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pip install imbriumStandard and Hybrid Deep Learning Multivariate-Multi-Step & Univariate-Multi-Step Time Series Forecasting.
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imbrium is a deep learning library that specializes in time series forecasting. Its primary objective is to provide a user-friendly repository of deep learning architectures for this purpose. The focus is on simplifying the process of creating and applying these architectures, with the goal of allowing users to create complex architectures without having to build them from scratch. Instead, the emphasis shifts to high-level configuration of the architectures.
imbrium is designed to simplify the application of deep learning models for time series forecasting. The library offers a variety of pre-built architectures. The user retains full control over the configuration of each layer, including the number of neurons, the type of activation function, loss function, optimizer, and metrics applied. This allows for the flexibility to adapt the architecture to the specific needs of the forecast task at hand. Imbrium also offers a user-friendly interface for training and evaluating these models, making it easy to quickly iterate and test different configurations.
imbrium uses the sliding window approach to generate forecasts. The sliding window approach in time series forecasting involves moving a fixed-size window (steps_past) through historical data, using the data within the window as input features. The next data points outside the window are used as the target variables (steps_future). This method allows the model to learn sequential patterns and trends in the data, enabling accurate predictions for future points in the time series.
- adapting
keras_core - removing internal hyperparameter tuning
- removing encoder-decoder architectures
- improve layer configuration
- split input data into target and feature numpy arrays
- overall lighten the library
Expand
Univariate Pure Predictors
from imbrium import PureUni
# create a PureUni object (numpy array expected)
predictor = PureUni(target = target_numpy_array)
# the following models are available for a PureUni objects;
# create and fit a muti-layer perceptron model
predictor.create_fit_mlp(
steps_past,
steps_future,
optimizer = "adam",
optimizer_args = None,
loss = "mean_squared_error",
metrics = "mean_squared_error",
dense_block_one = 1,
dense_block_two = 1,
dense_block_three = 1,
layer_config = {
"layer0": {
"config": {
"neurons": 50,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer1": {
"config": {
"neurons": 50,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer2": {
"config": {"neurons": 50, "activation": "relu", "regularization": 0.0}
},
},
epochs = 100,
show_progress = 1,
validation_split = 0.20,
board = False,
)
# create and fit a recurrent neural network model
predictor.create_fit_rnn(
steps_past,
steps_future,
optimizer = "adam",
optimizer_args = None,
loss = "mean_squared_error",
rnn_block_one = 1,
rnn_block_two = 1,
rnn_block_three = 1,
metrics = "mean_squared_error",
layer_config = {
"layer0": {
"config": {
"neurons": 50,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer1": {
"config": {
"neurons": 50,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer2": {
"config": {"neurons": 50, "activation": "relu", "regularization": 0.0}
},
},
epochs = 100,
show_progress = 1,
validation_split = 0.20,
board = False,
)
# create and fit a long short-term neural network model
predictor.create_fit_lstm(
steps_past,
steps_future,
optimizer = "adam",
optimizer_args = None,
loss = "mean_squared_error",
metrics = "mean_squared_error",
lstm_block_one = 1,
lstm_block_two = 1,
lstm_block_three = 1,
layer_config = {
"layer0": {
"config": {
"neurons": 50,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer1": {
"config": {
"neurons": 50,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer2": {
"config": {"neurons": 50, "activation": "relu", "regularization": 0.0}
},
},
epochs = 100,
show_progress = 1,
validation_split = 0.20,
board = False,
)
# create and fit a convolutional neural network
predictor = create_fit_cnn(
steps_past,
steps_future,
optimizer = "adam",
optimizer_args = None,
loss = "mean_squared_error",
metrics = "mean_squared_error",
conv_block_one = 1,
conv_block_two = 1,
dense_block_one = 1,
layer_config = {
"layer0": {
"config": {
"filters": 64,
"kernel_size": 1,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer1": {
"config": {
"filters": 32,
"kernel_size": 1,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer2": {
"config": {
"pool_size": 2,
}
},
"layer3": {
"config": {
"neurons": 32,
"activation": "relu",
"regularization": 0.0,
}
},
},
epochs = 100,
show_progress = 1,
validation_split = 0.20,
board = False,
)
# create and fit a gated recurrent unit neural network
predictor.create_fit_gru(
steps_past,
steps_future,
optimizer = "adam",
optimizer_args = None,
loss = "mean_squared_error",
metrics = "mean_squared_error",
gru_block_one = 1,
gru_block_two = 1,
gru_block_three = 1,
layer_config = {
"layer0": {
"config": {
"neurons": 50,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer1": {
"config": {
"neurons": 50,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer2": {
"config": {
"neurons": 50,
"activation": "relu",
"regularization": 0.0,
}
},
},
epochs = 100,
show_progress = 1,
validation_split = 0.20,
board = False,
)
# create and fit a bidirectional recurrent neural network
predictor.create_fit_birnn(
steps_past,
steps_future,
optimizer = "adam",
optimizer_args = None,
loss = "mean_squared_error",
metrics = "mean_squared_error",
birnn_block_one = 1,
rnn_block_one = 1,
layer_config = {
"layer0": {
"config": {
"neurons": 50,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer1": {
"config": {
"neurons": 50,
"activation": "relu",
"regularization": 0.0,
}
},
},
epochs = 100,
show_progress = 1,
validation_split = 0.20,
board = False,
)
# create and fit a bidirectional long short-term memory neural network
predictor.create_fit_bilstm(
steps_past,
steps_future,
optimizer = "adam",
optimizer_args = None,
loss = "mean_squared_error",
metrics = "mean_squared_error",
bilstm_block_one = 1,
lstm_block_one = 1,
layer_config = {
"layer0": {
"config": {
"neurons": 50,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer1": {
"config": {
"neurons": 50,
"activation": "relu",
"regularization": 0.0,
}
},
},
epochs = 100,
show_progress = 1,
validation_split = 0.20,
board = False,
)
# create and fit a bidirectional gated recurrent neural network
predictor.create_fit_bigru(
steps_past,
steps_future,
optimizer = "adam",
optimizer_args = None,
loss = "mean_squared_error",
metrics = "mean_squared_error",
bigru_block_one = 1,
gru_block_one = 1,
layer_config = {
"layer0": {
"config": {
"neurons": 50,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer1": {
"config": {
"neurons": 50,
"activation": "relu",
"regularization": 0.0,
}
},
},
epochs = 100,
show_progress = 1,
validation_split = 0.20,
board = False,
)
# you can add additional layer to the defualt layers by increasing the layer block count and adding the configuration for the layer in the layer_config dictionary. Please note that the last layer should not have a dropout key.
# in addition, you can add to all models early stopping arguments:
monitor='val_loss',
min_delta=0,
patience=0,
verbose=0,
mode='auto',
baseline=None,
restore_best_weights=False,
start_from_epoch=0
# instpect model structure
predictor.model_blueprint()
# insptect keras model performances via (access dictionary via history key):
predictor.show_performance()
# make predictions via (numpy array expected):
predictor.predict(data)
# save predictor via:
predictor.freeze(absolute_path)
# load saved predictor via:
predictor.retrieve(location)Multivariate Pure Predictors
from imbrium import PureMulti
# create a PureMulti object (numpy array expected)
predictor = PureMulti(target = target_numpy_array, features = features_numpy_array)
# the following models are available for a PureMulti objects;
# create and fit a muti-layer perceptron model
predictor.create_fit_mlp(
steps_past,
steps_future,
optimizer = "adam",
optimizer_args = None,
loss = "mean_squared_error",
metrics = "mean_squared_error",
dense_block_one = 1,
dense_block_two = 1,
dense_block_three = 1,
layer_config = {
"layer0": {
"config": {
"neurons": 50,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer1": {
"config": {
"neurons": 50,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer2": {
"config": {"neurons": 50, "activation": "relu", "regularization": 0.0}
},
},
epochs = 100,
show_progress = 1,
validation_split = 0.20,
board = False,
)
# create and fit a recurrent neural network model
predictor.create_fit_rnn(
steps_past,
steps_future,
optimizer = "adam",
optimizer_args = None,
loss = "mean_squared_error",
rnn_block_one = 1,
rnn_block_two = 1,
rnn_block_three = 1,
metrics = "mean_squared_error",
layer_config = {
"layer0": {
"config": {
"neurons": 50,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer1": {
"config": {
"neurons": 50,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer2": {
"config": {"neurons": 50, "activation": "relu", "regularization": 0.0}
},
},
epochs = 100,
show_progress = 1,
validation_split = 0.20,
board = False,
)
# create and fit a long short-term neural network model
predictor.create_fit_lstm(
steps_past,
steps_future,
optimizer = "adam",
optimizer_args = None,
loss = "mean_squared_error",
metrics = "mean_squared_error",
lstm_block_one = 1,
lstm_block_two = 1,
lstm_block_three = 1,
layer_config = {
"layer0": {
"config": {
"neurons": 50,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer1": {
"config": {
"neurons": 50,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer2": {
"config": {"neurons": 50, "activation": "relu", "regularization": 0.0}
},
},
epochs = 100,
show_progress = 1,
validation_split = 0.20,
board = False,
)
# create and fit a convolutional neural network
predictor = create_fit_cnn(
steps_past,
steps_future,
optimizer = "adam",
optimizer_args = None,
loss = "mean_squared_error",
metrics = "mean_squared_error",
conv_block_one = 1,
conv_block_two = 1,
dense_block_one = 1,
layer_config = {
"layer0": {
"config": {
"filters": 64,
"kernel_size": 1,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer1": {
"config": {
"filters": 32,
"kernel_size": 1,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer2": {
"config": {
"pool_size": 2,
}
},
"layer3": {
"config": {
"neurons": 32,
"activation": "relu",
"regularization": 0.0,
}
},
},
epochs = 100,
show_progress = 1,
validation_split = 0.20,
board = False,
)
# create and fit a gated recurrent unit neural network
predictor.create_fit_gru(
steps_past,
steps_future,
optimizer = "adam",
optimizer_args = None,
loss = "mean_squared_error",
metrics = "mean_squared_error",
gru_block_one = 1,
gru_block_two = 1,
gru_block_three = 1,
layer_config = {
"layer0": {
"config": {
"neurons": 50,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer1": {
"config": {
"neurons": 50,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer2": {
"config": {
"neurons": 50,
"activation": "relu",
"regularization": 0.0,
}
},
},
epochs = 100,
show_progress = 1,
validation_split = 0.20,
board = False,
)
# create and fit a bidirectional recurrent neural network
predictor.create_fit_birnn(
steps_past,
steps_future,
optimizer = "adam",
optimizer_args = None,
loss = "mean_squared_error",
metrics = "mean_squared_error",
birnn_block_one = 1,
rnn_block_one = 1,
layer_config = {
"layer0": {
"config": {
"neurons": 50,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer1": {
"config": {
"neurons": 50,
"activation": "relu",
"regularization": 0.0,
}
},
},
epochs = 100,
show_progress = 1,
validation_split = 0.20,
board = False,
)
# create and fit a bidirectional long short-term memory neural network
predictor.create_fit_bilstm(
steps_past,
steps_future,
optimizer = "adam",
optimizer_args = None,
loss = "mean_squared_error",
metrics = "mean_squared_error",
bilstm_block_one = 1,
lstm_block_one = 1,
layer_config = {
"layer0": {
"config": {
"neurons": 50,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer1": {
"config": {
"neurons": 50,
"activation": "relu",
"regularization": 0.0,
}
},
},
epochs = 100,
show_progress = 1,
validation_split = 0.20,
board = False,
)
# create and fit a bidirectional gated recurrent neural network
predictor.create_fit_bigru(
steps_past,
steps_future,
optimizer = "adam",
optimizer_args = None,
loss = "mean_squared_error",
metrics = "mean_squared_error",
bigru_block_one = 1,
gru_block_one = 1,
layer_config = {
"layer0": {
"config": {
"neurons": 50,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer1": {
"config": {
"neurons": 50,
"activation": "relu",
"regularization": 0.0,
}
},
},
epochs = 100,
show_progress = 1,
validation_split = 0.20,
board = False,
)
# you can add additional layer to the defualt layers by increasing the layer block count and adding the configuration for the layer in the layer_config dictionary. Please note that the last layer should not have a dropout key.
# in addition, you can add to all models early stopping arguments:
monitor='val_loss',
min_delta=0,
patience=0,
verbose=0,
mode='auto',
baseline=None,
restore_best_weights=False,
start_from_epoch=0
# instpect model structure
predictor.model_blueprint()
# insptect keras model performances via (access dictionary via history key):
predictor.show_performance()
# make predictions via (numpy array expected):
predictor.predict(data)
# save predictor via:
predictor.freeze(absolute_path)
# load saved predictor via:
predictor.retrieve(location)Univariate Hybrid Predictors
from imbrium import HybridUni
# create a HybridUni object (numpy array expected)
predictor = HybridUni(target = target_numpy_array)
# the following models are available for a HybridUni objects:
# create and fit a convolutional recurrent neural network
predictor.create_fit_cnnrnn(
sub_seq,
steps_past,
steps_future,
optimizer = "adam",
optimizer_args = None,
loss = "mean_squared_error",
metrics = "mean_squared_error",
conv_block_one = 1,
conv_block_two = 1,
rnn_block_one = 1,
rnn_block_two = 1,
layer_config = {
"layer0": {
"config": {
"filters": 64,
"kernel_size": 1,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer1": {
"config": {
"filters": 32,
"kernel_size": 1,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer2": {
"config": {
"pool_size": 2,
}
},
"layer3": {
"config": {
"neurons": 32,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer4": {
"config": {
"neurons": 32,
"activation": "relu",
"regularization": 0.0,
}
},
},
epochs = 100,
show_progress = 1,
validation_split = 0.20,
board = False,
)
# create and fit a convolutional long short-term memory neural network
predictor.create_fit_cnnlstm(
sub_seq,
steps_past,
steps_future,
optimizer = "adam",
optimizer_args = None,
loss = "mean_squared_error",
metrics = "mean_squared_error",
conv_block_one = 1,
conv_block_two = 1,
lstm_block_one = 1,
lstm_block_two = 1,
layer_config = {
"layer0": {
"config": {
"filters": 64,
"kernel_size": 1,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer1": {
"config": {
"filters": 32,
"kernel_size": 1,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer2": {
"config": {
"pool_size": 2,
}
},
"layer3": {
"config": {
"neurons": 32,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer4": {
"config": {
"neurons": 32,
"activation": "relu",
"regularization": 0.0,
}
},
},
epochs = 100,
show_progress = 1,
validation_split = 0.20,
board = False,
)
# create and fit a convolutional gated recurrent unit neural network
predictor.create_fit_cnngru(
sub_seq,
steps_past,
steps_future,
optimizer = "adam",
optimizer_args = None,
loss = "mean_squared_error",
metrics = "mean_squared_error",
conv_block_one = 1,
conv_block_two = 1,
gru_block_one = 1,
gru_block_two = 1,
layer_config = {
"layer0": {
"config": {
"filters": 64,
"kernel_size": 1,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer1": {
"config": {
"filters": 32,
"kernel_size": 1,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer2": {
"config": {
"pool_size": 2,
}
},
"layer3": {
"config": {
"neurons": 32,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer4": {
"config": {
"neurons": 32,
"activation": "relu",
"regularization": 0.0,
}
},
},
epochs = 100,
show_progress = 1,
validation_split = 0.20,
board = False,
)
# create and fit a convolutional bidirectional recurrent neural network
predictor.create_fit_cnnbirnn(
sub_seq,
steps_past,
steps_future,
optimizer = "adam",
optimizer_args = None,
loss = "mean_squared_error",
metrics = "mean_squared_error",
conv_block_one = 1,
conv_block_two = 1,
birnn_block_one = 1,
rnn_block_one = 1,
layer_config = {
"layer0": {
"config": {
"filters": 64,
"kernel_size": 1,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer1": {
"config": {
"filters": 32,
"kernel_size": 1,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer2": {
"config": {
"pool_size": 2,
}
},
"layer3": {
"config": {
"neurons": 32,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer4": {
"config": {
"neurons": 32,
"activation": "relu",
"regularization": 0.0,
}
},
},
epochs = 100,
show_progress = 1,
validation_split = 0.20,
board = False,
)
# create and fit a convolutional bidirectional long short-term neural network
predictor.create_fit_cnnbilstm(
sub_seq,
steps_past,
steps_future,
optimizer = "adam",
optimizer_args = None,
loss = "mean_squared_error",
metrics = "mean_squared_error",
conv_block_one = 1,
conv_block_two = 1,
bilstm_block_one = 1,
lstm_block_one = 1,
layer_config = {
"layer0": {
"config": {
"filters": 64,
"kernel_size": 1,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer1": {
"config": {
"filters": 32,
"kernel_size": 1,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer2": {
"config": {
"pool_size": 2,
}
},
"layer3": {
"config": {
"neurons": 32,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer4": {
"config": {
"neurons": 32,
"activation": "relu",
"regularization": 0.0,
}
},
},
epochs = 100,
show_progress = 1,
validation_split = 0.20,
board = False,
)
# create and fit a convolutional bidirectional gated recurrent neural network
predictor.create_fit_cnnbigru(
sub_seq,
steps_past,
steps_future,
optimizer = "adam",
optimizer_args = None,
loss = "mean_squared_error",
metrics = "mean_squared_error",
conv_block_one = 1,
conv_block_two = 1,
bigru_block_one = 1,
gru_block_one = 1,
layer_config = {
"layer0": {
"config": {
"filters": 64,
"kernel_size": 1,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer1": {
"config": {
"filters": 32,
"kernel_size": 1,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer2": {
"config": {
"pool_size": 2,
}
},
"layer3": {
"config": {
"neurons": 32,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer4": {
"config": {
"neurons": 32,
"activation": "relu",
"regularization": 0.0,
}
},
},
epochs = 100,
show_progress = 1,
validation_split = 0.20,
board = False,
)
# you can add additional layer to the defualt layers by increasing the layer block count and adding the configuration for the layer in the layer_config dictionary. Please note that the last layer should not have a dropout key.
# in addition, you can add to all models early stopping arguments:
monitor='val_loss',
min_delta=0,
patience=0,
verbose=0,
mode='auto',
baseline=None,
restore_best_weights=False,
start_from_epoch=0
# instpect model structure
predictor.model_blueprint()
# insptect keras model performances via (access dictionary via history key):
predictor.show_performance()
# make predictions via (numpy array expected):
# - when loading/retrieving a saved model, provide sub_seq, steps_past, steps_future in the predict method!
predictor.predict(data, sub_seq=None, steps_past=None, steps_future=None)
# save predictor via:
predictor.freeze(absolute_path)
# load saved predictor via:
predictor.retrieve(location)Multivariate Hybrid Predictors
from imbrium import HybridMulti
# create a HybridMulti object (numpy array expected)
predictor = HybridMulti(target = target_numpy_array, features = features_numpy_array)
# the following models are available for a HybridMulti objects:
# create and fit a convolutional recurrent neural network
predictor.create_fit_cnnrnn(
sub_seq,
steps_past,
steps_future,
optimizer = "adam",
optimizer_args = None,
loss = "mean_squared_error",
metrics = "mean_squared_error",
conv_block_one = 1,
conv_block_two = 1,
rnn_block_one = 1,
rnn_block_two = 1,
layer_config = {
"layer0": {
"config": {
"filters": 64,
"kernel_size": 1,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer1": {
"config": {
"filters": 32,
"kernel_size": 1,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer2": {
"config": {
"pool_size": 2,
}
},
"layer3": {
"config": {
"neurons": 32,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer4": {
"config": {
"neurons": 32,
"activation": "relu",
"regularization": 0.0,
}
},
},
epochs = 100,
show_progress = 1,
validation_split = 0.20,
board = False,
)
# create and fit a convolutional long short-term memory neural network
predictor.create_fit_cnnlstm(
sub_seq,
steps_past,
steps_future,
optimizer = "adam",
optimizer_args = None,
loss = "mean_squared_error",
metrics = "mean_squared_error",
conv_block_one = 1,
conv_block_two = 1,
lstm_block_one = 1,
lstm_block_two = 1,
layer_config = {
"layer0": {
"config": {
"filters": 64,
"kernel_size": 1,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer1": {
"config": {
"filters": 32,
"kernel_size": 1,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer2": {
"config": {
"pool_size": 2,
}
},
"layer3": {
"config": {
"neurons": 32,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer4": {
"config": {
"neurons": 32,
"activation": "relu",
"regularization": 0.0,
}
},
},
epochs = 100,
show_progress = 1,
validation_split = 0.20,
board = False,
)
# create and fit a convolutional gated recurrent unit neural network
predictor.create_fit_cnngru(
sub_seq,
steps_past,
steps_future,
optimizer = "adam",
optimizer_args = None,
loss = "mean_squared_error",
metrics = "mean_squared_error",
conv_block_one = 1,
conv_block_two = 1,
gru_block_one = 1,
gru_block_two = 1,
layer_config = {
"layer0": {
"config": {
"filters": 64,
"kernel_size": 1,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer1": {
"config": {
"filters": 32,
"kernel_size": 1,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer2": {
"config": {
"pool_size": 2,
}
},
"layer3": {
"config": {
"neurons": 32,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer4": {
"config": {
"neurons": 32,
"activation": "relu",
"regularization": 0.0,
}
},
},
epochs = 100,
show_progress = 1,
validation_split = 0.20,
board = False,
)
# create and fit a convolutional bidirectional recurrent neural network
predictor.create_fit_cnnbirnn(
sub_seq,
steps_past,
steps_future,
optimizer = "adam",
optimizer_args = None,
loss = "mean_squared_error",
metrics = "mean_squared_error",
conv_block_one = 1,
conv_block_two = 1,
birnn_block_one = 1,
rnn_block_one = 1,
layer_config = {
"layer0": {
"config": {
"filters": 64,
"kernel_size": 1,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer1": {
"config": {
"filters": 32,
"kernel_size": 1,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer2": {
"config": {
"pool_size": 2,
}
},
"layer3": {
"config": {
"neurons": 32,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer4": {
"config": {
"neurons": 32,
"activation": "relu",
"regularization": 0.0,
}
},
},
epochs = 100,
show_progress = 1,
validation_split = 0.20,
board = False,
)
# create and fit a convolutional bidirectional long short-term neural network
predictor.create_fit_cnnbilstm(
sub_seq,
steps_past,
steps_future,
optimizer = "adam",
optimizer_args = None,
loss = "mean_squared_error",
metrics = "mean_squared_error",
conv_block_one = 1,
conv_block_two = 1,
bilstm_block_one = 1,
lstm_block_one = 1,
layer_config = {
"layer0": {
"config": {
"filters": 64,
"kernel_size": 1,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer1": {
"config": {
"filters": 32,
"kernel_size": 1,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer2": {
"config": {
"pool_size": 2,
}
},
"layer3": {
"config": {
"neurons": 32,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer4": {
"config": {
"neurons": 32,
"activation": "relu",
"regularization": 0.0,
}
},
},
epochs = 100,
show_progress = 1,
validation_split = 0.20,
board = False,
)
# create and fit a convolutional bidirectional gated recurrent neural network
predictor.create_fit_cnnbigru(
sub_seq,
steps_past,
steps_future,
optimizer = "adam",
optimizer_args = None,
loss = "mean_squared_error",
metrics = "mean_squared_error",
conv_block_one = 1,
conv_block_two = 1,
bigru_block_one = 1,
gru_block_one = 1,
layer_config = {
"layer0": {
"config": {
"filters": 64,
"kernel_size": 1,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer1": {
"config": {
"filters": 32,
"kernel_size": 1,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer2": {
"config": {
"pool_size": 2,
}
},
"layer3": {
"config": {
"neurons": 32,
"activation": "relu",
"regularization": 0.0,
"dropout": 0.0,
}
},
"layer4": {
"config": {
"neurons": 32,
"activation": "relu",
"regularization": 0.0,
}
},
},
epochs = 100,
show_progress = 1,
validation_split = 0.20,
board = False,
)
# you can add additional layer to the defualt layers by increasing the layer block count and adding the configuration for the layer in the layer_config dictionary. Please note that the last layer should not have a dropout key.
# in addition, you can add to all models early stopping arguments:
monitor='val_loss',
min_delta=0,
patience=0,
verbose=0,
mode='auto',
baseline=None,
restore_best_weights=False,
start_from_epoch=0
# instpect model structure
predictor.model_blueprint()
# insptect keras model performances via (access dictionary via history key):
predictor.show_performance()
# make predictions via (numpy array expected):
# - when loading/retrieving a saved model, provide sub_seq, steps_past, steps_future in the predict method!
predictor.predict(data, sub_seq=None, steps_past=None, steps_future=None)
# save predictor via:
predictor.freeze(absolute_path)
# load saved predictor via:
predictor.retrieve(location)https://github.com/maxmekiska/ImbriumTesting-Demo/blob/main/use-case-1.ipynb
https://github.com/maxmekiska/ImbriumTesting-Demo/blob/main/IntegrationTest.ipynb
Expand
The library differentiates between two modes:
- Univariate-Multistep forecasting
- Multivariate-Multistep forecasting
These two main modes are further divided based on the complexity of the underlying model architectures:
- Pure
- Hybrid
Pure supports the following architectures:
- Multilayer perceptron (MLP)
- Recurrent neural network (RNN)
- Long short-term memory (LSTM)
- Gated recurrent unit (GRU)
- Convolutional neural network (CNN)
- Bidirectional recurrent neural network (BI-RNN)
- Bidirectional long-short term memory (BI-LSTM)
- Bidirectional gated recurrent unit (BI-GRU)
- Encoder-Decoder recurrent neural network
- Encoder-Decoder long-short term memory
- Encoder-Decoder convolutional neural network (Encoding via CNN, Decoding via GRU)
- Encoder-Decoder gated recurrent unit
Hybrid supports:
- Convolutional neural network + recurrent neural network (CNN-RNN)
- Convolutional neural network + Long short-term memory (CNN-LSTM)
- Convolutional neural network + Gated recurrent unit (CNN-GRU)
- Convolutional neural network + Bidirectional recurrent neural network (CNN-BI-RNN)
- Convolutional neural network + Bidirectional long-short term memory (CNN-BI-LSTM)
- Convolutional neural network + Bidirectional gated recurrent unit (CNN-BI-GRU)
Please note that each model is supported by a prior input data pre-processing procedure which allows to set a look-back period, look-forward period, sub-sequences division (only for hybrid architectures) and data scaling method.
The following scikit-learn scaling procedures are supported:
- StandardScaler
- MinMaxScaler
- MaxAbsScaler
- Normalizing ([0, 1])
- None (raw data input)
During training/fitting, callback conditions can be defined to guard against overfitting.
Trained models can furthermore be saved or loaded if the user wishes to do so.
Expand
Attention: Typing has been left in the below examples to ease the configuration readability.
Version 1.2.0 started supporting tensor board dashboards: https://www.tensorflow.org/tensorboard/get_started
Version 1.3.0 started supporting adjustable layer depth configurations for all architectures. If you wish to adjust the layer depth, please make sure to include a custom layer_config accounting for the correct number of layers. The last layer cannot contain a dropout parameter -> tuple needs to be of length 3: (neurons, activation, regularization).
- Univariate-Multistep forecasting - Pure architectures
from imbrium.predictors.univarpure import PureUni
predictor = PureUni(
steps_past: int,
steps_future: int,
data = pd.DataFrame(),
scale: str = ''
)
# Choose between one of the architectures:
predictor.create_mlp(
optimizer: str = 'adam',
optimizer_args: dict = None,
loss: str = 'mean_squared_error',
metrics: str = 'mean_squared_error',
dense_block_one: int = 1,
dense_block_two: int = 1,
dense_block_three: int = 1,
layer_config: dict =
{
'layer0': (50, 'relu', 0.0, 0.0), # (neurons, activation, regularization, dropout)
'layer1': (25,'relu', 0.0, 0.0), # (neurons, activation, regularization, dropout)
'layer2': (25, 'relu', 0.0) # (neurons, activation, regularization)
}
)
predictor.create_rnn(
optimizer: str = 'adam',
optimizer_args: dict = None,
loss: str = 'mean_squared_error',
metrics: str = 'mean_squared_error',
rnn_block_one: int = 1,
rnn_block_two: int = 1,
rnn_block_three: int = 1,
layer_config: dict =
{
'layer0': (40, 'relu', 0.0, 0.0), # (neurons, activation, regularization, dropout)
'layer1': (50,'relu', 0.0, 0.0), # (neurons, activation, regularization, dropout)
'layer2': (50, 'relu', 0.0) # (neurons, activation, regularization)
}
)
predictor.create_lstm(
optimizer: str = 'adam',
optimizer_args: dict = None,
loss: str = 'mean_squared_error',
metrics: str = 'mean_squared_error',
lstm_block_one: int = 1,
lstm_block_two: int = 1,
lstm_block_three: int = 1,
layer_config: dict =
{
'layer0': (40, 'relu', 0.0, 0.0), # (neurons, activation, regularization, dropout)
'layer1': (50,'relu', 0.0, 0.0), # (neurons, activation, regularization, dropout)
'layer2': (50, 'relu', 0.0) # (neurons, activation, regularization)
}
)
predictor.create_gru(
optimizer: str = 'adam',
optimizer_args: dict = None,
loss: str = 'mean_squared_error',
metrics: str = 'mean_squared_error',
gru_block_one: int = 1,
gru_block_two: int = 1,
gru_block_three: int = 1,
layer_config: dict =
{
'layer0': (40, 'relu', 0.0, 0.0), # (neurons, activation, regularization, dropout)
'layer1': (50,'relu', 0.0, 0.0), # (neurons, activation, regularization, dropout)
'layer2': (50, 'relu', 0.0) # (neurons, activation, regularization)
}
)
predictor.create_cnn(
optimizer: str = 'adam',
optimizer_args: dict = None,
loss: str = 'mean_squared_error',
metrics: str = 'mean_squared_error',
conv_block_one: int = 1,
conv_block_two: int = 1,
dense_block_one: int = 1,
layer_config: dict =
{
'layer0': (64, 1, 'relu', 0.0, 0.0), # (filter_size, kernel_size, activation, regularization, dropout)
'layer1': (32, 1, 'relu', 0.0, 0.0), # (filter_size, kernel_size, activation, regularization, dropout)
'layer2': (2), # (pool_size)
'layer3': (50, 'relu', 0.0) # (neurons, activation, regularization)
}
)
predictor.create_birnn(
optimizer: str = 'adam',
optimizer_args: dict = None,
loss: str = 'mean_squared_error',
metrics: str = 'mean_squared_error',
birnn_block_one: int = 1,
rnn_block_one: int = 1,
layer_config: dict =
{
'layer0': (50, 'relu', 0.0, 0.0), # (neurons, activation, regularization, dropout)
'layer1': (50, 'relu', 0.0) # (neurons, activation, regularization)
}
)
predictor.create_bilstm(
optimizer: str = 'adam',
optimizer_args: dict = None,
loss: str = 'mean_squared_error',
metrics: str = 'mean_squared_error',
bilstm_block_one: int = 1,
lstm_block_one: int = 1,
layer_config: dict =
{
'layer0': (50, 'relu', 0.0, 0.0), # (neurons, activation, regularization, dropout)
'layer1': (50, 'relu', 0.0) # (neurons, activation, regularization)
}
)
predictor.create_bigru(
optimizer: str = 'adam',
optimizer_args: dict = None,
loss: str = 'mean_squared_error',
metrics: str = 'mean_squared_error',
bigru_block_one: int = 1,
gru_block_one: int = 1,
layer_config: dict =
{
'layer0': (50, 'relu', 0.0, 0.0), # (neurons, activation, regularization, dropout)
'layer1': (50, 'relu', 0.0) # (neurons, activation, regularization)
}
)
predictor.create_encdec_rnn(
optimizer: str = 'adam',
optimizer_args: dict = None,
loss: str = 'mean_squared_error',
metrics: str = 'mean_squared_error',
enc_rnn_block_one: int = 1,
enc_rnn_block_two: int = 1,
dec_rnn_block_one: int = 1,
dec_rnn_block_two: int = 1,
layer_config: dict =
{
'layer0': (100, 'relu', 0.0, 0.0), # (neurons, activation, regularization, dropout)
'layer1': (50, 'relu', 0.0, 0.0), # (neurons, activation, regularization, dropout)
'layer2': (50, 'relu', 0.0, 0.0), # (neurons, activation, regularization, dropout)
'layer3': (100, 'relu', 0.0) # (neurons, activation, regularization)
}
)
predictor.create_encdec_lstm(
optimizer: str = 'adam',
optimizer_args: dict = None,
loss: str = 'mean_squared_error',
metrics: str = 'mean_squared_error',
enc_lstm_block_one: int = 1,
enc_lstm_block_two: int = 1,
dec_lstm_block_one: int = 1,
dec_lstm_block_two: int = 1,
layer_config: dict =
{
'layer0': (100, 'relu', 0.0, 0.0), # (neurons, activation, regularization, dropout)
'layer1': (50, 'relu', 0.0, 0.0), # (neurons, activation, regularization, dropout)
'layer2': (50, 'relu', 0.0, 0.0), # (neurons, activation, regularization, dropout)
'layer3': (100, 'relu', 0.0) # (neurons, activation, regularization)
}
)
predictor.create_encdec_cnn(
optimizer: str = 'adam',
optimizer_args: dict = None,
loss: str = 'mean_squared_error',
metrics: str = 'mean_squared_error',
enc_conv_block_one: int = 1,
enc_conv_block_two: int = 1,
dec_gru_block_one: int = 1,
dec_gru_block_two: int = 1,
layer_config: dict =
{
'layer0': (64, 1, 'relu', 0.0, 0.0), # (filter_size, kernel_size, activation, regularization, dropout)
'layer1': (32, 1, 'relu', 0.0, 0.0), # (filter_size, kernel_size, activation, regularization, dropout)
'layer2': (2), # (pool_size)
'layer3': (50, 'relu', 0.0, 0.0), # (neurons, activation, regularization, dropout)
'layer4': (100, 'relu', 0.0) # (neurons, activation, regularization)
}
)
predictor.create_encdec_gru(
optimizer: str = 'adam',
optimizer_args: dict = None,
loss: str = 'mean_squared_error',
metrics: str = 'mean_squared_error',
enc_gru_block_one: int = 1,
enc_gru_block_two: int = 1,
dec_gru_block_one: int = 1,
dec_gru_block_two: int = 1,
layer_config: dict =
{
'layer0': (100, 'relu', 0.0, 0.0), # (neurons, activation, regularization, dropout)
'layer1': (50, 'relu', 0.0, 0.0), # (neurons, activation, regularization, dropout)
'layer2': (50, 'relu', 0.0, 0.0), # (neurons, activation, regularization, dropout)
'layer3': (100, 'relu', 0.0) # (neurons, activation, regularization)
}
)
# Fit the predictor object - more callback settings at:
# https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/EarlyStopping
predictor.fit_model(
epochs: int,
show_progress: int = 1,
validation_split: float = 0.20,
board: bool = True, # record training progress in tensorboard
monitor='loss',
patience=3
)
# Have a look at the model performance
predictor.show_performance(metric_name: str = None) # optionally plot metric name against loss
# Make a prediction based on new unseen data
predictor.predict(data)
# Safe your model:
predictor.save_model()
# Load a model:
# Step 1: initialize a new predictor object with same characteristics as model to load
# Step 2: Do not pass in any data
# Step 3: Invoke the method load_model()
# optional Step 4: Use the setter method set_model_id(name: str) to give model a name
loading_predictor = PureUni(steps_past: int, steps_future: int)
loading_predictor.load_model(location: str)
loading_predictor.set_model_id(name: str)- Univariate-Multistep forecasting - Hybrid architectures
from imbrium.predictors.univarhybrid import HybridUni
predictor = HybridUni(
sub_seq: int,
steps_past: int,
steps_future: int, data = pd.DataFrame(),
scale: str = ''
)
# Choose between one of the architectures:
predictor.create_cnnrnn(
optimizer: str = 'adam',
optimizer_args: dict = None,
loss: str = 'mean_squared_error',
metrics: str = 'mean_squared_error',
conv_block_one: int = 1,
conv_block_two: int = 1,
rnn_block_one: int = 1,
rnn_block_two: int = 1,
layer_config =
{
'layer0': (64, 1, 'relu', 0.0, 0.0), # (filter_size, kernel_size, activation, regularization, dropout)
'layer1': (32, 1, 'relu', 0.0, 0.0), # (filter_size, kernel_size, activation, regularization, dropout)
'layer2': (2), # (pool_size)
'layer3': (50,'relu', 0.0, 0.0), # (neurons, activation, regularization, dropout)
'layer4': (25, 'relu', 0.0, 0.0) # (neurons, activation, regularization, dropout)
}
)
predictor.create_cnnlstm(
optimizer: str = 'adam',
optimizer_args: dict = None,
loss: str = 'mean_squared_error',
metrics: str = 'mean_squared_error',
conv_block_one: int = 1,
conv_block_two: int = 1,
lstm_block_one: int = 1,
lstm_block_two: int = 1,
layer_config =
{
'layer0': (64, 1, 'relu', 0.0, 0.0), # (filter_size, kernel_size, activation, regularization, dropout)
'layer1': (32, 1, 'relu', 0.0, 0.0), # (filter_size, kernel_size, activation, regularization, dropout)
'layer2': (2), # (pool_size)
'layer3': (50, 'relu', 0.0, 0.0), # (neurons, activation, regularization, dropout)
'layer4': (25, 'relu', 0.0) # (neurons, activation, regularization)
}
)
predictor.create_cnngru(
optimizer: str = 'adam',
optimizer_args: dict = None,
loss: str = 'mean_squared_error',
metrics: str = 'mean_squared_error',
conv_block_one: int = 1,
conv_block_two: int = 1,
gru_block_one: int = 1,
gru_block_two: int = 1,
layer_config =
{
'layer0': (64, 1, 'relu', 0.0, 0.0), # (filter_size, kernel_size, activation, regularization, dropout)
'layer1': (32, 1, 'relu', 0.0, 0.0), # (filter_size, kernel_size, activation, regularization, dropout)
'layer2': (2), # (pool_size)
'layer3': (50, 'relu', 0.0, 0.0), # (neurons, activation, regularization, dropout)
'layer4': (25, 'relu', 0.0) # (neurons, activation, regularization)
}
)
predictor.create_cnnbirnn(
optimizer: str = 'adam',
optimizer_args: dict = None,
loss: str = 'mean_squared_error',
metrics: str = 'mean_squared_error',
conv_block_one: int = 1,
conv_block_two: int = 1,
birnn_block_one: int = 1,
rnn_block_one: int = 1,
layer_config =
{
'layer0': (64, 1, 'relu', 0.0, 0.0), # (filter_size, kernel_size, activation, regularization, dropout)
'layer1': (32, 1, 'relu', 0.0, 0.0), # (filter_size, kernel_size, activation, regularization, dropout)
'layer2': (2), # (pool_size)
'layer3': (50, 'relu', 0.0, 0.0), # (neurons, activation, regularization, dropout)
'layer4': (25, 'relu', 0.0) # (neurons, activation, regularization)
}
)
predictor.create_cnnbilstm(
optimizer: str = 'adam',
optimizer_args: dict = None,
loss: str = 'mean_squared_error',
metrics: str = 'mean_squared_error',
conv_block_one: int = 1,
conv_block_two: int = 1,
bilstm_block_one: int = 1,
lstm_block_one: int = 1,
layer_config =
{
'layer0': (64, 1, 'relu', 0.0, 0.0), # (filter_size, kernel_size, activation, regularization, dropout)
'layer1': (32, 1, 'relu', 0.0, 0.0), # (filter_size, kernel_size, activation, regularization, dropout)
'layer2': (2), # (pool_size)
'layer3': (50, 'relu', 0.0, 0.0), # (neurons, activation, regularization, dropout)
'layer4': (25, 'relu', 0.0) # (neurons, activation, regularization)
}
)
predictor.create_cnnbigru(
optimizer: str = 'adam',
optimizer_args: dict = None,
loss: str = 'mean_squared_error',
metrics: str = 'mean_squared_error',
conv_block_one: int = 1,
conv_block_two: int = 1,
bigru_block_one: int = 1,
gru_block_one: int = 1,
layer_config =
{
'layer0': (64, 1, 'relu', 0.0, 0.0), # (filter_size, kernel_size, activation, regularization, dropout)
'layer1': (32, 1, 'relu', 0.0, 0.0), # (filter_size, kernel_size, activation, regularization, dropout)
'layer2': (2), # (pool_size)
'layer3': (50, 'relu', 0.0, 0.0), # (neurons, activation, regularization, dropout)
'layer4': (25, 'relu', 0.0) # (neurons, activation, regularization)
}
)
# Fit the predictor object - more callback settings at:
# https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/EarlyStopping
predictor.fit_model(
epochs: int,
show_progress: int = 1,
validation_split: float = 0.20,
board: bool = True, # record training progress in tensorboard
monitor='loss',
patience=3
)
# Have a look at the model performance
predictor.show_performance(metric_name: str = None) # optionally plot metric name against loss
# Make a prediction based on new unseen data
predictor.predict(data: array)
# Safe your model:
predictor.save_model()
# Load a model:
# Step 1: initialize a new predictor object with same characteristics as model to load
# Step 2: Do not pass in any data
# Step 3: Invoke the method load_model()
# optional Step 4: Use the setter method set_model_id(name: str) to give model a name
loading_predictor = HybridUni(sub_seq: int, steps_past: int, steps_future: int)
loading_predictor.load_model(location: str)
loading_predictor.set_model_id(name: str)- Multivariate-Multistep forecasting - Pure architectures
from imbrium.predictors.multivarpure import PureMulti
# please make sure that the target feature is the first variable in the feature list
predictor = PureMulti(steps_past: int, steps_future: int, data = DataFrame(), features = [], scale: str = '')
# Choose between one of the architectures:
predictor.create_mlp(
optimizer: str = 'adam',
optimizer_args: dict = None,
loss: str = 'mean_squared_error',
metrics: str = 'mean_squared_error',
dense_block_one: int = 1,
dense_block_two: int = 1,
dense_block_three: int = 1,
layer_config: dict =
{
'layer0': (50, 'relu', 0.0, 0.0), # (neurons, activation, regularization, dropout)
'layer1': (25,'relu', 0.0, 0.0), # (neurons, activation, regularization, dropout)
'layer2': (25, 'relu', 0.0) # (neurons, activation, regularization)
}
)
predictor.create_rnn(
optimizer: str = 'adam',
optimizer_args: dict = None,
loss: str = 'mean_squared_error',
metrics: str = 'mean_squared_error',
rnn_block_one: int = 1,
rnn_block_two: int = 1,
rnn_block_three: int = 1,
layer_config: dict =
{
'layer0': (40, 'relu', 0.0, 0.0), # (neurons, activation, regularization, dropout)
'layer1': (50,'relu', 0.0, 0.0), # (neurons, activation, regularization, dropout)
'layer2': (50, 'relu', 0.0) # (neurons, activation, regularization)
}
)
predictor.create_lstm(
optimizer: str = 'adam',
optimizer_args: dict = None,
loss: str = 'mean_squared_error',
metrics: str = 'mean_squared_error',
lstm_block_one: int = 1,
lstm_block_two: int = 1,
lstm_block_three: int = 1,
layer_config: dict =
{
'layer0': (40, 'relu', 0.0, 0.0), # (neurons, activation, regularization, dropout)
'layer1': (50,'relu', 0.0, 0.0), # (neurons, activation, regularization, dropout)
'layer2': (50, 'relu', 0.0) # (neurons, activation, regularization)
}
)
predictor.create_gru(
optimizer: str = 'adam',
optimizer_args: dict = None,
loss: str = 'mean_squared_error',
metrics: str = 'mean_squared_error',
gru_block_one: int = 1,
gru_block_two: int = 1,
gru_block_three: int = 1,
layer_config: dict =
{
'layer0': (40, 'relu', 0.0, 0.0), # (neurons, activation, regularization, dropout)
'layer1': (50,'relu', 0.0, 0.0), # (neurons, activation, regularization, dropout)
'layer2': (50, 'relu', 0.0) # (neurons, activation, regularization)
}
)
predictor.create_cnn(
optimizer: str = 'adam',
optimizer_args: dict = None,
loss: str = 'mean_squared_error',
metrics: str = 'mean_squared_error',
conv_block_one: int = 1,
conv_block_two: int = 1,
dense_block_one: int = 1,
layer_config: dict =
{
'layer0': (64, 1, 'relu', 0.0, 0.0), # (filter_size, kernel_size, activation, regularization, dropout)
'layer1': (32, 1, 'relu', 0.0, 0.0), # (filter_size, kernel_size, activation, regularization, dropout)
'layer2': (2), # (pool_size)
'layer3': (50, 'relu', 0.0) # (neurons, activation, regularization)
}
)
predictor.create_birnn(
optimizer: str = 'adam',
optimizer_args: dict = None,
loss: str = 'mean_squared_error',
metrics: str = 'mean_squared_error',
birnn_block_one: int = 1,
rnn_block_one: int = 1,
layer_config: dict =
{
'layer0': (50, 'relu', 0.0, 0.0), # (neurons, activation, regularization, dropout)
'layer1': (50, 'relu', 0.0) # (neurons, activation, regularization)
}
)
predictor.create_bilstm(
optimizer: str = 'adam',
optimizer_args: dict = None,
loss: str = 'mean_squared_error',
metrics: str = 'mean_squared_error',
bilstm_block_one: int = 1,
lstm_block_one: int = 1,
layer_config: dict =
{
'layer0': (50, 'relu', 0.0, 0.0), # (neurons, activation, regularization, dropout)
'layer1': (50, 'relu', 0.0) # (neurons, activation, regularization)
}
)
predictor.create_bigru(
optimizer: str = 'adam',
optimizer_args: dict = None,
loss: str = 'mean_squared_error',
metrics: str = 'mean_squared_error',
bigru_block_one: int = 1,
gru_block_one: int = 1,
layer_config: dict =
{
'layer0': (50, 'relu', 0.0, 0.0), # (neurons, activation, regularization, dropout)
'layer1': (50, 'relu', 0.0) # (neurons, activation, regularization)
}
)
predictor.create_encdec_rnn(
optimizer: str = 'adam',
optimizer_args: dict = None,
loss: str = 'mean_squared_error',
metrics: str = 'mean_squared_error',
enc_rnn_block_one: int = 1,
enc_rnn_block_two: int = 1,
dec_rnn_block_one: int = 1,
dec_rnn_block_two: int = 1,
layer_config: dict =
{
'layer0': (100, 'relu', 0.0, 0.0), # (neurons, activation, regularization, dropout)
'layer1': (50, 'relu', 0.0, 0.0), # (neurons, activation, regularization, dropout)
'layer2': (50, 'relu', 0.0, 0.0), # (neurons, activation, regularization, dropout)
'layer3': (100, 'relu', 0.0) # (neurons, activation, regularization)
}
)
predictor.create_encdec_lstm(
optimizer: str = 'adam',
optimizer_args: dict = None,
loss: str = 'mean_squared_error',
metrics: str = 'mean_squared_error',
enc_lstm_block_one: int = 1,
enc_lstm_block_two: int = 1,
dec_lstm_block_one: int = 1,
dec_lstm_block_two: int = 1,
layer_config: dict =
{
'layer0': (100, 'relu', 0.0, 0.0), # (neurons, activation, regularization, dropout)
'layer1': (50, 'relu', 0.0, 0.0), # (neurons, activation, regularization, dropout)
'layer2': (50, 'relu', 0.0, 0.0), # (neurons, activation, regularization, dropout)
'layer3': (100, 'relu', 0.0) # (neurons, activation, regularization)
}
)
predictor.create_encdec_cnn(
optimizer: str = 'adam',
optimizer_args: dict = None,
loss: str = 'mean_squared_error',
metrics: str = 'mean_squared_error',
enc_conv_block_one: int = 1,
enc_conv_block_two: int = 1,
dec_gru_block_one: int = 1,
dec_gru_block_two: int = 1,
layer_config: dict =
{
'layer0': (64, 1, 'relu', 0.0, 0.0), # (filter_size, kernel_size, activation, regularization, dropout)
'layer1': (32, 1, 'relu', 0.0, 0.0), # (filter_size, kernel_size, activation, regularization, dropout)
'layer2': (2), # (pool_size)
'layer3': (50, 'relu', 0.0, 0.0), # (neurons, activation, regularization, dropout)
'layer4': (100, 'relu', 0.0) # (neurons, activation, regularization)
}
)
predictor.create_encdec_gru(
optimizer: str = 'adam',
optimizer_args: dict = None,
loss: str = 'mean_squared_error',
metrics: str = 'mean_squared_error',
enc_gru_block_one: int = 1,
enc_gru_block_two: int = 1,
dec_gru_block_one: int = 1,
dec_gru_block_two: int = 1,
layer_config: dict =
{
'layer0': (100, 'relu', 0.0, 0.0), # (neurons, activation, regularization, dropout)
'layer1': (50, 'relu', 0.0, 0.0), # (neurons, activation, regularization, dropout)
'layer2': (50, 'relu', 0.0, 0.0), # (neurons, activation, regularization, dropout)
'layer3': (100, 'relu', 0.0) # (neurons, activation, regularization)
}
)
# Fit the predictor object - more callback settings at:
# https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/EarlyStopping
predictor.fit_model(
epochs: int,
show_progress: int = 1,
validation_split: float = 0.20,
board: bool = True, # record training progress in tensorboard
monitor='loss',
patience=3
)
# Have a look at the model performance
predictor.show_performance(metric_name: str = None) # optionally plot metric name against loss
# Make a prediction based on new unseen data
predictor.predict(data: array)
# Safe your model:
predictor.save_model()
# Load a model:
# Step 1: initialize a new predictor object with same characteristics as model to load
# Step 2: Do not pass in any data
# Step 3: Invoke the method load_model()
# optional Step 4: Use the setter method set_model_id(name: str) to give model a name
loading_predictor = PureMulti(steps_past: int, steps_future: int)
loading_predictor.load_model(location: str)
loading_predictor.set_model_id(name: str)- Multivariate-Multistep forecasting - Hybrid architectures
from imbrium.predictors.multivarhybrid import HybridMulti
# please make sure that the target feature is the first variable in the feature list
predictor = HybridMulti(sub_seq: int, steps_past: int, steps_future: int, data = DataFrame(), features:list = [], scale: str = '')
# Choose between one of the architectures:
predictor.create_cnnrnn(
optimizer: str = 'adam',
optimizer_args: dict = None,
loss: str = 'mean_squared_error',
metrics: str = 'mean_squared_error',
conv_block_one: int = 1,
conv_block_two: int = 1,
rnn_block_one: int = 1,
rnn_block_two: int = 1,
layer_config =
{
'layer0': (64, 1, 'relu', 0.0, 0.0), # (filter_size, kernel_size, activation, regularization, dropout)
'layer1': (32, 1, 'relu', 0.0, 0.0), # (filter_size, kernel_size, activation, regularization, dropout)
'layer2': (2), # (pool_size)
'layer3': (50, 'relu', 0.0, 0.0), # (neurons, activation, regularization, dropout)
'layer4': (25, 'relu', 0.0) # (neurons, activation, regularization)
}
)
predictor.create_cnnlstm(
optimizer: str = 'adam',
optimizer_args: dict = None,
loss: str = 'mean_squared_error',
metrics: str = 'mean_squared_error',
conv_block_one: int = 1,
conv_block_two: int = 1,
lstm_block_one: int = 1,
lstm_block_two: int = 1,
layer_config =
{
'layer0': (64, 1, 'relu', 0.0, 0.0), # (filter_size, kernel_size, activation, regularization, dropout)
'layer1': (32, 1, 'relu', 0.0, 0.0), # (filter_size, kernel_size, activation, regularization, dropout)
'layer2': (2), # (pool_size)
'layer3': (50, 'relu', 0.0, 0.0), # (neurons, activation, regularization, dropout)
'layer4': (25, 'relu', 0.0) # (neurons, activation, regularization)
}
)
predictor.create_cnngru(
optimizer: str = 'adam',
optimizer_args: dict = None,
loss: str = 'mean_squared_error',
metrics: str = 'mean_squared_error',
conv_block_one: int = 1,
conv_block_two: int = 1,
gru_block_one: int = 1,
gru_block_two: int = 1,
layer_config =
{
'layer0': (64, 1, 'relu', 0.0, 0.0), # (filter_size, kernel_size, activation, regularization, dropout)
'layer1': (32, 1, 'relu', 0.0, 0.0), # (filter_size, kernel_size, activation, regularization, dropout)
'layer2': (2), # (pool_size)
'layer3': (50, 'relu', 0.0, 0.0), # (neurons, activation, regularization, dropout)
'layer4': (25, 'relu', 0.0) # (neurons, activation, regularization)
}
)
predictor.create_cnnbirnn(
optimizer: str = 'adam',
optimizer_args: dict = None,
loss: str = 'mean_squared_error',
metrics: str = 'mean_squared_error',
conv_block_one: int = 1,
conv_block_two: int = 1,
birnn_block_one: int = 1,
rnn_block_one: int = 1,
layer_config =
{
'layer0': (64, 1, 'relu', 0.0, 0.0), # (filter_size, kernel_size, activation, regularization, dropout)
'layer1': (32, 1, 'relu', 0.0, 0.0), # (filter_size, kernel_size, activation, regularization, dropout)
'layer2': (2), # (pool_size)
'layer3': (50, 'relu', 0.0, 0.0), # (neurons, activation, regularization, dropout)
'layer4': (25, 'relu', 0.0) # (neurons, activation, regularization)
}
)
predictor.create_cnnbilstm(
optimizer: str = 'adam',
optimizer_args: dict = None,
loss: str = 'mean_squared_error',
metrics: str = 'mean_squared_error',
conv_block_one: int = 1,
conv_block_two: int = 1,
bilstm_block_one: int = 1,
lstm_block_one: int = 1,
layer_config =
{
'layer0': (64, 1, 'relu', 0.0, 0.0), # (filter_size, kernel_size, activation, regularization, dropout)
'layer1': (32, 1, 'relu', 0.0, 0.0), # (filter_size, kernel_size, activation, regularization, dropout)
'layer2': (2), # (pool_size)
'layer3': (50, 'relu', 0.0, 0.0), # (neurons, activation, regularization, dropout)
'layer4': (25, 'relu', 0.0) # (neurons, activation, regularization)
}
)
predictor.create_cnnbigru(
optimizer: str = 'adam',
optimizer_args: dict = None,
loss: str = 'mean_squared_error',
metrics: str = 'mean_squared_error',
conv_block_one: int = 1,
conv_block_two: int = 1,
bigru_block_one: int = 1,
gru_block_one: int = 1,
layer_config =
{
'layer0': (64, 1, 'relu', 0.0, 0.0), # (filter_size, kernel_size, activation, regularization, dropout)
'layer1': (32, 1, 'relu', 0.0, 0.0), # (filter_size, kernel_size, activation, regularization, dropout)
'layer2': (2), # (pool_size)
'layer3': (50, 'relu', 0.0, 0.0), # (neurons, activation, regularization, dropout)
'layer4': (25, 'relu', 0.0) # (neurons, activation, regularization)
}
)
# Fit the predictor object - more callback settings at:
# https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/EarlyStopping
predictor.fit_model(
epochs: int,
show_progress: int = 1,
validation_split: float = 0.20,
board: bool = True, # record training progress in tensorboard
monitor='loss',
patience=3
)
# Have a look at the model performance
predictor.show_performance(metric_name: str = None) # optionally plot metric name against loss
# Make a prediction based on new unseen data
predictor.predict(data: array)
# Safe your model:
predictor.save_model()
# Load a model:
# Step 1: initialize a new predictor object with same characteristics as model to load
# Step 2: Do not pass in any data
# Step 3: Invoke the method load_model()
# optional Step 4: Use the setter method set_model_id(name: str) to give model a name
loading_predictor = HybridMulti(sub_seq: int, steps_past: int, steps_future: int)
loading_predictor.load_model(location: str)
loading_predictor.set_model_id(name: str)Expand
Starting from version 1.1.0, imbrium will support experimental hyperparamerter optimization for the model layer config and optimizer arguments. The optimization process uses the Optuna library (https://optuna.org/).
To leverage Optimization, use the new classes OptimizePureUni, OptimizeHybridUni, OptimizePureMulti and OptimizeHybridMulti. These classes implement optimizable model architecture methods:
OptimizePureUni & OptimizePureMulti:
- create_fit_mlp
- create_fit_rnn
- create_fit_lstm
- create_fit_cnn
- create_fit_gru
- create_fit_birnn
- create_fit_bilstm
- create_fit_bigru
- create_fit_encdec_rnn
- create_fit_encdec_lstm
- create_fit_encdec_gru
- create_fit_encdec_cnn
OptimizeHybridUni & OptimizeHybridMulti:
- create_fit_cnnrnn
- create_fit_cnnlstm
- create_fit_cnngru
- create_fit_cnnbirnn
- create_fit_cnnbilstm
- create_fit_cnnbigru
from imbrium.predictors.univarpure import OptimizePureUni
from imbrium.utils.optimization import seeker
# initialize optimizable predictor object
predictor = OptimizePureUni(steps_past=5, steps_future=10, data=data, scale='standard')
# use seeker decorator on optimization harness
@seeker(optimizer_range=["adam", "sgd"],
layer_config_range= [
{
'layer0': (5, 'relu'),
'layer1': (10,'relu'),
'layer2': (5, 'relu')
},
{
'layer0': (2, 'relu'),
'layer1': (5, 'relu'),
'layer2': (2, 'relu')
}
],
optimizer_args_range = [
{
'learning_rate': 0.02,
},
{
'learning_rate': 0.0001,
}
]
optimization_target='minimize', n_trials = 2)
def create_fit_model(predictor: object, *args, **kwargs):
# use optimizable create_fit_xxx method
return predictor.create_fit_lstm(*args, **kwargs)
create_fit_model(
predictor,
loss='mean_squared_error',
metrics='mean_squared_error',
epochs=2,
show_progress=0,
validation_split=0.20,
board=True,
monitor='val_loss',
patience=2,
min_delta=0,
verbose=1
)
predictor.show_performance()
predictor.predict(data.tail(5))
predictor.model_blueprint()from imbrium.predictors.univarhybrid import OptimizeHybridUni
from imbrium.utils.optimization import seeker
predictor = OptimizeHybridUni(sub_seq = 2, steps_past = 10, steps_future = 5, data = data, scale = 'maxabs')
@seeker(optimizer_range=["adam", "sgd"],
layer_config_range= [
{
'layer0': (8, 1, 'relu'),
'layer1': (4, 1, 'relu'),
'layer2': (2),
'layer3': (25, 'relu'),
'layer4': (10, 'relu')
},
{
'layer0': (16, 1, 'relu'),
'layer1': (8, 1, 'relu'),
'layer2': (2)
'layer3': (55, 'relu'),
'layer4': (10, 'relu')
},
{
'layer0': (32, 1, 'relu'),
'layer1': (16, 1, 'relu'),
'layer2': (2),
'layer3': (25, 'relu'),
'layer4': (10, 'relu')
}
],
optimizer_args_range = [
{
'learning_rate': 0.02,
},
{
'learning_rate': 0.0001,
}
]
optimization_target='minimize', n_trials = 2)
def create_fit_model(predictor: object, *args, **kwargs):
return predictor.create_fit_cnnlstm(*args, **kwargs)
create_fit_model(
predictor,
loss='mean_squared_error',
metrics='mean_squared_error',
epochs=2,
show_progress=0,
validation_split=0.20,
board=True,
monitor='val_loss',
patience=2,
min_delta=0,
verbose=1
)
predictor.show_performance()
predictor.predict(data.tail(10))
predictor.model_blueprint()predictor = OptimizePureMulti(
steps_past = 5,
steps_future = 10,
data = data,
features = ['target', 'target', 'HouseAge', 'AveRooms', 'AveBedrms'],
scale = 'normalize'
)
@seeker(optimizer_range=["adam", "sgd"],
layer_config_range= [
{
'layer0': (5, 'relu'),
'layer1': (10,'relu'),
'layer2': (5, 'relu')
},
{
'layer0': (2, 'relu'),
'layer1': (5, 'relu'),
'layer2': (2, 'relu')
},
{
'layer0': (20, 'relu'),
'layer1': (50, 'relu'),
'layer2': (20, 'sigmoid')
}
],
optimizer_args_range = [
{
'learning_rate': 0.02,
},
{
'learning_rate': 0.0001,
}
]
optimization_target='minimize', n_trials = 3)
def create_fit_model(predictor: object, *args, **kwargs):
return predictor.create_fit_lstm(*args, **kwargs)
create_fit_model(
predictor,
loss='mean_squared_error',
metrics='mean_squared_error',
epochs=2,
show_progress=1,
validation_split=0.20,
board=True,
monitor='val_loss',
patience=2,
min_delta=0,
verbose=1
)
predictor.show_performance()
predictor.predict(data[['target', 'HouseAge', 'AveRooms', 'AveBedrms']].tail(5))
predictor.model_blueprint()predictor = OptimizeHybridMulti(
sub_seq = 2,
steps_past = 10,
steps_future = 5,
data = data,
features = ['target', 'target', 'HouseAge', 'AveRooms', 'AveBedrms'],
scale = 'normalize'
)
@seeker(optimizer_range=["adam", "sgd"],
layer_config_range= [
{
'layer0': (8, 1, 'relu'),
'layer1': (4, 1, 'relu'),
'layer2': (2),
'layer3': (5, 'relu'),
'layer4': (5, 'relu')
},
{
'layer0': (8, 1, 'relu'),
'layer1': (4, 1, 'relu'),
'layer2': (2),
'layer3': (5, 'relu'),
'layer4': (5, 'relu')
},
{
'layer0': (8, 1, 'relu'),
'layer1': (4, 1, 'relu'),
'layer2': (2),
'layer3': (5, 'relu'),
'layer4': (5, 'relu')
}
],
optimizer_args_range = [
{
'learning_rate': 0.02,
},
{
'learning_rate': 0.0001,
}
]
optimization_target='minimize', n_trials = 3)
def create_fit_model(predictor: object, *args, **kwargs):
return predictor.create_fit_cnnlstm(*args, **kwargs)
create_fit_model(
predictor,
loss='mean_squared_error',
metrics='mean_squared_error',
epochs=2,
show_progress=1,
validation_split=0.20,
board=True,
monitor='val_loss',
patience=2,
min_delta=0,
verbose=1
)
predictor.show_performance()
predictor.predict(data[['target', 'HouseAge', 'AveRooms', 'AveBedrms']].tail(10))
predictor.model_blueprint()predictor = OptimizePureMulti(...)
@seeker(optimizer_range=[...],
layer_config_range= [
{...},
{...},
{...}
],
optimizer_args_range = [
{...},
{...},
]
optimization_target = '...', n_trials = x)
def create_fit_model(predictor: object, *args, **kwargs): # seeker harness
return predictor.create_fit_xxx(*args, **kwargs)
create_fit_model(...) # execute seeker harness
predictor.show_performance()
predictor.predict(...)
predictor.model_blueprint()Expand
Brwonlee, J., 2016. Display deep learning model training history in keras [Online]. Available from: https://machinelearningmastery.com/display-deep- learning-model-training-history-in-keras/.
Brwonlee, J., 2018a. How to develop convolutional neural network models for time series forecasting [Online]. Available from: https://machinelearningmastery.com/how-to-develop-convolutional- neural-network-models-for-time-series-forecasting/.
Brwonlee, J., 2018b. How to develop lstm models for time series forecasting [Online]. Available from: https://machinelearningmastery.com/how-to-develop- lstm-models-for-time-series-forecasting/.
Brwonlee, J., 2018c. How to develop multilayer perceptron models for time series forecasting [Online]. Available from: https://machinelearningmastery.com/how-to-develop-multilayer- perceptron-models-for-time-series-forecasting/.