slearn.symbolicML
- class slearn.symbolicML(classifier_name='MLPClassifier', ws=3, random_seed=0, verbose=0)[source]
Classifier for symbolic sequences.
- classifier_name - str, default=MLPClassifier,
optional choices = {“KNeighborsClassifier”, “GaussianProcessClassifier” “QuadraticDiscriminantAnalysis”, “DecisionTreeClassifier”, “LogisticRegression”, “AdaBoostClassifier”, “RandomForestClassifier”, “GaussianNB”, “DeepForest”, “LGBM”, “SVC”, “RBF”}:
The classifier you specify for symbols prediction.
- ws - int, default=3:
The windows size for symbols to be the features, i.e, the dimensions of features. The larger the window, the more information about time series can be taken into account.
- random_seed - int, default=0:
The random state fixed for classifers in scikit-learn.
- verbose - int, default=0:
Whether to print progress messages to stdout.
Methods
__init__
([classifier_name, ws, random_seed, ...])construct_train
(series)Construct features and target labels for symbols.
encode
(string)Construct features and target labels for symbols and encode to numerical values.
forecast
(x, y[, step, inversehash, centers])init_classifier
()