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<!DOCTYPE html>
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<div class="sphx-glr-download-link-note admonition note">
<p class="first admonition-title">Note</p>
<p class="last">Click <a class="reference internal" href="#sphx-glr-download-auto-examples-customphi-py"><span class="std std-ref">here</span></a>
to download the full example code</p>
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<p class="sphx-glr-example-title" id="sphx-glr-auto-examples-customphi-py">An example of creating you own custom feature mappings</p>
<p>In this example, I am extending the Phi parent class
according to the needs of the mappings.
You can choose the best feature mapping class for extension
according to your requirements.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">load_iris</span>
<span class="kn">from</span> <span class="nn">sklearn.utils</span> <span class="kn">import</span> <span class="n">check_array</span>
<span class="kn">from</span> <span class="nn">MRCpy</span> <span class="kn">import</span> <span class="n">CMRC</span>
<span class="kn">from</span> <span class="nn">MRCpy.phi</span> <span class="kn">import</span> <span class="o">*</span>
<span class="c1"># Custom phi example: Generating the linear kernel</span>
<span class="c1"># modified by multiplying a constant</span>
<span class="k">class</span> <span class="nc">myPhi</span><span class="p">(</span><span class="n">BasePhi</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> This constructor is by default present in the parent Phi class.</span>
<span class="sd"> So, no need to redefine this constructor</span>
<span class="sd"> unless you need any extra parameters from the user.</span>
<span class="sd"> In our example here, we don't actually need this</span>
<span class="sd"> as we are not using any extra parameters here</span>
<span class="sd"> but it is defined here as an example.</span>
<span class="sd"> Removing this constructor doesn't have any affect on the performance.</span>
<span class="sd"> """</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">,</span> <span class="n">b</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
<span class="c1"># Calling the parent constructor.</span>
<span class="c1"># It is always better convention to call the parent constructor</span>
<span class="c1"># for primary variables initialization.</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">n_classes</span><span class="p">)</span>
<span class="c1"># Define any extra parameters for your own features</span>
<span class="c1"># Example : self.add_intercept = True/False</span>
<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">X</span><span class="p">,</span> <span class="n">Y</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Fit any extra parameter for your feature mappings</span>
<span class="sd"> and set the length of the feature mapping.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> X : array-like of shape (n_samples, n_dimensions)</span>
<span class="sd"> Unlabeled training instances</span>
<span class="sd"> used to learn the feature configurations</span>
<span class="sd"> Y : array-like of shape (n_samples,), default=None</span>
<span class="sd"> Labels corresponding to the unlabeled instances.</span>
<span class="sd"> """</span>
<span class="c1"># Check if the array is 2D numpy matrix or not.</span>
<span class="c1"># X is expected to be a numpy 2D matrix.</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">check_array</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">accept_sparse</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="c1"># Defining the length of the phi</span>
<span class="c1"># Defines the total length of the feature mapping automatically</span>
<span class="c1"># It is recommended to call this function at the end of fit</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">)</span>
<span class="c1"># Return the fitted feature mapping instance</span>
<span class="k">return</span> <span class="bp">self</span>
<span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Transform the given instances to the principal features if any.</span>
<span class="sd"> No need to give definition for this function</span>
<span class="sd"> if you are not calling it in the eval function.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> X : array-like of shape (n_samples, n_dimensions)</span>
<span class="sd"> Unlabeled training instances.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> X_feat : array-like of shape (n_samples, n_features)</span>
<span class="sd"> Transformed features from the given instances i.e.,</span>
<span class="sd"> the instances itself.</span>
<span class="sd"> """</span>
<span class="c1"># We want to use the linear kernel feature mapping (i.e., X itself)</span>
<span class="c1"># and transform it by multiplying by a factor 2</span>
<span class="c1"># Note: This is just an example of building custom feature mappings,</span>
<span class="c1"># so the results after using this feature mappings</span>
<span class="c1"># might not be satisfactory</span>
<span class="n">X_feat</span> <span class="o">=</span> <span class="n">X</span> <span class="o">*</span> <span class="mi">2</span>
<span class="c1"># Return the features</span>
<span class="k">return</span> <span class="n">X_feat</span>
<span class="k">def</span> <span class="nf">eval_xy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Computes the complete feature mapping vector</span>
<span class="sd"> corresponding to instance X.</span>
<span class="sd"> X can be a matrix in which case</span>
<span class="sd"> the function returns a matrix in which</span>
<span class="sd"> the rows represent the complete feature mapping vector</span>
<span class="sd"> corresponding to each instance.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> X : array-like of shape (n_samples, n_dimensions)</span>
<span class="sd"> Unlabeled training instances for developing the feature matrix</span>
<span class="sd"> Y : array-like of shape (n_samples,)</span>
<span class="sd"> Labels corresponding to the unlabeled training instances</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> phi : array-like of shape (n_samples, n_classes, n_features*n_classes)</span>
<span class="sd"> Matrix containing the complete feature vector as rows</span>
<span class="sd"> corresponding to each of the instance and their labels.</span>
<span class="sd"> The `eval` function of the BasePhi computes the feature mappings</span>
<span class="sd"> by calling the transform function to get the principal features</span>
<span class="sd"> and then appending zeros for the one-hot encoding.</span>
<span class="sd"> """</span>
<span class="c1"># Here in this example,</span>
<span class="c1"># we want to use the one-hot encoded feature mappings.</span>
<span class="c1"># So, we call the parent class eval function</span>
<span class="c1"># which does the one-hot encoding by default</span>
<span class="c1"># and also adds the intercept corresponding to each class</span>
<span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">eval_xy</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">)</span>
<span class="c1"># In case you don't want the one-hot encoding,</span>
<span class="c1"># you have to define you own eval function</span>
<span class="c1"># without calling the parent class eval function.</span>
<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">'__main__'</span><span class="p">:</span>
<span class="c1"># Loading the dataset</span>
<span class="n">X</span><span class="p">,</span> <span class="n">Y</span> <span class="o">=</span> <span class="n">load_iris</span><span class="p">(</span><span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">r</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.unique.html#numpy.unique" title="numpy.unique" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">unique</span></a><span class="p">(</span><span class="n">Y</span><span class="p">))</span>
<span class="c1"># Creating the custom phi object</span>
<span class="n">myphi</span> <span class="o">=</span> <span class="n">myPhi</span><span class="p">(</span><span class="n">n_classes</span><span class="o">=</span><span class="n">r</span><span class="p">)</span>
<span class="c1"># Fit the MRC model with the custom phi</span>
<span class="n">clf</span> <span class="o">=</span> <span class="n">CMRC</span><span class="p">(</span><span class="n">phi</span><span class="o">=</span><span class="n">myphi</span><span class="p">,</span> <span class="n">fit_intercept</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">)</span>
<span class="c1"># Prediction</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n\n</span><span class="s1">The predicted values for the first 3 instances are : '</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">3</span><span class="p">,</span> <span class="p">:]))</span>
<span class="c1"># Predicted probabilities</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n\n</span><span class="s1">The predicted probabilities for the first 3 instances are : '</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">3</span><span class="p">,</span> <span class="p">:]))</span>
<span class="c1"># Accuracy/Score of the model</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n\n</span><span class="s1">The score is : '</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">))</span>
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