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Machine Learning Tool Box

Project description

PyPI version License

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],
)

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