Recipe
Recipe
You can use Recipe
to choose some presets for the TimeSequencePredictor
fitting process by passing to the recipe
field in the fit
method.
SmokeRecipe
A very simple Recipe for smoke test that runs one epoch and one iteration with only 1 random sample.
SmokeRecipe()
LSTMRandomGridRecipe
A recipe mixing random and grid using LSTM Model only
LSTMGridRandomRecipe(num_rand_samples=1,epochs=5,training_iteration=10,look_back=2,lstm_1_units=[16, 32, 64, 128],lstm_2_units=[16, 32, 64], batch_size=[32, 64])
Arguments
 :param lstm_1_units: random search candidates for num of lstm_1_units
 :param lstm_2_units: grid search candidates for num of lstm_1_units
 :param batch_size: grid search candidates for batch size
 :param num_rand_samples: number of hyperparam configurations sampled randomly
 :param look_back: the length to look back, either a tuple with 2 int values, which is in format is (min len, max len), or a single int, which is a fixed length to look back.
 :param training_iteration: no. of iterations for training (n epochs) in trials
 :param epochs: no. of epochs to train in each iteration
MTNetRandomGridRecipe
A recipe mixing random and grid using MTNet Model only
MTNetGridRandomRecipe(num_rand_samples=1,
epochs=5,
training_iteration=10,
time_step=[3, 4],
long_num=[3, 4],
cnn_height=[2, 3],
cnn_hid_size=[32, 50, 100],
ar_size=[2, 3],
batch_size=[32, 64])
Arguments
 :param num_rand_samples: number of hyperparam configurations sampled randomly
 :param training_iteration: no. of iterations for training (n epochs) in trials
 :param epochs: no. of epochs to train in each iteration
 :param time_step: random search candidates for model param "time_step"
 :param long_num: random search candidates for model param "long_num"
 :param ar_size: random search candidates for model param "ar_size"
 :param batch_size: grid search candidates for batch size
 :param cnn_height: random search candidates for model param "cnn_height"
 :param cnn_hid_size: random search candidates for model param "cnn_hid_size"
RandomRecipe
Pure random sample Recipe. Often used as baseline.
RandomRecipe(num_rand_samples=1, look_back=2)
Arguments

num_rand_samples: number of hyperparam configurations sampled randomly.

look_back: The length to look back. It could be
 A single int, which is a fixed length to look back. Note that the look back value should be larger than 1 to take the series information into account.
 A tuple with 2 int values, which is in format is (min len, max len). The
min len
value should also be larger than 1.
GridRandomRecipe
A recipe involves both grid search and random search. The arguments are the same with RandomRecipe
.
GridRandomRecipe(num_rand_samples=1, look_back=2)
BayesRecipe
A recipe to search with Bayes Optimization. You need to preinstall bayesianoptimization
before using the recipe.
BayesRecipe(num_samples=1, look_back=2)