Python 基于类返回numpy数组的分发示例
请注意：我和numpy结婚就是为了完成这个任务 我试图编写一个函数来实现以下目标：Python 基于类返回numpy数组的分发示例,python,numpy,machine-learning,sampling,numpy-ndarray,Python,Numpy,Machine Learning,Sampling,Numpy Ndarray,请注意：我和numpy结婚就是为了完成这个任务 我试图编写一个函数来实现以下目标： 将数据集加载到numpy数组中 将数据集拆分为5个“相等”（或尽可能相等）的折叠 对于每个折叠，确保分别有80/20的数据用于培训和测试 这里有一个陷阱。原始输入数据集已“标记”，最后一列包含分类。折叠需要保持与输入集相同的类大小分布 例如，如果我有input=100个样本（行），并且有两个类（由最后一列中的值表示），A和B，具有33%和67%的分割，那么我应该创建5个折叠，每个折叠包含20个样本，其中6或7个样
input=100个样本（行）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()
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在采纳@AlexL的建议后，我查看了代码并开发了一个修改版本，其中包含以下两个功能：
# 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
在采纳@AlexL的建议后，我查看了代码并开发了一个修改版本，其中包含以下两个功能：
# 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
虽然你注定要独自去numpy，但我强烈建议你从scikit learn中搜索全班。这正好完成了您想要做的事情，在他们的代码（位于numpy之上）中看到这一点可能会有所帮助。他们利用了他们的
KFoldKFold