deletions | additions       diff --git a/figures/impute_comparison.pdf b/figures/impute_comparison.pdf  new file mode 100644  index 0000000..bbe2b12  Binary files /dev/null and b/figures/impute_comparison.pdf differ diff --git a/notebooks/paper plots.ipynb b/notebooks/paper plots.ipynb  new file mode 100644  index 0000000..1a5fc17  --- /dev/null  +++ b/notebooks/paper plots.ipynb 
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{  "cells": [  {  "cell_type": "code",  "execution_count": 1,  "metadata": {  "collapsed": false  },  "outputs": [  {  "name": "stderr",  "output_type": "stream",  "text": [  "Using Theano backend.\n",  "/Users/tim/venvs/analysis-venv-2.7/lib/python2.7/site-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",  " warnings.warn(self.msg_depr % (key, alt_key))\n"  ]  },  {  "name": "stdout",  "output_type": "stream",  "text": [  "Couldn't import dot_parser, loading of dot files will not be possible.\n"  ]  },  {  "name": "stderr",  "output_type": "stream",  "text": [  "/Users/tim/venvs/analysis-venv-2.7/lib/python2.7/site-packages/IPython/html.py:14: ShimWarning: The `IPython.html` package has been deprecated. You should import from `notebook` instead. `IPython.html.widgets` has moved to `ipywidgets`.\n",  " \"`IPython.html.widgets` has moved to `ipywidgets`.\", ShimWarning)\n"  ]  }  ],  "source": [  "import mhcflurry, seaborn, numpy, pandas, pickle, sklearn, collections, scipy, time\n",  "import mhcflurry.dataset\n",  "import fancyimpute, locale\n",  "from matplotlib import pyplot\n",  "\n",  "\n",  "import sklearn.metrics\n",  "import sklearn.cross_validation\n",  "%matplotlib inline\n",  "\n",  "\n",  "def print_full(x):\n",  " pandas.set_option('display.max_rows', len(x))\n",  " print(x)\n",  " pandas.reset_option('display.max_rows')"  ]  },  {  "cell_type": "code",  "execution_count": 2,  "metadata": {  "collapsed": false  },  "outputs": [  {  "data": {  "text/html": [  "

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	activation	allele	dropout_probability	embedding_output_dim	fit_time	fraction_negative	impute	layer_sizes	model_params	n_training_epochs	...	test_auc	test_f1	test_size	test_tau	train_auc	train_f1	train_size	train_tau	layer0_size	model_string
0	tanh	HLA-A0203	0.5	32	1.726369	0.2	False	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.787809	0.483003	5315	0.372769	0.998302	0.947368	50	0.770267	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
1	tanh	HLA-A0203	0.5	32	3.950002	0.2	True	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.815350	0.513711	5315	0.416518	0.998302	0.947368	50	0.824931	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
2	tanh	HLA-A0203	0.5	32	3.298754	0.2	False	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.782669	0.536396	5375	0.384346	1.000000	0.918919	50	0.804755	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
3	tanh	HLA-A0203	0.5	32	4.956206	0.2	True	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.790377	0.546405	5375	0.394875	1.000000	0.918919	50	0.831692	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
4	tanh	HLA-A0203	0.5	32	4.355083	0.2	True	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.813876	0.515342	5369	0.436850	0.992381	0.903226	50	0.872410	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
5	tanh	HLA-A0203	0.5	32	2.193765	0.2	False	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.794476	0.461988	5369	0.401830	0.996190	0.888889	50	0.842442	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
6	tanh	HLA-A0201	0.5	32	18.507652	0.2	True	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.937106	0.778107	6578	0.566801	0.965124	0.856716	1000	0.651183	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
7	tanh	HLA-A0201	0.5	32	19.386850	0.2	False	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.937746	0.783494	6578	0.566767	0.964381	0.864785	1000	0.649207	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
8	tanh	HLA-A0201	0.5	32	21.011101	0.2	True	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.939144	0.763819	6478	0.580869	0.964396	0.842809	1000	0.617125	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
9	tanh	HLA-A0201	0.5	32	17.923310	0.2	False	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.939693	0.768324	6478	0.581335	0.964512	0.836425	1000	0.616712	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
10	tanh	HLA-A0201	0.5	32	20.640437	0.2	False	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.937720	0.758129	6537	0.569621	0.958349	0.828179	1000	0.623817	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
11	tanh	HLA-A0201	0.5	32	21.971126	0.2	True	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.937999	0.765993	6537	0.571824	0.961578	0.841930	1000	0.633758	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
12	tanh	HLA-A0201	0.5	32	4.527541	0.2	True	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.761049	0.068106	9537	0.331257	1.000000	1.000000	10	1.000000	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
13	tanh	HLA-A0201	0.5	32	2.624935	0.2	False	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.510052	0.019291	9537	0.025539	1.000000	1.000000	10	0.955556	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
14	tanh	HLA-A0201	0.5	32	4.525949	0.2	True	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.798850	0.223807	9474	0.383791	1.000000	1.000000	10	0.955556	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
15	tanh	HLA-A0201	0.5	32	2.305879	0.2	False	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.624110	0.087189	9474	0.152300	1.000000	1.000000	10	0.955556	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
16	tanh	HLA-A0201	0.5	32	2.302873	0.2	False	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.696474	0.049908	9520	0.250387	1.000000	0.857143	10	1.000000	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
17	tanh	HLA-A0201	0.5	32	4.278460	0.2	True	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.803198	0.093205	9520	0.388869	1.000000	1.000000	10	1.000000	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
18	tanh	HLA-A0203	0.5	32	2.587237	0.2	False	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.678122	0.096665	5489	0.220779	1.000000	1.000000	10	0.966092	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
19	tanh	HLA-A0203	0.5	32	4.241571	0.2	True	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.757714	0.073638	5489	0.335474	1.000000	1.000000	10	0.966092	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
20	tanh	HLA-A0203	0.5	32	2.299999	0.2	False	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.715024	0.055145	5512	0.289235	1.000000	1.000000	10	0.816497	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
21	tanh	HLA-A0203	0.5	32	4.537592	0.2	True	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.795471	0.087374	5512	0.395721	1.000000	1.000000	10	0.816497	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
22	tanh	HLA-A0203	0.5	32	2.629939	0.2	False	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.732625	0.214759	5514	0.315640	1.000000	1.000000	10	0.920087	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
23	tanh	HLA-A0203	0.5	32	4.249131	0.2	True	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.790998	0.251858	5514	0.395806	1.000000	1.000000	10	0.920087	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
24	tanh	HLA-A0301	0.5	32	4.012164	0.2	False	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.835806	0.397087	5864	0.424022	0.995000	0.923077	100	0.673731	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
25	tanh	HLA-A0301	0.5	32	5.880508	0.2	True	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.847088	0.408085	5864	0.440685	0.995625	0.918919	100	0.671134	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
26	tanh	HLA-A0301	0.5	32	5.461747	0.2	True	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.843878	0.447610	5830	0.431866	1.000000	0.913043	100	0.741684	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
27	tanh	HLA-A0301	0.5	32	3.169109	0.2	False	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.837747	0.383466	5830	0.421623	0.994133	0.863636	100	0.730850	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
28	tanh	HLA-A0301	0.5	32	5.930596	0.2	True	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.815691	0.509604	5878	0.398072	1.000000	0.956522	100	0.697136	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
29	tanh	HLA-A0301	0.5	32	3.956572	0.2	False	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.820578	0.519844	5878	0.404652	1.000000	0.978723	100	0.688995	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
114	tanh	HLA-A0203	0.5	32	6.213101	0.2	True	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.878032	0.664391	4860	0.532049	0.968405	0.830508	200	0.723689	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
115	tanh	HLA-A0203	0.5	32	4.023137	0.2	False	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.875739	0.660449	4860	0.527987	0.968865	0.823529	200	0.718398	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
116	tanh	HLA-A0203	0.5	32	3.775757	0.2	False	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.872028	0.689700	4877	0.530505	0.978005	0.876923	200	0.739647	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
117	tanh	HLA-A0203	0.5	32	5.512664	0.2	True	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.873473	0.694524	4877	0.532519	0.979576	0.885496	200	0.747955	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
118	tanh	HLA-A0203	0.5	32	4.925610	0.2	True	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.871015	0.657324	4881	0.522220	0.986211	0.894309	200	0.702950	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
119	tanh	HLA-A0203	0.5	32	4.052275	0.2	False	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.870450	0.666667	4881	0.519812	0.985641	0.868852	200	0.691445	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
120	tanh	HLA-A0201	0.5	32	4.304954	0.2	True	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.879427	0.621351	8896	0.486743	0.989302	0.930693	150	0.727620	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
121	tanh	HLA-A0201	0.5	32	3.138064	0.2	False	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.876158	0.621445	8896	0.482924	0.985411	0.910891	150	0.715581	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
122	tanh	HLA-A0201	0.5	32	4.056201	0.2	True	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.902595	0.708851	8807	0.521010	0.993192	0.941176	150	0.756157	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
123	tanh	HLA-A0201	0.5	32	2.988854	0.2	False	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.900432	0.704242	8807	0.514215	0.991830	0.932039	150	0.747129	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
124	tanh	HLA-A0201	0.5	32	3.142403	0.2	False	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.901071	0.689683	8906	0.517426	0.982623	0.893617	150	0.771905	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
125	tanh	HLA-A0201	0.5	32	4.405436	0.2	True	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.904398	0.716724	8906	0.519347	0.987674	0.916667	150	0.784575	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
126	tanh	HLA-A0301	0.5	32	3.030754	0.2	True	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.782518	0.234125	6006	0.340898	1.000000	1.000000	50	0.585550	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
127	tanh	HLA-A0301	0.5	32	1.975326	0.2	False	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.765161	0.200000	6006	0.315647	1.000000	1.000000	50	0.538776	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
128	tanh	HLA-A0301	0.5	32	3.172742	0.2	True	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.818226	0.231626	6029	0.398189	0.990676	0.842105	50	0.803063	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
129	tanh	HLA-A0301	0.5	32	1.795594	0.2	False	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.817788	0.216676	6029	0.391441	0.995338	0.842105	50	0.813271	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
130	tanh	HLA-A0301	0.5	32	1.725392	0.2	False	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.803556	0.450473	5963	0.377142	1.000000	0.941176	50	0.759519	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
131	tanh	HLA-A0301	0.5	32	3.258902	0.2	True	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.815190	0.426157	5963	0.401121	1.000000	0.909091	50	0.754484	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
132	tanh	HLA-A0203	0.5	32	3.781140	0.2	True	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.821252	0.574111	5155	0.445329	0.995714	0.918033	100	0.792008	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
133	tanh	HLA-A0203	0.5	32	2.310814	0.2	False	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.810397	0.578774	5155	0.426463	0.994762	0.915254	100	0.779992	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
134	tanh	HLA-A0203	0.5	32	2.403544	0.2	False	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.844897	0.605942	5188	0.476586	0.996568	0.942857	100	0.825217	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
135	tanh	HLA-A0203	0.5	32	3.863117	0.2	True	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.836725	0.605171	5188	0.469238	0.996139	0.942857	100	0.823530	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
136	tanh	HLA-A0203	0.5	32	2.412391	0.2	False	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.841734	0.636049	5129	0.487334	0.998713	0.942857	100	0.764532	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
137	tanh	HLA-A0203	0.5	32	3.622578	0.2	True	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.851222	0.657169	5129	0.499891	0.999571	0.911765	100	0.762436	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
138	tanh	HLA-A0301	0.5	32	3.540570	0.2	False	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.866753	0.541187	5586	0.451763	0.964156	0.759259	200	0.641774	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
139	tanh	HLA-A0301	0.5	32	5.051757	0.2	True	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.878847	0.608206	5586	0.470898	0.967436	0.796610	200	0.641774	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
140	tanh	HLA-A0301	0.5	32	4.327855	0.2	False	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.864583	0.574333	5631	0.456183	0.979436	0.787234	200	0.624748	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
141	tanh	HLA-A0301	0.5	32	5.015249	0.2	True	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.864002	0.529172	5631	0.457263	0.987586	0.769231	200	0.628813	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
142	tanh	HLA-A0301	0.5	32	3.766955	0.2	False	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.873587	0.547294	5604	0.471515	0.983197	0.833333	200	0.620050	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...
143	tanh	HLA-A0301	0.5	32	5.047997	0.2	True	[64]	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...	250	...	0.872979	0.576398	5604	0.468377	0.988464	0.857143	200	0.642805	64	{'pretrain_decay': '1 / (1+epoch)**2', 'dropou...

\n",  "

144 rows × 21 columns

\n",  "

"  ],  "text/plain": [  " activation allele dropout_probability embedding_output_dim \\\n",  "0 tanh HLA-A0203 0.5 32 \n",  "1 tanh HLA-A0203 0.5 32 \n",  "2 tanh HLA-A0203 0.5 32 \n",  "3 tanh HLA-A0203 0.5 32 \n",  "4 tanh HLA-A0203 0.5 32 \n",  "5 tanh HLA-A0203 0.5 32 \n",  "6 tanh HLA-A0201 0.5 32 \n",  "7 tanh HLA-A0201 0.5 32 \n",  "8 tanh HLA-A0201 0.5 32 \n",  "9 tanh HLA-A0201 0.5 32 \n",  "10 tanh HLA-A0201 0.5 32 \n",  "11 tanh HLA-A0201 0.5 32 \n",  "12 tanh HLA-A0201 0.5 32 \n",  "13 tanh HLA-A0201 0.5 32 \n",  "14 tanh HLA-A0201 0.5 32 \n",  "15 tanh HLA-A0201 0.5 32 \n",  "16 tanh HLA-A0201 0.5 32 \n",  "17 tanh HLA-A0201 0.5 32 \n",  "18 tanh HLA-A0203 0.5 32 \n",  "19 tanh HLA-A0203 0.5 32 \n",  "20 tanh HLA-A0203 0.5 32 \n",  "21 tanh HLA-A0203 0.5 32 \n",  "22 tanh HLA-A0203 0.5 32 \n",  "23 tanh HLA-A0203 0.5 32 \n",  "24 tanh HLA-A0301 0.5 32 \n",  "25 tanh HLA-A0301 0.5 32 \n",  "26 tanh HLA-A0301 0.5 32 \n",  "27 tanh HLA-A0301 0.5 32 \n",  "28 tanh HLA-A0301 0.5 32 \n",  "29 tanh HLA-A0301 0.5 32 \n",  ".. ... ... ... ... \n",  "114 tanh HLA-A0203 0.5 32 \n",  "115 tanh HLA-A0203 0.5 32 \n",  "116 tanh HLA-A0203 0.5 32 \n",  "117 tanh HLA-A0203 0.5 32 \n",  "118 tanh HLA-A0203 0.5 32 \n",  "119 tanh HLA-A0203 0.5 32 \n",  "120 tanh HLA-A0201 0.5 32 \n",  "121 tanh HLA-A0201 0.5 32 \n",  "122 tanh HLA-A0201 0.5 32 \n",  "123 tanh HLA-A0201 0.5 32 \n",  "124 tanh HLA-A0201 0.5 32 \n",  "125 tanh HLA-A0201 0.5 32 \n",  "126 tanh HLA-A0301 0.5 32 \n",  "127 tanh HLA-A0301 0.5 32 \n",  "128 tanh HLA-A0301 0.5 32 \n",  "129 tanh HLA-A0301 0.5 32 \n",  "130 tanh HLA-A0301 0.5 32 \n",  "131 tanh HLA-A0301 0.5 32 \n",  "132 tanh HLA-A0203 0.5 32 \n",  "133 tanh HLA-A0203 0.5 32 \n",  "134 tanh HLA-A0203 0.5 32 \n",  "135 tanh HLA-A0203 0.5 32 \n",  "136 tanh HLA-A0203 0.5 32 \n",  "137 tanh HLA-A0203 0.5 32 \n",  "138 tanh HLA-A0301 0.5 32 \n",  "139 tanh HLA-A0301 0.5 32 \n",  "140 tanh HLA-A0301 0.5 32 \n",  "141 tanh HLA-A0301 0.5 32 \n",  "142 tanh HLA-A0301 0.5 32 \n",  "143 tanh HLA-A0301 0.5 32 \n",  "\n",  " fit_time fraction_negative impute layer_sizes \\\n",  "0 1.726369 0.2 False [64] \n",  "1 3.950002 0.2 True [64] \n",  "2 3.298754 0.2 False [64] \n",  "3 4.956206 0.2 True [64] \n",  "4 4.355083 0.2 True [64] \n",  "5 2.193765 0.2 False [64] \n",  "6 18.507652 0.2 True [64] \n",  "7 19.386850 0.2 False [64] \n",  "8 21.011101 0.2 True [64] \n",  "9 17.923310 0.2 False [64] \n",  "10 20.640437 0.2 False [64] \n",  "11 21.971126 0.2 True [64] \n",  "12 4.527541 0.2 True [64] \n",  "13 2.624935 0.2 False [64] \n",  "14 4.525949 0.2 True [64] \n",  "15 2.305879 0.2 False [64] \n",  "16 2.302873 0.2 False [64] \n",  "17 4.278460 0.2 True [64] \n",  "18 2.587237 0.2 False [64] \n",  "19 4.241571 0.2 True [64] \n",  "20 2.299999 0.2 False [64] \n",  "21 4.537592 0.2 True [64] \n",  "22 2.629939 0.2 False [64] \n",  "23 4.249131 0.2 True [64] \n",  "24 4.012164 0.2 False [64] \n",  "25 5.880508 0.2 True [64] \n",  "26 5.461747 0.2 True [64] \n",  "27 3.169109 0.2 False [64] \n",  "28 5.930596 0.2 True [64] \n",  "29 3.956572 0.2 False [64] \n",  ".. ... ... ... ... \n",  "114 6.213101 0.2 True [64] \n",  "115 4.023137 0.2 False [64] \n",  "116 3.775757 0.2 False [64] \n",  "117 5.512664 0.2 True [64] \n",  "118 4.925610 0.2 True [64] \n",  "119 4.052275 0.2 False [64] \n",  "120 4.304954 0.2 True [64] \n",  "121 3.138064 0.2 False [64] \n",  "122 4.056201 0.2 True [64] \n",  "123 2.988854 0.2 False [64] \n",  "124 3.142403 0.2 False [64] \n",  "125 4.405436 0.2 True [64] \n",  "126 3.030754 0.2 True [64] \n",  "127 1.975326 0.2 False [64] \n",  "128 3.172742 0.2 True [64] \n",  "129 1.795594 0.2 False [64] \n",  "130 1.725392 0.2 False [64] \n",  "131 3.258902 0.2 True [64] \n",  "132 3.781140 0.2 True [64] \n",  "133 2.310814 0.2 False [64] \n",  "134 2.403544 0.2 False [64] \n",  "135 3.863117 0.2 True [64] \n",  "136 2.412391 0.2 False [64] \n",  "137 3.622578 0.2 True [64] \n",  "138 3.540570 0.2 False [64] \n",  "139 5.051757 0.2 True [64] \n",  "140 4.327855 0.2 False [64] \n",  "141 5.015249 0.2 True [64] \n",  "142 3.766955 0.2 False [64] \n",  "143 5.047997 0.2 True [64] \n",  "\n",  " model_params n_training_epochs \\\n",  "0 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "1 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "2 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "3 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "4 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "5 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "6 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "7 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "8 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "9 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "10 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "11 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "12 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "13 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "14 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "15 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "16 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "17 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "18 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "19 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "20 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "21 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "22 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "23 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "24 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "25 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "26 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "27 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "28 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "29 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  ".. ... ... \n",  "114 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "115 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "116 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "117 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "118 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "119 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "120 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "121 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "122 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "123 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "124 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "125 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "126 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "127 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "128 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "129 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "130 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "131 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "132 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "133 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "134 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "135 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "136 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "137 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "138 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "139 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "140 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "141 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "142 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "143 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... 250 \n",  "\n",  " ... test_auc test_f1 \\\n",  "0 ... 0.787809 0.483003 \n",  "1 ... 0.815350 0.513711 \n",  "2 ... 0.782669 0.536396 \n",  "3 ... 0.790377 0.546405 \n",  "4 ... 0.813876 0.515342 \n",  "5 ... 0.794476 0.461988 \n",  "6 ... 0.937106 0.778107 \n",  "7 ... 0.937746 0.783494 \n",  "8 ... 0.939144 0.763819 \n",  "9 ... 0.939693 0.768324 \n",  "10 ... 0.937720 0.758129 \n",  "11 ... 0.937999 0.765993 \n",  "12 ... 0.761049 0.068106 \n",  "13 ... 0.510052 0.019291 \n",  "14 ... 0.798850 0.223807 \n",  "15 ... 0.624110 0.087189 \n",  "16 ... 0.696474 0.049908 \n",  "17 ... 0.803198 0.093205 \n",  "18 ... 0.678122 0.096665 \n",  "19 ... 0.757714 0.073638 \n",  "20 ... 0.715024 0.055145 \n",  "21 ... 0.795471 0.087374 \n",  "22 ... 0.732625 0.214759 \n",  "23 ... 0.790998 0.251858 \n",  "24 ... 0.835806 0.397087 \n",  "25 ... 0.847088 0.408085 \n",  "26 ... 0.843878 0.447610 \n",  "27 ... 0.837747 0.383466 \n",  "28 ... 0.815691 0.509604 \n",  "29 ... 0.820578 0.519844 \n",  ".. ... ... ... \n",  "114 ... 0.878032 0.664391 \n",  "115 ... 0.875739 0.660449 \n",  "116 ... 0.872028 0.689700 \n",  "117 ... 0.873473 0.694524 \n",  "118 ... 0.871015 0.657324 \n",  "119 ... 0.870450 0.666667 \n",  "120 ... 0.879427 0.621351 \n",  "121 ... 0.876158 0.621445 \n",  "122 ... 0.902595 0.708851 \n",  "123 ... 0.900432 0.704242 \n",  "124 ... 0.901071 0.689683 \n",  "125 ... 0.904398 0.716724 \n",  "126 ... 0.782518 0.234125 \n",  "127 ... 0.765161 0.200000 \n",  "128 ... 0.818226 0.231626 \n",  "129 ... 0.817788 0.216676 \n",  "130 ... 0.803556 0.450473 \n",  "131 ... 0.815190 0.426157 \n",  "132 ... 0.821252 0.574111 \n",  "133 ... 0.810397 0.578774 \n",  "134 ... 0.844897 0.605942 \n",  "135 ... 0.836725 0.605171 \n",  "136 ... 0.841734 0.636049 \n",  "137 ... 0.851222 0.657169 \n",  "138 ... 0.866753 0.541187 \n",  "139 ... 0.878847 0.608206 \n",  "140 ... 0.864583 0.574333 \n",  "141 ... 0.864002 0.529172 \n",  "142 ... 0.873587 0.547294 \n",  "143 ... 0.872979 0.576398 \n",  "\n",  " test_size test_tau train_auc train_f1 train_size train_tau \\\n",  "0 5315 0.372769 0.998302 0.947368 50 0.770267 \n",  "1 5315 0.416518 0.998302 0.947368 50 0.824931 \n",  "2 5375 0.384346 1.000000 0.918919 50 0.804755 \n",  "3 5375 0.394875 1.000000 0.918919 50 0.831692 \n",  "4 5369 0.436850 0.992381 0.903226 50 0.872410 \n",  "5 5369 0.401830 0.996190 0.888889 50 0.842442 \n",  "6 6578 0.566801 0.965124 0.856716 1000 0.651183 \n",  "7 6578 0.566767 0.964381 0.864785 1000 0.649207 \n",  "8 6478 0.580869 0.964396 0.842809 1000 0.617125 \n",  "9 6478 0.581335 0.964512 0.836425 1000 0.616712 \n",  "10 6537 0.569621 0.958349 0.828179 1000 0.623817 \n",  "11 6537 0.571824 0.961578 0.841930 1000 0.633758 \n",  "12 9537 0.331257 1.000000 1.000000 10 1.000000 \n",  "13 9537 0.025539 1.000000 1.000000 10 0.955556 \n",  "14 9474 0.383791 1.000000 1.000000 10 0.955556 \n",  "15 9474 0.152300 1.000000 1.000000 10 0.955556 \n",  "16 9520 0.250387 1.000000 0.857143 10 1.000000 \n",  "17 9520 0.388869 1.000000 1.000000 10 1.000000 \n",  "18 5489 0.220779 1.000000 1.000000 10 0.966092 \n",  "19 5489 0.335474 1.000000 1.000000 10 0.966092 \n",  "20 5512 0.289235 1.000000 1.000000 10 0.816497 \n",  "21 5512 0.395721 1.000000 1.000000 10 0.816497 \n",  "22 5514 0.315640 1.000000 1.000000 10 0.920087 \n",  "23 5514 0.395806 1.000000 1.000000 10 0.920087 \n",  "24 5864 0.424022 0.995000 0.923077 100 0.673731 \n",  "25 5864 0.440685 0.995625 0.918919 100 0.671134 \n",  "26 5830 0.431866 1.000000 0.913043 100 0.741684 \n",  "27 5830 0.421623 0.994133 0.863636 100 0.730850 \n",  "28 5878 0.398072 1.000000 0.956522 100 0.697136 \n",  "29 5878 0.404652 1.000000 0.978723 100 0.688995 \n",  ".. ... ... ... ... ... ... \n",  "114 4860 0.532049 0.968405 0.830508 200 0.723689 \n",  "115 4860 0.527987 0.968865 0.823529 200 0.718398 \n",  "116 4877 0.530505 0.978005 0.876923 200 0.739647 \n",  "117 4877 0.532519 0.979576 0.885496 200 0.747955 \n",  "118 4881 0.522220 0.986211 0.894309 200 0.702950 \n",  "119 4881 0.519812 0.985641 0.868852 200 0.691445 \n",  "120 8896 0.486743 0.989302 0.930693 150 0.727620 \n",  "121 8896 0.482924 0.985411 0.910891 150 0.715581 \n",  "122 8807 0.521010 0.993192 0.941176 150 0.756157 \n",  "123 8807 0.514215 0.991830 0.932039 150 0.747129 \n",  "124 8906 0.517426 0.982623 0.893617 150 0.771905 \n",  "125 8906 0.519347 0.987674 0.916667 150 0.784575 \n",  "126 6006 0.340898 1.000000 1.000000 50 0.585550 \n",  "127 6006 0.315647 1.000000 1.000000 50 0.538776 \n",  "128 6029 0.398189 0.990676 0.842105 50 0.803063 \n",  "129 6029 0.391441 0.995338 0.842105 50 0.813271 \n",  "130 5963 0.377142 1.000000 0.941176 50 0.759519 \n",  "131 5963 0.401121 1.000000 0.909091 50 0.754484 \n",  "132 5155 0.445329 0.995714 0.918033 100 0.792008 \n",  "133 5155 0.426463 0.994762 0.915254 100 0.779992 \n",  "134 5188 0.476586 0.996568 0.942857 100 0.825217 \n",  "135 5188 0.469238 0.996139 0.942857 100 0.823530 \n",  "136 5129 0.487334 0.998713 0.942857 100 0.764532 \n",  "137 5129 0.499891 0.999571 0.911765 100 0.762436 \n",  "138 5586 0.451763 0.964156 0.759259 200 0.641774 \n",  "139 5586 0.470898 0.967436 0.796610 200 0.641774 \n",  "140 5631 0.456183 0.979436 0.787234 200 0.624748 \n",  "141 5631 0.457263 0.987586 0.769231 200 0.628813 \n",  "142 5604 0.471515 0.983197 0.833333 200 0.620050 \n",  "143 5604 0.468377 0.988464 0.857143 200 0.642805 \n",  "\n",  " layer0_size model_string \n",  "0 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "1 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "2 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "3 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "4 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "5 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "6 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "7 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "8 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "9 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "10 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "11 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "12 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "13 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "14 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "15 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "16 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "17 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "18 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "19 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "20 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "21 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "22 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "23 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "24 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "25 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "26 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "27 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "28 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "29 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  ".. ... ... \n",  "114 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "115 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "116 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "117 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "118 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "119 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "120 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "121 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "122 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "123 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "124 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "125 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "126 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "127 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "128 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "129 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "130 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "131 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "132 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "133 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "134 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "135 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "136 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "137 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "138 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "139 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "140 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "141 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "142 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "143 64 {'pretrain_decay': '1 / (1+epoch)**2', 'dropou... \n",  "\n",  "[144 rows x 21 columns]"  ]  },  "execution_count": 2,  "metadata": {},  "output_type": "execute_result"  }  ],  "source": [  "impute_comparison = pandas.read_csv(\"../data/impute_comparison.csv\")\n",  "impute_comparison"  ]  },  {  "cell_type": "code",  "execution_count": 23,  "metadata": {  "collapsed": false  },  "outputs": [  {  "data": {  "image/png": 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eZUfGNgBGdTyXuwfdwwXdLqxx3rJyJSWwYoVZa7NmjfknsH17F7NmlXLddTU3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\nAElFTkSuQmCC\n",  "text/plain": [  ""  ]  },  "metadata": {},  "output_type": "display_data"  }  ],  "source": [  "pyplot.figure(figsize=(8,2))\n",  "\n",  "for (i, (allele, data)) in enumerate(impute_comparison.groupby(\"allele\")):\n",  " pyplot.subplot(1, 3, i + 1, sharey=pyplot.gca())\n",  " grouped = data.groupby([\"train_size\", \"impute\"]).test_auc.mean().to_frame().reset_index()\n",  " grouped[\"std_error\"] = data.groupby([\"train_size\", \"impute\"]).test_auc.std().to_frame().reset_index().test_auc\n",  " for impute in [True, False]:\n",  " sub = grouped[grouped.impute == impute]\n",  " pyplot.errorbar(\n",  " x=sub.train_size.values,\n",  " y=sub.test_auc.values,\n",  " yerr=sub.std_error.values,\n",  " label=(\"with\" if impute else \"without\") + \" imputation\",\n",  " color='green' if impute else 'blue')\n",  " if i == 0:\n",  " pyplot.legend(loc='lower right')\n",  " #pyplot.xlim(xmin=0, xmax=1000)\n",  " pyplot.title(allele)\n",  " pyplot.xscale(\"log\")\n",  " if i == 0:\n",  " pyplot.ylabel(\"AUC\")\n",  " if i == 1:\n",  " pyplot.xlabel(\"Train size\")\n",  " pyplot.savefig(\"../figures/impute_comparison.pdf\")\n",  "\n",  "pyplot.tight_layout()\n"  ]  }  ],  "metadata": {  "kernelspec": {  "display_name": "Python 2",  "language": "python",  "name": "python2"  },  "language_info": {  "codemirror_mode": {  "name": "ipython",  "version": 2  },  "file_extension": ".py",  "mimetype": "text/x-python",  "name": "python",  "nbconvert_exporter": "python",  "pygments_lexer": "ipython2",  "version": "2.7.10"  }  },  "nbformat": 4,  "nbformat_minor": 0  }  diff --git a/notebooks/validation results analysis.ipynb b/notebooks/validation results analysis.ipynb  index 2e738d8..a20cc70 100644  --- a/notebooks/validation results analysis.ipynb  +++ b/notebooks/validation results analysis.ipynb 
...
"cells": [  {  "cell_type": "code",  "execution_count": 42, 1,  "metadata": {  "collapsed": false  },  "outputs": [], [  {  "name": "stderr",  "output_type": "stream",  "text": [  "Using Theano backend.\n",  "/Users/tim/venvs/analysis-venv-2.7/lib/python2.7/site-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",  " warnings.warn(self.msg_depr % (key, alt_key))\n"  ]  },  {  "name": "stdout",  "output_type": "stream",  "text": [  "Couldn't import dot_parser, loading of dot files will not be possible.\n"  ]  },  {  "name": "stderr",  "output_type": "stream",  "text": [  "/Users/tim/venvs/analysis-venv-2.7/lib/python2.7/site-packages/IPython/html.py:14: ShimWarning: The `IPython.html` package has been deprecated. You should import from `notebook` instead. `IPython.html.widgets` has moved to `ipywidgets`.\n",  " \"`IPython.html.widgets` has moved to `ipywidgets`.\", ShimWarning)\n"  ]  }  ],  "source": [  "import mhcflurry, seaborn, numpy, pandas, pickle, sklearn, collections, scipy, time\n",  "import mhcflurry.dataset\n", 
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},  {  "cell_type": "code",  "execution_count": 13, 2,  "metadata": {  "collapsed": true  }, 
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},  {  "cell_type": "code",  "execution_count": 14, 3,  "metadata": {  "collapsed": false  }, 
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},  {  "cell_type": "code",  "execution_count": 15, 4,  "metadata": {  "collapsed": true  }, 
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},  {  "cell_type": "code",  "execution_count": 16, 5,  "metadata": {  "collapsed": false  }, 
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" \n",  " dropout_probability\n",  " embedding_output_dim\n",  " fraction_negative\n",  " impute\n",  " layer_sizes\n",  " layer_size\n", 
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" \n",  " \n",  " 0\n",  " 0.0\n", 0.5\n",  " 32\n",  " 0.0\n",  " False\n",  " [64]\n",  " 64\n",  " \n",  " \n",  " 1\n",  " 0.0\n",  " 8\n",  " False\n",  " [4]\n",  " 4\n",  " \n",  " \n",  " 2\n",  " 0.5\n",  " 32\n",  " False\n", 0.0\n",  " True\n",  " [64]\n",  " 64\n",  " \n",  " \n",  " 3\n", 2\n",  " 0.5\n",  " 8\n",  " False\n",  " [4]\n",  " 4\n",  " \n",  " \n",  " 4\n",  " 0.0\n",  " 32\n",  " True\n", 0.2\n",  " False\n",  " [64]\n",  " 64\n",  " \n",  " \n",  " 5\n",  " 0.0\n",  " 8\n",  " True\n",  " [4]\n",  " 4\n",  " \n",  " \n",  " 6\n", 3\n",  " 0.5\n",  " 32\n",  " 0.2\n",  " True\n",  " [64]\n",  " 64\n",  " \n",  " \n",  " 7\n",  " 0.5\n",  " 8\n",  " True\n",  " [4]\n",  " 4\n",  " \n",  " \n",  "\n",  ""  ],  "text/plain": [  " dropout_probability embedding_output_dim fraction_negative  impute layer_sizes layer_size\n", \\\n",  "0 0.0 0.5  32 0.0  False [64] 64\n", \n",  "1 0.5 32  0.0 8 False [4] 4\n", True \n",  "2 0.5 32 0.2  False [64] 64\n", \n",  "3 0.58 False [4] 4\n",  "4 0.0  32 0.2  True \n",  "\n",  " layer_sizes layer_size \n",  "0  [64] 64\n",  "5 0.0 8 True [4] 4\n",  "6 0.5 32 True 64 \n",  "1  [64] 64\n",  "7 0.5 8 True [4] 4" 64 \n",  "2 [64] 64 \n",  "3 [64] 64 "  ]  },  "execution_count": 16, 5,  "metadata": {},  "output_type": "execute_result"  } 
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},  {  "cell_type": "code",  "execution_count": 17, 6,  "metadata": {  "collapsed": false,  "scrolled": true 
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" \n",  " dropout_probability\n",  " embedding_output_dim\n",  " fraction_negative\n",  " impute\n",  " layer_sizes\n",  " layer_size\n", 
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" \n",  " \n",  " 0\n",  " 0.0\n", 0.5\n",  " 32\n",  " 0.0\n",  " False\n",  " [64]\n",  " 64\n",  " 0\n",  " big\n", big dropout\n",  " \n",  " \n",  " 1\n",  " 0.5\n",  " 32\n",  " 0.0\n",  " 8\n", True\n",  " False\n", [64]\n",  " [4]\n",  " 4\n", 64\n",  " 1\n",  " small\n", big dropout impute\n",  " \n",  " \n",  " 2\n",  " 0.5\n",  " 32\n",  " 0.2\n",  " False\n",  " [64]\n",  " 64\n", 
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" \n",  " 3\n",  " 0.5\n",  " 8\n",  " False\n",  " [4]\n",  " 4\n",  " 3\n",  " small dropout\n",  " \n",  " \n",  " 4\n",  " 0.0\n",  " 32\n",  " True\n",  " [64]\n",  " 64\n",  " 4\n",  " big impute\n",  " \n",  " \n",  " 5\n",  " 0.0\n",  " 8\n",  " True\n",  " [4]\n",  " 4\n",  " 5\n",  " small impute\n",  " \n",  " \n",  " 6\n",  " 0.5\n",  " 32\n",  " 0.2\n",  " True\n",  " [64]\n",  " 64\n",  " 6\n", 3\n",  " big dropout impute\n",  " \n",  " \n",  " 7\n",  " 0.5\n",  " 8\n",  " True\n",  " [4]\n",  " 4\n",  " 7\n",  " small dropout impute\n",  " \n",  " \n",  "\n",  ""  ],  "text/plain": [  " dropout_probability embedding_output_dim fraction_negative  imputelayer_sizes layer_size  \\\n", "0 0.0 0.5  32 0.0  False[64] 64  \n", "1 0.5 32  0.0 8 False [4] 4 True  \n", "2 0.5 32 0.2  False[64] 64  \n", "3 0.58 False [4] 4 \n",  "4 0.0 32 True [64] 64 \n",  "5 0.0 8 True [4] 4 \n",  "6 0.5  32 0.2  True[64] 64 \n",  "7 0.5 8 True [4] 4  \n", "\n",  " layer_sizes layer_size  num name \n", "0 [64] 64  0 big dropout  \n", "1 [64] 64  1 small big dropout impute  \n", "2 [64] 64  2 big dropout \n", "3 [64] 64  3small dropout \n",  "4 4 big impute \n",  "5 5 small impute \n",  "6 6  big dropout impute\n",  "7 7 small dropout impute  " ]  },  "execution_count": 17, 6,  "metadata": {},  "output_type": "execute_result"  } 
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},  {  "cell_type": "code",  "execution_count": 18, 7,  "metadata": {  "collapsed": false  }, 
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"data": {  "text/plain": [  "name\n",  "big[0]\n",  "big  dropout [2]\n", [0, 2]\n",  "big dropout impute [6]\n",  "big impute [4]\n",  "small [1]\n",  "small dropout [3]\n",  "small dropout impute [7]\n",  "small impute [5]\n", [1, 3]\n",  "Name: num, dtype: object"  ]  },  "execution_count": 18, 7,  "metadata": {},  "output_type": "execute_result"  } 
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},  {  "cell_type": "code",  "execution_count": 19, 8,  "metadata": {  "collapsed": false  }, 
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27680 rows × 15 11  columns

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"27679 Mamu-A02 YVVQMLARL 9 152.405275 232.273680 \n",  "\n",  " netmhcpan smmpmbec_cpp mhcflurry 0 mhcflurry 1 mhcflurry 2 \\\n",  "0 711.213514 438.530698 5.451339 101.031088 820.899047 1442.126816 1230.322823 1277.631488  \n", "1 785.235635 10351.421667 29611.681275 10194.929401 13355.273001 11473.595261 12327.145996 12685.090199  \n", "2 7.516229 28.054336 13.543497 75.988225 32.385539 18.691766 31.570829 21.826293  \n", "3 9.749896 25.703958 5.779730 10.814566 7.445489 4.279559 4.320936 5.741265  \n", "4 8.336812 28.773984 1.740462 4.376349 5.543891 3.503900 3.545383 4.403022  \n", "5 114.815362 187.068214 22.095282 50.841470 241.358596 325.846385 203.968140 378.604284  \n", "6 389.045145 200.909281 28.622502 24.963412 178.902143 38.287690 80.587676 100.644543  \n", "7 493.173804 295.120923 1296.377555 857.209411 488.717022 450.264145 471.683475 817.682084  \n", "8 77.268059 38.459178 174.309147 45.290250 130.499676 103.544944 116.724333 247.518551  \n", "9 597.035287 225.423921 6239.534375 4963.755498 720.436304 1401.468773 1236.987110 3250.940299  \n", "10 2032.357011 698.232404 13865.829459 1112.259791 2637.347063 2016.574129 2718.284664 1352.770844  \n", "11 214.783047 378.442585 19.963773 4647.659834 6820.757678 4188.596184 5007.824420 3391.821865  \n", "12 5176.068320 1545.254440 19773.885246 12361.514588 10056.465902 5005.722397 7991.819257 6685.943936  \n", "13 1023.292992 557.185749 942.769484 6507.812960 1035.591768 1337.240540 1731.062904 1986.621196  \n", "14 501.187234 822.242650 1039.066462 653.188436 282.606450 100.960679 123.063538 238.241607  \n", "15 1918.668741 1870.682140 147.843011 222.644999 3785.610395 680.326369 1587.502550 856.363458  \n", "16 228.559880 200.447203 12.736352 100.895525 393.284191 310.702631 288.458942 326.145448  \n", "17 623.734835 286.417797 187.385974 491.212649 705.914186 271.301464 156.373254 219.784353  \n", "18 5152.286446 679.203633 1030.169279 1593.111056 3143.834356 2515.938301 3103.319839 2768.676318  \n", "19 2172.701179 1927.524913 37.175585 3248.903081 3679.325951 1076.149383 1829.973751 781.507334  \n", "20 29.580125 63.826349 2.609560 14.783175 22.525098 13.751390 7.096296 41.477242  \n", "21 3206.269325 595.662144 18367.825270 8699.676803 3662.742351 4256.550204 2679.336713 3891.360910  \n", "22 679.203633 444.631267 1002.270321 271.628670 769.960883 247.510091 1257.020204 581.139814  \n", "23 1857.804455 325.087297 6694.233166 361.287857 2602.563945 2107.373160 4434.869234 3599.445945  \n", "24 46989.410861 440554.863507 36138.465971 39872.294037 34837.346341 38311.680156 37864.670399 40542.758854  \n", "25 12882.495517 7816.278046 32436.969144 12881.899896 17740.278650 15301.706435 19487.254968 19015.171428  \n", "26 202.768272 260.015956 221.733235 197.460982 153.420663 281.659673 230.446704 131.354673  \n", "27 9885.530947 26242.185434 30271.867532 14368.146625 21138.351414 23526.526949 23286.457871 20124.099341  \n", "28 12560.299637 35399.734108 44200.734066 34653.288740 27071.471077 29954.637899 31195.855765 30063.373727  \n", "29 2285.598803 1811.340093 15786.995089 72.916893 3410.631472 2575.449524 1534.354687 2611.968281  \n", "... ... ... ... ... ... \n",  "27650 10023.052381 48.865236 2065.341574 728.983555 2792.654265 1591.049576 1298.358917 16338.032773  \n", "27651 22233.098906 1409.288798 227.126009 606.293671 353.449644 261.780710 303.622055 6300.597643  \n", "27652 8709.635900 4477.133042 151.811211 4869.354056 1403.358566 2698.313836 1549.550832 9385.257012  \n", "27653 5105.050000 51.522864 209.030747 41.744716 60.784907 53.868203 36.758850 530.426107  \n", "27654 49.431069 6.324119 16.959545 8.121621 14.356819 8.342093 8.597771 5.255895  \n", "27655 15.346170 16.292960 9.174435 6.832824 25.246580 16.994110 14.152847 11.581538  \n", "27656 3863.669771 220.292646 79.919742 231.309007 53.090184 48.098444 55.920499 1522.610694  \n", "27657 3.090295 1.778279 2.600664 1.976217 6.495700 3.144389 6.683502 5.092176  \n", "27658 1753.880502 3111.716337 16.282788 2336.008591 532.048748 674.837597 555.696323 3025.303561  \n", "27659 2666.858665 42.559841 87.501604 439.277027 539.625724 300.914050 492.241431 2926.526438  \n", "27660 52.844525 91.833260 2.405268 4.438093 22.381857 30.779175 38.565790 20.406062  \n", "27661 1406.047524 14.487719 4.775581 11.403164 29.039302 53.892417 42.670705 302.341059  \n", "27662 11246.049740 588.843655 368.583423 114.116744 203.855168 223.802423 399.355915 2596.288840  \n", "27663 42.461956 40.550854 21.397735 145.943550 311.567850 146.972886 328.245742 227.028697  \n", "27664 13995.873226 3140.508694 18625.210534 13470.617022 10772.845684 12404.116293 9665.630283 18590.521692  \n", "27665 10046.157903 387.257645 2955.836543 7865.545579 6649.588325 6959.512936 5557.106811 12556.819304  \n", "27666 2349.632821 80.723503 246.849109 282.161004 301.224065 402.648574 718.399164 2726.886297  \n", "27667 5176.068320 1270.574105 37.992944 2084.788960 436.602256 693.193377 1000.767613 2026.422421  \n", "27668 5420.008904 797.994687 76.974552 1089.595023 261.575824 454.242824 904.169913 1463.422954  \n", "27669 7.744618 76.383578 160.400573 30.913585 66.086443 93.591134 61.590145 90.506850  \n", "27670 5701.642723 100.230524 58.603552 135.090210 49.812494 110.463817 286.472186 984.834053  \n", "27671 6025.595861 119.674053 4.324396 13.887901 6.294973 12.476188 15.759480 381.167625  \n", "27672 2844.461107 87.096359 3.220311 25.387794 9.983106 18.118964 32.871695 230.206292  \n", "27673 3872.576449 1954.339456 248.907741 1465.396277 1036.887847 807.577510 1192.040107 3950.178511  \n", "27674 6998.419960 32.734069 1284.004341 1146.690887 879.071962 627.329290 832.174798 3403.672519  \n", "27675 71.285303 108.143395 2853.412412 19.054451 275.667564 81.902887 124.100978 49.638987  \n", "27676 5610.479760 901.571138 9434.039198 24974.087871 5350.263772 6364.523809 10233.878501 14430.800761  \n", "27677 10046.157903 2600.159563 13239.877677 13230.273196 1927.724518 1830.258194 3487.883085 13554.295203  \n", "27678 6039.486294 10568.175092 7424.515180 13628.730953 13854.170383 13949.882776 11029.857942 20742.160346  \n", "27679 739.605275 105.681751 738.060365 2192.357405 1236.577935 1800.941914 1240.632578 1893.788242  \n", "\n",  " mhcflurry 3mhcflurry 4 mhcflurry 5 mhcflurry 6 mhcflurry 7  \n", "0 869.601741 59.834101 27.943191 608.394258 811.199864 364.773670  \n", "1 24772.895203 30971.969221 11948.433480 11875.604773 23080.234754 8186.470538  \n", "2 179.998789 315.922174 39.496692 19.949449 179.248142 17.622147  \n", "3 83.166802 2.613762 43.592036 5.861501 74.532864 4.149827  \n", "4 79.100063 4.624470 7.253820 4.515577 69.159665 3.479555  \n", "5 559.872375 550.332342 19.113564 287.854885 525.939006 133.562226  \n", "6 343.282060 18.001538 227.662890 110.146283 151.060043 31.089573  \n", "7 164.852471 4100.242979 557.652976 469.690670 116.277820 560.212784  \n", "8 211.171559 23.736367 34.675442 131.878046 175.714103 108.927801  \n", "9 503.218493 435.649890 1038.488658 651.353328 371.779976 1010.833977  \n", "10 2752.021063 12057.249762 585.894911 3369.383522 2528.991398 2722.914015  \n", "11 7011.871156 7122.612569 14412.432018 2920.347673 4482.395034 3764.403815  \n", "12 6799.103995 5418.650845 3587.423664 9205.116143 8595.917172 7121.915549  \n", "13 3993.317652 1981.230229 972.021179 1393.502636 4001.782051 1469.306302  \n", "14 1793.919036 557.954792 425.015655 222.671564 1341.140376 294.472511  \n", "15 2420.020910 174.925022 8753.176855 957.809744 2995.218818 483.283059  \n", "16 359.229730 745.285924 249.467407 163.333364 336.489205 231.132923  \n", "17 1106.050966 114.836547 142.075527 326.419418 1139.864466 96.644863  \n", "18 2424.417471 1388.073067 2320.410789 789.180375 1853.193862 1845.718431  \n", "19 2298.381230 424.242448 6223.620937 2154.508459 1658.177678 847.066732  \n", "20 253.259330 8.273311 59.201622 13.418418 259.264728 10.913079  \n", "21 7587.162092 25.805973 4508.746097 3429.652107 7870.240455 1372.866392  \n", "22 850.555894 1426.551051 1770.968157 575.079918 1104.023482 500.292469  \n", "23 2546.067959 16605.290279 3441.673321 2184.611795 8289.213593 2239.333205  \n", "24 33755.078310 42845.880010 43848.592509 35699.023249 32750.655338 40682.564705  \n", "25 27053.582736 44353.808145 14965.320060 16839.947094 27573.442469 17826.789421  \n", "26 478.963051 626.055500 69.118821 51.779863 375.973551 65.215567  \n", "27 26405.718159 32687.881124 23112.506599 21874.360223 25386.499837 19022.397273  \n", "28 29420.942415 29231.913839 28463.702047 28468.569166 29267.170707 31692.725154  \n", "29 4876.564589 28970.504950 148.173560 3717.315776 3904.625862 1343.507135  \n", "... ...... ... ... ...  \n", "27650 2108.415291 1089.314789 842.314932 2206.211966 1843.589845 20773.739040  \n", "27651 466.928279 1364.159023 578.793553 222.244468 367.454335 6083.006392  \n", "27652 2164.345626 3321.177031 7761.567568 1468.655916 1907.323170 11166.049632  \n", "27653 76.636019 371.454333 33.397569 82.581101 87.061756 452.322369  \n", "27654 30.118698 1.712349 4.407756 6.392747 37.085836 6.264340  \n", "27655 96.486682 98.324803 115.025999 14.946450 68.344064 10.941380  \n", "27656 114.949172 195.203057 10.991426 70.559725 108.703007 1633.521469  \n", "27657 25.555743 1.349125 5.080187 5.659219 23.263357 4.890292  \n", "27658 1530.365737 26.445342 2181.583492 500.952879 1294.191177 4587.142016  \n", "27659 897.089051 41.071072 488.572826 499.676471 855.272192 4100.385046  \n", "27660 40.885613 2.270725 5.268112 18.498812 36.389652 26.230448  \n", "27661 92.926272 40.752966 9.605551 35.842858 78.484408 732.230654  \n", "27662 305.630346 608.355582 340.968029 201.175200 243.791206 2072.687575  \n", "27663 398.864906 217.096787 155.443201 137.623483 236.299133 88.368151  \n", "27664 3235.366788 25285.992128 13285.242087 8539.736248 3084.491697 20819.981462  \n", "27665 2505.757913 8028.763257 6718.186614 4872.760597 2268.560672 17267.251833  \n", "27666 142.114753 137.068230 196.830786 307.109562 91.528719 2697.837580  \n", "27667 1043.930987 265.042232 499.003286 553.381852 879.689216 2606.895028  \n", "27668 799.875786 112.878412 1194.284269 438.603157 638.247050 2544.400702  \n", "27669 138.080623 40.750919 28.757507 75.340023 154.791104 69.346296  \n", "27670 39.532117 144.020068 96.212073 127.612192 25.804109 1610.278911  \n", "27671 26.782833 8.707189 18.227903 12.921459 19.729256 440.313942  \n", "27672 26.259662 18.379812 30.134358 16.790709 18.481367 849.650751  \n", "27673 1474.221609 162.130308 1218.408182 808.684758 1117.764150 4063.500410  \n", "27674 1194.848606 348.979832 78.475399 755.831662 1054.595956 3362.152015  \n", "27675 327.909013 2496.259480 32.585823 130.402003 248.016529 79.299313  \n", "27676 961.282868 17928.704583 4489.577760 5397.953890 774.583726 18974.479542  \n", "27677 299.745625 9076.175041 270.068237 2870.278654 173.133909 12282.559495  \n", "27678 9970.894235 28950.523005 13186.826643 11222.578955 9287.520258 25558.849591  \n", "27679 589.164269 4539.740974 756.158234 780.810863 384.069563 4769.034670  \n", "\n",  "[27680 rows x 15 11  columns]" ]  },  "execution_count": 19, 8,  "metadata": {},  "output_type": "execute_result"  } 
...
},  {  "cell_type": "code",  "execution_count": 20, 9,  "metadata": {  "collapsed": false  }, 
...
" mhcflurry 0\n",  " mhcflurry 1\n",  " mhcflurry 2\n",  " ...\n", mhcflurry 3\n",  " mhcflurry ensemble big dropout\n",  " mhcflurry ensemble big dropout impute\n",  " mhcflurry ensemble big impute\n",  " mhcflurry ensemble small\n",  " mhcflurry ensemble small dropout\n",  " mhcflurry ensemble small dropout impute\n",  " mhcflurry ensemble small impute\n",  " mhcflurry ensemble all\n",  " mhcflurry ensemble all impute\n",  " mhcflurry ensemble all not impute\n", 
...
" 154.881662\n",  " 711.213514\n",  " 438.530698\n",  " 5.451339\n", 1442.126816\n",  " 101.031088\n", 1230.322823\n",  " 820.899047\n", 1277.631488\n",  " ...\n", 364.773670\n",  " 820.899047\n", 1357.389638\n",  " 608.394258\n", 669.917436\n",  " 59.834101\n", 953.592673\n",  " 101.031088\n", 669.917436\n",  " 869.601741\n",  " 811.199864\n",  " 27.943191\n",  " 154.485692\n",  " 169.486237\n",  " 140.812784\n", 1357.389638\n",  " \n",  " \n",  " 1\n", 
...
" 6456.542290\n",  " 785.235635\n",  " 10351.421667\n",  " 29611.681275\n", 11473.595261\n",  " 10194.929401\n", 12327.145996\n",  " 13355.273001\n", 12685.090199\n",  " ...\n", 8186.470538\n",  " 13355.273001\n", 12064.144843\n",  " 11875.604773\n", 10045.686513\n",  " 30971.969222\n", 11008.751843\n",  " 10194.929401\n", 10045.686513\n",  " 24772.895203\n",  " 23080.234753\n",  " 11948.433480\n",  " 17811.749199\n",  " 17846.125248\n",  " 17777.439368\n", 12064.144843\n",  " \n",  " \n",  " 2\n", 
...
" 17.458222\n",  " 7.516229\n",  " 28.054336\n",  " 13.543497\n", 18.691766\n",  " 75.988225\n", 31.570829\n",  " 32.385539\n", 21.826293\n",  " ...\n", 17.622147\n",  " 32.385539\n", 20.198316\n",  " 19.949449\n", 23.586984\n",  " 315.922174\n", 21.826987\n",  " 75.988225\n", 23.586984\n",  " 179.998789\n",  " 179.248142\n",  " 39.496692\n",  " 63.599402\n",  " 81.730019\n",  " 49.490798\n", 20.198316\n",  " \n",  " \n",  " 3\n", 
...
" 9.638290\n",  " 9.749896\n",  " 25.703958\n",  " 5.779730\n", 4.279559\n",  " 10.814566\n", 4.320936\n",  " 7.445489\n", 5.741265\n",  " ...\n", 4.149827\n",  " 7.445489\n", 4.956822\n",  " 5.861501\n", 4.234517\n",  " 2.613762\n", 4.581457\n",  " 10.814566\n", 4.234517\n",  " 83.166802\n",  " 74.532864\n",  " 43.592036\n",  " 14.474338\n",  " 14.936797\n",  " 14.026198\n", 4.956822\n",  " \n",  " \n",  " 4\n", 
...
" 8.550667\n",  " 8.336812\n",  " 28.773984\n",  " 1.740462\n", 3.503900\n",  " 4.376349\n", 3.545383\n",  " 5.543891\n", 4.403022\n",  " ...\n", 3.479555\n",  " 5.543891\n", 3.927817\n",  " 4.515577\n", 3.512315\n",  " 4.624470\n", 3.714261\n",  " 4.376349\n", 3.512315\n",  " 79.100063\n",  " 69.159665\n",  " 7.253820\n",  " 8.769914\n",  " 10.116929\n",  " 7.602247\n", 3.927817\n",  " \n",  " \n",  " 5\n", 
...
" 252.348077\n",  " 114.815362\n",  " 187.068214\n",  " 22.095282\n", 325.846385\n",  " 50.841470\n", 203.968140\n",  " 241.358596\n", 378.604284\n",  " ...\n", 133.562226\n",  " 241.358596\n", 351.236156\n",  " 287.854885\n", 165.052836\n",  " 550.332342\n", 240.774840\n",  " 50.841470\n", 165.052836\n",  " 559.872375\n",  " 525.939006\n",  " 19.113564\n",  " 148.908039\n",  " 199.764781\n",  " 110.998566\n", 351.236156\n",  " \n",  " \n",  " 6\n", 
...
" 199.986187\n",  " 389.045145\n",  " 200.909281\n",  " 28.622502\n", 38.287690\n",  " 24.963412\n", 80.587676\n",  " 178.902143\n", 100.644543\n",  " ...\n", 31.089573\n",  " 178.902143\n", 62.076139\n",  " 110.146283\n", 50.054335\n",  " 18.001538\n", 55.742083\n",  " 24.963412\n", 50.054335\n",  " 343.282060\n",  " 151.060043\n",  " 227.662890\n",  " 86.000294\n",  " 90.872049\n",  " 81.389719\n", 62.076139\n",  " \n",  " \n",  " 7\n", 
...
" 1059.253725\n",  " 493.173804\n",  " 295.120923\n",  " 1296.377555\n", 450.264145\n",  " 857.209411\n", 471.683475\n",  " 488.717022\n", 817.682084\n",  " ...\n", 560.212784\n",  " 488.717022\n", 606.772547\n",  " 469.690670\n", 514.045827\n",  " 4100.242979\n", 558.488045\n",  " 857.209411\n", 514.045827\n",  " 164.852471\n",  " 116.277820\n",  " 557.652976\n",  " 570.238747\n",  " 594.457223\n",  " 547.006945\n", 606.772547\n",  " \n",  " \n",  " 8\n", 
...
" 66.374307\n",  " 77.268059\n",  " 38.459178\n",  " 174.309147\n", 103.544944\n",  " 45.290250\n", 116.724333\n",  " 130.499676\n", 247.518551\n",  " ...\n", 108.927801\n",  " 130.499676\n", 160.091519\n",  " 131.878046\n", 112.758702\n",  " 23.736367\n", 134.356659\n",  " 45.290250\n", 112.758702\n",  " 211.171559\n",  " 175.714103\n",  " 34.675442\n",  " 89.587650\n",  " 66.085145\n",  " 121.448579\n", 160.091519\n",  " \n",  " \n",  " 9\n", 
...
" 547.015963\n",  " 597.035287\n",  " 225.423921\n",  " 6239.534375\n", 1401.468773\n",  " 4963.755498\n", 1236.987110\n",  " 720.436304\n", 3250.940299\n",  " ...\n", 1010.833977\n",  " 720.436304\n", 2134.500249\n",  " 651.353328\n", 1118.207763\n",  " 435.649890\n", 1544.931956\n",  " 4963.755498\n", 1118.207763\n",  " 503.218493\n",  " 371.779976\n",  " 1038.488658\n",  " 1026.229732\n",  " 575.321362\n",  " 1830.537737\n", 2134.500249\n",  " \n",  " \n",  " 10\n", 
...
" 1686.553025\n",  " 2032.357011\n",  " 698.232404\n",  " 13865.829459\n", 2016.574129\n",  " 1112.259791\n", 2718.284664\n",  " 2637.347063\n", 1352.770844\n",  " ...\n", 2722.914015\n",  " 2637.347063\n", 1651.654530\n",  " 3369.383522\n", 2720.598355\n",  " 12057.249762\n", 2119.785036\n",  " 1112.259791\n", 2720.598355\n",  " 2752.021063\n",  " 2528.991398\n",  " 585.894911\n",  " 3010.004287\n",  " 2785.424850\n",  " 3252.690809\n", 1651.654530\n",  " \n",  " \n",  " 11\n", 
...
" 435.511874\n",  " 214.783047\n",  " 378.442585\n",  " 19.963773\n", 4188.596184\n",  " 4647.659834\n", 5007.824420\n",  " 6820.757678\n", 3391.821865\n",  " ...\n", 3764.403815\n",  " 6820.757678\n", 3769.213727\n",  " 2920.347673\n", 4341.828342\n",  " 7122.612569\n", 4045.402203\n",  " 4647.659834\n", 4341.828342\n",  " 7011.871156\n",  " 4482.395034\n",  " 14412.432018\n",  " 2964.374702\n",  " 6054.528390\n",  " 1451.395849\n", 3769.213727\n",  " \n",  " \n",  " 12\n", 
...
" 4055.085354\n",  " 5176.068320\n",  " 1545.254440\n",  " 19773.885246\n", 5005.722397\n",  " 12361.514588\n", 7991.819257\n",  " 10056.465902\n", 6685.943936\n",  " ...\n", 7121.915549\n",  " 10056.465902\n", 5785.151624\n",  " 9205.116143\n", 7544.339721\n",  " 5418.650845\n", 6606.447547\n",  " 12361.514588\n", 7544.339721\n",  " 6799.103995\n",  " 8595.917172\n",  " 3587.423664\n",  " 8438.339841\n",  " 6262.515773\n",  " 11370.123741\n", 5785.151624\n",  " \n",  " \n",  " 13\n", 
...
" 903.649474\n",  " 1023.292992\n",  " 557.185749\n",  " 942.769484\n", 1337.240540\n",  " 6507.812960\n", 1731.062904\n",  " 1035.591768\n", 1986.621196\n",  " ...\n", 1469.306302\n",  " 1035.591768\n", 1629.905028\n",  " 1393.502636\n", 1594.823386\n",  " 1981.230229\n", 1612.268792\n",  " 6507.812960\n", 1594.823386\n",  " 3993.317652\n",  " 4001.782051\n",  " 972.021179\n",  " 2015.658089\n",  " 1810.268419\n",  " 2244.350888\n", 1629.905028\n",  " \n",  " \n",  " 14\n", 
...
" 97.948999\n",  " 501.187234\n",  " 822.242650\n",  " 1039.066462\n", 100.960679\n",  " 653.188436\n", 123.063538\n",  " 282.606450\n", 238.241607\n",  " ...\n", 294.472511\n",  " 282.606450\n", 155.090407\n",  " 222.671564\n", 190.364989\n",  " 557.954792\n", 171.824863\n",  " 653.188436\n", 190.364989\n",  " 1793.919036\n",  " 1341.140376\n",  " 425.015655\n",  " 628.566465\n",  " 515.864609\n",  " 765.890496\n", 155.090407\n",  " \n",  " \n",  " 15\n", 
...
" 2654.605562\n",  " 1918.668741\n",  " 1870.682140\n",  " 147.843011\n", 680.326369\n",  " 222.644999\n", 1587.502550\n",  " 3785.610395\n", 856.363458\n",  " ...\n", 483.283059\n",  " 3785.610395\n", 763.286736\n",  " 957.809744\n", 875.907009\n",  " 174.925022\n", 817.660200\n",  " 222.644999\n", 875.907009\n",  " 2420.020910\n",  " 2995.218818\n",  " 8753.176855\n",  " 1035.766303\n",  " 1447.709292\n",  " 741.040927\n", 763.286736\n",  " \n",  " \n",  " 16\n", 
...
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...
" ...\n",  " ...\n",  " ...\n",  " ...\n",  " ...\n",  " ...\n",  " ...\n",  " ...\n",  " \n",  " \n",  " 27650\n", 
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" 1199.499303\n",  " 1753.880502\n",  " 3111.716337\n",  " 16.282788\n", 674.837597\n",  " 2336.008591\n", 555.696323\n",  " 532.048748\n", 3025.303561\n",  " ...\n", 4587.142016\n",  " 532.048748\n", 1428.841693\n",  " 500.952879\n", 1596.576948\n",  " 26.445342\n", 1510.382637\n",  " 2336.008591\n", 1596.576948\n",  " 1530.365737\n",  " 1294.191177\n",  " 2181.583492\n",  " 429.519887\n",  " 439.773531\n",  " 419.505315\n", 1428.841693\n",  " \n",  " \n",  " 27659\n", 
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" 671.428853\n",  " 2666.858665\n",  " 42.559841\n",  " 87.501604\n", 300.914050\n",  " 439.277027\n", 492.241431\n",  " 539.625724\n", 2926.526438\n",  " ...\n", 4100.385046\n",  " 539.625724\n", 938.420441\n",  " 499.676471\n", 1420.696801\n",  " 41.071072\n", 1154.647530\n",  " 439.277027\n", 1420.696801\n",  " 897.089051\n",  " 855.272192\n",  " 488.572826\n",  " 335.249132\n",  " 304.308897\n",  " 369.335179\n", 938.420441\n",  " \n",  " \n",  " 27660\n", 
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" 32.210688\n",  " 52.844525\n",  " 91.833260\n",  " 2.405268\n", 30.779175\n",  " 4.438093\n", 38.565790\n",  " 22.381857\n", 20.406062\n",  " ...\n", 26.230448\n",  " 22.381857\n", 25.061559\n",  " 18.498812\n", 31.805628\n",  " 2.270725\n", 28.232935\n",  " 4.438093\n", 31.805628\n",  " 40.885613\n",  " 36.389652\n",  " 5.268112\n",  " 9.704451\n",  " 9.472952\n",  " 9.941608\n", 25.061559\n",  " \n",  " \n",  " 27661\n", 
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" 236.591970\n",  " 1406.047524\n",  " 14.487719\n",  " 4.775581\n", 53.892417\n",  " 11.403164\n", 42.670705\n",  " 29.039302\n", 302.341059\n",  " ...\n", 732.230654\n",  " 29.039302\n", 127.647525\n",  " 35.842858\n", 176.761983\n",  " 40.752966\n", 150.210617\n",  " 11.403164\n", 176.761983\n",  " 92.926272\n",  " 78.484408\n",  " 9.605551\n",  " 25.184334\n",  " 32.394172\n",  " 19.579160\n", 127.647525\n",  " \n",  " \n",  " 27662\n", 
...
" 4345.102242\n",  " 11246.049740\n",  " 588.843655\n",  " 368.583423\n", 223.802423\n",  " 114.116744\n", 399.355915\n",  " 203.855168\n", 2596.288840\n",  " ...\n", 2072.687575\n",  " 203.855168\n", 762.270118\n",  " 201.175200\n", 909.802200\n",  " 608.355582\n", 832.775499\n",  " 114.116744\n", 909.802200\n",  " 305.630346\n",  " 243.791206\n",  " 340.968029\n",  " 268.060841\n",  " 317.589334\n",  " 226.256384\n", 762.270118\n",  " \n",  " \n",  " 27663\n", 
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" 354.813389\n",  " 42.461956\n",  " 40.550854\n",  " 21.397735\n", 146.972886\n",  " 145.943550\n", 328.245742\n",  " 311.567850\n", 227.028697\n",  " ...\n", 88.368151\n",  " 311.567850\n", 182.666534\n",  " 137.623483\n", 170.312857\n",  " 217.096787\n", 176.381573\n",  " 145.943550\n", 170.312857\n",  " 398.864906\n",  " 236.299133\n",  " 155.443201\n",  " 159.832050\n",  " 182.009874\n",  " 140.356584\n", 182.666534\n",  " \n",  " \n",  " 27664\n", 
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" 17947.336268\n",  " 13995.873226\n",  " 3140.508694\n",  " 18625.210534\n", 12404.116293\n",  " 13470.617022\n", 9665.630283\n",  " 10772.845684\n", 18590.521692\n",  " ...\n", 20819.981462\n",  " 10772.845684\n", 15185.486262\n",  " 8539.736248\n", 14185.846584\n",  " 25285.992128\n", 14677.158391\n",  " 13470.617022\n", 14185.846584\n",  " 3235.366788\n",  " 3084.491697\n",  " 13285.242087\n",  " 9684.507101\n",  " 9698.830812\n",  " 9670.204544\n", 15185.486262\n",  " \n",  " \n",  " 27665\n", 
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...
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" 232.273680\n",  " 739.605275\n",  " 105.681751\n",  " 738.060365\n", 1800.941914\n",  " 2192.357405\n", 1240.632578\n",  " 1236.577935\n", 1893.788242\n",  " ...\n", 4769.034670\n",  " 1236.577935\n", 1846.781693\n",  " 780.810863\n", 2432.410282\n",  " 4539.740974\n", 2119.464738\n",  " 2192.357405\n", 2432.410282\n",  " 589.164269\n",  " 384.069563\n",  " 756.158234\n",  " 1024.489658\n",  " 1007.279220\n",  " 1041.994156\n", 1846.781693\n",  " \n",  " \n",  "\n",  "

27680 rows × 26 16  columns

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...
"27679 Mamu-A02 YVVQMLARL 9 152.405275 232.273680 \n",  "\n",  " netmhcpan smmpmbec_cpp mhcflurry 0 mhcflurry 1 mhcflurry 2 \\\n",  "0 711.213514 438.530698 5.451339 101.031088 820.899047 1442.126816 1230.322823 1277.631488  \n", "1 785.235635 10351.421667 29611.681275 10194.929401 13355.273001 11473.595261 12327.145996 12685.090199  \n", "2 7.516229 28.054336 13.543497 75.988225 32.385539 18.691766 31.570829 21.826293  \n", "3 9.749896 25.703958 5.779730 10.814566 7.445489 4.279559 4.320936 5.741265  \n", "4 8.336812 28.773984 1.740462 4.376349 5.543891 3.503900 3.545383 4.403022  \n", "5 114.815362 187.068214 22.095282 50.841470 241.358596 325.846385 203.968140 378.604284  \n", "6 389.045145 200.909281 28.622502 24.963412 178.902143 38.287690 80.587676 100.644543  \n", "7 493.173804 295.120923 1296.377555 857.209411 488.717022 450.264145 471.683475 817.682084  \n", "8 77.268059 38.459178 174.309147 45.290250 130.499676 103.544944 116.724333 247.518551  \n", "9 597.035287 225.423921 6239.534375 4963.755498 720.436304 1401.468773 1236.987110 3250.940299  \n", "10 2032.357011 698.232404 13865.829459 1112.259791 2637.347063 2016.574129 2718.284664 1352.770844  \n", "11 214.783047 378.442585 19.963773 4647.659834 6820.757678 4188.596184 5007.824420 3391.821865  \n", "12 5176.068320 1545.254440 19773.885246 12361.514588 10056.465902 5005.722397 7991.819257 6685.943936  \n", "13 1023.292992 557.185749 942.769484 6507.812960 1035.591768 1337.240540 1731.062904 1986.621196  \n", "14 501.187234 822.242650 1039.066462 653.188436 282.606450 100.960679 123.063538 238.241607  \n", "15 1918.668741 1870.682140 147.843011 222.644999 3785.610395 680.326369 1587.502550 856.363458  \n", "16 228.559880 200.447203 12.736352 100.895525 393.284191 310.702631 288.458942 326.145448  \n", "17 623.734835 286.417797 187.385974 491.212649 705.914186 271.301464 156.373254 219.784353  \n", "18 5152.286446 679.203633 1030.169279 1593.111056 3143.834356 2515.938301 3103.319839 2768.676318  \n", "19 2172.701179 1927.524913 37.175585 3248.903081 3679.325951 1076.149383 1829.973751 781.507334  \n", "20 29.580125 63.826349 2.609560 14.783175 22.525098 13.751390 7.096296 41.477242  \n", "21 3206.269325 595.662144 18367.825270 8699.676803 3662.742351 4256.550204 2679.336713 3891.360910  \n", "22 679.203633 444.631267 1002.270321 271.628670 769.960883 247.510091 1257.020204 581.139814  \n", "23 1857.804455 325.087297 6694.233166 361.287857 2602.563945 2107.373160 4434.869234 3599.445945  \n", "24 46989.410861 440554.863507 36138.465971 39872.294037 34837.346341 38311.680156 37864.670399 40542.758854  \n", "25 12882.495517 7816.278046 32436.969144 12881.899896 17740.278650 15301.706435 19487.254968 19015.171428  \n", "26 202.768272 260.015956 221.733235 197.460982 153.420663 281.659673 230.446704 131.354673  \n", "27 9885.530947 26242.185434 30271.867532 14368.146625 21138.351414 23526.526949 23286.457871 20124.099341  \n", "28 12560.299637 35399.734108 44200.734066 34653.288740 27071.471077 29954.637899 31195.855765 30063.373727  \n", "29 2285.598803 1811.340093 15786.995089 72.916893 3410.631472 2575.449524 1534.354687 2611.968281  \n", "... ... ... ... ... ... \n",  "27650 10023.052381 48.865236 2065.341574 728.983555 2792.654265 1591.049576 1298.358917 16338.032773  \n", "27651 22233.098906 1409.288798 227.126009 606.293671 353.449644 261.780710 303.622055 6300.597643  \n", "27652 8709.635900 4477.133042 151.811211 4869.354056 1403.358566 2698.313836 1549.550832 9385.257012  \n", "27653 5105.050000 51.522864 209.030747 41.744716 60.784907 53.868203 36.758850 530.426107  \n", "27654 49.431069 6.324119 16.959545 8.121621 14.356819 8.342093 8.597771 5.255895  \n", "27655 15.346170 16.292960 9.174435 6.832824 25.246580 16.994110 14.152847 11.581538  \n", "27656 3863.669771 220.292646 79.919742 231.309007 53.090184 48.098444 55.920499 1522.610694  \n", "27657 3.090295 1.778279 2.600664 1.976217 6.495700 3.144389 6.683502 5.092176  \n", "27658 1753.880502 3111.716337 16.282788 2336.008591 532.048748 674.837597 555.696323 3025.303561  \n", "27659 2666.858665 42.559841 87.501604 439.277027 539.625724 300.914050 492.241431 2926.526438  \n", "27660 52.844525 91.833260 2.405268 4.438093 22.381857 30.779175 38.565790 20.406062  \n", "27661 1406.047524 14.487719 4.775581 11.403164 29.039302 53.892417 42.670705 302.341059  \n", "27662 11246.049740 588.843655 368.583423 114.116744 203.855168 223.802423 399.355915 2596.288840  \n", "27663 42.461956 40.550854 21.397735 145.943550 311.567850 146.972886 328.245742 227.028697  \n", "27664 13995.873226 3140.508694 18625.210534 13470.617022 10772.845684 12404.116293 9665.630283 18590.521692  \n", "27665 10046.157903 387.257645 2955.836543 7865.545579 6649.588325 6959.512936 5557.106811 12556.819304  \n", "27666 2349.632821 80.723503 246.849109 282.161004 301.224065 402.648574 718.399164 2726.886297  \n", "27667 5176.068320 1270.574105 37.992944 2084.788960 436.602256 693.193377 1000.767613 2026.422421  \n", "27668 5420.008904 797.994687 76.974552 1089.595023 261.575824 454.242824 904.169913 1463.422954  \n", "27669 7.744618 76.383578 160.400573 30.913585 66.086443 93.591134 61.590145 90.506850  \n", "27670 5701.642723 100.230524 58.603552 135.090210 49.812494 110.463817 286.472186 984.834053  \n", "27671 6025.595861 119.674053 4.324396 13.887901 6.294973 12.476188 15.759480 381.167625  \n", "27672 2844.461107 87.096359 3.220311 25.387794 9.983106 18.118964 32.871695 230.206292  \n", "27673 3872.576449 1954.339456 248.907741 1465.396277 1036.887847 807.577510 1192.040107 3950.178511  \n", "27674 6998.419960 32.734069 1284.004341 1146.690887 879.071962 627.329290 832.174798 3403.672519  \n", "27675 71.285303 108.143395 2853.412412 19.054451 275.667564 81.902887 124.100978 49.638987  \n", "27676 5610.479760 901.571138 9434.039198 24974.087871 5350.263772 6364.523809 10233.878501 14430.800761  \n", "27677 10046.157903 2600.159563 13239.877677 13230.273196 1927.724518 1830.258194 3487.883085 13554.295203  \n", "27678 6039.486294 10568.175092 7424.515180 13628.730953 13854.170383 13949.882776 11029.857942 20742.160346  \n", "27679 739.605275 105.681751 738.060365 2192.357405 1236.577935 \n",  "\n",  " ... mhcflurry ensemble big dropout \\\n",  "0 ... 820.899047 \n",  "1 ... 13355.273001 \n",  "2 ... 32.385539 \n",  "3 ... 7.445489 \n",  "4 ... 5.543891 \n",  "5 ... 241.358596 \n",  "6 ... 178.902143 \n",  "7 ... 488.717022 \n",  "8 ... 130.499676 \n",  "9 ... 720.436304 \n",  "10 ... 2637.347063 \n",  "11 ... 6820.757678 \n",  "12 ... 10056.465902 \n",  "13 ... 1035.591768 \n",  "14 ... 282.606450 \n",  "15 ... 3785.610395 \n",  "16 ... 393.284191 \n",  "17 ... 705.914186 \n",  "18 ... 3143.834356 \n",  "19 ... 3679.325951 \n",  "20 ... 22.525098 \n",  "21 ... 3662.742351 \n",  "22 ... 769.960883 \n",  "23 ... 2602.563945 \n",  "24 ... 34837.346341 \n",  "25 ... 17740.278650 \n",  "26 ... 153.420663 \n",  "27 ... 21138.351414 \n",  "28 ... 27071.471077 \n",  "29 ... 3410.631472 \n",  "... ... ... \n",  "27650 ... 2792.654265 \n",  "27651 ... 353.449644 \n",  "27652 ... 1403.358566 \n",  "27653 ... 60.784907 \n",  "27654 ... 14.356819 \n",  "27655 ... 25.246580 \n",  "27656 ... 53.090184 \n",  "27657 ... 6.495700 \n",  "27658 ... 532.048748 \n",  "27659 ... 539.625724 \n",  "27660 ... 22.381857 \n",  "27661 ... 29.039302 \n",  "27662 ... 203.855168 \n",  "27663 ... 311.567850 \n",  "27664 ... 10772.845684 \n",  "27665 ... 6649.588325 \n",  "27666 ... 301.224065 \n",  "27667 ... 436.602256 \n",  "27668 ... 261.575824 \n",  "27669 ... 66.086443 \n",  "27670 ... 49.812494 \n",  "27671 ... 6.294973 \n",  "27672 ... 9.983106 \n",  "27673 ... 1036.887847 \n",  "27674 ... 879.071962 \n",  "27675 ... 275.667564 \n",  "27676 ... 5350.263772 \n",  "27677 ... 1927.724518 \n",  "27678 ... 13854.170383 \n",  "27679 ... 1236.577935 \n",  "\n",  " mhcflurry ensemble big dropout impute mhcflurry ensemble big impute \\\n",  "0 608.394258 59.834101 \n",  "1 11875.604773 30971.969222 \n",  "2 19.949449 315.922174 \n",  "3 5.861501 2.613762 \n",  "4 4.515577 4.624470 \n",  "5 287.854885 550.332342 \n",  "6 110.146283 18.001538 \n",  "7 469.690670 4100.242979 \n",  "8 131.878046 23.736367 \n",  "9 651.353328 435.649890 \n",  "10 3369.383522 12057.249762 \n",  "11 2920.347673 7122.612569 \n",  "12 9205.116143 5418.650845 \n",  "13 1393.502636 1981.230229 \n",  "14 222.671564 557.954792 \n",  "15 957.809744 174.925022 \n",  "16 163.333364 745.285924 \n",  "17 326.419418 114.836547 \n",  "18 789.180375 1388.073067 \n",  "19 2154.508459 424.242448 \n",  "20 13.418418 8.273311 \n",  "21 3429.652107 25.805973 \n",  "22 575.079918 1426.551051 \n",  "23 2184.611795 16605.290279 \n",  "24 35699.023249 42845.880010 \n",  "25 16839.947094 44353.808145 \n",  "26 51.779863 626.055500 \n",  "27 21874.360223 32687.881124 \n",  "28 28468.569166 29231.913839 \n",  "29 3717.315776 28970.504950 \n",  "... ... ... \n",  "27650 2206.211966 1089.314789 \n",  "27651 222.244468 1364.159023 \n",  "27652 1468.655916 3321.177031 \n",  "27653 82.581101 371.454333 \n",  "27654 6.392747 1.712349 \n",  "27655 14.946450 98.324803 \n",  "27656 70.559725 195.203057 \n",  "27657 5.659219 1.349125 \n",  "27658 500.952879 26.445342 \n",  "27659 499.676471 41.071072 \n",  "27660 18.498812 2.270725 \n",  "27661 35.842858 40.752966 \n",  "27662 201.175200 608.355582 \n",  "27663 137.623483 217.096787 \n",  "27664 8539.736248 25285.992128 \n",  "27665 4872.760597 8028.763257 \n",  "27666 307.109562 137.068230 \n",  "27667 553.381852 265.042232 \n",  "27668 438.603157 112.878412 \n",  "27669 75.340023 40.750919 \n",  "27670 127.612192 144.020068 \n",  "27671 12.921459 8.707189 \n",  "27672 16.790709 18.379812 \n",  "27673 808.684758 162.130308 \n",  "27674 755.831662 348.979832 \n",  "27675 130.402003 2496.259480 \n",  "27676 5397.953890 17928.704583 \n",  "27677 2870.278654 9076.175041 \n",  "27678 11222.578955 28950.523005 \n",  "27679 780.810863 4539.740974 \n",  "\n",  " mhcflurry ensemble small mhcflurry ensemble small dropout \\\n",  "0 101.031088 869.601741 \n",  "1 10194.929401 24772.895203 \n",  "2 75.988225 179.998789 \n",  "3 10.814566 83.166802 \n",  "4 4.376349 79.100063 \n",  "5 50.841470 559.872375 \n",  "6 24.963412 343.282060 \n",  "7 857.209411 164.852471 \n",  "8 45.290250 211.171559 \n",  "9 4963.755498 503.218493 \n",  "10 1112.259791 2752.021063 \n",  "11 4647.659834 7011.871156 \n",  "12 12361.514588 6799.103995 \n",  "13 6507.812960 3993.317652 \n",  "14 653.188436 1793.919036 \n",  "15 222.644999 2420.020910 \n",  "16 100.895525 359.229730 \n",  "17 491.212649 1106.050966 \n",  "18 1593.111056 2424.417471 \n",  "19 3248.903081 2298.381230 \n",  "20 14.783175 253.259330 \n",  "21 8699.676803 7587.162092 \n",  "22 271.628670 850.555894 \n",  "23 361.287857 2546.067959 \n",  "24 39872.294037 33755.078310 \n",  "25 12881.899896 27053.582736 \n",  "26 197.460982 478.963051 \n",  "27 14368.146625 26405.718159 \n",  "28 34653.288740 29420.942415 \n",  "29 72.916893 4876.564589 \n",  "... ... ... \n",  "27650 728.983555 2108.415291 \n",  "27651 606.293671 466.928279 \n",  "27652 4869.354056 2164.345626 \n",  "27653 41.744716 76.636019 \n",  "27654 8.121621 30.118698 \n",  "27655 6.832824 96.486682 \n",  "27656 231.309007 114.949172 \n",  "27657 1.976217 25.555743 \n",  "27658 2336.008591 1530.365737 \n",  "27659 439.277027 897.089051 \n",  "27660 4.438093 40.885613 \n",  "27661 11.403164 92.926272 \n",  "27662 114.116744 305.630346 \n",  "27663 145.943550 398.864906 \n",  "27664 13470.617022 3235.366788 \n",  "27665 7865.545579 2505.757913 \n",  "27666 282.161004 142.114753 \n",  "27667 2084.788960 1043.930987 \n",  "27668 1089.595023 799.875786 \n",  "27669 30.913585 138.080623 \n",  "27670 135.090210 39.532117 \n",  "27671 13.887901 26.782833 \n",  "27672 25.387794 26.259662 \n",  "27673 1465.396277 1474.221609 \n",  "27674 1146.690887 1194.848606 \n",  "27675 19.054451 327.909013 \n",  "27676 24974.087871 961.282868 \n",  "27677 13230.273196 299.745625 \n",  "27678 13628.730953 9970.894235 \n",  "27679 2192.357405 589.164269 1800.941914 1240.632578 1893.788242  \n", "\n",  " mhcflurry 3 mhcflurry  ensemble small big  dropoutimpute  \\\n", "0 811.199864 364.773670 1357.389638  \n", "1 23080.234753 8186.470538 12064.144843  \n", "2 179.248142 17.622147 20.198316  \n", "3 74.532864 4.149827 4.956822  \n", "4 69.159665 3.479555 3.927817  \n", "5 525.939006 133.562226 351.236156  \n", "6 151.060043 31.089573 62.076139  \n", "7 116.277820 560.212784 606.772547  \n", "8 175.714103 108.927801 160.091519  \n", "9 371.779976 1010.833977 2134.500249  \n", "10 2528.991398 2722.914015 1651.654530  \n", "11 4482.395034 3764.403815 3769.213727  \n", "12 8595.917172 7121.915549 5785.151624  \n", "13 4001.782051 1469.306302 1629.905028  \n", "14 1341.140376 294.472511 155.090407  \n", "15 2995.218818 483.283059 763.286736  \n", "16 336.489205 231.132923 318.330408  \n", "17 1139.864466 96.644863 244.188077  \n", "18 1853.193862 1845.718431 2639.283765  \n", "19 1658.177678 847.066732 917.070682  \n", "20 259.264728 10.913079 23.882415  \n", "21 7870.240455 1372.866392 4069.861555  \n", "22 1104.023482 500.292469 379.259763  \n", "23 8289.213593 2239.333205 2754.156091  \n", "24 32750.655338 40682.564705 39411.435014  \n", "25 27573.442469 17826.789421 17057.683635  \n", "26 375.973551 65.215567 192.346859  \n", "27 25386.499837 19022.397273 21758.910025  \n", "28 29267.170707 31692.725154 30008.956563  \n", "29 3904.625862 1343.507135 2593.644630  \n", "... ... ...  \n", "27650 1843.589845 20773.739040 5098.491946  \n", "27651 367.454335 6083.006392 1284.279925  \n", "27652 1907.323170 11166.049632 5032.332347  \n", "27653 87.061756 452.322369 169.035798  \n", "27654 37.085836 6.264340 6.621568  \n", "27655 68.344064 10.941380 14.029181  \n", "27656 108.703007 1633.521469 270.620038  \n", "27657 23.263357 4.890292 4.001473  \n", "27658 1294.191177 4587.142016 1428.841693  \n", "27659 855.272192 4100.385046 938.420441  \n", "27660 36.389652 26.230448 25.061559  \n", "27661 78.484408 732.230654 127.647525  \n", "27662 243.791206 2072.687575 762.270118  \n", "27663 236.299133 88.368151 182.666534  \n", "27664 3084.491697 20819.981462 15185.486262  \n", "27665 2268.560672 17267.251833 9348.226911  \n", "27666 91.528719 2697.837580 1047.843919  \n", "27667 879.689216 2606.895028 1185.201502  \n", "27668 638.247050 2544.400702 815.321639  \n", "27669 154.791104 69.346296 92.036073  \n", "27670 25.804109 1610.278911 329.831061  \n", "27671 19.729256 440.313942 68.960271  \n", "27672 18.481367 849.650751 64.584051  \n", "27673 1117.764150 4063.500410 1786.078197  \n", "27674 1054.595956 3362.152015 1461.240385  \n", "27675 248.016529 79.299313 63.761872  \n", "27676 774.583726 18974.479542 9583.588839  \n", "27677 173.133909 12282.559495 4980.748925  \n", "27678 9287.520258 25558.849591 17010.311736  \n", "27679 384.069563 4769.034670 1846.781693  \n", "\n",  " mhcflurry ensemble small big dropout  impute mhcflurry ensemble all \\\n", "0 27.943191 154.485692 669.917436 953.592673  \n", "1 11948.433480 17811.749199 10045.686513 11008.751843  \n", "2 39.496692 63.599402 23.586984 21.826987  \n", "3 43.592036 14.474338 4.234517 4.581457  \n", "4 7.253820 8.769914 3.512315 3.714261  \n", "5 19.113564 148.908039 165.052836 240.774840  \n", "6 227.662890 86.000294 50.054335 55.742083  \n", "7 557.652976 570.238747 514.045827 558.488045  \n", "8 34.675442 89.587650 112.758702 134.356659  \n", "9 1038.488658 1026.229732 1118.207763 1544.931956  \n", "10 585.894911 3010.004287 2720.598355 2119.785036  \n", "11 14412.432018 2964.374702 4341.828342 4045.402203  \n", "12 3587.423664 8438.339841 7544.339721 6606.447547  \n", "13 972.021179 2015.658089 1594.823386 1612.268792  \n", "14 425.015655 628.566465 190.364989 171.824863  \n", "15 8753.176855 1035.766303 875.907009 817.660200  \n", "16 249.467407 192.108823 258.209911 286.698564  \n", "17 142.075527 380.163383 122.933607 173.259693  \n", "18 2320.410789 1664.512286 2393.293677 2513.280953  \n", "19 6223.620937 1327.326231 1245.034090 1068.543056  \n", "20 59.201622 27.973906 8.800138 14.497191  \n", "21 4508.746097 3296.534195 1917.908060 2793.854019  \n", "22 1770.968157 855.149552 793.018122 548.415778  \n", "23 3441.673321 3368.729698 3151.372707 2946.077449  \n", "24 43848.592509 37268.831119 39248.336315 39329.801120  \n", "25 14965.320060 22329.641778 18638.540466 17830.600851  \n", "26 69.118821 201.431860 122.591649 153.558193  \n", "27 23112.506599 23768.273345 21046.715960 21399.850441  \n", "28 28463.702047 30967.395008 31443.309029 30717.761879  \n", "29 148.173560 2424.245000 1435.763376 1929.730543  \n", "... ... ... \n", "27650 842.314932 1548.648296 5193.435214 5145.744612  \n", "27651 578.793553 442.327389 1359.019831 1321.121450  \n", "27652 7761.567568 1889.000333 4159.610739 4575.209686  \n", "27653 33.397569 88.088881 128.945143 147.635853  \n", "27654 4.407756 10.079920 7.338894 6.971010  \n", "27655 115.025999 33.948331 12.443941 13.212808  \n", "27656 10.991426 81.019251 302.237217 285.992041  \n", "27657 5.080187 5.442511 5.717017 4.782937  \n", "27658 2181.583492 429.519887 1596.576948 1510.382637  \n", "27659 488.572826 335.249132 1420.696801 1154.647530  \n", "27660 5.268112 9.704451 31.805628 28.232935  \n", "27661 9.605551 25.184334 176.761983 150.210617  \n", "27662 340.968029 268.060841 909.802200 832.775499  \n", "27663 155.443201 159.832050 170.312857 176.381573  \n", "27664 13285.242087 9684.507101 14185.846584 14677.158391  \n", "27665 6718.186614 4682.159085 9795.711447 9569.353863  \n", "27666 196.830786 196.921095 1392.165314 1207.796323  \n", "27667 499.003286 468.583438 1615.207762 1383.599171  \n", "27668 1194.284269 400.522069 1516.763186 1112.047592  \n", "27669 28.757507 70.616729 65.353259 77.555511  \n", "27670 96.212073 71.863560 679.190782 473.305627  \n", "27671 18.227903 11.927685 83.301374 75.792383  \n", "27672 30.134358 15.695873 167.121096 103.891084  \n", "27673 1218.408182 749.478729 2200.876067 1982.659012  \n", "27674 78.475399 654.710643 1672.691895 1563.395327  \n", "27675 32.585823 244.876955 99.202431 79.531960  \n", "27676 4489.577760 5026.992149 13934.938760 11556.241758  \n", "27677 270.068237 1825.436151 6545.237315 5709.657058  \n", "27678 13186.826643 12392.342651 16790.189997 16899.892484  \n", "27679 756.158234 1024.489658 2432.410282 2119.464738  \n", "\n",  " mhcflurry ensemble all impute mhcflurry ensemble all not impute \n",  "0 169.486237 140.812784 669.917436 1357.389638  \n", "1 17846.125248 17777.439368 10045.686513 12064.144843  \n", "2 81.730019 49.490798 23.586984 20.198316  \n", "3 14.936797 14.026198 4.234517 4.956822  \n", "4 10.116929 7.602247 3.512315 3.927817  \n", "5 199.764781 110.998566 165.052836 351.236156  \n", "6 90.872049 81.389719 50.054335 62.076139  \n", "7 594.457223 547.006945 514.045827 606.772547  \n", "8 66.085145 121.448579 112.758702 160.091519  \n", "9 575.321362 1830.537737 1118.207763 2134.500249  \n", "10 2785.424850 3252.690809 2720.598355 1651.654530  \n", "11 6054.528390 1451.395849 4341.828342 3769.213727  \n", "12 6262.515773 11370.123741 7544.339721 5785.151624  \n", "13 1810.268419 2244.350888 1594.823386 1629.905028  \n", "14 515.864609 765.890496 190.364989 155.090407  \n", "15 1447.709292 741.040927 875.907009 763.286736  \n", "16 317.940383 116.077736 258.209911 318.330408  \n", "17 279.130408 517.765868 122.933607 244.188077  \n", "18 1473.223315 1880.638951 2393.293677 2639.283765  \n", "19 1752.504453 1005.301254 1245.034090 917.070682  \n", "20 36.129706 21.659169 8.800138 23.882415  \n", "21 1331.231307 8163.222757 1917.908060 4069.861555  \n", "22 1125.385061 649.804926 793.018122 379.259763  \n", "23 5671.865589 2000.812537 3151.372707 2754.156091  \n", "24 38497.742496 36079.148619 39248.336315 39411.435014  \n", "25 23562.000395 21161.738968 18638.540466 17057.683635  \n", "26 170.365573 238.163108 122.591649 192.346859  \n", "27 25450.305572 22197.408050 21046.715960 21758.910025  \n", "28 28855.177741 33234.227914 31443.309029 30008.956563  \n", "29 2809.531564 2091.794908 1435.763376 2593.644630  \n", "... ... ... \n",  "27650 1389.904179 1725.522939 5193.435214 5098.491946  \n", "27651 503.913224 388.268276 1359.019831 1284.279925  \n", "27652 2915.052984 1224.102023 4159.610739 5032.332347  \n", "27653 97.181164 79.847274 128.945143 169.035798  \n", "27654 6.503938 15.622043 7.338894 6.621568  \n", "27655 58.300796 19.767983 12.443941 14.029181  \n", "27656 63.691984 103.060364 302.237217 270.620038  \n", "27657 5.480753 5.404535 5.717017 4.001473  \n", "27658 439.773531 419.505315 1596.576948 1428.841693  \n", "27659 304.308897 369.335179 1420.696801 938.420441  \n", "27660 9.472952 9.941608 31.805628 25.061559  \n", "27661 32.394172 19.579160 176.761983 127.647525  \n", "27662 317.589334 226.256384 909.802200 762.270118  \n", "27663 182.009874 140.356584 170.312857 182.666534  \n", "27664 9698.830812 9670.204544 14185.846584 15185.486262  \n", "27665 4941.474371 4436.451968 9795.711447 9348.226911  \n", "27666 165.947280 233.676127 1392.165314 1047.843919  \n", "27667 503.724607 435.893810 1615.207762 1185.201502  \n", "27668 440.752509 363.963731 1516.763186 815.321639  \n", "27669 60.801613 82.016286 65.353259 92.036073  \n", "27670 82.187999 62.836074 679.190782 329.831061  \n", "27671 14.182711 10.031205 83.301374 68.960271  \n", "27672 20.361114 12.099555 167.121096 64.584051  \n", "27673 650.049774 864.115930 2200.876067 1786.078197  \n", "27674 384.380424 1115.160918 1672.691895 1461.240385  \n", "27675 226.475291 264.773798 99.202431 63.761872  \n", "27676 4283.147070 5900.019227 13934.938760 9583.588839  \n", "27677 1050.559470 3171.850083 6545.237315 4980.748925  \n", "27678 14123.661118 10873.254115 16790.189997 17010.311736  \n", "27679 1007.279220 1041.994156 2432.410282 1846.781693  \n", "\n",  "[27680 rows x 26 16  columns]" ]  },  "execution_count": 20, 9,  "metadata": {},  "output_type": "execute_result"  } 
...
},  {  "cell_type": "code",  "execution_count": 22, 10,  "metadata": {  "collapsed": false  },  "outputs": [  {  "name": "stderr",  "output_type": "stream",  "text": [  "/Users/tim/venvs/analysis-venv-2.7/lib/python2.7/site-packages/sklearn/metrics/classification.py:1074: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no predicted samples.\n",  " 'precision', 'predicted', average, warn_for)\n",  "/Users/tim/venvs/analysis-venv-2.7/lib/python2.7/site-packages/sklearn/metrics/classification.py:1076: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true samples.\n",  " 'recall', 'true', average, warn_for)\n"  ]  },  {  "data": {  "text/html": [ 
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" \n",  " overall\n",  " overall\n",  " 0.882928\n", 0.927733\n",  " 0.704518\n", 0.780452\n",  " 0.496251\n", 0.575551\n",  " 0.918006\n", 0.926805\n",  " 0.780544\n", 0.781977\n",  " 0.561266\n", 0.576826\n",  " 0.929080\n", 0.929323\n",  " 0.787132\n", 0.770265\n",  " 0.571978\n", 0.577676\n",  " ...\n",  " 0.800909\n",  " 0.582258\n", 
...
" \n",  " H-2-DB\n",  " H-2-DB\n",  " 0.848124\n", 0.912222\n",  " 0.535433\n", 0.593886\n",  " 0.511345\n", 0.634828\n",  " 0.885038\n", 0.915309\n",  " 0.596078\n", 0.612766\n",  " 0.580237\n", 0.640775\n",  " 0.907967\n", 0.905614\n",  " 0.546296\n", 0.580357\n",  " 0.625583\n", 0.625557\n",  " ...\n",  " 0.621212\n",  " 0.600337\n", 
...
" \n",  " H-2-KB\n",  " H-2-KB\n",  " 0.849000\n", 0.907870\n",  " 0.762565\n", 0.812165\n",  " 0.471307\n", 0.589815\n",  " 0.895286\n", 0.908143\n",  " 0.806228\n", 0.819149\n",  " 0.586440\n", 0.591965\n",  " 0.909416\n", 0.909597\n",  " 0.836806\n", 0.802893\n",  " 0.587509\n", 0.593356\n",  " ...\n",  " 0.813675\n",  " 0.573199\n", 
...
" \n",  " H-2-KD\n",  " H-2-KD\n",  " 0.763398\n", 0.773768\n",  " 0.557143\n", 0.584615\n",  " 0.342118\n", 0.357916\n",  " 0.772275\n", 0.776589\n",  " 0.562963\n", 0.604651\n",  " 0.352598\n", 0.355835\n",  " 0.786212\n", 0.793761\n",  " 0.592593\n", 0.589147\n",  " 0.368319\n", 0.385118\n",  " ...\n",  " 0.657718\n",  " 0.403275\n", 
...
" \n",  " HLA-A0101\n",  " HLA-A0101\n",  " 0.849094\n", 0.912949\n",  " 0.531532\n", 0.605714\n",  " 0.409243\n", 0.519646\n",  " 0.910766\n", 0.903609\n",  " 0.610329\n", 0.631016\n",  " 0.508191\n", 0.514472\n",  " 0.915757\n", 0.909852\n",  " 0.592179\n", 0.613636\n",  " 0.521964\n", 0.516242\n",  " ...\n",  " 0.619565\n",  " 0.524866\n", 
...
" \n",  " HLA-A0201\n",  " HLA-A0201\n",  " 0.854361\n", 0.929486\n",  " 0.784367\n", 0.878578\n",  " 0.488418\n",  " 0.927671\n",  " 0.876843\n",  " 0.619872\n", 0.623194\n",  " 0.930293\n",  " 0.883262\n", 0.879897\n",  " 0.624985\n", 0.625150\n",  " 0.930269\n",  " 0.875983\n",  " 0.625099\n",  " ...\n",  " 0.884336\n",  " 0.635498\n", 
...
" \n",  " HLA-A0202\n",  " HLA-A0202\n",  " 0.767461\n",  " 0.639175\n",  " 0.422552\n",  " 0.874446\n",  " 0.719101\n",  " 0.596025\n",  " 0.895233\n",  " 0.769231\n", 0.755556\n",  " 0.611888\n",  " 0.900499\n",  " 0.755556\n",  " 0.615726\n",  " 0.905765\n",  " 0.761905\n",  " 0.605748\n",  " ...\n",  " 0.755556\n", 
...
" \n",  " HLA-A0203\n",  " HLA-A0203\n",  " 0.923881\n", 0.977392\n",  " 0.884058\n", 0.954976\n",  " 0.465846\n", 0.578919\n",  " 0.972561\n", 0.978112\n",  " 0.939645\n", 0.952494\n",  " 0.580955\n", 0.592381\n",  " 0.979134\n", 0.976223\n",  " 0.954869\n", 0.953293\n",  " 0.592506\n", 0.589924\n",  " ...\n",  " 0.948626\n",  " 0.586911\n", 
...
" \n",  " HLA-A0206\n",  " HLA-A0206\n",  " 0.829841\n", 0.907647\n",  " 0.778351\n", 0.870210\n",  " 0.415579\n", 0.540573\n",  " 0.900923\n", 0.903137\n",  " 0.866911\n", 0.870098\n",  " 0.510937\n", 0.532982\n",  " 0.906767\n", 0.906622\n",  " 0.869779\n", 0.867168\n",  " 0.528590\n", 0.537858\n",  " ...\n",  " 0.872902\n",  " 0.543184\n", 
...
" \n",  " HLA-A0301\n",  " HLA-A0301\n",  " 0.825286\n", 0.925179\n",  " 0.782222\n", 0.881178\n",  " 0.434382\n", 0.602198\n",  " 0.920225\n", 0.926072\n",  " 0.867521\n", 0.883281\n",  " 0.582132\n", 0.596442\n",  " 0.925394\n", 0.920753\n",  " 0.872727\n", 0.862366\n",  " 0.600378\n", 0.588700\n",  " ...\n",  " 0.900621\n",  " 0.629236\n", 
...
" \n",  " HLA-A1101\n",  " HLA-A1101\n",  " 0.880948\n", 0.938378\n",  " 0.820874\n", 0.882514\n",  " 0.501814\n", 0.613449\n",  " 0.930302\n", 0.938900\n",  " 0.875000\n", 0.884298\n",  " 0.611392\n", 0.614414\n",  " 0.941065\n", 0.936935\n",  " 0.887363\n", 0.885246\n",  " 0.615063\n", 0.610654\n",  " ...\n",  " 0.883402\n",  " 0.632199\n", 
...
" \n",  " HLA-A2301\n",  " HLA-A2301\n",  " 0.817591\n", 0.847377\n",  " 0.654434\n", 0.734104\n",  " 0.482072\n", 0.564802\n",  " 0.851253\n", 0.853138\n",  " 0.727273\n", 0.697248\n",  " 0.558449\n", 0.573883\n",  " 0.861713\n", 0.858740\n",  " 0.748571\n", 0.634812\n",  " 0.581984\n", 0.582354\n",  " ...\n",  " 0.773842\n",  " 0.605598\n", 
...
" \n",  " HLA-A2402\n",  " HLA-A2402\n",  " 0.805891\n", 0.857054\n",  " 0.575916\n", 0.571429\n",  " 0.457142\n", 0.565620\n",  " 0.835599\n", 0.867143\n",  " 0.629808\n", 0.567416\n",  " 0.537855\n", 0.573752\n",  " 0.857510\n", 0.859523\n",  " 0.575269\n", 0.416667\n",  " 0.562741\n", 0.562286\n",  " ...\n",  " 0.632911\n",  " 0.575710\n", 
...
" \n",  " HLA-A2501\n",  " HLA-A2501\n",  " 0.989781\n", 0.991241\n",  " 0.421053\n", 0.615385\n",  " 0.151074\n",  " 0.989294\n",  " 0.571429\n",  " 0.150775\n",  " 0.992701\n",  " 0.421053\n",  " 0.151673\n",  " 0.976642\n", 0.997080\n",  " 0.666667\n",  " 0.146880\n", 0.153321\n",  " ...\n",  " 0.666667\n",  " 0.151836\n", 
...
" \n",  " HLA-A2601\n",  " HLA-A2601\n",  " 0.902436\n", 0.929049\n",  " 0.525547\n", 0.548673\n",  " 0.372451\n", 0.443452\n",  " 0.925136\n", 0.931319\n",  " 0.566176\n", 0.582278\n",  " 0.419262\n", 0.446167\n",  " 0.934574\n", 0.933928\n",  " 0.571429\n", 0.563107\n",  " 0.447482\n", 0.453999\n",  " ...\n",  " 0.541176\n",  " 0.436646\n", 
...
" \n",  " HLA-A2602\n",  " HLA-A2602\n",  " 0.878531\n", 0.927684\n",  " 0.658333\n", 0.756219\n",  " 0.432259\n", 0.516947\n",  " 0.909927\n", 0.929454\n",  " 0.703704\n", 0.785714\n",  " 0.474037\n", 0.515487\n",  " 0.926912\n", 0.926848\n",  " 0.773869\n", 0.764045\n",  " 0.519182\n", 0.520612\n",  " ...\n",  " 0.766839\n",  " 0.525365\n", 
...
" \n",  " HLA-A2603\n",  " HLA-A2603\n",  " 0.846290\n", 0.899656\n",  " 0.500000\n", 0.571429\n",  " 0.299386\n", 0.343022\n",  " 0.864373\n", 0.888649\n",  " 0.500000\n", 0.596491\n",  " 0.334720\n", 0.336494\n",  " 0.894644\n", 0.900344\n",  " 0.526316\n", 0.581818\n",  " 0.346853\n", 0.356361\n",  " ...\n",  " 0.542373\n",  " 0.366317\n", 
...
" \n",  " HLA-A2902\n",  " HLA-A2902\n",  " 0.857957\n", 0.891331\n",  " 0.592593\n", 0.701754\n",  " 0.546467\n", 0.628437\n",  " 0.901099\n",  " 0.622951\n",  " 0.641024\n",  " 0.890517\n", 0.897436\n",  " 0.631579\n",  " 0.628130\n", 0.626902\n",  " 0.899471\n",  " 0.703704\n",  " 0.649621\n",  " ...\n",  " 0.644068\n",  " 0.619830\n", 
...
" \n",  " HLA-A3001\n",  " HLA-A3001\n",  " 0.873687\n", 0.901720\n",  " 0.709677\n", 0.749340\n",  " 0.515750\n", 0.531903\n",  " 0.885017\n", 0.898432\n",  " 0.743003\n", 0.746667\n",  " 0.507472\n", 0.523981\n",  " 0.892818\n", 0.896782\n",  " 0.746736\n", 0.752688\n",  " 0.521534\n", 0.501209\n",  " ...\n",  " 0.731959\n",  " 0.456482\n", 
...
" \n",  " HLA-A3002\n",  " HLA-A3002\n",  " 0.698121\n", 0.735361\n",  " 0.592391\n", 0.624685\n",  " 0.290363\n", 0.344846\n",  " 0.742389\n", 0.729837\n",  " 0.650794\n", 0.645161\n",  " 0.345972\n", 0.335080\n",  " 0.735662\n", 0.756559\n",  " 0.631043\n", 0.594752\n",  " 0.346248\n", 0.352958\n",  " ...\n",  " 0.663438\n",  " 0.342161\n", 
...
" \n",  " HLA-A3101\n",  " HLA-A3101\n",  " 0.795053\n", 0.847940\n",  " 0.769231\n", 0.816688\n",  " 0.385885\n", 0.494975\n",  " 0.849611\n", 0.847258\n",  " 0.821918\n", 0.822943\n",  " 0.481153\n", 0.496457\n",  " 0.851305\n", 0.849787\n",  " 0.819549\n", 0.798440\n",  " 0.504028\n", 0.493825\n",  " ...\n",  " 0.833958\n",  " 0.525026\n", 
...
" \n",  " HLA-A3201\n",  " HLA-A3201\n",  " 0.887849\n", 0.895410\n",  " 0.712991\n", 0.766234\n",  " 0.505632\n", 0.512027\n",  " 0.893634\n", 0.893538\n",  " 0.747541\n", 0.753247\n",  " 0.490116\n", 0.504569\n",  " 0.903860\n", 0.903836\n",  " 0.784566\n", 0.762590\n",  " 0.505111\n", 0.485866\n",  " ...\n",  " 0.772881\n",  " 0.448284\n", 
...
" \n",  " HLA-A3301\n",  " HLA-A3301\n",  " 0.847973\n", 0.918900\n",  " 0.670996\n", 0.776892\n",  " 0.450852\n", 0.590329\n",  " 0.905218\n", 0.907902\n",  " 0.819367\n", 0.774067\n",  " 0.580473\n", 0.575365\n",  " 0.919463\n", 0.920683\n",  " 0.785300\n", 0.677130\n",  " 0.588369\n", 0.581894\n",  " ...\n",  " 0.868327\n",  " 0.587112\n", 
...
" \n",  " HLA-A6801\n",  " HLA-A6801\n",  " 0.893033\n", 0.943450\n",  " 0.876437\n", 0.913889\n",  " 0.461775\n", 0.562651\n",  " 0.943820\n", 0.943482\n",  " 0.915160\n", 0.910097\n",  " 0.577124\n", 0.571558\n",  " 0.944046\n", 0.946299\n",  " 0.912378\n", 0.915254\n",  " 0.569317\n", 0.575288\n",  " ...\n",  " 0.924791\n",  " 0.571403\n", 
...
" \n",  " HLA-A6802\n",  " HLA-A6802\n",  " 0.901857\n", 0.968036\n",  " 0.795031\n", 0.891566\n",  " 0.537770\n", 0.635743\n",  " 0.963291\n", 0.966897\n",  " 0.877458\n", 0.882883\n",  " 0.602793\n", 0.631313\n",  " 0.969391\n", 0.967103\n",  " 0.884211\n", 0.874233\n",  " 0.637281\n", 0.635978\n",  " ...\n",  " 0.922636\n",  " 0.652332\n", 
...
" \n",  " HLA-A6901\n",  " HLA-A6901\n",  " 0.947505\n", 0.964657\n",  " 0.625000\n", 0.666667\n",  " 0.273077\n", 0.331780\n",  " 0.959113\n", 0.961192\n",  " 0.657143\n", 0.711864\n",  " 0.340376\n", 0.326690\n",  " 0.959979\n", 0.964917\n",  " 0.724138\n", 0.595745\n",  " 0.329593\n", 0.335286\n",  " ...\n",  " 0.698413\n",  " 0.335339\n", 
...
" \n",  " HLA-A8001\n",  " HLA-A8001\n",  " 0.958525\n", 0.986175\n",  " 0.266667\n", 0.454545\n",  " 0.175769\n", 0.182616\n",  " 0.985791\n", 0.984639\n",  " 0.413793\n", 0.454545\n",  " 0.198756\n", 0.173568\n",  " 0.985023\n", 0.980031\n",  " 0.500000\n", 0.400000\n",  " 0.183105\n", 0.177481\n",  " ...\n",  " 0.434783\n",  " 0.182374\n", 
...
" \n",  " HLA-B0702\n",  " HLA-B0702\n",  " 0.872906\n", 0.914436\n",  " 0.805708\n", 0.829545\n",  " 0.500195\n", 0.598865\n",  " 0.916789\n", 0.915921\n",  " 0.868085\n", 0.844693\n",  " 0.592265\n", 0.599692\n",  " 0.913410\n", 0.911246\n",  " 0.855592\n", 0.832018\n",  " 0.598378\n", 0.595233\n",  " ...\n",  " 0.869383\n",  " 0.606014\n", 
...
" \n",  " HLA-B0801\n",  " HLA-B0801\n",  " 0.913530\n", 0.945158\n",  " 0.676056\n", 0.737079\n",  " 0.504525\n", 0.605155\n",  " 0.946449\n", 0.945246\n",  " 0.776119\n", 0.712264\n",  " 0.607055\n", 0.604413\n",  " 0.949558\n", 0.945333\n",  " 0.763158\n", 0.715990\n",  " 0.626074\n", 0.608809\n",  " ...\n",  " 0.776053\n",  " 0.629519\n", 
...
" \n",  " HLA-B0802\n",  " HLA-B0802\n",  " 0.976013\n", 0.984159\n",  " 0.434783\n", 0.333333\n",  " 0.354449\n", 0.376994\n",  " 0.980199\n", 0.983367\n",  " 0.105263\n", 0.400000\n",  " 0.367794\n", 0.382026\n",  " 0.977597\n", 0.984499\n",  " 0.105263\n", 0.260870\n",  " 0.366811\n", 0.371586\n",  " ...\n",  " 0.190476\n",  " 0.393833\n", 
...
" \n",  " HLA-B0803\n",  " HLA-B0803\n",  " 0.934815\n",  " 0.000000\n",  " 0.252862\n",  " 0.908148\n", 0.944198\n",  " 0.000000\n",  " 0.268350\n", 0.299541\n",  " 0.945185\n", 0.931852\n",  " 0.181818\n",  " 0.295024\n",  " 0.935802\n",  " 0.000000\n",  " 0.304273\n", 0.293733\n",  " ...\n",  " 0.000000\n",  " 0.318249\n", 
...
" \n",  " HLA-B1501\n",  " HLA-B1501\n",  " 0.895093\n", 0.940036\n",  " 0.734491\n", 0.838095\n",  " 0.528943\n", 0.599721\n",  " 0.932714\n", 0.939101\n",  " 0.828704\n", 0.842105\n",  " 0.583925\n", 0.596248\n",  " 0.938055\n", 0.936564\n",  " 0.847059\n", 0.823821\n",  " 0.591207\n", 0.597447\n",  " ...\n",  " 0.846512\n",  " 0.591735\n", 
...
" \n",  " HLA-B1503\n",  " HLA-B1503\n",  " 0.771809\n", 0.825758\n",  " 0.485437\n", 0.519231\n",  " 0.356271\n", 0.421374\n",  " 0.813590\n", 0.829431\n",  " 0.473684\n", 0.500000\n",  " 0.363621\n", 0.426775\n",  " 0.838154\n", 0.840909\n",  " 0.525253\n", 0.666667\n",  " 0.420474\n", 0.490528\n",  " ...\n",  " 0.592593\n",  " 0.468922\n", 
...
" \n",  " HLA-B1509\n",  " HLA-B1509\n",  " 0.829480\n", 0.878087\n",  " 0.390244\n", 0.222222\n",  " 0.263071\n", 0.338780\n",  " 0.843841\n", 0.888503\n",  " 0.181818\n", 0.235294\n",  " 0.327942\n", 0.352934\n",  " 0.878797\n", 0.882585\n",  " 0.277778\n", 0.125000\n",  " 0.339481\n", 0.339131\n",  " ...\n",  " 0.187500\n",  " 0.354311\n", 
...
" \n",  " HLA-B1517\n",  " HLA-B1517\n",  " 0.912835\n", 0.902346\n",  " 0.617761\n", 0.702703\n",  " 0.407852\n", 0.383573\n",  " 0.916991\n", 0.897179\n",  " 0.675105\n", 0.693333\n",  " 0.421397\n", 0.365051\n",  " 0.914638\n", 0.902588\n",  " 0.675439\n", 0.720379\n",  " 0.410866\n", 0.378477\n",  " ...\n",  " 0.637931\n",  " 0.422497\n", 
...
" \n",  " HLA-B1801\n",  " HLA-B1801\n",  " 0.728271\n", 0.823684\n",  " 0.244898\n", 0.333333\n",  " 0.168027\n", 0.263029\n",  " 0.809323\n", 0.809774\n",  " 0.454545\n", 0.263158\n",  " 0.224997\n", 0.251219\n",  " 0.813759\n", 0.806541\n",  " 0.368421\n", 0.242424\n",  " 0.252797\n", 0.249062\n",  " ...\n",  " 0.358974\n",  " 0.237118\n", 
...
" HLA-B2703\n",  " NaN\n",  " 0.000000\n",  " 0.067344\n", -0.000918\n",  " NaN\n",  " 0.000000\n",  " 0.057242\n", 0.015306\n",  " NaN\n",  " 0.000000\n",  " 0.019285\n", -0.009795\n",  " ...\n",  " 0.000000\n",  " 0.053658\n", 
...
" \n",  " HLA-B2705\n",  " HLA-B2705\n",  " 0.878645\n", 0.949909\n",  " 0.491228\n", 0.510638\n",  " 0.253302\n", 0.421742\n",  " 0.930309\n", 0.947126\n",  " 0.571429\n", 0.425532\n",  " 0.429170\n", 0.411754\n",  " 0.948820\n", 0.948941\n",  " 0.592593\n", 0.488889\n",  " 0.444921\n", 0.405393\n",  " ...\n",  " 0.285714\n",  " 0.430561\n", 
...
" \n",  " HLA-B3501\n",  " HLA-B3501\n",  " 0.775841\n", 0.834629\n",  " 0.641921\n", 0.728745\n",  " 0.434887\n", 0.528165\n",  " 0.824142\n", 0.829498\n",  " 0.729783\n", 0.722449\n",  " 0.520458\n", 0.521581\n",  " 0.834516\n", 0.831705\n",  " 0.738523\n", 0.716484\n",  " 0.526153\n", 0.516995\n",  " ...\n",  " 0.712000\n",  " 0.515205\n", 
...
" \n",  " HLA-B3801\n",  " HLA-B3801\n",  " 0.891928\n", 0.920432\n",  " 0.093333\n", 0.000000\n",  " 0.486744\n", 0.508204\n",  " 0.621749\n", 0.928707\n",  " 0.000000\n",  " 0.129742\n", 0.519743\n",  " 0.926241\n", 0.909895\n",  " 0.000000\n",  " 0.521713\n", 0.478477\n",  " ...\n",  " 0.000000\n",  " 0.513269\n", 
...
" \n",  " HLA-B3901\n",  " HLA-B3901\n",  " 0.954221\n", 0.961234\n",  " 0.595745\n", 0.651163\n",  " 0.290531\n", 0.307182\n",  " 0.956429\n", 0.945390\n",  " 0.754717\n", 0.711111\n",  " 0.300792\n", 0.295269\n",  " 0.960974\n", 0.947532\n",  " 0.750000\n", 0.723404\n",  " 0.306478\n", 0.301767\n",  " ...\n",  " 0.750000\n",  " 0.305138\n", 
...
" \n",  " HLA-B4001\n",  " HLA-B4001\n",  " 0.848219\n", 0.912136\n",  " 0.589404\n", 0.699088\n",  " 0.463190\n", 0.591791\n",  " 0.878764\n", 0.914117\n",  " 0.727778\n", 0.706587\n",  " 0.556345\n", 0.591034\n",  " 0.921028\n", 0.926635\n",  " 0.705167\n", 0.683386\n",  " 0.616325\n", 0.610002\n",  " ...\n",  " 0.829787\n",  " 0.635308\n", 
...
" \n",  " HLA-B4002\n",  " HLA-B4002\n",  " 0.855159\n", 0.906746\n",  " 0.466667\n", 0.580645\n",  " 0.453044\n", 0.494300\n",  " 0.911706\n", 0.906746\n",  " 0.685714\n", 0.666667\n",  " 0.471726\n", 0.507533\n",  " 0.930556\n", 0.900794\n",  " 0.774194\n", 0.645161\n",  " 0.478732\n", 0.530886\n",  " ...\n",  " 0.758621\n",  " 0.456242\n", 
...
" \n",  " HLA-B4402\n",  " HLA-B4402\n",  " 0.787365\n", 0.910569\n",  " 0.468750\n", 0.574018\n",  " 0.394692\n", 0.567193\n",  " 0.871823\n", 0.902234\n",  " 0.601719\n", 0.571429\n",  " 0.521560\n", 0.548542\n",  " 0.895220\n", 0.916983\n",  " 0.555891\n", 0.491909\n",  " 0.546076\n", 0.573202\n",  " ...\n",  " 0.568807\n",  " 0.574916\n", 
...
" \n",  " HLA-B4403\n",  " HLA-B4403\n",  " 0.730159\n", 0.837484\n",  " 0.517350\n", 0.646341\n",  " 0.346724\n", 0.485605\n",  " 0.786895\n", 0.812021\n",  " 0.684350\n", 0.628399\n",  " 0.421291\n", 0.459152\n",  " 0.819601\n", 0.847085\n",  " 0.701370\n", 0.612903\n",  " 0.462955\n", 0.511578\n",  " ...\n",  " 0.766304\n",  " 0.541066\n", 
...
" \n",  " HLA-B4501\n",  " HLA-B4501\n",  " 0.990000\n", 0.993333\n",  " 0.888889\n", 0.571429\n",  " 0.269334\n", 0.253825\n",  " 0.976667\n",  " 0.666667\n",  " 0.269334\n",  " 1.000000\n", 0.993333\n",  " 0.571429\n",  " 0.257961\n", 0.263130\n",  " 0.996667\n",  " 0.571429\n",  " 0.285876\n",  " ...\n",  " 1.000000\n",  " 0.263574\n", 
...
" \n",  " HLA-B5101\n",  " HLA-B5101\n",  " 0.822785\n", 0.924288\n",  " 0.285714\n", 0.416667\n",  " 0.247637\n", 0.371285\n",  " 0.920332\n", 0.930103\n",  " 0.462963\n", 0.448980\n",  " 0.347800\n", 0.372668\n",  " 0.947567\n", 0.933584\n",  " 0.480000\n", 0.458333\n",  " 0.377706\n", 0.372841\n",  " ...\n",  " 0.428571\n",  " 0.365307\n", 
...
" \n",  " HLA-B5301\n",  " HLA-B5301\n",  " 0.783119\n", 0.868288\n",  " 0.627968\n", 0.723618\n",  " 0.410082\n", 0.551604\n",  " 0.818300\n", 0.872307\n",  " 0.688372\n", 0.734177\n",  " 0.485075\n", 0.561115\n",  " 0.854508\n", 0.859937\n",  " 0.731132\n", 0.672222\n",  " 0.542728\n", 0.526675\n",  " ...\n",  " 0.733813\n",  " 0.515585\n", 
...
" \n",  " HLA-B5401\n",  " HLA-B5401\n",  " 0.864865\n", 0.851351\n",  " 0.727273\n", 0.888889\n",  " 0.287001\n", 0.297251\n",  " 0.810811\n",  " 0.727273\n",  " 0.322876\n", 0.837838\n",  " 0.800000\n",  " 0.727273\n", 0.282608\n",  " 0.303108\n", 0.810811\n",  " 0.750000\n",  " 0.339715\n",  " ...\n",  " 0.800000\n",  " 0.300252\n", 
...
" \n",  " HLA-B5701\n",  " HLA-B5701\n",  " 0.905416\n", 0.916851\n",  " 0.556701\n", 0.731563\n",  " 0.477058\n", 0.533204\n",  " 0.917743\n", 0.917088\n",  " 0.753623\n", 0.735905\n",  " 0.526438\n", 0.539218\n",  " 0.916802\n", 0.889716\n",  " 0.755952\n", 0.707692\n",  " 0.539018\n", 0.465169\n",  " ...\n",  " 0.741840\n",  " 0.484975\n", 
...
" \n",  " HLA-B5801\n",  " HLA-B5801\n",  " 0.892351\n", 0.907342\n",  " 0.791774\n", 0.811224\n",  " 0.537777\n", 0.593507\n",  " 0.902817\n", 0.907140\n",  " 0.816121\n", 0.814249\n",  " 0.568854\n", 0.594791\n",  " 0.905746\n", 0.902089\n",  " 0.823529\n", 0.801034\n",  " 0.582108\n", 0.557516\n",  " ...\n",  " 0.827411\n",  " 0.545132\n", 
...
" \n",  " Mamu-A01\n",  " Mamu-A01\n",  " 0.813036\n", 0.906266\n",  " 0.569697\n", 0.671233\n",  " 0.422206\n", 0.516791\n",  " 0.885572\n", 0.904175\n",  " 0.700730\n", 0.691176\n",  " 0.484435\n", 0.507662\n",  " 0.917658\n", 0.924219\n",  " 0.686567\n", 0.660714\n",  " 0.528892\n", 0.561897\n",  " ...\n",  " 0.694915\n",  " 0.569423\n", 
...
" \n",  " Mamu-A02\n",  " Mamu-A02\n",  " 0.754829\n", 0.862996\n",  " 0.579545\n", 0.656891\n",  " 0.374816\n", 0.509122\n",  " 0.858251\n", 0.871361\n",  " 0.660870\n", 0.670554\n",  " 0.493879\n", 0.519787\n",  " 0.864487\n", 0.914008\n",  " 0.670520\n", 0.767528\n",  " 0.510146\n", 0.573650\n",  " ...\n",  " 0.767123\n",  " 0.598964\n", 
...
" \n",  " \n",  "\n",  "

54 rows × 69 39  columns

\n", ""  ],  "text/plain": [  " allele mhcflurry 0_auc mhcflurry 0_f1 mhcflurry 0_tau \\\n",  "allele \n",  "overall overall 0.882928 0.704518 0.496251 0.927733 0.780452 0.575551  \n", "H-2-DB H-2-DB 0.848124 0.535433 0.511345 0.912222 0.593886 0.634828  \n", "H-2-KB H-2-KB 0.849000 0.762565 0.471307 0.907870 0.812165 0.589815  \n", "H-2-KD H-2-KD 0.763398 0.557143 0.342118 0.773768 0.584615 0.357916  \n", "HLA-A0101 HLA-A0101 0.849094 0.531532 0.409243 0.912949 0.605714 0.519646  \n", "HLA-A0201 HLA-A0201 0.854361 0.784367 0.488418 0.929486 0.878578 0.623194  \n", "HLA-A0202 HLA-A0202 0.767461 0.639175 0.422552 0.895233 0.755556 0.611888  \n", "HLA-A0203 HLA-A0203 0.923881 0.884058 0.465846 0.977392 0.954976 0.578919  \n", "HLA-A0206 HLA-A0206 0.829841 0.778351 0.415579 0.907647 0.870210 0.540573  \n", "HLA-A0301 HLA-A0301 0.825286 0.782222 0.434382 0.925179 0.881178 0.602198  \n", "HLA-A1101 HLA-A1101 0.880948 0.820874 0.501814 0.938378 0.882514 0.613449  \n", "HLA-A2301 HLA-A2301 0.817591 0.654434 0.482072 0.847377 0.734104 0.564802  \n", "HLA-A2402 HLA-A2402 0.805891 0.575916 0.457142 0.857054 0.571429 0.565620  \n", "HLA-A2501 HLA-A2501 0.989781 0.421053 0.150775 0.991241 0.615385 0.151074  \n", "HLA-A2601 HLA-A2601 0.902436 0.525547 0.372451 0.929049 0.548673 0.443452  \n", "HLA-A2602 HLA-A2602 0.878531 0.658333 0.432259 0.927684 0.756219 0.516947  \n", "HLA-A2603 HLA-A2603 0.846290 0.500000 0.299386 0.899656 0.571429 0.343022  \n", "HLA-A2902 HLA-A2902 0.857957 0.592593 0.546467 0.891331 0.701754 0.628437  \n", "HLA-A3001 HLA-A3001 0.873687 0.709677 0.515750 0.901720 0.749340 0.531903  \n", "HLA-A3002 HLA-A3002 0.698121 0.592391 0.290363 0.735361 0.624685 0.344846  \n", "HLA-A3101 HLA-A3101 0.795053 0.769231 0.385885 0.847940 0.816688 0.494975  \n", "HLA-A3201 HLA-A3201 0.887849 0.712991 0.505632 0.895410 0.766234 0.512027  \n", "HLA-A3301 HLA-A3301 0.847973 0.670996 0.450852 0.918900 0.776892 0.590329  \n", "HLA-A6801 HLA-A6801 0.893033 0.876437 0.461775 0.943450 0.913889 0.562651  \n", "HLA-A6802 HLA-A6802 0.901857 0.795031 0.537770 0.968036 0.891566 0.635743  \n", "HLA-A6901 HLA-A6901 0.947505 0.625000 0.273077 0.964657 0.666667 0.331780  \n", "HLA-A8001 HLA-A8001 0.958525 0.266667 0.175769 0.986175 0.454545 0.182616  \n", "HLA-B0702 HLA-B0702 0.872906 0.805708 0.500195 0.914436 0.829545 0.598865  \n", "HLA-B0801 HLA-B0801 0.913530 0.676056 0.504525 0.945158 0.737079 0.605155  \n", "HLA-B0802 HLA-B0802 0.976013 0.434783 0.354449 0.984159 0.333333 0.376994  \n", "HLA-B0803 HLA-B0803 0.934815 0.944198  0.000000 0.252862 0.299541  \n", "HLA-B1501 HLA-B1501 0.895093 0.734491 0.528943 0.940036 0.838095 0.599721  \n", "HLA-B1503 HLA-B1503 0.771809 0.485437 0.356271 0.825758 0.519231 0.421374  \n", "HLA-B1509 HLA-B1509 0.829480 0.390244 0.263071 0.878087 0.222222 0.338780  \n", "HLA-B1517 HLA-B1517 0.912835 0.617761 0.407852 0.902346 0.702703 0.383573  \n", "HLA-B1801 HLA-B1801 0.728271 0.244898 0.168027 0.823684 0.333333 0.263029  \n", "HLA-B2703 HLA-B2703 NaN 0.000000 0.067344 -0.000918  \n", "HLA-B2705 HLA-B2705 0.878645 0.491228 0.253302 0.949909 0.510638 0.421742  \n", "HLA-B3501 HLA-B3501 0.775841 0.641921 0.434887 0.834629 0.728745 0.528165  \n", "HLA-B3801 HLA-B3801 0.891928 0.093333 0.486744 0.920432 0.000000 0.508204  \n", "HLA-B3901 HLA-B3901 0.954221 0.595745 0.290531 0.961234 0.651163 0.307182  \n", "HLA-B4001 HLA-B4001 0.848219 0.589404 0.463190 0.912136 0.699088 0.591791  \n", "HLA-B4002 HLA-B4002 0.855159 0.466667 0.453044 0.906746 0.580645 0.494300  \n", "HLA-B4402 HLA-B4402 0.787365 0.468750 0.394692 0.910569 0.574018 0.567193  \n", "HLA-B4403 HLA-B4403 0.730159 0.517350 0.346724 0.837484 0.646341 0.485605  \n", "HLA-B4501 HLA-B4501 0.990000 0.888889 0.269334 0.993333 0.571429 0.253825  \n", "HLA-B4601 HLA-B4601 NaN 0.000000 NaN \n",  "HLA-B5101 HLA-B5101 0.822785 0.285714 0.247637 0.924288 0.416667 0.371285  \n", "HLA-B5301 HLA-B5301 0.783119 0.627968 0.410082 0.868288 0.723618 0.551604  \n", "HLA-B5401 HLA-B5401 0.864865 0.727273 0.287001 0.851351 0.888889 0.297251  \n", "HLA-B5701 HLA-B5701 0.905416 0.556701 0.477058 0.916851 0.731563 0.533204  \n", "HLA-B5801 HLA-B5801 0.892351 0.791774 0.537777 0.907342 0.811224 0.593507  \n", "Mamu-A01 Mamu-A01 0.813036 0.569697 0.422206 0.906266 0.671233 0.516791  \n", "Mamu-A02 Mamu-A02 0.754829 0.579545 0.374816 0.862996 0.656891 0.509122  \n", "\n",  " mhcflurry 1_auc mhcflurry 1_f1 mhcflurry 1_tau mhcflurry 2_auc \\\n",  "allele \n",  "overall 0.918006 0.780544 0.561266 0.929080 0.926805 0.781977 0.576826 0.929323  \n", "H-2-DB 0.885038 0.596078 0.580237 0.907967 0.915309 0.612766 0.640775 0.905614  \n", "H-2-KB 0.895286 0.806228 0.586440 0.909416 0.908143 0.819149 0.591965 0.909597  \n", "H-2-KD 0.772275 0.562963 0.352598 0.786212 0.776589 0.604651 0.355835 0.793761  \n", "HLA-A0101 0.910766 0.610329 0.508191 0.915757 0.903609 0.631016 0.514472 0.909852  \n", "HLA-A02010.927671 0.876843 0.619872  0.930293 0.879897 0.625150 0.930269  \n", "HLA-A0202 0.874446 0.719101 0.596025 0.895233 0.900499 0.755556 0.615726 0.905765  \n", "HLA-A0203 0.972561 0.939645 0.580955 0.979134 0.978112 0.952494 0.592381 0.976223  \n", "HLA-A0206 0.900923 0.866911 0.510937 0.906767 0.903137 0.870098 0.532982 0.906622  \n", "HLA-A0301 0.920225 0.867521 0.582132 0.925394 0.926072 0.883281 0.596442 0.920753  \n", "HLA-A1101 0.930302 0.875000 0.611392 0.941065 0.938900 0.884298 0.614414 0.936935  \n", "HLA-A2301 0.851253 0.727273 0.558449 0.861713 0.853138 0.697248 0.573883 0.858740  \n", "HLA-A2402 0.835599 0.629808 0.537855 0.857510 0.867143 0.567416 0.573752 0.859523  \n", "HLA-A2501 0.992701 0.421053 0.151673 0.976642 0.989294 0.571429 0.150775 0.997080  \n", "HLA-A2601 0.925136 0.566176 0.419262 0.934574 0.931319 0.582278 0.446167 0.933928  \n", "HLA-A2602 0.909927 0.703704 0.474037 0.926912 0.929454 0.785714 0.515487 0.926848  \n", "HLA-A2603 0.864373 0.500000 0.334720 0.894644 0.888649 0.596491 0.336494 0.900344  \n", "HLA-A2902 0.901099 0.622951 0.641024 0.890517 0.897436 0.631579 0.626902 0.899471  \n", "HLA-A3001 0.885017 0.743003 0.507472 0.892818 0.898432 0.746667 0.523981 0.896782  \n", "HLA-A3002 0.742389 0.650794 0.345972 0.735662 0.729837 0.645161 0.335080 0.756559  \n", "HLA-A3101 0.849611 0.821918 0.481153 0.851305 0.847258 0.822943 0.496457 0.849787  \n", "HLA-A3201 0.893634 0.747541 0.490116 0.903860 0.893538 0.753247 0.504569 0.903836  \n", "HLA-A3301 0.905218 0.819367 0.580473 0.919463 0.907902 0.774067 0.575365 0.920683  \n", "HLA-A6801 0.943820 0.915160 0.577124 0.944046 0.943482 0.910097 0.571558 0.946299  \n", "HLA-A6802 0.963291 0.877458 0.602793 0.969391 0.966897 0.882883 0.631313 0.967103  \n", "HLA-A6901 0.959113 0.657143 0.340376 0.959979 0.961192 0.711864 0.326690 0.964917  \n", "HLA-A8001 0.985791 0.413793 0.198756 0.985023 0.984639 0.454545 0.173568 0.980031  \n", "HLA-B0702 0.916789 0.868085 0.592265 0.913410 0.915921 0.844693 0.599692 0.911246  \n", "HLA-B0801 0.946449 0.776119 0.607055 0.949558 0.945246 0.712264 0.604413 0.945333  \n", "HLA-B0802 0.980199 0.105263 0.367794 0.977597 0.983367 0.400000 0.382026 0.984499  \n", "HLA-B0803 0.908148 0.000000 0.268350 0.945185 0.931852 0.181818 0.295024 0.935802  \n", "HLA-B1501 0.932714 0.828704 0.583925 0.938055 0.939101 0.842105 0.596248 0.936564  \n", "HLA-B1503 0.813590 0.473684 0.363621 0.838154 0.829431 0.500000 0.426775 0.840909  \n", "HLA-B1509 0.843841 0.181818 0.327942 0.878797 0.888503 0.235294 0.352934 0.882585  \n", "HLA-B1517 0.916991 0.675105 0.421397 0.914638 0.897179 0.693333 0.365051 0.902588  \n", "HLA-B1801 0.809323 0.454545 0.224997 0.813759 0.809774 0.263158 0.251219 0.806541  \n", "HLA-B2703 NaN 0.000000 0.057242 0.015306  NaN \n", "HLA-B2705 0.930309 0.571429 0.429170 0.948820 0.947126 0.425532 0.411754 0.948941  \n", "HLA-B3501 0.824142 0.729783 0.520458 0.834516 0.829498 0.722449 0.521581 0.831705  \n", "HLA-B3801 0.621749 0.928707  0.000000 0.129742 0.926241 0.519743 0.909895  \n", "HLA-B3901 0.956429 0.754717 0.300792 0.960974 0.945390 0.711111 0.295269 0.947532  \n", "HLA-B4001 0.878764 0.727778 0.556345 0.921028 0.914117 0.706587 0.591034 0.926635  \n", "HLA-B4002 0.911706 0.685714 0.471726 0.930556 0.906746 0.666667 0.507533 0.900794  \n", "HLA-B4402 0.871823 0.601719 0.521560 0.895220 0.902234 0.571429 0.548542 0.916983  \n", "HLA-B4403 0.786895 0.684350 0.421291 0.819601 0.812021 0.628399 0.459152 0.847085  \n", "HLA-B4501 0.976667 0.666667 0.269334 1.000000 0.993333 0.571429 0.263130 0.996667  \n", "HLA-B4601 NaN 0.000000 NaN NaN \n",  "HLA-B5101 0.920332 0.462963 0.347800 0.947567 0.930103 0.448980 0.372668 0.933584  \n", "HLA-B5301 0.818300 0.688372 0.485075 0.854508 0.872307 0.734177 0.561115 0.859937  \n", "HLA-B5401 0.810811 0.727273 0.322876 0.837838  0.800000 0.282608 0.810811  \n", "HLA-B5701 0.917743 0.753623 0.526438 0.916802 0.917088 0.735905 0.539218 0.889716  \n", "HLA-B5801 0.902817 0.816121 0.568854 0.905746 0.907140 0.814249 0.594791 0.902089  \n", "Mamu-A01 0.885572 0.700730 0.484435 0.917658 0.904175 0.691176 0.507662 0.924219  \n", "Mamu-A02 0.858251 0.660870 0.493879 0.864487 0.871361 0.670554 0.519787 0.914008  \n", "\n",  " mhcflurry 2_f1 mhcflurry 2_tau ... netmhc_f1 netmhc_tau \\\n",  "allele ... \n",  "overall 0.787132 0.571978 0.770265 0.577676  ... 0.800909 0.582258 \n", "H-2-DB 0.546296 0.625583 0.580357 0.625557  ... 0.621212 0.600337 \n", "H-2-KB 0.836806 0.587509 0.802893 0.593356  ... 0.813675 0.573199 \n", "H-2-KD 0.592593 0.368319 0.589147 0.385118  ... 0.657718 0.403275 \n", "HLA-A0101 0.592179 0.521964 0.613636 0.516242  ... 0.619565 0.524866 \n", "HLA-A0201 0.883262 0.624985 0.875983 0.625099  ... 0.884336 0.635498 \n", "HLA-A0202 0.769231 0.761905  0.605748 ... 0.755556 0.627143 \n", "HLA-A0203 0.954869 0.592506 0.953293 0.589924  ... 0.948626 0.586911 \n", "HLA-A0206 0.869779 0.528590 0.867168 0.537858  ... 0.872902 0.543184 \n", "HLA-A0301 0.872727 0.600378 0.862366 0.588700  ... 0.900621 0.629236 \n", "HLA-A1101 0.887363 0.615063 0.885246 0.610654  ... 0.883402 0.632199 \n", "HLA-A2301 0.748571 0.581984 0.634812 0.582354  ... 0.773842 0.605598 \n", "HLA-A2402 0.575269 0.562741 0.416667 0.562286  ... 0.632911 0.575710 \n", "HLA-A2501 0.666667 0.146880 0.153321  ... 0.666667 0.151836 \n", "HLA-A2601 0.571429 0.447482 0.563107 0.453999  ... 0.541176 0.436646 \n", "HLA-A2602 0.773869 0.519182 0.764045 0.520612  ... 0.766839 0.525365 \n", "HLA-A2603 0.526316 0.346853 0.581818 0.356361  ... 0.542373 0.366317 \n", "HLA-A2902 0.631579 0.628130 0.703704 0.649621  ... 0.644068 0.619830 \n", "HLA-A3001 0.746736 0.521534 0.752688 0.501209  ... 0.731959 0.456482 \n", "HLA-A3002 0.631043 0.346248 0.594752 0.352958  ... 0.663438 0.342161 \n", "HLA-A3101 0.819549 0.504028 0.798440 0.493825  ... 0.833958 0.525026 \n", "HLA-A3201 0.784566 0.505111 0.762590 0.485866  ... 0.772881 0.448284 \n", "HLA-A3301 0.785300 0.588369 0.677130 0.581894  ... 0.868327 0.587112 \n", "HLA-A6801 0.912378 0.569317 0.915254 0.575288  ... 0.924791 0.571403 \n", "HLA-A6802 0.884211 0.637281 0.874233 0.635978  ... 0.922636 0.652332 \n", "HLA-A6901 0.724138 0.329593 0.595745 0.335286  ... 0.698413 0.335339 \n", "HLA-A8001 0.500000 0.183105 0.400000 0.177481  ... 0.434783 0.182374 \n", "HLA-B0702 0.855592 0.598378 0.832018 0.595233  ... 0.869383 0.606014 \n", "HLA-B0801 0.763158 0.626074 0.715990 0.608809  ... 0.776053 0.629519 \n", "HLA-B0802 0.105263 0.366811 0.260870 0.371586  ... 0.190476 0.393833 \n", "HLA-B0803 0.000000 0.304273 0.293733  ... 0.000000 0.318249 \n", "HLA-B1501 0.847059 0.591207 0.823821 0.597447  ... 0.846512 0.591735 \n", "HLA-B1503 0.525253 0.420474 0.666667 0.490528  ... 0.592593 0.468922 \n", "HLA-B1509 0.277778 0.339481 0.125000 0.339131  ... 0.187500 0.354311 \n", "HLA-B1517 0.675439 0.410866 0.720379 0.378477  ... 0.637931 0.422497 \n", "HLA-B1801 0.368421 0.252797 0.242424 0.249062  ... 0.358974 0.237118 \n", "HLA-B2703 0.000000 0.019285 -0.009795  ... 0.000000 0.053658 \n", "HLA-B2705 0.592593 0.444921 0.488889 0.405393  ... 0.285714 0.430561 \n", "HLA-B3501 0.738523 0.526153 0.716484 0.516995  ... 0.712000 0.515205 \n", "HLA-B3801 0.000000 0.521713 0.478477  ... 0.000000 0.513269 \n", "HLA-B3901 0.750000 0.306478 0.723404 0.301767  ... 0.750000 0.305138 \n", "HLA-B4001 0.705167 0.616325 0.683386 0.610002  ... 0.829787 0.635308 \n", "HLA-B4002 0.774194 0.478732 0.645161 0.530886  ... 0.758621 0.456242 \n", "HLA-B4402 0.555891 0.546076 0.491909 0.573202  ... 0.568807 0.574916 \n", "HLA-B4403 0.701370 0.462955 0.612903 0.511578  ... 0.766304 0.541066 \n", "HLA-B4501 0.571429 0.257961 0.285876  ... 1.000000 0.263574 \n", "HLA-B4601 0.000000 NaN ... 0.000000 NaN \n",  "HLA-B5101 0.480000 0.377706 0.458333 0.372841  ... 0.428571 0.365307 \n", "HLA-B5301 0.731132 0.542728 0.672222 0.526675  ... 0.733813 0.515585 \n", "HLA-B5401 0.727273 0.303108 0.750000 0.339715  ... 0.800000 0.300252 \n", "HLA-B5701 0.755952 0.539018 0.707692 0.465169  ... 0.741840 0.484975 \n", "HLA-B5801 0.823529 0.582108 0.801034 0.557516  ... 0.827411 0.545132 \n", "Mamu-A01 0.686567 0.528892 0.660714 0.561897  ... 0.694915 0.569423 \n", "Mamu-A02 0.670520 0.510146 0.767528 0.573650  ... 0.767123 0.598964 \n", "\n",  " netmhcpan_auc netmhcpan_f1 netmhcpan_tau smmpmbec_cpp_auc \\\n",  "allele \n", 
...
"Mamu-A01 0.609272 0.469975 274 2264 \n",  "Mamu-A02 0.640000 0.478589 388 938 \n",  "\n",  "[54 rows x 69 39  columns]" ]  },  "execution_count": 22, 10,  "metadata": {},  "output_type": "execute_result"  } 
...
},  {  "cell_type": "code",  "execution_count": 23, 11,  "metadata": {  "collapsed": false  },  "outputs": [  {  "data": {  "text/plain": [  "allele HLA-A3101\n",  "mhcflurry 0_auc 0.8479397\n",  "mhcflurry 0_f1 0.8166877\n",  "mhcflurry 0_tau 0.4949753\n",  "mhcflurry 1_auc 0.8472576\n",  "mhcflurry 1_f1 0.8229426\n",  "mhcflurry 1_tau 0.4964571\n",  "mhcflurry 2_auc 0.8497869\n",  "mhcflurry 2_f1 0.7984395\n",  "mhcflurry 2_tau 0.4938253\n",  "mhcflurry 3_auc 0.8507833\n",  "mhcflurry 3_f1 0.8092784\n",  "mhcflurry 3_tau 0.4997911\n",  "mhcflurry ensemble all impute_auc 0.8503771\n",  "mhcflurry ensemble all impute_f1 0.8226415\n",  "mhcflurry ensemble all impute_tau 0.5018517\n",  "mhcflurry ensemble all not impute_auc 0.8498099\n",  "mhcflurry ensemble all not impute_f1 0.8117048\n",  "mhcflurry ensemble all not impute_tau 0.4974372\n",  "mhcflurry ensemble all_auc 0.8500935\n",  "mhcflurry ensemble all_f1 0.8207071\n",  "mhcflurry ensemble all_tau 0.5001924\n",  "mhcflurry ensemble big dropout impute_auc 0.8503771\n",  "mhcflurry ensemble big dropout impute_f1 0.8226415\n",  "mhcflurry ensemble big dropout impute_tau 0.5018517\n",  "mhcflurry ensemble big dropout_auc 0.8498099\n",  "mhcflurry ensemble big dropout_f1 0.8117048\n",  "mhcflurry ensemble big dropout_tau 0.4974372\n",  "netmhc_auc 0.8626062\n",  "netmhc_f1 0.8339576\n",  "netmhc_tau 0.5250261\n",  "netmhcpan_auc 0.8592605\n",  "netmhcpan_f1 0.822335\n",  "netmhcpan_tau 0.5289405\n",  "smmpmbec_cpp_auc 0.85127\n",  "smmpmbec_cpp_f1 0.837037\n",  "smmpmbec_cpp_tau 0.5025855\n",  "test_size 724\n",  "train_size 4796\n",  "Name: HLA-A3101, dtype: object"  ]  },  "execution_count": 11,  "metadata": {},  "output_type": "execute_result"  }  ],  "source": [  "scores_df.ix[\"HLA-A3101\"]"  ]  },  {  "cell_type": "code",  "execution_count": 12,  "metadata": {  "collapsed": false  }, 
...
"output_type": "stream",  "text": [  "overall H-2-DB H-2-KB H-2-KD HLA-A0101 HLA-A0201 HLA-A0202 HLA-A0203 HLA-A0206 HLA-A0301 HLA-A1101 HLA-A2301 HLA-A2402 HLA-A2501 HLA-A2601 HLA-A2602 HLA-A2603 HLA-A2902 HLA-A3001 HLA-A3002 HLA-A3101 HLA-A3201 HLA-A3301 HLA-A6801 HLA-A6802 HLA-A6901 HLA-A8001 HLA-B0702 HLA-B0801 HLA-B0802 HLA-B0803 HLA-B1501 HLA-B1503 HLA-B1509 HLA-B1517 HLA-B1801 HLA-B2703 HLA-B2705 HLA-B3501 HLA-B3801 HLA-B3901 HLA-B4001 HLA-B4002 HLA-B4402 HLA-B4403 HLA-B4501 HLA-B4601 HLA-B5101 HLA-B5301 HLA-B5401 HLA-B5701 HLA-B5801 Mamu-A01 Mamu-A02\n",  "(54, 69)\n" 39)\n"  ]  }  ], 
...
},  {  "cell_type": "code",  "execution_count": 25, 66,  "metadata": {  "collapsed": false,  "scrolled": true 
...
"name": "stdout",  "output_type": "stream",  "text": [  "allele overall\n", "test_size 27680\n", "netmhcpan_auc 0.9329235\n","netmhc_auc 0.9299468\n",  "mhcflurry ensemble big dropout_auc 0.92908\n",  "mhcflurry 2_auc 0.92908\n",  "mhcflurry ensemble big dropout impute_auc 0.9287027\n",  "mhcflurry 6_auc 0.9287027\n",  "mhcflurry ensemble all_auc 0.9275826\n", 0.9307122\n",  "mhcflurry ensemble all impute_auc 0.9268115\n", big dropout_auc 0.9306644\n",  "mhcflurry ensemble all not impute_auc 0.9249937\n",  "mhcflurry ensemble small impute_auc 0.9205328\n",  "mhcflurry 5_auc 0.9205328\n",  "smmpmbec_cpp_auc 0.9192085\n",  "mhcflurry ensemble small_auc 0.9180064\n",  "mhcflurry 1_auc 0.9180064\n",  "mhcflurry 7_auc 0.9176498\n", 0.9306644\n",  "netmhc_auc 0.9299468\n",  "mhcflurry ensemble small big  dropout impute_auc 0.9176498\n",  "mhcflurry 3_auc 0.9122768\n",  "mhcflurry ensemble small dropout_auc 0.9122768\n", 0.9299198\n",  "mhcflurry ensemble big all  impute_auc 0.8890605\n", 0.9299198\n",  "mhcflurry 4_auc 0.8890605\n", 2_auc 0.9293233\n",  "mhcflurry ensemble big_auc 0.8829282\n", 3_auc 0.9287587\n",  "mhcflurry 0_auc 0.8829282\n", 0.9277326\n",  "mhcflurry 1_auc 0.9268053\n",  "smmpmbec_cpp_auc 0.9192085\n",  "netmhc_f1 0.800909\n", "netmhcpan_f1 0.79317\n","mhcflurry 6_f1 0.7875957\n",  "mhcflurry ensemble big dropout impute_f1 0.7875957\n",  "mhcflurry 2_f1 0.7871323\n",  "mhcflurry ensemble big dropout_f1 0.7871323\n",  "smmpmbec_cpp_f1 0.7842948\n", "mhcflurryensemble all_f1 0.7842049\n",  "mhcflurry 5_f1 0.7823755\n",  "mhcflurry ensemble small impute_f1 0.7823755\n",  "mhcflurry ensemble all impute_f1 0.7815531\n",  "mhcflurry  1_f1 0.7805442\n",  "mhcflurry ensemble small_f1 0.7805442\n", 0.7819771\n",  "mhcflurry ensemble allnot  impute_f1 0.7790492\n", 0.7812917\n",  "mhcflurry ensemble big dropout  impute_f1 0.7181063\n", 0.7812917\n",  "mhcflurry 4_f1 0.7181063\n", ensemble all_f1 0.7812046\n",  "mhcflurry 7_f1 0.7171487\n", 0_f1 0.7804517\n",  "mhcflurry ensemble small dropout all not  impute_f1 0.7171487\n", 0.780244\n",  "mhcflurry ensemble small big  dropout_f1 0.715679\n", 0.780244\n",  "mhcflurry 3_f1 0.715679\n", 0.7732276\n",  "mhcflurry 0_f1 0.7045182\n",  "mhcflurry ensemble big_f1 0.7045182\n", 2_f1 0.7702654\n",  "netmhc_tau 0.5822579\n","netmhcpan_tau 0.5795864\n",  "mhcflurry ensemble big dropout impute_tau 0.5788812\n",  "mhcflurry 6_tau 0.5788812\n",  "mhcflurry ensemble all_tau 0.5749651\n", 0.5819843\n",  "mhcflurry ensemble all impute_tau 0.5742558\n", 0.5815906\n",  "mhcflurry ensemble big dropout_tau 0.5719777\n",  "mhcflurry 2_tau 0.5719777\n",  "mhcflurry ensemble all not impute_tau 0.5689815\n",  "mhcflurry ensemble small dropout  impute_tau 0.5648743\n",  "mhcflurry 5_tau 0.5648743\n",  "smmpmbec_cpp_tau 0.5619957\n", 0.5815906\n",  "mhcflurry ensemble small_tau 0.5612657\n",  "mhcflurry 1_tau 0.5612657\n", big dropout_tau 0.5802612\n",  "mhcflurry ensemble small dropout all not  impute_tau 0.5490314\n",  "mhcflurry 7_tau 0.5490314\n", 0.5802612\n",  "netmhcpan_tau 0.5795864\n",  "mhcflurry 3_tau 0.5451891\n",  "mhcflurry ensemble small dropout_tau 0.5451891\n",  "mhcflurry 4_tau 0.5085733\n", 0.5788436\n",  "mhcflurry ensemble big impute_tau 0.5085733\n", 2_tau 0.5776759\n",  "mhcflurry ensemble big_tau 0.4962506\n", 1_tau 0.5768259\n",  "mhcflurry 0_tau 0.4962506\n", 0.5755507\n",  "smmpmbec_cpp_tau 0.5619957\n",  "train_size NaN\n", "Name: overall, dtype: object\n"  ]  } 
...
},  {  "cell_type": "code",  "execution_count": 26, 79,  "metadata": {  "collapsed": false  },  "outputs": [  {  "data": {  "text/plain": [  "(44, 39)"  ]  },  "execution_count": 79,  "metadata": {},  "output_type": "execute_result"  }  ],  "source": [  "scores_df.ix[(scores_df.index != \"overall\") & (scores_df.train_size >= 500)].shape"  ]  },  {  "cell_type": "code",  "execution_count": 80,  "metadata": {  "collapsed": false  },  "outputs": [  {  "name": "stdout", "output_type": "stream", "text": [ "train_size 3131.285714\n", 2656.227273\n",  "test_size 605.371429\n",  "mhcflurry 6_auc 0.903385\n",  "mhcflurry ensemble big dropout impute_auc 0.903385\n", 558.181818\n",  "mhcflurry ensemble all_auc 0.902820\n", 0.911521\n",  "mhcflurry ensemble big dropout_auc 0.902490\n",  "mhcflurry 2_auc 0.902490\n",  "mhcflurry ensemble all impute_auc 0.900867\n",  "netmhcpan_auc 0.900643\n",  "netmhc_auc 0.900374\n", 0.911310\n",  "mhcflurry ensemble all not impute_auc 0.899897\n",  "mhcflurry 3_auc 0.894749\n", 0.911310\n",  "mhcflurry ensemble small dropout_auc 0.894749\n",  "mhcflurry 5_auc 0.893936\n",  "mhcflurry ensemble small impute_auc 0.893936\n",  "mhcflurry ensemble small big  dropout impute_auc 0.893547\n",  "mhcflurry 7_auc 0.893547\n",  "mhcflurry ensemble small_auc 0.892490\n",  "mhcflurry 1_auc 0.892490\n",  "smmpmbec_cpp_auc 0.888191\n",  "mhcflurry 4_auc 0.852369\n", 0.910493\n",  "mhcflurry ensemble big all  impute_auc 0.852369\n", 0.910493\n",  "mhcflurry ensemble big_auc 0.845604\n", 3_auc 0.910256\n",  "netmhcpan_auc 0.909513\n",  "mhcflurry 0_auc 0.845604\n",  "netmhc_f1 0.714548\n", 2_auc 0.909055\n",  "netmhc_auc 0.908575\n",  "mhcflurry ensemble big dropout impute_f1 0.711891\n", 0_auc 0.908213\n",  "mhcflurry 6_f1 0.711891\n", 1_auc 0.906355\n",  "smmpmbec_cpp_auc 0.893122\n",  "netmhcpan_f1 0.711710\n",  "mhcflurry ensemble small_f1 0.711313\n", 0.720172\n",  "netmhc_f1 0.717352\n",  "smmpmbec_cpp_f1 0.696423\n",  "mhcflurry 1_f1 0.711313\n", 0_f1 0.690217\n",  "mhcflurry ensemble all_f1 0.711193\n", 0.689826\n",  "mhcflurry 2_f1 0.710673\n", 1_f1 0.688504\n",  "mhcflurry ensemble big dropout_f1 0.710673\n",  "mhcflurry 5_f1 0.709066\n",  "mhcflurry ensemble small dropout  impute_f1 0.709066\n", 0.687764\n",  "mhcflurry ensemble allnot  impute_f1 0.707238\n", 0.687764\n",  "mhcflurry ensemble all not  impute_f1 0.705761\n",  "smmpmbec_cpp_f1 0.697506\n",  "mhcflurry 4_f1 0.650743\n", 0.686037\n",  "mhcflurry ensemble bigimpute_f1 0.650743\n",  "mhcflurry 0_f1 0.631926\n",  "mhcflurry ensemble big_f1 0.631926\n",  "mhcflurry 7_f1 0.601342\n",  "mhcflurry ensemble small dropout impute_f1 0.601342\n",  "mhcflurry ensemble small  dropout_f1 0.595424\n", 0.686037\n",  "mhcflurry 3_f1 0.595424\n", 0.680760\n",  "mhcflurry ensemble all_tau 0.533006\n", 2_f1 0.673735\n",  "mhcflurry ensemble big dropout impute_tau 0.532926\n",  "mhcflurry 6_tau 0.532926\n",  "mhcflurry 2_tau 0.532192\n", all_tau 0.502062\n",  "mhcflurry ensemble big dropout_tau 0.532192\n",  "netmhc_tau 0.531214\n",  "netmhcpan_tau 0.529680\n", 0.501478\n",  "mhcflurry ensemble all not impute_tau 0.527773\n", 0.501478\n",  "mhcflurry ensemble all impute_tau 0.526555\n",  "mhcflurry ensemble small dropout_tau 0.518481\n",  "mhcflurry 3_tau 0.518481\n", 0.500492\n",  "mhcflurry ensemble small big  dropout impute_tau 0.518127\n",  "mhcflurry 7_tau 0.518127\n",  "mhcflurry 1_tau 0.515864\n",  "mhcflurry ensemble small_tau 0.515864\n",  "smmpmbec_cpp_tau 0.510021\n",  "mhcflurry 5_tau 0.509902\n",  "mhcflurry ensemble small impute_tau 0.509902\n",  "mhcflurry 4_tau 0.436853\n", 0.500492\n",  "netmhcpan_tau 0.499376\n",  "netmhc_tau 0.498401\n",  "mhcflurry ensemble big impute_tau 0.436853\n", 2_tau 0.497598\n",  "mhcflurry ensemble big_tau 0.427165\n", 3_tau 0.497076\n",  "mhcflurry 0_tau 0.427165\n", 0.495993\n",  "mhcflurry 1_tau 0.493647\n",  "smmpmbec_cpp_tau 0.476536\n",  "dtype: float64\n"  ]  }  ],  "source": [  "print_full(scores_df.ix[(scores_df.index != \"overall\") & (scores_df.train_size >= 1000)].mean(0).sort(inplace=False, 500)].mean(0).sort(inplace=False,  ascending=False))" ]  },  {  "cell_type": "code",  "execution_count": 46,  "metadata": {  "collapsed": false  },  "outputs": [  {  "data": {  "text/plain": [  "allele\n",  "overall NaN\n",  "H-2-DB 3216\n",  "H-2-KB 3407\n",  "H-2-KD 452\n",  "HLA-A0101 3725\n",  "HLA-A0201 9565\n",  "HLA-A0202 3919\n",  "HLA-A0203 5542\n",  "HLA-A0206 4827\n",  "HLA-A0301 6141\n",  "HLA-A1101 5399\n",  "HLA-A2301 2021\n",  "HLA-A2402 2533\n",  "HLA-A2501 519\n",  "HLA-A2601 2894\n",  "HLA-A2602 202\n",  "HLA-A2603 205\n",  "HLA-A2902 2397\n",  "HLA-A3001 2040\n",  "HLA-A3002 1430\n",  "HLA-A3101 4796\n",  "HLA-A3201 640\n",  "HLA-A3301 3040\n",  "HLA-A6801 3184\n",  "HLA-A6802 4768\n",  "HLA-A6901 2079\n",  "HLA-A8001 782\n",  "HLA-B0702 3412\n",  "HLA-B0801 2267\n",  "HLA-B0802 487\n",  "HLA-B0803 217\n",  "HLA-B1501 3213\n",  "HLA-B1503 429\n",  "HLA-B1509 346\n",  "HLA-B1517 846\n",  "HLA-B1801 2052\n",  "HLA-B2703 433\n",  "HLA-B2705 3028\n",  "HLA-B3501 2397\n",  "HLA-B3801 136\n",  "HLA-B3901 886\n",  "HLA-B4001 2718\n",  "HLA-B4002 866\n",  "HLA-B4402 1705\n",  "HLA-B4403 913\n",  "HLA-B4501 889\n",  "HLA-B4601 1424\n",  "HLA-B5101 1734\n",  "HLA-B5301 1018\n",  "HLA-B5401 1019\n",  "HLA-B5701 1857\n",  "HLA-B5801 2564\n",  "Mamu-A01 2264\n",  "Mamu-A02 938\n",  "Name: train_size, dtype: float64"  ]  },  "execution_count": 46,  "metadata": {},  "output_type": "execute_result"  }  ],  "source": [  "scores_df.train_size"  ]  },  {  "cell_type": "code",  "execution_count": 49,  "metadata": {  "collapsed": false  },  "outputs": [  {  "data": {  "text/html": [  "

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	dropout_probability	embedding_output_dim	fraction_negative	impute	layer_sizes	layer_size	num	name
0	0.5	32	0.0	False	[64]	64	0	big dropout
1	0.5	32	0.0	True	[64]	64	1	big dropout impute
2	0.5	32	0.2	False	[64]	64	2	big dropout
3	0.5	32	0.2	True	[64]	64	3	big dropout impute

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"  ],  "text/plain": [  " dropout_probability embedding_output_dim fraction_negative impute \\\n",  "0 0.5 32 0.0 False \n",  "1 0.5 32 0.0 True \n",  "2 0.5 32 0.2 False \n",  "3 0.5 32 0.2 True \n",  "\n",  " layer_sizes layer_size num name \n",  "0 [64] 64 0 big dropout \n",  "1 [64] 64 1 big dropout impute \n",  "2 [64] 64 2 big dropout \n",  "3 [64] 64 3 big dropout impute "  ]  },  "execution_count": 49,  "metadata": {},  "output_type": "execute_result"  }  ],  "source": [  "models"  ]  },  {  "cell_type": "code",  "execution_count": 63,  "metadata": {  "collapsed": false  },  "outputs": [  {  "data": {  "text/plain": [  "allele\n",  "H-2-DB True\n",  "H-2-KB True\n",  "H-2-KD True\n",  "HLA-A0101 True\n",  "HLA-A0201 True\n",  "HLA-A0202 True\n",  "HLA-A0203 True\n",  "HLA-A0206 True\n",  "HLA-A0301 True\n",  "HLA-A1101 True\n",  "HLA-A2301 True\n",  "HLA-A2402 True\n",  "HLA-A2501 True\n",  "HLA-A2601 True\n",  "HLA-A2602 True\n",  "HLA-A2603 True\n",  "HLA-A2902 True\n",  "HLA-A3001 True\n",  "HLA-A3002 True\n",  "HLA-A3101 True\n",  "HLA-A3201 True\n",  "HLA-A3301 True\n",  "HLA-A6801 True\n",  "HLA-A6802 True\n",  "HLA-A6901 True\n",  "HLA-A8001 True\n",  "HLA-B0702 True\n",  "HLA-B0801 True\n",  "HLA-B0802 True\n",  "HLA-B0803 True\n",  "HLA-B1501 True\n",  "HLA-B1503 True\n",  "HLA-B1509 True\n",  "HLA-B1517 True\n",  "HLA-B1801 True\n",  "HLA-B2703 False\n",  "HLA-B2705 True\n",  "HLA-B3501 True\n",  "HLA-B3801 True\n",  "HLA-B3901 True\n",  "HLA-B4001 True\n",  "HLA-B4002 True\n",  "HLA-B4402 True\n",  "HLA-B4403 True\n",  "HLA-B4501 True\n",  "HLA-B4601 False\n",  "HLA-B5101 True\n",  "HLA-B5301 True\n",  "HLA-B5401 True\n",  "HLA-B5701 True\n",  "HLA-B5801 True\n",  "Mamu-A01 True\n",  "Mamu-A02 True\n",  "dtype: bool"  ]  },  "execution_count": 63,  "metadata": {},  "output_type": "execute_result"  }  ],  "source": [  "validation_df_with_mhcflurry_results[\"binder\"] = validation_df_with_mhcflurry_results.meas < 500\n",  "grouped = validation_df_with_mhcflurry_results.groupby(\"allele\")\n",  "(grouped.meas.std() > 0) & (grouped.binder.std() > 0)"  ]  },  {  "cell_type": "code",  "execution_count": 76,  "metadata": {  "collapsed": false  },  "outputs": [  {  "data": {  "text/plain": [  "0.40170940170940173"  ]  },  "execution_count": 76,  "metadata": {},  "output_type": "execute_result"  }  ],  "source": [  "(validation_df_with_mhcflurry_results.ix[validation_df_with_mhcflurry_results.allele == \"HLA-B3801\"].meas < 500).mean()"  ]  },  {  "cell_type": "code",  "execution_count": 31, 68,  "metadata": {  "collapsed": false  },  "outputs": [  {  "data": {  "text/html": [  "

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	train_size	mhcflurry 2_auc	mhcflurry 3_auc	netmhc_auc	netmhcpan_auc
allele
HLA-B3801	136	0.909895	0.854171	0.925684	0.980074
HLA-A2602	202	0.926848	0.921058	0.931577	0.957811
HLA-A2603	205	0.900344	0.883735	0.890172	0.934300
HLA-B0803	217	0.935802	0.941728	0.968395	0.952346
HLA-B1509	346	0.882585	0.849996	0.901247	0.922907
HLA-B1503	429	0.840909	0.857438	0.864784	0.870064
HLA-B2703	433	NaN	NaN	NaN	NaN
H-2-KD	452	0.793761	0.792185	0.815331	0.819189
HLA-B0802	487	0.984499	0.984386	0.989930	0.989590

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"  ],  "text/plain": [  " train_size mhcflurry 2_auc mhcflurry 3_auc netmhc_auc \\\n",  "allele \n",  "HLA-B3801 136 0.909895 0.854171 0.925684 \n",  "HLA-A2602 202 0.926848 0.921058 0.931577 \n",  "HLA-A2603 205 0.900344 0.883735 0.890172 \n",  "HLA-B0803 217 0.935802 0.941728 0.968395 \n",  "HLA-B1509 346 0.882585 0.849996 0.901247 \n",  "HLA-B1503 429 0.840909 0.857438 0.864784 \n",  "HLA-B2703 433 NaN NaN NaN \n",  "H-2-KD 452 0.793761 0.792185 0.815331 \n",  "HLA-B0802 487 0.984499 0.984386 0.989930 \n",  "\n",  " netmhcpan_auc \n",  "allele \n",  "HLA-B3801 0.980074 \n",  "HLA-A2602 0.957811 \n",  "HLA-A2603 0.934300 \n",  "HLA-B0803 0.952346 \n",  "HLA-B1509 0.922907 \n",  "HLA-B1503 0.870064 \n",  "HLA-B2703 NaN \n",  "H-2-KD 0.819189 \n",  "HLA-B0802 0.989590 "  ]  },  "execution_count": 68,  "metadata": {},  "output_type": "execute_result"  }  ],  "source": [  "scores_df.ix[\n",  " scores_df.train_size < 500\n",  "][[\"train_size\", \"mhcflurry 2_auc\", \"mhcflurry 3_auc\", \"netmhc_auc\", \"netmhcpan_auc\"]].sort(\"train_size\", inplace=False)"  ]  },  {  "cell_type": "code",  "execution_count": 64,  "metadata": {  "collapsed": false,  "scrolled": false 
...
"name": "stdout",  "output_type": "stream",  "text": [  "train_size 565.888889\n", "test_size 360.666667\n", "netmhcpan_auc 0.936087\n", "netmhc_auc 0.926065\n", "mhcflurry 6_auc 0.914555\n", ensemble all not impute_auc 0.917750\n",  "mhcflurry ensemble big dropout impute_auc 0.914555\n", dropout_auc 0.917750\n",  "mhcflurry ensemble all_auc 0.913816\n", 0.917658\n",  "mhcflurry 2_auc 0.913501\n", 0.915545\n",  "mhcflurry ensemble bigdropout_auc 0.913501\n",  "mhcflurry ensemble all impute_auc 0.912783\n",  "mhcflurry ensemble small  dropout impute_auc 0.909942\n",  "mhcflurry 7_auc 0.909942\n", 0.915021\n",  "mhcflurry ensemble allnot impute_auc 0.908626\n",  "smmpmbec_cpp_auc 0.905258\n",  "mhcflurry 5_auc 0.899864\n",  "mhcflurry ensemble small impute_auc 0.899864\n",  "mhcflurry 1_auc 0.881951\n",  "mhcflurry ensemble small_auc 0.881951\n",  "mhcflurry 4_auc 0.879705\n",  "mhcflurry ensemble big  impute_auc 0.879705\n",  "mhcflurry ensemble big_auc 0.877978\n", 0.915021\n",  "mhcflurry 0_auc 0.877978\n",  "mhcflurry ensemble small dropout_auc 0.865762\n", 0.911218\n",  "mhcflurry 3_auc 0.865762\n", 0.910943\n",  "mhcflurry 1_auc 0.908827\n",  "smmpmbec_cpp_auc 0.905258\n",  "netmhcpan_f1 0.565589\n", "netmhc_f1 0.527323\n", "mhcflurry6_f1 0.515218\n",  "mhcflurry  ensemble big dropout impute_f1 0.515218\n",  "mhcflurry ensemble all_f1 0.509333\n", 0.510129\n",  "mhcflurry ensemble all impute_f1 0.508978\n",  "mhcflurry 2_f1 0.494181\n", 0.510129\n",  "mhcflurry ensemble big dropout_f1 0.494181\n", all_f1 0.504389\n",  "mhcflurry 1_f1 0.501371\n",  "mhcflurry 3_f1 0.500463\n",  "smmpmbec_cpp_f1 0.493544\n", "mhcflurry 5_f1 0.486568\n",  "mhcflurry ensemble small impute_f1 0.486568\n", 2_f1 0.492089\n",  "mhcflurry ensemble all not impute_f1 0.484503\n", 0.488826\n",  "mhcflurry ensemble big impute_f1 0.476241\n",  "mhcflurry 4_f1 0.476241\n",  "mhcflurry ensemble small_f1 0.457625\n",  "mhcflurry 1_f1 0.457625\n",  "mhcflurry ensemble small dropout impute_f1 0.455173\n",  "mhcflurry 7_f1 0.455173\n", dropout_f1 0.488826\n",  "mhcflurry 0_f1 0.454774\n",  "mhcflurry ensemble big_f1 0.454774\n",  "mhcflurry 3_f1 0.442731\n",  "mhcflurry ensemble small dropout_f1 0.442731\n", 0.479577\n",  "netmhcpan_tau 0.403266\n", "smmpmbec_cpp_tau 0.377134\n", "netmhc_tau 0.375954\n", "mhcflurry ensemble all_tau 0.364992\n",  "mhcflurry ensemble all impute_tau 0.360015\n", 2_tau 0.368036\n",  "mhcflurry ensemble big dropout_tau 0.359368\n",  "mhcflurry 2_tau 0.359368\n", 0.368003\n",  "mhcflurry ensemble all not impute_tau 0.358148\n", 0.368003\n",  "mhcflurry ensemble big dropout impute_tau 0.357670\n",  "mhcflurry 6_tau 0.357670\n",  "mhcflurry 7_tau 0.348081\n", all_tau 0.367299\n",  "mhcflurry ensemble small big  dropout impute_tau 0.348081\n", 0.362222\n",  "mhcflurry ensemble small impute_tau 0.347463\n",  "mhcflurry 5_tau 0.347463\n",  "mhcflurry ensemble small_tau 0.327501\n",  "mhcflurry 1_tau 0.327501\n",  "mhcflurry ensemble big all  impute_tau 0.327486\n",  "mhcflurry 4_tau 0.327486\n", 0.362222\n",  "mhcflurry 0_tau 0.323832\n", 0.357844\n",  "mhcflurry ensemble big_tau 0.323832\n", 1_tau 0.357692\n",  "mhcflurry 3_tau 0.304589\n",  "mhcflurry ensemble small dropout_tau 0.304589\n", 0.355994\n",  "dtype: float64\n"  ]  } 
...
},  {  "cell_type": "code",  "execution_count": 28, 67,  "metadata": {  "collapsed": false,  "scrolled": false 
...
"name": "stdout",  "output_type": "stream",  "text": [  "train_size 2260.018868\n", "test_size 522.264151\n", "netmhcpan_auc 0.912458\n","netmhc_auc 0.908938\n",  "mhcflurry 6_auc 0.907108\n",  "mhcflurry ensemble big dropout impute_auc 0.907108\n",  "mhcflurry ensemble all_auc 0.906485\n", 0.909909\n",  "mhcflurry ensemble big dropout_auc 0.906160\n",  "mhcflurry 2_auc 0.906160\n", 0.909660\n",  "mhcflurry ensemble all not  impute_auc 0.904839\n", 0.909660\n",  "netmhc_auc 0.908938\n",  "mhcflurry ensemble allnot  impute_auc 0.902807\n",  "mhcflurry 7_auc 0.899012\n", 0.908434\n",  "mhcflurry ensemble small big  dropout impute_auc 0.899012\n",  "mhcflurry ensemble small impute_auc 0.895912\n",  "mhcflurry 5_auc 0.895912\n",  "smmpmbec_cpp_auc 0.893880\n", 0.908434\n",  "mhcflurry ensemble small_auc 0.888977\n",  "mhcflurry 1_auc 0.888977\n", 2_auc 0.907137\n",  "mhcflurry 3_auc 0.885087\n",  "mhcflurry ensemble small dropout_auc 0.885087\n",  "mhcflurry 4_auc 0.861481\n",  "mhcflurry ensemble big impute_auc 0.861481\n",  "mhcflurry ensemble big_auc 0.856395\n", 0.906386\n",  "mhcflurry 0_auc 0.856395\n", 0.906017\n",  "mhcflurry 1_auc 0.904506\n",  "smmpmbec_cpp_auc 0.893880\n",  "netmhcpan_f1 0.662084\n", "netmhc_f1 0.650962\n", "mhcflurry ensemble big dropout impute_f1 0.645096\n",  "mhcflurry 6_f1 0.645096\n",  "mhcflurry ensemble all_f1 0.642637\n", 0.634057\n",  "mhcflurry ensemble all impute_f1 0.638929\n", 0.634057\n",  "mhcflurry 2_f1 0.637148\n", 1_f1 0.633927\n",  "mhcflurry ensemble big dropout_f1 0.637148\n",  "mhcflurry 5_f1 0.633501\n", all_f1 0.632375\n",  "mhcflurry ensemble small impute_f1 0.633501\n", 0_f1 0.629370\n",  "smmpmbec_cpp_f1 0.628236\n",  "mhcflurry ensemble all not impute_f1 0.631592\n",  "smmpmbec_cpp_f1 0.628236\n",  "mhcflurry 1_f1 0.625155\n",  "mhcflurry ensemble small_f1 0.625155\n",  "mhcflurry 4_f1 0.591478\n", 0.627128\n",  "mhcflurry ensemble big impute_f1 0.591478\n",  "mhcflurry 0_f1 0.571761\n",  "mhcflurry ensemble big_f1 0.571761\n",  "mhcflurry ensemble small dropout impute_f1 0.551700\n",  "mhcflurry 7_f1 0.551700\n", dropout_f1 0.627128\n",  "mhcflurry 3_f1 0.543566\n", 0.624452\n",  "mhcflurry ensemble small dropout_f1 0.543566\n", 2_f1 0.615696\n",  "netmhcpan_tau 0.485921\n", "netmhc_tau 0.477470\n", "mhcflurry ensemble all_tau 0.474847\n",  "mhcflurry ensemble big dropout_tau 0.472368\n",  "mhcflurry 2_tau 0.472368\n",  "mhcflurry 6_tau 0.472261\n",  "mhcflurry ensemble big dropout impute_tau 0.472261\n", 0.477411\n",  "mhcflurry ensemble all not impute_tau 0.469057\n", 0.476973\n",  "mhcflurry ensemble all impute_tau 0.468907\n",  "smmpmbec_cpp_tau 0.465725\n",  "mhcflurry 7_tau 0.459264\n", big dropout_tau 0.476973\n",  "mhcflurry ensemble small big  dropout impute_tau 0.459264\n", 0.474807\n",  "mhcflurry ensemble small all  impute_tau 0.453673\n", 0.474807\n",  "mhcflurry 5_tau 0.453673\n", 2_tau 0.473509\n",  "mhcflurry ensemble small_tau 0.450662\n", 0_tau 0.470953\n",  "mhcflurry 1_tau 0.450662\n", 0.469740\n",  "mhcflurry 3_tau 0.444442\n",  "mhcflurry ensemble small dropout_tau 0.444442\n",  "mhcflurry ensemble big impute_tau 0.398996\n",  "mhcflurry 4_tau 0.398996\n",  "mhcflurry 0_tau 0.391396\n",  "mhcflurry ensemble big_tau 0.391396\n", 0.469488\n",  "smmpmbec_cpp_tau 0.465725\n",  "dtype: float64\n"  ]  } 
...
},  {  "cell_type": "code",  "execution_count": 54, 45,  "metadata": {  "collapsed": false,  "scrolled": false 
...
{  "data": {  "text/plain": [  "(0.75, 1.0)" ""  ]  },  "execution_count": 54, 45,  "metadata": {},  "output_type": "execute_result"  },  {  "data": {  "image/png": 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J3bg1WFddeOFEXXjhxIjFSzYUhQAAQNS41kxUXt88td44G8mJMaQAAEgUFIUA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\n3y8/bltQ2O2hwbCBjqFFHBJ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jpEEhEREREREREZER0qCQiIiIiIiIiMgIaVBI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\n2WFYOwAAAABJRU5ErkJggg==\n", "iVBORw0KGgoAAAANSUhEUgAAAxkAAAIqCAYAAABIeodIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nu+++H5Wq6S0pn1yorYvUl3WR+rIeUlfWR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utgXZWVlZGcnERyctJVj9nY2BAc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D4XK5+Psr/yC3UFx9CwkMZtHshSPUkhiIpk9k2HptIxceBwqFgilTkjAai2lubsJs7sbPb+QV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Xx6nJ4idSZFqgwK1ZmTtq7tGTJxsO8kh/pPYrS0TWhdC6CSxbEidS6r0ysoUmeF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\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"text/plain": [  "0x116e69a50>" 0x11a53d250>"  ]  },  "metadata": {}, 
...
}  ],  "source": [  "pyplot.figure(figsize=(20,5))\n",  "scores_df[[\n", "#pyplot.figure(figsize=(20,5))\n",  "data=scores_df[[\n",  " \"mhcflurry ensemble big dropout impute_auc\",\n", 2_auc\",\n",  " \"mhcflurry ensemble big dropout_auc\",\n", 3_auc\",\n",  " \"netmhc_auc\",\n",  " \"netmhcpan_auc\"\n",  "]].plot(kind='bar', ax=pyplot.gca())\n",  "pyplot.ylim(ymin=.75, ymax=1.0)" "]].stack().to_frame().reset_index()\n",  "data.columns = [\"allele\", \"predictor\", \"auc\"]\n",  "data[\"train_size\"] = [scores_df.ix[scores_df.allele == a].train_size.unique()[0] for a in data.allele]\n",  "data = data[data.allele != \"overall\"]\n",  "#data.columns\n",  "#data\n",  "seaborn.set_context('poster')\n",  "seaborn.kdeplot(data.train_size, data.auc, hue=data.predictor)\n"  ]  },  {  "cell_type": "code",  "execution_count": 50, 18,  "metadata": {  "collapsed": true  }, 
...
},  {  "cell_type": "code",  "execution_count": null, 20,  "metadata": {  "collapsed": true false  },  "outputs": [], [  {  "ename": "KeyError",  "evalue": "\"['mhcflurry 6_auc'] not in index\"",  "output_type": "error",  "traceback": [  "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",  "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",  "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mscores_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"mhcflurry 6_auc\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"netmhc_auc\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"netmhcpan_auc\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",  "\u001b[0;32m/Users/tim/venvs/analysis-venv-2.7/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 1789\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1790\u001b[0m \u001b[0;31m# either boolean or fancy integer index\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1791\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1792\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1793\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",  "\u001b[0;32m/Users/tim/venvs/analysis-venv-2.7/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m_getitem_array\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 1833\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1834\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1835\u001b[0;31m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_convert_to_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1836\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1837\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",  "\u001b[0;32m/Users/tim/venvs/analysis-venv-2.7/lib/python2.7/site-packages/pandas/core/indexing.pyc\u001b[0m in \u001b[0;36m_convert_to_indexer\u001b[0;34m(self, obj, axis, is_setter)\u001b[0m\n\u001b[1;32m 1110\u001b[0m \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1111\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1112\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'%s not in index'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mobjarr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1113\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1114\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0m_values_from_object\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",  "\u001b[0;31mKeyError\u001b[0m: \"['mhcflurry 6_auc'] not in index\""  ]  }  ],  "source": [  "scores_df[[\"mhcflurry 6_auc\", \"netmhc_auc\", \"netmhcpan_auc\"]].plot"  ]  },  {  "cell_type": "code",  "execution_count": 29, null,  "metadata": {  "collapsed": false  },  "outputs": [  {  "data": {  "text/plain": [  "allele\n",  "overall 0.989877\n",  "H-2-DB 0.978362\n",  "H-2-KB 1.011331\n",  "H-2-KD 0.938034\n",  "HLA-A0101 0.983438\n",  "HLA-A0201 0.994114\n",  "HLA-A0202 0.992062\n",  "HLA-A0203 0.996301\n",  "HLA-A0206 0.982678\n",  "HLA-A0301 0.980282\n",  "HLA-A1101 0.986175\n",  "HLA-A2301 0.953784\n",  "HLA-A2402 0.983222\n",  "HLA-A2501 0.994112\n",  "HLA-A2601 0.991764\n",  "HLA-A2602 0.981905\n",  "HLA-A2603 0.996853\n",  "HLA-A2902 1.009836\n",  "HLA-A3001 1.015944\n",  "HLA-A3002 0.950338\n",  "HLA-A3101 0.994753\n",  "HLA-A3201 1.001196\n",  "HLA-A3301 0.986507\n",  "HLA-A6801 0.994634\n",  "HLA-A6802 0.993701\n",  "HLA-A6901 1.000722\n",  "HLA-A8001 0.992971\n",  "HLA-B0702 0.996995\n",  "HLA-B0801 1.000092\n",  "HLA-B0802 0.989599\n",  "HLA-B0803 0.975523\n",  "HLA-B1501 0.994391\n",  "HLA-B1503 0.940005\n",  "HLA-B1509 0.954165\n",  "HLA-B1517 0.995481\n",  "HLA-B1801 1.018311\n",  "HLA-B2703 NaN\n",  "HLA-B2705 0.981630\n",  "HLA-B3501 0.990696\n",  "HLA-B3801 0.985315\n",  "HLA-B3901 0.987083\n",  "HLA-B4001 0.971704\n",  "HLA-B4002 0.989224\n",  "HLA-B4402 0.953884\n",  "HLA-B4403 0.895020\n",  "HLA-B4501 0.970000\n",  "HLA-B4601 NaN\n",  "HLA-B5101 1.009608\n",  "HLA-B5301 1.001499\n",  "HLA-B5401 1.072848\n",  "HLA-B5701 1.013825\n",  "HLA-B5801 1.011590\n",  "Mamu-A01 0.965943\n",  "Mamu-A02 0.919299\n",  "dtype: float64"  ]  },  "execution_count": 29,  "metadata": {},  "output_type": "execute_result"  }  ], [],  "source": [  "scores_df.ix[:, \"mhcflurry ensemble small impute_auc\"] / scores_df.ix[:, \"netmhc_auc\"]"  ]  },  {  "cell_type": "code",  "execution_count": 30, null,  "metadata": {  "collapsed": false  },  "outputs": [  {  "data": {  "text/plain": [  "\n",  "Dimensions: 22 (items) x 54 (major_axis) x 3 (minor_axis)\n",  "Items axis: mhcflurry 0 to smmpmbec_cpp\n",  "Major_axis axis: overall to Mamu-A02\n",  "Minor_axis axis: auc to tau"  ]  },  "execution_count": 30,  "metadata": {},  "output_type": "execute_result"  }  ], [],  "source": [  "def sub_df(name):\n",  " result = scores_df[[c for c in list(scores_df.columns) if (name + \"_\") in c]].copy()\n",