
for idx in range(len(attribute_list)):
res[attribute_list[idx][0]] = idx
return res
attribute_dict = _attribute_list_to_dict(predictions_arff['attributes'])
# might throw KeyError!
predicted_idx = attribute_dict['prediction']
correct_idx = attribute_dict['correct']
repeat_idx = attribute_dict['repeat']
fold_idx = attribute_dict['fold']
sample_idx = attribute_dict['sample'] # TODO: this one might be zero
if predictions_arff['attributes'][predicted_idx][1] != predictions_arff['attributes'][correct_idx][1]:
pred = predictions_arff['attributes'][predicted_idx][1]
corr = predictions_arff['attributes'][correct_idx][1]
raise ValueError('Predicted and Correct do not have equal values: %s Vs. %s' %(str(pred), str(corr)))
# TODO: these could be cached
values_predict = {}
values_correct = {}
for line_idx, line in enumerate(predictions_arff['data']):
rep = line[repeat_idx]
fold = line[fold_idx]
samp = line[sample_idx]
# TODO: can be sped up bt preprocessing index, but OK for now.
prediction = predictions_arff['attributes'][predicted_idx][1].index(line[predicted_idx])
correct = predictions_arff['attributes'][predicted_idx][1].index(line[correct_idx])
if rep not in values_predict:
values_predict[rep] = dict()
values_correct[rep] = dict()
if fold not in values_predict[rep]:
values_predict[rep][fold] = dict()
values_correct[rep][fold] = dict()
if samp not in values_predict[rep][fold]:
values_predict[rep][fold][samp] = []
values_correct[rep][fold][samp] = []
values_predict[line[repeat_idx]][line[fold_idx]][line[sample_idx]].append(prediction)
values_correct[line[repeat_idx]][line[fold_idx]][line[sample_idx]].append(correct)
scores = []
for rep in values_predict.keys():
for fold in values_predict[rep].keys():
last_sample = len(values_predict[rep][fold]) - 1
y_pred = values_predict[rep][fold][last_sample]
y_true = values_correct[rep][fold][last_sample]
scores.append(sklearn_fn(y_true, y_pred, **kwargs))
return np.array(scores)
def test_encode_source(self):
obj = arff.loads(ARFF_SOURCE)
result = arff.dumps(obj)
expected = ARFF_DESTINY
self.assertEqual(result, expected)
def load_arff(filename):
f = open(filename, 'r')
arf = arff.loads(f.read())
f.close()
return arf
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-d', '--directory', help='directory of the dataset')
parser.add_argument('-m', '--mode', help='merge mode(0=merge by folders, 1=merge two arff files')
args = parser.parse_args()
if args.directory:
directory = args.directory
else:
parser.print_help()
if args.mode:
mode = args.mode
else:
parser.print_help()
obj = {
'description': u'',
'relation': 'testset',
'attributes': [
('apk_size', 'REAL'),
('dex_size', 'REAL'),
('min_andrversion', 'INTEGER'),
('max_andrversion', 'INTEGER'),
('target_andrversion', 'INTEGER'),
('security', ['-1','0']),
('methodCount', 'INTEGER'),
('classCount', 'INTEGER'),
('crypto_count', 'INTEGER'),
('dynCode_count', 'INTEGER'),
('native_count', 'INTEGER'),
('reflect_count', 'INTEGER'),
('sendSMS', ['1', '0']),
('deleteSMS', ['1', '0']),
('interruptSMS', ['1', '0']),
('httpPost', ['1', '0']),
('deviceId', ['1', '0']),
('simCountry', ['1', '0']),
('installedPkg', ['1', '0']),
('loadOtherCode', ['1', '0']),
('subprocess', ['1', '0']),
('executeOtherCode', ['1', '0']),
('jni', ['1', '0']),
('unix', ['1', '0']),
('buttonCount', 'INTEGER'),
('TextViewCount', 'INTEGER'),
('EditViewCount', 'INTEGER'),
('ImageButtonCount', 'INTEGER'),
('CheckBoxCount', 'INTEGER'),
('RadioGroupCount', 'INTEGER'),
('RadioButtonCount', 'INTEGER'),
('ToastCount', 'INTEGER'),
('SpinnerCount', 'INTEGER'),
('ListViewCount', 'INTEGER'),
('fileCount', 'INTEGER'),
('INTERNET', ['1', '0']),
('SET_DEBUG_APP', ['1', '0']),
('MODIFY_PHONE_STATE', ['1', '0']),
('RECORD_AUDIO', ['1', '0']),
('RECEIVE_BOOT_COMPLETED', ['1', '0']),
('RECEIVE_MMS', ['1', '0']),
('RECEIVE_SMS', ['1', '0']),
('RECEIVE_WAP_PUSH', ['1', '0']),
('SEND_SMS', ['1', '0']),
('CALL_PHONE', ['1', '0']),
('CALL_PRIVILEGED', ['1', '0']),
('PROCESS_OUTGOING_CALLS', ['1', '0']),
('READ_CALL_LOG', ['1', '0']),
('READ_EXTERNAL_STORAGE', ['1', '0']),
('READ_LOGS', ['1', '0']),
('ACCESS_COARSE_LOCATION', ['1', '0']),
('ACCESS_FINE_LOCATION', ['1', '0']),
('BLUETOOTH', ['1', '0']),
('CAMERA', ['1', '0']),
('INSTALL_PACKAGES', ['1', '0']),
('NFC', ['1', '0']),
('READ_CONTACTS', ['1', '0']),
('permissionCount', 'INTEGER'),
('activityCount', 'INTEGER'),
('serviceCount', 'INTEGER'),
('receiverCount', 'INTEGER'),
('providerCount', 'INTEGER'),
('exportedCount', 'INTEGER'),
('hPictureCount', 'INTEGER'),
('mPictureCount', 'INTEGER'),
('lPictureCount', 'INTEGER'),
('xPictureCount', 'INTEGER'),
('totalCount', 'INTEGER'),
],
'data': [],
}
#size = {
# 'anquan': 15,
# 'ditu': 11,
# 'liaotian': 12,
# 'meihua': 298,
# 'paishe': 39,
# 'richeng': 67,
# 'shangwu': 24,
# 'shiyong': 192,
# 'tongxun': 50,
# 'tuxiang': 43,
# 'wangluo': 77,
# 'xitong': 295,
# 'xuexi': 33,
# 'yingyin': 60,
# 'yuedu': 27,
#}
size = {
'anquan': 54,
'ditu': 39,
'liaotian': 42,
'meihua': 1064,
'paishe': 141,
'richeng': 238,
'shangwu': 87,
'shiyong': 684,
'tongxun': 178,
'tuxiang': 153,
'wangluo': 275,
'xitong': 1050,
'xuexi': 117,
'yingyin': 213,
'yuedu': 95,
}
# Part 1: merge arff by folders
if mode == '0':
for folder in os.listdir(directory):
if os.path.isdir(os.path.join(directory, folder)):
print 'Merging ' + folder
f = open(os.path.join(os.path.join(directory, folder), 'weka_testset.arff'), 'r')
file = f.read()
f.close()
d = arff.loads(file)
i = 0
for data in d['data']:
# if you want to shrink the dataset, uncomment two lines below
if i == size[folder]:
break
obj['data'].append(data)
i += 1
f = open(os.path.join(directory, 'weka_testset_4430.arff'), 'w')
arff.dump(obj, f)
f.close()
# Part 2: merge two arff files
if mode == '1':
f1 = open(os.path.join(directory, 'weka_testset_1.arff'), 'r')
file1 = f1.read()
f1.close()
f2 = open(os.path.join(directory, 'weka_testset_2.arff'), 'r')
file2 = f2.read()
f2.close()
d1 = arff.loads(file1)
d2 = arff.loads(file2)
for data in d1['data']:
obj['data'].append(data)
for data in d2['data']:
obj['data'].append(data)
f = open(os.path.join(directory, 'weka_testset.arff'), 'w')
arff.dump(obj, f)
f.close()
sys.path.append("D:\\repositories/openml-python/")
import openml
# benchmark configurations
small_config_url = "https://raw.githubusercontent.com/openml/automlbenchmark/master/resources/benchmarks/small-8c4h.yaml"
medium_config_url = "https://raw.githubusercontent.com/openml/automlbenchmark/master/resources/benchmarks/medium-8c4h.yaml"
# auto-sklearn problems
binary_url = "https://raw.githubusercontent.com/automl/auto-sklearn/master/autosklearn/metalearning/files/roc_auc_binary.classification_dense/algorithm_runs.arff"
multiclass_url = "https://raw.githubusercontent.com/automl/auto-sklearn/master/autosklearn/metalearning/files/log_loss_multiclass.classification_dense/algorithm_runs.arff"
print('loading files')
small_configuration = yaml.load(requests.get(small_config_url).text)
medium_configuration = yaml.load(requests.get(medium_config_url).text)
binary_configuration = arff.loads(requests.get(binary_url).text)
multiclass_configuration = arff.loads(requests.get(multiclass_url).text)
print('parsing files')
benchmark_tids = set(
[problem.get('openml_task_id') for problem in small_configuration] +
[problem.get('openml_task_id') for problem in medium_configuration])
autosklearn_tids = set(
[int(row[0]) for row in binary_configuration['data']] +
[int(row[0]) for row in multiclass_configuration['data']])
print('comparing tids')
print(benchmark_tids & autosklearn_tids)
print('retrieving and comparing dids')
def load(file_name):
# Load ARFF from web
response = requests.get(
'https://raw.githubusercontent.com/renatopp/arff-datasets/master/classification/'
+ file_name)
html = response.text
arff_f = arff.loads(html)
# Load ARFF from file
# with open('./datasets/arff-datasets-master/classification/' + file_name, 'r') as file:
# arff_f = arff.load(file)
# ARFF to pandas
attrs = arff_f['attributes']
attrs_t = []
for attr in attrs:
attrs_t.append(attr[0])
df = pd.DataFrame(data=arff_f['data'], columns=attrs_t)
# Target column estimation
if 'class' in list(df):
target = 'class'
elif 'Class' in list(df):
target = 'Class'
elif 'type' in list(df):
target = 'type'
elif 'TYPE' in list(df):
target = 'TYPE'
elif 'Type' in list(df):
target = 'Type'
elif 'symboling' in list(df):
target = 'symboling'
elif 'OVERALL_DIAGNOSIS' in list(df):
target = 'OVERALL_DIAGNOSIS'
elif 'LRS-class' in list(df):
target = 'LRS-class'
elif 'num' in list(df):
target = 'num'
elif 'Class_attribute' in list(df):
target = 'Class_attribute'
elif 'Contraceptive_method_used' in list(df):
target = 'Contraceptive_method_used'
elif 'surgical_lesion' in list(df):
target = 'surgical_lesion'
elif 'band_type' in list(df):
target = 'band_type'
elif 'Survival_status' in list(df):
target = 'Survival_status'
elif 'surgical lesion' in list(df):
target = 'surgical lesion'
elif 'decision' in list(df):
target = 'decision'
else:
print('Using the last column...', list(df)[-1])
target = list(df)[-1]
# Remove rows with a missing target value
# Justification: They are of no use for strictly supervised learning (semi-supervised learning would still benefit from them)
df = df.dropna(subset=[target])
# Get class metadata
y_unique, y_inversed = np.unique(df[target], return_inverse=True)
y_counts = np.bincount(y_inversed)
# Convert the problem into binary classification with {0,1} as class values.
# Justification: OneHotEncoding and TargetEncoder work only with binary numerical output.
# Approach: Take a majority class as 1 and the rest as 0.
majority_class = y_unique[np.argmax(y_counts)]
df[target] = (df[target] == majority_class).astype('uint8')
# Determine the count of folds that is not going to cause issues.
# We identify the least common class label and then return min(10, minority_class_count).
# Justification: If we have only 5 positive samples and 5 negative samples, with stratified cross-validation we can use at best 5 folds.
y_unique, y_inversed = np.unique(df[target], return_inverse=True)
y_counts = np.bincount(y_inversed)
fold_count = min(np.min(y_counts), 10)
# Target/features split. Encoders expect the target to be in pandas.Series and features in pandas.DataFrame.
y = df.loc[:, target]
X = df.drop(target, axis=1)
# Data type estimation
for col in X:
try:
X[col] = X[col].astype('float', copy=False)
except ValueError:
pass
return X, y, fold_count
def load(file_name):
# Load ARFF from web
response = requests.get('https://raw.githubusercontent.com/renatopp/arff-datasets/master/classification/' + file_name)
html = response.text
arff_f = arff.loads(html)
# Load ARFF from file
# with open('./datasets/arff-datasets-master/classification/' + file_name, 'r') as file:
# arff_f = arff.load(file)
# ARFF to pandas
attrs = arff_f['attributes']
attrs_t = []
for attr in attrs:
attrs_t.append(attr[0])
df = pd.DataFrame(data=arff_f['data'], columns=attrs_t)
# df = pd.read_csv('./datasets/article/' + file_name)
# Target column estimation
if 'class' in list(df):
target = 'class'
elif 'Class' in list(df):
target = 'Class'
elif 'type' in list(df):
target = 'type'
elif 'TYPE' in list(df):
target = 'TYPE'
elif 'Type' in list(df):
target = 'Type'
elif 'symboling' in list(df):
target = 'symboling'
elif 'OVERALL_DIAGNOSIS' in list(df):
target = 'OVERALL_DIAGNOSIS'
elif 'LRS-class' in list(df):
target = 'LRS-class'
elif 'num' in list(df):
target = 'num'
elif 'Class_attribute' in list(df):
target = 'Class_attribute'
elif 'Contraceptive_method_used' in list(df):
target = 'Contraceptive_method_used'
elif 'surgical_lesion' in list(df):
target = 'surgical_lesion'
elif 'band_type' in list(df):
target = 'band_type'
elif 'Survival_status' in list(df):
target = 'Survival_status'
elif 'surgical lesion' in list(df):
target = 'surgical lesion'
elif 'decision' in list(df):
target = 'decision'
elif 'ACTION' in list(df):
target = 'ACTION'
else:
print('Using the last column...', list(df)[-1])
target = list(df)[-1]
# Remove rows with a missing target value
# Justification: They are of no use for strictly supervised learning (semi-supervised learning would still benefit from them)
df = df.dropna(subset=[target])
# Get class metadata
y_unique, y_inversed = np.unique(df[target], return_inverse=True)
y_counts = np.bincount(y_inversed)
# Convert the problem into binary classification with {0,1} as class values.
# Justification: OneHotEncoding and TargetEncoder work only with binary numerical output.
# Approach: Take a majority class as 1 and the rest as 0.
majority_class = y_unique[np.argmax(y_counts)]
df[target] = (df[target]==majority_class).astype('uint8')
# Determine the count of folds that is not going to cause issues.
# We identify the least common class label and then return min(10, minority_class_count).
# Justification: If we have only 5 positive samples and 5 negative samples, we can use at best 5 folds with stratified cross-validation.
y_unique, y_inversed = np.unique(df[target], return_inverse=True)
y_counts = np.bincount(y_inversed)
fold_count = min(np.min(y_counts), 10)
# Target/features split. Encoders expect the target to be in pandas.Series and features in pandas.DataFrame.
y = df.loc[:, target]
X = df.drop(target, axis=1)
# Data type estimation
for col in X:
try:
X[col] = X[col].astype('float', copy=False)
except ValueError:
pass
return X, y, fold_count
wtag = False
targets_test = classes
entries_test = feature_vectors
elif learner == "proper":
pins = Proper({'context': train_context}, False)
output = pins.run()
elif learner == "tertius":
pins = Tertius()
else:
data = arff.loads(unicode(train_arff))
entries = []
targets = []
for entry in data:
en = list(entry)
features_target = en[-1]
features_train = en[0:len(en) - 1]
features_train = [1 if x == "+" else 0 for x in features_train]
entries.append(features_train)
targets.append(features_target)
# Map the *test set* using the features from the train set
if learner == 'aleph':
tmp_learner = learner
test_arff = mapper.domain_map(features,
import numpy
import pprint
import os
from sklearn import datasets
from sklearn.cross_validation import train_test_split
from sklearn.grid_search import GridSearchCV
from sklearn.metrics import classification_report
from sklearn.svm import SVC
rootdir = os.getcwd() + '/'
with open(rootdir + 'myfeatures_988_train.arff') as f:
data = f.read()
d = arff.loads(data)
main_data_list = d['data']
mdata = []
labels = []
for lists in main_data_list:
mdata.append(lists[1:-1])
labels.append(lists[-1])
# To apply an classifier on this data, we need to flatten the image, to
# turn the data in a (samples, feature) matrix:
n_samples = len(labels)
X = mdata
y = labels
def importarArff(filePath):
with open(filePath, 'r') as myfile:
text = myfile.read()
obj = arff.loads(text)
return obj