def test_rossler_dataset_creator():
x_0 = [2, 2, 2]
data = rossler_dataset_maker(x_0=x_0,
start_time=0.0,
end_time=30.0,
time_step=0.1)
first_five_x = np.array([2., 1.607831, 1.23093264, 0.8672437, 0.51370104])
first_five_y = np.array(
[2., 2.26131837, 2.49356973, 2.69709381, 2.8719731])
first_five_z = np.array(
[2., 1.58200549, 1.20471963, 0.88366574, 0.62337719])
np.testing.assert_allclose(first_five_x, data[0][0:5, [0]].reshape(5))
np.testing.assert_allclose(first_five_y, data[0][0:5, [1]].reshape(5))
np.testing.assert_allclose(first_five_z, data[0][0:5, [2]].reshape(5))
def load_model_and_viz():
loaded_model = keras.models.load_model(
f'/home/hossein/Desktop/Repos/extreme_events/saved_models/{MODEL_NAME}'
)
with open(MODEL_PATH / "meta_data", "rb") as meta_data_file:
metadata = pickle.load(meta_data_file)
test_time = 10.0
data = rossler_dataset_maker(
x_0=metadata.init_val,
start_time=metadata.start_time,
end_time=metadata.end_time +
test_time, # extra 10 time unites for test
time_step=metadata.time_step)
train_time_steps = int(
(metadata.end_time - metadata.start_time) / metadata.time_step)
test_time_steps = int(test_time / metadata.time_step)
tot_time_steps = train_time_steps + test_time_steps
all_x = data[:, :, :-1].reshape(1, tot_time_steps, 2)
all_y = data[:, :, -1].reshape(1, tot_time_steps)
all_y_if_flipped = if_flips_in_next_n_steps(all_y,
threshold=5.0,
n_time_steps=20)
plt.plot(data[0][0:tot_time_steps, 2:3].T.tolist()[0])
plt.plot([None] * train_time_steps +
all_y_if_flipped[0][train_time_steps:tot_time_steps].tolist())
results2 = []
for i in range(test_time_steps):
val = loaded_model.predict(all_x[:, i:train_time_steps + i, :])
if val[0][0] > 0:
results2.append(1)
else:
results2.append(0)
if i % 10 == 0:
print(f"done {i} iteration")
plt.plot([None] * train_time_steps + results2, "r")
plt.show()
from extreme_events.data_source.data_providers import rossler_dataset_maker, if_flips_in_next_n_steps
from extreme_events.utils.model_utils import set_seeds_and_clear_session, MetaData
from extreme_events.models.simple_rnn import SimpleTwoLayerRNN
if __name__ == "__main__":
set_seeds_and_clear_session()
x_0 = [2, 2, 2]
start_time = 0.0
end_time = 30.0
time_step = 0.01
loss_type = 'binary_crossentropy'
model_meta_data = MetaData(init_val=x_0, start_time=start_time, end_time=end_time, time_step=time_step, loss_type=loss_type)
data = rossler_dataset_maker(x_0=model_meta_data.init_val,
start_time=model_meta_data.start_time,
end_time=model_meta_data.end_time,
time_step=model_meta_data.time_step)
x_train = data[0][:, :-1].reshape(1, data.shape[1], 2) # both x and y
y_train = data[0][:, -1:].reshape(1, data.shape[1]) # assuming the last column is target
y_train = if_flips_in_next_n_steps(y_train, threshold=5.0, n_time_steps=20)
optimizer = keras.optimizers.Adam(lr=0.005)
# loss = keras.losses.Huber()
loss = keras.losses.BinaryCrossentropy(
from_logits=False, label_smoothing=0, reduction=keras.losses.Reduction.SUM,
)
# loss = SigmoidFocalCrossEntropy()
metrics = ["mse"]
model = SimpleTwoLayerRNN(loss_function=loss, optimizer=optimizer, metrics=metrics,
input_shape=(x_train.shape[1], x_train.shape[2]))
history = model.fit(X=x_train, y=y_train)
from tensorflow import keras
import numpy as np
from extreme_events.data_source.data_providers import rossler_dataset_maker
from extreme_events.utils.model_utils import set_seeds_and_clear_session
from extreme_events.models.lstm import LSTM2Layer
if __name__ == "__main__":
set_seeds_and_clear_session()
x_0 = [2, 2, 2]
start_time = 0.0
end_time = 30.0
time_step = 0.01
data = rossler_dataset_maker(x_0=x_0,
start_time=start_time,
end_time=end_time,
time_step=time_step)
optimizer = keras.optimizers.Adam(lr=0.005)
# def last_time_step_mse(Y_true, Y_pred):
# return keras.metrics.mean_squared_error(Y_true[:, -1], Y_pred[:, -1])
x_train = data[0][:, :-1].reshape(1, data.shape[1], 2) # both x and y
y_train = data[0][:, -1:].reshape(
1, data.shape[1]) # assuming the last column is target
loss = keras.losses.Huber()
metrics = ["mse"]
model = LSTM2Layer(loss_function=loss,
optimizer=optimizer,
metrics=metrics,
from tensorflow import keras
from extreme_events.data_source.data_providers import rossler_dataset_maker
from extreme_events.utils.model_utils import set_seeds_and_clear_session
from extreme_events.models.gru import GRU
if __name__ == "__main__":
set_seeds_and_clear_session()
x_0 = [2, 2, 2]
start_time = 0.0
end_time = 30.0
time_step = 0.01
data = rossler_dataset_maker(x_0=x_0,
start_time=start_time,
end_time=end_time,
time_step=time_step,
target_advance_time=10.0,
target_threshold_val=10.0)
x_train = data[0][:, :-1].reshape(1, data.shape[1], 2) # both x and y
y_train = data[0][:, -1:].reshape(
1, data.shape[1]) # assuming the last column is target
optimizer = keras.optimizers.Adam(lr=0.005)
loss = "mse"
metrics = ["mse"]
model = GRU(loss_function=loss,
optimizer=optimizer,
metrics=metrics,
input_shape=(x_train.shape[1], x_train.shape[2]))
model.fit(X=x_train, y=y_train)