add usecase examples

doc/tutorial/auto_examples/usecase/sg_execution_times.rst

+
+:orphan:
+
+.. _sphx_glr_tutorial_auto_examples_usecase_sg_execution_times:
+
+Computation times
+=================
+**01:09.574** total execution time for **tutorial_auto_examples_usecase** files:
+
++------------------------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorial_auto_examples_usecase_plot_usecase_exampleMVML.py` (``plot_usecase_exampleMVML.py``)     | 00:39.921 | 0.0 MB |
++------------------------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorial_auto_examples_usecase_plot_usecase_exampleMKL.py` (``plot_usecase_exampleMKL.py``)       | 00:12.697 | 0.0 MB |
++------------------------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorial_auto_examples_usecase_plot_usecase_exampleMuCuBo.py` (``plot_usecase_exampleMuCuBo.py``) | 00:11.436 | 0.0 MB |
++------------------------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorial_auto_examples_usecase_plot_usecase_exampleMumBo.py` (``plot_usecase_exampleMumBo.py``)   | 00:05.520 | 0.0 MB |
++------------------------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorial_auto_examples_usecase_usecase_function.py` (``usecase_function.py``)                     | 00:00.000 | 0.0 MB |
++------------------------------------------------------------------------------------------------------------------+-----------+--------+

doc/tutorial/auto_examples/usecase/usecase_function.ipynb

+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Use Case Function module\n\n\nFunction plot_subplot\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib._color_data as mcd\n\n\ndef plot_subplot(X, Y, Y_pred, vue, subplot, title):\n    cn = mcd.CSS4_COLORS\n    classes = np.unique(Y)\n    n_classes = len(np.unique(Y))\n    axs = plt.subplot(subplot[0],subplot[1],subplot[2])\n    axs.set_title(title)\n    #plt.scatter(X._extract_view(vue), X._extract_view(vue), s=40, c='gray',\n    #            edgecolors=(0, 0, 0))\n    for index, k in zip(range(n_classes), cn.keys()):\n         Y_class, = np.where(Y==classes[index])\n         Y_class_pred = np.intersect1d(np.where(Y_pred==classes[index])[0], np.where(Y_pred==Y)[0])\n         plt.scatter(X._extract_view(vue)[Y_class],\n                     X._extract_view(vue)[Y_class],\n                     s=40, c=cn[k], edgecolors='blue', linewidths=2, label=\"class real class: \"+str(index)) #\n         plt.scatter(X._extract_view(vue)[Y_class_pred],\n                     X._extract_view(vue)[Y_class_pred],\n                     s=160, edgecolors='orange', linewidths=2, label=\"class prediction: \"+str(index))\n\n\nif __name__ == '__main__':\n    pass"
+      ]
+    }
+  ],
+  "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/usecase_function.py

+# -*- coding: utf-8 -*-
+"""
+========================
+Use Case Function module
+========================
+
+Function plot_subplot
+
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib._color_data as mcd
+
+
+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))
+
+
+if __name__ == '__main__':
+    pass
\ No newline at end of file

doc/tutorial/auto_examples/usecase/usecase_function.rst

+.. note::
+    :class: sphx-glr-download-link-note
+
+    Click :ref:`here <sphx_glr_download_tutorial_auto_examples_usecase_usecase_function.py>` to download the full example code
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_tutorial_auto_examples_usecase_usecase_function.py:
+
+
+========================
+Use Case Function module
+========================
+
+Function plot_subplot
+
+
+
+.. code-block:: default
+
+
+    import numpy as np
+    import matplotlib.pyplot as plt
+    import matplotlib._color_data as mcd
+
+
+    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))
+
+
+    if __name__ == '__main__':
+        pass
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** ( 0 minutes  0.000 seconds)
+
+
+.. _sphx_glr_download_tutorial_auto_examples_usecase_usecase_function.py:
+
+
+.. only :: html
+
+ .. container:: sphx-glr-footer
+    :class: sphx-glr-footer-example
+
+
+
+  .. container:: sphx-glr-download
+
+     :download:`Download Python source code: usecase_function.py <usecase_function.py>`
+
+
+
+  .. container:: sphx-glr-download
+
+     :download:`Download Jupyter notebook: usecase_function.ipynb <usecase_function.ipynb>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_

doc/tutorial/times.rst

@@ -14,3 +14,4 @@ total execution time for **tutorial_auto_examples** files:
    auto_examples/mumbo/sg_execution_times
    auto_examples/cumbo/sg_execution_times
    auto_examples/mvml/sg_execution_times
+   auto_examples/usecase/sg_execution_times

examples/usecase/__init__.py

+
+

examples/usecase/plot_usecase_exampleMKL.py

+# -*- coding: utf-8 -*-
+"""
+============
+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
+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()
+

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()
+

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

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()

examples/usecase/usecase_function.py

+# -*- coding: utf-8 -*-
+"""
+========================
+Use Case Function module
+========================
+
+Function plot_subplot
+
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib._color_data as mcd
+
+
+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))
+
+
+if __name__ == '__main__':
+    pass
\ No newline at end of file