def test_full_problem(self):
from deephyper.nas.preprocessing import minmaxstdscaler
from deephyper.problem import NaProblem
pb = NaProblem()
def load_data(prop):
return ([[10]], [1]), ([10], [1])
pb.load_data(load_data, prop=1.0)
pb.preprocessing(minmaxstdscaler)
pb.search_space(OneLayerSpace)
pb.hyperparameters(
batch_size=64,
learning_rate=0.001,
optimizer="adam",
num_epochs=10,
loss_metric="mse",
)
with pytest.raises(NaProblemError):
pb.objective("r2")
pb.loss("mse")
pb.metrics(["r2"])
possible_objective = ["loss", "val_loss", "r2", "val_r2"]
for obj in possible_objective:
pb.objective(obj)
from deephyper.problem import NaProblem
from molnet.molnet.load_data import load_data
from molnet.molnet.search_space import create_search_space
from deephyper.search.nas.model.preprocessing import minmaxstdscaler
Problem = NaProblem(seed=2019)
Problem.load_data(load_data)
Problem.preprocessing(minmaxstdscaler)
Problem.search_space(create_search_space, num_layers=3)
Problem.hyperparameters(
batch_size=32,
learning_rate=0.01,
optimizer='adam',
num_epochs=20,
callbacks=dict(EarlyStopping=dict(
monitor='val_r2', # or 'val_acc' ?
mode='max',
verbose=0,
patience=5)))
Problem.loss('mse') # or 'categorical_crossentropy' ?
Problem.metrics(['r2']) # or 'acc' ?
Problem.objective('val_r2__last') # or 'val_acc__last' ?
# Just to print your problem, to test its definition and imports in the current python environment.
from deephyper.problem import NaProblem
from nas_1.polynome2.load_data import load_data
from nas_1.polynome2.search_space import create_search_space
from deephyper.nas.preprocessing import minmaxstdscaler, stdscaler
import numpy as np
import tensorflow as tf
tf.config.run_functions_eagerly(True)
# from deephyper.nas.train_utils import *
from sklearn import metrics
Problem = NaProblem(seed=2019)
Problem.load_data(load_data)
Problem.preprocessing(stdscaler)
Problem.search_space(create_search_space, num_layers=6)
Problem.hyperparameters(
batch_size=128,
learning_rate=0.001,
optimizer='adam',
num_epochs=20,
callbacks=dict(EarlyStopping=dict(
monitor='val_loss', # or 'val_r2' or 'val_acc' ?
# mode='max',
verbose=0,
patience=5,
restore_best_weights=True)))