Merge pull request #22 from beringresearch/ivis-supvised-softmax

Ivis supvised softmax

Author: idroz

.gitignore

@@ -7,3 +7,4 @@ __pycache__/
 *.swp
 .ipynb_checkpoints
 _build
+*.pyc

docs/_static/classification-weight-impact-mnist.png

@@ -13,7 +13,27 @@ Several examples of how ivis can be used in common machine learning tasks.
 
 .. raw:: html
 
-    <div class="sphx-glr-thumbcontainer" tooltip="Example of reducing dimensionality of the iris dataset using ivis. ">
+    <div class="sphx-glr-thumbcontainer" tooltip="Ivis can be applied easily applied to unstructured datasets, including images. Here we visualis...">
+
+.. only:: html
+
+    .. figure:: /auto_examples/images/thumb/sphx_glr_mnist_thumb.png
+
+        :ref:`sphx_glr_auto_examples_mnist.py`
+
+.. raw:: html
+
+    </div>
+
+
+.. toctree::
+   :hidden:
+
+   /auto_examples/mnist
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="Example of reducing dimensionality of the iris dataset using ivis.">
 
 .. only:: html
 

docs/_static/mnist-embedding-comparison_titled.png

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\niris dataset\n============\n\nExample of reducing dimensionality of the iris dataset using ivis.\n\n"
+        "\niris dataset\n============\n\nExample of reducing dimensionality of the iris dataset using ivis.\n"
       ]
     },
     {
@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "import seaborn as sns\nimport matplotlib.pyplot as plt\nfrom sklearn.datasets import load_iris\nfrom sklearn.preprocessing import MinMaxScaler\n\nfrom ivis import Ivis\n\nsns.set(context='paper', style='white')\n\nX = load_iris().data\nX = MinMaxScaler().fit_transform(X)\n\nivis = Ivis(k=3, batch_size=120, model='maaten')\nivis.fit(X)\n\nembeddings = ivis.transform(X)\n\nfig, ax = plt.subplots(figsize=(12, 10))\nplt.scatter(embeddings[:, 0],\n            embeddings[:, 1],\n            c=load_iris().target, cmap='Spectral', s=1)\nplt.setp(ax, xticks=[], yticks=[])\nplt.title('ivis embeddings of the iris dataset', fontsize=18)\n\nplt.show()"
+        "import seaborn as sns\nimport matplotlib.pyplot as plt\nfrom sklearn.datasets import load_iris\nfrom sklearn.preprocessing import MinMaxScaler\n\nfrom ivis import Ivis\n\nsns.set(context='paper', style='white')\n\nX = load_iris().data\nX = MinMaxScaler().fit_transform(X)\n\nivis = Ivis(k=5, model='maaten', verbose=0)\nivis.fit(X)\n\nembeddings = ivis.transform(X)\n\nplt.figure(figsize=(5, 5), dpi=100)\nplt.scatter(embeddings[:, 0],\n            embeddings[:, 1],\n            c=load_iris().target, s=20)\nplt.xlabel('ivis 1')\nplt.ylabel('ivis 2')\nplt.title('ivis embeddings of the iris dataset')\n\nplt.show()"
       ]
     }
   ],
@@ -46,7 +46,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.3"
     }
   },
   "nbformat": 4,

docs/auto_examples/auto_examples_jupyter.zip

@@ -17,16 +17,17 @@
 X = load_iris().data
 X = MinMaxScaler().fit_transform(X)
 
-ivis = Ivis(k=3, batch_size=120, model='maaten')
+ivis = Ivis(k=5, model='maaten', verbose=0)
 ivis.fit(X)
 
 embeddings = ivis.transform(X)
 
-fig, ax = plt.subplots(figsize=(12, 10))
+plt.figure(figsize=(5, 5), dpi=100)
 plt.scatter(embeddings[:, 0],
             embeddings[:, 1],
-            c=load_iris().target, cmap='Spectral', s=1)
-plt.setp(ax, xticks=[], yticks=[])
-plt.title('ivis embeddings of the iris dataset', fontsize=18)
+            c=load_iris().target, s=20)
+plt.xlabel('ivis 1')
+plt.ylabel('ivis 2')
+plt.title('ivis embeddings of the iris dataset')
 
 plt.show()

docs/auto_examples/auto_examples_python.zip

@@ -12,7 +12,6 @@ iris dataset
 
 Example of reducing dimensionality of the iris dataset using ivis.
 
-
 .. code-block:: default
 
 
@@ -28,17 +27,18 @@ Example of reducing dimensionality of the iris dataset using ivis.
     X = load_iris().data
     X = MinMaxScaler().fit_transform(X)
 
-    ivis = Ivis(k=3, batch_size=120, model='maaten')
+    ivis = Ivis(k=5, model='maaten', verbose=0)
     ivis.fit(X)
 
     embeddings = ivis.transform(X)
 
-    fig, ax = plt.subplots(figsize=(12, 10))
+    plt.figure(figsize=(5, 5), dpi=100)
     plt.scatter(embeddings[:, 0],
                 embeddings[:, 1],
-                c=load_iris().target, cmap='Spectral', s=1)
-    plt.setp(ax, xticks=[], yticks=[])
-    plt.title('ivis embeddings of the iris dataset', fontsize=18)
+                c=load_iris().target, s=20)
+    plt.xlabel('ivis 1')
+    plt.ylabel('ivis 2')
+    plt.title('ivis embeddings of the iris dataset')
 
     plt.show()