SupportVectorMachine(max_iter=1000, regularizer=1.0, verbose=False, use_gpu=False, class_weights=None, gpu_mem_limit=0, n_threads=-1, tol=0.001, return_training_history=None, labelColIndex=1)¶
Support Vector Machine classifier
This class implements regularized support vector machine using the IBM Snap ML solver. It can handle sparse and dense dataset formats. Use libsvm, snap or csv format for the Dual algorithm, or snap.t (transposed) format for the primal algorithm.
- max_iter (int, default : 1000) – Maximum number of iterations used by the solver to converge.
- regularizer (float, default : 1.0) – Regularization strength. It must be a positive float. Larger regularization values imply stronger regularization.
- verbose (boolean, default : False) – Flag for indicating if the training loss will be printed at each epoch.
- use_gpu (bool, default : False) – Flag for indicating the hardware platform used for training. If True, the training is performed using the GPU. If False, the training is performed using the CPU.
- class_weights ('balanced'/True or None/False, optional) – If set to ‘None’, all classes will have weight 1.
- gpu_mem_limit (int, default : 0) – Limit of the GPU memory. If set to the default value 0, the maximum possible memory is used.
- n_threads (int, default : -1 meaning that n_threads=256 if GPU is enabled, else 1) – Number of threads to be used.
- tol (float, default : 0.001) – The tolerance parameter. Training will finish when maximum change in model coefficients is less than tol.
- return_training_history (str or None, default : None) – How much information about the training should be collected and returned by the fit function. By default no information is returned (None), but this parameter can be set to “summary”, to obtain summary statistics at the end of training, or “full” to obtain a complete set of statistics for the entire training procedure. Note, enabling either option will result in slower training.
- labelColIndex (int, default : 1) – Denotes which column to be marked as label. It’s applicable only when Dataframe is passed in as input and its holding features in individual columns rather than in DenseVector or SparseVector
- coef (ndarray, shape (n_features,)) – Coefficients of the features in the trained model.
- pred_array (ndarray, shape(number_of_test_examples,)) – binary predictions written by the predict() function
Parameters: data (pyspark.sql.DataFrame/py4j.java_gateway.JavaObject which points to a memory address where the actual data is stored and it cannot be accessed by python as a python array.) – data to fit model Returns: double – final training loss of the last epoch
Returns: all the initialized parameters of the Support Vector Machine Model as a python dictionary
- data (pyspark.sql.DataFrame/py4j.java_gateway.JavaObject which points to a memory address where the actual data is stored and cannot be accessed by python as an array but only can passed as a parameter to this function in order to get the predictions) – data to make predictions
- num_threads – the number of threads to use for inference (default 0 means use all avaliable threads)
a pointer which points to a com.ibm.snap.ml.DatasetWithPredictions java object. This pointer cannot be accessed by python but the user can access the predictions from the pred_array_ field which is a python array.