We decided to archive this project and migrate the most important functionality to MLTB2.

Machine Learning Tool Box

This is the machine learning tool box. A collection of userful machine learning tools intended for reuse and extension. The toolbox contains the following modules:

hyperopt - Hyperopt tool to save and restart evaluations
keras - Keras (tf.keras) callback for various metrics and various other Keras tools
lightgbm - metric tool functions for LightGBM
metrics - several metric implementations
plot - plot and visualisation tools
tools - various (i.a. statistical) tools

Module: hyperopt

This module contains a tool function to save and restart Hyperopt evaluations. This is done by saving and loading the hyperopt.Trials objects. The usage looks like this:

from mltb.hyperopt import fmin
from hyperopt import tpe, hp, STATUS_OK


def objective(x):
    return {
        'loss': x ** 2,
        'status': STATUS_OK,
        'other_stuff': {'type': None, 'value': [0, 1, 2]},
        }


best, trials = fmin(objective,
    space=hp.uniform('x', -10, 10),
    algo=tpe.suggest,
    max_evals=100,
    filename='trials_file')

print('best:', best)
print('number of trials:', len(trials.trials))

Output of first run:

No trials file "trials_file" found. Created new trials object.
100%|██████████| 100/100 [00:00<00:00, 338.61it/s, best loss: 0.0007185087453453681]
best: {'x': 0.026805013436769026}
number of trials: 100

Output of second run:

100 evals loaded from trials file "trials_file".
100%|██████████| 100/100 [00:00<00:00, 219.65it/s, best loss: 0.00012259809712488858]
best: {'x': 0.011072402500130158}
number of trials: 200

Module: lightgbm

This module implements metric functions that are not included in LightGBM. At the moment this is the F1- and accuracy-score for binary and multi class problems. The usage looks like this:

bst = lgb.train(param,
                train_data,
                valid_sets=[validation_data]
                early_stopping_rounds=10,
                evals_result=evals_result,
                feval=mltb.lightgbm.multi_class_f1_score_factory(num_classes, 'macro'),
               )

Module: keras (for tf.keras)

BinaryClassifierMetricsCallback

This module provides custom metrics in form of a callback. Because the callback adds these values to the internal logs dictionary it is possible to use the EarlyStopping callback to do early stopping on these metrics.

Parameters

Parameter	Description	Type	Default values
val_data	Validation input	list
val_label	Validation output	list
pos_label	Which index is the positive label	Optional[int]	1
metrics	List of supported metric names or custom metric functions	List[Union[str, Callable]]	['val_roc_auc', 'val_average_precision', 'val_f1', 'val_acc']

Available metrics

val_roc_auc : ROC-AUC
val_f1 : F1-score
val_acc: Accuracy
val_average_precision: Average precision
val_mcc: Matthews correlation coefficient

The usage looks like this:

bcm_callback = mltb.keras.BinaryClassifierMetricsCallback(val_data, val_labels)
es_callback = callbacks.EarlyStopping(monitor='val_roc_auc', patience=5,  mode='max')

history = network.fit(train_data, train_labels,
                      epochs=1000,
                      batch_size=128,

                      #do not give validation_data here or validation will be done twice
                      #validation_data=(val_data, val_labels),

                      #always provide BinaryClassifierMetricsCallback before the EarlyStopping callback
                      callbacks=[bcm_callback, es_callback],
)

You can also define your own custom metric:

def custom_average_recall_score(y_true, y_pred, pos_label):
    rounded_pred = np.rint(y_pred)
    return sklearn.metrics.recall_score(y_true, rounded_pred, pos_label)


bcm_callback = mltb.keras.BinaryClassifierMetricsCallback(val_data, val_labels,metrics=[custom_average_recall_score])
es_callback = callbacks.EarlyStopping(monitor='custom_average_recall_score', patience=5,  mode='max')

history = network.fit(train_data, train_labels,
                      epochs=1000,
                      batch_size=128,

                      #do not give validation_data here or validation will be done twice
                      #validation_data=(val_data, val_labels),

                      #always provide BinaryClassifierMetricsCallback before the EarlyStopping callback
                      callbacks=[bcm_callback, es_callback],
)