def setUp(self):
# classification problem.
iris = pd.read_csv('iris.csv')
self.iris = Dataset(iris)
# Regression problem
tips = pd.read_csv('tips.csv')
self.tips = Dataset(tips, dependent_col='tip')
def generate_metafeatures_from_server(file_id, target_field, **kwargs):
# Read the data set into memory
raw_data = get_file_from_server(file_id)
df = pd.read_csv(StringIO(raw_data), sep=None, engine='python', **kwargs)
dataset = Dataset(df,
dependent_col=target_field,
prediction_type='classification')
return generate_metafeatures(dataset, target_field)
def get_metafeatures(df):
dataset = Dataset(df, dependent_col = 'class', prediction_type='classification')
meta_features = OrderedDict()
for i in dir(dataset):
result = getattr(dataset, i)
if not i.startswith('__') and not i.startswith('_') and hasattr(result, '__call__'):
meta_features[i] = result()
return meta_features
def generate_metafeatures_from_filepath(input_file, target_field, **kwargs):
"""Calls metafeature generating methods from dataset_describe"""
# Read the data set into memory
df = pd.read_csv(input_file, sep=None, engine='python', **kwargs)
dataset = Dataset(df,
dependent_col=target_field,
prediction_type='classification')
return generate_metafeatures(dataset, target_field)
