klib.describe.corr_plot()

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5

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Total Lines	146
Code Lines	47

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0

'''
def corr_plot(data, split=None, threshold=0, target=None, method='pearson', cmap='BrBG', figsize=(12, 10), annot=True,
              dev=False, **kwargs):
    '''
    Two-dimensional visualization of the correlation between feature-columns, excluding NA values.

    Parameters
    ----------
    data: 2D dataset that can be coerced into Pandas DataFrame. If a Pandas DataFrame is provided, the index/column \
    information is used to label the plots.

    split: {None, 'pos', 'neg', 'high', 'low'}, default None
        Type of split to be performed.

        * None: visualize all correlations between the feature-columns.
        * pos: visualize all positive correlations between the feature-columns above the threshold.
        * neg: visualize all negative correlations between the feature-columns below the threshold.
        * high: visualize all correlations between the feature-columns for which abs(corr) > threshold is True.
        * low: visualize all correlations between the feature-columns for which abs(corr) < threshold is True.

    threshold: float, default 0
        Value between 0 <= threshold <= 1

    target: string, list, np.array or pd.Series, default None
        Specify target for correlation. E.g. label column to generate only the correlations between each feature\
        and the label.

    method: {'pearson', 'spearman', 'kendall'}, default 'pearson'
        * pearson: measures linear relationships and requires normally distributed and homoscedastic data.
        * spearman: ranked/ordinal correlation, measures monotonic relationships.
        * kendall: ranked/ordinal correlation, measures monotonic relationships. Computationally more expensive but
                   more robust in smaller dataets than 'spearman'.

    cmap: matplotlib colormap name or object, or list of colors, default 'BrBG'
        The mapping from data values to color space.

    figsize: tuple, default (12, 10)
        Use to control the figure size.

    annot: bool, default True
        Use to show or hide annotations.

    dev: bool, default False
        Display figure settings in the plot by setting dev = True. If False, the settings are not displayed.s

    **kwargs: optional
        Additional elements to control the visualization of the plot, e.g.:

        * mask: bool, default True
        If set to False the entire correlation matrix, including the upper triangle is shown. Set dev = False in this \
        case to avoid overlap.
        * vmax: float, default is calculated from the given correlation coefficients.
        Value between -1 or vmin <= vmax <= 1, limits the range of the colorbar.
        * vmin: float, default is calculated from the given correlation coefficients.
        Value between -1 <= vmin <= 1 or vmax, limits the range of the colorbar.
        * linewidths: float, default 0.5
        Controls the line-width inbetween the squares.
        * annot_kws: dict, default {'size' : 10}
        Controls the font size of the annotations. Only available when annot = True.
        * cbar_kws: dict, default {'shrink': .95, 'aspect': 30}
        Controls the size of the colorbar.
        * Many more kwargs are available, i.e. 'alpha' to control blending, or options to adjust labels, ticks ...

        Kwargs can be supplied through a dictionary of key-value pairs (see above).

    Returns
    -------
    ax: matplotlib Axes
        Returns the Axes object with the plot for further tweaking.
    '''

    # Validate Inputs
    _validate_input_0_1(threshold, 'threshold')
    _validate_input_bool(annot, 'annot')
    _validate_input_bool(dev, 'dev')

    data = pd.DataFrame(data)
    mask = False
    square = False

    # Obtain correlations
    if isinstance(target, (str, list, pd.Series, np.ndarray)):
        if isinstance(target, str):
            target_data = data[target]
            data = data.drop(target, axis=1)

        elif isinstance(target, (list, pd.Series, np.ndarray)):
            target_data = pd.Series(target)

        corr = pd.DataFrame(data.corrwith(target_data))

The variable target_data does not seem to be defined for all execution paths.

        corr = _corr_selector(corr, split=split, threshold=threshold)
        vmax = np.round(np.nanmax(corr)-0.05, 2)
        vmin = np.round(np.nanmin(corr)+0.05, 2)

    else:
        corr = corr_mat(data, split=split, threshold=threshold, method=method).data

        mask = np.triu(np.ones_like(corr, dtype=np.bool))  # Generate mask for the upper triangle
        square = True

        vmax = np.round(np.nanmax(corr.where(~mask))-0.05, 2)
        vmin = np.round(np.nanmin(corr.where(~mask))+0.05, 2)

    fig, ax = plt.subplots(figsize=figsize)

    # Specify kwargs for the heatmap
    kwargs = {'mask': mask,
              'cmap': cmap,
              'annot': annot,
              'vmax': vmax,
              'vmin': vmin,
              'linewidths': .5,
              'annot_kws': {'size': 10},
              'cbar_kws': {'shrink': .95, 'aspect': 30},
              **kwargs}

    # Draw heatmap with mask and some default settings
    sns.heatmap(corr,
                center=0,
                square=square,
                fmt='.2f',
                **kwargs
                )

    ax.set_title(f'Feature-correlation ({method})', fontdict={'fontsize': 18})

    # Display settings
    if dev:
        fig.suptitle(f"\
            Settings (dev-mode): \n\
            - split-mode: {split} \n\
            - threshold: {threshold} \n\
            - method: {method} \n\
            - annotations: {annot} \n\
            - cbar: \n\
                - vmax: {vmax} \n\
                - vmin: {vmin} \n\
            - linewidths: {kwargs['linewidths']} \n\
            - annot_kws: {kwargs['annot_kws']} \n\
            - cbar_kws: {kwargs['cbar_kws']}",
                     fontsize=12,
                     color='gray',
                     x=0.35,
                     y=0.85,
                     ha='left')

    return ax