请注意：我已与numpy结婚。
我正在尝试编写一个实现以下目标的函数：
例如，如果我有input=100 samples(rows)，并且有两个类（由最后一列中的值表示），A和B，分别分为33％和67％，那么我应该创建5折每次包含20个样本，其中6个或7个样本为A，13个或14个样本为B。
这就是我正在努力实现的目标。我不知道如何正确地确保FOLD本身包含类的正确采样分布。
我有以下代码来显示到目前为止的尝试。到目前为止，我已经编写了两个函数，它们可以告诉我输入类的分布情况，并且可以创建5折。但是，我需要找到一种方法来组合这些并创建5个折叠以保持各自的分布。
import numpy
def csv_to_array(file):
# Open the file, and load it in delimiting on the ',' for a comma separated value file
data = open(file, 'r')
data = numpy.loadtxt(data, delimiter=',')
# Loop through the data in the array
for index in range(len(data)):
# Utilize a try catch to try and convert to float, if it can't convert to float, converts to 0
try:
data[index] = [float(x) for x in data[index]]
except Exception:
data[index] = 0
except ValueError:
data[index] = 0
# Return the now type-formatted data
return data
def class_distribution(dataset):
dataset = numpy.asarray(dataset)
num_total_rows = dataset.shape[0]
num_columns = dataset.shape[1]
classes = dataset[:,num_columns-1]
classes = numpy.unique(classes)
for aclass in classes:
total = 0
for row in dataset:
if numpy.array_equal(aclass, row[-1]):
total = total + 1
else:
continue
print(aclass, " Has: ", ((total/num_total_rows) * 100))
print(aclass, " : ", total)
def create_folds(dataset):
# print("DATASET", dataset)
numpy.random.shuffle(dataset)
num_rows = dataset.shape[0]
split_mark = int(num_rows / 5)
folds = []
fold_sets = []
temp1 = dataset[:split_mark]
# print("TEMP1", temp1)
temp2 = dataset[split_mark:split_mark*2]
# print("TEMP2", temp2)
temp3 = dataset[split_mark*2:split_mark*3]
# print("TEMP3", temp3)
temp4 = dataset[split_mark*3:split_mark*4]
# print("TEMP4", temp4)
temp5 = dataset[split_mark*4:]
# print("TEMP5", temp5)
folds.append(temp1)
folds.append(temp2)
folds.append(temp3)
folds.append(temp4)
folds.append(temp5)
folds = numpy.asarray(folds)
# print(folds)
return folds
def main():
print("BEGINNING CFV")
ecoli = csv_to_array('Classification/ecoli.csv')
# print(len(ecoli))
class_distribution(ecoli)
create_folds(ecoli)
main()
这是我正在使用的csv的示例，最后一列表示该类。它是对UCI机器学习存储库中的ecoli dataset的修改：
0.61,0.45,0.48,0.5,0.48,0.35,0.41,0
0.17,0.38,0.48,0.5,0.45,0.42,0.5,0
0.44,0.35,0.48,0.5,0.55,0.55,0.61,0
0.43,0.4,0.48,0.5,0.39,0.28,0.39,0
0.42,0.35,0.48,0.5,0.58,0.15,0.27,0
0.23,0.33,0.48,0.5,0.43,0.33,0.43,0
0.37,0.52,0.48,0.5,0.42,0.42,0.36,0
0.29,0.3,0.48,0.5,0.45,0.03,0.17,0
0.22,0.36,0.48,0.5,0.35,0.39,0.47,0
0.23,0.58,0.48,0.5,0.37,0.53,0.59,0
0.47,0.47,0.48,0.5,0.22,0.16,0.26,0
0.54,0.47,0.48,0.5,0.28,0.33,0.42,0
0.51,0.37,0.48,0.5,0.35,0.36,0.45,0
0.4,0.35,0.48,0.5,0.45,0.33,0.42,0
0.44,0.34,0.48,0.5,0.3,0.33,0.43,0
0.44,0.49,0.48,0.5,0.39,0.38,0.4,0
0.43,0.32,0.48,0.5,0.33,0.45,0.52,0
0.49,0.43,0.48,0.5,0.49,0.3,0.4,0
0.47,0.28,0.48,0.5,0.56,0.2,0.25,0
0.32,0.33,0.48,0.5,0.6,0.06,0.2,0
0.34,0.35,0.48,0.5,0.51,0.49,0.56,0
0.35,0.34,0.48,0.5,0.46,0.3,0.27,0
0.38,0.3,0.48,0.5,0.43,0.29,0.39,0
0.38,0.44,0.48,0.5,0.43,0.2,0.31,0
0.41,0.51,0.48,0.5,0.58,0.2,0.31,0
0.34,0.42,0.48,0.5,0.41,0.34,0.43,0
0.51,0.49,0.48,0.5,0.53,0.14,0.26,0
0.25,0.51,0.48,0.5,0.37,0.42,0.5,0
0.29,0.28,0.48,0.5,0.5,0.42,0.5,0
0.25,0.26,0.48,0.5,0.39,0.32,0.42,0
0.24,0.41,0.48,0.5,0.49,0.23,0.34,0
0.17,0.39,0.48,0.5,0.53,0.3,0.39,0
0.04,0.31,0.48,0.5,0.41,0.29,0.39,0
0.61,0.36,0.48,0.5,0.49,0.35,0.44,0
0.34,0.51,0.48,0.5,0.44,0.37,0.46,0
0.28,0.33,0.48,0.5,0.45,0.22,0.33,0
0.4,0.46,0.48,0.5,0.42,0.35,0.44,0
0.23,0.34,0.48,0.5,0.43,0.26,0.37,0
0.37,0.44,0.48,0.5,0.42,0.39,0.47,0
0,0.38,0.48,0.5,0.42,0.48,0.55,0
0.39,0.31,0.48,0.5,0.38,0.34,0.43,0
0.3,0.44,0.48,0.5,0.49,0.22,0.33,0
0.27,0.3,0.48,0.5,0.71,0.28,0.39,0
0.17,0.52,0.48,0.5,0.49,0.37,0.46,0
0.36,0.42,0.48,0.5,0.53,0.32,0.41,0
0.3,0.37,0.48,0.5,0.43,0.18,0.3,0
0.26,0.4,0.48,0.5,0.36,0.26,0.37,0
0.4,0.41,0.48,0.5,0.55,0.22,0.33,0
0.22,0.34,0.48,0.5,0.42,0.29,0.39,0
0.44,0.35,0.48,0.5,0.44,0.52,0.59,0
0.27,0.42,0.48,0.5,0.37,0.38,0.43,0
0.16,0.43,0.48,0.5,0.54,0.27,0.37,0
0.06,0.61,0.48,0.5,0.49,0.92,0.37,1
0.44,0.52,0.48,0.5,0.43,0.47,0.54,1
0.63,0.47,0.48,0.5,0.51,0.82,0.84,1
0.23,0.48,0.48,0.5,0.59,0.88,0.89,1
0.34,0.49,0.48,0.5,0.58,0.85,0.8,1
0.43,0.4,0.48,0.5,0.58,0.75,0.78,1
0.46,0.61,0.48,0.5,0.48,0.86,0.87,1
0.27,0.35,0.48,0.5,0.51,0.77,0.79,1
0.52,0.39,0.48,0.5,0.65,0.71,0.73,1
0.29,0.47,0.48,0.5,0.71,0.65,0.69,1
0.55,0.47,0.48,0.5,0.57,0.78,0.8,1
0.12,0.67,0.48,0.5,0.74,0.58,0.63,1
0.4,0.5,0.48,0.5,0.65,0.82,0.84,1
0.73,0.36,0.48,0.5,0.53,0.91,0.92,1
0.84,0.44,0.48,0.5,0.48,0.71,0.74,1
0.48,0.45,0.48,0.5,0.6,0.78,0.8,1
0.54,0.49,0.48,0.5,0.4,0.87,0.88,1
0.48,0.41,0.48,0.5,0.51,0.9,0.88,1
0.5,0.66,0.48,0.5,0.31,0.92,0.92,1
0.72,0.46,0.48,0.5,0.51,0.66,0.7,1
0.47,0.55,0.48,0.5,0.58,0.71,0.75,1
0.33,0.56,0.48,0.5,0.33,0.78,0.8,1
0.64,0.58,0.48,0.5,0.48,0.78,0.73,1
0.11,0.5,0.48,0.5,0.58,0.72,0.68,1
0.31,0.36,0.48,0.5,0.58,0.94,0.94,1
0.68,0.51,0.48,0.5,0.71,0.75,0.78,1
0.69,0.39,0.48,0.5,0.57,0.76,0.79,1
0.52,0.54,0.48,0.5,0.62,0.76,0.79,1
0.46,0.59,0.48,0.5,0.36,0.76,0.23,1
0.36,0.45,0.48,0.5,0.38,0.79,0.17,1
0,0.51,0.48,0.5,0.35,0.67,0.44,1
0.1,0.49,0.48,0.5,0.41,0.67,0.21,1
0.3,0.51,0.48,0.5,0.42,0.61,0.34,1
0.61,0.47,0.48,0.5,0,0.8,0.32,1
0.63,0.75,0.48,0.5,0.64,0.73,0.66,1
0.71,0.52,0.48,0.5,0.64,1,0.99,1
0.72,0.42,0.48,0.5,0.65,0.77,0.79,2
0.79,0.41,0.48,0.5,0.66,0.81,0.83,2
0.83,0.48,0.48,0.5,0.65,0.76,0.79,2
0.69,0.43,0.48,0.5,0.59,0.74,0.77,2
0.79,0.36,0.48,0.5,0.46,0.82,0.7,2
0.78,0.33,0.48,0.5,0.57,0.77,0.79,2
0.75,0.37,0.48,0.5,0.64,0.7,0.74,2
0.59,0.29,0.48,0.5,0.64,0.75,0.77,2
0.67,0.37,0.48,0.5,0.54,0.64,0.68,2
0.66,0.48,0.48,0.5,0.54,0.7,0.74,2
0.64,0.46,0.48,0.5,0.48,0.73,0.76,2
0.76,0.71,0.48,0.5,0.5,0.71,0.75,2
0.84,0.49,0.48,0.5,0.55,0.78,0.74,2
0.77,0.55,0.48,0.5,0.51,0.78,0.74,2
0.81,0.44,0.48,0.5,0.42,0.67,0.68,2
0.58,0.6,0.48,0.5,0.59,0.73,0.76,2
0.63,0.42,0.48,0.5,0.48,0.77,0.8,2
0.62,0.42,0.48,0.5,0.58,0.79,0.81,2
0.86,0.39,0.48,0.5,0.59,0.89,0.9,2
0.81,0.53,0.48,0.5,0.57,0.87,0.88,2
0.87,0.49,0.48,0.5,0.61,0.76,0.79,2
0.47,0.46,0.48,0.5,0.62,0.74,0.77,2
0.76,0.41,0.48,0.5,0.5,0.59,0.62,2
0.7,0.53,0.48,0.5,0.7,0.86,0.87,2
0.64,0.45,0.48,0.5,0.67,0.61,0.66,2
0.81,0.52,0.48,0.5,0.57,0.78,0.8,2
0.73,0.26,0.48,0.5,0.57,0.75,0.78,2
0.49,0.61,1,0.5,0.56,0.71,0.74,2
0.88,0.42,0.48,0.5,0.52,0.73,0.75,2
0.84,0.54,0.48,0.5,0.75,0.92,0.7,2
0.63,0.51,0.48,0.5,0.64,0.72,0.76,2
0.86,0.55,0.48,0.5,0.63,0.81,0.83,2
0.79,0.54,0.48,0.5,0.5,0.66,0.68,2
0.57,0.38,0.48,0.5,0.06,0.49,0.33,2
0.78,0.44,0.48,0.5,0.45,0.73,0.68,2
0.78,0.68,0.48,0.5,0.83,0.4,0.29,3
0.63,0.69,0.48,0.5,0.65,0.41,0.28,3
0.67,0.88,0.48,0.5,0.73,0.5,0.25,3
0.61,0.75,0.48,0.5,0.51,0.33,0.33,3
0.67,0.84,0.48,0.5,0.74,0.54,0.37,3
0.74,0.9,0.48,0.5,0.57,0.53,0.29,3
0.73,0.84,0.48,0.5,0.86,0.58,0.29,3
0.75,0.76,0.48,0.5,0.83,0.57,0.3,3
0.77,0.57,0.48,0.5,0.88,0.53,0.2,3
0.74,0.78,0.48,0.5,0.75,0.54,0.15,3
0.68,0.76,0.48,0.5,0.84,0.45,0.27,3
0.56,0.68,0.48,0.5,0.77,0.36,0.45,3
0.65,0.51,0.48,0.5,0.66,0.54,0.33,3
0.52,0.81,0.48,0.5,0.72,0.38,0.38,3
0.64,0.57,0.48,0.5,0.7,0.33,0.26,3
0.6,0.76,1,0.5,0.77,0.59,0.52,3
0.69,0.59,0.48,0.5,0.77,0.39,0.21,3
0.63,0.49,0.48,0.5,0.79,0.45,0.28,3
0.71,0.71,0.48,0.5,0.68,0.43,0.36,3
0.68,0.63,0.48,0.5,0.73,0.4,0.3,3
0.74,0.49,0.48,0.5,0.42,0.54,0.36,4
0.7,0.61,0.48,0.5,0.56,0.52,0.43,4
0.66,0.86,0.48,0.5,0.34,0.41,0.36,4
0.73,0.78,0.48,0.5,0.58,0.51,0.31,4
0.65,0.57,0.48,0.5,0.47,0.47,0.51,4
0.72,0.86,0.48,0.5,0.17,0.55,0.21,4
0.67,0.7,0.48,0.5,0.46,0.45,0.33,4
0.67,0.81,0.48,0.5,0.54,0.49,0.23,4
0.67,0.61,0.48,0.5,0.51,0.37,0.38,4
0.63,1,0.48,0.5,0.35,0.51,0.49,4
0.57,0.59,0.48,0.5,0.39,0.47,0.33,4
0.71,0.71,0.48,0.5,0.4,0.54,0.39,4
0.66,0.74,0.48,0.5,0.31,0.38,0.43,4
0.67,0.81,0.48,0.5,0.25,0.42,0.25,4
0.64,0.72,0.48,0.5,0.49,0.42,0.19,4
0.68,0.82,0.48,0.5,0.38,0.65,0.56,4
0.32,0.39,0.48,0.5,0.53,0.28,0.38,4
0.7,0.64,0.48,0.5,0.47,0.51,0.47,4
0.63,0.57,0.48,0.5,0.49,0.7,0.2,4
0.69,0.65,0.48,0.5,0.63,0.48,0.41,4
0.43,0.59,0.48,0.5,0.52,0.49,0.56,4
0.74,0.56,0.48,0.5,0.47,0.68,0.3,4
0.71,0.57,0.48,0.5,0.48,0.35,0.32,4
0.61,0.6,0.48,0.5,0.44,0.39,0.38,4
0.59,0.61,0.48,0.5,0.42,0.42,0.37,4
0.74,0.74,0.48,0.5,0.31,0.53,0.52,4
答案 0 :(得分：0)
在接受@AlexL的建议后，我查看了StratifiedKFold代码并开发了具有以下两个功能的修改版本：
# This function returns the list of classes, and their associated weights (i.e. distributions)
def class_distribution(dataset):
dataset = numpy.asarray(dataset)
num_total_rows = dataset.shape[0]
num_columns = dataset.shape[1]
classes = dataset[:, num_columns - 1]
classes = numpy.unique(classes)
class_weights = []
# Loop through the classes one by one
for aclass in classes:
total = 0
weight = 0
for row in dataset:
if numpy.array_equal(aclass, row[-1]):
total = total + 1
else:
continue
weight = float((total / num_total_rows))
class_weights.append(weight)
class_weights = numpy.asarray(class_weights)
return classes, class_weights
# This functions performs k cross fold validation for classification
def cross_fold_validation_classification(dataset, k):
temp_dataset = numpy.asarray(dataset)
classes, class_weights = class_distribution(temp_dataset)
total_num_rows = temp_dataset.shape[0]
data = numpy.copy(temp_dataset)
total_fold_array = []
for _ in range(k):
curr_fold_array = []
# Loop through each class and its associated weight
for a_class, a_class_weight in zip(classes, class_weights):
numpy.random.shuffle(data)
num_added = 0
num_to_add = float((((a_class_weight * total_num_rows)) / k))
tot = 0
for row in data:
curr = row[-1]
if num_added >= num_to_add:
break
else:
if (a_class == curr):
curr_fold_array.append(row)
num_added = num_added + 1
numpy.delete(data, tot)
tot = tot + 1
total_fold_array.append(curr_fold_array)
return total_fold_array