add usecase examples

doc/tutorial/auto_examples/usecase/plot_usecase_exampleMKL.ipynb

+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Use Case MKL\n\nUse case for all classifier of multimodallearn MKL\nmulti class digit from sklearn, multivue\n - vue 0 digit data (color of sklearn)\n - vue 1 gradiant of image in first direction\n - vue 2 gradiant of image in second direction\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from __future__ import absolute_import\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.multiclass import OneVsOneClassifier\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.tree import DecisionTreeClassifier\nfrom multimodal.datasets.base import load_dict, save_dict\nfrom multimodal.tests.data.get_dataset_path import get_dataset_path\nfrom multimodal.datasets.data_sample import MultiModalArray\nfrom multimodal.kernels.mvml import MVML\nfrom multimodal.kernels.lpMKL import MKL\n\nfrom usecase_function import plot_subplot\n\nif __name__ == '__main__':\n    # file = get_dataset_path(\"digit_histogram.npy\")\n    file = get_dataset_path(\"digit_col_grad.npy\")\n    y = np.load(get_dataset_path(\"digit_y.npy\"))\n    base_estimator = DecisionTreeClassifier(max_depth=4)\n    dic_digit = load_dict(file)\n    XX =MultiModalArray(dic_digit)\n    X_train, X_test, y_train, y_test = train_test_split(XX, y)\n\n    est4 = OneVsOneClassifier(MKL(lmbda=0.1, nystrom_param=0.2)).fit(X_train, y_train)\n    y_pred4 = est4.predict(X_test)\n    y_pred44 = est4.predict(X_train)\n    print(\"result of MKL on digit with oneversone\")\n    result4 = np.mean(y_pred4.ravel() == y_test.ravel()) * 100\n    print(result4)\n\n    fig = plt.figure(figsize=(12., 11.))\n    fig.suptitle(\"MKL : result\" + str(result4), fontsize=16)\n    plot_subplot(X_train, y_train, y_pred44  ,0, (4, 1, 1), \"train vue 0\" )\n    plot_subplot(X_test, y_test,y_pred4 , 0, (4, 1, 2), \"test vue 0\" )\n    plot_subplot(X_test, y_test, y_pred4,1, (4, 1, 3), \"test vue 1\" )\n    plot_subplot(X_test, y_test,y_pred4, 2, (4, 1, 4), \"test vue 2\" )\n    # plt.legend()\n    plt.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.8"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file

examples/usecase/usecase_example.py → doc/tutorial/auto_examples/usecase/plot_usecase_exampleMKL.py

 # -*- coding: utf-8 -*-
 """
-========
-Use Case
-========
-Use case for all classifier of multimodallearn (in file mvml.py) is intended to be used with very simple simulated dataset
-
+============
+Use Case MKL
+============
+Use case for all classifier of multimodallearn MKL
 multi class digit from sklearn, multivue
  - vue 0 digit data (color of sklearn)
  - vue 1 gradiant of image in first direction
  - vue 2 gradiant of image in second direction
 
 """
-
+from __future__ import absolute_import
 import numpy as np
 import matplotlib.pyplot as plt
-import matplotlib._color_data as mcd
-from sklearn import datasets
-from sklearn.multiclass import OneVsRestClassifier
 from sklearn.multiclass import OneVsOneClassifier
 from sklearn.model_selection import train_test_split
 from sklearn.tree import DecisionTreeClassifier
@@ -25,27 +21,8 @@ from multimodal.tests.data.get_dataset_path import get_dataset_path
 from multimodal.datasets.data_sample import MultiModalArray
 from multimodal.kernels.mvml import MVML
 from multimodal.kernels.lpMKL import MKL
-from multimodal.boosting.mumbo import MumboClassifier
-from multimodal.boosting.cumbo import MuCumboClassifier
-
-def plot_subplot(X, Y, Y_pred, vue, subplot, title):
-    cn = mcd.CSS4_COLORS
-    classes = np.unique(Y)
-    n_classes = len(np.unique(Y))
-    axs = plt.subplot(subplot[0],subplot[1],subplot[2])
-    axs.set_title(title)
-    #plt.scatter(X._extract_view(vue), X._extract_view(vue), s=40, c='gray',
-    #            edgecolors=(0, 0, 0))
-    for index, k in zip(range(n_classes), cn.keys()):
-         Y_class, = np.where(Y==classes[index])
-         Y_class_pred = np.intersect1d(np.where(Y_pred==classes[index])[0], np.where(Y_pred==Y)[0])
-         plt.scatter(X._extract_view(vue)[Y_class],
-                     X._extract_view(vue)[Y_class],
-                     s=40, c=cn[k], edgecolors='blue', linewidths=2, label="class real class: "+str(index)) #
-         plt.scatter(X._extract_view(vue)[Y_class_pred],
-                     X._extract_view(vue)[Y_class_pred],
-                     s=160, edgecolors='orange', linewidths=2, label="class prediction: "+str(index))
 
+from usecase_function import plot_subplot
 
 if __name__ == '__main__':
     # file = get_dataset_path("digit_histogram.npy")
@@ -55,25 +32,6 @@ if __name__ == '__main__':
     dic_digit = load_dict(file)
     XX =MultiModalArray(dic_digit)
     X_train, X_test, y_train, y_test = train_test_split(XX, y)
-    est1 = OneVsOneClassifier(MVML(lmbda=0.1, eta=1, nystrom_param=0.2)).fit(X_train, y_train)
-    y_pred1 = est1.predict(X_test)
-    y_pred11 = est1.predict(X_train)
-    print("result of MVML on digit with oneversone")
-    result1 = np.mean(y_pred1.ravel() == y_test.ravel()) * 100
-    print(result1)
-    est2 = MumboClassifier(base_estimator=base_estimator).fit(X_train, y_train)
-    y_pred2 = est2.predict(X_test)
-    y_pred22 = est2.predict(X_train)
-    print("result of MumboClassifier on digit ")
-    result2 = np.mean(y_pred2.ravel() == y_test.ravel()) * 100
-    print(result2)
-
-    est3 = MuCumboClassifier(base_estimator=base_estimator).fit(X_train, y_train)
-    y_pred3 = est3.predict(X_test)
-    y_pred33 = est3.predict(X_train)
-    print("result of MuCumboClassifier on digit ")
-    result3 = np.mean(y_pred3.ravel() == y_test.ravel()) * 100
-    print(result3)
 
     est4 = OneVsOneClassifier(MKL(lmbda=0.1, nystrom_param=0.2)).fit(X_train, y_train)
     y_pred4 = est4.predict(X_test)
@@ -90,30 +48,4 @@ if __name__ == '__main__':
     plot_subplot(X_test, y_test,y_pred4, 2, (4, 1, 4), "test vue 2" )
     # plt.legend()
     plt.show()
-    fig = plt.figure(figsize=(12., 11.))
-    fig.suptitle("MuCumbo: result" + str(result3), fontsize=16)
-    plot_subplot(X_train, y_train, y_pred33  ,0, (4, 1, 1), "train vue 0" )
-    plot_subplot(X_test, y_test,y_pred3 , 0, (4, 1, 2), "test vue 0" )
-    plot_subplot(X_test, y_test, y_pred3,1, (4, 1, 3), "test vue 1" )
-    plot_subplot(X_test, y_test,y_pred3, 2, (4, 1, 4), "test vue 2" )
-    # plt.legend()
-    plt.show()
-    fig = plt.figure(figsize=(12., 11.))
-    fig.suptitle("Mumbo: result" + str(result2), fontsize=16)
-    plot_subplot(X_train, y_train, y_pred22 , 0, (4, 1, 1), "train vue 0" )
-    plot_subplot(X_test, y_test,y_pred2, 0, (4, 1, 2), "test vue 0" )
-    plot_subplot(X_test, y_test, y_pred2, 1, (4, 1, 3), "test vue 1" )
-    plot_subplot(X_test, y_test,y_pred2, 2, (4, 1, 4), "test vue 2" )
-    # plt.legend()
-    plt.show()
-    fig = plt.figure(figsize=(12., 11.))
-    fig.suptitle("MVML: result" + str(result1), fontsize=16)
-    plot_subplot(X_train, y_train, y_pred11
-                 , 0, (4, 1, 1), "train vue 0" )
-    plot_subplot(X_test, y_test,y_pred1, 0, (4, 1, 2), "test vue 0" )
-    plot_subplot(X_test, y_test, y_pred1, 1, (4, 1, 3), "test vue 1" )
-    plot_subplot(X_test, y_test,y_pred1, 2, (4, 1, 4), "test vue 2" )
-    #plt.legend()
-    plt.show()
-    #mvml = MVML(lmbda=0.1, eta=1, nystrom_param=0.2)
-    #mvml.fit(dic_digit_histo, y)
+

doc/tutorial/auto_examples/usecase/plot_usecase_exampleMKL.py.md5

+4f807359096f5f5b3a7ee6b3ea540b91
\ No newline at end of file

doc/tutorial/auto_examples/usecase/plot_usecase_exampleMKL.rst

+.. note::
+    :class: sphx-glr-download-link-note
+
+    Click :ref:`here <sphx_glr_download_tutorial_auto_examples_usecase_plot_usecase_exampleMKL.py>` to download the full example code
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_tutorial_auto_examples_usecase_plot_usecase_exampleMKL.py:
+
+
+============
+Use Case MKL
+============
+Use case for all classifier of multimodallearn MKL
+multi class digit from sklearn, multivue
+ - vue 0 digit data (color of sklearn)
+ - vue 1 gradiant of image in first direction
+ - vue 2 gradiant of image in second direction
+
+
+
+
+.. image:: /tutorial/auto_examples/usecase/images/sphx_glr_plot_usecase_exampleMKL_001.png
+    :class: sphx-glr-single-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    result of MKL on digit with oneversone
+    98.44444444444444
+    /home/dominique/projets/ANR-Lives/scikit-multimodallearn/examples/usecase/plot_usecase_exampleMKL.py:50: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
+      plt.show()
+
+
+
+
+
+
+|
+
+
+.. code-block:: default
+
+    from __future__ import absolute_import
+    import numpy as np
+    import matplotlib.pyplot as plt
+    from sklearn.multiclass import OneVsOneClassifier
+    from sklearn.model_selection import train_test_split
+    from sklearn.tree import DecisionTreeClassifier
+    from multimodal.datasets.base import load_dict, save_dict
+    from multimodal.tests.data.get_dataset_path import get_dataset_path
+    from multimodal.datasets.data_sample import MultiModalArray
+    from multimodal.kernels.mvml import MVML
+    from multimodal.kernels.lpMKL import MKL
+
+    from usecase_function import plot_subplot
+
+    if __name__ == '__main__':
+        # file = get_dataset_path("digit_histogram.npy")
+        file = get_dataset_path("digit_col_grad.npy")
+        y = np.load(get_dataset_path("digit_y.npy"))
+        base_estimator = DecisionTreeClassifier(max_depth=4)
+        dic_digit = load_dict(file)
+        XX =MultiModalArray(dic_digit)
+        X_train, X_test, y_train, y_test = train_test_split(XX, y)
+
+        est4 = OneVsOneClassifier(MKL(lmbda=0.1, nystrom_param=0.2)).fit(X_train, y_train)
+        y_pred4 = est4.predict(X_test)
+        y_pred44 = est4.predict(X_train)
+        print("result of MKL on digit with oneversone")
+        result4 = np.mean(y_pred4.ravel() == y_test.ravel()) * 100
+        print(result4)
+
+        fig = plt.figure(figsize=(12., 11.))
+        fig.suptitle("MKL : result" + str(result4), fontsize=16)
+        plot_subplot(X_train, y_train, y_pred44  ,0, (4, 1, 1), "train vue 0" )
+        plot_subplot(X_test, y_test,y_pred4 , 0, (4, 1, 2), "test vue 0" )
+        plot_subplot(X_test, y_test, y_pred4,1, (4, 1, 3), "test vue 1" )
+        plot_subplot(X_test, y_test,y_pred4, 2, (4, 1, 4), "test vue 2" )
+        # plt.legend()
+        plt.show()
+
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** ( 0 minutes  12.697 seconds)
+
+
+.. _sphx_glr_download_tutorial_auto_examples_usecase_plot_usecase_exampleMKL.py:
+
+
+.. only :: html
+
+ .. container:: sphx-glr-footer
+    :class: sphx-glr-footer-example
+
+
+
+  .. container:: sphx-glr-download
+
+     :download:`Download Python source code: plot_usecase_exampleMKL.py <plot_usecase_exampleMKL.py>`
+
+
+
+  .. container:: sphx-glr-download
+
+     :download:`Download Jupyter notebook: plot_usecase_exampleMKL.ipynb <plot_usecase_exampleMKL.ipynb>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_

doc/tutorial/auto_examples/usecase/plot_usecase_exampleMVML.ipynb

+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Use Case of MVML\n\nUse case for all classifier of multimodallearn MVML\n\nmulti class digit from sklearn, multivue\n - vue 0 digit data (color of sklearn)\n - vue 1 gradiant of image in first direction\n - vue 2 gradiant of image in second direction\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from __future__ import absolute_import\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.multiclass import OneVsOneClassifier\nfrom sklearn.model_selection import train_test_split\nfrom multimodal.datasets.base import load_dict, save_dict\nfrom multimodal.tests.data.get_dataset_path import get_dataset_path\nfrom multimodal.datasets.data_sample import MultiModalArray\nfrom multimodal.kernels.mvml import MVML\nfrom usecase_function import plot_subplot\n\n\nif __name__ == '__main__':\n    # file = get_dataset_path(\"digit_histogram.npy\")\n    file = get_dataset_path(\"digit_col_grad.npy\")\n    y = np.load(get_dataset_path(\"digit_y.npy\"))\n    dic_digit = load_dict(file)\n    XX =MultiModalArray(dic_digit)\n    X_train, X_test, y_train, y_test = train_test_split(XX, y)\n    est1 = OneVsOneClassifier(MVML(lmbda=0.1, eta=1, nystrom_param=0.2)).fit(X_train, y_train)\n    y_pred1 = est1.predict(X_test)\n    y_pred11 = est1.predict(X_train)\n    print(\"result of MVML on digit with oneversone\")\n    result1 = np.mean(y_pred1.ravel() == y_test.ravel()) * 100\n    print(result1)\n\n    fig = plt.figure(figsize=(12., 11.))\n    fig.suptitle(\"MVML: result\" + str(result1), fontsize=16)\n    plot_subplot(X_train, y_train, y_pred11\n                 , 0, (4, 1, 1), \"train vue 0\" )\n    plot_subplot(X_test, y_test,y_pred1, 0, (4, 1, 2), \"test vue 0\" )\n    plot_subplot(X_test, y_test, y_pred1, 1, (4, 1, 3), \"test vue 1\" )\n    plot_subplot(X_test, y_test,y_pred1, 2, (4, 1, 4), \"test vue 2\" )\n    #plt.legend()\n    plt.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.8"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file

doc/tutorial/auto_examples/usecase/plot_usecase_exampleMVML.py

+# -*- coding: utf-8 -*-
+"""
+================
+Use Case of MVML
+================
+Use case for all classifier of multimodallearn MVML
+
+multi class digit from sklearn, multivue
+ - vue 0 digit data (color of sklearn)
+ - vue 1 gradiant of image in first direction
+ - vue 2 gradiant of image in second direction
+
+"""
+from __future__ import absolute_import
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.multiclass import OneVsOneClassifier
+from sklearn.model_selection import train_test_split
+from multimodal.datasets.base import load_dict, save_dict
+from multimodal.tests.data.get_dataset_path import get_dataset_path
+from multimodal.datasets.data_sample import MultiModalArray
+from multimodal.kernels.mvml import MVML
+from usecase_function import plot_subplot
+
+
+if __name__ == '__main__':
+    # file = get_dataset_path("digit_histogram.npy")
+    file = get_dataset_path("digit_col_grad.npy")
+    y = np.load(get_dataset_path("digit_y.npy"))
+    dic_digit = load_dict(file)
+    XX =MultiModalArray(dic_digit)
+    X_train, X_test, y_train, y_test = train_test_split(XX, y)
+    est1 = OneVsOneClassifier(MVML(lmbda=0.1, eta=1, nystrom_param=0.2)).fit(X_train, y_train)
+    y_pred1 = est1.predict(X_test)
+    y_pred11 = est1.predict(X_train)
+    print("result of MVML on digit with oneversone")
+    result1 = np.mean(y_pred1.ravel() == y_test.ravel()) * 100
+    print(result1)
+
+    fig = plt.figure(figsize=(12., 11.))
+    fig.suptitle("MVML: result" + str(result1), fontsize=16)
+    plot_subplot(X_train, y_train, y_pred11
+                 , 0, (4, 1, 1), "train vue 0" )
+    plot_subplot(X_test, y_test,y_pred1, 0, (4, 1, 2), "test vue 0" )
+    plot_subplot(X_test, y_test, y_pred1, 1, (4, 1, 3), "test vue 1" )
+    plot_subplot(X_test, y_test,y_pred1, 2, (4, 1, 4), "test vue 2" )
+    #plt.legend()
+    plt.show()
+

doc/tutorial/auto_examples/usecase/plot_usecase_exampleMVML.py.md5

+b4b4bb03418027ba62ce77c251085cf5
\ No newline at end of file

doc/tutorial/auto_examples/usecase/plot_usecase_exampleMVML.rst

+.. note::
+    :class: sphx-glr-download-link-note
+
+    Click :ref:`here <sphx_glr_download_tutorial_auto_examples_usecase_plot_usecase_exampleMVML.py>` to download the full example code
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_tutorial_auto_examples_usecase_plot_usecase_exampleMVML.py:
+
+
+================
+Use Case of MVML
+================
+Use case for all classifier of multimodallearn MVML
+
+multi class digit from sklearn, multivue
+ - vue 0 digit data (color of sklearn)
+ - vue 1 gradiant of image in first direction
+ - vue 2 gradiant of image in second direction
+
+
+
+
+.. image:: /tutorial/auto_examples/usecase/images/sphx_glr_plot_usecase_exampleMVML_001.png
+    :class: sphx-glr-single-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    result of MVML on digit with oneversone
+    98.88888888888889
+    /home/dominique/projets/ANR-Lives/scikit-multimodallearn/examples/usecase/plot_usecase_exampleMVML.py:48: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
+      plt.show()
+
+
+
+
+
+
+|
+
+
+.. code-block:: default
+
+    from __future__ import absolute_import
+    import numpy as np
+    import matplotlib.pyplot as plt
+    from sklearn.multiclass import OneVsOneClassifier
+    from sklearn.model_selection import train_test_split
+    from multimodal.datasets.base import load_dict, save_dict
+    from multimodal.tests.data.get_dataset_path import get_dataset_path
+    from multimodal.datasets.data_sample import MultiModalArray
+    from multimodal.kernels.mvml import MVML
+    from usecase_function import plot_subplot
+
+
+    if __name__ == '__main__':
+        # file = get_dataset_path("digit_histogram.npy")
+        file = get_dataset_path("digit_col_grad.npy")
+        y = np.load(get_dataset_path("digit_y.npy"))
+        dic_digit = load_dict(file)
+        XX =MultiModalArray(dic_digit)
+        X_train, X_test, y_train, y_test = train_test_split(XX, y)
+        est1 = OneVsOneClassifier(MVML(lmbda=0.1, eta=1, nystrom_param=0.2)).fit(X_train, y_train)
+        y_pred1 = est1.predict(X_test)
+        y_pred11 = est1.predict(X_train)
+        print("result of MVML on digit with oneversone")
+        result1 = np.mean(y_pred1.ravel() == y_test.ravel()) * 100
+        print(result1)
+
+        fig = plt.figure(figsize=(12., 11.))
+        fig.suptitle("MVML: result" + str(result1), fontsize=16)
+        plot_subplot(X_train, y_train, y_pred11
+                     , 0, (4, 1, 1), "train vue 0" )
+        plot_subplot(X_test, y_test,y_pred1, 0, (4, 1, 2), "test vue 0" )
+        plot_subplot(X_test, y_test, y_pred1, 1, (4, 1, 3), "test vue 1" )
+        plot_subplot(X_test, y_test,y_pred1, 2, (4, 1, 4), "test vue 2" )
+        #plt.legend()
+        plt.show()
+
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** ( 0 minutes  39.921 seconds)
+
+
+.. _sphx_glr_download_tutorial_auto_examples_usecase_plot_usecase_exampleMVML.py:
+
+
+.. only :: html
+
+ .. container:: sphx-glr-footer
+    :class: sphx-glr-footer-example
+
+
+
+  .. container:: sphx-glr-download
+
+     :download:`Download Python source code: plot_usecase_exampleMVML.py <plot_usecase_exampleMVML.py>`
+
+
+
+  .. container:: sphx-glr-download
+
+     :download:`Download Jupyter notebook: plot_usecase_exampleMVML.ipynb <plot_usecase_exampleMVML.ipynb>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_

doc/tutorial/auto_examples/usecase/plot_usecase_exampleMuCuBo.ipynb

+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Use Case MuCumBo\n\nUse case for all classifier of multimodallearn  MuCumBo\n\nmulti class digit from sklearn, multivue\n - vue 0 digit data (color of sklearn)\n - vue 1 gradiant of image in first direction\n - vue 2 gradiant of image in second direction\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from __future__ import absolute_import\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.tree import DecisionTreeClassifier\nfrom multimodal.datasets.base import load_dict, save_dict\nfrom multimodal.tests.data.get_dataset_path import get_dataset_path\nfrom multimodal.datasets.data_sample import MultiModalArray\n\nfrom multimodal.boosting.cumbo import MuCumboClassifier\nfrom usecase_function import plot_subplot\n\n\nif __name__ == '__main__':\n    # file = get_dataset_path(\"digit_histogram.npy\")\n    file = get_dataset_path(\"digit_col_grad.npy\")\n    y = np.load(get_dataset_path(\"digit_y.npy\"))\n    base_estimator = DecisionTreeClassifier(max_depth=4)\n    dic_digit = load_dict(file)\n    XX =MultiModalArray(dic_digit)\n    X_train, X_test, y_train, y_test = train_test_split(XX, y)\n    est3 = MuCumboClassifier(base_estimator=base_estimator).fit(X_train, y_train)\n    y_pred3 = est3.predict(X_test)\n    y_pred33 = est3.predict(X_train)\n    print(\"result of MuCumboClassifier on digit \")\n    result3 = np.mean(y_pred3.ravel() == y_test.ravel()) * 100\n    print(result3)\n\n    fig = plt.figure(figsize=(12., 11.))\n    fig.suptitle(\"MuCumbo: result\" + str(result3), fontsize=16)\n    plot_subplot(X_train, y_train, y_pred33  ,0, (4, 1, 1), \"train vue 0\" )\n    plot_subplot(X_test, y_test,y_pred3 , 0, (4, 1, 2), \"test vue 0\" )\n    plot_subplot(X_test, y_test, y_pred3,1, (4, 1, 3), \"test vue 1\" )\n    plot_subplot(X_test, y_test,y_pred3, 2, (4, 1, 4), \"test vue 2\" )\n    # plt.legend()\n    plt.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.8"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file

doc/tutorial/auto_examples/usecase/plot_usecase_exampleMuCuBo.py

+# -*- coding: utf-8 -*-
+"""
+================
+Use Case MuCumBo
+================
+Use case for all classifier of multimodallearn  MuCumBo
+
+multi class digit from sklearn, multivue
+ - vue 0 digit data (color of sklearn)
+ - vue 1 gradiant of image in first direction
+ - vue 2 gradiant of image in second direction
+
+"""
+from __future__ import absolute_import
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.model_selection import train_test_split
+from sklearn.tree import DecisionTreeClassifier
+from multimodal.datasets.base import load_dict, save_dict
+from multimodal.tests.data.get_dataset_path import get_dataset_path
+from multimodal.datasets.data_sample import MultiModalArray
+
+from multimodal.boosting.cumbo import MuCumboClassifier
+from usecase_function import plot_subplot
+
+
+if __name__ == '__main__':
+    # file = get_dataset_path("digit_histogram.npy")
+    file = get_dataset_path("digit_col_grad.npy")
+    y = np.load(get_dataset_path("digit_y.npy"))
+    base_estimator = DecisionTreeClassifier(max_depth=4)
+    dic_digit = load_dict(file)
+    XX =MultiModalArray(dic_digit)
+    X_train, X_test, y_train, y_test = train_test_split(XX, y)
+    est3 = MuCumboClassifier(base_estimator=base_estimator).fit(X_train, y_train)
+    y_pred3 = est3.predict(X_test)
+    y_pred33 = est3.predict(X_train)
+    print("result of MuCumboClassifier on digit ")
+    result3 = np.mean(y_pred3.ravel() == y_test.ravel()) * 100
+    print(result3)
+
+    fig = plt.figure(figsize=(12., 11.))
+    fig.suptitle("MuCumbo: result" + str(result3), fontsize=16)
+    plot_subplot(X_train, y_train, y_pred33  ,0, (4, 1, 1), "train vue 0" )
+    plot_subplot(X_test, y_test,y_pred3 , 0, (4, 1, 2), "test vue 0" )
+    plot_subplot(X_test, y_test, y_pred3,1, (4, 1, 3), "test vue 1" )
+    plot_subplot(X_test, y_test,y_pred3, 2, (4, 1, 4), "test vue 2" )
+    # plt.legend()
+    plt.show()
\ No newline at end of file

doc/tutorial/auto_examples/usecase/plot_usecase_exampleMuCuBo.py.md5

+41656262e29b2bcd048fa6cd8a96eaf4
\ No newline at end of file

doc/tutorial/auto_examples/usecase/plot_usecase_exampleMuCuBo.rst

+.. note::
+    :class: sphx-glr-download-link-note
+
+    Click :ref:`here <sphx_glr_download_tutorial_auto_examples_usecase_plot_usecase_exampleMuCuBo.py>` to download the full example code
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_tutorial_auto_examples_usecase_plot_usecase_exampleMuCuBo.py:
+
+
+================
+Use Case MuCumBo
+================
+Use case for all classifier of multimodallearn  MuCumBo
+
+multi class digit from sklearn, multivue
+ - vue 0 digit data (color of sklearn)
+ - vue 1 gradiant of image in first direction
+ - vue 2 gradiant of image in second direction
+
+
+
+
+.. image:: /tutorial/auto_examples/usecase/images/sphx_glr_plot_usecase_exampleMuCuBo_001.png
+    :class: sphx-glr-single-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    result of MuCumboClassifier on digit 
+    85.33333333333334
+    /home/dominique/projets/ANR-Lives/scikit-multimodallearn/examples/usecase/plot_usecase_exampleMuCuBo.py:49: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
+      plt.show()
+
+
+
+
+
+
+|
+
+
+.. code-block:: default
+
+    from __future__ import absolute_import
+    import numpy as np
+    import matplotlib.pyplot as plt
+    from sklearn.model_selection import train_test_split
+    from sklearn.tree import DecisionTreeClassifier
+    from multimodal.datasets.base import load_dict, save_dict
+    from multimodal.tests.data.get_dataset_path import get_dataset_path
+    from multimodal.datasets.data_sample import MultiModalArray
+
+    from multimodal.boosting.cumbo import MuCumboClassifier
+    from usecase_function import plot_subplot
+
+
+    if __name__ == '__main__':
+        # file = get_dataset_path("digit_histogram.npy")
+        file = get_dataset_path("digit_col_grad.npy")
+        y = np.load(get_dataset_path("digit_y.npy"))
+        base_estimator = DecisionTreeClassifier(max_depth=4)
+        dic_digit = load_dict(file)
+        XX =MultiModalArray(dic_digit)
+        X_train, X_test, y_train, y_test = train_test_split(XX, y)
+        est3 = MuCumboClassifier(base_estimator=base_estimator).fit(X_train, y_train)
+        y_pred3 = est3.predict(X_test)
+        y_pred33 = est3.predict(X_train)
+        print("result of MuCumboClassifier on digit ")
+        result3 = np.mean(y_pred3.ravel() == y_test.ravel()) * 100
+        print(result3)
+
+        fig = plt.figure(figsize=(12., 11.))
+        fig.suptitle("MuCumbo: result" + str(result3), fontsize=16)
+        plot_subplot(X_train, y_train, y_pred33  ,0, (4, 1, 1), "train vue 0" )
+        plot_subplot(X_test, y_test,y_pred3 , 0, (4, 1, 2), "test vue 0" )
+        plot_subplot(X_test, y_test, y_pred3,1, (4, 1, 3), "test vue 1" )
+        plot_subplot(X_test, y_test,y_pred3, 2, (4, 1, 4), "test vue 2" )
+        # plt.legend()
+        plt.show()
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** ( 0 minutes  11.436 seconds)
+
+
+.. _sphx_glr_download_tutorial_auto_examples_usecase_plot_usecase_exampleMuCuBo.py:
+
+
+.. only :: html
+
+ .. container:: sphx-glr-footer
+    :class: sphx-glr-footer-example
+
+
+
+  .. container:: sphx-glr-download
+
+     :download:`Download Python source code: plot_usecase_exampleMuCuBo.py <plot_usecase_exampleMuCuBo.py>`
+
+
+
+  .. container:: sphx-glr-download
+
+     :download:`Download Jupyter notebook: plot_usecase_exampleMuCuBo.ipynb <plot_usecase_exampleMuCuBo.ipynb>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_

doc/tutorial/auto_examples/usecase/plot_usecase_exampleMumBo.ipynb

+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Use Case MumBo\n\nUse case for all classifier of multimodallearn MumBo\n\nmulti class digit from sklearn, multivue\n - vue 0 digit data (color of sklearn)\n - vue 1 gradiant of image in first direction\n - vue 2 gradiant of image in second direction\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from __future__ import absolute_import\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.tree import DecisionTreeClassifier\nfrom multimodal.datasets.base import load_dict, save_dict\nfrom multimodal.tests.data.get_dataset_path import get_dataset_path\nfrom multimodal.datasets.data_sample import MultiModalArray\n\nfrom multimodal.boosting.mumbo import MumboClassifier\n\nfrom usecase_function import plot_subplot\n\n\nif __name__ == '__main__':\n    # file = get_dataset_path(\"digit_histogram.npy\")\n    file = get_dataset_path(\"digit_col_grad.npy\")\n    y = np.load(get_dataset_path(\"digit_y.npy\"))\n    base_estimator = DecisionTreeClassifier(max_depth=4)\n    dic_digit = load_dict(file)\n    XX =MultiModalArray(dic_digit)\n    X_train, X_test, y_train, y_test = train_test_split(XX, y)\n\n    est2 = MumboClassifier(base_estimator=base_estimator).fit(X_train, y_train)\n    y_pred2 = est2.predict(X_test)\n    y_pred22 = est2.predict(X_train)\n    print(\"result of MumboClassifier on digit \")\n    result2 = np.mean(y_pred2.ravel() == y_test.ravel()) * 100\n    print(result2)\n\n    fig = plt.figure(figsize=(12., 11.))\n    fig.suptitle(\"Mumbo: result\" + str(result2), fontsize=16)\n    plot_subplot(X_train, y_train, y_pred22 , 0, (4, 1, 1), \"train vue 0\" )\n    plot_subplot(X_test, y_test,y_pred2, 0, (4, 1, 2), \"test vue 0\" )\n    plot_subplot(X_test, y_test, y_pred2, 1, (4, 1, 3), \"test vue 1\" )\n    plot_subplot(X_test, y_test,y_pred2, 2, (4, 1, 4), \"test vue 2\" )\n    # plt.legend()\n    plt.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.8"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file

doc/tutorial/auto_examples/usecase/plot_usecase_exampleMumBo.py

+# -*- coding: utf-8 -*-
+"""
+==============
+Use Case MumBo
+==============
+Use case for all classifier of multimodallearn MumBo
+
+multi class digit from sklearn, multivue
+ - vue 0 digit data (color of sklearn)
+ - vue 1 gradiant of image in first direction
+ - vue 2 gradiant of image in second direction
+
+"""
+from __future__ import absolute_import
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.model_selection import train_test_split
+from sklearn.tree import DecisionTreeClassifier
+from multimodal.datasets.base import load_dict, save_dict
+from multimodal.tests.data.get_dataset_path import get_dataset_path
+from multimodal.datasets.data_sample import MultiModalArray
+
+from multimodal.boosting.mumbo import MumboClassifier
+
+from usecase_function import plot_subplot
+
+
+if __name__ == '__main__':
+    # file = get_dataset_path("digit_histogram.npy")
+    file = get_dataset_path("digit_col_grad.npy")
+    y = np.load(get_dataset_path("digit_y.npy"))
+    base_estimator = DecisionTreeClassifier(max_depth=4)
+    dic_digit = load_dict(file)
+    XX =MultiModalArray(dic_digit)
+    X_train, X_test, y_train, y_test = train_test_split(XX, y)
+
+    est2 = MumboClassifier(base_estimator=base_estimator).fit(X_train, y_train)
+    y_pred2 = est2.predict(X_test)
+    y_pred22 = est2.predict(X_train)
+    print("result of MumboClassifier on digit ")
+    result2 = np.mean(y_pred2.ravel() == y_test.ravel()) * 100
+    print(result2)
+
+    fig = plt.figure(figsize=(12., 11.))
+    fig.suptitle("Mumbo: result" + str(result2), fontsize=16)
+    plot_subplot(X_train, y_train, y_pred22 , 0, (4, 1, 1), "train vue 0" )
+    plot_subplot(X_test, y_test,y_pred2, 0, (4, 1, 2), "test vue 0" )
+    plot_subplot(X_test, y_test, y_pred2, 1, (4, 1, 3), "test vue 1" )
+    plot_subplot(X_test, y_test,y_pred2, 2, (4, 1, 4), "test vue 2" )
+    # plt.legend()
+    plt.show()

doc/tutorial/auto_examples/usecase/plot_usecase_exampleMumBo.py.md5

+2135145bcc76c1c0354a13e5fff1666c
\ No newline at end of file

doc/tutorial/auto_examples/usecase/plot_usecase_exampleMumBo.rst

+.. note::
+    :class: sphx-glr-download-link-note
+
+    Click :ref:`here <sphx_glr_download_tutorial_auto_examples_usecase_plot_usecase_exampleMumBo.py>` to download the full example code
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_tutorial_auto_examples_usecase_plot_usecase_exampleMumBo.py:
+
+
+==============
+Use Case MumBo
+==============
+Use case for all classifier of multimodallearn MumBo
+
+multi class digit from sklearn, multivue
+ - vue 0 digit data (color of sklearn)
+ - vue 1 gradiant of image in first direction
+ - vue 2 gradiant of image in second direction
+
+
+
+
+.. image:: /tutorial/auto_examples/usecase/images/sphx_glr_plot_usecase_exampleMumBo_001.png
+    :class: sphx-glr-single-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    result of MumboClassifier on digit 
+    96.0
+    /home/dominique/projets/ANR-Lives/scikit-multimodallearn/examples/usecase/plot_usecase_exampleMumBo.py:51: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
+      plt.show()
+
+
+
+
+
+
+|
+
+
+.. code-block:: default
+
+    from __future__ import absolute_import
+    import numpy as np
+    import matplotlib.pyplot as plt
+    from sklearn.model_selection import train_test_split
+    from sklearn.tree import DecisionTreeClassifier
+    from multimodal.datasets.base import load_dict, save_dict
+    from multimodal.tests.data.get_dataset_path import get_dataset_path
+    from multimodal.datasets.data_sample import MultiModalArray
+
+    from multimodal.boosting.mumbo import MumboClassifier
+
+    from usecase_function import plot_subplot
+
+
+    if __name__ == '__main__':
+        # file = get_dataset_path("digit_histogram.npy")
+        file = get_dataset_path("digit_col_grad.npy")
+        y = np.load(get_dataset_path("digit_y.npy"))
+        base_estimator = DecisionTreeClassifier(max_depth=4)
+        dic_digit = load_dict(file)
+        XX =MultiModalArray(dic_digit)
+        X_train, X_test, y_train, y_test = train_test_split(XX, y)
+
+        est2 = MumboClassifier(base_estimator=base_estimator).fit(X_train, y_train)
+        y_pred2 = est2.predict(X_test)
+        y_pred22 = est2.predict(X_train)
+        print("result of MumboClassifier on digit ")
+        result2 = np.mean(y_pred2.ravel() == y_test.ravel()) * 100
+        print(result2)
+
+        fig = plt.figure(figsize=(12., 11.))
+        fig.suptitle("Mumbo: result" + str(result2), fontsize=16)
+        plot_subplot(X_train, y_train, y_pred22 , 0, (4, 1, 1), "train vue 0" )
+        plot_subplot(X_test, y_test,y_pred2, 0, (4, 1, 2), "test vue 0" )
+        plot_subplot(X_test, y_test, y_pred2, 1, (4, 1, 3), "test vue 1" )
+        plot_subplot(X_test, y_test,y_pred2, 2, (4, 1, 4), "test vue 2" )
+        # plt.legend()
+        plt.show()
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** ( 0 minutes  5.520 seconds)
+
+
+.. _sphx_glr_download_tutorial_auto_examples_usecase_plot_usecase_exampleMumBo.py:
+
+
+.. only :: html
+
+ .. container:: sphx-glr-footer
+    :class: sphx-glr-footer-example
+
+
+
+  .. container:: sphx-glr-download
+
+     :download:`Download Python source code: plot_usecase_exampleMumBo.py <plot_usecase_exampleMumBo.py>`
+
+
+
+  .. container:: sphx-glr-download
+
+     :download:`Download Jupyter notebook: plot_usecase_exampleMumBo.ipynb <plot_usecase_exampleMumBo.ipynb>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_