add test

downloads-generation/data_curated/curate.py

@@ -145,7 +145,7 @@ def load_data_iedb(iedb_csv, include_qualitative=True, include_mass_spec=False):
     quantitative["measurement_type"] = "quantitative"
     quantitative["measurement_inequality"] = quantitative[
         "Measurement Inequality"
-    ].fillna("=")
+    ].fillna("=").map(lambda s: {">=": ">", "<=": "<"}.get(s, s))
     print("Quantitative measurements: %d" % len(quantitative))
 
     qualitative = iedb_df.ix[iedb_df["Units"].isnull()].copy()

mhcflurry/downloads.yml

@@ -41,7 +41,7 @@ releases:
               default: false
 
             - name: data_curated
-              url: https://github.com/openvax/mhcflurry/releases/download/pan-dev1/data_curated.20190514.tar.bz2
+              url: https://github.com/openvax/mhcflurry/releases/download/pan-dev1/data_curated.20190516.tar.bz2
               default: true
 
             # Older downloads

mhcflurry/train_pan_allele_models_command.py

@@ -246,12 +246,6 @@ def main(args):
 
     print("Data inequalities:")
     print(df.measurement_inequality.value_counts())
-    df.measurement_inequality = df.measurement_inequality.map(lambda s: {
-        ">=": ">", "<=": "<"
-    }.get(s, s))
-
-    print("Data inequalities after adjustment:")
-    print(df.measurement_inequality.value_counts())
 
     if args.ignore_inequalities and "measurement_inequality" in df.columns:
         print("Dropping measurement_inequality column")

test/test_class1_pan.py

+"""
+Tests for training and predicting using Class1 pan-allele models.
+"""
+
+import json
+import os
+import shutil
+import tempfile
+import subprocess
+from copy import deepcopy
+
+import pandas
+
+from numpy.testing import assert_array_less, assert_equal
+
+from mhcflurry import Class1AffinityPredictor,Class1NeuralNetwork
+from mhcflurry.allele_encoding import AlleleEncoding
+from mhcflurry.downloads import get_path
+
+
+HYPERPARAMETERS = {
+    'activation': 'tanh',
+    'allele_dense_layer_sizes': [],
+    'batch_normalization': False,
+    'dense_layer_l1_regularization': 0.0,
+    'dense_layer_l2_regularization': 0.0,
+    'dropout_probability': 0.5,
+    'early_stopping': True,
+    'init': 'glorot_uniform',
+    'layer_sizes': [64],
+    'learning_rate': None,
+    'locally_connected_layers': [],
+    'loss': 'custom:mse_with_inequalities',
+    'max_epochs': 5000,
+    'minibatch_size': 128,
+    'optimizer': 'rmsprop',
+    'output_activation': 'sigmoid',
+    'patience': 20,
+    'peptide_allele_merge_activation': '',
+    'peptide_allele_merge_method': 'concatenate',
+    'peptide_amino_acid_encoding': 'BLOSUM62',
+    'peptide_dense_layer_sizes': [],
+    'peptide_encoding': {
+        'alignment_method': 'left_pad_centered_right_pad',
+        'max_length': 15,
+        'vector_encoding_name': 'BLOSUM62',
+    },
+    'random_negative_affinity_max': 50000.0,
+    'random_negative_affinity_min': 20000.0,
+    'random_negative_constant': 25,
+    'random_negative_distribution_smoothing': 0.0,
+    'random_negative_match_distribution': True,
+    'random_negative_rate': 0.2,
+    'train_data': {},
+    'validation_split': 0.1,
+}
+
+
+ALLELE_TO_SEQUENCE = pandas.read_csv(
+    get_path(
+        "allele_sequences", "allele_sequences.csv"),
+    index_col=0).sequence.to_dict()
+
+
+TRAIN_DF = pandas.read_csv(
+    get_path(
+        "data_curated", "curated_training_data.no_mass_spec.csv.bz2"))
+
+TRAIN_DF = TRAIN_DF.loc[TRAIN_DF.allele.isin(ALLELE_TO_SEQUENCE)]
+TRAIN_DF = TRAIN_DF.loc[TRAIN_DF.peptide.str.len() >= 8]
+TRAIN_DF = TRAIN_DF.loc[TRAIN_DF.peptide.str.len() <= 15]
+
+
+def test_train_simple():
+    network = Class1NeuralNetwork(**HYPERPARAMETERS)
+    allele_encoding = AlleleEncoding(
+        TRAIN_DF.allele.values,
+        allele_to_sequence=ALLELE_TO_SEQUENCE)
+    network.fit(
+        TRAIN_DF.peptide.values,
+        affinities=TRAIN_DF.measurement_value.values,
+        allele_encoding=allele_encoding,
+        inequalities=TRAIN_DF.measurement_inequality.values)
+
+    predictions = network.predict(
+        peptides=TRAIN_DF.peptide.values,
+        allele_encoding=allele_encoding)
+
+    print(pandas.Series(predictions).describe())