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preprocessing.py
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import pandas as pd
import numpy as np
def process_breast_cancer_data():
"""
Imports breast cancer dataset, imputes missing values, and normalizes
:return: DataFrame
"""
df = pd.read_csv("data/breast-cancer-wisconsin.data", header=None, na_values=['?'])
df = df.fillna(np.random.randint(1, 11))
df.columns = ['id', 'clump_thickness', 'cell_size_uniformity', 'cell_shape_uniformity', 'marginal_adhesion',
'single_epithelial_size', 'bare_nuclei', 'bland_chromatin', 'normal_nucleoli', 'mitoses', 'class']
df['class'] = df['class'].apply(lambda x: 1 if x == 4 else 0)
normalized_df = (df.iloc[:, 1:-1] - df.iloc[:, 1:-1].mean()) / df.iloc[:, 1:-1].std()
normalized_df.insert(0, 'id', df['id'])
normalized_df.insert(len(df.columns) - 1, 'class', df['class'])
normalized_df = normalized_df.sample(frac=1)
return normalized_df
def process_glass_data():
"""
Imports glass data, and normalizes
:return: DataFrame
"""
df = pd.read_csv("data/glass.data", header=None)
df.columns = ['id', 'ri', 'na', 'mg', 'al', 'si', 'k', 'ca', 'ba', 'fe', 'class']
normalized_df = (df.iloc[:, 1:-1] - df.iloc[:, 1:-1].mean()) / df.iloc[:, 1:-1].std()
normalized_df.insert(0, 'id', df['id'])
normalized_df.insert(len(df.columns) - 1, 'class', df['class'])
normalized_df = normalized_df.sample(frac=1)
return normalized_df
def process_soybean_data():
"""
Imports soybean data, and normalizes
:return: DataFrame
"""
df = pd.read_csv("data/soybean-small.data", header=None)
df.columns = ['date', 'plant-stand', 'precip', 'temp', 'hail', 'crop-hist', 'area-damaged',
'severity', 'seed-tmt', 'germination', 'plant-growth', 'leaves', 'leafspots-halo',
'leafspots-marg', 'leafspot-size', 'leaf-shread', 'leaf-malf', 'leaf-mild', 'stem',
'lodging', 'stem-cankers', 'canker-lesion', 'fruiting-bodies', 'external decay',
'mycelium', 'int-discolor', 'slcerotia', 'fruit-pods', 'fruit spots', 'seed',
'mold-growth', 'seed-discolor', 'seed-size', 'shriveling', 'roots', 'class']
df['class'] = df['class'].apply(convert_soybean_to_numerical)
df = df.sample(frac=1)
return df
def convert_soybean_to_numerical(x):
"""
Helper function for soybean data. Converts class labels to numerical data.
:param x: Str
:return: Int
"""
if x == 'D1':
return 1
elif x == 'D2':
return 2
elif x == 'D3':
return 3
elif x == 'D4':
return 4
def process_abalone():
"""
Imports Abalone dataset
:return: DataFrame
"""
df = pd.read_csv("data/abalone.data", header=None)
df.columns = ['Sex', 'Length', 'Diameter', 'Height', 'Whole_weight', 'Shucked_weight', 'Viscera_weight',
'Shell_weight', 'class']
df['Sex'] = df['Sex'].apply(convert_abalone_to_numerical)
normalized_df = (df.iloc[:, :-1] - df.iloc[:, :-1].mean()) / df.iloc[:, :-1].std()
# normalized_df.insert(0, 'Sex', df['Sex'])
normalized_df.insert(len(df.columns) - 1, 'class', df['class'])
normalized_df = normalized_df.sample(frac=1)
return normalized_df
def convert_abalone_to_numerical(x):
"""
Helper function for abalone data. Converts sex labels to numerical data.
:param x: Str
:return: Int
"""
if x == 'M':
return 1
elif x == 'F':
return 2
elif x == 'I':
return 3
def process_machine():
"""
Imports Machine dataset
:return: DataFrame
"""
df = pd.read_csv("data/machine.data", header=None)
df.columns = ['Vendor', 'Id', 'MYCT', 'MMIN', 'MMAX', 'CACH', 'CHMIN',
'CHMAX', 'class', 'ERP']
df = df.drop(columns=['Vendor', 'Id', 'ERP'])
df = df[['MYCT', 'MMIN', 'MMAX', 'CACH', 'CHMIN',
'CHMAX', 'class']]
normalized_df = (df.iloc[:, :-1] - df.iloc[:, :-1].mean()) / df.iloc[:, :-1].std()
normalized_df.insert(len(df.columns) - 1, 'class', df['class'])
normalized_df = normalized_df.sample(frac=1)
return normalized_df
def process_forest_fires():
"""
Imports Forest Fires dataset
:return: DataFrame
"""
df = pd.read_csv("data/forestfires.csv", header=None)
df.columns = ['X', 'Y', 'Month', 'Day', 'FFMC', 'DMC', 'DC', 'ISI', 'Temp',
'RH', 'Wind', 'Rain', 'class']
df['Month'] = df['Month'].apply(change_month_to_number)
df['Day'] = df['Day'].apply(change_day_to_number)
normalized_df = (df.iloc[:, :-1] - df.iloc[:, :-1].mean()) / df.iloc[:, :-1].std()
normalized_df.insert(len(df.columns) - 1, 'class', df['class'])
normalized_df = normalized_df.sample(frac=1)
return normalized_df
def change_month_to_number(x):
"""
Helper function for forest fires data. Converts months labels to numerical data.
:param x: Str
:return: Int
"""
if x == 'jan':
return 1
elif x == 'feb':
return 2
elif x == 'mar':
return 3
elif x == 'apr':
return 4
elif x == 'may':
return 5
elif x == 'jun':
return 6
elif x == 'jul':
return 7
elif x == 'aug':
return 8
elif x == 'sep':
return 9
elif x == 'oct':
return 10
elif x == 'nov':
return 11
elif x == 'dec':
return 12
def change_day_to_number(x):
"""
Helper function for forest fires data. Converts day labels to numerical data.
:param x: Str
:return: Int
"""
if x == 'sun':
return 1
elif x == 'mon':
return 2
elif x == 'tue':
return 3
elif x == 'wed':
return 4
elif x == 'thu':
return 5
elif x == 'fri':
return 6
elif x == 'sat':
return 7