give the wrapper a better name

1 files changed, 0 insertions, 190 deletions

diff --git a/gensvm/models.py b/gensvm/models.py
deleted file mode 100644
index f06374b..0000000
--- a/gensvm/models.py
+++ /dev/null
@@ -1,190 +0,0 @@
-# -*- coding: utf-8 -*-
-
-"""
-"""
-
-from __future__ import print_function, division
-
-import numpy as np
-import warnings
-
-from sklearn.base import BaseEstimator
-from sklearn.exceptions import ConvergenceWarning, FitFailedWarning
-from sklearn.preprocessing import LabelEncoder
-from sklearn.utils import check_X_y, check_random_state
-from sklearn.utils.multiclass import type_of_target
-from sklearn.utils.validation import check_is_fitted
-
-from . import pyx_gensvm
-
-
-def _fit_gensvm(X, y, p, lmd, kappa, epsilon, weight_idx, kernel, gamma, coef, 
-        degree, kernel_eigen_cutoff, verbose, max_iter, random_state=None):
-
-    # process the random state
-    rnd = check_random_state(random_state)
-
-    # set the verbosity in GenSVM
-    pyx_gensvm.set_verbosity_wrap(verbose)
-
-    # run the actual training
-    raw_coef_, n_SV_, n_iter_, training_error_, status_ = pyx_gensvm.train_wrap(
-            X, y, p, lmd, kappa, epsilon, weight_idx, kernel, gamma, coef, 
-            degree, kernel_eigen_cutoff, max_iter, 
-            rnd.randint(np.iinfo('i').max))
-
-    # process output
-    if status_ == 1 and verbose > 0:
-        warnings.warn("GenSVM optimization prematurely ended due to a "
-                "incorrect step in the optimization algorithm.", 
-                FitFailedWarning)
-
-    if status_ == 2 and verbose > 0:
-        warnings.warn("GenSVM failed to converge, increase "
-                "the number of iterations.", ConvergenceWarning)
-
-    coef_ = raw_coef_[1:, :]
-    intercept_ = raw_coef_[0, :]
-
-    return coef_, intercept_, n_iter_, n_SV_
-
-
-class GenSVM(BaseEstimator):
-    """Generalized Multiclass Support Vector Machine Classification.
-
-    This class implements the basic GenSVM classifier. GenSVM is a generalized 
-    multiclass SVM which is flexible in the weighting of misclassification 
-    errors. It is this flexibility that makes it perform well on diverse 
-    datasets.
-
-    This methods of this class use the GenSVM C library for the actual 
-    computations.
-
-    Parameters
-    ----------
-    p : float, optional (default=1.0)
-        Parameter for the L_p norm of the loss function (1.0 <= p <= 2.0)
-
-    lmd : float, optional (default=1e-5)
-        Parameter for the regularization term of the loss function (lmd > 0)
-
-    kappa : float, optional (default=0.0)
-        Parameter for the hinge function in the loss function (kappa > -1.0)
-
-    weight_idx : int, optional (default=1)
-        Type of sample weights to use (1 = unit weights, 2 = size correction 
-        weights)
-
-    kernel : string, optional (default='linear')
-        Specify the kernel type to use in the classifier. It must be one of 
-        'linear', 'poly', 'rbf', or 'sigmoid'.
-
-    gamma : float, optional (default=1.0)
-        Kernel parameter for the rbf, poly, and sigmoid kernel
-
-    coef : float, optional (default=0.0)
-        Kernel parameter for the poly and sigmoid kernel
-
-    degree : float, optional (default=2.0)
-        Kernel parameter for the poly kernel
-
-    kernel_eigen_cutoff : float, optional (default=1e-8)
-        Cutoff point for the reduced eigendecomposition used with 
-        kernel-GenSVM. Eigenvectors for which the ratio between their 
-        corresponding eigenvalue and the largest eigenvalue is smaller than the 
-        cutoff will be dropped.
-
-    verbose : int, (default=0)
-        Enable verbose output
-
-    max_iter : int, (default=1e8)
-        The maximum number of iterations to be run.
-
-
-    Attributes
-    ----------
-    coef_ : array, shape = [n_features, n_classes-1]
-        Weights assigned to the features (coefficients in the primal problem)
-
-    intercept_ : array, shape = [n_classes]
-        Constants in the decision function
-
-    n_iter_ : int
-        The number of iterations that were run during training.
-
-    n_support_ : int
-        The number of support vectors that were found
-
-
-    References
-    ----------
-    * Van den Burg, G.J.J. and Groenen, P.J.F.. GenSVM: A Generalized 
-    Multiclass Support Vector Machine. Journal of Machine Learning Research, 
-    17(225):1--42, 2016.
-
-    """
-
-    def __init__(self, p=1.0, lmd=1e-5, kappa=0.0, epsilon=1e-6, weight_idx=1, 
-            kernel='linear', gamma=1.0, coef=0.0, degree=2.0, 
-            kernel_eigen_cutoff=1e-8, verbose=0, random_state=None, 
-            max_iter=1e8):
-        self.p = p
-        self.lmd = lmd
-        self.kappa = kappa
-        self.epsilon = epsilon
-        self.weight_idx = weight_idx
-        self.kernel = kernel
-        self.gamma = gamma
-        self.coef = coef
-        self.degree = degree
-        self.kernel_eigen_cutoff = kernel_eigen_cutoff
-        self.verbose = verbose
-        self.random_state = random_state
-        self.max_iter = max_iter
-
-
-    def fit(self, X, y):
-        if not 1.0 <= self.p <= 2.0:
-            raise ValueError("Value for p should be within [1, 2]; got p = %r)" 
-                    % self.p)
-        if not self.kappa > -1.0:
-            raise ValueError("Value for kappa should be larger than -1; got "
-                    "kappa = %r" % self.kappa)
-        if not self.lmd > 0:
-            raise ValueError("Value for lmd should be larger than 0; got "
-                    "lmd = %r" % self.lmd)
-        if not self.epsilon > 0:
-            raise ValueError("Value for epsilon should be larger than 0; got "
-                    "epsilon = %r" % self.epsilon)
-        X, y_org = check_X_y(X, y, accept_sparse=False, dtype=np.float64, 
-                order="C")
-
-        y_type = type_of_target(y_org)
-        if y_type not in ["binary", "multiclass"]:
-            raise ValueError("Label type not allowed for GenSVM: %r" % y_type)
-
-        # This is necessary because GenSVM expects classes to go from 1 to 
-        # n_class
-        self.encoder = LabelEncoder()
-        y = self.encoder.fit_transform(y_org)
-        y += 1
-
-        self.coef_, self.intercept_, self.n_iter_, self.n_support_ = \
-                _fit_gensvm(X, y, self.p, self.lmd, self.kappa, self.epsilon, 
-                        self.weight_idx, self.kernel, self.gamma, self.coef, 
-                        self.degree, self.kernel_eigen_cutoff, self.verbose, 
-                        self.max_iter, self.random_state)
-        return self
-
-
-    def predict(self, X):
-        check_is_fitted(self, "coef_")
-
-        V = np.vstack((self.intercept_, self.coef_))
-        predictions = pyx_gensvm.predict_wrap(X, V)
-
-        # Transform the classes back to the original form
-        predictions -= 1
-        outcome = self.encoder.inverse_transform(predictions)
-
-        return outcome