Text-Normalization/predictor.py at master · rudranshtripathi9/Text-Normalization · GitHub

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import pandas as pd
import numpy as np
import os
import pickle
import gc
import xgboost as xgb
import numpy as np
import re
import pandas as pd
from sklearn.model_selection import train_test_split

max_num_features = 10
pad_size = 1
boundary_letter = -1
space_letter = 0
max_data_size = 320 # 320000

out_path = r'output'
df = pd.read_csv(r'input/en_train.csv', encoding='utf8')
df_test = pd.read_csv(r'input/en_test_2.csv', encoding='utf8')

#x_data = []
y_data =  pd.factorize(df['class'])
labels = y_data[1]
#y_data = y_data[0]
#gc.collect()
#for x in df['before'].values:
#    x_row = np.ones(max_num_features, dtype=int) * space_letter
#    for xi, i in zip(list(str(x)), np.arange(max_num_features)):
#        x_row[i] = ord(xi)
#    x_data.append(x_row)

x_test = []
for x in df_test['before'].values:
    x_row = np.ones(max_num_features, dtype=int) * space_letter
    for xi, i in zip(list(str(x)), np.arange(max_num_features)):
        x_row[i] = ord(xi)
    x_test.append(x_row)

def context_window_transform(data, pad_size):
    pre = np.zeros(max_num_features)
    pre = [pre for x in np.arange(pad_size)]
    data = pre + data + pre
    neo_data = []
    for i in np.arange(len(data) - pad_size * 2):
        row = []
        for x in data[i : i + pad_size * 2 + 1]:
            row.append([boundary_letter])
            row.append(x)
        row.append([boundary_letter])
        neo_data.append([int(x) for y in row for x in y])
    return neo_data

#x_data = x_data[:max_data_size]
#y_data = y_data[:max_data_size]
#x_data = np.array(context_window_transform(x_data, pad_size))
#gc.collect()
#x_data = np.array(x_data)
#y_data = np.array(y_data)

#x_test = x_test[:max_data_size]
x_test = np.array(context_window_transform(x_test, pad_size))
x_test = np.array(x_test)

#print('Total number of samples:', len(x_data))
#print('Use: ', max_data_size)
print('Total number of test samples:', len(x_test))

#print('x_data sample:')
#print(x_data[0])
#print('y_data sample:')
#print(y_data[0])
#print('labels:')
print(labels)

#x_train = x_data
#y_train = y_data
#gc.collect()
#
#x_train, x_valid, y_train, y_valid= train_test_split(x_train, y_train,
#                                                      test_size=0.1, random_state=2017)
#gc.collect()
#num_class = len(labels)
#dtrain = xgb.DMatrix(x_train, label=y_train)
#dvalid = xgb.DMatrix(x_valid, label=y_valid)
#watchlist = [(dvalid, 'valid'), (dtrain, 'train')]

dtest = xgb.DMatrix(x_test)

#param = {'objective':'multi:softmax',
#         'eta':'0.3', 'max_depth':10,
#         'silent':1, 'nthread':-1,
#         'num_class':num_class,
#         'eval_metric':'merror'}
#model = xgb.train(param, dtrain, 50, watchlist, early_stopping_rounds=20,
#                  verbose_eval=10)
#gc.collect()

model = xgb.Booster({'nthread':4})
model.load_model(os.path.join(out_path, 'xgb_model'))

#pred = model.predict(dvalid)
#pred = [labels[int(x)] for x in pred]
#y_valid = [labels[x] for x in y_valid]
#x_valid = [ [ chr(x) for x in y[2 + max_num_features: 2 + max_num_features * 2]] for y in x_valid]
#x_valid = [''.join(x) for x in x_valid]
#x_valid = [re.sub('a+$', '', x) for x in x_valid]

pred_test = model.predict(dtest)
pred_test = [labels[int(x)] for x in pred_test]

x_test = [ [ chr(x) for x in y[2 + max_num_features: 2 + max_num_features * 2]] for y in x_test]
x_test = [''.join(x) for x in x_test]
x_test = [re.sub('a+$', '', x) for x in x_test]

gc.collect()

#df_pred = pd.DataFrame(columns=['data', 'predict', 'target'])
#df_pred['data'] = x_valid
#df_pred['predict'] = pred
#df_pred['target'] = y_valid
#df_pred.to_csv(os.path.join(out_path, 'pred.csv'), encoding='utf8')

df_pred_test = pd.DataFrame(columns=['data', 'predict'])
df_pred_test['data'] = x_test
df_pred_test['predict'] = pred_test
df_pred_test.to_csv(os.path.join(out_path, 'pred_test_2.csv'), encoding='utf8')

#df_erros = df_pred.loc[df_pred['predict'] != df_pred['target']]
#df_erros.to_csv(os.path.join(out_path, 'errors.csv'), index=False, encoding='utf8')
#
#model.save_model(os.path.join(out_path, 'xgb_model'))