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utils.html
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utils.html
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<!DOCTYPE html>
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<div class="section" id="module-rep.utils">
<span id="utilities"></span><span id="utils"></span><h1>Utilities<a class="headerlink" href="#module-rep.utils" title="Permalink to this headline">¶</a></h1>
<p>Different helpful functions, objects, methods are collected here.</p>
<dl class="class">
<dt id="rep.utils.Binner">
<em class="property">class </em><code class="descclassname">rep.utils.</code><code class="descname">Binner</code><span class="sig-paren">(</span><em>values</em>, <em>bins_number</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/rep/utils.html#Binner"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#rep.utils.Binner" title="Permalink to this definition">¶</a></dt>
<dd><p>Binner is a class that helps to split the values into several bins.
Initially an array of values is given, which is then splitted into ‘bins_number’ equal parts,
and thus we are computing limits (boundaries of bins).</p>
<dl class="attribute">
<dt id="rep.utils.Binner.bins_number">
<code class="descname">bins_number</code><a class="headerlink" href="#rep.utils.Binner.bins_number" 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">number of bins</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="rep.utils.Binner.get_bins">
<code class="descname">get_bins</code><span class="sig-paren">(</span><em>values</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/rep/utils.html#Binner.get_bins"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#rep.utils.Binner.get_bins" title="Permalink to this definition">¶</a></dt>
<dd><p>Given the values of feature, compute the index of bin</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>values</strong> – array of shape [n_samples]</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">array of shape [n_samples]</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="rep.utils.Binner.set_limits">
<code class="descname">set_limits</code><span class="sig-paren">(</span><em>limits</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/rep/utils.html#Binner.set_limits"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#rep.utils.Binner.set_limits" title="Permalink to this definition">¶</a></dt>
<dd><p>Change the thresholds inside bins.</p>
</dd></dl>
<dl class="method">
<dt id="rep.utils.Binner.split_into_bins">
<code class="descname">split_into_bins</code><span class="sig-paren">(</span><em>*arrays</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/rep/utils.html#Binner.split_into_bins"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#rep.utils.Binner.split_into_bins" 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">Parameters:</th><td class="field-body"><strong>arrays</strong> – data to be splitted, the first array corresponds</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">sequence of length [n_bins] with values corresponding to each bin.</td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="rep.utils.Flattener">
<em class="property">class </em><code class="descclassname">rep.utils.</code><code class="descname">Flattener</code><span class="sig-paren">(</span><em>data</em>, <em>sample_weight=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/rep/utils.html#Flattener"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#rep.utils.Flattener" title="Permalink to this definition">¶</a></dt>
<dd><p>Prepares normalization function for some set of values
transforms it to uniform distribution from [0, 1].</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>list or numpy.array</em>) – predictions</li>
<li><strong>sample_weight</strong> (<em>None or list or numpy.array</em>) – weights</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Return func:</th><td class="field-body"><p class="first last">normalization function</p>
</td>
</tr>
</tbody>
</table>
<p>Example of usage:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">normalizer</span> <span class="o">=</span> <span class="n">Flattener</span><span class="p">(</span><span class="n">signal</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">hist</span><span class="p">(</span><span class="n">normalizer</span><span class="p">(</span><span class="n">background</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">hist</span><span class="p">(</span><span class="n">normalizer</span><span class="p">(</span><span class="n">signal</span><span class="p">))</span>
</pre></div>
</div>
</dd></dl>
<dl class="class">
<dt id="rep.utils.Stopwatch">
<em class="property">class </em><code class="descclassname">rep.utils.</code><code class="descname">Stopwatch</code><a class="reference internal" href="_modules/rep/utils.html#Stopwatch"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#rep.utils.Stopwatch" title="Permalink to this definition">¶</a></dt>
<dd><p>Simple tool to measure time.
If your internet connection is reliable, use %time magic.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="k">with</span> <span class="n">Stopwatch</span><span class="p">()</span> <span class="k">as</span> <span class="n">timer</span><span class="p">:</span>
<span class="gp">>>> </span> <span class="c1"># do something here</span>
<span class="gp">>>> </span> <span class="n">classifier</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="gp">>>> </span><span class="c1"># print how much time was spent</span>
<span class="gp">>>> </span><span class="k">print</span><span class="p">(</span><span class="n">timer</span><span class="p">)</span>
</pre></div>
</div>
<dl class="attribute">
<dt id="rep.utils.Stopwatch.elapsed">
<code class="descname">elapsed</code><a class="headerlink" href="#rep.utils.Stopwatch.elapsed" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
</dd></dl>
<dl class="function">
<dt id="rep.utils.calc_ROC">
<code class="descclassname">rep.utils.</code><code class="descname">calc_ROC</code><span class="sig-paren">(</span><em>prediction</em>, <em>signal</em>, <em>sample_weight=None</em>, <em>max_points=10000</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/rep/utils.html#calc_ROC"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#rep.utils.calc_ROC" title="Permalink to this definition">¶</a></dt>
<dd><p>Calculate roc curve, returns limited number of points.
This is needed for interactive plots, which suffer</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>prediction</strong> (<em>numpy.ndarray or list</em>) – predictions</li>
<li><strong>signal</strong> (<em>array or list</em>) – true labels</li>
<li><strong>sample_weight</strong> (<em>None or array or list</em>) – weights</li>
<li><strong>max_points</strong> (<em>int</em>) – maximum of used points on roc curve</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">(tpr, tnr), (err_tnr, err_tpr), thresholds</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="rep.utils.calc_feature_correlation_matrix">
<code class="descclassname">rep.utils.</code><code class="descname">calc_feature_correlation_matrix</code><span class="sig-paren">(</span><em>df</em>, <em>weights=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/rep/utils.html#calc_feature_correlation_matrix"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#rep.utils.calc_feature_correlation_matrix" title="Permalink to this definition">¶</a></dt>
<dd><p>Calculate correlation matrix</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>df</strong> (<em>pandas.DataFrame</em>) – data of shape [n_samples, n_features]</li>
<li><strong>weights</strong> – weights of shape [n_samples] (optional)</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">correlation matrix for dataFrame of shape [n_features, n_features]</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">numpy.ndarray</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="rep.utils.calc_hist_with_errors">
<code class="descclassname">rep.utils.</code><code class="descname">calc_hist_with_errors</code><span class="sig-paren">(</span><em>x</em>, <em>weight=None</em>, <em>bins=60</em>, <em>normed=True</em>, <em>x_range=None</em>, <em>ignored_sideband=0.0</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/rep/utils.html#calc_hist_with_errors"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#rep.utils.calc_hist_with_errors" title="Permalink to this definition">¶</a></dt>
<dd><p>Calculate data for error bar (for plot pdf with errors)</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>list or numpy.array</em>) – data</li>
<li><strong>weight</strong> (<em>None or list or numpy.array</em>) – weights</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">tuple (x-points (list), y-points (list), y points errors (list), x points errors (list))</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="rep.utils.check_arrays">
<code class="descclassname">rep.utils.</code><code class="descname">check_arrays</code><span class="sig-paren">(</span><em>*arrays</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/rep/utils.html#check_arrays"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#rep.utils.check_arrays" title="Permalink to this definition">¶</a></dt>
<dd><p>Left for consistency, version of <cite>sklearn.validation.check_arrays</cite></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>arrays</strong> (<em>list[iterable]</em>) – arrays with same length of first dimension.</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="rep.utils.check_sample_weight">
<code class="descclassname">rep.utils.</code><code class="descname">check_sample_weight</code><span class="sig-paren">(</span><em>y_true</em>, <em>sample_weight</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/rep/utils.html#check_sample_weight"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#rep.utils.check_sample_weight" title="Permalink to this definition">¶</a></dt>
<dd><p>Checks the weights, if None, returns array.</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>y_true</strong> – labels (or any array of length [n_samples])</li>
<li><strong>sample_weight</strong> – None or array of length [n_samples]</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">numpy.array of shape [n_samples]</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="rep.utils.fit_metric">
<code class="descclassname">rep.utils.</code><code class="descname">fit_metric</code><span class="sig-paren">(</span><em>metric</em>, <em>*args</em>, <em>**kargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/rep/utils.html#fit_metric"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#rep.utils.fit_metric" title="Permalink to this definition">¶</a></dt>
<dd><p>Metric can implement one of two interfaces (function or object).
This function fits metrics, if it is required (by simply checking presence of fit method).</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>metric</strong> – metric function, following REP conventions</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="rep.utils.get_columns_dict">
<code class="descclassname">rep.utils.</code><code class="descname">get_columns_dict</code><span class="sig-paren">(</span><em>columns</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/rep/utils.html#get_columns_dict"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#rep.utils.get_columns_dict" title="Permalink to this definition">¶</a></dt>
<dd><p>Get (new column: old column) dict expressions.
This function is used to process names of features, which can contain expressions.</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>columns</strong> (<em>list[str]</em>) – columns names</td>
</tr>
<tr class="field-even field"><th class="field-name">Return type:</th><td class="field-body">dict</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="rep.utils.get_columns_in_df">
<code class="descclassname">rep.utils.</code><code class="descname">get_columns_in_df</code><span class="sig-paren">(</span><em>df</em>, <em>columns</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/rep/utils.html#get_columns_in_df"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#rep.utils.get_columns_in_df" title="Permalink to this definition">¶</a></dt>
<dd><p>Get columns in data frame using <em>numexpr</em> evaluation</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>df</strong> (<em>pandas.DataFrame</em>) – data</li>
<li><strong>columns</strong> – necessary columns</li>
<li><strong>columns</strong> – None or list[str]</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">data frame with pointed columns</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="rep.utils.get_efficiencies">
<code class="descclassname">rep.utils.</code><code class="descname">get_efficiencies</code><span class="sig-paren">(</span><em>prediction</em>, <em>spectator</em>, <em>sample_weight=None</em>, <em>bins_number=20</em>, <em>thresholds=None</em>, <em>errors=False</em>, <em>ignored_sideband=0.0</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/rep/utils.html#get_efficiencies"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#rep.utils.get_efficiencies" title="Permalink to this definition">¶</a></dt>
<dd><p>Construct efficiency function dependent on spectator for each threshold</p>
<p>Different score functions available: Efficiency, Precision, Recall, F1Score,
and other things from sklearn.metrics</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>prediction</strong> – list of probabilities</li>
<li><strong>spectator</strong> – list of spectator’s values</li>
<li><strong>bins_number</strong> – int, count of bins for plot</li>
<li><strong>thresholds</strong> – <p>list of prediction’s threshold</p>
<p>(default=prediction’s cuts for which efficiency will be [0.2, 0.4, 0.5, 0.6, 0.8])</p>
</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"><p>if errors=False
OrderedDict threshold -> (x_values, y_values)</p>
<p>if errors=True
OrderedDict threshold -> (x_values, y_values, y_err, x_err)</p>
<p>All the parts: x_values, y_values, y_err, x_err are numpy.arrays of the same length.</p>
</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="rep.utils.reorder_by_first">
<code class="descclassname">rep.utils.</code><code class="descname">reorder_by_first</code><span class="sig-paren">(</span><em>*arrays</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/rep/utils.html#reorder_by_first"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#rep.utils.reorder_by_first" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies the same permutation to all passed arrays,
permutation sorts the first passed array</p>
</dd></dl>
<dl class="function">
<dt id="rep.utils.take_last">
<code class="descclassname">rep.utils.</code><code class="descname">take_last</code><span class="sig-paren">(</span><em>sequence</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/rep/utils.html#take_last"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#rep.utils.take_last" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the last element in sequence or raises an error</p>
</dd></dl>
<dl class="function">
<dt id="rep.utils.train_test_split">
<code class="descclassname">rep.utils.</code><code class="descname">train_test_split</code><span class="sig-paren">(</span><em>*arrays</em>, <em>**kw_args</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/rep/utils.html#train_test_split"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#rep.utils.train_test_split" title="Permalink to this definition">¶</a></dt>
<dd><p>Does the same thing as sklearn.cross_validation.train_test_split.
Additionally has ‘allow_none’ parameter.</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 last simple">
<li><strong>arrays</strong> (<em>list[numpy.array] or list[pandas.DataFrame]</em>) – arrays to split with same first dimension</li>
<li><strong>allow_none</strong> (<em>bool</em>) – default False, is set to True, allows
non-first arguments to be None (in this case, both resulting train and test parts are None).</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="rep.utils.train_test_split_group">
<code class="descclassname">rep.utils.</code><code class="descname">train_test_split_group</code><span class="sig-paren">(</span><em>group_column</em>, <em>*arrays</em>, <em>**kw_args</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/rep/utils.html#train_test_split_group"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#rep.utils.train_test_split_group" title="Permalink to this definition">¶</a></dt>
<dd><p>Modification of <a class="reference internal" href="#rep.utils.train_test_split" title="rep.utils.train_test_split"><code class="xref py py-class docutils literal"><span class="pre">train_test_split</span></code></a> which alters splitting rule.</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 last simple">
<li><strong>group_column</strong> – array-like of shape [n_samples] with indices of groups,
events from one group will be kept together (all events in train or all events in test).
If <cite>group_column</cite> is used, train_size and test_size will refer to number of groups, not events</li>
<li><strong>arrays</strong> (<em>list[numpy.array] or list[pandas.DataFrame]</em>) – arrays to split</li>
<li><strong>allow_none</strong> (<em>bool</em>) – default False
(useful for sample_weight - after splitting train and test of <cite>None</cite> are again <cite>None</cite>)</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="rep.utils.weighted_quantile">
<code class="descclassname">rep.utils.</code><code class="descname">weighted_quantile</code><span class="sig-paren">(</span><em>array</em>, <em>quantiles</em>, <em>sample_weight=None</em>, <em>array_sorted=False</em>, <em>old_style=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/rep/utils.html#weighted_quantile"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#rep.utils.weighted_quantile" title="Permalink to this definition">¶</a></dt>
<dd><p>Computing quantiles of array. Unlike the numpy.percentile, this function supports weights,
but it is inefficient and performs complete sorting.</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>array</strong> – distribution, array of shape [n_samples]</li>
<li><strong>quantiles</strong> – floats from range [0, 1] with quantiles of shape [n_quantiles]</li>
<li><strong>sample_weight</strong> – optional weights of samples, array of shape [n_samples]</li>
<li><strong>array_sorted</strong> – if True, the sorting step will be skipped</li>
<li><strong>old_style</strong> – if True, will correct output to be consistent with numpy.percentile.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">array of shape [n_quantiles]</p>
</td>
</tr>
</tbody>
</table>
<p>Example:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">weighted_quantile</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.5</span><span class="p">])</span>
<span class="go">Out: array([ 3.])</span>
<span class="gp">>>> </span><span class="n">weighted_quantile</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.5</span><span class="p">],</span> <span class="n">sample_weight</span><span class="o">=</span><span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
<span class="go">Out: array([ 2.])</span>
</pre></div>
</div>
</dd></dl>
</div>
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