def fn(pipe):
p = pipe \
.split(5) \
.pipe(kmeans(clusters)) \
.y(seeding_centroids(0.1)) \
.y(label_consensus()) \
.pipe(knn(neighbors)) \
.pipe(predict()) \
.pipe(evaluate()) \
.merge('evaluation', average('evaluation'))
return p
def fn(pipe):
p = pipe \
.split(5) \
.pipe(agglomerative_l_method()) \
.pipe(copy('y', 'y_bak')) \
.y(seeding_random(0.1)) \
.y(label_consensus()) \
.pipe(knn(neighbors)) \
.pipe(predict()) \
.pipe(copy('y_bak', 'y')) \
.pipe(evaluate()) \
.merge('evaluation', average('evaluation'))
return p
from pipe import Pipe
from wrapper import knn
from pipetools import predict, dump, load_y, copy, evaluate, echo
from utils import load_x, load_y
from multipipetools import average
from splitter import cross
file = './datasets/iris/iris.data'
a = Pipe() \
.x(load_x(file)) \
.y(load_y(file))\
.split(5, cross()) \
.pipe(knn(1)) \
.pipe(copy('x_test', 'x')) \
.pipe(copy('y_test', 'y')) \
.pipe(predict()) \
.pipe(evaluate()) \
.merge('evaluation', average('evaluation'))\
.pipe(dump('evaluation'))