Model Interpretation

class ai4water.postprocessing.interpret.Interpret(model, save: bool = False, show: bool = True)

Bases: Plot

Interprets the ai4water Model. The Interpret class is different than the methods in explain module. The Interpret class explains the behaviour of the model by using consituents of the model itself for example attention weights or feature importance.

__init__(model, save: bool = False, show: bool = True)

Parameters:

• model – an instance of ai4water’s Model

• save (bool) –

• show (bool) –

compare_xgb_f_imp(calculation_method='all', rescale=True, figsize: Optional[tuple] = None, backend: str = 'matplotlib', **kwargs)

compare various feature importance calculations methods that are built in in XGBoost

f_importances_svm(coef, names)

feature_importance()

get_enc_var_selection_weights(data, data_type: str = 'test')

Returns encoder variable selection weights of TFT model

interpret_attention_lstm(x=None, data=None, data_type: str = 'test')

Parameters:

• x – input data. If not given, data argument must be given.

• data – the data to use to interpret model. It is only required when x is not given.

• data_type – either training, test, validation or all. It is only useful when data argument is used.

interpret_example_tft(example_index: int, x=None, data=None, data_type='test')

interprets a single example using TFT model.

Parameters:

• example_index (int) – index of example to be explained

• x – input data, if not given, data must be given

• data – the data whose example to interpret.

• data_type (str) – either training, test, validation or all. It is only useful when data argument is used.

interpret_ft_transformer(x=None, data=None, data_type: str = 'test')

Parameters:

• x – input data. If not given, data argument must be given.

• data – the data to use to interpret model. It is only required when x is not given.

• data_type – either training, test, validation or all. It is only useful when data argument is used.

interpret_tab_transformer(x=None, data=None, data_type: str = 'test')

Parameters:

• x – input data. If not given, data argument must be given.

• data – the data to use to interpret model. It is only required when x is not given.

• data_type – either training, test, validation or all. It is only useful when data argument is used.

interpret_tft(x=None, y=None, data=None, data_type='test')

global interpretation of TFT model.

Parameters:

• x – input data. If not given, data argument must be given.

• y – labels/target/true data corresponding to x. It is only used for plotting.

• data – the data to use to interpret model. It is only required when x is not given.

• data_type – either training, test, validation or all. It is only useful when data argument is used.

property model

plot_feature_importance(importance=None, use_xgb=False, max_num_features=20, figsize=None, **kwargs)

plots feature importance when the model is tree based.

tft_attention_components(x=None, data=None, data_type: str = 'test')

Gets attention components of tft layer from ai4water’s Model.

Parameters:

• x – the input data to the model

• data – raw data from which x/inputs are extracted.

• data_type – the data to use to calculate attention components

Returns:

• dict – dictionary containing attention components of tft as numpy arrays. Following four attention components are present in the dictionary

  • decoder_self_attn: (attention_heads, ?, total_time_steps, 22)

  • static_variable_selection_weights:

  • encoder_variable_selection_weights: (?, encoder_steps, input_features)

  • decoder_variable_selection_weights: (?, decoder_steps, input_features)

• str – a string indicating which data was used