def _read_url_files(url, data=None, file_dictionary=None, file_elements=None):
"""do a post request to url with data, file content of
file_dictionary and sending file_elements as files"""
data = {} if data is None else data
data['api_key'] = config.apikey
if file_elements is None:
file_elements = {}
if file_dictionary is not None:
for key, path in file_dictionary.items():
path = os.path.abspath(path)
if os.path.exists(path):
try:
if key is 'dataset':
# check if arff is valid?
decoder = arff.ArffDecoder()
with io.open(path, encoding='utf8') as fh:
decoder.decode(fh, encode_nominal=True)
except:
raise ValueError("The file you have provided is not a valid arff file")
file_elements[key] = open(path, 'rb')
else:
raise ValueError("File doesn't exist")
# Using requests.post sets header 'Accept-encoding' automatically to
# 'gzip,deflate'
response = requests.post(url, data=data, files=file_elements)
if response.status_code != 200:
raise _parse_server_exception(response, url=url)
if 'Content-Encoding' not in response.headers or \
response.headers['Content-Encoding'] != 'gzip':
warnings.warn('Received uncompressed content from OpenML for %s.' % url)
return response.text
def read_data(self, filename):
file_ = codecs.open(filename, 'rb', 'utf-8')
decoder = arff.ArffDecoder()
dataset = decoder.decode(file_.readlines(), encode_nominal=True)
file_.close()
data = dataset['data']
return self.normlize_data(np.mat(data))
def load_arff_data(filepath, is_regression=True):
from sklearn.model_selection import train_test_split
with open(filepath, 'r') as f:
decoder = arff.ArffDecoder()
d = decoder.decode(f, encode_nominal=True)
data = np.array(d['data'])
X = data[:, :-1]
y = data[:, -1]
rng = np.random.RandomState(0)
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2,
random_state=rng) # TODO: Test size should be an arg
n_dim = X_train.shape[1]
n_train = X_train.shape[0]
n_test = X_test.shape[0]
n_class = 1 if is_regression else np.unique(y_train)
data = {
'x_train': X_train,
'y_train': y_train,
'n_class': n_class,
'n_dim': n_dim,
'n_train': n_train,
'x_test': X_test,
'y_test': y_test,
'n_test': n_test,
'is_sparse': False
}
return data
def scrape_data():
# decode the .arff data and change text labels into numerical
decoder = arff.ArffDecoder()
data = decoder.decode(file, encode_nominal=True)
# split the raw data into data and labels
vals = [val[0:-1] for val in data['data']]
labels = [label[-1] for label in data['data']]
for val in labels:
if labels[val] != 0:
labels[val] = 1
# split the labels and data into traning and validation sets
training_data = vals[0:int(.9 * len(vals))]
training_labels = labels[0:int(.9 * len(vals))]
validation_data = vals[int(.9 * len(vals)):]
validation_labels = labels[int(.9 * len(vals)):]
print(training_labels)
# flatten labels with one hot encoding
training_labels = to_categorical(training_labels, 5)
validation_labels = to_categorical(validation_labels, 5)
# save all arrays with numpy
np.save('saved-files/vals', np.asarray(vals))
np.save('saved-files/labels', np.asarray(labels))
np.save('saved-files/training_data', np.asarray(training_data))
np.save('saved-files/validation_data', np.asarray(validation_data))
np.save('saved-files/training_labels', np.asarray(training_labels))
np.save('saved-files/validation_labels', np.asarray(validation_labels))
def test_encode_adding_quotes_with_spaces(self):
# regression tests for https://github.com/renatopp/liac-arff/issues/87
encoder = self.get_encoder()
# \u3000 corresponds to an ideographic space. It should be treated as
# a space.
fixture = {
'relation': 'name',
'attributes': [('A', 'STRING'), ('B', 'STRING')],
'data': [['a', 'b'], ['b\u3000e', 'a']],
}
expected_data = """@RELATION name
@ATTRIBUTE A STRING
@ATTRIBUTE B STRING
@DATA
a,b
'b\u3000e',a
"""
arff_data = encoder.encode(fixture)
self.assertEqual(arff_data, expected_data)
decoder = arff.ArffDecoder()
arff_object = decoder.decode(arff_data)
self.assertEqual(arff_object['data'], fixture['data'])
def meta_train_data_transformed(request):
tests_dir = __file__
os.chdir(os.path.dirname(tests_dir))
decoder = arff.ArffDecoder()
with open(os.path.join("datasets", "dataset.arff")) as fh:
dataset = decoder.decode(fh, encode_nominal=True)
# -1 because the last attribute is the class
attribute_types = [
'numeric' if type(type_) != list else 'nominal'
for name, type_ in dataset['attributes'][:-1]]
categorical = {i: True if attribute == 'nominal' else False
for i, attribute in enumerate(attribute_types)}
data = np.array(dataset['data'], dtype=np.float64)
X = data[:, :-1]
y = data[:, -1].reshape((-1,))
logger = logging.getLogger('Meta')
meta_features.helper_functions.set_value(
"MissingValues", meta_features.helper_functions["MissingValues"](X, y, logger, categorical),
)
meta_features.helper_functions.set_value(
"NumSymbols",
meta_features.helper_functions["NumSymbols"](X, y, logger, categorical),
)
meta_features.helper_functions.set_value(
"ClassOccurences",
meta_features.helper_functions["ClassOccurences"](X, y, logger),
)
DPP = FeatTypeSplit(feat_type={
col: 'categorical' if category else 'numerical' for col, category in categorical.items()
})
X_transformed = DPP.fit_transform(X)
number_numerical = np.sum(~np.array(list(categorical.values())))
categorical_transformed = {i: True if i < (X_transformed.shape[1] - number_numerical) else False
for i in range(X_transformed.shape[1])}
# pre-compute values for transformed inputs
meta_features.helper_functions.set_value(
"PCA", meta_features.helper_functions["PCA"](X_transformed, y, logger),
)
meta_features.helper_functions.set_value(
"Skewnesses", meta_features.helper_functions["Skewnesses"](
X_transformed, y, logger, categorical_transformed),
)
meta_features.helper_functions.set_value(
"Kurtosisses", meta_features.helper_functions["Kurtosisses"](
X_transformed, y, logger, categorical_transformed)
)
if request.param == 'numpy':
return X_transformed, y, categorical_transformed
elif request.param == 'pandas':
return pd.DataFrame(X_transformed), y, categorical_transformed
else:
raise ValueError(request.param)
def return_arff(self):
filename = self.directory
with io.open(filename) as fh:
decoder = arff.ArffDecoder()
return decoder.decode(fh,
encode_nominal=True,
return_type=arff.DENSE)
def read_data(self, filename):
file_ = codecs.open(filename, 'rb', 'utf-8')
decoder = arff.ArffDecoder()
dataset = decoder.decode(file_.readlines(), encode_nominal=True)
file_.close()
self.__data = dataset['data']
if (self.__data is not None and self.__data[0] is not None):
self.__dim_size = len(self.__data[0])
def setUp(self):
self.cwd = os.getcwd()
tests_dir = __file__
os.chdir(os.path.dirname(tests_dir))
decoder = arff.ArffDecoder()
with open(os.path.join("datasets", "dataset.arff")) as fh:
dataset = decoder.decode(fh, encode_nominal=True)
# -1 because the last attribute is the class
self.attribute_types = [
'numeric' if type(type_) != list else 'nominal'
for name, type_ in dataset['attributes'][:-1]]
self.categorical = [True if attribute == 'nominal' else False
for attribute in self.attribute_types]
data = np.array(dataset['data'], dtype=np.float64)
X = data[:,:-1]
y = data[:,-1].reshape((-1,))
ohe = OneHotEncoder(self.categorical)
X_transformed = ohe.fit_transform(X)
imp = Imputer(copy=False)
X_transformed = imp.fit_transform(X_transformed)
center = not scipy.sparse.isspmatrix((X_transformed))
standard_scaler = StandardScaler(with_mean=center)
X_transformed = standard_scaler.fit_transform(X_transformed)
X_transformed = X_transformed.todense()
# Transform the array which indicates the categorical metafeatures
number_numerical = np.sum(~np.array(self.categorical))
categorical_transformed = [True] * (X_transformed.shape[1] -
number_numerical) + \
[False] * number_numerical
self.categorical_transformed = categorical_transformed
self.X = X
self.X_transformed = X_transformed
self.y = y
self.mf = meta_features.metafeatures
self.helpers = meta_features.helper_functions
# Precompute some helper functions
self.helpers.set_value("PCA", self.helpers["PCA"]
(self.X_transformed, self.y))
self.helpers.set_value("MissingValues", self.helpers[
"MissingValues"](self.X, self.y, self.categorical))
self.helpers.set_value("NumSymbols", self.helpers["NumSymbols"](
self.X, self.y, self.categorical))
self.helpers.set_value("ClassOccurences",
self.helpers["ClassOccurences"](self.X, self.y))
self.helpers.set_value("Skewnesses",
self.helpers["Skewnesses"](self.X_transformed, self.y,
self.categorical_transformed))
self.helpers.set_value("Kurtosisses",
self.helpers["Kurtosisses"](self.X_transformed, self.y,
self.categorical_transformed))
def load_arff_data(filename):
with open(filename) as f:
decoder = arff.ArffDecoder()
arff_obj = decoder.decode(f, encode_nominal=True)
# feat_num = len([v for v in arff_obj['attributes'] if v[0] != 'class'])
data = np.array(arff_obj['data'])
X = data[:, :-1]
y = data[:, -1]
return X, y
def arff2df(filepath):
decoder = arff.ArffDecoder()
arff_file = open(filepath)
decoded_arff = decoder.decode(arff_file, return_type=arff.LOD)
data = decoded_arff['data']
column_names = list(map(lambda x: x[0], decoded_arff['attributes']))
df = pd.DataFrame.from_records(data,
columns=list(range(len(column_names))))
df = df.fillna(0)
df.columns = column_names
return df
def load_arff_data(filepath):
with open(filepath, 'r') as f:
decoder = arff.ArffDecoder()
d = decoder.decode(f, encode_nominal=True)
# tvas: We are assuming the target/dependent is the last column
data = np.array(d['data'])
X = data[:, :-1]
y = data[:, -1]
return X, y
def read_data(self, filename):
"""
Read data from file.
:param filename: filename
:return: normalized data
"""
file_ = codecs.open(filename, 'rb', 'utf-8')
decoder = arff.ArffDecoder()
dataset = decoder.decode(file_.readlines(), encode_nominal=True)
file_.close()
data = dataset['data']
return self.normalize_data(np.mat(data))
def sparse_data():
tests_dir = __file__
os.chdir(os.path.dirname(tests_dir))
decoder = arff.ArffDecoder()
with open(os.path.join("datasets", "dataset.arff")) as fh:
dataset = decoder.decode(fh, encode_nominal=True)
# -1 because the last attribute is the class
attribute_types = [
'numeric' if type(type_) != list else 'nominal'
for name, type_ in dataset['attributes'][:-1]
]
categorical = {
i: True if attribute == 'nominal' else False
for i, attribute in enumerate(attribute_types)
}
data = np.array(dataset['data'], dtype=np.float64)
X = data[:, :-1]
y = data[:, -1].reshape((-1, ))
# First, swap NaNs and zeros, because when converting an encoded
# dense matrix to sparse, the values which are encoded to zero are lost
X_sparse = X.copy()
NaNs = ~np.isfinite(X_sparse)
X_sparse[NaNs] = 0
X_sparse = sparse.csr_matrix(X_sparse)
X = X_sparse
y = y
mf = meta_features.metafeatures
helpers = meta_features.helper_functions
logger = logging.getLogger()
# Precompute some helper functions
helpers.set_value(
"MissingValues",
helpers["MissingValues"](X, y, logger, categorical),
)
mf.set_value(
"NumberOfMissingValues",
mf["NumberOfMissingValues"](X, y, logger, categorical),
)
helpers.set_value(
"NumSymbols",
helpers["NumSymbols"](X, y, logger, categorical),
)
helpers.set_value(
"ClassOccurences",
helpers["ClassOccurences"](X, y, logger),
)
return X, y, categorical
def test_date(self):
file_ = '''@RELATION employee
@ATTRIBUTE Name STRING
@ATTRIBUTE start_date DATE
@ATTRIBUTE end_date DATE '%Y/%m/%dT%H:%M:%S'
@ATTRIBUTE simple_date DATE '%Y/%m/%d'
@DATA
Lulu,'2011-05-20T12:34:56','2014/06/21T12:34:56','2018/03/04'
Daisy,'2012-09-30T12:34:56','2015/11/21T12:34:56','2018/03/04'
Brie,'2013-05-01T12:34:56','2016/12/21T12:34:56','2018/03/04'
'''
decoder = arff.ArffDecoder()
d = decoder.decode(file_, encode_nominal=True)
reconstituted = arff.dumps(d)
decoder2 = arff.ArffDecoder()
d2 = decoder2.decode(reconstituted, encode_nominal=True)
self.assertEqual(d['data'][1][1], d2['data'][1][1])
self.assertEqual(d['data'][1][2], d2['data'][1][2])
self.assertEqual(d['data'][1][3], d2['data'][1][3])
self.assertEqual(d['data'][2][1], d2['data'][2][1])
self.assertEqual(d['data'][2][2], d2['data'][2][2])
self.assertEqual(d['data'][2][3], d2['data'][2][3])
def retrieve_class_labels_for_dataset(self, dataset):
"""Reads the datasets arff to determine the class-labels, and returns those.
If the task has no class labels (for example a regression problem) it returns None."""
# TODO improve performance, currently reads the whole file
# Should make a method that only reads the attributes
arffFileName = dataset.data_file
with open(arffFileName) as fh:
arffData = arff.ArffDecoder().decode(fh)
dataAttributes = dict(arffData['attributes'])
if('class' in dataAttributes):
return dataAttributes['class']
else:
return None
def read_data(self, filename):
"""
Read data from file.
:param filename: Name of the file to read
:return: no return
"""
file_ = codecs.open(filename, 'rb', 'utf-8')
decoder = arff.ArffDecoder()
dataset = decoder.decode(file_.readlines(), encode_nominal=True)
file_.close()
self.__data = dataset['data']
if self.__data is not None and self.__data[0] is not None:
self.__dim_size = len(self.__data[0])
def test_get_online_dataset_arff(self):
dataset_id = 100 # Australian
# lazy loading not used as arff file is checked.
dataset = openml.datasets.get_dataset(dataset_id)
decoder = arff.ArffDecoder()
# check if the arff from the dataset is
# the same as the arff from _get_arff function
d_format = (dataset.format).lower()
self.assertEqual(
dataset._get_arff(d_format),
decoder.decode(
_get_online_dataset_arff(dataset_id),
encode_nominal=True,
return_type=arff.DENSE if d_format == 'arff' else arff.COO),
"ARFF files are not equal")
def scrape_data():
# 解码arff数据，文本标签转变为二进制数据
decoder = arff.ArffDecoder()
data = decoder.decode(file, encode_nominal=True)
# 将原始数据分解为数据和标签
vals = [val[0: -1] for val in data['data']]
labels = [label[-1] for label in data['data']]
for val in labels:
if labels[val] != 0:
labels[val] = 1
#将标签和数据分成训练和验证集
training_data = vals[0: int(.9 * len(vals))]
training_labels = labels[0: int(.9 * len(vals))]
validation_data = vals[int(.9 * len(vals)):]
validation_labels = labels[int(.9 * len(vals)):]
a = np.asarray(training_data, dtype=float)
scaler = preprocessing.StandardScaler().fit(a)
scaler.mean_
scaler.var_
scaler.scale_
training_data = scaler.transform(a)
b = np.asarray(validation_data, dtype=float)
scaler = preprocessing.StandardScaler().fit(b)
scaler.mean_
scaler.var_
scaler.scale_
validation_data = scaler.transform(b)
#print(training_labels)
# 将原有的类别向量转换为独热编码的形式
training_labels = to_categorical(training_labels, 5)
validation_labels = to_categorical(validation_labels, 5)
# 用numpy保存所有数组
np.save('saved-files/vals', np.asarray(vals))
np.save('saved-files/labels', np.asarray(labels))
np.save('saved-files/training_data', np.asarray(training_data))
np.save('saved-files/validation_data', np.asarray(validation_data))
np.save('saved-files/training_labels', np.asarray(training_labels))
np.save('saved-files/validation_labels', np.asarray(validation_labels))
def meta_train_data(request):
tests_dir = __file__
os.chdir(os.path.dirname(tests_dir))
decoder = arff.ArffDecoder()
with open(os.path.join("datasets", "dataset.arff")) as fh:
dataset = decoder.decode(fh, encode_nominal=True)
# -1 because the last attribute is the class
attribute_types = [
'numeric' if type(type_) != list else 'nominal'
for name, type_ in dataset['attributes'][:-1]
]
categorical = {
i: True if attribute == 'nominal' else False
for i, attribute in enumerate(attribute_types)
}
data = np.array(dataset['data'], dtype=np.float64)
X = data[:, :-1]
y = data[:, -1].reshape((-1, ))
logger = logging.getLogger('Meta')
meta_features.helper_functions.set_value(
"MissingValues",
meta_features.helper_functions["MissingValues"](X, y, logger,
categorical),
)
meta_features.helper_functions.set_value(
"NumSymbols",
meta_features.helper_functions["NumSymbols"](X, y, logger,
categorical),
)
meta_features.helper_functions.set_value(
"ClassOccurences",
meta_features.helper_functions["ClassOccurences"](X, y, logger),
)
if request.param == 'numpy':
return X, y, categorical
elif request.param == 'pandas':
return pd.DataFrame(X), y, categorical
else:
raise ValueError(request.param)
def publish(self):
"""Publish the dataset on the OpenML server.
Upload the dataset description and dataset content to openml.
Returns
-------
dataset_id: int
Id of the dataset uploaded to the server.
"""
file_elements = {'description': self._to_xml()}
# the arff dataset string is available
if self._dataset is not None:
file_elements['dataset'] = self._dataset
else:
# the path to the arff dataset is given
if self.data_file is not None:
path = os.path.abspath(self.data_file)
if os.path.exists(path):
try:
with io.open(path, encoding='utf8') as fh:
# check if arff is valid
decoder = arff.ArffDecoder()
decoder.decode(fh, encode_nominal=True)
except arff.ArffException:
raise ValueError("The file you have provided is not "
"a valid arff file.")
with open(path, 'rb') as fp:
file_elements['dataset'] = fp.read()
else:
if self.url is None:
raise ValueError("No url/path to the data file was given")
return_value = openml._api_calls._perform_api_call(
"data/",
'post',
file_elements=file_elements,
)
response = xmltodict.parse(return_value)
self.dataset_id = int(response['oml:upload_data_set']['oml:id'])
return self.dataset_id
def detect_dialect(self):
# The arff package loads an arff file into a dict with the the keys:
# description (description of dataset)
# relation (name of dataset)
# attributes (list of tuples with name and type of attribute)
# data (list with the data rows)
decoder = arff.ArffDecoder()
if self.contents is None:
file = open(self.path, 'r')
weka = decoder.decode(file)
file.close()
else:
weka = decoder.decode(self.decoded_contents)
# The decoded contents are no longer needed and should not waste memory
self.decoded_contents = None
self.name = weka['relation']
self.description = weka['description']
# Attribute types are either 'REAL', 'INTEGER', 'NUMERIC' or a list of values (NOMINAL???)
self.attributes = weka['attributes']
self.data = weka['data']
def _get_file_elements(self) -> Dict:
""" Adds the 'dataset' to file elements. """
file_elements = {}
path = None if self.data_file is None else os.path.abspath(
self.data_file)
if self._dataset is not None:
file_elements['dataset'] = self._dataset
elif path is not None and os.path.exists(path):
with open(path, 'rb') as fp:
file_elements['dataset'] = fp.read()
try:
dataset_utf8 = str(file_elements['dataset'], 'utf8')
arff.ArffDecoder().decode(dataset_utf8, encode_nominal=True)
except arff.ArffException:
raise ValueError(
"The file you have provided is not a valid arff file.")
elif self.url is None:
raise ValueError("No valid url/path to the data file was given.")
return file_elements
def _load_arff(filename, target):
with open(filename) as fh:
decoder = arff.ArffDecoder()
arff_object = decoder.decode(fh, encode_nominal=True)
dataset_name = arff_object['relation']
attributes = arff_object['attributes']
data = arff_object['data']
if isinstance(data, list):
data = np.array(data)
elif isinstance(data, tuple):
data = sparse.coo_matrix(data)
else:
raise ValueError('arff returned unknown data format of type %s' %
str(type(data)))
target_attribute = -1
for i, attribute in enumerate(attributes):
if attribute[0] == target:
target_attribute = i
break
if target_attribute < 0:
raise ValueError(
'Target feature %s not found. Available features '
'are: %s' %
(target, str([attribute[0] for attribute in attributes])))
y = data[:, target_attribute]
X = data[:, np.arange(data.shape[1]) != target_attribute]
# Do not add the target to the feat_type list
feat_type = [
'Categorical' if type(attribute[1]) in (list, tuple) else 'Numerical'
for attribute in attributes[:-1]
]
return X, y, dataset_name, feat_type
def retrieve_class_labels(self, target_name='class'):
"""Reads the datasets arff to determine the class-labels.
If the task has no class labels (for example a regression problem)
it returns None. Necessary because the data returned by get_data
only contains the indices of the classes, while OpenML needs the real
classname when uploading the results of a run.
Parameters
----------
target_name : str
Name of the target attribute
Returns
-------
list
"""
# TODO improve performance, currently reads the whole file
# Should make a method that only reads the attributes
arffFileName = self.data_file
if self.format.lower() == 'arff':
return_type = arff.DENSE
elif self.format.lower() == 'sparse_arff':
return_type = arff.COO
else:
raise ValueError('Unknown data format %s' % self.format)
with io.open(arffFileName, encoding='utf8') as fh:
arffData = arff.ArffDecoder().decode(fh, return_type=return_type)
dataAttributes = dict(arffData['attributes'])
if target_name in dataAttributes:
return dataAttributes[target_name]
else:
return None
def sparse_data_transformed():
tests_dir = __file__
os.chdir(os.path.dirname(tests_dir))
decoder = arff.ArffDecoder()
with open(os.path.join("datasets", "dataset.arff")) as fh:
dataset = decoder.decode(fh, encode_nominal=True)
# -1 because the last attribute is the class
attribute_types = [
'numeric' if type(type_) != list else 'nominal'
for name, type_ in dataset['attributes'][:-1]
]
categorical = {
i: True if attribute == 'nominal' else False
for i, attribute in enumerate(attribute_types)
}
data = np.array(dataset['data'], dtype=np.float64)
X = data[:, :-1]
y = data[:, -1].reshape((-1, ))
# First, swap NaNs and zeros, because when converting an encoded
# dense matrix to sparse, the values which are encoded to zero are lost
X_sparse = X.copy()
NaNs = ~np.isfinite(X_sparse)
X_sparse[NaNs] = 0
X_sparse = sparse.csr_matrix(X_sparse)
ohe = FeatTypeSplit(
feat_type={
col: 'categorical' if category else 'numerical'
for col, category in categorical.items()
})
X_transformed = X_sparse.copy()
X_transformed = ohe.fit_transform(X_transformed)
imp = SimpleImputer(copy=False)
X_transformed = imp.fit_transform(X_transformed)
standard_scaler = StandardScaler(with_mean=False)
X_transformed = standard_scaler.fit_transform(X_transformed)
# Transform the array which indicates the categorical metafeatures
number_numerical = np.sum(~np.array(list(categorical.values())))
categorical_transformed = {
i: True if i < (X_transformed.shape[1] - number_numerical) else False
for i in range(X_transformed.shape[1])
}
X = X_sparse
X_transformed = X_transformed
y = y
mf = meta_features.metafeatures
helpers = meta_features.helper_functions
logger = logging.getLogger()
# Precompute some helper functions
helpers.set_value(
"PCA",
helpers["PCA"](X_transformed, y, logger),
)
helpers.set_value(
"MissingValues",
helpers["MissingValues"](X, y, logger, categorical),
)
mf.set_value(
"NumberOfMissingValues",
mf["NumberOfMissingValues"](X, y, logger, categorical),
)
helpers.set_value(
"NumSymbols",
helpers["NumSymbols"](X, y, logger, categorical),
)
helpers.set_value(
"ClassOccurences",
helpers["ClassOccurences"](X, y, logger),
)
helpers.set_value(
"Skewnesses",
helpers["Skewnesses"](X_transformed, y, logger,
categorical_transformed),
)
helpers.set_value(
"Kurtosisses",
helpers["Kurtosisses"](X_transformed, y, logger,
categorical_transformed),
)
return X_transformed, y, categorical_transformed
def get_decoder(self):
decoder = arff.ArffDecoder()
return decoder
def get_decoder(self, conversors):
decoder = arff.ArffDecoder()
decoder._conversors = conversors
return decoder
model.fit(x_train,y_train)
predictionsLR=model.predict(x_test)
a1=accuracy_score(y_test,predictionsLR)
return(a1)
# In[155]:
df_all=[]
df_all=pd.DataFrame(df_all)
files_to_read = [("Combined1.arff","FileName"),("Combined2.arff","FileName"),("Combined3.arff","FileName"),("Combined5a.arff","FileName"),("Combined5b.arff","FileName")]
for (file, file_data) in files_to_read:
df1=[]
with open (file) as f:
decoder=arff.ArffDecoder()
datadictionary=decoder.decode(f,encode_nominal=True,return_type=arff.LOD)
data=datadictionary['data']
arff.ArffDecoder
df1=pd.DataFrame(data)
#df[3094] = np.where(df[3094]==1.0, 'human', 'worm')
df1=df1.replace(-np.inf,np.nan)
df1.fillna(df.mean(),inplace=True)
b=extract_metafeature(df1)
b=pd.DataFrame(b)
df_all = df_all.append(b)
print(df_all)
# In[156]:
def setUp(self):
self.cwd = os.getcwd()
tests_dir = __file__
os.chdir(os.path.dirname(tests_dir))
decoder = arff.ArffDecoder()
with open(os.path.join("datasets", "dataset.arff")) as fh:
dataset = decoder.decode(fh, encode_nominal=True)
# -1 because the last attribute is the class
self.attribute_types = [
'numeric' if type(type_) != list else 'nominal'
for name, type_ in dataset['attributes'][:-1]
]
self.categorical = [
True if attribute == 'nominal' else False
for attribute in self.attribute_types
]
data = np.array(dataset['data'], dtype=np.float64)
X = data[:, :-1]
y = data[:, -1].reshape((-1, ))
# First, swap NaNs and zeros, because when converting an encoded
# dense matrix to sparse, the values which are encoded to zero are lost
X_sparse = X.copy()
NaNs = ~np.isfinite(X_sparse)
X_sparse[NaNs] = 0
X_sparse = sparse.csr_matrix(X_sparse)
ohe = OneHotEncoder(self.categorical)
X_transformed = X_sparse.copy()
X_transformed = ohe.fit_transform(X_transformed)
imp = SimpleImputer(copy=False)
X_transformed = imp.fit_transform(X_transformed)
standard_scaler = StandardScaler(with_mean=False)
X_transformed = standard_scaler.fit_transform(X_transformed)
# Transform the array which indicates the categorical metafeatures
number_numerical = np.sum(~np.array(self.categorical))
categorical_transformed = [True] * (X_transformed.shape[1] -
number_numerical) + \
[False] * number_numerical
self.categorical_transformed = categorical_transformed
self.X = X_sparse
self.X_transformed = X_transformed
self.y = y
self.mf = meta_features.metafeatures
self.helpers = meta_features.helper_functions
# Precompute some helper functions
self.helpers.set_value("PCA", self.helpers["PCA"](self.X_transformed,
self.y))
self.helpers.set_value(
"MissingValues", self.helpers["MissingValues"](self.X, self.y,
self.categorical))
self.mf.set_value(
"NumberOfMissingValues",
self.mf["NumberOfMissingValues"](self.X, self.y, self.categorical))
self.helpers.set_value(
"NumSymbols", self.helpers["NumSymbols"](self.X, self.y,
self.categorical))
self.helpers.set_value("ClassOccurences",
self.helpers["ClassOccurences"](self.X, self.y))
self.helpers.set_value(
"Skewnesses",
self.helpers["Skewnesses"](self.X_transformed, self.y,
self.categorical_transformed))
self.helpers.set_value(
"Kurtosisses",
self.helpers["Kurtosisses"](self.X_transformed, self.y,
self.categorical_transformed))