From 11be9a6038b78ee6bcea699f0242696fcf4b86df Mon Sep 17 00:00:00 2001
From: Kai Staats <github@overthesun.com>
Date: Wed, 14 Sep 2016 19:49:38 -0600
Subject: [PATCH] name change

---
 tools/karoo_sort.py | 67 +++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 67 insertions(+)
 create mode 100644 tools/karoo_sort.py

diff --git a/tools/karoo_sort.py b/tools/karoo_sort.py
new file mode 100644
index 0000000..145f32d
--- /dev/null
+++ b/tools/karoo_sort.py
@@ -0,0 +1,67 @@
+# Karoo Dataset Builder
+# by Kai Staats, MSc UCT / AIMS and Arun Kumar, PhD
+# version 0.9.2.0
+
+import sys
+import numpy as np
+
+np.set_printoptions(linewidth = 320) # set the terminal to print 320 characters before line-wrapping in order to view Trees
+
+'''
+In machine learning, it is often the case that your engaged dataset is derived from a larger parent. In constructing 
+the subset, if we grab a series of datapoints (rows in a .csv) from the larger dataset in sequential order, only from 
+the top, middle, or bottom, we will likely bias the new dataset and incorrectly train the machine learning algorithm. 
+Therefore, it is imperative that we engage a random function, guided only by the number of data points for each class.
+
+This script can be used *before* karoo_normalise.py, and assumes no header has yet been applied to the .csv.
+'''
+
+### USER INTERACTION ###
+if len(sys.argv) == 1: print '\n\t\033[31mERROR! You have not assigned an input file. Try again ...\033[0;0m'; sys.exit()
+elif len(sys.argv) > 2: print '\n\t\033[31mERROR! You have assigned too many command line arguments. Try again ...\033[0;0m'; sys.exit()
+else: filename = sys.argv[1]
+
+#n = range(1,101)
+#while True:
+#	try:
+#		labels = raw_input('\n\tEnter number of unique class labels, or 0 for a regression dataset (default 2): ')
+#		if labels not in str(n) and labels not in '': raise ValueError()
+#		# if labels == '0': labels = 1; break
+#		labels = labels or 2; labels = int(labels); break
+#	except ValueError: print '\n\t\033[32mEnter a number from 0 including 100. Try again ...\033[0;0m'
+
+n = range(10,10001)
+while True:
+	try:
+		samples = raw_input('\n\tEnter number of desired datapoints per class (default 100): ')
+		if samples not in str(n) and samples not in '': raise ValueError()
+		if samples == '0': samples = 10; break
+		samples = samples or 100; samples = int(samples); break
+	except ValueError: print '\n\t\033[32mEnter a number from 10 including 10000. Try again ...\033[0;0m'
+
+
+### LOAD THE ORIGINAL DATASET ###
+print '\n\t\033[36m\n\tLoading dataset:', filename, '\033[0;0m\n'
+data = np.loadtxt(filename, delimiter = ',') # load data
+data_sort = np.empty(shape = [0, data.shape[1]]) # build an empty array of the proper dimensions
+
+
+### SORT DATA by LABEL ###
+labels = len(np.unique(data[:,-1]))
+
+for label in range(labels):
+	data_list = np.where(data[:,-1] == label) # build a list of all rows which end in the current label
+
+	data_select = np.random.choice(data_list[0], samples, replace = False) # select user defined 'samples' from list
+	print data_select
+	
+	data_sort = np.append(data_sort, data[data_select], axis = 0)
+
+
+### SAVE THE SORTED DATASET ###
+file_tmp = filename.split('.')[0]
+np.savetxt(file_tmp + '-SORT.csv', data_sort, delimiter = ',')
+
+print '\n\t\033[36mThe sorted dataset has been written to the file:', file_tmp + '-SORT.csv', '\033[0;0m'
+
+