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<div class="section" id="logisticregression">
<span id="logreg-api-doc"></span><h1>LogisticRegression<a class="headerlink" href="#logisticregression" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="pai4sk.LogisticRegression">
<em class="property">class </em><code class="descclassname">pai4sk.</code><code class="descname">LogisticRegression</code><span class="sig-paren">(</span><em>max_iter=1000</em>, <em>regularizer=1.0</em>, <em>device_ids=[]</em>, <em>verbose=False</em>, <em>use_gpu=False</em>, <em>class_weight=None</em>, <em>dual=True</em>, <em>num_threads=1</em>, <em>penalty='l2'</em>, <em>tol=0.001</em>, <em>return_training_history=None</em>, <em>privacy=False</em>, <em>eta=0.3</em>, <em>batch_size=100</em>, <em>privacy_epsilon=10</em>, <em>grad_clip=1</em>, <em>fit_intercept=False</em>, <em>intercept_scaling=1.0</em><span class="sig-paren">)</span><a class="headerlink" href="#pai4sk.LogisticRegression" title="Permalink to this definition">¶</a></dt>
<dd><p>Logistic Regression classifier</p>
<p>This class implements regularized logistic regression using the IBM Snap ML solver.
It supports both local and distributed(MPI) methods of the Snap ML solver. It
can be used for both binary and multi-class classification problems. For multi-class
classification it predicts only classes (no probabilities). It handles both dense and
sparse matrix inputs. Use csr, csc, ndarray, deviceNDArray or SnapML data partition format
for training and csr, ndarray or SnapML data partition format for prediction.
DeviceNDArray input data format is currently not supported for training with MPI implementation.
We recommend the user to first normalize the input 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>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>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>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>device_ids</strong> (<em>array-like of int</em><em>, </em><em>default :</em><em> [</em><em>]</em>) – If use_gpu is True, it indicates the IDs of the GPUs used for training.
For single-GPU training, set device_ids to the GPU ID to be used for training, e.g., [0].
For multi-GPU training, set device_ids to a list of GPU IDs to be used for training, e.g., [0, 1].</li>
<li><strong>class_weight</strong> (<em>'balanced'</em><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>optional</em>) – If set to ‘None’, all classes will have weight 1.</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.</li>
<li><strong>verbose</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>) – If True, it prints the training cost, one per iteration. Warning: this will increase the
training time. For performance evaluation, use verbose=False.</li>
<li><strong>num_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</em>) – The number of threads used for running the training. The value of this parameter
should be a multiple of 32 if the training is performed on GPU (use_gpu=True).</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 (LogisticRegression)
or “l1” for L1 regularization (SparseLogisticRegression). 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.
return_training_history is not supported for DeviceNDArray input format.</li>
<li><strong>privacy</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>) – Train the model using a differentially private algorithm.
Currently not supported for MPI implementation.</li>
<li><strong>eta</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 : 0.3</em>) – Learning rate for the differentially private training algorithm.
Currently not supported for MPI implementation.</li>
<li><strong>batch_size</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 : 100</em>) – Mini-batch size for the differentially private training algorithm.
Currently not supported for MPI implementation.</li>
<li><strong>privacy_epsilon</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 : 10.0</em>) – Target privacy gaurantee. Learned model will be (privacy_epsilon, 0.01)-private.
Currently not supported for MPI implementation.</li>
<li><strong>grad_clip</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>) – Gradient clipping parameter for the differentially private training algorithm.
Currently not supported for MPI implementation.</li>
<li><strong>fit_intercept</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>) – Add bias term – note, may affect speed of convergence, especially for sparse datasets.</li>
<li><strong>intercept_scaling</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>) – Scaling of bias term. The inclusion of a bias term is implemented by appending an additional feature to the
dataset. This feature has a constant value, that can be set using this parameter.</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>array-like</em><em>, </em><em>shape</em><em> (</em><em>n_features</em><em>, </em><em>1</em><em>) </em><em>for binary classification or</em>) – (n_features, n_classes) for multi-class classification.
Coefficients of the features in the trained model.</li>
<li><strong>support</strong> (<em>array-like</em>) – Indices of the features that contribute to the decision. (only available for L1)
Currently not supported for MPI implementation.</li>
<li><strong>model_sparsity</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.7)"><em>float</em></a>) – fraction of non-zeros in the model parameters. (only available for L1)
Currently not supported for MPI implementation.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="pai4sk.LogisticRegression.fit">
<code class="descname">fit</code><span class="sig-paren">(</span><em>X_train</em>, <em>y_train=None</em><span class="sig-paren">)</span><a class="headerlink" href="#pai4sk.LogisticRegression.fit" title="Permalink to this definition">¶</a></dt>
<dd><p>Fit the model according to the given train data.</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>X_train</strong> (<em>Train dataset. Supports the following input data-types :</em>) – <ol class="arabic">
<li>Sparse matrix (csr_matrix, csc_matrix) or dense matrix (ndarray)</li>
<li>DeviceNDArray. Not supported for MPI execution.</li>
<li>SnapML data partition of type DensePartition, SparsePartition or ConstantValueSparsePartition</li>
</ol>
</li>
<li><strong>y_train</strong> (<em>The target corresponding to X_train.</em>) – If X_train is sparse matrix or dense matrix, y_train should be array-like of shape = (n_samples,)
In case of deviceNDArray, y_train should be array-like of shape = (n_samples, 1)
For binary classification the labels should be {-1, 1} or {0, 1}.
If X_train is SnapML data partition type, then y_train is not required (i.e. None).</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>self</strong></p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.7)">object</a></p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="pai4sk.LogisticRegression.get_params">
<code class="descname">get_params</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pai4sk.LogisticRegression.get_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Get the values of the model parameters.</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">Returns:</th><td class="field-body"><strong>params</strong></td>
</tr>
<tr class="field-even field"><th class="field-name">Return type:</th><td class="field-body"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.7)">dict</a></td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="pai4sk.LogisticRegression.predict">
<code class="descname">predict</code><span class="sig-paren">(</span><em>X</em>, <em>num_threads=0</em><span class="sig-paren">)</span><a class="headerlink" href="#pai4sk.LogisticRegression.predict" title="Permalink to this definition">¶</a></dt>
<dd><p>Class predictions</p>
<p>The returned class estimates.</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>X</strong> (<em>Dataset used for predicting class estimates. Supports the following input data-types :</em>) – <ol class="arabic">
<li>Sparse matrix (csr_matrix, csc_matrix) or dense matrix (ndarray)</li>
<li>SnapML data partition of type DensePartition, SparsePartition or ConstantValueSparsePartition</li>
</ol>
</li>
<li><strong>num_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 : 0</em>) – Number of threads used to run inference.
By default inference runs with maximum number of available threads.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>proba</strong> – Returns the predicted class of the sample.</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">array-like, shape = (n_samples,)</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="pai4sk.LogisticRegression.predict_log_proba">
<code class="descname">predict_log_proba</code><span class="sig-paren">(</span><em>X</em>, <em>num_threads=0</em><span class="sig-paren">)</span><a class="headerlink" href="#pai4sk.LogisticRegression.predict_log_proba" title="Permalink to this definition">¶</a></dt>
<dd><p>Log of probability estimates</p>
<p>The returned log-probability estimates for the two classes.
Only for binary classification.</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>X</strong> (<em>Dataset used for predicting log-probability estimates. Supports the following input data-types :</em>) – <ol class="arabic">
<li>Sparse matrix (csr_matrix, csc_matrix) or dense matrix (ndarray)</li>
<li>SnapML data partition of type DensePartition, SparsePartition or ConstantValueSparsePartition</li>
</ol>
</li>
<li><strong>num_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 : 0</em>) – Number of threads used to run inference.
By default inference runs with maximum number of available threads.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><p><strong>proba</strong> – array-like of shape = (n_samples, 2)
Returns the log-probability of the sample to be a positive example for MPI :</p>
<blockquote>
<div><p>array-like of shape = (n_samples,)</p>
</div></blockquote>
</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">Returns the log-probability of the sample of each of the two classes for local implementation :</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="pai4sk.LogisticRegression.predict_proba">
<code class="descname">predict_proba</code><span class="sig-paren">(</span><em>X</em>, <em>num_threads=0</em><span class="sig-paren">)</span><a class="headerlink" href="#pai4sk.LogisticRegression.predict_proba" title="Permalink to this definition">¶</a></dt>
<dd><p>Probability estimates</p>
<p>The returned probability estimates for the two classes.
Only for binary classification.</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>X</strong> (<em>Dataset used for predicting probability estimates. Supports the following input data-types :</em>) – <ol class="arabic">
<li>Sparse matrix (csr_matrix, csc_matrix) or dense matrix (ndarray)</li>
<li>SnapML data partition of type DensePartition, SparsePartition or ConstantValueSparsePartition</li>
</ol>
</li>
<li><strong>num_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 : 0</em>) – Number of threads used to run inference.
By default inference runs with maximum number of available threads.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><p><strong>proba</strong> – array-like of shape = (n_samples, 2)
Returns the probability of the sample to be a positive example for MPI :</p>
<blockquote>
<div><p>array-like of shape = (n_samples,)</p>
</div></blockquote>
</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">Returns the probability of the sample of each of the two classes for local implementation :</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
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