Skip to content

Commit

Permalink
Deployed fb518b9 with MkDocs version: 1.5.3
Browse files Browse the repository at this point in the history
  • Loading branch information
@KarelZe
KarelZe committed Nov 20, 2023
1 parent 06b83ee commit 2472136
Show file tree
Hide file tree
Showing 3 changed files with 23 additions and 35 deletions.
56 changes: 22 additions & 34 deletions reference/index.html
View file
Original file line number Diff line number Diff line change
Expand Up @@ -899,10 +899,7 @@ <h1>API reference</h1>
<span class="normal">520</span>
<span class="normal">521</span>
<span class="normal">522</span>
<span class="normal">523</span>
<span class="normal">524</span>
<span class="normal">525</span>
<span class="normal">526</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="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>
<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 @@ -942,9 +939,7 @@ <h1>API reference</h1>

<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</span>
<span class="sd"> columns. Required to match columns in feature matrix with label.</span>
<span class="sd"> Can be `None`, if `pd.DataFrame` is passed. Defaults to None.</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>
<span class="sd"> random_state (float | None, optional): random seed. Defaults to 42.</span>
<span class="sd"> strategy (Literal[&amp;quot;random&amp;quot;, &amp;quot;const&amp;quot;], optional): Strategy to fill unclassfied. Randomly with uniform probability or with constant 0. Defaults to &amp;quot;random&amp;quot;.</span>
<span class="sd"> &quot;&quot;&quot;</span>
Expand Down Expand Up @@ -1257,8 +1252,7 @@ <h1>API reference</h1>
<span class="sd"> Args:</span>
<span class="sd"> X (npt.NDArray | pd.DataFrame): features</span>
<span class="sd"> y (npt.NDArray | pd.Series): ground truth (ignored)</span>
<span class="sd"> sample_weight (npt.NDArray | None, optional): Sample weights.</span>
<span class="sd"> Defaults to None.</span>
<span class="sd"> sample_weight (npt.NDArray | None, optional): Sample weights. Defaults to None.</span>

<span class="sd"> Raises:</span>
<span class="sd"> ValueError: Unknown subset e. g., &#39;ise&#39;</span>
Expand Down Expand Up @@ -1458,7 +1452,7 @@ <h2 id="tclf.classical_classifier.ClassicalClassifier.__init__" class="doc doc-h
</td>
<td>
<div class="doc-md-description">
<p>List of feature names in order of</p>
<p>List of feature names in order of columns. Required to match columns in feature matrix with label. Can be <code>None</code>, if <code>pd.DataFrame</code> is passed. Defaults to None.</p>
</div>
</td>
<td>
Expand Down Expand Up @@ -1522,9 +1516,7 @@ <h2 id="tclf.classical_classifier.ClassicalClassifier.__init__" class="doc doc-h
<span class="normal">80</span>
<span class="normal">81</span>
<span class="normal">82</span>
<span class="normal">83</span>
<span class="normal">84</span>
<span class="normal">85</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
<span class="normal">83</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
<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 @@ -1541,9 +1533,7 @@ <h2 id="tclf.classical_classifier.ClassicalClassifier.__init__" class="doc doc-h

<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</span>
<span class="sd"> columns. Required to match columns in feature matrix with label.</span>
<span class="sd"> Can be `None`, if `pd.DataFrame` is passed. Defaults to None.</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>
<span class="sd"> random_state (float | None, optional): random seed. Defaults to 42.</span>
<span class="sd"> strategy (Literal[&amp;quot;random&amp;quot;, &amp;quot;const&amp;quot;], optional): Strategy to fill unclassfied. Randomly with uniform probability or with constant 0. Defaults to &amp;quot;random&amp;quot;.</span>
<span class="sd"> &quot;&quot;&quot;</span>
Expand Down Expand Up @@ -1621,7 +1611,7 @@ <h2 id="tclf.classical_classifier.ClassicalClassifier.fit" class="doc doc-headin
</td>
<td>
<div class="doc-md-description">
<p>Sample weights.</p>
<p>Sample weights. Defaults to None.</p>
</div>
</td>
<td>
Expand Down Expand Up @@ -1701,7 +1691,9 @@ <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>
<div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">380</span>
<div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">378</span>
<span class="normal">379</span>
<span class="normal">380</span>
<span class="normal">381</span>
<span class="normal">382</span>
<span class="normal">383</span>
Expand Down Expand Up @@ -1774,10 +1766,7 @@ <h2 id="tclf.classical_classifier.ClassicalClassifier.fit" class="doc doc-headin
<span class="normal">450</span>
<span class="normal">451</span>
<span class="normal">452</span>
<span class="normal">453</span>
<span class="normal">454</span>
<span class="normal">455</span>
<span class="normal">456</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">fit</span><span class="p">(</span>
<span class="normal">453</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">fit</span><span class="p">(</span>
<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 All @@ -1788,8 +1777,7 @@ <h2 id="tclf.classical_classifier.ClassicalClassifier.fit" class="doc doc-headin
<span class="sd"> Args:</span>
<span class="sd"> X (npt.NDArray | pd.DataFrame): features</span>
<span class="sd"> y (npt.NDArray | pd.Series): ground truth (ignored)</span>
<span class="sd"> sample_weight (npt.NDArray | None, optional): Sample weights.</span>
<span class="sd"> Defaults to None.</span>
<span class="sd"> sample_weight (npt.NDArray | None, optional): Sample weights. Defaults to None.</span>

<span class="sd"> Raises:</span>
<span class="sd"> ValueError: Unknown subset e. g., &#39;ise&#39;</span>
Expand Down Expand Up @@ -1932,7 +1920,10 @@ <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>
<div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">458</span>
<div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">455</span>
<span class="normal">456</span>
<span class="normal">457</span>
<span class="normal">458</span>
<span class="normal">459</span>
<span class="normal">460</span>
<span class="normal">461</span>
Expand Down Expand Up @@ -1967,10 +1958,7 @@ <h2 id="tclf.classical_classifier.ClassicalClassifier.predict" class="doc doc-he
<span class="normal">490</span>
<span class="normal">491</span>
<span class="normal">492</span>
<span class="normal">493</span>
<span class="normal">494</span>
<span class="normal">495</span>
<span class="normal">496</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><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="o">-&gt;</span> <span class="n">npt</span><span class="o">.</span><span class="n">NDArray</span><span class="p">:</span>
<span class="normal">493</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><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="o">-&gt;</span> <span class="n">npt</span><span class="o">.</span><span class="n">NDArray</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Perform classification on test vectors `X`.</span>

<span class="sd"> Args:</span>
Expand Down Expand Up @@ -2089,7 +2077,10 @@ <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>
<div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">498</span>
<div class="codehilite"><table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">495</span>
<span class="normal">496</span>
<span class="normal">497</span>
<span class="normal">498</span>
<span class="normal">499</span>
<span class="normal">500</span>
<span class="normal">501</span>
Expand All @@ -2114,10 +2105,7 @@ <h2 id="tclf.classical_classifier.ClassicalClassifier.predict_proba" class="doc
<span class="normal">520</span>
<span class="normal">521</span>
<span class="normal">522</span>
<span class="normal">523</span>
<span class="normal">524</span>
<span class="normal">525</span>
<span class="normal">526</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">predict_proba</span><span class="p">(</span><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="o">-&gt;</span> <span class="n">npt</span><span class="o">.</span><span class="n">NDArray</span><span class="p">:</span>
<span class="normal">523</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">predict_proba</span><span class="p">(</span><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="o">-&gt;</span> <span class="n">npt</span><span class="o">.</span><span class="n">NDArray</span><span class="p">:</span>
<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
Loading

0 comments on commit 2472136

Please sign in to comment.