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50  <h1>Source code for park.fitmc</h1><div class="highlight"><pre>
51<span class="c"># The job queue needs to be in a transaction/rollback protected database.</span>
52<span class="c"># If the server is rebooted, long running jobs should continue to work.</span>
53<span class="c">#</span>
54<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">division</span>
55<span class="kn">import</span> <span class="nn">numpy</span>
56
57<span class="kn">import</span> <span class="nn">simplex</span>
58
59<span class="kn">import</span> <span class="nn">fitresult</span><span class="o">,</span> <span class="nn">pmap</span><span class="o">,</span> <span class="nn">fit</span>
60
61<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s">&#39;fitmc&#39;</span><span class="p">,</span> <span class="s">&#39;FitMCJob&#39;</span><span class="p">]</span>
62
63<span class="k">class</span> <span class="nc">LocalFit</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
64    <span class="sd">&quot;&quot;&quot;</span>
65<span class="sd">    Abstract interface for a local minimizer</span>
66
67<span class="sd">    See `park.fitmc.FitSimplex` for a concrete implementation.</span>
68<span class="sd">    &quot;&quot;&quot;</span>
69    <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">objective</span><span class="p">,</span> <span class="n">x0</span><span class="p">):</span>
70        <span class="sd">&quot;&quot;&quot;</span>
71<span class="sd">        Minimize the value of a fitness function.</span>
72
73<span class="sd">        See `park.fitmc.Fitness` for the definition of the goodness of fit</span>
74<span class="sd">        object.  x0 is a vector containing the initial value for the fit.</span>
75<span class="sd">        &quot;&quot;&quot;</span>
76    <span class="k">def</span> <span class="nf">abort</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
77        <span class="sd">&quot;&quot;&quot;</span>
78<span class="sd">        Cancel the fit.  This will be called from a separate execution</span>
79<span class="sd">        thread.  The fit should terminate as soon as possible after this</span>
80<span class="sd">        function has been called.  Ideally this would interrupt the</span>
81<span class="sd">        cur</span>
82<span class="sd">        &quot;&quot;&quot;</span>
83
84<span class="k">class</span> <span class="nc">FitSimplex</span><span class="p">(</span><span class="n">LocalFit</span><span class="p">):</span>
85    <span class="sd">&quot;&quot;&quot;</span>
86<span class="sd">    Local minimizer using Nelder-Mead simplex algorithm.</span>
87
88<span class="sd">    Simplex is robust and derivative free, though not very efficient.</span>
89
90<span class="sd">    This class wraps the bounds contrained Nelder-Mead simplex</span>
91<span class="sd">    implementation for `park.simplex.simplex`.</span>
92<span class="sd">    &quot;&quot;&quot;</span>
93    <span class="n">radius</span> <span class="o">=</span> <span class="mf">0.05</span>
94    <span class="sd">&quot;&quot;&quot;Size of the initial simplex; this is a portion between 0 and 1&quot;&quot;&quot;</span>
95    <span class="n">xtol</span> <span class="o">=</span> <span class="mi">1</span>
96    <span class="c">#xtol = 1e-4</span>
97    <span class="sd">&quot;&quot;&quot;Stop when simplex vertices are within xtol of each other&quot;&quot;&quot;</span>
98    <span class="n">ftol</span> <span class="o">=</span> <span class="mf">1e-4</span>
99    <span class="sd">&quot;&quot;&quot;Stop when vertex values are within ftol of each other&quot;&quot;&quot;</span>
100    <span class="n">maxiter</span> <span class="o">=</span> <span class="bp">None</span>
101    <span class="sd">&quot;&quot;&quot;Maximum number of iterations before fit terminates&quot;&quot;&quot;</span>
102    <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">fitness</span><span class="p">,</span> <span class="n">x0</span><span class="p">):</span>
103        <span class="sd">&quot;&quot;&quot;Run the fit&quot;&quot;&quot;</span>
104        <span class="bp">self</span><span class="o">.</span><span class="n">cancel</span> <span class="o">=</span> <span class="bp">False</span>
105        <span class="n">pars</span> <span class="o">=</span> <span class="n">fitness</span><span class="o">.</span><span class="n">fit_parameters</span><span class="p">()</span>
106        <span class="n">bounds</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">p</span><span class="o">.</span><span class="n">range</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">pars</span><span class="p">])</span><span class="o">.</span><span class="n">T</span>
107        <span class="n">result</span> <span class="o">=</span> <span class="n">simplex</span><span class="o">.</span><span class="n">simplex</span><span class="p">(</span><span class="n">fitness</span><span class="p">,</span> <span class="n">x0</span><span class="p">,</span> <span class="n">bounds</span><span class="o">=</span><span class="n">bounds</span><span class="p">,</span>
108                                 <span class="n">radius</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">radius</span><span class="p">,</span> <span class="n">xtol</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">xtol</span><span class="p">,</span>
109                                 <span class="n">ftol</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">ftol</span><span class="p">,</span> <span class="n">maxiter</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">maxiter</span><span class="p">,</span>
110                                 <span class="n">abort_test</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_iscancelled</span><span class="p">)</span>
111        <span class="c">#print &quot;calls:&quot;,result.calls</span>
112        <span class="c">#print &quot;simplex returned&quot;,result.x,result.fx</span>
113        <span class="c"># Need to make our own copy of the fit results so that the</span>
114        <span class="c"># values don&#39;t get stomped on by the next fit iteration.</span>
115        <span class="n">fitpars</span> <span class="o">=</span> <span class="p">[</span><span class="n">fitresult</span><span class="o">.</span><span class="n">FitParameter</span><span class="p">(</span><span class="n">pars</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">name</span><span class="p">,</span><span class="n">pars</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">range</span><span class="p">,</span><span class="n">v</span><span class="p">)</span>
116                   <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">v</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">result</span><span class="o">.</span><span class="n">x</span><span class="p">)]</span>
117        <span class="n">res</span> <span class="o">=</span> <span class="n">fitresult</span><span class="o">.</span><span class="n">FitResult</span><span class="p">(</span><span class="n">fitpars</span><span class="p">,</span> <span class="n">result</span><span class="o">.</span><span class="n">calls</span><span class="p">,</span> <span class="n">result</span><span class="o">.</span><span class="n">fx</span><span class="p">)</span>
118        <span class="c"># Compute the parameter uncertainties from the jacobian</span>
119        <span class="n">res</span><span class="o">.</span><span class="n">calc_cov</span><span class="p">(</span><span class="n">fitness</span><span class="p">)</span>
120        <span class="k">return</span> <span class="n">res</span>
121
122    <span class="k">def</span> <span class="nf">abort</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
123        <span class="sd">&quot;&quot;&quot;Cancel the fit in progress from another thread of execution&quot;&quot;&quot;</span>
124        <span class="c"># Effectively an atomic operation; rely on GIL to protect it.</span>
125        <span class="bp">self</span><span class="o">.</span><span class="n">cancel</span> <span class="o">=</span> <span class="bp">True</span>
126        <span class="c"># Abort the current function evaluation if possible.</span>
127        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">fitness</span><span class="p">,</span><span class="s">&#39;abort&#39;</span><span class="p">):</span> <span class="bp">self</span><span class="o">.</span><span class="n">fitness</span><span class="o">.</span><span class="n">abort</span><span class="p">()</span>
128
129    <span class="k">def</span> <span class="nf">_iscancelled</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">cancel</span>
130
131<span class="k">class</span> <span class="nc">MapMC</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
132    <span class="sd">&quot;&quot;&quot;</span>
133<span class="sd">    Evaluate a local fit at a particular start point.</span>
134
135<span class="sd">    This is the function to be mapped across a set of start points for the</span>
136<span class="sd">    monte carlo map-reduce implementation.</span>
137
138<span class="sd">    See `park.pmap.Mapper` for required interface.</span>
139<span class="sd">    &quot;&quot;&quot;</span>
140    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">minimizer</span><span class="p">,</span> <span class="n">fitness</span><span class="p">):</span>
141        <span class="bp">self</span><span class="o">.</span><span class="n">minimizer</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">fitness</span> <span class="o">=</span> <span class="n">minimizer</span><span class="p">,</span> <span class="n">fitness</span>
142    <span class="k">def</span> <span class="nf">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x0</span><span class="p">):</span>
143        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">minimizer</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fitness</span><span class="p">,</span><span class="n">x0</span><span class="p">)</span>
144    <span class="k">def</span> <span class="nf">abort</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
145        <span class="bp">self</span><span class="o">.</span><span class="n">minimizer</span><span class="o">.</span><span class="n">abort</span><span class="p">()</span>
146
147<span class="k">class</span> <span class="nc">CollectMC</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
148    <span class="sd">&quot;&quot;&quot;</span>
149<span class="sd">    Collect the results from multiple start points in a Monte Carlo fit engine.</span>
150
151<span class="sd">    See `park.pmap.Collector` for required interface.</span>
152<span class="sd">    &quot;&quot;&quot;</span>
153    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">number_expected</span><span class="p">,</span> <span class="n">handler</span><span class="p">):</span>
154        <span class="bp">self</span><span class="o">.</span><span class="n">number_expected</span> <span class="o">=</span> <span class="n">number_expected</span>
155        <span class="sd">&quot;&quot;&quot;Number of starting points to check with local optimizer&quot;&quot;&quot;</span>
156        <span class="bp">self</span><span class="o">.</span><span class="n">iterations</span> <span class="o">=</span> <span class="mi">0</span>
157        <span class="bp">self</span><span class="o">.</span><span class="n">best</span> <span class="o">=</span> <span class="bp">None</span>
158        <span class="bp">self</span><span class="o">.</span><span class="n">calls</span> <span class="o">=</span> <span class="mi">0</span>
159        <span class="bp">self</span><span class="o">.</span><span class="n">handler</span> <span class="o">=</span> <span class="n">handler</span>
160        <span class="bp">self</span><span class="o">.</span><span class="n">handler</span><span class="o">.</span><span class="n">done</span> <span class="o">=</span> <span class="bp">False</span>
161    <span class="k">def</span> <span class="nf">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">result</span><span class="p">):</span>
162        <span class="c"># Keep track of the amount of work done</span>
163        <span class="bp">self</span><span class="o">.</span><span class="n">iterations</span> <span class="o">+=</span> <span class="mi">1</span>
164        <span class="bp">self</span><span class="o">.</span><span class="n">calls</span> <span class="o">+=</span> <span class="n">result</span><span class="o">.</span><span class="n">calls</span>
165        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">best</span> <span class="ow">is</span> <span class="bp">None</span> <span class="ow">or</span> <span class="n">result</span><span class="o">.</span><span class="n">fitness</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">best</span><span class="o">.</span><span class="n">fitness</span><span class="p">:</span>
166            <span class="bp">self</span><span class="o">.</span><span class="n">best</span> <span class="o">=</span> <span class="n">result</span>
167            <span class="bp">self</span><span class="o">.</span><span class="n">handler</span><span class="o">.</span><span class="n">result</span> <span class="o">=</span> <span class="n">result</span>
168            <span class="bp">self</span><span class="o">.</span><span class="n">handler</span><span class="o">.</span><span class="n">improvement</span><span class="p">()</span>
169        <span class="c"># Progress should go after improvement in case the fit handler</span>
170        <span class="c"># wants to suppress intermediate improvements</span>
171        <span class="bp">self</span><span class="o">.</span><span class="n">handler</span><span class="o">.</span><span class="n">progress</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">iterations</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">number_expected</span><span class="p">)</span>
172    <span class="k">def</span> <span class="nf">abort</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
173        <span class="bp">self</span><span class="o">.</span><span class="n">handler</span><span class="o">.</span><span class="n">done</span> <span class="o">=</span> <span class="bp">True</span>
174        <span class="bp">self</span><span class="o">.</span><span class="n">handler</span><span class="o">.</span><span class="n">abort</span><span class="p">()</span>
175    <span class="k">def</span> <span class="nf">finalize</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
176        <span class="bp">self</span><span class="o">.</span><span class="n">handler</span><span class="o">.</span><span class="n">result</span><span class="o">.</span><span class="n">calls</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">calls</span>
177        <span class="bp">self</span><span class="o">.</span><span class="n">handler</span><span class="o">.</span><span class="n">done</span> <span class="o">=</span> <span class="bp">True</span>
178        <span class="bp">self</span><span class="o">.</span><span class="n">handler</span><span class="o">.</span><span class="n">finalize</span><span class="p">()</span>
179    <span class="k">def</span> <span class="nf">error</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">msg</span><span class="p">):</span>
180        <span class="bp">self</span><span class="o">.</span><span class="n">handler</span><span class="o">.</span><span class="n">done</span> <span class="o">=</span> <span class="bp">True</span>
181        <span class="bp">self</span><span class="o">.</span><span class="n">handler</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="n">msg</span><span class="p">)</span>
182
183<span class="k">def</span> <span class="nf">fitmc</span><span class="p">(</span><span class="n">fitness</span><span class="p">,</span> <span class="n">x0</span><span class="p">,</span> <span class="n">bounds</span><span class="p">,</span> <span class="n">localfit</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">handler</span><span class="p">):</span>
184<div class="viewcode-block" id="fitmc"><a class="viewcode-back" href="../../dev/api/park.html#park.fitmc.fitmc">[docs]</a>    <span class="sd">&quot;&quot;&quot;</span>
185<span class="sd">    Run a monte carlo fit.</span>
186
187<span class="sd">    This procedure maps a local optimizer across a set of n initial points.</span>
188<span class="sd">    The initial parameter value defined by the fitness parameters defines</span>
189<span class="sd">    one initial point.  The remainder are randomly generated within the</span>
190<span class="sd">    bounds of the problem.</span>
191
192<span class="sd">    localfit is the local optimizer to use.  It should be a bounded</span>
193<span class="sd">    optimizer following the `park.fitmc.LocalFit` interface.</span>
194
195<span class="sd">    handler accepts updates to the current best set of fit parameters.</span>
196<span class="sd">    See `park.fitresult.FitHandler` for details.</span>
197<span class="sd">    &quot;&quot;&quot;</span>
198    <span class="c"># Generate random number within bounds.  If bounds are indefinite, use [0,1]</span>
199    <span class="c"># If bounds are semi-definite, use [low,low+1] or [high-1,high], depending</span>
200    <span class="c"># on which limit is unbounded.</span>
201    <span class="n">lo</span><span class="p">,</span><span class="n">hi</span> <span class="o">=</span> <span class="n">bounds</span>
202    <span class="n">inf_lo</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">isinf</span><span class="p">(</span><span class="n">lo</span><span class="p">)</span>
203    <span class="n">inf_hi</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">isinf</span><span class="p">(</span><span class="n">hi</span><span class="p">)</span>
204    <span class="n">delta</span> <span class="o">=</span> <span class="n">hi</span><span class="o">-</span><span class="n">lo</span>
205    <span class="n">delta</span><span class="p">[</span><span class="n">inf_lo</span><span class="o">|</span><span class="n">inf_hi</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1.0</span>
206    <span class="n">lo</span><span class="p">[</span><span class="n">inf_lo</span><span class="p">]</span> <span class="o">=</span> <span class="n">hi</span><span class="p">[</span><span class="n">inf_lo</span><span class="p">]</span> <span class="o">-</span> <span class="mf">1.0</span>
207    <span class="n">lo</span><span class="p">[</span><span class="n">inf_lo</span><span class="o">&amp;</span><span class="n">inf_hi</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.0</span>
208    <span class="n">P</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">n</span><span class="p">,</span><span class="nb">len</span><span class="p">(</span><span class="n">x0</span><span class="p">))</span><span class="o">*</span><span class="n">delta</span><span class="o">+</span><span class="n">lo</span>
209    <span class="c">#print &quot;Population&quot;,P</span>
210    <span class="n">P</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">x0</span>
211
212    <span class="n">pmap</span><span class="o">.</span><span class="n">pmapreduce</span><span class="p">(</span><span class="n">MapMC</span><span class="p">(</span><span class="n">localfit</span><span class="p">,</span><span class="n">fitness</span><span class="p">),</span>
213                    <span class="n">CollectMC</span><span class="p">(</span><span class="n">n</span><span class="p">,</span><span class="n">handler</span><span class="p">),</span>
214                    <span class="n">P</span><span class="p">)</span>
215
216<span class="k">class</span> <span class="nc">FitMC</span><span class="p">(</span><span class="n">fit</span><span class="o">.</span><span class="n">Fitter</span><span class="p">):</span></div>
217    <span class="sd">&quot;&quot;&quot;</span>
218<span class="sd">    Monte Carlo optimizer.</span>
219
220<span class="sd">    This implements `park.fit.Fitter`.</span>
221<span class="sd">    &quot;&quot;&quot;</span>
222    <span class="n">localfit</span> <span class="o">=</span> <span class="n">FitSimplex</span><span class="p">()</span>
223    <span class="n">start_points</span> <span class="o">=</span> <span class="mi">10</span>
224
225    <span class="k">def</span> <span class="nf">_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">objective</span><span class="p">,</span> <span class="n">x0</span><span class="p">,</span> <span class="n">bounds</span><span class="p">):</span>
226        <span class="sd">&quot;&quot;&quot;</span>
227<span class="sd">        Run a monte carlo fit.</span>
228
229<span class="sd">        This procedure maps a local optimizer across a set of initial points.</span>
230<span class="sd">        &quot;&quot;&quot;</span>
231        <span class="n">fitmc</span><span class="p">(</span><span class="n">objective</span><span class="p">,</span> <span class="n">x0</span><span class="p">,</span> <span class="n">bounds</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">localfit</span><span class="p">,</span>
232              <span class="bp">self</span><span class="o">.</span><span class="n">start_points</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">handler</span><span class="p">)</span>
233
234<span class="k">if</span> <span class="n">__name__</span> <span class="o">==</span> <span class="s">&quot;__main__&quot;</span><span class="p">:</span>
235    <span class="n">fit</span><span class="o">.</span><span class="n">demo2</span><span class="p">(</span><span class="n">FitMC</span><span class="p">(</span><span class="n">localfit</span><span class="o">=</span><span class="n">FitSimplex</span><span class="p">(),</span><span class="n">start_points</span><span class="o">=</span><span class="mi">10</span><span class="p">))</span>
236</pre></div>
237
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