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preprocessing.py
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122 lines (95 loc) · 3.47 KB
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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import tensorflow as tf
def lymphography_dataset():
path = "./datasets/lymphography.csv"
df = pd.read_csv(path)
df.Label[df.Label == 3] = 0
df.Label[df.Label == 4] = 1
df.Label[df.Label == 1] = 1
df.Label[df.Label == 2] = 0
df_norm = df[df.Label == 0]
df_anom = df[df.Label == 1]
ds_norm = df_norm.values
ds_anom = df_anom.values
X_train = ds_norm[:100, :-1]
Y_train = ds_norm[:100, -1]
l = ds_norm.shape[0] - X_train.shape[0]
no_of_test_samples = l + ds_anom.shape[0]
no_of_features = X_train.shape[1]
X_test = np.zeros((no_of_test_samples, no_of_features))
Y_test = np.zeros((no_of_test_samples,))
X_test[:l, :] = ds_norm[100:, :-1]
X_test[l:, :] = ds_anom[:,:-1]
print(X_test.shape)
Y_test[:l,] = ds_norm[100:, -1]
Y_test[l:,] = ds_anom[:, -1]
return X_train, Y_train, X_test, Y_test, ds_anom, ds_norm
def pageblocks_dataset():
path = "./datasets/page-blocks.csv"
df = pd.read_csv(path)
df.label[df.label == 1] = 0
df.label[df.label == 2] = 1
df.label[df.label == 3] = 1
df.label[df.label == 4] = 1
df.label[df.label == 5] = 1
df_norm = df[df.label == 0]
df_anom = df[df.label == 1]
ds_norm = df_norm.values
ds_anom = df_anom.values
X_train = ds_norm[:4700, :-1]
Y_train = ds_norm[:4700, -1]
l = ds_norm.shape[0] - X_train.shape[0]
no_of_test_samples = l + ds_anom.shape[0]
no_of_features = X_train.shape[1]
X_test = np.zeros((no_of_test_samples, no_of_features))
Y_test = np.zeros((no_of_test_samples,))
X_test[:l, :] = ds_norm[4700:, :-1]
X_test[l:, :] = ds_anom[:,:-1]
print(X_test.shape)
Y_test[:l,] = ds_norm[4700:, -1]
Y_test[l:,] = ds_anom[:, -1]
return X_train, Y_train, X_test, Y_test, ds_anom, ds_norm
def postoperative_dataset():
path = "./datasets/postop.csv"
df = pd.read_csv(path)
df_norm = df[df.Label == 0]
df_anom = df[df.Label == 1]
ds_norm = df_norm.values
ds_anom = df_anom.values
X_train = ds_norm[:50, :-1]
Y_train = ds_norm[:50, -1]
l = ds_norm.shape[0] - X_train.shape[0]
no_of_test_samples = l + ds_anom.shape[0]
no_of_features = X_train.shape[1]
X_test = np.zeros((no_of_test_samples, no_of_features))
Y_test = np.zeros((no_of_test_samples,))
X_test[:l, :] = ds_norm[50:, :-1]
X_test[l:, :] = ds_anom[:,:-1]
print(X_test.shape)
Y_test[:l,] = ds_norm[50:, -1]
Y_test[l:,] = ds_anom[:, -1]
return X_train, Y_train, X_test, Y_test, ds_anom, ds_norm
def cancer_dataset():
path = "./datasets/cancer.csv"
df = pd.read_csv(path)
df.Label[df.Label==1] = 0
df.Label[df.Label==-1] = 1
df_norm = df[df.Label == 0]
df_anom = df[df.Label == 1]
ds_norm = df_norm.values
ds_anom = df_anom.values
X_train = ds_norm[:400, :-1]
Y_train = ds_norm[:400, -1]
l = ds_norm.shape[0] - X_train.shape[0]
no_of_test_samples = l + ds_anom.shape[0]
no_of_features = X_train.shape[1]
X_test = np.zeros((no_of_test_samples, no_of_features))
Y_test = np.zeros((no_of_test_samples,))
X_test[:l, :] = ds_norm[400:, :-1]
X_test[l:, :] = ds_anom[:,:-1]
print(X_test.shape)
Y_test[:l,] = ds_norm[400:, -1]
Y_test[l:,] = ds_anom[:, -1]
return X_train, Y_train, X_test, Y_test, ds_anom, ds_norm