Python DeepTCR_SS.Train 예제들

예제 #1

               Load_Prev_Data=True,
               aggregate_by_aa=True,
               aa_column_beta=0,
               count_column=1,
               v_beta_column=2,
               j_beta_column=3)

AUC = []
Class = []
Method = []
folds = 100
seeds = np.array(range(folds))
for i in range(folds):
    np.random.seed(seeds[i])
    DTCRS.Get_Train_Valid_Test()
    DTCRS.Train(graph_seed=graph_seed)
    DTCRS.AUC_Curve(plot=False)
    AUC.extend(DTCRS.AUC_DF['AUC'].tolist())
    Class.extend(DTCRS.AUC_DF['Class'].tolist())
    Method.extend(['Sup-Seq-VDJ'] * len(DTCRS.AUC_DF))

df_s = pd.DataFrame()
df_s['Class'] = Class
df_s['AUC'] = AUC
df_s['Method'] = Method
df_s['Type'] = 'Supervised'

df_comp = pd.concat((df_u, df_s), axis=0)

dir_results = 'Sup_V_Unsup_Results'
if not os.path.exists(dir_results):

예제 #2

파일: DeepTCR_v_SVM.py 프로젝트: BlingArida/DeepTCR

               aa_column_beta=0,
               count_column=1,
               v_beta_column=2,
               j_beta_column=3)
kmer_features = kmer_search(DTCRS.beta_sequences)
clf_svm = SVC(probability=True)
clf_rf = RandomForestClassifier(n_estimators=100)

y_test_list = []
y_pred_list_dtcr = []
y_pred_list_svm = []
y_pred_list_rf = []

for i in range(100):
    DTCRS.Get_Train_Valid_Test()
    DTCRS.Train(use_only_seq=True, num_fc_layers=0, units_fc=256)
    y_pred_list_dtcr.append(DTCRS.y_pred)

    #Kmer
    clf_svm.fit(kmer_features[DTCRS.train[6]], np.argmax(DTCRS.train[-1], -1))
    svm_pred = clf_svm.predict_proba(kmer_features[DTCRS.test[6]])
    y_pred_list_svm.append(svm_pred)

    #RF
    clf_rf.fit(kmer_features[DTCRS.train[6]], np.argmax(DTCRS.train[-1], -1))
    rf_pred = clf_rf.predict_proba(kmer_features[DTCRS.test[6]])
    y_pred_list_rf.append(rf_pred)

    y_test_list.append(DTCRS.test[-1])

auc = []

예제 #3

파일: sup_v_unsup.py 프로젝트: jude-hey/DeepTCR

DTCRS = DeepTCR_SS('Sequence_C')
DTCRS.Get_Data(directory='../../Data/Murine_Antigens',
               Load_Prev_Data=True,
               aggregate_by_aa=True,
               aa_column_beta=0,
               count_column=1,
               v_beta_column=2,
               j_beta_column=3)

AUC = []
Class = []
Method = []
for i in range(10):
    DTCRS.Get_Train_Valid_Test()

    DTCRS.Train(use_only_seq=True)
    DTCRS.AUC_Curve(plot=False)
    AUC.extend(DTCRS.AUC_DF['AUC'].tolist())
    Class.extend(DTCRS.AUC_DF['Class'].tolist())
    Method.extend(['Sup-Seq'] * len(DTCRS.AUC_DF))

    DTCRS.Train(use_only_gene=True)
    DTCRS.AUC_Curve(plot=False)
    AUC.extend(DTCRS.AUC_DF['AUC'].tolist())
    Class.extend(DTCRS.AUC_DF['Class'].tolist())
    Method.extend(['Sup-VDJ'] * len(DTCRS.AUC_DF))

    DTCRS.Train()
    DTCRS.AUC_Curve(plot=False)
    AUC.extend(DTCRS.AUC_DF['AUC'].tolist())
    Class.extend(DTCRS.AUC_DF['Class'].tolist())

예제 #4

파일: sup_v_unsup.py 프로젝트: pablasimarjot/DeepTCR

df_u['AUC'] = df_metrics['Value']
df_u['Method'] = df_metrics['Algorithm']
df_u['Type'] = 'Unsupervised'

#Run Supervised Sequence Classifier
DTCRS = DeepTCR_SS('Sequence_C')
DTCRS.Get_Data(directory='../../Data/Murine_Antigens',Load_Prev_Data=True,aggregate_by_aa=True,
               aa_column_beta=0,count_column=1,v_beta_column=2,j_beta_column=3)

AUC = []
Class = []
Method = []
for i in range(10):
    DTCRS.Get_Train_Valid_Test()

    DTCRS.Train(use_only_seq=True,num_fc_layers=1,units_fc=256)
    DTCRS.AUC_Curve(plot=False)
    AUC.extend(DTCRS.AUC_DF['AUC'].tolist())
    Class.extend(DTCRS.AUC_DF['Class'].tolist())
    Method.extend(['Sup-Seq']*len(DTCRS.AUC_DF))

    DTCRS.Train(use_only_gene=True,num_fc_layers=1,units_fc=256)
    DTCRS.AUC_Curve(plot=False)
    AUC.extend(DTCRS.AUC_DF['AUC'].tolist())
    Class.extend(DTCRS.AUC_DF['Class'].tolist())
    Method.extend(['Sup-VDJ']*len(DTCRS.AUC_DF))

    DTCRS.Train(num_fc_layers=1,units_fc=256)
    DTCRS.AUC_Curve(plot=False)
    AUC.extend(DTCRS.AUC_DF['AUC'].tolist())
    Class.extend(DTCRS.AUC_DF['Class'].tolist())