Source code for hail.experimental.plots

import json
import numpy as np
import pandas as pd

import hail as hl
from bokeh.layouts import gridplot
from bokeh.models import Title, ColumnDataSource, HoverTool, Div, Tabs, Panel
from bokeh.palettes import Spectral8
from bokeh.plotting import figure
from bokeh.transform import factor_cmap
from hail.typecheck import typecheck
from hail.utils.hadoop_utils import hadoop_open, hadoop_ls
from hail.utils.java import warning


[docs]def plot_roc_curve(ht, scores, tp_label='tp', fp_label='fp', colors=None, title='ROC Curve', hover_mode='mouse'): """Create ROC curve from Hail Table. One or more `score` fields must be provided, which are assessed against `tp_label` and `fp_label` as truth data. High scores should correspond to true positives. Parameters ---------- ht : :class:`.Table` Table with required data scores : :obj:`str` or :obj:`list` of :obj:`.str` Top-level location of scores in ht against which to generate PR curves. tp_label : :obj:`str` Top-level location of true positives in ht. fp_label : :obj:`str` Top-level location of false positives in ht. colors : :obj:`dict` of :obj:`str` Optional colors to use (score -> desired color). title : :obj:`str` Title of plot. hover_mode : :obj:`str` Hover mode; one of 'mouse' (default), 'vline' or 'hline' Returns ------- :obj:`tuple` of :class:`.Figure` and :obj:`list` of :obj:`str` Figure, and list of AUCs corresponding to scores. """ if colors is None: # Get a palette automatically from bokeh.palettes import d3 palette = d3['Category10'][max(3, len(scores))] colors = {score: palette[i] for i, score in enumerate(scores)} if isinstance(scores, str): scores = [scores] total_tp, total_fp = ht.aggregate((hl.agg.count_where(ht[tp_label]), hl.agg.count_where(ht[fp_label]))) p = figure(title=title, x_axis_label='FPR', y_axis_label='TPR', tools="hover,save,pan,box_zoom,reset,wheel_zoom") p.add_layout(Title(text=f'Based on {total_tp} TPs and {total_fp} FPs'), 'above') aucs = [] for score in scores: ordered_ht = ht.key_by(_score=-ht[score]) ordered_ht = ordered_ht.select( score_name=score, score=ordered_ht[score], tpr=hl.scan.count_where(ordered_ht[tp_label]) / total_tp, fpr=hl.scan.count_where(ordered_ht[fp_label]) / total_fp, ).key_by().drop('_score') last_row = hl.utils.range_table(1).key_by().select(score_name=score, score=hl.float64(float('-inf')), tpr=hl.float32(1.0), fpr=hl.float32(1.0)) ordered_ht = ordered_ht.union(last_row) ordered_ht = ordered_ht.annotate( auc_contrib=hl.or_else((ordered_ht.fpr - hl.scan.max(ordered_ht.fpr)) * ordered_ht.tpr, 0.0) ) auc = ordered_ht.aggregate(hl.agg.sum(ordered_ht.auc_contrib)) aucs.append(auc) df = ordered_ht.annotate(score_name=ordered_ht.score_name + f' (AUC = {auc:.4f})').to_pandas() p.line(x='fpr', y='tpr', legend='score_name', source=ColumnDataSource(df), color=colors[score], line_width=3) p.legend.location = 'bottom_right' p.legend.click_policy = 'hide' p.select_one(HoverTool).tooltips = [(x, f"@{x}") for x in ('score_name', 'score', 'tpr', 'fpr')] p.select_one(HoverTool).mode = hover_mode return p, aucs
[docs]@typecheck(t_path=str) def hail_metadata(t_path): """Create a metadata plot for a Hail Table or MatrixTable. Parameters ---------- t_path : str Path to the Hail Table or MatrixTable files. Returns ------- :class:`bokeh.plotting.figure.Figure` or :class:`bokeh.models.widgets.panels.Tabs` or :class:`bokeh.models.layouts.Column` """ def get_rows_data(rows_files): file_sizes = [] partition_bounds = [] parts_file = [x['path'] for x in rows_files if x['path'].endswith('parts')] if parts_file: parts = hadoop_ls(parts_file[0]) for i, x in enumerate(parts): index = x['path'].split(f'{parts_file[0]}/part-')[1].split('-')[0] if i < len(parts) - 1: test_index = parts[i + 1]['path'].split(f'{parts_file[0]}/part-')[1].split('-')[0] if test_index == index: continue file_sizes.append(x['size_bytes']) metadata_file = [x['path'] for x in rows_files if x['path'].endswith('metadata.json.gz')] if metadata_file: with hadoop_open(metadata_file[0], 'rb') as f: rows_meta = json.loads(f.read()) try: partition_bounds = [ (x['start']['locus']['contig'], x['start']['locus']['position'], x['end']['locus']['contig'], x['end']['locus']['position']) for x in rows_meta['jRangeBounds']] except KeyError: pass return partition_bounds, file_sizes def scale_file_sizes(file_sizes): min_file_size = min(file_sizes) * 1.1 total_file_size = sum(file_sizes) all_scales = [ ('T', 1e12), ('G', 1e9), ('M', 1e6), ('K', 1e3), ('', 1e0) ] for overall_scale, overall_factor in all_scales: if total_file_size > overall_factor: total_file_size /= overall_factor break for scale, factor in all_scales: if min_file_size > factor: file_sizes = [x / factor for x in file_sizes] break total_file_size = f'{total_file_size:.1f} {overall_scale}B' return total_file_size, file_sizes, scale files = hadoop_ls(t_path) rows_file = [x['path'] for x in files if x['path'].endswith('rows')] entries_file = [x['path'] for x in files if x['path'].endswith('entries')] success_file = [x['modification_time'] for x in files if x['path'].endswith('SUCCESS')] metadata_file = [x['path'] for x in files if x['path'].endswith('metadata.json.gz')] if not metadata_file: raise FileNotFoundError('No metadata.json.gz file found.') with hadoop_open(metadata_file[0], 'rb') as f: overall_meta = json.loads(f.read()) rows_per_partition = overall_meta['components']['partition_counts']['counts'] if not rows_file: raise FileNotFoundError('No rows directory found.') rows_files = hadoop_ls(rows_file[0]) data_type = 'Table' if entries_file: data_type = 'MatrixTable' rows_file = [x['path'] for x in rows_files if x['path'].endswith('rows')] rows_files = hadoop_ls(rows_file[0]) row_partition_bounds, row_file_sizes = get_rows_data(rows_files) total_file_size, row_file_sizes, row_scale = scale_file_sizes(row_file_sizes) panel_size = 480 subpanel_size = 120 if not row_partition_bounds: warning('Table is not partitioned. Only plotting file sizes') row_file_sizes_hist, row_file_sizes_edges = np.histogram(row_file_sizes, bins=50) p_file_size = figure(plot_width=panel_size, plot_height=panel_size) p_file_size.quad(right=row_file_sizes_hist, left=0, bottom=row_file_sizes_edges[:-1], top=row_file_sizes_edges[1:], fill_color="#036564", line_color="#033649") p_file_size.yaxis.axis_label = f'File size ({row_scale}B)' return p_file_size all_data = { 'partition_widths': [-1 if x[0] != x[2] else x[3] - x[1] for x in row_partition_bounds], 'partition_bounds': [f'{x[0]}:{x[1]}-{x[2]}:{x[3]}' for x in row_partition_bounds], 'spans_chromosome': ['Spans chromosomes' if x[0] != x[2] else 'Within chromosome' for x in row_partition_bounds], 'row_file_sizes': row_file_sizes, 'row_file_sizes_human': [f'{x:.1f} {row_scale}B' for x in row_file_sizes], 'rows_per_partition': rows_per_partition, 'index': list(range(len(rows_per_partition))) } if entries_file: entries_rows_files = hadoop_ls(entries_file[0]) entries_rows_file = [x['path'] for x in entries_rows_files if x['path'].endswith('rows')] if entries_rows_file: entries_files = hadoop_ls(entries_rows_file[0]) entry_partition_bounds, entry_file_sizes = get_rows_data(entries_files) total_entry_file_size, entry_file_sizes, entry_scale = scale_file_sizes(entry_file_sizes) all_data['entry_file_sizes'] = entry_file_sizes all_data['entry_file_sizes_human'] = [f'{x:.1f} {entry_scale}B' for x in row_file_sizes] title = f'{data_type}: {t_path}' msg = f"Rows: {sum(all_data['rows_per_partition']):,}<br/>Partitions: {len(all_data['rows_per_partition']):,}<br/>Size: {total_file_size}<br/>" if success_file[0]: msg += success_file[0] tools = "hover,save,pan,box_zoom,reset,wheel_zoom" source = ColumnDataSource(pd.DataFrame(all_data)) p = figure(tools=tools, plot_width=panel_size, plot_height=panel_size) p.title.text = title p.xaxis.axis_label = 'Number of rows' p.yaxis.axis_label = f'File size ({row_scale}B)' color_map = factor_cmap('spans_chromosome', palette=Spectral8, factors=list(set(all_data['spans_chromosome']))) p.scatter('rows_per_partition', 'row_file_sizes', color=color_map, legend='spans_chromosome', source=source) p.legend.location = 'bottom_right' p.select_one(HoverTool).tooltips = [(x, f'@{x}') for x in ('rows_per_partition', 'row_file_sizes_human', 'partition_bounds', 'index')] p_stats = Div(text=msg) p_rows_per_partition = figure(x_range=p.x_range, plot_width=panel_size, plot_height=subpanel_size) p_file_size = figure(y_range=p.y_range, plot_width=subpanel_size, plot_height=panel_size) rows_per_partition_hist, rows_per_partition_edges = np.histogram(all_data['rows_per_partition'], bins=50) p_rows_per_partition.quad(top=rows_per_partition_hist, bottom=0, left=rows_per_partition_edges[:-1], right=rows_per_partition_edges[1:], fill_color="#036564", line_color="#033649") row_file_sizes_hist, row_file_sizes_edges = np.histogram(all_data['row_file_sizes'], bins=50) p_file_size.quad(right=row_file_sizes_hist, left=0, bottom=row_file_sizes_edges[:-1], top=row_file_sizes_edges[1:], fill_color="#036564", line_color="#033649") rows_grid = gridplot([[p_rows_per_partition, p_stats], [p, p_file_size]]) if 'entry_file_sizes' in all_data: title = f'Statistics for {data_type}: {t_path}' msg = f"Rows: {sum(all_data['rows_per_partition']):,}<br/>Partitions: {len(all_data['rows_per_partition']):,}<br/>Size: {total_entry_file_size}<br/>" if success_file[0]: msg += success_file[0] source = ColumnDataSource(pd.DataFrame(all_data)) p = figure(tools=tools, plot_width=panel_size, plot_height=panel_size) p.title.text = title p.xaxis.axis_label = 'Number of rows' p.yaxis.axis_label = f'File size ({entry_scale}B)' color_map = factor_cmap('spans_chromosome', palette=Spectral8, factors=list(set(all_data['spans_chromosome']))) p.scatter('rows_per_partition', 'entry_file_sizes', color=color_map, legend='spans_chromosome', source=source) p.legend.location = 'bottom_right' p.select_one(HoverTool).tooltips = [(x, f'@{x}') for x in ('rows_per_partition', 'entry_file_sizes_human', 'partition_bounds', 'index')] p_stats = Div(text=msg) p_rows_per_partition = figure(x_range=p.x_range, plot_width=panel_size, plot_height=subpanel_size) p_rows_per_partition.quad(top=rows_per_partition_hist, bottom=0, left=rows_per_partition_edges[:-1], right=rows_per_partition_edges[1:], fill_color="#036564", line_color="#033649") p_file_size = figure(y_range=p.y_range, plot_width=subpanel_size, plot_height=panel_size) row_file_sizes_hist, row_file_sizes_edges = np.histogram(all_data['entry_file_sizes'], bins=50) p_file_size.quad(right=row_file_sizes_hist, left=0, bottom=row_file_sizes_edges[:-1], top=row_file_sizes_edges[1:], fill_color="#036564", line_color="#033649") entries_grid = gridplot([[p_rows_per_partition, p_stats], [p, p_file_size]]) return Tabs(tabs=[Panel(child=entries_grid, title='Entries'), Panel(child=rows_grid, title='Rows')]) else: return rows_grid