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],
)