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evaluation.py
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evaluation.py
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"""
Script for statistically evaluating various aspects of tsinfer performance.
"""
import argparse
import random
import concurrent.futures
import time
import warnings
import os.path
import json
import logging
import numpy as np
import pandas as pd
import matplotlib as mp
# Force matplotlib to not use any Xwindows backend.
mp.use("Agg") # NOQA
import matplotlib.pyplot as plt
from matplotlib import collections as mc
import seaborn as sns
import tqdm
import daiquiri
import colorama
import msprime
import tskit
import tsinfer
import tsinfer.cli as cli
# Set by the CLI.
global _output_format
_output_format = None
def save_figure(basename):
plt.savefig(basename + "." + _output_format)
plt.clf()
def make_errors(v, p):
"""
For each sample an error occurs with probability p. Errors are generated by
sampling values from the stationary distribution, that is, if we have an
allele frequency of f, a 1 is emitted with probability f and a
0 with probability 1 - f. Thus, there is a possibility that an 'error'
will in fact result in the same value.
"""
w = np.copy(v)
if p > 0:
m = v.shape[0]
frequency = np.sum(v) / m
# Randomly choose samples with probability p
samples = np.where(np.random.random(m) < p)[0]
# Generate observations from the stationary distribution.
errors = (np.random.random(samples.shape[0]) < frequency).astype(int)
w[samples] = errors
return w
def make_errors_genotype_model(g, error_probs):
"""
Given an empirically estimated error probability matrix, resample for a particular
variant. Determine variant frequency and true genotype (g0, g1, or g2),
then return observed genotype based on row in error_probs with nearest
frequency. Treat each pair of alleles as a diploid individual.
"""
w = np.copy(g)
# Make diploid (iterate each pair of alleles)
genos = [(w[i], w[i + 1]) for i in range(0, w.shape[0], 2)]
# Record the true genotypes
g0 = [i for i, x in enumerate(genos) if x == (0, 0)]
g1a = [i for i, x in enumerate(genos) if x == (1, 0)]
g1b = [i for i, x in enumerate(genos) if x == (0, 1)]
g2 = [i for i, x in enumerate(genos) if x == (1, 1)]
for idx in g0:
result = [(0, 0), (1, 0), (1, 1)][
np.random.choice(3, p=error_probs[["p00", "p01", "p02"]].values[0])
]
if result == (1, 0):
genos[idx] = [(0, 1), (1, 0)][np.random.choice(2)]
else:
genos[idx] = result
for idx in g1a:
genos[idx] = [(0, 0), (1, 0), (1, 1)][
np.random.choice(3, p=error_probs[["p10", "p11", "p12"]].values[0])
]
for idx in g1b:
genos[idx] = [(0, 0), (0, 1), (1, 1)][
np.random.choice(3, p=error_probs[["p10", "p11", "p12"]].values[0])
]
for idx in g2:
result = [(0, 0), (1, 0), (1, 1)][
np.random.choice(3, p=error_probs[["p20", "p21", "p22"]].values[0])
]
if result == (1, 0):
genos[idx] = [(0, 1), (1, 0)][np.random.choice(2)]
else:
genos[idx] = result
return np.array(sum(genos, ()))
def generate_samples(ts, error_param=0):
"""
Generate a samples file from a simulated ts based on the empirically estimated
error matrix saved in self.error_matrix.
Reject any variants that result in a fixed column.
"""
assert ts.num_sites != 0
sd = tsinfer.SampleData(sequence_length=ts.sequence_length)
try:
e = float(error_param)
for v in ts.variants():
g = v.genotypes if error_param == 0 else make_errors(v.genotypes, e)
sd.add_site(position=v.site.position, alleles=v.alleles, genotypes=g)
except ValueError:
error_matrix = pd.read_csv(error_param)
# Error_param is not a number => is a error file
# First record the allele frequency
for v in ts.variants():
m = v.genotypes.shape[0]
frequency = np.sum(v.genotypes) / m
# Find closest row in error matrix file
closest_row = (error_matrix["freq"] - frequency).abs().argsort()[:1]
closest_freq = error_matrix.iloc[closest_row]
g = make_errors_genotype_model(v.genotypes, closest_freq)
sd.add_site(position=v.site.position, alleles=v.alleles, genotypes=g)
sd.finalise()
return sd
def run_infer(
ts, engine=tsinfer.C_ENGINE, path_compression=True, exact_ancestors=False
):
"""
Runs the perfect inference process on the specified tree sequence.
"""
sample_data = tsinfer.SampleData.from_tree_sequence(ts)
if exact_ancestors:
ancestor_data = tsinfer.AncestorData(sample_data)
tsinfer.build_simulated_ancestors(sample_data, ancestor_data, ts)
ancestor_data.finalise()
else:
ancestor_data = tsinfer.generate_ancestors(sample_data, engine=engine)
ancestors_ts = tsinfer.match_ancestors(
sample_data, ancestor_data, path_compression=path_compression, engine=engine
)
inferred_ts = tsinfer.match_samples(
sample_data, ancestors_ts, path_compression=path_compression, engine=engine
)
return inferred_ts
def edges_performance_worker(args):
simulation_args, tree_metrics, engine = args
before = time.perf_counter()
smc_ts = msprime.simulate(**simulation_args)
sim_time = time.perf_counter() - before
tmp_ts = tsinfer.strip_singletons(smc_ts)
if tmp_ts.num_sites == 0:
warnings.warn("Dropping simulation with no variants")
return {}
before = time.perf_counter()
estimated_ancestors_ts = run_infer(smc_ts, exact_ancestors=False, engine=engine)
estimated_ancestors_time = time.perf_counter() - before
num_children = []
for edgeset in estimated_ancestors_ts.edgesets():
num_children.append(len(edgeset.children))
estimated_ancestors_num_children = np.array(num_children)
before = time.perf_counter()
exact_ancestors_ts = run_infer(smc_ts, exact_ancestors=True, engine=engine)
exact_ancestors_time = time.perf_counter() - before
num_children = []
for edgeset in exact_ancestors_ts.edgesets():
num_children.append(len(edgeset.children))
exact_ancestors_num_children = np.array(num_children)
results = {
"sim_time": sim_time,
"estimated_anc_time": estimated_ancestors_time,
"exact_anc_time": exact_ancestors_time,
"num_sites": smc_ts.num_sites,
"source_num_trees": smc_ts.num_trees,
"estimated_anc_trees": estimated_ancestors_ts.num_trees,
"exact_anc_trees": exact_ancestors_ts.num_trees,
"source_edges": smc_ts.num_edges,
"estimated_anc_edges": estimated_ancestors_ts.num_edges,
"exact_anc_edges": exact_ancestors_ts.num_edges,
"estimated_anc_max_children": np.max(estimated_ancestors_num_children),
"estimated_anc_mean_children": np.mean(estimated_ancestors_num_children),
"exact_anc_max_children": np.max(exact_ancestors_num_children),
"exact_anc_mean_children": np.mean(exact_ancestors_num_children),
}
results.update(simulation_args)
if tree_metrics:
before = time.perf_counter()
breakpoints, kc_distance = tsinfer.compare(smc_ts, exact_ancestors_ts)
d = breakpoints[1:] - breakpoints[:-1]
d /= breakpoints[-1]
exact_anc_kc_distance_weighted = np.sum(kc_distance * d)
exact_anc_perfect_trees = np.sum((kc_distance == 0) * d)
exact_anc_kc_mean = np.mean(kc_distance)
breakpoints, kc_distance = tsinfer.compare(smc_ts, estimated_ancestors_ts)
d = breakpoints[1:] - breakpoints[:-1]
d /= breakpoints[-1]
estimated_anc_kc_distance_weighted = np.sum(kc_distance * d)
estimated_anc_perfect_trees = np.sum((kc_distance == 0) * d)
estimated_anc_kc_mean = np.mean(kc_distance)
tree_metrics_time = time.perf_counter() - before
results.update(
{
"tree_metrics_time": tree_metrics_time,
"exact_anc_kc_distance_weighted": exact_anc_kc_distance_weighted,
"exact_anc_perfect_trees": exact_anc_perfect_trees,
"exact_anc_kc_mean": exact_anc_kc_mean,
"estimated_anc_kc_distance_weighted": estimated_anc_kc_distance_weighted,
"estimated_anc_perfect_trees": estimated_anc_perfect_trees,
"estimated_anc_kc_mean": estimated_anc_kc_mean,
}
)
return results
def run_edges_performance(args):
num_lengths = 10
MB = 10 ** 6
work = []
rng = random.Random()
rng.seed(args.random_seed)
for L in np.linspace(0, args.length, num_lengths + 1)[1:]:
for _ in range(args.num_replicates):
sim_args = {
"sample_size": args.sample_size,
"length": L * MB,
"recombination_rate": args.recombination_rate,
"mutation_rate": args.mutation_rate,
"Ne": args.Ne,
"model": "smc_prime",
"random_seed": rng.randint(1, 2 ** 30),
}
work.append((sim_args, args.compute_tree_metrics, args.engine))
random.shuffle(work)
progress = tqdm.tqdm(total=len(work), disable=not args.progress)
results = []
try:
with concurrent.futures.ProcessPoolExecutor(args.num_processes) as executor:
for result in executor.map(edges_performance_worker, work):
results.append(result)
progress.update()
except KeyboardInterrupt:
pass
progress.close()
df = pd.DataFrame(results)
df.length /= MB
dfg = df.groupby(df.length).mean()
# print(dfg.estimated_anc_edges.describe())
print(dfg)
name_format = os.path.join(
args.destination_dir,
"ancestors_n={}_L={}_mu={}_rho={}_{{}}".format(
args.sample_size, args.length, args.mutation_rate, args.recombination_rate
),
)
plt.plot(
dfg.num_sites,
dfg.estimated_anc_edges / dfg.source_edges,
label="estimated ancestors",
)
plt.plot(
dfg.num_sites, dfg.exact_anc_edges / dfg.source_edges, label="exact ancestors"
)
plt.title(
"n = {}, mut_rate={}, rec_rate={}, reps={}".format(
args.sample_size,
args.mutation_rate,
args.recombination_rate,
args.num_replicates,
)
)
plt.ylabel("inferred # edges / source # edges")
plt.xlabel("Num sites")
plt.legend()
save_figure(name_format.format("edges"))
plt.plot(
dfg.num_sites,
dfg.estimated_anc_mean_children,
label="estimated ancestors mean",
color="blue",
)
plt.plot(
dfg.num_sites,
dfg.estimated_anc_max_children,
label="estimated ancestors max",
color="blue",
linestyle=":",
)
plt.title(
"n = {}, mut_rate={}, rec_rate={}, reps={}".format(
args.sample_size,
args.mutation_rate,
args.recombination_rate,
args.num_replicates,
)
)
plt.plot(
dfg.num_sites,
dfg.exact_anc_mean_children,
label="exact ancestors mean",
color="red",
)
plt.plot(
dfg.num_sites,
dfg.exact_anc_max_children,
label="exact ancestors max",
color="red",
linestyle=":",
)
plt.title(
"n = {}, mut_rate={}, rec_rate={}, reps={}".format(
args.sample_size,
args.mutation_rate,
args.recombination_rate,
args.num_replicates,
)
)
plt.ylabel("num_children")
plt.xlabel("Num sites")
plt.legend()
save_figure(name_format.format("num_children"))
plt.clf()
if args.compute_tree_metrics:
plt.plot(
dfg.num_sites,
dfg.estimated_anc_kc_distance_weighted,
label="estimated ancestors",
)
plt.plot(
dfg.num_sites, dfg.exact_anc_kc_distance_weighted, label="exact ancestors"
)
plt.title(
"n = {}, mut_rate={}, rec_rate={}, reps={}".format(
args.sample_size,
args.mutation_rate,
args.recombination_rate,
args.num_replicates,
)
)
plt.ylabel("Distance weighted KC metric")
plt.xlabel("Num sites")
plt.legend()
save_figure(name_format.format("kc_distance_weighted"))
plt.clf()
plt.plot(dfg.num_sites, dfg.estimated_anc_kc_mean, label="estimated ancestors")
plt.plot(dfg.num_sites, dfg.exact_anc_kc_mean, label="exact ancestors")
plt.title(
"n = {}, mut_rate={}, rec_rate={}, reps={}".format(
args.sample_size,
args.mutation_rate,
args.recombination_rate,
args.num_replicates,
)
)
plt.ylabel("Mean KC metric")
plt.xlabel("Num sites")
plt.legend()
save_figure(name_format.format("kc_mean"))
plt.clf()
plt.plot(
dfg.num_sites, dfg.estimated_anc_perfect_trees, label="estimated ancestors"
)
plt.plot(dfg.num_sites, dfg.exact_anc_perfect_trees, label="exact ancestors")
plt.title(
"n = {}, mut_rate={}, rec_rate={}, reps={}".format(
args.sample_size,
args.mutation_rate,
args.recombination_rate,
args.num_replicates,
)
)
plt.ylabel("Mean KC metric")
plt.xlabel("Num sites")
plt.legend()
save_figure(name_format.format("perfect_trees"))
plt.clf()
def unrank(samples, n):
"""
Unranks the specified set of samples from a possible n into its position
in a lexicographically sorted list of bitstrings.
"""
bitstring = np.zeros(n, dtype=int)
for s in samples:
bitstring[s] = 1
mult = 2 ** np.arange(n, dtype=int)
unranked = np.sum(mult * bitstring)
return unranked
def edge_plot(ts, filename):
n = ts.num_samples
pallete = sns.color_palette("husl", 2 ** n - 1)
lines = []
colours = []
for tree in ts.trees():
left, right = tree.interval
for u in tree.nodes():
children = tree.children(u)
# Don't bother plotting unary nodes, which will all have the same
# samples under them as their next non-unary descendant
if len(children) > 1:
for c in children:
lines.append([(left, c), (right, c)])
colours.append(pallete[unrank(tree.samples(c), n)])
lc = mc.LineCollection(lines, linewidths=2, colors=colours)
fig, ax = plt.subplots()
ax.add_collection(lc)
ax.autoscale()
save_figure(filename)
def run_hotspot_analysis(args):
MB = 10 ** 6
L = args.length * MB
rng = random.Random()
if args.random_seed is not None:
rng.seed(args.random_seed)
breakpoints = np.linspace(0, L, args.num_hotspots + 2)
end = breakpoints[1:-1] + L * args.hotspot_width
breakpoints = np.hstack([breakpoints, end])
breakpoints.sort()
rates = np.zeros_like(breakpoints)
rates[:-1] = args.recombination_rate
# Set the odd elements of the array to be hotspots.
rates[1::2] *= args.hotspot_intensity
recomb_map = msprime.RecombinationMap(list(breakpoints), list(rates))
sim_args = {
"sample_size": args.sample_size,
"recombination_map": recomb_map,
"mutation_rate": args.mutation_rate,
"Ne": args.Ne,
"random_seed": rng.randint(1, 2 ** 30),
}
ts = msprime.simulate(**sim_args)
print("simulated ", ts.num_trees, "trees and", ts.num_sites, "sites")
inferred_ts = run_infer(ts)
num_bins = 100
hotspot_breakpoints = breakpoints
for density in [True, False]:
for x in hotspot_breakpoints[1:-1]:
plt.axvline(x=x, color="k", ls=":")
breakpoints = np.array(list(inferred_ts.breakpoints()))
v, bin_edges = np.histogram(breakpoints, num_bins, density=density)
plt.plot(bin_edges[:-1], v, label="inferred")
breakpoints = np.array(list(ts.breakpoints()))
v, bin_edges = np.histogram(breakpoints, num_bins, density=density)
plt.plot(bin_edges[:-1], v, label="source")
plt.ylabel("Number of breakpoints")
plt.legend()
name_format = os.path.join(
args.destination_dir,
"hotspots_n={}_L={}_mu={}_rho={}_N={}_I={}_W={}_{{}}".format(
args.sample_size,
args.length,
args.mutation_rate,
args.recombination_rate,
args.num_hotspots,
args.hotspot_intensity,
args.hotspot_width,
),
)
save_figure(name_format.format("breakpoints_density={}".format(density)))
plt.clf()
print("Generating edge plots")
# TODO add option for colour mapping.
edge_plot(ts, name_format.format("source_edges"))
edge_plot(inferred_ts, name_format.format("dest_edges"))
def ancestor_properties_worker(args):
simulation_args, compute_exact = args
ts = msprime.simulate(**simulation_args)
sample_data = tsinfer.SampleData.from_tree_sequence(ts)
estimated_anc = tsinfer.generate_ancestors(sample_data)
# Show lengths as a fraction of the total.
estimated_anc_length = estimated_anc.ancestors_length / ts.sequence_length
focal_sites = estimated_anc.ancestors_focal_sites[:]
estimated_anc_focal_distance = np.zeros(estimated_anc.num_ancestors)
pos = np.hstack([estimated_anc.sites_position[:] / ts.sequence_length] + [1])
for j in range(estimated_anc.num_ancestors):
focal = focal_sites[j]
if len(focal) > 0:
estimated_anc_focal_distance[j] = pos[focal[-1]] - pos[focal[0]]
results = {
"num_sites": ts.num_sites,
"num_trees": ts.num_trees,
"estimated_anc_num": estimated_anc.num_ancestors,
"estimated_anc_mean_len": np.mean(estimated_anc_length),
"estimated_anc_mean_focal_distance": np.mean(estimated_anc_focal_distance),
}
if compute_exact:
exact_anc = tsinfer.AncestorData(sample_data)
tsinfer.build_simulated_ancestors(sample_data, exact_anc, ts)
exact_anc.finalise()
# Show lengths as a fraction of the total.
exact_anc_length = exact_anc.ancestors_end[:] - exact_anc.ancestors_start[:]
focal_sites = exact_anc.ancestors_focal_sites[:]
pos = np.hstack([exact_anc.sites_position[:] / ts.sequence_length] + [1])
exact_anc_focal_distance = np.zeros(exact_anc.num_ancestors)
for j in range(exact_anc.num_ancestors):
focal = focal_sites[j]
if len(focal) > 0:
exact_anc_focal_distance[j] = pos[focal[-1]] - pos[focal[0]]
results.update(
{
"exact_anc_num": exact_anc.num_ancestors,
"exact_anc_mean_len": np.mean(exact_anc_length),
"exact_anc_mean_focal_distance": np.mean(exact_anc_focal_distance),
}
)
results.update(simulation_args)
return results
def run_ancestor_properties(args):
num_lengths = 10
MB = 10 ** 6
work = []
rng = random.Random()
if args.random_seed is not None:
rng.seed(args.random_seed)
for L in np.linspace(0, args.length, num_lengths + 1)[1:]:
for _ in range(args.num_replicates):
sim_args = {
"sample_size": args.sample_size,
"length": L * MB,
"recombination_rate": args.recombination_rate,
"mutation_rate": args.mutation_rate,
"Ne": args.Ne,
"model": "smc_prime",
"random_seed": rng.randint(1, 2 ** 30),
}
work.append((sim_args, not args.skip_exact))
random.shuffle(work)
progress = tqdm.tqdm(total=len(work), disable=not args.progress)
results = []
try:
with concurrent.futures.ProcessPoolExecutor(args.num_processes) as executor:
for result in executor.map(ancestor_properties_worker, work):
results.append(result)
progress.update()
except KeyboardInterrupt:
pass
progress.close()
df = pd.DataFrame(results)
dfg = df.groupby(df.length).mean()
print(dfg)
name_format = os.path.join(
args.destination_dir,
"anc-prop_n={}_L={}_mu={}_rho={}_{{}}".format(
args.sample_size, args.length, args.mutation_rate, args.recombination_rate
),
)
plt.plot(dfg.num_sites, dfg.estimated_anc_num, label="estimated ancestors")
if not args.skip_exact:
plt.plot(dfg.num_sites, dfg.exact_anc_num, label="exact ancestors")
plt.title(
"n = {}, mut_rate={}, rec_rate={}, reps={}".format(
args.sample_size,
args.mutation_rate,
args.recombination_rate,
args.num_replicates,
)
)
# plt.ylabel("inferred # ancestors / exact # ancestors")
plt.xlabel("Num sites")
plt.legend()
save_figure(name_format.format("num"))
plt.clf()
plt.plot(dfg.num_sites, dfg.estimated_anc_mean_len, label="estimated ancestors")
if not args.skip_exact:
plt.plot(dfg.num_sites, dfg.exact_anc_mean_len, label="exact ancestors")
plt.title(
"n = {}, mut_rate={}, rec_rate={}, reps={}".format(
args.sample_size,
args.mutation_rate,
args.recombination_rate,
args.num_replicates,
)
)
# plt.ylabel("inferred # ancestors / exact # ancestors")
plt.xlabel("Num sites")
plt.legend()
save_figure(name_format.format("mean_len"))
plt.clf()
plt.plot(
dfg.num_sites,
dfg.estimated_anc_mean_focal_distance,
label="estimated ancestors",
)
if not args.skip_exact:
plt.plot(
dfg.num_sites, dfg.exact_anc_mean_focal_distance, label="exact ancestors"
)
plt.title(
"n = {}, mut_rate={}, rec_rate={}, reps={}".format(
args.sample_size,
args.mutation_rate,
args.recombination_rate,
args.num_replicates,
)
)
# plt.ylabel("inferred # ancestors / exact # ancestors")
plt.xlabel("Num sites")
plt.legend()
save_figure(name_format.format("mean_focal_distance"))
plt.clf()
def imputation_accuracy_worker(args):
simulation_args, missing_proportion = args
ts = msprime.simulate(**simulation_args)
np.random.seed(simulation_args["random_seed"])
G = ts.genotype_matrix()
missing = np.random.rand(ts.num_sites, ts.num_samples) < missing_proportion
G[missing] = tskit.MISSING_DATA
with tsinfer.SampleData(ts.sequence_length) as sample_data:
for var in ts.variants():
sample_data.add_site(
var.site.position, alleles=var.alleles, genotypes=G[var.site.id]
)
ts_inferred = tsinfer.infer(sample_data)
assert ts_inferred.num_sites == ts.num_sites
total_missing = np.sum(missing)
num_correct = 0
for v1, v2 in zip(ts.variants(), ts_inferred.variants()):
site_id = v1.site.id
a1 = np.array(v1.alleles)[v1.genotypes]
a2 = np.array(v2.alleles)[v2.genotypes]
original = a1[missing[site_id]]
inferred = a2[missing[site_id]]
num_correct += np.sum(original == inferred)
accuracy = 1
if total_missing > 0:
accuracy = num_correct / total_missing
results = {
"num_trees": ts.num_trees,
"num_sites": ts.num_sites,
"num_samples": ts.num_samples,
"missing_proportion": missing_proportion,
"accuracy": accuracy,
}
return results
def run_imputation_accuracy(args):
MB = 10 ** 6
work = []
rng = random.Random()
if args.random_seed is not None:
rng.seed(args.random_seed)
for missing_proportion in np.linspace(0.01, 0.1, 10):
for _ in range(args.num_replicates):
sim_args = {
"sample_size": args.sample_size,
"length": args.length * MB,
"recombination_rate": args.recombination_rate,
"mutation_rate": args.mutation_rate,
"Ne": args.Ne,
"random_seed": rng.randint(1, 2 ** 30),
}
work.append((sim_args, missing_proportion))
# imputation_accuracy_worker((sim_args, missing_proportion))
rng.shuffle(work)
progress = tqdm.tqdm(total=len(work), disable=not args.progress)
results = []
try:
with concurrent.futures.ProcessPoolExecutor(args.num_processes) as executor:
for result in executor.map(imputation_accuracy_worker, work):
results.append(result)
progress.update()
except KeyboardInterrupt:
pass
progress.close()
df = pd.DataFrame(results)
dfg = df.groupby(df.missing_proportion).mean()
print(dfg)
name_format = os.path.join(
args.destination_dir,
"imputation-accuracy_n={}_L={}_mu={}_rho={}_{{}}".format(
args.sample_size, args.length, args.mutation_rate, args.recombination_rate
),
)
sns.lineplot(x="missing_proportion", y="accuracy", data=df)
plt.title(
"n = {}, mut_rate={}, rec_rate={}, reps={}".format(
args.sample_size,
args.mutation_rate,
args.recombination_rate,
args.num_replicates,
)
)
plt.ylabel("Fraction of missing genotypes imputed correctly")
plt.xlabel("Fraction of genotypes missing")
save_figure(name_format.format("num"))
def running_mean(x, N):
cumsum = np.cumsum(np.insert(x, 0, 0))
return (cumsum[N:] - cumsum[:-N]) / float(N)
def running_median(x, N):
idx = np.arange(N) + np.arange(len(x) - N + 1)[:, None]
b = [row[row > 0] for row in x[idx]]
return np.array(list(map(np.median, b)))
class MidpointNormalize(mp.colors.Normalize):
def __init__(self, vmin=None, vmax=None, midpoint=None, clip=False):
self.midpoint = midpoint
mp.colors.Normalize.__init__(self, vmin, vmax, clip)
def __call__(self, value, clip=None):
# I'm ignoring masked values and all kinds of edge cases to make a
# simple example...
x, y = [self.vmin, self.midpoint, self.vmax], [0, 0.5, 1]
return np.ma.masked_array(np.interp(value, x, y))
class NormalizeBandWidths(mp.colors.Normalize):
"""
normalise a range into 0..1 where ranges of integers are banded
into a single colour. The init parameter band_widths needs to be
a numpy vector of length the maximum integer encountered
"""
def __init__(self, vmin=None, vmax=None, band_widths=None, clip=False):
self.bands = np.cumsum(band_widths) / np.sum(band_widths)
self.x = np.arange(len(self.bands))
mp.colors.Normalize.__init__(self, vmin, vmax, clip)
def __call__(self, value, clip=None):
return np.ma.masked_array(np.interp(value, self.x, self.bands))
def sim_true_and_inferred_ancestors(args):
"""
Run a simulation under args and return the samples, plus the true and the inferred
ancestors
"""
MB = 10 ** 6
rng = random.Random(args.random_seed)
np.random.seed(args.random_seed)
sim_args = {
"sample_size": args.sample_size,
"length": args.length * MB,
"recombination_rate": args.recombination_rate,
"mutation_rate": args.mutation_rate,
"Ne": args.Ne,
"model": "smc_prime",
"random_seed": rng.randint(1, 2 ** 30),
}
ts = msprime.simulate(**sim_args)
sample_data = generate_samples(ts, args.error)
inferred_anc = tsinfer.generate_ancestors(sample_data, engine=args.engine)
true_anc = tsinfer.AncestorData(sample_data)
tsinfer.build_simulated_ancestors(sample_data, true_anc, ts)
true_anc.finalise()
return sample_data, true_anc, inferred_anc
def ancestor_data_by_pos(anc1, anc2):
"""
Return indexes into ancestor data, keyed by focal site position, returning only
those indexes where positions are the same for both ancestors. This is useful
e.g. for plotting length v length scatterplots.
"""
anc_by_focal_pos = []
for anc in (anc1, anc2):
position_to_index = {
anc.sites_position[:][site_index]: i
for i, sites in enumerate(anc.ancestors_focal_sites[:])
for site_index in sites
}
anc_by_focal_pos.append(position_to_index)
# NB with error we may not have exactly the same focal sites in exact & estimated
shared_indices = set.intersection(*[set(a.keys()) for a in anc_by_focal_pos])
return {
pos: np.array([anc_by_focal_pos[0][pos], anc_by_focal_pos[1][pos]], np.int)
for pos in shared_indices
}
def run_ancestor_comparison(args):
sample_data, exact_anc, estimated_anc = sim_true_and_inferred_ancestors(args)
# Convert lengths to kb.
estimated_anc_length = estimated_anc.ancestors_length / 1000
exact_anc_length = exact_anc.ancestors_length / 1000
max_length = sample_data.sequence_length / 1000
try:
err = float(args.error)
except ValueError:
err = args.error.replace("/", "_")
if err.endswith(".csv"):
err = err[: -len(".csv")]
name_format = os.path.join(
args.destination_dir,
"anc-qual_n={}_Ne={}_L={}_mu={}_rho={}_err={}_{{}}".format(
args.sample_size,
args.Ne,
args.length,
args.mutation_rate,
args.recombination_rate,
err,
),
)
if args.store_data:
# TODO Are we using this option for anything?
filename = name_format.format("length.json")
# Don't store the longest (root) ancestor
data = {
"exact_ancestors": exact_anc_length[1:].tolist(),
"estimated_ancestors": estimated_anc_length[1:].tolist(),
}
with open(filename, "w") as f:
json.dump(data, f)
plt.hist(
[exact_anc_length[1:], estimated_anc_length[1:]], label=["Exact", "Estimated"]
)
plt.ylabel("Length (kb)")
plt.legend()
save_figure(name_format.format("length-dist"))
plt.clf()
# NB ancestors_time is not exactly the same as frequency, because frequency
# categories that are not represented in the data will be missed out. If we want a
# true frequency, we therefore need to get it directly from the samples
pos_to_ancestor = {}
estimated_anc.ancestors_focal_freq = np.zeros(estimated_anc.num_ancestors, np.int)
ancestor_site_position = estimated_anc.sites_position
for a, focal_sites in enumerate(estimated_anc.ancestors_focal_sites[:]):
for focal_site in focal_sites:
pos_to_ancestor[ancestor_site_position[focal_site]] = a
for var in sample_data.variants(ancestor_site_position):
# for i, g in sample_data.genotypes(inference_sites=True):
# pos = sample_data.sites_position[:][i]
pos = var.site.position
freq = np.sum(var.genotypes)
if estimated_anc.ancestors_focal_freq[pos_to_ancestor[pos]]:
# check all focal sites in an ancestor have the same freq
assert freq == estimated_anc.ancestors_focal_freq[pos_to_ancestor[pos]]
estimated_anc.ancestors_focal_freq[pos_to_ancestor[pos]] = freq
print("mean estimated ancestor length", np.mean(estimated_anc_length))
# Get the number of ancestors that have the maximum length
max_len = np.max(estimated_anc_length)
num_max_len = np.sum(estimated_anc_length == max_len)
print("max_len = ", max_len)
print(
"fraction of ancestors with max length = ",
num_max_len / estimated_anc.num_ancestors,
)
plt.hist(estimated_anc_length[estimated_anc.ancestors_focal_freq == 2], bins=50)
plt.xlabel("doubleton ancestor length")
save_figure(name_format.format("doubleton-length-dist"))
plt.clf()
anc_indexes = ancestor_data_by_pos(exact_anc, estimated_anc)
# convert to a 2d numpy array for convenience
exact_v_estimated_indexes = np.array([v for v in anc_indexes.values()])
for colorscale in ("Frequency", "True_time"):
fig = plt.figure(figsize=(10, 10), dpi=100)
if args.length_scale == "log":
plt.yscale("log")
plt.xscale("log")
if colorscale != "Frequency":
cs = exact_anc.ancestors_time[:][exact_v_estimated_indexes[:, 0]]
else:
cs = estimated_anc.ancestors_focal_freq[exact_v_estimated_indexes[:, 1]]
plt.scatter(
exact_anc_length[exact_v_estimated_indexes[:, 0]],
estimated_anc_length[exact_v_estimated_indexes[:, 1]],
c=cs,
cmap="brg",
s=2,
norm=NormalizeBandWidths(band_widths=np.bincount(cs)),
)
plt.plot([1, max_length], [1, max_length], "-", color="grey", zorder=-1)
plt.xlim(1, max_length)
plt.ylim(1, max_length)
cbar = plt.colorbar()
cbar.set_label(colorscale, rotation=270)
plt.xlabel("True ancestor length per variant (kb)")
plt.ylabel("Inferred ancestor length per variant (kb)")
save_figure(name_format.format("length-scatter_{}".format(colorscale.lower())))
# plot exact ancestors ordered by time, and estimated ancestors in frequency bands
# one point per variable site, so these should be directly comparable
# the exact ancestors have ancestors_time from 1..n_ancestors, ordered by real time
# in the simulation, so that each time is unique for a set of site on one ancestor
for ancestors_are_estimated, anc in enumerate([exact_anc, estimated_anc]):
time = anc.ancestors_time[:] + (1 if ancestors_are_estimated else 0)
df = pd.DataFrame(
{
"start": anc.ancestors_start[:],
"end": anc.ancestors_end[:],
"l": anc.ancestors_length / 1000,
"time": time,
"nsites": [len(x) for x in anc.ancestors_focal_sites[:]],
}
)
df_all = pd.DataFrame(
{
"lengths_per_site": np.repeat(df.l.values, df.nsites.values),
"time": np.repeat(df.time.values, df.nsites.values),
"const": 1,
}
).sort_values(by=["time"])
sum_per_timeslice = df_all.groupby("time").sum().const.values
df_all["x_pos"] = range(df_all.shape[0])
df_all["mean_x_pos"] = np.repeat(
df_all.groupby("time").mean().x_pos.values, sum_per_timeslice
)
df_all["width"] = np.repeat(sum_per_timeslice, sum_per_timeslice)
mean_by_anc_time = (
df.iloc[df["nsites"].nonzero()].groupby("time", sort=True).mean()
)
median_by_anc_time = (
df.iloc[df["nsites"].nonzero()].groupby("time", sort=True).median()
)
sum_by_anc_time = (
df.iloc[df["nsites"].nonzero()].groupby("time", sort=True).sum()
)
line_x = np.insert(np.cumsum(sum_by_anc_time["nsites"]).values, 0, 0)
if ancestors_are_estimated:
# averaging over times is probably more-or-less OK
lines_y = [mean_by_anc_time.l, median_by_anc_time.l]
names = ["Mean", "Median"]
linestyles = ["-", ":"]