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@KarelZe
KarelZe committed Nov 20, 2023
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Expand Up @@ -899,7 +899,13 @@ <h1>API reference</h1>
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<span class="normal">523</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">class</span> <span class="nc">ClassicalClassifier</span><span class="p">(</span><span class="n">ClassifierMixin</span><span class="p">,</span> <span class="n">BaseEstimator</span><span class="p">):</span>
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<span class="normal">529</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">class</span> <span class="nc">ClassicalClassifier</span><span class="p">(</span><span class="n">ClassifierMixin</span><span class="p">,</span> <span class="n">BaseEstimator</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;ClassicalClassifier implements several trade classification rules.</span>

<span class="sd"> Including:</span>
Expand Down Expand Up @@ -937,6 +943,12 @@ <h1>API reference</h1>
<span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Initialize a ClassicalClassifier.</span>

<span class="sd"> Examples:</span>
<span class="sd"> &gt;&gt;&gt; clf = ClassicalClassifier(layers=[(&quot;lr&quot;, &quot;all&quot;)], strategy=&quot;const&quot;)</span>

<span class="sd"> Or equivalent:</span>
<span class="sd"> &gt;&gt;&gt; clf = ClassicalClassifier(layers=[(&quot;quote&quot;, &quot;all&quot;), (&quot;tick&quot;, &quot;all&quot;)], strategy=&quot;const&quot;)</span>

<span class="sd"> Args:</span>
<span class="sd"> layers (List[ tuple[ str, str, ] ]): Layers of classical rule.</span>
<span class="sd"> features (List[str] | None, optional): List of feature names in order of columns. Required to match columns in feature matrix with label. Can be `None`, if `pd.DataFrame` is passed. Defaults to None.</span>
Expand Down Expand Up @@ -1343,7 +1355,7 @@ <h1>API reference</h1>
<span class="n">func</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">func_mapping_</span><span class="p">[</span><span class="n">func_str</span><span class="p">]</span>
<span class="n">pred</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span>
<span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">pred</span><span class="p">),</span>
<span class="n">func</span><span class="p">(</span><span class="n">subset</span><span class="p">),</span>
<span class="n">func</span><span class="p">(</span><span class="n">subset</span><span class="p">),</span> <span class="c1"># type: ignore [operator]</span>
<span class="n">pred</span><span class="p">,</span>
<span class="p">)</span>

Expand Down Expand Up @@ -1420,6 +1432,15 @@ <h2 id="tclf.classical_classifier.ClassicalClassifier.__init__" class="doc doc-h



<p><strong>Examples:</strong></p>
<div class="codehilite"><pre><span></span><code><span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span> <span class="o">=</span> <span class="n">ClassicalClassifier</span><span class="p">(</span><span class="n">layers</span><span class="o">=</span><span class="p">[(</span><span class="s2">&quot;lr&quot;</span><span class="p">,</span> <span class="s2">&quot;all&quot;</span><span class="p">)],</span> <span class="n">strategy</span><span class="o">=</span><span class="s2">&quot;const&quot;</span><span class="p">)</span>
</code></pre></div>
<p>Or equivalent:</p>
<div class="codehilite"><pre><span></span><code><span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span> <span class="o">=</span> <span class="n">ClassicalClassifier</span><span class="p">(</span><span class="n">layers</span><span class="o">=</span><span class="p">[(</span><span class="s2">&quot;quote&quot;</span><span class="p">,</span> <span class="s2">&quot;all&quot;</span><span class="p">),</span> <span class="p">(</span><span class="s2">&quot;tick&quot;</span><span class="p">,</span> <span class="s2">&quot;all&quot;</span><span class="p">)],</span> <span class="n">strategy</span><span class="o">=</span><span class="s2">&quot;const&quot;</span><span class="p">)</span>
</code></pre></div>



<p><strong>Parameters:</strong></p>
<table>
<thead>
Expand Down Expand Up @@ -1516,7 +1537,13 @@ <h2 id="tclf.classical_classifier.ClassicalClassifier.__init__" class="doc doc-h
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<span class="bp">self</span><span class="p">,</span>
<span class="o">*</span><span class="p">,</span>
<span class="n">layers</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span>
Expand All @@ -1531,6 +1558,12 @@ <h2 id="tclf.classical_classifier.ClassicalClassifier.__init__" class="doc doc-h
<span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Initialize a ClassicalClassifier.</span>

<span class="sd"> Examples:</span>
<span class="sd"> &gt;&gt;&gt; clf = ClassicalClassifier(layers=[(&quot;lr&quot;, &quot;all&quot;)], strategy=&quot;const&quot;)</span>

<span class="sd"> Or equivalent:</span>
<span class="sd"> &gt;&gt;&gt; clf = ClassicalClassifier(layers=[(&quot;quote&quot;, &quot;all&quot;), (&quot;tick&quot;, &quot;all&quot;)], strategy=&quot;const&quot;)</span>

<span class="sd"> Args:</span>
<span class="sd"> layers (List[ tuple[ str, str, ] ]): Layers of classical rule.</span>
<span class="sd"> features (List[str] | None, optional): List of feature names in order of columns. Required to match columns in feature matrix with label. Can be `None`, if `pd.DataFrame` is passed. Defaults to None.</span>
Expand Down Expand Up @@ -1691,13 +1724,7 @@ <h2 id="tclf.classical_classifier.ClassicalClassifier.fit" class="doc doc-headin

<details class="quote">
<summary>Source code in <code>src/tclf/classical_classifier.py</code></summary>
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<span class="bp">self</span><span class="p">,</span>
<span class="n">X</span><span class="p">:</span> <span class="n">npt</span><span class="o">.</span><span class="n">NDArray</span> <span class="o">|</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span>
<span class="n">y</span><span class="p">:</span> <span class="n">npt</span><span class="o">.</span><span class="n">NDArray</span> <span class="o">|</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">,</span>
Expand Down Expand Up @@ -1920,13 +1953,7 @@ <h2 id="tclf.classical_classifier.ClassicalClassifier.predict" class="doc doc-he

<details class="quote">
<summary>Source code in <code>src/tclf/classical_classifier.py</code></summary>
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Expand Down Expand Up @@ -1958,7 +1985,13 @@ <h2 id="tclf.classical_classifier.ClassicalClassifier.predict" class="doc doc-he
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<span class="w"> </span><span class="sd">&quot;&quot;&quot;Perform classification on test vectors `X`.</span>

<span class="sd"> Args:</span>
Expand All @@ -1983,7 +2016,7 @@ <h2 id="tclf.classical_classifier.ClassicalClassifier.predict" class="doc doc-he
<span class="n">func</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">func_mapping_</span><span class="p">[</span><span class="n">func_str</span><span class="p">]</span>
<span class="n">pred</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span>
<span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">pred</span><span class="p">),</span>
<span class="n">func</span><span class="p">(</span><span class="n">subset</span><span class="p">),</span>
<span class="n">func</span><span class="p">(</span><span class="n">subset</span><span class="p">),</span> <span class="c1"># type: ignore [operator]</span>
<span class="n">pred</span><span class="p">,</span>
<span class="p">)</span>

Expand Down Expand Up @@ -2077,13 +2110,7 @@ <h2 id="tclf.classical_classifier.ClassicalClassifier.predict_proba" class="doc

<details class="quote">
<summary>Source code in <code>src/tclf/classical_classifier.py</code></summary>
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<span class="w"> </span><span class="sd">&quot;&quot;&quot;Predict class probabilities for X.</span>

<span class="sd"> Probabilities are either 0 or 1 depending on the class.</span>
Expand Down
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