visualization.py

from functools import partial

import pandas as pd
import numpy as np
from scipy.cluster.hierarchy import dendrogram, linkage
from scipy.spatial.distance import squareform
from matplotlib import pyplot as plt

import pysubgroup_mod as ps


def plot_sgbars(result_df, _, ylabel="target share", title="Discovered Subgroups", dynamic_widths=False, _suffix=""):
    shares_sg = result_df["target_share_sg"]
    shares_compl = result_df["target_share_complement"]
    sg_relative_sizes = result_df["relative_size_sg"]
    x = np.arange(len(result_df))

    base_width = 0.8
    if dynamic_widths:
        width_sg = 0.02 + base_width * sg_relative_sizes
        width_compl = base_width - width_sg
    else:
        width_sg = base_width / 2
        width_compl = base_width / 2

    fig, ax = plt.subplots()
    rects1 = ax.bar(x, shares_sg, width_sg, align='edge')
    rects2 = ax.bar(x + width_sg, shares_compl, width_compl, align='edge', color='#61b76f')

    ax.set_ylabel(ylabel)
    ax.set_title(title)
    ax.set_xticks(x + base_width / 2)
    ax.set_xticklabels(result_df.index, rotation=90)

    ax.legend((rects1[0], rects2[0]), ('subgroup', 'complement'))
    fig.set_size_inches(12, len(result_df))

    return fig


def plot_roc(result_df, data, qf=ps.StandardQF(0.5), levels=40, annotate=False):
    instances_dataset = len(data)
    positives_dataset = np.max(result_df['positives_dataset'])
    negatives_dataset = instances_dataset - positives_dataset

    xlist = np.linspace(0.01, 0.99, 100)
    ylist = np.linspace(0.01, 0.99, 100)
    X, Y = np.meshgrid(xlist, ylist)
    f = np.vectorize(partial(qf.evaluate, instances_dataset, positives_dataset), otypes=[np.float])
    Z = f(X * negatives_dataset + Y * positives_dataset, Y * positives_dataset)
    max_val = np.max([np.max(Z), -np.min(Z)])

    fig, ax = plt.subplots()
    cm = plt.cm.get_cmap("bwr")

    plt.contourf(X, Y, Z, levels, cmap=cm, vmin=-max_val, vmax=max_val)

    for i, sg in result_df.iterrows():
        rel_positives_sg = sg['positives_sg'] / positives_dataset
        rel_negatives_sg = (sg['size_sg'] - sg['positives_sg']) / negatives_dataset
        ax.plot(rel_negatives_sg, rel_positives_sg, 'ro', color='black')
        if annotate:
            label_margin = 0.01
            ax.annotate(str(i), (rel_negatives_sg + label_margin, rel_positives_sg + label_margin))

    # plt.colorbar(cp)
    plt.title('Discovered subgroups')
    plt.xlabel('False Positive Rate')
    plt.ylabel('True Positive Rate')

    return fig


def plot_npspace(result_df, data, annotate=True, fixed_limits=False):

    fig, ax = plt.subplots()

    for i, sg in result_df.iterrows():
        target_share_sg = sg['target_share_sg']
        size_sg = sg['size_sg']
        ax.plot(size_sg, target_share_sg, 'ro', color='black')
        if annotate:
            ax.annotate(str(i), (size_sg + 5, target_share_sg + 0.001))

    if fixed_limits:
        plt.xlim((0, len(data)))
        plt.ylim((0, 1))

    plt.title('Discovered subgroups')
    plt.xlabel('Size of Subgroup')
    plt.ylabel('Target Share Subgroup')

    return fig


def plot_distribution_numeric(sg, data, bins):
    fig, _ = plt.subplots()
    target_values_sg = data[sg.covers(data)][sg.target.get_attributes()].values
    target_values_data = data[sg.target.get_attributes()].values
    plt.hist(target_values_sg, bins, alpha=0.5, label=str(sg.subgroup_description), density=True)
    plt.hist(target_values_data, bins, alpha=0.5, label="Overall Data", density=True)
    plt.legend(loc='upper right')
    return fig


def compare_distributions_numeric(sgs, data, bins):
    fig, _ = plt.subplots()
    for sg in sgs:
        target_values_sg = data[sg.covers(data)][sg.target.get_attributes()].values
        plt.hist(target_values_sg, bins, alpha=0.3, label=str(sg.subgroup_description), density=True)
    plt.legend(loc='upper right')
    return fig


def similarity_sgs(sgd_results, data, color=True):
    sgs = [x[1] for x in sgd_results]
    #sgNames = [str(sg.subgroup_description) for sg in sgs]
    dists = [[ps.overlap(sg, sg2, data) for sg2 in sgs] for sg in sgs]
    dist_df = pd.DataFrame(dists)
    if color:
        dist_df = dist_df.style.background_gradient()
    return dist_df


def similarity_dendrogram(result, data):
    fig, _ = plt.subplots()
    dist_df = similarity_sgs(result, data, color=False)
    mat = 1 - dist_df.values
    dists = squareform(mat)
    linkage_matrix = linkage(dists, "single")
    dendrogram(linkage_matrix, labels=dist_df.index)
    return fig

def supportSetVisualization(result, in_order=True, drop_empty=True):
    df = result.task.data
    n_items = len(result.task.data)
    n_SGDs = len(result.results)
    covs = np.zeros((n_items, n_SGDs), dtype=bool)
    for i, (_, r, _) in enumerate(result.to_subgroups):
        covs[:, i] = r.covers(df)

    img_arr = covs.copy()

    sort_inds_x = np.argsort(np.sum(covs, axis=1))[::-1]
    img_arr = img_arr[sort_inds_x, :]
    if not in_order:
        sort_inds_y = np.argsort(np.sum(covs, axis=0))
        img_arr = img_arr[:, sort_inds_y]
    if drop_empty:
        keep_entities = np.sum(img_arr, axis=1) > 0
        print("Discarding {} entities that are not covered".format(n_items - np.count_nonzero(keep_entities)))
        img_arr = img_arr[keep_entities, :]
    return img_arr.T