Python 中的加权基尼系数
Weighted Gini coefficient in Python
下面是 Python 中基尼系数的简单实现,来自 :
def gini(x):
# Mean absolute difference.
mad = np.abs(np.subtract.outer(x, x)).mean()
# Relative mean absolute difference
rmad = mad / np.mean(x)
# Gini coefficient is half the relative mean absolute difference.
return 0.5 * rmad
如何调整以将权重数组作为第二个向量?这应该采用非整数权重,所以不要只是按权重炸毁数组。
示例:
gini([1, 2, 3]) # No weight: 0.22.
gini([1, 1, 1, 2, 2, 3]) # Manually weighted: 0.23.
gini([1, 2, 3], weight=[3, 2, 1]) # Should also give 0.23.
mad
的计算可以替换为:
x = np.array([1, 2, 3, 6])
c = np.array([2, 3, 1, 2])
count = np.multiply.outer(c, c)
mad = np.abs(np.subtract.outer(x, x) * count).sum() / count.sum()
np.mean(x)
可以替换为:
np.average(x, weights=c)
这是完整的函数:
def gini(x, weights=None):
if weights is None:
weights = np.ones_like(x)
count = np.multiply.outer(weights, weights)
mad = np.abs(np.subtract.outer(x, x) * count).sum() / count.sum()
rmad = mad / np.average(x, weights=weights)
return 0.5 * rmad
检查结果,gini2()
使用numpy.repeat()
重复元素:
def gini2(x, weights=None):
if weights is None:
weights = np.ones(x.shape[0], dtype=int)
x = np.repeat(x, weights)
mad = np.abs(np.subtract.outer(x, x)).mean()
rmad = mad / np.mean(x)
return 0.5 * rmad
下面是 Python 中基尼系数的简单实现,来自
def gini(x):
# Mean absolute difference.
mad = np.abs(np.subtract.outer(x, x)).mean()
# Relative mean absolute difference
rmad = mad / np.mean(x)
# Gini coefficient is half the relative mean absolute difference.
return 0.5 * rmad
如何调整以将权重数组作为第二个向量?这应该采用非整数权重,所以不要只是按权重炸毁数组。
示例:
gini([1, 2, 3]) # No weight: 0.22.
gini([1, 1, 1, 2, 2, 3]) # Manually weighted: 0.23.
gini([1, 2, 3], weight=[3, 2, 1]) # Should also give 0.23.
mad
的计算可以替换为:
x = np.array([1, 2, 3, 6])
c = np.array([2, 3, 1, 2])
count = np.multiply.outer(c, c)
mad = np.abs(np.subtract.outer(x, x) * count).sum() / count.sum()
np.mean(x)
可以替换为:
np.average(x, weights=c)
这是完整的函数:
def gini(x, weights=None):
if weights is None:
weights = np.ones_like(x)
count = np.multiply.outer(weights, weights)
mad = np.abs(np.subtract.outer(x, x) * count).sum() / count.sum()
rmad = mad / np.average(x, weights=weights)
return 0.5 * rmad
检查结果,gini2()
使用numpy.repeat()
重复元素:
def gini2(x, weights=None):
if weights is None:
weights = np.ones(x.shape[0], dtype=int)
x = np.repeat(x, weights)
mad = np.abs(np.subtract.outer(x, x)).mean()
rmad = mad / np.mean(x)
return 0.5 * rmad