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diff --git a/docs/cls_gridsearch.txt b/docs/cls_gridsearch.txt deleted file mode 100644 index 6a2c05e..0000000 --- a/docs/cls_gridsearch.txt +++ /dev/null @@ -1,285 +0,0 @@ - -.. py:class:: GenSVMGridSearchCV(param_grid='tiny', scoring=None, iid=True, cv=None, refit=True, verbose=0, return_train_score=True) - :noindex: - :module: gensvm.gridsearch - - GenSVM cross validated grid search - - This class implements efficient GenSVM grid search with cross validation. - One of the strong features of GenSVM is that seeding the classifier - properly can greatly reduce total training time. This class ensures that - the grid search is done in the most efficient way possible. - - The implementation of this class is based on the `GridSearchCV`_ class in - scikit-learn. The documentation of the various parameters is therefore - mostly the same. This is done to provide the user with a familiar and - easy-to-use interface to doing a grid search with GenSVM. A separate class - was needed to benefit from the fast low-level C implementation of grid - search in the GenSVM library. - - :param param_grid: If a string, it must be either 'tiny', 'small', or 'full' to load the - predefined parameter grids (see the functions :func:`load_grid_tiny`, - :func:`load_grid_small`, and :func:`load_grid_full`). - - Otherwise, a dictionary of parameter names (strings) as keys and lists - of parameter settings to evaluate as values, or a list of such dicts. - The GenSVM model will be evaluated at all combinations of the - parameters. - :type param_grid: string, dict, or list of dicts - :param scoring: A single string (see :ref:`scoring_parameter`) or a callable (see - :ref:`scoring`) to evaluate the predictions on the test set. - - For evaluating multiple metrics, either give a list of (unique) strings - or a dict with names as keys and callables as values. - - NOTE that when using custom scorers, each scorer should return a single - value. Metric functions returning a list/array of values can be wrapped - into multiple scorers that return one value each. - - If None, the `accuracy_score`_ is used. - :type scoring: string, callable, list/tuple, dict or None - :param iid: If True, the data is assumed to be identically distributed across the - folds, and the loss minimized is the total loss per sample and not the - mean loss across the folds. - :type iid: boolean, default=True - :param cv: Determines the cross-validation splitting strategy. Possible inputs for - cv are: - - - None, to use the default 5-fold cross validation, - - integer, to specify the number of folds in a `(Stratified)KFold`, - - An object to be used as a cross-validation generator. - - An iterable yielding train, test splits. - - For integer/None inputs, :class:`StratifiedKFold - <sklearn.model_selection.StratifiedKFold>` is used. In all other - cases, :class:`KFold <sklearn.model_selection.KFold>` is used. - - Refer to the `scikit-learn User Guide on cross validation`_ for the - various strategies that can be used here. - - NOTE: At the moment, the ShuffleSplit and StratifiedShuffleSplit are - not supported in this class. If you need these, you can use the GenSVM - classifier directly with the GridSearchCV object from scikit-learn. - (these methods require significant changes in the low-level library - before they can be supported). - :type cv: int, cross-validation generator or an iterable, optional - :param refit: Refit the GenSVM estimator with the best found parameters on the whole - dataset. - - For multiple metric evaluation, this needs to be a string denoting the - scorer to be used to find the best parameters for refitting the - estimator at the end. - - The refitted estimator is made available at the `:attr:best_estimator_ - <.GenSVMGridSearchCV.best_estimator_>` attribute and allows the user to - use the :func:`~GenSVMGridSearchCV.predict` method directly on this - :class:`.GenSVMGridSearchCV` instance. - - Also for multiple metric evaluation, the attributes :attr:`best_index_ - <.GenSVMGridSearchCV.best_index_>`, :attr:`best_score_ - <.GenSVMGridSearchCV.best_score_>` and :attr:`best_params_ - <.GenSVMGridSearchCV:best_params_>` will only be available if ``refit`` - is set and all of them will be determined w.r.t this specific scorer. - - See ``scoring`` parameter to know more about multiple metric - evaluation. - :type refit: boolean, or string, default=True - :param verbose: Controls the verbosity: the higher, the more messages. - :type verbose: integer - :param return_train_score: If ``False``, the :attr:`cv_results_ <.GenSVMGridSearchCV.cv_results_>` - attribute will not include training scores. - :type return_train_score: boolean, default=True - - .. rubric:: Examples - - >>> from gensvm import GenSVMGridSearchCV - >>> from sklearn.datasets import load_iris - >>> iris = load_iris() - >>> param_grid = {'p': [1.0, 2.0], 'kappa': [-0.9, 0.0, 1.0]} - >>> clf = GenSVMGridSearchCV(param_grid) - >>> clf.fit(iris.data, iris.target) - GenSVMGridSearchCV(cv=None, iid=True, - param_grid={'p': [1.0, 2.0], 'kappa': [-0.9, 0.0, 1.0]}, - refit=True, return_train_score=True, scoring=None, verbose=0) - - .. attribute:: cv_results_ - - *dict of numpy (masked) ndarrays* -- A dict with keys as column headers and values as columns, that can be - imported into a pandas `DataFrame`_. - - For instance the below given table - - +------------+-----------+------------+-----------------+---+---------+ - |param_kernel|param_gamma|param_degree|split0_test_score|...|rank_t...| - +============+===========+============+=================+===+=========+ - | 'poly' | -- | 2 | 0.8 |...| 2 | - +------------+-----------+------------+-----------------+---+---------+ - | 'poly' | -- | 3 | 0.7 |...| 4 | - +------------+-----------+------------+-----------------+---+---------+ - | 'rbf' | 0.1 | -- | 0.8 |...| 3 | - +------------+-----------+------------+-----------------+---+---------+ - | 'rbf' | 0.2 | -- | 0.9 |...| 1 | - +------------+-----------+------------+-----------------+---+---------+ - - will be represented by a ``cv_results_`` dict of:: - - { - 'param_kernel': masked_array(data = ['poly', 'poly', 'rbf', 'rbf'], - mask = [False False False False]...) - 'param_gamma': masked_array(data = [-- -- 0.1 0.2], - mask = [ True True False False]...), - 'param_degree': masked_array(data = [2.0 3.0 -- --], - mask = [False False True True]...), - 'split0_test_score' : [0.8, 0.7, 0.8, 0.9], - 'split1_test_score' : [0.82, 0.5, 0.7, 0.78], - 'mean_test_score' : [0.81, 0.60, 0.75, 0.82], - 'std_test_score' : [0.02, 0.01, 0.03, 0.03], - 'rank_test_score' : [2, 4, 3, 1], - 'split0_train_score' : [0.8, 0.9, 0.7], - 'split1_train_score' : [0.82, 0.5, 0.7], - 'mean_train_score' : [0.81, 0.7, 0.7], - 'std_train_score' : [0.03, 0.03, 0.04], - 'mean_fit_time' : [0.73, 0.63, 0.43, 0.49], - 'std_fit_time' : [0.01, 0.02, 0.01, 0.01], - 'mean_score_time' : [0.007, 0.06, 0.04, 0.04], - 'std_score_time' : [0.001, 0.002, 0.003, 0.005], - 'params' : [{'kernel': 'poly', 'degree': 2}, ...], - } - - NOTE: - - The key ``'params'`` is used to store a list of parameter settings - dicts for all the parameter candidates. - - The ``mean_fit_time``, ``std_fit_time``, ``mean_score_time`` and - ``std_score_time`` are all in seconds. - - For multi-metric evaluation, the scores for all the scorers are - available in the :attr:`cv_results_ <.GenSVMGridSearchCV.cv_results_>` - dict at the keys ending with that scorer's name (``'_<scorer_name>'``) - instead of ``'_score'`` shown above. ('split0_test_precision', - 'mean_train_precision' etc.) - - .. attribute:: best_estimator_ - - *estimator or dict* -- Estimator that was chosen by the search, i.e. estimator which gave - highest score (or smallest loss if specified) on the left out data. Not - available if ``refit=False``. - - See ``refit`` parameter for more information on allowed values. - - .. attribute:: best_score_ - - *float* -- Mean cross-validated score of the best_estimator - - For multi-metric evaluation, this is present only if ``refit`` is - specified. - - .. attribute:: best_params_ - - *dict* -- Parameter setting that gave the best results on the hold out data. - - For multi-metric evaluation, this is present only if ``refit`` is - specified. - - .. attribute:: best_index_ - - *int* -- The index (of the ``cv_results_`` arrays) which corresponds to the best - candidate parameter setting. - - The dict at ``search.cv_results_['params'][search.best_index_]`` gives - the parameter setting for the best model, that gives the highest mean - score (``search.best_score_``). - - For multi-metric evaluation, this is present only if ``refit`` is - specified. - - .. attribute:: scorer_ - - *function or a dict* -- Scorer function used on the held out data to choose the best parameters - for the model. - - For multi-metric evaluation, this attribute holds the validated - ``scoring`` dict which maps the scorer key to the scorer callable. - - .. attribute:: n_splits_ - - *int* -- The number of cross-validation splits (folds/iterations). - - .. rubric:: Notes - - The parameters selected are those that maximize the score of the left out - data, unless an explicit score is passed in which case it is used instead. - - .. seealso:: - - `ParameterGrid`_: - Generates all the combinations of a hyperparameter grid. - - :class:`.GenSVM`: - The GenSVM classifier - - .. _GridSearchCV: - http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html - .. _accuracy_score: - http://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html - .. _scikit-learn User Guide on cross validation: - http://scikit-learn.org/stable/modules/cross_validation.html - - .. _ParameterGrid: - http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.ParameterGrid.html - .. _DataFrame: - https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html - - - .. py:method:: GenSVMGridSearchCV.fit(X, y, groups=None) - :noindex: - :module: gensvm.gridsearch - - Run GenSVM grid search with all sets of parameters - - :param X: Training data, where n_samples is the number of observations and - n_features is the number of features. - :type X: array-like, shape = (n_samples, n_features) - :param y: Target vector for the training data. - :type y: array-like, shape = (n_samples, ) - :param groups: Group labels for the samples used while splitting the dataset into - train/test sets. - :type groups: array-like, with shape (n_samples, ), optional - - :returns: **self** -- Return self. - :rtype: object - - - .. py:method:: GenSVMGridSearchCV.predict(X, trainX=None) - :noindex: - :module: gensvm.gridsearch - - Predict the class labels on the test data - - :param X: Test data, where n_samples is the number of observations and - n_features is the number of features. - :type X: array-like, shape = (n_samples, n_features) - :param trainX: Only for nonlinear prediction with kernels: the training data used - to train the model. - :type trainX: array, shape = [n_train_samples, n_features] - - :returns: **y_pred** -- Predicted class labels of the data in X. - :rtype: array-like, shape = (n_samples, ) - - - .. py:method:: GenSVMGridSearchCV.score(X, y) - :noindex: - :module: gensvm.gridsearch - - Compute the score on the test data given the true labels - - :param X: Test data, where n_samples is the number of observations and - n_features is the number of features. - :type X: array-like, shape = (n_samples, n_features) - :param y: True labels for the test data. - :type y: array-like, shape = (n_samples, ) - - :returns: **score** - :rtype: float - |