From aab2c59cb50962252ceeb1b8cbda505eec68aadd Mon Sep 17 00:00:00 2001 From: BenKaehler Date: Sat, 8 Jul 2017 06:49:23 +1000 Subject: [PATCH] Made some slight alterations to the runtime notebooks --- ipynb/runtime/analysis.ipynb | 245 +++++++++++++++------------ ipynb/runtime/compute-runtimes.ipynb | 56 +++--- setup.py | 2 +- 3 files changed, 158 insertions(+), 145 deletions(-) diff --git a/ipynb/runtime/analysis.ipynb b/ipynb/runtime/analysis.ipynb index 76152df6cf7..8a8abbef7c2 100644 --- a/ipynb/runtime/analysis.ipynb +++ b/ipynb/runtime/analysis.ipynb @@ -11,7 +11,9 @@ { "cell_type": "code", "execution_count": 1, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "from os.path import expandvars\n", @@ -30,20 +32,22 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ - "runtime_results = expandvars('$HOME/Desktop/projects/tax-credit-runtime/runtime_results.txt')\n", - "outdir = expandvars('$HOME/Desktop/plots/')" + "runtime_results = '../../temp_results_runtime/runtime_results.txt'\n", + "outdir = '../../plots/'" ] }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, + "execution_count": 3, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "df = pd.read_csv(runtime_results, header=None, sep='\\t', \n", @@ -56,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": { "collapsed": true }, @@ -79,14 +83,14 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { - "image/png": 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vX6KChSippAu7+OSbvuRc/Jn7gus4g0ytD8G3xzIJMlFhtB4a6jYPKPS9us0D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\nW5qlb5LotFgxczgcJCcnB/ZNJhM+n68Z76h2kpKSSE5OxuFwcP/99zN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Y6JoCZv/+/WzevJldu3bx2muvuV5z++23c/vtt9f624xl/rjuv/9+unXrRlxc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\nKvHII4+cUH4pZBKJRCJpizBeEEUikUgkEj9SzCQSiUQS9kgxk0gkEkn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0blast0.0000603.4770150.7222470.1682070.0000330.0001384.1900310.9331376.556498e-030.000026
1blast+0.0000744.2427230.7728250.1254570.0000350.0001263.3217370.9880582.130735e-040.000010
2naive-bayes-0.0001465.777330-0.4407800.4575250.0001720.0004653.3360250.9834554.083326e-040.000043
3rdp0.0008585.5070770.9953490.0003810.0000480.0015825.9934070.9976838.047788e-060.000054
4sortmerna0.0009455.0023670.9992160.0000260.0000220.0064063.5263100.9765258.201595e-040.000706
5uclust0.0000273.2074200.9665440.0073090.0000040.0002683.6825150.9061401.280112e-020.000062
6vsearch0.0000394.2103870.5308170.3573810.0000360.0028323.7888570.9994354.784837e-070.000048
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" ], "text/plain": [ - " Method Slope Intercept R P-val Std Error\n", - "0 blast 0.000060 3.477015 0.722247 0.168207 0.000033\n", - "1 blast+ 0.000074 4.242723 0.772825 0.125457 0.000035\n", - "2 naive-bayes -0.000146 5.777330 -0.440780 0.457525 0.000172\n", - "3 rdp 0.000858 5.507077 0.995349 0.000381 0.000048\n", - "4 sortmerna 0.000945 5.002367 0.999216 0.000026 0.000022\n", - "5 uclust 0.000027 3.207420 0.966544 0.007309 0.000004\n", - "6 vsearch 0.000039 4.210387 0.530817 0.357381 0.000036" + " Method Slope Intercept R P-val Std Error\n", + "0 blast 0.000138 4.190031 0.933137 6.556498e-03 0.000026\n", + "1 blast+ 0.000126 3.321737 0.988058 2.130735e-04 0.000010\n", + "2 naive-bayes 0.000465 3.336025 0.983455 4.083326e-04 0.000043\n", + "3 rdp 0.001582 5.993407 0.997683 8.047788e-06 0.000054\n", + "4 sortmerna 0.006406 3.526310 0.976525 8.201595e-04 0.000706\n", + "5 uclust 0.000268 3.682515 0.906140 1.280112e-02 0.000062\n", + "6 vsearch 0.002832 3.788857 0.999435 4.784837e-07 0.000048" ] }, - "execution_count": 7, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -201,7 +218,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "metadata": { "collapsed": true }, @@ -220,14 +237,14 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { - "image/png": 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Hs2bNGuzt7Rk5ciTPPvus5a0VopCtm7WH5eN3AKC2U/FOZF86BDZRuCpRnHUd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\nLFy40FNmSWqJWuastdJVYzKZWLlyJcuWLWPbtm2NWqdDhw707t0bgE6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UzLp1RS43q1uX7mvWoC6YvUGIiialJYT4SwadjozY2CKf02dlyUUXolJJaQkh/pJao8Gy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\n1ZmrP1w7ZdzL363FZ5rKlHFhT5K07KAsM5OEiAh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6vsearch0.0233394.3417470.9999875.842472e-080.00007011.4369871141.1010640.9997578.871686e-080.126143
\n", "
" ], "text/plain": [ - " Method Slope Intercept R P-val Std Error\n", - "0 blast 0.142721 5.560904 0.999994 1.969482e-08 0.000296\n", - "1 blast+ 0.025660 5.533586 0.999991 3.239439e-08 0.000063\n", - "2 naive-bayes 0.024100 11.128306 0.997689 1.333428e-04 0.000948\n", - "3 rdp 0.002543 13.722656 0.997643 1.373013e-04 0.000101\n", - "4 sortmerna 0.021109 14.571249 0.999638 8.261863e-06 0.000328\n", - "5 uclust 0.002660 3.708455 0.999688 6.603372e-06 0.000038\n", - "6 vsearch 0.023339 4.341747 0.999987 5.842472e-08 0.000070" + " Method Slope Intercept R P-val Std Error\n", + "0 blast 1.628019 13.196796 0.999999 2.001489e-12 0.001237\n", + "1 blast+ 0.205456 22.293103 0.999830 4.346064e-08 0.001896\n", + "2 naive-bayes 0.023456 10.853440 0.998150 5.133200e-06 0.000714\n", + "3 rdp 0.020598 25.548071 0.999112 1.182753e-06 0.000434\n", + "4 sortmerna 0.014828 72.197167 0.992448 8.533166e-05 0.000916\n", + "5 uclust 0.008074 6.916588 0.999386 5.652671e-07 0.000142\n", + "6 vsearch 11.436987 1141.101064 0.999757 8.871686e-08 0.126143" ] }, - "execution_count": 11, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -342,7 +372,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 8, "metadata": { "collapsed": true }, @@ -350,15 +380,6 @@ "source": [ "lm.savefig(join(outdir, 'runtime_by_querycount.pdf'))" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/ipynb/runtime/compute-runtimes.ipynb b/ipynb/runtime/compute-runtimes.ipynb index d754954fc29..baafdaf6c19 100644 --- a/ipynb/runtime/compute-runtimes.ipynb +++ b/ipynb/runtime/compute-runtimes.ipynb @@ -39,13 +39,13 @@ "project_dir = '../..'\n", "data_dir = join(project_dir, \"data\")\n", "\n", - "results_dir = join(project_dir, 'temp_dir_runtime')\n", + "results_dir = join(project_dir, 'temp_results_runtime')\n", "runtime_results = join(results_dir, 'runtime_results.txt')\n", "tmpdir = join(results_dir, 'tmp')\n", "\n", "ref_db_dir = join(project_dir, 'data/ref_dbs/gg_13_8_otus')\n", - "ref_seqs = join(ref_db_dir, '99_otus.fasta')\n", - "ref_taxa = join(ref_db_dir, '99_otu_taxonomy.txt')\n", + "ref_seqs = join(ref_db_dir, '99_otus_clean.fasta')\n", + "ref_taxa = join(ref_db_dir, '99_otu_taxonomy_clean.tsv')\n", "\n", "num_iters = 1\n", "sampling_depths = [1] + list(range(2000,10001,2000))" @@ -62,9 +62,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "runtime_make_test_data(ref_seqs, tmpdir, sampling_depths)" @@ -86,12 +84,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[32mSaved TaxonomicClassifier to: ../../temp_dir_runtime/tmp/1.fna.nb.qza\u001b[0m\n", - "\u001b[32mSaved TaxonomicClassifier to: ../../temp_dir_runtime/tmp/2000.fna.nb.qza\u001b[0m\n", - "\u001b[32mSaved TaxonomicClassifier to: ../../temp_dir_runtime/tmp/4000.fna.nb.qza\u001b[0m\n", - "\u001b[32mSaved TaxonomicClassifier to: ../../temp_dir_runtime/tmp/6000.fna.nb.qza\u001b[0m\n", - "\u001b[32mSaved TaxonomicClassifier to: ../../temp_dir_runtime/tmp/8000.fna.nb.qza\u001b[0m\n", - "\u001b[32mSaved TaxonomicClassifier to: ../../temp_dir_runtime/tmp/10000.fna.nb.qza\u001b[0m\n" + "\u001b[33mQIIME is caching your current deployment for improved performance. This may take a few moments and should only happen once per deployment.\u001b[0m\n", + "\u001b[32mSaved TaxonomicClassifier to: ../../temp_results_runtime/tmp/1.fna.nb.qza\u001b[0m\n", + "\u001b[32mSaved TaxonomicClassifier to: ../../temp_results_runtime/tmp/2000.fna.nb.qza\u001b[0m\n", + "\u001b[32mSaved TaxonomicClassifier to: ../../temp_results_runtime/tmp/4000.fna.nb.qza\u001b[0m\n", + "\u001b[32mSaved TaxonomicClassifier to: ../../temp_results_runtime/tmp/6000.fna.nb.qza\u001b[0m\n", + "\u001b[32mSaved TaxonomicClassifier to: ../../temp_results_runtime/tmp/8000.fna.nb.qza\u001b[0m\n", + "\u001b[32mSaved TaxonomicClassifier to: ../../temp_results_runtime/tmp/10000.fna.nb.qza\u001b[0m\n" ] } ], @@ -124,15 +123,15 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "qiime1_setup = join(results_dir, '.bashrc')\n", - "qiime1_template = ('source activate qiime1; source ' + qiime1_setup + '; '\n", - " 'assign_taxonomy.py -i {1} -o {0} -r {2} -t {3} -m {4} {5}')\n", + "qiime1_template = ('bash -c \"source activate qiime1; source ' + qiime1_setup + '; '\n", + " 'assign_taxonomy.py -i {1} -o {0} -r {2} -t {3} -m {4} {5}\"')\n", "blast_template = ('qiime feature-classifier classify-consensus-blast --i-query {1}.qza --o-classification '\n", " '{0}/assign.tmp --i-reference-reads {2}.qza --i-reference-taxonomy {3}.qza {5}')\n", "vsearch_template = ('qiime feature-classifier classify-consensus-vsearch --i-query {1}.qza '\n", @@ -146,7 +145,9 @@ " 'uclust': (qiime1_template, '--min_consensus_fraction 0.51 --similarity 0.8 --uclust_max_accepts 3'),\n", " 'sortmerna': (qiime1_template, '--sortmerna_e_value 0.001 --min_consensus_fraction 0.51 --similarity 0.8 '\n", " '--sortmerna_best_N_alignments 3 --sortmerna_coverage 0.8'),\n", - " 'blast' : (qiime1_template, '-e 0.001'),\n", + " 'blast' : (qiime1_template, '-e 0.001')\n", + " }\n", + "qiime2_methods = {\n", " 'blast+' : (blast_template, '--p-evalue 0.001'),\n", " 'vsearch' : (vsearch_template, '--p-perc-identity 0.90'),\n", " 'naive-bayes': (naive_bayes_template, '--p-confidence 0.7')\n", @@ -169,7 +170,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": { "collapsed": true }, @@ -188,7 +189,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": { "collapsed": true }, @@ -207,16 +208,16 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "84\n", - "('qiime feature-classifier classify-sklearn --o-classification ../../temp_dir_runtime/tmp/assign.tmp --i-classifier ../../temp_dir_runtime/tmp/2000.fna.nb.qza --i-reads ../../temp_dir_runtime/tmp/1.fna.qza --p-confidence 0.7', 'naive-bayes', '1', '2000', 0)\n", - "('source activate qiime1; source ../../temp_dir_runtime/.bashrc; assign_taxonomy.py -i ../../temp_dir_runtime/tmp/10000.fna -o ../../temp_dir_runtime/tmp -r ../../temp_dir_runtime/tmp/10000.fna -t ../../data/ref_dbs/gg_13_8_otus/99_otu_taxonomy.txt -m rdp --confidence 0.5 --rdp_max_memory 16000', 'rdp', '10000', '10000', 0)\n" + "48\n", + "('bash -c \"source activate qiime1; source ../../temp_results_runtime/.bashrc; assign_taxonomy.py -i ../../temp_results_runtime/tmp/1.fna -o ../../temp_results_runtime/tmp -r ../../temp_results_runtime/tmp/2000.fna -t ../../data/ref_dbs/gg_13_8_otus/99_otu_taxonomy_clean.tsv -m blast -e 0.001\"', 'blast', '1', '2000', 0)\n", + "('bash -c \"source activate qiime1; source ../../temp_results_runtime/.bashrc; assign_taxonomy.py -i ../../temp_results_runtime/tmp/10000.fna -o ../../temp_results_runtime/tmp -r ../../temp_results_runtime/tmp/10000.fna -t ../../data/ref_dbs/gg_13_8_otus/99_otu_taxonomy_clean.tsv -m sortmerna --sortmerna_e_value 0.001 --min_consensus_fraction 0.51 --similarity 0.8 --sortmerna_best_N_alignments 3 --sortmerna_coverage 0.8\"', 'sortmerna', '10000', '10000', 0)\n" ] } ], @@ -234,17 +235,8 @@ }, "outputs": [], "source": [ - "Parallel(n_jobs=1)(delayed(clock_runtime)(command, runtime_results, force=False) for command in (list(set(commands_a + commands_b))))" + "Parallel(n_jobs=21)(delayed(clock_runtime)(command, runtime_results, force=False) for command in (list(set(commands_a + commands_b))))" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/setup.py b/setup.py index 604c5a1c263..1e09da1a06b 100644 --- a/setup.py +++ b/setup.py @@ -17,7 +17,7 @@ packages=find_packages(), install_requires=['biom-format', 'pandas', 'statsmodels', 'bokeh', 'scipy', 'jupyter', 'scikit-bio', 'seaborn', - 'scikit-learn'], + 'scikit-learn', 'joblib'], author="Nicholas Bokulich", author_email="nbokulich@gmail.com", description="Systematic benchmarking of taxonomic classification methods",