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cm

Confusion Matrix Plotting API

This module provides utility functions to plot confusion matrices.

Functions:

Functions

multilabel_confusion_matrix_plot 

multilabel_confusion_matrix_plot(y_true: npt.NDArray, y_pred: npt.NDArray, labels: list[str], save_path: os.PathLike | None = None, normalize: Literal['true', 'pred', 'all'] | None = False, max_cols: int = 5, **kwargs) -> tuple[plt.Figure, plt.Axes] | None

Generate multilabel confusion matrix plot via matplotlib/seaborn

Parameters:

  • y_true

    (NDArray) –

    True y labels

  • y_pred

    (NDArray) –

    Predicted y labels

  • labels

    (list[str]) –

    Label names

  • save_path

    (str | None, default: None ) –

    Path to save plot. Defaults to None.

  • normalize

    (Literal['true', 'pred', 'all'] | None, default: False ) –

    Normalize. Defaults to False.

  • max_cols

    (int, default: 5 ) –

    Max columns. Defaults to 5.

Returns:

  • tuple[Figure, Axes] | None –

    tuple[plt.Figure, plt.Axes] | None: Figure and axes

Source code in neuralspot_edge/plotting/cm.py 
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def multilabel_confusion_matrix_plot(
    y_true: npt.NDArray,
    y_pred: npt.NDArray,
    labels: list[str],
    save_path: os.PathLike | None = None,
    normalize: Literal["true", "pred", "all"] | None = False,
    max_cols: int = 5,
    **kwargs,
) -> tuple[plt.Figure, plt.Axes] | None:
    """Generate multilabel confusion matrix plot via matplotlib/seaborn

    Args:
        y_true (npt.NDArray): True y labels
        y_pred (npt.NDArray): Predicted y labels
        labels (list[str]): Label names
        save_path (str | None): Path to save plot. Defaults to None.
        normalize (Literal["true", "pred", "all"] | None): Normalize. Defaults to False.
        max_cols (int): Max columns. Defaults to 5.

    Returns:
        tuple[plt.Figure, plt.Axes] | None: Figure and axes
    """
    cms = multilabel_confusion_matrix(y_true, y_pred)
    ncols = min(len(labels), max_cols)
    nrows = len(labels) // ncols + (len(labels) % ncols > 0)
    width = 10
    hdim = width / ncols
    fig, ax = plt.subplots(figsize=kwargs.get("figsize", (width, hdim * nrows)), nrows=nrows, ncols=ncols)
    for i, label in enumerate(labels):
        r = i // ncols
        c = i % ncols
        ann, fmt = True, "g"
        cm = cms[i]  # cm will be 2x2
        cmn = cm
        if normalize == "true":
            cmn = cmn / cmn.sum(axis=1, keepdims=True)
        elif normalize == "pred":
            cmn = cmn / cmn.sum(axis=0, keepdims=True)
        elif normalize == "all":
            cmn = cmn / cmn.sum()
        cmn = np.nan_to_num(cmn)
        if normalize:
            ann = np.asarray([f"{c:g}{os.linesep}{nc:.2%}" for c, nc in zip(cm.flatten(), cmn.flatten())]).reshape(
                cm.shape
            )
            fmt = ""
        # END IF
        cm_ax = ax[c] if nrows <= 1 else ax[r][c]
        sns.heatmap(cmn, xticklabels=False, yticklabels=False, annot=ann, fmt=fmt, ax=cm_ax)
        cm_ax.set_title(label)
    # END FOR
    # Remove unused subplots
    for i in range(len(labels), nrows * ncols):
        r = i // ncols
        c = i % ncols
        cm_ax = ax[c] if nrows <= 1 else ax[r][c]
        fig.delaxes(cm_ax)
    # END FOR
    fig.text(0.5, 0.04, "Prediction", ha="center")
    fig.text(0, 0.5, "Label", va="center", rotation="vertical")
    fig.tight_layout()
    if save_path:
        fig.savefig(save_path, bbox_inches="tight")
        plt.close(fig)
        return None
    # END IF
    return fig, ax

confusion_matrix_plot 

confusion_matrix_plot(y_true: npt.NDArray, y_pred: npt.NDArray, labels: list[str], save_path: os.PathLike | None = None, normalize: Literal['true', 'pred', 'all'] | None = False, **kwargs) -> tuple[plt.Figure, plt.Axes] | None

Generate confusion matrix plot via matplotlib/seaborn

Parameters:

  • y_true

    (NDArray) –

    True y labels

  • y_pred

    (NDArray) –

    Predicted y labels

  • labels

    (list[str]) –

    Label names

  • save_path

    (str | None, default: None ) –

    Path to save plot. Defaults to None.

Returns:

  • tuple[Figure, Axes] | None –

    tuple[plt.Figure, plt.Axes] | None: Figure and axes

Source code in neuralspot_edge/plotting/cm.py 
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def confusion_matrix_plot(
    y_true: npt.NDArray,
    y_pred: npt.NDArray,
    labels: list[str],
    save_path: os.PathLike | None = None,
    normalize: Literal["true", "pred", "all"] | None = False,
    **kwargs,
) -> tuple[plt.Figure, plt.Axes] | None:
    """Generate confusion matrix plot via matplotlib/seaborn

    Args:
        y_true (npt.NDArray): True y labels
        y_pred (npt.NDArray): Predicted y labels
        labels (list[str]): Label names
        save_path (str | None): Path to save plot. Defaults to None.

    Returns:
        tuple[plt.Figure, plt.Axes] | None: Figure and axes
    """

    cm = confusion_matrix(y_true, y_pred)
    cmn = cm
    ann = True
    fmt = "g"
    if normalize:
        cmn = confusion_matrix(y_true, y_pred, normalize=normalize)
        ann = np.asarray([f"{c:g}{os.linesep}{nc:.2%}" for c, nc in zip(cm.flatten(), cmn.flatten())]).reshape(cm.shape)
        fmt = ""
    # END IF
    fig, ax = plt.subplots(figsize=kwargs.get("figsize", (10, 8)))
    sns.heatmap(cmn, xticklabels=labels, yticklabels=labels, annot=ann, fmt=fmt, ax=ax)
    ax.set_xlabel("Prediction")
    ax.set_ylabel("Label")
    if save_path:
        fig.savefig(save_path, bbox_inches="tight")
        plt.close(fig)
        return None
    # END IF
    return fig, ax

px_plot_confusion_matrix 

px_plot_confusion_matrix(y_true: npt.NDArray, y_pred: npt.NDArray, labels: list[str], normalize: Literal['true', 'pred', 'all'] | None = False, save_path: os.PathLike | None = None, title: str | None = None, width: int | None = None, height: int | None = 400, bg_color: str = 'rgba(38,42,50,1.0)') -> go.Figure

Generate confusion matrix plot via plotly

Parameters:

  • y_true

    (NDArray) –

    True y labels

  • y_pred

    (NDArray) –

    Predicted y labels

  • labels

    (list[str]) –

    Label names

  • normalize

    (Literal['true', 'pred', 'all'] | None, default: False ) –

    Normalize. Defaults to False.

  • save_path

    (PathLike | None, default: None ) –

    Path to save plot. Defaults to None.

  • title

    (str | None, default: None ) –

    Title. Defaults to None.

  • width

    (int | None, default: None ) –

    Width. Defaults to None.

  • height

    (int | None, default: 400 ) –

    Height. Defaults to 400.

  • bg_color

    (str, default: 'rgba(38,42,50,1.0)' ) –

    Background color. Defaults to "rgba(38,42,50,1.0)".

Returns:

  • Figure –

    go.Figure: Plotly figure

Source code in neuralspot_edge/plotting/cm.py 
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def px_plot_confusion_matrix(
    y_true: npt.NDArray,
    y_pred: npt.NDArray,
    labels: list[str],
    normalize: Literal["true", "pred", "all"] | None = False,
    save_path: os.PathLike | None = None,
    title: str | None = None,
    width: int | None = None,
    height: int | None = 400,
    bg_color: str = "rgba(38,42,50,1.0)",
) -> go.Figure:
    """Generate confusion matrix plot via plotly

    Args:
        y_true (npt.NDArray): True y labels
        y_pred (npt.NDArray): Predicted y labels
        labels (list[str]): Label names
        normalize (Literal["true", "pred", "all"] | None): Normalize. Defaults to False.
        save_path (os.PathLike | None): Path to save plot. Defaults to None.
        title (str | None): Title. Defaults to None.
        width (int | None): Width. Defaults to None.
        height (int | None): Height. Defaults to 400.
        bg_color (str): Background color. Defaults to "rgba(38,42,50,1.0)".

    Returns:
        go.Figure: Plotly figure
    """

    cm = confusion_matrix(y_true, y_pred)
    cmn = cm
    ann = None
    if normalize:
        cmn = confusion_matrix(y_true, y_pred, normalize=normalize)
        ann = np.asarray([f"{c:g}<br>{nc:.2%}" for c, nc in zip(cm.flatten(), cmn.flatten())]).reshape(cm.shape)

    cmn = pd.DataFrame(cmn, index=labels, columns=labels)
    fig = px.imshow(
        cmn,
        labels=dict(x="Predicted", y="Actual", color="Count", text_auto=True),
        title=title,
        template="plotly_dark",
        color_continuous_scale="Plotly3",
    )
    if ann is not None:
        fig.update_traces(text=ann, texttemplate="%{text}")

    fig.update_layout(
        plot_bgcolor=bg_color,
        paper_bgcolor=bg_color,
        margin=dict(l=20, r=5, t=40, b=20),
        height=height,
        width=width,
        title=title,
    )
    if save_path is not None:
        fig.write_html(save_path, include_plotlyjs="cdn", full_html=False)

    return fig