fixed class1_binding_predictor tests

mhcflurry/class1_allele_specific_kmer_ic50_predictor_base.py

@@ -109,7 +109,7 @@ class Class1AlleleSpecificKmerIC50PredictorBase(IC50PredictorBase):
         if any(len(peptide) != self.kmer_size for peptide in peptides):
             raise ValueError("Can only predict 9mer peptides")
         X, _ = self.encode_peptides(peptides)
-        return self.predict_scores_for_kmer_array(X)
+        return self.predict_scores_for_kmer_encoded_array(X)
 
     def predict_kmer_peptides_ic50(self, peptides):
         scores = self.predict_scores_for_kmer_peptides(peptides)
@@ -132,7 +132,8 @@ class Class1AlleleSpecificKmerIC50PredictorBase(IC50PredictorBase):
         # peptides of lengths other than self.kmer_size get multiple predictions,
         # which are then combined with the combine_fn argument
         multiple_predictions_dict = defaultdict(list)
-        fixed_length_predictions = self.predict(input_matrix)
+        fixed_length_predictions = \
+            self.predict_scores_for_kmer_encoded_array(input_matrix)
         for i, yi in enumerate(fixed_length_predictions):
             original_peptide_index = original_peptide_indices[i]
             original_peptide = peptides[original_peptide_index]

mhcflurry/class1_binding_predictor.py

@@ -314,7 +314,7 @@ class Class1BindingPredictor(Class1AlleleSpecificKmerIC50PredictorBase):
         alleles = alleles_with_models.intersection(alleles_with_weights)
         return list(sorted(alleles))
 
-    def predict_scores_for_kmer_array(self, X):
+    def predict_scores_for_kmer_encoded_array(self, X):
         """
         Given an encoded array of amino acid indices, returns a vector
         of predicted log IC50 values.

mhcflurry/dataset.py

@@ -62,7 +62,7 @@ class Dataset(object):
         # make allele and peptide columns the index, and copy it
         # so we can add a column without any observable side-effect in
         # the calling code
-        df = df.set_index(["allele", "peptide"])
+        df = df.set_index(["allele", "peptide"], drop=False)
 
         if "sample_weight" not in columns:
             df["sample_weight"] = np.ones(len(df), dtype=float)
@@ -209,6 +209,7 @@ class Dataset(object):
                     "Wrong length for column '%s', expected %d but got %d" % (
                         column_name, column))
             df[column_name] = np.asarray(column)
+        print(df)
         return cls(df)
 
     @classmethod

test/dummy_predictors.py

-# Copyright (c) 2016. Mount Sinai School of Medicine
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import numpy as np
-from mhcflurry import Class1BindingPredictor
-
-class Dummy9merIndexEncodingModel(object):
-    """
-    Dummy molde used for testing the pMHC binding predictor.
-    """
-    def __init__(self, constant_output_value=0):
-        self.constant_output_value = constant_output_value
-
-    def predict(self, X, verbose=False):
-        assert isinstance(X, np.ndarray)
-        assert len(X.shape) == 2
-        n_rows, n_cols = X.shape
-        n_cols == 9, "Expected 9mer index input input, got %d columns" % (
-            n_cols,)
-        return np.ones(n_rows, dtype=float) * self.constant_output_value
-
-always_zero_predictor_with_unknown_AAs = Class1BindingPredictor(
-    model=Dummy9merIndexEncodingModel(0),
-    allow_unknown_amino_acids=True)
-
-always_zero_predictor_without_unknown_AAs = Class1BindingPredictor(
-    model=Dummy9merIndexEncodingModel(0),
-    allow_unknown_amino_acids=False)
-
-
-always_one_predictor_with_unknown_AAs = Class1BindingPredictor(
-    model=Dummy9merIndexEncodingModel(1),
-    allow_unknown_amino_acids=True)
-
-always_one_predictor_without_unknown_AAs = Class1BindingPredictor(
-    model=Dummy9merIndexEncodingModel(1),
-    allow_unknown_amino_acids=False)

test/test_class1_binding_predictor.py → test/test_class1_binding_predictor_with_dummy_models.py

@@ -15,7 +15,7 @@
 
 import numpy as np
 
-from dummy_predictors import always_zero_predictor_with_unknown_AAs
+from dummy_models import always_zero_predictor_with_unknown_AAs
 
 
 def test_always_zero_9mer_inputs():