"""
@author : Anjin Liu, Zhaoqing Liu
@email : anjin.liu@uts.edu.au, Zhaoqing.Liu-1@student.uts.edu.au
"""
import os
import numpy as np
import pandas as pd
from scipy.io import arff
from sklearn import datasets
# ==================================================================================
# Functions for loading specific datasets.
# ==================================================================================
# Change it to your folder path.
DATA_FOLDER_PATH = os.path.abspath(os.path.dirname(os.getcwd())) + '/Datasets/'
# print("+++++++++++++++++++++++++++++++++++++++++++++", DATA_FOLDER_PATH)
[docs]def load_vehicle():
"""
Turing Institute Research Memorandum TIRM-87-018 "Vehicle
Recognition Using Rule Based Methods" by Siebert,JP (March 1987)
"""
file_list = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i']
file_list = ['xa{}.dat'.format(i) for i in file_list]
column_names = ['COMPACTNESS',
'CIRCULARITY',
'DISTANCE CIRCULARITY',
'RADIUS RATIO',
'PR.AXIS ASPECT RATIO',
'MAX.LENGTH ASPECT RATIO',
'SCATTER RATIO',
'ELONGATEDNESS',
'PR.AXIS RECTANGULARITY',
'MAX.LENGTH RECTANGULARITY',
'SCALED VARIANCE ALONG MAJOR AXIS',
'SCALED VARIANCE ALONG MINOR AXIS',
'SCALED RADIUS OF GYRATION',
'SKEWNESS ABOUT MAJOR AXIS',
'SKEWNESS ABOUT MINOR AXIS',
'KURTOSIS ABOUT MINOR AXIS',
'KURTOSIS ABOUT MAJOR AXIS',
'HOLLOWS RATIO',
'CLASS']
data_df_list = []
for file in file_list:
data = np.genfromtxt(DATA_FOLDER_PATH + 'Vehicle/{}'.format(file), dtype='unicode', delimiter=' ')
data_df = pd.DataFrame(data)
data_df_list.append(data_df)
data_df_merge = pd.concat(data_df_list)
data_df_merge.columns = column_names
label_encoder_dict = {'opel': 0, 'saab': 1, 'bus': 2, 'van': 3}
data_df_merge['CLASS'].replace(label_encoder_dict, inplace=True)
for col_name in column_names:
data_df_merge[col_name] = data_df_merge[col_name].astype(int)
return data_df_merge
[docs]def load_German_credit():
"""
Professor Dr. Hans Hofmann
Institut f"ur Statistik und "Okonometrie
Universit"at Hamburg
FB Wirtschaftswissenschaften
Von-Melle-Park 5
2000 Hamburg 13
"""
df = pd.read_csv(DATA_FOLDER_PATH + 'German Credit/german.data-numeric', skipinitialspace=True, sep=' ',
header=None)
return df.iloc[:, :-1]
[docs]def load_chess():
"""
(a) Database originally generated and described by Alen Shapiro.
(b) Donor/Coder: Rob Holte (holte@uottawa.bitnet). The database
was supplied to Holte by Peter Clark of the Turing Institute
in Glasgow (pete@turing.ac.uk).
(c) Date: 1 August 1989
"""
df = pd.read_csv(DATA_FOLDER_PATH + 'Chess/kr-vs-kp.data', header=None)
return df
[docs]def load_diabetes():
data = arff.loadarff(DATA_FOLDER_PATH + 'Diabetes/dataset_37_diabetes.arff')
df = pd.DataFrame(data[0])
df['class'] = df['class'].str.decode('utf-8')
class_replace_dict = {'tested_negative': 0, 'tested_positive': 1}
df['class'].replace(class_replace_dict, inplace=True)
return df
[docs]def load_iris():
# dataset = datasets.load_iris()
# X = dataset.data
# y = dataset.target
#
# y = np.expand_dims(y, axis=1)
# ds = np.concatenate((X, y), axis=1)
#
# return pd.DataFrame(ds)
return datasets.load_iris(as_frame=True).frame
[docs]def load_wine():
# dataset = datasets.load_wine()
# X = dataset.data
# y = dataset.target
#
# y = np.expand_dims(y, axis=1)
# ds = np.concatenate((X, y), axis=1)
#
# return pd.DataFrame(ds)
return datasets.load_wine(as_frame=True).frame
# ==================================================================================
# Datasets available provided by this module.
# ==================================================================================
# Datasets (k: dataset name, v: function for getting data) on which the model is being trained.
DS_LOAD_FUNC_CLF = {"Vehicle": load_vehicle, "German_Credit": load_German_credit, "Diabetes": load_diabetes,
"Iris": load_iris, "Wine": load_wine}
DS_LOAD_FUNC_REG = {} # To be set when running regression experiments.
[docs]def load_data_clf(ds_name):
"""
Load a dataset by the specified name.
Parameters
----------
ds_name : str
Returns
-------
data : DataFrame
"""
ds_load_func = None
if ds_name in DS_LOAD_FUNC_CLF.keys():
ds_load_func = DS_LOAD_FUNC_CLF[ds_name]
return None if ds_load_func is None else ds_load_func()
# if __name__ == "__main__":
# print(DATA_FOLDER_PATH)
#
# print('Loading Vehicle')
# data_vehicle = load_vehicle()
# print('Loading Vehicle, shape', data_vehicle.shape)
#
# print('Loading Waveform')
# data_waveform = load_waveform()
# print('Loading Waveform, shape', data_waveform.shape)
#
# print('Loading German_credit')
# data_gc = load_German_credit()
# print('Loading German_credit, shape', data_gc.shape)
#
# print('Loading Chess')
# data_chess = load_chess()
# print('Loading Chess, shape', data_chess.shape)
#
# print('Loading Diabetes')
# data_diabetes = load_diabetes()
# print('Loading Diabetes, shape', data_diabetes.shape)
#
# print('Loading Iris')
# data_iris = load_iris()
# print('Loading Iris, shape', data_iris.shape)
#
# print('Loading Wine')
# data_wine = load_wine()
# print('Loading Wine, shape', data_wine.shape)
#
# data, target = datasets.load_boston(return_X_y=True)
# print(type(data))
# print(type(target))