{
    "info": {
        "author": "Fabio Sigrist",
        "author_email": "fabiosigrist@gmail.com",
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        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3"
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        "description": "# KTBoost - A Python Package for Boosting\n\nThis Python package implements several boosting algorithms with different combinations of base learners, optimization algorithms, and loss functions.\n\n## Description\n\nConcerning **base learners**, KTboost includes:\n\n* Trees \n* Reproducing kernel Hilbert space (RKHS) ridge regression functions (i.e., posterior means of Gaussian processes)\n* A combination of the two (the KTBoost algorithm) \n\n\nConcerning the **optimization** step for finding the boosting updates, the package supports:\n\n* Gradient descent\n* Newton's method (if applicable)\n* A hybrid gradient-Newton version for trees as base learners (if applicable)\n\n\nThe package implements the following **loss functions**:\n\n * Continuous data (\"regression\"): quadratic loss (L2 loss), absolute error (L1 loss), Huber loss, quantile regression loss, Gamma regression loss, negative Gaussian log-likelihood with both the mean and the standard deviation as functions of features\n* Count data (\"regression\"): Poisson regression loss\n* (Unorderd) Categorical data (\"classification\"): logistic regression loss (log loss), exponential loss, cross entropy loss with softmax\n* Mixed continuous-categorical data (\"censored regression\"): negative Tobit likelihood (the Grabit model)\n\n\n\n\n## Installation\n\nIt can be **installed** using \n```\npip install -U KTBoost\n```\nand then loaded using \n```python\nimport KTBoost.KTBoost as KTBoost\n```\n\n## Author\nFabio Sigrist\n\n## References\n\n* Friedman, J. H. (2001). Greedy function approximation: a gradient boosting machine. The Annals of Statistics, 1189-1232.\n* [Sigrist, F., & Hirnschall, C. (2019).](https://arxiv.org/abs/1711.08695) Grabit: Gradient Tree Boosted Tobit Models for Default Prediction. Journal of Banking & Finance\n* [Sigrist, F. (2018)](https://arxiv.org/abs/1808.03064). Gradient and Newton Boosting for Classification and Regression. arXiv preprint arXiv:1808.03064.\n* [Sigrist, F. (2019).](https://arxiv.org/abs/1902.03999) KTBoost: Combined Kernel and Tree Boosting. arXiv preprint arXiv:1902.03999.\n\n\n## Usage and examples\nThe package is build as an extension of the scikit-learn implementation of boosting algorithms and its workflow is very similar to that of scikit-learn.\n\nThe two main classes are `KTBoost.BoostingClassifier` and `KTBoost.BoostingRegressor`. The following **code examples** show how the package can be used. See also below for more information on the main parameters.\n\n\n#### Define models, train models, make predictions\n```python\nimport KTBoost.KTBoost as KTBoost\n\n################################################\n## Define model (see below for more examples) ##\n################################################\n## Standard tree-boosting for regression with quadratic loss and hybrid gradient-Newton updates as in Friedman (2001)\nmodel = KTBoost.BoostingRegressor(loss='ls')\n\n##################\n## Train models ##\n##################\nmodel.fit(Xtrain,ytrain)\n\n######################\n## Make predictions ##\n######################\nmodel.predict(Xpred)\n```\n\n#### More examples of models\n```python\n#############################\n## More examples of models ##\n#############################\n## Boosted Tobit model, i.e. Grabit model (Sigrist and Hirnschall, 2017), \n## with lower and upper limits at 0 and 100\nmodel = KTBoost.BoostingRegressor(loss='tobit',yl=0,yu=100)\n## KTBoost algorithm (combined kernel and tree boosting) for classification with Newton updates\nmodel = KTBoost.BoostingClassifier(loss='deviance',base_learner='combined',\n                                    update_step='newton',theta=1)\n## Gradient boosting for classification with trees as base learners\nmodel = KTBoost.BoostingClassifier(loss='deviance',update_step='gradient')\n## Newton boosting for classification model with trees as base learners\nmodel = KTBoost.BoostingClassifier(loss='deviance',update_step='newton')\n## Hybrid gradient-Newton boosting (Friedman, 2001) for classification with \n## trees as base learners (this is the version that scikit-learn implements)\nmodel = KTBoost.BoostingClassifier(loss='deviance',update_step='hybrid')\n## Kernel boosting for regression with quadratic loss\nmodel = KTBoost.BoostingRegressor(loss='ls',base_learner='kernel',theta=1)\n## Kernel boosting with the Nystroem method and the range parameter theta chosen \n## as the average distance to the 100-nearest neighbors (of the Nystroem samples)\nmodel = KTBoost.BoostingRegressor(loss='ls',base_learner='kernel',nystroem=True,\n                                  n_components=1000,theta=None,n_neighbors=100)\n## Regression model where both the mean and the standard deviation depend \n## on the covariates / features\nmodel = KTBoost.BoostingRegressor(loss='msr')\n```\n\n#### Feature importances and partial dependence plots\n```python\n#########################\n## Feature importances ## (only defined for trees as base learners)\n#########################\nXtrain=np.random.rand(1000,10)\nytrain=2*Xtrain[:,0]+2*Xtrain[:,1]+np.random.rand(1000)\n\nmodel = KTBoost.BoostingRegressor()\nmodel.fit(Xtrain,ytrain)\n## Extract feature importances calculated as described in Friedman (2001)\nfeat_imp = model.feature_importances_\n\n## Alternatively, plot feature importances directly\nKTBoost.plot_feature_importances(model=model,feature_names=feature_names,maxFeat=10)\n\n##############################\n## Partial dependence plots ## (currently only implemented for trees as base learners)\n##############################\nfrom KTBoost.partial_dependence import plot_partial_dependence\nimport matplotlib.pyplot as plt\nfeatures = [0,1,2,3,4,5]\nfig, axs = plot_partial_dependence(model,Xtrain,features,percentiles=(0,1),figsize=(8,6))\nplt.subplots_adjust(top=0.9)\nfig.suptitle('Partial dependence plots')\n\n## Alternatively, get partial dependencies in numerical form\nfrom KTBoost.partial_dependence import partial_dependence\nkwargs = dict(X=Xtrain, percentiles=(0, 1))\npartial_dependence(model,[0],**kwargs)\n```\n\n#### Summary of main parameters\nIn the following, we describe the most important parameters of the constructors of the two classes `KTBoost.BoostingClassifier` and `KTBoost.BoostingRegressor`.\n\n* **loss** : loss function to be optimized.\n    * `KTBoost.BoostingClassifier`\n    {'deviance', 'exponential'}, optional (default='deviance')\n\n        'deviance' refers to the logistic regression loss for binary classification, and the cross-entropy        loss with the softmax function for multiclass classification.\n\n    * `KTBoost.BoostingRegressor`\n    {'ls', 'lad', 'huber', 'quantile', 'poisson', 'gamma', 'tobit', 'msr'}, optional (default='ls')\n\n        'ls' refers to the squarred loss. 'lad' (least absolute deviation) is a robust\n        version. 'huber' is a combination of the former two. 'quantile'\n        allows quantile regression (use 'alpha' to specify the quantile).\n        'tobit' corresponds to the [Grabit model](https://arxiv.org/abs/1711.08695) with a Tobit loss.            'msr' is a linear regression model where both the mean and the logarithm of the standard deviation         are varying.\n\n* **update_step** : string, default=\"hybrid\"\n\n    Defines how boosting updates are calculated. Use either \"gradient\" for gradient boosting\n    or \"newton\" for Newton boosting (if applicable). \"hybrid\" uses a gradient step for finding the structur of trees and a Newton step for finding the leaf values. For kernel boosting, \"hybrid\" uses\n    gradient descent. See the [reference paper](https://arxiv.org/abs/1808.03064) for more information.\n\n* **base_learner** : string, default=\"tree\"\n\n    Base learners used in boosting updates. Choose among \"tree\" for trees, \"kernel\" for\n    reproducing kernel Hilbert space (RKHS) regression functions, and \"combined\" for a combination of the two. See the [reference paper](https://arxiv.org/abs/1902.03999) for more information.\n\n* **learning_rate** : float, optional (default=0.1)\n\n    The learning rate shrinks the contribution of each base learner by 'learning_rate'.\n    There is a trade-off between learning_rate and n_estimators.\n\n* **n_estimators** : int (default=100)\n\n    The number of boosting iterations to perform.\n\n* **max_depth** : integer, optional (default=5)\n\n    Maximum depth of the regression trees. The maximum\n    depth limits the number of nodes in the tree. This value determines the interaction\n    of the predictor variables.\n\n* **min_samples_leaf** : int, float, optional (default=1)\n\n    The minimum number of samples required to be at a leaf node:\n\n    - If int, then consider `min_samples_leaf` as the minimum number.\n    - If float, then `min_samples_leaf` is a percentage and\n      `ceil(min_samples_leaf * n_samples)` are the minimum\n      number of samples for each node.\n\n* **min_weight_leaf** : float, optional (default=1.)\n\n    The minimum number of weighted samples required to be at a leaf node.\n    If Newton boosting is used, this corresponds to the equivalent (i.e.,\n    normalized) number of weighted samples where the weights are determined\n    based on the second derivatives / Hessians.\n\n* **criterion** : string, optional (default=\"mse\")\n\n    The function to measure the quality of a split. Supported criteria\n    are \"friedman_mse\" for the mean squared error with improvement\n    score by Friedman, \"mse\" for mean squared error, and \"mae\" for\n    the mean absolute error.\n\n* **random_state** : int, RandomState instance or None, optional (default=None)\n\n    If int, random_state is the seed used by the random number generator;\n    If RandomState instance, random_state is the random number generator;\n    If None, the random number generator is the RandomState instance used\n    by `np.random`.\n\n* **kernel** : string, default=\"rbf\"\n\n    Kernel function used for kernel boosting. Currently, supports \"laplace\", \"rbf\", and \"GW\"\n    (generalied Wendland with \"smoothness parameter\" mu=1).\n\n* **theta** : float, default: 1.\n\n    Range parameter of the kernel functions which determines how fast the kernel function\n    decays with distance.\n\n* **n_neighbors** : int, default: None\n\n    If the range parameter 'theta' is not given, it can be determined from the data using this\n    parameter. The parameter 'theta' is chosen as the average distance of the 'n_neighbors'\n    nearest neighbors distances. The parameter 'range_adjust' can be used to modify this.\n    If range_adjust=3 or range_adjust=4.6, 'theta' is chosen such that the kernel function has\n    decayed to essentially zero (0.05 or 0.01, respectively) at the average distance of the\n    'n_neighbors' nearest neighbors (for rbf and laplace kernel).\n\n* **alphaReg** : float, default: 1.\n\n    Regularization parameter for kernel Ridge regression boosting updates.\n\n* **nystroem** : boolean, default=None\n\n    Indicates whether Nystroem sampling is used or not for kernel boosting.\n\n* **n_components** : int, detault = 100\n\n    Number of data points used in Nystroem sampling for kernel boosting.\n\n\n",
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