from pipe import Pipe from wrapper import agglomerative from pipetools import dump, evaluate from utils import load_x file = './datasets/iris/iris.data' Pipe()\ .x(load_x(file, delimiter=','))\ .pipe(agglomerative(n_clusters=3))\ .pipe(dump('prediction'))
clusters_count = 10 # file = './datasets/iris/iris.data' # file_test = './datasets/iris/iris.data' file = './datasets/pendigits/pendigits.tra' file_test = './datasets/pendigits/pendigits.tes' def kmeans_ssl(clusters, neighbors): 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 return fn p = Pipe() \ .x(load_x(file)) \ .y(load_y(file)) \ .x_test(load_x(file_test))\ .y_test(load_y(file_test))\ .connect(start_timer()) \ .connect(kmeans_ssl(clusters=clusters_count, neighbors=1)) \ .connect(stop_timer()) \ .pipe(dump('evaluation'))
from utils import load_x, load_y from wrapper import * file = './datasets/pendigits/pendigits.tra' file_test = './datasets/pendigits/pendigits.tes' X = load_x(file) Y = load_y(file) X_test = load_x(file_test) Y_test = load_y(file_test) goodK = Pipe()\ .x(X)\ .y(Y)\ .x_test(X_test)\ .y_test(Y_test)\ .pipe(good_K_for_KNN())\ .connect(stop()) print('goodK:', good_K_for_KNN)
from multipipetools import average from pipe import Pipe from pipetools import * from ssltools import * from utils import load_x, load_y from wrapper import agglomerative_l_method, knn file = './datasets/iris/iris.data' # file = './datasets/pendigits/pendigits.tra' points = load_x(file, delimiter=',') target = load_y(file, delimiter=',') def l_method(neighbors): 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 return fn p = Pipe() \ .x(points) \ .y(target) \
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'))