.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/plot_abnormality_scores.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_abnormality_scores.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_plot_abnormality_scores.py:


==============
Anomaly scores
==============

calculating anomaly scores

.. GENERATED FROM PYTHON SOURCE LINES 10-20

.. code-block:: Python


    import numpy as np
    import matplotlib.pyplot as plt
    from sknormod import MassUnivNormodOLS
    from sknormod.datasets import make_gaussian
    from sknormod.plotting import plot_scatter_with_lines
    from sknormod.atypicality import multi_atypicality_scorer, multi_roc_auc_score

    from sklearn.model_selection import train_test_split








.. GENERATED FROM PYTHON SOURCE LINES 21-24

Generate synthetic data
-----------------------
We generate synthetic data for multiple variables across a range of ages.

.. GENERATED FROM PYTHON SOURCE LINES 24-35

.. code-block:: Python

    n_subj = 2000  # Number of subjects
    n_cols = 200    # Number of variables

    age = np.linspace(0, 100, n_subj) 
    X = np.column_stack((age, age**2))  # Age and age squared as features
    # Generate synthetic Y data for each variable
    Y = np.column_stack([make_gaussian(n_subj, 
            interpolate_mu = np.random.normal(loc=100, scale=2, size=3))[1] 
                         for _ in range(n_cols)])  









.. GENERATED FROM PYTHON SOURCE LINES 36-37

train test split and generate test data

.. GENERATED FROM PYTHON SOURCE LINES 37-52

.. code-block:: Python

    X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3)

    n_test = X_test.shape[0]
    diag_labels = np.array([0]*(n_test//2) + [1]*(n_test//2))

    num_cols = 50
    rows_to_modify = np.where(diag_labels == 1)[0]

    # For each row that meets the condition, subtract 0.1 from a random column
    for row in rows_to_modify:
        # Randomly select a column index
        col_index = np.random.choice(num_cols)
        # Subtract 0.1 from the selected column in the current row
        Y_test[row, col_index] -= 3








.. GENERATED FROM PYTHON SOURCE LINES 53-55

Fit and get z-scores and abnormality scores
-------------------------------------------

.. GENERATED FROM PYTHON SOURCE LINES 55-70

.. code-block:: Python


    # Fit MassUnivNormodOLS model
    mass_normod = MassUnivNormodOLS()
    mass_normod.fit(X_train, Y_train)

    # Calculate z-scores 
    Z_test = mass_normod.transform_to_z(X_test, Y_test)  
    # Calculate abnormality scores
    atypicality_scores = multi_atypicality_scorer(Z_test)

    # Check how well they discirminate between groups
    auc_scores = multi_roc_auc_score(diag_labels, atypicality_scores)
    for key, value, in auc_scores.items():
        print(f"{key}: {value:.2f}")





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

 .. code-block:: none

    mean_z: 0.56
    mean_abs_z: 0.57
    count<-3: 0.70
    count>3: 0.51
    count_abs>3: 0.68
    max_z: 0.54
    min_z: 0.74
    max_abs_z: 0.72
    trimmed_mean_top_z: 0.55




.. GENERATED FROM PYTHON SOURCE LINES 71-73

Ploting
-------

.. GENERATED FROM PYTHON SOURCE LINES 73-101

.. code-block:: Python

    n_columns = len(atypicality_scores)
    # Number of rows and columns for subplots
    n_rows = (n_columns + 2) // 3
    n_cols = min(n_columns, 3)

    # Create a figure with subplots (3 columns)
    fig, axes = plt.subplots(n_rows, n_cols, figsize=(12, 4 * n_rows), tight_layout=True)
    # Flatten the axes array
    axes = axes.flatten()
    # Plot a histogram for each score type in the dictionary
    for i, (score_name, scores) in enumerate(atypicality_scores.items()):
        ax = axes[i]
        min_value = np.min(scores)
        max_value = np.max(scores)
        ax.hist(scores[diag_labels == 0], range=(min_value, max_value),
                color="blue", alpha=0.5, label='Label 0')
        ax.hist(scores[diag_labels == 1], range=(min_value, max_value), 
                color="orange", alpha=0.5, label='Label 1')
        ax.set_title(f'Histogram of {score_name}')
        ax.set_xlabel('Score')
        ax.set_ylabel('Frequency')

    # Hide empty subplots if there are fewer scores than subplots
    for ax in axes[n_columns:]:
        ax.axis('off')

    # Show the plot
    plt.show()



.. image-sg:: /auto_examples/images/sphx_glr_plot_abnormality_scores_001.png
   :alt: Histogram of mean_z, Histogram of mean_abs_z, Histogram of count<-3, Histogram of count>3, Histogram of count_abs>3, Histogram of max_z, Histogram of min_z, Histogram of max_abs_z, Histogram of trimmed_mean_top_z
   :srcset: /auto_examples/images/sphx_glr_plot_abnormality_scores_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 0.470 seconds)


.. _sphx_glr_download_auto_examples_plot_abnormality_scores.py:

.. only:: html

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

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

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

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

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


.. only:: html

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

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