vigra - vigra/random_forest/rf_preprocessing.hxx Source File

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rf_preprocessing.hxx |

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35
36 #ifndef VIGRA_RF_PREPROCESSING_HXX
37 #define VIGRA_RF_PREPROCESSING_HXX
38
39 #include <limits>
40 #include <vigra/mathutil.hxx>
41 #include "rf_common.hxx"
42
43 namespace vigra
44 {
45
46 /** Class used while preprocessing (currently used only during learn)
47 *
48 * This class is internally used by the Random Forest learn function.
49 * Different split functors may need to process the data in different manners
50 * (i.e., regression labels that should not be touched and classification
51 * labels that must be converted into a integral format)
52 *
53 * This Class only exists in specialized versions, where the Tag class is
54 * fixed.
55 *
56 * The Tag class is determined by Splitfunctor::Preprocessor_t . Currently
57 * it can either be ClassificationTag or RegressionTag. look At the
58 * RegressionTag specialisation for the basic interface if you ever happen
59 * to care.... - or need some sort of vague new preprocessor.
60 * new preprocessor ( Soft labels or whatever)
61 */
62 template<class Tag, class LabelType, class T1, class C1, class T2, class C2>
63 class Processor;
64
65 namespace detail
66 {
67
68 /* Common helper function used in all Processors.
69 * This function analyses the options struct and calculates the real
70 * values needed for the current problem (data)
71 */
72 template<class T>
73 void fill_external_parameters(RandomForestOptions const & options,
74 ProblemSpec<T> & ext_param)
75 {
76 // set correct value for mtry
77 switch(options.mtry_switch_)
78 {
79 case RF_SQRT:
80 ext_param.actual_mtry_ =
81 int(std::floor(
82 std::sqrt(double(ext_param.column_count_))
83 + 0.5));
84 break;
85 case RF_LOG:
86 // this is in Breimans original paper
87 ext_param.actual_mtry_ =
88 int(1+(std::log(double(ext_param.column_count_))
89 /std::log(2.0)));
90 break;
91 case RF_FUNCTION:
92 ext_param.actual_mtry_ =
93 options.mtry_func_(ext_param.column_count_);
94 break;
95 case RF_ALL:
96 ext_param.actual_mtry_ = ext_param.column_count_;
97 break;
98 default:
99 ext_param.actual_mtry_ =
100 options.mtry_;
101 }
102 // set correct value for msample
103 switch(options.training_set_calc_switch_)
104 {
105 case RF_CONST:
106 ext_param.actual_msample_ =
107 options.training_set_size_;
108 break;
109 case RF_PROPORTIONAL:
110 ext_param.actual_msample_ =
111 static_cast<int>(std::ceil(options.training_set_proportion_ *
112 ext_param.row_count_));
113 break;
114 case RF_FUNCTION:
115 ext_param.actual_msample_ =
116 options.training_set_func_(ext_param.row_count_);
117 break;
118 default:
119 vigra_precondition(1!= 1, "unexpected error");
120
121 }
122
123 }
124
125 /* Returns true if MultiArray contains NaNs
126 */
127 template<unsigned int N, class T, class C>
128 bool contains_nan(MultiArrayView<N, T, C> const & in)
129 {
130 typedef typename MultiArrayView<N, T, C>::const_iterator Iter;
131 Iter i = in.begin(), end = in.end();
132 for(; i != end; ++i)
133 if(isnan(NumericTraits<T>::toRealPromote(*i)))
134 return true;
135 return false;
136 }
137
138 /* Returns true if MultiArray contains Infs
139 */
140 template<unsigned int N, class T, class C>
141 bool contains_inf(MultiArrayView<N, T, C> const & in)
142 {
143 if(!std::numeric_limits<T>::has_infinity)
144 return false;
145 typedef typename MultiArrayView<N, T, C>::const_iterator Iter;
146 Iter i = in.begin(), end = in.end();
147 for(; i != end; ++i)
148 if(abs(*i) == std::numeric_limits<T>::infinity())
149 return true;
150 return false;
151 }
152 } // namespace detail
153
154
155
156 /** Preprocessor used during Classification
157 *
158 * This class converts the labels int Integral labels which are used by the
159 * standard split functor to address memory in the node objects.
160 */
161 template<class LabelType, class T1, class C1, class T2, class C2>
162 class Processor<ClassificationTag, LabelType, T1, C1, T2, C2>
163 {
164 public:
165 typedef Int32 LabelInt;
166 typedef MultiArrayView<2, T1, C1> Feature_t;
167 typedef MultiArray<2, T1> FeatureWithMemory_t;
168 typedef MultiArrayView<2,LabelInt> Label_t;
169 MultiArrayView<2, T1, C1>const & features_;
170 MultiArray<2, LabelInt> intLabels_;
171 MultiArrayView<2, LabelInt> strata_;
172
173 template<class T>
174 Processor(MultiArrayView<2, T1, C1>const & features,
175 MultiArrayView<2, T2, C2>const & response,
176 RandomForestOptions &options,
177 ProblemSpec<T> &ext_param)
178 :
179 features_( features) // do not touch the features.
180 {
181 vigra_precondition(!detail::contains_nan(features), "RandomForest(): Feature matrix "
182 "contains NaNs");
183 vigra_precondition(!detail::contains_nan(response), "RandomForest(): Response "
184 "contains NaNs");
185 vigra_precondition(!detail::contains_inf(features), "RandomForest(): Feature matrix "
186 "contains inf");
187 vigra_precondition(!detail::contains_inf(response), "RandomForest(): Response "
188 "contains inf");
189 // set some of the problem specific parameters
190 ext_param.column_count_ = features.shape(1);
191 ext_param.row_count_ = features.shape(0);
192 ext_param.problem_type_ = CLASSIFICATION;
193 ext_param.used_ = true;
194 intLabels_.reshape(response.shape());
195
196 //get the class labels
197 if(ext_param.class_count_ == 0)
198 {
199 // fill up a map with the current labels and then create the
200 // integral labels.
201 std::set<T2> labelToInt;
202 for(MultiArrayIndex k = 0; k < features.shape(0); ++k)
203 labelToInt.insert(response(k,0));
204 std::vector<T2> tmp_(labelToInt.begin(), labelToInt.end());
205 ext_param.classes_(tmp_.begin(), tmp_.end());
206 }
207 for(MultiArrayIndex k = 0; k < features.shape(0); ++k)
208 {
209 if(std::find(ext_param.classes.begin(), ext_param.classes.end(), response(k,0)) == ext_param.classes.end())
210 {
211 throw std::runtime_error("RandomForest(): invalid label in training data.");
212 }
213 else
214 intLabels_(k, 0) = std::find(ext_param.classes.begin(), ext_param.classes.end(), response(k,0))
215 - ext_param.classes.begin();
216 }
217 // set class weights
218 if(ext_param.class_weights_.size() == 0)
219 {
220 ArrayVector<T2>
221 tmp(static_cast<std::size_t>(ext_param.class_count_),
222 NumericTraits<T2>::one());
223 ext_param.class_weights(tmp.begin(), tmp.end());
224 }
225
226 // set mtry and msample
227 detail::fill_external_parameters(options, ext_param);
228
229 // set strata
230 strata_ = intLabels_;
231
232 }
233
234 /** Access the processed features
235 */
236 MultiArrayView<2, T1, C1>const & features()
237 {
238 return features_;
239 }
240
241 /** Access processed labels
242 */
243 MultiArrayView<2, LabelInt> response()
244 {
245 return MultiArrayView<2, LabelInt>(intLabels_);
246 }
247
248 /** Access processed strata
249 */
250 ArrayVectorView < LabelInt> strata()
251 {
252 return ArrayVectorView<LabelInt>(intLabels_.size(), intLabels_.data());
253 }
254
255 /** Access strata fraction sized - not used currently
256 */
257 ArrayVectorView< double> strata_prob()
258 {
259 return ArrayVectorView< double>();
260 }
261 };
262
263
264
265 /** Regression Preprocessor - This basically does not do anything with the
266 * data.
267 */
268 template<class LabelType, class T1, class C1, class T2, class C2>
269 class Processor<RegressionTag,LabelType, T1, C1, T2, C2>
270 {
271 public:
272 // only views are created - no data copied.
273 MultiArrayView<2, T1, C1> features_;
274 MultiArrayView<2, T2, C2> response_;
275 RandomForestOptions const & options_;
276 ProblemSpec<LabelType> const &
277 ext_param_;
278 // will only be filled if needed
279 MultiArray<2, int> strata_;
280 bool strata_filled;
281
282 // copy the views.
283 template<class T>
284 Processor( MultiArrayView<2, T1, C1> features,
285 MultiArrayView<2, T2, C2> response,
286 RandomForestOptions const & options,
287 ProblemSpec<T>& ext_param)
288 :
289 features_(features),
290 response_(response),
291 options_(options),
292 ext_param_(ext_param)
293 {
294 // set some of the problem specific parameters
295 ext_param.column_count_ = features.shape(1);
296 ext_param.row_count_ = features.shape(0);
297 ext_param.problem_type_ = REGRESSION;
298 ext_param.used_ = true;
299 detail::fill_external_parameters(options, ext_param);
300 vigra_precondition(!detail::contains_nan(features), "Processor(): Feature Matrix "
301 "Contains NaNs");
302 vigra_precondition(!detail::contains_nan(response), "Processor(): Response "
303 "Contains NaNs");
304 vigra_precondition(!detail::contains_inf(features), "Processor(): Feature Matrix "
305 "Contains inf");
306 vigra_precondition(!detail::contains_inf(response), "Processor(): Response "
307 "Contains inf");
308 strata_ = MultiArray<2, int> (MultiArrayShape<2>::type(response_.shape(0), 1));
309 ext_param.response_size_ = response.shape(1);
310 ext_param.class_count_ = response_.shape(1);
311 std::vector<T2> tmp_(ext_param.class_count_, 0);
312 ext_param.classes_(tmp_.begin(), tmp_.end());
313 }
314
315 /** access preprocessed features
316 */
317 MultiArrayView<2, T1, C1> & features()
318 {
319 return features_;
320 }
321
322 /** access preprocessed response
323 */
324 MultiArrayView<2, T2, C2> & response()
325 {
326 return response_;
327 }
328
329 /** access strata - this is not used currently
330 */
331 MultiArray<2, int> & strata()
332 {
333 return strata_;
334 }
335 };
336 }
337 #endif //VIGRA_RF_PREPROCESSING_HXX
338
339
340
vigra::Processor< ClassificationTag, LabelType, T1, C1, T2, C2 >::strata_prob
ArrayVectorView< double > strata_prob()
Definition: rf_preprocessing.hxx:257
vigra::Processor< ClassificationTag, LabelType, T1, C1, T2, C2 >::response
MultiArrayView< 2, LabelInt > response()
Definition: rf_preprocessing.hxx:243
vigra::Processor
Definition: rf_preprocessing.hxx:63
vigra::MultiArrayView::shape
const difference_type & shape() const
Definition: multi_array.hxx:1648
vigra::ArrayVectorView
Definition: array_vector.hxx:76
vigra::ArrayVectorView::begin
const_iterator begin() const
Definition: array_vector.hxx:223
vigra::ProblemSpec
problem specification class for the random forest.
Definition: rf_common.hxx:538
vigra::Processor< RegressionTag, LabelType, T1, C1, T2, C2 >::features
MultiArrayView< 2, T1, C1 > & features()
Definition: rf_preprocessing.hxx:317
vigra::MultiArray
Main MultiArray class containing the memory management.
Definition: multi_array.hxx:2474
vigra::MultiArrayIndex
std::ptrdiff_t MultiArrayIndex
Definition: multi_fwd.hxx:60
vigra::Processor< ClassificationTag, LabelType, T1, C1, T2, C2 >::features
MultiArrayView< 2, T1, C1 > const & features()
Definition: rf_preprocessing.hxx:236
vigra::ProblemSpec::classes_
ProblemSpec & classes_(C_Iter begin, C_Iter end)
supply with class labels -
Definition: rf_common.hxx:828
vigra::ArrayVector
Definition: array_vector.hxx:58
vigra::Int32
detail::SelectIntegerType< 32, detail::SignedIntTypes >::type Int32
32-bit signed int
Definition: sized_int.hxx:175
vigra::Processor< RegressionTag, LabelType, T1, C1, T2, C2 >::response
MultiArrayView< 2, T2, C2 > & response()
Definition: rf_preprocessing.hxx:324
vigra::MultiArrayShape::type
TinyVector< MultiArrayIndex, N > type
Definition: multi_shape.hxx:272
vigra::ProblemSpec::class_weights
ProblemSpec & class_weights(W_Iter begin, W_Iter end)
supply with class weights -
Definition: rf_common.hxx:844
vigra::linalg::log
linalg::TemporaryMatrix< T > log(MultiArrayView< 2, T, C > const &v)
vigra::Processor< RegressionTag, LabelType, T1, C1, T2, C2 >::strata
MultiArray< 2, int > & strata()
Definition: rf_preprocessing.hxx:331
vigra::Processor< ClassificationTag, LabelType, T1, C1, T2, C2 >::strata
ArrayVectorView< LabelInt > strata()
Definition: rf_preprocessing.hxx:250
vigra::abs
FFTWComplex< R >::NormType abs(const FFTWComplex< R > &a)
absolute value (= magnitude)
Definition: fftw3.hxx:1002
vigra::MultiArrayView< 2, T1, C1 >
vigra::RandomForestOptions
Options object for the random forest.
Definition: rf_common.hxx:170
vigra::ArrayVectorView::end
const_iterator end() const
Definition: array_vector.hxx:237
vigra::ArrayVectorView::size
size_type size() const
Definition: array_vector.hxx:358
vigra::ceil
int ceil(FixedPoint< IntBits, FracBits > v)
rounding up.
Definition: fixedpoint.hxx:675
vigra::floor
int floor(FixedPoint< IntBits, FracBits > v)
rounding down.
Definition: fixedpoint.hxx:667
vigra::sqrt
SquareRootTraits< FixedPoint< IntBits, FracBits > >::SquareRootResult sqrt(FixedPoint< IntBits, FracBits > v)
square root.
Definition: fixedpoint.hxx:616

© Ullrich Köthe (ullrich.koethe@iwr.uni-heidelberg.de) Heidelberg Collaboratory for Image Processing, University of Heidelberg, Germany | html generated using doxygen and Python vigra 1.11.0 (Fri May 19 2017)