.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/plot_nmf.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_plot_nmf.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_plot_nmf.py:


Custom Operator for NMF Decomposition
=====================================

`NMF <https://scikit-learn.org/stable/modules/generated/
sklearn.decomposition.NMF.html>`_ factorizes an input matrix
into two matrices *W, H* of rank *k* so that :math:`WH \sim M``.
:math:`M=(m_{ij})` may be a binary matrix where *i* is a user
and *j* a product he bought. The prediction
function depends on whether or not the user needs a
recommandation for an existing user or a new user.
This example addresses the first case.

The second case is more complex as it theoretically
requires the estimation of a new matrix *W* with a
gradient descent.

Building a simple model
+++++++++++++++++++++++

.. GENERATED FROM PYTHON SOURCE LINES 25-59

.. code-block:: default


    import os
    import skl2onnx
    import onnxruntime
    import sklearn
    from sklearn.decomposition import NMF
    import numpy as np
    import matplotlib.pyplot as plt
    from onnx.tools.net_drawer import GetPydotGraph, GetOpNodeProducer
    import onnx
    from skl2onnx.algebra.onnx_ops import OnnxArrayFeatureExtractor, OnnxMul, OnnxReduceSum
    from skl2onnx.common.data_types import FloatTensorType
    from onnxruntime import InferenceSession


    mat = np.array(
        [[1, 0, 0, 0], [1, 0, 0, 0], [1, 0, 0, 0], [1, 0, 0, 0], [1, 0, 0, 0]],
        dtype=np.float64,
    )
    mat[: mat.shape[1], :] += np.identity(mat.shape[1])

    mod = NMF(n_components=2)
    W = mod.fit_transform(mat)
    H = mod.components_
    pred = mod.inverse_transform(W)

    print("original predictions")
    exp = []
    for i in range(mat.shape[0]):
        for j in range(mat.shape[1]):
            exp.append((i, j, pred[i, j]))

    print(exp)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    original predictions
    [(0, 0, 1.8940507356352687), (0, 1, 0.10912372262184848), (0, 2, 0.3072453141962623), (0, 3, 0.3072453141962623), (1, 0, 1.014673742790866), (1, 1, 0.9848866016414943), (1, 2, 0.0), (1, 3, 0.0), (2, 0, 1.1066115912409111), (2, 1, 0.0), (2, 2, 0.19083752823558645), (2, 3, 0.19083752823558645), (3, 0, 1.1066115912409111), (3, 1, 0.0), (3, 2, 0.19083752823558645), (3, 3, 0.19083752823558645), (4, 0, 0.9470253678176344), (4, 1, 0.05456186131092424), (4, 2, 0.15362265709813114), (4, 3, 0.15362265709813114)]




.. GENERATED FROM PYTHON SOURCE LINES 60-62

Let's rewrite the prediction in a way it is closer
to the function we need to convert into ONNX.

.. GENERATED FROM PYTHON SOURCE LINES 62-76

.. code-block:: default



    def predict(W, H, row_index, col_index):
        return np.dot(W[row_index, :], H[:, col_index])


    got = []
    for i in range(mat.shape[0]):
        for j in range(mat.shape[1]):
            got.append((i, j, predict(W, H, i, j)))

    print(got)






.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    [(0, 0, 1.8940507356352687), (0, 1, 0.10912372262184848), (0, 2, 0.3072453141962623), (0, 3, 0.3072453141962623), (1, 0, 1.014673742790866), (1, 1, 0.9848866016414943), (1, 2, 0.0), (1, 3, 0.0), (2, 0, 1.1066115912409111), (2, 1, 0.0), (2, 2, 0.19083752823558645), (2, 3, 0.19083752823558645), (3, 0, 1.1066115912409111), (3, 1, 0.0), (3, 2, 0.19083752823558645), (3, 3, 0.19083752823558645), (4, 0, 0.9470253678176344), (4, 1, 0.05456186131092424), (4, 2, 0.15362265709813114), (4, 3, 0.15362265709813114)]




.. GENERATED FROM PYTHON SOURCE LINES 77-87

Conversion into ONNX
++++++++++++++++++++

There is no implemented converter for
`NMF <https://scikit-learn.org/stable/modules/generated/
sklearn.decomposition.NMF.html>`_ as the function we plan
to convert is not transformer or a predictor.
The following converter does not need to be registered,
it just creates an ONNX graph equivalent to function
*predict* implemented above.

.. GENERATED FROM PYTHON SOURCE LINES 87-113

.. code-block:: default



    def nmf_to_onnx(W, H, op_version=12):
        """
        The function converts a NMF described by matrices
        *W*, *H* (*WH* approximate training data *M*).
        into a function which takes two indices *(i, j)*
        and returns the predictions for it. It assumes
        these indices applies on the training data.
        """
        col = OnnxArrayFeatureExtractor(H, "col")
        row = OnnxArrayFeatureExtractor(W.T, "row")
        dot = OnnxMul(col, row, op_version=op_version)
        res = OnnxReduceSum(dot, output_names="rec", op_version=op_version)
        indices_type = np.array([0], dtype=np.int64)
        onx = res.to_onnx(
            inputs={"col": indices_type, "row": indices_type},
            outputs=[("rec", FloatTensorType((None, 1)))],
            target_opset=op_version,
        )
        return onx


    model_onnx = nmf_to_onnx(W.astype(np.float32), H.astype(np.float32))
    print(model_onnx)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    ir_version: 7
    opset_import {
      domain: ""
      version: 12
    }
    opset_import {
      domain: "ai.onnx.ml"
      version: 1
    }
    producer_name: "skl2onnx"
    producer_version: "1.16.0"
    domain: "ai.onnx"
    model_version: 0
    graph {
      node {
        input: "Ar_ArrayFeatureExtractorcst"
        input: "col"
        output: "Ar_Z0"
        name: "Ar_ArrayFeatureExtractor"
        op_type: "ArrayFeatureExtractor"
        domain: "ai.onnx.ml"
      }
      node {
        input: "Ar_ArrayFeatureExtractorcst1"
        input: "row"
        output: "Ar_Z02"
        name: "Ar_ArrayFeatureExtractor1"
        op_type: "ArrayFeatureExtractor"
        domain: "ai.onnx.ml"
      }
      node {
        input: "Ar_Z0"
        input: "Ar_Z02"
        output: "Mu_C0"
        name: "Mu_Mul"
        op_type: "Mul"
        domain: ""
      }
      node {
        input: "Mu_C0"
        output: "rec"
        name: "Re_ReduceSum"
        op_type: "ReduceSum"
        domain: ""
      }
      name: "OnnxReduceSum"
      initializer {
        dims: 2
        dims: 4
        data_type: 1
        float_data: 1.98630548
        float_data: 0
        float_data: 0.342542619
        float_data: 0.342542619
        float_data: 0.900879145
        float_data: 0.874432623
        float_data: 0
        float_data: 0
        name: "Ar_ArrayFeatureExtractorcst"
      }
      initializer {
        dims: 2
        dims: 5
        data_type: 1
        float_data: 0.896955
        float_data: 0
        float_data: 0.557120502
        float_data: 0.557120502
        float_data: 0.448477507
        float_data: 0.124793746
        float_data: 1.126315
        float_data: 0
        float_data: 0
        float_data: 0.0623968728
        name: "Ar_ArrayFeatureExtractorcst1"
      }
      input {
        name: "col"
        type {
          tensor_type {
            elem_type: 7
            shape {
              dim {
              }
            }
          }
        }
      }
      input {
        name: "row"
        type {
          tensor_type {
            elem_type: 7
            shape {
              dim {
              }
            }
          }
        }
      }
      output {
        name: "rec"
        type {
          tensor_type {
            elem_type: 1
            shape {
              dim {
              }
              dim {
                dim_value: 1
              }
            }
          }
        }
      }
    }





.. GENERATED FROM PYTHON SOURCE LINES 114-115

Let's compute prediction with it.

.. GENERATED FROM PYTHON SOURCE LINES 115-137

.. code-block:: default


    sess = InferenceSession(
        model_onnx.SerializeToString(), providers=["CPUExecutionProvider"]
    )


    def predict_onnx(sess, row_indices, col_indices):
        res = sess.run(None, {"col": col_indices, "row": row_indices})
        return res


    onnx_preds = []
    for i in range(mat.shape[0]):
        for j in range(mat.shape[1]):
            row_indices = np.array([i], dtype=np.int64)
            col_indices = np.array([j], dtype=np.int64)
            pred = predict_onnx(sess, row_indices, col_indices)[0]
            onnx_preds.append((i, j, pred[0, 0]))

    print(onnx_preds)






.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    [(0, 0, 1.8940508), (0, 1, 0.10912372), (0, 2, 0.3072453), (0, 3, 0.3072453), (1, 0, 1.0146737), (1, 1, 0.9848866), (1, 2, 0.0), (1, 3, 0.0), (2, 0, 1.1066115), (2, 1, 0.0), (2, 2, 0.19083752), (2, 3, 0.19083752), (3, 0, 1.1066115), (3, 1, 0.0), (3, 2, 0.19083752), (3, 3, 0.19083752), (4, 0, 0.9470254), (4, 1, 0.05456186), (4, 2, 0.15362266), (4, 3, 0.15362266)]




.. GENERATED FROM PYTHON SOURCE LINES 138-139

The ONNX graph looks like the following.

.. GENERATED FROM PYTHON SOURCE LINES 139-151

.. code-block:: default

    pydot_graph = GetPydotGraph(
        model_onnx.graph,
        name=model_onnx.graph.name,
        rankdir="TB",
        node_producer=GetOpNodeProducer("docstring"),
    )
    pydot_graph.write_dot("graph_nmf.dot")
    os.system("dot -O -Tpng graph_nmf.dot")
    image = plt.imread("graph_nmf.dot.png")
    plt.imshow(image)
    plt.axis("off")




.. image-sg:: /auto_examples/images/sphx_glr_plot_nmf_001.png
   :alt: plot nmf
   :srcset: /auto_examples/images/sphx_glr_plot_nmf_001.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    (-0.5, 1654.5, 846.5, -0.5)



.. GENERATED FROM PYTHON SOURCE LINES 152-153

**Versions used for this example**

.. GENERATED FROM PYTHON SOURCE LINES 153-159

.. code-block:: default


    print("numpy:", np.__version__)
    print("scikit-learn:", sklearn.__version__)
    print("onnx: ", onnx.__version__)
    print("onnxruntime: ", onnxruntime.__version__)
    print("skl2onnx: ", skl2onnx.__version__)




.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    numpy: 1.23.5
    scikit-learn: 1.4.dev0
    onnx:  1.15.0
    onnxruntime:  1.16.0+cu118
    skl2onnx:  1.16.0





.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.298 seconds)


.. _sphx_glr_download_auto_examples_plot_nmf.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example




    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_nmf.py <plot_nmf.py>`

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_nmf.ipynb <plot_nmf.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_