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" <th>model_layer_sizes</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>HLA-A3301</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2026</td>\n",
" <td>1014</td>\n",
" <td>None</td>\n",
" <td>0.710233</td>\n",
" <td>0.989589</td>\n",
" <td>0.902256</td>\n",
" <td>0.429803</td>\n",
" <td>...</td>\n",
" <td>0.1</td>\n",
" <td>True</td>\n",
" <td>0.0</td>\n",
" <td>glorot_uniform</td>\n",
" <td>tanh</td>\n",
" <td>128</td>\n",
" <td>False</td>\n",
" <td>9</td>\n",
" <td>50000.0</td>\n",
" <td>[8]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>HLA-A3301</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2026</td>\n",
" <td>1014</td>\n",
" <td>None</td>\n",
" <td>0.747597</td>\n",
" <td>0.993938</td>\n",
" <td>0.919708</td>\n",
" <td>0.425610</td>\n",
" <td>...</td>\n",
" <td>0.1</td>\n",
" <td>True</td>\n",
" <td>0.0</td>\n",
" <td>glorot_uniform</td>\n",
" <td>tanh</td>\n",
" <td>128</td>\n",
" <td>False</td>\n",
" <td>9</td>\n",
" <td>50000.0</td>\n",
" <td>[12]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>HLA-A3301</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>2027</td>\n",
" <td>1013</td>\n",
" <td>None</td>\n",
" <td>0.705507</td>\n",
" <td>0.990185</td>\n",
" <td>0.882466</td>\n",
" <td>0.430678</td>\n",
" <td>...</td>\n",
" <td>0.1</td>\n",
" <td>True</td>\n",
" <td>0.0</td>\n",
" <td>glorot_uniform</td>\n",
" <td>tanh</td>\n",
" <td>128</td>\n",
" <td>False</td>\n",
" <td>9</td>\n",
" <td>50000.0</td>\n",
" <td>[8]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>HLA-A3301</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2027</td>\n",
" <td>1013</td>\n",
" <td>None</td>\n",
" <td>0.745532</td>\n",
" <td>0.993875</td>\n",
" <td>0.924812</td>\n",
" <td>0.395103</td>\n",
" <td>...</td>\n",
" <td>0.1</td>\n",
" <td>True</td>\n",
" <td>0.0</td>\n",
" <td>glorot_uniform</td>\n",
" <td>tanh</td>\n",
" <td>128</td>\n",
" <td>False</td>\n",
" <td>9</td>\n",
" <td>50000.0</td>\n",
" <td>[12]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>HLA-A3301</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>2027</td>\n",
" <td>1013</td>\n",
" <td>None</td>\n",
" <td>0.709275</td>\n",
" <td>0.992395</td>\n",
" <td>0.894531</td>\n",
" <td>0.441365</td>\n",
" <td>...</td>\n",
" <td>0.1</td>\n",
" <td>True</td>\n",
" <td>0.0</td>\n",
" <td>glorot_uniform</td>\n",
" <td>tanh</td>\n",
" <td>128</td>\n",
" <td>False</td>\n",
" <td>9</td>\n",
" <td>50000.0</td>\n",
" <td>[8]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>HLA-A3301</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2027</td>\n",
" <td>1013</td>\n",
" <td>None</td>\n",
" <td>0.743498</td>\n",
" <td>0.994674</td>\n",
" <td>0.873518</td>\n",
" <td>0.439221</td>\n",
" <td>...</td>\n",
" <td>0.1</td>\n",
" <td>True</td>\n",
" <td>0.0</td>\n",
" <td>glorot_uniform</td>\n",
" <td>tanh</td>\n",
" <td>128</td>\n",
" <td>False</td>\n",
" <td>9</td>\n",
" <td>50000.0</td>\n",
" <td>[12]</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>6 rows × 31 columns</p>\n",
"</div>"
],
"text/plain": [
" allele fold_num model_num train_size test_size imputed_train_size \\\n",
"0 HLA-A3301 0 0 2026 1014 None \n",
"1 HLA-A3301 0 1 2026 1014 None \n",
"2 HLA-A3301 1 0 2027 1013 None \n",
"3 HLA-A3301 1 1 2027 1013 None \n",
"4 HLA-A3301 2 0 2027 1013 None \n",
"5 HLA-A3301 2 1 2027 1013 None \n",
"\n",
" train_tau train_auc train_f1 test_tau ... \\\n",
"0 0.710233 0.989589 0.902256 0.429803 ... \n",
"1 0.747597 0.993938 0.919708 0.425610 ... \n",
"2 0.705507 0.990185 0.882466 0.430678 ... \n",
"3 0.745532 0.993875 0.924812 0.395103 ... \n",
"4 0.709275 0.992395 0.894531 0.441365 ... \n",
"5 0.743498 0.994674 0.873518 0.439221 ... \n",
"\n",
" model_fraction_negative model_batch_normalization \\\n",
"0 0.1 True \n",
"1 0.1 True \n",
"2 0.1 True \n",
"3 0.1 True \n",
"4 0.1 True \n",
"5 0.1 True \n",
"\n",
" model_dropout_probability model_init model_activation \\\n",
"0 0.0 glorot_uniform tanh \n",
"1 0.0 glorot_uniform tanh \n",
"2 0.0 glorot_uniform tanh \n",
"3 0.0 glorot_uniform tanh \n",
"4 0.0 glorot_uniform tanh \n",
"5 0.0 glorot_uniform tanh \n",
"\n",
" model_batch_size model_impute model_kmer_size model_max_ic50 \\\n",
"0 128 False 9 50000.0 \n",
"1 128 False 9 50000.0 \n",
"2 128 False 9 50000.0 \n",
"3 128 False 9 50000.0 \n",
"4 128 False 9 50000.0 \n",
"5 128 False 9 50000.0 \n",
"\n",
" model_layer_sizes \n",
"0 [8] \n",
"1 [12] \n",
"2 [8] \n",
"3 [12] \n",
"4 [8] \n",
"5 [12] \n",
"\n",
"[6 rows x 31 columns]"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"results_df = mhcflurry.class1_allele_specific.train.train_across_models_and_folds(\n",
" folds,\n",
" models_to_search,\n",
" return_predictors=True)\n",
"results_df"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 Class1BindingPredictor(name=None, max_ic50=500...\n",
"1 Class1BindingPredictor(name=None, max_ic50=500...\n",
"2 Class1BindingPredictor(name=None, max_ic50=500...\n",
"3 Class1BindingPredictor(name=None, max_ic50=500...\n",
"4 Class1BindingPredictor(name=None, max_ic50=500...\n",
"5 Class1BindingPredictor(name=None, max_ic50=500...\n",
"Name: predictor, dtype: object"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# The trained predictors are in the 'predictor' column\n",
"results_df.predictor"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"model_num\n",
"0 0.859859\n",
"1 0.847004\n",
"Name: test_auc, dtype: float64"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Which model had the best average AUC across folds?\n",
"results_df.groupby(\"model_num\").test_auc.mean()"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [py3k]",
"language": "python",
"name": "Python [py3k]"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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"nbformat": 4,
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