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<div class="section" id="using-functiontransformer-to-select-columns">
<span id="sphx-glr-auto-examples-preprocessing-plot-function-transformer-py"></span><h1>Using FunctionTransformer to select columns<a class="headerlink" href="#using-functiontransformer-to-select-columns" title="Permalink to this headline">¶</a></h1>
<p>Shows how to use a function transformer in a pipeline. If you know your
dataset’s first principle component is irrelevant for a classification task,
you can use the FunctionTransformer to select all but the first column of the
PCA transformed data.</p>
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<div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="View documentation for sklearn.model_selection.train_test_split"><span class="n">train_test_split</span></a>
<span class="kn">from</span> <span class="nn">sklearn.decomposition</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.decomposition.PCA.html#sklearn.decomposition.PCA" title="View documentation for sklearn.decomposition.PCA"><span class="n">PCA</span></a>
<span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="View documentation for sklearn.pipeline.make_pipeline"><span class="n">make_pipeline</span></a>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.preprocessing.FunctionTransformer.html#sklearn.preprocessing.FunctionTransformer" title="View documentation for sklearn.preprocessing.FunctionTransformer"><span class="n">FunctionTransformer</span></a>
<span class="k">def</span> <span class="nf">_generate_vector</span><span class="p">(</span><span class="n">shift</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">noise</span><span class="o">=</span><span class="mi">15</span><span class="p">):</span>
<span class="k">return</span> <a href="http://docs.scipy.org/doc/numpy-1.8.1/reference/generated/numpy.arange.html#numpy.arange" title="View documentation for numpy.arange"><span class="n">np</span><span class="o">.</span><span class="n">arange</span></a><span class="p">(</span><span class="mi">1000</span><span class="p">)</span> <span class="o">+</span> <span class="p">(</span><a href="http://docs.scipy.org/doc/numpy-1.8.1/reference/generated/numpy.random.rand.html#numpy.random.rand" title="View documentation for numpy.random.rand"><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span></a><span class="p">(</span><span class="mi">1000</span><span class="p">)</span> <span class="o">-</span> <span class="n">shift</span><span class="p">)</span> <span class="o">*</span> <span class="n">noise</span>
<span class="k">def</span> <span class="nf">generate_dataset</span><span class="p">():</span>
<span class="sd">"""</span>
<span class="sd"> This dataset is two lines with a slope ~ 1, where one has</span>
<span class="sd"> a y offset of ~100</span>
<span class="sd"> """</span>
<span class="k">return</span> <a href="http://docs.scipy.org/doc/numpy-1.8.1/reference/generated/numpy.vstack.html#numpy.vstack" title="View documentation for numpy.vstack"><span class="n">np</span><span class="o">.</span><span class="n">vstack</span></a><span class="p">((</span>
<a href="http://docs.scipy.org/doc/numpy-1.8.1/reference/generated/numpy.vstack.html#numpy.vstack" title="View documentation for numpy.vstack"><span class="n">np</span><span class="o">.</span><span class="n">vstack</span></a><span class="p">((</span>
<span class="n">_generate_vector</span><span class="p">(),</span>
<span class="n">_generate_vector</span><span class="p">()</span> <span class="o">+</span> <span class="mi">100</span><span class="p">,</span>
<span class="p">))</span><span class="o">.</span><span class="n">T</span><span class="p">,</span>
<a href="http://docs.scipy.org/doc/numpy-1.8.1/reference/generated/numpy.vstack.html#numpy.vstack" title="View documentation for numpy.vstack"><span class="n">np</span><span class="o">.</span><span class="n">vstack</span></a><span class="p">((</span>
<span class="n">_generate_vector</span><span class="p">(),</span>
<span class="n">_generate_vector</span><span class="p">(),</span>
<span class="p">))</span><span class="o">.</span><span class="n">T</span><span class="p">,</span>
<span class="p">)),</span> <a href="http://docs.scipy.org/doc/numpy-1.8.1/reference/generated/numpy.hstack.html#numpy.hstack" title="View documentation for numpy.hstack"><span class="n">np</span><span class="o">.</span><span class="n">hstack</span></a><span class="p">((</span><a href="http://docs.scipy.org/doc/numpy-1.8.1/reference/generated/numpy.zeros.html#numpy.zeros" title="View documentation for numpy.zeros"><span class="n">np</span><span class="o">.</span><span class="n">zeros</span></a><span class="p">(</span><span class="mi">1000</span><span class="p">),</span> <a href="http://docs.scipy.org/doc/numpy-1.8.1/reference/generated/numpy.ones.html#numpy.ones" title="View documentation for numpy.ones"><span class="n">np</span><span class="o">.</span><span class="n">ones</span></a><span class="p">(</span><span class="mi">1000</span><span class="p">)))</span>
<span class="k">def</span> <span class="nf">all_but_first_column</span><span class="p">(</span><span class="n">X</span><span class="p">):</span>
<span class="k">return</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">:]</span>
<span class="k">def</span> <span class="nf">drop_first_component</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"> Create a pipeline with PCA and the column selector and use it to</span>
<span class="sd"> transform the dataset.</span>
<span class="sd"> """</span>
<span class="n">pipeline</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="View documentation for sklearn.pipeline.make_pipeline"><span class="n">make_pipeline</span></a><span class="p">(</span>
<a href="../../modules/generated/sklearn.decomposition.PCA.html#sklearn.decomposition.PCA" title="View documentation for sklearn.decomposition.PCA"><span class="n">PCA</span></a><span class="p">(),</span> <a href="../../modules/generated/sklearn.preprocessing.FunctionTransformer.html#sklearn.preprocessing.FunctionTransformer" title="View documentation for sklearn.preprocessing.FunctionTransformer"><span class="n">FunctionTransformer</span></a><span class="p">(</span><span class="n">all_but_first_column</span><span class="p">),</span>
<span class="p">)</span>
<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="View documentation for sklearn.model_selection.train_test_split"><span class="n">train_test_split</span></a><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">pipeline</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="k">return</span> <span class="n">pipeline</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_test</span><span class="p">),</span> <span class="n">y_test</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="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">generate_dataset</span><span class="p">()</span>
<span class="n">lw</span> <span class="o">=</span> <span class="mi">0</span>
<a href="http://matplotlib.org/api/_as_gen/matplotlib.figure.AxesStack.html#matplotlib.figure" title="View documentation for matplotlib.pyplot.figure"><span class="n">plt</span><span class="o">.</span><span class="n">figure</span></a><span class="p">()</span>
<a href="http://matplotlib.org/api/_as_gen/matplotlib.pyplot.scatter.html#matplotlib.pyplot.scatter" title="View documentation for matplotlib.pyplot.scatter"><span class="n">plt</span><span class="o">.</span><span class="n">scatter</span></a><span class="p">(</span><span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="n">lw</span><span class="p">)</span>
<a href="http://matplotlib.org/api/_as_gen/matplotlib.figure.AxesStack.html#matplotlib.figure" title="View documentation for matplotlib.pyplot.figure"><span class="n">plt</span><span class="o">.</span><span class="n">figure</span></a><span class="p">()</span>
<span class="n">X_transformed</span><span class="p">,</span> <span class="n">y_transformed</span> <span class="o">=</span> <span class="n">drop_first_component</span><span class="p">(</span><span class="o">*</span><span class="n">generate_dataset</span><span class="p">())</span>
<a href="http://matplotlib.org/api/_as_gen/matplotlib.pyplot.scatter.html#matplotlib.pyplot.scatter" title="View documentation for matplotlib.pyplot.scatter"><span class="n">plt</span><span class="o">.</span><span class="n">scatter</span></a><span class="p">(</span>
<span class="n">X_transformed</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span>
<a href="http://docs.scipy.org/doc/numpy-1.8.1/reference/generated/numpy.zeros.html#numpy.zeros" title="View documentation for numpy.zeros"><span class="n">np</span><span class="o">.</span><span class="n">zeros</span></a><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">X_transformed</span><span class="p">)),</span>
<span class="n">c</span><span class="o">=</span><span class="n">y_transformed</span><span class="p">,</span>
<span class="n">lw</span><span class="o">=</span><span class="n">lw</span><span class="p">,</span>
<span class="n">s</span><span class="o">=</span><span class="mi">60</span>
<span class="p">)</span>
<a href="http://matplotlib.org/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="View documentation for matplotlib.pyplot.show"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
</pre></div>
</div>
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