def test_fit_3():
"""
This function tests the thrid way of fitting the descriptor: the data is passed directly to the fit function.
"""
test_dir = os.path.dirname(os.path.realpath(__file__))
data = np.load(test_dir + "/data/local_slatm_ch4cn_light.npz")
descriptor = data["arr_0"]
classes = data["arr_1"]
energies = data["arr_2"]
estimator = ARMP()
estimator.fit(x=descriptor, y=energies, classes=classes)
def test_predict_3():
test_dir = os.path.dirname(os.path.realpath(__file__))
data = np.load(test_dir + "/data/local_slatm_ch4cn_light.npz")
descriptor = data["arr_0"]
classes = data["arr_1"]
energies = data["arr_2"]
estimator = ARMP()
estimator.fit(x=descriptor, y=energies, classes=classes)
energies_pred = estimator.predict(x=descriptor, classes=classes)
assert energies.shape == energies_pred.shape
def test_fit_2():
"""
This function tests the second way of fitting the descriptor: the data is passed by storing the compounds in the
class.
"""
test_dir = os.path.dirname(os.path.realpath(__file__))
data = np.load(test_dir + "/data/local_slatm_ch4cn_light.npz")
descriptor = data["arr_0"]
classes = data["arr_1"]
energies = data["arr_2"]
estimator = ARMP()
estimator.set_representations(representations=descriptor)
estimator.set_classes(classes=classes)
estimator.set_properties(energies)
idx = np.arange(0, 100)
estimator.fit(idx)
def test_fit_1():
"""
This function tests the first way of fitting the descriptor: the data is passed by first creating compounds and then
the descriptors are created from the compounds.
"""
test_dir = os.path.dirname(os.path.realpath(__file__))
filenames = glob.glob(test_dir + "/CN_isobutane/*.xyz")
energies = np.loadtxt(test_dir + '/CN_isobutane/prop_kjmol_training.txt',
usecols=[1])
filenames.sort()
estimator = ARMP(representation="acsf")
estimator.generate_compounds(filenames[:50])
estimator.set_properties(energies[:50])
estimator.generate_representation()
idx = np.arange(0, 50)
estimator.fit(idx)
def test_score_3():
"""
This function tests that all the scoring functions work.
"""
test_dir = os.path.dirname(os.path.realpath(__file__))
data = np.load(test_dir + "/data/local_slatm_ch4cn_light.npz")
descriptor = data["arr_0"]
classes = data["arr_1"]
energies = data["arr_2"]
estimator_1 = ARMP(scoring_function='mae')
estimator_1.fit(x=descriptor, y=energies, classes=classes)
estimator_1.score(x=descriptor, y=energies, classes=classes)
estimator_2 = ARMP(scoring_function='r2')
estimator_2.fit(x=descriptor, y=energies, classes=classes)
estimator_2.score(x=descriptor, y=energies, classes=classes)
estimator_3 = ARMP(scoring_function='rmse')
estimator_3.fit(x=descriptor, y=energies, classes=classes)
estimator_3.score(x=descriptor, y=energies, classes=classes)
import os
import numpy as np
## ------------- ** Loading the data ** ---------------
current_dir = os.path.dirname(os.path.realpath(__file__))
data = np.load(current_dir + '/../test/data/local_slatm_ch4cn_light.npz')
representation = data["arr_0"]
zs = data["arr_1"]
energies = data["arr_2"]
## ------------- ** Setting up the estimator ** ---------------
estimator = ARMP(iterations=150,
l2_reg=0.0,
learning_rate=0.005,
hidden_layer_sizes=(40, 20, 10))
## ------------- ** Fitting to the data ** ---------------
estimator.fit(x=representation, y=energies, classes=zs)
## ------------- ** Predicting and scoring ** ---------------
score = estimator.score(x=representation, y=energies, classes=zs)
print("The mean absolute error is %s kJ/mol." % (str(-score)))
energies_predict = estimator.predict(x=representation, classes=zs)
## ------------- ** Loading the data ** ---------------
current_dir = os.path.dirname(os.path.realpath(__file__))
data = np.load(current_dir + '/../test/data/local_slatm_ch4cn_light.npz')
descriptor = data["arr_0"]
zs = data["arr_1"]
energies = data["arr_2"]
## ------------- ** Setting up the estimator ** ---------------
estimator = ARMP(iterations=100, l2_reg=0.0)
estimator.set_representations(representations=descriptor)
estimator.set_classes(zs)
estimator.set_properties(energies)
## ------------- ** Fitting to the data ** ---------------
idx = np.arange(0, 100)
estimator.fit(idx)
## ------------- ** Predicting and scoring ** ---------------
score = estimator.score(idx)
print("The mean absolute error is %s kJ/mol." % (str(-score)))
energies_predict = estimator.predict(idx)
store_frequency=2,
hidden_layer_sizes=(50, 30, 10),
batch_size=200)
estimator.set_properties(ene_isopent)
estimator.generate_representation(pad_xyz, pad_zs, method='fortran')
print("Generated the representations")
print(estimator.representation.shape)
idx = list(range(n_samples))
idx_train, idx_test = modsel.train_test_split(idx,
random_state=42,
shuffle=True)
estimator.fit(idx_train)
data_squal = h5py.File(
"/Volumes/Transcend/data_sets/CN_squalane/dft/squalane_cn_dft.hdf5", "r")
xyz_squal = np.array(data_squal.get("xyz")[:10])
zs_squal = np.array(data_squal.get("zs")[:10], dtype=np.int32)
ene_squal = np.array(data_squal.get("ene")[:10]) * 2625.50
ene_squal = ene_squal - ref_ene
estimator.score(idx_test)
pred1 = estimator.predict_from_xyz(xyz_squal, zs_squal)
print("Done squal pred")
pred2 = estimator.predict(idx_test)
# Training the model on 3 folds of n data points
for n in n_samples:
cv_idx = idx_train[:n]
splitter = modsel.KFold(n_splits=3, random_state=42, shuffle=True)
indices = splitter.split(cv_idx)
scores_per_fold = []
traj_scores_per_fold = []
for item in indices:
idx_train_fold = cv_idx[item[0]]
idx_test_fold = cv_idx[item[1]]
estimator.fit(idx_train_fold)
score = estimator.score(idx_test_fold)
traj_score = estimator.score(idx_test)
scores_per_fold.append(score)
traj_scores_per_fold.append(traj_score)
tf.reset_default_graph()
scores.append(scores_per_fold)
traj_scores.append(traj_scores_per_fold)
# Saving the data to an .npz file
np.savez("./plot/scores_vr.npz", np.asarray(n_samples), np.asarray(scores),
np.asarray(traj_scores))
l2_reg=0.0,
hidden_layer_sizes=(40, 20, 10),
tensorboard=True,
store_frequency=10,
# batch_size=400,
batch_size=n_train,
learning_rate=0.1,
# scoring_function="mae",
)
estimator.set_representations(representations=X)
estimator.set_classes(Z)
estimator.set_properties(Y)
# idx = np.arange(0,100)
# estimator.fit(idx)
# score = estimator.score(idx)
# estimator.fit(x=representation, y=energies, classes=zs)
estimator.fit(x=X, y=Y, classes=Z)
## ------------- ** Predicting and scoring ** ---------------
score = estimator.score(x=X, y=Y, classes=Z)
print("The mean absolute error is %s kJ/mol." % (str(-score)))
# energies_predict = estimator.predict(idx)
# print(energies_predict)
[1, 2, 3]])
ene_true = np.array([0.5, 0.9, 1.0])
estimator = ARMP(iterations=10, l1_reg=0.0001, l2_reg=0.005, learning_rate=0.0005, representation='acsf',
representation_params={"radial_rs": np.arange(0, 10, 5), "angular_rs": np.arange(0, 10, 5),
"theta_s": np.arange(0, 3.14, 3)},
tensorboard=True, store_frequency=10
)
estimator.set_properties(ene_true)
estimator.generate_representation(xyz, zs)
idx = list(range(xyz.shape[0]))
estimator.fit(idx)
estimator.save_nn(save_dir="temp")
pred1 = estimator.predict(idx)
estimator.loaded_model = True
estimator.fit(idx)
pred2 = estimator.predict(idx)
estimator.session.close()
tf.reset_default_graph()
new_estimator = ARMP(iterations=10, l1_reg=0.0001, l2_reg=0.005, learning_rate=0.0005, representation='acsf',
representation_params={"radial_rs": np.arange(0, 10, 5), "angular_rs": np.arange(0, 10, 5),
"theta_s": np.arange(0, 3.14, 3)},
ene_isopent = ene_isopent - ref_ene
zs_isopent = np.array(data.get("zs"), dtype=np.int32)
traj_idx = np.array(data.get("traj_idx"))
file_idx = np.array(data.get("Filenumber"))
# Finding the indices of the last trajectory so that it can be used as a test set
idx_test = np.where(traj_idx==22)[0]
idx_train = np.where(traj_idx!=22)[0]
shuffle(idx_train)
# Making sure that the model is trained on the same number of samples as the MD-NN
idx_train_half = idx_train[:7621]
# Creating the estimator
acsf_params = {"nRs2":14, "nRs3":14, "nTs":14, "rcut":3.29, "acut":3.29, "zeta":100.06564927139748, "eta":39.81824764370754}
estimator = ARMP(iterations=2633, batch_size=22, l1_reg=1.46e-05, l2_reg=0.0001, learning_rate=0.0013, representation_name='acsf',
representation_params=acsf_params, tensorboard=True, store_frequency=25, hidden_layer_sizes=(185,))
estimator.set_properties(ene_isopent)
estimator.generate_representation(xyz_isopent, zs_isopent, method="fortran")
# Fitting the estimator and scoring it
estimator.fit(idx_train_half)
score = estimator.score(idx_test)
# Saving the model for later reuse
model_name = "vr-nn"
estimator.save_nn(model_name)
print(score)