import numpy as np
from bokeh.models import Band, HoverTool
from bokeh.plotting import ColumnDataSource, figure
from scipy import interp
from sklearn import metrics
from sklearn.metrics import confusion_matrix, roc_auc_score
from sklearn.utils import resample
from ..utils import binary_metrics


def roc_plot(fpr, tpr, tpr_ci, width=450, height=350, xlabel="1-Specificity", ylabel="Sensitivity", legend=True, label_font_size="13pt", title="", errorbar=False):
    """Creates a rocplot using Bokeh.

    Parameters
    ----------
    fpr : array-like, shape = [n_samples]
        False positive rates. Calculate using roc_calculate.

    tpr : array-like, shape = [n_samples]
        True positive rates. Calculate using roc_calculate.

    tpr_ci : array-like, shape = [n_samples, 2]
        True positive rates 95% confidence intervals [lowci, uppci]. Calculate using roc_calculate.
    """

    # Get CI
    tpr_lowci = tpr_ci[0]
    tpr_uppci = tpr_ci[1]
    auc = metrics.auc(fpr, tpr)

    # specificity and ci-interval for HoverTool
    spec = 1 - fpr
    ci = (tpr_uppci - tpr_lowci) / 2

    # Figure
    data = {"x": fpr, "y": tpr, "lowci": tpr_lowci, "uppci": tpr_uppci, "spec": spec, "ci": ci}
    source = ColumnDataSource(data=data)
    fig = figure(title=title, plot_width=width, plot_height=height, x_axis_label=xlabel, y_axis_label=ylabel, x_range=(-0.06, 1.06), y_range=(-0.06, 1.06))

    # Figure: add line
    fig.line([0, 1], [0, 1], color="black", line_dash="dashed", line_width=2.5, legend="Equal distribution line")
    figline = fig.line("x", "y", color="green", line_width=3.5, alpha=0.6, legend="ROC Curve (Train)", source=source)
    fig.add_tools(HoverTool(renderers=[figline], tooltips=[("Specificity", "@spec{1.111}"), ("Sensitivity", "@y{1.111} (+/- @ci{1.111})")]))

    # Figure: add 95CI band
    figband = Band(base="x", lower="lowci", upper="uppci", level="underlay", fill_alpha=0.1, line_width=1, line_color="black", fill_color="green", source=source)
    fig.add_layout(figband)

    # Figure: add errorbar  spec =  1 - fpr
    if errorbar is not False:
        idx = np.abs(fpr - (1 - errorbar)).argmin()  # this find the closest value in fpr to errorbar fpr
        fpr_eb = fpr[idx]
        tpr_eb = tpr[idx]
        tpr_lowci_eb = tpr_lowci[idx]
        tpr_uppci_eb = tpr_uppci[idx]

        # Edge case: If this is a perfect roc curve, and specificity >= 1, make sure error_bar is at (0,1) not (0,0)
        if errorbar >= 1:
            for i in range(len(fpr)):
                if fpr[i] == 0 and tpr[i] == 1:
                    fpr_eb = 0
                    tpr_eb = 1
                    tpr_lowci_eb = 1
                    tpr_uppci_eb = 1

        roc_whisker_line = fig.multi_line([[fpr_eb, fpr_eb]], [[tpr_lowci_eb, tpr_uppci_eb]], line_alpha=1, line_color="black")
        roc_whisker_bot = fig.multi_line([[fpr_eb - 0.03, fpr_eb + 0.03]], [[tpr_lowci_eb, tpr_lowci_eb]], line_color="black")
        roc_whisker_top = fig.multi_line([[fpr_eb - 0.03, fpr_eb + 0.03]], [[tpr_uppci_eb, tpr_uppci_eb]], line_alpha=1, line_color="black")
        fig.circle([fpr_eb], [tpr_eb], size=8, fill_alpha=1, line_alpha=1, line_color="black", fill_color="white")

    # Change font size
    fig.title.text_font_size = "11pt"
    fig.xaxis.axis_label_text_font_size = label_font_size
    fig.yaxis.axis_label_text_font_size = label_font_size
    fig.legend.label_text_font = "10pt"

    # Extra padding
    fig.min_border_left = 20
    fig.min_border_right = 20
    fig.min_border_top = 20
    fig.min_border_bottom = 20

    # Edit legend
    fig.legend.location = "bottom_right"
    fig.legend.label_text_font_size = "10pt"
    if legend is False:
        fig.legend.visible = False
    return fig


def roc_calculate(Ytrue, Yscore, bootnum=1000, metric=None, val=None):
    """Calculates required metrics for the roc plot function (fpr, tpr, and tpr_ci).

    Parameters
    ----------
    Ytrue : array-like, shape = [n_samples]
        Binary label for samples (0s and 1s)

    Yscore : array-like, shape = [n_samples]
        Predicted y score for samples

    Returns
    ----------------------------------
    fpr : array-like, shape = [n_samples]
        False positive rates.

    tpr : array-like, shape = [n_samples]
        True positive rates.

    tpr_ci : array-like, shape = [n_samples, 2]
        True positive rates 95% confidence intervals [lowci, uppci].
    """

    # Get fpr, tpr
    fpr, tpr, threshold = metrics.roc_curve(Ytrue, Yscore, pos_label=1, drop_intermediate=False)

    # fpr, tpr with drop_intermediates for fpr = 0 (useful for plot... since we plot specificity on x-axis, we don't need intermediates when fpr=0)
    tpr0 = tpr[fpr == 0][-1]
    tpr = np.concatenate([[tpr0], tpr[fpr > 0]])
    fpr = np.concatenate([[0], fpr[fpr > 0]])

    # if metric is provided, calculate stats
    if metric is not None:
        specificity, sensitivity, threshold = get_spec_sens_cuttoff(Ytrue, Yscore, metric, val)
        stats = get_stats(Ytrue, Yscore, specificity)
        stats["val_specificity"] = specificity
        stats["val_sensitivity"] = specificity
        stats["val_cutoffscore"] = threshold

    # bootstrap using vertical averaging
    tpr_boot = []
    boot_stats = []
    for i in range(bootnum):
        # Resample and get tpr, fpr
        Ytrue_res, Yscore_res = resample(Ytrue, Yscore)
        fpr_res, tpr_res, threshold_res = metrics.roc_curve(Ytrue_res, Yscore_res, pos_label=1, drop_intermediate=False)

        # Drop intermediates when fpr=0
        tpr0_res = tpr_res[fpr_res == 0][-1]
        tpr_res = np.concatenate([[tpr0_res], tpr_res[fpr_res > 0]])
        fpr_res = np.concatenate([[0], fpr_res[fpr_res > 0]])

        # Vertical averaging... use closest fpr_res to fpr, and append the corresponding tpr
        idx = [np.abs(i - fpr_res).argmin() for i in fpr]
        tpr_list = tpr_res[idx]
        tpr_boot.append(tpr_list)

        # if metric is provided, calculate stats
        if metric is not None:
            stats_res = get_stats(Ytrue_res, Yscore_res, specificity)
            boot_stats.append(stats_res)

    # Get CI for bootstat
    if metric is not None:
        bootci_stats = {}
        for i in boot_stats[0].keys():
            stats_i = [k[i] for k in boot_stats]
            stats_i = np.array(stats_i)
            stats_i = stats_i[~np.isnan(stats_i)]  # Remove nans
            try:
                lowci = np.percentile(stats_i, 2.5)
                uppci = np.percentile(stats_i, 97.5)
            except IndexError:
                lowci = np.nan
                uppci = np.nan
            bootci_stats[i] = [lowci, uppci]

    # Get CI for tpr
    tpr_lowci = np.percentile(tpr_boot, 2.5, axis=0)
    tpr_uppci = np.percentile(tpr_boot, 97.5, axis=0)

    # Add the starting 0
    tpr = np.insert(tpr, 0, 0)
    fpr = np.insert(fpr, 0, 0)
    tpr_lowci = np.insert(tpr_lowci, 0, 0)
    tpr_uppci = np.insert(tpr_uppci, 0, 0)

    # Concatenate tpr_ci
    tpr_ci = np.array([tpr_lowci, tpr_uppci])

    if metric is None:
        return fpr, tpr, tpr_ci
    else:
        return fpr, tpr, tpr_ci, stats, bootci_stats


def get_sens_spec(Ytrue, Yscore, cuttoff_val):
    """Get sensitivity and specificity from cutoff value."""
    Yscore_round = np.where(np.array(Yscore) > cuttoff_val, 1, 0)
    tn, fp, fn, tp = metrics.confusion_matrix(Ytrue, Yscore_round).ravel()
    sensitivity = tp / (tp + fn)
    specificity = tn / (tn + fp)
    return sensitivity, specificity


def get_sens_cuttoff(Ytrue, Yscore, specificity_val):
    """Get sensitivity and cuttoff value from specificity."""
    fpr0 = 1 - specificity_val
    fpr, sensitivity, thresholds = metrics.roc_curve(Ytrue, Yscore, pos_label=1, drop_intermediate=False)
    idx = np.abs(fpr - fpr0).argmin()  # this find the closest value in fpr to fpr0
    # Check that this is not a perfect roc curve
    # If it is perfect, allow sensitivity = 1, rather than 0
    if specificity_val == 1 and sensitivity[idx] == 0:
        for i in range(len(fpr)):
            if fpr[i] == 1 and sensitivity[i] == 1:
                return 1, 0.5
    return sensitivity[idx], thresholds[idx]


def get_spec_sens_cuttoff(Ytrue, Yscore, metric, val):
    """Return specificity, sensitivity, cutoff value provided the metric and value used."""
    if metric == "specificity":
        specificity = val
        sensitivity, threshold = get_sens_cuttoff(Ytrue, Yscore, val)
    elif metric == "cutoffscore":
        threshold = val
        sensitivity, specificity = get_sens_spec(Ytrue, Yscore, val)
    return specificity, sensitivity, threshold


def get_stats(Ytrue, Yscore, specificity):
    """Calculates binary metrics given the specificity."""
    sensitivity, cutoffscore = get_sens_cuttoff(Ytrue, Yscore, specificity)
    stats = binary_metrics(Ytrue, Yscore, cut_off=cutoffscore)
    return stats

import numpy as np
from bokeh.models import Band, HoverTool
from bokeh.plotting import ColumnDataSource, figure
from scipy import interp
from sklearn import metrics
from sklearn.metrics import confusion_matrix, roc_auc_score
from sklearn.utils import resample
from ..utils import binary_metrics


def roc_plot(fpr, tpr, tpr_ci, width=450, height=350, xlabel="1-Specificity", ylabel="Sensitivity", legend=True, label_font_size="13pt", title="", errorbar=False):
    """Creates a rocplot using Bokeh.

    Parameters
    ----------
    fpr : array-like, shape = [n_samples]
        False positive rates. Calculate using roc_calculate.

    tpr : array-like, shape = [n_samples]
        True positive rates. Calculate using roc_calculate.

    tpr_ci : array-like, shape = [n_samples, 2]
        True positive rates 95% confidence intervals [lowci, uppci]. Calculate using roc_calculate.
    """

    # Get CI
    tpr_lowci = tpr_ci[0]
    tpr_uppci = tpr_ci[1]
    auc = metrics.auc(fpr, tpr)

    # specificity and ci-interval for HoverTool
    spec = 1 - fpr
    ci = (tpr_uppci - tpr_lowci) / 2

    # Figure
    data = {"x": fpr, "y": tpr, "lowci": tpr_lowci, "uppci": tpr_uppci, "spec": spec, "ci": ci}
    source = ColumnDataSource(data=data)
    fig = figure(title=title, plot_width=width, plot_height=height, x_axis_label=xlabel, y_axis_label=ylabel, x_range=(-0.06, 1.06), y_range=(-0.06, 1.06))

    # Figure: add line
    fig.line([0, 1], [0, 1], color="black", line_dash="dashed", line_width=2.5, legend="Equal distribution line")
    figline = fig.line("x", "y", color="green", line_width=3.5, alpha=0.6, legend="ROC Curve (Train)", source=source)
    fig.add_tools(HoverTool(renderers=[figline], tooltips=[("Specificity", "@spec{1.111}"), ("Sensitivity", "@y{1.111} (+/- @ci{1.111})")]))

    # Figure: add 95CI band
    figband = Band(base="x", lower="lowci", upper="uppci", level="underlay", fill_alpha=0.1, line_width=1, line_color="black", fill_color="green", source=source)
    fig.add_layout(figband)

    # Figure: add errorbar  spec =  1 - fpr
    if errorbar is not False:
        idx = np.abs(fpr - (1 - errorbar)).argmin()  # this find the closest value in fpr to errorbar fpr
        fpr_eb = fpr[idx]
        tpr_eb = tpr[idx]
        tpr_lowci_eb = tpr_lowci[idx]
        tpr_uppci_eb = tpr_uppci[idx]

        # Edge case: If this is a perfect roc curve, and specificity >= 1, make sure error_bar is at (0,1) not (0,0)
        if errorbar >= 1:
            for i in range(len(fpr)):
                if fpr[i] == 0 and tpr[i] == 1:
                    fpr_eb = 0
                    tpr_eb = 1
                    tpr_lowci_eb = 1
                    tpr_uppci_eb = 1

        roc_whisker_line = fig.multi_line([[fpr_eb, fpr_eb]], [[tpr_lowci_eb, tpr_uppci_eb]], line_alpha=1, line_color="black")
        roc_whisker_bot = fig.multi_line([[fpr_eb - 0.03, fpr_eb + 0.03]], [[tpr_lowci_eb, tpr_lowci_eb]], line_color="black")
        roc_whisker_top = fig.multi_line([[fpr_eb - 0.03, fpr_eb + 0.03]], [[tpr_uppci_eb, tpr_uppci_eb]], line_alpha=1, line_color="black")
        fig.circle([fpr_eb], [tpr_eb], size=8, fill_alpha=1, line_alpha=1, line_color="black", fill_color="white")

    # Change font size
    fig.title.text_font_size = "11pt"
    fig.xaxis.axis_label_text_font_size = label_font_size
    fig.yaxis.axis_label_text_font_size = label_font_size
    fig.legend.label_text_font = "10pt"

    # Extra padding
    fig.min_border_left = 20
    fig.min_border_right = 20
    fig.min_border_top = 20
    fig.min_border_bottom = 20

    # Edit legend
    fig.legend.location = "bottom_right"
    fig.legend.label_text_font_size = "10pt"
    if legend is False:
        fig.legend.visible = False
    return fig


def roc_calculate(Ytrue, Yscore, bootnum=1000, metric=None, val=None):
    """Calculates required metrics for the roc plot function (fpr, tpr, and tpr_ci).

    Parameters
    ----------
    Ytrue : array-like, shape = [n_samples]
        Binary label for samples (0s and 1s)

    Yscore : array-like, shape = [n_samples]
        Predicted y score for samples

    Returns
    ----------------------------------
    fpr : array-like, shape = [n_samples]
        False positive rates.

    tpr : array-like, shape = [n_samples]
        True positive rates.

    tpr_ci : array-like, shape = [n_samples, 2]
        True positive rates 95% confidence intervals [lowci, uppci].
    """

    # Get fpr, tpr
    fpr, tpr, threshold = metrics.roc_curve(Ytrue, Yscore, pos_label=1, drop_intermediate=False)

    # fpr, tpr with drop_intermediates for fpr = 0 (useful for plot... since we plot specificity on x-axis, we don't need intermediates when fpr=0)
    tpr0 = tpr[fpr == 0][-1]
    tpr = np.concatenate([[tpr0], tpr[fpr > 0]])
    fpr = np.concatenate([[0], fpr[fpr > 0]])

    # if metric is provided, calculate stats
    if metric is not None:
        specificity, sensitivity, threshold = get_spec_sens_cuttoff(Ytrue, Yscore, metric, val)
        stats = get_stats(Ytrue, Yscore, specificity)
        stats["val_specificity"] = specificity
        stats["val_sensitivity"] = specificity
        stats["val_cutoffscore"] = threshold

    # bootstrap using vertical averaging
    tpr_boot = []
    boot_stats = []
    for i in range(bootnum):
        # Resample and get tpr, fpr
        Ytrue_res, Yscore_res = resample(Ytrue, Yscore)
        fpr_res, tpr_res, threshold_res = metrics.roc_curve(Ytrue_res, Yscore_res, pos_label=1, drop_intermediate=False)

        # Drop intermediates when fpr=0
        tpr0_res = tpr_res[fpr_res == 0][-1]
        tpr_res = np.concatenate([[tpr0_res], tpr_res[fpr_res > 0]])
        fpr_res = np.concatenate([[0], fpr_res[fpr_res > 0]])

        # Vertical averaging... use closest fpr_res to fpr, and append the corresponding tpr
        idx = [np.abs(i - fpr_res).argmin() for i in fpr]
        tpr_list = tpr_res[idx]
        tpr_boot.append(tpr_list)

        # if metric is provided, calculate stats
        if metric is not None:
            stats_res = get_stats(Ytrue_res, Yscore_res, specificity)
            boot_stats.append(stats_res)

    # Get CI for bootstat
    if metric is not None:
        bootci_stats = {}
        for i in boot_stats[0].keys():
            stats_i = [k[i] for k in boot_stats]
            stats_i = np.array(stats_i)
            stats_i = stats_i[~np.isnan(stats_i)]  # Remove nans
            try:
                lowci = np.percentile(stats_i, 2.5)
                uppci = np.percentile(stats_i, 97.5)
            except IndexError:
                lowci = np.nan
                uppci = np.nan
            bootci_stats[i] = [lowci, uppci]

    # Get CI for tpr
    tpr_lowci = np.percentile(tpr_boot, 2.5, axis=0)
    tpr_uppci = np.percentile(tpr_boot, 97.5, axis=0)

    # Add the starting 0
    tpr = np.insert(tpr, 0, 0)
    fpr = np.insert(fpr, 0, 0)
    tpr_lowci = np.insert(tpr_lowci, 0, 0)
    tpr_uppci = np.insert(tpr_uppci, 0, 0)

    # Concatenate tpr_ci
    tpr_ci = np.array([tpr_lowci, tpr_uppci])

    if metric is None:
        return fpr, tpr, tpr_ci
    else:
        return fpr, tpr, tpr_ci, stats, bootci_stats


def get_sens_spec(Ytrue, Yscore, cuttoff_val):
    """Get sensitivity and specificity from cutoff value."""
    Yscore_round = np.where(np.array(Yscore) > cuttoff_val, 1, 0)
    tn, fp, fn, tp = metrics.confusion_matrix(Ytrue, Yscore_round).ravel()
    sensitivity = tp / (tp + fn)
    specificity = tn / (tn + fp)
    return sensitivity, specificity


def get_sens_cuttoff(Ytrue, Yscore, specificity_val):
    """Get sensitivity and cuttoff value from specificity."""
    fpr0 = 1 - specificity_val
    fpr, sensitivity, thresholds = metrics.roc_curve(Ytrue, Yscore, pos_label=1, drop_intermediate=False)
    idx = np.abs(fpr - fpr0).argmin()  # this find the closest value in fpr to fpr0
    # Check that this is not a perfect roc curve
    # If it is perfect, allow sensitivity = 1, rather than 0
    if specificity_val == 1 and sensitivity[idx] == 0:
        for i in range(len(fpr)):
            if fpr[i] == 1 and sensitivity[i] == 1:
                return 1, 0.5
    return sensitivity[idx], thresholds[idx]


def get_spec_sens_cuttoff(Ytrue, Yscore, metric, val):
    """Return specificity, sensitivity, cutoff value provided the metric and value used."""
    if metric == "specificity":
        specificity = val
        sensitivity, threshold = get_sens_cuttoff(Ytrue, Yscore, val)
    elif metric == "cutoffscore":
        threshold = val
        sensitivity, specificity = get_sens_spec(Ytrue, Yscore, val)
    return specificity, sensitivity, threshold


def get_stats(Ytrue, Yscore, specificity):
    """Calculates binary metrics given the specificity."""
    sensitivity, cutoffscore = get_sens_cuttoff(Ytrue, Yscore, specificity)
    stats = binary_metrics(Ytrue, Yscore, cut_off=cutoffscore)
    return stats

Creates a rocplot using Bokeh.      Parameters     ----------     fpr : array-like, shape = [n_samples]         False positive rates. Calculate using roc_calculate.      tpr : array-like, shape = [n_samples]         True positive rates. Calculate using roc_calculate.      tpr_ci : array-like, shape = [n_samples, 2]         True positive rates 95% confidence intervals [lowci, uppci]. Calculate using roc_calculate. # Get CI # specificity and ci-interval for HoverTool # Figure # Figure: add line # Figure: add 95CI band # Figure: add errorbar  spec =  1 - fpr # this find the closest value in fpr to errorbar fpr # Edge case: If this is a perfect roc curve, and specificity >= 1, make sure error_bar is at (0,1) not (0,0) # Change font size # Extra padding # Edit legend Calculates required metrics for the roc plot function (fpr, tpr, and tpr_ci).      Parameters     ----------     Ytrue : array-like, shape = [n_samples]         Binary label for samples (0s and 1s)      Yscore : array-like, shape = [n_samples]         Predicted y score for samples      Returns     ----------------------------------     fpr : array-like, shape = [n_samples]         False positive rates.      tpr : array-like, shape = [n_samples]         True positive rates.      tpr_ci : array-like, shape = [n_samples, 2]         True positive rates 95% confidence intervals [lowci, uppci]. # Get fpr, tpr # fpr, tpr with drop_intermediates for fpr = 0 (useful for plot... since we plot specificity on x-axis, we don't need intermediates when fpr=0) # if metric is provided, calculate stats # bootstrap using vertical averaging # Resample and get tpr, fpr # Drop intermediates when fpr=0 # Vertical averaging... use closest fpr_res to fpr, and append the corresponding tpr # if metric is provided, calculate stats # Get CI for bootstat # Remove nans # Get CI for tpr # Add the starting 0 # Concatenate tpr_ci Get sensitivity and specificity from cutoff value. Get sensitivity and cuttoff value from specificity. # this find the closest value in fpr to fpr0 # Check that this is not a perfect roc curve # If it is perfect, allow sensitivity = 1, rather than 0 Return specificity, sensitivity, cutoff value provided the metric and value used. Calculates binary metrics given the specificity.