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How to use test_clean method in Kiwi

Best Python code snippet using Kiwi_python

LHS_NLP_ML.py

Source:LHS_NLP_ML.py

1#!/usr/bin/env python2# coding: utf-83# In[14]:4import pandas as pd5df_training_tweet = pd.read_csv("/Users/arjunanandapadmanabhan/Downloads/wn22_data/wn22_PA_training_tweets.txt", sep=",")6df_labels = pd.read_csv("/Users/arjunanandapadmanabhan/Downloads/wn22_data/wn22_PA_training_labels.txt", sep=",")7final_df = pd.merge(df_training_tweet, df_labels, on="TweetID")8final_df9df_testing_tweet = pd.read_csv("/Users/arjunanandapadmanabhan/Downloads/wn22_data/wn22_PA_testing_tweets.txt", sep=",")10df_testing_tweet11import re12import nltk13from nltk.corpus import stopwords14def clean_text(df, text_field):15    df[text_field] = df[text_field].str.lower()16    df[text_field] = df[text_field].apply(lambda x: re.sub(r"(@[A-Za-z0-9]+)|(\d+)|([^0-9A-Za-z \t])|(\w+:\/\/\S+)|^rt|http.+?", "", x)) 17    return df18test_clean = clean_text(df_testing_tweet, "Tweet")19train_clean = clean_text(final_df, "Tweet")20train_clean.Tweet[4]21import pandas as pd22import numpy as np23import nltk24import string25import contractions26from nltk.tokenize import word_tokenize27from nltk.corpus import stopwords, wordnet28from nltk.stem import WordNetLemmatizer29train_clean['no_contract'] = train_clean['Tweet'].apply(lambda x: [contractions.fix(word) for word in x.split()])30train_clean['Tweet'] = [' '.join(map(str, l)) for l in train_clean['no_contract']]31test_clean['no_contract'] = test_clean['Tweet'].apply(lambda x: [contractions.fix(word) for word in x.split()])32test_clean['Tweet'] = [' '.join(map(str, l)) for l in test_clean['no_contract']]33train_clean['tokenized'] = train_clean['Tweet'].apply(word_tokenize)34train_clean['tokenized'] = train_clean['tokenized'].apply(lambda x: [word.lower() for word in x])35test_clean['tokenized'] = test_clean['Tweet'].apply(word_tokenize)36test_clean['tokenized'] = test_clean['tokenized'].apply(lambda x: [word.lower() for word in x])37punc = string.punctuation38train_clean['no_punc'] = train_clean['tokenized'].apply(lambda x: [word for word in x if word not in punc])39test_clean['no_punc'] = test_clean['tokenized'].apply(lambda x: [word for word in x if word not in punc])40stop_words = set(stopwords.words('english'))41train_clean['tokenized']  = train_clean['no_punc'] .apply(lambda x: [word for word in x if word not in stop_words])42test_clean['tokenized']  = test_clean['no_punc'] .apply(lambda x: [word for word in x if word not in stop_words])43train_clean['pos_tags'] = train_clean['tokenized'].apply(nltk.tag.pos_tag)44test_clean['pos_tags'] = test_clean['tokenized'].apply(nltk.tag.pos_tag)45def get_wordnet_pos(tag):46    if tag.startswith('J'):47        return wordnet.ADJ48    elif tag.startswith('V'):49        return wordnet.VERB50    elif tag.startswith('N'):51        return wordnet.NOUN52    elif tag.startswith('R'):53        return wordnet.ADV54    else:55        return wordnet.NOUN56train_clean['wordnet_pos'] = train_clean['pos_tags'].apply(lambda x: [(word, get_wordnet_pos(pos_tag)) for (word, pos_tag) in x])57test_clean['wordnet_pos'] = test_clean['pos_tags'].apply(lambda x: [(word, get_wordnet_pos(pos_tag)) for (word, pos_tag) in x])58wnl = WordNetLemmatizer()59train_clean['lemmatized'] = train_clean['wordnet_pos'].apply(lambda x: [wnl.lemmatize(word, tag) for word, tag in x])60test_clean['lemmatized'] = test_clean['wordnet_pos'].apply(lambda x: [wnl.lemmatize(word, tag) for word, tag in x])61train_clean['Tweet'] = [' '.join(map(str, l)) for l in train_clean['lemmatized']]62test_clean['Tweet']  = [' '.join(map(str, l)) for l in test_clean['lemmatized']]63# Can be used to check the frequency of a term64# d = {}65# for word in train_clean['Tweet']:66#     for item in word.split():67#         if item in d:68#             d[item] = d[item] + 169#         else:70#             d[item] = 171# for i, word in enumerate(train_clean['Tweet']):72#     for item in word.split():73#         if d[item] <=10:74#             train_clean['Tweet'][i] = train_clean['Tweet'][i].replace(item, '')75#             train_clean['Tweet'][i] = re.sub(' +', ' ', train_clean['Tweet'][i])76            77            78# train_clean['Tweet'][0]79from sklearn.utils import resample80train_majority = train_clean[train_clean.Label==0]81train_minority = train_clean[train_clean.Label==1]82train_minority_upsampled = resample(train_minority, 83                                 replace=True,    84                                 n_samples=len(train_majority),   85                                 random_state=123)86train_upsampled = pd.concat([train_minority_upsampled, train_majority])87train_upsampled['Label'].value_counts()88from sklearn.feature_extraction.text import TfidfVectorizer89from sklearn.pipeline import Pipeline90from sklearn.feature_extraction.text import CountVectorizer91from sklearn.feature_extraction.text import TfidfTransformer92# from sklearn.linear_model import SGDClassifier93from sklearn.neural_network import MLPClassifier94# from sklearn.neighbors import KNeighborsClassifier95# from sklearn.ensemble import RandomForestClassifier96from sklearn.ensemble import GradientBoostingClassifier97from sklearn import svm98# from sklearn import tree99# pipeline_svc = Pipeline([100#     ('vect', CountVectorizer()),101#     ('tfidf',  TfidfTransformer()),102#     ('nb', SGDClassifier(learning_rate = 'constant', eta0=0.96, epsilon=0.0004, max_iter=5000, validation_fraction=0.8, loss='log')),103# ])104# pipeline_svc = Pipeline([105#     ('vect', CountVectorizer()),106#     ('tfidf',  TfidfTransformer()),107#     ('nb', MLPClassifier(hidden_layer_sizes = (2,),solver='adam', learning_rate='adaptive', max_iter=1000, epsilon=1e-6, tol=0.0001))108# ])109# pipeline_svc = Pipeline([110#     ('vect', CountVectorizer(ngram_range=(1,3) )),111#     ('tfidf',  TfidfTransformer()),112#     ('nb', svm.SVC(kernel='poly', C=2.5, degree=2, coef0=0.18, break_ties=True))113# ])114pipeline_svc = Pipeline([115    ('vect', CountVectorizer()),116    ('tfidf',  TfidfTransformer()),117    ('nb', svm.SVC(gamma='scale'))118])119# pipeline_svc = Pipeline([120#     ('vect', CountVectorizer()),121#     ('tfidf',  TfidfTransformer()),122#     ('nb', GradientBoostingClassifier(n_estimators=10000, subsample=1))123# ])124# from sklearn.model_selection import train_test_split125# from sklearn.metrics import f1_score126# from numpy import arange127# ep = [0.001, 0.0002, 0.0003, 0.0004, 0.0007, 0.005, 0.03, 0.01, 0.003, 0.000004, 0.00000006, 0.005]128# max_num = 0.0129# ep_max = 0.0130# et_max = 0.0131# for e in ep:132#     for et in arange(0.01,1, 0.01):133#         pipeline_svc = Pipeline([134#         ('vect', CountVectorizer()),135#         ('tfidf',  TfidfTransformer()),136#         ('nb', SGDClassifier(learning_rate = 'constant', eta0=et, epsilon=e, max_iter=5000, validation_fraction=0.8, loss='log')),137#     ])138#         X_train, X_test, y_train, y_test = train_test_split(train_upsampled['Tweet'], train_upsampled['Label'],random_state = 0)139#         model = pipeline_svc.fit(X_train, y_train)140#         y_predict = model.predict(X_test)141#         x = f1_score(y_test, y_predict)142#         if x > max_num:143#             max_num = x144#             ep_max = e145#             et_max = et146            147            148# print(max_num, ep_max, et_max)149# In[124]:150# from sklearn.model_selection import train_test_split151# X_train, X_test, y_train, y_test = train_test_split(train_upsampled['Tweet'], train_upsampled['Label'],random_state = 123)152# In[137]:153X_train = train_upsampled['Tweet']154y_train = train_upsampled['Label']155# In[138]:156model = pipeline_svc.fit(X_train, y_train)157# y_predict = model.predict(X_test)158# from sklearn.metrics import f1_score159# f1_score(y_test, y_predict)160# In[140]:161x_valid = df_testing_tweet['Tweet']162y_predict_1 = model.predict(x_valid)163y_predict_1164# In[141]:165# from sklearn.feature_extraction.text import TfidfVectorizer166# tfidf_vec = TfidfVectorizer()167# x_training = tfidf_vec.fit_transform(X_train)168# x_validation = tfidf_vec.transform(X_test)169# In[142]:170# from sklearn import svm171# model_svm = svm.SVC()172# model_svm.fit(x_training,y_train)173# In[143]:174# pred_svm = model_svm.predict(x_validation)175# In[144]:176# from sklearn.metrics import f1_score177# f1_score(y_test, pred_svm)178# In[145]:179# x_valid = df_testing_tweet['Tweet']180# x_test = tfidf_vec.transform(x_valid)181# y_predict_1 = model_svm.predict(x_test)182# y_predict_1183# In[146]:184pred = pd.DataFrame(y_predict_1)185pred.columns = ['Label']186pred187data = [df_testing_tweet['TweetID'], pred['Label']]188headers = ["TweetID", "Label"]189Final = pd.concat(data, axis=1, keys=headers)190Final.to_csv(r'/Users/arjunanandapadmanabhan/Downloads/wn22_data/output_2.txt', header=True, index=None, sep=',', mode='a')191# Confusion Matrix192from sklearn.metrics import confusion_matrix193import matplotlib.pyplot as plt194import itertools195import numpy as np196def plot_confusion_matrix(cm, classes,197                          normalize=False,198                          title='Confusion matrix',199                          cmap=plt.cm.Blues):200    """201    This function prints and plots the confusion matrix.202    Normalization can be applied by setting `normalize=True`.203    """204    if normalize:205        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]206        print("Normalized confusion matrix")207    else:208        print('Confusion matrix, without normalization')209        210    plt.imshow(cm, interpolation='nearest', cmap=cmap)211    plt.title(title)212    plt.colorbar()213    tick_marks = np.arange(len(classes))214    plt.xticks(tick_marks, classes, rotation=45)215    plt.yticks(tick_marks, classes)216    fmt = '.2f' if normalize else 'd'217    thresh = cm.max() / 2.218    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):219        plt.text(j, i, format(cm[i, j], fmt),220                 horizontalalignment="center",221                 color="white" if cm[i, j] > thresh else "black")222    plt.ylabel('True label')223    plt.xlabel('Predicted label')224    plt.tight_layout()225classes=list(set(y_test))226cm = confusion_matrix(y_test, y_predic, labels=classes)...

Ion XGBoost.py

Source:Ion XGBoost.py

1#!/usr/bin/env python32# -*- coding: utf-8 -*-3"""4Created on Mon May  4 14:03:53 20205@author: feichang6"""7import pandas as pd8import numpy as np9import matplotlib.pyplot as plt10train = pd.read_csv('train.csv')11test = pd.read_csv('test.csv')12type(train.shape[0])13train_clean = train14"""15#take a look at the train16plt.figure(figsize=(5,5)); res = 100017plt.plot(range(0,train.shape[0],res),train.signal[0::res])18for i in range(11): plt.plot([i*500000,i*500000],[-5,12.5],'r')19plt.show()20#take a look at the test21plt.figure(figsize=(10,5)); res = 100022plt.plot(range(0,test.shape[0],res),test.signal[0::res])23plt.show()24"""25#first, fix the first drift in seg 2, 500000-60000026a = 50000027b = 60000028seg_1 = train.loc[train.index[a:b], 'signal'].values29time_1 = train.loc[train.index[a:b], 'time'].values30from sklearn.linear_model import LinearRegression31regressor = LinearRegression()32regressor.fit(time_1.reshape(-1,1),seg_1)33train_clean.loc[train.index[a:b], 'signal'] = train_clean.signal[a:b].values - regressor.coef_*(train_clean.time.values[a:b] - 50)34"""35plt.figure(figsize=(5,5)); res = 100036plt.plot(range(0,train_clean.shape[0],res),train.signal[0::res])37for i in range(11): plt.plot([i*500000,i*500000],[-5,12.5],'r')38plt.show()39"""40#then fix the polynomial drift41a = 0 42while a < 4500001:43    b = a + 50000044    seg_2 = train.loc[train.index[a:b], 'signal'].values45    time_2 = train.loc[train.index[a:b], 'time'].values46    47    from sklearn.preprocessing import PolynomialFeatures48    poly_reg = PolynomialFeatures(degree = 2)49    time_poly = poly_reg.fit_transform(time_2.reshape(-1,1))50    #define poly regressor51    lin_reg2 = LinearRegression()52    lin_reg2.fit(time_poly,seg_2)53    drift_0 = lin_reg2.predict(time_poly)[0]54    drift = lin_reg2.predict(time_poly) - drift_055    train_clean.loc[train.index[a:b], 'signal'] = train_clean.signal[a:b].values - drift56    a += 50000057#now the signal data is clean, look: 58"""59res = 100060plt.plot(range(0,train.shape[0],res),train.signal[0::res])61"""62"""63We also need to take the average current of the "phase" into consideration64"""65a = 0 66train_clean['Mean'] = 0.67train_clean['stdev'] = 0.68while a < 4500001:69    b = a + 50000070    avg = np.mean(train_clean.signal[a:b].values, dtype = 'float32')71    std = np.std(train_clean.signal[a:b].values, dtype = 'float32')72    train_clean.Mean[a:b].values.fill(avg) 73    train_clean.stdev[a:b].values.fill(std)74    a += 50000075"""76train_clean.head()77"""78from sklearn.model_selection import train_test_split79X_train, X_test, y_train, y_test = train_test_split(train_clean[['signal','Mean','stdev']], train_clean['open_channels'], test_size = 0.25, random_state = 0)80"""81from sklearn.tree import DecisionTreeClassifier82classifier_1 = DecisionTreeClassifier(random_state = 0, max_depth = 11, 83                                      min_samples_split = 32, min_samples_leaf = 5)84classifier_1.fit(X_train, y_train)85"""86from xgboost import XGBClassifier87classifier_1 = XGBClassifier()88classifier_1.fit(X_train, y_train)89prediction = classifier_1.predict(X_test)90from sklearn.metrics import f1_score91F1 = f1_score(y_test, prediction, average = 'macro')92print('F1 score:', F1)93"""94now it's very close. We are ready to move to the next step95plt.plot(prediction,'red')96plt.plot(train_clean['open_channels'])97"""98"""99now we clean the test data100first take a look101plt.plot(test['signal'])102"""103#every 100000 points, a phase, till 1000000104test_clean = test105a = 0106while a < 900001:107    b = a + 100000108    seg_1 = test.loc[train.index[a:b], 'signal'].values109    time_1 = test.loc[train.index[a:b], 'time'].values110    regressor_3 = LinearRegression()111    regressor_3.fit(time_1.reshape(-1,1),seg_1)112    drift_0 = regressor_3.predict(time_1.reshape(-1,1))[0]113    drift = regressor_3.predict(time_1.reshape(-1,1)) - drift_0114    115    test_clean.loc[train.index[a:b], 'signal'] = test_clean.signal[a:b].values - drift116    a += 100000117"""118take a look119plt.plot(test_clean['signal'])120"""121#1000000 to 1500000 a polynomial122a = 1000000123b = 1500000124seg_2 = test.loc[train.index[a:b], 'signal'].values125time_2 = test.loc[train.index[a:b], 'time'].values126    127poly_reg = PolynomialFeatures(degree = 2)128time_poly = poly_reg.fit_transform(time_2.reshape(-1,1))129lin_reg2 = LinearRegression()130lin_reg2.fit(time_poly,seg_2)131drift_0 = lin_reg2.predict(time_poly)[0]132drift = lin_reg2.predict(time_poly) - drift_0133test_clean.loc[test_clean.index[a:b], 'signal'] = test_clean.signal[a:b].values - drift + 0.25134"""135take a look136plt.figure(figsize=(20,5)); res = 1000137plt.plot(range(0,test_clean.shape[0],res),test_clean.signal[0::res])138plt.plot(test_clean['signal'])139plt.figure(figsize=(20,5)); res = 1000140plt.plot(pd.read_csv('test.csv')['signal'])141"""142a = 0 143test_clean['Mean'] = 0.144test_clean['stdev'] = 0.145while a < 1900001:146    b = a + 100000147    avg = np.mean(test_clean.signal[a:b].values, dtype = 'float32')148    std = np.std(test_clean.signal[a:b].values, dtype = 'float32')149    test_clean.Mean[a:b].values.fill(avg) 150    test_clean.stdev[a:b].values.fill(std)151    a += 100000152test_clean.head()153prediction = classifier_1.predict(test_clean[['signal','Mean','stdev']])154"""155Take a look at the mean156plt.plot(test_clean['Mean'])157plt.plot(train_clean['Mean'])158"""159"""160output = pd.DataFrame({'time': test_clean['time'], 'open_channels': prediction})161output.to_csv('my_submission.csv', index=False)162print("Your submission was successfully saved!")163"""164samplesubmission = pd.read_csv('sample_submission.csv', dtype={'time': 'str'})165samplesubmission.info()166output = pd.DataFrame({'time': samplesubmission.time, 'open_channels': prediction})167output.to_csv('submission.csv', index=False)168sub = pd.read_csv('submission.csv')...

baseline.py

Source:baseline.py

1import pandas as pd2from sklearn.tree import DecisionTreeRegressor3from sklearn.ensemble import RandomForestRegressor4from sklearn.model_selection import train_test_split5from sklearn.metrics import mean_absolute_error6def basic_cleaning(train_feature_path, train_labels_path, test_feature_path, export=False, out_path=None):7    train_features=pd.read_csv(train_feature_path)8    train_labels=pd.read_csv(train_labels_path)9    train_features=pd.merge(train_features, train_labels, on=['city', 'year','weekofyear'])10    test_features=pd.read_csv(test_feature_path)11    #convert kelvin to celsius12    train_features[['reanalysis_air_temp_k', 'reanalysis_avg_temp_k', 'reanalysis_dew_point_temp_k', 'reanalysis_max_air_temp_k', 'reanalysis_min_air_temp_k', 'reanalysis_tdtr_k']]=train_features[['reanalysis_air_temp_k', 'reanalysis_avg_temp_k', 'reanalysis_dew_point_temp_k', 'reanalysis_max_air_temp_k', 'reanalysis_min_air_temp_k', 'reanalysis_tdtr_k']]-273.1513    test_features[['reanalysis_air_temp_k', 'reanalysis_avg_temp_k', 'reanalysis_dew_point_temp_k', 'reanalysis_max_air_temp_k', 'reanalysis_min_air_temp_k', 'reanalysis_tdtr_k']]=test_features[['reanalysis_air_temp_k', 'reanalysis_avg_temp_k', 'reanalysis_dew_point_temp_k', 'reanalysis_max_air_temp_k', 'reanalysis_min_air_temp_k', 'reanalysis_tdtr_k']]-273.1514    #dropping rows where no temperature data is available15    train_clean=train_features.dropna(subset=['reanalysis_air_temp_k', 'reanalysis_avg_temp_k','station_avg_temp_c'], how='all')16    # inputation for submission17    test_clean=test_features.copy(deep=True)18    #inputation - temperature data19    train_clean['station_avg_temp_c'].fillna(train_clean['reanalysis_avg_temp_k'], inplace=True)20    train_clean['station_diur_temp_rng_c'].fillna(train_clean['reanalysis_tdtr_k'], inplace=True)21    train_clean['station_max_temp_c'].fillna(train_clean['reanalysis_max_air_temp_k'], inplace=True)22    train_clean['station_min_temp_c'].fillna(train_clean['reanalysis_min_air_temp_k'], inplace=True)23    test_clean['station_avg_temp_c'].fillna(test_clean['reanalysis_avg_temp_k'], inplace=True)24    test_clean['station_diur_temp_rng_c'].fillna(test_clean['reanalysis_tdtr_k'], inplace=True)25    test_clean['station_max_temp_c'].fillna(test_clean['reanalysis_max_air_temp_k'], inplace=True)26    test_clean['station_min_temp_c'].fillna(test_clean['reanalysis_min_air_temp_k'], inplace=True)27    #inputation - vegetation index28    for i in ['ndvi_ne','ndvi_sw','ndvi_nw','ndvi_se']:29        train_clean[i]=train_clean[i].interpolate()30        test_clean[i]=test_clean[i].interpolate()31    #inputation - precipitation level32    train_clean['station_precip_mm'].fillna(train_clean['reanalysis_sat_precip_amt_mm'], inplace=True)33    test_clean['station_precip_mm'].fillna(test_clean['reanalysis_sat_precip_amt_mm'], inplace=True)34    #dropping duplicate columns35    train_clean.drop(['precipitation_amt_mm','reanalysis_sat_precip_amt_mm'],axis=1, inplace=True)36    test_clean.drop(['precipitation_amt_mm','reanalysis_sat_precip_amt_mm'],axis=1, inplace=True)37    #drop useless column38    train_clean.drop('week_start_date', axis=1, inplace=True)39    test_clean.drop('week_start_date', axis=1, inplace=True)40    #encode city as binary variable41    train_clean['city']=train_clean['city'].map({'sj':1, 'iq':0})42    test_clean['city']=test_clean['city'].map({'sj':1, 'iq':0})43    for i in test_clean.columns:44        test_clean[i]=test_clean[i].interpolate()45    if export:46        train_clean.to_csv('train_'+out_path)47        test_clean.to_csv('test_'+out_path)48    return train_clean, test_clean49def export_submission(test_clean, out_path, model):50    test_clean['total_cases']=model.predict(test_clean)51    test_clean['total_cases']=test_clean['total_cases'].astype(int)52    submission=test_clean[['city', 'year', 'weekofyear', 'total_cases']]53    submission['city']=submission['city'].map({1:'sj', 0:'iq'})54    submission.to_csv(out_path, index=False)55    return submission56cleaned=basic_cleaning('../data/dengue_features_train.csv', '../data/dengue_labels_train.csv','../data/dengue_features_test.csv' )57print(cleaned[0].head())58X=cleaned[0].drop('total_cases', axis=1)59Y=cleaned[0]['total_cases']60X_train, X_test, y_train , y_test=train_test_split(X,Y, random_state=42)61dtr=DecisionTreeRegressor(random_state=420)62dtr.fit(X_train, y_train)63rfr=RandomForestRegressor(random_state=420)64rfr.fit(X_train, y_train)65print(mean_absolute_error(y_test, dtr.predict(X_test)))...

calculate_bleu_for_bt.py

Source:calculate_bleu_for_bt.py

1from nltk.translate.bleu_score import corpus_bleu2from experiments.utils import get_daily_dialog, get_mutual_friends, get_babi_dialog3import os4import argparse5from config import *6if __name__ == "__main__":7    parser = argparse.ArgumentParser()8    parser.add_argument("--dataset_clean", type=str, required=True)9    parser.add_argument("--dataset_back", type=str, required=True)10    args = parser.parse_args()11    if "dailydialog" in args.dataset_clean.lower():12        test_clean = get_daily_dialog(os.path.join(args.dataset_clean, "test.json"))13        test_translated = get_daily_dialog(os.path.join(args.dataset_back, "test.json"))14        list_of_hypothesis = []15        list_of_references = []16        assert len(test_clean) == len(test_translated)17        for ground_truth, translation in zip(test_clean, test_translated):18            assert len(ground_truth.dialog) == len(translation.dialog)19            for utterance_ground, utterance_translation in zip(ground_truth.dialog, translation.dialog):20                references = [utterance_ground[TEXT].split(" ")]21                list_of_references.append(references)22                hypothesis = utterance_translation[TEXT].split(" ")23                list_of_hypothesis.append(hypothesis)24        print(corpus_bleu(list_of_references, list_of_hypothesis))25    elif "mutualfriends" in args.dataset_clean.lower():26        test_clean = get_mutual_friends(os.path.join(args.dataset_clean, "test.json"))27        test_translated = get_mutual_friends(os.path.join(args.dataset_back, "test.json"))28        list_of_hypothesis = []29        list_of_references = []30        assert len(test_clean) == len(test_translated)31        for ground_truth, translation in zip(test_clean, test_translated):32            assert ground_truth.uuid == translation.uuid33            assert len(ground_truth.dialog) == len(translation.dialog)34            for utterance_ground, utterance_translation in zip(ground_truth.dialog, translation.dialog):35                if utterance_ground["action"] == "message":36                    references = [utterance_ground["data"].split(" ")]37                    list_of_references.append(references)38                    hypothesis = utterance_translation["data"].split(" ")39                    list_of_hypothesis.append(hypothesis)40                    print(utterance_ground, utterance_translation)41        print(corpus_bleu(list_of_references, list_of_hypothesis))42    elif "babi" in args.dataset_clean.lower():43        test_clean = get_babi_dialog(os.path.join(args.dataset_clean, "dialog-babi-task5-full-dialogs-tst.txt"))44        test_translated = get_babi_dialog(os.path.join(args.dataset_back, "dialog-babi-task5-full-dialogs-tst.txt"))45        list_of_hypothesis = []46        list_of_references = []47        assert len(test_clean) == len(test_translated)48        for ground_truth, translation in zip(test_clean, test_translated):49            assert len(ground_truth.dialog) == len(translation.dialog)50            for utterance_ground, utterance_translation in zip(ground_truth.dialog, translation.dialog):51                key = list(utterance_ground.keys())[0]52                if key in ["User", "Bot"]:53                    references = [utterance_ground[key].split(" ")]54                    list_of_references.append(references)55                    hypothesis = utterance_translation[key].split(" ")56                    list_of_hypothesis.append(hypothesis)...

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