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<!DOCTYPE html>
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<h1>Source code for MRCpy.base_mrc</h1><div class="highlight"><pre>
<span></span><span class="sd">'''Super class for Minimax Risk Classifiers.'''</span>
<span class="kn">import</span> <span class="nn">cvxpy</span> <span class="k">as</span> <span class="nn">cvx</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.base</span> <span class="kn">import</span> <span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">ClassifierMixin</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="p">,</span> <span class="n">check_X_y</span>
<span class="kn">from</span> <span class="nn">sklearn.utils.validation</span> <span class="kn">import</span> <span class="n">check_is_fitted</span>
<span class="c1"># Import the feature mapping</span>
<span class="kn">from</span> <span class="nn">MRCpy.phi</span> <span class="kn">import</span> \
<span class="n">BasePhi</span><span class="p">,</span> \
<span class="n">RandomFourierPhi</span><span class="p">,</span> \
<span class="n">RandomReLUPhi</span><span class="p">,</span> \
<span class="n">ThresholdPhi</span>
<div class="viewcode-block" id="BaseMRC"><a class="viewcode-back" href="../../generated/MRCpy.BaseMRC.html#MRCpy.BaseMRC">[docs]</a><span class="k">class</span> <span class="nc">BaseMRC</span><span class="p">(</span><span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">ClassifierMixin</span><span class="p">):</span>
<span class="sd">'''</span>
<span class="sd"> Base class for different minimax risk classifiers.</span>
<span class="sd"> This class is a parent class for different MRCs</span>
<span class="sd"> implemented in the library.</span>
<span class="sd"> It defines the different parameters and the layout.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> loss : `str` {'0-1', 'log'}, default='0-1'</span>
<span class="sd"> The type of loss function to use for the risk minimization.</span>
<span class="sd"> s : float, default=0.3</span>
<span class="sd"> For tuning the estimation of expected values</span>
<span class="sd"> of feature mapping function.</span>
<span class="sd"> Must be a positive float value and</span>
<span class="sd"> expected to be in the 0 to 1 in general cases.</span>
<span class="sd"> deterministic : bool, default=None</span>
<span class="sd"> For determining if the prediction of the labels</span>
<span class="sd"> should be done in a deterministic way or not.</span>
<span class="sd"> For '0-1' loss, the non-deterministic ('False') approach</span>
<span class="sd"> works well.</span>
<span class="sd"> For 'log' loss, the deterministic ('True') approach</span>
<span class="sd"> works well.</span>
<span class="sd"> If the user does not specify the value, the default value</span>
<span class="sd"> is set according to loss function.</span>
<span class="sd"> random_state : int, RandomState instance, default=None</span>
<span class="sd"> Used when 'fourier' and 'relu' options for feature mappings are used</span>
<span class="sd"> to produce the random weights.</span>
<span class="sd"> fit_intercept : bool, default=True</span>
<span class="sd"> Whether to calculate the intercept for MRCs</span>
<span class="sd"> If set to false, no intercept will be used in calculations</span>
<span class="sd"> (i.e. data is expected to be already centered).</span>
<span class="sd"> warm_start : bool, default=False</span>
<span class="sd"> When set to True,</span>
<span class="sd"> reuse the solution of the previous call to fit as initialization,</span>
<span class="sd"> otherwise, just erase the previous solution.</span>
<span class="sd"> use_cvx : bool, default=False</span>
<span class="sd"> If True, use CVXpy library for the optimization</span>
<span class="sd"> instead of the subgradient methods.</span>
<span class="sd"> solver : str {'SCS', 'ECOS', 'MOSEK'}, default='MOSEK'</span>
<span class="sd"> The type of CVX solver to use for solving the problem.</span>
<span class="sd"> In some cases, one solver might not work,</span>
<span class="sd"> so you might need to change solver depending on the problem.</span>
<span class="sd"> 'MOSEK' is a commercial solver for which one might need to</span>
<span class="sd"> request for a license. A free license can be requested</span>
<span class="sd"> `here <https://www.mosek.com/products/academic-licenses/>`_</span>
<span class="sd"> max_iters : int, default=10000</span>
<span class="sd"> The maximum number of iterations to use</span>
<span class="sd"> for finding the solution of optimization</span>
<span class="sd"> using the subgradient approach.</span>
<span class="sd"> phi : str {'fourier', 'relu', 'threshold', 'linear'} or</span>
<span class="sd"> `BasePhi` instance (custom features), default='linear'</span>
<span class="sd"> The type of feature mapping function to use for mapping the input data</span>
<span class="sd"> 'fourier', 'relu', 'threshold' and 'linear'</span>
<span class="sd"> are the currenlty available feature mapping methods.</span>
<span class="sd"> The users can also implement their own feature mapping object</span>
<span class="sd"> (should be a `BasePhi` instance) and pass it to this argument.</span>
<span class="sd"> To implement a feature mapping, please go through the</span>
<span class="sd"> :ref:`Feature Mapping` section.</span>
<span class="sd"> **phi_kwargs : Additional parameters for feature mappings.</span>
<span class="sd"> Groups the multiple optional parameters</span>
<span class="sd"> for the corresponding feature mappings(phi).</span>
<span class="sd"> For example in case of fourier features,</span>
<span class="sd"> the number of features is given by `n_components`</span>
<span class="sd"> parameter which can be passed as argument -</span>
<span class="sd"> `MRC(loss='log', phi='fourier', n_components=500)`</span>
<span class="sd"> The list of arguments for each feature mappings class</span>
<span class="sd"> can be found in the corresponding documentation.</span>
<span class="sd"> Attributes</span>
<span class="sd"> ----------</span>
<span class="sd"> is_fitted_ : bool</span>
<span class="sd"> True if the classifier is fitted i.e., the parameters are learnt.</span>
<span class="sd"> tau_ : array-like of shape (n_features)</span>
<span class="sd"> The mean estimates for the expectations of feature mappings.</span>
<span class="sd"> lambda_ : array-like of shape (n_features)</span>
<span class="sd"> The variance in the mean estimates for the expectations</span>
<span class="sd"> of the feature mappings.</span>
<span class="sd"> classes_ : array-like of shape (n_classes)</span>
<span class="sd"> Labels in the given dataset.</span>
<span class="sd"> If the labels Y are not given during fit</span>
<span class="sd"> i.e., tau and lambda are given as input,</span>
<span class="sd"> then this array is None.</span>
<span class="sd"> '''</span>
<div class="viewcode-block" id="BaseMRC.__init__"><a class="viewcode-back" href="../../generated/MRCpy.BaseMRC.html#MRCpy.BaseMRC.__init__">[docs]</a> <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">loss</span><span class="o">=</span><span class="s1">'0-1'</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span>
<span class="n">deterministic</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">fit_intercept</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">warm_start</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">use_cvx</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">solver</span><span class="o">=</span><span class="s1">'MOSEK'</span><span class="p">,</span> <span class="n">max_iters</span><span class="o">=</span><span class="mi">10000</span><span class="p">,</span> <span class="n">phi</span><span class="o">=</span><span class="s1">'linear'</span><span class="p">,</span> <span class="o">**</span><span class="n">phi_kwargs</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">loss</span> <span class="o">=</span> <span class="n">loss</span>
<span class="bp">self</span><span class="o">.</span><span class="n">s</span> <span class="o">=</span> <span class="n">s</span>
<span class="bp">self</span><span class="o">.</span><span class="n">deterministic</span> <span class="o">=</span> <span class="n">deterministic</span>
<span class="bp">self</span><span class="o">.</span><span class="n">random_state</span> <span class="o">=</span> <span class="n">random_state</span>
<span class="bp">self</span><span class="o">.</span><span class="n">fit_intercept</span> <span class="o">=</span> <span class="n">fit_intercept</span>
<span class="bp">self</span><span class="o">.</span><span class="n">warm_start</span> <span class="o">=</span> <span class="n">warm_start</span>
<span class="bp">self</span><span class="o">.</span><span class="n">use_cvx</span> <span class="o">=</span> <span class="n">use_cvx</span>
<span class="bp">self</span><span class="o">.</span><span class="n">solver</span> <span class="o">=</span> <span class="n">solver</span>
<span class="bp">self</span><span class="o">.</span><span class="n">max_iters</span> <span class="o">=</span> <span class="n">max_iters</span>
<span class="c1"># Feature mappings</span>
<span class="k">if</span> <span class="n">phi</span> <span class="o">==</span> <span class="s1">'fourier'</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">phi</span> <span class="o">=</span> <span class="n">RandomFourierPhi</span><span class="p">(</span><span class="n">n_classes</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
<span class="n">fit_intercept</span><span class="o">=</span><span class="n">fit_intercept</span><span class="p">,</span>
<span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span><span class="p">,</span>
<span class="o">**</span><span class="n">phi_kwargs</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">phi</span> <span class="o">==</span> <span class="s1">'linear'</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">phi</span> <span class="o">=</span> <span class="n">BasePhi</span><span class="p">(</span><span class="n">n_classes</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
<span class="n">fit_intercept</span><span class="o">=</span><span class="n">fit_intercept</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">phi</span> <span class="o">==</span> <span class="s1">'threshold'</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">phi</span> <span class="o">=</span> <span class="n">ThresholdPhi</span><span class="p">(</span><span class="n">n_classes</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
<span class="n">fit_intercept</span><span class="o">=</span><span class="n">fit_intercept</span><span class="p">,</span>
<span class="o">**</span><span class="n">phi_kwargs</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">phi</span> <span class="o">==</span> <span class="s1">'relu'</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">phi</span> <span class="o">=</span> <span class="n">RandomReLUPhi</span><span class="p">(</span><span class="n">n_classes</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
<span class="n">fit_intercept</span><span class="o">=</span><span class="n">fit_intercept</span><span class="p">,</span>
<span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span><span class="p">,</span>
<span class="o">**</span><span class="n">phi_kwargs</span><span class="p">)</span>
<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">phi</span><span class="p">,</span> <span class="n">BasePhi</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">phi</span> <span class="o">=</span> <span class="n">phi</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'Unexpected feature mapping type ... '</span><span class="p">)</span>
<span class="c1"># Solver list available in cvxpy</span>
<span class="bp">self</span><span class="o">.</span><span class="n">solvers</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'MOSEK'</span><span class="p">,</span> <span class="s1">'SCS'</span><span class="p">,</span> <span class="s1">'ECOS'</span><span class="p">]</span></div>
<div class="viewcode-block" id="BaseMRC.fit"><a class="viewcode-back" href="../../generated/MRCpy.BaseMRC.html#MRCpy.BaseMRC.fit">[docs]</a> <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="p">):</span>
<span class="sd">'''</span>
<span class="sd"> Fit the MRC model.</span>
<span class="sd"> Computes the parameters required for the minimax risk optimization</span>
<span class="sd"> and then calls the `minimax_risk` function to solve the optimization.</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"> Training instances used in</span>
<span class="sd"> - Calculating the expectation estimates</span>
<span class="sd"> that constrain the uncertainty set</span>
<span class="sd"> for the minimax risk classification</span>
<span class="sd"> - Solving the minimax risk optimization problem.</span>
<span class="sd"> n_samples is the number of samples and</span>
<span class="sd"> n_features is the number of features.</span>
<span class="sd"> Y : array-like of shape (n_samples1), default = None</span>
<span class="sd"> Labels corresponding to the training instances</span>
<span class="sd"> used only to compute the expectation estimates.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> self :</span>
<span class="sd"> Fitted estimator</span>
<span class="sd"> '''</span>
<span class="n">X</span><span class="p">,</span> <span class="n">Y</span> <span class="o">=</span> <span class="n">check_X_y</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="n">accept_sparse</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="c1"># Obtaining the number of classes and mapping the labels to integers</span>
<span class="n">origY</span> <span class="o">=</span> <span class="n">Y</span>
<span class="bp">self</span><span class="o">.</span><span class="n">classes_</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">origY</span><span class="p">)</span>
<span class="n">n_classes</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
<span class="n">Y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">origY</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">int</span><span class="p">)</span>
<span class="c1"># Map the values of Y from 0 to n_classes-1</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">y</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">classes_</span><span class="p">):</span>
<span class="n">Y</span><span class="p">[</span><span class="n">origY</span> <span class="o">==</span> <span class="n">y</span><span class="p">]</span> <span class="o">=</span> <span class="n">i</span>
<span class="c1"># Set the number of classes in phi</span>
<span class="bp">self</span><span class="o">.</span><span class="n">phi</span><span class="o">.</span><span class="n">n_classes</span> <span class="o">=</span> <span class="n">n_classes</span>
<span class="c1"># Fit the feature mappings</span>
<span class="bp">self</span><span class="o">.</span><span class="n">phi</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"># Compute the expectation estimates</span>
<span class="n">tau_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">phi</span><span class="o">.</span><span class="n">est_exp</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="n">lambda_</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">s</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">phi</span><span class="o">.</span><span class="n">est_std</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="o">/</span> \
<span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="c1"># Limit the number of training samples used in the optimization</span>
<span class="c1"># for large datasets</span>
<span class="c1"># Reduces the training time and use of memory</span>
<span class="n">n_max</span> <span class="o">=</span> <span class="mi">5000</span>
<span class="n">n</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="k">if</span> <span class="n">n_max</span> <span class="o"><</span> <span class="n">n</span><span class="p">:</span>
<span class="n">n</span> <span class="o">=</span> <span class="n">n_max</span>
<span class="c1"># Fit the MRC classifier</span>
<span class="bp">self</span><span class="o">.</span><span class="n">minimax_risk</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="n">n</span><span class="p">],</span> <span class="n">tau_</span><span class="p">,</span> <span class="n">lambda_</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span></div>
<div class="viewcode-block" id="BaseMRC.minimax_risk"><a class="viewcode-back" href="../../generated/MRCpy.BaseMRC.html#MRCpy.BaseMRC.minimax_risk">[docs]</a> <span class="k">def</span> <span class="nf">minimax_risk</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">tau_</span><span class="p">,</span> <span class="n">lambda_</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">):</span>
<span class="sd">'''</span>
<span class="sd"> Abstract function for sub-classes implementing</span>
<span class="sd"> the different MRCs.</span>
<span class="sd"> Solves the minimax risk optimization problem</span>
<span class="sd"> for the corresponding variant of MRC.</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"> Training instances used for solving</span>
<span class="sd"> the minimax risk optimization problem.</span>
<span class="sd"> tau_ : array-like of shape (n_features * n_classes)</span>
<span class="sd"> The mean estimates</span>
<span class="sd"> for the expectations of feature mappings.</span>
<span class="sd"> lambda_ : array-like of shape (n_features * n_classes)</span>
<span class="sd"> The variance in the mean estimates</span>
<span class="sd"> for the expectations of the feature mappings.</span>
<span class="sd"> n_classes : int</span>
<span class="sd"> Number of labels in the dataset.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> self :</span>
<span class="sd"> Fitted estimator</span>
<span class="sd"> '''</span>
<span class="c1"># Variants of MRCs inheriting from this class should</span>
<span class="c1"># extend this function to implement the solution to their</span>
<span class="c1"># minimax risk optimization problem.</span>
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">'BaseMRC is not an implemented classifier.'</span> <span class="o">+</span>
<span class="s1">' It is base class for different MRCs.'</span> <span class="o">+</span>
<span class="s1">' This functions needs to be implemented'</span> <span class="o">+</span>
<span class="s1">' by a sub-class implementing a MRC.'</span><span class="p">)</span></div>
<span class="k">def</span> <span class="nf">try_solvers</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">constraints</span><span class="p">,</span> <span class="n">mu</span><span class="p">):</span>
<span class="sd">'''</span>
<span class="sd"> Solves the MRC problem</span>
<span class="sd"> using different types of solvers available in CVXpy</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> objective : cvxpy variable of float value</span>
<span class="sd"> Defines the minimization problem of the MRC.</span>
<span class="sd"> constraints : array-like of shape (n_constraints)</span>
<span class="sd"> Defines the constraints for the MRC optimization.</span>
<span class="sd"> mu : cvxpy array of shape (number of featuers in phi)</span>
<span class="sd"> Parameters used in the optimization problem</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> mu_ : array-like of shape (number of featuers in phi)</span>
<span class="sd"> The value of the parameters</span>
<span class="sd"> corresponding to the optimum value of the objective function.</span>
<span class="sd"> objective_value : float</span>
<span class="sd"> The optimized objective value.</span>
<span class="sd"> '''</span>
<span class="c1"># Reuse the solution from previous call to fit.</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">warm_start</span><span class="p">:</span>
<span class="c1"># Use a previous solution if it exists.</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">mu</span><span class="o">.</span><span class="n">value</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">mu_</span>
<span class="k">except</span> <span class="ne">AttributeError</span><span class="p">:</span>
<span class="k">pass</span>
<span class="c1"># Solve the problem</span>
<span class="n">prob</span> <span class="o">=</span> <span class="n">cvx</span><span class="o">.</span><span class="n">Problem</span><span class="p">(</span><span class="n">objective</span><span class="p">,</span> <span class="n">constraints</span><span class="p">)</span>
<span class="n">prob</span><span class="o">.</span><span class="n">solve</span><span class="p">(</span><span class="n">solver</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">solver</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">warm_start</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">warm_start</span><span class="p">)</span>
<span class="n">mu_</span> <span class="o">=</span> <span class="n">mu</span><span class="o">.</span><span class="n">value</span>
<span class="c1"># if the solver could not find values of mu for the given solver</span>
<span class="k">if</span> <span class="n">mu_</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="c1"># try with a different solver for solution</span>
<span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">solvers</span><span class="p">:</span>
<span class="k">if</span> <span class="n">s</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">solver</span><span class="p">:</span>
<span class="c1"># Reuse the solution from previous call to fit.</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">warm_start</span><span class="p">:</span>
<span class="c1"># Use a previous solution if it exists.</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">mu</span><span class="o">.</span><span class="n">value</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">mu_</span>
<span class="k">except</span> <span class="ne">AttributeError</span><span class="p">:</span>
<span class="k">pass</span>
<span class="c1"># Solve the problem</span>
<span class="n">prob</span><span class="o">.</span><span class="n">solve</span><span class="p">(</span><span class="n">solver</span><span class="o">=</span><span class="n">s</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">warm_start</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">warm_start</span><span class="p">)</span>
<span class="c1"># Check the values</span>
<span class="n">mu_</span> <span class="o">=</span> <span class="n">mu</span><span class="o">.</span><span class="n">value</span>
<span class="c1"># Break the loop once the solution is obtained</span>
<span class="k">if</span> <span class="n">mu_</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">break</span>
<span class="c1"># If no solution can be found for the optimization.</span>
<span class="k">if</span> <span class="n">mu_</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'CVXpy solver couldn</span><span class="se">\'</span><span class="s1">t find a solution .... '</span> <span class="o">+</span>
<span class="s1">'The problem is '</span><span class="p">,</span> <span class="n">prob</span><span class="o">.</span><span class="n">status</span><span class="p">)</span>
<span class="n">objective_value</span> <span class="o">=</span> <span class="n">prob</span><span class="o">.</span><span class="n">value</span>
<span class="k">return</span> <span class="n">mu_</span><span class="p">,</span> <span class="n">objective_value</span>
<div class="viewcode-block" id="BaseMRC.predict_proba"><a class="viewcode-back" href="../../generated/MRCpy.BaseMRC.html#MRCpy.BaseMRC.predict_proba">[docs]</a> <span class="k">def</span> <span class="nf">predict_proba</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"> Abstract function for sub-classes implementing</span>
<span class="sd"> the different MRCs.</span>
<span class="sd"> Computes conditional probabilities corresponding</span>
<span class="sd"> to each class for the given unlabeled instances.</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"> Testing instances for which</span>
<span class="sd"> the prediction probabilities are calculated for each class.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> hy_x : array-like of shape (n_samples, n_classes)</span>
<span class="sd"> The conditional probabilities (p(y|x))</span>
<span class="sd"> corresponding to each class.</span>
<span class="sd"> '''</span>
<span class="c1"># Variants of MRCs inheriting from this class</span>
<span class="c1"># implement this function to compute the conditional</span>
<span class="c1"># probabilities using the classifier obtained from minimax risk</span>
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">'BaseMRC is not an implemented classifier.'</span> <span class="o">+</span>
<span class="s1">' It is base class for different MRCs.'</span> <span class="o">+</span>
<span class="s1">' This functions needs to be implemented'</span> <span class="o">+</span>
<span class="s1">' by a sub-class implementing a MRC.'</span><span class="p">)</span></div>
<div class="viewcode-block" id="BaseMRC.predict"><a class="viewcode-back" href="../../generated/MRCpy.BaseMRC.html#MRCpy.BaseMRC.predict">[docs]</a> <span class="k">def</span> <span class="nf">predict</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"> Returns the predicted classes for the given instances</span>
<span class="sd"> using the probabilities given by the function `predict_proba`.</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"> Test instances for which the labels are to be predicted</span>
<span class="sd"> by the MRC model.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> y_pred : array-like of shape (n_samples)</span>
<span class="sd"> The predicted labels corresponding to the given instances.</span>
<span class="sd"> '''</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="n">check_is_fitted</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">"is_fitted_"</span><span class="p">)</span>
<span class="c1"># Get the prediction probabilities for the classifier</span>
<span class="n">proba</span> <span class="o">=</span> <span class="bp">self</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="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">deterministic</span><span class="p">:</span>
<span class="n">y_pred</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">proba</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">y_pred</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">n_classes</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="n">pc</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
<span class="k">for</span> <span class="n">pc</span> <span class="ow">in</span> <span class="n">proba</span><span class="p">]</span>
<span class="c1"># Check if the labels were provided for fitting</span>
<span class="c1"># (labels might be omitted if fitting is done through minimax_risk)</span>
<span class="c1"># Otherwise return the default labels i.e., from 0 to n_classes-1.</span>
<span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">'classes_'</span><span class="p">):</span>
<span class="n">y_pred</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">[</span><span class="n">label</span><span class="p">]</span> <span class="k">for</span> <span class="n">label</span> <span class="ow">in</span> <span class="n">y_pred</span><span class="p">])</span>
<span class="k">return</span> <span class="n">y_pred</span></div></div>
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