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 sas.sascalc.fit.AbstractFitEngine import Model, FitHandler |
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9 | from sas.sascalc.dataloader.loader import Loader |
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10 | from sas.sascalc.fit.BumpsFitting import BumpsFit as Fit |
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11 | from sas.models.LineModel import LineModel |
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12 | from sas.models.Constant import Constant |
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13 | |
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14 | from bumps import fitters |
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15 | try: |
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16 | from bumps.options import FIT_CONFIG |
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17 | def set_fitter(alg, opts): |
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18 | FIT_CONFIG.selected_id = alg |
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19 | FIT_CONFIG.values[alg].update(opts, monitors=[]) |
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20 | except: |
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21 | # CRUFT: Bumps changed its handling of fit options around 0.7.5.6 |
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22 | def set_fitter(alg, opts): |
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23 | #print "fitting",alg,opts |
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24 | #print "options",fitters.FIT_OPTIONS[alg].__dict__ |
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25 | fitters.FIT_DEFAULT = alg |
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26 | fitters.FIT_OPTIONS[alg].options.update(opts, monitors=[]) |
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27 | |
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28 | |
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29 | class testFitModule(unittest.TestCase): |
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30 | """ test fitting """ |
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31 | |
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32 | def test_bad_pars(self): |
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33 | fitter = Fit() |
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34 | |
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35 | data = Loader().load("testdata_line.txt") |
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36 | data.name = data.filename |
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37 | fitter.set_data(data,1) |
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38 | |
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39 | model1 = LineModel() |
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40 | model1.name = "M1" |
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41 | model = Model(model1, data) |
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42 | pars1= ['param1','param2'] |
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43 | try: |
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44 | fitter.set_model(model,1,pars1) |
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45 | except ValueError,exc: |
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46 | #print "ValueError was correctly raised: "+str(msg) |
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47 | assert str(exc).startswith('parameter param1') |
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48 | else: |
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49 | raise AssertionError("No error raised for fitting with wrong parameters name to fit") |
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50 | |
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51 | def fit_single(self, isdream=False): |
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52 | fitter = Fit() |
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53 | |
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54 | data = Loader().load("testdata_line.txt") |
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55 | data.name = data.filename |
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56 | fitter.set_data(data,1) |
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57 | |
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58 | # Receives the type of model for the fitting |
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59 | model1 = LineModel() |
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60 | model1.name = "M1" |
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61 | model = Model(model1,data) |
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62 | |
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63 | pars1= ['A','B'] |
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64 | fitter.set_model(model,1,pars1) |
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65 | fitter.select_problem_for_fit(id=1,value=1) |
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66 | result1, = fitter.fit(handler=FitHandler()) |
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67 | |
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68 | # The target values were generated from the following statements |
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69 | p,s,fx = result1.pvec, result1.stderr, result1.fitness |
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70 | #print "p0,p1,s0,s1,fx = %g, %g, %g, %g, %g"%(p[0],p[1],s[0],s[1],fx) |
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71 | p0,p1,s0,s1,fx_ = 3.68353, 2.61004, 0.336186, 0.105244, 1.20189 |
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72 | |
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73 | if isdream: |
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74 | # Dream is not a minimizer: just check that the fit is within |
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75 | # uncertainty |
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76 | self.assertTrue( abs(p[0]-p0) <= s0 ) |
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77 | self.assertTrue( abs(p[1]-p1) <= s1 ) |
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78 | else: |
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79 | self.assertTrue( abs(p[0]-p0) <= 1e-5 ) |
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80 | self.assertTrue( abs(p[1]-p1) <= 1e-5 ) |
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81 | self.assertTrue( abs(fx-fx_) <= 1e-5 ) |
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82 | |
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83 | def fit_bumps(self, alg, **opts): |
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84 | set_fitter(alg, opts) |
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85 | self.fit_single(isdream=(alg=='dream')) |
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86 | |
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87 | def test_bumps_de(self): |
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88 | self.fit_bumps('de') |
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89 | |
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90 | def test_bumps_dream(self): |
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91 | self.fit_bumps('dream', burn=500, steps=100) |
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92 | |
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93 | def test_bumps_amoeba(self): |
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94 | self.fit_bumps('amoeba') |
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95 | |
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96 | def test_bumps_newton(self): |
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97 | self.fit_bumps('newton') |
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98 | |
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99 | def test_bumps_lm(self): |
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100 | self.fit_bumps('lm') |
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101 | |
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102 | def test2(self): |
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103 | """ fit 2 data and 2 model with no constrainst""" |
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104 | #load data |
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105 | l = Loader() |
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106 | data1=l.load("testdata_line.txt") |
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107 | data1.name = data1.filename |
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108 | |
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109 | data2=l.load("testdata_line1.txt") |
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110 | data2.name = data2.filename |
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111 | |
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112 | #Importing the Fit module |
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113 | fitter = Fit() |
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114 | # Receives the type of model for the fitting |
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115 | model11 = LineModel() |
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116 | model11.name= "M1" |
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117 | model22 = LineModel() |
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118 | model11.name= "M2" |
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119 | |
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120 | model1 = Model(model11,data1) |
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121 | model2 = Model(model22,data2) |
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122 | pars1= ['A','B'] |
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123 | fitter.set_data(data1,1) |
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124 | fitter.set_model(model1,1,pars1) |
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125 | fitter.select_problem_for_fit(id=1,value=0) |
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126 | fitter.set_data(data2,2) |
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127 | fitter.set_model(model2,2,pars1) |
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128 | fitter.select_problem_for_fit(id=2,value=0) |
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129 | |
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130 | try: result1, = fitter.fit(handler=FitHandler()) |
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131 | except RuntimeError,msg: |
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132 | assert str(msg)=="Nothing to fit" |
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133 | else: raise AssertionError,"No error raised for fitting with no model" |
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134 | fitter.select_problem_for_fit(id=1,value=1) |
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135 | fitter.select_problem_for_fit(id=2,value=1) |
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136 | R1,R2 = fitter.fit(handler=FitHandler()) |
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137 | |
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138 | self.assertTrue( math.fabs(R1.pvec[0]-4)/3 <= R1.stderr[0] ) |
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139 | self.assertTrue( math.fabs(R1.pvec[1]-2.5)/3 <= R1.stderr[1] ) |
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140 | self.assertTrue( R1.fitness/(len(data1.x)+len(data2.x)) < 2) |
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141 | |
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142 | |
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143 | def test_constraints(self): |
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144 | """ fit 2 data and 2 model with 1 constrainst""" |
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145 | #load data |
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146 | l = Loader() |
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147 | data1= l.load("testdata_line.txt") |
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148 | data1.name = data1.filename |
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149 | data2= l.load("testdata_cst.txt") |
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150 | data2.name = data2.filename |
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151 | |
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152 | # Receives the type of model for the fitting |
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153 | model11 = LineModel() |
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154 | model11.name= "line" |
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155 | model11.setParam("A", 1.0) |
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156 | model11.setParam("B",1.0) |
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157 | |
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158 | model22 = Constant() |
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159 | model22.name= "cst" |
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160 | model22.setParam("value", 1.0) |
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161 | |
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162 | model1 = Model(model11,data1) |
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163 | model2 = Model(model22,data2) |
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164 | model1.set(A=4) |
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165 | model1.set(B=3) |
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166 | # Constraint the constant value to be equal to parameter B (the real value is 2.5) |
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167 | #model2.set(value='line.B') |
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168 | pars1= ['A','B'] |
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169 | pars2= ['value'] |
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170 | |
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171 | #Importing the Fit module |
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172 | fitter = Fit() |
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173 | fitter.set_data(data1,1) |
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174 | fitter.set_model(model1,1,pars1) |
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175 | fitter.set_data(data2,2,smearer=None) |
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176 | fitter.set_model(model2,2,pars2,constraints=[("value","line.B")]) |
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177 | fitter.select_problem_for_fit(id=1,value=1) |
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178 | fitter.select_problem_for_fit(id=2,value=1) |
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179 | |
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180 | R1,R2 = fitter.fit(handler=FitHandler()) |
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181 | self.assertTrue( math.fabs(R1.pvec[0]-4.0)/3. <= R1.stderr[0]) |
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182 | self.assertTrue( math.fabs(R1.pvec[1]-2.5)/3. <= R1.stderr[1]) |
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183 | self.assertTrue( R1.fitness/(len(data1.x)+len(data2.x)) < 2) |
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184 | |
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185 | |
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186 | def test4(self): |
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187 | """ fit 2 data concatenates with limited range of x and one model """ |
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188 | #load data |
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189 | l = Loader() |
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190 | data1 = l.load("testdata_line.txt") |
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191 | data1.name = data1.filename |
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192 | data2 = l.load("testdata_line1.txt") |
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193 | data2.name = data2.filename |
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194 | |
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195 | # Receives the type of model for the fitting |
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196 | model1 = LineModel() |
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197 | model1.name= "M1" |
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198 | model1.setParam("A", 1.0) |
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199 | model1.setParam("B",1.0) |
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200 | model = Model(model1,data1) |
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201 | |
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202 | pars1= ['A','B'] |
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203 | #Importing the Fit module |
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204 | |
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205 | fitter = Fit() |
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206 | fitter.set_data(data1,1,qmin=0, qmax=7) |
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207 | fitter.set_model(model,1,pars1) |
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208 | fitter.set_data(data2,1,qmin=1,qmax=10) |
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209 | fitter.select_problem_for_fit(id=1,value=1) |
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210 | result2, = fitter.fit(handler=FitHandler()) |
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211 | |
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212 | self.assert_(result2) |
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213 | self.assertTrue( math.fabs(result2.pvec[0]-4)/3 <= result2.stderr[0] ) |
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214 | self.assertTrue( math.fabs(result2.pvec[1]-2.5)/3 <= result2.stderr[1] ) |
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215 | self.assertTrue( result2.fitness/len(data1.x) < 2) |
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216 | |
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217 | |
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218 | if __name__ == "__main__": |
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219 | unittest.main() |
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220 | |
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