Mercurial > pylearn
annotate pylearn/external/wrap_libsvm.py @ 1492:e7c4d031d333
Fix for Windows paths
| author | Olivier Delalleau <delallea@iro> |
|---|---|
| date | Tue, 16 Aug 2011 15:44:01 -0400 |
| parents | 80123baa4544 |
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| rev | line source |
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| 517 | 1 """Run an experiment using libsvm. |
| 2 """ | |
| 3 import numpy | |
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4 from ..datasets import make_dataset |
| 517 | 5 |
| 6 # libsvm currently has no python installation instructions/convention. | |
| 7 # | |
| 8 # This module uses a specific convention for libsvm's installation. | |
| 9 # I base this on installing libsvm-2.88. | |
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10 # To install libsvm's python module, do the following: |
| 517 | 11 # 1. Build libsvm (run make in both the root dir and the python subdir). |
| 12 # 2. touch a '__init__.py' file in the python subdir | |
| 13 # 3. add a symbolic link to a PYTHONPATH location that looks like this: | |
| 14 # libsvm -> <your root path>/libsvm-2.88/python/ | |
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15 # 4. modify the svm_model class in python/svm.py to inherit from object |
| 517 | 16 # |
| 17 # That is the sort of thing that this module expects from 'import libsvm' | |
| 18 | |
| 19 import libsvm | |
| 20 | |
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21 class svm_model(libsvm.svm_model): |
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22 """ |
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23 This class is a picklable drop-in replacement for libsvm.svm_model. |
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24 """ |
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25 def __getstate__(self): |
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26 return PicklableSVM.svm_to_str(self) |
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27 |
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28 def __setstate__(self, svm_str): |
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29 PicklableSVM.str_to_svm(svm_str, self=self) |
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30 |
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31 @staticmethod |
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32 def str_to_svm(s, self=None): |
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33 fname = tempfile.mktemp() |
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34 f = open(fname,'w') |
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35 f.write(s) |
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36 f.close() |
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37 rval = self |
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38 try: |
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39 if self: |
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40 self.__init__(fname) |
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41 else: |
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42 rval = libsvm.svm_model(fname) |
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43 finally: |
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44 os.remove(fname) |
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45 return rval |
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46 |
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47 @staticmethod |
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48 def svm_to_str(svm): |
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49 fname = tempfile.mktemp() |
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50 svm.save(fname) |
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51 rval = open(fname, 'r').read() |
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52 os.remove(fname) |
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53 return rval |
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54 |
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55 def predict(self, x): |
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56 if type(x) != numpy.ndarray: |
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57 raise TypeError(x) |
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58 if x.ndim != 1: |
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59 raise TypeError(x) |
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60 return libsvm.svm_model.predict(self, numpy.asarray(x, dtype='float64')) |
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61 |
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62 def predict_probability(self, x): |
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63 if x.ndim != 1: |
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64 raise TypeError(x) |
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65 return libsvm.svm_model.predict_probability(self, numpy.asarray(x, dtype='float64')) |
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66 |
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67 svm_problem = libsvm.svm_problem |
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68 svm_parameter = libsvm.svm_parameter |
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69 RBF = libsvm.RBF |
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70 |
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71 |
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72 #################################### |
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73 # Extra stuff that is less essential |
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74 # |
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75 # TODO: Move stuff below to a file |
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76 # in algorithms |
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77 #################################### |
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78 |
| 517 | 79 def score_01(x, y, model): |
| 80 assert len(x) == len(y) | |
| 81 size = len(x) | |
| 82 errors = 0 | |
| 83 for i in range(size): | |
| 84 prediction = model.predict(x[i]) | |
| 85 #probability = model.predict_probability | |
| 86 if (y[i] != prediction): | |
| 87 errors = errors + 1 | |
| 88 return float(errors)/size | |
| 89 | |
| 90 #this is the dbdict experiment interface... if you happen to use dbdict | |
| 91 class State(object): | |
| 92 #TODO: parametrize to get all the kernel types, not hardcode for RBF | |
| 93 dataset = 'MNIST_1k' | |
| 94 C = 10.0 | |
| 95 kernel = 'RBF' | |
| 96 # rel_gamma is related to the procedure Jerome used. He mentioned why in | |
| 97 # quadratic_neurons/neuropaper/draft3.pdf. | |
| 98 rel_gamma = 1.0 | |
| 99 | |
| 100 def __init__(self, **kwargs): | |
| 101 for k, v in kwargs: | |
| 102 setattr(self, k, type(getattr(self, k))(v)) | |
| 103 | |
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104 def state_run_svm_experiment(state, channel=lambda *args, **kwargs:None): |
| 517 | 105 """Parameters are described in state, and returned in state. |
| 106 | |
| 107 :param state: object instance to store parameters and return values | |
| 108 :param channel: not used | |
| 109 | |
| 110 :returns: None | |
| 111 | |
| 112 This is the kind of function that dbdict-run can use. | |
| 113 | |
| 114 """ | |
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115 dataset = make_dataset(**state.dataset) |
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116 |
| 517 | 117 |
| 118 #libsvm needs stuff in int32 on a 32bit machine | |
| 119 #TODO: test this on a 64bit machine | |
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120 # -> Both int32 and int64 (default) seem to be OK |
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121 train_y = numpy.asarray(dataset.train.y, dtype='int32') |
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122 valid_y = numpy.asarray(dataset.valid.y, dtype='int32') |
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123 test_y = numpy.asarray(dataset.test.y, dtype='int32') |
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124 problem = libsvm.svm_problem(train_y, dataset.train.x); |
| 517 | 125 |
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126 gamma0 = 0.5 / numpy.sum(numpy.var(dataset.train.x, axis=0)) |
| 517 | 127 |
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128 param = libsvm.svm_parameter(C=state['C'], |
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129 kernel_type=getattr(libsvm, state['kernel']), |
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130 gamma=state['rel_gamma'] * gamma0) |
| 517 | 131 |
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132 model = libsvm.svm_model(problem, param) #this is the expensive part |
| 517 | 133 |
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134 state['train_01'] = score_01(dataset.train.x, train_y, model) |
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135 state['valid_01'] = score_01(dataset.valid.x, valid_y, model) |
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136 state['test_01'] = score_01(dataset.test.x, test_y, model) |
| 517 | 137 |
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138 state['n_train'] = len(train_y) |
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139 state['n_valid'] = len(valid_y) |
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140 state['n_test'] = len(test_y) |
| 517 | 141 |
| 142 def run_svm_experiment(**kwargs): | |
| 143 """Python-friendly interface to dbdict_run_svm_experiment | |
| 144 | |
| 145 Parameters are used to construct a `State` instance, which is returned after running | |
| 146 `dbdict_run_svm_experiment` on it. | |
| 147 | |
| 148 .. code-block:: python | |
| 149 results = run_svm_experiment(dataset='MNIST_1k', C=100.0, rel_gamma=0.01) | |
| 150 print results.n_train | |
| 151 # 1000 | |
| 152 print results.valid_01, results.test_01 | |
| 153 # 0.14, 0.10 #.. or something... | |
| 154 | |
| 155 """ | |
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156 state_run_svm_experiment(state=kwargs) |
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157 return kwargs |
| 517 | 158 |
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159 def train_rbf_model(train_X, train_Y, C, gamma): |
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160 param = libsvm.svm_parameter(C=C, kernel_type=libsvm.RBF, gamma=gamma) |
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161 problem = libsvm.svm_problem(train_Y, train_X) |
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162 model = svm_model(problem, param) |
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163 |
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164 #save_filename = state.save_filename |
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165 #model.save(save_filename) |
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166 |
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167 |
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168 def jobman_train_model(state, channel): |
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169 """ |
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170 |
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171 According to the given validation set, |
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172 What is the best libsvm parameter setting to train on? |
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173 """ |
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174 (train_X, train_Y) = jobman.tools.make(state.train_set) |
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175 (valid_X, valid_Y) = jobman.tools.make(state.valid_set) |
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176 |
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177 C_grid = [1,2,3] |
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178 gamma_grid = [0.1, 1, 10] |
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179 |
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180 grid = [dict( |
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181 train_set=None, |
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182 svm_param=dict(kernel='RBF', C=C, gamma=g), |
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183 save_filename='model_RBF_C%f_G%f.libsvm') |
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184 for C in C_grid |
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185 for g in gamma_grid] |
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186 |
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187 # will return quickly if jobs have already run |
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188 # and the rootpath is populated with results |
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189 grid = jobman.map( |
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190 jobman_train_model_given_all_params, |
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191 grid, |
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192 path=jobman.rootpath(state)+'/gridmap', |
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193 cleanup=False) |
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194 |
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195 # evaluate all these sub_state models on our validation_set |
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196 valid_perf = [] |
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197 for sub_state in grid: |
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198 # create a file in this state-space called model.tmp |
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199 # with the same contents as the |
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200 # save_filename file in the sub_state |
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201 jobman.link('model.tmp', jobman.rootpath(sub_state)+'/'+sub_state.save_filename) |
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202 model = svm.model('model.tmp') |
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203 valid_perf.append((score_01(valid_X, valid_Y, model), sub_state)) |
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204 jobman.unlink('model.tmp') |
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205 |
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206 # calculate the return value |
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207 valid_perf.sort() #lowest first |
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208 state.lowest_valid_err = valid_perf[0][0] |
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209 state.lowest_valid_svm_param = valid_perf[0][1].svm_param |
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210 |
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211 |
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212 |