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:
