1 | """ |
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2 | Unit tests for fitting module |
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3 | @author Gervaise Alina |
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4 | """ |
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5 | import unittest |
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6 | import math |
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7 | |
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8 | from sans.fit.AbstractFitEngine import Model, FitHandler |
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9 | from sans.dataloader.loader import Loader |
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10 | from sans.fit.Fitting import Fit |
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11 | from sans.models.LineModel import LineModel |
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12 | from sans.models.Constant import Constant |
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13 | |
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14 | class testFitModule(unittest.TestCase): |
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15 | """ test fitting """ |
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16 | |
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17 | def test_bad_pars(self): |
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18 | fitter = Fit('bumps') |
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19 | |
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20 | data = Loader().load("testdata_line.txt") |
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21 | data.name = data.filename |
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22 | fitter.set_data(data,1) |
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23 | |
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24 | model1 = LineModel() |
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25 | model1.name = "M1" |
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26 | model = Model(model1, data) |
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27 | pars1= ['param1','param2'] |
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28 | try: |
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29 | fitter.set_model(model,1,pars1) |
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30 | except ValueError,exc: |
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31 | #print "ValueError was correctly raised: "+str(msg) |
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32 | assert str(exc).startswith('wrong parameter') |
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33 | else: |
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34 | raise AssertionError("No error raised for scipy fitting with wrong parameters name to fit") |
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35 | |
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36 | def fit_single(self, fitter_name, isdream=False): |
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37 | fitter = Fit(fitter_name) |
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38 | |
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39 | data = Loader().load("testdata_line.txt") |
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40 | data.name = data.filename |
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41 | fitter.set_data(data,1) |
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42 | |
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43 | # Receives the type of model for the fitting |
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44 | model1 = LineModel() |
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45 | model1.name = "M1" |
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46 | model = Model(model1,data) |
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47 | #fit with scipy test |
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48 | |
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49 | pars1= ['A','B'] |
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50 | fitter.set_model(model,1,pars1) |
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51 | fitter.select_problem_for_fit(id=1,value=1) |
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52 | result1, = fitter.fit(handler=FitHandler()) |
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53 | |
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54 | # The target values were generated from the following statements |
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55 | p,s,fx = result1.pvec, result1.stderr, result1.fitness |
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56 | #print "p0,p1,s0,s1,fx_ = %g, %g, %g, %g, %g"%(p[0],p[1],s[0],s[1],fx) |
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57 | p0,p1,s0,s1,fx_ = 3.68353, 2.61004, 0.336186, 0.105244, 1.20189 |
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58 | |
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59 | if isdream: |
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60 | # Dream is not a minimizer: just check that the fit is within |
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61 | # uncertainty |
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62 | self.assertTrue( abs(p[0]-p0) <= s0 ) |
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63 | self.assertTrue( abs(p[1]-p1) <= s1 ) |
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64 | else: |
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65 | self.assertTrue( abs(p[0]-p0) <= 1e-5 ) |
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66 | self.assertTrue( abs(p[1]-p1) <= 1e-5 ) |
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67 | self.assertTrue( abs(fx-fx_) <= 1e-2 ) |
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68 | |
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69 | def fit_bumps(self, alg, **opts): |
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70 | #Importing the Fit module |
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71 | from bumps import fitters |
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72 | fitters.FIT_DEFAULT = alg |
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73 | fitters.FIT_OPTIONS[alg].options.update(opts) |
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74 | fitters.FIT_OPTIONS[alg].options.update(monitors=[]) |
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75 | #print "fitting",alg,opts |
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76 | #kprint "options",fitters.FIT_OPTIONS[alg].__dict__ |
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77 | self.fit_single('bumps', isdream=(alg=='dream')) |
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78 | |
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79 | def test_bumps_de(self): |
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80 | self.fit_bumps('de') |
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81 | |
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82 | def test_bumps_dream(self): |
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83 | self.fit_bumps('dream', burn=500, steps=100) |
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84 | |
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85 | def test_bumps_amoeba(self): |
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86 | self.fit_bumps('amoeba') |
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87 | |
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88 | def test_bumps_newton(self): |
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89 | self.fit_bumps('newton') |
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90 | |
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91 | def test_scipy(self): |
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92 | #print "fitting scipy" |
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93 | self.fit_single('scipy') |
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94 | |
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95 | def test_park(self): |
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96 | #print "fitting park" |
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97 | self.fit_single('park') |
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98 | |
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99 | |
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100 | def test2(self): |
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101 | """ fit 2 data and 2 model with no constrainst""" |
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102 | #load data |
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103 | l = Loader() |
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104 | data1=l.load("testdata_line.txt") |
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105 | data1.name = data1.filename |
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106 | |
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107 | data2=l.load("testdata_line1.txt") |
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108 | data2.name = data2.filename |
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109 | |
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110 | #Importing the Fit module |
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111 | fitter = Fit('scipy') |
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112 | # Receives the type of model for the fitting |
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113 | model11 = LineModel() |
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114 | model11.name= "M1" |
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115 | model22 = LineModel() |
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116 | model11.name= "M2" |
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117 | |
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118 | model1 = Model(model11,data1) |
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119 | model2 = Model(model22,data2) |
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120 | #fit with scipy test |
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121 | pars1= ['A','B'] |
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122 | fitter.set_data(data1,1) |
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123 | fitter.set_model(model1,1,pars1) |
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124 | fitter.select_problem_for_fit(id=1,value=0) |
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125 | fitter.set_data(data2,2) |
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126 | fitter.set_model(model2,2,pars1) |
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127 | fitter.select_problem_for_fit(id=2,value=0) |
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128 | |
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129 | try: result1, = fitter.fit(handler=FitHandler()) |
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130 | except RuntimeError,msg: |
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131 | assert str(msg)=="No Assembly scheduled for Scipy fitting." |
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132 | else: raise AssertionError,"No error raised for scipy fitting with no model" |
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133 | fitter.select_problem_for_fit(id=1,value=1) |
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134 | fitter.select_problem_for_fit(id=2,value=1) |
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135 | try: result1, = fitter.fit(handler=FitHandler()) |
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136 | except RuntimeError,msg: |
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137 | assert str(msg)=="Scipy can't fit more than a single fit problem at a time." |
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138 | else: raise AssertionError,"No error raised for scipy fitting with more than 2 models" |
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139 | |
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140 | #fit with park test |
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141 | fitter = Fit('park') |
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142 | fitter.set_data(data1,1) |
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143 | fitter.set_model(model1,1,pars1) |
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144 | fitter.set_data(data2,2) |
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145 | fitter.set_model(model2,2,pars1) |
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146 | fitter.select_problem_for_fit(id=1,value=1) |
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147 | fitter.select_problem_for_fit(id=2,value=1) |
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148 | R1,R2 = fitter.fit(handler=FitHandler()) |
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149 | |
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150 | self.assertTrue( math.fabs(R1.pvec[0]-4)/3 <= R1.stderr[0] ) |
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151 | self.assertTrue( math.fabs(R1.pvec[1]-2.5)/3 <= R1.stderr[1] ) |
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152 | self.assertTrue( R1.fitness/(len(data1.x)+len(data2.x)) < 2) |
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153 | |
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154 | |
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155 | def test3(self): |
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156 | """ fit 2 data and 2 model with 1 constrainst""" |
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157 | #load data |
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158 | l = Loader() |
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159 | data1= l.load("testdata_line.txt") |
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160 | data1.name = data1.filename |
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161 | data2= l.load("testdata_cst.txt") |
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162 | data2.name = data2.filename |
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163 | |
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164 | # Receives the type of model for the fitting |
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165 | model11 = LineModel() |
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166 | model11.name= "line" |
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167 | model11.setParam("A", 1.0) |
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168 | model11.setParam("B",1.0) |
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169 | |
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170 | model22 = Constant() |
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171 | model22.name= "cst" |
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172 | model22.setParam("value", 1.0) |
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173 | |
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174 | model1 = Model(model11,data1) |
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175 | model2 = Model(model22,data2) |
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176 | model1.set(A=4) |
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177 | model1.set(B=3) |
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178 | # Constraint the constant value to be equal to parameter B (the real value is 2.5) |
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179 | model2.set(value='line.B') |
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180 | #fit with scipy test |
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181 | pars1= ['A','B'] |
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182 | pars2= ['value'] |
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183 | |
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184 | #Importing the Fit module |
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185 | fitter = Fit('park') |
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186 | fitter.set_data(data1,1) |
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187 | fitter.set_model(model1,1,pars1) |
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188 | fitter.set_data(data2,2,smearer=None) |
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189 | fitter.set_model(model2,2,pars2) |
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190 | fitter.select_problem_for_fit(id=1,value=1) |
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191 | fitter.select_problem_for_fit(id=2,value=1) |
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192 | |
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193 | R1,R2 = fitter.fit(handler=FitHandler()) |
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194 | self.assertTrue( math.fabs(R1.pvec[0]-4.0)/3. <= R1.stderr[0]) |
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195 | self.assertTrue( math.fabs(R1.pvec[1]-2.5)/3. <= R1.stderr[1]) |
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196 | self.assertTrue( R1.fitness/(len(data1.x)+len(data2.x)) < 2) |
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197 | |
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198 | |
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199 | def test4(self): |
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200 | """ fit 2 data concatenates with limited range of x and one model """ |
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201 | #load data |
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202 | l = Loader() |
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203 | data1 = l.load("testdata_line.txt") |
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204 | data1.name = data1.filename |
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205 | data2 = l.load("testdata_line1.txt") |
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206 | data2.name = data2.filename |
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207 | |
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208 | # Receives the type of model for the fitting |
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209 | model1 = LineModel() |
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210 | model1.name= "M1" |
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211 | model1.setParam("A", 1.0) |
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212 | model1.setParam("B",1.0) |
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213 | model = Model(model1,data1) |
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214 | |
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215 | #fit with scipy test |
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216 | pars1= ['A','B'] |
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217 | #Importing the Fit module |
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218 | fitter = Fit('scipy') |
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219 | fitter.set_data(data1,1,qmin=0, qmax=7) |
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220 | fitter.set_model(model,1,pars1) |
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221 | fitter.set_data(data2,1,qmin=1,qmax=10) |
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222 | fitter.select_problem_for_fit(id=1,value=1) |
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223 | |
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224 | result1, = fitter.fit(handler=FitHandler()) |
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225 | #print(result1) |
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226 | self.assert_(result1) |
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227 | |
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228 | self.assertTrue( math.fabs(result1.pvec[0]-4)/3 <= result1.stderr[0] ) |
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229 | self.assertTrue( math.fabs(result1.pvec[1]-2.5)/3 <= result1.stderr[1]) |
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230 | self.assertTrue( result1.fitness/len(data1.x) < 2 ) |
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231 | |
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232 | #fit with park test |
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233 | fitter = Fit('park') |
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234 | fitter.set_data(data1,1,qmin=0, qmax=7) |
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235 | fitter.set_model(model,1,pars1) |
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236 | fitter.set_data(data2,1,qmin=1,qmax=10) |
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237 | fitter.select_problem_for_fit(id=1,value=1) |
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238 | result2, = fitter.fit(handler=FitHandler()) |
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239 | |
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240 | self.assert_(result2) |
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241 | self.assertTrue( math.fabs(result2.pvec[0]-4)/3 <= result2.stderr[0] ) |
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242 | self.assertTrue( math.fabs(result2.pvec[1]-2.5)/3 <= result2.stderr[1] ) |
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243 | self.assertTrue( result2.fitness/len(data1.x) < 2) |
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244 | # compare fit result result for scipy and park |
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245 | self.assertAlmostEquals( result1.pvec[0], result2.pvec[0] ) |
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246 | self.assertAlmostEquals( result1.pvec[1],result2.pvec[1] ) |
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247 | self.assertAlmostEquals( result1.stderr[0],result2.stderr[0] ) |
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248 | self.assertAlmostEquals( result1.stderr[1],result2.stderr[1] ) |
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249 | self.assertTrue( result2.fitness/(len(data2.x)+len(data1.x)) < 2 ) |
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250 | |
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251 | |
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252 | if __name__ == "__main__": |
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253 | unittest.main() |
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254 | |
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