diff --git a/.hgignore b/.hgignore index a827dd3..c6bbb66 100644 --- a/.hgignore +++ b/.hgignore @@ -35,6 +35,4 @@ doc/draw* dist/* working/* .ruff_cache/* -manuscript/* -paper/* jats/* diff --git a/paper/figs/compute_time-ctree.pdf b/paper/figs/compute_time-ctree.pdf new file mode 100644 index 0000000..a7cdc2b Binary files /dev/null and b/paper/figs/compute_time-ctree.pdf differ diff --git a/paper/figs/compute_time.pdf b/paper/figs/compute_time.pdf new file mode 100644 index 0000000..f74dbb7 Binary files /dev/null and b/paper/figs/compute_time.pdf differ diff --git a/paper/figs/jsd_v_dist.pdf b/paper/figs/jsd_v_dist.pdf new file mode 100644 index 0000000..7218775 Binary files /dev/null and b/paper/figs/jsd_v_dist.pdf differ diff --git a/paper/figs/likelihood_vs_k_for_ss.pdf b/paper/figs/likelihood_vs_k_for_ss.pdf new file mode 100644 index 0000000..7e95637 Binary files /dev/null and b/paper/figs/likelihood_vs_k_for_ss.pdf differ diff --git a/paper/figs/likelihood_vs_ss_for_k.pdf b/paper/figs/likelihood_vs_ss_for_k.pdf new file mode 100644 index 0000000..f480f17 Binary files /dev/null and b/paper/figs/likelihood_vs_ss_for_k.pdf differ diff --git a/paper/figs/max.pdf b/paper/figs/max.pdf new file mode 100644 index 0000000..5a4fa6c Binary files /dev/null and b/paper/figs/max.pdf differ diff --git a/paper/figs/max.tex b/paper/figs/max.tex new file mode 100644 index 0000000..879aa79 --- /dev/null +++ b/paper/figs/max.tex @@ -0,0 +1,47 @@ +\documentclass{article} +\usepackage{graphicx} +\usepackage{caption} +\usepackage{minted} +\usepackage[a4paper]{geometry} + +\geometry{ + paperwidth=12cm, + paperheight=9.5cm, + left=0.1cm, % Adjust margins as needed + right=0.1cm, + top=0.1cm, + bottom=0.1cm +} +\pagestyle{empty} % no page numbers + +\begin{document} + +\begin{figure}[h] + \centering + \begin{minted}{python} +# A list of sequences converted into k-mer counts. +records: list[KmerSeq] +shuffle(records) + +# The minimum size of the divergent set. +min_size: int +# The maximum size of the divergent set. +max_size: int + +sr = SummedRecords.from_records(records[:min_size]) +for r in records: + if sr.increases_jsd(r): + # Adding r to the N-1 set increased JSD over sr.jsd. + # We define a new SummedRecords instance of {N} & {r}. + nsr = sr + r + # Has adding r increased the standard deviation? + sr = nsr if nsr.std > sr.std else sr.replaced_lowest(r) + if sr.size > max_size: + # We stay within the user specified set size + # by dropping the record with lowest delta-JSD. + sr = sr.dropped_lowest() + \end{minted} + +\end{figure} + +\end{document} \ No newline at end of file diff --git a/paper/figs/nmost.pdf b/paper/figs/nmost.pdf new file mode 100644 index 0000000..bf5cf5f Binary files /dev/null and b/paper/figs/nmost.pdf differ diff --git a/paper/figs/nmost.tex b/paper/figs/nmost.tex new file mode 100644 index 0000000..bb2fb5b --- /dev/null +++ b/paper/figs/nmost.tex @@ -0,0 +1,42 @@ +\documentclass{article} +\usepackage{graphicx} +\usepackage{caption} +\usepackage{minted} +\usepackage[a4paper]{geometry} + +\geometry{ + paperwidth=12cm, + paperheight=7cm, + left=0.1cm, % Adjust margins as needed + right=0.1cm, + top=0.1cm, + bottom=0.1cm +} +\pagestyle{empty} % no page numbers + +\begin{document} + +\begin{figure}[h] + \centering + \begin{minted}{python} +# A list of sequences converted into k-mer counts. +records: list[KmerSeq] +# Randomise the order of the records +shuffle(records) + +# The size of the divergent set. +n: int + +# SummedRecords sorts records by their delta-JSD. The record +# with the lowest delta-JSD is excluded from the N-1 set. +sr = SummedRecords.from_records(records[:n]) +for r in records: + # Is JSD({N-1} & {r}) > JSD({N})? + if sr.increases_jsd(r): + # Create a new SummedRecords instance from {N-1} & {r}. + sr = sr.replaced_lowest(r) + \end{minted} + +\end{figure} + +\end{document} \ No newline at end of file diff --git a/paper/figs/selected_edges.pdf b/paper/figs/selected_edges.pdf new file mode 100644 index 0000000..4cb9270 Binary files /dev/null and b/paper/figs/selected_edges.pdf differ diff --git a/paper/figs/synthetic_known_bar.pdf b/paper/figs/synthetic_known_bar.pdf new file mode 100644 index 0000000..a94ee28 Binary files /dev/null and b/paper/figs/synthetic_known_bar.pdf differ diff --git a/paper/nbks/benchmark.ipynb b/paper/nbks/benchmark.ipynb new file mode 100644 index 0000000..59b1f63 --- /dev/null +++ b/paper/nbks/benchmark.ipynb @@ -0,0 +1,242 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Summarising compute speed\n", + "\n", + "## The select algorithms: max and nmost\n", + "\n", + "`benchmark.py` was run against the REFSOIL collection -- 960 whole bacterial genomes. We separately recorded times to:\n", + "- load the sequences from compressed files and convert them into `SeqRecord` objects\n", + "- identify the divergent set\n", + "\n", + "Each condition was run 5 times with the sequences randomly drawn without replacement.\n", + "\n", + "# Synopsis\n", + "\n", + "Performance is approximately linear with the number of sequences for both the divergent `prep` and `max` steps." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import plotly.express as px\n", + "import project_path\n", + "from cogent3 import load_table, make_table\n", + "\n", + "write_pdf = project_path.pdf_writer()\n", + "\n", + "\n", + "def _cast_element(row):\n", + " try:\n", + " return tuple(row)\n", + " except TypeError:\n", + " return int(row)\n", + "\n", + "\n", + "def group_by(table, columns, quant_col, sort_cols):\n", + " columns = tuple(columns)\n", + " distinct = table.distinct_values(columns)\n", + " one_col = len(columns) == 1\n", + " results = []\n", + " for group in distinct:\n", + " subtable = table.filtered(lambda x: _cast_element(x) == group, columns=columns)\n", + " quants = subtable.columns[quant_col]\n", + " mean, std = quants.mean(), quants.std(ddof=1)\n", + " results.append(\n", + " ([group] if one_col else list(group)) + [mean, std, quants.shape[0]],\n", + " )\n", + "\n", + " table = make_table(\n", + " header=list(columns) + [f\"mean_{quant_col}\", f\"std_{quant_col}\", \"n\"],\n", + " rows=results,\n", + " )\n", + " table = table.sorted(columns=sort_cols)\n", + " return table\n", + "\n", + "\n", + "table = load_table(project_path.RESULT_DIR / \"benchmark-max.tsv\")\n", + "st = table.filtered(lambda x: x == \"prep\", columns=\"command\").get_columns(\n", + " (\"numseqs\", \"time(s)\"),\n", + ")\n", + "prep_time = group_by(st, (\"numseqs\",), \"time(s)\", \"numseqs\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "px.scatter(\n", + " prep_time,\n", + " x=\"numseqs\",\n", + " y=\"mean_time(s)\",\n", + " error_y=\"std_time(s)\",\n", + " labels={\"mean_time(s)\": \"Seconds\", \"numseqs\": \"Number of sequences\"},\n", + " trendline=\"ols\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "st = table.filtered(lambda x: x == max, columns=\"command\").get_columns(\n", + " (\"numseqs\", \"k\", \"time(s)\"),\n", + ")\n", + "max_time = group_by(st, (\"numseqs\", \"k\"), \"time(s)\", (\"numseqs\", \"k\"))\n", + "st = max_time.filtered(lambda x: str(x) in \"28\", columns=\"k\")\n", + "st.columns[\"k\"] = st.columns[\"k\"].astype(str)\n", + "\n", + "tickfont = dict(size=16)\n", + "titlefont = dict(size=20)\n", + "legend = dict(title=dict(text=\"k\"), font=dict(size=17), tracegroupgap=10)\n", + "\n", + "fig = px.scatter(\n", + " st,\n", + " x=\"numseqs\",\n", + " y=\"mean_time(s)\",\n", + " error_y=\"std_time(s)\",\n", + " color=\"k\",\n", + " labels={\"mean_time(s)\": \"Seconds\", \"numseqs\": \"Number of sequences\"},\n", + " trendline=\"ols\",\n", + ")\n", + "fig.update_layout(\n", + " height=600,\n", + " width=1400,\n", + " xaxis=dict(\n", + " title=\"Number of Sequences\",\n", + " titlefont=titlefont,\n", + " tickfont=tickfont,\n", + " ),\n", + " yaxis=dict(\n", + " title=\"Mean time (seconds)\",\n", + " titlefont=titlefont,\n", + " tickfont=tickfont,\n", + " ),\n", + " legend=legend,\n", + ")\n", + "fig.update_traces(marker=dict(size=12))\n", + "fig.show()\n", + "outpath = project_path.FIG_DIR / \"compute_time.pdf\"\n", + "# write_pdf(fig, outpath)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "st = max_time.filtered(lambda x: str(x) == \"200\", columns=\"numseqs\")\n", + "st.columns[\"numseqs\"] = st.columns[\"numseqs\"].astype(str)\n", + "px.scatter(\n", + " st,\n", + " x=\"k\",\n", + " y=\"mean_time(s)\",\n", + " error_y=\"std_time(s)\",\n", + " color=\"numseqs\",\n", + " labels={\"mean_time(s)\": \"Seconds\", \"k\": \"k\"},\n", + " trendline=\"ols\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The cluster algorithm: ctree\n", + "\n", + "`benchmark_ctree.py` was run against the REFSOIL collection. We recorded the time to resolve the tree.\n", + "\n", + "Each condition was run 3 times.\n", + "\n", + "# Synopsis\n", + "\n", + "Performance is approximately linear with the number of sequences as the algorithm scales proportional to the total of sequence lengths." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "table = load_table(project_path.RESULT_DIR / \"benchmark-ctree.tsv\")\n", + "ctree_time = group_by(table, (\"numseqs\", \"k\"), \"time(s)\", (\"numseqs\", \"k\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ctree_time.columns[\"k\"] = ctree_time.columns[\"k\"].astype(str)\n", + "\n", + "tickfont = dict(size=16)\n", + "titlefont = dict(size=20)\n", + "legend = dict(title=dict(text=\"k\"), font=dict(size=17), tracegroupgap=10)\n", + "\n", + "fig = px.scatter(\n", + " ctree_time,\n", + " x=\"numseqs\",\n", + " y=\"mean_time(s)\",\n", + " error_y=\"std_time(s)\",\n", + " color=\"k\",\n", + " labels={\"mean_time(s)\": \"Seconds\", \"numseqs\": \"Number of sequences\"},\n", + " trendline=\"ols\",\n", + ")\n", + "fig.update_layout(\n", + " height=600,\n", + " width=1400,\n", + " xaxis=dict(\n", + " title=\"Number of Sequences\",\n", + " titlefont=titlefont,\n", + " tickfont=tickfont,\n", + " ),\n", + " yaxis=dict(\n", + " title=\"Mean time (seconds)\",\n", + " titlefont=titlefont,\n", + " tickfont=tickfont,\n", + " ),\n", + " legend=legend,\n", + ")\n", + "fig.update_traces(marker=dict(size=12))\n", + "fig.show()\n", + "outpath = project_path.FIG_DIR / \"compute_time-ctree.pdf\"\n", + "# write_pdf(fig, outpath)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "dvgt", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.4" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/paper/nbks/benchmark.py b/paper/nbks/benchmark.py new file mode 100644 index 0000000..0cec673 --- /dev/null +++ b/paper/nbks/benchmark.py @@ -0,0 +1,133 @@ +import os +import shutil +import tempfile +import time +from pathlib import Path + +import click +from click.testing import CliRunner +from cogent3 import make_table +from rich import progress as rich_progress + +from diverse_seq import cli as dvs_cli +from diverse_seq import util as dvs_util + +RUNNER = CliRunner() + + +class TempWorkingDir: + def __enter__(self): + self.original_directory = os.getcwd() + self.temp_directory = tempfile.mkdtemp() + os.chdir(self.temp_directory) + return Path(self.temp_directory) + + def __exit__(self, exc_type, exc_value, traceback): + os.chdir(self.original_directory) + shutil.rmtree(self.temp_directory) + + +class TimeIt: + def __enter__(self): + self.start_time = time.time() + return self + + def __exit__(self, exc_type, exc_value, traceback): + self.end_time = time.time() + self.elapsed_time = self.end_time - self.start_time + + def get_elapsed_time(self): + return self.elapsed_time + + +_click_command_opts = dict( + no_args_is_help=True, + context_settings={"show_default": True}, +) + + +def run_prep(temp_dir, seqdir, dvs_file, suffix, num_seqs): + # run the prep command + args = f"-s {seqdir} -o {dvs_file} -sf {suffix} -L {num_seqs} -np 1 -hp".split() + with TimeIt() as timer: + r = RUNNER.invoke(dvs_cli.prep, args, catch_exceptions=False) + assert r.exit_code == 0, r.output + return timer.get_elapsed_time() + + +def run_max(seqfile, outpath, k): + args = ( + f"-s {seqfile} -o {outpath} --min_size 5 --max_size 10 -k {k} -np 1 -hp".split() + ) + with TimeIt() as timer: + r = RUNNER.invoke(dvs_cli.max, args, catch_exceptions=False) + assert r.exit_code == 0, r.output + return timer.get_elapsed_time() + + +def run_nmost(seqfile, outpath, k): + args = f"-s {seqfile} -o {outpath} --number 5 -k {k} -np 1 -hp".split() + with TimeIt() as timer: + r = RUNNER.invoke(dvs_cli.nmost, args, catch_exceptions=False) + assert r.exit_code == 0, r.output + return timer.get_elapsed_time() + + +@click.command(**_click_command_opts) +@click.argument("seqdir", type=Path) +@click.argument("outpath", type=Path) +@click.option("-c", "--command", type=click.Choice(["max", "nmost"]), default="max") +@click.option("-s", "--suffix", type=str, default="gb") +def run(seqdir, suffix, outpath, command): + seqdir = seqdir.absolute() + reps = [1, 2, 3] + kmer_sizes = [2, 3, 4, 5, 6, 7, 8] + num_seqs = [50, 100, 150, 200] + results = [] + run_func = run_max if command == "max" else run_nmost + with dvs_util.keep_running(): + with rich_progress.Progress( + rich_progress.TextColumn("[progress.description]{task.description}"), + rich_progress.BarColumn(), + rich_progress.TaskProgressColumn(), + rich_progress.TimeRemainingColumn(), + rich_progress.TimeElapsedColumn(), + ) as progress: + repeats = progress.add_task("Doing reps", total=len(reps)) + for _ in reps: + seqnum = progress.add_task("Doing num seqs", total=len(num_seqs)) + for num in num_seqs: + with TempWorkingDir() as temp_dir: + dvs_file = temp_dir / f"dvs_L{num}.dvseqs" + elapsed_time = run_prep( + temp_dir, + seqdir, + dvs_file, + suffix, + num, + ) + results.append(("prep", num, None, elapsed_time)) + + kmers = progress.add_task( + "Doing kmers", + total=len(kmer_sizes), + transient=True, + ) + for k in kmer_sizes: + kout = temp_dir / f"selected-{k}.tsv" + elapsed_time = run_func(dvs_file, kout, k) + results.append((command, num, k, elapsed_time)) + progress.update(kmers, advance=1, refresh=True) + progress.remove_task(kmers) + + progress.update(seqnum, advance=1, refresh=True) + progress.remove_task(seqnum) + progress.update(repeats, advance=1, refresh=True) + + table = make_table(header=("command", "numseqs", "k", "time(s)"), data=results) + print(table) + table.write(outpath) + + +if __name__ == "__main__": + run() diff --git a/paper/nbks/benchmark_ctree.py b/paper/nbks/benchmark_ctree.py new file mode 100644 index 0000000..4ae0f80 --- /dev/null +++ b/paper/nbks/benchmark_ctree.py @@ -0,0 +1,63 @@ +import itertools +from pathlib import Path + +import cogent3 +from benchmark import TempWorkingDir, TimeIt +from click.testing import CliRunner +from rich import progress as rich_progress + +from diverse_seq import cli as dvs_cli +from diverse_seq import util as dvs_util + +RUNNER = CliRunner() + + +def main(): + in_file = Path("../data/soil.dvseqs").absolute() + assert in_file.exists() + reps = [1, 2, 3] + kmer_sizes = list(range(10, 17, 2)) + num_seqs = [50, 100, 150, 200] + + results = {"k": [], "numseqs": [], "time(s)": []} + with dvs_util.keep_running(): + with rich_progress.Progress( + rich_progress.TextColumn("[progress.description]{task.description}"), + rich_progress.BarColumn(), + rich_progress.TaskProgressColumn(), + rich_progress.TimeRemainingColumn(), + rich_progress.TimeElapsedColumn(), + ) as progress: + repeats = progress.add_task("Doing reps", total=len(reps)) + for _ in reps: + combo = progress.add_task( + "Doing k x num_seq", + total=len(num_seqs) * len(kmer_sizes), + ) + for k, num_seq in itertools.product(kmer_sizes, num_seqs): + with TempWorkingDir() as temp_dir: + out_tree_file = temp_dir / f"{in_file.name}.tre" + args = f"-s {in_file} -o {out_tree_file} -k {k} --distance mash --sketch-size 3000 -hp --limit {num_seq}".split() + with TimeIt() as timer: + r = RUNNER.invoke( + dvs_cli.ctree, + args, + catch_exceptions=False, + ) + assert r.exit_code == 0, r.output + results["time(s)"].append(timer.get_elapsed_time()) + results["k"].append(k) + results["numseqs"].append(num_seq) + progress.update(combo, advance=1, refresh=True) + + progress.remove_task(combo) + progress.update(repeats, advance=1, refresh=True) + + table = cogent3.make_table(data=results) + outpath = "benchmark-ctree.tsv" + table.write(outpath) + dvs_util.print_colour(f"Wrote {outpath}!", "green") + + +if __name__ == "__main__": + main() diff --git a/paper/nbks/ctree.ipynb b/paper/nbks/ctree.ipynb new file mode 100644 index 0000000..710bb30 --- /dev/null +++ b/paper/nbks/ctree.ipynb @@ -0,0 +1,132 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import cogent3\n", + "import plotly.express as px\n", + "import project_path\n", + "\n", + "color_discrete_sequence = px.colors.sequential.Rainbow\n", + "\n", + "FIG_DIMS = dict(width=800, height=600)\n", + "\n", + "write_pdf = project_path.pdf_writer()\n", + "\n", + "\n", + "def load_table(fname, header):\n", + " from cogent3.util.table import cast_str_to_array\n", + "\n", + " inpath = project_path.PAPER_DIR / \"nbks\" / \"ctree\" / \"out\" / fname\n", + " lines = [l.split(\"\\t\") for l in inpath.read_text().splitlines()]\n", + " data = dict(zip(header, zip(*lines, strict=False), strict=False))\n", + " for name, vals in data.items():\n", + " data[name] = cast_str_to_array(vals)\n", + " return cogent3.make_table(header=header, data=data)\n", + "\n", + "\n", + "def get_plot_for(*, x_axis, limit_to, colour, legend_title, log_x):\n", + " table = load_table(\n", + " \"results_with_ls.tsv\",\n", + " [\"k\", \"ss\", \"cpus\", \"time\", \"lnL\", \"tree\"],\n", + " )\n", + " table = table.get_columns([c for c in table.header if c != \"tree\"])\n", + " table = table.filtered(lambda x: int(x) in limit_to, columns=colour)\n", + " iqtree = load_table(\"iqtree_with_ls.tsv\", [\"time\", \"lnL\", \"tree\"])\n", + " iqtree_lnL = iqtree.columns[\"lnL\"][0]\n", + " fig = px.line(\n", + " table,\n", + " x=x_axis,\n", + " y=\"lnL\",\n", + " color=colour,\n", + " labels={\"ss\": \"Sketch Size\", \"k\": \"k\"},\n", + " color_discrete_sequence=color_discrete_sequence,\n", + " log_x=log_x,\n", + " )\n", + "\n", + " fig.add_hline(\n", + " y=iqtree_lnL,\n", + " line_dash=\"dash\",\n", + " line_color=\"black\",\n", + " annotation_text=\"IQ-TREE\",\n", + " annotation_position=\"bottom left\",\n", + " )\n", + "\n", + " fig.update_layout(legend_title_text=legend_title)\n", + "\n", + " diff = 0.005\n", + " max_val = (1 - diff) * iqtree_lnL\n", + " min_val = (1 + diff) * table.columns[\"lnL\"].min()\n", + " fig.update_yaxes(autorange=False, range=[min_val, max_val])\n", + " fig.update_layout(**FIG_DIMS)\n", + " return fig\n", + "\n", + "\n", + "def make_likelihood_vs_k():\n", + " return get_plot_for(\n", + " x_axis=\"ss\",\n", + " colour=\"k\",\n", + " legend_title=\"k\",\n", + " limit_to=[8, 10, 12, 14, 16],\n", + " log_x=True,\n", + " )\n", + "\n", + "\n", + "def make_likelihood_vs_ss():\n", + " return get_plot_for(\n", + " x_axis=\"k\",\n", + " colour=\"ss\",\n", + " legend_title=\"Sketch Size\",\n", + " limit_to=[100, 1000, 10_000, 25_000, 100000],\n", + " log_x=False,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = make_likelihood_vs_k()\n", + "fig.show()\n", + "# write_pdf(fig, project_path.FIG_DIR / \"likelihood_vs_ss_for_k.pdf\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = make_likelihood_vs_ss()\n", + "fig.show()\n", + "# write_pdf(fig, project_path.FIG_DIR / \"likelihood_vs_k_for_ss.pdf\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "dvgt", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/paper/nbks/ctree/README.md b/paper/nbks/ctree/README.md new file mode 100644 index 0000000..405189c --- /dev/null +++ b/paper/nbks/ctree/README.md @@ -0,0 +1,13 @@ +# Files used for the ctree experiments + +## `experiment.py` + +Calls ctree on the mammals alignment for different values of k and sketch size. + +## `iq_experiment.py` + +Uses iqtree to find maximum likelihood tree on mammals alignment. + +## `likelihoods.py` + +Find likelihood of all generated trees from `experiment.py` and `iq_experiment.py`. diff --git a/paper/nbks/ctree/data/concat/.gitkeep b/paper/nbks/ctree/data/concat/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/paper/nbks/ctree/data/mammals-aligned/.gitkeep b/paper/nbks/ctree/data/mammals-aligned/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/paper/nbks/ctree/data/mammals-aligned/README.md b/paper/nbks/ctree/data/mammals-aligned/README.md new file mode 100644 index 0000000..e422a81 --- /dev/null +++ b/paper/nbks/ctree/data/mammals-aligned/README.md @@ -0,0 +1 @@ +fasta files for the mammals alignment should be placed in this directory. \ No newline at end of file diff --git a/paper/nbks/ctree/experiment.py b/paper/nbks/ctree/experiment.py new file mode 100644 index 0000000..652ee26 --- /dev/null +++ b/paper/nbks/ctree/experiment.py @@ -0,0 +1,98 @@ +import multiprocessing +import os +import time +from pathlib import Path + +from cogent3 import load_aligned_seqs, make_aligned_seqs + +from diverse_seq.cluster import dvs_par_ctree + +MAMMALS_PATH = Path("data/mammals-aligned") +OUT_FILE = Path("out/results.tsv") + + +KS = list(range(4, 21)) +SKETCH_SIZES = [ + 100, + 250, + 500, + 750, + 1000, + 2500, + 5000, + 7500, + 10000, + 25000, + 50000, + 75000, + 100000, +] + + +def load_alignment(directory: Path): + fasta_files = filter( + lambda file_name: file_name.endswith(".fa"), + os.listdir(directory), + ) + + seqs = {} + + for file in fasta_files: + aln = load_aligned_seqs(directory / file, moltype="dna") + aln_dict = aln.to_dict() + for seq_name in aln_dict: + if seq_name in seqs: + seqs[seq_name] = seqs[seq_name] + "-" + aln_dict[seq_name] + else: + seqs[seq_name] = aln_dict[seq_name] + + aln = make_aligned_seqs(seqs, moltype="dna") + return aln + + +def get_completed() -> set[tuple[int, int]]: + completed = set() + with OUT_FILE.open() as f: + for line in f: + k, ss = line.strip().split("\t")[:2] + k = int(k) + ss = int(ss) + completed.add((k, ss)) + return completed + + +def run_experiment(aln, k, sketch_size): + print(f"Doing {k=} {sketch_size=}") + ctree_app = dvs_par_ctree( + k=k, + sketch_size=sketch_size, + parallel=True, + ) + start_time = time.time() + ctree = ctree_app(aln) + total_time = time.time() - start_time + + to_write = [ + str(k), + str(sketch_size), + str(multiprocessing.cpu_count()), + str(total_time), + str(ctree), + ] + with OUT_FILE.open("a") as f: + f.write("\t".join(to_write) + "\n") + + +def run_experiments(): + completed = get_completed() + aln = load_alignment(MAMMALS_PATH) + for k in KS: + for sketch_size in SKETCH_SIZES: + if (k, sketch_size) in completed: + print(f"Skipping {k=} {sketch_size=}") + continue + run_experiment(aln, k, sketch_size) + + +if __name__ == "__main__": + run_experiments() diff --git a/paper/nbks/ctree/iq_experiment.py b/paper/nbks/ctree/iq_experiment.py new file mode 100644 index 0000000..66b0149 --- /dev/null +++ b/paper/nbks/ctree/iq_experiment.py @@ -0,0 +1,54 @@ +import os +import time +from pathlib import Path + +from cogent3 import load_aligned_seqs, make_aligned_seqs +from piqtree2 import build_tree + +MAMMALS_PATH = Path("data/mammals-aligned") + +CONCAT_PATH = Path("data/concat/concat.fasta") +OUT_FILE = Path("out/iqtree.tsv") + + +def load_alignment(directory: Path): + fasta_files = filter( + lambda file_name: file_name.endswith(".fa"), + os.listdir(directory), + ) + + seqs = {} + + for file in fasta_files: + aln = load_aligned_seqs(directory / file, moltype="dna") + aln_dict = aln.to_dict() + for seq_name in aln_dict: + if seq_name in seqs: + seqs[seq_name] = seqs[seq_name] + "-" + aln_dict[seq_name] + else: + seqs[seq_name] = aln_dict[seq_name] + + aln = make_aligned_seqs(seqs, moltype="dna") + return aln + + +def do_iqtree(): + if OUT_FILE.exists(): + raise FileExistsError("Might overwrite file") + + print("Loading") + aln = load_alignment(MAMMALS_PATH) + aln.write(CONCAT_PATH) + + print("Running IQ-TREE") + start = time.time() + tree = build_tree(aln, "GTR", rand_seed=1) + end = time.time() + + print("Saving results") + with OUT_FILE.open("w") as f: + f.write("\t".join([str(end - start), str(tree)])) + + +if __name__ == "__main__": + do_iqtree() diff --git a/paper/nbks/ctree/likelihoods.py b/paper/nbks/ctree/likelihoods.py new file mode 100644 index 0000000..0d3d4ad --- /dev/null +++ b/paper/nbks/ctree/likelihoods.py @@ -0,0 +1,71 @@ +import os +from pathlib import Path + +from cogent3 import load_aligned_seqs, make_aligned_seqs, make_tree +from piqtree2 import fit_tree + +MAMMALS_PATH = Path("data/mammals-aligned") + +CONCAT_PATH = Path("data/concat/concat.fasta") +RESULTS_FILE = Path("out/results.tsv") +IQ_FILE = Path("out/iqtree.tsv") +OUT_FILE = Path("out/results_with_ls.tsv") +OUT_IQ_FILE = Path("out/iqtree_with_ls.tsv") + + +def load_alignment(directory: Path): + fasta_files = filter( + lambda file_name: file_name.endswith(".fa"), + os.listdir(directory), + ) + + seqs = {} + + for file in fasta_files: + aln = load_aligned_seqs(directory / file, moltype="dna") + aln_dict = aln.to_dict() + for seq_name in aln_dict: + if seq_name in seqs: + seqs[seq_name] = seqs[seq_name] + "-" + aln_dict[seq_name] + else: + seqs[seq_name] = aln_dict[seq_name] + + aln = make_aligned_seqs(seqs, moltype="dna") + return aln + + +def do_likelihoods(): + if OUT_FILE.exists() or OUT_IQ_FILE.exists(): + raise FileExistsError("Might overwrite file") + with OUT_FILE.open("w") as f: + f.write("") + with OUT_IQ_FILE.open("w") as f: + f.write("") + + print("Loading") + aln = load_alignment(MAMMALS_PATH) + + with RESULTS_FILE.open() as f: + for line in f: + k, ss, cpus, time, tree = line.strip().split("\t") + print(f"Doing {k} {ss}") + tree = make_tree(tree).unrooted() + tree = fit_tree(aln, tree, "GTR", rand_seed=1) + likelihood = tree.params["lnL"] + with OUT_FILE.open("a") as f: + f.write( + "\t".join([k, ss, cpus, time, str(likelihood), str(tree)]) + "\n", + ) + with IQ_FILE.open() as f: + for line in f: + time, tree = line.strip().split("\t") + print("Doing iq") + tree = make_tree(tree).unrooted() + tree = fit_tree(aln, tree, "GTR", rand_seed=1) + likelihood = tree.params["lnL"] + with OUT_IQ_FILE.open("a") as f: + f.write("\t".join([time, str(likelihood), str(tree)]) + "\n") + + +if __name__ == "__main__": + do_likelihoods() diff --git a/paper/nbks/ctree/out/iqtree.tsv b/paper/nbks/ctree/out/iqtree.tsv new file mode 100644 index 0000000..28d0b89 --- /dev/null +++ b/paper/nbks/ctree/out/iqtree.tsv @@ -0,0 +1 @@ +655.1481719017029 (Platypus:0.1980301436,Wallaby:0.1191527873,(((Elephant:0.0358218601,Hyrax:0.0605419732):0.011391009,Tenrec:0.0961679508):0.01525657163,((Armadillo:0.0600424226,Sloth:0.0442643023):0.02103895428,((((Pika:0.0725051939,Rabbit:0.0482012328):0.03549673931,(Squirrel:0.0523954477,(Rat:0.0408106421,Mouse:0.0339639561):0.08308803936):0.01294145588):0.005594380951,((((((Chimp:0.0040948927,Gorilla:0.0042681031):0.001057520244,Human:0.0029979133):0.003504745723,Orangutan:0.0081440637):0.004860938415,Macaque:0.0162054466):0.007434746397,Marmoset:0.026475292):0.02368246129,(Tarsier:0.0613649001,Bushbaby:0.0679831987):0.007245090196):0.006993359519):0.008028021123,((Hedgehog:0.0769769377,Shrew:0.0961128472):0.02069224504,(((Microbat:0.0588027131,Megabat:0.0402080439):0.01102099503,Horse:0.0515907398):0.002668084828,((Alpaca:0.0473195068,((Cow:0.0495403783,Dolphin:0.0268515024):0.007809417817,Pig:0.047013653):0.004336967106):0.0141890121,(Dog:0.0468850846,Cat:0.0357324471):0.01849949786):0.00212596777):0.004283734631):0.007051284005):0.00951365831):0.006811166723):0.08613639279); \ No newline at end of file diff --git a/paper/nbks/ctree/out/iqtree_with_ls.tsv b/paper/nbks/ctree/out/iqtree_with_ls.tsv new file mode 100644 index 0000000..b7aac18 --- /dev/null +++ b/paper/nbks/ctree/out/iqtree_with_ls.tsv @@ -0,0 +1 @@ +655.1481719017029 -2502925.29249 (Platypus:0.1980308653,Wallaby:0.1191530578,(((Elephant:0.0358218972,Hyrax:0.0605419424):0.01139086181,Tenrec:0.0961679148):0.01525657575,((Armadillo:0.0600424856,Sloth:0.0442642846):0.02103896162,((((Pika:0.0725054577,Rabbit:0.048201197):0.03549680139,(Squirrel:0.0523955656,(Rat:0.0408106935,Mouse:0.0339640019):0.08308823611):0.01294113704):0.00559443405,((((((Chimp:0.0040950479,Gorilla:0.0042682237):0.001057566512,Human:0.0029977418):0.003504631519,Orangutan:0.0081439037):0.004860978577,Macaque:0.0162053552):0.007434877301,Marmoset:0.0264752485):0.02368241929,(Tarsier:0.0613649259,Bushbaby:0.0679832721):0.007245143123):0.006993413386):0.008027835817,((Hedgehog:0.0769770362,Shrew:0.0961130197):0.02069223852,(((Microbat:0.0588029055,Megabat:0.0402079716):0.01102111325,Horse:0.051591009):0.002668025121,((Alpaca:0.0473194195,((Cow:0.0495406753,Dolphin:0.0268515612):0.007809204421,Pig:0.0470136204):0.004336782976):0.01418908638,(Dog:0.0468851069,Cat:0.0357323123):0.01849962134):0.002125933215):0.004283832057):0.007051199809):0.009513613404):0.00681120086):0.08613654022); diff --git a/paper/nbks/ctree/out/results.tsv b/paper/nbks/ctree/out/results.tsv new file mode 100644 index 0000000..d096e3c --- /dev/null +++ b/paper/nbks/ctree/out/results.tsv @@ -0,0 +1,221 @@ +4 100 8 4.2377097606658936 (Cat,(Dog,(Pig,(Dolphin,(Cow,(Alpaca,(Horse,(Megabat,(Microbat,(Shrew,(Hedgehog,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Human,(Chimp,(Mouse,(Rat,(Squirrel,(Rabbit,(Pika,(Sloth,(Armadillo,(Tenrec,(Hyrax,(Elephant,(Platypus,Wallaby)))))))))))))))))))))))))))))); +4 250 8 4.084033489227295 (Cat,(Dog,(Pig,(Dolphin,(Cow,(Alpaca,(Horse,(Megabat,(Microbat,(Shrew,(Hedgehog,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Human,(Chimp,(Mouse,(Rat,(Squirrel,(Rabbit,(Pika,(Sloth,(Armadillo,(Tenrec,(Hyrax,(Elephant,(Platypus,Wallaby)))))))))))))))))))))))))))))); +4 500 8 4.0288474559783936 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(Cat,(Dog,(Pig,(Dolphin,(Cow,(Alpaca,(Horse,(Megabat,(Microbat,(Shrew,(Hedgehog,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Human,(Chimp,(Mouse,(Rat,(Squirrel,(Rabbit,(Pika,(Sloth,(Armadillo,(Tenrec,(Hyrax,(Elephant,(Platypus,Wallaby)))))))))))))))))))))))))))))); +5 75000 8 4.000338315963745 (Cat,(Dog,(Pig,(Dolphin,(Cow,(Alpaca,(Horse,(Megabat,(Microbat,(Shrew,(Hedgehog,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Human,(Chimp,(Mouse,(Rat,(Squirrel,(Rabbit,(Pika,(Sloth,(Armadillo,(Tenrec,(Hyrax,(Elephant,(Platypus,Wallaby)))))))))))))))))))))))))))))); +5 100000 8 4.068085670471191 (Cat,(Dog,(Pig,(Dolphin,(Cow,(Alpaca,(Horse,(Megabat,(Microbat,(Shrew,(Hedgehog,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Human,(Chimp,(Mouse,(Rat,(Squirrel,(Rabbit,(Pika,(Sloth,(Armadillo,(Tenrec,(Hyrax,(Elephant,(Platypus,Wallaby)))))))))))))))))))))))))))))); +6 100 8 4.093605995178223 (Cat,(Dog,(Pig,(Dolphin,(Cow,(Alpaca,(Horse,(Megabat,(Microbat,(Shrew,(Hedgehog,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Human,(Chimp,(Mouse,(Rat,(Squirrel,(Rabbit,(Pika,(Sloth,(Armadillo,(Tenrec,(Hyrax,(Elephant,(Platypus,Wallaby)))))))))))))))))))))))))))))); +6 250 8 4.074373006820679 (Horse,(Cat,(Dog,(Pig,(Dolphin,(Cow,(Alpaca,(Megabat,(Microbat,(Shrew,(Hedgehog,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Human,(Chimp,(Mouse,(Rat,(Squirrel,(Rabbit,(Pika,(Sloth,(Armadillo,(Tenrec,(Hyrax,(Elephant,(Platypus,Wallaby)))))))))))))))))))))))))))))); +6 500 8 4.136301279067993 (Horse,(Sloth,(Cat,(Dog,(Pig,(Dolphin,(Cow,(Alpaca,(Megabat,(Microbat,(Shrew,(Hedgehog,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Human,(Chimp,(Mouse,(Rat,(Squirrel,(Rabbit,(Pika,(Armadillo,(Tenrec,(Hyrax,(Elephant,(Platypus,Wallaby)))))))))))))))))))))))))))))); +6 750 8 4.061798810958862 (Horse,(Sloth,(Cat,(Dog,(Pig,(Dolphin,(Cow,(Alpaca,(Megabat,(Microbat,(Shrew,(Hedgehog,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Human,(Chimp,(Mouse,(Rat,(Squirrel,(Rabbit,(Pika,(Armadillo,(Tenrec,(Hyrax,(Elephant,(Platypus,Wallaby)))))))))))))))))))))))))))))); +6 1000 8 4.046265363693237 ((Marmoset,Alpaca),((Wallaby,Sloth),(Horse,(Tenrec,(Cat,(Dog,(Pig,(Dolphin,(Cow,(Megabat,(Microbat,(Shrew,(Hedgehog,(Bushbaby,(Tarsier,(Macaque,(Orangutan,(Gorilla,(Human,(Chimp,(Mouse,(Rat,(Squirrel,(Rabbit,(Pika,(Armadillo,(Hyrax,(Platypus,Elephant)))))))))))))))))))))))))))); +6 2500 8 4.198445558547974 ((Wallaby,Sloth),(Squirrel,((Marmoset,Alpaca),(Horse,(Tenrec,(Hyrax,(Cat,(Dog,(Pig,(Dolphin,(Cow,(Megabat,(Microbat,(Shrew,(Hedgehog,(Bushbaby,(Tarsier,(Macaque,(Orangutan,(Gorilla,(Human,(Chimp,(Mouse,(Rat,(Rabbit,(Pika,(Armadillo,(Platypus,Elephant)))))))))))))))))))))))))))); +6 5000 8 4.3241801261901855 ((Squirrel,(Hedgehog,((Marmoset,Alpaca),(Horse,(Pika,(Tenrec,(Hyrax,(Cat,(Dog,(Pig,(Dolphin,(Cow,(Megabat,(Microbat,(Shrew,(Bushbaby,(Tarsier,(Macaque,(Orangutan,(Gorilla,(Human,(Chimp,(Mouse,(Rat,(Rabbit,(Armadillo,(Platypus,Elephant))))))))))))))))))))))))))),(Wallaby,Sloth)); +6 7500 8 4.472481727600098 ((Squirrel,(Hedgehog,((Marmoset,Alpaca),(Horse,(Pika,(Tenrec,(Hyrax,(Cat,(Dog,(Pig,(Dolphin,(Cow,(Megabat,(Microbat,(Shrew,(Bushbaby,(Tarsier,(Macaque,(Orangutan,(Gorilla,(Human,(Chimp,(Mouse,(Rat,(Rabbit,(Armadillo,(Platypus,Elephant))))))))))))))))))))))))))),(Wallaby,Sloth)); +6 10000 8 4.1822240352630615 ((Squirrel,(Hedgehog,((Marmoset,Alpaca),(Horse,(Pika,(Tenrec,(Hyrax,(Cat,(Dog,(Pig,(Dolphin,(Cow,(Megabat,(Microbat,(Shrew,(Bushbaby,(Tarsier,(Macaque,(Orangutan,(Gorilla,(Human,(Chimp,(Mouse,(Rat,(Rabbit,(Armadillo,(Platypus,Elephant))))))))))))))))))))))))))),(Wallaby,Sloth)); +6 25000 8 4.151824712753296 ((Squirrel,(Hedgehog,((Marmoset,Alpaca),(Horse,(Pika,(Tenrec,(Hyrax,(Cat,(Dog,(Pig,(Dolphin,(Cow,(Megabat,(Microbat,(Shrew,(Bushbaby,(Tarsier,(Macaque,(Orangutan,(Gorilla,(Human,(Chimp,(Mouse,(Rat,(Rabbit,(Armadillo,(Platypus,Elephant))))))))))))))))))))))))))),(Wallaby,Sloth)); +6 50000 8 4.163990020751953 ((Squirrel,(Hedgehog,((Marmoset,Alpaca),(Horse,(Pika,(Tenrec,(Hyrax,(Cat,(Dog,(Pig,(Dolphin,(Cow,(Megabat,(Microbat,(Shrew,(Bushbaby,(Tarsier,(Macaque,(Orangutan,(Gorilla,(Human,(Chimp,(Mouse,(Rat,(Rabbit,(Armadillo,(Platypus,Elephant))))))))))))))))))))))))))),(Wallaby,Sloth)); +6 75000 8 4.213173151016235 ((Squirrel,(Hedgehog,((Marmoset,Alpaca),(Horse,(Pika,(Tenrec,(Hyrax,(Cat,(Dog,(Pig,(Dolphin,(Cow,(Megabat,(Microbat,(Shrew,(Bushbaby,(Tarsier,(Macaque,(Orangutan,(Gorilla,(Human,(Chimp,(Mouse,(Rat,(Rabbit,(Armadillo,(Platypus,Elephant))))))))))))))))))))))))))),(Wallaby,Sloth)); +6 100000 8 4.155531644821167 ((Squirrel,(Hedgehog,((Marmoset,Alpaca),(Horse,(Pika,(Tenrec,(Hyrax,(Cat,(Dog,(Pig,(Dolphin,(Cow,(Megabat,(Microbat,(Shrew,(Bushbaby,(Tarsier,(Macaque,(Orangutan,(Gorilla,(Human,(Chimp,(Mouse,(Rat,(Rabbit,(Armadillo,(Platypus,Elephant))))))))))))))))))))))))))),(Wallaby,Sloth)); +7 100 8 4.051084041595459 (Mouse,(Sloth,(Hedgehog,(Dolphin,(Cow,((Armadillo,Alpaca),((Tenrec,Squirrel),((Bushbaby,(Rat,Macaque)),((Cat,(Dog,(Platypus,Tarsier))),(Hyrax,(Elephant,(Wallaby,(Pig,(Horse,(Megabat,(Microbat,(Shrew,(Marmoset,(Orangutan,(Gorilla,(Human,(Chimp,(Pika,Rabbit))))))))))))))))))))))); +7 250 8 4.033607721328735 (Mouse,(Hedgehog,(Sloth,(Armadillo,(Dolphin,(Alpaca,((Elephant,Cow),((Tenrec,Squirrel),((Wallaby,Bushbaby),(Rat,(Shrew,(Hyrax,((Orangutan,(Macaque,(Pika,(Chimp,(Human,Gorilla))))),((Marmoset,(Pig,(Microbat,Horse))),(((Dog,Cat),(Platypus,Tarsier)),(Rabbit,Megabat)))))))))))))))); +7 500 8 3.889801263809204 (Sloth,(Hedgehog,(Squirrel,(Mouse,(Wallaby,(Armadillo,(Shrew,((Elephant,Cow),(((Orangutan,(Gorilla,(Chimp,Human))),((Tenrec,Bushbaby),(Rabbit,(Rat,((Pika,Macaque),(Hyrax,(Marmoset,(Megabat,(Platypus,((Dog,Cat),(Pig,(Microbat,Horse)))))))))))),(Dolphin,(Tarsier,Alpaca))))))))))); +7 750 8 4.102148532867432 (Hedgehog,(Sloth,(Wallaby,(Squirrel,(Mouse,(Armadillo,((Orangutan,(Macaque,(Gorilla,(Chimp,Human)))),(Shrew,(Rabbit,((Tarsier,Alpaca),((Elephant,Cow),(Tenrec,(Dolphin,(Bushbaby,(Rat,(Marmoset,(Hyrax,(Megabat,(Microbat,(Pika,((Horse,Pig),(Platypus,(Dog,Cat))))))))))))))))))))))); +7 1000 8 3.989675760269165 (Sloth,(Wallaby,(Hedgehog,(Alpaca,(Mouse,((Tenrec,Squirrel),(Hyrax,(Rabbit,(Armadillo,(Shrew,((Orangutan,(Macaque,(Gorilla,(Chimp,Human)))),(Bushbaby,((Elephant,Cow),(Dolphin,(Microbat,(Pika,(Megabat,((Tarsier,(Cat,(Platypus,Dog))),(Marmoset,(Rat,(Horse,Pig))))))))))))))))))))); +7 2500 8 4.007145881652832 (Sloth,(Wallaby,(Hedgehog,(Squirrel,(Alpaca,(Shrew,(Armadillo,(Tenrec,(Pika,(Mouse,(Elephant,(Hyrax,((Rabbit,(Marmoset,(Orangutan,(Macaque,(Gorilla,(Chimp,Human)))))),(Tarsier,(Dolphin,(Microbat,((Bushbaby,Cow),(Megabat,(((Platypus,Horse),(Dog,Cat)),(Rat,Pig)))))))))))))))))))); +7 5000 8 4.009132146835327 (Wallaby,(Sloth,(Hedgehog,(Squirrel,(Alpaca,(Shrew,(Tenrec,(Hyrax,(Pika,(Armadillo,(Dolphin,(Elephant,(Tarsier,((Marmoset,(Orangutan,(Macaque,(Gorilla,(Chimp,Human))))),(Megabat,(Bushbaby,((Rabbit,(Dog,(Microbat,Pig))),(Cat,((Rat,Mouse),(Cow,(Platypus,Horse))))))))))))))))))))); +7 7500 8 4.169933080673218 (Wallaby,(Sloth,(Hedgehog,(Squirrel,(Alpaca,(Shrew,(Tenrec,(Hyrax,(Pika,(Armadillo,(Dolphin,((Marmoset,(Orangutan,(Macaque,(Gorilla,(Chimp,Human))))),(Tarsier,(Elephant,(Megabat,(Bushbaby,((Rat,Mouse),((Rabbit,(Cow,(Cat,(Horse,(Platypus,Pig))))),(Microbat,Dog))))))))))))))))))); +7 10000 8 4.997243642807007 (Wallaby,(Sloth,(Hedgehog,(Squirrel,(Shrew,(Alpaca,(Hyrax,(Tenrec,(Pika,((Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human))))),(Armadillo,(Tarsier,(Dolphin,(Megabat,(Elephant,(Bushbaby,((Rat,Mouse),(Dog,(Microbat,(Rabbit,(Cow,(Pig,(Cat,(Platypus,Horse)))))))))))))))))))))))); +7 25000 8 4.92268967628479 (Wallaby,(Sloth,(Hedgehog,(Shrew,(Squirrel,(Alpaca,(Tenrec,(Hyrax,(Pika,(Tarsier,((Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human))))),(Armadillo,(Elephant,(Bushbaby,(Dolphin,(Megabat,((Rat,Mouse),(Dog,(Microbat,(Rabbit,(Cow,(Pig,(Cat,(Platypus,Horse)))))))))))))))))))))))); +7 50000 8 4.710857152938843 (Wallaby,(Sloth,(Hedgehog,(Shrew,(Squirrel,(Alpaca,(Tenrec,(Hyrax,(Pika,(Tarsier,((Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human))))),(Armadillo,(Elephant,(Bushbaby,(Dolphin,(Megabat,((Rat,Mouse),(Dog,(Microbat,(Rabbit,(Cow,(Pig,(Cat,(Platypus,Horse)))))))))))))))))))))))); +7 75000 8 4.5595972537994385 (Wallaby,(Sloth,(Hedgehog,(Shrew,(Squirrel,(Alpaca,(Tenrec,(Hyrax,(Pika,(Tarsier,((Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human))))),(Armadillo,(Elephant,(Bushbaby,(Dolphin,(Megabat,((Rat,Mouse),(Dog,(Microbat,(Rabbit,(Cow,(Pig,(Cat,(Platypus,Horse)))))))))))))))))))))))); +7 100000 8 4.60521936416626 (Wallaby,(Sloth,(Hedgehog,(Shrew,(Squirrel,(Alpaca,(Tenrec,(Hyrax,(Pika,(Tarsier,((Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human))))),(Armadillo,(Elephant,(Bushbaby,(Dolphin,(Megabat,((Rat,Mouse),(Dog,(Microbat,(Rabbit,(Cow,(Pig,(Cat,(Platypus,Horse)))))))))))))))))))))))); +8 100 8 4.101529598236084 ((Sloth,Hedgehog),(Rat,((Tenrec,Pika),(Armadillo,(((Wallaby,Alpaca),((Platypus,(Rabbit,Cow)),((Mouse,Dog),(Elephant,(Dolphin,Pig))))),((Marmoset,(Shrew,Cat)),(Hyrax,((Tarsier,(Macaque,(Gorilla,(Orangutan,(Chimp,Human))))),(Bushbaby,((Squirrel,Horse),(Microbat,Megabat))))))))))); +8 250 8 4.047252416610718 (Sloth,(Wallaby,(Hedgehog,(Tenrec,(Hyrax,(Rat,(Shrew,((Pika,(Platypus,Rabbit)),(Armadillo,(Squirrel,((Mouse,Dog),((Elephant,Pig),((Megabat,Alpaca),((Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))),((Bushbaby,Dolphin),((Microbat,Horse),(Cow,Cat))))))))))))))))); +8 500 8 4.1712470054626465 (Sloth,(Wallaby,(Hedgehog,(Tenrec,(Platypus,(Hyrax,(Shrew,((Pika,Rabbit),((Tarsier,(Rat,Mouse)),((Elephant,Armadillo),((Alpaca,(Bushbaby,(Cow,(Dolphin,(Microbat,(Horse,Pig)))))),((Megabat,(Dog,Cat)),(Squirrel,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +8 750 8 4.045953989028931 (Wallaby,(Sloth,(Shrew,(Hedgehog,(Pika,(Hyrax,(Tenrec,((Platypus,Rabbit),((Tarsier,(Rat,Mouse)),(Armadillo,(Alpaca,((Squirrel,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))),((Megabat,(Dog,Cat)),(Bushbaby,(Cow,(Dolphin,(Elephant,(Microbat,(Horse,Pig))))))))))))))))))); +8 1000 8 3.977060317993164 (Wallaby,(Sloth,(Shrew,(Hedgehog,(Tenrec,(Hyrax,(((Rat,Mouse),(Armadillo,(Alpaca,(Tarsier,((Squirrel,(Bushbaby,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human))))))),((Megabat,(Dog,Cat)),(Elephant,(Cow,(Dolphin,(Microbat,(Horse,Pig))))))))))),(Pika,(Platypus,Rabbit))))))))); +8 2500 8 4.148793935775757 (Wallaby,(Sloth,(Hedgehog,(Shrew,(Tenrec,(Pika,(Platypus,(Hyrax,(Alpaca,((Rat,Mouse),(Tarsier,((Bushbaby,(Squirrel,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human))))))),(Armadillo,(Rabbit,((Cow,Dolphin),(Elephant,(Megabat,((Dog,Cat),(Microbat,(Horse,Pig)))))))))))))))))))); +8 5000 8 4.183537483215332 (Wallaby,(Sloth,(Hedgehog,(Tenrec,(Shrew,(Pika,(Platypus,(Hyrax,(Alpaca,((Rat,Mouse),(Armadillo,((Rabbit,(Elephant,(Megabat,(Microbat,((Dog,Cat),((Cow,Dolphin),(Horse,Pig))))))),(Tarsier,(Squirrel,(Bushbaby,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))))); +8 7500 8 4.383633613586426 (Wallaby,(Sloth,(Hedgehog,(Tenrec,(Shrew,(Pika,(Platypus,(Hyrax,(Alpaca,((Rat,Mouse),(Armadillo,(Tarsier,(((Megabat,((Dog,Cat),(Microbat,(Horse,(Pig,(Cow,Dolphin)))))),(Elephant,Rabbit)),(Squirrel,(Bushbaby,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))))); +8 10000 8 4.3561413288116455 (Wallaby,(Sloth,(Hedgehog,(Tenrec,(Shrew,(Pika,(Platypus,(Hyrax,(Alpaca,((Rat,Mouse),(Squirrel,(Tarsier,(Armadillo,(((Elephant,Rabbit),(Megabat,(Microbat,((Dolphin,(Cow,Pig)),(Horse,(Dog,Cat)))))),(Bushbaby,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))))); +8 25000 8 5.825811862945557 (Wallaby,(Hedgehog,(Sloth,(Shrew,(Tenrec,(Pika,(Hyrax,(Platypus,(Alpaca,((Rat,Mouse),(Squirrel,(Armadillo,(Tarsier,(Elephant,((Megabat,(Rabbit,(Microbat,((Horse,(Dog,Cat)),(Dolphin,(Cow,Pig)))))),(Bushbaby,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human))))))))))))))))))))); +8 50000 8 5.692780256271362 (Wallaby,(Hedgehog,(Sloth,(Shrew,(Tenrec,(Pika,(Hyrax,(Platypus,(Alpaca,((Rat,Mouse),(Squirrel,(Armadillo,(Elephant,((Megabat,(Rabbit,(Microbat,((Horse,(Dog,Cat)),(Dolphin,(Cow,Pig)))))),(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human))))))))))))))))))))); +8 75000 8 6.036278963088989 (Wallaby,(Hedgehog,(Sloth,(Shrew,(Tenrec,(Pika,(Hyrax,(Platypus,(Alpaca,((Rat,Mouse),(Squirrel,(Armadillo,(Bushbaby,(Megabat,(Elephant,((Rabbit,(Microbat,(Dolphin,((Horse,(Dog,Cat)),(Cow,Pig))))),(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))))))); +8 100000 8 6.079356670379639 (Wallaby,(Hedgehog,(Sloth,(Shrew,(Tenrec,(Pika,(Hyrax,(Platypus,(Alpaca,((Rat,Mouse),(Squirrel,(Armadillo,(Bushbaby,(Megabat,(Elephant,((Rabbit,(Microbat,(Dolphin,((Horse,(Dog,Cat)),(Cow,Pig))))),(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))))))); +9 100 8 4.248380184173584 (Wallaby,(Shrew,(Hedgehog,((Platypus,Armadillo),(Tenrec,(((Rat,Mouse),((Pika,Rabbit),(Squirrel,(((Horse,(Microbat,(Megabat,(Marmoset,(Macaque,(Orangutan,(Chimp,(Human,Gorilla)))))))),(Tarsier,Bushbaby)),((Alpaca,Cat),(Cow,(Dog,(Dolphin,Pig)))))))),(Sloth,(Elephant,Hyrax)))))))); +9 250 8 4.220959901809692 (Hedgehog,(Wallaby,(Shrew,(Platypus,(Tenrec,((Rat,Mouse),((Elephant,Hyrax),((Armadillo,Sloth),((Pika,Rabbit),(Alpaca,(Squirrel,((Cow,Dolphin),(Microbat,(Bushbaby,(Horse,(Pig,((Dog,Cat),(Megabat,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))))))))); +9 500 8 4.407300233840942 (Wallaby,(Platypus,(Hedgehog,(Tenrec,(Shrew,((Rat,Mouse),(Pika,((Armadillo,Sloth),((Elephant,Hyrax),(Squirrel,(Microbat,(((Cow,Dolphin),(Alpaca,Pig)),(Rabbit,(Bushbaby,((Dog,Cat),((Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))),(Megabat,Horse))))))))))))))))); +9 750 8 4.170255899429321 (Wallaby,(Platypus,(Hedgehog,(Tenrec,(Shrew,(Sloth,(Pika,((Rat,Mouse),(Hyrax,((Squirrel,((Rabbit,(Bushbaby,((Megabat,Horse),((Dog,Cat),(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))),(Elephant,Armadillo))),(Microbat,(Alpaca,(Pig,(Cow,Dolphin)))))))))))))); +9 1000 8 4.173167705535889 (Wallaby,(Platypus,(Tenrec,(Hedgehog,(Shrew,(Pika,((Rat,Mouse),(Hyrax,(Sloth,(Alpaca,(Squirrel,(((Rabbit,((Tarsier,(Bushbaby,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human))))))),(Megabat,(Horse,(Dog,Cat))))),(Microbat,(Dolphin,(Cow,Pig)))),(Elephant,Armadillo))))))))))))); +9 2500 8 4.189305067062378 (Wallaby,(Platypus,(Tenrec,(Hedgehog,(Shrew,(Pika,(Hyrax,((Rat,Mouse),(Sloth,(Alpaca,(Rabbit,((Elephant,Armadillo),(Squirrel,(Microbat,((Pig,(Cow,Dolphin)),((Megabat,(Dog,Cat)),(Bushbaby,(Tarsier,(Horse,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))))))))); +9 5000 8 4.385913133621216 (Wallaby,(Platypus,(Tenrec,(Hedgehog,(Shrew,(Pika,(Hyrax,(Sloth,((Rat,Mouse),(Alpaca,(Rabbit,((Elephant,Armadillo),(Microbat,(((Pig,(Cow,Dolphin)),(Megabat,(Horse,(Dog,Cat)))),(Squirrel,(Tarsier,(Bushbaby,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))))))); +9 7500 8 4.375309467315674 (Wallaby,(Platypus,(Tenrec,(Shrew,(Hedgehog,(Pika,(Hyrax,(Sloth,((Rat,Mouse),(Alpaca,(Rabbit,(((Squirrel,(Tarsier,(Bushbaby,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))),(Microbat,((Cow,(Dolphin,Pig)),(Megabat,(Horse,(Dog,Cat)))))),(Elephant,Armadillo))))))))))))); +9 10000 8 4.957677841186523 (Wallaby,(Platypus,(Tenrec,(Shrew,(Hedgehog,(Pika,(Sloth,(Hyrax,((Rat,Mouse),(Alpaca,(Rabbit,(((Squirrel,(Tarsier,(Bushbaby,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))),(((Pig,(Cow,Dolphin)),(Horse,(Dog,Cat))),(Microbat,Megabat))),(Elephant,Armadillo))))))))))))); +9 25000 8 5.171752214431763 (Wallaby,(Platypus,(Tenrec,(Shrew,(Hedgehog,(Pika,(Hyrax,(Sloth,((Rat,Mouse),(Alpaca,(Rabbit,(Armadillo,(Elephant,((Microbat,Megabat),((Pig,(Cow,Dolphin)),(Tarsier,(Bushbaby,(Squirrel,((Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human))))),(Horse,(Dog,Cat))))))))))))))))))))); +9 50000 8 5.793044567108154 (Wallaby,(Platypus,(Tenrec,(Shrew,(Hedgehog,(Pika,(Sloth,(Hyrax,((Rat,Mouse),(Alpaca,(Rabbit,(Armadillo,(Elephant,((Tarsier,(Bushbaby,(Squirrel,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))),(((Pig,(Cow,Dolphin)),(Horse,(Dog,Cat))),(Microbat,Megabat)))))))))))))))); +9 75000 8 6.596000671386719 (Wallaby,(Platypus,(Tenrec,(Shrew,(Hedgehog,(Pika,(Sloth,(Hyrax,((Rat,Mouse),(Alpaca,(Rabbit,(Armadillo,(Elephant,((Tarsier,(Bushbaby,(Squirrel,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))),(((Pig,(Cow,Dolphin)),(Horse,(Dog,Cat))),(Microbat,Megabat)))))))))))))))); +9 100000 8 7.384727954864502 (Wallaby,(Platypus,(Tenrec,(Shrew,(Hedgehog,(Pika,(Sloth,(Hyrax,((Rat,Mouse),(Alpaca,(Rabbit,(Armadillo,(Tarsier,(Elephant,((Microbat,Megabat),((Pig,(Cow,Dolphin)),(Bushbaby,(Squirrel,((Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human))))),(Horse,(Dog,Cat))))))))))))))))))))); +10 100 8 4.2603631019592285 ((Platypus,Wallaby),(Tenrec,(Shrew,(Hedgehog,(Pika,(((Armadillo,Sloth),((((Elephant,Hyrax),(Alpaca,Pig)),((Megabat,Dog),(Tarsier,((Squirrel,(Marmoset,(Macaque,(Orangutan,(Chimp,(Human,Gorilla)))))),(Cow,(Bushbaby,(Horse,Dolphin))))))),(Microbat,Cat))),(Rabbit,(Rat,Mouse)))))))); +10 250 8 4.294339179992676 ((Tenrec,(Shrew,(Hedgehog,(Pika,((Rat,Mouse),((Armadillo,Sloth),(((Elephant,Hyrax),((Tarsier,(Squirrel,(Bushbaby,(Horse,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human))))))))),((Dog,Cat),(Alpaca,(Cow,(Pig,(Megabat,Dolphin))))))),(Rabbit,Microbat)))))))),(Platypus,Wallaby)); +10 500 8 4.3072028160095215 ((Tenrec,(Shrew,(Pika,((Rat,Mouse),(Hedgehog,(Hyrax,((Armadillo,Sloth),(Alpaca,(Rabbit,(Tarsier,(Bushbaby,(Cow,((Dog,Cat),((Elephant,(Squirrel,(Marmoset,(Macaque,(Orangutan,(Chimp,(Human,Gorilla))))))),(Microbat,(Megabat,(Horse,(Dolphin,Pig)))))))))))))))))),(Platypus,Wallaby)); +10 750 8 4.2647082805633545 (Platypus,(Wallaby,(Tenrec,(Shrew,((Rat,Mouse),(Hedgehog,((Pika,Rabbit),((Armadillo,Sloth),((Elephant,Hyrax),(Alpaca,(Squirrel,(((Cow,(Dolphin,Pig)),(Microbat,(Megabat,Horse))),((Dog,Cat),(Tarsier,(Bushbaby,(Marmoset,(Macaque,(Orangutan,(Chimp,(Human,Gorilla)))))))))))))))))))); +10 1000 8 4.252018213272095 ((Tenrec,(Shrew,(Pika,(Hedgehog,((Rat,Mouse),((Armadillo,Sloth),((Elephant,Hyrax),(Alpaca,(Rabbit,(Squirrel,((Tarsier,(Bushbaby,(Marmoset,(Macaque,(Orangutan,(Chimp,(Human,Gorilla))))))),((Dog,Cat),((Cow,(Dolphin,Pig)),(Microbat,(Megabat,Horse))))))))))))))),(Platypus,Wallaby)); +10 2500 8 4.2079856395721436 (Platypus,(Wallaby,(Tenrec,(Shrew,(Hedgehog,(Pika,((Rat,Mouse),((Elephant,Hyrax),((Armadillo,Sloth),(Alpaca,(Rabbit,(((Pig,(Cow,Dolphin)),((Dog,Cat),(Microbat,(Megabat,Horse)))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))))); +10 5000 8 4.431292295455933 (Platypus,(Wallaby,(Tenrec,(Shrew,(Hedgehog,(Pika,((Rat,Mouse),((Elephant,Hyrax),((Armadillo,Sloth),(Rabbit,((Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))),(((Horse,(Dog,Cat)),(Microbat,Megabat)),(Alpaca,(Pig,(Cow,Dolphin))))))))))))))); +10 7500 8 4.985644340515137 (Platypus,(Wallaby,(Tenrec,(Shrew,(Hedgehog,(Pika,((Rat,Mouse),((Elephant,Hyrax),((Armadillo,Sloth),(Rabbit,(Alpaca,(((Microbat,Megabat),((Pig,(Cow,Dolphin)),(Horse,(Dog,Cat)))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))))); +10 10000 8 4.946542739868164 (Platypus,(Wallaby,(Tenrec,(Shrew,(Hedgehog,(Pika,((Rat,Mouse),((Armadillo,Sloth),((Elephant,Hyrax),(Rabbit,((Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))),((Alpaca,(Pig,(Cow,Dolphin))),((Horse,(Dog,Cat)),(Microbat,Megabat)))))))))))))); +10 25000 8 5.126317024230957 (Platypus,(Wallaby,(Tenrec,(Shrew,(Hedgehog,(Pika,((Rat,Mouse),((Elephant,Hyrax),((Armadillo,Sloth),(Rabbit,(Alpaca,(((Microbat,Megabat),((Pig,(Cow,Dolphin)),(Horse,(Dog,Cat)))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))))); +10 50000 8 5.867666959762573 (Platypus,(Wallaby,(Tenrec,(Shrew,(Pika,(Hedgehog,((Rat,Mouse),((Elephant,Hyrax),((Armadillo,Sloth),(Rabbit,(Alpaca,(((Microbat,Megabat),((Pig,(Cow,Dolphin)),(Horse,(Dog,Cat)))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))))); +10 75000 8 6.523652791976929 (Platypus,(Wallaby,(Tenrec,(Shrew,(Pika,(Hedgehog,((Rat,Mouse),((Elephant,Hyrax),((Armadillo,Sloth),(Rabbit,(Alpaca,(((Microbat,Megabat),((Pig,(Cow,Dolphin)),(Horse,(Dog,Cat)))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))))); +10 100000 8 7.124992609024048 (Platypus,(Wallaby,(Tenrec,(Shrew,(Pika,(Hedgehog,((Rat,Mouse),((Elephant,Hyrax),((Armadillo,Sloth),(Rabbit,(Alpaca,(((Microbat,Megabat),((Pig,(Cow,Dolphin)),(Horse,(Dog,Cat)))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))))); +11 100 8 4.310589790344238 (Platypus,(Shrew,(Wallaby,((Squirrel,(Rat,Mouse)),((Tenrec,Hedgehog),((Elephant,Hyrax),((Armadillo,Sloth),((Pika,Rabbit),(Microbat,(((Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human))))),(Tarsier,Bushbaby)),((Megabat,Horse),(Cow,((Dog,Cat),(Pig,(Alpaca,Dolphin))))))))))))))); +11 250 8 4.318761110305786 (Platypus,(Wallaby,(Tenrec,(Shrew,(Pika,((Rat,Mouse),(Hedgehog,((Armadillo,Sloth),(Rabbit,((Elephant,Hyrax),(Alpaca,(Microbat,((((Dog,Cat),(Cow,(Dolphin,Pig))),(Horse,(Bushbaby,Megabat))),(Squirrel,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))))); +11 500 8 4.295088291168213 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,(Pika,((Armadillo,Sloth),((Elephant,Hyrax),(Rabbit,(Microbat,(((Alpaca,(Cow,(Dolphin,Pig))),(Horse,(Megabat,(Dog,Cat)))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))))); +11 750 8 4.32359766960144 (Platypus,(Wallaby,(Tenrec,((Rat,Mouse),(Shrew,(Pika,(Hedgehog,((Armadillo,Sloth),((Elephant,Hyrax),(Rabbit,(Alpaca,(Microbat,((Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))),((Cow,(Dolphin,Pig)),(Megabat,(Horse,(Dog,Cat))))))))))))))))); +11 1000 8 4.319476366043091 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Armadillo,Sloth),((Elephant,Hyrax),(Alpaca,(Microbat,(((Cow,(Dolphin,Pig)),(Horse,(Megabat,(Dog,Cat)))),(Squirrel,(Tarsier,(Bushbaby,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))))); +11 2500 8 4.354321718215942 (Platypus,(Wallaby,(Tenrec,((Rat,Mouse),(Shrew,(Pika,(Hedgehog,((Elephant,Hyrax),(Rabbit,((Armadillo,Sloth),((Squirrel,(Tarsier,(Bushbaby,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))),((Alpaca,(Cow,(Dolphin,Pig))),(Microbat,(Megabat,(Horse,(Dog,Cat)))))))))))))))); +11 5000 8 5.142577171325684 (Platypus,(Wallaby,(Tenrec,((Rat,Mouse),(Shrew,(Pika,(Hedgehog,((Elephant,Hyrax),((Armadillo,Sloth),(Rabbit,(((Alpaca,(Cow,(Dolphin,Pig))),(Microbat,(Megabat,(Horse,(Dog,Cat))))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human))))))))))))))))))); +11 7500 8 4.688713312149048 (Platypus,(Wallaby,(Tenrec,((Rat,Mouse),(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Cow,(Dolphin,Pig))),(Microbat,(Megabat,(Horse,(Dog,Cat))))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +11 10000 8 4.80496072769165 (Platypus,(Wallaby,(Tenrec,((Rat,Mouse),(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Cow,(Dolphin,Pig))),((Horse,(Dog,Cat)),(Microbat,Megabat))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +11 25000 8 5.332113027572632 (Platypus,(Wallaby,(Tenrec,(Shrew,((Rat,Mouse),(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),((Horse,(Dog,Cat)),(Microbat,Megabat))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +11 50000 8 5.90401554107666 (Platypus,(Wallaby,(Tenrec,((Rat,Mouse),(Shrew,(Pika,(Hedgehog,((Elephant,Hyrax),((Armadillo,Sloth),(Rabbit,(((Alpaca,(Pig,(Cow,Dolphin))),((Horse,(Dog,Cat)),(Microbat,Megabat))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human))))))))))))))))))); +11 75000 8 6.6420745849609375 (Platypus,(Wallaby,(Tenrec,((Rat,Mouse),(Shrew,(Pika,(Hedgehog,((Elephant,Hyrax),((Armadillo,Sloth),(Rabbit,(((Alpaca,(Pig,(Cow,Dolphin))),((Horse,(Dog,Cat)),(Microbat,Megabat))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human))))))))))))))))))); +11 100000 8 7.214916467666626 (Platypus,(Wallaby,(Tenrec,((Rat,Mouse),(Shrew,(Pika,(Hedgehog,((Elephant,Hyrax),((Armadillo,Sloth),(Rabbit,(((Alpaca,(Pig,(Cow,Dolphin))),((Horse,(Dog,Cat)),(Microbat,Megabat))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human))))))))))))))))))); +12 100 8 4.302613258361816 ((Pika,(Tenrec,(Shrew,((Rat,Mouse),(Hedgehog,(Rabbit,((Armadillo,Sloth),((Elephant,Hyrax),((Cow,(Dolphin,Pig)),((Bushbaby,(Squirrel,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Chimp,(Human,Gorilla)))))))),((Microbat,Megabat),(Alpaca,(Cat,(Horse,Dog)))))))))))))),(Platypus,Wallaby)); +12 250 8 4.322468519210815 ((Tenrec,((Rat,Mouse),(Shrew,((Pika,Rabbit),(Hedgehog,((Armadillo,Sloth),((Elephant,Hyrax),((((Microbat,Megabat),(Horse,(Dog,Cat))),(Alpaca,(Pig,(Cow,Dolphin)))),(Squirrel,(Tarsier,(Bushbaby,(Marmoset,(Macaque,(Orangutan,(Chimp,(Human,Gorilla)))))))))))))))),(Platypus,Wallaby)); +12 500 8 4.309951305389404 (Platypus,(Wallaby,(Tenrec,((Rat,Mouse),(Shrew,(Hedgehog,((Pika,Rabbit),((Armadillo,Sloth),((Elephant,Hyrax),(((Alpaca,(Pig,(Cow,Dolphin))),((Microbat,Megabat),(Horse,(Dog,Cat)))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +12 750 8 4.316399335861206 (Platypus,(Wallaby,(Tenrec,((Rat,Mouse),(Shrew,(Hedgehog,((Pika,Rabbit),((Armadillo,Sloth),((Elephant,Hyrax),((Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))),((Alpaca,(Pig,(Cow,Dolphin))),((Dog,Cat),(Horse,(Microbat,Megabat)))))))))))))); +12 1000 8 4.468536853790283 (Platypus,(Wallaby,(Tenrec,((Rat,Mouse),(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),((Dog,Cat),(Horse,(Microbat,Megabat)))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +12 2500 8 5.167824983596802 (Platypus,(Wallaby,(Tenrec,((Rat,Mouse),(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Microbat,Megabat),((Horse,(Dog,Cat)),(Alpaca,(Cow,(Dolphin,Pig))))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +12 5000 8 4.647489309310913 (Platypus,(Wallaby,(Tenrec,((Rat,Mouse),(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Microbat,Megabat),((Horse,(Dog,Cat)),(Alpaca,(Pig,(Cow,Dolphin))))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +12 7500 8 4.757613182067871 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),((Horse,(Dog,Cat)),(Microbat,Megabat))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +12 10000 8 4.867581367492676 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),((Horse,(Dog,Cat)),(Microbat,Megabat))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +12 25000 8 5.266693353652954 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),((Horse,(Dog,Cat)),(Microbat,Megabat))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +12 50000 8 5.890510082244873 (Platypus,(Wallaby,(Tenrec,((Rat,Mouse),(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),((Horse,(Dog,Cat)),(Microbat,Megabat))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +12 75000 8 6.406416893005371 (Platypus,(Wallaby,(Tenrec,((Rat,Mouse),(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),((Horse,(Dog,Cat)),(Microbat,Megabat))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +12 100000 8 7.173907279968262 (Platypus,(Wallaby,(Tenrec,((Rat,Mouse),(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),((Horse,(Dog,Cat)),(Microbat,Megabat))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +13 100 8 4.2796900272369385 (Platypus,(Wallaby,(Shrew,(Hedgehog,((Rat,Mouse),(Tenrec,(Sloth,((Elephant,Hyrax),((Pika,Rabbit),(Alpaca,(Bushbaby,(Tarsier,(Squirrel,(((Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human))))),(Armadillo,(Horse,(Dog,Cat)))),((Pig,(Cow,Dolphin)),(Microbat,Megabat)))))))))))))))); +13 250 8 4.2732179164886475 (Platypus,(Wallaby,(Shrew,((Rat,Mouse),(Hedgehog,(Tenrec,((Pika,Rabbit),(Sloth,(Alpaca,((Elephant,Hyrax),(Megabat,((Squirrel,(Tarsier,(Bushbaby,(Marmoset,(Macaque,(Orangutan,(Human,(Chimp,Gorilla)))))))),((Cow,(Dolphin,Pig)),(Armadillo,(Microbat,(Horse,(Dog,Cat))))))))))))))))); +13 500 8 4.3290181159973145 (Platypus,(Wallaby,((Rat,Mouse),(Hedgehog,(Shrew,(Tenrec,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(Alpaca,((Tarsier,(Bushbaby,(Squirrel,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))),((Microbat,Megabat),((Cow,(Dolphin,Pig)),(Horse,(Dog,Cat))))))))))))))); +13 750 8 4.616049766540527 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),((Bushbaby,(Squirrel,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))),((Alpaca,(Pig,(Cow,Dolphin))),((Horse,(Dog,Cat)),(Microbat,Megabat))))))))))))); +13 1000 8 4.93379545211792 (Platypus,(Wallaby,(Tenrec,((Rat,Mouse),(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Cow,(Dolphin,Pig))),((Microbat,Megabat),(Horse,(Dog,Cat)))),(Bushbaby,(Squirrel,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +13 2500 8 4.522557735443115 (Platypus,(Wallaby,(Tenrec,((Rat,Mouse),(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),((Horse,(Dog,Cat)),(Microbat,Megabat))),(Bushbaby,(Squirrel,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +13 5000 8 4.497307538986206 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Cow,(Dolphin,Pig))),((Microbat,Megabat),(Horse,(Dog,Cat)))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +13 7500 8 4.5639259815216064 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Cow,(Dolphin,Pig))),((Microbat,Megabat),(Horse,(Dog,Cat)))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +13 10000 8 4.726675271987915 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Cow,(Dolphin,Pig))),((Microbat,Megabat),(Horse,(Dog,Cat)))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +13 25000 8 5.078895330429077 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),((Horse,(Dog,Cat)),(Microbat,Megabat))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +13 50000 8 5.7769365310668945 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),((Horse,(Dog,Cat)),(Microbat,Megabat))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +13 75000 8 6.315812110900879 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),((Horse,(Dog,Cat)),(Microbat,Megabat))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +13 100000 8 7.32050895690918 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),((Horse,(Dog,Cat)),(Microbat,Megabat))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +14 100 8 6.192691087722778 (Hedgehog,((Tenrec,((Rat,Mouse),(Pika,(((Cow,(Alpaca,Dolphin)),((Dog,Cat),(Microbat,(Horse,(Megabat,Pig))))),((Armadillo,Sloth),(((Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human))))),(Squirrel,(Tarsier,Bushbaby))),(Rabbit,(Elephant,Hyrax)))))))),(Wallaby,(Platypus,Shrew)))); +14 250 8 4.641571760177612 (Wallaby,(Platypus,((Rat,Mouse),(Shrew,(Hedgehog,(Tenrec,((Pika,Rabbit),((Armadillo,Sloth),((Elephant,Hyrax),(Squirrel,((((Dog,Cat),(Microbat,Megabat)),(Horse,(Cow,(Pig,(Alpaca,Dolphin))))),(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +14 500 8 5.3850202560424805 (Wallaby,(Platypus,((Rat,Mouse),(Tenrec,(Hedgehog,(Shrew,((Pika,Rabbit),((Armadillo,Sloth),((Elephant,Hyrax),((((Dog,Cat),(Microbat,Megabat)),(Horse,(Cow,(Pig,(Alpaca,Dolphin))))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +14 750 8 4.479605436325073 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Armadillo,Sloth),((Elephant,Hyrax),(((Pig,(Alpaca,(Cow,Dolphin))),(Microbat,((Dog,Cat),(Megabat,Horse)))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +14 1000 8 4.568563461303711 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),(Microbat,((Dog,Cat),(Megabat,Horse)))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +14 2500 8 4.444180250167847 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Cow,(Dolphin,Pig))),((Horse,(Dog,Cat)),(Microbat,Megabat))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +14 5000 8 4.656815528869629 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),((Horse,(Dog,Cat)),(Microbat,Megabat))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +14 7500 8 4.670673131942749 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),((Horse,(Dog,Cat)),(Microbat,Megabat))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +14 10000 8 4.692760705947876 (Platypus,(Wallaby,(Tenrec,((Rat,Mouse),(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Cow,(Dolphin,Pig))),((Horse,(Dog,Cat)),(Microbat,Megabat))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +14 25000 8 5.111825942993164 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),((Horse,(Dog,Cat)),(Microbat,Megabat))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +14 50000 8 5.764783620834351 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),((Horse,(Dog,Cat)),(Microbat,Megabat))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +14 75000 8 6.557143211364746 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),((Horse,(Dog,Cat)),(Microbat,Megabat))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +14 100000 8 7.3096091747283936 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),((Horse,(Dog,Cat)),(Microbat,Megabat))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +15 100 8 4.866623401641846 (Wallaby,(Platypus,((Hyrax,Tenrec),((Rat,Mouse),(Pika,(Bushbaby,(Hedgehog,(Shrew,((Armadillo,Sloth),(Alpaca,(Rabbit,((Pig,(Cow,Dolphin)),(Elephant,(Cat,((Squirrel,Horse),(Tarsier,(Microbat,((Marmoset,(Macaque,(Orangutan,(Human,(Chimp,Gorilla))))),(Megabat,Dog))))))))))))))))))); +15 250 8 4.881701707839966 (Platypus,(Wallaby,(Tenrec,((Rat,Mouse),(Pika,(Hyrax,(Shrew,(Hedgehog,(Bushbaby,((Armadillo,Sloth),(Rabbit,(Squirrel,((Horse,(Alpaca,(Pig,(Cow,Dolphin)))),(Tarsier,(Elephant,((Microbat,Megabat),((Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human))))),(Dog,Cat)))))))))))))))))); +15 500 8 4.375967741012573 (Platypus,(Wallaby,(Tenrec,((Rat,Mouse),(Pika,(Shrew,(Hedgehog,((Elephant,Hyrax),((Armadillo,Sloth),(Bushbaby,(Squirrel,(Rabbit,((Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))),((Alpaca,(Pig,(Cow,Dolphin))),((Microbat,Megabat),(Horse,(Dog,Cat))))))))))))))))); +15 750 8 4.413890600204468 (Platypus,(Wallaby,(Tenrec,((Rat,Mouse),(Pika,(Shrew,(Hedgehog,((Elephant,Hyrax),((Armadillo,Sloth),(Rabbit,(Bushbaby,(Squirrel,((Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))),(Alpaca,((Pig,(Cow,Dolphin)),((Microbat,Megabat),(Horse,(Dog,Cat)))))))))))))))))); +15 1000 8 4.474238395690918 (Platypus,(Wallaby,(Tenrec,((Rat,Mouse),(Pika,(Shrew,(Hedgehog,((Elephant,Hyrax),((Armadillo,Sloth),(Rabbit,(Squirrel,(Bushbaby,((Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))),((Alpaca,(Pig,(Cow,Dolphin))),((Microbat,Megabat),(Horse,(Dog,Cat))))))))))))))))); +15 2500 8 4.485032320022583 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),((Horse,(Dog,Cat)),(Microbat,Megabat))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +15 5000 8 4.616722345352173 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Cow,(Dolphin,Pig))),((Horse,(Dog,Cat)),(Microbat,Megabat))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +15 7500 8 4.849466323852539 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Cow,(Dolphin,Pig))),((Horse,(Dog,Cat)),(Microbat,Megabat))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +15 10000 8 4.710943698883057 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),((Horse,(Dog,Cat)),(Microbat,Megabat))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +15 25000 8 5.0572285652160645 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),((Horse,(Dog,Cat)),(Microbat,Megabat))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +15 50000 8 5.68668270111084 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),((Horse,(Dog,Cat)),(Microbat,Megabat))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +15 75000 8 6.349209785461426 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),((Horse,(Dog,Cat)),(Microbat,Megabat))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +15 100000 8 7.556539535522461 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),((Horse,(Dog,Cat)),(Microbat,Megabat))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +16 100 8 4.493891954421997 ((Wallaby,Hedgehog),(Platypus,((Shrew,(Pika,Rabbit)),(Tenrec,((Rat,Mouse),(((Cow,(Dolphin,Pig)),(Alpaca,(Megabat,(Microbat,(Horse,(Dog,Cat)))))),((Armadillo,Sloth),(Hyrax,((Tarsier,(Marmoset,(Macaque,(Orangutan,(Chimp,(Human,Gorilla)))))),(Elephant,(Squirrel,Bushbaby))))))))))); +16 250 8 4.461766958236694 (Wallaby,(Platypus,(Hedgehog,(Shrew,((Rat,Mouse),((Hyrax,Tenrec),((Pika,Rabbit),((Armadillo,Sloth),((((Cow,Dolphin),(Alpaca,Pig)),(Microbat,(Megabat,(Horse,(Dog,Cat))))),(Elephant,(Bushbaby,(Tarsier,(Squirrel,(Marmoset,(Macaque,(Orangutan,(Human,(Chimp,Gorilla)))))))))))))))))); +16 500 8 4.444768190383911 ((Tenrec,((Rat,Mouse),(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Cow,(Dolphin,Pig))),(Microbat,(Megabat,(Horse,(Dog,Cat))))),(Bushbaby,(Tarsier,(Squirrel,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))),(Platypus,Wallaby)); +16 750 8 4.393027067184448 (Wallaby,(Platypus,(Tenrec,((Rat,Mouse),(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),((Bushbaby,(Squirrel,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))),(Microbat,((Megabat,(Horse,(Dog,Cat))),(Alpaca,(Cow,(Dolphin,Pig))))))))))))))); +16 1000 8 4.439895391464233 (Wallaby,(Platypus,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),((Bushbaby,(Squirrel,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))),(Microbat,((Megabat,(Horse,(Dog,Cat))),(Alpaca,(Cow,(Dolphin,Pig))))))))))))))); +16 2500 8 4.271305561065674 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),(Microbat,((Dog,Cat),(Megabat,Horse)))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +16 5000 8 4.4932801723480225 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),(Microbat,((Dog,Cat),(Megabat,Horse)))),(Bushbaby,(Squirrel,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +16 7500 8 4.602348804473877 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),(Microbat,((Dog,Cat),(Megabat,Horse)))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +16 10000 8 4.680637359619141 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),(Microbat,((Dog,Cat),(Megabat,Horse)))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +16 25000 8 5.019838333129883 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),((Microbat,Megabat),(Horse,(Dog,Cat)))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +16 50000 8 5.696374893188477 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),((Microbat,Megabat),(Horse,(Dog,Cat)))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +16 75000 8 6.847574472427368 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),((Microbat,Megabat),(Horse,(Dog,Cat)))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +16 100000 8 7.806044578552246 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),((Horse,(Dog,Cat)),(Microbat,Megabat))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +17 100 8 4.471745014190674 (Platypus,(Wallaby,(Hedgehog,(Tenrec,(Shrew,((Rat,Mouse),((Pika,Rabbit),((Elephant,Hyrax),(Bushbaby,((Alpaca,(Cow,(Dolphin,Pig))),(Tarsier,(((Dog,Cat),(Megabat,(Microbat,Horse))),(Armadillo,(Squirrel,(Sloth,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))))); +17 250 8 4.478341102600098 ((Hedgehog,(Tenrec,((Pika,Rabbit),(Shrew,((Rat,Mouse),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),((Microbat,Horse),(Megabat,(Dog,Cat)))),(Squirrel,(Tarsier,(Bushbaby,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))),(Platypus,Wallaby)); +17 500 8 4.328781366348267 ((Tenrec,(Shrew,(Hedgehog,((Rat,Mouse),((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(Squirrel,((Alpaca,(Pig,(Cow,Dolphin))),((Tarsier,(Horse,(Bushbaby,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))),((Dog,Cat),(Microbat,Megabat)))))))))))),(Platypus,Wallaby)); +17 750 8 4.427735805511475 (Platypus,(Wallaby,(Tenrec,(Shrew,((Rat,Mouse),(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(Squirrel,((Tarsier,(Bushbaby,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human))))))),((Alpaca,(Cow,(Dolphin,Pig))),(Microbat,(Megabat,(Horse,(Dog,Cat)))))))))))))))); +17 1000 8 4.642756938934326 (Platypus,(Wallaby,(Pika,((Rat,Mouse),(Tenrec,(Hedgehog,(Shrew,((Elephant,Hyrax),((Armadillo,Sloth),(Rabbit,(Squirrel,((Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human))))))),((Alpaca,(Pig,(Cow,Dolphin))),((Horse,(Dog,Cat)),(Microbat,Megabat))))))))))))))); +17 2500 8 4.672106027603149 (Platypus,(Wallaby,(Tenrec,((Rat,Mouse),(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(Squirrel,((Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human))))))),((Alpaca,(Pig,(Cow,Dolphin))),((Horse,(Dog,Cat)),(Microbat,Megabat)))))))))))))); +17 5000 8 4.582099914550781 (Platypus,(Wallaby,(Tenrec,((Rat,Mouse),(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),(Microbat,(Megabat,(Horse,(Dog,Cat))))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +17 7500 8 4.508077144622803 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),(Microbat,((Dog,Cat),(Megabat,Horse)))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +17 10000 8 4.642721176147461 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),((Horse,(Dog,Cat)),(Microbat,Megabat))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +17 25000 8 5.2407310009002686 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),((Horse,(Dog,Cat)),(Microbat,Megabat))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +17 50000 8 6.153024196624756 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),((Horse,(Dog,Cat)),(Microbat,Megabat))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +17 75000 8 7.238961696624756 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),((Microbat,Megabat),(Horse,(Dog,Cat)))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +17 100000 8 7.233076810836792 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),((Microbat,Megabat),(Horse,(Dog,Cat)))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +18 100 8 4.307050943374634 ((Platypus,Wallaby),(Tenrec,(Pika,((Rat,Mouse),(Shrew,(Hedgehog,(Hyrax,(Bushbaby,(((Pig,(Cow,(Alpaca,Dolphin))),(Microbat,Megabat)),((Dog,Cat),((Sloth,(Elephant,(Armadillo,Squirrel))),(Rabbit,(Tarsier,(Horse,(Macaque,(Marmoset,(Orangutan,(Gorilla,(Chimp,Human))))))))))))))))))); +18 250 8 4.502177476882935 ((Platypus,Wallaby),(Tenrec,(Pika,((Rat,Mouse),(Shrew,(Hedgehog,(((Tarsier,Bushbaby),((Squirrel,(Rabbit,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human))))))),((Microbat,Megabat),((Dog,Cat),(Alpaca,((Cow,Dolphin),(Horse,Pig))))))),((Armadillo,Sloth),(Elephant,Hyrax))))))))); +18 500 8 4.492775917053223 (Platypus,(Wallaby,(Tenrec,((Rat,Mouse),(Shrew,((Pika,Rabbit),(Hedgehog,((Elephant,Hyrax),((Armadillo,Sloth),(Bushbaby,(((Pig,(Alpaca,(Cow,Dolphin))),((Dog,Cat),(Megabat,(Microbat,Horse)))),(Squirrel,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +18 750 8 4.559913635253906 (Platypus,(Wallaby,((Rat,Mouse),(Pika,(Tenrec,(Shrew,(Hedgehog,((Elephant,Hyrax),(Rabbit,((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),((Dog,Cat),(Microbat,(Megabat,Horse)))),(Bushbaby,(Squirrel,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human))))))))))))))))))); +18 1000 8 4.451488256454468 (Platypus,(Wallaby,((Rat,Mouse),(Pika,(Tenrec,(Shrew,(Hedgehog,((Elephant,Hyrax),((Armadillo,Sloth),(Rabbit,(((Cow,(Alpaca,(Dolphin,Pig))),((Dog,Cat),(Microbat,(Megabat,Horse)))),(Bushbaby,(Tarsier,(Squirrel,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human))))))))))))))))))); +18 2500 8 4.438739538192749 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),((Microbat,Megabat),(Horse,(Dog,Cat)))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +18 5000 8 4.745427846908569 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),((Microbat,Megabat),(Horse,(Dog,Cat)))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +18 7500 8 4.72693395614624 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),((Dog,Cat),(Microbat,(Megabat,Horse)))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +18 10000 8 4.8844969272613525 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),((Dog,Cat),(Microbat,(Megabat,Horse)))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +18 25000 8 5.502027988433838 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),(Microbat,((Dog,Cat),(Megabat,Horse)))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +18 50000 8 6.14923620223999 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),(Microbat,((Dog,Cat),(Megabat,Horse)))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +18 75000 8 6.476955890655518 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),((Horse,(Dog,Cat)),(Microbat,Megabat))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +18 100000 8 7.188019514083862 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),((Horse,(Dog,Cat)),(Microbat,Megabat))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +19 100 8 4.483212947845459 (Wallaby,(Platypus,(Hedgehog,(Shrew,((Pika,Rabbit),((Rat,Mouse),(Tenrec,(((Squirrel,Dog),((Microbat,Megabat),((Horse,((Alpaca,Pig),(Cow,Dolphin))),((Elephant,(Tarsier,Bushbaby)),(Cat,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))),(Hyrax,(Armadillo,Sloth)))))))))); +19 250 8 4.2962260246276855 ((Hedgehog,((Rat,Mouse),(Shrew,(Tenrec,((Pika,Rabbit),((Microbat,((Alpaca,(Cow,(Dolphin,Pig))),((Horse,(Megabat,(Dog,Cat))),((Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human))))),(Squirrel,(Tarsier,Bushbaby)))))),((Armadillo,Sloth),(Elephant,Hyrax)))))))),(Platypus,Wallaby)); +19 500 8 4.342597246170044 ((Hedgehog,((Rat,Mouse),(Shrew,(Tenrec,((Pika,Rabbit),(((Squirrel,((Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human))))),(Tarsier,Bushbaby))),(Microbat,((Alpaca,(Cow,(Dolphin,Pig))),((Dog,Cat),(Megabat,Horse))))),((Armadillo,Sloth),(Elephant,Hyrax)))))))),(Platypus,Wallaby)); +19 750 8 4.277270555496216 ((Hedgehog,((Rat,Mouse),(Shrew,(Tenrec,((Pika,Rabbit),(((Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))),((Microbat,Megabat),((Dog,Cat),(Cow,(Alpaca,(Horse,(Dolphin,Pig))))))),((Elephant,Hyrax),(Armadillo,Sloth)))))))),(Platypus,Wallaby)); +19 1000 8 4.3547587394714355 (((Rat,Mouse),(Hedgehog,(Tenrec,(Shrew,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),((Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))),((Microbat,Megabat),((Dog,Cat),(Cow,(Alpaca,(Horse,(Dolphin,Pig)))))))))))))),(Platypus,Wallaby)); +19 2500 8 4.443521022796631 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Hedgehog,(Shrew,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),((Horse,(Dog,Cat)),(Microbat,Megabat))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +19 5000 8 4.53224515914917 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),((Horse,(Dog,Cat)),(Microbat,Megabat))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +19 7500 8 4.6904284954071045 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),((Microbat,Megabat),(Horse,(Dog,Cat)))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +19 10000 8 5.4336042404174805 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),((Microbat,Megabat),(Horse,(Dog,Cat)))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +19 25000 8 5.360120534896851 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),((Microbat,Megabat),(Horse,(Dog,Cat)))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +19 50000 8 5.782797336578369 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),(Microbat,((Dog,Cat),(Megabat,Horse)))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +19 75000 8 6.516418933868408 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),(Microbat,((Dog,Cat),(Megabat,Horse)))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +19 100000 8 7.3305933475494385 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),((Horse,(Dog,Cat)),(Microbat,Megabat))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +20 100 8 4.428686141967773 ((Platypus,Wallaby),((Hyrax,Tenrec),((Rat,Mouse),(Shrew,(Hedgehog,((Pika,Rabbit),((Armadillo,Sloth),(Elephant,(Tarsier,((((Cow,Dolphin),(Pig,(Dog,Cat))),(Alpaca,(Megabat,Horse))),(Microbat,(Squirrel,(Bushbaby,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +20 250 8 4.334704399108887 ((Tenrec,((Rat,Mouse),((Elephant,Hyrax),(((Pika,Rabbit),((Squirrel,(Armadillo,Sloth)),((Alpaca,(Pig,(Cow,Dolphin))),(Microbat,((Megabat,Horse),(Tarsier,((Dog,Cat),(Bushbaby,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human))))))))))))),(Hedgehog,Shrew))))),(Platypus,Wallaby)); +20 500 8 4.309459209442139 ((Tenrec,((Rat,Mouse),(((Elephant,Hyrax),((Pika,Rabbit),((Microbat,((Alpaca,(((Dog,Cat),(Megabat,Horse)),(Pig,(Cow,Dolphin)))),(Tarsier,(Bushbaby,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human))))))))),(Squirrel,(Armadillo,Sloth))))),(Hedgehog,Shrew)))),(Platypus,Wallaby)); +20 750 8 4.282939910888672 ((Tenrec,((Rat,Mouse),(Hedgehog,(Shrew,((Pika,Rabbit),((Elephant,Hyrax),(((Tarsier,(Bushbaby,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human))))))),(Microbat,((Megabat,(Horse,(Dog,Cat))),((Cow,Dolphin),(Alpaca,Pig))))),(Squirrel,(Armadillo,Sloth))))))))),(Platypus,Wallaby)); +20 1000 8 4.337608575820923 ((Tenrec,((Rat,Mouse),(Hedgehog,(Shrew,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(Squirrel,((Tarsier,(Bushbaby,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human))))))),(((Cow,Dolphin),(Alpaca,Pig)),(Microbat,(Megabat,(Horse,(Dog,Cat)))))))))))))),(Platypus,Wallaby)); +20 2500 8 4.430898666381836 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(Squirrel,(((Pig,(Alpaca,(Cow,Dolphin))),(Microbat,((Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human))))),(Megabat,(Horse,(Dog,Cat)))))),(Tarsier,Bushbaby)))))))))))); +20 5000 8 4.736349105834961 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),(Microbat,(Megabat,(Horse,(Dog,Cat))))),(Squirrel,(Tarsier,(Bushbaby,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +20 7500 8 5.407204627990723 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),((Horse,(Dog,Cat)),(Microbat,Megabat))),(Squirrel,(Tarsier,(Bushbaby,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +20 10000 8 4.80083703994751 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),((Horse,(Dog,Cat)),(Microbat,Megabat))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +20 25000 8 5.455485582351685 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),((Horse,(Dog,Cat)),(Microbat,Megabat))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +20 50000 8 6.0050578117370605 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),((Horse,(Dog,Cat)),(Microbat,Megabat))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +20 75000 8 6.727431297302246 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),((Horse,(Dog,Cat)),(Microbat,Megabat))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); +20 100000 8 7.562861442565918 (Platypus,(Wallaby,((Rat,Mouse),(Tenrec,(Shrew,(Hedgehog,((Pika,Rabbit),((Elephant,Hyrax),((Armadillo,Sloth),(((Alpaca,(Pig,(Cow,Dolphin))),((Horse,(Dog,Cat)),(Microbat,Megabat))),(Squirrel,(Bushbaby,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Chimp,Human)))))))))))))))))); diff --git a/paper/nbks/ctree/out/results_with_ls.tsv b/paper/nbks/ctree/out/results_with_ls.tsv new file mode 100644 index 0000000..9986adc --- /dev/null +++ b/paper/nbks/ctree/out/results_with_ls.tsv @@ -0,0 +1,221 @@ +4 100 8 4.2377097606658936 -2623556.83509 (Platypus:0.1949835741,Wallaby:0.1198371913,(Elephant:0.0412968214,(Hyrax:0.0639512545,(Tenrec:0.0964437211,(Armadillo:0.0737116854,(Sloth:0.0573007218,(Pika:0.0945153998,(Rabbit:0.0738814197,(Squirrel:0.0585943549,(Rat:0.1062475093,(Mouse:0.1025639417,(Chimp:0.0044275129,(Human:0.0033532574,(Gorilla:0.0045537614,(Orangutan:0.0083777414,(Macaque:0.0162751686,(Marmoset:0.0264481813,(Tarsier:0.0644538594,(Bushbaby:0.0709996333,(Hedgehog:0.0935309523,(Shrew:0.1124474979,(Microbat:0.0661237808,(Megabat:0.0489362871,(Horse:0.051557288,(Alpaca:0.0486803247,(Cow:0.0580120257,(Dolphin:0.0346033558,(Pig:0.0491097786,(Dog:0.0468917073,Cat:0.0354553176):0.02760248538):0.002154724833):0.0004209772826):0.003500642909):0.01181708236):0.003495767344):0.001529826455):0.004749537296):0.002289592327):0.01549156783):0.005226996913):0.02148191305):0.00734992778):0.004711769528):0.003309634113):0.0004666048719):0.0001995485506):0.05488711221):8.047662332e-07):0.01126693151):0.01378868525):0.001730552682):0.02173780589):0.001417993921):0.0198039565):0.008608329148):0.002044841812):0.09201180224); 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(Platypus:0.1949835741,Wallaby:0.1198371913,(Elephant:0.0412968214,(Hyrax:0.0639512545,(Tenrec:0.0964437211,(Armadillo:0.0737116854,(Sloth:0.0573007218,(Pika:0.0945153998,(Rabbit:0.0738814197,(Squirrel:0.0585943549,(Rat:0.1062475093,(Mouse:0.1025639417,(Chimp:0.0044275129,(Human:0.0033532574,(Gorilla:0.0045537614,(Orangutan:0.0083777414,(Macaque:0.0162751686,(Marmoset:0.0264481813,(Tarsier:0.0644538594,(Bushbaby:0.0709996333,(Hedgehog:0.0935309523,(Shrew:0.1124474979,(Microbat:0.0661237808,(Megabat:0.0489362871,(Horse:0.051557288,(Alpaca:0.0486803247,(Cow:0.0580120257,(Dolphin:0.0346033558,(Pig:0.0491097786,(Dog:0.0468917073,Cat:0.0354553176):0.02760248538):0.002154724833):0.0004209772826):0.003500642909):0.01181708236):0.003495767344):0.001529826455):0.004749537296):0.002289592327):0.01549156783):0.005226996913):0.02148191305):0.00734992778):0.004711769528):0.003309634113):0.0004666048719):0.0001995485506):0.05488711221):8.047662332e-07):0.01126693151):0.01378868525):0.001730552682):0.02173780589):0.001417993921):0.0198039565):0.008608329148):0.002044841812):0.09201180224); 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(Platypus:0.1949835741,Wallaby:0.1198371913,(Elephant:0.0412968214,(Hyrax:0.0639512545,(Tenrec:0.0964437211,(Armadillo:0.0737116854,(Sloth:0.0573007218,(Pika:0.0945153998,(Rabbit:0.0738814197,(Squirrel:0.0585943549,(Rat:0.1062475093,(Mouse:0.1025639417,(Chimp:0.0044275129,(Human:0.0033532574,(Gorilla:0.0045537614,(Orangutan:0.0083777414,(Macaque:0.0162751686,(Marmoset:0.0264481813,(Tarsier:0.0644538594,(Bushbaby:0.0709996333,(Hedgehog:0.0935309523,(Shrew:0.1124474979,(Microbat:0.0661237808,(Megabat:0.0489362871,(Horse:0.051557288,(Alpaca:0.0486803247,(Cow:0.0580120257,(Dolphin:0.0346033558,(Pig:0.0491097786,(Dog:0.0468917073,Cat:0.0354553176):0.02760248538):0.002154724833):0.0004209772826):0.003500642909):0.01181708236):0.003495767344):0.001529826455):0.004749537296):0.002289592327):0.01549156783):0.005226996913):0.02148191305):0.00734992778):0.004711769528):0.003309634113):0.0004666048719):0.0001995485506):0.05488711221):8.047662332e-07):0.01126693151):0.01378868525):0.001730552682):0.02173780589):0.001417993921):0.0198039565):0.008608329148):0.002044841812):0.09201180224); +4 750 8 4.448108911514282 -2623556.83509 (Platypus:0.1949835741,Wallaby:0.1198371913,(Elephant:0.0412968214,(Hyrax:0.0639512545,(Tenrec:0.0964437211,(Armadillo:0.0737116854,(Sloth:0.0573007218,(Pika:0.0945153998,(Rabbit:0.0738814197,(Squirrel:0.0585943549,(Rat:0.1062475093,(Mouse:0.1025639417,(Chimp:0.0044275129,(Human:0.0033532574,(Gorilla:0.0045537614,(Orangutan:0.0083777414,(Macaque:0.0162751686,(Marmoset:0.0264481813,(Tarsier:0.0644538594,(Bushbaby:0.0709996333,(Hedgehog:0.0935309523,(Shrew:0.1124474979,(Microbat:0.0661237808,(Megabat:0.0489362871,(Horse:0.051557288,(Alpaca:0.0486803247,(Cow:0.0580120257,(Dolphin:0.0346033558,(Pig:0.0491097786,(Dog:0.0468917073,Cat:0.0354553176):0.02760248538):0.002154724833):0.0004209772826):0.003500642909):0.01181708236):0.003495767344):0.001529826455):0.004749537296):0.002289592327):0.01549156783):0.005226996913):0.02148191305):0.00734992778):0.004711769528):0.003309634113):0.0004666048719):0.0001995485506):0.05488711221):8.047662332e-07):0.01126693151):0.01378868525):0.001730552682):0.02173780589):0.001417993921):0.0198039565):0.008608329148):0.002044841812):0.09201180224); 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(Platypus:0.1949835741,Wallaby:0.1198371913,(Elephant:0.0412968214,(Hyrax:0.0639512545,(Tenrec:0.0964437211,(Armadillo:0.0737116854,(Sloth:0.0573007218,(Pika:0.0945153998,(Rabbit:0.0738814197,(Squirrel:0.0585943549,(Rat:0.1062475093,(Mouse:0.1025639417,(Chimp:0.0044275129,(Human:0.0033532574,(Gorilla:0.0045537614,(Orangutan:0.0083777414,(Macaque:0.0162751686,(Marmoset:0.0264481813,(Tarsier:0.0644538594,(Bushbaby:0.0709996333,(Hedgehog:0.0935309523,(Shrew:0.1124474979,(Microbat:0.0661237808,(Megabat:0.0489362871,(Horse:0.051557288,(Alpaca:0.0486803247,(Cow:0.0580120257,(Dolphin:0.0346033558,(Pig:0.0491097786,(Dog:0.0468917073,Cat:0.0354553176):0.02760248538):0.002154724833):0.0004209772826):0.003500642909):0.01181708236):0.003495767344):0.001529826455):0.004749537296):0.002289592327):0.01549156783):0.005226996913):0.02148191305):0.00734992778):0.004711769528):0.003309634113):0.0004666048719):0.0001995485506):0.05488711221):8.047662332e-07):0.01126693151):0.01378868525):0.001730552682):0.02173780589):0.001417993921):0.0198039565):0.008608329148):0.002044841812):0.09201180224); 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(Platypus:0.1949835741,Wallaby:0.1198371913,(Elephant:0.0412968214,(Hyrax:0.0639512545,(Tenrec:0.0964437211,(Armadillo:0.0737116854,(Sloth:0.0573007218,(Pika:0.0945153998,(Rabbit:0.0738814197,(Squirrel:0.0585943549,(Rat:0.1062475093,(Mouse:0.1025639417,(Chimp:0.0044275129,(Human:0.0033532574,(Gorilla:0.0045537614,(Orangutan:0.0083777414,(Macaque:0.0162751686,(Marmoset:0.0264481813,(Tarsier:0.0644538594,(Bushbaby:0.0709996333,(Hedgehog:0.0935309523,(Shrew:0.1124474979,(Microbat:0.0661237808,(Megabat:0.0489362871,(Horse:0.051557288,(Alpaca:0.0486803247,(Cow:0.0580120257,(Dolphin:0.0346033558,(Pig:0.0491097786,(Dog:0.0468917073,Cat:0.0354553176):0.02760248538):0.002154724833):0.0004209772826):0.003500642909):0.01181708236):0.003495767344):0.001529826455):0.004749537296):0.002289592327):0.01549156783):0.005226996913):0.02148191305):0.00734992778):0.004711769528):0.003309634113):0.0004666048719):0.0001995485506):0.05488711221):8.047662332e-07):0.01126693151):0.01378868525):0.001730552682):0.02173780589):0.001417993921):0.0198039565):0.008608329148):0.002044841812):0.09201180224); 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(Platypus:0.1949835741,Wallaby:0.1198371913,(Elephant:0.0412968214,(Hyrax:0.0639512545,(Tenrec:0.0964437211,(Armadillo:0.0737116854,(Sloth:0.0573007218,(Pika:0.0945153998,(Rabbit:0.0738814197,(Squirrel:0.0585943549,(Rat:0.1062475093,(Mouse:0.1025639417,(Chimp:0.0044275129,(Human:0.0033532574,(Gorilla:0.0045537614,(Orangutan:0.0083777414,(Macaque:0.0162751686,(Marmoset:0.0264481813,(Tarsier:0.0644538594,(Bushbaby:0.0709996333,(Hedgehog:0.0935309523,(Shrew:0.1124474979,(Microbat:0.0661237808,(Megabat:0.0489362871,(Horse:0.051557288,(Alpaca:0.0486803247,(Cow:0.0580120257,(Dolphin:0.0346033558,(Pig:0.0491097786,(Dog:0.0468917073,Cat:0.0354553176):0.02760248538):0.002154724833):0.0004209772826):0.003500642909):0.01181708236):0.003495767344):0.001529826455):0.004749537296):0.002289592327):0.01549156783):0.005226996913):0.02148191305):0.00734992778):0.004711769528):0.003309634113):0.0004666048719):0.0001995485506):0.05488711221):8.047662332e-07):0.01126693151):0.01378868525):0.001730552682):0.02173780589):0.001417993921):0.0198039565):0.008608329148):0.002044841812):0.09201180224); 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(Platypus:0.1949835741,Wallaby:0.1198371913,(Elephant:0.0412968214,(Hyrax:0.0639512545,(Tenrec:0.0964437211,(Armadillo:0.0737116854,(Sloth:0.0573007218,(Pika:0.0945153998,(Rabbit:0.0738814197,(Squirrel:0.0585943549,(Rat:0.1062475093,(Mouse:0.1025639417,(Chimp:0.0044275129,(Human:0.0033532574,(Gorilla:0.0045537614,(Orangutan:0.0083777414,(Macaque:0.0162751686,(Marmoset:0.0264481813,(Tarsier:0.0644538594,(Bushbaby:0.0709996333,(Hedgehog:0.0935309523,(Shrew:0.1124474979,(Microbat:0.0661237808,(Megabat:0.0489362871,(Horse:0.051557288,(Alpaca:0.0486803247,(Cow:0.0580120257,(Dolphin:0.0346033558,(Pig:0.0491097786,(Dog:0.0468917073,Cat:0.0354553176):0.02760248538):0.002154724833):0.0004209772826):0.003500642909):0.01181708236):0.003495767344):0.001529826455):0.004749537296):0.002289592327):0.01549156783):0.005226996913):0.02148191305):0.00734992778):0.004711769528):0.003309634113):0.0004666048719):0.0001995485506):0.05488711221):8.047662332e-07):0.01126693151):0.01378868525):0.001730552682):0.02173780589):0.001417993921):0.0198039565):0.008608329148):0.002044841812):0.09201180224); 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(Platypus:0.1950901956,Wallaby:0.1202588206,(((((((Elephant:0.036564036,Hyrax:0.0606569671):0.03313519697,((Armadillo:0.0600665231,Sloth:0.0443293244):0.02691577674,((Squirrel:0.0659385402,(((((((Chimp:0.0042501113,Human:0.0033991342):0.0006171303222,Gorilla:0.0042816665):0.003461431286,Orangutan:0.0084593937):0.004720102935,Macaque:0.0162591997):0.007373794652,Marmoset:0.0263877771):0.02156123661,Tarsier:0.0647825226):0.004399284853,Bushbaby:0.0726047727):0.005954820625):0.007860931412,(((Microbat:0.0589380936,Megabat:0.0402617412):0.01201073585,(Horse:0.0518038903,(Dog:0.0469828901,Cat:0.0355999414):0.01796813044):0.002218184048):0.001600678674,(Alpaca:0.047257951,((Cow:0.0496001468,Dolphin:0.0267901269):0.0077788327,Pig:0.047052511):0.004389736894):0.01471733223):0.01092157897):0.003661143124):0.002302980837):0.004972467407,(Pika:0.0720272954,Rabbit:0.0483049044):0.03929006345):0.004696362268,Hedgehog:0.0939886676):0.003322347909,Shrew:0.1106092106):0.005308560413,Tenrec:0.1075698502):0.01377091196,(Rat:0.0410146681,Mouse:0.0335711713):0.07939141915):0.07681137058); +20 50000 8 6.0050578117370605 -2523483.69196 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+20 100000 8 7.562861442565918 -2523483.69196 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diff --git a/paper/nbks/get_data_sets.py b/paper/nbks/get_data_sets.py new file mode 100644 index 0000000..bda0998 --- /dev/null +++ b/paper/nbks/get_data_sets.py @@ -0,0 +1,70 @@ +import pathlib +import re +import sys +import zipfile + +import requests +from rich import progress as rich_progress + +DATA_DIR = pathlib.Path(__file__).parent.parent / "data" + +if not DATA_DIR.exists(): + if DATA_DIR.parent.name != "paper": + print( + f"cannot find the data directory {DATA_DIR}, this file belongs in the `paper/nbks` directory", + ) + sys.exit(1) + else: + DATA_DIR.mkdir() + +_filename = re.compile(r"/([^/]+\.[^/?]+)(?:\?|$)") + + +def extract_zip(zip_file_path, extract_dir): + """ + Extracts the contents of a ZIP archive to a specified directory. + + Args: + zip_file_path (str): Path to the ZIP archive file. + extract_dir (str): Directory to extract the contents to. + """ + with zipfile.ZipFile(zip_file_path, "r") as zip_ref: + zip_ref.extractall(extract_dir) + + +def fetch_data_sets(): + urls = [ + # the following two are required for checking the dvs prep command + # "https://zenodo.org/records/14052787/files/refsoil.zip?download=1", + # "https://zenodo.org/records/14052787/files/wol.zip?download=1", + # the following is required for the tree-based comparisons + "https://zenodo.org/records/14052787/files/mammals-aligned.zip?download=1", + # the following files are required for checking performance against large + # data set sizes. They are around(1GB and 9GB) and can take some time to download! + # you can grab them directly from zenodo and place into the paper/data directory + # "https://zenodo.org/records/14052787/files/soil.dvseqs?download=1", + # "https://zenodo.org/records/14052787/files/wol.dvseqs?download=1", + ] + block_size = 4096 + + with rich_progress.Progress() as progress: + get_url = progress.add_task("Downloading data sets", total=len(urls)) + for url in urls: + name = _filename.search(url).group(1) + dest = DATA_DIR / name + response = requests.get(url, stream=True) + + total_size = int(response.headers.get("content-length", 0)) + get_content = progress.add_task(f"Getting {name}", total=total_size) + + with dest.open("wb") as out: + for data in response.iter_content(block_size): + progress.update(get_content, advance=len(data), refresh=True) + out.write(data) + progress.remove_task(get_content) + progress.update(get_url, advance=1, refresh=True) + if name.endswith(".zip"): + extract_zip(dest, DATA_DIR) + + +fetch_data_sets() diff --git a/paper/nbks/jsd_v_dist.ipynb b/paper/nbks/jsd_v_dist.ipynb new file mode 100644 index 0000000..2369345 --- /dev/null +++ b/paper/nbks/jsd_v_dist.ipynb @@ -0,0 +1,286 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Sequences selected by divergent exhibit large conventional genetic distance\n", + "\n", + "We test the expectation that the minimum pairwise genetic distance sequences between sequences selected by divergent max is greater than the corresponding minimum between a random selection of the same number of sequences.\n", + "\n", + "The data was from alignments of 106 genes from exactly 31 diverse mammal species. The matrix of all pairwise genetic distances were computed between the aligned sequences using the Paralinear distance of Lake. If the JSD measure reflects genetic distance, we expect the mean of the pairwise distances between the species identified by `divergent max` to be larger than the same number of species selected randomly. We set a minimal set size of 5 and maximal set size of 10." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import plotly.express as px\n", + "import project_path\n", + "from cogent3 import load_table\n", + "from plotly.subplots import make_subplots\n", + "\n", + "write_pdf = project_path.pdf_writer()\n", + "\n", + "# style settings\n", + "cols = {\n", + " \"stdev\": dict(color=\"blue\"),\n", + " \"cov\": dict(color=\"red\"),\n", + " \"JSD\": dict(color=\"cyan\"),\n", + "}\n", + "opacity = 0.6\n", + "marker = dict(size=18, opacity=opacity)\n", + "d_jsd = \"δJSD\"\n", + "stat_names = dict(stdev=f\"std({d_jsd})\", cov=f\"cov({d_jsd})\", JSD=r\"JSD(F)\")\n", + "tickfont = dict(size=20)\n", + "titlefont = dict(size=26)\n", + "legend = dict(title=dict(text=\"\"), font=dict(size=28), tracegroupgap=10)\n", + "\n", + "\n", + "table = load_table(project_path.RESULT_DIR / \"jsd_v_dist-stats.tsv\")\n", + "# eliminating rows where min_size == max_size\n", + "table = table.filtered(lambda x: len(set(x)) > 1, columns=[\"min_size\", \"max_size\"])\n", + "other = load_table(project_path.RESULT_DIR / \"jsd_v_dist-nmost.tsv\")\n", + "table = table.appended(None, other)\n", + "table" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The value of $k$ impacts on the dispersal of the selected sequences\n", + "\n", + "The performance of divergent `max` in recovering representative sequences is a function of $k$ and the selected statistic." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig_stat = px.scatter(\n", + " table,\n", + " x=\"k\",\n", + " y=\"(p-value<0.05)%\",\n", + " color=\"stat\",\n", + " trendline=\"lowess\",\n", + " labels={\n", + " \"k\": \"k\",\n", + " \"(p-value<0.05)%\": \"Significant %\",\n", + " \"fixed_size\": \"Fixed Set Size\",\n", + " },\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The distribution of the number of selected sequences\n", + "\n", + "Both $k$ and the statistic impact on the number of sequences selected by divergent `max`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import statsmodels.api as sm\n", + "from cogent3 import load_table, make_table\n", + "from plotly.graph_objects import Figure, Scatter\n", + "\n", + "\n", + "def make_trendline(x, y, name):\n", + " lowess = sm.nonparametric.lowess(x, y)\n", + " lowess_x = lowess[:, 0]\n", + " lowess_y = lowess[:, 1]\n", + " return Scatter(\n", + " x=lowess_x,\n", + " y=lowess_y,\n", + " mode=\"lines\",\n", + " name=name,\n", + " line=cols[name],\n", + " showlegend=False,\n", + " opacity=0.5,\n", + " )\n", + "\n", + "\n", + "def make_trace(table, name, fig):\n", + " st = Scatter(\n", + " x=table.columns[\"k\"],\n", + " y=table.columns[\"mean_size\"],\n", + " mode=\"markers\",\n", + " name=name,\n", + " error_y=dict(\n", + " type=\"data\",\n", + " array=table.columns[\"error_above\"].tolist(),\n", + " arrayminus=table.columns[\"error_below\"].tolist(),\n", + " ),\n", + " marker=cols[name] | marker,\n", + " )\n", + " fig.add_trace(st)\n", + " fig.add_trace(\n", + " make_trendline(table.columns[\"mean_size\"], table.columns[\"k\"], name),\n", + " )\n", + "\n", + "\n", + "def make_plot(table):\n", + " # this has to be done manually because plotly express has a\n", + " # bug with error bars and multiple traces\n", + " stdev = table.filtered(lambda x: x == \"stdev\", columns=\"stat\")\n", + " cov = table.filtered(lambda x: x == \"cov\", columns=\"stat\")\n", + " fig = Figure()\n", + " make_trace(stdev, \"stdev\", fig)\n", + " make_trace(cov, \"cov\", fig)\n", + " return fig\n", + "\n", + "\n", + "def calc_upper_lower_bounds(x, y, lower, upper):\n", + " assert lower <= x <= upper, f\"invalid {x=}, {lower=}, {upper=}\"\n", + " lo = x - y\n", + " hi = x + y\n", + " if lo >= lower and hi <= upper:\n", + " return y, y\n", + " if lo <= lower and hi >= upper:\n", + " return x - lower, upper - x\n", + " if lo < lower:\n", + " diff = x - lower\n", + " return x - lower, y - diff\n", + " diff = upper - x\n", + " return (y - diff), upper - x\n", + "\n", + "\n", + "def group_by(table, one_stat):\n", + " columns = [\"k\", \"min_size\", \"max_size\", \"stat\"]\n", + " distinct = table.distinct_values(columns)\n", + "\n", + " results = []\n", + " for group in distinct:\n", + " _, lower, upper, _ = group\n", + " subtable = table.filtered(lambda x: tuple(x) == group, columns=columns)\n", + " sizes = subtable.columns[\"size\"]\n", + " mean, std = sizes.mean(), sizes.std(ddof=1)\n", + " lo, hi = calc_upper_lower_bounds(mean, std, lower, upper)\n", + " results.append(list(group) + [mean, std, lo, hi, sizes.shape[0]])\n", + "\n", + " table = make_table(\n", + " header=columns + [\"mean_size\", \"std_size\", \"error_below\", \"error_above\", \"n\"],\n", + " rows=results,\n", + " )\n", + " table = table.sorted(columns=(\"stat\", \"k\"))\n", + " if one_stat:\n", + " table = table.filtered(lambda x: x == \"stdev\", columns=\"stat\")\n", + " return table\n", + "\n", + "\n", + "table = load_table(\n", + " project_path.RESULT_DIR / \"jsd_v_dist-sizes.tsv\",\n", + ").filtered(lambda x: x == 5, columns=\"min_size\")\n", + "table = group_by(table, one_stat=False)\n", + "fig_size = make_plot(table)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = make_subplots(rows=2, cols=1, shared_xaxes=True, vertical_spacing=0.05)\n", + "for trace in fig_stat.data:\n", + " fig.add_trace(trace, row=1, col=1)\n", + "\n", + "for trace in fig_size.data:\n", + " trace.showlegend = False\n", + " fig.add_trace(trace, row=2, col=1)\n", + "\n", + "fig.update_yaxes(title_text=\"Significant %\", row=1, col=1, title_font=titlefont)\n", + "fig.update_yaxes(\n", + " title_text=\"Mean Number Selected\",\n", + " row=2,\n", + " col=1,\n", + " title_font=titlefont,\n", + ")\n", + "fig.update_xaxes(title_text=\"k\", row=2, col=1, title_font=titlefont)\n", + "fig.update_layout(legend=legend, width=1600, height=1200)\n", + "fig.for_each_trace(lambda t: t.update(name=stat_names.get(t.name, t.name)))\n", + "fig.update_traces(\n", + " selector=dict(name=stat_names[\"cov\"]),\n", + " marker=cols[\"cov\"] | marker,\n", + " line=cols[\"cov\"],\n", + ")\n", + "fig.update_traces(\n", + " selector=dict(name=stat_names[\"stdev\"]),\n", + " marker=cols[\"stdev\"] | marker,\n", + " line=cols[\"stdev\"],\n", + ")\n", + "fig.update_traces(\n", + " selector=dict(name=stat_names[\"JSD\"]),\n", + " marker=cols[\"JSD\"] | marker,\n", + " line=cols[\"JSD\"],\n", + ")\n", + "fig.update_traces(opacity=opacity, selector=dict(mode=\"lines\"))\n", + "fig.update_traces(marker=marker, selector=dict(mode=\"markers\"))\n", + "fig.update_yaxes(tickfont=tickfont, row=1, col=1)\n", + "fig.update_yaxes(tickfont=tickfont, row=2, col=1)\n", + "fig.update_xaxes(tickfont=tickfont, row=2, col=1)\n", + "\n", + "x, y = 0.2, 1.05\n", + "fig.add_annotation(\n", + " xref=\"x1\",\n", + " yref=\"y1\",\n", + " x=x * 10,\n", + " y=y * 100,\n", + " text=\"(a)\",\n", + " showarrow=False,\n", + " row=1,\n", + " col=1,\n", + " font=titlefont,\n", + ")\n", + "\n", + "fig.add_annotation(\n", + " xref=\"paper\",\n", + " yref=\"paper\",\n", + " x=x * 10,\n", + " y=y * 30,\n", + " text=\"(b)\",\n", + " showarrow=False,\n", + " row=2,\n", + " col=1,\n", + " font=titlefont,\n", + ")\n", + "outpath = project_path.FIG_DIR / \"jsd_v_dist.pdf\"\n", + "fig.show()\n", + "# write_pdf(fig, outpath)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "dvgt", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.4" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/paper/nbks/jsd_v_dist.py b/paper/nbks/jsd_v_dist.py new file mode 100644 index 0000000..f01cd2f --- /dev/null +++ b/paper/nbks/jsd_v_dist.py @@ -0,0 +1,294 @@ +from itertools import product +from pathlib import Path + +import click +from cogent3 import make_table +from cogent3.app import io +from cogent3.app import typing as c3_types +from cogent3.app.composable import define_app +from numpy import array, isnan +from numpy.random import choice +from rich.progress import track +from scipy import special + +from diverse_seq import util as dvs_utils +from diverse_seq.records import dvs_max, dvs_nmost + + +def get_combinations(all_vals, choose, number): + """generates random combinations of indices that will be used to + obtain sets of names""" + indices = set() + interval = range(len(all_vals)) + max_size = special.binom(len(all_vals), choose) + number = min(number, max_size) + while len(indices) < number: + candidate = tuple(sorted(choice(interval, size=choose, replace=False))) + if candidate in indices: + continue + indices.add(candidate) + return [[all_vals[i] for i in combo] for combo in indices] + + +class min_dist: + """returns minimum pairwise genetic distance from a set of names""" + + def __init__(self, dists): + self.dists = dists + self.num = len(dists.names) + + def __call__(self, names: list[str]) -> float: + dists = self.dists.take_dists(names) + values = array([v for v in dists.to_dict().values() if not isnan(v)]) + return values.min() + + +@define_app +class compare_sets: + """compares the minimum genetic distance from a sets of sequences identified + by dvs_max against randomly drawn sets of the same size""" + + def __init__( + self, + *, + app, + dist_size: int = 1000, + ) -> None: + self.dist_size = dist_size + self.dvgt = app + + def main(self, aln: c3_types.AlignedSeqsType) -> dict: + calc_min = min_dist(aln.distance_matrix(calc="paralinear", drop_invalid=False)) + divergent = self.dvgt(aln.degap()) + num = divergent.num_seqs + combinations = get_combinations(aln.names, num, self.dist_size) + + stats = {"observed": [True], "mean(dist)": [calc_min(divergent.names)]} + for names in combinations: + stats["observed"].append(False) + stats["mean(dist)"].append(calc_min(names)) + + dists = stats["mean(dist)"] + obs = dists[0] + # we estimate the probability the minimum genetic distance from + # the num sequences identified by dvs_max equals that of a random + # draw of num sequences by counting the number of times the min distance from + # the latter is greater than or equal to the diverged set. + gt = sum(v >= obs for v in dists[1:]) + return { + "stats": stats, + "divergent": len(divergent.names), # size of the divergent set + "dist_size": len(combinations), + "num_gt": gt, + "source": aln.info.source, + } + + +def run_nmost( + store_path: Path, + k: int = 4, + size: int = 10, + dist_size: int = 1000, + limit: int | None = None, + serial: bool = False, +): + """runs a specific combination of parameters""" + with dvs_utils.keep_running(): + loader = io.load_aligned(moltype="dna") + app = dvs_nmost(k=k, n=size) + make_distn = compare_sets( + dist_size=dist_size, + app=app, + ) + + app = loader + make_distn + dstore = io.open_data_store(store_path, suffix="fa", limit=limit) + + results = [] + for result in app.as_completed(dstore, parallel=not serial): + if not result: + continue + result = result.obj # what get's returned is a source proxy object + result["pval"] = result["num_gt"] / result["dist_size"] + result["num_divergent"] = result.pop("divergent") + result.pop("stats") + results.append(result) + + return results + + +def run_max( + store_path: Path, + stat, + k: int = 4, + min_size: int = 7, + max_size: int = 10, + dist_size: int = 1000, + limit: int | None = None, + serial: bool = False, +): + """runs a specific combination of parameters""" + with dvs_utils.keep_running(): + loader = io.load_aligned(moltype="dna") + app = dvs_max( + k=k, + min_size=min_size, + max_size=max_size, + stat=stat, + ) + make_distn = compare_sets(app=app, dist_size=dist_size) + + app = loader + make_distn + dstore = io.open_data_store(store_path, suffix="fa", limit=limit) + + results = [] + for result in app.as_completed(dstore, parallel=not serial): + if not result: + continue + result = result.obj # what get's returned is a source proxy object + result["pval"] = result["num_gt"] / result["dist_size"] + result["num_divergent"] = result.pop("divergent") + result.pop("stats") + results.append(result) + + return results + + +_click_command_opts = dict( + no_args_is_help=True, + context_settings={"show_default": True}, +) + + +@click.group() +def main(): + """run experiments assessing dvgt""" + + +@main.command(**_click_command_opts) +@click.argument("data_path", type=Path) +@click.option( + "-os", + "--outpath_stats", + type=Path, + default=Path("jsd_v_dist-stats.tsv"), + help="writes stat output to this file", +) +@click.option( + "-oz", + "--outpath_sizes", + type=Path, + default=Path("jsd_v_dist-sizes.tsv"), + help="writes size data output to this file", +) +@click.option( + "-x", + "--max_k", + type=int, + default=11, + help="upper bound for k-mer size, not inclusive", +) +@click.option("-r", "--repeats", type=int, default=3, help="repeats per condition") +@click.option("-D", "--dry_run", is_flag=True, help="don't write output") +@click.option("-S", "--serial", is_flag=True, help="run on 1 CPU") +def max(data_path, outpath_stats, outpath_sizes, dry_run, max_k, serial, repeats): + """measures performance of dvgt max using a directory containg fasta + formatted alignment files of DNA sequences""" + settings = ( + list( + product( + range(2, max_k), # k values + (5, 10), # min_size values + (30,), # max_size value + ("stdev", "cov"), # stat values + ), + ) + * repeats + ) + + order = "k", "min_size", "max_size", "stat" + stat_results = [] + size_results = [] + for config in track(settings, description="Working on setting..."): + setting = dict(zip(order, config, strict=False)) + result = run_max(data_path, dist_size=1000, serial=serial, **setting) + size_results.extend([list(config) + [r["num_divergent"]] for r in result]) + + stat_results.append( + list(config) + [100 * sum(r["pval"] < 0.05 for r in result) / len(result)], + ) + + stat_table = make_table( + header=list(order) + ["(p-value<0.05)%"], + data=stat_results, + ) + print(stat_table) + size_table = make_table( + header=list(order) + ["size"], + data=size_results, + ) + print(size_table[:10]) + if not dry_run: + stat_table.write(outpath_stats) + size_table.write(outpath_sizes) + + +@main.command(**_click_command_opts) +@click.argument("data_path", type=Path) +@click.option( + "-os", + "--outpath_stats", + type=Path, + default=Path("jsd_v_dist-nmost.tsv"), + help="writes stat output to this file", +) +@click.option( + "-x", + "--max_k", + type=int, + default=11, + help="upper bound for k-mer size, not inclusive", +) +@click.option("-r", "--repeats", type=int, default=3, help="repeats per condition") +@click.option("-D", "--dry_run", is_flag=True, help="don't write output") +@click.option("-S", "--serial", is_flag=True, help="run on 1 CPU") +def nmost(data_path, outpath_stats, dry_run, max_k, serial, repeats): + """measures performance of dvgt nmost using a directory containg fasta + formatted alignment files of DNA sequences""" + settings = ( + list( + product( + range(2, max_k), # k values + (10,), # size value + ), + ) + * repeats + ) + + order = "k", "size" + stat_results = [] + for config in track(settings, description="Working on setting..."): + setting = dict(zip(order, config, strict=False)) + + result = run_nmost(data_path, dist_size=1000, serial=serial, **setting) + # the two sizes are there so this table can be merged with the one for max + size = setting.pop("size") + setting["min_size"] = setting["max_size"] = size + + stat_results.append( + list(config) + + [size, "JSD"] + + [100 * sum(r["pval"] < 0.05 for r in result) / len(result)], + ) + + stat_table = make_table( + header=["k", "min_size", "max_size", "stat"] + ["(p-value<0.05)%"], + data=stat_results, + ) + print(stat_table) + if not dry_run: + stat_table.write(outpath_stats) + + +if __name__ == "__main__": + main() diff --git a/paper/nbks/plugin_demo.ipynb b/paper/nbks/plugin_demo.ipynb new file mode 100644 index 0000000..b526842 --- /dev/null +++ b/paper/nbks/plugin_demo.ipynb @@ -0,0 +1,206 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We make apps available for use within cogent3. They are obtained by the standard mechanism for getting apps." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import project_path\n", + "from cogent3 import available_apps" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The available apps can be listed using the `available_apps()` function with the `name_filter` argument set to `\"dvs\"`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "available_apps(name_filter=\"dvs\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using a `dvs` app to select representative sequences\n", + "\n", + "You can get help using the Cogent 3 `app_help()` system." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from cogent3.app import app_help\n", + "\n", + "app_help(\"dvs_max\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can copy the vignette from the help display to create your app instance." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from cogent3.app import get_app\n", + "\n", + "app = get_app(\n", + " \"dvs_max\",\n", + " min_size=5,\n", + " max_size=40,\n", + " stat=\"stdev\",\n", + " moltype=\"dna\",\n", + " k=6,\n", + " seed=None,\n", + ")\n", + "app" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now load a single alignment from the included sample data and apply the Divergent MAX plug-in to those sequences after removing gaps. (This alignment was chosen because the estimated tree was not completely terrible!)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from cogent3 import get_app, open_data_store\n", + "\n", + "in_data = open_data_store(\n", + " project_path.DATA_DIR / \"mammals-aligned.zip\",\n", + " suffix=\"fa\",\n", + " mode=\"r\",\n", + ")\n", + "loader = get_app(\"load_aligned\", moltype=\"dna\")\n", + "aln = loader(in_data[64])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> **Note**\n", + "> Successive calls to the app can return different results as the sequence order is randomised." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "selected = app(aln)\n", + "selected" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We show how the sampled sequences are dispersed across the phylogeny by first estimating a tree using NJ based on the paralinear distance." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import project_path\n", + "\n", + "write_pdf = project_path.pdf_writer()\n", + "\n", + "dnd = (\n", + " aln.quick_tree(calc=\"paralinear\")\n", + " .rooted_with_tip(\"Platypus\")\n", + " .get_figure(width=1400, height=1600)\n", + ")\n", + "dnd.tip_font = dict(size=32, family=\"Inconsolata\")\n", + "dnd.label_pad = 0.003\n", + "dnd.line_width = 3\n", + "dnd.scale_bar = None\n", + "dnd.style_edges(edges=selected.names, line={\"color\": \"red\", \"width\": 3})\n", + "outpath = project_path.FIG_DIR / \"selected_edges.pdf\"\n", + "dnd.show()\n", + "# write_pdf(dnd.plotly_figure, outpath)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using ``a `dvs` app to estimate a phylogeny from unaligned sequences" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "app_help(\"dvs_ctree\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ctree = get_app(\"dvs_ctree\")\n", + "tree = ctree(aln)\n", + "tree.get_figure(width=700, height=800).show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "dvgt", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/paper/nbks/project_path.py b/paper/nbks/project_path.py new file mode 100644 index 0000000..844d3a7 --- /dev/null +++ b/paper/nbks/project_path.py @@ -0,0 +1,31 @@ +import pathlib +import time + +from plotly.io import write_image + +PAPER_DIR = pathlib.Path(__file__).parent.parent + +DATA_DIR = PAPER_DIR / "data" +RESULT_DIR = PAPER_DIR / "results" + +FIG_DIR = PAPER_DIR / "figs" +TABLE_DIR = PAPER_DIR / "tables" +FIG_DIR.mkdir(exist_ok=True) + + +class pdf_writer: + """class that handles super annoying mathjax warning box in plotly pdf's""" + + def __init__(self) -> None: + self._done_once = False + + def __call__(self, fig, path): + # the sleep, plus successive write, is ESSENTIAL to avoid the super annoying + # "[MathJax]/extensions/MathMenu.js" text box error + # but we only need to do this once + if not self._done_once: + write_image(fig, path) + time.sleep(2) + self._done_once = True + + write_image(fig, path) diff --git a/paper/nbks/synthetic_known.py b/paper/nbks/synthetic_known.py new file mode 100644 index 0000000..214abe0 --- /dev/null +++ b/paper/nbks/synthetic_known.py @@ -0,0 +1,174 @@ +from itertools import product + +import project_path +from cogent3 import make_table, make_unaligned_seqs +from cogent3.app import typing as c3_types +from cogent3.app.composable import NotCompleted, define_app +from numpy import array +from numpy.random import choice, shuffle +from rich.progress import track + +from diverse_seq import util as dvs_utils +from diverse_seq.record import KmerSeq, SeqArray, seqarray_to_kmerseq +from diverse_seq.records import max_divergent + +POOL = {"a": "ACGGGGGT", "b": "ACCCCCGT", "c": "AAAAACGT", "d": "ACGTTTTT"} +STATS = "stdev", "cov" + + +def seqs_from_pool(pool: str, num_seqs: int, seq_len: int) -> dict: + """generate synthetic sequences from a pool""" + return { + f"{pool}-{i}": "".join(choice(list(POOL[pool]), size=seq_len)) + for i in range(num_seqs) + } + + +@define_app +class make_sample: + def __init__(self, pool_sizes: dict, seq_len: int): + self.pool_sizes = pool_sizes + self.seq_len = seq_len + + def main(self, num: int) -> c3_types.UnalignedSeqsType: + seqs = {} + for pool, num_seqs in self.pool_sizes.items(): + seqs |= seqs_from_pool(pool, num_seqs, self.seq_len) + return make_unaligned_seqs(seqs, moltype="dna", source=f"rep-{num}") + + +@define_app +class seqcoll_to_records: + def __init__(self, k: int): + self.s2a = dvs_utils.str2arr(moltype="dna") + self.a2k = seqarray_to_kmerseq(k=k, moltype="dna") + + def main(self, seqs: c3_types.UnalignedSeqsType) -> list[KmerSeq]: + records = [] + for s in seqs.seqs: + sa = self.s2a(str(s)) + ks = self.a2k( + SeqArray(seqid=s.name, data=sa, moltype="dna", source=seqs.info.source), + ) + records.append(ks) + shuffle(records) + return records + + +@define_app +class true_positive: + def __init__( + self, + expected: set[str], + stat: str, + label_2_pool: callable = lambda x: x.split("-")[0], + size: int = 2, + ) -> None: + self.expected = expected + self.label2pool = label_2_pool + self.size = size + self.stat = stat + + def main(self, records: list[KmerSeq]) -> bool: + result = max_divergent( + records, + min_size=self.size, + stat=self.stat, + verbose=False, + max_set=True, + ) + if len(result.record_names) != len(self.expected): + return NotCompleted( + "FAIL", + self, + f"number of records {len(result.record_names)} != {len(self.expected)}", + ) + + found_pools = {self.label2pool(n) for n in result.record_names} + if self.expected != found_pools: + return NotCompleted( + "FAIL", + self, + f"found pool {found_pools} != {self.expected}", + ) + return True + + +def do_run(pool, num_reps, seq_len, k=3, stat="stdev"): + make_seqs = make_sample(pool, seq_len=seq_len) + s2r = seqcoll_to_records(k=k) + finds_true = true_positive(set(POOL), stat=stat, size=2) + app = make_seqs + s2r + finds_true + return list(map(app, range(num_reps))) + + +@define_app +class eval_condition: + def __init__(self, num_reps, k, repeats, pools) -> None: + self.num_reps = num_reps + self.k = k + self.repeats = repeats + self.pools = pools + + def main(self, settings: tuple[str, str, int]) -> c3_types.TabularType: + title, stat, seq_len = settings + pool = self.pools[title] + num_correct = [] + for _ in range(self.repeats): + r = do_run( + pool=pool, + num_reps=self.num_reps, + seq_len=seq_len, + k=self.k, + stat=stat, + ) + num_correct.append(self.num_reps - sum(r)) + + num_correct = array(num_correct) + mean = num_correct.mean() + stdev = num_correct.std(ddof=1) + return make_table( + header=[ + "pop_size", + "repeats", + "seq_len", + "stat", + "mean(correct)", + "stdv(correct)", + ], + data=[[self.num_reps, self.repeats, seq_len, stat, mean, stdev]], + title=title, + ) + + +def main(): + BALANCED = dict(a=25, b=25, c=25, d=25) + IMBALANCED = dict(a=1, b=49, c=25, d=25) + config = dict( + num_reps=50, + k=1, + repeats=5, + pools=dict(balanced=BALANCED, imbalanced=IMBALANCED), + ) + app = eval_condition(**config) + pools = "balanced", "imbalanced" + seq_len = 200, 1000 + + result_tables = [] + settings = list(product(pools, STATS, seq_len)) + for t in track(map(app, settings), total=len(settings)): + if not t: + print(t) + result_tables.append(t) + + table = result_tables[0].appended("pool", result_tables[1:]) + table = table.sorted(columns=["seq_len", "pool", "stat"]) + table.columns["correct%"] = ( + 100 * table.columns["mean(correct)"] / config["num_reps"] + ) + table.write(project_path.RESULT_DIR / "synthetic_known_summary.tsv") + print(table) + + +if __name__ == "__main__": + main() diff --git a/paper/paper.bib b/paper/paper.bib new file mode 100644 index 0000000..73b8f6c --- /dev/null +++ b/paper/paper.bib @@ -0,0 +1,239 @@ + +@article{balaban.2019.plosone, + author = {Balaban, Metin and Moshiri, Niema and Mai, Uyen and Jia, Xingfan and Mirarab, Siavash}, + doi = {10.1371/journal.pone.0221068}, + editor = {Bozdag, Serdar}, + issn = {1932-6203}, + journal = {PLOS ONE}, + language = {en}, + month = aug, + number = {8}, + pages = {e0221068}, + shorttitle = {{TreeCluster}}, + title = {{TreeCluster}: {Clustering} biological sequences using phylogenetic trees}, + url = {https://dx.plos.org/10.1371/journal.pone.0221068}, + urldate = {2024-08-14}, + volume = {14}, + year = {2019}} + 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Math. 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As the size of DNA sequence datasets continues to increase, there is a need for a tool that can effectively lessen the computational burden associated with this widely used analysis. + +`diverse-seq` implements computationally efficient alignment-free algorithms that enable efficient prototyping for phylogenetic workflows. It can accelerate parameter selection searches for sequence alignment and phylogeny estimation by identifying a subset of sequences that are representative of the diversity in a collection. We show that selecting representative sequences with an entropy measure of $k$-mer frequencies correspond well to sampling via conventional genetic distances. The computational performance is linear with respect to the number of sequences and can be run in parallel. Applied to a collection of 10.5k whole microbial genomes on a laptop took ~8 minutes to prepare the data and 4 minutes to select 100 representatives. `diverse-seq` can further boost the performance of phylogenetic estimation by providing a seed phylogeny that can be further refined by a more sophisticated algorithm. For ~1k whole microbial genomes on a laptop, it takes ~1.8 minutes to estimate a bifurcating tree from mash distances. + +The `diverse-seq` algorithms are not limited to homologous sequences. As such, they can improve the performance of other workflows. For instance, machine learning projects that involve non-homologous sequences can benefit as representative sampling can mitigate biases from imbalanced groups. + +`diverse-seq` is a BSD-3 licensed Python package that provides both a command-line interface and `cogent3` plugins. The latter simplifies integration by users into their own analyses. It is available via the Python Package Index and GitHub. + +# Statement of need + +Accurately selecting a representative subset of biological sequences can improve the statistical accuracy and computational performance of data sampling workflows. In many cases, the reliability of such analyses is contingent on the sample capturing the full diversity of the original collection [e.g. estimating large phylogenies @parks.2018.natbiotechnol; @zhu.2019.nat.commun]. Additionally, the computation time of algorithms reliant on numerical optimisation, such as phylogenetic estimation, can be markedly reduced by having a good initial estimate. + +Existing tools to the data sampling problem require input data in formats that themselves can be computationally costly to acquire. For instance, tree-based sequence selection procedures can be efficient, but they rely on a phylogenetic tree or a pairwise genetic distance matrix, both of which require alignment of homologous sequences [e.g. @widmann.2006.molcellproteomics; @balaban.2019.plosone]. Adding both the time for sequence alignment and tree estimation presents a barrier to their use. + +The `diverse-seq` sequence selection algorithms are linear in time for the number of sequences and more flexible than published approaches. While the algorithms do not require sequences to be homologous, when applied to homologous sequences, the set selected is comparable to what would be expected based on genetic distance. The `diverse-seq` clustering algorithm is linear in time for the combined sequence length. For homologous sequences, the estimated trees are approximations to that estimated from an alignment by IQ-TREE2 [@minh.2020.iq]. + +# Definitions + +A $k$-mer is a subsequence of length $k$, and the frequency of each $k$-mer in a sequence forms a $k$-mer probability vector. The Shannon entropy is a measure of "uncertainty" in a probability vector and is commonly used in sequence analysis [e.g. @schneider.1990.nucleicacidsres]. The Shannon entropy is calculated as $H=-\sum_i p_i \log_2 p_i$ where $p_i$ is the probability of the $i$-th $k$-mer. As an indication of the interpretability of Shannon entropy, a DNA sequence with equifrequent nucleotides has the maximum possible $H=2$ (high uncertainty) while a sequence with a single nucleotide has $H=0$ (no uncertainty). + +Shannon entropy is integral to other statistical measures that quantify uncertainty [@lin.1991.ieeetrans.inf.theory], including Jensen-Shannon divergence (JSD), which we employ in this work. As illustrated in Table 1, the magnitude of JSD reflects the level of relatedness amongst sequences via the similarity between their $k$-mer probability vectors. For a collection of $N$ DNA sequences $\mathbb{S}$, define $f_i$ as the $k$-mer frequency vector for sequence $s_i$. The JSD for the resulting set of vectors, $\mathbb{F}$, is + +\begin{equation*} +JSD(\mathbb{F})=H \left( \frac{1}{N}\sum_i^N f_i \right) - \overline{H(\mathbb{F})}, +\end{equation*} + +where the first term corresponds to the Shannon entropy of the mean of the $N$ probability vectors and the second term $\overline{H(\mathbb{F})}$ is the mean of their corresponding Shannon entropies. For vector $f_i \in \mathbb{F}$ its contribution to the total JSD of $\mathbb{F}$ is + +\begin{equation*} +\delta_{JSD}(i)=JSD(\mathbb{F})-JSD(\mathbb{F} - \{f_i\}). +\end{equation*}\label{eqn:delta-jsd} + +From the equation, it is apparent that to update the JSD of a collection efficiently, we need only track $k$-mer counts, total Shannon entropy and the number of sequences. Thus, the algorithm can be implemented with a single pass through the data. + +To facilitate the description below, we define the record with the minimum $\delta_{JSD}$ as $$lowest = \argmin_{i \in N} \delta_{JSD}(i).$$ + +# Algorithms + +*`dvs` subcommand: `prep` converts sequences into `numpy` arrays for faster processing.* + +The `prep` sub-command converts plain text sequence data into an on-disk storage format for efficient access in the other steps. A user can provide either fasta or GenBank formatted DNA sequence files. The sequences are converted into unsigned 8-bit integer `numpy` arrays and stored in a single HDF5 file on disk. The resulting `.dvseqs` file is required for all the sub-commands. + +## Selection of representative sequences + +*`dvs` subcommands: `nmost` samples the $n$ sequences that increase JSD most; `max` samples sequences that maximise a user specified statistic, either the standard deviation or the coefficient of variation of $\delta_{JSD}$.* + +The following optimisations have been employed to make the algorithm for computing the JSD scalable in terms of the number of sequences. + +1. Sequence data is BLOSC2 compressed as unsigned-8 bit integers and saved in HDF5 format on disk. +2. `numba`, a just-in-time compiler, is used for the core algorithms producing $k$-mers and their counts, providing a significant speed up over a pure python implementation [@numba]. +3. Sequence loading and $k$-mer counting is triggered when a sequence record is considered for inclusion in the divergent set, reducing the memory required to that for the user-nominated size. + +The `nmost` algorithm defines an exact number of sequences to be selected that maximise the JSD. The order of input sequences is randomised and the selected set is initialised with the first $n$ sequences. As shown in \autoref{algo:nmost}, for each of the remaining sequences, if adding it to the set $\mathbb{F} - \{lowest\}$ increases JSD, it replaces $lowest$. The `max` algorithm differs from `nmost` by defining lower and upper bounds for the number of sequences in the divergent set. It further amends the within-loop condition (\autoref{algo:max}), allowing the number of sequences in the set to change when a statistical measure of $\delta_{JSD}$ variance increases. We provide users a choice of two measures of variance in $\delta_{JSD}$: the standard deviation or the coefficient of variation. + +## Constructing a tree from $k$-mers + +*`dvs` subcommand: `ctree` estimates a phylogenetic tree from unaligned sequences using mash distances.* + +The mash distance [@ondov.2016.mash] estimates the proportion of changes between two sequences and can be computed in near linear time. It approximates the Jaccard distance between the $k$-mer sets of the two sequences (the proportion of shared $k$-mers) from a subset of all $k$-mers in the two sequences. This subset, the MinHash sketch, is the *sketch size* smallest $k$-mers when sorted by a hash function. We apply agglomerative clustering with average linkage [@murtagh.2012.algorithms] to the pairwise mash distances to estimate a phylogenetic tree from unaligned sequences. We allow the user to select the distance metric, $k$ and the sketch size. + +## `dvs` cogent3 apps + +We provide `dvs_nmost`, `dvs_max`, `dvs_ctree` and `dvs_par_ctree` as cogent3 apps. For users with cogent3 installed, these are available at runtime via the cogent3 function `get_app()`. The apps mirror the settings from their command-line implementation but differ in that they operate directly on a sequence collection, skipping conversion to disk storage. The `dvs_nmost` and `dvs_max` directly return the selected subset of sequences, `dvs_ctree` and `dvs_par_ctree` returns the estimated phylogenetic tree. The `dvs_par_ctree` app runs in parallel on a single alignment. The `dvs_max` and `dvs_ctree` apps are demonstrated in the `plugin_demo.ipynb` notebook. + +# Performance + +## Selection of representative sequences +### Recovery of representatives from synthetic knowns + +We evaluate the ability of `dvs max` to recover known divergent lineages using simulated data. The experimental design was intended to assess whether rare sequences can be recovered. We defined four distinct sequence compositions and two distinct "pool" compositions: *balanced*, in which each sequence family was present at equal frequency, or *imbalanced*, where one sequence occurred at 1%, another 49% and the remainder at 25% each. In each scenario, we simulated a total of 50 sequences. If `dvs max` identifies a set of precisely 4 sequences with one pool representative, this is counted as a success. As shown in \autoref{fig:synthetic-knowns}, the primary determinant of the success was the length of the simulated sequences. + +### The selected sequences are phylogenetically diverse + +For homologous DNA sequences, increasing the amount of elapsed time since they shared a common ancestor increases their genetic distance due to time-dependent accumulation of sequence changes. We expect that the JSD between two sequences will also increase proportional to the time since they last shared a common ancestor. We therefore pose the null hypothesis that if JSD is not informative, then the minimum pairwise genetic distance amongst $n$ sequences chosen by `diverse_seq` will be approximately equal to the minimum pairwise genetic distance between a random selection of $n$ sequences. Under the alternate hypothesis that JSD is informative, the minimum genetic distance between sequences chosen by `diverse_seq` will be larger than between randomly selected sequences. We test this hypothesis using a resampling statistic [@sokal.1995, 808], estimating the probability of the algorithmic choice being consistent with the null hypothesis. This probability is calculated as the proportion of 1000 randomly selected sets of sequences whose minimum genetic distance was greater or equal to that obtained from the sequences chosen by `dvs max`. We further summarised the performance of the `dvs` commands as the percentage of loci which gave a $p$-value less than 0.05. A bigger percentage is better. + +We addressed the above hypothesis using 106 alignments of protein coding DNA sequences from the following 31 mammals: Alpaca, Armadillo, Bushbaby, Cat, Chimp, Cow, Dog, Dolphin, Elephant, Gorilla, Hedgehog, Horse, Human, Hyrax, Macaque, Marmoset, Megabat, Microbat, Mouse, Orangutan, Pig, Pika, Platypus, Rabbit, Rat, Shrew, Sloth, Squirrel, Tarsier, Tenrec and Wallaby. The sequences were obtained from Ensembl.org [@harrison.2024.nucleicacidsresearch] and aligned using cogent3's codon aligner [@knight.2007.genomebiol]. The genetic distance between the sequences was calculated using the paralinear distance [@lake.1994.procnatlacadsciua]. + +The results of the analysis (\autoref{fig:jsd-v-dist}) indicated the sucess of `dvs max` in identifying genetically diverse sequences was principally sensitive to the choice of $k$. While \autoref{fig:jsd-v-dist}(a) showed close equivalence between the statistics, \autoref{fig:jsd-v-dist}(b) indicates the size of the selected set using the standard deviation was systematically lower than for the coefficient of variation. The result from the `dvs nmost` analysis, which performed using the minimum set size argument given to `dvs max` is represented by the $JSD(\mathbb{F})$ statistic. + +### Computational performance + +As shown in \autoref{fig:compute-time}, the compute time was linear with respect to the number of sequences, shown using random samples of microbial genomes from the 960 REFSOIL dataset [@choi.2017.ismej]. We further trialled the algorithm on the dataset of @zhu.2019.nat.commun, which consists of 10,560 whole microbial genomes. Using 10 cores on a MacBook Pro M2 Max, application of `dvs prep` followed by `dvs nmost` took 8'9" and 3'45" (to select 100 sequences) respectively. The RAM memory per process was ~300MB. + +## Constructing trees from $k$-mers + +We use the mammals dataset to evaluate the statistical performance of the `ctree` method. All sequences were concatenated and a phylogenetic tree was estimated from this alignment with different $k$-mer sizes and sketch sizes. The trees generated by `dvs ctree` are compared to the maximum likelihood tree found by IQ-TREE2 [@minh.2020.iq] using a general time-reversible model [@tavare.1986.some] on the concatenated alignment. + +The likelihood of the generated trees changes with $k$ (\autoref{fig:ctree-k}). When $k \leq 5$, the $k$-mers are non-unique (all mash-distances are zero) and the method generates a caterpillar tree. It is not until $k=8$ when the caterpillar tree is statistically outperformed. As $k$ increases further, the likelihood approaches that of IQ-TREE2 but plateaus before it at $k=12$. \autoref{fig:ctree-ss} shows how the likelihood of the generated trees changes as the sketch size increases for varying $k \geq 8$. + +Sketch size also impacts on the tree likelihood. An upward trend with increasing sketch size was evident with the likelihood approaching but not reaching that found by IQ-TREE2 on the aligned data. For the best performing values of $k$, there was no benefit to increasing sketch size beyond ~2,500. + +### Computational performance + +While the time complexity of the standard algorithm for agglomerative clustering is $\mathcal{O}(n^3)$, with respect to the number of sequences, the number of sequences is often small in comparison to the sequence lengths. As such, it was found that the most time consuming step of the algorithm was within the distance calculation. The pairwise distance calculation is done in two steps. First, is the constructing the MinHash sketch for each sequence (a subset of sketch size $k$-mers), followed by the computation of distances from these sketches [@ondov.2016.mash]. The expected runtime for constructing the MinHash sketch for a sequence is $\mathcal{O}(l + s\log s \log l)$ where $l$ is the length of the sequence, and $s$ is the sketch size. This is linear with respect to $l$ when $l > > s$. Hence, the time complexity for constructing all MinHash sketches is linear with respect to the combined length of all sequences. The time complexity for calculating the distance between two sequences from the MinHash sketch is $\mathcal{O}(s)$. Hence, the time complexity for calculating the pairwise distance matrix between all pairs of sequences from the sketches is $\mathcal{O} (sn^2)$. For suitable applications of this algorithm however, both the sketch size and the number of sequences are numerically dominated by the combined length of the sequences. Thus, the expected time to run the algorithm is linear with respect to the combined length of the sequences. This has been verified empirically with the 960 REFSOIL dataset (\autoref{fig:ctree-time}). The figure shows that as the number of sequences grows (and hence the combined length of the sequences), the time taken to construct the cluster tree grows linearly. When applied to the full REFSOIL dataset using 10 cores on a MacBook Pro M2 Max, `ctree` took ~3.5 minutes. + +# Recommendations + +For selecting representative sequences for large-scale analyses, we recommend use of the `nmost` command line tool. The choice of $k$ should be guided by the maximum number of unique $k$-mers in a DNA sequence of length $L$, indicated as the result of the expression $log(L/4)$. For instance, $k\approx 12$ for bacterial genomes (which are of the order $10^6$bp). For individual protein coding genes, as \autoref{fig:jsd-v-dist} indicates, $k=6$ for `nmost` gives a reasonable accuracy. For estimating a phylogeny, we recommend $k=12$ and a sketch size of $3000$. + +# Availability + +`diverse-seq` can be installed from the Python Package Index. The GitHub repository for `diverse-seq` contains all the scripts used to generate the results in this work. A script for downloading the data sets used in this work, which are available from Zenodo **10.5281/zenodo.14052787**, is also included. + +# Figures + +![The `diverse-seq nmost` algorithm.](figs/nmost.pdf){#algo:nmost} + +![The `diverse-seq max` algorithm. This includes upper and lower bounds for the size of the divergent set and amends the within-loop condition of `nmost`. The set size is increased when a record that increases JSD also increases the standard deviation of $\delta_{JSD}$.](figs/max.pdf){#algo:max} + + +![Identification of representatives of known groups is affected by sequence length. `dvs max` identified representatives of known groups in both *balanced*, and *imbalanced* pools.](figs/synthetic_known_bar.pdf){#fig:synthetic-knowns} + +![The statistical performance of `dvs max` in recovering representative sequences is a function of $k$ and the chosen statistic. The minimum and maximum allowed set sizes were 5 and 30, respectively. `dvs nmost` is represented by $JSD(\mathbb{F})$ run with n=5. Trendlines were estimated using LOWESS [@cleveland.1979.j.am.stat.assoc]. (a) *Significant %* is the percentage of cases where `dvs max` was significantly better ($p-\text{value} \le 0.05$) at selecting divergent sequences than a random selection process. (b) The mean and standard deviations of the number of sequences selected by `dvs max`.](figs/jsd_v_dist.pdf){#fig:jsd-v-dist} + +![Result of applying the `dvs_max` app to a single sequence alignment. The phylogenetic tree was estimated using Neighbour-Joining [@saitou.1987.mol.biol.evol] from the pairwise paralinear distances [@lake.1994.procnatlacadsciua]. The branch to the sequence selected by `dvs_max` are shown in red. See the `plugin_demo` notebook for the code used to produce this figure.](figs/selected_edges.pdf){#fig:selected-edges} + +![`dvs max` exhibits linear time performance with respect to the number of microbial genome sequences. Three replicates were performed for each condition. For each repeat, sequences were randomly sampled without replacement from the 960 REFSOIL microbial dataset [@choi.2017.ismej].](figs/compute_time.pdf){#fig:compute-time} + +![Statistical performance of the `dvs_ctree` app on the concatenated mammals alignment as the $k$-mer size increases. The likelihood of trees, represented as the log-likelihood (lnL), generated by the app is compared to the maximum likelihood tree found by IQ-TREE2 [@minh.2020.iq]. For large enough sketch sizes, the likelihood approaches that of IQ-TREE2 and plateaus beyond a $k$-mer size of ~12.](figs/likelihood_vs_k_for_ss.pdf){#fig:ctree-k} + +![Statistical performance of the `dvs_ctree` app on the concatenated mammals alignment as the sketch size increases. The likelihood of trees, represented as the log-likelihood (lnL), generated by the app is compared to the maximum likelihood tree found by IQ-TREE2 [@minh.2020.iq]. For optimal $k$-mer sizes, the likelihood approaches that of IQ-TREE2 and plateaus beyond a sketch size of ~2500.](figs/likelihood_vs_ss_for_k.pdf){#fig:ctree-ss} + +![Computational performance of the `dvs_ctree` app on 960 REFSOIL microbial dataset using 8 cores. The wall time taken to run the algorithm grows linearly with respect to the number of sequences.](figs/compute_time-ctree.pdf){#fig:ctree-time} + +# Tables + +**Table 1** Jensen-Shanon Divergence (JSD) for different relationships between two sequences with $k=1$.\label{JSD-examples} + ++--------------+--------+--------+-----+ +| Relationship | seq1 | seq2 | JSD | ++==============+========+========+=====+ +| Identical | `ATCG` | `TCGA` | 0.0 | ++--------------+--------+--------+-----+ +| No overlap | `AAAA` | `TTTT` | 1.0 | ++--------------+--------+--------+-----+ +| Intermediate | `ATCG` | `ATCC` | 0.5 | ++==============+========+========+=====+ + + +# Acknowledgements + +We thank Yapeng Lang for comments on the manuscript. + +# References \ No newline at end of file diff --git a/paper/results/benchmark-ctree.tsv b/paper/results/benchmark-ctree.tsv new file mode 100644 index 0000000..56113a3 --- /dev/null +++ b/paper/results/benchmark-ctree.tsv @@ -0,0 +1,49 @@ +k numseqs time(s) +10 50 20.400996208190918 +10 100 36.59555625915527 +10 150 56.67584800720215 +10 200 78.90142583847046 +12 50 20.54346203804016 +12 100 39.98088002204895 +12 150 60.63236713409424 +12 200 83.78770923614502 +14 50 21.90869402885437 +14 100 41.23087000846863 +14 150 64.63227701187134 +14 200 88.33604693412781 +16 50 22.182900190353394 +16 100 42.87033820152283 +16 150 66.58072185516357 +16 200 91.89087104797363 +10 50 19.027562856674194 +10 100 38.29978919029236 +10 150 58.920490980148315 +10 200 79.70972418785095 +12 50 20.86475419998169 +12 100 40.769277811050415 +12 150 63.52880001068115 +12 200 85.12522029876709 +14 50 21.13470697402954 +14 100 41.061113119125366 +14 150 66.06328225135803 +14 200 89.36060500144958 +16 50 22.011647939682007 +16 100 43.3382408618927 +16 150 67.48384284973145 +16 200 92.61627078056335 +10 50 18.95676016807556 +10 100 37.24605417251587 +10 150 58.52467679977417 +10 200 79.65758538246155 +12 50 20.64378309249878 +12 100 39.46475791931152 +12 150 60.93326187133789 +12 200 85.05353879928589 +14 50 20.980711221694946 +14 100 40.59002494812012 +14 150 64.87178015708923 +14 200 91.31307172775269 +16 50 22.075860261917114 +16 100 43.57241606712341 +16 150 68.67832493782043 +16 200 92.59029603004456 diff --git a/paper/results/benchmark-max.tsv b/paper/results/benchmark-max.tsv new file mode 100644 index 0000000..e200b11 --- /dev/null +++ b/paper/results/benchmark-max.tsv @@ -0,0 +1,97 @@ +command numseqs k time(s) +prep 50 2.2197999954223633 +max 50 2 5.199400186538696 +max 50 3 5.2096288204193115 +max 50 4 5.5462563037872314 +max 50 5 5.516131162643433 +max 50 6 5.598415851593018 +max 50 7 5.7647411823272705 +max 50 8 6.270074129104614 +prep 100 4.931617021560669 +max 100 2 9.868052959442139 +max 100 3 10.37977123260498 +max 100 4 10.096950054168701 +max 100 5 10.737774848937988 +max 100 6 10.699741840362549 +max 100 7 10.86460018157959 +max 100 8 11.00886082649231 +prep 150 8.820343017578125 +max 150 2 16.354172945022583 +max 150 3 17.321242094039917 +max 150 4 17.0143620967865 +max 150 5 17.315665006637573 +max 150 6 17.77433967590332 +max 150 7 18.351871013641357 +max 150 8 18.018709182739258 +prep 200 11.976114988327026 +max 200 2 20.208352088928223 +max 200 3 21.91296672821045 +max 200 4 22.267865896224976 +max 200 5 23.107195138931274 +max 200 6 23.228440046310425 +max 200 7 23.709959030151367 +max 200 8 23.813808917999268 +prep 50 3.833455801010132 +max 50 2 5.611738204956055 +max 50 3 6.108872890472412 +max 50 4 6.100829124450684 +max 50 5 6.231610059738159 +max 50 6 6.249242067337036 +max 50 7 6.4174721240997314 +max 50 8 6.271366119384766 +prep 100 6.561968803405762 +max 100 2 10.741018772125244 +max 100 3 10.782916784286499 +max 100 4 11.238109111785889 +max 100 5 11.628036260604858 +max 100 6 11.743911027908325 +max 100 7 11.816339015960693 +max 100 8 11.788946151733398 +prep 150 11.709703922271729 +max 150 2 16.35231375694275 +max 150 3 16.90885090827942 +max 150 4 16.562835931777954 +max 150 5 17.232949018478394 +max 150 6 17.247282028198242 +max 150 7 17.654213190078735 +max 150 8 17.724243879318237 +prep 200 12.580319166183472 +max 200 2 22.556401014328003 +max 200 3 23.27392315864563 +max 200 4 22.588284015655518 +max 200 5 23.38051676750183 +max 200 6 23.92368483543396 +max 200 7 23.34791111946106 +max 200 8 23.720210313796997 +prep 50 4.620098114013672 +max 50 2 5.269720077514648 +max 50 3 5.432962894439697 +max 50 4 5.289083957672119 +max 50 5 5.5486249923706055 +max 50 6 5.542788028717041 +max 50 7 5.873444318771362 +max 50 8 5.606462240219116 +prep 100 7.519195079803467 +max 100 2 11.77892518043518 +max 100 3 12.362501859664917 +max 100 4 12.666976690292358 +max 100 5 12.91024899482727 +max 100 6 12.775952100753784 +max 100 7 12.750930309295654 +max 100 8 13.01595401763916 +prep 150 9.455878734588623 +max 150 2 16.96672797203064 +max 150 3 17.033857822418213 +max 150 4 16.730255126953125 +max 150 5 17.259427070617676 +max 150 6 17.547806978225708 +max 150 7 17.51994013786316 +max 150 8 17.780198097229004 +prep 200 11.793842315673828 +max 200 2 22.392876148223877 +max 200 3 23.075665950775146 +max 200 4 22.63230609893799 +max 200 5 23.395052909851074 +max 200 6 23.778443813323975 +max 200 7 24.022155046463013 +max 200 8 23.861922025680542 diff --git a/paper/results/benchmark-nmost.tsv b/paper/results/benchmark-nmost.tsv new file mode 100644 index 0000000..78a4da1 --- /dev/null +++ b/paper/results/benchmark-nmost.tsv @@ -0,0 +1,97 @@ +command numseqs k time(s) +prep 50 2.3646609783172607 +nmost 50 2 4.456601142883301 +nmost 50 3 4.36300802230835 +nmost 50 4 4.5952489376068115 +nmost 50 5 4.679824113845825 +nmost 50 6 4.6786932945251465 +nmost 50 7 4.952332973480225 +nmost 50 8 5.050390005111694 +prep 100 5.123053073883057 +nmost 100 2 9.154332876205444 +nmost 100 3 9.834275007247925 +nmost 100 4 9.85277271270752 +nmost 100 5 10.160806894302368 +nmost 100 6 10.236114025115967 +nmost 100 7 10.609652042388916 +nmost 100 8 10.624366760253906 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10.490545988082886 +nmost 150 2 17.30330181121826 +nmost 150 3 17.559410333633423 +nmost 150 4 17.046263694763184 +nmost 150 5 18.048783779144287 +nmost 150 6 18.41697382926941 +nmost 150 7 18.967029094696045 +nmost 150 8 18.533120155334473 +prep 200 15.23118281364441 +nmost 200 2 21.48833394050598 +nmost 200 3 22.141258001327515 +nmost 200 4 22.609405755996704 +nmost 200 5 22.908766984939575 +nmost 200 6 23.804404973983765 +nmost 200 7 23.998902082443237 +nmost 200 8 23.95554494857788 diff --git a/paper/results/benchmark.tsv b/paper/results/benchmark.tsv new file mode 100644 index 0000000..901fd1a --- /dev/null +++ b/paper/results/benchmark.tsv @@ -0,0 +1,97 @@ +command numseqs k time(s) +prep 50 2.7535879611968994 +max 50 2 5.192910671234131 +max 50 3 5.253834009170532 +max 50 4 5.538035154342651 +max 50 5 5.388818025588989 +max 50 6 5.456940174102783 +max 50 7 5.844316720962524 +max 50 8 6.0705718994140625 +prep 100 6.304960012435913 +max 100 2 11.004969120025635 +max 100 3 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