🐛 Fix typo in multi_aso() code example and fix random seed for multi_…

…aso() tests

README.md

@@ -250,9 +250,9 @@ M = 3  # Number of different models / algorithms
 
 # Simulate different model scores by sampling from normal distributions with increasing means
 # Here, we will sample from N(0.1, 0.8), N(0.15, 0.8), N(0.2, 0.8)
-my_models_scores = [np.random.normal(loc=loc, scale=0.8, size=N) for loc in np.arange(0.1, 0.1 + 0.05 * M, step=0.05)]
+my_models_scores = np.array([np.random.normal(loc=loc, scale=0.8, size=N) for loc in np.arange(0.1, 0.1 + 0.05 * M, step=0.05)])
 
-eps_min = multi_aso(my_model_scores, confidence_level=0.05)
+eps_min = multi_aso(my_models_scores, confidence_level=0.05)
 
 # eps_min =
 # array([[1., 1., 1.],
@@ -287,7 +287,7 @@ my_models_scores = {
 #   ...
 # }
 
-eps_min = multi_aso(my_model_scores, confidence_level=0.05, return_df=True)
+eps_min = multi_aso(my_models_scores, confidence_level=0.05, return_df=True)
 
 # This is now a DataFrame!
 # eps_min =

README_RAW.md

@@ -260,7 +260,7 @@ M = 3  # Number of different models / algorithms
 
 # Simulate different model scores by sampling from normal distributions with increasing means
 # Here, we will sample from N(0.1, 0.8), N(0.15, 0.8), N(0.2, 0.8)
-my_models_scores = [np.random.normal(loc=loc, scale=0.8, size=N) for loc in np.arange(0.1, 0.1 + 0.05 * M, step=0.05)]
+my_models_scores = np.array([np.random.normal(loc=loc, scale=0.8, size=N) for loc in np.arange(0.1, 0.1 + 0.05 * M, step=0.05)])
 
 eps_min = multi_aso(my_models_scores, confidence_level=0.05)
 

deepsig/tests/test_aso.py

@@ -156,6 +156,7 @@ def setUp(self) -> None:
         }
         self.num_models = 3
         self.num_seeds = 100
+        np.random.seed(5678)
         self.scores = [
             np.random.normal(loc=0.3, scale=0.2, size=self.num_seeds).tolist()
             for _ in range(self.num_models)

docs/README_DOCS.md

@@ -250,9 +250,9 @@ M = 3  # Number of different models / algorithms
 
 # Simulate different model scores by sampling from normal distributions with increasing means
 # Here, we will sample from N(0.1, 0.8), N(0.15, 0.8), N(0.2, 0.8)
-my_models_scores = [np.random.normal(loc=loc, scale=0.8, size=N) for loc in np.arange(0.1, 0.1 + 0.05 * M, step=0.05)]
+my_models_scores = np.array([np.random.normal(loc=loc, scale=0.8, size=N) for loc in np.arange(0.1, 0.1 + 0.05 * M, step=0.05)])
 
-eps_min = multi_aso(my_model_scores, confidence_level=0.05)
+eps_min = multi_aso(my_models_scores, confidence_level=0.05)
 
 # eps_min =
 # array([[1., 1., 1.],
@@ -287,7 +287,7 @@ my_models_scores = {
 #   ...
 # }
 
-eps_min = multi_aso(my_model_scores, confidence_level=0.05, return_df=True)
+eps_min = multi_aso(my_models_scores, confidence_level=0.05, return_df=True)
 
 # This is now a DataFrame!
 # eps_min =

docs/build/html/README_DOCS.html

@@ -358,9 +358,9 @@ <h3>Scenario 4 - Comparing more than two models<a class="headerlink" href="#scen
 
 <span class="c1"># Simulate different model scores by sampling from normal distributions with increasing means</span>
 <span class="c1"># Here, we will sample from N(0.1, 0.8), N(0.15, 0.8), N(0.2, 0.8)</span>
-<span class="n">my_models_scores</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="n">loc</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">0.8</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">N</span><span class="p">)</span> <span class="k">for</span> <span class="n">loc</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span> <span class="o">+</span> <span class="mf">0.05</span> <span class="o">*</span> <span class="n">M</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="mf">0.05</span><span class="p">)]</span>
+<span class="n">my_models_scores</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="n">loc</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">0.8</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">N</span><span class="p">)</span> <span class="k">for</span> <span class="n">loc</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span> <span class="o">+</span> <span class="mf">0.05</span> <span class="o">*</span> <span class="n">M</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="mf">0.05</span><span class="p">)])</span>
 
-<span class="n">eps_min</span> <span class="o">=</span> <span class="n">multi_aso</span><span class="p">(</span><span class="n">my_model_scores</span><span class="p">,</span> <span class="n">confidence_level</span><span class="o">=</span><span class="mf">0.05</span><span class="p">)</span>
+<span class="n">eps_min</span> <span class="o">=</span> <span class="n">multi_aso</span><span class="p">(</span><span class="n">my_models_scores</span><span class="p">,</span> <span class="n">confidence_level</span><span class="o">=</span><span class="mf">0.05</span><span class="p">)</span>
 
 <span class="c1"># eps_min =</span>
 <span class="c1"># array([[1., 1., 1.],</span>
@@ -392,7 +392,7 @@ <h3>Scenario 4 - Comparing more than two models<a class="headerlink" href="#scen
 <span class="c1">#   ...</span>
 <span class="c1"># }</span>
 
-<span class="n">eps_min</span> <span class="o">=</span> <span class="n">multi_aso</span><span class="p">(</span><span class="n">my_model_scores</span><span class="p">,</span> <span class="n">confidence_level</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span> <span class="n">return_df</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">eps_min</span> <span class="o">=</span> <span class="n">multi_aso</span><span class="p">(</span><span class="n">my_models_scores</span><span class="p">,</span> <span class="n">confidence_level</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span> <span class="n">return_df</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 
 <span class="c1"># This is now a DataFrame!</span>
 <span class="c1"># eps_min =</span>

docs/build/html/_sources/README_DOCS.md.txt

@@ -250,9 +250,9 @@ M = 3  # Number of different models / algorithms
 
 # Simulate different model scores by sampling from normal distributions with increasing means
 # Here, we will sample from N(0.1, 0.8), N(0.15, 0.8), N(0.2, 0.8)
-my_models_scores = [np.random.normal(loc=loc, scale=0.8, size=N) for loc in np.arange(0.1, 0.1 + 0.05 * M, step=0.05)]
+my_models_scores = np.array([np.random.normal(loc=loc, scale=0.8, size=N) for loc in np.arange(0.1, 0.1 + 0.05 * M, step=0.05)])
 
-eps_min = multi_aso(my_model_scores, confidence_level=0.05)
+eps_min = multi_aso(my_models_scores, confidence_level=0.05)
 
 # eps_min =
 # array([[1., 1., 1.],
@@ -287,7 +287,7 @@ my_models_scores = {
 #   ...
 # }
 
-eps_min = multi_aso(my_model_scores, confidence_level=0.05, return_df=True)
+eps_min = multi_aso(my_models_scores, confidence_level=0.05, return_df=True)
 
 # This is now a DataFrame!
 # eps_min =

docs/build/html/genindex.html

@@ -117,8 +117,131 @@ <h4>Table Of Contents</h4>
 <h1 id="index">Index</h1>
 
 <div class="genindex-jumpbox">
+ <a href="#A"><strong>A</strong></a>
+ | <a href="#B"><strong>B</strong></a>
+ | <a href="#C"><strong>C</strong></a>
+ | <a href="#D"><strong>D</strong></a>
+ | <a href="#G"><strong>G</strong></a>
+ | <a href="#M"><strong>M</strong></a>
+ | <a href="#P"><strong>P</strong></a>
 
 </div>
+<h2 id="A">A</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="index.html#deepsig.aso.aso">aso() (in module deepsig.aso)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="B">B</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="index.html#deepsig.correction.bonferroni_correction">bonferroni_correction() (in module deepsig.correction)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="index.html#deepsig.bootstrap.bootstrap_test">bootstrap_test() (in module deepsig.bootstrap)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="C">C</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="index.html#deepsig.correction.calculate_partial_conjunction">calculate_partial_conjunction() (in module deepsig.correction)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="index.html#deepsig.aso.compute_violation_ratio">compute_violation_ratio() (in module deepsig.aso)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="D">D</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li>
+    deepsig
+
+      <ul>
+        <li><a href="index.html#module-deepsig">module</a>
+</li>
+      </ul></li>
+      <li>
+    deepsig.aso
+
+      <ul>
+        <li><a href="index.html#module-deepsig.aso">module</a>
+</li>
+      </ul></li>
+      <li>
+    deepsig.bootstrap
+
+      <ul>
+        <li><a href="index.html#module-deepsig.bootstrap">module</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li>
+    deepsig.correction
+
+      <ul>
+        <li><a href="index.html#module-deepsig.correction">module</a>
+</li>
+      </ul></li>
+      <li>
+    deepsig.permutation
+
+      <ul>
+        <li><a href="index.html#module-deepsig.permutation">module</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="G">G</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="index.html#deepsig.aso.get_quantile_function">get_quantile_function() (in module deepsig.aso)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="M">M</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li>
+    module
+
+      <ul>
+        <li><a href="index.html#module-deepsig">deepsig</a>
+</li>
+        <li><a href="index.html#module-deepsig.aso">deepsig.aso</a>
+</li>
+        <li><a href="index.html#module-deepsig.bootstrap">deepsig.bootstrap</a>
+</li>
+        <li><a href="index.html#module-deepsig.correction">deepsig.correction</a>
+</li>
+        <li><a href="index.html#module-deepsig.permutation">deepsig.permutation</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="index.html#deepsig.aso.multi_aso">multi_aso() (in module deepsig.aso)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="P">P</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="index.html#deepsig.permutation.permutation_test">permutation_test() (in module deepsig.permutation)</a>
+</li>
+  </ul></td>
+</tr></table>
+
 
 
           </div>