support balanced class_weight #141

python/README.md

@@ -69,8 +69,8 @@ The usage of thundersvm scikit interface is similar to sklearn.svm.
 *probability*: boolean, optional(default=False)\
  whether to train a SVC or SVR model for probability estimates, True or False
 
-*class_weight*: {dict}, optional(default=None)\
- set the parameter C of class i to weight*C, for C-SVC
+*class_weight*: {dict, 'balanced'}, optional(default=None)\
+ set the parameter C of class i to weight*C, for C-SVC. If not given, all classes are supposed to have weight one. The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as ```n_samples / (n_classes * np.bincount(y))```
 
 *shrinking*: boolean, optional (default=False, not supported yet for True)\
  whether to use the shrinking heuristic.

python/thundersvm/thundersvm.py

@@ -185,12 +185,30 @@ def _dense_fit(self, X, y, solver_type, kernel):
  if self.class_weight is None:
  weight_size = 0
  self.class_weight = dict()
+ weight_label = (c_int * weight_size)()
+ weight_label[:] = list(self.class_weight.keys())
+ weight = (c_float * weight_size)()
+ weight[:] = list(self.class_weight.values())
+ elif self.class_weight == 'balanced':
+ y_unique = np.unique(y)
+ y_count = np.bincount(y.astype(int))
+ weight_label_list = []
+ weight_list = []
+ for n in range(0, len(y_count)):
+ if y_count[n] != 0:
+ weight_label_list.append(n)
+ weight_list.append(samples/(len(y_unique)*y_count[n]))
+ weight_size=len(weight_list)
+ weight_label = (c_int * weight_size)()
+ weight_label[:] = weight_label_list
+ weight = (c_float * weight_size)()
+ weight[:] = weight_list
  else:
  weight_size = len(self.class_weight)
- weight_label = (c_int * weight_size)()
- weight_label[:] = list(self.class_weight.keys())
- weight = (c_float * weight_size)()
- weight[:] = list(self.class_weight.values())
+  weight_label = (c_int * weight_size)()
+  weight_label[:] = list(self.class_weight.keys())
+  weight = (c_float * weight_size)()
+  weight[:] = list(self.class_weight.values())
 
  n_features = (c_int * 1)()
  n_classes = (c_int * 1)()
@@ -228,12 +246,30 @@ def _sparse_fit(self, X, y, solver_type, kernel):
  if self.class_weight is None:
  weight_size = 0
  self.class_weight = dict()
+ weight_label = (c_int * weight_size)()
+ weight_label[:] = list(self.class_weight.keys())
+ weight = (c_float * weight_size)()
+ weight[:] = list(self.class_weight.values())
+ elif self.class_weight == 'balanced':
+ y_unique = np.unique(y)
+ y_count = np.bincount(y.astype(int))
+ weight_label_list = []
+ weight_list = []
+ for n in range(0, len(y_count)):
+ if y_count[n] != 0:
+ weight_label_list.append(n)
+ weight_list.append(X.shape[0]/(len(y_unique)*y_count[n]))
+ weight_size=len(weight_list)
+ weight_label = (c_int * weight_size)()
+ weight_label[:] = weight_label_list
+ weight = (c_float * weight_size)()
+ weight[:] = weight_list
  else:
  weight_size = len(self.class_weight)
- weight_label = (c_int * weight_size)()
- weight_label[:] = list(self.class_weight.keys())
- weight = (c_float * weight_size)()
- weight[:] = list(self.class_weight.values())
+  weight_label = (c_int * weight_size)()
+  weight_label[:] = list(self.class_weight.keys())
+  weight = (c_float * weight_size)()
+  weight[:] = list(self.class_weight.values())
 
  n_features = (c_int * 1)()
  n_classes = (c_int * 1)()

src/thundersvm/thundersvm-scikit.cpp

@@ -221,7 +221,7 @@ extern "C" {
  param_cmd.max_mem_size = static_cast<size_t>(max(max_mem_size, 0)) << 20;
  if(weight_size != 0) {
  param_cmd.nr_weight = weight_size;
- param_cmd.weight = (float_type *) malloc(weight_size * sizeof(float_type));
+ param_cmd.weight = (float_type *) malloc(weight_size * sizeof(double));
  param_cmd.weight_label = (int *) malloc(weight_size * sizeof(int));
  for (int i = 0; i < weight_size; i++) {
  param_cmd.weight[i] = weight[i];