Deployed 9bef900 with MkDocs version: 1.5.3

api_modules/mlxtend.classifier/StackingCVClassifier/index.html

@@ -753,6 +753,10 @@ <h2 id="stackingcvclassifier">StackingCVClassifier</h2>
 <p>Fitted meta-classifier (clone of the original meta-estimator)</p>
 </li>
 <li>
+<p><code>classes_</code> : ndarray of shape (n_classes,) or list of ndarray if <code>y</code>             is of type <code>"multilabel-indicator"</code>.</p>
+<p>Class labels.</p>
+</li>
+<li>
 <p><code>train_meta_features</code> : numpy array, shape = [n_samples, n_classifiers]</p>
 <p>meta-features for training data, where n_samples is the
 number of samples

api_modules/mlxtend.classifier/StackingClassifier/index.html

@@ -714,6 +714,10 @@ <h2 id="stackingclassifier">StackingClassifier</h2>
 <p>Fitted meta-classifier (clone of the original meta-estimator)</p>
 </li>
 <li>
+<p><code>classes_</code> : ndarray of shape (n_classes,) or list of ndarray if <code>y</code>             is of type <code>"multilabel-indicator"</code>.</p>
+<p>Class labels.</p>
+</li>
+<li>
 <p><code>train_meta_features</code> : numpy array, shape = [n_samples, n_classifiers]</p>
 <p>meta-features for training data, where n_samples is the
 number of samples

api_modules/mlxtend.frequent_patterns/association_rules/index.html

@@ -617,7 +617,7 @@
                     <div class="col-md-9" role="main">
 
 <h2 id="association_rules">association_rules</h2>
-<p><em>association_rules(df, metric='confidence', min_threshold=0.8, support_only=False)</em></p>
+<p><em>association_rules(df: pandas.core.frame.DataFrame, num_itemsets: Union[int, NoneType] = 1, df_orig: Union[pandas.core.frame.DataFrame, NoneType] = None, null_values=False, metric='confidence', min_threshold=0.8, support_only=False, return_metrics: list = ['antecedent support', 'consequent support', 'support', 'confidence', 'lift', 'representativity', 'leverage', 'conviction', 'zhangs_metric', 'jaccard', 'certainty', 'kulczynski']) -&gt; pandas.core.frame.DataFrame</em></p>
 <p>Generates a DataFrame of association rules including the
     metrics 'score', 'confidence', and 'lift'</p>
 <p><strong>Parameters</strong></p>
@@ -628,6 +628,18 @@ <h2 id="association_rules">association_rules</h2>
 with columns ['support', 'itemsets']</p>
 </li>
 <li>
+<p><code>df_orig</code> : pandas DataFrame (default: None)</p>
+<p>DataFrame with original input data. Only provided when null_values exist</p>
+</li>
+<li>
+<p><code>num_itemsets</code> : int (default: 1)</p>
+<p>Number of transactions in original input data (df_orig)</p>
+</li>
+<li>
+<p><code>null_values</code> : bool (default: False)</p>
+<p>In case there are null values as NaNs in the original input data</p>
+</li>
+<li>
 <p><code>metric</code> : string (default: 'confidence')</p>
 <p>Metric to evaluate if a rule is of interest.
 <strong>Automatically set to 'support' if <code>support_only=True</code>.</strong>

api_modules/mlxtend.frequent_patterns/fpgrowth/index.html

@@ -617,7 +617,7 @@
                     <div class="col-md-9" role="main">
 
 <h2 id="fpgrowth">fpgrowth</h2>
-<p><em>fpgrowth(df, min_support=0.5, use_colnames=False, max_len=None, verbose=0)</em></p>
+<p><em>fpgrowth(df, min_support=0.5, null_values=False, use_colnames=False, max_len=None, verbose=0)</em></p>
 <p>Get frequent itemsets from a one-hot DataFrame</p>
 <p><strong>Parameters</strong></p>
 <ul>
@@ -647,6 +647,10 @@ <h2 id="fpgrowth">fpgrowth</h2>
 transactions_where_item(s)_occur / total_transactions.</p>
 </li>
 <li>
+<p><code>null_values</code> : bool (default: False)</p>
+<p>In case there are null values as NaNs in the original input data</p>
+</li>
+<li>
 <p><code>use_colnames</code> : bool (default: False)</p>
 <p>If true, uses the DataFrames' column names in the returned DataFrame
 instead of column indices.</p>

api_modules/mlxtend.frequent_patterns/fpmax/index.html

@@ -617,7 +617,7 @@
                     <div class="col-md-9" role="main">
 
 <h2 id="fpmax">fpmax</h2>
-<p><em>fpmax(df, min_support=0.5, use_colnames=False, max_len=None, verbose=0)</em></p>
+<p><em>fpmax(df, min_support=0.5, null_values=False, use_colnames=False, max_len=None, verbose=0)</em></p>
 <p>Get maximal frequent itemsets from a one-hot DataFrame</p>
 <p><strong>Parameters</strong></p>
 <ul>
@@ -648,6 +648,10 @@ <h2 id="fpmax">fpmax</h2>
 transactions_where_item(s)_occur / total_transactions.</p>
 </li>
 <li>
+<p><code>null_values</code> : bool (default: True)</p>
+<p>In case there are null values as NaNs in the original input data</p>
+</li>
+<li>
 <p><code>use_colnames</code> : bool (default: False)</p>
 <p>If true, uses the DataFrames' column names in the returned DataFrame
 instead of column indices.</p>

api_modules/mlxtend.preprocessing/TransactionEncoder/index.html

@@ -656,6 +656,17 @@ <h3 id="methods">Methods</h3>
 <p>Fit a TransactionEncoder encoder and transform a dataset.</p>
 <hr>
 
+<p><em>get_feature_names_out()</em></p>
+<p>Used to get the column names of pandas output.</p>
+<pre><code>This method combined with the `TransformerMixin` exposes the
+set_output API to the `TransactionEncoder`. This allows the user
+to set the transformed output to a `pandas.DataFrame` by default.
+
+See  https://scikit-learn.org/stable/developers/develop.html#developer-api-set-output
+for more details.
+</code></pre>
+<hr>
+
 <p><em>get_params(deep=True)</em></p>
 <p>Get parameters for this estimator.</p>
 <p><strong>Parameters</strong></p>

api_subpackages/mlxtend._base/index.html

@@ -611,7 +611,7 @@
 </div></div>
                     <div class="col-md-9" role="main">
 
-<p>mlxtend version: 0.23.1 </p></div>
+<p>mlxtend version: 0.23.4 </p></div>
             </div>
         </div>
 

api_subpackages/mlxtend.classifier/index.html

@@ -666,7 +666,7 @@
 </div></div>
                     <div class="col-md-9" role="main">
 
-<p>mlxtend version: 0.23.1 </p>
+<p>mlxtend version: 0.23.4 </p>
 <h2 id="adaline">Adaline</h2>
 <p><em>Adaline(eta=0.01, epochs=50, minibatches=None, random_seed=None, print_progress=0)</em></p>
 <p>ADAptive LInear NEuron classifier.</p>
@@ -2174,6 +2174,10 @@ <h2 id="stackingcvclassifier">StackingCVClassifier</h2>
 <p>Fitted meta-classifier (clone of the original meta-estimator)</p>
 </li>
 <li>
+<p><code>classes_</code> : ndarray of shape (n_classes,) or list of ndarray if <code>y</code>             is of type <code>"multilabel-indicator"</code>.</p>
+<p>Class labels.</p>
+</li>
+<li>
 <p><code>train_meta_features</code> : numpy array, shape = [n_samples, n_classifiers]</p>
 <p>meta-features for training data, where n_samples is the
 number of samples
@@ -2494,6 +2498,10 @@ <h2 id="stackingclassifier">StackingClassifier</h2>
 <p>Fitted meta-classifier (clone of the original meta-estimator)</p>
 </li>
 <li>
+<p><code>classes_</code> : ndarray of shape (n_classes,) or list of ndarray if <code>y</code>             is of type <code>"multilabel-indicator"</code>.</p>
+<p>Class labels.</p>
+</li>
+<li>
 <p><code>train_meta_features</code> : numpy array, shape = [n_samples, n_classifiers]</p>
 <p>meta-features for training data, where n_samples is the
 number of samples

api_subpackages/mlxtend.cluster/index.html

@@ -626,7 +626,7 @@
 </div></div>
                     <div class="col-md-9" role="main">
 
-<p>mlxtend version: 0.23.1 </p>
+<p>mlxtend version: 0.23.4 </p>
 <h2 id="kmeans">Kmeans</h2>
 <p><em>Kmeans(k, max_iter=10, convergence_tolerance=1e-05, random_seed=None, print_progress=0)</em></p>
 <p>K-means clustering class.</p>

api_subpackages/mlxtend.data/index.html

@@ -661,7 +661,7 @@
 </div></div>
                     <div class="col-md-9" role="main">
 
-<p>mlxtend version: 0.23.1 </p>
+<p>mlxtend version: 0.23.4 </p>
 <h2 id="autompg_data">autompg_data</h2>
 <p><em>autompg_data()</em></p>
 <p>Auto MPG dataset.</p>

api_subpackages/mlxtend.evaluate/index.html

@@ -736,7 +736,7 @@
 </div></div>
                     <div class="col-md-9" role="main">
 
-<p>mlxtend version: 0.23.1 </p>
+<p>mlxtend version: 0.23.4 </p>
 <h2 id="bootstrapoutofbag">BootstrapOutOfBag</h2>
 <p><em>BootstrapOutOfBag(n_splits=200, random_seed=None)</em></p>
 <p><strong>Parameters</strong></p>

api_subpackages/mlxtend.externals/index.html

@@ -611,7 +611,7 @@
 </div></div>
                     <div class="col-md-9" role="main">
 
-<p>mlxtend version: 0.23.1 </p></div>
+<p>mlxtend version: 0.23.4 </p></div>
             </div>
         </div>
 

api_subpackages/mlxtend.feature_extraction/index.html

@@ -636,7 +636,7 @@
 </div></div>
                     <div class="col-md-9" role="main">
 
-<p>mlxtend version: 0.23.1 </p>
+<p>mlxtend version: 0.23.4 </p>
 <h2 id="lineardiscriminantanalysis">LinearDiscriminantAnalysis</h2>
 <p><em>LinearDiscriminantAnalysis(n_discriminants=None)</em></p>
 <p>Linear Discriminant Analysis Class</p>

api_subpackages/mlxtend.feature_selection/index.html

@@ -636,7 +636,7 @@
 </div></div>
                     <div class="col-md-9" role="main">
 
-<p>mlxtend version: 0.23.1 </p>
+<p>mlxtend version: 0.23.4 </p>
 <h2 id="columnselector">ColumnSelector</h2>
 <p><em>ColumnSelector(cols=None, drop_axis=False)</em></p>
 <p>Object for selecting specific columns from a data set.</p>

api_subpackages/mlxtend.file_io/index.html

@@ -631,7 +631,7 @@
 </div></div>
                     <div class="col-md-9" role="main">
 
-<p>mlxtend version: 0.23.1 </p>
+<p>mlxtend version: 0.23.4 </p>
 <h2 id="find_filegroups">find_filegroups</h2>
 <p><em>find_filegroups(paths, substring='', extensions=None, validity_check=True, ignore_invisible=True, rstrip='', ignore_substring=None)</em></p>
 <p>Find and collect files from different directories in a python dictionary.</p>

api_subpackages/mlxtend.frequent_patterns/index.html

@@ -646,7 +646,7 @@
 </div></div>
                     <div class="col-md-9" role="main">
 
-<p>mlxtend version: 0.23.1 </p>
+<p>mlxtend version: 0.23.4 </p>
 <h2 id="apriori">apriori</h2>
 <p><em>apriori(df, min_support=0.5, use_colnames=False, max_len=None, verbose=0, low_memory=False)</em></p>
 <p>Get frequent itemsets from a one-hot DataFrame</p>
@@ -717,7 +717,7 @@ <h2 id="apriori">apriori</h2>
 <p>For usage examples, please see
     https://rasbt.github.io/mlxtend/user_guide/frequent_patterns/apriori/</p>
 <h2 id="association_rules">association_rules</h2>
-<p><em>association_rules(df, metric='confidence', min_threshold=0.8, support_only=False)</em></p>
+<p><em>association_rules(df: pandas.core.frame.DataFrame, num_itemsets: Union[int, NoneType] = 1, df_orig: Union[pandas.core.frame.DataFrame, NoneType] = None, null_values=False, metric='confidence', min_threshold=0.8, support_only=False, return_metrics: list = ['antecedent support', 'consequent support', 'support', 'confidence', 'lift', 'representativity', 'leverage', 'conviction', 'zhangs_metric', 'jaccard', 'certainty', 'kulczynski']) -&gt; pandas.core.frame.DataFrame</em></p>
 <p>Generates a DataFrame of association rules including the
     metrics 'score', 'confidence', and 'lift'</p>
 <p><strong>Parameters</strong></p>
@@ -728,6 +728,18 @@ <h2 id="association_rules">association_rules</h2>
 with columns ['support', 'itemsets']</p>
 </li>
 <li>
+<p><code>df_orig</code> : pandas DataFrame (default: None)</p>
+<p>DataFrame with original input data. Only provided when null_values exist</p>
+</li>
+<li>
+<p><code>num_itemsets</code> : int (default: 1)</p>
+<p>Number of transactions in original input data (df_orig)</p>
+</li>
+<li>
+<p><code>null_values</code> : bool (default: False)</p>
+<p>In case there are null values as NaNs in the original input data</p>
+</li>
+<li>
 <p><code>metric</code> : string (default: 'confidence')</p>
 <p>Metric to evaluate if a rule is of interest.
 <strong>Automatically set to 'support' if <code>support_only=True</code>.</strong>
@@ -787,7 +799,7 @@ <h2 id="association_rules">association_rules</h2>
 <p>For usage examples, please see
     https://rasbt.github.io/mlxtend/user_guide/frequent_patterns/association_rules/</p>
 <h2 id="fpgrowth">fpgrowth</h2>
-<p><em>fpgrowth(df, min_support=0.5, use_colnames=False, max_len=None, verbose=0)</em></p>
+<p><em>fpgrowth(df, min_support=0.5, null_values=False, use_colnames=False, max_len=None, verbose=0)</em></p>
 <p>Get frequent itemsets from a one-hot DataFrame</p>
 <p><strong>Parameters</strong></p>
 <ul>
@@ -817,6 +829,10 @@ <h2 id="fpgrowth">fpgrowth</h2>
 transactions_where_item(s)_occur / total_transactions.</p>
 </li>
 <li>
+<p><code>null_values</code> : bool (default: False)</p>
+<p>In case there are null values as NaNs in the original input data</p>
+</li>
+<li>
 <p><code>use_colnames</code> : bool (default: False)</p>
 <p>If true, uses the DataFrames' column names in the returned DataFrame
 instead of column indices.</p>
@@ -844,7 +860,7 @@ <h2 id="fpgrowth">fpgrowth</h2>
 <p>For usage examples, please see
     https://rasbt.github.io/mlxtend/user_guide/frequent_patterns/fpgrowth/</p>
 <h2 id="fpmax">fpmax</h2>
-<p><em>fpmax(df, min_support=0.5, use_colnames=False, max_len=None, verbose=0)</em></p>
+<p><em>fpmax(df, min_support=0.5, null_values=False, use_colnames=False, max_len=None, verbose=0)</em></p>
 <p>Get maximal frequent itemsets from a one-hot DataFrame</p>
 <p><strong>Parameters</strong></p>
 <ul>
@@ -875,6 +891,10 @@ <h2 id="fpmax">fpmax</h2>
 transactions_where_item(s)_occur / total_transactions.</p>
 </li>
 <li>
+<p><code>null_values</code> : bool (default: True)</p>
+<p>In case there are null values as NaNs in the original input data</p>
+</li>
+<li>
 <p><code>use_colnames</code> : bool (default: False)</p>
 <p>If true, uses the DataFrames' column names in the returned DataFrame
 instead of column indices.</p>

api_subpackages/mlxtend.image/index.html

@@ -621,7 +621,7 @@
 </div></div>
                     <div class="col-md-9" role="main">
 
-<p>mlxtend version: 0.23.1 </p>
+<p>mlxtend version: 0.23.4 </p>
 <h2 id="eyepadalign">EyepadAlign</h2>
 <p><em>EyepadAlign(verbose=0)</em></p>
 <p>Class to align/transform face images to facial landmarks,

api_subpackages/mlxtend.math/index.html

@@ -636,7 +636,7 @@
 </div></div>
                     <div class="col-md-9" role="main">
 
-<p>mlxtend version: 0.23.1 </p>
+<p>mlxtend version: 0.23.4 </p>
 <h2 id="factorial">factorial</h2>
 <p><em>factorial(n)</em></p>
 <p>None</p>

api_subpackages/mlxtend.plotting/index.html

@@ -696,7 +696,7 @@
 </div></div>
                     <div class="col-md-9" role="main">
 
-<p>mlxtend version: 0.23.1 </p>
+<p>mlxtend version: 0.23.4 </p>
 <h2 id="category_scatter">category_scatter</h2>
 <p><em>category_scatter(x, y, label_col, data, markers='sxo^v', colors=('blue', 'green', 'red', 'purple', 'gray', 'cyan'), alpha=0.7, markersize=20.0, legend_loc='best')</em></p>
 <p>Scatter plot to plot categories in different colors/markerstyles.</p>

api_subpackages/mlxtend.preprocessing/index.html

@@ -661,7 +661,7 @@
 </div></div>
                     <div class="col-md-9" role="main">
 
-<p>mlxtend version: 0.23.1 </p>
+<p>mlxtend version: 0.23.4 </p>
 <h2 id="copytransformer">CopyTransformer</h2>
 <p><em>CopyTransformer()</em></p>
 <p>Transformer that returns a copy of the input array</p>
@@ -977,6 +977,17 @@ <h3 id="methods_3">Methods</h3>
 <p>Fit a TransactionEncoder encoder and transform a dataset.</p>
 <hr>
 
+<p><em>get_feature_names_out()</em></p>
+<p>Used to get the column names of pandas output.</p>
+<pre><code>This method combined with the `TransformerMixin` exposes the
+set_output API to the `TransactionEncoder`. This allows the user
+to set the transformed output to a `pandas.DataFrame` by default.
+
+See  https://scikit-learn.org/stable/developers/develop.html#developer-api-set-output
+for more details.
+</code></pre>
+<hr>
+
 <p><em>get_params(deep=True)</em></p>
 <p>Get parameters for this estimator.</p>
 <p><strong>Parameters</strong></p>

api_subpackages/mlxtend.regressor/index.html

@@ -636,7 +636,7 @@
 </div></div>
                     <div class="col-md-9" role="main">
 
-<p>mlxtend version: 0.23.1 </p>
+<p>mlxtend version: 0.23.4 </p>
 <h2 id="linearregression">LinearRegression</h2>
 <p><em>LinearRegression(method='direct', eta=0.01, epochs=50, minibatches=None, random_seed=None, print_progress=0)</em></p>
 <p>Ordinary least squares linear regression.</p>

api_subpackages/mlxtend.text/index.html

@@ -641,7 +641,7 @@
 </div></div>
                     <div class="col-md-9" role="main">
 
-<p>mlxtend version: 0.23.1 </p>
+<p>mlxtend version: 0.23.4 </p>
 <h2 id="generalize_names">generalize_names</h2>
 <p><em>generalize_names(name, output_sep=' ', firstname_output_letters=1)</em></p>
 <p>Generalize a person's first and last name.</p>

api_subpackages/mlxtend.utils/index.html

@@ -641,7 +641,7 @@
 </div></div>
                     <div class="col-md-9" role="main">
 
-<p>mlxtend version: 0.23.1 </p>
+<p>mlxtend version: 0.23.4 </p>
 <h2 id="counter">Counter</h2>
 <p><em>Counter(stderr=False, start_newline=True, precision=0, name=None)</em></p>
 <p>Class to display the progress of for-loop iterators.</p>

index.html

@@ -820,5 +820,5 @@ <h4 class="modal-title" id="keyboardModalLabel">Keyboard Shortcuts</h4>
 
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