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            "Development Status :: 3 - Alpha",
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            "Intended Audience :: Education",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: BSD License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering",
            "Topic :: Software Development"
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        "description": "# Machine Learning Tool Box\nThis is the machine learning tool box. A collection of userful machine learning tools intended for reuse and extension.\nThe toolbox contains the following modules:\n* hyperopt - Hyperopt tool to save and restart evaluations\n* keras - Keras callback for various metrics and various other Keras tools\n* lightgbm - metric tool functions for LightGBM\n* metrics - several metric implementations \n* plot - plot and visualisation tools\n* tools - various (i.a. statistical) tools\n\n## Module: hyperopt\nThis module contains a tool function to save and restart Hyperopt evaluations.\nThis is done by saving and loading the ``hyperopt.Trials`` objects.\nThe usage looks like this:\n```\nfrom mltb.hyperopt import fmin\nfrom hyperopt import tpe, hp, STATUS_OK\n\n\ndef objective(x):\n    return {\n        'loss': x ** 2,\n        'status': STATUS_OK,\n        'other_stuff': {'type': None, 'value': [0, 1, 2]},\n        }\n\n\nbest, trials = fmin(objective,\n    space=hp.uniform('x', -10, 10),\n    algo=tpe.suggest,\n    max_evals=100,\n    filename='trials_file')\n\nprint('best:', best)\nprint('number of trials:', len(trials.trials))\n```\n\nOutput of first run:\n```\nNo trials file \"trials_file\" found. Created new trials object.\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 100/100 [00:00<00:00, 338.61it/s, best loss: 0.0007185087453453681]\nbest: {'x': 0.026805013436769026}\nnumber of trials: 100\n```\n\nOutput of second run:\n```\n100 evals loaded from trials file \"trials_file\".\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 100/100 [00:00<00:00, 219.65it/s, best loss: 0.00012259809712488858]\nbest: {'x': 0.011072402500130158}\nnumber of trials: 200\n```\n\n## Module: keras\nThis module provides ROC-AUC- and F1-metrics (which are not included in Keras) \nin form of a callback. \nBecause the callback adds these values to the internal `logs` dictionary it is \npossible to use the `EarlyStopping` callback\nto do early stopping on these metrics. The usage looks like this:\n```\nbcm_callback = mltb.keras.BinaryClassifierMetricsCallback(val_data, val_labels)\nes_callback = callbacks.EarlyStopping(monitor='roc_auc', patience=5,  mode='max')\n\nhistory = network.fit(train_data, train_labels, \n                      epochs=1000, \n                      batch_size=128, \n\n                      #do not give validation_data here or validation will be done twice\n                      #validation_data=(val_data, val_labels),\n\n                      #always provide BinaryClassifierMetricsCallback before the EarlyStopping callback\n                      callbacks=[bcm_callback, es_callback],\n)\n```\n\n## Module: lightgbm\nThis module implements metric functions that are not included in LightGBM. \nAt the moment this is the F1- and accuracy-score for binary and multi class problems.\nThe usage looks like this:\n```\nbst = lgb.train(param, \n                train_data, \n                valid_sets=[validation_data]\n                early_stopping_rounds=10,\n                evals_result=evals_result,\n                feval=mltb.lightgbm.multi_class_f1_score_factory(num_classes, 'macro'),\n               )\n```\n\n\n",
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