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# pandas is an open source library providing high-performance,
# easy-to-use data structures and data analysis tools.
import pandas as pd
# NumPy is the fundamental package for scientific computing with Python.
import numpy as np
# Seaborn is a Python visualization library based on matplotlib.
import seaborn as sns
# matplotlib is a python 2D plotting library which produces publication quality
# figures in a variety of hardcopy formats and interactive environments across platforms.
from matplotlib import pyplot
from pocket_classifier import PocketClassifier
from perceptron_classifier import PerceptronClassifier
from collections import Counter
from urllib import request
from sklearn import preprocessing
from sklearn import model_selection
# Set aesthetic parameters in one step.
def imputer_by_most_frequent(missing_values=np.nan, data=[]):
'''Input missing value by frequency, i.e., the value appeared most often.
The missing value can be np.nan, '?', or whatever character which indicates missing value.
data: one dimension list
The list of the encoded data based on one hot encoding approach
# Find the value appeared most often by using Counter.
most = Counter(data).most_common(1)[0][0]
complete_list = []
for item in data:
if item is missing_values:
item = most
return complete_list
def one_hot_encoder(data=[]):
'''Transfer categorical data to numerical data based on one hot encoding approach.
data: one dimension list
The list of the encoded data based on one hot encoding approach
# Since scikit-learn's OneHotEncoder only accepts numerical data, use LabelEncoder to transfer the
# categorical data to numerical by using simple encoding approach.
# For example, t -> 0; f -> 1
LABEL_ENCODER = preprocessing.LabelEncoder()
numerical_data = LABEL_ENCODER.fit_transform(data)
two_d_array = [[item] for item in numerical_data]
# Use scikit-learn OneHotEncoder to encode the A9 feature
encoder = preprocessing.OneHotEncoder()
return encoder.transform(two_d_array).toarray()
if __name__ == '__main__':
# Download Japanese Credit Data Set from
URL = ''
request.urlretrieve(URL, '')
# Use pandas.read_csv module to load adult data set
crx_data = pd.read_csv('', header=None)
crx_data.replace('?', np.nan, inplace=True)
# Transfer the category data to numerical data and input missing data:
# A1: b, a. (missing)
# A2: continuous. (missing) mean
# A3: continuous.
# A4: u, y, l, t. (missing) frequency
# A5: g, p, gg. (missing) frequency
# A6: c, d, cc, i, j, k, m, r, q, w, x, e, aa, ff. (missing) frequency
# A7: v, h, bb, j, n, z, dd, ff, o. (missing) frequency
# A8: continuous.
# A9: t, f.
#A10: t, f.
#A11: continuous.
#A12: t, f.
#A13: g, p, s.
#A14: continuous. (missing) mean
#A15: continuous.
#A16: +,- (class label)
A1_no_missing = imputer_by_most_frequent(np.nan, crx_data.iloc[:, 0].values)
A1_encoded = one_hot_encoder(A1_no_missing)
imputer = preprocessing.Imputer(missing_values=np.nan, strategy='mean', axis=0)
A2_two_d = np.array([[item] for item in crx_data.iloc[:, 1].values])
A2_no_missing = imputer.fit_transform(A2_two_d)
A3 = crx_data.iloc[:, 2].values
A4_no_missing = imputer_by_most_frequent(np.nan, crx_data.iloc[:, 3].values)
A4_encoded = one_hot_encoder(A4_no_missing)
A5_no_missing = imputer_by_most_frequent(np.nan, crx_data.iloc[:, 4].values)
A5_encoded = one_hot_encoder(A5_no_missing)
A6_no_missing = imputer_by_most_frequent(np.nan, crx_data.iloc[:, 5].values)
A6_encoded = one_hot_encoder(A6_no_missing)
A7_no_missing = imputer_by_most_frequent(np.nan, crx_data.iloc[:, 6].values)
A7_encoded = one_hot_encoder(A7_no_missing)
A8 = crx_data.iloc[:, 7].values
A9_encoded = one_hot_encoder(crx_data.iloc[:, 8].values)
A10_encoded = one_hot_encoder(crx_data.iloc[:, 9].values)
A11 = crx_data.iloc[:, 10].values
A12_encoded = one_hot_encoder(crx_data.iloc[:, 11].values)
A13_encoded = one_hot_encoder(crx_data.iloc[:, 12].values)
A14_two_d = np.array([[item] for item in crx_data.iloc[:, 13].values])
A14_no_missing = imputer.fit_transform(A14_two_d)
A15 = crx_data.iloc[:, 14].values
# Aggregate all the encoded data together to a two-dimension set
data = list()
label = list()
for index in range(690):
temp = np.append(A1_encoded[index], A2_no_missing[index])
temp = np.append(temp, A3[index])
temp = np.append(temp, A4_encoded[index])
temp = np.append(temp, A5_encoded[index])
temp = np.append(temp, A6_encoded[index])
temp = np.append(temp, A7_encoded[index])
temp = np.append(temp, A8[index])
temp = np.append(temp, A9_encoded[index])
temp = np.append(temp, A10_encoded[index])
temp = np.append(temp, A11[index])
temp = np.append(temp, A12_encoded[index])
temp = np.append(temp, A14_no_missing[index])
temp = np.append(temp, A15[index])
# Use scikit-learn's MinMaxScaler to scale the training data set.
min_max_scaler = preprocessing.MinMaxScaler()
data_minmax = min_max_scaler.fit_transform(data)
features = len(data[0])
# Use scikit-learn's train_test_split function to separate the Iris Data Set
# to a training subset (75% of the data) and a test subst (25% of the data)
DATA_TRAIN, DATA_TEST, LABELS_TRAIN, LABELS_TEST = model_selection.train_test_split(data_minmax, label, test_size=0.25, random_state=1000)
pocket_classifier = PocketClassifier(features, ('+', '-'))
pocket_classifier.train(DATA_TRAIN, LABELS_TRAIN, 100)
result = pocket_classifier.classify(DATA_TEST)
misclassify = 0
for predict, answer in zip(result, LABELS_TEST):
if predict != answer:
misclassify += 1
print("Accuracy rate: %2.2f" % (100 * (len(result) - misclassify) / len(result)) + '%')