实现代码

import numpy as np

import pandas as pd

from collections import Counter

def chimerge(data, attr, label, max_intervals):

distinct_vals = sorted(set(data[attr])) # Sort the distinct values

labels = sorted(set(data[label])) # Get all possible labels

empty_count = {l: 0 for l in labels} # A helper function for padding the Counter()

intervals = [[distinct_vals[i], distinct_vals[i]] for i in range(len(distinct_vals))] # Initialize the intervals for each attribute

while len(intervals) > max_intervals: # While loop

chi = []

for i in range(len(intervals)-1):

# Calculate the Chi2 value

obs0 = data[data[attr].between(intervals[i][0], intervals[i][1])]

obs1 = data[data[attr].between(intervals[i+1][0], intervals[i+1][1])]

total = len(obs0) + len(obs1)

count_0 = np.array([v for i, v in {**empty_count, **Counter(obs0[label])}.items()])

count_1 = np.array([v for i, v in {**empty_count, **Counter(obs1[label])}.items()])

count_total = count_0 + count_1

expected_0 = count_total*sum(count_0)/total

expected_1 = count_total*sum(count_1)/total

chi_ = (count_0 - expected_0)**2/expected_0 + (count_1 - expected_1)**2/expected_1

chi_ = np.nan_to_num(chi_) # Deal with the zero counts

chi.append(sum(chi_)) # Finally do the summation for Chi2

min_chi = min(chi) # Find the minimal Chi2 for current iteration

for i, v in enumerate(chi):

if v == min_chi:

min_chi_index = i # Find the index of the interval to be merged

break

new_intervals = [] # Prepare for the merged new data array

skip = False

done = False

for i in range(len(intervals)):

if skip:

skip = False

continue

if i == min_chi_index and not done: # Merge the intervals

t = intervals[i] + intervals[i+1]

new_intervals.append([min(t), max(t)])

skip = True

done = True

else:

new_intervals.append(intervals[i])

intervals = new_intervals

for i in intervals:

print(‘[‘, i[0], ‘,‘, i[1], ‘]‘, sep=‘‘)

使用例子

iris = pd.read_csv(‘http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data‘, header=None)

iris.columns = [‘sepal_l‘, ‘sepal_w‘, ‘petal_l‘, ‘petal_w‘, ‘type‘]

for attr in [‘sepal_l‘, ‘sepal_w‘, ‘petal_l‘, ‘petal_w‘]:

print(‘Interval for‘, attr)

chimerge(data=iris, attr=attr, label=‘type‘, max_intervals=3)

结果:

原文：https://www.cnblogs.com/hichens/p/13585854.html