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params_default.yaml
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params_default.yaml
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profile:
dataset: name_of_dataset
clean:
target: name_of_target_variable
classification: False
onehot_encode_target: False
combine_files: False
percentage_zeros_threshold: 1.0
correlation_metric: pearson
input_max_correlation_threshold: 1.0
featurize:
# If empty, all input variables are used
variables_to_include:
# - variable1
# - variable2
# By setting this to True, the add_-options below are overrided.
use_all_engineered_features_on_all_variables: False
# List the variables below each add_* to add engineered feature
add_sum:
# - variable1
add_gradient:
# - variable1
add_mean:
# - variable1
add_maximum:
# - variable1
add_minimum:
# - variable1
add_min_max_range:
# - variable1
add_slope:
# - variable1
add_slope_sin:
# - variable1
add_slope_cos:
# - variable1
add_standard_deviation:
# - variable1
add_variance:
# - variable1
add_peak_frequency:
# - variable1
rolling_window_size_sum: 10
rolling_window_size_mean: 10
rolling_window_size_max_min: 10
rolling_window_size_standard_deviation: 10
# List features here to remove the raw variables after engineering features
# from them
remove_features:
# - variable1
target_min_correlation_threshold: 0.0
split:
train_split: 0.5
shuffle_files: False
shuffle_samples_before_split: False
scale:
input_method: minmax
output_method:
sequentialize:
window_size: 10
overlap: 0
target_size: 1
shuffle_samples: False
future_predict: False
train:
seed: 2020
learning_method: dnn
ensemble: False
hyperparameter_tuning: False
# Parameters for deep learning (dnn, cnn, lstm etc):
n_epochs: 800
early_stopping: True
patience: 50
activation_function: relu
batch_size: 256
n_layers: 2
n_neurons: [16, 16]
dropout: 0.1
# Parameters for cnn and rnn
n_flattened_layers: 1
n_flattened_nodes: 16
# Parameters for cnn:
kernel_size: 3
maxpooling: False
maxpooling_size: 4
# Parameters for rnn:
unit_type: LSTM
# Parameters for transformer:
ff_dim: 4
n_transformer_blocks: 4
n_heads: 4
head_size: 256
evaluate:
performance_metric: r2
threshold_for_ensemble_models: -1000
dropout_uncertainty_estimation: True
uncertainty_estimation_sampling_size: 100
show_inputs: True
explain:
generate_explanations: True
number_of_background_samples: 10
number_of_summary_samples: 20
explanation_method: shap
seed: 2020
combine_explanations:
combination_method: weighted
weighting_method: softmax
agreement_method: spearman