import numpy as np import warnings try: import matplotlib.pyplot as plt from matplotlib import lines from matplotlib.font_manager import FontProperties from matplotlib.path import Path from matplotlib.patches import PathPatch import matplotlib except ImportError: warnings.warn("matplotlib could not be loaded!") pass def draw_bars(out_value, features, feature_type, width_separators, width_bar): """Draw the bars and separators.""" rectangle_list = [] separator_list = [] pre_val = out_value for index, features in zip(range(len(features)), features): if feature_type == 'positive': left_bound = float(features[0]) right_bound = pre_val pre_val = left_bound separator_indent = np.abs(width_separators) separator_pos = left_bound colors = ['#FF0D57', '#FFC3D5'] else: left_bound = pre_val right_bound = float(features[0]) pre_val = right_bound separator_indent = - np.abs(width_separators) separator_pos = right_bound colors = ['#1E88E5', '#D1E6FA'] # Create rectangle if index == 0: if feature_type == 'positive': points_rectangle = [[left_bound, 0], [right_bound, 0], [right_bound, width_bar], [left_bound, width_bar], [left_bound + separator_indent, (width_bar / 2)] ] else: points_rectangle = [[right_bound, 0], [left_bound, 0], [left_bound, width_bar], [right_bound, width_bar], [right_bound + separator_indent, (width_bar / 2)] ] else: points_rectangle = [[left_bound, 0], [right_bound, 0], [right_bound + separator_indent * 0.90, (width_bar / 2)], [right_bound, width_bar], [left_bound, width_bar], [left_bound + separator_indent * 0.90, (width_bar / 2)]] line = plt.Polygon(points_rectangle, closed=True, fill=True, facecolor=colors[0], linewidth=0) rectangle_list += [line] # Create seperator points_separator = [[separator_pos, 0], [separator_pos + separator_indent, (width_bar / 2)], [separator_pos, width_bar]] line = plt.Polygon(points_separator, closed=None, fill=None, edgecolor=colors[1], lw=3) separator_list += [line] return rectangle_list, separator_list def draw_labels(fig, ax, out_value, features, feature_type, offset_text, total_effect=0, min_perc=0.05, text_rotation=0): start_text = out_value pre_val = out_value # Define variables specific to positive and negative effect features if feature_type == 'positive': colors = ['#FF0D57', '#FFC3D5'] alignement = 'right' sign = 1 else: colors = ['#1E88E5', '#D1E6FA'] alignement = 'left' sign = -1 # Draw initial line if feature_type == 'positive': x, y = np.array([[pre_val, pre_val], [0, -0.18]]) line = lines.Line2D(x, y, lw=1., alpha=0.5, color=colors[0]) line.set_clip_on(False) ax.add_line(line) start_text = pre_val box_end = out_value val = out_value for feature in features: # Exclude all labels that do not contribute at least 10% to the total feature_contribution = np.abs(float(feature[0]) - pre_val) / np.abs(total_effect) if feature_contribution < min_perc: break # Compute value for current feature val = float(feature[0]) # Draw labels. if feature[1] == "": text = feature[2] else: text = feature[2] + ' = ' + feature[1] text_out_val = plt.text(start_text - sign * offset_text, -0.15, text, fontsize=12, color=colors[0], horizontalalignment=alignement, rotation=text_rotation) text_out_val.set_bbox(dict(facecolor='none', edgecolor='none')) # We need to draw the plot to be able to get the size of the # text box fig.canvas.draw() box_size = text_out_val.get_bbox_patch().get_extents()\ .transformed(ax.transData.inverted()) if feature_type == 'positive': box_end_ = box_size.get_points()[0][0] else: box_end_ = box_size.get_points()[1][0] # If the feature goes over the side of the plot, we remove that label # and stop drawing labels if box_end_ > ax.get_xlim()[1]: text_out_val.remove() break # Create end line if (sign * box_end_) > (sign * val): x, y = np.array([[val, val], [0, -0.18]]) line = lines.Line2D(x, y, lw=1., alpha=0.5, color=colors[0]) line.set_clip_on(False) ax.add_line(line) start_text = val box_end = val else: box_end = box_end_ - sign * offset_text x, y = np.array([[val, box_end, box_end], [0, -0.08, -0.18]]) line = lines.Line2D(x, y, lw=1., alpha=0.5, color=colors[0]) line.set_clip_on(False) ax.add_line(line) start_text = box_end # Update previous value pre_val = float(feature[0]) # Create line for labels extent_shading = [out_value, box_end, 0, -0.31] path = [[out_value, 0], [pre_val, 0], [box_end, -0.08], [box_end, -0.2], [out_value, -0.2], [out_value, 0]] path = Path(path) patch = PathPatch(path, facecolor='none', edgecolor='none') ax.add_patch(patch) # Extend axis if needed lower_lim, upper_lim = ax.get_xlim() if (box_end < lower_lim): ax.set_xlim(box_end, upper_lim) if (box_end > upper_lim): ax.set_xlim(lower_lim, box_end) # Create shading if feature_type == 'positive': colors = np.array([(255, 13, 87), (255, 255, 255)]) / 255. else: colors = np.array([(30, 136, 229), (255, 255, 255)]) / 255. cm = matplotlib.colors.LinearSegmentedColormap.from_list('cm', colors) Z, Z2 = np.meshgrid(np.linspace(0, 10), np.linspace(-10, 10)) im = plt.imshow(Z2, interpolation='quadric', cmap=cm, vmax=0.01, alpha=0.3, origin='lower', extent=extent_shading, clip_path=patch, clip_on=True, aspect='auto') im.set_clip_path(patch) return fig, ax def format_data(data): """Format data.""" # Format negative features neg_features = np.array([[data['features'][x]['effect'], data['features'][x]['value'], data['featureNames'][x]] for x in data['features'].keys() if data['features'][x]['effect'] < 0]) neg_features = np.array(sorted(neg_features, key=lambda x: float(x[0]), reverse=False)) # Format postive features pos_features = np.array([[data['features'][x]['effect'], data['features'][x]['value'], data['featureNames'][x]] for x in data['features'].keys() if data['features'][x]['effect'] >= 0]) pos_features = np.array(sorted(pos_features, key=lambda x: float(x[0]), reverse=True)) # Define link function if data['link'] == 'identity': convert_func = lambda x: x elif data['link'] == 'logit': convert_func = lambda x: 1 / (1 + np.exp(-x)) else: assert False, "ERROR: Unrecognized link function: " + str(data['link']) # Convert negative feature values to plot values neg_val = data['outValue'] for i in neg_features: val = float(i[0]) neg_val = neg_val + np.abs(val) i[0] = convert_func(neg_val) if len(neg_features) > 0: total_neg = np.max(neg_features[:, 0].astype(float)) - \ np.min(neg_features[:, 0].astype(float)) else: total_neg = 0 # Convert positive feature values to plot values pos_val = data['outValue'] for i in pos_features: val = float(i[0]) pos_val = pos_val - np.abs(val) i[0] = convert_func(pos_val) if len(pos_features) > 0: total_pos = np.max(pos_features[:, 0].astype(float)) - \ np.min(pos_features[:, 0].astype(float)) else: total_pos = 0 # Convert output value and base value data['outValue'] = convert_func(data['outValue']) data['baseValue'] = convert_func(data['baseValue']) return neg_features, total_neg, pos_features, total_pos def draw_output_element(out_name, out_value, ax): # Add output value x, y = np.array([[out_value, out_value], [0, 0.24]]) line = lines.Line2D(x, y, lw=2., color='#F2F2F2') line.set_clip_on(False) ax.add_line(line) font0 = FontProperties() font = font0.copy() font.set_weight('bold') text_out_val = plt.text(out_value, 0.25, '{0:.2f}'.format(out_value), fontproperties=font, fontsize=14, horizontalalignment='center') text_out_val.set_bbox(dict(facecolor='white', edgecolor='white')) text_out_val = plt.text(out_value, 0.33, out_name, fontsize=12, alpha=0.5, horizontalalignment='center') text_out_val.set_bbox(dict(facecolor='white', edgecolor='white')) def draw_base_element(base_value, ax): x, y = np.array([[base_value, base_value], [0.13, 0.25]]) line = lines.Line2D(x, y, lw=2., color='#F2F2F2') line.set_clip_on(False) ax.add_line(line) text_out_val = plt.text(base_value, 0.33, 'base value', fontsize=12, alpha=0.5, horizontalalignment='center') text_out_val.set_bbox(dict(facecolor='white', edgecolor='white')) def draw_higher_lower_element(out_value, offset_text): plt.text(out_value - offset_text, 0.405, 'higher', fontsize=13, color='#FF0D57', horizontalalignment='right') plt.text(out_value + offset_text, 0.405, 'lower', fontsize=13, color='#1E88E5', horizontalalignment='left') plt.text(out_value, 0.4, r'$\leftarrow$', fontsize=13, color='#1E88E5', horizontalalignment='center') plt.text(out_value, 0.425, r'$\rightarrow$', fontsize=13, color='#FF0D57', horizontalalignment='center') def update_axis_limits(ax, total_pos, pos_features, total_neg, neg_features, base_value): ax.set_ylim(-0.5, 0.15) padding = np.max([np.abs(total_pos) * 0.2, np.abs(total_neg) * 0.2]) if len(pos_features) > 0: min_x = min(np.min(pos_features[:, 0].astype(float)), base_value) - padding else: min_x = 0 if len(neg_features) > 0: max_x = max(np.max(neg_features[:, 0].astype(float)), base_value) + padding else: max_x = 0 ax.set_xlim(min_x, max_x) plt.tick_params(top=True, bottom=False, left=False, right=False, labelleft=False, labeltop=True, labelbottom=False) plt.locator_params(axis='x', nbins=12) for key, spine in zip(plt.gca().spines.keys(), plt.gca().spines.values()): if key != 'top': spine.set_visible(False) def draw_additive_plot(data, figsize, show, text_rotation=0): """Draw additive plot.""" # Turn off interactive plot if show == False: plt.ioff() # Format data neg_features, total_neg, pos_features, total_pos = format_data(data) # Compute overall metrics base_value = data['baseValue'] out_value = data['outValue'] offset_text = (np.abs(total_neg) + np.abs(total_pos)) * 0.04 # Define plots fig, ax = plt.subplots(figsize=figsize) # Compute axis limit update_axis_limits(ax, total_pos, pos_features, total_neg, neg_features, base_value) # Define width of bar width_bar = 0.1 width_separators = (ax.get_xlim()[1] - ax.get_xlim()[0]) / 200 # Create bar for negative shap values rectangle_list, separator_list = draw_bars(out_value, neg_features, 'negative', width_separators, width_bar) for i in rectangle_list: ax.add_patch(i) for i in separator_list: ax.add_patch(i) # Create bar for positive shap values rectangle_list, separator_list = draw_bars(out_value, pos_features, 'positive', width_separators, width_bar) for i in rectangle_list: ax.add_patch(i) for i in separator_list: ax.add_patch(i) # Add labels total_effect = np.abs(total_neg) + total_pos fig, ax = draw_labels(fig, ax, out_value, neg_features, 'negative', offset_text, total_effect, min_perc=0.05, text_rotation=text_rotation) fig, ax = draw_labels(fig, ax, out_value, pos_features, 'positive', offset_text, total_effect, min_perc=0.05, text_rotation=text_rotation) # higher lower legend draw_higher_lower_element(out_value, offset_text) # Add label for base value draw_base_element(base_value, ax) # Add output label out_names = data['outNames'][0] draw_output_element(out_names, out_value, ax) if show: plt.show() else: return plt.gcf()