Merge branch 'adapt-python:master' into master

Author: antoinedemathelin

adapt/instance_based/_kliep.py

@@ -54,7 +54,7 @@ class KLIEP(BaseAdaptEstimator):
     
     .. math::
     
-        \sum_{x_k \in X_S} \sum_{x_j \in X_T} \\alpha_i K(x_j, x_k)) = n_S
+        \sum_{x_j \in X_S} \sum_{x_i \in X_T} \\alpha_i K(x_j, x_i)) = n_S
         
     Where:
     

adapt/instance_based/_kmm.py

@@ -20,10 +20,10 @@ class KMM(BaseAdaptEstimator):
     KMM: Kernel Mean Matching
     
     KMM is a sample bias correction method for domain adaptation based on the
-    minimization of the **Maximum Mean Discreapancy** (MMD) between source
+    minimization of the **Maximum Mean Discrepancy** (MMD) between source
     and target domains.
     
-    The algorithm corrects input source and taregt distributions differences by
+    The algorithm corrects the difference between the input source and target distributions by
     **reweighting** the source instances such that the means of the source and target
     instances in a **reproducing kernel Hilbert space** (RKHS) are "close".
     

docs/_static/doctools.js

@@ -154,9 +154,7 @@ var Documentation = {
     this.fixFirefoxAnchorBug();
     this.highlightSearchWords();
     this.initIndexTable();
-    if (DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) {
-      this.initOnKeyListeners();
-    }
+    this.initOnKeyListeners();
   },
 
   /**
@@ -269,6 +267,13 @@ var Documentation = {
     window.history.replaceState({}, '', url);
   },
 
+   /**
+   * helper function to focus on search bar
+   */
+  focusSearchBar : function() {
+    $('input[name=q]').first().focus();
+  },
+
   /**
    * make the url absolute
    */
@@ -291,27 +296,54 @@ var Documentation = {
   },
 
   initOnKeyListeners: function() {
+    // only install a listener if it is really needed
+    if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS &&
+        !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS)
+        return;
+
     $(document).keydown(function(event) {
       var activeElementType = document.activeElement.tagName;
       // don't navigate when in search box, textarea, dropdown or button
       if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT'
-          && activeElementType !== 'BUTTON' && !event.altKey && !event.ctrlKey && !event.metaKey
-          && !event.shiftKey) {
-        switch (event.keyCode) {
-          case 37: // left
-            var prevHref = $('link[rel="prev"]').prop('href');
-            if (prevHref) {
-              window.location.href = prevHref;
-              return false;
-            }
-            break;
-          case 39: // right
-            var nextHref = $('link[rel="next"]').prop('href');
-            if (nextHref) {
-              window.location.href = nextHref;
-              return false;
-            }
-            break;
+          && activeElementType !== 'BUTTON') {
+        if (event.altKey || event.ctrlKey || event.metaKey)
+          return;
+
+          if (!event.shiftKey) {
+            switch (event.key) {
+              case 'ArrowLeft':
+                if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS)
+                  break;
+                var prevHref = $('link[rel="prev"]').prop('href');
+                if (prevHref) {
+                  window.location.href = prevHref;
+                  return false;
+                }
+                break;
+              case 'ArrowRight':
+                if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS)
+                  break;
+                var nextHref = $('link[rel="next"]').prop('href');
+                if (nextHref) {
+                  window.location.href = nextHref;
+                  return false;
+                }
+                break;
+              case 'Escape':
+                if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS)
+                  break;
+                Documentation.hideSearchWords();
+                return false;
+          }
+        }
+
+        // some keyboard layouts may need Shift to get /
+        switch (event.key) {
+          case '/':
+            if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS)
+              break;
+            Documentation.focusSearchBar();
+            return false;
         }
       }
     });

docs/_static/documentation_options.js

@@ -8,5 +8,7 @@ var DOCUMENTATION_OPTIONS = {
     LINK_SUFFIX: '.html',
     HAS_SOURCE: true,
     SOURCELINK_SUFFIX: '.txt',
-    NAVIGATION_WITH_KEYS: false
+    NAVIGATION_WITH_KEYS: false,
+    SHOW_SEARCH_SUMMARY: true,
+    ENABLE_SEARCH_SHORTCUTS: true,
 };

docs/_static/searchtools.js

@@ -172,10 +172,6 @@ var Search = {
       }
       // stem the word
       var word = stemmer.stemWord(tmp[i].toLowerCase());
-      // prevent stemmer from cutting word smaller than two chars
-      if(word.length < 3 && tmp[i].length >= 3) {
-        word = tmp[i];
-      }
       var toAppend;
       // select the correct list
       if (word[0] == '-') {
@@ -276,7 +272,7 @@ var Search = {
           setTimeout(function() {
             displayNextItem();
           }, 5);
-        } else if (DOCUMENTATION_OPTIONS.HAS_SOURCE) {
+        } else if (DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY) {
           $.ajax({url: requestUrl,
                   dataType: "text",
                   complete: function(jqxhr, textstatus) {
@@ -293,7 +289,7 @@ var Search = {
                     }, 5);
                   }});
         } else {
-          // no source available, just display title
+          // just display title
           Search.output.append(listItem);
           setTimeout(function() {
             displayNextItem();

docs/contents.html

@@ -84,6 +84,7 @@
 <li class="toctree-l2"><a class="reference internal" href="generated/adapt.parameter_based.RegularTransferNN.html">RegularTransferNN</a></li>
 <li class="toctree-l2"><a class="reference internal" href="generated/adapt.parameter_based.FineTuning.html">FineTuning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="generated/adapt.parameter_based.TransferTreeClassifier.html">TransferTreeClassifier</a></li>
+<li class="toctree-l2"><a class="reference internal" href="generated/adapt.parameter_based.TransferForestClassifier.html">TransferForestClassifier</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="#adapt-metrics">Metrics</a><ul>
@@ -262,7 +263,7 @@ <h1>ADAPT<a class="headerlink" href="#adapt" title="Permalink to this headline">
 <img alt="alternate text" src="_images/feature_based.png" />
 </figure>
 <p class="rubric">Methods</p>
-<table class="longtable docutils align-default">
+<table class="autosummary longtable docutils align-default">
 <colgroup>
 <col style="width: 10%" />
 <col style="width: 90%" />
@@ -317,7 +318,7 @@ <h1>ADAPT<a class="headerlink" href="#adapt" title="Permalink to this headline">
 <img alt="alternate text" src="_images/instance_based.png" />
 </figure>
 <p class="rubric">Methods</p>
-<table class="longtable docutils align-default">
+<table class="autosummary longtable docutils align-default">
 <colgroup>
 <col style="width: 10%" />
 <col style="width: 90%" />
@@ -358,7 +359,7 @@ <h1>ADAPT<a class="headerlink" href="#adapt" title="Permalink to this headline">
 <img alt="alternate text" src="_images/parameter_based.png" />
 </figure>
 <p class="rubric">Methods</p>
-<table class="longtable docutils align-default">
+<table class="autosummary longtable docutils align-default">
 <colgroup>
 <col style="width: 10%" />
 <col style="width: 90%" />
@@ -377,15 +378,18 @@ <h1>ADAPT<a class="headerlink" href="#adapt" title="Permalink to this headline">
 <td><p>FineTuning : finetunes pretrained networks on target data.</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="generated/adapt.parameter_based.TransferTreeClassifier.html#adapt.parameter_based.TransferTreeClassifier" title="adapt.parameter_based.TransferTreeClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameter_based.TransferTreeClassifier</span></code></a>([...])</p></td>
-<td><p>TBA</p></td>
+<td><p>TransferTreeClassifier: Modify a source Decision tree on a target dataset.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/adapt.parameter_based.TransferForestClassifier.html#adapt.parameter_based.TransferForestClassifier" title="adapt.parameter_based.TransferForestClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameter_based.TransferForestClassifier</span></code></a>([...])</p></td>
+<td><p>TransferForestClassifier: Modify a source Random Forest on a target dataset.</p></td>
 </tr>
 </tbody>
 </table>
 </section>
 <section id="adapt-metrics-metrics">
 <span id="adapt-metrics"></span><h2><a class="reference internal" href="#adapt-metrics"><span class="std std-ref">adapt.metrics</span></a>: Metrics<a class="headerlink" href="#adapt-metrics-metrics" title="Permalink to this headline"></a></h2>
 <p>This module contains functions to compute adaptation metrics.</p>
-<table class="longtable docutils align-default">
+<table class="autosummary longtable docutils align-default">
 <colgroup>
 <col style="width: 10%" />
 <col style="width: 90%" />
@@ -424,7 +428,7 @@ <h1>ADAPT<a class="headerlink" href="#adapt" title="Permalink to this headline">
 <section id="adapt-utils-utility-functions">
 <span id="adapt-utils"></span><h2><a class="reference internal" href="#adapt-utils"><span class="std std-ref">adapt.utils</span></a>: Utility Functions<a class="headerlink" href="#adapt-utils-utility-functions" title="Permalink to this headline"></a></h2>
 <p>This module contains utility functions used in the previous modules.</p>
-<table class="longtable docutils align-default">
+<table class="autosummary longtable docutils align-default">
 <colgroup>
 <col style="width: 10%" />
 <col style="width: 90%" />

docs/examples/Classification.html

@@ -84,6 +84,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../generated/adapt.parameter_based.RegularTransferNN.html">RegularTransferNN</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../generated/adapt.parameter_based.FineTuning.html">FineTuning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../generated/adapt.parameter_based.TransferTreeClassifier.html">TransferTreeClassifier</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/adapt.parameter_based.TransferForestClassifier.html">TransferForestClassifier</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../contents.html#adapt-metrics">Metrics</a><ul>
@@ -477,8 +478,8 @@
 </style>
 <section id="Classification">
 <h1>Classification<a class="headerlink" href="#Classification" title="Permalink to this headline"></a></h1>
-<div class="btn btn-notebook" role="button"><p><img alt="a966f76b77f24009aa977763ca649bb9" src="../_images/colab_logo_32px.png" /> <a class="reference external" href="https://colab.research.google.com/drive/1ANQUix9Y6V4RXu-vAaCFGmU979d5m4bO?usp=sharing">Run in Google Colab</a></p>
-</div><div class="btn btn-notebook" role="button"><p><img alt="4335e175433742a2ad920bb842514876" src="../_images/github_logo_32px.png" /> <a class="reference external" href="https://github.com/adapt-python/notebooks/blob/d0364973c642ea4880756cef4e9f2ee8bb5e8495/Classification.ipynb">View on GitHub</a></p>
+<div class="btn btn-notebook" role="button"><p><img alt="7d3a3b28b0f74e78bfa0fc52c3456b1e" src="../_images/colab_logo_32px.png" /> <a class="reference external" href="https://colab.research.google.com/drive/1ANQUix9Y6V4RXu-vAaCFGmU979d5m4bO?usp=sharing">Run in Google Colab</a></p>
+</div><div class="btn btn-notebook" role="button"><p><img alt="185060b5f0d34e42bf0fe659ce25e540" src="../_images/github_logo_32px.png" /> <a class="reference external" href="https://github.com/adapt-python/notebooks/blob/d0364973c642ea4880756cef4e9f2ee8bb5e8495/Classification.ipynb">View on GitHub</a></p>
 </div><p>You will find here the application of DA methods from the ADAPT package on a simple two dimensional DA classification problem.</p>
 <p>First we import packages needed in the following. We will use <code class="docutils literal notranslate"><span class="pre">matplotlib</span> <span class="pre">Animation</span></code> tools in order to get a visual understanding of the mselected methods:</p>
 <div class="nbinput nblast docutils container">

docs/examples/Multi_fidelity.html

@@ -84,6 +84,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../generated/adapt.parameter_based.RegularTransferNN.html">RegularTransferNN</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../generated/adapt.parameter_based.FineTuning.html">FineTuning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../generated/adapt.parameter_based.TransferTreeClassifier.html">TransferTreeClassifier</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/adapt.parameter_based.TransferForestClassifier.html">TransferForestClassifier</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../contents.html#adapt-metrics">Metrics</a><ul>
@@ -477,8 +478,8 @@
 </style>
 <section id="Multi-Fidelity">
 <h1>Multi-Fidelity<a class="headerlink" href="#Multi-Fidelity" title="Permalink to this headline"></a></h1>
-<div class="btn btn-notebook" role="button"><p><img alt="30ffe81bd05f45ea916075bb1f6386f1" src="../_images/colab_logo_32px.png" /> <a class="reference external" href="https://colab.research.google.com/drive/1Cc9TVY_Tl_boVzZDNisQnqe6Qx78svqe?usp=sharing">Run in Google Colab</a></p>
-</div><div class="btn btn-notebook" role="button"><p><img alt="d04db52ebb1f471a93a750710c96c5a7" src="../_images/github_logo_32px.png" /> <a class="reference external" href="https://github.com/adapt-python/notebooks/blob/d0364973c642ea4880756cef4e9f2ee8bb5e8495/Multi_fidelity.ipynb">View on GitHub</a></p>
+<div class="btn btn-notebook" role="button"><p><img alt="2cb04df9d25e4a5d9f33c4ca995536ec" src="../_images/colab_logo_32px.png" /> <a class="reference external" href="https://colab.research.google.com/drive/1Cc9TVY_Tl_boVzZDNisQnqe6Qx78svqe?usp=sharing">Run in Google Colab</a></p>
+</div><div class="btn btn-notebook" role="button"><p><img alt="532b34ba0bb048abb10480d897b56cfd" src="../_images/github_logo_32px.png" /> <a class="reference external" href="https://github.com/adapt-python/notebooks/blob/d0364973c642ea4880756cef4e9f2ee8bb5e8495/Multi_fidelity.ipynb">View on GitHub</a></p>
 </div><p>The following example is a 1D regression multi-fidelity issue. Blue points are low fidelity observations and orange points are high fidelity observations. The goal is to use both datasets to learn the task on the [0, 1] interval.</p>
 <p>To tackle this challenge, we use here the parameter-based method: <a class="reference external" href="#RegularTransferNN">RegularTransferNN</a></p>
 <div class="nbinput nblast docutils container">

docs/examples/Regression.html

@@ -84,6 +84,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../generated/adapt.parameter_based.RegularTransferNN.html">RegularTransferNN</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../generated/adapt.parameter_based.FineTuning.html">FineTuning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../generated/adapt.parameter_based.TransferTreeClassifier.html">TransferTreeClassifier</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/adapt.parameter_based.TransferForestClassifier.html">TransferForestClassifier</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../contents.html#adapt-metrics">Metrics</a><ul>
@@ -477,8 +478,8 @@
 </style>
 <section id="Toy-Regression">
 <h1>Toy Regression<a class="headerlink" href="#Toy-Regression" title="Permalink to this headline"></a></h1>
-<div class="btn btn-notebook" role="button"><p><img alt="b653644c0aeb42a582c3f958545c1ad0" src="../_images/colab_logo_32px.png" /> <a class="reference external" href="https://colab.research.google.com/drive/1adhqoV6b0uEavLDmMfkiwtRjam0DrXux?usp=sharing">Run in Google Colab</a></p>
-</div><div class="btn btn-notebook" role="button"><p><img alt="0048eac286214a80b6c3a06c47700b07" src="../_images/github_logo_32px.png" /> <a class="reference external" href="https://github.com/adapt-python/notebooks/blob/d0364973c642ea4880756cef4e9f2ee8bb5e8495/Regression.ipynb">View on GitHub</a></p>
+<div class="btn btn-notebook" role="button"><p><img alt="67ee9f06163c4803af3baf522a13f11c" src="../_images/colab_logo_32px.png" /> <a class="reference external" href="https://colab.research.google.com/drive/1adhqoV6b0uEavLDmMfkiwtRjam0DrXux?usp=sharing">Run in Google Colab</a></p>
+</div><div class="btn btn-notebook" role="button"><p><img alt="2062a87673f04f2ba0e0e4f78a94694f" src="../_images/github_logo_32px.png" /> <a class="reference external" href="https://github.com/adapt-python/notebooks/blob/d0364973c642ea4880756cef4e9f2ee8bb5e8495/Regression.ipynb">View on GitHub</a></p>
 </div><p>You will find here the application of DA methods from the ADAPT package on a simple one dimensional DA regression problem.</p>
 <p>First we import packages needed in the following. We will use <code class="docutils literal notranslate"><span class="pre">matplotlib</span> <span class="pre">Animation</span></code> tools in order to get a visual understanding of the selected methods:</p>
 <div class="nbinput nblast docutils container">

docs/examples/Rotation.html

@@ -84,6 +84,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../generated/adapt.parameter_based.RegularTransferNN.html">RegularTransferNN</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../generated/adapt.parameter_based.FineTuning.html">FineTuning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../generated/adapt.parameter_based.TransferTreeClassifier.html">TransferTreeClassifier</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/adapt.parameter_based.TransferForestClassifier.html">TransferForestClassifier</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../contents.html#adapt-metrics">Metrics</a><ul>
@@ -477,8 +478,8 @@
 </style>
 <section id="Rotation">
 <h1>Rotation<a class="headerlink" href="#Rotation" title="Permalink to this headline"></a></h1>
-<div class="btn btn-notebook" role="button"><p><img alt="3f548eeb62484142857cf0d514cc73f7" src="../_images/colab_logo_32px.png" /> <a class="reference external" href="https://colab.research.google.com/drive/1XePW12UF80PKzvLu9cyRJKWQoZIxk_J2?usp=sharing">Run in Google Colab</a></p>
-</div><div class="btn btn-notebook" role="button"><p><img alt="51599fe3c6584045a915b0fe378ac67b" src="../_images/github_logo_32px.png" /> <a class="reference external" href="https://github.com/adapt-python/notebooks/blob/d0364973c642ea4880756cef4e9f2ee8bb5e8495/Rotation.ipynb">View on GitHub</a></p>
+<div class="btn btn-notebook" role="button"><p><img alt="cf8e6b5ca909497caf99c1844f5b6462" src="../_images/colab_logo_32px.png" /> <a class="reference external" href="https://colab.research.google.com/drive/1XePW12UF80PKzvLu9cyRJKWQoZIxk_J2?usp=sharing">Run in Google Colab</a></p>
+</div><div class="btn btn-notebook" role="button"><p><img alt="e0fff568b24a40b8833e086904618346" src="../_images/github_logo_32px.png" /> <a class="reference external" href="https://github.com/adapt-python/notebooks/blob/d0364973c642ea4880756cef4e9f2ee8bb5e8495/Rotation.ipynb">View on GitHub</a></p>
 </div><div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
 </pre></div>