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rasbt/mlxtend: Version 0.17.0

Sebastian Raschka; James Bourbeau; Reiichiro Nakano; Zach Griffith; Kota Mori; Will McGinnis; JJLWHarrison; Guillaume Poirier-Morency; Daniel; Qiang Gu; Floris Hoogenboom; Colin; Vahid Mirjalili; selay01; Christos Aridas; Steve Harenberg; Pablo Fernandez; Oliver Tomic; Laurens Geffert; Janpreet Singh; Alejandro Correa Bahnsen; Benjamin Lee; Batuhan Bardak; Arfon Smith; Anton Loss; Anebi; Ajinkya Kale; Adam Erickson; Adam Cooper; Ackerley Tng


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{
  "publisher": "Zenodo", 
  "DOI": "10.5281/zenodo.3343208", 
  "title": "rasbt/mlxtend: Version 0.17.0", 
  "issued": {
    "date-parts": [
      [
        2019, 
        7, 
        19
      ]
    ]
  }, 
  "abstract": "New Features\n<ul>\n<li>Added an enhancement to the existing <code>iris_data()</code> such that both the UCI Repository version of the Iris dataset as well as the corrected, original\nversion of the dataset can be loaded, which has a slight difference in two data points (consistent with Fisher's paper; this is also the same as in R). (via <a href=\"https://github.com/rasbt/mlxtend/pull/532\">#539</a> via <a href=\"https://github.com/janismdhanbad\">janismdhanbad</a>)</li>\n<li>Added optional <code>groups</code> parameter to <code>SequentialFeatureSelector</code> and <code>ExhaustiveFeatureSelector</code> <code>fit()</code> methods for forwarding to sklearn CV (<a href=\"https://github.com/rasbt/mlxtend/pull/537\">#537</a> via <a href=\"https://github.com/qiaguhttps://github.com/arc12\">arc12</a>)</li>\n<li>Added a new <code>plot_pca_correlation_graph</code> function to the <code>mlxtend.plotting</code> submodule for plotting a PCA correlation graph. (<a href=\"https://github.com/rasbt/mlxtend/pull/544\">#544</a> via <a href=\"https://github.com/qiaguhttps://github.com/Gabriel-Azevedo-Ferreira\">Gabriel-Azevedo-Ferreira</a>)</li>\n<li>Added a <code>zoom_factor</code> parameter to the <code>mlxten.plotting.plot_decision_region</code> function that allows users to zoom in and out of the decision region plots. (<a href=\"https://github.com/rasbt/mlxtend/pull/545\">#545</a>)</li>\n<li>Added a function <code>fpgrowth</code> that implements the FP-Growth algorithm for mining frequent itemsets as a drop-in replacement for the existing <code>apriori</code> algorithm. (<a href=\"https://github.com/rasbt/mlxtend/pull/550\">#550</a> via <a href=\"https://github.com/harenbergsd\">Steve Harenberg</a>)</li>\n<li>New <code>heatmap</code> function in <code>mlxtend.plotting</code>.  (<a href=\"https://github.com/rasbt/mlxtend/pull/552\">#552</a>)</li>\n<li>Added a function <code>fpmax</code> that implements the FP-Max algorithm for mining maximal itemsets as a drop-in replacement for the <code>fpgrowth</code> algorithm. (<a href=\"https://github.com/rasbt/mlxtend/pull/553\">#553</a> via <a href=\"https://github.com/harenbergsd\">Steve Harenberg</a>)</li>\n<li>New <code>figsize</code> parameter for the <code>plot_decision_regions</code> function in <code>mlxtend.plotting</code>. (<a href=\"https://github.com/rasbt/mlxtend/pull/555\">#555</a> via <a href=\"https://github.com/kazyka\">Mirza Hasanbasic</a>)</li>\n<li>New <code>low_memory</code> option for the <code>apriori</code> frequent itemset generating function. Setting <code>low_memory=False</code> (default) uses a substantially optimized version of the algorithm that is 3-6x faster than the original implementation (<code>low_memory=True</code>). (<a href=\"https://github.com/rasbt/mlxtend/pull/567\">#567</a> via <a href=\"https://github.com/jmayse\">jmayse</a>)</li>\n</ul>\nChanges\n<ul>\n<li>Now uses the latest joblib library under the hood for multiprocessing instead of <code>sklearn.externals.joblib</code>. (<a href=\"https://github.com/rasbt/mlxtend/pull/547\">#547</a>)</li>\n<li>Changes to <code>StackingCVClassifier</code> and <code>StackingCVRegressor</code> such that first-level models are allowed to generate output of non-numeric type. (<a href=\"https://github.com/rasbt/mlxtend/pull/562\">#562</a>)</li>\n</ul>\nBug Fixes\n<ul>\n<li>Fixed documentation of <code>iris_data()</code> under <code>iris.py</code> by adding a note about differences in the iris data in R and UCI machine learning repo.</li>\n<li>Make sure that if the <code>'svd'</code> mode is used in PCA, the number of eigenvalues is the same as when using <code>'eigen'</code> (append 0's zeros in that case) (<a href=\"https://github.com/rasbt/mlxtend/pull/565\">#565</a>)</li>\n</ul>", 
  "author": [
    {
      "family": "Sebastian Raschka"
    }, 
    {
      "family": "James Bourbeau"
    }, 
    {
      "family": "Reiichiro Nakano"
    }, 
    {
      "family": "Zach Griffith"
    }, 
    {
      "family": "Kota Mori"
    }, 
    {
      "family": "Will McGinnis"
    }, 
    {
      "family": "JJLWHarrison"
    }, 
    {
      "family": "Guillaume Poirier-Morency"
    }, 
    {
      "family": "Daniel"
    }, 
    {
      "family": "Qiang Gu"
    }, 
    {
      "family": "Floris Hoogenboom"
    }, 
    {
      "family": "Colin"
    }, 
    {
      "family": "Vahid Mirjalili"
    }, 
    {
      "family": "selay01"
    }, 
    {
      "family": "Christos Aridas"
    }, 
    {
      "family": "Steve Harenberg"
    }, 
    {
      "family": "Pablo Fernandez"
    }, 
    {
      "family": "Oliver Tomic"
    }, 
    {
      "family": "Laurens Geffert"
    }, 
    {
      "family": "Janpreet Singh"
    }, 
    {
      "family": "Alejandro Correa Bahnsen"
    }, 
    {
      "family": "Benjamin Lee"
    }, 
    {
      "family": "Batuhan Bardak"
    }, 
    {
      "family": "Arfon Smith"
    }, 
    {
      "family": "Anton Loss"
    }, 
    {
      "family": "Anebi"
    }, 
    {
      "family": "Ajinkya Kale"
    }, 
    {
      "family": "Adam Erickson"
    }, 
    {
      "family": "Adam Cooper"
    }, 
    {
      "family": "Ackerley Tng"
    }
  ], 
  "version": "v0.17.0", 
  "type": "article", 
  "id": "3343208"
}
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