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<div class="section" id="cluster-dbscan">
<span id="dbscan-doc"></span><h1>cluster.DBSCAN<a class="headerlink" href="#cluster-dbscan" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="pai4sk.cluster.DBSCAN">
<em class="property">class </em><code class="descclassname">pai4sk.cluster.</code><code class="descname">DBSCAN</code><span class="sig-paren">(</span><em>eps=0.5</em>, <em>min_samples=5</em>, <em>metric='euclidean'</em>, <em>metric_params=None</em>, <em>algorithm='auto'</em>, <em>leaf_size=30</em>, <em>p=None</em>, <em>n_jobs=None</em>, <em>use_gpu=True</em><span class="sig-paren">)</span><a class="headerlink" href="#pai4sk.cluster.DBSCAN" title="Permalink to this definition">¶</a></dt>
<dd><p>Perform DBSCAN clustering from vector array or distance matrix.</p>
<p>DBSCAN - Density-Based Spatial Clustering of Applications with Noise. Finds
core samples of high density and expands clusters from them. Good for data
which contains clusters of similar density.</p>
<p>If cuml is installed and if the input data is cudf dataframe and if possible,
then the accelerated DBSCAN algorithm from cuML will be used. Otherwise,
scikit-learn’s DBSCAN algorithm will be used.</p>
<p>cuML in pai4sk is currently supported only
| (a) with python 3.6 and
| (b) without MPI.
| If DBSCAN from cuML is run, then the return values from the APIs will be
cudf dataframe and cudf Series objects instead of the return types of
scikit-learn API.</p>
<dl class="docutils">
<dt>eps <span class="classifier-delimiter">:</span> <span class="classifier">float, optional</span></dt>
<dd>The maximum distance between two samples for them to be considered as
in the same neighborhood.</dd>
<dt>min_samples <span class="classifier-delimiter">:</span> <span class="classifier">int, optional</span></dt>
<dd>The number of samples (or total weight) in a neighborhood for a point
to be considered as a core point. This includes the point itself.</dd>
<dt>metric <span class="classifier-delimiter">:</span> <span class="classifier">string, or callable</span></dt>
<dd>The metric to use when calculating distance between instances in a
feature array. If metric is a string or callable, it must be one of
the options allowed by pai4sk.metrics.pairwise_distances for its
metric parameter. If metric is ‘precomputed’, X is assumed to be a
distance matrix and must be square. X may be a sparse matrix, in which
case only nonzero elements may be considered neighbors for DBSCAN.
New in version 0.17: metric precomputed to accept precomputed sparse matrix.</dd>
<dt>metric_params <span class="classifier-delimiter">:</span> <span class="classifier">dict, optional</span></dt>
<dd>Additional keyword arguments for the metric function.
New in version 0.19.</dd>
<dt>algorithm <span class="classifier-delimiter">:</span> <span class="classifier">{‘auto’, ‘ball_tree’, ‘kd_tree’, ‘brute’, ‘cuml’}, optional</span></dt>
<dd><p class="first">The algorithm to be used by the NearestNeighbors module to compute
pointwise distances and find nearest neighbors. See NearestNeighbors
module documentation for details.</p>
<p class="last">If cuml is installed and if cudf dataframe is given as input, if either
(1) algorithm is set to “cuml” or
(2) algorithm is “auto”, then pai4sk will try to use DBSCAN algorithm
from RAPIDS cuML if possible. cuML in pai4sk is currently supported
only (a) with python 3.6 and (b) without MPI.</p>
</dd>
<dt>leaf_size <span class="classifier-delimiter">:</span> <span class="classifier">int, optional (default = 30)</span></dt>
<dd>Leaf size passed to BallTree or cKDTree. This can affect the speed of
the construction and query, as well as the memory required to store the
tree. The optimal value depends on the nature of the problem.</dd>
<dt>p <span class="classifier-delimiter">:</span> <span class="classifier">float, optional</span></dt>
<dd>The power of the Minkowski metric to be used to calculate distance between points.</dd>
<dt>n_jobs <span class="classifier-delimiter">:</span> <span class="classifier">int or None, optional (default=None)</span></dt>
<dd>The number of parallel jobs to run. None means 1 unless in a
joblib.parallel_backend context. -1 means using all processors.
See Glossary for more details.</dd>
<dt>use_gpu <span class="classifier-delimiter">:</span> <span class="classifier">boolean, Default is True</span></dt>
<dd>If True, cuML will use GPU 0. Applicable only for cuML.</dd>
</dl>
<p>Attributes:
<a href="#id1"><span class="problematic" id="id2">core_sample_indices_</span></a> : array, shape = [n_core_samples]</p>
<blockquote>
<div>Indices of core samples.</div></blockquote>
<dl class="docutils">
<dt><a href="#id3"><span class="problematic" id="id4">components_</span></a> <span class="classifier-delimiter">:</span> <span class="classifier">array, shape = [n_core_samples, n_features]</span></dt>
<dd>Copy of each core sample found by training.</dd>
<dt><a href="#id5"><span class="problematic" id="id6">labels_</span></a> <span class="classifier-delimiter">:</span> <span class="classifier">array, shape = [n_samples]</span></dt>
<dd>Cluster labels for each point in the dataset given to fit(). Noisy
samples are given the label -1.</dd>
</dl>
<dl class="method">
<dt id="pai4sk.cluster.DBSCAN.fit">
<code class="descname">fit</code><span class="sig-paren">(</span><em>X</em>, <em>y=None</em>, <em>sample_weight=None</em><span class="sig-paren">)</span><a class="headerlink" href="#pai4sk.cluster.DBSCAN.fit" title="Permalink to this definition">¶</a></dt>
<dd><p>Perform DBSCAN clustering from features or distance matrix.
Parameters:
———-
X : array or sparse (CSR) matrix of shape (n_samples, n_features), or array of shape (n_samples, n_samples) or cuDF dataframe</p>
<blockquote>
<div>A feature array, or array of distances between samples if metric=’precomputed’.</div></blockquote>
<dl class="docutils">
<dt>sample_weight <span class="classifier-delimiter">:</span> <span class="classifier">array, shape (n_samples,), optional</span></dt>
<dd>Weight of each sample, such that a sample with a weight of at least min_samples is by itself a core sample; a sample with negative weight may inhibit its eps-neighbor from being core. Note that weights are absolute, and default to 1.</dd>
</dl>
<p>y : Ignored</p>
<dl class="docutils">
<dt>self <span class="classifier-delimiter">:</span> <span class="classifier">object</span></dt>
<dd>If DBSCAN from cuML is run, then this fit method saves the computed
labels as cudf Series object instead of array.</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="pai4sk.cluster.DBSCAN.fit_predict">
<code class="descname">fit_predict</code><span class="sig-paren">(</span><em>X</em>, <em>y=None</em>, <em>sample_weight=None</em><span class="sig-paren">)</span><a class="headerlink" href="#pai4sk.cluster.DBSCAN.fit_predict" title="Permalink to this definition">¶</a></dt>
<dd><p>Performs clustering on X and returns cluster labels.</p>
<p>Parameters:
X : array or sparse (CSR) matrix of shape (n_samples, n_features), or array of shape (n_samples, n_samples) or cudf dataframe</p>
<blockquote>
<div>A feature array, or array of distances between samples if metric=’precomputed’.</div></blockquote>
<dl class="docutils">
<dt>sample_weight <span class="classifier-delimiter">:</span> <span class="classifier">array, shape (n_samples,), optional</span></dt>
<dd>Weight of each sample, such that a sample with a weight of at least min_samples is by itself a core sample; a sample with negative weight may inhibit its eps-neighbor from being core. Note that weights are absolute, and default to 1.</dd>
</dl>
<p>y : Ignored</p>
<dl class="docutils">
<dt>y <span class="classifier-delimiter">:</span> <span class="classifier">ndarray, shape (n_samples,) or cudf Series</span></dt>
<dd>If DBSCAN from cuML is run, then this fit method returns the computed
labels as cudf Series object instead of ndarray.</dd>
</dl>
</dd></dl>
</dd></dl>
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