In [1]:

    

import pandas as pd
df = pd.read_csv('queryset_CNN.csv')
print(df.shape)
print(df.dtypes)


    

    




(32, 3)
doc_id         int64
author_id      int64
authorList    object
dtype: object

In [2]:

    

for index, row in df.iterrows():
    doc_id = row.doc_id

    author_id = row.author_id

    import ast
    authorList = ast.literal_eval(row.authorList)
    
    candidate = len(authorList)
    
    algo = "multi_nb"
    
    test = str("pretrained_feat_" + algo) # change before run
    
    test = str("pretrained_feat_" + algo) # change before run
    
    level = "word"
    
    iterations = 10

    dropout = 0.5

    samples = 3200

    dimensions = 200

    loc = authorList.index(author_id)

    printstate = (("doc_id = %s, candidate = %s, ") % (str(doc_id), str(candidate)))
    printstate += (("dimensions = %s, samples = %s, ") % (str(dimensions), str(samples)))
    printstate += (("\niterations = %s, dropout = %s, test = %s") % (str(iterations), str(dropout), str(test)))

    print("Current test: %s" % (str(printstate)))
    """
    from sshtunnel import SSHTunnelForwarder
    with SSHTunnelForwarder(('144.214.121.15', 22),
                            ssh_username='ninadt',
                            ssh_password='Ninad123',
                            remote_bind_address=('localhost', 3306),
                            local_bind_address=('localhost', 3302)):
    """
    import UpdateDB as db
    case = db.checkOldCNNDiff(doc_id = doc_id, candidate = candidate, dimensions = dimensions,
                              samples = samples, iterations = iterations, dropout = dropout,
                              test = test)

    if case == False:

        print("Running: %12s" % (str(printstate)))

        import StyloNeuralML as Stylo
        (labels_index, train_acc, val_acc, samples) = Stylo.getResults(
            doc_id = doc_id, authorList = authorList[:], algo = algo,
            level = level, glove = '../../glove/', dimensions = dimensions, 
            samples = samples, nb_epoch = iterations, dropout = dropout, batch_size = 10 )
        
        (predY, testY) = Stylo.getTestResults(
            doc_id = doc_id, authorList = authorList[:], labels_index = labels_index, algo = algo,
            level = level, glove = '../../glove/', dimensions = dimensions, 
            samples = samples, nb_epoch = iterations, dropout = dropout, batch_size = 10 )
            
        loc = testY
        
        test_acc = 0.0
        
        test_acc = predY[loc]

        test_bin = 0

        if(predY.tolist().index(max(predY)) == testY):
            test_bin = 1
        
        """
        from sshtunnel import SSHTunnelForwarder
        with SSHTunnelForwarder(('144.214.121.15', 22),
                                ssh_username='ninadt',
                                ssh_password='Ninad123',
                                remote_bind_address=('localhost', 3306),
                                local_bind_address=('localhost', 3302)):
        """
        import UpdateDB as db
        case = db.updateresultOldCNNDiff(doc_id = doc_id, candidate = candidate, dimensions = dimensions,
                                         samples = samples, iterations = iterations, dropout = dropout, 
                                         train_acc = train_acc, val_acc = val_acc,
                                         test_acc = test_acc, test_bin = test_bin,
                                         test = test)
        del Stylo

        #from keras import backend as K
        #K.clear_session()
        
        import time
        time.sleep(10)

        from IPython.display import clear_output

        clear_output()

    else:
        print("Skipped: %12s" % (str(printstate)))

# import pandas as pd
# df = pd.DataFrame(output)
# df.to_csv("styloout.csv", index = False, encoding='utf-8')

import time
time.sleep(10)


    

In [3]:

    

%tb


    

    




No traceback available to show.

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