Python load_x Examples

Example #1

File: agglomerative-cluster-pipe.py Project: phizaz/seeding-strategy-ssl

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'))

Example #2

File: kmeans-ssl-pipe.py Project: phizaz/seeding-strategy-ssl

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'))

Example #3

File: test_goodK.py Project: phizaz/seeding-strategy-ssl

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)

Example #4

File: l_method-ssl-pipe.py Project: phizaz/seeding-strategy-ssl

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) \

Example #5

File: knn-pipe.py Project: phizaz/seeding-strategy-ssl

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'))