diff --git a/mlsauce-docs/search.js b/mlsauce-docs/search.js index 1195586..e4c1fe4 100644 --- a/mlsauce-docs/search.js +++ b/mlsauce-docs/search.js @@ -1,6 +1,6 @@ window.pdocSearch = (function(){ /** elasticlunr - http://weixsong.github.io * Copyright (C) 2017 Oliver Nightingale * Copyright (C) 2017 Wei Song * MIT Licensed */!function(){function e(e){if(null===e||"object"!=typeof e)return e;var t=e.constructor();for(var n in e)e.hasOwnProperty(n)&&(t[n]=e[n]);return t}var t=function(e){var n=new t.Index;return n.pipeline.add(t.trimmer,t.stopWordFilter,t.stemmer),e&&e.call(n,n),n};t.version="0.9.5",lunr=t,t.utils={},t.utils.warn=function(e){return function(t){e.console&&console.warn&&console.warn(t)}}(this),t.utils.toString=function(e){return void 0===e||null===e?"":e.toString()},t.EventEmitter=function(){this.events={}},t.EventEmitter.prototype.addListener=function(){var e=Array.prototype.slice.call(arguments),t=e.pop(),n=e;if("function"!=typeof t)throw new TypeError("last argument must be a function");n.forEach(function(e){this.hasHandler(e)||(this.events[e]=[]),this.events[e].push(t)},this)},t.EventEmitter.prototype.removeListener=function(e,t){if(this.hasHandler(e)){var n=this.events[e].indexOf(t);-1!==n&&(this.events[e].splice(n,1),0==this.events[e].length&&delete this.events[e])}},t.EventEmitter.prototype.emit=function(e){if(this.hasHandler(e)){var t=Array.prototype.slice.call(arguments,1);this.events[e].forEach(function(e){e.apply(void 0,t)},this)}},t.EventEmitter.prototype.hasHandler=function(e){return e in this.events},t.tokenizer=function(e){if(!arguments.length||null===e||void 0===e)return[];if(Array.isArray(e)){var n=e.filter(function(e){return null===e||void 0===e?!1:!0});n=n.map(function(e){return t.utils.toString(e).toLowerCase()});var i=[];return n.forEach(function(e){var n=e.split(t.tokenizer.seperator);i=i.concat(n)},this),i}return e.toString().trim().toLowerCase().split(t.tokenizer.seperator)},t.tokenizer.defaultSeperator=/[\s\-]+/,t.tokenizer.seperator=t.tokenizer.defaultSeperator,t.tokenizer.setSeperator=function(e){null!==e&&void 0!==e&&"object"==typeof e&&(t.tokenizer.seperator=e)},t.tokenizer.resetSeperator=function(){t.tokenizer.seperator=t.tokenizer.defaultSeperator},t.tokenizer.getSeperator=function(){return t.tokenizer.seperator},t.Pipeline=function(){this._queue=[]},t.Pipeline.registeredFunctions={},t.Pipeline.registerFunction=function(e,n){n in t.Pipeline.registeredFunctions&&t.utils.warn("Overwriting existing registered function: "+n),e.label=n,t.Pipeline.registeredFunctions[n]=e},t.Pipeline.getRegisteredFunction=function(e){return e in t.Pipeline.registeredFunctions!=!0?null:t.Pipeline.registeredFunctions[e]},t.Pipeline.warnIfFunctionNotRegistered=function(e){var n=e.label&&e.label in this.registeredFunctions;n||t.utils.warn("Function is not registered with pipeline. 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e.elements=this.toArray(),e.length=e.elements.length,e},lunr.SortedSet.prototype.union=function(e){var t,n,i;this.length>=e.length?(t=this,n=e):(t=e,n=this),i=t.clone();for(var o=0,r=n.toArray();o

\n"}, "mlsauce.AdaOpt": {"fullname": "mlsauce.AdaOpt", "modulename": "mlsauce", "qualname": "AdaOpt", "kind": "class", "doc": "

AdaOpt classifier.

\n\n

Attributes:

\n\n
n_iterations: int\n    number of iterations of the optimizer at training time.\n\nlearning_rate: float\n    controls the speed of the optimizer at training time.\n\nreg_lambda: float\n    L2 regularization parameter for successive errors in the optimizer\n    (at training time).\n\nreg_alpha: float\n    L1 regularization parameter for successive errors in the optimizer\n    (at training time).\n\neta: float\n    controls the slope in gradient descent (at training time).\n\ngamma: float\n    controls the step size in gradient descent (at training time).\n\nk: int\n    number of nearest neighbors selected at test time for classification.\n\ntolerance: float\n    controls early stopping in gradient descent (at training time).\n\nn_clusters: int\n    number of clusters, if MiniBatch k-means is used at test time\n    (for faster prediction).\n\nbatch_size: int\n    size of the batch, if MiniBatch k-means is used at test time\n    (for faster prediction).\n\nrow_sample: float\n    percentage of rows chosen from training set (by stratified subsampling,\n    for faster prediction).\n\ntype_dist: str\n    distance used for finding the nearest neighbors; currently `euclidean-f`\n    (euclidean distances calculated as whole), `euclidean` (euclidean distances\n    calculated row by row), `cosine` (cosine distance).\n\nn_jobs: int\n    number of cpus for parallel processing (default: None)\n\nverbose: int\n    progress bar for parallel processing (yes = 1) or not (no = 0)\n\ncache: boolean\n    if the nearest neighbors are cached or not, for faster retrieval in\n    subsequent calls.\n\nn_clusters_input: int\n    number of clusters (a priori) for clustering the features\n\nclustering_method: str\n    clustering method: currently 'kmeans', 'gmm'\n\ncluster_scaling: str\n    scaling method for clustering: currently 'standard', 'robust', 'minmax'\n\nseed: int\n    reproducibility seed for nodes_sim=='uniform', clustering and dropout.\n
\n", "bases": "sklearn.base.BaseEstimator, sklearn.base.ClassifierMixin"}, "mlsauce.AdaOpt.__init__": {"fullname": "mlsauce.AdaOpt.__init__", "modulename": "mlsauce", "qualname": "AdaOpt.__init__", "kind": "function", "doc": "

\n", "signature": "(\tn_iterations=50,\tlearning_rate=0.3,\treg_lambda=0.1,\treg_alpha=0.5,\teta=0.01,\tgamma=0.01,\tk=3,\ttolerance=0,\tn_clusters=0,\tbatch_size=100,\trow_sample=0.8,\ttype_dist='euclidean-f',\tn_jobs=None,\tverbose=0,\tcache=True,\tn_clusters_input=0,\tclustering_method='kmeans',\tcluster_scaling='standard',\tseed=123)"}, "mlsauce.AdaOpt.n_iterations": {"fullname": "mlsauce.AdaOpt.n_iterations", "modulename": "mlsauce", "qualname": "AdaOpt.n_iterations", "kind": "variable", "doc": "

\n"}, "mlsauce.AdaOpt.learning_rate": {"fullname": "mlsauce.AdaOpt.learning_rate", "modulename": "mlsauce", "qualname": "AdaOpt.learning_rate", "kind": "variable", "doc": "

\n"}, "mlsauce.AdaOpt.reg_lambda": {"fullname": "mlsauce.AdaOpt.reg_lambda", "modulename": "mlsauce", "qualname": "AdaOpt.reg_lambda", "kind": "variable", "doc": "

\n"}, "mlsauce.AdaOpt.reg_alpha": {"fullname": "mlsauce.AdaOpt.reg_alpha", "modulename": "mlsauce", "qualname": "AdaOpt.reg_alpha", "kind": "variable", "doc": "

\n"}, "mlsauce.AdaOpt.eta": {"fullname": "mlsauce.AdaOpt.eta", "modulename": "mlsauce", "qualname": "AdaOpt.eta", "kind": "variable", "doc": "

\n"}, "mlsauce.AdaOpt.gamma": {"fullname": "mlsauce.AdaOpt.gamma", "modulename": "mlsauce", "qualname": "AdaOpt.gamma", "kind": "variable", "doc": "

\n"}, "mlsauce.AdaOpt.k": {"fullname": "mlsauce.AdaOpt.k", "modulename": "mlsauce", "qualname": "AdaOpt.k", "kind": "variable", "doc": "

\n"}, "mlsauce.AdaOpt.tolerance": {"fullname": "mlsauce.AdaOpt.tolerance", "modulename": "mlsauce", "qualname": "AdaOpt.tolerance", "kind": "variable", "doc": "

\n"}, "mlsauce.AdaOpt.n_clusters": {"fullname": "mlsauce.AdaOpt.n_clusters", "modulename": "mlsauce", "qualname": "AdaOpt.n_clusters", "kind": "variable", "doc": "

\n"}, "mlsauce.AdaOpt.batch_size": {"fullname": "mlsauce.AdaOpt.batch_size", "modulename": "mlsauce", "qualname": "AdaOpt.batch_size", "kind": "variable", "doc": "

\n"}, "mlsauce.AdaOpt.row_sample": {"fullname": "mlsauce.AdaOpt.row_sample", "modulename": "mlsauce", "qualname": "AdaOpt.row_sample", "kind": "variable", "doc": "

\n"}, "mlsauce.AdaOpt.type_dist": {"fullname": "mlsauce.AdaOpt.type_dist", "modulename": "mlsauce", "qualname": "AdaOpt.type_dist", "kind": "variable", "doc": "

\n"}, "mlsauce.AdaOpt.n_jobs": {"fullname": "mlsauce.AdaOpt.n_jobs", "modulename": "mlsauce", "qualname": "AdaOpt.n_jobs", "kind": "variable", "doc": "

\n"}, "mlsauce.AdaOpt.cache": {"fullname": "mlsauce.AdaOpt.cache", "modulename": "mlsauce", "qualname": "AdaOpt.cache", "kind": "variable", "doc": "

\n"}, "mlsauce.AdaOpt.verbose": {"fullname": "mlsauce.AdaOpt.verbose", "modulename": "mlsauce", "qualname": "AdaOpt.verbose", "kind": "variable", "doc": "

\n"}, "mlsauce.AdaOpt.n_clusters_input": {"fullname": "mlsauce.AdaOpt.n_clusters_input", "modulename": "mlsauce", "qualname": "AdaOpt.n_clusters_input", "kind": "variable", "doc": "

\n"}, "mlsauce.AdaOpt.clustering_method": {"fullname": "mlsauce.AdaOpt.clustering_method", "modulename": "mlsauce", "qualname": "AdaOpt.clustering_method", "kind": "variable", "doc": "

\n"}, "mlsauce.AdaOpt.cluster_scaling": {"fullname": "mlsauce.AdaOpt.cluster_scaling", "modulename": "mlsauce", "qualname": "AdaOpt.cluster_scaling", "kind": "variable", "doc": "

\n"}, "mlsauce.AdaOpt.seed": {"fullname": "mlsauce.AdaOpt.seed", "modulename": "mlsauce", "qualname": "AdaOpt.seed", "kind": "variable", "doc": "

\n"}, "mlsauce.AdaOpt.fit": {"fullname": "mlsauce.AdaOpt.fit", "modulename": "mlsauce", "qualname": "AdaOpt.fit", "kind": "function", "doc": "

Fit AdaOpt to training data (X, y)

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n    Target values.\n\n**kwargs: additional parameters to be passed to self.cook_training_set.\n
\n\n

Returns:

\n\n
self: object.\n
\n", "signature": "(self, X, y, **kwargs):", "funcdef": "def"}, "mlsauce.AdaOpt.predict": {"fullname": "mlsauce.AdaOpt.predict", "modulename": "mlsauce", "qualname": "AdaOpt.predict", "kind": "function", "doc": "

Predict test data X.

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to `predict_proba`\n
\n\n

Returns:

\n\n
model predictions: {array-like}\n
\n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.AdaOpt.predict_proba": {"fullname": "mlsauce.AdaOpt.predict_proba", "modulename": "mlsauce", "qualname": "AdaOpt.predict_proba", "kind": "function", "doc": "

Predict probabilities for test data X.

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to\n    self.cook_test_set\n
\n\n

Returns:

\n\n
probability estimates for test data: {array-like}\n
\n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.AdaOpt.set_score_request": {"fullname": "mlsauce.AdaOpt.set_score_request", "modulename": "mlsauce", "qualname": "AdaOpt.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.LSBoostClassifier": {"fullname": "mlsauce.LSBoostClassifier", "modulename": "mlsauce", "qualname": "LSBoostClassifier", "kind": "class", "doc": "

LSBoost classifier.

\n\n
Attributes:\n\n    n_estimators: int\n        number of boosting iterations.\n\n    learning_rate: float\n        controls the learning speed at training time.\n\n    n_hidden_features: int\n        number of nodes in successive hidden layers.\n\n    reg_lambda: float\n        L2 regularization parameter for successive errors in the optimizer\n        (at training time).\n\n    alpha: float\n        compromise between L1 and L2 regularization (must be in [0, 1]),\n        for `solver` == 'enet'.\n\n    row_sample: float\n        percentage of rows chosen from the training set.\n\n    col_sample: float\n        percentage of columns chosen from the training set.\n\n    dropout: float\n        percentage of nodes dropped from the training set.\n\n    tolerance: float\n        controls early stopping in gradient descent (at training time).\n\n    direct_link: bool\n        indicates whether the original features are included (True) in model's\n        fitting or not (False).\n\n    verbose: int\n        progress bar (yes = 1) or not (no = 0) (currently).\n\n    seed: int\n        reproducibility seed for nodes_sim=='uniform', clustering and dropout.\n\n    backend: str\n        type of backend; must be in ('cpu', 'gpu', 'tpu')\n\n    solver: str\n        type of 'weak' learner; currently in ('ridge', 'lasso', 'enet').\n        'enet' is a combination of 'ridge' and 'lasso' called Elastic Net.\n\n    activation: str\n        activation function: currently 'relu', 'relu6', 'sigmoid', 'tanh'\n\n    n_clusters: int\n        number of clusters for clustering the features\n\n    clustering_method: str\n        clustering method: currently 'kmeans', 'gmm'\n\n    cluster_scaling: str\n        scaling method for clustering: currently 'standard', 'robust', 'minmax'\n\n    degree: int\n        degree of features interactions to include in the model\n\n    weights_distr: str\n        distribution of weights for constructing the model's hidden layer;\n        currently 'uniform', 'gaussian'\n\nExamples:\n\n    ```python\n    import numpy as np\n    from sklearn.datasets import load_digits, load_breast_cancer, load_wine, load_iris\n    from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\n    from sklearn.tree import DecisionTreeRegressor\n    from sklearn.kernel_ridge import KernelRidge\n    from time import time\n    from os import chdir\n    from sklearn import metrics\n    import os\n\n    import mlsauce as ms\n\n    print(\"\n
\n\n

\")\n print(\"GenericBoosting Decision tree -----\")\n print(\"\n\")

\n\n
    print(\"\n
\n\n

\")\n print(\"breast_cancer data -----\")

\n\n
    # data 1\n    breast_cancer = load_breast_cancer()\n    X = breast_cancer.data\n    y = breast_cancer.target\n    # split data into training test and test set\n    np.random.seed(15029)\n    X_train, X_test, y_train, y_test = train_test_split(X, y,\n                                                        test_size=0.2)\n\n    clf = DecisionTreeRegressor()\n    clf2 = KernelRidge()\n\n    obj = ms.GenericBoostingClassifier(clf, tolerance=1e-2)\n    print(obj.get_params())\n    start = time()\n    obj.fit(X_train, y_train)\n    print(time()-start)\n    start = time()\n    print(obj.score(X_test, y_test))\n    print(time()-start)\n\n    print(obj.obj['loss'])\n\n    obj = ms.GenericBoostingClassifier(clf, tolerance=1e-2, n_clusters=2)\n    print(obj.get_params())\n    start = time()\n    obj.fit(X_train, y_train)\n    print(time()-start)\n    start = time()\n    print(obj.score(X_test, y_test))\n    print(time()-start)\n\n    print(obj.obj['loss'])\n\n\n    # data 2\n    print(\"\n
\n\n

\")\n print(\"wine data -----\")

\n\n
    wine = load_wine()\n    Z = wine.data\n    t = wine.target\n    np.random.seed(879423)\n    X_train, X_test, y_train, y_test = train_test_split(Z, t,\n                                                        test_size=0.2)\n\n    obj = ms.GenericBoostingClassifier(clf)\n    print(obj.get_params())\n    start = time()\n    obj.fit(X_train, y_train)\n    print(time()-start)\n    start = time()\n    print(obj.score(X_test, y_test))\n    print(time()-start)\n\n    print(obj.obj['loss'])\n\n    obj = ms.GenericBoostingClassifier(clf, n_clusters=3)\n    print(obj.get_params())\n    start = time()\n    obj.fit(X_train, y_train)\n    print(time()-start)\n    start = time()\n    print(obj.score(X_test, y_test))\n    print(time()-start)\n\n    print(obj.obj['loss'])\n\n    # data 3\n    print(\"\n
\n\n

\")\n print(\"iris data -----\")

\n\n
    iris = load_iris()\n    Z = iris.data\n    t = iris.target\n    np.random.seed(734563)\n    X_train, X_test, y_train, y_test = train_test_split(Z, t,\n                                                        test_size=0.2)\n\n\n    obj = ms.GenericBoostingClassifier(clf)\n    print(obj.get_params())\n    start = time()\n    obj.fit(X_train, y_train)\n    print(time()-start)\n    start = time()\n    print(obj.score(X_test, y_test))\n    print(time()-start)\n\n    print(obj.obj['loss'])\n\n\n    print(\"\n
\n\n

\")\n print(\"GenericBoosting KRR -----\")\n print(\"\n\")

\n\n
    obj = ms.GenericBoostingClassifier(clf2, tolerance=1e-2)\n    print(obj.get_params())\n    start = time()\n    obj.fit(X_train, y_train)\n    print(time()-start)\n    start = time()\n    print(obj.score(X_test, y_test))\n    print(time()-start)\n\n    print(obj.obj['loss'])\n\n    obj = ms.GenericBoostingClassifier(clf2, tolerance=1e-2, n_clusters=2)\n    print(obj.get_params())\n    start = time()\n    obj.fit(X_train, y_train)\n    print(time()-start)\n    start = time()\n    print(obj.score(X_test, y_test))\n    print(time()-start)\n\n    print(obj.obj['loss'])\n\n\n    # data 2\n    print(\"\n
\n\n

\")\n print(\"wine data -----\")

\n\n
    wine = load_wine()\n    Z = wine.data\n    t = wine.target\n    np.random.seed(879423)\n    X_train, X_test, y_train, y_test = train_test_split(Z, t,\n                                                        test_size=0.2)\n\n    obj = ms.GenericBoostingClassifier(clf2)\n    print(obj.get_params())\n    start = time()\n    obj.fit(X_train, y_train)\n    print(time()-start)\n    start = time()\n    print(obj.score(X_test, y_test))\n    print(time()-start)\n\n    print(obj.obj['loss'])\n\n    obj = ms.GenericBoostingClassifier(clf2, n_clusters=3)\n    print(obj.get_params())\n    start = time()\n    obj.fit(X_train, y_train)\n    print(time()-start)\n    start = time()\n    print(obj.score(X_test, y_test))\n    print(time()-start)\n\n    print(obj.obj['loss'])\n\n    # data 3\n    print(\"\n
\n\n

\")\n print(\"iris data -----\")

\n\n
    iris = load_iris()\n    Z = iris.data\n    t = iris.target\n    np.random.seed(734563)\n    X_train, X_test, y_train, y_test = train_test_split(Z, t,\n                                                        test_size=0.2)\n\n\n    obj = ms.GenericBoostingClassifier(clf2)\n    print(obj.get_params())\n    start = time()\n    obj.fit(X_train, y_train)\n    print(time()-start)\n    start = time()\n    print(obj.score(X_test, y_test))\n    print(time()-start)\n\n    print(obj.obj['loss'])\n```\n
\n", "bases": "sklearn.base.BaseEstimator, sklearn.base.ClassifierMixin"}, "mlsauce.LSBoostClassifier.__init__": {"fullname": "mlsauce.LSBoostClassifier.__init__", "modulename": "mlsauce", "qualname": "LSBoostClassifier.__init__", "kind": "function", "doc": "

\n", "signature": "(\tn_estimators=100,\tlearning_rate=0.1,\tn_hidden_features=5,\treg_lambda=0.1,\talpha=0.5,\trow_sample=1,\tcol_sample=1,\tdropout=0,\ttolerance=0.0001,\tdirect_link=1,\tverbose=1,\tseed=123,\tbackend='cpu',\tsolver='ridge',\tactivation='relu',\tn_clusters=0,\tclustering_method='kmeans',\tcluster_scaling='standard',\tdegree=None,\tweights_distr='uniform',\tbase_model=None)"}, "mlsauce.LSBoostClassifier.base_model": {"fullname": "mlsauce.LSBoostClassifier.base_model", "modulename": "mlsauce", "qualname": "LSBoostClassifier.base_model", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostClassifier.n_estimators": {"fullname": "mlsauce.LSBoostClassifier.n_estimators", "modulename": "mlsauce", "qualname": "LSBoostClassifier.n_estimators", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostClassifier.learning_rate": {"fullname": "mlsauce.LSBoostClassifier.learning_rate", "modulename": "mlsauce", "qualname": "LSBoostClassifier.learning_rate", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostClassifier.n_hidden_features": {"fullname": "mlsauce.LSBoostClassifier.n_hidden_features", "modulename": "mlsauce", "qualname": "LSBoostClassifier.n_hidden_features", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostClassifier.reg_lambda": {"fullname": "mlsauce.LSBoostClassifier.reg_lambda", "modulename": "mlsauce", "qualname": "LSBoostClassifier.reg_lambda", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostClassifier.alpha": {"fullname": "mlsauce.LSBoostClassifier.alpha", "modulename": "mlsauce", "qualname": "LSBoostClassifier.alpha", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostClassifier.row_sample": {"fullname": "mlsauce.LSBoostClassifier.row_sample", "modulename": "mlsauce", "qualname": "LSBoostClassifier.row_sample", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostClassifier.col_sample": {"fullname": "mlsauce.LSBoostClassifier.col_sample", "modulename": "mlsauce", "qualname": "LSBoostClassifier.col_sample", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostClassifier.dropout": {"fullname": "mlsauce.LSBoostClassifier.dropout", "modulename": "mlsauce", "qualname": "LSBoostClassifier.dropout", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostClassifier.tolerance": {"fullname": "mlsauce.LSBoostClassifier.tolerance", "modulename": "mlsauce", "qualname": "LSBoostClassifier.tolerance", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostClassifier.direct_link": {"fullname": "mlsauce.LSBoostClassifier.direct_link", "modulename": "mlsauce", "qualname": "LSBoostClassifier.direct_link", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostClassifier.verbose": {"fullname": "mlsauce.LSBoostClassifier.verbose", "modulename": "mlsauce", "qualname": "LSBoostClassifier.verbose", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostClassifier.seed": {"fullname": "mlsauce.LSBoostClassifier.seed", "modulename": "mlsauce", "qualname": "LSBoostClassifier.seed", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostClassifier.backend": {"fullname": "mlsauce.LSBoostClassifier.backend", "modulename": "mlsauce", "qualname": "LSBoostClassifier.backend", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostClassifier.obj": {"fullname": "mlsauce.LSBoostClassifier.obj", "modulename": "mlsauce", "qualname": "LSBoostClassifier.obj", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostClassifier.solver": {"fullname": "mlsauce.LSBoostClassifier.solver", "modulename": "mlsauce", "qualname": "LSBoostClassifier.solver", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostClassifier.activation": {"fullname": "mlsauce.LSBoostClassifier.activation", "modulename": "mlsauce", "qualname": "LSBoostClassifier.activation", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostClassifier.n_clusters": {"fullname": "mlsauce.LSBoostClassifier.n_clusters", "modulename": "mlsauce", "qualname": "LSBoostClassifier.n_clusters", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostClassifier.clustering_method": {"fullname": "mlsauce.LSBoostClassifier.clustering_method", "modulename": "mlsauce", "qualname": "LSBoostClassifier.clustering_method", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostClassifier.cluster_scaling": {"fullname": "mlsauce.LSBoostClassifier.cluster_scaling", "modulename": "mlsauce", "qualname": "LSBoostClassifier.cluster_scaling", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostClassifier.degree": {"fullname": "mlsauce.LSBoostClassifier.degree", "modulename": "mlsauce", "qualname": "LSBoostClassifier.degree", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostClassifier.poly_": {"fullname": "mlsauce.LSBoostClassifier.poly_", "modulename": "mlsauce", "qualname": "LSBoostClassifier.poly_", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostClassifier.weights_distr": {"fullname": "mlsauce.LSBoostClassifier.weights_distr", "modulename": "mlsauce", "qualname": "LSBoostClassifier.weights_distr", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostClassifier.fit": {"fullname": "mlsauce.LSBoostClassifier.fit", "modulename": "mlsauce", "qualname": "LSBoostClassifier.fit", "kind": "function", "doc": "

Fit Booster (classifier) to training data (X, y)

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n    Target values.\n\n**kwargs: additional parameters to be passed to self.cook_training_set.\n
\n\n

Returns:

\n\n
self: object.\n
\n", "signature": "(self, X, y, **kwargs):", "funcdef": "def"}, "mlsauce.LSBoostClassifier.predict": {"fullname": "mlsauce.LSBoostClassifier.predict", "modulename": "mlsauce", "qualname": "LSBoostClassifier.predict", "kind": "function", "doc": "

Predict test data X.

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to `predict_proba`\n
\n\n

Returns:

\n\n
model predictions: {array-like}\n
\n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.LSBoostClassifier.predict_proba": {"fullname": "mlsauce.LSBoostClassifier.predict_proba", "modulename": "mlsauce", "qualname": "LSBoostClassifier.predict_proba", "kind": "function", "doc": "

Predict probabilities for test data X.

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to\n    self.cook_test_set\n
\n\n

Returns:

\n\n
probability estimates for test data: {array-like}\n
\n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.LSBoostClassifier.set_score_request": {"fullname": "mlsauce.LSBoostClassifier.set_score_request", "modulename": "mlsauce", "qualname": "LSBoostClassifier.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.GenericBoostingClassifier": {"fullname": "mlsauce.GenericBoostingClassifier", "modulename": "mlsauce", "qualname": "GenericBoostingClassifier", "kind": "class", "doc": "

Generic Boosting classifier (using any classifier as base learner).

\n\n

Attributes:

\n\n
obj: object\n    model object.\n\nn_estimators: int\n    number of boosting iterations.\n\nlearning_rate: float\n    controls the learning speed at training time.\n\nn_hidden_features: int\n    number of nodes in successive hidden layers.\n\nreg_lambda: float\n    L2 regularization parameter for successive errors in the optimizer\n    (at training time).\n\nalpha: float\n    compromise between L1 and L2 regularization (must be in [0, 1]),\n    for `solver` == 'enet'.\n\nrow_sample: float\n    percentage of rows chosen from the training set.\n\ncol_sample: float\n    percentage of columns chosen from the training set.\n\ndropout: float\n    percentage of nodes dropped from the training set.\n\ntolerance: float\n    controls early stopping in gradient descent (at training time).\n\ndirect_link: bool\n    indicates whether the original features are included (True) in model's\n    fitting or not (False).\n\nverbose: int\n    progress bar (yes = 1) or not (no = 0) (currently).\n\nseed: int\n    reproducibility seed for nodes_sim=='uniform', clustering and dropout.\n\nbackend: str\n    type of backend; must be in ('cpu', 'gpu', 'tpu')\n\nsolver: str\n    type of 'weak' learner; currently in ('ridge', 'lasso', 'enet').\n    'enet' is a combination of 'ridge' and 'lasso' called Elastic Net.\n\nactivation: str\n    activation function: currently 'relu', 'relu6', 'sigmoid', 'tanh'\n\nn_clusters: int\n    number of clusters for clustering the features\n\nclustering_method: str\n    clustering method: currently 'kmeans', 'gmm'\n\ncluster_scaling: str\n    scaling method for clustering: currently 'standard', 'robust', 'minmax'\n\ndegree: int\n    degree of features interactions to include in the model\n\nweights_distr: str\n    distribution of weights for constructing the model's hidden layer;\n    currently 'uniform', 'gaussian'\n
\n", "bases": "mlsauce.booster._booster_classifier.LSBoostClassifier"}, "mlsauce.GenericBoostingClassifier.__init__": {"fullname": "mlsauce.GenericBoostingClassifier.__init__", "modulename": "mlsauce", "qualname": "GenericBoostingClassifier.__init__", "kind": "function", "doc": "

\n", "signature": "(\tobj,\tn_estimators=100,\tlearning_rate=0.1,\tn_hidden_features=5,\treg_lambda=0.1,\talpha=0.5,\trow_sample=1,\tcol_sample=1,\tdropout=0,\ttolerance=0.0001,\tdirect_link=1,\tverbose=1,\tseed=123,\tbackend='cpu',\tsolver='ridge',\tactivation='relu',\tn_clusters=0,\tclustering_method='kmeans',\tcluster_scaling='standard',\tdegree=None,\tweights_distr='uniform')"}, "mlsauce.GenericBoostingClassifier.base_model": {"fullname": "mlsauce.GenericBoostingClassifier.base_model", "modulename": "mlsauce", "qualname": "GenericBoostingClassifier.base_model", "kind": "variable", "doc": "

\n"}, "mlsauce.GenericBoostingClassifier.set_score_request": {"fullname": "mlsauce.GenericBoostingClassifier.set_score_request", "modulename": "mlsauce", "qualname": "GenericBoostingClassifier.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.GenericBoostingRegressor": {"fullname": "mlsauce.GenericBoostingRegressor", "modulename": "mlsauce", "qualname": "GenericBoostingRegressor", "kind": "class", "doc": "

LSBoost regressor.

\n\n

Attributes:

\n\n
n_estimators: int\n    number of boosting iterations.\n\nlearning_rate: float\n    controls the learning speed at training time.\n\nn_hidden_features: int\n    number of nodes in successive hidden layers.\n\nreg_lambda: float\n    L2 regularization parameter for successive errors in the optimizer\n    (at training time).\n\nalpha: float\n    compromise between L1 and L2 regularization (must be in [0, 1]),\n    for `solver` == 'enet'\n\nrow_sample: float\n    percentage of rows chosen from the training set.\n\ncol_sample: float\n    percentage of columns chosen from the training set.\n\ndropout: float\n    percentage of nodes dropped from the training set.\n\ntolerance: float\n    controls early stopping in gradient descent (at training time).\n\ndirect_link: bool\n    indicates whether the original features are included (True) in model's\n    fitting or not (False).\n\nverbose: int\n    progress bar (yes = 1) or not (no = 0) (currently).\n\nseed: int\n    reproducibility seed for nodes_sim=='uniform', clustering and dropout.\n\nbackend: str\n    type of backend; must be in ('cpu', 'gpu', 'tpu')\n\nsolver: str\n    type of 'weak' learner; currently in ('ridge', 'lasso')\n\nactivation: str\n    activation function: currently 'relu', 'relu6', 'sigmoid', 'tanh'\n\ntype_pi: str.\n    type of prediction interval; currently \"kde\" (default) or \"bootstrap\".\n    Used only in `self.predict`, for `self.replications` > 0 and `self.kernel`\n    in ('gaussian', 'tophat'). Default is `None`.\n\nreplications: int.\n    number of replications (if needed) for predictive simulation.\n    Used only in `self.predict`, for `self.kernel` in ('gaussian',\n    'tophat') and `self.type_pi = 'kde'`. Default is `None`.\n\nn_clusters: int\n    number of clusters for clustering the features\n\nclustering_method: str\n    clustering method: currently 'kmeans', 'gmm'\n\ncluster_scaling: str\n    scaling method for clustering: currently 'standard', 'robust', 'minmax'\n\ndegree: int\n    degree of features interactions to include in the model\n\nweights_distr: str\n    distribution of weights for constructing the model's hidden layer;\n    either 'uniform' or 'gaussian'\n
\n", "bases": "mlsauce.booster._booster_regressor.LSBoostRegressor"}, "mlsauce.GenericBoostingRegressor.__init__": {"fullname": "mlsauce.GenericBoostingRegressor.__init__", "modulename": "mlsauce", "qualname": "GenericBoostingRegressor.__init__", "kind": "function", "doc": "

\n", "signature": "(\tobj,\tn_estimators=100,\tlearning_rate=0.1,\tn_hidden_features=5,\treg_lambda=0.1,\talpha=0.5,\trow_sample=1,\tcol_sample=1,\tdropout=0,\ttolerance=0.0001,\tdirect_link=1,\tverbose=1,\tseed=123,\tbackend='cpu',\tsolver='ridge',\tactivation='relu',\ttype_pi=None,\treplications=None,\tkernel=None,\tn_clusters=0,\tclustering_method='kmeans',\tcluster_scaling='standard',\tdegree=None,\tweights_distr='uniform')"}, "mlsauce.GenericBoostingRegressor.base_model": {"fullname": "mlsauce.GenericBoostingRegressor.base_model", "modulename": "mlsauce", "qualname": "GenericBoostingRegressor.base_model", "kind": "variable", "doc": "

\n"}, "mlsauce.GenericBoostingRegressor.set_predict_request": {"fullname": "mlsauce.GenericBoostingRegressor.set_predict_request", "modulename": "mlsauce", "qualname": "GenericBoostingRegressor.set_predict_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.GenericBoostingRegressor.set_score_request": {"fullname": "mlsauce.GenericBoostingRegressor.set_score_request", "modulename": "mlsauce", "qualname": "GenericBoostingRegressor.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.StumpClassifier": {"fullname": "mlsauce.StumpClassifier", "modulename": "mlsauce", "qualname": "StumpClassifier", "kind": "class", "doc": "

Stump classifier.

\n\n

Attributes:

\n\n
bins: int\n    Number of histogram bins; as in numpy.histogram.\n
\n", "bases": "sklearn.base.BaseEstimator, sklearn.base.ClassifierMixin"}, "mlsauce.StumpClassifier.__init__": {"fullname": "mlsauce.StumpClassifier.__init__", "modulename": "mlsauce", "qualname": "StumpClassifier.__init__", "kind": "function", "doc": "

\n", "signature": "(bins='auto')"}, "mlsauce.StumpClassifier.bins": {"fullname": "mlsauce.StumpClassifier.bins", "modulename": "mlsauce", "qualname": "StumpClassifier.bins", "kind": "variable", "doc": "

\n"}, "mlsauce.StumpClassifier.obj": {"fullname": "mlsauce.StumpClassifier.obj", "modulename": "mlsauce", "qualname": "StumpClassifier.obj", "kind": "variable", "doc": "

\n"}, "mlsauce.StumpClassifier.fit": {"fullname": "mlsauce.StumpClassifier.fit", "modulename": "mlsauce", "qualname": "StumpClassifier.fit", "kind": "function", "doc": "

Fit Stump to training data (X, y)

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n    Target values.\n\nsample_weight: array_like, shape = [n_samples]\n    Observations weights.\n
\n\n

Returns:

\n\n
self: object.\n
\n", "signature": "(self, X, y, sample_weight=None, **kwargs):", "funcdef": "def"}, "mlsauce.StumpClassifier.predict": {"fullname": "mlsauce.StumpClassifier.predict", "modulename": "mlsauce", "qualname": "StumpClassifier.predict", "kind": "function", "doc": "

Predict test data X.

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to `predict_proba`\n
\n\n

Returns:

\n\n
model predictions: {array-like}\n
\n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.StumpClassifier.predict_proba": {"fullname": "mlsauce.StumpClassifier.predict_proba", "modulename": "mlsauce", "qualname": "StumpClassifier.predict_proba", "kind": "function", "doc": "

Predict probabilities for test data X.

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to\n    self.cook_test_set\n
\n\n

Returns:

\n\n
probability estimates for test data: {array-like}\n
\n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.StumpClassifier.set_fit_request": {"fullname": "mlsauce.StumpClassifier.set_fit_request", "modulename": "mlsauce", "qualname": "StumpClassifier.set_fit_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.StumpClassifier.set_score_request": {"fullname": "mlsauce.StumpClassifier.set_score_request", "modulename": "mlsauce", "qualname": "StumpClassifier.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.ElasticNetRegressor": {"fullname": "mlsauce.ElasticNetRegressor", "modulename": "mlsauce", "qualname": "ElasticNetRegressor", "kind": "class", "doc": "

Elasticnet.

\n\n

Attributes:

\n\n
reg_lambda: float\n    regularization parameter.\n\nalpha: float\n    compromise between L1 and L2 regularization (must be in [0, 1]),\n    for `solver` == 'enet'.\n\nbackend: str\n    type of backend; must be in ('cpu', 'gpu', 'tpu')\n
\n", "bases": "sklearn.base.BaseEstimator, sklearn.base.RegressorMixin"}, "mlsauce.ElasticNetRegressor.__init__": {"fullname": "mlsauce.ElasticNetRegressor.__init__", "modulename": "mlsauce", "qualname": "ElasticNetRegressor.__init__", "kind": "function", "doc": "

\n", "signature": "(reg_lambda=0.1, alpha=0.5, backend='cpu')"}, "mlsauce.ElasticNetRegressor.reg_lambda": {"fullname": "mlsauce.ElasticNetRegressor.reg_lambda", "modulename": "mlsauce", "qualname": "ElasticNetRegressor.reg_lambda", "kind": "variable", "doc": "

\n"}, "mlsauce.ElasticNetRegressor.alpha": {"fullname": "mlsauce.ElasticNetRegressor.alpha", "modulename": "mlsauce", "qualname": "ElasticNetRegressor.alpha", "kind": "variable", "doc": "

\n"}, "mlsauce.ElasticNetRegressor.backend": {"fullname": "mlsauce.ElasticNetRegressor.backend", "modulename": "mlsauce", "qualname": "ElasticNetRegressor.backend", "kind": "variable", "doc": "

\n"}, "mlsauce.ElasticNetRegressor.fit": {"fullname": "mlsauce.ElasticNetRegressor.fit", "modulename": "mlsauce", "qualname": "ElasticNetRegressor.fit", "kind": "function", "doc": "

Fit matrixops (classifier) to training data (X, y)

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n    Target values.\n\n**kwargs: additional parameters to be passed to self.cook_training_set.\n
\n\n

Returns:

\n\n
self: object.\n
\n", "signature": "(self, X, y, **kwargs):", "funcdef": "def"}, "mlsauce.ElasticNetRegressor.predict": {"fullname": "mlsauce.ElasticNetRegressor.predict", "modulename": "mlsauce", "qualname": "ElasticNetRegressor.predict", "kind": "function", "doc": "

Predict test data X.

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to `predict_proba`\n
\n\n

Returns:

\n\n
model predictions: {array-like}\n
\n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.ElasticNetRegressor.set_score_request": {"fullname": "mlsauce.ElasticNetRegressor.set_score_request", "modulename": "mlsauce", "qualname": "ElasticNetRegressor.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.LassoRegressor": {"fullname": "mlsauce.LassoRegressor", "modulename": "mlsauce", "qualname": "LassoRegressor", "kind": "class", "doc": "

Lasso.

\n\n

Attributes:

\n\n
reg_lambda: float\n    L1 regularization parameter.\n\nmax_iter: int\n    number of iterations of lasso shooting algorithm.\n\ntol: float\n    tolerance for convergence of lasso shooting algorithm.\n\nbackend: str\n    type of backend; must be in ('cpu', 'gpu', 'tpu').\n
\n", "bases": "sklearn.base.BaseEstimator, sklearn.base.RegressorMixin"}, "mlsauce.LassoRegressor.__init__": {"fullname": "mlsauce.LassoRegressor.__init__", "modulename": "mlsauce", "qualname": "LassoRegressor.__init__", "kind": "function", "doc": "

\n", "signature": "(reg_lambda=0.1, max_iter=10, tol=0.001, backend='cpu')"}, "mlsauce.LassoRegressor.reg_lambda": {"fullname": "mlsauce.LassoRegressor.reg_lambda", "modulename": "mlsauce", "qualname": "LassoRegressor.reg_lambda", "kind": "variable", "doc": "

\n"}, "mlsauce.LassoRegressor.max_iter": {"fullname": "mlsauce.LassoRegressor.max_iter", "modulename": "mlsauce", "qualname": "LassoRegressor.max_iter", "kind": "variable", "doc": "

\n"}, "mlsauce.LassoRegressor.tol": {"fullname": "mlsauce.LassoRegressor.tol", "modulename": "mlsauce", "qualname": "LassoRegressor.tol", "kind": "variable", "doc": "

\n"}, "mlsauce.LassoRegressor.backend": {"fullname": "mlsauce.LassoRegressor.backend", "modulename": "mlsauce", "qualname": "LassoRegressor.backend", "kind": "variable", "doc": "

\n"}, "mlsauce.LassoRegressor.fit": {"fullname": "mlsauce.LassoRegressor.fit", "modulename": "mlsauce", "qualname": "LassoRegressor.fit", "kind": "function", "doc": "

Fit matrixops (classifier) to training data (X, y)

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n    Target values.\n\n**kwargs: additional parameters to be passed to self.cook_training_set.\n
\n\n

Returns:

\n\n
self: object.\n
\n", "signature": "(self, X, y, **kwargs):", "funcdef": "def"}, "mlsauce.LassoRegressor.predict": {"fullname": "mlsauce.LassoRegressor.predict", "modulename": "mlsauce", "qualname": "LassoRegressor.predict", "kind": "function", "doc": "

Predict test data X.

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to `predict_proba`\n
\n\n

Returns:

\n\n
model predictions: {array-like}\n
\n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.LassoRegressor.set_score_request": {"fullname": "mlsauce.LassoRegressor.set_score_request", "modulename": "mlsauce", "qualname": "LassoRegressor.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.LSBoostRegressor": {"fullname": "mlsauce.LSBoostRegressor", "modulename": "mlsauce", "qualname": "LSBoostRegressor", "kind": "class", "doc": "

LSBoost regressor.

\n\n

Attributes:

\n\n
n_estimators: int\n    number of boosting iterations.\n\nlearning_rate: float\n    controls the learning speed at training time.\n\nn_hidden_features: int\n    number of nodes in successive hidden layers.\n\nreg_lambda: float\n    L2 regularization parameter for successive errors in the optimizer\n    (at training time).\n\nalpha: float\n    compromise between L1 and L2 regularization (must be in [0, 1]),\n    for `solver` == 'enet'\n\nrow_sample: float\n    percentage of rows chosen from the training set.\n\ncol_sample: float\n    percentage of columns chosen from the training set.\n\ndropout: float\n    percentage of nodes dropped from the training set.\n\ntolerance: float\n    controls early stopping in gradient descent (at training time).\n\ndirect_link: bool\n    indicates whether the original features are included (True) in model's\n    fitting or not (False).\n\nverbose: int\n    progress bar (yes = 1) or not (no = 0) (currently).\n\nseed: int\n    reproducibility seed for nodes_sim=='uniform', clustering and dropout.\n\nbackend: str\n    type of backend; must be in ('cpu', 'gpu', 'tpu')\n\nsolver: str\n    type of 'weak' learner; currently in ('ridge', 'lasso')\n\nactivation: str\n    activation function: currently 'relu', 'relu6', 'sigmoid', 'tanh'\n\ntype_pi: str.\n    type of prediction interval; currently \"kde\" (default) or \"bootstrap\".\n    Used only in `self.predict`, for `self.replications` > 0 and `self.kernel`\n    in ('gaussian', 'tophat'). Default is `None`.\n\nreplications: int.\n    number of replications (if needed) for predictive simulation.\n    Used only in `self.predict`, for `self.kernel` in ('gaussian',\n    'tophat') and `self.type_pi = 'kde'`. Default is `None`.\n\nn_clusters: int\n    number of clusters for clustering the features\n\nclustering_method: str\n    clustering method: currently 'kmeans', 'gmm'\n\ncluster_scaling: str\n    scaling method for clustering: currently 'standard', 'robust', 'minmax'\n\ndegree: int\n    degree of features interactions to include in the model\n\nweights_distr: str\n    distribution of weights for constructing the model's hidden layer;\n    either 'uniform' or 'gaussian'\n
\n\n

Examples:

\n\n
\n
import subprocess\nimport sys\nimport os\n\nsubprocess.check_call([sys.executable, "-m", "pip", "install", "matplotlib"])\n\nimport mlsauce as ms\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.datasets import load_diabetes\nfrom sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\nfrom sklearn.tree import DecisionTreeRegressor\nfrom time import time\nfrom os import chdir\nfrom sklearn import metrics\n\nregr = DecisionTreeRegressor()\n\ndiabetes = load_diabetes()\nX = diabetes.data\ny = diabetes.target\n# split data into training test and test set\nnp.random.seed(15029)\nX_train, X_test, y_train, y_test = train_test_split(X, y,\n                                                    test_size=0.2)\n\nobj = ms.GenericBoostingRegressor(regr, col_sample=0.9, row_sample=0.9)\nprint(obj.get_params())\nstart = time()\nobj.fit(X_train, y_train)\nprint(time()-start)\nstart = time()\nprint(np.sqrt(np.mean(np.square(obj.predict(X_test) - y_test))))\nprint(time()-start)\n\nprint(obj.obj['loss'])\n\nobj = ms.GenericBoostingRegressor(regr, col_sample=0.9, row_sample=0.9, n_clusters=2)\nprint(obj.get_params())\nstart = time()\nobj.fit(X_train, y_train)\nprint(time()-start)\nstart = time()\nprint(np.sqrt(np.mean(np.square(obj.predict(X_test) - y_test))))\nprint(time()-start)\n\nprint(obj.obj['loss'])\n
\n
\n
\n", "bases": "sklearn.base.BaseEstimator, sklearn.base.RegressorMixin"}, "mlsauce.LSBoostRegressor.__init__": {"fullname": "mlsauce.LSBoostRegressor.__init__", "modulename": "mlsauce", "qualname": "LSBoostRegressor.__init__", "kind": "function", "doc": "

\n", "signature": "(\tn_estimators=100,\tlearning_rate=0.1,\tn_hidden_features=5,\treg_lambda=0.1,\talpha=0.5,\trow_sample=1,\tcol_sample=1,\tdropout=0,\ttolerance=0.0001,\tdirect_link=1,\tverbose=1,\tseed=123,\tbackend='cpu',\tsolver='ridge',\tactivation='relu',\ttype_pi=None,\treplications=None,\tkernel=None,\tn_clusters=0,\tclustering_method='kmeans',\tcluster_scaling='standard',\tdegree=None,\tweights_distr='uniform',\tbase_model=None)"}, "mlsauce.LSBoostRegressor.base_model": {"fullname": "mlsauce.LSBoostRegressor.base_model", "modulename": "mlsauce", "qualname": "LSBoostRegressor.base_model", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostRegressor.n_estimators": {"fullname": "mlsauce.LSBoostRegressor.n_estimators", "modulename": "mlsauce", "qualname": "LSBoostRegressor.n_estimators", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostRegressor.learning_rate": {"fullname": "mlsauce.LSBoostRegressor.learning_rate", "modulename": "mlsauce", "qualname": "LSBoostRegressor.learning_rate", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostRegressor.n_hidden_features": {"fullname": "mlsauce.LSBoostRegressor.n_hidden_features", "modulename": "mlsauce", "qualname": "LSBoostRegressor.n_hidden_features", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostRegressor.reg_lambda": {"fullname": "mlsauce.LSBoostRegressor.reg_lambda", "modulename": "mlsauce", "qualname": "LSBoostRegressor.reg_lambda", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostRegressor.alpha": {"fullname": "mlsauce.LSBoostRegressor.alpha", "modulename": "mlsauce", "qualname": "LSBoostRegressor.alpha", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostRegressor.row_sample": {"fullname": "mlsauce.LSBoostRegressor.row_sample", "modulename": "mlsauce", "qualname": "LSBoostRegressor.row_sample", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostRegressor.col_sample": {"fullname": "mlsauce.LSBoostRegressor.col_sample", "modulename": "mlsauce", "qualname": "LSBoostRegressor.col_sample", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostRegressor.dropout": {"fullname": "mlsauce.LSBoostRegressor.dropout", "modulename": "mlsauce", "qualname": "LSBoostRegressor.dropout", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostRegressor.tolerance": {"fullname": "mlsauce.LSBoostRegressor.tolerance", "modulename": "mlsauce", "qualname": "LSBoostRegressor.tolerance", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostRegressor.direct_link": {"fullname": "mlsauce.LSBoostRegressor.direct_link", "modulename": "mlsauce", "qualname": "LSBoostRegressor.direct_link", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostRegressor.verbose": {"fullname": "mlsauce.LSBoostRegressor.verbose", "modulename": "mlsauce", "qualname": "LSBoostRegressor.verbose", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostRegressor.seed": {"fullname": "mlsauce.LSBoostRegressor.seed", "modulename": "mlsauce", "qualname": "LSBoostRegressor.seed", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostRegressor.backend": {"fullname": "mlsauce.LSBoostRegressor.backend", "modulename": "mlsauce", "qualname": "LSBoostRegressor.backend", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostRegressor.obj": {"fullname": "mlsauce.LSBoostRegressor.obj", "modulename": "mlsauce", "qualname": "LSBoostRegressor.obj", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostRegressor.solver": {"fullname": "mlsauce.LSBoostRegressor.solver", "modulename": "mlsauce", "qualname": "LSBoostRegressor.solver", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostRegressor.activation": {"fullname": "mlsauce.LSBoostRegressor.activation", "modulename": "mlsauce", "qualname": "LSBoostRegressor.activation", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostRegressor.type_pi": {"fullname": "mlsauce.LSBoostRegressor.type_pi", "modulename": "mlsauce", "qualname": "LSBoostRegressor.type_pi", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostRegressor.replications": {"fullname": "mlsauce.LSBoostRegressor.replications", "modulename": "mlsauce", "qualname": "LSBoostRegressor.replications", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostRegressor.kernel": {"fullname": "mlsauce.LSBoostRegressor.kernel", "modulename": "mlsauce", "qualname": "LSBoostRegressor.kernel", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostRegressor.n_clusters": {"fullname": "mlsauce.LSBoostRegressor.n_clusters", "modulename": "mlsauce", "qualname": "LSBoostRegressor.n_clusters", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostRegressor.clustering_method": {"fullname": "mlsauce.LSBoostRegressor.clustering_method", "modulename": "mlsauce", "qualname": "LSBoostRegressor.clustering_method", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostRegressor.cluster_scaling": {"fullname": "mlsauce.LSBoostRegressor.cluster_scaling", "modulename": "mlsauce", "qualname": "LSBoostRegressor.cluster_scaling", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostRegressor.degree": {"fullname": "mlsauce.LSBoostRegressor.degree", "modulename": "mlsauce", "qualname": "LSBoostRegressor.degree", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostRegressor.poly_": {"fullname": "mlsauce.LSBoostRegressor.poly_", "modulename": "mlsauce", "qualname": "LSBoostRegressor.poly_", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostRegressor.weights_distr": {"fullname": "mlsauce.LSBoostRegressor.weights_distr", "modulename": "mlsauce", "qualname": "LSBoostRegressor.weights_distr", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostRegressor.fit": {"fullname": "mlsauce.LSBoostRegressor.fit", "modulename": "mlsauce", "qualname": "LSBoostRegressor.fit", "kind": "function", "doc": "

Fit Booster (regressor) to training data (X, y)

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n   Target values.\n\n**kwargs: additional parameters to be passed to self.cook_training_set.\n
\n\n

Returns:

\n\n
self: object.\n
\n", "signature": "(self, X, y, **kwargs):", "funcdef": "def"}, "mlsauce.LSBoostRegressor.predict": {"fullname": "mlsauce.LSBoostRegressor.predict", "modulename": "mlsauce", "qualname": "LSBoostRegressor.predict", "kind": "function", "doc": "

Predict probabilities for test data X.

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\nlevel: int\n    Level of confidence (default = 95)\n\nmethod: str\n    `None`, or 'splitconformal', 'localconformal'\n    prediction (if you specify `return_pi = True`)\n\n**kwargs: additional parameters to be passed to\n    self.cook_test_set\n
\n\n

Returns:

\n\n
probability estimates for test data: {array-like}\n
\n", "signature": "(self, X, level=95, method=None, **kwargs):", "funcdef": "def"}, "mlsauce.LSBoostRegressor.set_predict_request": {"fullname": "mlsauce.LSBoostRegressor.set_predict_request", "modulename": "mlsauce", "qualname": "LSBoostRegressor.set_predict_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.LSBoostRegressor.set_score_request": {"fullname": "mlsauce.LSBoostRegressor.set_score_request", "modulename": "mlsauce", "qualname": "LSBoostRegressor.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.RidgeRegressor": {"fullname": "mlsauce.RidgeRegressor", "modulename": "mlsauce", "qualname": "RidgeRegressor", "kind": "class", "doc": "

Ridge.

\n\n

Attributes:

\n\n
reg_lambda: float\n    regularization parameter.\n\nbackend: str\n    type of backend; must be in ('cpu', 'gpu', 'tpu')\n
\n", "bases": "sklearn.base.BaseEstimator, sklearn.base.RegressorMixin"}, "mlsauce.RidgeRegressor.__init__": {"fullname": "mlsauce.RidgeRegressor.__init__", "modulename": "mlsauce", "qualname": "RidgeRegressor.__init__", "kind": "function", "doc": "

\n", "signature": "(reg_lambda=0.1, backend='cpu')"}, "mlsauce.RidgeRegressor.reg_lambda": {"fullname": "mlsauce.RidgeRegressor.reg_lambda", "modulename": "mlsauce", "qualname": "RidgeRegressor.reg_lambda", "kind": "variable", "doc": "

\n"}, "mlsauce.RidgeRegressor.backend": {"fullname": "mlsauce.RidgeRegressor.backend", "modulename": "mlsauce", "qualname": "RidgeRegressor.backend", "kind": "variable", "doc": "

\n"}, "mlsauce.RidgeRegressor.fit": {"fullname": "mlsauce.RidgeRegressor.fit", "modulename": "mlsauce", "qualname": "RidgeRegressor.fit", "kind": "function", "doc": "

Fit matrixops (classifier) to training data (X, y)

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n    Target values.\n\n**kwargs: additional parameters to be passed to self.cook_training_set.\n
\n\n

Returns:

\n\n
self: object.\n
\n", "signature": "(self, X, y, **kwargs):", "funcdef": "def"}, "mlsauce.RidgeRegressor.predict": {"fullname": "mlsauce.RidgeRegressor.predict", "modulename": "mlsauce", "qualname": "RidgeRegressor.predict", "kind": "function", "doc": "

Predict test data X.

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to `predict_proba`\n
\n\n

Returns:

\n\n
model predictions: {array-like}\n
\n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.RidgeRegressor.set_score_request": {"fullname": "mlsauce.RidgeRegressor.set_score_request", "modulename": "mlsauce", "qualname": "RidgeRegressor.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.LazyBoostingClassifier": {"fullname": "mlsauce.LazyBoostingClassifier", "modulename": "mlsauce", "qualname": "LazyBoostingClassifier", "kind": "class", "doc": "

Fitting -- almost -- all the classification algorithms\n and returning their scores.

\n\n
Parameters:\n\n    verbose: int, optional (default=0)\n        Any positive number for verbosity.\n\n    ignore_warnings: bool, optional (default=True)\n        When set to True, the warning related to algorigms that are not\n        able to run are ignored.\n\n    custom_metric: function, optional (default=None)\n        When function is provided, models are evaluated based on the custom\n          evaluation metric provided.\n\n    predictions: bool, optional (default=False)\n        When set to True, the predictions of all the models models are\n        returned as data frame.\n\n    sort_by: string, optional (default='Accuracy')\n        Sort models by a metric. Available options are 'Accuracy',\n        'Balanced Accuracy', 'ROC AUC', 'F1 Score' or a custom metric\n        identified by its name and provided by custom_metric.\n\n    random_state: int, optional (default=42)\n        Reproducibiility seed.\n\n    estimators: list, optional (default='all')\n        list of Estimators names or just 'all' for > 90 classifiers\n        (default='all')\n\n    preprocess: bool, preprocessing is done when set to True\n\n    n_jobs: int, when possible, run in parallel\n        For now, only used by individual models that support it.\n\n    n_layers: int, optional (default=3)\n        Number of layers of GenericBoostingClassifiers to be used.\n\n    All the other parameters are the same as GenericBoostingClassifier's.\n\nAttributes:\n\n    models_: dict-object\n        Returns a dictionary with each model pipeline as value\n        with key as name of models.\n\n    best_model_: object\n        Returns the best model pipeline.\n\nExamples\n\n\n    
\n
        import os\n        import mlsauce as ms\n        from sklearn.datasets import load_breast_cancer, load_iris, load_wine, load_digits\n        from sklearn.model_selection import train_test_split\n        from time import time\n\n        load_models = [load_breast_cancer, load_iris, load_wine]\n\n        for model in load_models:\n\n            data = model()\n            X = data.data\n            y= data.target\n\n            X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .2, random_state = 13)\n\n            clf = ms.LazyBoostingClassifier(verbose=1, ignore_warnings=False,\n                                            custom_metric=None, preprocess=False)\n\n            start = time()\n            models, predictioms = clf.fit(X_train, X_test, y_train, y_test)\n            print(f"\nElapsed: {time() - start} seconds\n")\n\n            print(models)\n
\n
\n
\n", "bases": "sklearn.base.ClassifierMixin"}, "mlsauce.LazyBoostingClassifier.__init__": {"fullname": "mlsauce.LazyBoostingClassifier.__init__", "modulename": "mlsauce", "qualname": "LazyBoostingClassifier.__init__", "kind": "function", "doc": "

\n", "signature": "(\tverbose=0,\tignore_warnings=True,\tcustom_metric=None,\tpredictions=False,\tsort_by='Accuracy',\trandom_state=42,\testimators='all',\tpreprocess=False,\tn_jobs=None)"}, "mlsauce.LazyBoostingClassifier.verbose": {"fullname": "mlsauce.LazyBoostingClassifier.verbose", "modulename": "mlsauce", "qualname": "LazyBoostingClassifier.verbose", "kind": "variable", "doc": "

\n"}, "mlsauce.LazyBoostingClassifier.ignore_warnings": {"fullname": "mlsauce.LazyBoostingClassifier.ignore_warnings", "modulename": "mlsauce", "qualname": "LazyBoostingClassifier.ignore_warnings", "kind": "variable", "doc": "

\n"}, "mlsauce.LazyBoostingClassifier.custom_metric": {"fullname": "mlsauce.LazyBoostingClassifier.custom_metric", "modulename": "mlsauce", "qualname": "LazyBoostingClassifier.custom_metric", "kind": "variable", "doc": "

\n"}, "mlsauce.LazyBoostingClassifier.predictions": {"fullname": "mlsauce.LazyBoostingClassifier.predictions", "modulename": "mlsauce", "qualname": "LazyBoostingClassifier.predictions", "kind": "variable", "doc": "

\n"}, "mlsauce.LazyBoostingClassifier.sort_by": {"fullname": "mlsauce.LazyBoostingClassifier.sort_by", "modulename": "mlsauce", "qualname": "LazyBoostingClassifier.sort_by", "kind": "variable", "doc": "

\n"}, "mlsauce.LazyBoostingClassifier.models_": {"fullname": "mlsauce.LazyBoostingClassifier.models_", "modulename": "mlsauce", "qualname": "LazyBoostingClassifier.models_", "kind": "variable", "doc": "

\n"}, "mlsauce.LazyBoostingClassifier.best_model_": {"fullname": "mlsauce.LazyBoostingClassifier.best_model_", "modulename": "mlsauce", "qualname": "LazyBoostingClassifier.best_model_", "kind": "variable", "doc": "

\n"}, "mlsauce.LazyBoostingClassifier.random_state": {"fullname": "mlsauce.LazyBoostingClassifier.random_state", "modulename": "mlsauce", "qualname": "LazyBoostingClassifier.random_state", "kind": "variable", "doc": "

\n"}, "mlsauce.LazyBoostingClassifier.estimators": {"fullname": "mlsauce.LazyBoostingClassifier.estimators", "modulename": "mlsauce", "qualname": "LazyBoostingClassifier.estimators", "kind": "variable", "doc": "

\n"}, "mlsauce.LazyBoostingClassifier.preprocess": {"fullname": "mlsauce.LazyBoostingClassifier.preprocess", "modulename": "mlsauce", "qualname": "LazyBoostingClassifier.preprocess", "kind": "variable", "doc": "

\n"}, "mlsauce.LazyBoostingClassifier.n_jobs": {"fullname": "mlsauce.LazyBoostingClassifier.n_jobs", "modulename": "mlsauce", "qualname": "LazyBoostingClassifier.n_jobs", "kind": "variable", "doc": "

\n"}, "mlsauce.LazyBoostingClassifier.fit": {"fullname": "mlsauce.LazyBoostingClassifier.fit", "modulename": "mlsauce", "qualname": "LazyBoostingClassifier.fit", "kind": "function", "doc": "

Fit classifiers to X_train and y_train, predict and score on X_test,\ny_test.

\n\n

Parameters:

\n\n
X_train: array-like,\n    Training vectors, where rows is the number of samples\n    and columns is the number of features.\n\nX_test: array-like,\n    Testing vectors, where rows is the number of samples\n    and columns is the number of features.\n\ny_train: array-like,\n    Training vectors, where rows is the number of samples\n    and columns is the number of features.\n\ny_test: array-like,\n    Testing vectors, where rows is the number of samples\n    and columns is the number of features.\n\n**kwargs: dict,\n    Additional arguments to be passed to the fit GenericBoostingClassifier.\n
\n\n

Returns:

\n\n
scores: Pandas DataFrame\n    Returns metrics of all the models in a Pandas DataFrame.\n\npredictions: Pandas DataFrame\n    Returns predictions of all the models in a Pandas DataFrame.\n
\n", "signature": "(self, X_train, X_test, y_train, y_test, **kwargs):", "funcdef": "def"}, "mlsauce.LazyBoostingClassifier.get_best_model": {"fullname": "mlsauce.LazyBoostingClassifier.get_best_model", "modulename": "mlsauce", "qualname": "LazyBoostingClassifier.get_best_model", "kind": "function", "doc": "

This function returns the best model pipeline based on the sort_by metric.

\n\n

Returns:

\n\n
best_model: object,\n    Returns the best model pipeline based on the sort_by metric.\n
\n", "signature": "(self):", "funcdef": "def"}, "mlsauce.LazyBoostingClassifier.provide_models": {"fullname": "mlsauce.LazyBoostingClassifier.provide_models", "modulename": "mlsauce", "qualname": "LazyBoostingClassifier.provide_models", "kind": "function", "doc": "

Returns all the model objects trained. If fit hasn't been called yet,\nthen it's called to return the models.

\n\n

Parameters:

\n\n

X_train: array-like,\n Training vectors, where rows is the number of samples\n and columns is the number of features.

\n\n

X_test: array-like,\n Testing vectors, where rows is the number of samples\n and columns is the number of features.

\n\n

y_train: array-like,\n Training vectors, where rows is the number of samples\n and columns is the number of features.

\n\n

y_test: array-like,\n Testing vectors, where rows is the number of samples\n and columns is the number of features.

\n\n

Returns:

\n\n
models: dict-object,\n    Returns a dictionary with each model's pipeline as value\n    and key = name of the model.\n
\n", "signature": "(self, X_train, X_test, y_train, y_test):", "funcdef": "def"}, "mlsauce.LazyBoostingRegressor": {"fullname": "mlsauce.LazyBoostingRegressor", "modulename": "mlsauce", "qualname": "LazyBoostingRegressor", "kind": "class", "doc": "

Fitting -- almost -- all the regression algorithms\n and returning their scores.

\n\n

Parameters:

\n\n
verbose: int, optional (default=0)\n    Any positive number for verbosity.\n\nignore_warnings: bool, optional (default=True)\n    When set to True, the warning related to algorigms that are not able to run are ignored.\n\ncustom_metric: function, optional (default=None)\n    When function is provided, models are evaluated based on the custom evaluation metric provided.\n\npredictions: bool, optional (default=False)\n    When set to True, the predictions of all the models models are returned as dataframe.\n\nsort_by: string, optional (default='RMSE')\n    Sort models by a metric. Available options are 'R-Squared', 'Adjusted R-Squared', 'RMSE', 'Time Taken' and 'Custom Metric'.\n    or a custom metric identified by its name and provided by custom_metric.\n\nrandom_state: int, optional (default=42)\n    Reproducibiility seed.\n\nestimators: list, optional (default='all')\n    list of Estimators names or just 'all' (default='all')\n\npreprocess: bool\n    preprocessing is done when set to True\n\nn_jobs : int, when possible, run in parallel\n    For now, only used by individual models that support it.\n\nn_layers: int, optional (default=3)\n    Number of layers of CustomRegressors to be used.\n\nAll the other parameters are the same as CustomRegressor's.\n
\n\n

Attributes:

\n\n
models_: dict-object\n    Returns a dictionary with each model pipeline as value\n    with key as name of models.\n\nbest_model_: object\n    Returns the best model pipeline based on the sort_by metric.\n
\n\n

Examples:

\n\n
\n
import os\nimport mlsauce as ms\nfrom sklearn.datasets import load_diabetes\nfrom sklearn.model_selection import train_test_split\n\ndata = load_diabetes()\nX = data.data\ny= data.target\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .2, random_state = 123)\n\nregr = ms.LazyBoostingRegressor(verbose=0, ignore_warnings=True,\n                                custom_metric=None, preprocess=True)\nmodels, predictioms = regr.fit(X_train, X_test, y_train, y_test)\nmodel_dictionary = regr.provide_models(X_train, X_test, y_train, y_test)\nprint(models)\n
\n
\n
\n", "bases": "sklearn.base.RegressorMixin"}, "mlsauce.LazyBoostingRegressor.__init__": {"fullname": "mlsauce.LazyBoostingRegressor.__init__", "modulename": "mlsauce", "qualname": "LazyBoostingRegressor.__init__", "kind": "function", "doc": "

\n", "signature": "(\tverbose=0,\tignore_warnings=True,\tcustom_metric=None,\tpredictions=False,\tsort_by='RMSE',\trandom_state=42,\testimators='all',\tpreprocess=False,\tn_jobs=None)"}, "mlsauce.LazyBoostingRegressor.verbose": {"fullname": "mlsauce.LazyBoostingRegressor.verbose", "modulename": "mlsauce", "qualname": "LazyBoostingRegressor.verbose", "kind": "variable", "doc": "

\n"}, "mlsauce.LazyBoostingRegressor.ignore_warnings": {"fullname": "mlsauce.LazyBoostingRegressor.ignore_warnings", "modulename": "mlsauce", "qualname": "LazyBoostingRegressor.ignore_warnings", "kind": "variable", "doc": "

\n"}, "mlsauce.LazyBoostingRegressor.custom_metric": {"fullname": "mlsauce.LazyBoostingRegressor.custom_metric", "modulename": "mlsauce", "qualname": "LazyBoostingRegressor.custom_metric", "kind": "variable", "doc": "

\n"}, "mlsauce.LazyBoostingRegressor.predictions": {"fullname": "mlsauce.LazyBoostingRegressor.predictions", "modulename": "mlsauce", "qualname": "LazyBoostingRegressor.predictions", "kind": "variable", "doc": "

\n"}, "mlsauce.LazyBoostingRegressor.sort_by": {"fullname": "mlsauce.LazyBoostingRegressor.sort_by", "modulename": "mlsauce", "qualname": "LazyBoostingRegressor.sort_by", "kind": "variable", "doc": "

\n"}, "mlsauce.LazyBoostingRegressor.models_": {"fullname": "mlsauce.LazyBoostingRegressor.models_", "modulename": "mlsauce", "qualname": "LazyBoostingRegressor.models_", "kind": "variable", "doc": "

\n"}, "mlsauce.LazyBoostingRegressor.best_model_": {"fullname": "mlsauce.LazyBoostingRegressor.best_model_", "modulename": "mlsauce", "qualname": "LazyBoostingRegressor.best_model_", "kind": "variable", "doc": "

\n"}, "mlsauce.LazyBoostingRegressor.random_state": {"fullname": "mlsauce.LazyBoostingRegressor.random_state", "modulename": "mlsauce", "qualname": "LazyBoostingRegressor.random_state", "kind": "variable", "doc": "

\n"}, "mlsauce.LazyBoostingRegressor.estimators": {"fullname": "mlsauce.LazyBoostingRegressor.estimators", "modulename": "mlsauce", "qualname": "LazyBoostingRegressor.estimators", "kind": "variable", "doc": "

\n"}, "mlsauce.LazyBoostingRegressor.preprocess": {"fullname": "mlsauce.LazyBoostingRegressor.preprocess", "modulename": "mlsauce", "qualname": "LazyBoostingRegressor.preprocess", "kind": "variable", "doc": "

\n"}, "mlsauce.LazyBoostingRegressor.n_jobs": {"fullname": "mlsauce.LazyBoostingRegressor.n_jobs", "modulename": "mlsauce", "qualname": "LazyBoostingRegressor.n_jobs", "kind": "variable", "doc": "

\n"}, "mlsauce.LazyBoostingRegressor.fit": {"fullname": "mlsauce.LazyBoostingRegressor.fit", "modulename": "mlsauce", "qualname": "LazyBoostingRegressor.fit", "kind": "function", "doc": "

Fit Regression algorithms to X_train and y_train, predict and score on X_test, y_test.

\n\n

Parameters:

\n\n
X_train : array-like,\n    Training vectors, where rows is the number of samples\n    and columns is the number of features.\n\nX_test : array-like,\n    Testing vectors, where rows is the number of samples\n    and columns is the number of features.\n\ny_train : array-like,\n    Training vectors, where rows is the number of samples\n    and columns is the number of features.\n\ny_test : array-like,\n    Testing vectors, where rows is the number of samples\n    and columns is the number of features.\n\n**kwargs: dict,\n    Additional parameters to be passed to the GenericBoostingRegressor.\n
\n\n

Returns:

\n\n

scores: Pandas DataFrame\n Returns metrics of all the models in a Pandas DataFrame.

\n\n

predictions : Pandas DataFrame\n Returns predictions of all the models in a Pandas DataFrame.

\n", "signature": "(self, X_train, X_test, y_train, y_test, **kwargs):", "funcdef": "def"}, "mlsauce.LazyBoostingRegressor.get_best_model": {"fullname": "mlsauce.LazyBoostingRegressor.get_best_model", "modulename": "mlsauce", "qualname": "LazyBoostingRegressor.get_best_model", "kind": "function", "doc": "

This function returns the best model pipeline based on the sort_by metric.

\n\n

Returns:

\n\n
best_model: object,\n    Returns the best model pipeline based on the sort_by metric.\n
\n", "signature": "(self):", "funcdef": "def"}, "mlsauce.LazyBoostingRegressor.provide_models": {"fullname": "mlsauce.LazyBoostingRegressor.provide_models", "modulename": "mlsauce", "qualname": "LazyBoostingRegressor.provide_models", "kind": "function", "doc": "

This function returns all the model objects trained in fit function.\nIf fit is not called already, then we call fit and then return the models.

\n\n

Parameters:

\n\n
X_train : array-like,\n    Training vectors, where rows is the number of samples\n    and columns is the number of features.\n\nX_test : array-like,\n    Testing vectors, where rows is the number of samples\n    and columns is the number of features.\n\ny_train : array-like,\n    Training vectors, where rows is the number of samples\n    and columns is the number of features.\n\ny_test : array-like,\n    Testing vectors, where rows is the number of samples\n    and columns is the number of features.\n
\n\n

Returns:

\n\n
models: dict-object,\n    Returns a dictionary with each model pipeline as value\n    with key as name of models.\n
\n", "signature": "(self, X_train, X_test, y_train, y_test):", "funcdef": "def"}, "mlsauce.download": {"fullname": "mlsauce.download", "modulename": "mlsauce", "qualname": "download", "kind": "function", "doc": "

\n", "signature": "(\tpkgname='MASS',\tdataset='Boston',\tsource='https://cran.r-universe.dev/',\t**kwargs):", "funcdef": "def"}, "mlsauce.get_config": {"fullname": "mlsauce.get_config", "modulename": "mlsauce", "qualname": "get_config", "kind": "function", "doc": "

Retrieve current values for configuration set by set_config()

\n\n

Returns

\n\n

config : dict\n Keys are parameter names that can be passed to set_config().

\n\n

See Also

\n\n

config_context: Context manager for global mlsauce configuration\nset_config: Set global mlsauce configuration

\n", "signature": "():", "funcdef": "def"}, "mlsauce.set_config": {"fullname": "mlsauce.set_config", "modulename": "mlsauce", "qualname": "set_config", "kind": "function", "doc": "

Set global mlsauce configuration

\n\n

New in version 0.3.0.

\n\n

Parameters

\n\n

assume_finite : bool, optional\n If True, validation for finiteness will be skipped,\n saving time, but leading to potential crashes. If\n False, validation for finiteness will be performed,\n avoiding error. Global default: False.

\n\n
*New in version 0.3.0.*\n
\n\n

working_memory : int, optional\n If set, mlsauce will attempt to limit the size of temporary arrays\n to this number of MiB (per job when parallelised), often saving both\n computation time and memory on expensive operations that can be\n performed in chunks. Global default: 1024.

\n\n
*New in version 0.3.0.*\n
\n\n

print_changed_only : bool, optional\n If True, only the parameters that were set to non-default\n values will be printed when printing an estimator. For example,\n print(SVC()) while True will only print 'SVC()' while the default\n behaviour would be to print 'SVC(C=1.0, cache_size=200, ...)' with\n all the non-changed parameters.

\n\n
*New in version 0.3.0.*\n
\n\n

display : {'text', 'diagram'}, optional\n If 'diagram', estimators will be displayed as text in a jupyter lab\n of notebook context. If 'text', estimators will be displayed as\n text. Default is 'text'.

\n\n
*New in version 0.3.0.*\n
\n\n

See Also

\n\n

config_context: Context manager for global mlsauce configuration\nget_config: Retrieve current values of the global configuration

\n", "signature": "(\tassume_finite=None,\tworking_memory=None,\tprint_changed_only=None,\tdisplay=None):", "funcdef": "def"}, "mlsauce.config_context": {"fullname": "mlsauce.config_context", "modulename": "mlsauce", "qualname": "config_context", "kind": "function", "doc": "

Context manager for global mlsauce configuration

\n\n

Parameters

\n\n

assume_finite : bool, optional\n If True, validation for finiteness will be skipped,\n saving time, but leading to potential crashes. If\n False, validation for finiteness will be performed,\n avoiding error. Global default: False.

\n\n

working_memory : int, optional\n If set, mlsauce will attempt to limit the size of temporary arrays\n to this number of MiB (per job when parallelised), often saving both\n computation time and memory on expensive operations that can be\n performed in chunks. Global default: 1024.

\n\n

print_changed_only : bool, optional\n If True, only the parameters that were set to non-default\n values will be printed when printing an estimator. For example,\n print(SVC()) while True will only print 'SVC()', but would print\n 'SVC(C=1.0, cache_size=200, ...)' with all the non-changed parameters\n when False. Default is True.

\n\n
*New in version 0.3.0.*\n
\n\n

display : {'text', 'diagram'}, optional\n If 'diagram', estimators will be displayed as text in a jupyter lab\n of notebook context. If 'text', estimators will be displayed as\n text. Default is 'text'.

\n\n
*New in version 0.3.0.*\n
\n\n

Notes

\n\n

All settings, not just those presently modified, will be returned to\ntheir previous values when the context manager is exited. This is not\nthread-safe.

\n\n

Examples

\n\n
\n
>>> import mlsauce\n>>> from mlsauce.utils.validation import assert_all_finite\n>>> with mlsauce.config_context(assume_finite=True):\n...     assert_all_finite([float('nan')])\n>>> with mlsauce.config_context(assume_finite=True):\n...     with mlsauce.config_context(assume_finite=False):\n...         assert_all_finite([float('nan')])\nTraceback (most recent call last):\n...\nValueError: Input contains NaN, ...\n
\n
\n\n

See Also

\n\n

set_config: Set global mlsauce configuration\nget_config: Retrieve current values of the global configuration

\n", "signature": "(**new_config):", "funcdef": "def"}, "mlsauce.adaopt": {"fullname": "mlsauce.adaopt", "modulename": "mlsauce.adaopt", "kind": "module", "doc": "

\n"}, "mlsauce.adaopt.AdaOpt": {"fullname": "mlsauce.adaopt.AdaOpt", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt", "kind": "class", "doc": "

AdaOpt classifier.

\n\n

Attributes:

\n\n
n_iterations: int\n    number of iterations of the optimizer at training time.\n\nlearning_rate: float\n    controls the speed of the optimizer at training time.\n\nreg_lambda: float\n    L2 regularization parameter for successive errors in the optimizer\n    (at training time).\n\nreg_alpha: float\n    L1 regularization parameter for successive errors in the optimizer\n    (at training time).\n\neta: float\n    controls the slope in gradient descent (at training time).\n\ngamma: float\n    controls the step size in gradient descent (at training time).\n\nk: int\n    number of nearest neighbors selected at test time for classification.\n\ntolerance: float\n    controls early stopping in gradient descent (at training time).\n\nn_clusters: int\n    number of clusters, if MiniBatch k-means is used at test time\n    (for faster prediction).\n\nbatch_size: int\n    size of the batch, if MiniBatch k-means is used at test time\n    (for faster prediction).\n\nrow_sample: float\n    percentage of rows chosen from training set (by stratified subsampling,\n    for faster prediction).\n\ntype_dist: str\n    distance used for finding the nearest neighbors; currently `euclidean-f`\n    (euclidean distances calculated as whole), `euclidean` (euclidean distances\n    calculated row by row), `cosine` (cosine distance).\n\nn_jobs: int\n    number of cpus for parallel processing (default: None)\n\nverbose: int\n    progress bar for parallel processing (yes = 1) or not (no = 0)\n\ncache: boolean\n    if the nearest neighbors are cached or not, for faster retrieval in\n    subsequent calls.\n\nn_clusters_input: int\n    number of clusters (a priori) for clustering the features\n\nclustering_method: str\n    clustering method: currently 'kmeans', 'gmm'\n\ncluster_scaling: str\n    scaling method for clustering: currently 'standard', 'robust', 'minmax'\n\nseed: int\n    reproducibility seed for nodes_sim=='uniform', clustering and dropout.\n
\n", "bases": "sklearn.base.BaseEstimator, sklearn.base.ClassifierMixin"}, "mlsauce.adaopt.AdaOpt.__init__": {"fullname": "mlsauce.adaopt.AdaOpt.__init__", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.__init__", "kind": "function", "doc": "

\n", "signature": "(\tn_iterations=50,\tlearning_rate=0.3,\treg_lambda=0.1,\treg_alpha=0.5,\teta=0.01,\tgamma=0.01,\tk=3,\ttolerance=0,\tn_clusters=0,\tbatch_size=100,\trow_sample=0.8,\ttype_dist='euclidean-f',\tn_jobs=None,\tverbose=0,\tcache=True,\tn_clusters_input=0,\tclustering_method='kmeans',\tcluster_scaling='standard',\tseed=123)"}, "mlsauce.adaopt.AdaOpt.n_iterations": {"fullname": "mlsauce.adaopt.AdaOpt.n_iterations", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.n_iterations", "kind": "variable", "doc": "

\n"}, "mlsauce.adaopt.AdaOpt.learning_rate": {"fullname": "mlsauce.adaopt.AdaOpt.learning_rate", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.learning_rate", "kind": "variable", "doc": "

\n"}, "mlsauce.adaopt.AdaOpt.reg_lambda": {"fullname": "mlsauce.adaopt.AdaOpt.reg_lambda", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.reg_lambda", "kind": "variable", "doc": "

\n"}, "mlsauce.adaopt.AdaOpt.reg_alpha": {"fullname": "mlsauce.adaopt.AdaOpt.reg_alpha", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.reg_alpha", "kind": "variable", "doc": "

\n"}, "mlsauce.adaopt.AdaOpt.eta": {"fullname": "mlsauce.adaopt.AdaOpt.eta", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.eta", "kind": "variable", "doc": "

\n"}, "mlsauce.adaopt.AdaOpt.gamma": {"fullname": "mlsauce.adaopt.AdaOpt.gamma", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.gamma", "kind": "variable", "doc": "

\n"}, "mlsauce.adaopt.AdaOpt.k": {"fullname": "mlsauce.adaopt.AdaOpt.k", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.k", "kind": "variable", "doc": "

\n"}, "mlsauce.adaopt.AdaOpt.tolerance": {"fullname": "mlsauce.adaopt.AdaOpt.tolerance", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.tolerance", "kind": "variable", "doc": "

\n"}, "mlsauce.adaopt.AdaOpt.n_clusters": {"fullname": "mlsauce.adaopt.AdaOpt.n_clusters", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.n_clusters", "kind": "variable", "doc": "

\n"}, "mlsauce.adaopt.AdaOpt.batch_size": {"fullname": "mlsauce.adaopt.AdaOpt.batch_size", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.batch_size", "kind": "variable", "doc": "

\n"}, "mlsauce.adaopt.AdaOpt.row_sample": {"fullname": "mlsauce.adaopt.AdaOpt.row_sample", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.row_sample", "kind": "variable", "doc": "

\n"}, "mlsauce.adaopt.AdaOpt.type_dist": {"fullname": "mlsauce.adaopt.AdaOpt.type_dist", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.type_dist", "kind": "variable", "doc": "

\n"}, "mlsauce.adaopt.AdaOpt.n_jobs": {"fullname": "mlsauce.adaopt.AdaOpt.n_jobs", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.n_jobs", "kind": "variable", "doc": "

\n"}, "mlsauce.adaopt.AdaOpt.cache": {"fullname": "mlsauce.adaopt.AdaOpt.cache", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.cache", "kind": "variable", "doc": "

\n"}, "mlsauce.adaopt.AdaOpt.verbose": {"fullname": "mlsauce.adaopt.AdaOpt.verbose", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.verbose", "kind": "variable", "doc": "

\n"}, "mlsauce.adaopt.AdaOpt.n_clusters_input": {"fullname": "mlsauce.adaopt.AdaOpt.n_clusters_input", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.n_clusters_input", "kind": "variable", "doc": "

\n"}, "mlsauce.adaopt.AdaOpt.clustering_method": {"fullname": "mlsauce.adaopt.AdaOpt.clustering_method", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.clustering_method", "kind": "variable", "doc": "

\n"}, "mlsauce.adaopt.AdaOpt.cluster_scaling": {"fullname": "mlsauce.adaopt.AdaOpt.cluster_scaling", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.cluster_scaling", "kind": "variable", "doc": "

\n"}, "mlsauce.adaopt.AdaOpt.seed": {"fullname": "mlsauce.adaopt.AdaOpt.seed", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.seed", "kind": "variable", "doc": "

\n"}, "mlsauce.adaopt.AdaOpt.fit": {"fullname": "mlsauce.adaopt.AdaOpt.fit", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.fit", "kind": "function", "doc": "

Fit AdaOpt to training data (X, y)

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n    Target values.\n\n**kwargs: additional parameters to be passed to self.cook_training_set.\n
\n\n

Returns:

\n\n
self: object.\n
\n", "signature": "(self, X, y, **kwargs):", "funcdef": "def"}, "mlsauce.adaopt.AdaOpt.predict": {"fullname": "mlsauce.adaopt.AdaOpt.predict", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.predict", "kind": "function", "doc": "

Predict test data X.

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to `predict_proba`\n
\n\n

Returns:

\n\n
model predictions: {array-like}\n
\n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.adaopt.AdaOpt.predict_proba": {"fullname": "mlsauce.adaopt.AdaOpt.predict_proba", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.predict_proba", "kind": "function", "doc": "

Predict probabilities for test data X.

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to\n    self.cook_test_set\n
\n\n

Returns:

\n\n
probability estimates for test data: {array-like}\n
\n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.adaopt.AdaOpt.set_score_request": {"fullname": "mlsauce.adaopt.AdaOpt.set_score_request", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.booster": {"fullname": "mlsauce.booster", "modulename": "mlsauce.booster", "kind": "module", "doc": "

\n"}, "mlsauce.booster.LSBoostClassifier": {"fullname": "mlsauce.booster.LSBoostClassifier", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier", "kind": "class", "doc": "

LSBoost classifier.

\n\n
Attributes:\n\n    n_estimators: int\n        number of boosting iterations.\n\n    learning_rate: float\n        controls the learning speed at training time.\n\n    n_hidden_features: int\n        number of nodes in successive hidden layers.\n\n    reg_lambda: float\n        L2 regularization parameter for successive errors in the optimizer\n        (at training time).\n\n    alpha: float\n        compromise between L1 and L2 regularization (must be in [0, 1]),\n        for `solver` == 'enet'.\n\n    row_sample: float\n        percentage of rows chosen from the training set.\n\n    col_sample: float\n        percentage of columns chosen from the training set.\n\n    dropout: float\n        percentage of nodes dropped from the training set.\n\n    tolerance: float\n        controls early stopping in gradient descent (at training time).\n\n    direct_link: bool\n        indicates whether the original features are included (True) in model's\n        fitting or not (False).\n\n    verbose: int\n        progress bar (yes = 1) or not (no = 0) (currently).\n\n    seed: int\n        reproducibility seed for nodes_sim=='uniform', clustering and dropout.\n\n    backend: str\n        type of backend; must be in ('cpu', 'gpu', 'tpu')\n\n    solver: str\n        type of 'weak' learner; currently in ('ridge', 'lasso', 'enet').\n        'enet' is a combination of 'ridge' and 'lasso' called Elastic Net.\n\n    activation: str\n        activation function: currently 'relu', 'relu6', 'sigmoid', 'tanh'\n\n    n_clusters: int\n        number of clusters for clustering the features\n\n    clustering_method: str\n        clustering method: currently 'kmeans', 'gmm'\n\n    cluster_scaling: str\n        scaling method for clustering: currently 'standard', 'robust', 'minmax'\n\n    degree: int\n        degree of features interactions to include in the model\n\n    weights_distr: str\n        distribution of weights for constructing the model's hidden layer;\n        currently 'uniform', 'gaussian'\n\nExamples:\n\n    ```python\n    import numpy as np\n    from sklearn.datasets import load_digits, load_breast_cancer, load_wine, load_iris\n    from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\n    from sklearn.tree import DecisionTreeRegressor\n    from sklearn.kernel_ridge import KernelRidge\n    from time import time\n    from os import chdir\n    from sklearn import metrics\n    import os\n\n    import mlsauce as ms\n\n    print(\"\n
\n\n

\")\n print(\"GenericBoosting Decision tree -----\")\n print(\"\n\")

\n\n
    print(\"\n
\n\n

\")\n print(\"breast_cancer data -----\")

\n\n
    # data 1\n    breast_cancer = load_breast_cancer()\n    X = breast_cancer.data\n    y = breast_cancer.target\n    # split data into training test and test set\n    np.random.seed(15029)\n    X_train, X_test, y_train, y_test = train_test_split(X, y,\n                                                        test_size=0.2)\n\n    clf = DecisionTreeRegressor()\n    clf2 = KernelRidge()\n\n    obj = ms.GenericBoostingClassifier(clf, tolerance=1e-2)\n    print(obj.get_params())\n    start = time()\n    obj.fit(X_train, y_train)\n    print(time()-start)\n    start = time()\n    print(obj.score(X_test, y_test))\n    print(time()-start)\n\n    print(obj.obj['loss'])\n\n    obj = ms.GenericBoostingClassifier(clf, tolerance=1e-2, n_clusters=2)\n    print(obj.get_params())\n    start = time()\n    obj.fit(X_train, y_train)\n    print(time()-start)\n    start = time()\n    print(obj.score(X_test, y_test))\n    print(time()-start)\n\n    print(obj.obj['loss'])\n\n\n    # data 2\n    print(\"\n
\n\n

\")\n print(\"wine data -----\")

\n\n
    wine = load_wine()\n    Z = wine.data\n    t = wine.target\n    np.random.seed(879423)\n    X_train, X_test, y_train, y_test = train_test_split(Z, t,\n                                                        test_size=0.2)\n\n    obj = ms.GenericBoostingClassifier(clf)\n    print(obj.get_params())\n    start = time()\n    obj.fit(X_train, y_train)\n    print(time()-start)\n    start = time()\n    print(obj.score(X_test, y_test))\n    print(time()-start)\n\n    print(obj.obj['loss'])\n\n    obj = ms.GenericBoostingClassifier(clf, n_clusters=3)\n    print(obj.get_params())\n    start = time()\n    obj.fit(X_train, y_train)\n    print(time()-start)\n    start = time()\n    print(obj.score(X_test, y_test))\n    print(time()-start)\n\n    print(obj.obj['loss'])\n\n    # data 3\n    print(\"\n
\n\n

\")\n print(\"iris data -----\")

\n\n
    iris = load_iris()\n    Z = iris.data\n    t = iris.target\n    np.random.seed(734563)\n    X_train, X_test, y_train, y_test = train_test_split(Z, t,\n                                                        test_size=0.2)\n\n\n    obj = ms.GenericBoostingClassifier(clf)\n    print(obj.get_params())\n    start = time()\n    obj.fit(X_train, y_train)\n    print(time()-start)\n    start = time()\n    print(obj.score(X_test, y_test))\n    print(time()-start)\n\n    print(obj.obj['loss'])\n\n\n    print(\"\n
\n\n

\")\n print(\"GenericBoosting KRR -----\")\n print(\"\n\")

\n\n
    obj = ms.GenericBoostingClassifier(clf2, tolerance=1e-2)\n    print(obj.get_params())\n    start = time()\n    obj.fit(X_train, y_train)\n    print(time()-start)\n    start = time()\n    print(obj.score(X_test, y_test))\n    print(time()-start)\n\n    print(obj.obj['loss'])\n\n    obj = ms.GenericBoostingClassifier(clf2, tolerance=1e-2, n_clusters=2)\n    print(obj.get_params())\n    start = time()\n    obj.fit(X_train, y_train)\n    print(time()-start)\n    start = time()\n    print(obj.score(X_test, y_test))\n    print(time()-start)\n\n    print(obj.obj['loss'])\n\n\n    # data 2\n    print(\"\n
\n\n

\")\n print(\"wine data -----\")

\n\n
    wine = load_wine()\n    Z = wine.data\n    t = wine.target\n    np.random.seed(879423)\n    X_train, X_test, y_train, y_test = train_test_split(Z, t,\n                                                        test_size=0.2)\n\n    obj = ms.GenericBoostingClassifier(clf2)\n    print(obj.get_params())\n    start = time()\n    obj.fit(X_train, y_train)\n    print(time()-start)\n    start = time()\n    print(obj.score(X_test, y_test))\n    print(time()-start)\n\n    print(obj.obj['loss'])\n\n    obj = ms.GenericBoostingClassifier(clf2, n_clusters=3)\n    print(obj.get_params())\n    start = time()\n    obj.fit(X_train, y_train)\n    print(time()-start)\n    start = time()\n    print(obj.score(X_test, y_test))\n    print(time()-start)\n\n    print(obj.obj['loss'])\n\n    # data 3\n    print(\"\n
\n\n

\")\n print(\"iris data -----\")

\n\n
    iris = load_iris()\n    Z = iris.data\n    t = iris.target\n    np.random.seed(734563)\n    X_train, X_test, y_train, y_test = train_test_split(Z, t,\n                                                        test_size=0.2)\n\n\n    obj = ms.GenericBoostingClassifier(clf2)\n    print(obj.get_params())\n    start = time()\n    obj.fit(X_train, y_train)\n    print(time()-start)\n    start = time()\n    print(obj.score(X_test, y_test))\n    print(time()-start)\n\n    print(obj.obj['loss'])\n```\n
\n", "bases": "sklearn.base.BaseEstimator, sklearn.base.ClassifierMixin"}, "mlsauce.booster.LSBoostClassifier.__init__": {"fullname": "mlsauce.booster.LSBoostClassifier.__init__", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.__init__", "kind": "function", "doc": "

\n", "signature": "(\tn_estimators=100,\tlearning_rate=0.1,\tn_hidden_features=5,\treg_lambda=0.1,\talpha=0.5,\trow_sample=1,\tcol_sample=1,\tdropout=0,\ttolerance=0.0001,\tdirect_link=1,\tverbose=1,\tseed=123,\tbackend='cpu',\tsolver='ridge',\tactivation='relu',\tn_clusters=0,\tclustering_method='kmeans',\tcluster_scaling='standard',\tdegree=None,\tweights_distr='uniform',\tbase_model=None)"}, "mlsauce.booster.LSBoostClassifier.base_model": {"fullname": "mlsauce.booster.LSBoostClassifier.base_model", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.base_model", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostClassifier.n_estimators": {"fullname": "mlsauce.booster.LSBoostClassifier.n_estimators", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.n_estimators", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostClassifier.learning_rate": {"fullname": "mlsauce.booster.LSBoostClassifier.learning_rate", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.learning_rate", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostClassifier.n_hidden_features": {"fullname": "mlsauce.booster.LSBoostClassifier.n_hidden_features", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.n_hidden_features", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostClassifier.reg_lambda": {"fullname": "mlsauce.booster.LSBoostClassifier.reg_lambda", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.reg_lambda", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostClassifier.alpha": {"fullname": "mlsauce.booster.LSBoostClassifier.alpha", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.alpha", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostClassifier.row_sample": {"fullname": "mlsauce.booster.LSBoostClassifier.row_sample", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.row_sample", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostClassifier.col_sample": {"fullname": "mlsauce.booster.LSBoostClassifier.col_sample", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.col_sample", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostClassifier.dropout": {"fullname": "mlsauce.booster.LSBoostClassifier.dropout", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.dropout", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostClassifier.tolerance": {"fullname": "mlsauce.booster.LSBoostClassifier.tolerance", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.tolerance", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostClassifier.direct_link": {"fullname": "mlsauce.booster.LSBoostClassifier.direct_link", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.direct_link", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostClassifier.verbose": {"fullname": "mlsauce.booster.LSBoostClassifier.verbose", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.verbose", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostClassifier.seed": {"fullname": "mlsauce.booster.LSBoostClassifier.seed", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.seed", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostClassifier.backend": {"fullname": "mlsauce.booster.LSBoostClassifier.backend", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.backend", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostClassifier.obj": {"fullname": "mlsauce.booster.LSBoostClassifier.obj", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.obj", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostClassifier.solver": {"fullname": "mlsauce.booster.LSBoostClassifier.solver", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.solver", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostClassifier.activation": {"fullname": "mlsauce.booster.LSBoostClassifier.activation", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.activation", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostClassifier.n_clusters": {"fullname": "mlsauce.booster.LSBoostClassifier.n_clusters", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.n_clusters", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostClassifier.clustering_method": {"fullname": "mlsauce.booster.LSBoostClassifier.clustering_method", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.clustering_method", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostClassifier.cluster_scaling": {"fullname": "mlsauce.booster.LSBoostClassifier.cluster_scaling", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.cluster_scaling", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostClassifier.degree": {"fullname": "mlsauce.booster.LSBoostClassifier.degree", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.degree", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostClassifier.poly_": {"fullname": "mlsauce.booster.LSBoostClassifier.poly_", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.poly_", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostClassifier.weights_distr": {"fullname": "mlsauce.booster.LSBoostClassifier.weights_distr", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.weights_distr", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostClassifier.fit": {"fullname": "mlsauce.booster.LSBoostClassifier.fit", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.fit", "kind": "function", "doc": "

Fit Booster (classifier) to training data (X, y)

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n    Target values.\n\n**kwargs: additional parameters to be passed to self.cook_training_set.\n
\n\n

Returns:

\n\n
self: object.\n
\n", "signature": "(self, X, y, **kwargs):", "funcdef": "def"}, "mlsauce.booster.LSBoostClassifier.predict": {"fullname": "mlsauce.booster.LSBoostClassifier.predict", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.predict", "kind": "function", "doc": "

Predict test data X.

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to `predict_proba`\n
\n\n

Returns:

\n\n
model predictions: {array-like}\n
\n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.booster.LSBoostClassifier.predict_proba": {"fullname": "mlsauce.booster.LSBoostClassifier.predict_proba", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.predict_proba", "kind": "function", "doc": "

Predict probabilities for test data X.

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to\n    self.cook_test_set\n
\n\n

Returns:

\n\n
probability estimates for test data: {array-like}\n
\n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.booster.LSBoostClassifier.set_score_request": {"fullname": "mlsauce.booster.LSBoostClassifier.set_score_request", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.booster.LSBoostRegressor": {"fullname": "mlsauce.booster.LSBoostRegressor", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor", "kind": "class", "doc": "

LSBoost regressor.

\n\n

Attributes:

\n\n
n_estimators: int\n    number of boosting iterations.\n\nlearning_rate: float\n    controls the learning speed at training time.\n\nn_hidden_features: int\n    number of nodes in successive hidden layers.\n\nreg_lambda: float\n    L2 regularization parameter for successive errors in the optimizer\n    (at training time).\n\nalpha: float\n    compromise between L1 and L2 regularization (must be in [0, 1]),\n    for `solver` == 'enet'\n\nrow_sample: float\n    percentage of rows chosen from the training set.\n\ncol_sample: float\n    percentage of columns chosen from the training set.\n\ndropout: float\n    percentage of nodes dropped from the training set.\n\ntolerance: float\n    controls early stopping in gradient descent (at training time).\n\ndirect_link: bool\n    indicates whether the original features are included (True) in model's\n    fitting or not (False).\n\nverbose: int\n    progress bar (yes = 1) or not (no = 0) (currently).\n\nseed: int\n    reproducibility seed for nodes_sim=='uniform', clustering and dropout.\n\nbackend: str\n    type of backend; must be in ('cpu', 'gpu', 'tpu')\n\nsolver: str\n    type of 'weak' learner; currently in ('ridge', 'lasso')\n\nactivation: str\n    activation function: currently 'relu', 'relu6', 'sigmoid', 'tanh'\n\ntype_pi: str.\n    type of prediction interval; currently \"kde\" (default) or \"bootstrap\".\n    Used only in `self.predict`, for `self.replications` > 0 and `self.kernel`\n    in ('gaussian', 'tophat'). Default is `None`.\n\nreplications: int.\n    number of replications (if needed) for predictive simulation.\n    Used only in `self.predict`, for `self.kernel` in ('gaussian',\n    'tophat') and `self.type_pi = 'kde'`. Default is `None`.\n\nn_clusters: int\n    number of clusters for clustering the features\n\nclustering_method: str\n    clustering method: currently 'kmeans', 'gmm'\n\ncluster_scaling: str\n    scaling method for clustering: currently 'standard', 'robust', 'minmax'\n\ndegree: int\n    degree of features interactions to include in the model\n\nweights_distr: str\n    distribution of weights for constructing the model's hidden layer;\n    either 'uniform' or 'gaussian'\n
\n\n

Examples:

\n\n
\n
import subprocess\nimport sys\nimport os\n\nsubprocess.check_call([sys.executable, "-m", "pip", "install", "matplotlib"])\n\nimport mlsauce as ms\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.datasets import load_diabetes\nfrom sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\nfrom sklearn.tree import DecisionTreeRegressor\nfrom time import time\nfrom os import chdir\nfrom sklearn import metrics\n\nregr = DecisionTreeRegressor()\n\ndiabetes = load_diabetes()\nX = diabetes.data\ny = diabetes.target\n# split data into training test and test set\nnp.random.seed(15029)\nX_train, X_test, y_train, y_test = train_test_split(X, y,\n                                                    test_size=0.2)\n\nobj = ms.GenericBoostingRegressor(regr, col_sample=0.9, row_sample=0.9)\nprint(obj.get_params())\nstart = time()\nobj.fit(X_train, y_train)\nprint(time()-start)\nstart = time()\nprint(np.sqrt(np.mean(np.square(obj.predict(X_test) - y_test))))\nprint(time()-start)\n\nprint(obj.obj['loss'])\n\nobj = ms.GenericBoostingRegressor(regr, col_sample=0.9, row_sample=0.9, n_clusters=2)\nprint(obj.get_params())\nstart = time()\nobj.fit(X_train, y_train)\nprint(time()-start)\nstart = time()\nprint(np.sqrt(np.mean(np.square(obj.predict(X_test) - y_test))))\nprint(time()-start)\n\nprint(obj.obj['loss'])\n
\n
\n
\n", "bases": "sklearn.base.BaseEstimator, sklearn.base.RegressorMixin"}, "mlsauce.booster.LSBoostRegressor.__init__": {"fullname": "mlsauce.booster.LSBoostRegressor.__init__", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.__init__", "kind": "function", "doc": "

\n", "signature": "(\tn_estimators=100,\tlearning_rate=0.1,\tn_hidden_features=5,\treg_lambda=0.1,\talpha=0.5,\trow_sample=1,\tcol_sample=1,\tdropout=0,\ttolerance=0.0001,\tdirect_link=1,\tverbose=1,\tseed=123,\tbackend='cpu',\tsolver='ridge',\tactivation='relu',\ttype_pi=None,\treplications=None,\tkernel=None,\tn_clusters=0,\tclustering_method='kmeans',\tcluster_scaling='standard',\tdegree=None,\tweights_distr='uniform',\tbase_model=None)"}, "mlsauce.booster.LSBoostRegressor.base_model": {"fullname": "mlsauce.booster.LSBoostRegressor.base_model", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.base_model", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostRegressor.n_estimators": {"fullname": "mlsauce.booster.LSBoostRegressor.n_estimators", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.n_estimators", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostRegressor.learning_rate": {"fullname": "mlsauce.booster.LSBoostRegressor.learning_rate", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.learning_rate", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostRegressor.n_hidden_features": {"fullname": "mlsauce.booster.LSBoostRegressor.n_hidden_features", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.n_hidden_features", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostRegressor.reg_lambda": {"fullname": "mlsauce.booster.LSBoostRegressor.reg_lambda", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.reg_lambda", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostRegressor.alpha": {"fullname": "mlsauce.booster.LSBoostRegressor.alpha", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.alpha", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostRegressor.row_sample": {"fullname": "mlsauce.booster.LSBoostRegressor.row_sample", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.row_sample", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostRegressor.col_sample": {"fullname": "mlsauce.booster.LSBoostRegressor.col_sample", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.col_sample", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostRegressor.dropout": {"fullname": "mlsauce.booster.LSBoostRegressor.dropout", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.dropout", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostRegressor.tolerance": {"fullname": "mlsauce.booster.LSBoostRegressor.tolerance", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.tolerance", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostRegressor.direct_link": {"fullname": "mlsauce.booster.LSBoostRegressor.direct_link", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.direct_link", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostRegressor.verbose": {"fullname": "mlsauce.booster.LSBoostRegressor.verbose", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.verbose", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostRegressor.seed": {"fullname": "mlsauce.booster.LSBoostRegressor.seed", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.seed", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostRegressor.backend": {"fullname": "mlsauce.booster.LSBoostRegressor.backend", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.backend", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostRegressor.obj": {"fullname": "mlsauce.booster.LSBoostRegressor.obj", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.obj", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostRegressor.solver": {"fullname": "mlsauce.booster.LSBoostRegressor.solver", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.solver", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostRegressor.activation": {"fullname": "mlsauce.booster.LSBoostRegressor.activation", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.activation", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostRegressor.type_pi": {"fullname": "mlsauce.booster.LSBoostRegressor.type_pi", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.type_pi", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostRegressor.replications": {"fullname": "mlsauce.booster.LSBoostRegressor.replications", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.replications", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostRegressor.kernel": {"fullname": "mlsauce.booster.LSBoostRegressor.kernel", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.kernel", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostRegressor.n_clusters": {"fullname": "mlsauce.booster.LSBoostRegressor.n_clusters", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.n_clusters", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostRegressor.clustering_method": {"fullname": "mlsauce.booster.LSBoostRegressor.clustering_method", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.clustering_method", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostRegressor.cluster_scaling": {"fullname": "mlsauce.booster.LSBoostRegressor.cluster_scaling", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.cluster_scaling", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostRegressor.degree": {"fullname": "mlsauce.booster.LSBoostRegressor.degree", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.degree", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostRegressor.poly_": {"fullname": "mlsauce.booster.LSBoostRegressor.poly_", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.poly_", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostRegressor.weights_distr": {"fullname": "mlsauce.booster.LSBoostRegressor.weights_distr", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.weights_distr", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostRegressor.fit": {"fullname": "mlsauce.booster.LSBoostRegressor.fit", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.fit", "kind": "function", "doc": "

Fit Booster (regressor) to training data (X, y)

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n   Target values.\n\n**kwargs: additional parameters to be passed to self.cook_training_set.\n
\n\n

Returns:

\n\n
self: object.\n
\n", "signature": "(self, X, y, **kwargs):", "funcdef": "def"}, "mlsauce.booster.LSBoostRegressor.predict": {"fullname": "mlsauce.booster.LSBoostRegressor.predict", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.predict", "kind": "function", "doc": "

Predict probabilities for test data X.

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\nlevel: int\n    Level of confidence (default = 95)\n\nmethod: str\n    `None`, or 'splitconformal', 'localconformal'\n    prediction (if you specify `return_pi = True`)\n\n**kwargs: additional parameters to be passed to\n    self.cook_test_set\n
\n\n

Returns:

\n\n
probability estimates for test data: {array-like}\n
\n", "signature": "(self, X, level=95, method=None, **kwargs):", "funcdef": "def"}, "mlsauce.booster.LSBoostRegressor.set_predict_request": {"fullname": "mlsauce.booster.LSBoostRegressor.set_predict_request", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.set_predict_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.booster.LSBoostRegressor.set_score_request": {"fullname": "mlsauce.booster.LSBoostRegressor.set_score_request", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.booster.GenericBoostingClassifier": {"fullname": "mlsauce.booster.GenericBoostingClassifier", "modulename": "mlsauce.booster", "qualname": "GenericBoostingClassifier", "kind": "class", "doc": "

Generic Boosting classifier (using any classifier as base learner).

\n\n

Attributes:

\n\n
obj: object\n    model object.\n\nn_estimators: int\n    number of boosting iterations.\n\nlearning_rate: float\n    controls the learning speed at training time.\n\nn_hidden_features: int\n    number of nodes in successive hidden layers.\n\nreg_lambda: float\n    L2 regularization parameter for successive errors in the optimizer\n    (at training time).\n\nalpha: float\n    compromise between L1 and L2 regularization (must be in [0, 1]),\n    for `solver` == 'enet'.\n\nrow_sample: float\n    percentage of rows chosen from the training set.\n\ncol_sample: float\n    percentage of columns chosen from the training set.\n\ndropout: float\n    percentage of nodes dropped from the training set.\n\ntolerance: float\n    controls early stopping in gradient descent (at training time).\n\ndirect_link: bool\n    indicates whether the original features are included (True) in model's\n    fitting or not (False).\n\nverbose: int\n    progress bar (yes = 1) or not (no = 0) (currently).\n\nseed: int\n    reproducibility seed for nodes_sim=='uniform', clustering and dropout.\n\nbackend: str\n    type of backend; must be in ('cpu', 'gpu', 'tpu')\n\nsolver: str\n    type of 'weak' learner; currently in ('ridge', 'lasso', 'enet').\n    'enet' is a combination of 'ridge' and 'lasso' called Elastic Net.\n\nactivation: str\n    activation function: currently 'relu', 'relu6', 'sigmoid', 'tanh'\n\nn_clusters: int\n    number of clusters for clustering the features\n\nclustering_method: str\n    clustering method: currently 'kmeans', 'gmm'\n\ncluster_scaling: str\n    scaling method for clustering: currently 'standard', 'robust', 'minmax'\n\ndegree: int\n    degree of features interactions to include in the model\n\nweights_distr: str\n    distribution of weights for constructing the model's hidden layer;\n    currently 'uniform', 'gaussian'\n
\n", "bases": "mlsauce.booster._booster_classifier.LSBoostClassifier"}, "mlsauce.booster.GenericBoostingClassifier.__init__": {"fullname": "mlsauce.booster.GenericBoostingClassifier.__init__", "modulename": "mlsauce.booster", "qualname": "GenericBoostingClassifier.__init__", "kind": "function", "doc": "

\n", "signature": "(\tobj,\tn_estimators=100,\tlearning_rate=0.1,\tn_hidden_features=5,\treg_lambda=0.1,\talpha=0.5,\trow_sample=1,\tcol_sample=1,\tdropout=0,\ttolerance=0.0001,\tdirect_link=1,\tverbose=1,\tseed=123,\tbackend='cpu',\tsolver='ridge',\tactivation='relu',\tn_clusters=0,\tclustering_method='kmeans',\tcluster_scaling='standard',\tdegree=None,\tweights_distr='uniform')"}, "mlsauce.booster.GenericBoostingClassifier.base_model": {"fullname": "mlsauce.booster.GenericBoostingClassifier.base_model", "modulename": "mlsauce.booster", "qualname": "GenericBoostingClassifier.base_model", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.GenericBoostingClassifier.set_score_request": {"fullname": "mlsauce.booster.GenericBoostingClassifier.set_score_request", "modulename": "mlsauce.booster", "qualname": "GenericBoostingClassifier.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.booster.GenericBoostingRegressor": {"fullname": "mlsauce.booster.GenericBoostingRegressor", "modulename": "mlsauce.booster", "qualname": "GenericBoostingRegressor", "kind": "class", "doc": "

LSBoost regressor.

\n\n

Attributes:

\n\n
n_estimators: int\n    number of boosting iterations.\n\nlearning_rate: float\n    controls the learning speed at training time.\n\nn_hidden_features: int\n    number of nodes in successive hidden layers.\n\nreg_lambda: float\n    L2 regularization parameter for successive errors in the optimizer\n    (at training time).\n\nalpha: float\n    compromise between L1 and L2 regularization (must be in [0, 1]),\n    for `solver` == 'enet'\n\nrow_sample: float\n    percentage of rows chosen from the training set.\n\ncol_sample: float\n    percentage of columns chosen from the training set.\n\ndropout: float\n    percentage of nodes dropped from the training set.\n\ntolerance: float\n    controls early stopping in gradient descent (at training time).\n\ndirect_link: bool\n    indicates whether the original features are included (True) in model's\n    fitting or not (False).\n\nverbose: int\n    progress bar (yes = 1) or not (no = 0) (currently).\n\nseed: int\n    reproducibility seed for nodes_sim=='uniform', clustering and dropout.\n\nbackend: str\n    type of backend; must be in ('cpu', 'gpu', 'tpu')\n\nsolver: str\n    type of 'weak' learner; currently in ('ridge', 'lasso')\n\nactivation: str\n    activation function: currently 'relu', 'relu6', 'sigmoid', 'tanh'\n\ntype_pi: str.\n    type of prediction interval; currently \"kde\" (default) or \"bootstrap\".\n    Used only in `self.predict`, for `self.replications` > 0 and `self.kernel`\n    in ('gaussian', 'tophat'). Default is `None`.\n\nreplications: int.\n    number of replications (if needed) for predictive simulation.\n    Used only in `self.predict`, for `self.kernel` in ('gaussian',\n    'tophat') and `self.type_pi = 'kde'`. Default is `None`.\n\nn_clusters: int\n    number of clusters for clustering the features\n\nclustering_method: str\n    clustering method: currently 'kmeans', 'gmm'\n\ncluster_scaling: str\n    scaling method for clustering: currently 'standard', 'robust', 'minmax'\n\ndegree: int\n    degree of features interactions to include in the model\n\nweights_distr: str\n    distribution of weights for constructing the model's hidden layer;\n    either 'uniform' or 'gaussian'\n
\n", "bases": "mlsauce.booster._booster_regressor.LSBoostRegressor"}, "mlsauce.booster.GenericBoostingRegressor.__init__": {"fullname": "mlsauce.booster.GenericBoostingRegressor.__init__", "modulename": "mlsauce.booster", "qualname": "GenericBoostingRegressor.__init__", "kind": "function", "doc": "

\n", "signature": "(\tobj,\tn_estimators=100,\tlearning_rate=0.1,\tn_hidden_features=5,\treg_lambda=0.1,\talpha=0.5,\trow_sample=1,\tcol_sample=1,\tdropout=0,\ttolerance=0.0001,\tdirect_link=1,\tverbose=1,\tseed=123,\tbackend='cpu',\tsolver='ridge',\tactivation='relu',\ttype_pi=None,\treplications=None,\tkernel=None,\tn_clusters=0,\tclustering_method='kmeans',\tcluster_scaling='standard',\tdegree=None,\tweights_distr='uniform')"}, "mlsauce.booster.GenericBoostingRegressor.base_model": {"fullname": "mlsauce.booster.GenericBoostingRegressor.base_model", "modulename": "mlsauce.booster", "qualname": "GenericBoostingRegressor.base_model", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.GenericBoostingRegressor.set_predict_request": {"fullname": "mlsauce.booster.GenericBoostingRegressor.set_predict_request", "modulename": "mlsauce.booster", "qualname": "GenericBoostingRegressor.set_predict_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.booster.GenericBoostingRegressor.set_score_request": {"fullname": "mlsauce.booster.GenericBoostingRegressor.set_score_request", "modulename": "mlsauce.booster", "qualname": "GenericBoostingRegressor.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.datasets": {"fullname": "mlsauce.datasets", "modulename": "mlsauce.datasets", "kind": "module", "doc": "

\n"}, "mlsauce.datasets.dowload": {"fullname": "mlsauce.datasets.dowload", "modulename": "mlsauce.datasets.dowload", "kind": "module", "doc": "

\n"}, "mlsauce.datasets.dowload.download": {"fullname": "mlsauce.datasets.dowload.download", "modulename": "mlsauce.datasets.dowload", "qualname": "download", "kind": "function", "doc": "

\n", "signature": "(\tpkgname='MASS',\tdataset='Boston',\tsource='https://cran.r-universe.dev/',\t**kwargs):", "funcdef": "def"}, "mlsauce.demo": {"fullname": "mlsauce.demo", "modulename": "mlsauce.demo", "kind": "module", "doc": "

\n"}, "mlsauce.elasticnet": {"fullname": "mlsauce.elasticnet", "modulename": "mlsauce.elasticnet", "kind": "module", "doc": "

\n"}, "mlsauce.elasticnet.ElasticNetRegressor": {"fullname": "mlsauce.elasticnet.ElasticNetRegressor", "modulename": "mlsauce.elasticnet", "qualname": "ElasticNetRegressor", "kind": "class", "doc": "

Elasticnet.

\n\n

Attributes:

\n\n
reg_lambda: float\n    regularization parameter.\n\nalpha: float\n    compromise between L1 and L2 regularization (must be in [0, 1]),\n    for `solver` == 'enet'.\n\nbackend: str\n    type of backend; must be in ('cpu', 'gpu', 'tpu')\n
\n", "bases": "sklearn.base.BaseEstimator, sklearn.base.RegressorMixin"}, "mlsauce.elasticnet.ElasticNetRegressor.__init__": {"fullname": "mlsauce.elasticnet.ElasticNetRegressor.__init__", "modulename": "mlsauce.elasticnet", "qualname": "ElasticNetRegressor.__init__", "kind": "function", "doc": "

\n", "signature": "(reg_lambda=0.1, alpha=0.5, backend='cpu')"}, "mlsauce.elasticnet.ElasticNetRegressor.reg_lambda": {"fullname": "mlsauce.elasticnet.ElasticNetRegressor.reg_lambda", "modulename": "mlsauce.elasticnet", "qualname": "ElasticNetRegressor.reg_lambda", "kind": "variable", "doc": "

\n"}, "mlsauce.elasticnet.ElasticNetRegressor.alpha": {"fullname": "mlsauce.elasticnet.ElasticNetRegressor.alpha", "modulename": "mlsauce.elasticnet", "qualname": "ElasticNetRegressor.alpha", "kind": "variable", "doc": "

\n"}, "mlsauce.elasticnet.ElasticNetRegressor.backend": {"fullname": "mlsauce.elasticnet.ElasticNetRegressor.backend", "modulename": "mlsauce.elasticnet", "qualname": "ElasticNetRegressor.backend", "kind": "variable", "doc": "

\n"}, "mlsauce.elasticnet.ElasticNetRegressor.fit": {"fullname": "mlsauce.elasticnet.ElasticNetRegressor.fit", "modulename": "mlsauce.elasticnet", "qualname": "ElasticNetRegressor.fit", "kind": "function", "doc": "

Fit matrixops (classifier) to training data (X, y)

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n    Target values.\n\n**kwargs: additional parameters to be passed to self.cook_training_set.\n
\n\n

Returns:

\n\n
self: object.\n
\n", "signature": "(self, X, y, **kwargs):", "funcdef": "def"}, "mlsauce.elasticnet.ElasticNetRegressor.predict": {"fullname": "mlsauce.elasticnet.ElasticNetRegressor.predict", "modulename": "mlsauce.elasticnet", "qualname": "ElasticNetRegressor.predict", "kind": "function", "doc": "

Predict test data X.

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to `predict_proba`\n
\n\n

Returns:

\n\n
model predictions: {array-like}\n
\n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.elasticnet.ElasticNetRegressor.set_score_request": {"fullname": "mlsauce.elasticnet.ElasticNetRegressor.set_score_request", "modulename": "mlsauce.elasticnet", "qualname": "ElasticNetRegressor.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.lasso": {"fullname": "mlsauce.lasso", "modulename": "mlsauce.lasso", "kind": "module", "doc": "

\n"}, "mlsauce.lasso.LassoRegressor": {"fullname": "mlsauce.lasso.LassoRegressor", "modulename": "mlsauce.lasso", "qualname": "LassoRegressor", "kind": "class", "doc": "

Lasso.

\n\n

Attributes:

\n\n
reg_lambda: float\n    L1 regularization parameter.\n\nmax_iter: int\n    number of iterations of lasso shooting algorithm.\n\ntol: float\n    tolerance for convergence of lasso shooting algorithm.\n\nbackend: str\n    type of backend; must be in ('cpu', 'gpu', 'tpu').\n
\n", "bases": "sklearn.base.BaseEstimator, sklearn.base.RegressorMixin"}, "mlsauce.lasso.LassoRegressor.__init__": {"fullname": "mlsauce.lasso.LassoRegressor.__init__", "modulename": "mlsauce.lasso", "qualname": "LassoRegressor.__init__", "kind": "function", "doc": "

\n", "signature": "(reg_lambda=0.1, max_iter=10, tol=0.001, backend='cpu')"}, "mlsauce.lasso.LassoRegressor.reg_lambda": {"fullname": "mlsauce.lasso.LassoRegressor.reg_lambda", "modulename": "mlsauce.lasso", "qualname": "LassoRegressor.reg_lambda", "kind": "variable", "doc": "

\n"}, "mlsauce.lasso.LassoRegressor.max_iter": {"fullname": "mlsauce.lasso.LassoRegressor.max_iter", "modulename": "mlsauce.lasso", "qualname": "LassoRegressor.max_iter", "kind": "variable", "doc": "

\n"}, "mlsauce.lasso.LassoRegressor.tol": {"fullname": "mlsauce.lasso.LassoRegressor.tol", "modulename": "mlsauce.lasso", "qualname": "LassoRegressor.tol", "kind": "variable", "doc": "

\n"}, "mlsauce.lasso.LassoRegressor.backend": {"fullname": "mlsauce.lasso.LassoRegressor.backend", "modulename": "mlsauce.lasso", "qualname": "LassoRegressor.backend", "kind": "variable", "doc": "

\n"}, "mlsauce.lasso.LassoRegressor.fit": {"fullname": "mlsauce.lasso.LassoRegressor.fit", "modulename": "mlsauce.lasso", "qualname": "LassoRegressor.fit", "kind": "function", "doc": "

Fit matrixops (classifier) to training data (X, y)

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n    Target values.\n\n**kwargs: additional parameters to be passed to self.cook_training_set.\n
\n\n

Returns:

\n\n
self: object.\n
\n", "signature": "(self, X, y, **kwargs):", "funcdef": "def"}, "mlsauce.lasso.LassoRegressor.predict": {"fullname": "mlsauce.lasso.LassoRegressor.predict", "modulename": "mlsauce.lasso", "qualname": "LassoRegressor.predict", "kind": "function", "doc": "

Predict test data X.

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to `predict_proba`\n
\n\n

Returns:

\n\n
model predictions: {array-like}\n
\n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.lasso.LassoRegressor.set_score_request": {"fullname": "mlsauce.lasso.LassoRegressor.set_score_request", "modulename": "mlsauce.lasso", "qualname": "LassoRegressor.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.lazybooster": {"fullname": "mlsauce.lazybooster", "modulename": "mlsauce.lazybooster", "kind": "module", "doc": "

\n"}, "mlsauce.lazybooster.LazyBoostingClassifier": {"fullname": "mlsauce.lazybooster.LazyBoostingClassifier", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingClassifier", "kind": "class", "doc": "

Fitting -- almost -- all the classification algorithms\n and returning their scores.

\n\n
Parameters:\n\n    verbose: int, optional (default=0)\n        Any positive number for verbosity.\n\n    ignore_warnings: bool, optional (default=True)\n        When set to True, the warning related to algorigms that are not\n        able to run are ignored.\n\n    custom_metric: function, optional (default=None)\n        When function is provided, models are evaluated based on the custom\n          evaluation metric provided.\n\n    predictions: bool, optional (default=False)\n        When set to True, the predictions of all the models models are\n        returned as data frame.\n\n    sort_by: string, optional (default='Accuracy')\n        Sort models by a metric. Available options are 'Accuracy',\n        'Balanced Accuracy', 'ROC AUC', 'F1 Score' or a custom metric\n        identified by its name and provided by custom_metric.\n\n    random_state: int, optional (default=42)\n        Reproducibiility seed.\n\n    estimators: list, optional (default='all')\n        list of Estimators names or just 'all' for > 90 classifiers\n        (default='all')\n\n    preprocess: bool, preprocessing is done when set to True\n\n    n_jobs: int, when possible, run in parallel\n        For now, only used by individual models that support it.\n\n    n_layers: int, optional (default=3)\n        Number of layers of GenericBoostingClassifiers to be used.\n\n    All the other parameters are the same as GenericBoostingClassifier's.\n\nAttributes:\n\n    models_: dict-object\n        Returns a dictionary with each model pipeline as value\n        with key as name of models.\n\n    best_model_: object\n        Returns the best model pipeline.\n\nExamples\n\n\n    
\n
        import os\n        import mlsauce as ms\n        from sklearn.datasets import load_breast_cancer, load_iris, load_wine, load_digits\n        from sklearn.model_selection import train_test_split\n        from time import time\n\n        load_models = [load_breast_cancer, load_iris, load_wine]\n\n        for model in load_models:\n\n            data = model()\n            X = data.data\n            y= data.target\n\n            X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .2, random_state = 13)\n\n            clf = ms.LazyBoostingClassifier(verbose=1, ignore_warnings=False,\n                                            custom_metric=None, preprocess=False)\n\n            start = time()\n            models, predictioms = clf.fit(X_train, X_test, y_train, y_test)\n            print(f"\nElapsed: {time() - start} seconds\n")\n\n            print(models)\n
\n
\n
\n", "bases": "sklearn.base.ClassifierMixin"}, "mlsauce.lazybooster.LazyBoostingClassifier.__init__": {"fullname": "mlsauce.lazybooster.LazyBoostingClassifier.__init__", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingClassifier.__init__", "kind": "function", "doc": "

\n", "signature": "(\tverbose=0,\tignore_warnings=True,\tcustom_metric=None,\tpredictions=False,\tsort_by='Accuracy',\trandom_state=42,\testimators='all',\tpreprocess=False,\tn_jobs=None)"}, "mlsauce.lazybooster.LazyBoostingClassifier.verbose": {"fullname": "mlsauce.lazybooster.LazyBoostingClassifier.verbose", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingClassifier.verbose", "kind": "variable", "doc": "

\n"}, "mlsauce.lazybooster.LazyBoostingClassifier.ignore_warnings": {"fullname": "mlsauce.lazybooster.LazyBoostingClassifier.ignore_warnings", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingClassifier.ignore_warnings", "kind": "variable", "doc": "

\n"}, "mlsauce.lazybooster.LazyBoostingClassifier.custom_metric": {"fullname": "mlsauce.lazybooster.LazyBoostingClassifier.custom_metric", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingClassifier.custom_metric", "kind": "variable", "doc": "

\n"}, "mlsauce.lazybooster.LazyBoostingClassifier.predictions": {"fullname": "mlsauce.lazybooster.LazyBoostingClassifier.predictions", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingClassifier.predictions", "kind": "variable", "doc": "

\n"}, "mlsauce.lazybooster.LazyBoostingClassifier.sort_by": {"fullname": "mlsauce.lazybooster.LazyBoostingClassifier.sort_by", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingClassifier.sort_by", "kind": "variable", "doc": "

\n"}, "mlsauce.lazybooster.LazyBoostingClassifier.models_": {"fullname": "mlsauce.lazybooster.LazyBoostingClassifier.models_", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingClassifier.models_", "kind": "variable", "doc": "

\n"}, "mlsauce.lazybooster.LazyBoostingClassifier.best_model_": {"fullname": "mlsauce.lazybooster.LazyBoostingClassifier.best_model_", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingClassifier.best_model_", "kind": "variable", "doc": "

\n"}, "mlsauce.lazybooster.LazyBoostingClassifier.random_state": {"fullname": "mlsauce.lazybooster.LazyBoostingClassifier.random_state", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingClassifier.random_state", "kind": "variable", "doc": "

\n"}, "mlsauce.lazybooster.LazyBoostingClassifier.estimators": {"fullname": "mlsauce.lazybooster.LazyBoostingClassifier.estimators", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingClassifier.estimators", "kind": "variable", "doc": "

\n"}, "mlsauce.lazybooster.LazyBoostingClassifier.preprocess": {"fullname": "mlsauce.lazybooster.LazyBoostingClassifier.preprocess", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingClassifier.preprocess", "kind": "variable", "doc": "

\n"}, "mlsauce.lazybooster.LazyBoostingClassifier.n_jobs": {"fullname": "mlsauce.lazybooster.LazyBoostingClassifier.n_jobs", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingClassifier.n_jobs", "kind": "variable", "doc": "

\n"}, "mlsauce.lazybooster.LazyBoostingClassifier.fit": {"fullname": "mlsauce.lazybooster.LazyBoostingClassifier.fit", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingClassifier.fit", "kind": "function", "doc": "

Fit classifiers to X_train and y_train, predict and score on X_test,\ny_test.

\n\n

Parameters:

\n\n
X_train: array-like,\n    Training vectors, where rows is the number of samples\n    and columns is the number of features.\n\nX_test: array-like,\n    Testing vectors, where rows is the number of samples\n    and columns is the number of features.\n\ny_train: array-like,\n    Training vectors, where rows is the number of samples\n    and columns is the number of features.\n\ny_test: array-like,\n    Testing vectors, where rows is the number of samples\n    and columns is the number of features.\n\n**kwargs: dict,\n    Additional arguments to be passed to the fit GenericBoostingClassifier.\n
\n\n

Returns:

\n\n
scores: Pandas DataFrame\n    Returns metrics of all the models in a Pandas DataFrame.\n\npredictions: Pandas DataFrame\n    Returns predictions of all the models in a Pandas DataFrame.\n
\n", "signature": "(self, X_train, X_test, y_train, y_test, **kwargs):", "funcdef": "def"}, "mlsauce.lazybooster.LazyBoostingClassifier.get_best_model": {"fullname": "mlsauce.lazybooster.LazyBoostingClassifier.get_best_model", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingClassifier.get_best_model", "kind": "function", "doc": "

This function returns the best model pipeline based on the sort_by metric.

\n\n

Returns:

\n\n
best_model: object,\n    Returns the best model pipeline based on the sort_by metric.\n
\n", "signature": "(self):", "funcdef": "def"}, "mlsauce.lazybooster.LazyBoostingClassifier.provide_models": {"fullname": "mlsauce.lazybooster.LazyBoostingClassifier.provide_models", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingClassifier.provide_models", "kind": "function", "doc": "

Returns all the model objects trained. If fit hasn't been called yet,\nthen it's called to return the models.

\n\n

Parameters:

\n\n

X_train: array-like,\n Training vectors, where rows is the number of samples\n and columns is the number of features.

\n\n

X_test: array-like,\n Testing vectors, where rows is the number of samples\n and columns is the number of features.

\n\n

y_train: array-like,\n Training vectors, where rows is the number of samples\n and columns is the number of features.

\n\n

y_test: array-like,\n Testing vectors, where rows is the number of samples\n and columns is the number of features.

\n\n

Returns:

\n\n
models: dict-object,\n    Returns a dictionary with each model's pipeline as value\n    and key = name of the model.\n
\n", "signature": "(self, X_train, X_test, y_train, y_test):", "funcdef": "def"}, "mlsauce.lazybooster.LazyBoostingRegressor": {"fullname": "mlsauce.lazybooster.LazyBoostingRegressor", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingRegressor", "kind": "class", "doc": "

Fitting -- almost -- all the regression algorithms\n and returning their scores.

\n\n

Parameters:

\n\n
verbose: int, optional (default=0)\n    Any positive number for verbosity.\n\nignore_warnings: bool, optional (default=True)\n    When set to True, the warning related to algorigms that are not able to run are ignored.\n\ncustom_metric: function, optional (default=None)\n    When function is provided, models are evaluated based on the custom evaluation metric provided.\n\npredictions: bool, optional (default=False)\n    When set to True, the predictions of all the models models are returned as dataframe.\n\nsort_by: string, optional (default='RMSE')\n    Sort models by a metric. Available options are 'R-Squared', 'Adjusted R-Squared', 'RMSE', 'Time Taken' and 'Custom Metric'.\n    or a custom metric identified by its name and provided by custom_metric.\n\nrandom_state: int, optional (default=42)\n    Reproducibiility seed.\n\nestimators: list, optional (default='all')\n    list of Estimators names or just 'all' (default='all')\n\npreprocess: bool\n    preprocessing is done when set to True\n\nn_jobs : int, when possible, run in parallel\n    For now, only used by individual models that support it.\n\nn_layers: int, optional (default=3)\n    Number of layers of CustomRegressors to be used.\n\nAll the other parameters are the same as CustomRegressor's.\n
\n\n

Attributes:

\n\n
models_: dict-object\n    Returns a dictionary with each model pipeline as value\n    with key as name of models.\n\nbest_model_: object\n    Returns the best model pipeline based on the sort_by metric.\n
\n\n

Examples:

\n\n
\n
import os\nimport mlsauce as ms\nfrom sklearn.datasets import load_diabetes\nfrom sklearn.model_selection import train_test_split\n\ndata = load_diabetes()\nX = data.data\ny= data.target\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .2, random_state = 123)\n\nregr = ms.LazyBoostingRegressor(verbose=0, ignore_warnings=True,\n                                custom_metric=None, preprocess=True)\nmodels, predictioms = regr.fit(X_train, X_test, y_train, y_test)\nmodel_dictionary = regr.provide_models(X_train, X_test, y_train, y_test)\nprint(models)\n
\n
\n
\n", "bases": "sklearn.base.RegressorMixin"}, "mlsauce.lazybooster.LazyBoostingRegressor.__init__": {"fullname": "mlsauce.lazybooster.LazyBoostingRegressor.__init__", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingRegressor.__init__", "kind": "function", "doc": "

\n", "signature": "(\tverbose=0,\tignore_warnings=True,\tcustom_metric=None,\tpredictions=False,\tsort_by='RMSE',\trandom_state=42,\testimators='all',\tpreprocess=False,\tn_jobs=None)"}, "mlsauce.lazybooster.LazyBoostingRegressor.verbose": {"fullname": "mlsauce.lazybooster.LazyBoostingRegressor.verbose", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingRegressor.verbose", "kind": "variable", "doc": "

\n"}, "mlsauce.lazybooster.LazyBoostingRegressor.ignore_warnings": {"fullname": "mlsauce.lazybooster.LazyBoostingRegressor.ignore_warnings", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingRegressor.ignore_warnings", "kind": "variable", "doc": "

\n"}, "mlsauce.lazybooster.LazyBoostingRegressor.custom_metric": {"fullname": "mlsauce.lazybooster.LazyBoostingRegressor.custom_metric", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingRegressor.custom_metric", "kind": "variable", "doc": "

\n"}, "mlsauce.lazybooster.LazyBoostingRegressor.predictions": {"fullname": "mlsauce.lazybooster.LazyBoostingRegressor.predictions", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingRegressor.predictions", "kind": "variable", "doc": "

\n"}, "mlsauce.lazybooster.LazyBoostingRegressor.sort_by": {"fullname": "mlsauce.lazybooster.LazyBoostingRegressor.sort_by", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingRegressor.sort_by", "kind": "variable", "doc": "

\n"}, "mlsauce.lazybooster.LazyBoostingRegressor.models_": {"fullname": "mlsauce.lazybooster.LazyBoostingRegressor.models_", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingRegressor.models_", "kind": "variable", "doc": "

\n"}, "mlsauce.lazybooster.LazyBoostingRegressor.best_model_": {"fullname": "mlsauce.lazybooster.LazyBoostingRegressor.best_model_", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingRegressor.best_model_", "kind": "variable", "doc": "

\n"}, "mlsauce.lazybooster.LazyBoostingRegressor.random_state": {"fullname": "mlsauce.lazybooster.LazyBoostingRegressor.random_state", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingRegressor.random_state", "kind": "variable", "doc": "

\n"}, "mlsauce.lazybooster.LazyBoostingRegressor.estimators": {"fullname": "mlsauce.lazybooster.LazyBoostingRegressor.estimators", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingRegressor.estimators", "kind": "variable", "doc": "

\n"}, "mlsauce.lazybooster.LazyBoostingRegressor.preprocess": {"fullname": "mlsauce.lazybooster.LazyBoostingRegressor.preprocess", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingRegressor.preprocess", "kind": "variable", "doc": "

\n"}, "mlsauce.lazybooster.LazyBoostingRegressor.n_jobs": {"fullname": "mlsauce.lazybooster.LazyBoostingRegressor.n_jobs", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingRegressor.n_jobs", "kind": "variable", "doc": "

\n"}, "mlsauce.lazybooster.LazyBoostingRegressor.fit": {"fullname": "mlsauce.lazybooster.LazyBoostingRegressor.fit", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingRegressor.fit", "kind": "function", "doc": "

Fit Regression algorithms to X_train and y_train, predict and score on X_test, y_test.

\n\n

Parameters:

\n\n
X_train : array-like,\n    Training vectors, where rows is the number of samples\n    and columns is the number of features.\n\nX_test : array-like,\n    Testing vectors, where rows is the number of samples\n    and columns is the number of features.\n\ny_train : array-like,\n    Training vectors, where rows is the number of samples\n    and columns is the number of features.\n\ny_test : array-like,\n    Testing vectors, where rows is the number of samples\n    and columns is the number of features.\n\n**kwargs: dict,\n    Additional parameters to be passed to the GenericBoostingRegressor.\n
\n\n

Returns:

\n\n

scores: Pandas DataFrame\n Returns metrics of all the models in a Pandas DataFrame.

\n\n

predictions : Pandas DataFrame\n Returns predictions of all the models in a Pandas DataFrame.

\n", "signature": "(self, X_train, X_test, y_train, y_test, **kwargs):", "funcdef": "def"}, "mlsauce.lazybooster.LazyBoostingRegressor.get_best_model": {"fullname": "mlsauce.lazybooster.LazyBoostingRegressor.get_best_model", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingRegressor.get_best_model", "kind": "function", "doc": "

This function returns the best model pipeline based on the sort_by metric.

\n\n

Returns:

\n\n
best_model: object,\n    Returns the best model pipeline based on the sort_by metric.\n
\n", "signature": "(self):", "funcdef": "def"}, "mlsauce.lazybooster.LazyBoostingRegressor.provide_models": {"fullname": "mlsauce.lazybooster.LazyBoostingRegressor.provide_models", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingRegressor.provide_models", "kind": "function", "doc": "

This function returns all the model objects trained in fit function.\nIf fit is not called already, then we call fit and then return the models.

\n\n

Parameters:

\n\n
X_train : array-like,\n    Training vectors, where rows is the number of samples\n    and columns is the number of features.\n\nX_test : array-like,\n    Testing vectors, where rows is the number of samples\n    and columns is the number of features.\n\ny_train : array-like,\n    Training vectors, where rows is the number of samples\n    and columns is the number of features.\n\ny_test : array-like,\n    Testing vectors, where rows is the number of samples\n    and columns is the number of features.\n
\n\n

Returns:

\n\n
models: dict-object,\n    Returns a dictionary with each model pipeline as value\n    with key as name of models.\n
\n", "signature": "(self, X_train, X_test, y_train, y_test):", "funcdef": "def"}, "mlsauce.nonconformist": {"fullname": "mlsauce.nonconformist", "modulename": "mlsauce.nonconformist", "kind": "module", "doc": "

docstring

\n"}, "mlsauce.nonconformist.AbsErrorErrFunc": {"fullname": "mlsauce.nonconformist.AbsErrorErrFunc", "modulename": "mlsauce.nonconformist", "qualname": "AbsErrorErrFunc", "kind": "class", "doc": "

Calculates absolute error nonconformity for regression problems.

\n\n

For each correct output in y, nonconformity is defined as

\n\n

$$| y_i - \\hat{y}_i |$$

\n", "bases": "mlsauce.nonconformist.nc.RegressionErrFunc"}, "mlsauce.nonconformist.AbsErrorErrFunc.apply": {"fullname": "mlsauce.nonconformist.AbsErrorErrFunc.apply", "modulename": "mlsauce.nonconformist", "qualname": "AbsErrorErrFunc.apply", "kind": "function", "doc": "

Apply the nonconformity function.

\n\n

Parameters

\n\n

prediction : numpy array of shape [n_samples, n_classes]\n Class probability estimates for each sample.

\n\n

y : numpy array of shape [n_samples]\n True output labels of each sample.

\n\n

Returns

\n\n

nc : numpy array of shape [n_samples]\n Nonconformity scores of the samples.

\n", "signature": "(self, prediction, y):", "funcdef": "def"}, "mlsauce.nonconformist.AbsErrorErrFunc.apply_inverse": {"fullname": "mlsauce.nonconformist.AbsErrorErrFunc.apply_inverse", "modulename": "mlsauce.nonconformist", "qualname": "AbsErrorErrFunc.apply_inverse", "kind": "function", "doc": "

Apply the inverse of the nonconformity function (i.e.,\ncalculate prediction interval).

\n\n

Parameters

\n\n

nc : numpy array of shape [n_calibration_samples]\n Nonconformity scores obtained for conformal predictor.

\n\n

significance : float\n Significance level (0, 1).

\n\n

Returns

\n\n

interval : numpy array of shape [n_samples, 2]\n Minimum and maximum interval boundaries for each prediction.

\n", "signature": "(self, nc, significance):", "funcdef": "def"}, "mlsauce.nonconformist.QuantileRegErrFunc": {"fullname": "mlsauce.nonconformist.QuantileRegErrFunc", "modulename": "mlsauce.nonconformist", "qualname": "QuantileRegErrFunc", "kind": "class", "doc": "

Calculates conformalized quantile regression error.

\n\n

For each correct output in y, nonconformity is defined as

\n\n

$$max{\\hat{q}_low - y, y - \\hat{q}_high}$$

\n", "bases": "mlsauce.nonconformist.nc.RegressionErrFunc"}, "mlsauce.nonconformist.QuantileRegErrFunc.apply": {"fullname": "mlsauce.nonconformist.QuantileRegErrFunc.apply", "modulename": "mlsauce.nonconformist", "qualname": "QuantileRegErrFunc.apply", "kind": "function", "doc": "

Apply the nonconformity function.

\n\n

Parameters

\n\n

prediction : numpy array of shape [n_samples, n_classes]\n Class probability estimates for each sample.

\n\n

y : numpy array of shape [n_samples]\n True output labels of each sample.

\n\n

Returns

\n\n

nc : numpy array of shape [n_samples]\n Nonconformity scores of the samples.

\n", "signature": "(self, prediction, y):", "funcdef": "def"}, "mlsauce.nonconformist.QuantileRegErrFunc.apply_inverse": {"fullname": "mlsauce.nonconformist.QuantileRegErrFunc.apply_inverse", "modulename": "mlsauce.nonconformist", "qualname": "QuantileRegErrFunc.apply_inverse", "kind": "function", "doc": "

Apply the inverse of the nonconformity function (i.e.,\ncalculate prediction interval).

\n\n

Parameters

\n\n

nc : numpy array of shape [n_calibration_samples]\n Nonconformity scores obtained for conformal predictor.

\n\n

significance : float\n Significance level (0, 1).

\n\n

Returns

\n\n

interval : numpy array of shape [n_samples, 2]\n Minimum and maximum interval boundaries for each prediction.

\n", "signature": "(self, nc, significance):", "funcdef": "def"}, "mlsauce.nonconformist.RegressorAdapter": {"fullname": "mlsauce.nonconformist.RegressorAdapter", "modulename": "mlsauce.nonconformist", "qualname": "RegressorAdapter", "kind": "class", "doc": "

Base class for all estimators in scikit-learn.

\n\n

Inheriting from this class provides default implementations of:

\n\n
    \n
  • setting and getting parameters used by GridSearchCV and friends;
    • \n
  • textual and HTML representation displayed in terminals and IDEs;
    • \n
  • estimator serialization;
    • \n
  • parameters validation;
    • \n
  • data validation;
    • \n
  • feature names validation.
    • \n
\n\n

Read more in the :ref:User Guide <rolling_your_own_estimator>.

\n\n

Notes

\n\n

All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

\n\n

Examples

\n\n
\n
>>> import numpy as np\n>>> from sklearn.base import BaseEstimator\n>>> class MyEstimator(BaseEstimator):\n...     def __init__(self, *, param=1):\n...         self.param = param\n...     def fit(self, X, y=None):\n...         self.is_fitted_ = True\n...         return self\n...     def predict(self, X):\n...         return np.full(shape=X.shape[0], fill_value=self.param)\n>>> estimator = MyEstimator(param=2)\n>>> estimator.get_params()\n{'param': 2}\n>>> X = np.array([[1, 2], [2, 3], [3, 4]])\n>>> y = np.array([1, 0, 1])\n>>> estimator.fit(X, y).predict(X)\narray([2, 2, 2])\n>>> estimator.set_params(param=3).fit(X, y).predict(X)\narray([3, 3, 3])\n
\n
\n", "bases": "mlsauce.nonconformist.base.BaseModelAdapter"}, "mlsauce.nonconformist.RegressorAdapter.__init__": {"fullname": "mlsauce.nonconformist.RegressorAdapter.__init__", "modulename": "mlsauce.nonconformist", "qualname": "RegressorAdapter.__init__", "kind": "function", "doc": "

\n", "signature": "(model, fit_params=None)"}, "mlsauce.nonconformist.RegressorAdapter.set_fit_request": {"fullname": "mlsauce.nonconformist.RegressorAdapter.set_fit_request", "modulename": "mlsauce.nonconformist", "qualname": "RegressorAdapter.set_fit_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.nonconformist.RegressorAdapter.set_predict_request": {"fullname": "mlsauce.nonconformist.RegressorAdapter.set_predict_request", "modulename": "mlsauce.nonconformist", "qualname": "RegressorAdapter.set_predict_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.nonconformist.RegressorNc": {"fullname": "mlsauce.nonconformist.RegressorNc", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNc", "kind": "class", "doc": "

Nonconformity scorer using an underlying regression model.

\n\n

Parameters

\n\n

model : RegressorAdapter\n Underlying regression model used for calculating nonconformity scores.

\n\n

err_func : RegressionErrFunc\n Error function object.

\n\n

normalizer : BaseScorer\n Normalization model.

\n\n

beta : float\n Normalization smoothing parameter. As the beta-value increases,\n the normalized nonconformity function approaches a non-normalized\n equivalent.

\n\n

Attributes

\n\n

model : RegressorAdapter\n Underlying model object.

\n\n

err_func : RegressionErrFunc\n Scorer function used to calculate nonconformity scores.

\n\n

See also

\n\n

ProbEstClassifierNc, NormalizedRegressorNc

\n", "bases": "mlsauce.nonconformist.nc.BaseModelNc"}, "mlsauce.nonconformist.RegressorNc.__init__": {"fullname": "mlsauce.nonconformist.RegressorNc.__init__", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNc.__init__", "kind": "function", "doc": "

\n", "signature": "(\tmodel,\terr_func=<mlsauce.nonconformist.nc.AbsErrorErrFunc object>,\tnormalizer=None,\tbeta=1e-06)"}, "mlsauce.nonconformist.RegressorNc.predict": {"fullname": "mlsauce.nonconformist.RegressorNc.predict", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNc.predict", "kind": "function", "doc": "

Constructs prediction intervals for a set of test examples.

\n\n

Predicts the output of each test pattern using the underlying model,\nand applies the (partial) inverse nonconformity function to each\nprediction, resulting in a prediction interval for each test pattern.

\n\n

Parameters

\n\n

x : numpy array of shape [n_samples, n_features]\n Inputs of patters for which to predict output values.

\n\n

significance : float\n Significance level (maximum allowed error rate) of predictions.\n Should be a float between 0 and 1. If None, then intervals for\n all significance levels (0.01, 0.02, ..., 0.99) are output in a\n 3d-matrix.

\n\n

Returns

\n\n

p : numpy array of shape [n_samples, 2] or [n_samples, 2, 99]\n If significance is None, then p contains the interval (minimum\n and maximum boundaries) for each test pattern, and each significance\n level (0.01, 0.02, ..., 0.99). If significance is a float between\n 0 and 1, then p contains the prediction intervals (minimum and\n maximum boundaries) for the set of test patterns at the chosen\n significance level.

\n", "signature": "(self, x, nc, significance=None):", "funcdef": "def"}, "mlsauce.nonconformist.RegressorNc.set_fit_request": {"fullname": "mlsauce.nonconformist.RegressorNc.set_fit_request", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNc.set_fit_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.nonconformist.RegressorNc.set_predict_request": {"fullname": "mlsauce.nonconformist.RegressorNc.set_predict_request", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNc.set_predict_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.nonconformist.RegressorNc.set_score_request": {"fullname": "mlsauce.nonconformist.RegressorNc.set_score_request", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNc.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.nonconformist.RegressorNormalizer": {"fullname": "mlsauce.nonconformist.RegressorNormalizer", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNormalizer", "kind": "class", "doc": "

Base class for all estimators in scikit-learn.

\n\n

Inheriting from this class provides default implementations of:

\n\n
    \n
  • setting and getting parameters used by GridSearchCV and friends;
    • \n
  • textual and HTML representation displayed in terminals and IDEs;
    • \n
  • estimator serialization;
    • \n
  • parameters validation;
    • \n
  • data validation;
    • \n
  • feature names validation.
    • \n
\n\n

Read more in the :ref:User Guide <rolling_your_own_estimator>.

\n\n

Notes

\n\n

All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

\n\n

Examples

\n\n
\n
>>> import numpy as np\n>>> from sklearn.base import BaseEstimator\n>>> class MyEstimator(BaseEstimator):\n...     def __init__(self, *, param=1):\n...         self.param = param\n...     def fit(self, X, y=None):\n...         self.is_fitted_ = True\n...         return self\n...     def predict(self, X):\n...         return np.full(shape=X.shape[0], fill_value=self.param)\n>>> estimator = MyEstimator(param=2)\n>>> estimator.get_params()\n{'param': 2}\n>>> X = np.array([[1, 2], [2, 3], [3, 4]])\n>>> y = np.array([1, 0, 1])\n>>> estimator.fit(X, y).predict(X)\narray([2, 2, 2])\n>>> estimator.set_params(param=3).fit(X, y).predict(X)\narray([3, 3, 3])\n
\n
\n", "bases": "mlsauce.nonconformist.nc.BaseScorer"}, "mlsauce.nonconformist.RegressorNormalizer.__init__": {"fullname": "mlsauce.nonconformist.RegressorNormalizer.__init__", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNormalizer.__init__", "kind": "function", "doc": "

\n", "signature": "(base_model, normalizer_model, err_func)"}, "mlsauce.nonconformist.RegressorNormalizer.base_model": {"fullname": "mlsauce.nonconformist.RegressorNormalizer.base_model", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNormalizer.base_model", "kind": "variable", "doc": "

\n"}, "mlsauce.nonconformist.RegressorNormalizer.normalizer_model": {"fullname": "mlsauce.nonconformist.RegressorNormalizer.normalizer_model", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNormalizer.normalizer_model", "kind": "variable", "doc": "

\n"}, "mlsauce.nonconformist.RegressorNormalizer.err_func": {"fullname": "mlsauce.nonconformist.RegressorNormalizer.err_func", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNormalizer.err_func", "kind": "variable", "doc": "

\n"}, "mlsauce.nonconformist.RegressorNormalizer.fit": {"fullname": "mlsauce.nonconformist.RegressorNormalizer.fit", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNormalizer.fit", "kind": "function", "doc": "

\n", "signature": "(self, x, y):", "funcdef": "def"}, "mlsauce.nonconformist.RegressorNormalizer.score": {"fullname": "mlsauce.nonconformist.RegressorNormalizer.score", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNormalizer.score", "kind": "function", "doc": "

\n", "signature": "(self, x, y=None):", "funcdef": "def"}, "mlsauce.nonconformist.RegressorNormalizer.set_fit_request": {"fullname": "mlsauce.nonconformist.RegressorNormalizer.set_fit_request", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNormalizer.set_fit_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.nonconformist.RegressorNormalizer.set_score_request": {"fullname": "mlsauce.nonconformist.RegressorNormalizer.set_score_request", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNormalizer.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.nonconformist.IcpRegressor": {"fullname": "mlsauce.nonconformist.IcpRegressor", "modulename": "mlsauce.nonconformist", "qualname": "IcpRegressor", "kind": "class", "doc": "

Inductive conformal regressor.

\n\n

Parameters

\n\n

nc_function : BaseScorer\n Nonconformity scorer object used to calculate nonconformity of\n calibration examples and test patterns. Should implement fit(x, y),\n calc_nc(x, y) and predict(x, nc_scores, significance).

\n\n

Attributes

\n\n

cal_x : numpy array of shape [n_cal_examples, n_features]\n Inputs of calibration set.

\n\n

cal_y : numpy array of shape [n_cal_examples]\n Outputs of calibration set.

\n\n

nc_function : BaseScorer\n Nonconformity scorer object used to calculate nonconformity scores.

\n\n

See also

\n\n

IcpClassifier

\n\n

References

\n\n

Examples

\n\n
\n
>>> import numpy as np\n>>> from sklearn.datasets import load_boston\n>>> from sklearn.tree import DecisionTreeRegressor\n>>> from nonconformist.base import RegressorAdapter\n>>> from nonconformist.icp import IcpRegressor\n>>> from nonconformist.nc import RegressorNc, AbsErrorErrFunc\n>>> boston = load_boston()\n>>> idx = np.random.permutation(boston.target.size)\n>>> train = idx[:int(idx.size / 3)]\n>>> cal = idx[int(idx.size / 3):int(2 * idx.size / 3)]\n>>> test = idx[int(2 * idx.size / 3):]\n>>> model = RegressorAdapter(DecisionTreeRegressor())\n>>> nc = RegressorNc(model, AbsErrorErrFunc())\n>>> icp = IcpRegressor(nc)\n>>> icp.fit(boston.data[train, :], boston.target[train])\n>>> icp.calibrate(boston.data[cal, :], boston.target[cal])\n>>> icp.predict(boston.data[test, :], significance=0.10)\n...     # doctest: +SKIP\narray([[  5. ,  20.6],\n        [ 15.5,  31.1],\n        ...,\n        [ 14.2,  29.8],\n        [ 11.6,  27.2]])\n
\n
\n\n
\n
\n
    \n
\n
\n", "bases": "mlsauce.nonconformist.icp.BaseIcp, mlsauce.nonconformist.base.RegressorMixin"}, "mlsauce.nonconformist.IcpRegressor.__init__": {"fullname": "mlsauce.nonconformist.IcpRegressor.__init__", "modulename": "mlsauce.nonconformist", "qualname": "IcpRegressor.__init__", "kind": "function", "doc": "

\n", "signature": "(nc_function, condition=None)"}, "mlsauce.nonconformist.IcpRegressor.predict": {"fullname": "mlsauce.nonconformist.IcpRegressor.predict", "modulename": "mlsauce.nonconformist", "qualname": "IcpRegressor.predict", "kind": "function", "doc": "

Predict the output values for a set of input patterns.

\n\n

Parameters

\n\n

x : numpy array of shape [n_samples, n_features]\n Inputs of patters for which to predict output values.

\n\n

significance : float\n Significance level (maximum allowed error rate) of predictions.\n Should be a float between 0 and 1. If None, then intervals for\n all significance levels (0.01, 0.02, ..., 0.99) are output in a\n 3d-matrix.

\n\n

Returns

\n\n

p : numpy array of shape [n_samples, 2] or [n_samples, 2, 99}\n If significance is None, then p contains the interval (minimum\n and maximum boundaries) for each test pattern, and each significance\n level (0.01, 0.02, ..., 0.99). If significance is a float between\n 0 and 1, then p contains the prediction intervals (minimum and\n maximum boundaries) for the set of test patterns at the chosen\n significance level.

\n", "signature": "(self, x, significance=None):", "funcdef": "def"}, "mlsauce.nonconformist.IcpRegressor.set_fit_request": {"fullname": "mlsauce.nonconformist.IcpRegressor.set_fit_request", "modulename": "mlsauce.nonconformist", "qualname": "IcpRegressor.set_fit_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.nonconformist.IcpRegressor.set_predict_request": {"fullname": "mlsauce.nonconformist.IcpRegressor.set_predict_request", "modulename": "mlsauce.nonconformist", "qualname": "IcpRegressor.set_predict_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.predictioninterval": {"fullname": "mlsauce.predictioninterval", "modulename": "mlsauce.predictioninterval", "kind": "module", "doc": "

\n"}, "mlsauce.predictioninterval.PredictionInterval": {"fullname": "mlsauce.predictioninterval.PredictionInterval", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval", "kind": "class", "doc": "

Class PredictionInterval: Obtain prediction intervals.

\n\n

Attributes:

\n\n
obj: an object;\n    fitted object containing methods `fit` and `predict`\n\nmethod: a string;\n    method for constructing the prediction intervals.\n    Currently \"splitconformal\" (default) and \"localconformal\"\n\nlevel: a float;\n    Confidence level for prediction intervals. Default is 95,\n    equivalent to a miscoverage error of 5 (%)\n\nreplications: an integer;\n    Number of replications for simulated conformal (default is `None`)\n\ntype_pi: a string;\n    type of prediction interval: currently \"kde\" (default) or \"bootstrap\"\n\nseed: an integer;\n    Reproducibility of fit (there's a random split between fitting and calibration data)\n
\n", "bases": "sklearn.base.BaseEstimator, sklearn.base.RegressorMixin"}, "mlsauce.predictioninterval.PredictionInterval.__init__": {"fullname": "mlsauce.predictioninterval.PredictionInterval.__init__", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.__init__", "kind": "function", "doc": "

\n", "signature": "(\tobj,\tmethod='splitconformal',\tlevel=95,\ttype_pi='bootstrap',\treplications=None,\tkernel=None,\tagg='mean',\tseed=123)"}, "mlsauce.predictioninterval.PredictionInterval.obj": {"fullname": "mlsauce.predictioninterval.PredictionInterval.obj", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.obj", "kind": "variable", "doc": "

\n"}, "mlsauce.predictioninterval.PredictionInterval.method": {"fullname": "mlsauce.predictioninterval.PredictionInterval.method", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.method", "kind": "variable", "doc": "

\n"}, "mlsauce.predictioninterval.PredictionInterval.level": {"fullname": "mlsauce.predictioninterval.PredictionInterval.level", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.level", "kind": "variable", "doc": "

\n"}, "mlsauce.predictioninterval.PredictionInterval.type_pi": {"fullname": "mlsauce.predictioninterval.PredictionInterval.type_pi", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.type_pi", "kind": "variable", "doc": "

\n"}, "mlsauce.predictioninterval.PredictionInterval.replications": {"fullname": "mlsauce.predictioninterval.PredictionInterval.replications", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.replications", "kind": "variable", "doc": "

\n"}, "mlsauce.predictioninterval.PredictionInterval.kernel": {"fullname": "mlsauce.predictioninterval.PredictionInterval.kernel", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.kernel", "kind": "variable", "doc": "

\n"}, "mlsauce.predictioninterval.PredictionInterval.agg": {"fullname": "mlsauce.predictioninterval.PredictionInterval.agg", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.agg", "kind": "variable", "doc": "

\n"}, "mlsauce.predictioninterval.PredictionInterval.seed": {"fullname": "mlsauce.predictioninterval.PredictionInterval.seed", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.seed", "kind": "variable", "doc": "

\n"}, "mlsauce.predictioninterval.PredictionInterval.alpha_": {"fullname": "mlsauce.predictioninterval.PredictionInterval.alpha_", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.alpha_", "kind": "variable", "doc": "

\n"}, "mlsauce.predictioninterval.PredictionInterval.quantile_": {"fullname": "mlsauce.predictioninterval.PredictionInterval.quantile_", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.quantile_", "kind": "variable", "doc": "

\n"}, "mlsauce.predictioninterval.PredictionInterval.icp_": {"fullname": "mlsauce.predictioninterval.PredictionInterval.icp_", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.icp_", "kind": "variable", "doc": "

\n"}, "mlsauce.predictioninterval.PredictionInterval.calibrated_residuals_": {"fullname": "mlsauce.predictioninterval.PredictionInterval.calibrated_residuals_", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.calibrated_residuals_", "kind": "variable", "doc": "

\n"}, "mlsauce.predictioninterval.PredictionInterval.scaled_calibrated_residuals_": {"fullname": "mlsauce.predictioninterval.PredictionInterval.scaled_calibrated_residuals_", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.scaled_calibrated_residuals_", "kind": "variable", "doc": "

\n"}, "mlsauce.predictioninterval.PredictionInterval.calibrated_residuals_scaler_": {"fullname": "mlsauce.predictioninterval.PredictionInterval.calibrated_residuals_scaler_", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.calibrated_residuals_scaler_", "kind": "variable", "doc": "

\n"}, "mlsauce.predictioninterval.PredictionInterval.kde_": {"fullname": "mlsauce.predictioninterval.PredictionInterval.kde_", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.kde_", "kind": "variable", "doc": "

\n"}, "mlsauce.predictioninterval.PredictionInterval.fit": {"fullname": "mlsauce.predictioninterval.PredictionInterval.fit", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.fit", "kind": "function", "doc": "

Fit the method to training data (X, y).

\n\n

Args:

\n\n
X: array-like, shape = [n_samples, n_features];\n    Training set vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples, ]; Target values.\n
\n", "signature": "(self, X, y):", "funcdef": "def"}, "mlsauce.predictioninterval.PredictionInterval.predict": {"fullname": "mlsauce.predictioninterval.PredictionInterval.predict", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.predict", "kind": "function", "doc": "

Obtain predictions and prediction intervals

\n\n

Args:

\n\n
X: array-like, shape = [n_samples, n_features];\n    Testing set vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\nreturn_pi: boolean\n    Whether the prediction interval is returned or not.\n    Default is False, for compatibility with other _estimators_.\n    If True, a tuple containing the predictions + lower and upper\n    bounds is returned.\n
\n", "signature": "(self, X, return_pi=False):", "funcdef": "def"}, "mlsauce.predictioninterval.PredictionInterval.set_predict_request": {"fullname": "mlsauce.predictioninterval.PredictionInterval.set_predict_request", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.set_predict_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.predictioninterval.PredictionInterval.set_score_request": {"fullname": "mlsauce.predictioninterval.PredictionInterval.set_score_request", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.ridge": {"fullname": "mlsauce.ridge", "modulename": "mlsauce.ridge", "kind": "module", "doc": "

\n"}, "mlsauce.ridge.RidgeRegressor": {"fullname": "mlsauce.ridge.RidgeRegressor", "modulename": "mlsauce.ridge", "qualname": "RidgeRegressor", "kind": "class", "doc": "

Ridge.

\n\n

Attributes:

\n\n
reg_lambda: float\n    regularization parameter.\n\nbackend: str\n    type of backend; must be in ('cpu', 'gpu', 'tpu')\n
\n", "bases": "sklearn.base.BaseEstimator, sklearn.base.RegressorMixin"}, "mlsauce.ridge.RidgeRegressor.__init__": {"fullname": "mlsauce.ridge.RidgeRegressor.__init__", "modulename": "mlsauce.ridge", "qualname": "RidgeRegressor.__init__", "kind": "function", "doc": "

\n", "signature": "(reg_lambda=0.1, backend='cpu')"}, "mlsauce.ridge.RidgeRegressor.reg_lambda": {"fullname": "mlsauce.ridge.RidgeRegressor.reg_lambda", "modulename": "mlsauce.ridge", "qualname": "RidgeRegressor.reg_lambda", "kind": "variable", "doc": "

\n"}, "mlsauce.ridge.RidgeRegressor.backend": {"fullname": "mlsauce.ridge.RidgeRegressor.backend", "modulename": "mlsauce.ridge", "qualname": "RidgeRegressor.backend", "kind": "variable", "doc": "

\n"}, "mlsauce.ridge.RidgeRegressor.fit": {"fullname": "mlsauce.ridge.RidgeRegressor.fit", "modulename": "mlsauce.ridge", "qualname": "RidgeRegressor.fit", "kind": "function", "doc": "

Fit matrixops (classifier) to training data (X, y)

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n    Target values.\n\n**kwargs: additional parameters to be passed to self.cook_training_set.\n
\n\n

Returns:

\n\n
self: object.\n
\n", "signature": "(self, X, y, **kwargs):", "funcdef": "def"}, "mlsauce.ridge.RidgeRegressor.predict": {"fullname": "mlsauce.ridge.RidgeRegressor.predict", "modulename": "mlsauce.ridge", "qualname": "RidgeRegressor.predict", "kind": "function", "doc": "

Predict test data X.

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to `predict_proba`\n
\n\n

Returns:

\n\n
model predictions: {array-like}\n
\n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.ridge.RidgeRegressor.set_score_request": {"fullname": "mlsauce.ridge.RidgeRegressor.set_score_request", "modulename": "mlsauce.ridge", "qualname": "RidgeRegressor.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.setup": {"fullname": "mlsauce.setup", "modulename": "mlsauce.setup", "kind": "module", "doc": "

\n"}, "mlsauce.stump": {"fullname": "mlsauce.stump", "modulename": "mlsauce.stump", "kind": "module", "doc": "

\n"}, "mlsauce.stump.StumpClassifier": {"fullname": "mlsauce.stump.StumpClassifier", "modulename": "mlsauce.stump", "qualname": "StumpClassifier", "kind": "class", "doc": "

Stump classifier.

\n\n

Attributes:

\n\n
bins: int\n    Number of histogram bins; as in numpy.histogram.\n
\n", "bases": "sklearn.base.BaseEstimator, sklearn.base.ClassifierMixin"}, "mlsauce.stump.StumpClassifier.__init__": {"fullname": "mlsauce.stump.StumpClassifier.__init__", "modulename": "mlsauce.stump", "qualname": "StumpClassifier.__init__", "kind": "function", "doc": "

\n", "signature": "(bins='auto')"}, "mlsauce.stump.StumpClassifier.bins": {"fullname": "mlsauce.stump.StumpClassifier.bins", "modulename": "mlsauce.stump", "qualname": "StumpClassifier.bins", "kind": "variable", "doc": "

\n"}, "mlsauce.stump.StumpClassifier.obj": {"fullname": "mlsauce.stump.StumpClassifier.obj", "modulename": "mlsauce.stump", "qualname": "StumpClassifier.obj", "kind": "variable", "doc": "

\n"}, "mlsauce.stump.StumpClassifier.fit": {"fullname": "mlsauce.stump.StumpClassifier.fit", "modulename": "mlsauce.stump", "qualname": "StumpClassifier.fit", "kind": "function", "doc": "

Fit Stump to training data (X, y)

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n    Target values.\n\nsample_weight: array_like, shape = [n_samples]\n    Observations weights.\n
\n\n

Returns:

\n\n
self: object.\n
\n", "signature": "(self, X, y, sample_weight=None, **kwargs):", "funcdef": "def"}, "mlsauce.stump.StumpClassifier.predict": {"fullname": "mlsauce.stump.StumpClassifier.predict", "modulename": "mlsauce.stump", "qualname": "StumpClassifier.predict", "kind": "function", "doc": "

Predict test data X.

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to `predict_proba`\n
\n\n

Returns:

\n\n
model predictions: {array-like}\n
\n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.stump.StumpClassifier.predict_proba": {"fullname": "mlsauce.stump.StumpClassifier.predict_proba", "modulename": "mlsauce.stump", "qualname": "StumpClassifier.predict_proba", "kind": "function", "doc": "

Predict probabilities for test data X.

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to\n    self.cook_test_set\n
\n\n

Returns:

\n\n
probability estimates for test data: {array-like}\n
\n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.stump.StumpClassifier.set_fit_request": {"fullname": "mlsauce.stump.StumpClassifier.set_fit_request", "modulename": "mlsauce.stump", "qualname": "StumpClassifier.set_fit_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.stump.StumpClassifier.set_score_request": {"fullname": "mlsauce.stump.StumpClassifier.set_score_request", "modulename": "mlsauce.stump", "qualname": "StumpClassifier.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.utils": {"fullname": "mlsauce.utils", "modulename": "mlsauce.utils", "kind": "module", "doc": "

\n"}, "mlsauce.utils.cluster": {"fullname": "mlsauce.utils.cluster", "modulename": "mlsauce.utils", "qualname": "cluster", "kind": "function", "doc": "

\n", "signature": "(\tX,\tn_clusters=None,\tmethod='kmeans',\ttype_scaling='standard',\ttraining=True,\tscaler=None,\tlabel_encoder=None,\tclusterer=None,\tseed=123):", "funcdef": "def"}, "mlsauce.utils.subsample": {"fullname": "mlsauce.utils.subsample", "modulename": "mlsauce.utils", "qualname": "subsample", "kind": "function", "doc": "

\n", "signature": "(y, row_sample=0.8, seed=123):", "funcdef": "def"}, "mlsauce.utils.merge_two_dicts": {"fullname": "mlsauce.utils.merge_two_dicts", "modulename": "mlsauce.utils", "qualname": "merge_two_dicts", "kind": "function", "doc": "

\n", "signature": "(x, y):", "funcdef": "def"}, "mlsauce.utils.flatten": {"fullname": "mlsauce.utils.flatten", "modulename": "mlsauce.utils", "qualname": "flatten", "kind": "function", "doc": "

\n", "signature": "(l):", "funcdef": "def"}, "mlsauce.utils.is_float": {"fullname": "mlsauce.utils.is_float", "modulename": "mlsauce.utils", "qualname": "is_float", "kind": "function", "doc": "

\n", "signature": "(x):", "funcdef": "def"}, "mlsauce.utils.is_factor": {"fullname": "mlsauce.utils.is_factor", "modulename": "mlsauce.utils", "qualname": "is_factor", "kind": "function", "doc": "

\n", "signature": "(y):", "funcdef": "def"}, "mlsauce.utils.Progbar": {"fullname": "mlsauce.utils.Progbar", "modulename": "mlsauce.utils", "qualname": "Progbar", "kind": "class", "doc": "

Displays a progress bar.

\n\n

Arguments

\n\n
target: Total number of steps expected, None if unknown.\nwidth: Progress bar width on screen.\nverbose: Verbosity mode, 0 (silent), 1 (verbose), 2 (semi-verbose)\nstateful_metrics: Iterable of string names of metrics that\n    should *not* be averaged over time. Metrics in this list\n    will be displayed as-is. All others will be averaged\n    by the progbar before display.\ninterval: Minimum visual progress update interval (in seconds).\n
\n"}, "mlsauce.utils.Progbar.__init__": {"fullname": "mlsauce.utils.Progbar.__init__", "modulename": "mlsauce.utils", "qualname": "Progbar.__init__", "kind": "function", "doc": "

\n", "signature": "(target, width=30, verbose=1, interval=0.05, stateful_metrics=None)"}, "mlsauce.utils.Progbar.target": {"fullname": "mlsauce.utils.Progbar.target", "modulename": "mlsauce.utils", "qualname": "Progbar.target", "kind": "variable", "doc": "

\n"}, "mlsauce.utils.Progbar.width": {"fullname": "mlsauce.utils.Progbar.width", "modulename": "mlsauce.utils", "qualname": "Progbar.width", "kind": "variable", "doc": "

\n"}, "mlsauce.utils.Progbar.verbose": {"fullname": "mlsauce.utils.Progbar.verbose", "modulename": "mlsauce.utils", "qualname": "Progbar.verbose", "kind": "variable", "doc": "

\n"}, "mlsauce.utils.Progbar.interval": {"fullname": "mlsauce.utils.Progbar.interval", "modulename": "mlsauce.utils", "qualname": "Progbar.interval", "kind": "variable", "doc": "

\n"}, "mlsauce.utils.Progbar.update": {"fullname": "mlsauce.utils.Progbar.update", "modulename": "mlsauce.utils", "qualname": "Progbar.update", "kind": "function", "doc": "

Updates the progress bar.

\n\n

Arguments

\n\n
current: Index of current step.\nvalues: List of tuples:\n    `(name, value_for_last_step)`.\n    If `name` is in `stateful_metrics`,\n    `value_for_last_step` will be displayed as-is.\n    Else, an average of the metric over time will be displayed.\n
\n", "signature": "(self, current, values=None):", "funcdef": "def"}, "mlsauce.utils.Progbar.add": {"fullname": "mlsauce.utils.Progbar.add", "modulename": "mlsauce.utils", "qualname": "Progbar.add", "kind": "function", "doc": "

\n", "signature": "(self, n, values=None):", "funcdef": "def"}, "mlsauce.utils.get_beta": {"fullname": "mlsauce.utils.get_beta", "modulename": "mlsauce.utils.get_beta", "kind": "module", "doc": "

\n"}, "mlsauce.utils.check_and_install": {"fullname": "mlsauce.utils.check_and_install", "modulename": "mlsauce.utils", "qualname": "check_and_install", "kind": "function", "doc": "

Check if a package is installed; if not, install it.

\n", "signature": "(package_name):", "funcdef": "def"}, "mlsauce.utils.get_beta.get_beta": {"fullname": "mlsauce.utils.get_beta.get_beta", "modulename": "mlsauce.utils.get_beta", "qualname": "get_beta", "kind": "function", "doc": "

\n", "signature": "(X, y):", "funcdef": "def"}}, "docInfo": {"mlsauce": {"qualname": 0, "fullname": 1, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mlsauce.AdaOpt": {"qualname": 1, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 6, "doc": 277}, "mlsauce.AdaOpt.__init__": {"qualname": 3, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 245, "bases": 0, "doc": 3}, "mlsauce.AdaOpt.n_iterations": {"qualname": 3, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mlsauce.AdaOpt.learning_rate": {"qualname": 3, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mlsauce.AdaOpt.reg_lambda": {"qualname": 3, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mlsauce.AdaOpt.reg_alpha": {"qualname": 3, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mlsauce.AdaOpt.eta": {"qualname": 2, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mlsauce.AdaOpt.gamma": {"qualname": 2, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mlsauce.AdaOpt.k": {"qualname": 2, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mlsauce.AdaOpt.tolerance": {"qualname": 2, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mlsauce.AdaOpt.n_clusters": {"qualname": 3, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mlsauce.AdaOpt.batch_size": {"qualname": 3, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mlsauce.AdaOpt.row_sample": {"qualname": 3, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mlsauce.AdaOpt.type_dist": {"qualname": 3, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mlsauce.AdaOpt.n_jobs": {"qualname": 3, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mlsauce.AdaOpt.cache": {"qualname": 2, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mlsauce.AdaOpt.verbose": {"qualname": 2, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mlsauce.AdaOpt.n_clusters_input": {"qualname": 4, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mlsauce.AdaOpt.clustering_method": {"qualname": 3, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mlsauce.AdaOpt.cluster_scaling": {"qualname": 3, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mlsauce.AdaOpt.seed": {"qualname": 2, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mlsauce.AdaOpt.fit": {"qualname": 2, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 28, "bases": 0, "doc": 73}, "mlsauce.AdaOpt.predict": {"qualname": 2, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 23, "bases": 0, "doc": 62}, "mlsauce.AdaOpt.predict_proba": {"qualname": 3, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 23, "bases": 0, "doc": 69}, "mlsauce.AdaOpt.set_score_request": {"qualname": 4, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 11, "bases": 0, "doc": 198}, "mlsauce.LSBoostClassifier": {"qualname": 1, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 6, "doc": 980}, "mlsauce.LSBoostClassifier.__init__": {"qualname": 3, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 276, "bases": 0, "doc": 3}, "mlsauce.LSBoostClassifier.base_model": {"qualname": 3, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, 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AdaOpt classifier.

\n\n

Attributes:

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\n"}, "mlsauce.AdaOpt.n_clusters": {"fullname": "mlsauce.AdaOpt.n_clusters", "modulename": "mlsauce", "qualname": "AdaOpt.n_clusters", "kind": "variable", "doc": "

\n"}, "mlsauce.AdaOpt.batch_size": {"fullname": "mlsauce.AdaOpt.batch_size", "modulename": "mlsauce", "qualname": "AdaOpt.batch_size", "kind": "variable", "doc": "

\n"}, "mlsauce.AdaOpt.row_sample": {"fullname": "mlsauce.AdaOpt.row_sample", "modulename": "mlsauce", "qualname": "AdaOpt.row_sample", "kind": "variable", "doc": "

\n"}, "mlsauce.AdaOpt.type_dist": {"fullname": "mlsauce.AdaOpt.type_dist", "modulename": "mlsauce", "qualname": "AdaOpt.type_dist", "kind": "variable", "doc": "

\n"}, "mlsauce.AdaOpt.n_jobs": {"fullname": "mlsauce.AdaOpt.n_jobs", "modulename": "mlsauce", "qualname": "AdaOpt.n_jobs", "kind": "variable", "doc": "

\n"}, "mlsauce.AdaOpt.cache": {"fullname": "mlsauce.AdaOpt.cache", "modulename": "mlsauce", "qualname": "AdaOpt.cache", "kind": "variable", "doc": "

\n"}, "mlsauce.AdaOpt.verbose": {"fullname": "mlsauce.AdaOpt.verbose", "modulename": "mlsauce", "qualname": "AdaOpt.verbose", "kind": "variable", "doc": "

\n"}, "mlsauce.AdaOpt.n_clusters_input": {"fullname": "mlsauce.AdaOpt.n_clusters_input", "modulename": "mlsauce", "qualname": "AdaOpt.n_clusters_input", "kind": "variable", "doc": "

\n"}, "mlsauce.AdaOpt.clustering_method": {"fullname": "mlsauce.AdaOpt.clustering_method", "modulename": "mlsauce", "qualname": "AdaOpt.clustering_method", "kind": "variable", "doc": "

\n"}, "mlsauce.AdaOpt.cluster_scaling": {"fullname": "mlsauce.AdaOpt.cluster_scaling", "modulename": "mlsauce", "qualname": "AdaOpt.cluster_scaling", "kind": "variable", "doc": "

\n"}, "mlsauce.AdaOpt.seed": {"fullname": "mlsauce.AdaOpt.seed", "modulename": "mlsauce", "qualname": "AdaOpt.seed", "kind": "variable", "doc": "

\n"}, "mlsauce.AdaOpt.fit": {"fullname": "mlsauce.AdaOpt.fit", "modulename": "mlsauce", "qualname": "AdaOpt.fit", "kind": "function", "doc": "

Fit AdaOpt to training data (X, y)

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n    Target values.\n\n**kwargs: additional parameters to be passed to self.cook_training_set.\n
\n\n

Returns:

\n\n
self: object.\n
\n", "signature": "(self, X, y, **kwargs):", "funcdef": "def"}, "mlsauce.AdaOpt.predict": {"fullname": "mlsauce.AdaOpt.predict", "modulename": "mlsauce", "qualname": "AdaOpt.predict", "kind": "function", "doc": "

Predict test data X.

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to `predict_proba`\n
\n\n

Returns:

\n\n
model predictions: {array-like}\n
\n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.AdaOpt.predict_proba": {"fullname": "mlsauce.AdaOpt.predict_proba", "modulename": "mlsauce", "qualname": "AdaOpt.predict_proba", "kind": "function", "doc": "

Predict probabilities for test data X.

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to\n    self.cook_test_set\n
\n\n

Returns:

\n\n
probability estimates for test data: {array-like}\n
\n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.AdaOpt.set_score_request": {"fullname": "mlsauce.AdaOpt.set_score_request", "modulename": "mlsauce", "qualname": "AdaOpt.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.LSBoostClassifier": {"fullname": "mlsauce.LSBoostClassifier", "modulename": "mlsauce", "qualname": "LSBoostClassifier", "kind": "class", "doc": "

LSBoost classifier.

\n\n
Attributes:\n\n    n_estimators: int\n        number of boosting iterations.\n\n    learning_rate: float\n        controls the learning speed at training time.\n\n    n_hidden_features: int\n        number of nodes in successive hidden layers.\n\n    reg_lambda: float\n        L2 regularization parameter for successive errors in the optimizer\n        (at training time).\n\n    alpha: float\n        compromise between L1 and L2 regularization (must be in [0, 1]),\n        for `solver` == 'enet'.\n\n    row_sample: float\n        percentage of rows chosen from the training set.\n\n    col_sample: float\n        percentage of columns chosen from the training set.\n\n    dropout: float\n        percentage of nodes dropped from the training set.\n\n    tolerance: float\n        controls early stopping in gradient descent (at training time).\n\n    direct_link: bool\n        indicates whether the original features are included (True) in model's\n        fitting or not (False).\n\n    verbose: int\n        progress bar (yes = 1) or not (no = 0) (currently).\n\n    seed: int\n        reproducibility seed for nodes_sim=='uniform', clustering and dropout.\n\n    backend: str\n        type of backend; must be in ('cpu', 'gpu', 'tpu')\n\n    solver: str\n        type of 'weak' learner; currently in ('ridge', 'lasso', 'enet').\n        'enet' is a combination of 'ridge' and 'lasso' called Elastic Net.\n\n    activation: str\n        activation function: currently 'relu', 'relu6', 'sigmoid', 'tanh'\n\n    n_clusters: int\n        number of clusters for clustering the features\n\n    clustering_method: str\n        clustering method: currently 'kmeans', 'gmm'\n\n    cluster_scaling: str\n        scaling method for clustering: currently 'standard', 'robust', 'minmax'\n\n    degree: int\n        degree of features interactions to include in the model\n\n    weights_distr: str\n        distribution of weights for constructing the model's hidden layer;\n        currently 'uniform', 'gaussian'\n\nExamples:\n\n    ```python\n    import numpy as np\n    from sklearn.datasets import load_digits, load_breast_cancer, load_wine, load_iris\n    from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\n    from sklearn.tree import DecisionTreeRegressor\n    from sklearn.kernel_ridge import KernelRidge\n    from time import time\n    from os import chdir\n    from sklearn import metrics\n    import os\n\n    import mlsauce as ms\n\n    print(\"\n
\n\n

\")\n print(\"GenericBoosting Decision tree -----\")\n print(\"\n\")

\n\n
    print(\"\n
\n\n

\")\n print(\"breast_cancer data -----\")

\n\n
    # data 1\n    breast_cancer = load_breast_cancer()\n    X = breast_cancer.data\n    y = breast_cancer.target\n    # split data into training test and test set\n    np.random.seed(15029)\n    X_train, X_test, y_train, y_test = train_test_split(X, y,\n                                                        test_size=0.2)\n\n    clf = DecisionTreeRegressor()\n    clf2 = KernelRidge()\n\n    obj = ms.GenericBoostingClassifier(clf, tolerance=1e-2)\n    print(obj.get_params())\n    start = time()\n    obj.fit(X_train, y_train)\n    print(time()-start)\n    start = time()\n    print(obj.score(X_test, y_test))\n    print(time()-start)\n\n    print(obj.obj['loss'])\n\n    obj = ms.GenericBoostingClassifier(clf, tolerance=1e-2, n_clusters=2)\n    print(obj.get_params())\n    start = time()\n    obj.fit(X_train, y_train)\n    print(time()-start)\n    start = time()\n    print(obj.score(X_test, y_test))\n    print(time()-start)\n\n    print(obj.obj['loss'])\n\n\n    # data 2\n    print(\"\n
\n\n

\")\n print(\"wine data -----\")

\n\n
    wine = load_wine()\n    Z = wine.data\n    t = wine.target\n    np.random.seed(879423)\n    X_train, X_test, y_train, y_test = train_test_split(Z, t,\n                                                        test_size=0.2)\n\n    obj = ms.GenericBoostingClassifier(clf)\n    print(obj.get_params())\n    start = time()\n    obj.fit(X_train, y_train)\n    print(time()-start)\n    start = time()\n    print(obj.score(X_test, y_test))\n    print(time()-start)\n\n    print(obj.obj['loss'])\n\n    obj = ms.GenericBoostingClassifier(clf, n_clusters=3)\n    print(obj.get_params())\n    start = time()\n    obj.fit(X_train, y_train)\n    print(time()-start)\n    start = time()\n    print(obj.score(X_test, y_test))\n    print(time()-start)\n\n    print(obj.obj['loss'])\n\n    # data 3\n    print(\"\n
\n\n

\")\n print(\"iris data -----\")

\n\n
    iris = load_iris()\n    Z = iris.data\n    t = iris.target\n    np.random.seed(734563)\n    X_train, X_test, y_train, y_test = train_test_split(Z, t,\n                                                        test_size=0.2)\n\n\n    obj = ms.GenericBoostingClassifier(clf)\n    print(obj.get_params())\n    start = time()\n    obj.fit(X_train, y_train)\n    print(time()-start)\n    start = time()\n    print(obj.score(X_test, y_test))\n    print(time()-start)\n\n    print(obj.obj['loss'])\n\n\n    print(\"\n
\n\n

\")\n print(\"GenericBoosting KRR -----\")\n print(\"\n\")

\n\n
    obj = ms.GenericBoostingClassifier(clf2, tolerance=1e-2)\n    print(obj.get_params())\n    start = time()\n    obj.fit(X_train, y_train)\n    print(time()-start)\n    start = time()\n    print(obj.score(X_test, y_test))\n    print(time()-start)\n\n    print(obj.obj['loss'])\n\n    obj = ms.GenericBoostingClassifier(clf2, tolerance=1e-2, n_clusters=2)\n    print(obj.get_params())\n    start = time()\n    obj.fit(X_train, y_train)\n    print(time()-start)\n    start = time()\n    print(obj.score(X_test, y_test))\n    print(time()-start)\n\n    print(obj.obj['loss'])\n\n\n    # data 2\n    print(\"\n
\n\n

\")\n print(\"wine data -----\")

\n\n
    wine = load_wine()\n    Z = wine.data\n    t = wine.target\n    np.random.seed(879423)\n    X_train, X_test, y_train, y_test = train_test_split(Z, t,\n                                                        test_size=0.2)\n\n    obj = ms.GenericBoostingClassifier(clf2)\n    print(obj.get_params())\n    start = time()\n    obj.fit(X_train, y_train)\n    print(time()-start)\n    start = time()\n    print(obj.score(X_test, y_test))\n    print(time()-start)\n\n    print(obj.obj['loss'])\n\n    obj = ms.GenericBoostingClassifier(clf2, n_clusters=3)\n    print(obj.get_params())\n    start = time()\n    obj.fit(X_train, y_train)\n    print(time()-start)\n    start = time()\n    print(obj.score(X_test, y_test))\n    print(time()-start)\n\n    print(obj.obj['loss'])\n\n    # data 3\n    print(\"\n
\n\n

\")\n print(\"iris data -----\")

\n\n
    iris = load_iris()\n    Z = iris.data\n    t = iris.target\n    np.random.seed(734563)\n    X_train, X_test, y_train, y_test = train_test_split(Z, t,\n                                                        test_size=0.2)\n\n\n    obj = ms.GenericBoostingClassifier(clf2)\n    print(obj.get_params())\n    start = time()\n    obj.fit(X_train, y_train)\n    print(time()-start)\n    start = time()\n    print(obj.score(X_test, y_test))\n    print(time()-start)\n\n    print(obj.obj['loss'])\n```\n
\n", "bases": "sklearn.base.BaseEstimator, sklearn.base.ClassifierMixin"}, "mlsauce.LSBoostClassifier.__init__": {"fullname": "mlsauce.LSBoostClassifier.__init__", "modulename": "mlsauce", "qualname": "LSBoostClassifier.__init__", "kind": "function", "doc": "

\n", "signature": "(\tn_estimators=100,\tlearning_rate=0.1,\tn_hidden_features=5,\treg_lambda=0.1,\talpha=0.5,\trow_sample=1,\tcol_sample=1,\tdropout=0,\ttolerance=0.0001,\tdirect_link=1,\tverbose=1,\tseed=123,\tbackend='cpu',\tsolver='ridge',\tactivation='relu',\tn_clusters=0,\tclustering_method='kmeans',\tcluster_scaling='standard',\tdegree=None,\tweights_distr='uniform',\tbase_model=None)"}, "mlsauce.LSBoostClassifier.base_model": {"fullname": "mlsauce.LSBoostClassifier.base_model", "modulename": "mlsauce", "qualname": "LSBoostClassifier.base_model", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostClassifier.n_estimators": {"fullname": "mlsauce.LSBoostClassifier.n_estimators", "modulename": "mlsauce", "qualname": "LSBoostClassifier.n_estimators", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostClassifier.learning_rate": {"fullname": "mlsauce.LSBoostClassifier.learning_rate", "modulename": "mlsauce", "qualname": "LSBoostClassifier.learning_rate", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostClassifier.n_hidden_features": {"fullname": "mlsauce.LSBoostClassifier.n_hidden_features", "modulename": "mlsauce", "qualname": "LSBoostClassifier.n_hidden_features", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostClassifier.reg_lambda": {"fullname": "mlsauce.LSBoostClassifier.reg_lambda", "modulename": "mlsauce", "qualname": "LSBoostClassifier.reg_lambda", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostClassifier.alpha": {"fullname": "mlsauce.LSBoostClassifier.alpha", "modulename": "mlsauce", "qualname": "LSBoostClassifier.alpha", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostClassifier.row_sample": {"fullname": "mlsauce.LSBoostClassifier.row_sample", "modulename": "mlsauce", "qualname": "LSBoostClassifier.row_sample", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostClassifier.col_sample": {"fullname": "mlsauce.LSBoostClassifier.col_sample", "modulename": "mlsauce", "qualname": "LSBoostClassifier.col_sample", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostClassifier.dropout": {"fullname": "mlsauce.LSBoostClassifier.dropout", "modulename": "mlsauce", "qualname": "LSBoostClassifier.dropout", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostClassifier.tolerance": {"fullname": "mlsauce.LSBoostClassifier.tolerance", "modulename": "mlsauce", "qualname": "LSBoostClassifier.tolerance", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostClassifier.direct_link": {"fullname": "mlsauce.LSBoostClassifier.direct_link", "modulename": "mlsauce", "qualname": "LSBoostClassifier.direct_link", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostClassifier.verbose": {"fullname": "mlsauce.LSBoostClassifier.verbose", "modulename": "mlsauce", "qualname": "LSBoostClassifier.verbose", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostClassifier.seed": {"fullname": "mlsauce.LSBoostClassifier.seed", "modulename": "mlsauce", "qualname": "LSBoostClassifier.seed", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostClassifier.backend": {"fullname": "mlsauce.LSBoostClassifier.backend", "modulename": "mlsauce", "qualname": "LSBoostClassifier.backend", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostClassifier.obj": {"fullname": "mlsauce.LSBoostClassifier.obj", "modulename": "mlsauce", "qualname": "LSBoostClassifier.obj", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostClassifier.solver": {"fullname": "mlsauce.LSBoostClassifier.solver", "modulename": "mlsauce", "qualname": "LSBoostClassifier.solver", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostClassifier.activation": {"fullname": "mlsauce.LSBoostClassifier.activation", "modulename": "mlsauce", "qualname": "LSBoostClassifier.activation", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostClassifier.n_clusters": {"fullname": "mlsauce.LSBoostClassifier.n_clusters", "modulename": "mlsauce", "qualname": "LSBoostClassifier.n_clusters", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostClassifier.clustering_method": {"fullname": "mlsauce.LSBoostClassifier.clustering_method", "modulename": "mlsauce", "qualname": "LSBoostClassifier.clustering_method", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostClassifier.cluster_scaling": {"fullname": "mlsauce.LSBoostClassifier.cluster_scaling", "modulename": "mlsauce", "qualname": "LSBoostClassifier.cluster_scaling", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostClassifier.degree": {"fullname": "mlsauce.LSBoostClassifier.degree", "modulename": "mlsauce", "qualname": "LSBoostClassifier.degree", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostClassifier.poly_": {"fullname": "mlsauce.LSBoostClassifier.poly_", "modulename": "mlsauce", "qualname": "LSBoostClassifier.poly_", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostClassifier.weights_distr": {"fullname": "mlsauce.LSBoostClassifier.weights_distr", "modulename": "mlsauce", "qualname": "LSBoostClassifier.weights_distr", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostClassifier.fit": {"fullname": "mlsauce.LSBoostClassifier.fit", "modulename": "mlsauce", "qualname": "LSBoostClassifier.fit", "kind": "function", "doc": "

Fit Booster (classifier) to training data (X, y)

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n    Target values.\n\n**kwargs: additional parameters to be passed to self.cook_training_set.\n
\n\n

Returns:

\n\n
self: object.\n
\n", "signature": "(self, X, y, **kwargs):", "funcdef": "def"}, "mlsauce.LSBoostClassifier.predict": {"fullname": "mlsauce.LSBoostClassifier.predict", "modulename": "mlsauce", "qualname": "LSBoostClassifier.predict", "kind": "function", "doc": "

Predict test data X.

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to `predict_proba`\n
\n\n

Returns:

\n\n
model predictions: {array-like}\n
\n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.LSBoostClassifier.predict_proba": {"fullname": "mlsauce.LSBoostClassifier.predict_proba", "modulename": "mlsauce", "qualname": "LSBoostClassifier.predict_proba", "kind": "function", "doc": "

Predict probabilities for test data X.

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to\n    self.cook_test_set\n
\n\n

Returns:

\n\n
probability estimates for test data: {array-like}\n
\n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.LSBoostClassifier.update": {"fullname": "mlsauce.LSBoostClassifier.update", "modulename": "mlsauce", "qualname": "LSBoostClassifier.update", "kind": "function", "doc": "

Update model with new data.

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples=1, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: float = [n_samples=1]\n   Target value.\n\neta: float\n    Inverse power applied to number of observations\n    (defines a learning rate).\n
\n\n

Returns:

\n\n
self: object.\n
\n", "signature": "(self, X, y, eta=0.9):", "funcdef": "def"}, "mlsauce.LSBoostClassifier.set_score_request": {"fullname": "mlsauce.LSBoostClassifier.set_score_request", "modulename": "mlsauce", "qualname": "LSBoostClassifier.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.GenericBoostingClassifier": {"fullname": "mlsauce.GenericBoostingClassifier", "modulename": "mlsauce", "qualname": "GenericBoostingClassifier", "kind": "class", "doc": "

Generic Boosting classifier (using any classifier as base learner).

\n\n

Attributes:

\n\n
base_model: object\n    base learner (default is ExtraTreeRegressor) to be boosted.\n\nn_estimators: int\n    number of boosting iterations.\n\nlearning_rate: float\n    controls the learning speed at training time.\n\nn_hidden_features: int\n    number of nodes in successive hidden layers.\n\nreg_lambda: float\n    L2 regularization parameter for successive errors in the optimizer\n    (at training time).\n\nalpha: float\n    compromise between L1 and L2 regularization (must be in [0, 1]),\n    for `solver` == 'enet'.\n\nrow_sample: float\n    percentage of rows chosen from the training set.\n\ncol_sample: float\n    percentage of columns chosen from the training set.\n\ndropout: float\n    percentage of nodes dropped from the training set.\n\ntolerance: float\n    controls early stopping in gradient descent (at training time).\n\ndirect_link: bool\n    indicates whether the original features are included (True) in model's\n    fitting or not (False).\n\nverbose: int\n    progress bar (yes = 1) or not (no = 0) (currently).\n\nseed: int\n    reproducibility seed for nodes_sim=='uniform', clustering and dropout.\n\nbackend: str\n    type of backend; must be in ('cpu', 'gpu', 'tpu')\n\nsolver: str\n    type of 'weak' learner; currently in ('ridge', 'lasso', 'enet').\n    'enet' is a combination of 'ridge' and 'lasso' called Elastic Net.\n\nactivation: str\n    activation function: currently 'relu', 'relu6', 'sigmoid', 'tanh'\n\nn_clusters: int\n    number of clusters for clustering the features\n\nclustering_method: str\n    clustering method: currently 'kmeans', 'gmm'\n\ncluster_scaling: str\n    scaling method for clustering: currently 'standard', 'robust', 'minmax'\n\ndegree: int\n    degree of features interactions to include in the model\n\nweights_distr: str\n    distribution of weights for constructing the model's hidden layer;\n    currently 'uniform', 'gaussian'\n
\n", "bases": "mlsauce.booster._booster_classifier.LSBoostClassifier"}, "mlsauce.GenericBoostingClassifier.__init__": {"fullname": "mlsauce.GenericBoostingClassifier.__init__", "modulename": "mlsauce", "qualname": "GenericBoostingClassifier.__init__", "kind": "function", "doc": "

\n", "signature": "(\tbase_model=ExtraTreeRegressor(),\tn_estimators=100,\tlearning_rate=0.1,\tn_hidden_features=5,\treg_lambda=0.1,\talpha=0.5,\trow_sample=1,\tcol_sample=1,\tdropout=0,\ttolerance=0.0001,\tdirect_link=1,\tverbose=1,\tseed=123,\tbackend='cpu',\tsolver='ridge',\tactivation='relu',\tn_clusters=0,\tclustering_method='kmeans',\tcluster_scaling='standard',\tdegree=None,\tweights_distr='uniform')"}, "mlsauce.GenericBoostingClassifier.base_model": {"fullname": "mlsauce.GenericBoostingClassifier.base_model", "modulename": "mlsauce", "qualname": "GenericBoostingClassifier.base_model", "kind": "variable", "doc": "

\n"}, "mlsauce.GenericBoostingClassifier.set_score_request": {"fullname": "mlsauce.GenericBoostingClassifier.set_score_request", "modulename": "mlsauce", "qualname": "GenericBoostingClassifier.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.GenericBoostingRegressor": {"fullname": "mlsauce.GenericBoostingRegressor", "modulename": "mlsauce", "qualname": "GenericBoostingRegressor", "kind": "class", "doc": "

LSBoost regressor.

\n\n

Attributes:

\n\n
base_model: object\n    base learner (default is ExtraTreeRegressor) to be boosted.\n\nn_estimators: int\n    number of boosting iterations.\n\nlearning_rate: float\n    controls the learning speed at training time.\n\nn_hidden_features: int\n    number of nodes in successive hidden layers.\n\nreg_lambda: float\n    L2 regularization parameter for successive errors in the optimizer\n    (at training time).\n\nalpha: float\n    compromise between L1 and L2 regularization (must be in [0, 1]),\n    for `solver` == 'enet'\n\nrow_sample: float\n    percentage of rows chosen from the training set.\n\ncol_sample: float\n    percentage of columns chosen from the training set.\n\ndropout: float\n    percentage of nodes dropped from the training set.\n\ntolerance: float\n    controls early stopping in gradient descent (at training time).\n\ndirect_link: bool\n    indicates whether the original features are included (True) in model's\n    fitting or not (False).\n\nverbose: int\n    progress bar (yes = 1) or not (no = 0) (currently).\n\nseed: int\n    reproducibility seed for nodes_sim=='uniform', clustering and dropout.\n\nbackend: str\n    type of backend; must be in ('cpu', 'gpu', 'tpu')\n\nsolver: str\n    type of 'weak' learner; currently in ('ridge', 'lasso')\n\nactivation: str\n    activation function: currently 'relu', 'relu6', 'sigmoid', 'tanh'\n\ntype_pi: str.\n    type of prediction interval; currently \"kde\" (default) or \"bootstrap\".\n    Used only in `self.predict`, for `self.replications` > 0 and `self.kernel`\n    in ('gaussian', 'tophat'). Default is `None`.\n\nreplications: int.\n    number of replications (if needed) for predictive simulation.\n    Used only in `self.predict`, for `self.kernel` in ('gaussian',\n    'tophat') and `self.type_pi = 'kde'`. Default is `None`.\n\nn_clusters: int\n    number of clusters for clustering the features\n\nclustering_method: str\n    clustering method: currently 'kmeans', 'gmm'\n\ncluster_scaling: str\n    scaling method for clustering: currently 'standard', 'robust', 'minmax'\n\ndegree: int\n    degree of features interactions to include in the model\n\nweights_distr: str\n    distribution of weights for constructing the model's hidden layer;\n    either 'uniform' or 'gaussian'\n
\n", "bases": "mlsauce.booster._booster_regressor.LSBoostRegressor"}, "mlsauce.GenericBoostingRegressor.__init__": {"fullname": "mlsauce.GenericBoostingRegressor.__init__", "modulename": "mlsauce", "qualname": "GenericBoostingRegressor.__init__", "kind": "function", "doc": "

\n", "signature": "(\tbase_model=ExtraTreeRegressor(),\tn_estimators=100,\tlearning_rate=0.1,\tn_hidden_features=5,\treg_lambda=0.1,\talpha=0.5,\trow_sample=1,\tcol_sample=1,\tdropout=0,\ttolerance=0.0001,\tdirect_link=1,\tverbose=1,\tseed=123,\tbackend='cpu',\tsolver='ridge',\tactivation='relu',\ttype_pi=None,\treplications=None,\tkernel=None,\tn_clusters=0,\tclustering_method='kmeans',\tcluster_scaling='standard',\tdegree=None,\tweights_distr='uniform')"}, "mlsauce.GenericBoostingRegressor.base_model": {"fullname": "mlsauce.GenericBoostingRegressor.base_model", "modulename": "mlsauce", "qualname": "GenericBoostingRegressor.base_model", "kind": "variable", "doc": "

\n"}, "mlsauce.GenericBoostingRegressor.set_predict_request": {"fullname": "mlsauce.GenericBoostingRegressor.set_predict_request", "modulename": "mlsauce", "qualname": "GenericBoostingRegressor.set_predict_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.GenericBoostingRegressor.set_score_request": {"fullname": "mlsauce.GenericBoostingRegressor.set_score_request", "modulename": "mlsauce", "qualname": "GenericBoostingRegressor.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.StumpClassifier": {"fullname": "mlsauce.StumpClassifier", "modulename": "mlsauce", "qualname": "StumpClassifier", "kind": "class", "doc": "

Stump classifier.

\n\n

Attributes:

\n\n
bins: int\n    Number of histogram bins; as in numpy.histogram.\n
\n", "bases": "sklearn.base.BaseEstimator, sklearn.base.ClassifierMixin"}, "mlsauce.StumpClassifier.__init__": {"fullname": "mlsauce.StumpClassifier.__init__", "modulename": "mlsauce", "qualname": "StumpClassifier.__init__", "kind": "function", "doc": "

\n", "signature": "(bins='auto')"}, "mlsauce.StumpClassifier.bins": {"fullname": "mlsauce.StumpClassifier.bins", "modulename": "mlsauce", "qualname": "StumpClassifier.bins", "kind": "variable", "doc": "

\n"}, "mlsauce.StumpClassifier.obj": {"fullname": "mlsauce.StumpClassifier.obj", "modulename": "mlsauce", "qualname": "StumpClassifier.obj", "kind": "variable", "doc": "

\n"}, "mlsauce.StumpClassifier.fit": {"fullname": "mlsauce.StumpClassifier.fit", "modulename": "mlsauce", "qualname": "StumpClassifier.fit", "kind": "function", "doc": "

Fit Stump to training data (X, y)

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n    Target values.\n\nsample_weight: array_like, shape = [n_samples]\n    Observations weights.\n
\n\n

Returns:

\n\n
self: object.\n
\n", "signature": "(self, X, y, sample_weight=None, **kwargs):", "funcdef": "def"}, "mlsauce.StumpClassifier.predict": {"fullname": "mlsauce.StumpClassifier.predict", "modulename": "mlsauce", "qualname": "StumpClassifier.predict", "kind": "function", "doc": "

Predict test data X.

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to `predict_proba`\n
\n\n

Returns:

\n\n
model predictions: {array-like}\n
\n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.StumpClassifier.predict_proba": {"fullname": "mlsauce.StumpClassifier.predict_proba", "modulename": "mlsauce", "qualname": "StumpClassifier.predict_proba", "kind": "function", "doc": "

Predict probabilities for test data X.

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to\n    self.cook_test_set\n
\n\n

Returns:

\n\n
probability estimates for test data: {array-like}\n
\n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.StumpClassifier.set_fit_request": {"fullname": "mlsauce.StumpClassifier.set_fit_request", "modulename": "mlsauce", "qualname": "StumpClassifier.set_fit_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.StumpClassifier.set_score_request": {"fullname": "mlsauce.StumpClassifier.set_score_request", "modulename": "mlsauce", "qualname": "StumpClassifier.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.ElasticNetRegressor": {"fullname": "mlsauce.ElasticNetRegressor", "modulename": "mlsauce", "qualname": "ElasticNetRegressor", "kind": "class", "doc": "

Elasticnet.

\n\n

Attributes:

\n\n
reg_lambda: float\n    regularization parameter.\n\nalpha: float\n    compromise between L1 and L2 regularization (must be in [0, 1]),\n    for `solver` == 'enet'.\n\nbackend: str\n    type of backend; must be in ('cpu', 'gpu', 'tpu')\n
\n", "bases": "sklearn.base.BaseEstimator, sklearn.base.RegressorMixin"}, "mlsauce.ElasticNetRegressor.__init__": {"fullname": "mlsauce.ElasticNetRegressor.__init__", "modulename": "mlsauce", "qualname": "ElasticNetRegressor.__init__", "kind": "function", "doc": "

\n", "signature": "(reg_lambda=0.1, alpha=0.5, backend='cpu')"}, "mlsauce.ElasticNetRegressor.reg_lambda": {"fullname": "mlsauce.ElasticNetRegressor.reg_lambda", "modulename": "mlsauce", "qualname": "ElasticNetRegressor.reg_lambda", "kind": "variable", "doc": "

\n"}, "mlsauce.ElasticNetRegressor.alpha": {"fullname": "mlsauce.ElasticNetRegressor.alpha", "modulename": "mlsauce", "qualname": "ElasticNetRegressor.alpha", "kind": "variable", "doc": "

\n"}, "mlsauce.ElasticNetRegressor.backend": {"fullname": "mlsauce.ElasticNetRegressor.backend", "modulename": "mlsauce", "qualname": "ElasticNetRegressor.backend", "kind": "variable", "doc": "

\n"}, "mlsauce.ElasticNetRegressor.fit": {"fullname": "mlsauce.ElasticNetRegressor.fit", "modulename": "mlsauce", "qualname": "ElasticNetRegressor.fit", "kind": "function", "doc": "

Fit matrixops (classifier) to training data (X, y)

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n    Target values.\n\n**kwargs: additional parameters to be passed to self.cook_training_set.\n
\n\n

Returns:

\n\n
self: object.\n
\n", "signature": "(self, X, y, **kwargs):", "funcdef": "def"}, "mlsauce.ElasticNetRegressor.predict": {"fullname": "mlsauce.ElasticNetRegressor.predict", "modulename": "mlsauce", "qualname": "ElasticNetRegressor.predict", "kind": "function", "doc": "

Predict test data X.

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to `predict_proba`\n
\n\n

Returns:

\n\n
model predictions: {array-like}\n
\n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.ElasticNetRegressor.set_score_request": {"fullname": "mlsauce.ElasticNetRegressor.set_score_request", "modulename": "mlsauce", "qualname": "ElasticNetRegressor.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.LassoRegressor": {"fullname": "mlsauce.LassoRegressor", "modulename": "mlsauce", "qualname": "LassoRegressor", "kind": "class", "doc": "

Lasso.

\n\n

Attributes:

\n\n
reg_lambda: float\n    L1 regularization parameter.\n\nmax_iter: int\n    number of iterations of lasso shooting algorithm.\n\ntol: float\n    tolerance for convergence of lasso shooting algorithm.\n\nbackend: str\n    type of backend; must be in ('cpu', 'gpu', 'tpu').\n
\n", "bases": "sklearn.base.BaseEstimator, sklearn.base.RegressorMixin"}, "mlsauce.LassoRegressor.__init__": {"fullname": "mlsauce.LassoRegressor.__init__", "modulename": "mlsauce", "qualname": "LassoRegressor.__init__", "kind": "function", "doc": "

\n", "signature": "(reg_lambda=0.1, max_iter=10, tol=0.001, backend='cpu')"}, "mlsauce.LassoRegressor.reg_lambda": {"fullname": "mlsauce.LassoRegressor.reg_lambda", "modulename": "mlsauce", "qualname": "LassoRegressor.reg_lambda", "kind": "variable", "doc": "

\n"}, "mlsauce.LassoRegressor.max_iter": {"fullname": "mlsauce.LassoRegressor.max_iter", "modulename": "mlsauce", "qualname": "LassoRegressor.max_iter", "kind": "variable", "doc": "

\n"}, "mlsauce.LassoRegressor.tol": {"fullname": "mlsauce.LassoRegressor.tol", "modulename": "mlsauce", "qualname": "LassoRegressor.tol", "kind": "variable", "doc": "

\n"}, "mlsauce.LassoRegressor.backend": {"fullname": "mlsauce.LassoRegressor.backend", "modulename": "mlsauce", "qualname": "LassoRegressor.backend", "kind": "variable", "doc": "

\n"}, "mlsauce.LassoRegressor.fit": {"fullname": "mlsauce.LassoRegressor.fit", "modulename": "mlsauce", "qualname": "LassoRegressor.fit", "kind": "function", "doc": "

Fit matrixops (classifier) to training data (X, y)

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n    Target values.\n\n**kwargs: additional parameters to be passed to self.cook_training_set.\n
\n\n

Returns:

\n\n
self: object.\n
\n", "signature": "(self, X, y, **kwargs):", "funcdef": "def"}, "mlsauce.LassoRegressor.predict": {"fullname": "mlsauce.LassoRegressor.predict", "modulename": "mlsauce", "qualname": "LassoRegressor.predict", "kind": "function", "doc": "

Predict test data X.

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to `predict_proba`\n
\n\n

Returns:

\n\n
model predictions: {array-like}\n
\n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.LassoRegressor.set_score_request": {"fullname": "mlsauce.LassoRegressor.set_score_request", "modulename": "mlsauce", "qualname": "LassoRegressor.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.LSBoostRegressor": {"fullname": "mlsauce.LSBoostRegressor", "modulename": "mlsauce", "qualname": "LSBoostRegressor", "kind": "class", "doc": "

LSBoost regressor.

\n\n

Attributes:

\n\n
n_estimators: int\n    number of boosting iterations.\n\nlearning_rate: float\n    controls the learning speed at training time.\n\nn_hidden_features: int\n    number of nodes in successive hidden layers.\n\nreg_lambda: float\n    L2 regularization parameter for successive errors in the optimizer\n    (at training time).\n\nalpha: float\n    compromise between L1 and L2 regularization (must be in [0, 1]),\n    for `solver` == 'enet'\n\nrow_sample: float\n    percentage of rows chosen from the training set.\n\ncol_sample: float\n    percentage of columns chosen from the training set.\n\ndropout: float\n    percentage of nodes dropped from the training set.\n\ntolerance: float\n    controls early stopping in gradient descent (at training time).\n\ndirect_link: bool\n    indicates whether the original features are included (True) in model's\n    fitting or not (False).\n\nverbose: int\n    progress bar (yes = 1) or not (no = 0) (currently).\n\nseed: int\n    reproducibility seed for nodes_sim=='uniform', clustering and dropout.\n\nbackend: str\n    type of backend; must be in ('cpu', 'gpu', 'tpu')\n\nsolver: str\n    type of 'weak' learner; currently in ('ridge', 'lasso')\n\nactivation: str\n    activation function: currently 'relu', 'relu6', 'sigmoid', 'tanh'\n\ntype_pi: str.\n    type of prediction interval; currently \"kde\" (default) or \"bootstrap\".\n    Used only in `self.predict`, for `self.replications` > 0 and `self.kernel`\n    in ('gaussian', 'tophat'). Default is `None`.\n\nreplications: int.\n    number of replications (if needed) for predictive simulation.\n    Used only in `self.predict`, for `self.kernel` in ('gaussian',\n    'tophat') and `self.type_pi = 'kde'`. Default is `None`.\n\nn_clusters: int\n    number of clusters for clustering the features\n\nclustering_method: str\n    clustering method: currently 'kmeans', 'gmm'\n\ncluster_scaling: str\n    scaling method for clustering: currently 'standard', 'robust', 'minmax'\n\ndegree: int\n    degree of features interactions to include in the model\n\nweights_distr: str\n    distribution of weights for constructing the model's hidden layer;\n    either 'uniform' or 'gaussian'\n
\n\n

Examples:

\n\n
\n
import subprocess\nimport sys\nimport os\n\nsubprocess.check_call([sys.executable, "-m", "pip", "install", "matplotlib"])\n\nimport mlsauce as ms\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.datasets import load_diabetes\nfrom sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\nfrom sklearn.tree import DecisionTreeRegressor\nfrom time import time\nfrom os import chdir\nfrom sklearn import metrics\n\nregr = DecisionTreeRegressor()\n\ndiabetes = load_diabetes()\nX = diabetes.data\ny = diabetes.target\n# split data into training test and test set\nnp.random.seed(15029)\nX_train, X_test, y_train, y_test = train_test_split(X, y,\n                                                    test_size=0.2)\n\nobj = ms.GenericBoostingRegressor(regr, col_sample=0.9, row_sample=0.9)\nprint(obj.get_params())\nstart = time()\nobj.fit(X_train, y_train)\nprint(time()-start)\nstart = time()\nprint(np.sqrt(np.mean(np.square(obj.predict(X_test) - y_test))))\nprint(time()-start)\n\nprint(obj.obj['loss'])\n\nobj = ms.GenericBoostingRegressor(regr, col_sample=0.9, row_sample=0.9, n_clusters=2)\nprint(obj.get_params())\nstart = time()\nobj.fit(X_train, y_train)\nprint(time()-start)\nstart = time()\nprint(np.sqrt(np.mean(np.square(obj.predict(X_test) - y_test))))\nprint(time()-start)\n\nprint(obj.obj['loss'])\n
\n
\n
\n", "bases": "sklearn.base.BaseEstimator, sklearn.base.RegressorMixin"}, "mlsauce.LSBoostRegressor.__init__": {"fullname": "mlsauce.LSBoostRegressor.__init__", "modulename": "mlsauce", "qualname": "LSBoostRegressor.__init__", "kind": "function", "doc": "

\n", "signature": "(\tn_estimators=100,\tlearning_rate=0.1,\tn_hidden_features=5,\treg_lambda=0.1,\talpha=0.5,\trow_sample=1,\tcol_sample=1,\tdropout=0,\ttolerance=0.0001,\tdirect_link=1,\tverbose=1,\tseed=123,\tbackend='cpu',\tsolver='ridge',\tactivation='relu',\ttype_pi=None,\treplications=None,\tkernel=None,\tn_clusters=0,\tclustering_method='kmeans',\tcluster_scaling='standard',\tdegree=None,\tweights_distr='uniform',\tbase_model=None)"}, "mlsauce.LSBoostRegressor.base_model": {"fullname": "mlsauce.LSBoostRegressor.base_model", "modulename": "mlsauce", "qualname": "LSBoostRegressor.base_model", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostRegressor.n_estimators": {"fullname": "mlsauce.LSBoostRegressor.n_estimators", "modulename": "mlsauce", "qualname": "LSBoostRegressor.n_estimators", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostRegressor.learning_rate": {"fullname": "mlsauce.LSBoostRegressor.learning_rate", "modulename": "mlsauce", "qualname": "LSBoostRegressor.learning_rate", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostRegressor.n_hidden_features": {"fullname": "mlsauce.LSBoostRegressor.n_hidden_features", "modulename": "mlsauce", "qualname": "LSBoostRegressor.n_hidden_features", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostRegressor.reg_lambda": {"fullname": "mlsauce.LSBoostRegressor.reg_lambda", "modulename": "mlsauce", "qualname": "LSBoostRegressor.reg_lambda", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostRegressor.alpha": {"fullname": "mlsauce.LSBoostRegressor.alpha", "modulename": "mlsauce", "qualname": "LSBoostRegressor.alpha", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostRegressor.row_sample": {"fullname": "mlsauce.LSBoostRegressor.row_sample", "modulename": "mlsauce", "qualname": "LSBoostRegressor.row_sample", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostRegressor.col_sample": {"fullname": "mlsauce.LSBoostRegressor.col_sample", "modulename": "mlsauce", "qualname": "LSBoostRegressor.col_sample", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostRegressor.dropout": {"fullname": "mlsauce.LSBoostRegressor.dropout", "modulename": "mlsauce", "qualname": "LSBoostRegressor.dropout", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostRegressor.tolerance": {"fullname": "mlsauce.LSBoostRegressor.tolerance", "modulename": "mlsauce", "qualname": "LSBoostRegressor.tolerance", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostRegressor.direct_link": {"fullname": "mlsauce.LSBoostRegressor.direct_link", "modulename": "mlsauce", "qualname": "LSBoostRegressor.direct_link", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostRegressor.verbose": {"fullname": "mlsauce.LSBoostRegressor.verbose", "modulename": "mlsauce", "qualname": "LSBoostRegressor.verbose", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostRegressor.seed": {"fullname": "mlsauce.LSBoostRegressor.seed", "modulename": "mlsauce", "qualname": "LSBoostRegressor.seed", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostRegressor.backend": {"fullname": "mlsauce.LSBoostRegressor.backend", "modulename": "mlsauce", "qualname": "LSBoostRegressor.backend", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostRegressor.obj": {"fullname": "mlsauce.LSBoostRegressor.obj", "modulename": "mlsauce", "qualname": "LSBoostRegressor.obj", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostRegressor.solver": {"fullname": "mlsauce.LSBoostRegressor.solver", "modulename": "mlsauce", "qualname": "LSBoostRegressor.solver", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostRegressor.activation": {"fullname": "mlsauce.LSBoostRegressor.activation", "modulename": "mlsauce", "qualname": "LSBoostRegressor.activation", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostRegressor.type_pi": {"fullname": "mlsauce.LSBoostRegressor.type_pi", "modulename": "mlsauce", "qualname": "LSBoostRegressor.type_pi", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostRegressor.replications": {"fullname": "mlsauce.LSBoostRegressor.replications", "modulename": "mlsauce", "qualname": "LSBoostRegressor.replications", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostRegressor.kernel": {"fullname": "mlsauce.LSBoostRegressor.kernel", "modulename": "mlsauce", "qualname": "LSBoostRegressor.kernel", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostRegressor.n_clusters": {"fullname": "mlsauce.LSBoostRegressor.n_clusters", "modulename": "mlsauce", "qualname": "LSBoostRegressor.n_clusters", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostRegressor.clustering_method": {"fullname": "mlsauce.LSBoostRegressor.clustering_method", "modulename": "mlsauce", "qualname": "LSBoostRegressor.clustering_method", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostRegressor.cluster_scaling": {"fullname": "mlsauce.LSBoostRegressor.cluster_scaling", "modulename": "mlsauce", "qualname": "LSBoostRegressor.cluster_scaling", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostRegressor.degree": {"fullname": "mlsauce.LSBoostRegressor.degree", "modulename": "mlsauce", "qualname": "LSBoostRegressor.degree", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostRegressor.poly_": {"fullname": "mlsauce.LSBoostRegressor.poly_", "modulename": "mlsauce", "qualname": "LSBoostRegressor.poly_", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostRegressor.weights_distr": {"fullname": "mlsauce.LSBoostRegressor.weights_distr", "modulename": "mlsauce", "qualname": "LSBoostRegressor.weights_distr", "kind": "variable", "doc": "

\n"}, "mlsauce.LSBoostRegressor.fit": {"fullname": "mlsauce.LSBoostRegressor.fit", "modulename": "mlsauce", "qualname": "LSBoostRegressor.fit", "kind": "function", "doc": "

Fit Booster (regressor) to training data (X, y)

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n   Target values.\n\n**kwargs: additional parameters to be passed to self.cook_training_set.\n
\n\n

Returns:

\n\n
self: object.\n
\n", "signature": "(self, X, y, **kwargs):", "funcdef": "def"}, "mlsauce.LSBoostRegressor.predict": {"fullname": "mlsauce.LSBoostRegressor.predict", "modulename": "mlsauce", "qualname": "LSBoostRegressor.predict", "kind": "function", "doc": "

Predict probabilities for test data X.

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\nlevel: int\n    Level of confidence (default = 95)\n\nmethod: str\n    `None`, or 'splitconformal', 'localconformal'\n    prediction (if you specify `return_pi = True`)\n\n**kwargs: additional parameters to be passed to\n    self.cook_test_set\n
\n\n

Returns:

\n\n
probability estimates for test data: {array-like}\n
\n", "signature": "(self, X, level=95, method=None, **kwargs):", "funcdef": "def"}, "mlsauce.LSBoostRegressor.update": {"fullname": "mlsauce.LSBoostRegressor.update", "modulename": "mlsauce", "qualname": "LSBoostRegressor.update", "kind": "function", "doc": "

Update model with new data.

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples=1, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: float = [n_samples=1]\n   Target value.\n\neta: float\n    Inverse power applied to number of observations\n    (defines a learning rate).\n
\n\n

Returns:

\n\n
self: object.\n
\n", "signature": "(self, X, y, eta=0.9):", "funcdef": "def"}, "mlsauce.LSBoostRegressor.set_predict_request": {"fullname": "mlsauce.LSBoostRegressor.set_predict_request", "modulename": "mlsauce", "qualname": "LSBoostRegressor.set_predict_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.LSBoostRegressor.set_score_request": {"fullname": "mlsauce.LSBoostRegressor.set_score_request", "modulename": "mlsauce", "qualname": "LSBoostRegressor.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.RidgeRegressor": {"fullname": "mlsauce.RidgeRegressor", "modulename": "mlsauce", "qualname": "RidgeRegressor", "kind": "class", "doc": "

Ridge.

\n\n

Attributes:

\n\n
reg_lambda: float\n    regularization parameter.\n\nbackend: str\n    type of backend; must be in ('cpu', 'gpu', 'tpu')\n
\n", "bases": "sklearn.base.BaseEstimator, sklearn.base.RegressorMixin"}, "mlsauce.RidgeRegressor.__init__": {"fullname": "mlsauce.RidgeRegressor.__init__", "modulename": "mlsauce", "qualname": "RidgeRegressor.__init__", "kind": "function", "doc": "

\n", "signature": "(reg_lambda=0.1, backend='cpu')"}, "mlsauce.RidgeRegressor.reg_lambda": {"fullname": "mlsauce.RidgeRegressor.reg_lambda", "modulename": "mlsauce", "qualname": "RidgeRegressor.reg_lambda", "kind": "variable", "doc": "

\n"}, "mlsauce.RidgeRegressor.backend": {"fullname": "mlsauce.RidgeRegressor.backend", "modulename": "mlsauce", "qualname": "RidgeRegressor.backend", "kind": "variable", "doc": "

\n"}, "mlsauce.RidgeRegressor.fit": {"fullname": "mlsauce.RidgeRegressor.fit", "modulename": "mlsauce", "qualname": "RidgeRegressor.fit", "kind": "function", "doc": "

Fit matrixops (classifier) to training data (X, y)

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n    Target values.\n\n**kwargs: additional parameters to be passed to self.cook_training_set.\n
\n\n

Returns:

\n\n
self: object.\n
\n", "signature": "(self, X, y, **kwargs):", "funcdef": "def"}, "mlsauce.RidgeRegressor.predict": {"fullname": "mlsauce.RidgeRegressor.predict", "modulename": "mlsauce", "qualname": "RidgeRegressor.predict", "kind": "function", "doc": "

Predict test data X.

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to `predict_proba`\n
\n\n

Returns:

\n\n
model predictions: {array-like}\n
\n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.RidgeRegressor.set_score_request": {"fullname": "mlsauce.RidgeRegressor.set_score_request", "modulename": "mlsauce", "qualname": "RidgeRegressor.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.LazyBoostingClassifier": {"fullname": "mlsauce.LazyBoostingClassifier", "modulename": "mlsauce", "qualname": "LazyBoostingClassifier", "kind": "class", "doc": "

Fitting -- almost -- all the classification algorithms\n and returning their scores.

\n\n
Parameters:\n\n    verbose: int, optional (default=0)\n        Any positive number for verbosity.\n\n    ignore_warnings: bool, optional (default=True)\n        When set to True, the warning related to algorigms that are not\n        able to run are ignored.\n\n    custom_metric: function, optional (default=None)\n        When function is provided, models are evaluated based on the custom\n          evaluation metric provided.\n\n    predictions: bool, optional (default=False)\n        When set to True, the predictions of all the models models are\n        returned as data frame.\n\n    sort_by: string, optional (default='Accuracy')\n        Sort models by a metric. Available options are 'Accuracy',\n        'Balanced Accuracy', 'ROC AUC', 'F1 Score' or a custom metric\n        identified by its name and provided by custom_metric.\n\n    random_state: int, optional (default=42)\n        Reproducibiility seed.\n\n    estimators: list, optional (default='all')\n        list of Estimators names or just 'all' for > 90 classifiers\n        (default='all')\n\n    preprocess: bool, preprocessing is done when set to True\n\n    n_jobs: int, when possible, run in parallel\n        For now, only used by individual models that support it.\n\n    n_layers: int, optional (default=3)\n        Number of layers of GenericBoostingClassifiers to be used.\n\n    All the other parameters are the same as GenericBoostingClassifier's.\n\nAttributes:\n\n    models_: dict-object\n        Returns a dictionary with each model pipeline as value\n        with key as name of models.\n\n    best_model_: object\n        Returns the best model pipeline.\n\nExamples\n\n\n    
\n
        import os\n        import mlsauce as ms\n        from sklearn.datasets import load_breast_cancer, load_iris, load_wine, load_digits\n        from sklearn.model_selection import train_test_split\n        from time import time\n\n        load_models = [load_breast_cancer, load_iris, load_wine]\n\n        for model in load_models:\n\n            data = model()\n            X = data.data\n            y= data.target\n\n            X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .2, random_state = 13)\n\n            clf = ms.LazyBoostingClassifier(verbose=1, ignore_warnings=False,\n                                            custom_metric=None, preprocess=False)\n\n            start = time()\n            models, predictioms = clf.fit(X_train, X_test, y_train, y_test)\n            print(f"\nElapsed: {time() - start} seconds\n")\n\n            print(models)\n
\n
\n
\n", "bases": "sklearn.base.ClassifierMixin"}, "mlsauce.LazyBoostingClassifier.__init__": {"fullname": "mlsauce.LazyBoostingClassifier.__init__", "modulename": "mlsauce", "qualname": "LazyBoostingClassifier.__init__", "kind": "function", "doc": "

\n", "signature": "(\tverbose=0,\tignore_warnings=True,\tcustom_metric=None,\tpredictions=False,\tsort_by='Accuracy',\trandom_state=42,\testimators='all',\tpreprocess=False,\tn_jobs=None)"}, "mlsauce.LazyBoostingClassifier.verbose": {"fullname": "mlsauce.LazyBoostingClassifier.verbose", "modulename": "mlsauce", "qualname": "LazyBoostingClassifier.verbose", "kind": "variable", "doc": "

\n"}, "mlsauce.LazyBoostingClassifier.ignore_warnings": {"fullname": "mlsauce.LazyBoostingClassifier.ignore_warnings", "modulename": "mlsauce", "qualname": "LazyBoostingClassifier.ignore_warnings", "kind": "variable", "doc": "

\n"}, "mlsauce.LazyBoostingClassifier.custom_metric": {"fullname": "mlsauce.LazyBoostingClassifier.custom_metric", "modulename": "mlsauce", "qualname": "LazyBoostingClassifier.custom_metric", "kind": "variable", "doc": "

\n"}, "mlsauce.LazyBoostingClassifier.predictions": {"fullname": "mlsauce.LazyBoostingClassifier.predictions", "modulename": "mlsauce", "qualname": "LazyBoostingClassifier.predictions", "kind": "variable", "doc": "

\n"}, "mlsauce.LazyBoostingClassifier.sort_by": {"fullname": "mlsauce.LazyBoostingClassifier.sort_by", "modulename": "mlsauce", "qualname": "LazyBoostingClassifier.sort_by", "kind": "variable", "doc": "

\n"}, "mlsauce.LazyBoostingClassifier.models_": {"fullname": "mlsauce.LazyBoostingClassifier.models_", "modulename": "mlsauce", "qualname": "LazyBoostingClassifier.models_", "kind": "variable", "doc": "

\n"}, "mlsauce.LazyBoostingClassifier.best_model_": {"fullname": "mlsauce.LazyBoostingClassifier.best_model_", "modulename": "mlsauce", "qualname": "LazyBoostingClassifier.best_model_", "kind": "variable", "doc": "

\n"}, "mlsauce.LazyBoostingClassifier.random_state": {"fullname": "mlsauce.LazyBoostingClassifier.random_state", "modulename": "mlsauce", "qualname": "LazyBoostingClassifier.random_state", "kind": "variable", "doc": "

\n"}, "mlsauce.LazyBoostingClassifier.estimators": {"fullname": "mlsauce.LazyBoostingClassifier.estimators", "modulename": "mlsauce", "qualname": "LazyBoostingClassifier.estimators", "kind": "variable", "doc": "

\n"}, "mlsauce.LazyBoostingClassifier.preprocess": {"fullname": "mlsauce.LazyBoostingClassifier.preprocess", "modulename": "mlsauce", "qualname": "LazyBoostingClassifier.preprocess", "kind": "variable", "doc": "

\n"}, "mlsauce.LazyBoostingClassifier.n_jobs": {"fullname": "mlsauce.LazyBoostingClassifier.n_jobs", "modulename": "mlsauce", "qualname": "LazyBoostingClassifier.n_jobs", "kind": "variable", "doc": "

\n"}, "mlsauce.LazyBoostingClassifier.fit": {"fullname": "mlsauce.LazyBoostingClassifier.fit", "modulename": "mlsauce", "qualname": "LazyBoostingClassifier.fit", "kind": "function", "doc": "

Fit classifiers to X_train and y_train, predict and score on X_test,\ny_test.

\n\n

Parameters:

\n\n
X_train: array-like,\n    Training vectors, where rows is the number of samples\n    and columns is the number of features.\n\nX_test: array-like,\n    Testing vectors, where rows is the number of samples\n    and columns is the number of features.\n\ny_train: array-like,\n    Training vectors, where rows is the number of samples\n    and columns is the number of features.\n\ny_test: array-like,\n    Testing vectors, where rows is the number of samples\n    and columns is the number of features.\n\n**kwargs: dict,\n    Additional arguments to be passed to the fit GenericBoostingClassifier.\n
\n\n

Returns:

\n\n
scores: Pandas DataFrame\n    Returns metrics of all the models in a Pandas DataFrame.\n\npredictions: Pandas DataFrame\n    Returns predictions of all the models in a Pandas DataFrame.\n
\n", "signature": "(self, X_train, X_test, y_train, y_test, **kwargs):", "funcdef": "def"}, "mlsauce.LazyBoostingClassifier.get_best_model": {"fullname": "mlsauce.LazyBoostingClassifier.get_best_model", "modulename": "mlsauce", "qualname": "LazyBoostingClassifier.get_best_model", "kind": "function", "doc": "

This function returns the best model pipeline based on the sort_by metric.

\n\n

Returns:

\n\n
best_model: object,\n    Returns the best model pipeline based on the sort_by metric.\n
\n", "signature": "(self):", "funcdef": "def"}, "mlsauce.LazyBoostingClassifier.provide_models": {"fullname": "mlsauce.LazyBoostingClassifier.provide_models", "modulename": "mlsauce", "qualname": "LazyBoostingClassifier.provide_models", "kind": "function", "doc": "

Returns all the model objects trained. If fit hasn't been called yet,\nthen it's called to return the models.

\n\n

Parameters:

\n\n

X_train: array-like,\n Training vectors, where rows is the number of samples\n and columns is the number of features.

\n\n

X_test: array-like,\n Testing vectors, where rows is the number of samples\n and columns is the number of features.

\n\n

y_train: array-like,\n Training vectors, where rows is the number of samples\n and columns is the number of features.

\n\n

y_test: array-like,\n Testing vectors, where rows is the number of samples\n and columns is the number of features.

\n\n

Returns:

\n\n
models: dict-object,\n    Returns a dictionary with each model's pipeline as value\n    and key = name of the model.\n
\n", "signature": "(self, X_train, X_test, y_train, y_test):", "funcdef": "def"}, "mlsauce.LazyBoostingClassifier.train_model": {"fullname": "mlsauce.LazyBoostingClassifier.train_model", "modulename": "mlsauce", "qualname": "LazyBoostingClassifier.train_model", "kind": "function", "doc": "

Function to train a single model and return its results.

\n", "signature": "(\tself,\tname,\tmodel,\tX_train,\ty_train,\tX_test,\ty_test,\tuse_preprocessing=False,\tpreprocessor=None,\t**kwargs):", "funcdef": "def"}, "mlsauce.LazyBoostingRegressor": {"fullname": "mlsauce.LazyBoostingRegressor", "modulename": "mlsauce", "qualname": "LazyBoostingRegressor", "kind": "class", "doc": "

Fitting -- almost -- all the regression algorithms\n and returning their scores.

\n\n

Parameters:

\n\n
verbose: int, optional (default=0)\n    Any positive number for verbosity.\n\nignore_warnings: bool, optional (default=True)\n    When set to True, the warning related to algorigms that are not able to run are ignored.\n\ncustom_metric: function, optional (default=None)\n    When function is provided, models are evaluated based on the custom evaluation metric provided.\n\npredictions: bool, optional (default=False)\n    When set to True, the predictions of all the models models are returned as dataframe.\n\nsort_by: string, optional (default='RMSE')\n    Sort models by a metric. Available options are 'R-Squared', 'Adjusted R-Squared', 'RMSE', 'Time Taken' and 'Custom Metric'.\n    or a custom metric identified by its name and provided by custom_metric.\n\nrandom_state: int, optional (default=42)\n    Reproducibiility seed.\n\nestimators: list, optional (default='all')\n    list of Estimators names or just 'all' (default='all')\n\npreprocess: bool\n    preprocessing is done when set to True\n\nn_jobs : int, when possible, run in parallel\n    For now, only used by individual models that support it.\n\nn_layers: int, optional (default=3)\n    Number of layers of CustomRegressors to be used.\n\nAll the other parameters are the same as CustomRegressor's.\n
\n\n

Attributes:

\n\n
models_: dict-object\n    Returns a dictionary with each model pipeline as value\n    with key as name of models.\n\nbest_model_: object\n    Returns the best model pipeline based on the sort_by metric.\n
\n\n

Examples:

\n\n
\n
import os\nimport mlsauce as ms\nfrom sklearn.datasets import load_diabetes\nfrom sklearn.model_selection import train_test_split\n\ndata = load_diabetes()\nX = data.data\ny= data.target\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .2, random_state = 123)\n\nregr = ms.LazyBoostingRegressor(verbose=0, ignore_warnings=True,\n                                custom_metric=None, preprocess=True)\nmodels, predictioms = regr.fit(X_train, X_test, y_train, y_test)\nmodel_dictionary = regr.provide_models(X_train, X_test, y_train, y_test)\nprint(models)\n
\n
\n
\n", "bases": "sklearn.base.RegressorMixin"}, "mlsauce.LazyBoostingRegressor.__init__": {"fullname": "mlsauce.LazyBoostingRegressor.__init__", "modulename": "mlsauce", "qualname": "LazyBoostingRegressor.__init__", "kind": "function", "doc": "

\n", "signature": "(\tverbose=0,\tignore_warnings=True,\tcustom_metric=None,\tpredictions=False,\tsort_by='RMSE',\trandom_state=42,\testimators='all',\tpreprocess=False,\tn_jobs=None)"}, "mlsauce.LazyBoostingRegressor.verbose": {"fullname": "mlsauce.LazyBoostingRegressor.verbose", "modulename": "mlsauce", "qualname": "LazyBoostingRegressor.verbose", "kind": "variable", "doc": "

\n"}, "mlsauce.LazyBoostingRegressor.ignore_warnings": {"fullname": "mlsauce.LazyBoostingRegressor.ignore_warnings", "modulename": "mlsauce", "qualname": "LazyBoostingRegressor.ignore_warnings", "kind": "variable", "doc": "

\n"}, "mlsauce.LazyBoostingRegressor.custom_metric": {"fullname": "mlsauce.LazyBoostingRegressor.custom_metric", "modulename": "mlsauce", "qualname": "LazyBoostingRegressor.custom_metric", "kind": "variable", "doc": "

\n"}, "mlsauce.LazyBoostingRegressor.predictions": {"fullname": "mlsauce.LazyBoostingRegressor.predictions", "modulename": "mlsauce", "qualname": "LazyBoostingRegressor.predictions", "kind": "variable", "doc": "

\n"}, "mlsauce.LazyBoostingRegressor.sort_by": {"fullname": "mlsauce.LazyBoostingRegressor.sort_by", "modulename": "mlsauce", "qualname": "LazyBoostingRegressor.sort_by", "kind": "variable", "doc": "

\n"}, "mlsauce.LazyBoostingRegressor.models_": {"fullname": "mlsauce.LazyBoostingRegressor.models_", "modulename": "mlsauce", "qualname": "LazyBoostingRegressor.models_", "kind": "variable", "doc": "

\n"}, "mlsauce.LazyBoostingRegressor.best_model_": {"fullname": "mlsauce.LazyBoostingRegressor.best_model_", "modulename": "mlsauce", "qualname": "LazyBoostingRegressor.best_model_", "kind": "variable", "doc": "

\n"}, "mlsauce.LazyBoostingRegressor.random_state": {"fullname": "mlsauce.LazyBoostingRegressor.random_state", "modulename": "mlsauce", "qualname": "LazyBoostingRegressor.random_state", "kind": "variable", "doc": "

\n"}, "mlsauce.LazyBoostingRegressor.estimators": {"fullname": "mlsauce.LazyBoostingRegressor.estimators", "modulename": "mlsauce", "qualname": "LazyBoostingRegressor.estimators", "kind": "variable", "doc": "

\n"}, "mlsauce.LazyBoostingRegressor.preprocess": {"fullname": "mlsauce.LazyBoostingRegressor.preprocess", "modulename": "mlsauce", "qualname": "LazyBoostingRegressor.preprocess", "kind": "variable", "doc": "

\n"}, "mlsauce.LazyBoostingRegressor.n_jobs": {"fullname": "mlsauce.LazyBoostingRegressor.n_jobs", "modulename": "mlsauce", "qualname": "LazyBoostingRegressor.n_jobs", "kind": "variable", "doc": "

\n"}, "mlsauce.LazyBoostingRegressor.fit": {"fullname": "mlsauce.LazyBoostingRegressor.fit", "modulename": "mlsauce", "qualname": "LazyBoostingRegressor.fit", "kind": "function", "doc": "

Fit Regression algorithms to X_train and y_train, predict and score on X_test, y_test.

\n\n

Parameters:

\n\n
X_train : array-like,\n    Training vectors, where rows is the number of samples\n    and columns is the number of features.\n\nX_test : array-like,\n    Testing vectors, where rows is the number of samples\n    and columns is the number of features.\n\ny_train : array-like,\n    Training vectors, where rows is the number of samples\n    and columns is the number of features.\n\ny_test : array-like,\n    Testing vectors, where rows is the number of samples\n    and columns is the number of features.\n\n**kwargs: dict,\n    Additional parameters to be passed to the GenericBoostingRegressor.\n
\n\n

Returns:

\n\n

scores: Pandas DataFrame\n Returns metrics of all the models in a Pandas DataFrame.

\n\n

predictions : Pandas DataFrame\n Returns predictions of all the models in a Pandas DataFrame.

\n", "signature": "(self, X_train, X_test, y_train, y_test, **kwargs):", "funcdef": "def"}, "mlsauce.LazyBoostingRegressor.get_best_model": {"fullname": "mlsauce.LazyBoostingRegressor.get_best_model", "modulename": "mlsauce", "qualname": "LazyBoostingRegressor.get_best_model", "kind": "function", "doc": "

This function returns the best model pipeline based on the sort_by metric.

\n\n

Returns:

\n\n
best_model: object,\n    Returns the best model pipeline based on the sort_by metric.\n
\n", "signature": "(self):", "funcdef": "def"}, "mlsauce.LazyBoostingRegressor.provide_models": {"fullname": "mlsauce.LazyBoostingRegressor.provide_models", "modulename": "mlsauce", "qualname": "LazyBoostingRegressor.provide_models", "kind": "function", "doc": "

This function returns all the model objects trained in fit function.\nIf fit is not called already, then we call fit and then return the models.

\n\n

Parameters:

\n\n
X_train : array-like,\n    Training vectors, where rows is the number of samples\n    and columns is the number of features.\n\nX_test : array-like,\n    Testing vectors, where rows is the number of samples\n    and columns is the number of features.\n\ny_train : array-like,\n    Training vectors, where rows is the number of samples\n    and columns is the number of features.\n\ny_test : array-like,\n    Testing vectors, where rows is the number of samples\n    and columns is the number of features.\n
\n\n

Returns:

\n\n
models: dict-object,\n    Returns a dictionary with each model pipeline as value\n    with key as name of models.\n
\n", "signature": "(self, X_train, X_test, y_train, y_test):", "funcdef": "def"}, "mlsauce.LazyBoostingRegressor.train_model": {"fullname": "mlsauce.LazyBoostingRegressor.train_model", "modulename": "mlsauce", "qualname": "LazyBoostingRegressor.train_model", "kind": "function", "doc": "

Function to train a single regression model and return its results.

\n", "signature": "(\tself,\tname,\tregr,\tX_train,\ty_train,\tX_test,\ty_test,\tuse_preprocessing=False,\tpreprocessor=None,\t**kwargs):", "funcdef": "def"}, "mlsauce.MultiTaskRegressor": {"fullname": "mlsauce.MultiTaskRegressor", "modulename": "mlsauce", "qualname": "MultiTaskRegressor", "kind": "class", "doc": "

A class for multi-task regression

\n\n

Parameters

\n\n

regr: object\n A regressor object

\n\n

Attributes

\n\n

objs: list\n A list containing the fitted regressor objects

\n", "bases": "sklearn.base.BaseEstimator, sklearn.base.RegressorMixin"}, "mlsauce.MultiTaskRegressor.__init__": {"fullname": "mlsauce.MultiTaskRegressor.__init__", "modulename": "mlsauce", "qualname": "MultiTaskRegressor.__init__", "kind": "function", "doc": "

\n", "signature": "(regr)"}, "mlsauce.MultiTaskRegressor.regr": {"fullname": "mlsauce.MultiTaskRegressor.regr", "modulename": "mlsauce", "qualname": "MultiTaskRegressor.regr", "kind": "variable", "doc": "

\n"}, "mlsauce.MultiTaskRegressor.objs": {"fullname": "mlsauce.MultiTaskRegressor.objs", "modulename": "mlsauce", "qualname": "MultiTaskRegressor.objs", "kind": "variable", "doc": "

\n"}, "mlsauce.MultiTaskRegressor.fit": {"fullname": "mlsauce.MultiTaskRegressor.fit", "modulename": "mlsauce", "qualname": "MultiTaskRegressor.fit", "kind": "function", "doc": "

Fit the regressor

\n\n

Parameters

\n\n

X: array-like\n The input data\ny: array-like\n The target values

\n", "signature": "(self, X, y):", "funcdef": "def"}, "mlsauce.MultiTaskRegressor.predict": {"fullname": "mlsauce.MultiTaskRegressor.predict", "modulename": "mlsauce", "qualname": "MultiTaskRegressor.predict", "kind": "function", "doc": "

Predict the target values

\n\n

Parameters

\n\n

X: array-like\n The input data

\n\n

Returns

\n\n

y_pred: array-like\n The predicted target values

\n", "signature": "(self, X):", "funcdef": "def"}, "mlsauce.MultiTaskRegressor.set_score_request": {"fullname": "mlsauce.MultiTaskRegressor.set_score_request", "modulename": "mlsauce", "qualname": "MultiTaskRegressor.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.download": {"fullname": "mlsauce.download", "modulename": "mlsauce", "qualname": "download", "kind": "function", "doc": "

\n", "signature": "(\tpkgname='MASS',\tdataset='Boston',\tsource='https://cran.r-universe.dev/',\t**kwargs):", "funcdef": "def"}, "mlsauce.get_config": {"fullname": "mlsauce.get_config", "modulename": "mlsauce", "qualname": "get_config", "kind": "function", "doc": "

Retrieve current values for configuration set by set_config()

\n\n

Returns

\n\n

config : dict\n Keys are parameter names that can be passed to set_config().

\n\n

See Also

\n\n

config_context: Context manager for global mlsauce configuration\nset_config: Set global mlsauce configuration

\n", "signature": "():", "funcdef": "def"}, "mlsauce.set_config": {"fullname": "mlsauce.set_config", "modulename": "mlsauce", "qualname": "set_config", "kind": "function", "doc": "

Set global mlsauce configuration

\n\n

New in version 0.3.0.

\n\n

Parameters

\n\n

assume_finite : bool, optional\n If True, validation for finiteness will be skipped,\n saving time, but leading to potential crashes. If\n False, validation for finiteness will be performed,\n avoiding error. Global default: False.

\n\n
*New in version 0.3.0.*\n
\n\n

working_memory : int, optional\n If set, mlsauce will attempt to limit the size of temporary arrays\n to this number of MiB (per job when parallelised), often saving both\n computation time and memory on expensive operations that can be\n performed in chunks. Global default: 1024.

\n\n
*New in version 0.3.0.*\n
\n\n

print_changed_only : bool, optional\n If True, only the parameters that were set to non-default\n values will be printed when printing an estimator. For example,\n print(SVC()) while True will only print 'SVC()' while the default\n behaviour would be to print 'SVC(C=1.0, cache_size=200, ...)' with\n all the non-changed parameters.

\n\n
*New in version 0.3.0.*\n
\n\n

display : {'text', 'diagram'}, optional\n If 'diagram', estimators will be displayed as text in a jupyter lab\n of notebook context. If 'text', estimators will be displayed as\n text. Default is 'text'.

\n\n
*New in version 0.3.0.*\n
\n\n

See Also

\n\n

config_context: Context manager for global mlsauce configuration\nget_config: Retrieve current values of the global configuration

\n", "signature": "(\tassume_finite=None,\tworking_memory=None,\tprint_changed_only=None,\tdisplay=None):", "funcdef": "def"}, "mlsauce.config_context": {"fullname": "mlsauce.config_context", "modulename": "mlsauce", "qualname": "config_context", "kind": "function", "doc": "

Context manager for global mlsauce configuration

\n\n

Parameters

\n\n

assume_finite : bool, optional\n If True, validation for finiteness will be skipped,\n saving time, but leading to potential crashes. If\n False, validation for finiteness will be performed,\n avoiding error. Global default: False.

\n\n

working_memory : int, optional\n If set, mlsauce will attempt to limit the size of temporary arrays\n to this number of MiB (per job when parallelised), often saving both\n computation time and memory on expensive operations that can be\n performed in chunks. Global default: 1024.

\n\n

print_changed_only : bool, optional\n If True, only the parameters that were set to non-default\n values will be printed when printing an estimator. For example,\n print(SVC()) while True will only print 'SVC()', but would print\n 'SVC(C=1.0, cache_size=200, ...)' with all the non-changed parameters\n when False. Default is True.

\n\n
*New in version 0.3.0.*\n
\n\n

display : {'text', 'diagram'}, optional\n If 'diagram', estimators will be displayed as text in a jupyter lab\n of notebook context. If 'text', estimators will be displayed as\n text. Default is 'text'.

\n\n
*New in version 0.3.0.*\n
\n\n

Notes

\n\n

All settings, not just those presently modified, will be returned to\ntheir previous values when the context manager is exited. This is not\nthread-safe.

\n\n

Examples

\n\n
\n
>>> import mlsauce\n>>> from mlsauce.utils.validation import assert_all_finite\n>>> with mlsauce.config_context(assume_finite=True):\n...     assert_all_finite([float('nan')])\n>>> with mlsauce.config_context(assume_finite=True):\n...     with mlsauce.config_context(assume_finite=False):\n...         assert_all_finite([float('nan')])\nTraceback (most recent call last):\n...\nValueError: Input contains NaN, ...\n
\n
\n\n

See Also

\n\n

set_config: Set global mlsauce configuration\nget_config: Retrieve current values of the global configuration

\n", "signature": "(**new_config):", "funcdef": "def"}, "mlsauce.adaopt": {"fullname": "mlsauce.adaopt", "modulename": "mlsauce.adaopt", "kind": "module", "doc": "

\n"}, "mlsauce.adaopt.AdaOpt": {"fullname": "mlsauce.adaopt.AdaOpt", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt", "kind": "class", "doc": "

AdaOpt classifier.

\n\n

Attributes:

\n\n
n_iterations: int\n    number of iterations of the optimizer at training time.\n\nlearning_rate: float\n    controls the speed of the optimizer at training time.\n\nreg_lambda: float\n    L2 regularization parameter for successive errors in the optimizer\n    (at training time).\n\nreg_alpha: float\n    L1 regularization parameter for successive errors in the optimizer\n    (at training time).\n\neta: float\n    controls the slope in gradient descent (at training time).\n\ngamma: float\n    controls the step size in gradient descent (at training time).\n\nk: int\n    number of nearest neighbors selected at test time for classification.\n\ntolerance: float\n    controls early stopping in gradient descent (at training time).\n\nn_clusters: int\n    number of clusters, if MiniBatch k-means is used at test time\n    (for faster prediction).\n\nbatch_size: int\n    size of the batch, if MiniBatch k-means is used at test time\n    (for faster prediction).\n\nrow_sample: float\n    percentage of rows chosen from training set (by stratified subsampling,\n    for faster prediction).\n\ntype_dist: str\n    distance used for finding the nearest neighbors; currently `euclidean-f`\n    (euclidean distances calculated as whole), `euclidean` (euclidean distances\n    calculated row by row), `cosine` (cosine distance).\n\nn_jobs: int\n    number of cpus for parallel processing (default: None)\n\nverbose: int\n    progress bar for parallel processing (yes = 1) or not (no = 0)\n\ncache: boolean\n    if the nearest neighbors are cached or not, for faster retrieval in\n    subsequent calls.\n\nn_clusters_input: int\n    number of clusters (a priori) for clustering the features\n\nclustering_method: str\n    clustering method: currently 'kmeans', 'gmm'\n\ncluster_scaling: str\n    scaling method for clustering: currently 'standard', 'robust', 'minmax'\n\nseed: int\n    reproducibility seed for nodes_sim=='uniform', clustering and dropout.\n
\n", "bases": "sklearn.base.BaseEstimator, sklearn.base.ClassifierMixin"}, "mlsauce.adaopt.AdaOpt.__init__": {"fullname": "mlsauce.adaopt.AdaOpt.__init__", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.__init__", "kind": "function", "doc": "

\n", "signature": "(\tn_iterations=50,\tlearning_rate=0.3,\treg_lambda=0.1,\treg_alpha=0.5,\teta=0.01,\tgamma=0.01,\tk=3,\ttolerance=0,\tn_clusters=0,\tbatch_size=100,\trow_sample=0.8,\ttype_dist='euclidean-f',\tn_jobs=None,\tverbose=0,\tcache=True,\tn_clusters_input=0,\tclustering_method='kmeans',\tcluster_scaling='standard',\tseed=123)"}, "mlsauce.adaopt.AdaOpt.n_iterations": {"fullname": "mlsauce.adaopt.AdaOpt.n_iterations", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.n_iterations", "kind": "variable", "doc": "

\n"}, "mlsauce.adaopt.AdaOpt.learning_rate": {"fullname": "mlsauce.adaopt.AdaOpt.learning_rate", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.learning_rate", "kind": "variable", "doc": "

\n"}, "mlsauce.adaopt.AdaOpt.reg_lambda": {"fullname": "mlsauce.adaopt.AdaOpt.reg_lambda", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.reg_lambda", "kind": "variable", "doc": "

\n"}, "mlsauce.adaopt.AdaOpt.reg_alpha": {"fullname": "mlsauce.adaopt.AdaOpt.reg_alpha", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.reg_alpha", "kind": "variable", "doc": "

\n"}, "mlsauce.adaopt.AdaOpt.eta": {"fullname": "mlsauce.adaopt.AdaOpt.eta", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.eta", "kind": "variable", "doc": "

\n"}, "mlsauce.adaopt.AdaOpt.gamma": {"fullname": "mlsauce.adaopt.AdaOpt.gamma", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.gamma", "kind": "variable", "doc": "

\n"}, "mlsauce.adaopt.AdaOpt.k": {"fullname": "mlsauce.adaopt.AdaOpt.k", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.k", "kind": "variable", "doc": "

\n"}, "mlsauce.adaopt.AdaOpt.tolerance": {"fullname": "mlsauce.adaopt.AdaOpt.tolerance", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.tolerance", "kind": "variable", "doc": "

\n"}, "mlsauce.adaopt.AdaOpt.n_clusters": {"fullname": "mlsauce.adaopt.AdaOpt.n_clusters", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.n_clusters", "kind": "variable", "doc": "

\n"}, "mlsauce.adaopt.AdaOpt.batch_size": {"fullname": "mlsauce.adaopt.AdaOpt.batch_size", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.batch_size", "kind": "variable", "doc": "

\n"}, "mlsauce.adaopt.AdaOpt.row_sample": {"fullname": "mlsauce.adaopt.AdaOpt.row_sample", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.row_sample", "kind": "variable", "doc": "

\n"}, "mlsauce.adaopt.AdaOpt.type_dist": {"fullname": "mlsauce.adaopt.AdaOpt.type_dist", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.type_dist", "kind": "variable", "doc": "

\n"}, "mlsauce.adaopt.AdaOpt.n_jobs": {"fullname": "mlsauce.adaopt.AdaOpt.n_jobs", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.n_jobs", "kind": "variable", "doc": "

\n"}, "mlsauce.adaopt.AdaOpt.cache": {"fullname": "mlsauce.adaopt.AdaOpt.cache", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.cache", "kind": "variable", "doc": "

\n"}, "mlsauce.adaopt.AdaOpt.verbose": {"fullname": "mlsauce.adaopt.AdaOpt.verbose", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.verbose", "kind": "variable", "doc": "

\n"}, "mlsauce.adaopt.AdaOpt.n_clusters_input": {"fullname": "mlsauce.adaopt.AdaOpt.n_clusters_input", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.n_clusters_input", "kind": "variable", "doc": "

\n"}, "mlsauce.adaopt.AdaOpt.clustering_method": {"fullname": "mlsauce.adaopt.AdaOpt.clustering_method", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.clustering_method", "kind": "variable", "doc": "

\n"}, "mlsauce.adaopt.AdaOpt.cluster_scaling": {"fullname": "mlsauce.adaopt.AdaOpt.cluster_scaling", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.cluster_scaling", "kind": "variable", "doc": "

\n"}, "mlsauce.adaopt.AdaOpt.seed": {"fullname": "mlsauce.adaopt.AdaOpt.seed", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.seed", "kind": "variable", "doc": "

\n"}, "mlsauce.adaopt.AdaOpt.fit": {"fullname": "mlsauce.adaopt.AdaOpt.fit", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.fit", "kind": "function", "doc": "

Fit AdaOpt to training data (X, y)

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n    Target values.\n\n**kwargs: additional parameters to be passed to self.cook_training_set.\n
\n\n

Returns:

\n\n
self: object.\n
\n", "signature": "(self, X, y, **kwargs):", "funcdef": "def"}, "mlsauce.adaopt.AdaOpt.predict": {"fullname": "mlsauce.adaopt.AdaOpt.predict", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.predict", "kind": "function", "doc": "

Predict test data X.

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to `predict_proba`\n
\n\n

Returns:

\n\n
model predictions: {array-like}\n
\n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.adaopt.AdaOpt.predict_proba": {"fullname": "mlsauce.adaopt.AdaOpt.predict_proba", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.predict_proba", "kind": "function", "doc": "

Predict probabilities for test data X.

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to\n    self.cook_test_set\n
\n\n

Returns:

\n\n
probability estimates for test data: {array-like}\n
\n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.adaopt.AdaOpt.set_score_request": {"fullname": "mlsauce.adaopt.AdaOpt.set_score_request", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.booster": {"fullname": "mlsauce.booster", "modulename": "mlsauce.booster", "kind": "module", "doc": "

\n"}, "mlsauce.booster.LSBoostClassifier": {"fullname": "mlsauce.booster.LSBoostClassifier", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier", "kind": "class", "doc": "

LSBoost classifier.

\n\n
Attributes:\n\n    n_estimators: int\n        number of boosting iterations.\n\n    learning_rate: float\n        controls the learning speed at training time.\n\n    n_hidden_features: int\n        number of nodes in successive hidden layers.\n\n    reg_lambda: float\n        L2 regularization parameter for successive errors in the optimizer\n        (at training time).\n\n    alpha: float\n        compromise between L1 and L2 regularization (must be in [0, 1]),\n        for `solver` == 'enet'.\n\n    row_sample: float\n        percentage of rows chosen from the training set.\n\n    col_sample: float\n        percentage of columns chosen from the training set.\n\n    dropout: float\n        percentage of nodes dropped from the training set.\n\n    tolerance: float\n        controls early stopping in gradient descent (at training time).\n\n    direct_link: bool\n        indicates whether the original features are included (True) in model's\n        fitting or not (False).\n\n    verbose: int\n        progress bar (yes = 1) or not (no = 0) (currently).\n\n    seed: int\n        reproducibility seed for nodes_sim=='uniform', clustering and dropout.\n\n    backend: str\n        type of backend; must be in ('cpu', 'gpu', 'tpu')\n\n    solver: str\n        type of 'weak' learner; currently in ('ridge', 'lasso', 'enet').\n        'enet' is a combination of 'ridge' and 'lasso' called Elastic Net.\n\n    activation: str\n        activation function: currently 'relu', 'relu6', 'sigmoid', 'tanh'\n\n    n_clusters: int\n        number of clusters for clustering the features\n\n    clustering_method: str\n        clustering method: currently 'kmeans', 'gmm'\n\n    cluster_scaling: str\n        scaling method for clustering: currently 'standard', 'robust', 'minmax'\n\n    degree: int\n        degree of features interactions to include in the model\n\n    weights_distr: str\n        distribution of weights for constructing the model's hidden layer;\n        currently 'uniform', 'gaussian'\n\nExamples:\n\n    ```python\n    import numpy as np\n    from sklearn.datasets import load_digits, load_breast_cancer, load_wine, load_iris\n    from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\n    from sklearn.tree import DecisionTreeRegressor\n    from sklearn.kernel_ridge import KernelRidge\n    from time import time\n    from os import chdir\n    from sklearn import metrics\n    import os\n\n    import mlsauce as ms\n\n    print(\"\n
\n\n

\")\n print(\"GenericBoosting Decision tree -----\")\n print(\"\n\")

\n\n
    print(\"\n
\n\n

\")\n print(\"breast_cancer data -----\")

\n\n
    # data 1\n    breast_cancer = load_breast_cancer()\n    X = breast_cancer.data\n    y = breast_cancer.target\n    # split data into training test and test set\n    np.random.seed(15029)\n    X_train, X_test, y_train, y_test = train_test_split(X, y,\n                                                        test_size=0.2)\n\n    clf = DecisionTreeRegressor()\n    clf2 = KernelRidge()\n\n    obj = ms.GenericBoostingClassifier(clf, tolerance=1e-2)\n    print(obj.get_params())\n    start = time()\n    obj.fit(X_train, y_train)\n    print(time()-start)\n    start = time()\n    print(obj.score(X_test, y_test))\n    print(time()-start)\n\n    print(obj.obj['loss'])\n\n    obj = ms.GenericBoostingClassifier(clf, tolerance=1e-2, n_clusters=2)\n    print(obj.get_params())\n    start = time()\n    obj.fit(X_train, y_train)\n    print(time()-start)\n    start = time()\n    print(obj.score(X_test, y_test))\n    print(time()-start)\n\n    print(obj.obj['loss'])\n\n\n    # data 2\n    print(\"\n
\n\n

\")\n print(\"wine data -----\")

\n\n
    wine = load_wine()\n    Z = wine.data\n    t = wine.target\n    np.random.seed(879423)\n    X_train, X_test, y_train, y_test = train_test_split(Z, t,\n                                                        test_size=0.2)\n\n    obj = ms.GenericBoostingClassifier(clf)\n    print(obj.get_params())\n    start = time()\n    obj.fit(X_train, y_train)\n    print(time()-start)\n    start = time()\n    print(obj.score(X_test, y_test))\n    print(time()-start)\n\n    print(obj.obj['loss'])\n\n    obj = ms.GenericBoostingClassifier(clf, n_clusters=3)\n    print(obj.get_params())\n    start = time()\n    obj.fit(X_train, y_train)\n    print(time()-start)\n    start = time()\n    print(obj.score(X_test, y_test))\n    print(time()-start)\n\n    print(obj.obj['loss'])\n\n    # data 3\n    print(\"\n
\n\n

\")\n print(\"iris data -----\")

\n\n
    iris = load_iris()\n    Z = iris.data\n    t = iris.target\n    np.random.seed(734563)\n    X_train, X_test, y_train, y_test = train_test_split(Z, t,\n                                                        test_size=0.2)\n\n\n    obj = ms.GenericBoostingClassifier(clf)\n    print(obj.get_params())\n    start = time()\n    obj.fit(X_train, y_train)\n    print(time()-start)\n    start = time()\n    print(obj.score(X_test, y_test))\n    print(time()-start)\n\n    print(obj.obj['loss'])\n\n\n    print(\"\n
\n\n

\")\n print(\"GenericBoosting KRR -----\")\n print(\"\n\")

\n\n
    obj = ms.GenericBoostingClassifier(clf2, tolerance=1e-2)\n    print(obj.get_params())\n    start = time()\n    obj.fit(X_train, y_train)\n    print(time()-start)\n    start = time()\n    print(obj.score(X_test, y_test))\n    print(time()-start)\n\n    print(obj.obj['loss'])\n\n    obj = ms.GenericBoostingClassifier(clf2, tolerance=1e-2, n_clusters=2)\n    print(obj.get_params())\n    start = time()\n    obj.fit(X_train, y_train)\n    print(time()-start)\n    start = time()\n    print(obj.score(X_test, y_test))\n    print(time()-start)\n\n    print(obj.obj['loss'])\n\n\n    # data 2\n    print(\"\n
\n\n

\")\n print(\"wine data -----\")

\n\n
    wine = load_wine()\n    Z = wine.data\n    t = wine.target\n    np.random.seed(879423)\n    X_train, X_test, y_train, y_test = train_test_split(Z, t,\n                                                        test_size=0.2)\n\n    obj = ms.GenericBoostingClassifier(clf2)\n    print(obj.get_params())\n    start = time()\n    obj.fit(X_train, y_train)\n    print(time()-start)\n    start = time()\n    print(obj.score(X_test, y_test))\n    print(time()-start)\n\n    print(obj.obj['loss'])\n\n    obj = ms.GenericBoostingClassifier(clf2, n_clusters=3)\n    print(obj.get_params())\n    start = time()\n    obj.fit(X_train, y_train)\n    print(time()-start)\n    start = time()\n    print(obj.score(X_test, y_test))\n    print(time()-start)\n\n    print(obj.obj['loss'])\n\n    # data 3\n    print(\"\n
\n\n

\")\n print(\"iris data -----\")

\n\n
    iris = load_iris()\n    Z = iris.data\n    t = iris.target\n    np.random.seed(734563)\n    X_train, X_test, y_train, y_test = train_test_split(Z, t,\n                                                        test_size=0.2)\n\n\n    obj = ms.GenericBoostingClassifier(clf2)\n    print(obj.get_params())\n    start = time()\n    obj.fit(X_train, y_train)\n    print(time()-start)\n    start = time()\n    print(obj.score(X_test, y_test))\n    print(time()-start)\n\n    print(obj.obj['loss'])\n```\n
\n", "bases": "sklearn.base.BaseEstimator, sklearn.base.ClassifierMixin"}, "mlsauce.booster.LSBoostClassifier.__init__": {"fullname": "mlsauce.booster.LSBoostClassifier.__init__", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.__init__", "kind": "function", "doc": "

\n", "signature": "(\tn_estimators=100,\tlearning_rate=0.1,\tn_hidden_features=5,\treg_lambda=0.1,\talpha=0.5,\trow_sample=1,\tcol_sample=1,\tdropout=0,\ttolerance=0.0001,\tdirect_link=1,\tverbose=1,\tseed=123,\tbackend='cpu',\tsolver='ridge',\tactivation='relu',\tn_clusters=0,\tclustering_method='kmeans',\tcluster_scaling='standard',\tdegree=None,\tweights_distr='uniform',\tbase_model=None)"}, "mlsauce.booster.LSBoostClassifier.base_model": {"fullname": "mlsauce.booster.LSBoostClassifier.base_model", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.base_model", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostClassifier.n_estimators": {"fullname": "mlsauce.booster.LSBoostClassifier.n_estimators", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.n_estimators", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostClassifier.learning_rate": {"fullname": "mlsauce.booster.LSBoostClassifier.learning_rate", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.learning_rate", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostClassifier.n_hidden_features": {"fullname": "mlsauce.booster.LSBoostClassifier.n_hidden_features", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.n_hidden_features", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostClassifier.reg_lambda": {"fullname": "mlsauce.booster.LSBoostClassifier.reg_lambda", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.reg_lambda", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostClassifier.alpha": {"fullname": "mlsauce.booster.LSBoostClassifier.alpha", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.alpha", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostClassifier.row_sample": {"fullname": "mlsauce.booster.LSBoostClassifier.row_sample", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.row_sample", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostClassifier.col_sample": {"fullname": "mlsauce.booster.LSBoostClassifier.col_sample", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.col_sample", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostClassifier.dropout": {"fullname": "mlsauce.booster.LSBoostClassifier.dropout", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.dropout", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostClassifier.tolerance": {"fullname": "mlsauce.booster.LSBoostClassifier.tolerance", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.tolerance", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostClassifier.direct_link": {"fullname": "mlsauce.booster.LSBoostClassifier.direct_link", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.direct_link", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostClassifier.verbose": {"fullname": "mlsauce.booster.LSBoostClassifier.verbose", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.verbose", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostClassifier.seed": {"fullname": "mlsauce.booster.LSBoostClassifier.seed", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.seed", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostClassifier.backend": {"fullname": "mlsauce.booster.LSBoostClassifier.backend", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.backend", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostClassifier.obj": {"fullname": "mlsauce.booster.LSBoostClassifier.obj", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.obj", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostClassifier.solver": {"fullname": "mlsauce.booster.LSBoostClassifier.solver", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.solver", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostClassifier.activation": {"fullname": "mlsauce.booster.LSBoostClassifier.activation", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.activation", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostClassifier.n_clusters": {"fullname": "mlsauce.booster.LSBoostClassifier.n_clusters", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.n_clusters", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostClassifier.clustering_method": {"fullname": "mlsauce.booster.LSBoostClassifier.clustering_method", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.clustering_method", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostClassifier.cluster_scaling": {"fullname": "mlsauce.booster.LSBoostClassifier.cluster_scaling", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.cluster_scaling", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostClassifier.degree": {"fullname": "mlsauce.booster.LSBoostClassifier.degree", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.degree", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostClassifier.poly_": {"fullname": "mlsauce.booster.LSBoostClassifier.poly_", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.poly_", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostClassifier.weights_distr": {"fullname": "mlsauce.booster.LSBoostClassifier.weights_distr", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.weights_distr", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostClassifier.fit": {"fullname": "mlsauce.booster.LSBoostClassifier.fit", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.fit", "kind": "function", "doc": "

Fit Booster (classifier) to training data (X, y)

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n    Target values.\n\n**kwargs: additional parameters to be passed to self.cook_training_set.\n
\n\n

Returns:

\n\n
self: object.\n
\n", "signature": "(self, X, y, **kwargs):", "funcdef": "def"}, "mlsauce.booster.LSBoostClassifier.predict": {"fullname": "mlsauce.booster.LSBoostClassifier.predict", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.predict", "kind": "function", "doc": "

Predict test data X.

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to `predict_proba`\n
\n\n

Returns:

\n\n
model predictions: {array-like}\n
\n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.booster.LSBoostClassifier.predict_proba": {"fullname": "mlsauce.booster.LSBoostClassifier.predict_proba", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.predict_proba", "kind": "function", "doc": "

Predict probabilities for test data X.

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to\n    self.cook_test_set\n
\n\n

Returns:

\n\n
probability estimates for test data: {array-like}\n
\n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.booster.LSBoostClassifier.update": {"fullname": "mlsauce.booster.LSBoostClassifier.update", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.update", "kind": "function", "doc": "

Update model with new data.

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples=1, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: float = [n_samples=1]\n   Target value.\n\neta: float\n    Inverse power applied to number of observations\n    (defines a learning rate).\n
\n\n

Returns:

\n\n
self: object.\n
\n", "signature": "(self, X, y, eta=0.9):", "funcdef": "def"}, "mlsauce.booster.LSBoostClassifier.set_score_request": {"fullname": "mlsauce.booster.LSBoostClassifier.set_score_request", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.booster.LSBoostRegressor": {"fullname": "mlsauce.booster.LSBoostRegressor", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor", "kind": "class", "doc": "

LSBoost regressor.

\n\n

Attributes:

\n\n
n_estimators: int\n    number of boosting iterations.\n\nlearning_rate: float\n    controls the learning speed at training time.\n\nn_hidden_features: int\n    number of nodes in successive hidden layers.\n\nreg_lambda: float\n    L2 regularization parameter for successive errors in the optimizer\n    (at training time).\n\nalpha: float\n    compromise between L1 and L2 regularization (must be in [0, 1]),\n    for `solver` == 'enet'\n\nrow_sample: float\n    percentage of rows chosen from the training set.\n\ncol_sample: float\n    percentage of columns chosen from the training set.\n\ndropout: float\n    percentage of nodes dropped from the training set.\n\ntolerance: float\n    controls early stopping in gradient descent (at training time).\n\ndirect_link: bool\n    indicates whether the original features are included (True) in model's\n    fitting or not (False).\n\nverbose: int\n    progress bar (yes = 1) or not (no = 0) (currently).\n\nseed: int\n    reproducibility seed for nodes_sim=='uniform', clustering and dropout.\n\nbackend: str\n    type of backend; must be in ('cpu', 'gpu', 'tpu')\n\nsolver: str\n    type of 'weak' learner; currently in ('ridge', 'lasso')\n\nactivation: str\n    activation function: currently 'relu', 'relu6', 'sigmoid', 'tanh'\n\ntype_pi: str.\n    type of prediction interval; currently \"kde\" (default) or \"bootstrap\".\n    Used only in `self.predict`, for `self.replications` > 0 and `self.kernel`\n    in ('gaussian', 'tophat'). Default is `None`.\n\nreplications: int.\n    number of replications (if needed) for predictive simulation.\n    Used only in `self.predict`, for `self.kernel` in ('gaussian',\n    'tophat') and `self.type_pi = 'kde'`. Default is `None`.\n\nn_clusters: int\n    number of clusters for clustering the features\n\nclustering_method: str\n    clustering method: currently 'kmeans', 'gmm'\n\ncluster_scaling: str\n    scaling method for clustering: currently 'standard', 'robust', 'minmax'\n\ndegree: int\n    degree of features interactions to include in the model\n\nweights_distr: str\n    distribution of weights for constructing the model's hidden layer;\n    either 'uniform' or 'gaussian'\n
\n\n

Examples:

\n\n
\n
import subprocess\nimport sys\nimport os\n\nsubprocess.check_call([sys.executable, "-m", "pip", "install", "matplotlib"])\n\nimport mlsauce as ms\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.datasets import load_diabetes\nfrom sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\nfrom sklearn.tree import DecisionTreeRegressor\nfrom time import time\nfrom os import chdir\nfrom sklearn import metrics\n\nregr = DecisionTreeRegressor()\n\ndiabetes = load_diabetes()\nX = diabetes.data\ny = diabetes.target\n# split data into training test and test set\nnp.random.seed(15029)\nX_train, X_test, y_train, y_test = train_test_split(X, y,\n                                                    test_size=0.2)\n\nobj = ms.GenericBoostingRegressor(regr, col_sample=0.9, row_sample=0.9)\nprint(obj.get_params())\nstart = time()\nobj.fit(X_train, y_train)\nprint(time()-start)\nstart = time()\nprint(np.sqrt(np.mean(np.square(obj.predict(X_test) - y_test))))\nprint(time()-start)\n\nprint(obj.obj['loss'])\n\nobj = ms.GenericBoostingRegressor(regr, col_sample=0.9, row_sample=0.9, n_clusters=2)\nprint(obj.get_params())\nstart = time()\nobj.fit(X_train, y_train)\nprint(time()-start)\nstart = time()\nprint(np.sqrt(np.mean(np.square(obj.predict(X_test) - y_test))))\nprint(time()-start)\n\nprint(obj.obj['loss'])\n
\n
\n
\n", "bases": "sklearn.base.BaseEstimator, sklearn.base.RegressorMixin"}, "mlsauce.booster.LSBoostRegressor.__init__": {"fullname": "mlsauce.booster.LSBoostRegressor.__init__", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.__init__", "kind": "function", "doc": "

\n", "signature": "(\tn_estimators=100,\tlearning_rate=0.1,\tn_hidden_features=5,\treg_lambda=0.1,\talpha=0.5,\trow_sample=1,\tcol_sample=1,\tdropout=0,\ttolerance=0.0001,\tdirect_link=1,\tverbose=1,\tseed=123,\tbackend='cpu',\tsolver='ridge',\tactivation='relu',\ttype_pi=None,\treplications=None,\tkernel=None,\tn_clusters=0,\tclustering_method='kmeans',\tcluster_scaling='standard',\tdegree=None,\tweights_distr='uniform',\tbase_model=None)"}, "mlsauce.booster.LSBoostRegressor.base_model": {"fullname": "mlsauce.booster.LSBoostRegressor.base_model", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.base_model", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostRegressor.n_estimators": {"fullname": "mlsauce.booster.LSBoostRegressor.n_estimators", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.n_estimators", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostRegressor.learning_rate": {"fullname": "mlsauce.booster.LSBoostRegressor.learning_rate", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.learning_rate", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostRegressor.n_hidden_features": {"fullname": "mlsauce.booster.LSBoostRegressor.n_hidden_features", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.n_hidden_features", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostRegressor.reg_lambda": {"fullname": "mlsauce.booster.LSBoostRegressor.reg_lambda", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.reg_lambda", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostRegressor.alpha": {"fullname": "mlsauce.booster.LSBoostRegressor.alpha", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.alpha", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostRegressor.row_sample": {"fullname": "mlsauce.booster.LSBoostRegressor.row_sample", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.row_sample", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostRegressor.col_sample": {"fullname": "mlsauce.booster.LSBoostRegressor.col_sample", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.col_sample", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostRegressor.dropout": {"fullname": "mlsauce.booster.LSBoostRegressor.dropout", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.dropout", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostRegressor.tolerance": {"fullname": "mlsauce.booster.LSBoostRegressor.tolerance", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.tolerance", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostRegressor.direct_link": {"fullname": "mlsauce.booster.LSBoostRegressor.direct_link", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.direct_link", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostRegressor.verbose": {"fullname": "mlsauce.booster.LSBoostRegressor.verbose", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.verbose", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostRegressor.seed": {"fullname": "mlsauce.booster.LSBoostRegressor.seed", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.seed", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostRegressor.backend": {"fullname": "mlsauce.booster.LSBoostRegressor.backend", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.backend", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostRegressor.obj": {"fullname": "mlsauce.booster.LSBoostRegressor.obj", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.obj", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostRegressor.solver": {"fullname": "mlsauce.booster.LSBoostRegressor.solver", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.solver", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostRegressor.activation": {"fullname": "mlsauce.booster.LSBoostRegressor.activation", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.activation", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostRegressor.type_pi": {"fullname": "mlsauce.booster.LSBoostRegressor.type_pi", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.type_pi", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostRegressor.replications": {"fullname": "mlsauce.booster.LSBoostRegressor.replications", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.replications", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostRegressor.kernel": {"fullname": "mlsauce.booster.LSBoostRegressor.kernel", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.kernel", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostRegressor.n_clusters": {"fullname": "mlsauce.booster.LSBoostRegressor.n_clusters", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.n_clusters", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostRegressor.clustering_method": {"fullname": "mlsauce.booster.LSBoostRegressor.clustering_method", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.clustering_method", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostRegressor.cluster_scaling": {"fullname": "mlsauce.booster.LSBoostRegressor.cluster_scaling", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.cluster_scaling", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostRegressor.degree": {"fullname": "mlsauce.booster.LSBoostRegressor.degree", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.degree", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostRegressor.poly_": {"fullname": "mlsauce.booster.LSBoostRegressor.poly_", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.poly_", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostRegressor.weights_distr": {"fullname": "mlsauce.booster.LSBoostRegressor.weights_distr", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.weights_distr", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.LSBoostRegressor.fit": {"fullname": "mlsauce.booster.LSBoostRegressor.fit", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.fit", "kind": "function", "doc": "

Fit Booster (regressor) to training data (X, y)

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n   Target values.\n\n**kwargs: additional parameters to be passed to self.cook_training_set.\n
\n\n

Returns:

\n\n
self: object.\n
\n", "signature": "(self, X, y, **kwargs):", "funcdef": "def"}, "mlsauce.booster.LSBoostRegressor.predict": {"fullname": "mlsauce.booster.LSBoostRegressor.predict", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.predict", "kind": "function", "doc": "

Predict probabilities for test data X.

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\nlevel: int\n    Level of confidence (default = 95)\n\nmethod: str\n    `None`, or 'splitconformal', 'localconformal'\n    prediction (if you specify `return_pi = True`)\n\n**kwargs: additional parameters to be passed to\n    self.cook_test_set\n
\n\n

Returns:

\n\n
probability estimates for test data: {array-like}\n
\n", "signature": "(self, X, level=95, method=None, **kwargs):", "funcdef": "def"}, "mlsauce.booster.LSBoostRegressor.update": {"fullname": "mlsauce.booster.LSBoostRegressor.update", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.update", "kind": "function", "doc": "

Update model with new data.

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples=1, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: float = [n_samples=1]\n   Target value.\n\neta: float\n    Inverse power applied to number of observations\n    (defines a learning rate).\n
\n\n

Returns:

\n\n
self: object.\n
\n", "signature": "(self, X, y, eta=0.9):", "funcdef": "def"}, "mlsauce.booster.LSBoostRegressor.set_predict_request": {"fullname": "mlsauce.booster.LSBoostRegressor.set_predict_request", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.set_predict_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.booster.LSBoostRegressor.set_score_request": {"fullname": "mlsauce.booster.LSBoostRegressor.set_score_request", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.booster.GenericBoostingClassifier": {"fullname": "mlsauce.booster.GenericBoostingClassifier", "modulename": "mlsauce.booster", "qualname": "GenericBoostingClassifier", "kind": "class", "doc": "

Generic Boosting classifier (using any classifier as base learner).

\n\n

Attributes:

\n\n
base_model: object\n    base learner (default is ExtraTreeRegressor) to be boosted.\n\nn_estimators: int\n    number of boosting iterations.\n\nlearning_rate: float\n    controls the learning speed at training time.\n\nn_hidden_features: int\n    number of nodes in successive hidden layers.\n\nreg_lambda: float\n    L2 regularization parameter for successive errors in the optimizer\n    (at training time).\n\nalpha: float\n    compromise between L1 and L2 regularization (must be in [0, 1]),\n    for `solver` == 'enet'.\n\nrow_sample: float\n    percentage of rows chosen from the training set.\n\ncol_sample: float\n    percentage of columns chosen from the training set.\n\ndropout: float\n    percentage of nodes dropped from the training set.\n\ntolerance: float\n    controls early stopping in gradient descent (at training time).\n\ndirect_link: bool\n    indicates whether the original features are included (True) in model's\n    fitting or not (False).\n\nverbose: int\n    progress bar (yes = 1) or not (no = 0) (currently).\n\nseed: int\n    reproducibility seed for nodes_sim=='uniform', clustering and dropout.\n\nbackend: str\n    type of backend; must be in ('cpu', 'gpu', 'tpu')\n\nsolver: str\n    type of 'weak' learner; currently in ('ridge', 'lasso', 'enet').\n    'enet' is a combination of 'ridge' and 'lasso' called Elastic Net.\n\nactivation: str\n    activation function: currently 'relu', 'relu6', 'sigmoid', 'tanh'\n\nn_clusters: int\n    number of clusters for clustering the features\n\nclustering_method: str\n    clustering method: currently 'kmeans', 'gmm'\n\ncluster_scaling: str\n    scaling method for clustering: currently 'standard', 'robust', 'minmax'\n\ndegree: int\n    degree of features interactions to include in the model\n\nweights_distr: str\n    distribution of weights for constructing the model's hidden layer;\n    currently 'uniform', 'gaussian'\n
\n", "bases": "mlsauce.booster._booster_classifier.LSBoostClassifier"}, "mlsauce.booster.GenericBoostingClassifier.__init__": {"fullname": "mlsauce.booster.GenericBoostingClassifier.__init__", "modulename": "mlsauce.booster", "qualname": "GenericBoostingClassifier.__init__", "kind": "function", "doc": "

\n", "signature": "(\tbase_model=ExtraTreeRegressor(),\tn_estimators=100,\tlearning_rate=0.1,\tn_hidden_features=5,\treg_lambda=0.1,\talpha=0.5,\trow_sample=1,\tcol_sample=1,\tdropout=0,\ttolerance=0.0001,\tdirect_link=1,\tverbose=1,\tseed=123,\tbackend='cpu',\tsolver='ridge',\tactivation='relu',\tn_clusters=0,\tclustering_method='kmeans',\tcluster_scaling='standard',\tdegree=None,\tweights_distr='uniform')"}, "mlsauce.booster.GenericBoostingClassifier.base_model": {"fullname": "mlsauce.booster.GenericBoostingClassifier.base_model", "modulename": "mlsauce.booster", "qualname": "GenericBoostingClassifier.base_model", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.GenericBoostingClassifier.set_score_request": {"fullname": "mlsauce.booster.GenericBoostingClassifier.set_score_request", "modulename": "mlsauce.booster", "qualname": "GenericBoostingClassifier.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.booster.GenericBoostingRegressor": {"fullname": "mlsauce.booster.GenericBoostingRegressor", "modulename": "mlsauce.booster", "qualname": "GenericBoostingRegressor", "kind": "class", "doc": "

LSBoost regressor.

\n\n

Attributes:

\n\n
base_model: object\n    base learner (default is ExtraTreeRegressor) to be boosted.\n\nn_estimators: int\n    number of boosting iterations.\n\nlearning_rate: float\n    controls the learning speed at training time.\n\nn_hidden_features: int\n    number of nodes in successive hidden layers.\n\nreg_lambda: float\n    L2 regularization parameter for successive errors in the optimizer\n    (at training time).\n\nalpha: float\n    compromise between L1 and L2 regularization (must be in [0, 1]),\n    for `solver` == 'enet'\n\nrow_sample: float\n    percentage of rows chosen from the training set.\n\ncol_sample: float\n    percentage of columns chosen from the training set.\n\ndropout: float\n    percentage of nodes dropped from the training set.\n\ntolerance: float\n    controls early stopping in gradient descent (at training time).\n\ndirect_link: bool\n    indicates whether the original features are included (True) in model's\n    fitting or not (False).\n\nverbose: int\n    progress bar (yes = 1) or not (no = 0) (currently).\n\nseed: int\n    reproducibility seed for nodes_sim=='uniform', clustering and dropout.\n\nbackend: str\n    type of backend; must be in ('cpu', 'gpu', 'tpu')\n\nsolver: str\n    type of 'weak' learner; currently in ('ridge', 'lasso')\n\nactivation: str\n    activation function: currently 'relu', 'relu6', 'sigmoid', 'tanh'\n\ntype_pi: str.\n    type of prediction interval; currently \"kde\" (default) or \"bootstrap\".\n    Used only in `self.predict`, for `self.replications` > 0 and `self.kernel`\n    in ('gaussian', 'tophat'). Default is `None`.\n\nreplications: int.\n    number of replications (if needed) for predictive simulation.\n    Used only in `self.predict`, for `self.kernel` in ('gaussian',\n    'tophat') and `self.type_pi = 'kde'`. Default is `None`.\n\nn_clusters: int\n    number of clusters for clustering the features\n\nclustering_method: str\n    clustering method: currently 'kmeans', 'gmm'\n\ncluster_scaling: str\n    scaling method for clustering: currently 'standard', 'robust', 'minmax'\n\ndegree: int\n    degree of features interactions to include in the model\n\nweights_distr: str\n    distribution of weights for constructing the model's hidden layer;\n    either 'uniform' or 'gaussian'\n
\n", "bases": "mlsauce.booster._booster_regressor.LSBoostRegressor"}, "mlsauce.booster.GenericBoostingRegressor.__init__": {"fullname": "mlsauce.booster.GenericBoostingRegressor.__init__", "modulename": "mlsauce.booster", "qualname": "GenericBoostingRegressor.__init__", "kind": "function", "doc": "

\n", "signature": "(\tbase_model=ExtraTreeRegressor(),\tn_estimators=100,\tlearning_rate=0.1,\tn_hidden_features=5,\treg_lambda=0.1,\talpha=0.5,\trow_sample=1,\tcol_sample=1,\tdropout=0,\ttolerance=0.0001,\tdirect_link=1,\tverbose=1,\tseed=123,\tbackend='cpu',\tsolver='ridge',\tactivation='relu',\ttype_pi=None,\treplications=None,\tkernel=None,\tn_clusters=0,\tclustering_method='kmeans',\tcluster_scaling='standard',\tdegree=None,\tweights_distr='uniform')"}, "mlsauce.booster.GenericBoostingRegressor.base_model": {"fullname": "mlsauce.booster.GenericBoostingRegressor.base_model", "modulename": "mlsauce.booster", "qualname": "GenericBoostingRegressor.base_model", "kind": "variable", "doc": "

\n"}, "mlsauce.booster.GenericBoostingRegressor.set_predict_request": {"fullname": "mlsauce.booster.GenericBoostingRegressor.set_predict_request", "modulename": "mlsauce.booster", "qualname": "GenericBoostingRegressor.set_predict_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.booster.GenericBoostingRegressor.set_score_request": {"fullname": "mlsauce.booster.GenericBoostingRegressor.set_score_request", "modulename": "mlsauce.booster", "qualname": "GenericBoostingRegressor.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.datasets": {"fullname": "mlsauce.datasets", "modulename": "mlsauce.datasets", "kind": "module", "doc": "

\n"}, "mlsauce.datasets.dowload": {"fullname": "mlsauce.datasets.dowload", "modulename": "mlsauce.datasets.dowload", "kind": "module", "doc": "

\n"}, "mlsauce.datasets.dowload.download": {"fullname": "mlsauce.datasets.dowload.download", "modulename": "mlsauce.datasets.dowload", "qualname": "download", "kind": "function", "doc": "

\n", "signature": "(\tpkgname='MASS',\tdataset='Boston',\tsource='https://cran.r-universe.dev/',\t**kwargs):", "funcdef": "def"}, "mlsauce.demo": {"fullname": "mlsauce.demo", "modulename": "mlsauce.demo", "kind": "module", "doc": "

\n"}, "mlsauce.elasticnet": {"fullname": "mlsauce.elasticnet", "modulename": "mlsauce.elasticnet", "kind": "module", "doc": "

\n"}, "mlsauce.elasticnet.ElasticNetRegressor": {"fullname": "mlsauce.elasticnet.ElasticNetRegressor", "modulename": "mlsauce.elasticnet", "qualname": "ElasticNetRegressor", "kind": "class", "doc": "

Elasticnet.

\n\n

Attributes:

\n\n
reg_lambda: float\n    regularization parameter.\n\nalpha: float\n    compromise between L1 and L2 regularization (must be in [0, 1]),\n    for `solver` == 'enet'.\n\nbackend: str\n    type of backend; must be in ('cpu', 'gpu', 'tpu')\n
\n", "bases": "sklearn.base.BaseEstimator, sklearn.base.RegressorMixin"}, "mlsauce.elasticnet.ElasticNetRegressor.__init__": {"fullname": "mlsauce.elasticnet.ElasticNetRegressor.__init__", "modulename": "mlsauce.elasticnet", "qualname": "ElasticNetRegressor.__init__", "kind": "function", "doc": "

\n", "signature": "(reg_lambda=0.1, alpha=0.5, backend='cpu')"}, "mlsauce.elasticnet.ElasticNetRegressor.reg_lambda": {"fullname": "mlsauce.elasticnet.ElasticNetRegressor.reg_lambda", "modulename": "mlsauce.elasticnet", "qualname": "ElasticNetRegressor.reg_lambda", "kind": "variable", "doc": "

\n"}, "mlsauce.elasticnet.ElasticNetRegressor.alpha": {"fullname": "mlsauce.elasticnet.ElasticNetRegressor.alpha", "modulename": "mlsauce.elasticnet", "qualname": "ElasticNetRegressor.alpha", "kind": "variable", "doc": "

\n"}, "mlsauce.elasticnet.ElasticNetRegressor.backend": {"fullname": "mlsauce.elasticnet.ElasticNetRegressor.backend", "modulename": "mlsauce.elasticnet", "qualname": "ElasticNetRegressor.backend", "kind": "variable", "doc": "

\n"}, "mlsauce.elasticnet.ElasticNetRegressor.fit": {"fullname": "mlsauce.elasticnet.ElasticNetRegressor.fit", "modulename": "mlsauce.elasticnet", "qualname": "ElasticNetRegressor.fit", "kind": "function", "doc": "

Fit matrixops (classifier) to training data (X, y)

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n    Target values.\n\n**kwargs: additional parameters to be passed to self.cook_training_set.\n
\n\n

Returns:

\n\n
self: object.\n
\n", "signature": "(self, X, y, **kwargs):", "funcdef": "def"}, "mlsauce.elasticnet.ElasticNetRegressor.predict": {"fullname": "mlsauce.elasticnet.ElasticNetRegressor.predict", "modulename": "mlsauce.elasticnet", "qualname": "ElasticNetRegressor.predict", "kind": "function", "doc": "

Predict test data X.

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to `predict_proba`\n
\n\n

Returns:

\n\n
model predictions: {array-like}\n
\n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.elasticnet.ElasticNetRegressor.set_score_request": {"fullname": "mlsauce.elasticnet.ElasticNetRegressor.set_score_request", "modulename": "mlsauce.elasticnet", "qualname": "ElasticNetRegressor.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.lasso": {"fullname": "mlsauce.lasso", "modulename": "mlsauce.lasso", "kind": "module", "doc": "

\n"}, "mlsauce.lasso.LassoRegressor": {"fullname": "mlsauce.lasso.LassoRegressor", "modulename": "mlsauce.lasso", "qualname": "LassoRegressor", "kind": "class", "doc": "

Lasso.

\n\n

Attributes:

\n\n
reg_lambda: float\n    L1 regularization parameter.\n\nmax_iter: int\n    number of iterations of lasso shooting algorithm.\n\ntol: float\n    tolerance for convergence of lasso shooting algorithm.\n\nbackend: str\n    type of backend; must be in ('cpu', 'gpu', 'tpu').\n
\n", "bases": "sklearn.base.BaseEstimator, sklearn.base.RegressorMixin"}, "mlsauce.lasso.LassoRegressor.__init__": {"fullname": "mlsauce.lasso.LassoRegressor.__init__", "modulename": "mlsauce.lasso", "qualname": "LassoRegressor.__init__", "kind": "function", "doc": "

\n", "signature": "(reg_lambda=0.1, max_iter=10, tol=0.001, backend='cpu')"}, "mlsauce.lasso.LassoRegressor.reg_lambda": {"fullname": "mlsauce.lasso.LassoRegressor.reg_lambda", "modulename": "mlsauce.lasso", "qualname": "LassoRegressor.reg_lambda", "kind": "variable", "doc": "

\n"}, "mlsauce.lasso.LassoRegressor.max_iter": {"fullname": "mlsauce.lasso.LassoRegressor.max_iter", "modulename": "mlsauce.lasso", "qualname": "LassoRegressor.max_iter", "kind": "variable", "doc": "

\n"}, "mlsauce.lasso.LassoRegressor.tol": {"fullname": "mlsauce.lasso.LassoRegressor.tol", "modulename": "mlsauce.lasso", "qualname": "LassoRegressor.tol", "kind": "variable", "doc": "

\n"}, "mlsauce.lasso.LassoRegressor.backend": {"fullname": "mlsauce.lasso.LassoRegressor.backend", "modulename": "mlsauce.lasso", "qualname": "LassoRegressor.backend", "kind": "variable", "doc": "

\n"}, "mlsauce.lasso.LassoRegressor.fit": {"fullname": "mlsauce.lasso.LassoRegressor.fit", "modulename": "mlsauce.lasso", "qualname": "LassoRegressor.fit", "kind": "function", "doc": "

Fit matrixops (classifier) to training data (X, y)

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n    Target values.\n\n**kwargs: additional parameters to be passed to self.cook_training_set.\n
\n\n

Returns:

\n\n
self: object.\n
\n", "signature": "(self, X, y, **kwargs):", "funcdef": "def"}, "mlsauce.lasso.LassoRegressor.predict": {"fullname": "mlsauce.lasso.LassoRegressor.predict", "modulename": "mlsauce.lasso", "qualname": "LassoRegressor.predict", "kind": "function", "doc": "

Predict test data X.

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to `predict_proba`\n
\n\n

Returns:

\n\n
model predictions: {array-like}\n
\n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.lasso.LassoRegressor.set_score_request": {"fullname": "mlsauce.lasso.LassoRegressor.set_score_request", "modulename": "mlsauce.lasso", "qualname": "LassoRegressor.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.lazybooster": {"fullname": "mlsauce.lazybooster", "modulename": "mlsauce.lazybooster", "kind": "module", "doc": "

\n"}, "mlsauce.lazybooster.LazyBoostingClassifier": {"fullname": "mlsauce.lazybooster.LazyBoostingClassifier", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingClassifier", "kind": "class", "doc": "

Fitting -- almost -- all the classification algorithms\n and returning their scores.

\n\n
Parameters:\n\n    verbose: int, optional (default=0)\n        Any positive number for verbosity.\n\n    ignore_warnings: bool, optional (default=True)\n        When set to True, the warning related to algorigms that are not\n        able to run are ignored.\n\n    custom_metric: function, optional (default=None)\n        When function is provided, models are evaluated based on the custom\n          evaluation metric provided.\n\n    predictions: bool, optional (default=False)\n        When set to True, the predictions of all the models models are\n        returned as data frame.\n\n    sort_by: string, optional (default='Accuracy')\n        Sort models by a metric. Available options are 'Accuracy',\n        'Balanced Accuracy', 'ROC AUC', 'F1 Score' or a custom metric\n        identified by its name and provided by custom_metric.\n\n    random_state: int, optional (default=42)\n        Reproducibiility seed.\n\n    estimators: list, optional (default='all')\n        list of Estimators names or just 'all' for > 90 classifiers\n        (default='all')\n\n    preprocess: bool, preprocessing is done when set to True\n\n    n_jobs: int, when possible, run in parallel\n        For now, only used by individual models that support it.\n\n    n_layers: int, optional (default=3)\n        Number of layers of GenericBoostingClassifiers to be used.\n\n    All the other parameters are the same as GenericBoostingClassifier's.\n\nAttributes:\n\n    models_: dict-object\n        Returns a dictionary with each model pipeline as value\n        with key as name of models.\n\n    best_model_: object\n        Returns the best model pipeline.\n\nExamples\n\n\n    
\n
        import os\n        import mlsauce as ms\n        from sklearn.datasets import load_breast_cancer, load_iris, load_wine, load_digits\n        from sklearn.model_selection import train_test_split\n        from time import time\n\n        load_models = [load_breast_cancer, load_iris, load_wine]\n\n        for model in load_models:\n\n            data = model()\n            X = data.data\n            y= data.target\n\n            X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .2, random_state = 13)\n\n            clf = ms.LazyBoostingClassifier(verbose=1, ignore_warnings=False,\n                                            custom_metric=None, preprocess=False)\n\n            start = time()\n            models, predictioms = clf.fit(X_train, X_test, y_train, y_test)\n            print(f"\nElapsed: {time() - start} seconds\n")\n\n            print(models)\n
\n
\n
\n", "bases": "sklearn.base.ClassifierMixin"}, "mlsauce.lazybooster.LazyBoostingClassifier.__init__": {"fullname": "mlsauce.lazybooster.LazyBoostingClassifier.__init__", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingClassifier.__init__", "kind": "function", "doc": "

\n", "signature": "(\tverbose=0,\tignore_warnings=True,\tcustom_metric=None,\tpredictions=False,\tsort_by='Accuracy',\trandom_state=42,\testimators='all',\tpreprocess=False,\tn_jobs=None)"}, "mlsauce.lazybooster.LazyBoostingClassifier.verbose": {"fullname": "mlsauce.lazybooster.LazyBoostingClassifier.verbose", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingClassifier.verbose", "kind": "variable", "doc": "

\n"}, "mlsauce.lazybooster.LazyBoostingClassifier.ignore_warnings": {"fullname": "mlsauce.lazybooster.LazyBoostingClassifier.ignore_warnings", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingClassifier.ignore_warnings", "kind": "variable", "doc": "

\n"}, "mlsauce.lazybooster.LazyBoostingClassifier.custom_metric": {"fullname": "mlsauce.lazybooster.LazyBoostingClassifier.custom_metric", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingClassifier.custom_metric", "kind": "variable", "doc": "

\n"}, "mlsauce.lazybooster.LazyBoostingClassifier.predictions": {"fullname": "mlsauce.lazybooster.LazyBoostingClassifier.predictions", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingClassifier.predictions", "kind": "variable", "doc": "

\n"}, "mlsauce.lazybooster.LazyBoostingClassifier.sort_by": {"fullname": "mlsauce.lazybooster.LazyBoostingClassifier.sort_by", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingClassifier.sort_by", "kind": "variable", "doc": "

\n"}, "mlsauce.lazybooster.LazyBoostingClassifier.models_": {"fullname": "mlsauce.lazybooster.LazyBoostingClassifier.models_", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingClassifier.models_", "kind": "variable", "doc": "

\n"}, "mlsauce.lazybooster.LazyBoostingClassifier.best_model_": {"fullname": "mlsauce.lazybooster.LazyBoostingClassifier.best_model_", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingClassifier.best_model_", "kind": "variable", "doc": "

\n"}, "mlsauce.lazybooster.LazyBoostingClassifier.random_state": {"fullname": "mlsauce.lazybooster.LazyBoostingClassifier.random_state", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingClassifier.random_state", "kind": "variable", "doc": "

\n"}, "mlsauce.lazybooster.LazyBoostingClassifier.estimators": {"fullname": "mlsauce.lazybooster.LazyBoostingClassifier.estimators", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingClassifier.estimators", "kind": "variable", "doc": "

\n"}, "mlsauce.lazybooster.LazyBoostingClassifier.preprocess": {"fullname": "mlsauce.lazybooster.LazyBoostingClassifier.preprocess", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingClassifier.preprocess", "kind": "variable", "doc": "

\n"}, "mlsauce.lazybooster.LazyBoostingClassifier.n_jobs": {"fullname": "mlsauce.lazybooster.LazyBoostingClassifier.n_jobs", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingClassifier.n_jobs", "kind": "variable", "doc": "

\n"}, "mlsauce.lazybooster.LazyBoostingClassifier.fit": {"fullname": "mlsauce.lazybooster.LazyBoostingClassifier.fit", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingClassifier.fit", "kind": "function", "doc": "

Fit classifiers to X_train and y_train, predict and score on X_test,\ny_test.

\n\n

Parameters:

\n\n
X_train: array-like,\n    Training vectors, where rows is the number of samples\n    and columns is the number of features.\n\nX_test: array-like,\n    Testing vectors, where rows is the number of samples\n    and columns is the number of features.\n\ny_train: array-like,\n    Training vectors, where rows is the number of samples\n    and columns is the number of features.\n\ny_test: array-like,\n    Testing vectors, where rows is the number of samples\n    and columns is the number of features.\n\n**kwargs: dict,\n    Additional arguments to be passed to the fit GenericBoostingClassifier.\n
\n\n

Returns:

\n\n
scores: Pandas DataFrame\n    Returns metrics of all the models in a Pandas DataFrame.\n\npredictions: Pandas DataFrame\n    Returns predictions of all the models in a Pandas DataFrame.\n
\n", "signature": "(self, X_train, X_test, y_train, y_test, **kwargs):", "funcdef": "def"}, "mlsauce.lazybooster.LazyBoostingClassifier.get_best_model": {"fullname": "mlsauce.lazybooster.LazyBoostingClassifier.get_best_model", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingClassifier.get_best_model", "kind": "function", "doc": "

This function returns the best model pipeline based on the sort_by metric.

\n\n

Returns:

\n\n
best_model: object,\n    Returns the best model pipeline based on the sort_by metric.\n
\n", "signature": "(self):", "funcdef": "def"}, "mlsauce.lazybooster.LazyBoostingClassifier.provide_models": {"fullname": "mlsauce.lazybooster.LazyBoostingClassifier.provide_models", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingClassifier.provide_models", "kind": "function", "doc": "

Returns all the model objects trained. If fit hasn't been called yet,\nthen it's called to return the models.

\n\n

Parameters:

\n\n

X_train: array-like,\n Training vectors, where rows is the number of samples\n and columns is the number of features.

\n\n

X_test: array-like,\n Testing vectors, where rows is the number of samples\n and columns is the number of features.

\n\n

y_train: array-like,\n Training vectors, where rows is the number of samples\n and columns is the number of features.

\n\n

y_test: array-like,\n Testing vectors, where rows is the number of samples\n and columns is the number of features.

\n\n

Returns:

\n\n
models: dict-object,\n    Returns a dictionary with each model's pipeline as value\n    and key = name of the model.\n
\n", "signature": "(self, X_train, X_test, y_train, y_test):", "funcdef": "def"}, "mlsauce.lazybooster.LazyBoostingClassifier.train_model": {"fullname": "mlsauce.lazybooster.LazyBoostingClassifier.train_model", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingClassifier.train_model", "kind": "function", "doc": "

Function to train a single model and return its results.

\n", "signature": "(\tself,\tname,\tmodel,\tX_train,\ty_train,\tX_test,\ty_test,\tuse_preprocessing=False,\tpreprocessor=None,\t**kwargs):", "funcdef": "def"}, "mlsauce.lazybooster.LazyBoostingRegressor": {"fullname": "mlsauce.lazybooster.LazyBoostingRegressor", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingRegressor", "kind": "class", "doc": "

Fitting -- almost -- all the regression algorithms\n and returning their scores.

\n\n

Parameters:

\n\n
verbose: int, optional (default=0)\n    Any positive number for verbosity.\n\nignore_warnings: bool, optional (default=True)\n    When set to True, the warning related to algorigms that are not able to run are ignored.\n\ncustom_metric: function, optional (default=None)\n    When function is provided, models are evaluated based on the custom evaluation metric provided.\n\npredictions: bool, optional (default=False)\n    When set to True, the predictions of all the models models are returned as dataframe.\n\nsort_by: string, optional (default='RMSE')\n    Sort models by a metric. Available options are 'R-Squared', 'Adjusted R-Squared', 'RMSE', 'Time Taken' and 'Custom Metric'.\n    or a custom metric identified by its name and provided by custom_metric.\n\nrandom_state: int, optional (default=42)\n    Reproducibiility seed.\n\nestimators: list, optional (default='all')\n    list of Estimators names or just 'all' (default='all')\n\npreprocess: bool\n    preprocessing is done when set to True\n\nn_jobs : int, when possible, run in parallel\n    For now, only used by individual models that support it.\n\nn_layers: int, optional (default=3)\n    Number of layers of CustomRegressors to be used.\n\nAll the other parameters are the same as CustomRegressor's.\n
\n\n

Attributes:

\n\n
models_: dict-object\n    Returns a dictionary with each model pipeline as value\n    with key as name of models.\n\nbest_model_: object\n    Returns the best model pipeline based on the sort_by metric.\n
\n\n

Examples:

\n\n
\n
import os\nimport mlsauce as ms\nfrom sklearn.datasets import load_diabetes\nfrom sklearn.model_selection import train_test_split\n\ndata = load_diabetes()\nX = data.data\ny= data.target\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .2, random_state = 123)\n\nregr = ms.LazyBoostingRegressor(verbose=0, ignore_warnings=True,\n                                custom_metric=None, preprocess=True)\nmodels, predictioms = regr.fit(X_train, X_test, y_train, y_test)\nmodel_dictionary = regr.provide_models(X_train, X_test, y_train, y_test)\nprint(models)\n
\n
\n
\n", "bases": "sklearn.base.RegressorMixin"}, "mlsauce.lazybooster.LazyBoostingRegressor.__init__": {"fullname": "mlsauce.lazybooster.LazyBoostingRegressor.__init__", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingRegressor.__init__", "kind": "function", "doc": "

\n", "signature": "(\tverbose=0,\tignore_warnings=True,\tcustom_metric=None,\tpredictions=False,\tsort_by='RMSE',\trandom_state=42,\testimators='all',\tpreprocess=False,\tn_jobs=None)"}, "mlsauce.lazybooster.LazyBoostingRegressor.verbose": {"fullname": "mlsauce.lazybooster.LazyBoostingRegressor.verbose", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingRegressor.verbose", "kind": "variable", "doc": "

\n"}, "mlsauce.lazybooster.LazyBoostingRegressor.ignore_warnings": {"fullname": "mlsauce.lazybooster.LazyBoostingRegressor.ignore_warnings", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingRegressor.ignore_warnings", "kind": "variable", "doc": "

\n"}, "mlsauce.lazybooster.LazyBoostingRegressor.custom_metric": {"fullname": "mlsauce.lazybooster.LazyBoostingRegressor.custom_metric", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingRegressor.custom_metric", "kind": "variable", "doc": "

\n"}, "mlsauce.lazybooster.LazyBoostingRegressor.predictions": {"fullname": "mlsauce.lazybooster.LazyBoostingRegressor.predictions", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingRegressor.predictions", "kind": "variable", "doc": "

\n"}, "mlsauce.lazybooster.LazyBoostingRegressor.sort_by": {"fullname": "mlsauce.lazybooster.LazyBoostingRegressor.sort_by", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingRegressor.sort_by", "kind": "variable", "doc": "

\n"}, "mlsauce.lazybooster.LazyBoostingRegressor.models_": {"fullname": "mlsauce.lazybooster.LazyBoostingRegressor.models_", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingRegressor.models_", "kind": "variable", "doc": "

\n"}, "mlsauce.lazybooster.LazyBoostingRegressor.best_model_": {"fullname": "mlsauce.lazybooster.LazyBoostingRegressor.best_model_", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingRegressor.best_model_", "kind": "variable", "doc": "

\n"}, "mlsauce.lazybooster.LazyBoostingRegressor.random_state": {"fullname": "mlsauce.lazybooster.LazyBoostingRegressor.random_state", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingRegressor.random_state", "kind": "variable", "doc": "

\n"}, "mlsauce.lazybooster.LazyBoostingRegressor.estimators": {"fullname": "mlsauce.lazybooster.LazyBoostingRegressor.estimators", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingRegressor.estimators", "kind": "variable", "doc": "

\n"}, "mlsauce.lazybooster.LazyBoostingRegressor.preprocess": {"fullname": "mlsauce.lazybooster.LazyBoostingRegressor.preprocess", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingRegressor.preprocess", "kind": "variable", "doc": "

\n"}, "mlsauce.lazybooster.LazyBoostingRegressor.n_jobs": {"fullname": "mlsauce.lazybooster.LazyBoostingRegressor.n_jobs", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingRegressor.n_jobs", "kind": "variable", "doc": "

\n"}, "mlsauce.lazybooster.LazyBoostingRegressor.fit": {"fullname": "mlsauce.lazybooster.LazyBoostingRegressor.fit", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingRegressor.fit", "kind": "function", "doc": "

Fit Regression algorithms to X_train and y_train, predict and score on X_test, y_test.

\n\n

Parameters:

\n\n
X_train : array-like,\n    Training vectors, where rows is the number of samples\n    and columns is the number of features.\n\nX_test : array-like,\n    Testing vectors, where rows is the number of samples\n    and columns is the number of features.\n\ny_train : array-like,\n    Training vectors, where rows is the number of samples\n    and columns is the number of features.\n\ny_test : array-like,\n    Testing vectors, where rows is the number of samples\n    and columns is the number of features.\n\n**kwargs: dict,\n    Additional parameters to be passed to the GenericBoostingRegressor.\n
\n\n

Returns:

\n\n

scores: Pandas DataFrame\n Returns metrics of all the models in a Pandas DataFrame.

\n\n

predictions : Pandas DataFrame\n Returns predictions of all the models in a Pandas DataFrame.

\n", "signature": "(self, X_train, X_test, y_train, y_test, **kwargs):", "funcdef": "def"}, "mlsauce.lazybooster.LazyBoostingRegressor.get_best_model": {"fullname": "mlsauce.lazybooster.LazyBoostingRegressor.get_best_model", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingRegressor.get_best_model", "kind": "function", "doc": "

This function returns the best model pipeline based on the sort_by metric.

\n\n

Returns:

\n\n
best_model: object,\n    Returns the best model pipeline based on the sort_by metric.\n
\n", "signature": "(self):", "funcdef": "def"}, "mlsauce.lazybooster.LazyBoostingRegressor.provide_models": {"fullname": "mlsauce.lazybooster.LazyBoostingRegressor.provide_models", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingRegressor.provide_models", "kind": "function", "doc": "

This function returns all the model objects trained in fit function.\nIf fit is not called already, then we call fit and then return the models.

\n\n

Parameters:

\n\n
X_train : array-like,\n    Training vectors, where rows is the number of samples\n    and columns is the number of features.\n\nX_test : array-like,\n    Testing vectors, where rows is the number of samples\n    and columns is the number of features.\n\ny_train : array-like,\n    Training vectors, where rows is the number of samples\n    and columns is the number of features.\n\ny_test : array-like,\n    Testing vectors, where rows is the number of samples\n    and columns is the number of features.\n
\n\n

Returns:

\n\n
models: dict-object,\n    Returns a dictionary with each model pipeline as value\n    with key as name of models.\n
\n", "signature": "(self, X_train, X_test, y_train, y_test):", "funcdef": "def"}, "mlsauce.lazybooster.LazyBoostingRegressor.train_model": {"fullname": "mlsauce.lazybooster.LazyBoostingRegressor.train_model", "modulename": "mlsauce.lazybooster", "qualname": "LazyBoostingRegressor.train_model", "kind": "function", "doc": "

Function to train a single regression model and return its results.

\n", "signature": "(\tself,\tname,\tregr,\tX_train,\ty_train,\tX_test,\ty_test,\tuse_preprocessing=False,\tpreprocessor=None,\t**kwargs):", "funcdef": "def"}, "mlsauce.multitaskregressor": {"fullname": "mlsauce.multitaskregressor", "modulename": "mlsauce.multitaskregressor", "kind": "module", "doc": "

\n"}, "mlsauce.multitaskregressor.MultiTaskRegressor": {"fullname": "mlsauce.multitaskregressor.MultiTaskRegressor", "modulename": "mlsauce.multitaskregressor", "qualname": "MultiTaskRegressor", "kind": "class", "doc": "

A class for multi-task regression

\n\n

Parameters

\n\n

regr: object\n A regressor object

\n\n

Attributes

\n\n

objs: list\n A list containing the fitted regressor objects

\n", "bases": "sklearn.base.BaseEstimator, sklearn.base.RegressorMixin"}, "mlsauce.multitaskregressor.MultiTaskRegressor.__init__": {"fullname": "mlsauce.multitaskregressor.MultiTaskRegressor.__init__", "modulename": "mlsauce.multitaskregressor", "qualname": "MultiTaskRegressor.__init__", "kind": "function", "doc": "

\n", "signature": "(regr)"}, "mlsauce.multitaskregressor.MultiTaskRegressor.regr": {"fullname": "mlsauce.multitaskregressor.MultiTaskRegressor.regr", "modulename": "mlsauce.multitaskregressor", "qualname": "MultiTaskRegressor.regr", "kind": "variable", "doc": "

\n"}, "mlsauce.multitaskregressor.MultiTaskRegressor.objs": {"fullname": "mlsauce.multitaskregressor.MultiTaskRegressor.objs", "modulename": "mlsauce.multitaskregressor", "qualname": "MultiTaskRegressor.objs", "kind": "variable", "doc": "

\n"}, "mlsauce.multitaskregressor.MultiTaskRegressor.fit": {"fullname": "mlsauce.multitaskregressor.MultiTaskRegressor.fit", "modulename": "mlsauce.multitaskregressor", "qualname": "MultiTaskRegressor.fit", "kind": "function", "doc": "

Fit the regressor

\n\n

Parameters

\n\n

X: array-like\n The input data\ny: array-like\n The target values

\n", "signature": "(self, X, y):", "funcdef": "def"}, "mlsauce.multitaskregressor.MultiTaskRegressor.predict": {"fullname": "mlsauce.multitaskregressor.MultiTaskRegressor.predict", "modulename": "mlsauce.multitaskregressor", "qualname": "MultiTaskRegressor.predict", "kind": "function", "doc": "

Predict the target values

\n\n

Parameters

\n\n

X: array-like\n The input data

\n\n

Returns

\n\n

y_pred: array-like\n The predicted target values

\n", "signature": "(self, X):", "funcdef": "def"}, "mlsauce.multitaskregressor.MultiTaskRegressor.set_score_request": {"fullname": "mlsauce.multitaskregressor.MultiTaskRegressor.set_score_request", "modulename": "mlsauce.multitaskregressor", "qualname": "MultiTaskRegressor.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.nonconformist": {"fullname": "mlsauce.nonconformist", "modulename": "mlsauce.nonconformist", "kind": "module", "doc": "

docstring

\n"}, "mlsauce.nonconformist.AbsErrorErrFunc": {"fullname": "mlsauce.nonconformist.AbsErrorErrFunc", "modulename": "mlsauce.nonconformist", "qualname": "AbsErrorErrFunc", "kind": "class", "doc": "

Calculates absolute error nonconformity for regression problems.

\n\n

For each correct output in y, nonconformity is defined as

\n\n

$$| y_i - \\hat{y}_i |$$

\n", "bases": "mlsauce.nonconformist.nc.RegressionErrFunc"}, "mlsauce.nonconformist.AbsErrorErrFunc.apply": {"fullname": "mlsauce.nonconformist.AbsErrorErrFunc.apply", "modulename": "mlsauce.nonconformist", "qualname": "AbsErrorErrFunc.apply", "kind": "function", "doc": "

Apply the nonconformity function.

\n\n

Parameters

\n\n

prediction : numpy array of shape [n_samples, n_classes]\n Class probability estimates for each sample.

\n\n

y : numpy array of shape [n_samples]\n True output labels of each sample.

\n\n

Returns

\n\n

nc : numpy array of shape [n_samples]\n Nonconformity scores of the samples.

\n", "signature": "(self, prediction, y):", "funcdef": "def"}, "mlsauce.nonconformist.AbsErrorErrFunc.apply_inverse": {"fullname": "mlsauce.nonconformist.AbsErrorErrFunc.apply_inverse", "modulename": "mlsauce.nonconformist", "qualname": "AbsErrorErrFunc.apply_inverse", "kind": "function", "doc": "

Apply the inverse of the nonconformity function (i.e.,\ncalculate prediction interval).

\n\n

Parameters

\n\n

nc : numpy array of shape [n_calibration_samples]\n Nonconformity scores obtained for conformal predictor.

\n\n

significance : float\n Significance level (0, 1).

\n\n

Returns

\n\n

interval : numpy array of shape [n_samples, 2]\n Minimum and maximum interval boundaries for each prediction.

\n", "signature": "(self, nc, significance):", "funcdef": "def"}, "mlsauce.nonconformist.QuantileRegErrFunc": {"fullname": "mlsauce.nonconformist.QuantileRegErrFunc", "modulename": "mlsauce.nonconformist", "qualname": "QuantileRegErrFunc", "kind": "class", "doc": "

Calculates conformalized quantile regression error.

\n\n

For each correct output in y, nonconformity is defined as

\n\n

$$max{\\hat{q}_low - y, y - \\hat{q}_high}$$

\n", "bases": "mlsauce.nonconformist.nc.RegressionErrFunc"}, "mlsauce.nonconformist.QuantileRegErrFunc.apply": {"fullname": "mlsauce.nonconformist.QuantileRegErrFunc.apply", "modulename": "mlsauce.nonconformist", "qualname": "QuantileRegErrFunc.apply", "kind": "function", "doc": "

Apply the nonconformity function.

\n\n

Parameters

\n\n

prediction : numpy array of shape [n_samples, n_classes]\n Class probability estimates for each sample.

\n\n

y : numpy array of shape [n_samples]\n True output labels of each sample.

\n\n

Returns

\n\n

nc : numpy array of shape [n_samples]\n Nonconformity scores of the samples.

\n", "signature": "(self, prediction, y):", "funcdef": "def"}, "mlsauce.nonconformist.QuantileRegErrFunc.apply_inverse": {"fullname": "mlsauce.nonconformist.QuantileRegErrFunc.apply_inverse", "modulename": "mlsauce.nonconformist", "qualname": "QuantileRegErrFunc.apply_inverse", "kind": "function", "doc": "

Apply the inverse of the nonconformity function (i.e.,\ncalculate prediction interval).

\n\n

Parameters

\n\n

nc : numpy array of shape [n_calibration_samples]\n Nonconformity scores obtained for conformal predictor.

\n\n

significance : float\n Significance level (0, 1).

\n\n

Returns

\n\n

interval : numpy array of shape [n_samples, 2]\n Minimum and maximum interval boundaries for each prediction.

\n", "signature": "(self, nc, significance):", "funcdef": "def"}, "mlsauce.nonconformist.RegressorAdapter": {"fullname": "mlsauce.nonconformist.RegressorAdapter", "modulename": "mlsauce.nonconformist", "qualname": "RegressorAdapter", "kind": "class", "doc": "

Base class for all estimators in scikit-learn.

\n\n

Inheriting from this class provides default implementations of:

\n\n
    \n
  • setting and getting parameters used by GridSearchCV and friends;
    • \n
  • textual and HTML representation displayed in terminals and IDEs;
    • \n
  • estimator serialization;
    • \n
  • parameters validation;
    • \n
  • data validation;
    • \n
  • feature names validation.
    • \n
\n\n

Read more in the :ref:User Guide <rolling_your_own_estimator>.

\n\n

Notes

\n\n

All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

\n\n

Examples

\n\n
\n
>>> import numpy as np\n>>> from sklearn.base import BaseEstimator\n>>> class MyEstimator(BaseEstimator):\n...     def __init__(self, *, param=1):\n...         self.param = param\n...     def fit(self, X, y=None):\n...         self.is_fitted_ = True\n...         return self\n...     def predict(self, X):\n...         return np.full(shape=X.shape[0], fill_value=self.param)\n>>> estimator = MyEstimator(param=2)\n>>> estimator.get_params()\n{'param': 2}\n>>> X = np.array([[1, 2], [2, 3], [3, 4]])\n>>> y = np.array([1, 0, 1])\n>>> estimator.fit(X, y).predict(X)\narray([2, 2, 2])\n>>> estimator.set_params(param=3).fit(X, y).predict(X)\narray([3, 3, 3])\n
\n
\n", "bases": "mlsauce.nonconformist.base.BaseModelAdapter"}, "mlsauce.nonconformist.RegressorAdapter.__init__": {"fullname": "mlsauce.nonconformist.RegressorAdapter.__init__", "modulename": "mlsauce.nonconformist", "qualname": "RegressorAdapter.__init__", "kind": "function", "doc": "

\n", "signature": "(model, fit_params=None)"}, "mlsauce.nonconformist.RegressorAdapter.set_fit_request": {"fullname": "mlsauce.nonconformist.RegressorAdapter.set_fit_request", "modulename": "mlsauce.nonconformist", "qualname": "RegressorAdapter.set_fit_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.nonconformist.RegressorAdapter.set_predict_request": {"fullname": "mlsauce.nonconformist.RegressorAdapter.set_predict_request", "modulename": "mlsauce.nonconformist", "qualname": "RegressorAdapter.set_predict_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.nonconformist.RegressorNc": {"fullname": "mlsauce.nonconformist.RegressorNc", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNc", "kind": "class", "doc": "

Nonconformity scorer using an underlying regression model.

\n\n

Parameters

\n\n

model : RegressorAdapter\n Underlying regression model used for calculating nonconformity scores.

\n\n

err_func : RegressionErrFunc\n Error function object.

\n\n

normalizer : BaseScorer\n Normalization model.

\n\n

beta : float\n Normalization smoothing parameter. As the beta-value increases,\n the normalized nonconformity function approaches a non-normalized\n equivalent.

\n\n

Attributes

\n\n

model : RegressorAdapter\n Underlying model object.

\n\n

err_func : RegressionErrFunc\n Scorer function used to calculate nonconformity scores.

\n\n

See also

\n\n

ProbEstClassifierNc, NormalizedRegressorNc

\n", "bases": "mlsauce.nonconformist.nc.BaseModelNc"}, "mlsauce.nonconformist.RegressorNc.__init__": {"fullname": "mlsauce.nonconformist.RegressorNc.__init__", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNc.__init__", "kind": "function", "doc": "

\n", "signature": "(\tmodel,\terr_func=<mlsauce.nonconformist.nc.AbsErrorErrFunc object>,\tnormalizer=None,\tbeta=1e-06)"}, "mlsauce.nonconformist.RegressorNc.predict": {"fullname": "mlsauce.nonconformist.RegressorNc.predict", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNc.predict", "kind": "function", "doc": "

Constructs prediction intervals for a set of test examples.

\n\n

Predicts the output of each test pattern using the underlying model,\nand applies the (partial) inverse nonconformity function to each\nprediction, resulting in a prediction interval for each test pattern.

\n\n

Parameters

\n\n

x : numpy array of shape [n_samples, n_features]\n Inputs of patters for which to predict output values.

\n\n

significance : float\n Significance level (maximum allowed error rate) of predictions.\n Should be a float between 0 and 1. If None, then intervals for\n all significance levels (0.01, 0.02, ..., 0.99) are output in a\n 3d-matrix.

\n\n

Returns

\n\n

p : numpy array of shape [n_samples, 2] or [n_samples, 2, 99]\n If significance is None, then p contains the interval (minimum\n and maximum boundaries) for each test pattern, and each significance\n level (0.01, 0.02, ..., 0.99). If significance is a float between\n 0 and 1, then p contains the prediction intervals (minimum and\n maximum boundaries) for the set of test patterns at the chosen\n significance level.

\n", "signature": "(self, x, nc, significance=None):", "funcdef": "def"}, "mlsauce.nonconformist.RegressorNc.set_fit_request": {"fullname": "mlsauce.nonconformist.RegressorNc.set_fit_request", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNc.set_fit_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.nonconformist.RegressorNc.set_predict_request": {"fullname": "mlsauce.nonconformist.RegressorNc.set_predict_request", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNc.set_predict_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.nonconformist.RegressorNc.set_score_request": {"fullname": "mlsauce.nonconformist.RegressorNc.set_score_request", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNc.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.nonconformist.RegressorNormalizer": {"fullname": "mlsauce.nonconformist.RegressorNormalizer", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNormalizer", "kind": "class", "doc": "

Base class for all estimators in scikit-learn.

\n\n

Inheriting from this class provides default implementations of:

\n\n
    \n
  • setting and getting parameters used by GridSearchCV and friends;
    • \n
  • textual and HTML representation displayed in terminals and IDEs;
    • \n
  • estimator serialization;
    • \n
  • parameters validation;
    • \n
  • data validation;
    • \n
  • feature names validation.
    • \n
\n\n

Read more in the :ref:User Guide <rolling_your_own_estimator>.

\n\n

Notes

\n\n

All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

\n\n

Examples

\n\n
\n
>>> import numpy as np\n>>> from sklearn.base import BaseEstimator\n>>> class MyEstimator(BaseEstimator):\n...     def __init__(self, *, param=1):\n...         self.param = param\n...     def fit(self, X, y=None):\n...         self.is_fitted_ = True\n...         return self\n...     def predict(self, X):\n...         return np.full(shape=X.shape[0], fill_value=self.param)\n>>> estimator = MyEstimator(param=2)\n>>> estimator.get_params()\n{'param': 2}\n>>> X = np.array([[1, 2], [2, 3], [3, 4]])\n>>> y = np.array([1, 0, 1])\n>>> estimator.fit(X, y).predict(X)\narray([2, 2, 2])\n>>> estimator.set_params(param=3).fit(X, y).predict(X)\narray([3, 3, 3])\n
\n
\n", "bases": "mlsauce.nonconformist.nc.BaseScorer"}, "mlsauce.nonconformist.RegressorNormalizer.__init__": {"fullname": "mlsauce.nonconformist.RegressorNormalizer.__init__", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNormalizer.__init__", "kind": "function", "doc": "

\n", "signature": "(base_model, normalizer_model, err_func)"}, "mlsauce.nonconformist.RegressorNormalizer.base_model": {"fullname": "mlsauce.nonconformist.RegressorNormalizer.base_model", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNormalizer.base_model", "kind": "variable", "doc": "

\n"}, "mlsauce.nonconformist.RegressorNormalizer.normalizer_model": {"fullname": "mlsauce.nonconformist.RegressorNormalizer.normalizer_model", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNormalizer.normalizer_model", "kind": "variable", "doc": "

\n"}, "mlsauce.nonconformist.RegressorNormalizer.err_func": {"fullname": "mlsauce.nonconformist.RegressorNormalizer.err_func", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNormalizer.err_func", "kind": "variable", "doc": "

\n"}, "mlsauce.nonconformist.RegressorNormalizer.fit": {"fullname": "mlsauce.nonconformist.RegressorNormalizer.fit", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNormalizer.fit", "kind": "function", "doc": "

\n", "signature": "(self, x, y):", "funcdef": "def"}, "mlsauce.nonconformist.RegressorNormalizer.score": {"fullname": "mlsauce.nonconformist.RegressorNormalizer.score", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNormalizer.score", "kind": "function", "doc": "

\n", "signature": "(self, x, y=None):", "funcdef": "def"}, "mlsauce.nonconformist.RegressorNormalizer.set_fit_request": {"fullname": "mlsauce.nonconformist.RegressorNormalizer.set_fit_request", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNormalizer.set_fit_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.nonconformist.RegressorNormalizer.set_score_request": {"fullname": "mlsauce.nonconformist.RegressorNormalizer.set_score_request", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNormalizer.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.nonconformist.IcpRegressor": {"fullname": "mlsauce.nonconformist.IcpRegressor", "modulename": "mlsauce.nonconformist", "qualname": "IcpRegressor", "kind": "class", "doc": "

Inductive conformal regressor.

\n\n

Parameters

\n\n

nc_function : BaseScorer\n Nonconformity scorer object used to calculate nonconformity of\n calibration examples and test patterns. Should implement fit(x, y),\n calc_nc(x, y) and predict(x, nc_scores, significance).

\n\n

Attributes

\n\n

cal_x : numpy array of shape [n_cal_examples, n_features]\n Inputs of calibration set.

\n\n

cal_y : numpy array of shape [n_cal_examples]\n Outputs of calibration set.

\n\n

nc_function : BaseScorer\n Nonconformity scorer object used to calculate nonconformity scores.

\n\n

See also

\n\n

IcpClassifier

\n\n

References

\n\n

Examples

\n\n
\n
>>> import numpy as np\n>>> from sklearn.datasets import load_boston\n>>> from sklearn.tree import DecisionTreeRegressor\n>>> from nonconformist.base import RegressorAdapter\n>>> from nonconformist.icp import IcpRegressor\n>>> from nonconformist.nc import RegressorNc, AbsErrorErrFunc\n>>> boston = load_boston()\n>>> idx = np.random.permutation(boston.target.size)\n>>> train = idx[:int(idx.size / 3)]\n>>> cal = idx[int(idx.size / 3):int(2 * idx.size / 3)]\n>>> test = idx[int(2 * idx.size / 3):]\n>>> model = RegressorAdapter(DecisionTreeRegressor())\n>>> nc = RegressorNc(model, AbsErrorErrFunc())\n>>> icp = IcpRegressor(nc)\n>>> icp.fit(boston.data[train, :], boston.target[train])\n>>> icp.calibrate(boston.data[cal, :], boston.target[cal])\n>>> icp.predict(boston.data[test, :], significance=0.10)\n...     # doctest: +SKIP\narray([[  5. ,  20.6],\n        [ 15.5,  31.1],\n        ...,\n        [ 14.2,  29.8],\n        [ 11.6,  27.2]])\n
\n
\n\n
\n
\n
    \n
\n
\n", "bases": "mlsauce.nonconformist.icp.BaseIcp, mlsauce.nonconformist.base.RegressorMixin"}, "mlsauce.nonconformist.IcpRegressor.__init__": {"fullname": "mlsauce.nonconformist.IcpRegressor.__init__", "modulename": "mlsauce.nonconformist", "qualname": "IcpRegressor.__init__", "kind": "function", "doc": "

\n", "signature": "(nc_function, condition=None)"}, "mlsauce.nonconformist.IcpRegressor.predict": {"fullname": "mlsauce.nonconformist.IcpRegressor.predict", "modulename": "mlsauce.nonconformist", "qualname": "IcpRegressor.predict", "kind": "function", "doc": "

Predict the output values for a set of input patterns.

\n\n

Parameters

\n\n

x : numpy array of shape [n_samples, n_features]\n Inputs of patters for which to predict output values.

\n\n

significance : float\n Significance level (maximum allowed error rate) of predictions.\n Should be a float between 0 and 1. If None, then intervals for\n all significance levels (0.01, 0.02, ..., 0.99) are output in a\n 3d-matrix.

\n\n

Returns

\n\n

p : numpy array of shape [n_samples, 2] or [n_samples, 2, 99}\n If significance is None, then p contains the interval (minimum\n and maximum boundaries) for each test pattern, and each significance\n level (0.01, 0.02, ..., 0.99). If significance is a float between\n 0 and 1, then p contains the prediction intervals (minimum and\n maximum boundaries) for the set of test patterns at the chosen\n significance level.

\n", "signature": "(self, x, significance=None):", "funcdef": "def"}, "mlsauce.nonconformist.IcpRegressor.set_fit_request": {"fullname": "mlsauce.nonconformist.IcpRegressor.set_fit_request", "modulename": "mlsauce.nonconformist", "qualname": "IcpRegressor.set_fit_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.nonconformist.IcpRegressor.set_predict_request": {"fullname": "mlsauce.nonconformist.IcpRegressor.set_predict_request", "modulename": "mlsauce.nonconformist", "qualname": "IcpRegressor.set_predict_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.predictioninterval": {"fullname": "mlsauce.predictioninterval", "modulename": "mlsauce.predictioninterval", "kind": "module", "doc": "

\n"}, "mlsauce.predictioninterval.PredictionInterval": {"fullname": "mlsauce.predictioninterval.PredictionInterval", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval", "kind": "class", "doc": "

Class PredictionInterval: Obtain prediction intervals.

\n\n

Attributes:

\n\n
obj: an object;\n    fitted object containing methods `fit` and `predict`\n\nmethod: a string;\n    method for constructing the prediction intervals.\n    Currently \"splitconformal\" (default) and \"localconformal\"\n\nlevel: a float;\n    Confidence level for prediction intervals. Default is 95,\n    equivalent to a miscoverage error of 5 (%)\n\nreplications: an integer;\n    Number of replications for simulated conformal (default is `None`)\n\ntype_pi: a string;\n    type of prediction interval: currently \"kde\" (default) or \"bootstrap\"\n\nseed: an integer;\n    Reproducibility of fit (there's a random split between fitting and calibration data)\n
\n", "bases": "sklearn.base.BaseEstimator, sklearn.base.RegressorMixin"}, "mlsauce.predictioninterval.PredictionInterval.__init__": {"fullname": "mlsauce.predictioninterval.PredictionInterval.__init__", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.__init__", "kind": "function", "doc": "

\n", "signature": "(\tobj,\tmethod='splitconformal',\tlevel=95,\ttype_pi='bootstrap',\treplications=None,\tkernel=None,\tagg='mean',\tseed=123)"}, "mlsauce.predictioninterval.PredictionInterval.obj": {"fullname": "mlsauce.predictioninterval.PredictionInterval.obj", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.obj", "kind": "variable", "doc": "

\n"}, "mlsauce.predictioninterval.PredictionInterval.method": {"fullname": "mlsauce.predictioninterval.PredictionInterval.method", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.method", "kind": "variable", "doc": "

\n"}, "mlsauce.predictioninterval.PredictionInterval.level": {"fullname": "mlsauce.predictioninterval.PredictionInterval.level", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.level", "kind": "variable", "doc": "

\n"}, "mlsauce.predictioninterval.PredictionInterval.type_pi": {"fullname": "mlsauce.predictioninterval.PredictionInterval.type_pi", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.type_pi", "kind": "variable", "doc": "

\n"}, "mlsauce.predictioninterval.PredictionInterval.replications": {"fullname": "mlsauce.predictioninterval.PredictionInterval.replications", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.replications", "kind": "variable", "doc": "

\n"}, "mlsauce.predictioninterval.PredictionInterval.kernel": {"fullname": "mlsauce.predictioninterval.PredictionInterval.kernel", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.kernel", "kind": "variable", "doc": "

\n"}, "mlsauce.predictioninterval.PredictionInterval.agg": {"fullname": "mlsauce.predictioninterval.PredictionInterval.agg", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.agg", "kind": "variable", "doc": "

\n"}, "mlsauce.predictioninterval.PredictionInterval.seed": {"fullname": "mlsauce.predictioninterval.PredictionInterval.seed", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.seed", "kind": "variable", "doc": "

\n"}, "mlsauce.predictioninterval.PredictionInterval.alpha_": {"fullname": "mlsauce.predictioninterval.PredictionInterval.alpha_", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.alpha_", "kind": "variable", "doc": "

\n"}, "mlsauce.predictioninterval.PredictionInterval.quantile_": {"fullname": "mlsauce.predictioninterval.PredictionInterval.quantile_", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.quantile_", "kind": "variable", "doc": "

\n"}, "mlsauce.predictioninterval.PredictionInterval.icp_": {"fullname": "mlsauce.predictioninterval.PredictionInterval.icp_", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.icp_", "kind": "variable", "doc": "

\n"}, "mlsauce.predictioninterval.PredictionInterval.calibrated_residuals_": {"fullname": "mlsauce.predictioninterval.PredictionInterval.calibrated_residuals_", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.calibrated_residuals_", "kind": "variable", "doc": "

\n"}, "mlsauce.predictioninterval.PredictionInterval.scaled_calibrated_residuals_": {"fullname": "mlsauce.predictioninterval.PredictionInterval.scaled_calibrated_residuals_", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.scaled_calibrated_residuals_", "kind": "variable", "doc": "

\n"}, "mlsauce.predictioninterval.PredictionInterval.calibrated_residuals_scaler_": {"fullname": "mlsauce.predictioninterval.PredictionInterval.calibrated_residuals_scaler_", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.calibrated_residuals_scaler_", "kind": "variable", "doc": "

\n"}, "mlsauce.predictioninterval.PredictionInterval.kde_": {"fullname": "mlsauce.predictioninterval.PredictionInterval.kde_", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.kde_", "kind": "variable", "doc": "

\n"}, "mlsauce.predictioninterval.PredictionInterval.fit": {"fullname": "mlsauce.predictioninterval.PredictionInterval.fit", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.fit", "kind": "function", "doc": "

Fit the method to training data (X, y).

\n\n

Args:

\n\n
X: array-like, shape = [n_samples, n_features];\n    Training set vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples, ]; Target values.\n
\n", "signature": "(self, X, y):", "funcdef": "def"}, "mlsauce.predictioninterval.PredictionInterval.predict": {"fullname": "mlsauce.predictioninterval.PredictionInterval.predict", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.predict", "kind": "function", "doc": "

Obtain predictions and prediction intervals

\n\n

Args:

\n\n
X: array-like, shape = [n_samples, n_features];\n    Testing set vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\nreturn_pi: boolean\n    Whether the prediction interval is returned or not.\n    Default is False, for compatibility with other _estimators_.\n    If True, a tuple containing the predictions + lower and upper\n    bounds is returned.\n
\n", "signature": "(self, X, return_pi=False):", "funcdef": "def"}, "mlsauce.predictioninterval.PredictionInterval.set_predict_request": {"fullname": "mlsauce.predictioninterval.PredictionInterval.set_predict_request", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.set_predict_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.predictioninterval.PredictionInterval.set_score_request": {"fullname": "mlsauce.predictioninterval.PredictionInterval.set_score_request", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.ridge": {"fullname": "mlsauce.ridge", "modulename": "mlsauce.ridge", "kind": "module", "doc": "

\n"}, "mlsauce.ridge.RidgeRegressor": {"fullname": "mlsauce.ridge.RidgeRegressor", "modulename": "mlsauce.ridge", "qualname": "RidgeRegressor", "kind": "class", "doc": "

Ridge.

\n\n

Attributes:

\n\n
reg_lambda: float\n    regularization parameter.\n\nbackend: str\n    type of backend; must be in ('cpu', 'gpu', 'tpu')\n
\n", "bases": "sklearn.base.BaseEstimator, sklearn.base.RegressorMixin"}, "mlsauce.ridge.RidgeRegressor.__init__": {"fullname": "mlsauce.ridge.RidgeRegressor.__init__", "modulename": "mlsauce.ridge", "qualname": "RidgeRegressor.__init__", "kind": "function", "doc": "

\n", "signature": "(reg_lambda=0.1, backend='cpu')"}, "mlsauce.ridge.RidgeRegressor.reg_lambda": {"fullname": "mlsauce.ridge.RidgeRegressor.reg_lambda", "modulename": "mlsauce.ridge", "qualname": "RidgeRegressor.reg_lambda", "kind": "variable", "doc": "

\n"}, "mlsauce.ridge.RidgeRegressor.backend": {"fullname": "mlsauce.ridge.RidgeRegressor.backend", "modulename": "mlsauce.ridge", "qualname": "RidgeRegressor.backend", "kind": "variable", "doc": "

\n"}, "mlsauce.ridge.RidgeRegressor.fit": {"fullname": "mlsauce.ridge.RidgeRegressor.fit", "modulename": "mlsauce.ridge", "qualname": "RidgeRegressor.fit", "kind": "function", "doc": "

Fit matrixops (classifier) to training data (X, y)

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n    Target values.\n\n**kwargs: additional parameters to be passed to self.cook_training_set.\n
\n\n

Returns:

\n\n
self: object.\n
\n", "signature": "(self, X, y, **kwargs):", "funcdef": "def"}, "mlsauce.ridge.RidgeRegressor.predict": {"fullname": "mlsauce.ridge.RidgeRegressor.predict", "modulename": "mlsauce.ridge", "qualname": "RidgeRegressor.predict", "kind": "function", "doc": "

Predict test data X.

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to `predict_proba`\n
\n\n

Returns:

\n\n
model predictions: {array-like}\n
\n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.ridge.RidgeRegressor.set_score_request": {"fullname": "mlsauce.ridge.RidgeRegressor.set_score_request", "modulename": "mlsauce.ridge", "qualname": "RidgeRegressor.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.setup": {"fullname": "mlsauce.setup", "modulename": "mlsauce.setup", "kind": "module", "doc": "

\n"}, "mlsauce.stump": {"fullname": "mlsauce.stump", "modulename": "mlsauce.stump", "kind": "module", "doc": "

\n"}, "mlsauce.stump.StumpClassifier": {"fullname": "mlsauce.stump.StumpClassifier", "modulename": "mlsauce.stump", "qualname": "StumpClassifier", "kind": "class", "doc": "

Stump classifier.

\n\n

Attributes:

\n\n
bins: int\n    Number of histogram bins; as in numpy.histogram.\n
\n", "bases": "sklearn.base.BaseEstimator, sklearn.base.ClassifierMixin"}, "mlsauce.stump.StumpClassifier.__init__": {"fullname": "mlsauce.stump.StumpClassifier.__init__", "modulename": "mlsauce.stump", "qualname": "StumpClassifier.__init__", "kind": "function", "doc": "

\n", "signature": "(bins='auto')"}, "mlsauce.stump.StumpClassifier.bins": {"fullname": "mlsauce.stump.StumpClassifier.bins", "modulename": "mlsauce.stump", "qualname": "StumpClassifier.bins", "kind": "variable", "doc": "

\n"}, "mlsauce.stump.StumpClassifier.obj": {"fullname": "mlsauce.stump.StumpClassifier.obj", "modulename": "mlsauce.stump", "qualname": "StumpClassifier.obj", "kind": "variable", "doc": "

\n"}, "mlsauce.stump.StumpClassifier.fit": {"fullname": "mlsauce.stump.StumpClassifier.fit", "modulename": "mlsauce.stump", "qualname": "StumpClassifier.fit", "kind": "function", "doc": "

Fit Stump to training data (X, y)

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n    Target values.\n\nsample_weight: array_like, shape = [n_samples]\n    Observations weights.\n
\n\n

Returns:

\n\n
self: object.\n
\n", "signature": "(self, X, y, sample_weight=None, **kwargs):", "funcdef": "def"}, "mlsauce.stump.StumpClassifier.predict": {"fullname": "mlsauce.stump.StumpClassifier.predict", "modulename": "mlsauce.stump", "qualname": "StumpClassifier.predict", "kind": "function", "doc": "

Predict test data X.

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to `predict_proba`\n
\n\n

Returns:

\n\n
model predictions: {array-like}\n
\n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.stump.StumpClassifier.predict_proba": {"fullname": "mlsauce.stump.StumpClassifier.predict_proba", "modulename": "mlsauce.stump", "qualname": "StumpClassifier.predict_proba", "kind": "function", "doc": "

Predict probabilities for test data X.

\n\n

Args:

\n\n
X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to\n    self.cook_test_set\n
\n\n

Returns:

\n\n
probability estimates for test data: {array-like}\n
\n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.stump.StumpClassifier.set_fit_request": {"fullname": "mlsauce.stump.StumpClassifier.set_fit_request", "modulename": "mlsauce.stump", "qualname": "StumpClassifier.set_fit_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.stump.StumpClassifier.set_score_request": {"fullname": "mlsauce.stump.StumpClassifier.set_score_request", "modulename": "mlsauce.stump", "qualname": "StumpClassifier.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n

Parameters

\n\n

name : str\n The name of the method for which the request function should be\n created, e.g. \"fit\" would create a set_fit_request function.

\n\n

keys : list of str\n A list of strings which are accepted parameters by the created\n function, e.g. [\"sample_weight\"] if the corresponding method\n accepts it as a metadata.

\n\n

validate_keys : bool, default=True\n Whether to check if the requested parameters fit the actual parameters\n of the method.

\n\n

Notes

\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.utils": {"fullname": "mlsauce.utils", "modulename": "mlsauce.utils", "kind": "module", "doc": "

\n"}, "mlsauce.utils.cluster": {"fullname": "mlsauce.utils.cluster", "modulename": "mlsauce.utils", "qualname": "cluster", "kind": "function", "doc": "

\n", "signature": "(\tX,\tn_clusters=None,\tmethod='kmeans',\ttype_scaling='standard',\ttraining=True,\tscaler=None,\tlabel_encoder=None,\tclusterer=None,\tseed=123):", "funcdef": "def"}, "mlsauce.utils.subsample": {"fullname": "mlsauce.utils.subsample", "modulename": "mlsauce.utils", "qualname": "subsample", "kind": "function", "doc": "

\n", "signature": "(y, row_sample=0.8, seed=123):", "funcdef": "def"}, "mlsauce.utils.merge_two_dicts": {"fullname": "mlsauce.utils.merge_two_dicts", "modulename": "mlsauce.utils", "qualname": "merge_two_dicts", "kind": "function", "doc": "

\n", "signature": "(x, y):", "funcdef": "def"}, "mlsauce.utils.flatten": {"fullname": "mlsauce.utils.flatten", "modulename": "mlsauce.utils", "qualname": "flatten", "kind": "function", "doc": "

\n", "signature": "(l):", "funcdef": "def"}, "mlsauce.utils.is_float": {"fullname": "mlsauce.utils.is_float", "modulename": "mlsauce.utils", "qualname": "is_float", "kind": "function", "doc": "

\n", "signature": "(x):", "funcdef": "def"}, "mlsauce.utils.is_factor": {"fullname": "mlsauce.utils.is_factor", "modulename": "mlsauce.utils", "qualname": "is_factor", "kind": "function", "doc": "

\n", "signature": "(y):", "funcdef": "def"}, "mlsauce.utils.Progbar": {"fullname": "mlsauce.utils.Progbar", "modulename": "mlsauce.utils", "qualname": "Progbar", "kind": "class", "doc": "

Displays a progress bar.

\n\n

Arguments

\n\n
target: Total number of steps expected, None if unknown.\nwidth: Progress bar width on screen.\nverbose: Verbosity mode, 0 (silent), 1 (verbose), 2 (semi-verbose)\nstateful_metrics: Iterable of string names of metrics that\n    should *not* be averaged over time. Metrics in this list\n    will be displayed as-is. All others will be averaged\n    by the progbar before display.\ninterval: Minimum visual progress update interval (in seconds).\n
\n"}, "mlsauce.utils.Progbar.__init__": {"fullname": "mlsauce.utils.Progbar.__init__", "modulename": "mlsauce.utils", "qualname": "Progbar.__init__", "kind": "function", "doc": "

\n", "signature": "(target, width=30, verbose=1, interval=0.05, stateful_metrics=None)"}, "mlsauce.utils.Progbar.target": {"fullname": "mlsauce.utils.Progbar.target", "modulename": "mlsauce.utils", "qualname": "Progbar.target", "kind": "variable", "doc": "

\n"}, "mlsauce.utils.Progbar.width": {"fullname": "mlsauce.utils.Progbar.width", "modulename": "mlsauce.utils", "qualname": "Progbar.width", "kind": "variable", "doc": "

\n"}, "mlsauce.utils.Progbar.verbose": {"fullname": "mlsauce.utils.Progbar.verbose", "modulename": "mlsauce.utils", "qualname": "Progbar.verbose", "kind": "variable", "doc": "

\n"}, "mlsauce.utils.Progbar.interval": {"fullname": "mlsauce.utils.Progbar.interval", "modulename": "mlsauce.utils", "qualname": "Progbar.interval", "kind": "variable", "doc": "

\n"}, "mlsauce.utils.Progbar.update": {"fullname": "mlsauce.utils.Progbar.update", "modulename": "mlsauce.utils", "qualname": "Progbar.update", "kind": "function", "doc": "

Updates the progress bar.

\n\n

Arguments

\n\n
current: Index of current step.\nvalues: List of tuples:\n    `(name, value_for_last_step)`.\n    If `name` is in `stateful_metrics`,\n    `value_for_last_step` will be displayed as-is.\n    Else, an average of the metric over time will be displayed.\n
\n", "signature": "(self, current, values=None):", "funcdef": "def"}, "mlsauce.utils.Progbar.add": {"fullname": "mlsauce.utils.Progbar.add", "modulename": "mlsauce.utils", "qualname": "Progbar.add", "kind": "function", "doc": "

\n", "signature": "(self, n, values=None):", "funcdef": "def"}, "mlsauce.utils.get_beta": {"fullname": "mlsauce.utils.get_beta", "modulename": "mlsauce.utils.get_beta", "kind": "module", "doc": "

\n"}, "mlsauce.utils.check_and_install": {"fullname": "mlsauce.utils.check_and_install", "modulename": "mlsauce.utils", "qualname": "check_and_install", "kind": "function", "doc": "

Check if a package is installed; if not, install it.

\n", "signature": "(package_name):", "funcdef": "def"}, "mlsauce.utils.is_multitask_estimator": {"fullname": "mlsauce.utils.is_multitask_estimator", "modulename": "mlsauce.utils", "qualname": "is_multitask_estimator", "kind": "function", "doc": "

\n", "signature": "(estimator):", "funcdef": "def"}, "mlsauce.utils.get_beta.get_beta": {"fullname": "mlsauce.utils.get_beta.get_beta", "modulename": "mlsauce.utils.get_beta", "qualname": "get_beta", "kind": "function", "doc": "

\n", "signature": "(X, y):", "funcdef": "def"}}, "docInfo": {"mlsauce": {"qualname": 0, "fullname": 1, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mlsauce.AdaOpt": {"qualname": 1, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 6, "doc": 277}, "mlsauce.AdaOpt.__init__": {"qualname": 3, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 245, "bases": 0, "doc": 3}, "mlsauce.AdaOpt.n_iterations": {"qualname": 3, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mlsauce.AdaOpt.learning_rate": {"qualname": 3, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mlsauce.AdaOpt.reg_lambda": {"qualname": 3, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mlsauce.AdaOpt.reg_alpha": {"qualname": 3, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mlsauce.AdaOpt.eta": {"qualname": 2, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mlsauce.AdaOpt.gamma": {"qualname": 2, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mlsauce.AdaOpt.k": {"qualname": 2, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mlsauce.AdaOpt.tolerance": {"qualname": 2, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mlsauce.AdaOpt.n_clusters": {"qualname": 3, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mlsauce.AdaOpt.batch_size": {"qualname": 3, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mlsauce.AdaOpt.row_sample": {"qualname": 3, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mlsauce.AdaOpt.type_dist": {"qualname": 3, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mlsauce.AdaOpt.n_jobs": {"qualname": 3, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mlsauce.AdaOpt.cache": {"qualname": 2, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mlsauce.AdaOpt.verbose": {"qualname": 2, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mlsauce.AdaOpt.n_clusters_input": {"qualname": 4, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mlsauce.AdaOpt.clustering_method": {"qualname": 3, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mlsauce.AdaOpt.cluster_scaling": {"qualname": 3, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mlsauce.AdaOpt.seed": {"qualname": 2, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mlsauce.AdaOpt.fit": {"qualname": 2, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 28, "bases": 0, "doc": 73}, "mlsauce.AdaOpt.predict": {"qualname": 2, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 23, "bases": 0, "doc": 62}, "mlsauce.AdaOpt.predict_proba": {"qualname": 3, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 23, "bases": 0, "doc": 69}, "mlsauce.AdaOpt.set_score_request": {"qualname": 4, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 11, "bases": 0, "doc": 198}, "mlsauce.LSBoostClassifier": {"qualname": 1, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 6, "doc": 980}, "mlsauce.LSBoostClassifier.__init__": {"qualname": 3, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 276, "bases": 0, "doc": 3}, "mlsauce.LSBoostClassifier.base_model": {"qualname": 3, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mlsauce.LSBoostClassifier.n_estimators": {"qualname": 3, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mlsauce.LSBoostClassifier.learning_rate": {"qualname": 3, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mlsauce.LSBoostClassifier.n_hidden_features": {"qualname": 4, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mlsauce.LSBoostClassifier.reg_lambda": {"qualname": 3, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mlsauce.LSBoostClassifier.alpha": {"qualname": 2, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mlsauce.LSBoostClassifier.row_sample": {"qualname": 3, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "mlsauce.LSBoostClassifier.col_sample": {"qualname": 3, "fullname": 4, "annotation": 0, 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update(self, X, y, eta=0.9): y: float = [n_samples=1] Target value. - + eta: float - Inverse power applied to number of observations + Inverse power applied to number of observations (defines a learning rate). Returns: @@ -540,10 +540,10 @@ def update(self, X, y, eta=0.9): seed=self.seed, ), ) - ) - + ) + self.obj = boosterc.update_booster( - self.obj, np.asarray(X, order="C"), np.asarray(y, order="C"), eta + self.obj, np.asarray(X, order="C"), np.asarray(y, order="C"), eta ) return self diff --git a/mlsauce/booster/_booster_regressor.py b/mlsauce/booster/_booster_regressor.py index 71f8b71..09a2ec5 100644 --- a/mlsauce/booster/_booster_regressor.py +++ b/mlsauce/booster/_booster_regressor.py @@ -373,13 +373,13 @@ def predict(self, X, level=95, method=None, **kwargs): self.y_ = None preds = self.pi.predict(X, return_pi=True) return preds - #print(f"\n in predict self: {self} \n") - #print(f"\n in predict self.obj: {self.obj} \n") - #try: + # print(f"\n in predict self: {self} \n") + # print(f"\n in predict self.obj: {self.obj} \n") + # try: return boosterc.predict_booster_regressor( self.obj, np.asarray(X, order="C") ) - #except ValueError: + # except ValueError: # pass def update(self, X, y, eta=0.9): @@ -393,9 +393,9 @@ def update(self, X, y, eta=0.9): y: float = [n_samples=1] Target value. - + eta: float - Inverse power applied to number of observations + Inverse power applied to number of observations (defines a learning rate). Returns: @@ -422,9 +422,8 @@ def update(self, X, y, eta=0.9): seed=self.seed, ), ) - ) + ) - self.obj = boosterc.update_booster( self.obj, np.asarray(X, order="C"), np.asarray(y, order="C"), eta ) diff --git a/mlsauce/lazybooster/config.py b/mlsauce/lazybooster/config.py index f297f97..5102a10 100644 --- a/mlsauce/lazybooster/config.py +++ b/mlsauce/lazybooster/config.py @@ -18,12 +18,12 @@ "IsotonicRegression", "MLPRegressor", "KernelRidge", - #"MultiOutputRegressor", - #"MultiTaskElasticNet", + # "MultiOutputRegressor", + # "MultiTaskElasticNet", "MultiTaskElasticNetCV", - #"MultiTaskLasso", + # "MultiTaskLasso", "MultiTaskLassoCV", - "NuSVR", + "NuSVR", "OrthogonalMatchingPursuit", "OrthogonalMatchingPursuitCV", "PLSCanonical", @@ -49,7 +49,7 @@ MTASKREGRESSORS = [ ( - "GenericBooster(MultiTask(" + est[0] + "))", + "GenericBooster(MultiTask(" + est[0] + "))", partial(MultiTaskRegressor, regr=est[1]()), ) for est in all_estimators() diff --git a/mlsauce/lazybooster/lazyboosterclassif.py b/mlsauce/lazybooster/lazyboosterclassif.py index a82cde3..b76ceab 100644 --- a/mlsauce/lazybooster/lazyboosterclassif.py +++ b/mlsauce/lazybooster/lazyboosterclassif.py @@ -343,7 +343,10 @@ def fit(self, X_train, X_test, y_train, y_test, **kwargs): and (est[0] in self.estimators) ) ] + [ - ("GBoostClassifier(MultiTask(" + est[0] + "))", partial(MultiTaskRegressor, regr=est[1]())) + ( + "GBoostClassifier(MultiTask(" + est[0] + "))", + partial(MultiTaskRegressor, regr=est[1]()), + ) for est in all_estimators() if ( issubclass(est[1], RegressorMixin) @@ -352,7 +355,7 @@ def fit(self, X_train, X_test, y_train, y_test, **kwargs): ] if self.preprocess is True: - + if self.n_jobs is None: for name, model in tqdm(self.classifiers): # do parallel exec @@ -376,7 +379,9 @@ def fit(self, X_train, X_test, y_train, y_test, **kwargs): fitted_clf = GenericBoostingClassifier( {**other_args, **kwargs}, verbose=self.verbose, - base_model=model(random_state=self.random_state), + base_model=model( + random_state=self.random_state + ), ) else: @@ -401,7 +406,9 @@ def fit(self, X_train, X_test, y_train, y_test, **kwargs): pipe.fit(X_train, y_train) self.models_[name] = pipe y_pred = pipe.predict(X_test) - accuracy = accuracy_score(y_test, y_pred, normalize=True) + accuracy = accuracy_score( + y_test, y_pred, normalize=True + ) b_accuracy = balanced_accuracy_score(y_test, y_pred) f1 = f1_score(y_test, y_pred, average="weighted") try: @@ -409,7 +416,9 @@ def fit(self, X_train, X_test, y_train, y_test, **kwargs): except Exception as exception: roc_auc = None if self.ignore_warnings is False: - print("ROC AUC couldn't be calculated for " + name) + print( + "ROC AUC couldn't be calculated for " + name + ) print(exception) names.append(name) Accuracy.append(accuracy) @@ -452,15 +461,24 @@ def fit(self, X_train, X_test, y_train, y_test, **kwargs): print(exception) else: - - # train_model(self, name, model, X_train, y_train, X_test, y_test, - #use_preprocessing=False, preprocessor=None, + + # train_model(self, name, model, X_train, y_train, X_test, y_test, + # use_preprocessing=False, preprocessor=None, # **kwargs): - results = Parallel(n_jobs=self.n_jobs)(delayed(self.train_model)( - name, model, X_train, y_train, X_test, y_test, - use_preprocessing=True, preprocessor=preprocessor, **kwargs - ) for name, model in tqdm(self.classifiers) - ) + results = Parallel(n_jobs=self.n_jobs)( + delayed(self.train_model)( + name, + model, + X_train, + y_train, + X_test, + y_test, + use_preprocessing=True, + preprocessor=preprocessor, + **kwargs + ) + for name, model in tqdm(self.classifiers) + ) Accuracy = [res["accuracy"] for res in results] B_Accuracy = [res["balanced_accuracy"] for res in results] ROC_AUC = [res["roc_auc"] for res in results] @@ -470,11 +488,12 @@ def fit(self, X_train, X_test, y_train, y_test, **kwargs): if self.custom_metric is not None: CUSTOM_METRIC = [res["custom_metric"] for res in results] if self.predictions: - predictions = {res["name"]: res["predictions"] for res in results} - + predictions = { + res["name"]: res["predictions"] for res in results + } else: # no preprocessing - + if self.n_jobs is None: for name, model in tqdm(self.classifiers): # do parallel exec @@ -482,21 +501,27 @@ def fit(self, X_train, X_test, y_train, y_test, **kwargs): try: if "random_state" in model().get_params().keys(): fitted_clf = GenericBoostingClassifier( - base_model=model(random_state=self.random_state), + base_model=model( + random_state=self.random_state + ), verbose=self.verbose, **kwargs ) else: fitted_clf = GenericBoostingClassifier( - base_model=model(), verbose=self.verbose, **kwargs + base_model=model(), + verbose=self.verbose, + **kwargs ) fitted_clf.fit(X_train, y_train) self.models_[name] = fitted_clf y_pred = fitted_clf.predict(X_test) - accuracy = accuracy_score(y_test, y_pred, normalize=True) + accuracy = accuracy_score( + y_test, y_pred, normalize=True + ) b_accuracy = balanced_accuracy_score(y_test, y_pred) f1 = f1_score(y_test, y_pred, average="weighted") try: @@ -504,7 +529,9 @@ def fit(self, X_train, X_test, y_train, y_test, **kwargs): except Exception as exception: roc_auc = None if self.ignore_warnings is False: - print("ROC AUC couldn't be calculated for " + name) + print( + "ROC AUC couldn't be calculated for " + name + ) print(exception) names.append(name) Accuracy.append(accuracy) @@ -546,13 +573,21 @@ def fit(self, X_train, X_test, y_train, y_test, **kwargs): print(name + " model failed to execute") print(exception) - else: + else: - results = Parallel(n_jobs=self.n_jobs)(delayed(self.train_model)( - name, model, X_train, y_train, X_test, y_test, - use_preprocessing=False, **kwargs - ) for name, model in tqdm(self.classifiers) - ) + results = Parallel(n_jobs=self.n_jobs)( + delayed(self.train_model)( + name, + model, + X_train, + y_train, + X_test, + y_test, + use_preprocessing=False, + **kwargs + ) + for name, model in tqdm(self.classifiers) + ) Accuracy = [res["accuracy"] for res in results] B_Accuracy = [res["balanced_accuracy"] for res in results] ROC_AUC = [res["roc_auc"] for res in results] @@ -562,8 +597,9 @@ def fit(self, X_train, X_test, y_train, y_test, **kwargs): if self.custom_metric is not None: CUSTOM_METRIC = [res["custom_metric"] for res in results] if self.predictions: - predictions = {res["name"]: res["predictions"] for res in results} - + predictions = { + res["name"]: res["predictions"] for res in results + } if self.custom_metric is None: scores = pd.DataFrame( @@ -643,10 +679,18 @@ def provide_models(self, X_train, X_test, y_train, y_test): return self.models_ - - def train_model(self, name, model, X_train, y_train, X_test, y_test, - use_preprocessing=False, preprocessor=None, - **kwargs): + def train_model( + self, + name, + model, + X_train, + y_train, + X_test, + y_test, + use_preprocessing=False, + preprocessor=None, + **kwargs + ): """ Function to train a single model and return its results. """ @@ -654,7 +698,10 @@ def train_model(self, name, model, X_train, y_train, X_test, y_test, # Handle n_jobs parameter try: - if "n_jobs" in model().get_params().keys() and "LogisticRegression" not in name: + if ( + "n_jobs" in model().get_params().keys() + and "LogisticRegression" not in name + ): other_args["n_jobs"] = self.n_jobs except Exception: pass @@ -688,13 +735,21 @@ def train_model(self, name, model, X_train, y_train, X_test, y_test, ] ) if self.verbose > 0: - print("\n Fitting pipeline with preprocessing for " + name + " model...") + print( + "\n Fitting pipeline with preprocessing for " + + name + + " model..." + ) pipe.fit(X_train, y_train) y_pred = pipe.predict(X_test) else: # Case with no preprocessing if self.verbose > 0: - print("\n Fitting model without preprocessing for " + name + " model...") + print( + "\n Fitting model without preprocessing for " + + name + + " model..." + ) y_pred = fitted_clf.predict(X_test) accuracy = accuracy_score(y_test, y_pred, normalize=True) @@ -728,4 +783,4 @@ def train_model(self, name, model, X_train, y_train, X_test, y_test, if self.ignore_warnings is False: print(name + " model failed to execute") print(exception) - return None \ No newline at end of file + return None diff --git a/mlsauce/lazybooster/lazyboosterregression.py b/mlsauce/lazybooster/lazyboosterregression.py index a75f6c5..2957383 100644 --- a/mlsauce/lazybooster/lazyboosterregression.py +++ b/mlsauce/lazybooster/lazyboosterregression.py @@ -19,8 +19,7 @@ from sklearn.compose import ColumnTransformer from sklearn.base import RegressorMixin from sklearn.metrics import ( - r2_score, - root_mean_squared_error, + r2_score ) from .config import REGRESSORS from ..booster import GenericBoostingRegressor @@ -74,7 +73,7 @@ ) -# Helper function +# Helper functions def get_card_split(df, cols, n=11): @@ -87,6 +86,9 @@ def get_card_split(df, cols, n=11): def adjusted_rsquared(r2, n, p): return 1 - (1 - r2) * ((n - 1) / (n - p - 1)) +def root_mean_squared_error(y_true, y_pred): + return np.sqrt(np.mean((y_true - y_pred) ** 2)) + class LazyBoostingRegressor(RegressorMixin): """ @@ -334,7 +336,7 @@ def fit(self, X_train, X_test, y_train, y_test, **kwargs): ] if self.preprocess is True: - + if self.n_jobs is None: for name, regr in tqdm(self.regressors): # do parallel exec @@ -398,30 +400,55 @@ def fit(self, X_train, X_test, y_train, y_test, **kwargs): if self.ignore_warnings is False: print(name + " model failed to execute") print(exception) - - else: - results = Parallel(n_jobs=self.n_jobs)(delayed(self.train_model)( - name, model, X_train, y_train, X_test, y_test, - use_preprocessing=True, preprocessor=preprocessor, **kwargs - ) for name, model in tqdm(self.regressors) - ) - R2 = [result["r_squared"] for result in results if result is not None] - ADJR2 = [result["adj_rsquared"] for result in results if result is not None] - RMSE = [result["rmse"] for result in results if result is not None] - TIME = [result["time"] for result in results if result is not None] - names = [result["name"] for result in results if result is not None] + else: + + results = Parallel(n_jobs=self.n_jobs)( + delayed(self.train_model)( + name, + model, + X_train, + y_train, + X_test, + y_test, + use_preprocessing=True, + preprocessor=preprocessor, + **kwargs + ) + for name, model in tqdm(self.regressors) + ) + R2 = [ + result["r_squared"] + for result in results + if result is not None + ] + ADJR2 = [ + result["adj_rsquared"] + for result in results + if result is not None + ] + RMSE = [ + result["rmse"] for result in results if result is not None + ] + TIME = [ + result["time"] for result in results if result is not None + ] + names = [ + result["name"] for result in results if result is not None + ] if self.custom_metric: CUSTOM_METRIC = [ - result["custom_metric"] for result in results if result is not None + result["custom_metric"] + for result in results + if result is not None ] if self.predictions: predictions = { - result["name"]: result["predictions"] for result in results if result is not None + result["name"]: result["predictions"] + for result in results + if result is not None } - - else: # self.preprocess is False; no preprocessing if self.n_jobs is None: @@ -476,29 +503,54 @@ def fit(self, X_train, X_test, y_train, y_test, **kwargs): if self.ignore_warnings is False: print(name + " model failed to execute") print(exception) - - else: - - results = Parallel(n_jobs=self.n_jobs)(delayed(self.train_model)( - name, model, X_train, y_train, X_test, y_test, - use_preprocessing=False, **kwargs - ) for name, model in tqdm(self.regressors) - ) - R2 = [result["r_squared"] for result in results if result is not None] - ADJR2 = [result["adj_rsquared"] for result in results if result is not None] - RMSE = [result["rmse"] for result in results if result is not None] - TIME = [result["time"] for result in results if result is not None] - names = [result["name"] for result in results if result is not None] + + else: + + results = Parallel(n_jobs=self.n_jobs)( + delayed(self.train_model)( + name, + model, + X_train, + y_train, + X_test, + y_test, + use_preprocessing=False, + **kwargs + ) + for name, model in tqdm(self.regressors) + ) + R2 = [ + result["r_squared"] + for result in results + if result is not None + ] + ADJR2 = [ + result["adj_rsquared"] + for result in results + if result is not None + ] + RMSE = [ + result["rmse"] for result in results if result is not None + ] + TIME = [ + result["time"] for result in results if result is not None + ] + names = [ + result["name"] for result in results if result is not None + ] if self.custom_metric: CUSTOM_METRIC = [ - result["custom_metric"] for result in results if result is not None + result["custom_metric"] + for result in results + if result is not None ] if self.predictions: predictions = { - result["name"]: result["predictions"] for result in results if result is not None + result["name"]: result["predictions"] + for result in results + if result is not None } - scores = { "Model": names, "Adjusted R-Squared": ADJR2, @@ -568,15 +620,27 @@ def provide_models(self, X_train, X_test, y_train, y_test): return self.models_ - def train_model(self, name, regr, X_train, y_train, X_test, y_test, - use_preprocessing=False, preprocessor=None, **kwargs): + def train_model( + self, + name, + regr, + X_train, + y_train, + X_test, + y_test, + use_preprocessing=False, + preprocessor=None, + **kwargs + ): """ Function to train a single regression model and return its results. """ start = time.time() try: - model = GenericBoostingRegressor(base_model=regr(), verbose=self.verbose, **kwargs) + model = GenericBoostingRegressor( + base_model=regr(), verbose=self.verbose, **kwargs + ) if use_preprocessing and preprocessor is not None: pipe = Pipeline( @@ -586,20 +650,30 @@ def train_model(self, name, regr, X_train, y_train, X_test, y_test, ] ) if self.verbose > 0: - print("\n Fitting boosted " + name + " model with preprocessing...") + print( + "\n Fitting boosted " + + name + + " model with preprocessing..." + ) pipe.fit(X_train, y_train) y_pred = pipe.predict(X_test) fitted_model = pipe else: # Case with no preprocessing if self.verbose > 0: - print("\n Fitting boosted " + name + " model without preprocessing...") + print( + "\n Fitting boosted " + + name + + " model without preprocessing..." + ) model.fit(X_train, y_train) y_pred = model.predict(X_test) fitted_model = model r_squared = r2_score(y_test, y_pred) - adj_rsquared = adjusted_rsquared(r_squared, X_test.shape[0], X_test.shape[1]) + adj_rsquared = adjusted_rsquared( + r_squared, X_test.shape[0], X_test.shape[1] + ) rmse = root_mean_squared_error(y_test, y_pred) custom_metric = None @@ -622,4 +696,3 @@ def train_model(self, name, regr, X_train, y_train, X_test, y_test, print(name + " model failed to execute") print(exception) return None - diff --git a/mlsauce/multitaskregressor/__init__.py b/mlsauce/multitaskregressor/__init__.py index 55dc1a6..032172d 100644 --- a/mlsauce/multitaskregressor/__init__.py +++ b/mlsauce/multitaskregressor/__init__.py @@ -1,3 +1,3 @@ from .mtaskregr import MultiTaskRegressor -__all__ = ['MultiTaskRegressor'] \ No newline at end of file +__all__ = ["MultiTaskRegressor"] diff --git a/mlsauce/multitaskregressor/mtaskregr.py b/mlsauce/multitaskregressor/mtaskregr.py index 7bd41d3..a4d5384 100644 --- a/mlsauce/multitaskregressor/mtaskregr.py +++ b/mlsauce/multitaskregressor/mtaskregr.py @@ -4,6 +4,7 @@ from sklearn.base import BaseEstimator, RegressorMixin from ..utils import is_multitask_estimator + class MultiTaskRegressor(BaseEstimator, RegressorMixin): """ A class for multi-task regression @@ -19,8 +20,11 @@ class MultiTaskRegressor(BaseEstimator, RegressorMixin): A list containing the fitted regressor objects """ + def __init__(self, regr): - assert is_multitask_estimator(regr) == False, "The regressor is already a multi-task regressor" + assert ( + is_multitask_estimator(regr) == False + ), "The regressor is already a multi-task regressor" self.regr = regr self.objs = [] @@ -36,12 +40,12 @@ def fit(self, X, y): The target values """ - n_tasks = y.shape[1] + n_tasks = y.shape[1] assert n_tasks > 1, "The number of columns in y must be greater than 1" - self.n_outputs_ = n_tasks - try: + self.n_outputs_ = n_tasks + try: for i in range(n_tasks): - self.regr.fit(X, y.iloc[:, i].values) + self.regr.fit(X, y.iloc[:, i].values) self.objs.append(deepcopy(self.regr)) except Exception: for i in range(n_tasks): @@ -68,4 +72,4 @@ def predict(self, X): y_pred = np.zeros((X.shape[0], self.n_outputs_)) for i in range(self.n_outputs_): y_pred[:, i] = self.objs[i].predict(X) - return y_pred \ No newline at end of file + return y_pred diff --git a/mlsauce/utils/__init__.py b/mlsauce/utils/__init__.py index 184a574..99dec6d 100644 --- a/mlsauce/utils/__init__.py +++ b/mlsauce/utils/__init__.py @@ -6,7 +6,7 @@ is_float, is_factor, check_and_install, - is_multitask_estimator + is_multitask_estimator, ) from .progress_bar import Progbar from .get_beta import get_beta @@ -21,5 +21,5 @@ "Progbar", "get_beta", "check_and_install", - "is_multitask_estimator" + "is_multitask_estimator", ] diff --git a/mlsauce/utils/misc/__init__.py b/mlsauce/utils/misc/__init__.py index 3b55c32..6fce534 100644 --- a/mlsauce/utils/misc/__init__.py +++ b/mlsauce/utils/misc/__init__.py @@ -5,7 +5,7 @@ is_float, is_factor, check_and_install, - is_multitask_estimator + is_multitask_estimator, ) @@ -16,5 +16,5 @@ "is_float", "is_factor", "check_and_install", - "is_multitask_estimator" + "is_multitask_estimator", ] diff --git a/mlsauce/utils/misc/misc.py b/mlsauce/utils/misc/misc.py index 677fa1c..d188792 100644 --- a/mlsauce/utils/misc/misc.py +++ b/mlsauce/utils/misc/misc.py @@ -134,8 +134,10 @@ def install_package(package_name): [sys.executable, "-m", "pip", "install", package_name] ) + def is_multitask_estimator(estimator): - return estimator._get_tags().get('multioutput', False) + return estimator._get_tags().get("multioutput", False) + def check_and_install(package_name): """Check if a package is installed; if not, install it.""" diff --git a/setup.py b/setup.py index 0c92809..188ab86 100644 --- a/setup.py +++ b/setup.py @@ -37,7 +37,7 @@ MAINTAINER_EMAIL = 'thierry.moudiki@gmail.com' LICENSE = 'BSD3 Clause Clear' -__version__ = '0.22.3' +__version__ = '0.22.4' VERSION = __version__