Use all data in imputation

downloads-generation/models_class1_allele_specific_ensemble/GENERATE.sh

@@ -36,7 +36,7 @@ cp $SCRIPT_DIR/models.py .
 python models.py > models.json
 
 time mhcflurry-class1-allele-specific-ensemble-train \
-    --ensemble-size 8 \
+    --ensemble-size 16 \
     --model-architectures models.json \
     --train-data "$(mhcflurry-downloads path data_combined_iedb_kim2014)/combined_human_class1_dataset.csv" \
     --min-samples-per-allele 200 \

downloads-generation/models_class1_allele_specific_ensemble/README.md

@@ -21,13 +21,8 @@ To generate this download we run:
 ```
 
 To debug locally:
+```
 ./GENERATE.sh \
     --parallel-backend local-threads \
-    --target-tasks 1 \
-    --backend kubernetes \
-    --storage-prefix gs://kubeface-tim \
-    --worker-image hammerlab/mhcflurry-misc:latest \
-    --kubernetes-task-resources-memory-mb 10000 \
-    --worker-path-prefix venv-py3/bin \
-    --max-simultaneous-tasks 200 \
+    --target-tasks 1
 ```

downloads-generation/models_class1_allele_specific_ensemble/models.py

@@ -17,8 +17,8 @@ models = HYPERPARAMETER_DEFAULTS.models_grid(
         # Arguments specific to imputation method (mice)
         {"n_burn_in": 5, "n_imputations": 50, "n_nearest_columns": 25}
     ],
-    impute_min_observations_per_peptide=[5],
-    impute_min_observations_per_allele=[100])
+    impute_min_observations_per_peptide=[1],
+    impute_min_observations_per_allele=[1])
 
 sys.stderr.write("Models: %d\n" % len(models))
 print(json.dumps(models, indent=4))

mhcflurry/class1_allele_specific_ensemble/class1_ensemble_multi_allele_predictor.py

@@ -307,7 +307,7 @@ class Class1EnsembleMultiAllelePredictor(object):
 
         assert len(splits) == self.ensemble_size, len(splits)
 
-        alleles = measurement_collection.df.allele.unique()
+        alleles = set(measurement_collection.df.allele.unique())
 
         total_work = (
             len(alleles) *
@@ -335,8 +335,18 @@ class Class1EnsembleMultiAllelePredictor(object):
                 task_alleles.clear()
                 task_models.clear()
 
+            assert all(
+                allele in set(train_split.df.allele.unique())
+                for allele in alleles), (
+                "%s not in %s" % (
+                    alleles, set(train_split.df.allele.unique())))
+            assert all(
+                allele in set(test_split.df.allele.unique())
+                for allele in alleles), (
+                "%s not in %s" % (
+                    alleles, set(test_split.df.allele.unique())))
+
             for allele in alleles:
-                task_alleles.append(allele)
                 for model in self.hyperparameters_to_search:
                     task_models.append(model)
                     if len(task_alleles) * len(task_models) > work_per_task:

mhcflurry/class1_allele_specific_ensemble/measurement_collection.py

@@ -68,7 +68,8 @@ class MeasurementCollection(object):
         """
         assert isinstance(allele, str), type(allele)
         assert len(self.df) > 0
-        assert allele in self.df.allele.unique()
+        alleles = set(self.df.allele.unique())
+        assert allele in alleles, "%s not in %s" % (allele, alleles)
         return MeasurementCollection(
             self.df.ix[self.df.allele == allele],
             check=False)
@@ -100,6 +101,8 @@ class MeasurementCollection(object):
                     None if random_state is None
                     else random_state + len(results)))
             stratification_groups = self.df.allele + self.df.measurement_type
+            assert len(stratification_groups.unique()) > 1, (
+                stratification_groups.unique())
             (indices1, indices2) = next(
                 cv.split(self.df.values, stratification_groups))
             assert len(indices1) > 0