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<div class="section" id="linearregression">
<span id="sp-lin-reg-doc"></span><h1>LinearRegression<a class="headerlink" href="#linearregression" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="snap_ml_spark.LinearRegression.LinearRegression">
<em class="property">class </em><code class="descclassname">snap_ml_spark.LinearRegression.</code><code class="descname">LinearRegression</code><span class="sig-paren">(</span><em>max_iter=1000</em>, <em>dual=True</em>, <em>regularizer=1.0</em>, <em>verbose=False</em>, <em>use_gpu=False</em>, <em>class_weights=None</em>, <em>gpu_mem_limit=0</em>, <em>n_threads=-1</em>, <em>penalty='l2'</em>, <em>tol=0.001</em>, <em>return_training_history=None</em><span class="sig-paren">)</span><a class="headerlink" href="#snap_ml_spark.LinearRegression.LinearRegression" title="Permalink to this definition">¶</a></dt>
<dd><p>Linear Regression classifier</p>
<p>This class implements Regularized Linear regression using the IBM Snap ML distributed solver. It can handle sparse and dense dataset formats. Please use libsvm, snap or csv format for the Dual algorithm, or snap.t (transposed) format for the primal algorithm.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>max_iter</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a><em>, </em><em>default : 1000</em>) – Maximum number of iterations used by the solver to converge.</li>
<li><strong>dual</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a><em>, </em><em>default : True</em>) – Dual or primal formulation.
Recommendation: if n_samples > n_features use dual=True, else dual=False.</li>
<li><strong>regularizer</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.7)"><em>float</em></a><em>, </em><em>default : 1.0</em>) – Regularization strength. It must be a positive float.
Larger regularization values imply stronger regularization.</li>
<li><strong>verbose</strong> (<em>boolean</em><em>, </em><em>default : False</em>) – Flag for indicating if the training loss will be printed at each epoch.</li>
<li><strong>use_gpu</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a><em>, </em><em>default : False</em>) – Flag for indicating the hardware platform used for training. If True, the training
is performed using the GPU. If False, the training is performed using the CPU.</li>
<li><strong>class_weights</strong> (<em>'balanced'/True</em><em> or </em><em>None/False</em><em>, </em><em>optional</em>) – If set to ‘None’, all classes will have weight = 1.</li>
<li><strong>gpu_mem_limit</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a><em>, </em><em>default : 0</em>) – Limit of the GPU memory. If set to the default value 0,
the maximum possible memory is used.</li>
<li><strong>n_threads</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a><em>, </em><em>default : -1 meaning that n_threads=256 if GPU is enabled</em><em>, </em><em>else 1</em>) – Number of threads to be used.</li>
<li><strong>penalty</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.7)"><em>str</em></a><em>, </em><em>default : "l2"</em>) – The regularization / penalty type. Possible values are “l2” for L2 regularization (RidgeRegression)
or “l1” for L1 regularization (LassoRegression). L1 regularization is possible only for the primal
optimization problem (dual=False).</li>
<li><strong>tol</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.7)"><em>float</em></a><em>, </em><em>default : 0.001</em>) – The tolerance parameter. Training will finish when maximum change in model coefficients is less than tol.</li>
<li><strong>return_training_history</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.7)"><em>str</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.7)"><em>None</em></a><em>, </em><em>default : None</em>) – How much information about the training should be collected and returned by the fit function. By
default no information is returned (None), but this parameter can be set to “summary”, to obtain
summary statistics at the end of training, or “full” to obtain a complete set of statistics
for the entire training procedure. Note, enabling either option will result in slower training.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Variables:</th><td class="field-body"><ul class="first last simple">
<li><strong>coef</strong> (<em>ndarray</em><em>, </em><em>shape</em><em> (</em><em>n_features</em><em>,</em><em>)</em>) – Coefficients of the features in the trained model.</li>
<li><strong>pred_array</strong> (<em>ndarray</em><em>, </em><em>shape</em><em>(</em><em>number_of_test_examples</em><em>,</em><em>)</em>) – linear predictions written by the predict() function of this class</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="snap_ml_spark.LinearRegression.LinearRegression.fit">
<code class="descname">fit</code><span class="sig-paren">(</span><em>data</em><span class="sig-paren">)</span><a class="headerlink" href="#snap_ml_spark.LinearRegression.LinearRegression.fit" title="Permalink to this definition">¶</a></dt>
<dd><p>learn model</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>data</strong> (<em>py4j.java_gateway.JavaObject</em><em>, </em><em>pointer which points to a memory address where the actual data is stored. The data cannot be accessed by python as a python array.</em>) – data to fit model</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">double – final training loss of the last epoch</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="snap_ml_spark.LinearRegression.LinearRegression.get_params">
<code class="descname">get_params</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#snap_ml_spark.LinearRegression.LinearRegression.get_params" title="Permalink to this definition">¶</a></dt>
<dd><table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">all the initialized parameters of the Linear Regression model as a python dictionary</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="snap_ml_spark.LinearRegression.LinearRegression.predict">
<code class="descname">predict</code><span class="sig-paren">(</span><em>data</em>, <em>num_threads=0</em><span class="sig-paren">)</span><a class="headerlink" href="#snap_ml_spark.LinearRegression.LinearRegression.predict" title="Permalink to this definition">¶</a></dt>
<dd><p>Predict regression values</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<em>py4j.java_gateway.JavaObject</em><em>, </em><em>pointer which points to a memory address where the actual data is stored. Cannot be accessed by python as an array but only can passed as a parameter to this function in order to get the predictions</em>) – data to make predictions</li>
<li><strong>num_threads</strong> – the number of threads to use for inference (default 0 means use all avaliable threads)</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a pointer which points to a com.ibm.snap.ml.DatasetWithPredictions java object. This pointer cannot be accessed by python but the user can access the predictions from the <a href="#id1"><span class="problematic" id="id2">pred_array_</span></a> field which is a python array.</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
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