# Copyright (c) 2014. 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.

"""

Instead of looking at positionally invariant n-grams (n=1,2,3),

let's split each string into 8-mers and treat each one as a

distinct sample (each weighted inversely with number of 8-mers

originating from single peptide).

"""

import numpy as np

import pandas as pd

import sklearn

import sklearn.cross_validation

from sklearn.ensemble import RandomForestClassifier

from epitopes import \

(fritsch_neoepitopes, iedb, features,

reduced_alphabet, reference,

hiv_frahm)

import eval_dataset

from balanced_ensemble import BalancedEnsembleClassifier

df = fritsch_neoepitopes.load_dataframe()

pos_peptides = df['Mutated Epitope']

neg_peptides = df['Native Epitope']

kmer_length = 9

# sweet over filtering criteria and n-gram transformation

# parameters and for all parameter combos evaluate

# - cross-validation accuracy

# - cross-validation area under ROC curve

# - accuracy on validation set of cancer peptides

params = []

aucs = []

accs = []

recalls = []

d = {

}

best_model = None

best_vectorizer = None

best_params = None

param_count = 0

for assay in ('cytotoxicity', None, ):

for mhc_class in (1, None):

for min_count in (3, 5, None):

imm, non = iedb.load_tcell_classes(

assay_group = assay,

human = True,

mhc_class = mhc_class,

min_count = min_count)

for alphabet in \

('hp2', 'gbmr4', 'hp_vs_aromatic', 'sdm12', 'hsdm17'):

transformer = reduced_alphabet.make_alphabet_transformer(alphabet)

param_str = \

"%d: Assay = '%s', min_count %s, alphabet %s, mhc_class %s" % \

(param_count, assay, min_count, alphabet, mhc_class)

print param_str

d['assay'].append(assay)

d['alphabet'].append(alphabet)

d['mhc'].append(mhc_class)

d['min_count'].append(min_count)

param_count += 1

X_combined = []

Y_combined = []

W_combined = []

dtype = 'S%d' % kmer_length

def expand(peptide_set):

return X, Counts, Indices

X_imm, Counts_imm, _ = expand(imm)

print "Min/Max/Median counts_imm", \

Counts_imm.min(), Counts_imm.max(), np.median(Counts_imm)

W_imm = 1.0 / np.array(Counts_imm)

X_combined.extend(X_imm)

W_combined.extend(W_imm)

Y_combined.extend([1] * len(X_imm))

X_non, Counts_non, _ = expand(non)

print "Min/Max/Median counts_non", \

Counts_non.min(), Counts_non.max(), np.median(Counts_non)

W_non = 1.0 / np.array(Counts_non)

X_combined.extend(X_non)

W_combined.extend(W_non)

Y_combined.extend([0] * len(X_non))

def strings_to_array(strings):

return X

X = strings_to_array(X_combined)

Y = np.array(Y_combined)

W = np.array(W_combined)

print "# imm = %d, # non = %d" % (len(imm), len(non))

print "Data shape", X.shape, "n_true", np.sum(Y)

rf = BalancedEnsembleClassifier(n_estimators = 200)

#aucs = sklearn.cross_validation.cross_val_score(

# rf, X, Y, cv = 10, scoring='roc_auc')

#print "CV AUC %0.4f (std %0.4f)" % (np.mean(aucs), np.std(aucs))

#d['cv_auc'].append(np.mean(aucs))

#rf = RandomForestClassifier(n_estimators = 100)

rf.fit(X, Y, W)

def predict(peptides):

return Y_pred >= 0

Y_pos_pred = predict(pos_peptides)

pos_acc = np.mean(Y_pos_pred)

print "Tumor antigen accuracy %0.4f" % (pos_acc,)

d['pos_acc'].append(pos_acc)

Y_neg_pred = predict(neg_peptides)

neg_acc = 1.0 - np.mean(Y_neg_pred)

print "Non-immunogenic accuracy %0.4f" % (neg_acc,)

d['neg_acc'].append(neg_acc)

tp = np.sum(Y_pos_pred)

fp = np.sum(Y_neg_pred)

fn = np.sum(~Y_pos_pred)

tn = np.sum(~Y_neg_pred)

precision = tp / float(tp + fp)

recall = tp / float(tp + fn)

print "tp = %d, fp = %d, fn = %d, tn = %d" % \

(tp, fp, fn, tn)

f1_score = 2 * (precision * recall) / (precision + recall)

print "F-1 score: %0.4f" % f1_score

d['f1_score'].append(f1_score)

f_half_score = 1.25 * \

(precision * recall) / ((0.25 * precision) + recall)

print "F-0.5 score: %0.4f" % f_half_score

d['f_score'].append(f_half_score)

acc = np.sqrt(pos_acc * neg_acc)

print "sqrt(pos_acc * neg_acc) =", acc

d['acc'].append(acc)

print "---"

print

df = pd.DataFrame(d)

df = df.sort('acc', ascending=False)

print df.to_string()

with open('May13_sliding_kmers.csv', 'w') as f:

df.to_csv(f)

with open('May13_sliding_kmers.html', 'w') as f:

df.to_html(f)