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  <h1>Source code for macop.evaluators.discrete.multi</h1><div class="highlight"><pre>
<span></span><span class="sd">&quot;&quot;&quot;Multi-objective evaluators classes </span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="c1"># main imports</span>
<span class="kn">from</span> <span class="nn">..base</span> <span class="kn">import</span> <span class="n">Evaluator</span>


<div class="viewcode-block" id="WeightedSum"><a class="viewcode-back" href="../../../../macop/macop.evaluators.discrete.multi.html#macop.evaluators.discrete.multi.WeightedSum">[docs]</a><span class="k">class</span> <span class="nc">WeightedSum</span><span class="p">(</span><span class="n">Evaluator</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;Weighted-sum sub-evaluator class which enables to compute solution using specific `_data`</span>

<span class="sd">    - stores into its `_data` dictionary attritute required measures when computing a solution</span>
<span class="sd">    - `_data[&#39;evaluators&#39;]` current evaluator to use</span>
<span class="sd">    - `_data[&#39;weights&#39;]` Associated weight to use</span>
<span class="sd">    - `compute` method enables to compute and associate a tuples of scores to a given solution</span>
<span class="sd">    </span>
<span class="sd">    &gt;&gt;&gt; import random</span>
<span class="sd">    &gt;&gt;&gt; # binary solution import</span>
<span class="sd">    &gt;&gt;&gt; from macop.solutions.discrete import BinarySolution</span>
<span class="sd">    &gt;&gt;&gt; # evaluators imports</span>
<span class="sd">    &gt;&gt;&gt; from macop.evaluators.discrete.mono import KnapsackEvaluator</span>
<span class="sd">    &gt;&gt;&gt; from macop.evaluators.discrete.multi import WeightedSum</span>
<span class="sd">    &gt;&gt;&gt; solution_data = [1, 0, 0, 1, 1, 0, 1, 0]</span>
<span class="sd">    &gt;&gt;&gt; size = len(solution_data)</span>
<span class="sd">    &gt;&gt;&gt; solution = BinarySolution(solution_data, size)</span>
<span class="sd">    &gt;&gt;&gt; # evaluator 1 initialization (worths objects passed into data)</span>
<span class="sd">    &gt;&gt;&gt; worths1 = [ random.randint(5, 20) for i in range(size) ]</span>
<span class="sd">    &gt;&gt;&gt; evaluator1 = KnapsackEvaluator(data={&#39;worths&#39;: worths1})</span>
<span class="sd">    &gt;&gt;&gt; # evaluator 2 initialization (worths objects passed into data)</span>
<span class="sd">    &gt;&gt;&gt; worths2 = [ random.randint(10, 15) for i in range(size) ]</span>
<span class="sd">    &gt;&gt;&gt; evaluator2 = KnapsackEvaluator(data={&#39;worths&#39;: worths2})</span>
<span class="sd">    &gt;&gt;&gt; weighted_evaluator = WeightedSum(data={&#39;evaluators&#39;: [evaluator1, evaluator2], &#39;weights&#39;: [0.3, 0.7]})</span>
<span class="sd">    &gt;&gt;&gt; weighted_score = weighted_evaluator.compute(solution)</span>
<span class="sd">    &gt;&gt;&gt; expected_score = evaluator1.compute(solution) * 0.3 + evaluator2.compute(solution) * 0.7</span>
<span class="sd">    &gt;&gt;&gt; weighted_score == expected_score</span>
<span class="sd">    True</span>
<span class="sd">    &gt;&gt;&gt; weighted_score</span>
<span class="sd">    50.8</span>
<span class="sd">    &quot;&quot;&quot;</span>

<div class="viewcode-block" id="WeightedSum.compute"><a class="viewcode-back" href="../../../../macop/macop.evaluators.discrete.multi.html#macop.evaluators.discrete.multi.WeightedSum.compute">[docs]</a>    <span class="k">def</span> <span class="nf">compute</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">solution</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;Apply the computation of fitness from solution</span>

<span class="sd">        - Associate tuple of fitness scores for each objective to the current solution</span>
<span class="sd">        - Compute weighted-sum for these objectives</span>

<span class="sd">        Args:</span>
<span class="sd">            solution: {Solution} -- Solution instance</span>
<span class="sd">    </span>
<span class="sd">        Returns:</span>
<span class="sd">            {float} -- weighted-sum of the fitness scores</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="n">scores</span> <span class="o">=</span> <span class="p">[</span><span class="n">evaluator</span><span class="o">.</span><span class="n">compute</span><span class="p">(</span><span class="n">solution</span><span class="p">)</span> <span class="k">for</span> <span class="n">evaluator</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="p">[</span><span class="s1">&#39;evaluators&#39;</span><span class="p">]]</span>

        <span class="c1"># associate objectives scores to solution</span>
        <span class="n">solution</span><span class="o">.</span><span class="n">_scores</span> <span class="o">=</span> <span class="n">scores</span>

        <span class="k">return</span> <span class="nb">sum</span><span class="p">([</span><span class="n">scores</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="n">w</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">w</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="p">[</span><span class="s1">&#39;weights&#39;</span><span class="p">])])</span></div></div>
</pre></div>

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