코딩탕탕

TensorFlow 기초 44 - LSTM을 사용한 삼성전자 주가 예측(종가)

코딩탕탕 — Tue, 20 Dec 2022 12:34:44 +0900

# LSTM을 사용한 삼성전자 주가 예측(종가)
# KRX: 005930
# !pip install finance-datareader

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import FinanceDataReader as fdr

STOCK_CODE = '005930'
stock_data = fdr.DataReader(STOCK_CODE)
print(stock_data.head())
print(stock_data.tail())

print('상관관계 : \n,', stock_data.corr(method='pearson'))

stock_data.reset_index(inplace=True)
stock_data.drop(['Change'], axis='columns', inplace=True)
print(stock_data.head(3))
print(stock_data.info())

# Data열을 연, 월, 일로 분리
stock_data['year'] = stock_data['Date'].dt.year
stock_data['month'] = stock_data['Date'].dt.month
stock_data['day'] = stock_data['Date'].dt.day
print(stock_data.head(3))
print(stock_data.shape) # (6000, 9)

# 1998년 이후 주가 흐름 시각화
df = stock_data.loc[stock_data['year'] >= 1998]
plt.figure(figsize=(6,4))
sns.lineplot(y=df['Close'], x=df.year)
plt.xlabel('year')
plt.ylabel('Close')
plt.legend()
plt.show()

# 스케일링
from sklearn.preprocessing import MinMaxScaler

scaler = MinMaxScaler()
scale_cols = ['Open', 'High', 'Low', 'Close', 'Volume']
df_scaled = scaler.fit_transform(stock_data[scale_cols])

df_scaled = pd.DataFrame(df_scaled)
df_scaled.columns = scale_cols
print(df_scaled.head(3))

only_close = ['Close']
close_scaled = scaler.fit_transform(stock_data[only_close]) # predict을 위함
print('스케일 값 :', close_scaled[:5].ravel())
print('복원 값 :', scaler.inverse_transform(close_scaled[:5]).ravel())
print('최초 값 :', stock_data['Close'].values[:5])

# 이전 20일을 기준으로 다음날 종가 예측
TEST_SIZE = 200 # 학습은 200일
train = df_scaled[:-TEST_SIZE] # 관찰값 처음부터 200일 이전까지
test = df_scaled[-TEST_SIZE:]  # 최근 200일
print(train.shape) # (5800, 5)
print(test.shape)  # (200, 5)

def make_dataset(data, label, window_size = 20):
    feature_list = []
    label_list = []
    for i in range(len(data) - window_size):
        feature_list.append(np.array(data.iloc[i:i + window_size]))
        label_list.append(np.array(label.iloc[i + window_size]))
        
    return np.array(feature_list), np.array(label_list)
    
# feature, label
feature_cols = ['Open', 'High', 'Low', 'Volume']
label_cols = ['Close']

train_feature = train[feature_cols]
train_label = train[label_cols]
test_feature = test[feature_cols]
test_label = test[label_cols]

train_feature, train_label = make_dataset(train_feature, train_label, 20)
print(train_feature[:2])
print(train_label[:2])
print(train_feature.shape, train_label.shape) # (5780, 20, 4) (5780, 1)

# train / test split
from sklearn.model_selection import train_test_split

x_train, x_test, y_train, y_test = train_test_split(train_feature, train_label, test_size=0.2, shuffle=False, random_state=12)
print(x_train.shape, x_test.shape, y_train.shape, y_test.shape) # (4624, 20, 4) (1156, 20, 4) (4624, 1) (1156, 1)

test_feature, test_label = make_dataset(test_feature, test_label, 20)

from keras.models import Sequential
from keras.layers import Dense, LSTM
from keras.callbacks import EarlyStopping, ModelCheckpoint

model = Sequential()
model.add(LSTM(units=16, activation='tanh', input_shape=(train_feature.shape[1], train_feature.shape[2]), return_sequences=False))
model.add(Dense(16, activation='relu'))
model.add(Dense(1, activation='linear'))

# 'mse'는 이상치에 민감하다. Huber loss는 모든 지점에서 미분이 가능하면서 이상치에 강건한(robust) 성격을 보인다.
from keras.losses import Huber
loss = Huber()
# model.compile(optimizer='adam', loss='mse', metrics=['mse'])
model.compile(optimizer='adam', loss=loss, metrics=['mse'])

es = EarlyStopping(monitor='val_loss', mode='auto', patience=3)
mchkpoint = ModelCheckpoint('nlp18.h5', monitor='val_loss', save_best_only=True, verbose=0)

history = model.fit(x_train, y_train, epochs=50, batch_size=8, validation_data=(x_test,y_test), verbose=2,
                    callbacks=[es, mchkpoint])

# 시각화
plt.figure(figsize=(6, 4))
plt.plot(history.history['loss'], label='loss')
plt.plot(history.history['val_loss'], label='val_loss')
plt.legend()
plt.show()

# predict
from sklearn.metrics import r2_score
pred = model.predict(test_feature, verbose=0)
print('결정계수(설명력) :', r2_score(test_label, pred))

print('pred :', np.round(pred[:10].flatten(), 2))
print('pred(스케일 원복) :', scaler.inverse_transform(pred[:10]).flatten())
print('real(스케일 원복) :', scaler.inverse_transform(test_label[:10]).flatten())

# 시각화
plt.figure(figsize=(6, 4))
plt.plot(test_label[:20], label='real')
plt.plot(pred[:20].flatten(), label='pred')
plt.legend()
plt.show()


<console>
            Open  High  Low  Close   Volume    Change
Date                                                 
1998-09-18   727   747  680    690  1432270       NaN
1998-09-19   660   706  653    697   794390  0.010145
1998-09-21   689   689  661    669   828650 -0.040172
1998-09-22   657   669  637    638  1026950 -0.046338
1998-09-23   643   662  624    649  1719730  0.017241
             Open   High    Low  Close    Volume    Change
Date                                                      
2022-12-14  59800  60600  59800  60500   8207485  0.013400
2022-12-15  59800  60200  59300  59300   8716039 -0.019835
2022-12-16  58300  59500  58300  59500  13033596  0.003373
2022-12-19  59500  59900  59100  59500   7696187  0.000000
2022-12-20  59000  59100  58600  58800   5367011 -0.011765
상관관계 : 
,             Open      High       Low     Close    Volume    Change
Open    1.000000  0.999884  0.999915  0.998152  0.722377 -0.025527
High    0.999884  1.000000  0.999882  0.998273  0.725217 -0.020439
Low     0.999915  0.999882  1.000000  0.998274  0.720598 -0.021182
Close   0.998152  0.998273  0.998274  1.000000  0.721871 -0.015309
Volume  0.722377  0.725217  0.720598  0.721871  1.000000 -0.003857
Change -0.025527 -0.020439 -0.021182 -0.015309 -0.003857  1.000000
        Date  Open  High  Low  Close   Volume
0 1998-09-18   727   747  680    690  1432270
1 1998-09-19   660   706  653    697   794390
2 1998-09-21   689   689  661    669   828650
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 6000 entries, 0 to 5999
Data columns (total 6 columns):
 #   Column  Non-Null Count  Dtype         
---  ------  --------------  -----         
 0   Date    6000 non-null   datetime64[ns]
 1   Open    6000 non-null   int64         
 2   High    6000 non-null   int64         
 3   Low     6000 non-null   int64         
 4   Close   6000 non-null   int64         
 5   Volume  6000 non-null   int64         
dtypes: datetime64[ns](1), int64(5)
memory usage: 281.4 KB
None
        Date  Open  High  Low  Close   Volume  year  month  day
0 1998-09-18   727   747  680    690  1432270  1998      9   18
1 1998-09-19   660   706  653    697   794390  1998      9   19
2 1998-09-21   689   689  661    669   828650  1998      9   21
(6000, 9)
       Open      High       Low     Close    Volume
0  0.008051  0.007717  0.007598  0.000575  0.015860
1  0.007309  0.007293  0.007296  0.000653  0.008797
2  0.007630  0.007118  0.007385  0.000343  0.009176
스케일 값 : [0.00057546 0.00065293 0.00034306 0.         0.00012173]
복원 값 : [690. 697. 669. 638. 649.]
최초 값 : [690 697 669 638 649]
(5800, 5)
(200, 5)
[[[0.00805094 0.00771694 0.00759777 0.01586016]
  [0.00730897 0.00729339 0.00729609 0.00879663]
  [0.00763012 0.00711777 0.00738547 0.00917601]
  [0.00727575 0.00691116 0.00711732 0.01137187]
  [0.00712071 0.00683884 0.00697207 0.01904333]
  [0.00803987 0.0075     0.00791061 0.01235021]
  [0.00827243 0.00786157 0.00801117 0.01733912]
  [0.00795127 0.00757231 0.00795531 0.0033217 ]
  [0.00820598 0.00780992 0.00795531 0.00601365]
  [0.00791805 0.00771694 0.00792179 0.00650742]
  [0.00816168 0.00769628 0.00804469 0.00534227]
  [0.00797342 0.00747934 0.00782123 0.00431831]
  [0.00771872 0.00733471 0.00767598 0.00396551]
  [0.00797342 0.00760331 0.00791061 0.00868069]
  [0.00805094 0.00769628 0.00811173 0.00855689]
  [0.00848283 0.00840909 0.00830168 0.01628106]
  [0.00894795 0.00852273 0.00887151 0.00901599]
  [0.00885936 0.00876033 0.00858101 0.01349   ]
  [0.00922481 0.00897727 0.00900559 0.01499311]
  [0.00922481 0.0088843  0.00909497 0.01206717]]

 [[0.00730897 0.00729339 0.00729609 0.00879663]
  [0.00763012 0.00711777 0.00738547 0.00917601]
  [0.00727575 0.00691116 0.00711732 0.01137187]
  [0.00712071 0.00683884 0.00697207 0.01904333]
  [0.00803987 0.0075     0.00791061 0.01235021]
  [0.00827243 0.00786157 0.00801117 0.01733912]
  [0.00795127 0.00757231 0.00795531 0.0033217 ]
  [0.00820598 0.00780992 0.00795531 0.00601365]
  [0.00791805 0.00771694 0.00792179 0.00650742]
  [0.00816168 0.00769628 0.00804469 0.00534227]
  [0.00797342 0.00747934 0.00782123 0.00431831]
  [0.00771872 0.00733471 0.00767598 0.00396551]
  [0.00797342 0.00760331 0.00791061 0.00868069]
  [0.00805094 0.00769628 0.00811173 0.00855689]
  [0.00848283 0.00840909 0.00830168 0.01628106]
  [0.00894795 0.00852273 0.00887151 0.00901599]
  [0.00885936 0.00876033 0.00858101 0.01349   ]
  [0.00922481 0.00897727 0.00900559 0.01499311]
  [0.00922481 0.0088843  0.00909497 0.01206717]
  [0.00971207 0.00987603 0.00975419 0.01851357]]]
[[0.00353025]
 [0.00417211]]
(5780, 20, 4) (5780, 1)
(4624, 20, 4) (1156, 20, 4) (4624, 1) (1156, 1)
2022-12-20 12:32:53.248658: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
Epoch 1/50
578/578 - 5s - loss: 3.2635e-04 - mse: 6.5270e-04 - val_loss: 0.0061 - val_mse: 0.0121 - 5s/epoch - 9ms/step
Epoch 2/50
578/578 - 2s - loss: 2.4637e-05 - mse: 4.9273e-05 - val_loss: 0.0035 - val_mse: 0.0070 - 2s/epoch - 3ms/step
Epoch 3/50
578/578 - 2s - loss: 2.3148e-05 - mse: 4.6295e-05 - val_loss: 0.0025 - val_mse: 0.0050 - 2s/epoch - 4ms/step
Epoch 4/50
578/578 - 2s - loss: 2.1270e-05 - mse: 4.2541e-05 - val_loss: 0.0015 - val_mse: 0.0030 - 2s/epoch - 4ms/step
Epoch 5/50
578/578 - 2s - loss: 1.9913e-05 - mse: 3.9827e-05 - val_loss: 0.0011 - val_mse: 0.0023 - 2s/epoch - 4ms/step
Epoch 6/50
578/578 - 2s - loss: 1.8818e-05 - mse: 3.7635e-05 - val_loss: 9.4901e-04 - val_mse: 0.0019 - 2s/epoch - 3ms/step
Epoch 7/50
578/578 - 2s - loss: 1.7018e-05 - mse: 3.4036e-05 - val_loss: 6.5133e-04 - val_mse: 0.0013 - 2s/epoch - 4ms/step
Epoch 8/50
578/578 - 2s - loss: 1.7787e-05 - mse: 3.5574e-05 - val_loss: 4.7323e-04 - val_mse: 9.4647e-04 - 2s/epoch - 4ms/step
Epoch 9/50
578/578 - 2s - loss: 1.5573e-05 - mse: 3.1145e-05 - val_loss: 5.5514e-04 - val_mse: 0.0011 - 2s/epoch - 3ms/step
Epoch 10/50
578/578 - 2s - loss: 1.5467e-05 - mse: 3.0933e-05 - val_loss: 9.2030e-04 - val_mse: 0.0018 - 2s/epoch - 3ms/step
Epoch 11/50
578/578 - 2s - loss: 1.5910e-05 - mse: 3.1819e-05 - val_loss: 9.5229e-04 - val_mse: 0.0019 - 2s/epoch - 3ms/step

결정계수(설명력) : 0.5338959831493618
pred : [0.73 0.73 0.72 0.73 0.72 0.71 0.71 0.71 0.71 0.71]
pred(스케일 원복) : [66518.586 66166.58  66002.34  66203.74  65722.97  65136.285 64868.406
 64739.223 64469.695 64387.508]
real(스케일 원복) : [69100. 69300. 69200. 68500. 68000. 67800. 67900. 67000. 68700. 67500.]

1998년 이후 주가 흐름 시각화

loss, val_loss 시각화

실제값과 예측값 시각화

TensorFlow 기초 43 - 케라스에서 제공하는 로이터 뉴스 데이터를 LSTM을 이용하여 텍스트 분류를 진행

코딩탕탕 — Mon, 19 Dec 2022 13:20:28 +0900

# 케라스에서 제공하는 로이터 뉴스 데이터를 LSTM을 이용하여 텍스트 분류를 진행해보겠습니다.
# 로이터 뉴스 기사 데이터는 총 11,258개의 뉴스 기사가 46개의 뉴스 카테고리로 분류되는 뉴스 기사 데이터입니다.

from keras.datasets import reuters
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense, Embedding, Flatten
from keras.utils import pad_sequences
import matplotlib.pyplot as plt

max_features = 10000

(x_train, y_train), (x_test, y_test) = reuters.load_data(num_words=max_features)
print(x_train.shape, y_train.shape, x_test.shape, y_test.shape) # (8982,) (8982,) (2246,) (2246,)
print(len(set(y_train)))
print(x_train[100])
print(y_train[100])

# train / validation split
x_val = x_train[7000:]
y_val = y_train[7000:]
x_train = x_train[:7000]
y_train = y_train[:7000]

print(x_train.shape, y_train.shape, x_val.shape, y_val.shape)

# 문장 길이 맞추기
text_max_words = 120
x_train = pad_sequences(x_train, maxlen=text_max_words)
x_val = pad_sequences(x_val, maxlen=text_max_words)
x_test = pad_sequences(x_test, maxlen=text_max_words)
print(x_train[0], len(x_train[0]))

# one-hot
y_train = np_utils.to_categorical(y_train)
y_val = np_utils.to_categorical(y_val)
y_test = np_utils.to_categorical(y_test)
print(y_train[0])

# 모델 구성 방법 1 : Dense로만 구성
model = Sequential()
model.add(Embedding(max_features, 128, input_length=text_max_words))
model.add(Flatten())
model.add(Dense(256, activation='relu'))
model.add(Dense(46, activation='softmax'))
print(model.summary()) # Trainable params: 5,224,238

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

hist = model.fit(x_train, y_train, epochs=10, batch_size=64, validation_data=(x_val, y_val), verbose=2)

# 시각화
def plt_func():
    fig, loss_ax = plt.subplots()
    acc_ax = loss_ax.twinx()
    loss_ax.plot(hist.history['loss'], c='y', label='train loss')
    loss_ax.plot(hist.history['val_loss'], c='r', label='val loss')
    loss_ax.set_ylim([0,3])
    
    acc_ax.plot(hist.history['accuracy'], c='y', label='train accuracy')
    acc_ax.plot(hist.history['val_accuracy'], c='r', label='val accuracy')
    acc_ax.set_ylim([0,1])

    loss_ax.legend()
    acc_ax.legend()

    plt.show()
    
    loss_metrics = model.evaluate(x_test, y_test, batch_size=64)
    print('loss_metrics :', loss_metrics)

plt_func()


<console>
(8982,) (8982,) (2246,) (2246,)
46
[1, 367, 1394, 169, 65, 87, 209, 30, 306, 228, 10, 803, 305, 96, 5, 196, 15, 10, 523, 2, 3006, 293, 484, 2, 1440, 5825, 8, 145, 7, 10, 1670, 6, 10, 294, 517, 237, 2, 367, 8042, 7, 2477, 1177, 483, 1440, 5825, 8, 367, 1394, 4, 169, 387, 66, 209, 30, 2344, 652, 1496, 9, 209, 30, 2564, 228, 10, 803, 305, 96, 5, 196, 15, 51, 36, 1457, 24, 1345, 5, 4, 196, 150, 10, 523, 320, 64, 992, 6373, 13, 367, 190, 297, 64, 85, 1692, 6, 8656, 122, 9, 36, 1457, 24, 269, 4753, 27, 367, 212, 114, 45, 30, 3292, 7, 126, 2203, 13, 367, 6, 1818, 4, 169, 65, 96, 28, 432, 23, 189, 1254, 4, 9725, 320, 5, 196, 15, 10, 523, 25, 730, 190, 57, 64, 6, 9953, 2016, 6373, 7, 2, 122, 1440, 5825, 8, 269, 4753, 1217, 7, 608, 2203, 30, 3292, 1440, 5825, 8, 43, 339, 43, 231, 9, 667, 1820, 126, 212, 4197, 21, 1709, 249, 311, 13, 260, 489, 9, 65, 4753, 64, 1209, 4397, 249, 954, 36, 152, 1440, 5825, 506, 24, 135, 367, 311, 34, 420, 4, 8407, 200, 1519, 13, 137, 730, 190, 7, 104, 570, 52, 64, 2492, 7725, 4, 642, 5, 405, 7725, 2492, 24, 76, 847, 1435, 4446, 6, 10, 548, 320, 34, 325, 136, 694, 1440, 5825, 8, 10, 5184, 847, 7, 4, 169, 76, 2378, 10, 4933, 3447, 5, 141, 1082, 36, 152, 36, 8, 126, 358, 367, 65, 814, 190, 64, 2575, 10, 969, 3161, 92, 48, 6, 2245, 31, 367, 51, 570, 4753, 292, 27, 405, 212, 62, 3740, 922, 9, 2464, 27, 367, 77, 62, 4397, 7, 316, 5, 874, 36, 152, 4, 936, 1243, 5, 358, 367, 398, 57, 45, 3680, 7367, 6, 2394, 1343, 13, 373, 4504, 36, 8, 1440, 5825, 8, 42, 196, 150, 10, 523, 96, 34, 9725, 43, 16, 1261, 205, 7, 4, 65, 182, 1351, 367, 6, 351, 184, 45, 6081, 2286, 197, 1245, 13, 3187, 2, 274, 419, 714, 1351, 367, 269, 10, 96, 41, 129, 1104, 1673, 1419, 578, 36, 152, 2, 1440, 7615, 367, 1683, 484, 293, 75, 6557, 4, 8042, 152, 24, 5222, 34, 325, 834, 6, 1356, 2, 2406, 7, 4, 65, 76, 1082, 164, 1574, 212, 9, 861, 34, 8340, 13, 286, 1930, 1440, 7615, 8, 787, 36, 1830, 1082, 41, 3751, 616, 6, 382, 2, 2, 1574, 6928, 17, 12]
20
(7000,) (7000,) (1982,) (1982,)
[   0    0    0    0    0    0    0    0    0    0    0    0    0    0
    0    0    0    0    0    0    0    0    0    0    0    0    0    0
    0    0    0    0    0    1    2    2    8   43   10  447    5   25
  207  270    5 3095  111   16  369  186   90   67    7   89    5   19
  102    6   19  124   15   90   67   84   22  482   26    7   48    4
   49    8  864   39  209  154    6  151    6   83   11   15   22  155
   11   15    7   48    9 4579 1005  504    6  258    6  272   11   15
   22  134   44   11   15   16    8  197 1245   90   67   52   29  209
   30   32  132    6  109   15   17   12] 120
[0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 embedding (Embedding)       (None, 120, 128)          1280000   
                                                                 
 flatten (Flatten)           (None, 15360)             0         
                                                                 
 dense (Dense)               (None, 256)               3932416   
                                                                 
 dense_1 (Dense)             (None, 46)                11822     
                                                                 
=================================================================
Total params: 5,224,238
Trainable params: 5,224,238
Non-trainable params: 0
_________________________________________________________________
None
Epoch 1/10
110/110 - 3s - loss: 1.9523 - accuracy: 0.5139 - val_loss: 1.4971 - val_accuracy: 0.6372 - 3s/epoch - 31ms/step
Epoch 2/10
110/110 - 3s - loss: 0.9231 - accuracy: 0.7911 - val_loss: 1.2598 - val_accuracy: 0.7094 - 3s/epoch - 27ms/step
Epoch 3/10
110/110 - 3s - loss: 0.3035 - accuracy: 0.9439 - val_loss: 1.3157 - val_accuracy: 0.7013 - 3s/epoch - 27ms/step
Epoch 4/10
110/110 - 3s - loss: 0.1877 - accuracy: 0.9600 - val_loss: 1.3787 - val_accuracy: 0.6958 - 3s/epoch - 27ms/step
Epoch 5/10
110/110 - 3s - loss: 0.1563 - accuracy: 0.9616 - val_loss: 1.3500 - val_accuracy: 0.7053 - 3s/epoch - 28ms/step
Epoch 6/10
110/110 - 3s - loss: 0.1321 - accuracy: 0.9630 - val_loss: 1.4119 - val_accuracy: 0.6902 - 3s/epoch - 27ms/step
Epoch 7/10
110/110 - 3s - loss: 0.1281 - accuracy: 0.9620 - val_loss: 1.4077 - val_accuracy: 0.6932 - 3s/epoch - 27ms/step
Epoch 8/10
110/110 - 3s - loss: 0.1126 - accuracy: 0.9649 - val_loss: 1.3556 - val_accuracy: 0.7018 - 3s/epoch - 27ms/step
Epoch 9/10
110/110 - 3s - loss: 0.1085 - accuracy: 0.9633 - val_loss: 1.4004 - val_accuracy: 0.6932 - 3s/epoch - 27ms/step
Epoch 10/10
110/110 - 3s - loss: 0.0953 - accuracy: 0.9644 - val_loss: 1.4192 - val_accuracy: 0.6993 - 3s/epoch - 27ms/step

 1/36 [..............................] - ETA: 0s - loss: 1.7320 - accuracy: 0.6406
17/36 [=============>................] - ETA: 0s - loss: 1.4034 - accuracy: 0.6939
32/36 [=========================>....] - ETA: 0s - loss: 1.4222 - accuracy: 0.6870
36/36 [==============================] - 0s 3ms/step - loss: 1.4476 - accuracy: 0.6861
loss_metrics : [1.4476467370986938, 0.6861086487770081]

TensorFlow 기초 42 - IMDB 리뷰 감성 분류하기(IMDB Movie Review Sentiment Analysis)

코딩탕탕 — Mon, 19 Dec 2022 12:57:58 +0900

# IMDB 리뷰 감성 분류하기(IMDB Movie Review Sentiment Analysis)
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras.datasets import imdb

(X_train, y_train), (X_test, y_test) = imdb.load_data()

print('훈련용 리뷰 개수 : {}'.format(len(X_train)))
print('테스트용 리뷰 개수 : {}'.format(len(X_test)))
num_classes = len(set(y_train))
print('카테고리 : {}'.format(num_classes))
print(set(y_train)) # {0, 1}
print(X_train[:1])
print(y_train[:1])

reviews_length = [len(review) for review in X_train]

print('리뷰의 최대 길이 : {}'.format(np.max(reviews_length)))
print('리뷰의 평균 길이 : {}'.format(np.mean(reviews_length)))

len_result = [len(i) for i in X_train]

plt.subplot(1,2,1)
plt.boxplot(reviews_length)
plt.subplot(1,2,2)
plt.hist(len_result, bins=50)
plt.show()

unique_elements, counts_elements = np.unique(y_train, return_counts=True)
print("각 레이블에 대한 빈도수:")
print(np.asarray((unique_elements, counts_elements)))

word_to_index = imdb.get_word_index()
index_to_word = {}
for key, value in word_to_index.items():
    index_to_word[value+3] = key # index_to_word에 인덱스를 집어넣으면 전처리 전에 어떤 단어였는지 확인할 수 있다.

print('빈도수 상위 1등 단어 : {}'.format(index_to_word[4])) # IMDB 리뷰 데이터셋에서는 0, 1, 2, 3은 특별 토큰으로 취급하고 있다
print('빈도수 상위 3938등 단어 : {}'.format(index_to_word[3941]))

# 첫번째 훈련용 리뷰의 X_train[0]의 각 단어가 정수로 바뀌기 전에 어떤 단어들이었는지 확인해보겠습니다.
for index, token in enumerate(("<pad>", "<sos>", "<unk>")):
  index_to_word[index] = token

print(' '.join([index_to_word[index] for index in X_train[0]]))


# LSTM, GRU로 IMDB 리뷰 감성 분류하기
import re
from keras.datasets import imdb
from keras.utils import pad_sequences
from keras.models import Sequential
from keras.layers import Dense, GRU, Embedding
from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.models import load_model

vocab_size = 10000
max_len = 500

(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=vocab_size)

X_train = pad_sequences(X_train, maxlen=max_len)
X_test = pad_sequences(X_test, maxlen=max_len)

embedding_dim = 100
hidden_units = 128

# # model 생성 1 : RNN 
# model = Sequential()
# model.add(Embedding(vocab_size, embedding_dim, input_length=max_len))
# model.add(GRU(hidden_units, activation='tanh'))
# model.add(Dense(1, activation='sigmoid'))

# model 생성 2 : CNN - Conv1D
from keras.layers import Conv1D, GlobalMaxPooling1D, MaxPooling1D, Dropout
model = Sequential()
model.add(Embedding(vocab_size, embedding_dim, input_length=max_len))
model.add(Conv1D(filters=256, kernel_size=3, padding='valid', strides=1, activation='relu'))
model.add(GlobalMaxPooling1D())
model.add(Dropout(0.3))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.3))
model.add(Dense(1, activation='sigmoid'))
print(model.summary())

es = EarlyStopping(monitor='val_loss', mode='min', verbose=0, patience=3, baseline=0.01)
mc = ModelCheckpoint('rnn16_model.h5', monitor='val_acc', mode='max', verbose=0, save_best_only=True)

model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['acc'])
history = model.fit(X_train, y_train, epochs=50, callbacks=[es, mc], batch_size=64, validation_split=0.2)
print('evaluate : ', model.evaluate(X_test, y_test))


es = EarlyStopping(monitor='val_loss', mode='min', verbose=0, patience=3, baseline=0.01)
mc = ModelCheckpoint('rnn16_model.h5', monitor='val_acc', mode='max', verbose=0, save_best_only=True)

model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['acc'])
history = model.fit(X_train, y_train, epochs=50, callbacks=[es, mc], batch_size=64, validation_split=0.2)
print('evaluate : ', model.evaluate(X_test, y_test))

pred = model.predict(X_test[:20])
print('예측값 :', np.where(pred > 0.5, 1, 0).flatten())
print('실제값 :', y_test[:20])

def sentiment_predict(new_sentence):
  # 알파벳과 숫자를 제외하고 모두 제거 및 알파벳 소문자화
  new_sentence = re.sub('[^0-9a-zA-Z ]', '', new_sentence).lower()
  encoded = []

  # 띄어쓰기 단위 토큰화 후 정수 인코딩
  for word in new_sentence.split():
    try :
      # 단어 집합의 크기를 10,000으로 제한.
      if word_to_index[word] <= 10000:
        encoded.append(word_to_index[word]+3)
      else:
      # 10,000 이상의 숫자는 <unk> 토큰으로 변환.
        encoded.append(2)
    # 단어 집합에 없는 단어는 <unk> 토큰으로 변환.
    except KeyError:
      encoded.append(2)

  pad_sequence = pad_sequences([encoded], maxlen=max_len)
  score = float(model.predict(pad_sequence)) # 예측

  if(score > 0.5):
    print("{:.2f}% 확률로 긍정 리뷰입니다.".format(score * 100))
  else:
    print("{:.2f}% 확률로 부정 리뷰입니다.".format((1 - score) * 100))
    
test_input = "This movie was just way too overrated. The fighting was not professional and in slow motion. I was expecting more from a 200 million budget movie. The little sister of T.Challa was just trying too hard to be funny. The story was really dumb as well. Don't watch this movie if you are going because others say its great unless you are a Black Panther fan or Marvels fan."
sentiment_predict(test_input)

test_input = " I was lucky enough to be included in the group to see the advanced screening in Melbourne on the 15th of April, 2012. And, firstly, I need to say a big thank-you to Disney and Marvel Studios. \
Now, the film... how can I even begin to explain how I feel about this film? It is, as the title of this review says a 'comic book triumph'. I went into the film with very, very high expectations and I was not disappointed. \
Seeing Joss Whedon's direction and envisioning of the film come to life on the big screen is perfect. The script is amazingly detailed and laced with sharp wit a humor. The special effects are literally mind-blowing and the action scenes are both hard-hitting and beautifully choreographed."
sentiment_predict(test_input)


<console>
훈련용 리뷰 개수 : 25000
테스트용 리뷰 개수 : 25000
카테고리 : 2
{0, 1}
[list([1, 14, 22, 16, 43, 530, 973, 1622, 1385, 65, 458, 4468, 66, 3941, 4, 173, 36, 256, 5, 25, 100, 43, 838, 112, 50, 670, 22665, 9, 35, 480, 284, 5, 150, 4, 172, 112, 167, 21631, 336, 385, 39, 4, 172, 4536, 1111, 17, 546, 38, 13, 447, 4, 192, 50, 16, 6, 147, 2025, 19, 14, 22, 4, 1920, 4613, 469, 4, 22, 71, 87, 12, 16, 43, 530, 38, 76, 15, 13, 1247, 4, 22, 17, 515, 17, 12, 16, 626, 18, 19193, 5, 62, 386, 12, 8, 316, 8, 106, 5, 4, 2223, 5244, 16, 480, 66, 3785, 33, 4, 130, 12, 16, 38, 619, 5, 25, 124, 51, 36, 135, 48, 25, 1415, 33, 6, 22, 12, 215, 28, 77, 52, 5, 14, 407, 16, 82, 10311, 8, 4, 107, 117, 5952, 15, 256, 4, 31050, 7, 3766, 5, 723, 36, 71, 43, 530, 476, 26, 400, 317, 46, 7, 4, 12118, 1029, 13, 104, 88, 4, 381, 15, 297, 98, 32, 2071, 56, 26, 141, 6, 194, 7486, 18, 4, 226, 22, 21, 134, 476, 26, 480, 5, 144, 30, 5535, 18, 51, 36, 28, 224, 92, 25, 104, 4, 226, 65, 16, 38, 1334, 88, 12, 16, 283, 5, 16, 4472, 113, 103, 32, 15, 16, 5345, 19, 178, 32])]
[1]
리뷰의 최대 길이 : 2494
리뷰의 평균 길이 : 238.71364
각 레이블에 대한 빈도수:
[[    0     1]
 [12500 12500]]
빈도수 상위 1등 단어 : the
빈도수 상위 3938등 단어 : suited

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 embedding (Embedding)       (None, 500, 100)          1000000   
                                                                 
 conv1d (Conv1D)             (None, 498, 256)          77056     
                                                                 
 global_max_pooling1d (Globa  (None, 256)              0         
 lMaxPooling1D)                                                  
                                                                 
 dropout (Dropout)           (None, 256)               0         
                                                                 
 dense (Dense)               (None, 64)                16448     
                                                                 
 dropout_1 (Dropout)         (None, 64)                0         
                                                                 
 dense_1 (Dense)             (None, 1)                 65        
                                                                 
=================================================================
Total params: 1,093,569
Trainable params: 1,093,569
Non-trainable params: 0
_________________________________________________________________
None
evaluate :  [0.32060739398002625, 0.8897200226783752]

1/1 [==============================] - ETA: 0s
1/1 [==============================] - 0s 61ms/step
예측값 : [0 1 1 0 1 1 1 0 1 1 1 0 0 1 1 0 1 0 0 0]
실제값 : [0 1 1 0 1 1 1 0 0 1 1 0 0 0 1 0 1 0 0 0]

1/1 [==============================] - ETA: 0s
1/1 [==============================] - 0s 9ms/step
99.97% 확률로 부정 리뷰입니다.

1/1 [==============================] - ETA: 0s
1/1 [==============================] - 0s 9ms/step
99.64% 확률로 긍정 리뷰입니다.

TensorFlow 기초 41 - RNN으로 스펨 메일 분류 (이항 분류)

코딩탕탕 — Mon, 19 Dec 2022 11:07:01 +0900

# RNN으로 스펨 메일 분류 (이항 분류)
import pandas as pd
from nltk.util import pad_sequence

data = pd.read_csv('spam.csv', encoding='latin1')
print(data.head())
print('샘플 수 :', len(data))

del data['Unnamed: 2']
del data['Unnamed: 3']
del data['Unnamed: 4']

print(data.head(2))
print(data['v1'].unique())
data['v1'] = data['v1'].replace(['ham', 'spam'], [0, 1])
print(data.head(3))

print(data.info())
print(data.isnull().values.any())
print(data['v2'].nunique()) # 유일 값 5169 : 중복 자료가 있음을 의미
data.drop_duplicates(subset= ['v2'], inplace=True)
print('샘플 수 :', len(data))
print(data['v1'].value_counts()) # 0 = 4516, 1 = 653
print(data.groupby('v1').size().reset_index(name='count'))

# feature / label 분리
x_data = data['v2']
y_data = data['v1']
print(x_data[:1])
print(y_data[:1])

# x_data에 대해 Token 처리
from keras.preprocessing.text import Tokenizer
tok = Tokenizer()
tok.fit_on_texts(x_data)
sequences = tok.texts_to_sequences(x_data) # 정수 인덱싱
print(sequences[:5])

word_to_index = tok.word_index # 각 단어에 부여된 인덱스를 확인
print(word_to_index)

vocab_size = len(word_to_index) + 1
print('vocab_size :', vocab_size) # 8921

# pad_sequences
x_data = sequences
print('메일의 최대 길이 : %d'%max(len(i) for i in x_data)) # 189
print('메일의 평균 길이 : %f'%(sum(map(len, x_data)) / len(x_data))) # 15.610370


from keras.utils import pad_sequences
max_len = max(len(i) for i in x_data)
# print(max_len)
data = pad_sequences(x_data, maxlen=max_len)
print(data[:1])
print('훈련 데이터의 크기(shape) :', data.shape) # (5169, 189)

# train / test split
import numpy as np
n_of_train = int(len(sequences) * 0.8)
n_of_test = int(len(sequences) - n_of_train)
print('train 수 :', n_of_train)
print('test 수 :', n_of_test)

x_train = data[:n_of_train]
y_train = np.array(y_data[:n_of_train])
x_test = data[n_of_train:]
y_test = np.array(y_data[n_of_train:])
print(x_train.shape, y_train.shape, x_test.shape, y_test.shape) # (4135, 189) (4135,) (1034, 189) (1034,)

# 스펨 메일 분류기 작성
from keras.layers import LSTM, Embedding, Dense, Dropout
from keras.models import Sequential 

model = Sequential()
model.add(Embedding(vocab_size, 32))
model.add(LSTM(32, activation='tanh'))
model.add(Dense(32, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(1, activation='sigmoid'))

print(model.summary())
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])

history = model.fit(x_train, y_train, epochs=5, batch_size=64, validation_split=0.2, verbose=2)
print('evaluate :', model.evaluate(x_test, y_test))

# 시각화
import matplotlib.pyplot as plt

epochs = range(1, len(history.history['acc']) + 1)
plt.plot(epochs, history.history['loss'], label='loss')
plt.plot(epochs, history.history['val_loss'], label='val_loss')
plt.legend()
plt.show()

plt.plot(epochs, history.history['acc'], label='acc')
plt.plot(epochs, history.history['val_acc'], label='val_acc')
plt.legend()
plt.show()

# 예측
pred = model.predict(x_test[:20])
print('예측값 :', np.where(pred > 0.5, 1, 0).flatten())
print('실제값 :', y_test[:20])



<console>
{'i': 1, 'to': 2, 'you': 3, 'a': 4, 'the': 5, 'u': 6, 'and': 7, 'in': 8, 'is': 9, 'me': 10, 'my': 11, 'for': 12, 'your': 13, 'it': 14, 'of': 15, 'have': 16, 'call': 17, 'that': 18, 'on': 19, '2': 20, 'are': 21, 'now': 22, 'so': 23, 'but': 24, 'not': 25, 'can': 26, 'at': 27, 'or': 28, "i'm": 29, 'do': 30, 'be': 31, 'get': 32, 'will': 33, 'just': 34, 'if': 35, 'with': 36, 'we': 37, 'no': 38, 'this': 39, 'ur': 40, 'up': 41, 'gt': 42, 'lt': 43, '4': 44, 'how': 45, 'when': 46, 'go': 47, 'from': 48, 'ok': 49, 'out': 50, 'all': 51, 'what': 52, 'free': 53, 'know': 54, 'like': 55, 'then': 56, 'got': 57, 'good': 58, 'come': 59, 'am': 60, 'was': 61, 'time': 62, 'its': 63, 'only': 64, 'day': 65, 'want': 66, 'love': 67, 'there': 68, 'he': 69, 'text': 70, 'send': 71, 'going': 72, 'one': 73, 'need': 74, 'by': 75, 'lor': 76, 'home': 77, 'as': 78, 'about': 79, 'still': 80, 'see': 81, 'txt': 82, 'back': 83, 'r': 84, 'stop': 85, 'da': 86, 'k': 87, 'today': 88, 'our': 89, "i'll": 90, 'dont': 91, 'reply': 92, 'take': 93, 'n': 94, 'hi': 95, 'think': 96, "don't": 97, 'tell': 98, 'any': 99, 'sorry': 100, 'new': 101, 'please': 102, 'mobile': 103, 'she': 104, 'here': 105, 'ì': 106, 'her': 107, 'been': 108, 'they': 109, 'some': 110, 'did': 111, 'well': 112, 'hey': 113, 'much': 114, 'phone': 115, 'oh': 116, 'hope': 117, 'night': 118, 'too': 119, 'him': 120, 'great': 121, 'week': 122, 'where': 123, 'an': 124, 'has': 125, 'more': 126, 'dear': 127, 'later': 128, 'pls': 129, '1': 130, 'make': 131, 'msg': 132, 'give': 133, 'claim': 134, 'way': 135, 'happy': 136, 'wat': 137, 'c': 138, 'had': 139, 'should': 140, "it's": 141, 'e': 142, 'yes': 143, 'who': 144, 'already': 145, 'number': 146, 'ask': 147, 'work': 148, 'yeah': 149, 'www': 150, 'really': 151, 'say': 152, 'after': 153, 'prize': 154, 'doing': 155, 'tomorrow': 156, 'im': 157, 'right': 158, 'meet': 159, 'babe': 160, 'why': 161, 'cash': 162, 'thanks': 163, 'message': 164, 'said': 165, 'cos': 166, 'find': 167, '3': 168, 'lol': 169, 'them': 170, 'would': 171, 'last': 172, 'b': 173, 'very': 174, 'miss': 175, 'life': 176, 'also': 177, 'let': 178, 'sure': 179, 'anything': 180, 'morning': 181, 'something': 182, 'd': 183, 'keep': 184, 't': 185, "i've": 186, 'every': 187, '150p': 188, 'again': 189, 'before': 190, 'urgent': 191, 'wait': 192, 'care': 193, 'next': 194, 'over': 195, 'us': 196, 'buy': 197, 'around': 198, 'first': 199, 'contact': 200, 'min': 201, 'sent': 202, 'thing': 203, 'were': 204, 'pick': 205, 'money': 206, 'off': 207, 's': 208, 'tonight': 209, 'won': 210, 'com': 211, 'could': 212, 'gonna': 213, 'soon': 214, 'his': 215, 'feel': 216, 'amp': 217, 'nokia': 218, "can't": 219, 'win': 220, 'even': 221, 'ya': 222, 'which': 223, 'late': 224, 'sleep': 225, 'dun': 226, 'down': 227, "that's": 228, 'uk': 229, 'someone': 230, 'leave': 231, 'wan': 232, 'many': 233, 'v': 234, 'always': 235, 'place': 236, 'service': 237, 'ìï': 238, 'cant': 239, 'other': 240, "you're": 241, 'things': 242, 'haha': 243, '5': 244, 'chat': 245, 'told': 246, 'per': 247, 'getting': 248, 'x': 249, 'nice': 250, 'waiting': 251, 'same': 252, 'yet': 253, 'thk': 254, 'coming': 255, 'help': 256, 'went': 257, '50': 258, 'customer': 259, 'done': 260, 'hello': 261, 'thought': 262, 'friend': 263, 'fine': 264, 'try': 265, 'sms': 266, 'may': 267, 'stuff': 268, 'class': 269, 'year': 270, 'mins': 271, 'wish': 272, 'tone': 273, 'co': 274, 'talk': 275, 'use': 276, "didn't": 277, 'gud': 278, '18': 279, 'y': 280, 'better': 281, 'friends': 282, 'name': 283, 'finish': 284, 'trying': 285, 'bit': 286, 'best': 287, 'cool': 288, 'guaranteed': 289, 'long': 290, 'ill': 291, 'yup': 292, '6': 293, 'people': 294, '16': 295, 'being': 296, 'never': 297, 'ready': 298, 'few': 299, 'lunch': 300, 'car': 301, 'end': 302, 'yo': 303, 'draw': 304, 'enjoy': 305, 'lar': 306, 'eat': 307, 'meeting': 308, 'wanna': 309, 'thats': 310, 'job': 311, 'nothing': 312, 'holiday': 313, 'house': 314, 'than': 315, 'line': 316, 'having': 317, 'live': 318, 'check': 319, 'guess': 320, 'liao': 321, 'cs': 322, 'days': 323, 'dinner': 324, 'man': 325, 'half': 326, 'wk': 327, 'because': 328, 'shit': 329, 'problem': 330, '7': 331, 'month': 332, 'special': 333, 'ah': 334, 'real': 335, 'another': 336, 'account': 337, 'jus': 338, 'big': 339, 'might': 340, 'quite': 341, 'mind': 342, 'smile': 343, 'lot': 344, 'shows': 345, 'dat': 346, 'early': 347, 'word': 348, 'å£1': 349, 'room': 350, 'once': 351, 'aight': 352, 'sir': 353, 'guys': 354, 'person': 355, 'video': 356, 'heart': 357, 'probably': 358, 'receive': 359, 'xxx': 360, 'latest': 361, 'wont': 362, 'remember': 363, 'maybe': 364, 'bed': 365, 'll': 366, 'start': 367, 'into': 368, 'watch': 369, 'chance': 370, 'den': 371, 'watching': 372, 'pay': 373, 'left': 374, 'does': 375, 'weekend': 376, 'play': 377, 'awarded': 378, 'hear': 379, 'offer': 380, 'thanx': 381, 'baby': 382, 'box': 383, 'dunno': 384, 'ever': 385, 'sat': 386, 'actually': 387, 'bad': 388, 'princess': 389, 'kiss': 390, 'fun': 391, 'speak': 392, 'cost': 393, 'look': 394, 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'yifeng': 7055, "they'll": 7056, 'slurp': 7057, '3miles': 7058, 'ing': 7059, 'brainless': 7060, 'doll': 7061, 'vehicle': 7062, 'sariyag': 7063, 'madoke': 7064, 'barolla': 7065, '07090201529': 7066, 'postponed': 7067, 'stocked': 7068, 'tiime': 7069, 'tears': 7070, 'afternon': 7071, 'interviews': 7072, 'resizing': 7073, '09066364349': 7074, 'box434sk38wp150ppm18': 7075, 'opposed': 7076, 'shortcode': 7077, '83332': 7078, '08081263000': 7079, 'refunded': 7080, 'somerset': 7081, 'overtime': 7082, 'nigpun': 7083, 'dismissial': 7084, 'screwd': 7085, '08712402972': 7086, 'bull': 7087, 'floating': 7088, '09058095201': 7089, 'heehee': 7090, 'arithmetic': 7091, 'percentages': 7092, "'an": 7093, "quote''": 7094, 'chillaxin': 7095, 'das': 7096, 'iknow': 7097, 'wellda': 7098, 'peril': 7099, 'studentfinancial': 7100, 'monster': 7101, 'ias': 7102, 'obey': 7103, 'uhhhhrmm': 7104, '600': 7105, '400': 7106, 'deltomorrow': 7107, '09066368470': 7108, '24m': 7109, 'smartcall': 7110, '68866': 7111, 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7580, 'dontcha': 7581, 'babygoodbye': 7582, 'golddigger': 7583, 'webeburnin': 7584, 'perform': 7585, 'cards': 7586, 'rebooting': 7587, 'nigh': 7588, 'nooooooo': 7589, 'cable': 7590, 'outage': 7591, 'sos': 7592, 'playin': 7593, 'guoyang': 7594, 'rahul': 7595, 'dengra': 7596, 'antelope': 7597, 'toplay': 7598, 'fieldof': 7599, 'selfindependence': 7600, 'contention': 7601, 'gnarls': 7602, 'barkleys': 7603, 'borderline': 7604, '545': 7605, 'nightnight': 7606, 'possibility': 7607, 'grooved': 7608, 'mising': 7609, 'secured': 7610, 'unsecured': 7611, '195': 7612, '6669': 7613, 'lanre': 7614, "fakeye's": 7615, 'eckankar': 7616, '3000': 7617, 'degrees': 7618, 'dodgey': 7619, 'seing': 7620, 'asssssholeeee': 7621, 'ceri': 7622, 'rebel': 7623, 'dreamz': 7624, 'buddy': 7625, 'blokes': 7626, '84484': 7627, 'nationwide': 7628, 'newport': 7629, 'juliana': 7630, 'nachos': 7631, 'dizzamn': 7632, 'suitemates': 7633, 'nimbomsons': 7634, 'continent': 7635, 'housewives': 7636, '0871750': 7637, '77': 7638, 'landlines': 7639, '087104711148': 7640, 'emerging': 7641, 'fiend': 7642, 'impede': 7643, 'hesitant': 7644, '60': 7645, '400thousad': 7646, 'nose': 7647, 'essay': 7648, 'tram': 7649, 'vic': 7650, 'coherently': 7651, 'triple': 7652, 'echo': 7653, 'gran': 7654, 'onlyfound': 7655, 'afew': 7656, 'cusoon': 7657, 'honi': 7658, 'bx526': 7659, 'southern': 7660, 'rayan': 7661, 'macleran': 7662, 'balls': 7663, 'olave': 7664, 'mandara': 7665, 'trishul': 7666, 'woo': 7667, 'hoo': 7668, 'panties': 7669, 'thout': 7670, '09066364311': 7671, 'flatter': 7672, 'pints': 7673, 'carlin': 7674, 'ciao': 7675, 'starve': 7676, 'impression': 7677, 'motivate': 7678, 'darkness': 7679, 'wknd': 7680, 'yalrigu': 7681, 'heltini': 7682, 'iyo': 7683, 'shared': 7684, 'uttered': 7685, 'trusting': 7686, 'noice': 7687, 'esaplanade': 7688, 'accessible': 7689, '08709501522': 7690, '139': 7691, 'la3': 7692, '2wu': 7693, 'occurs': 7694, 'enna': 7695, 'kalaachutaarama': 7696, 'coco': 7697, 'sporadically': 7698, '09064017305': 7699, 'pobox75ldns7': 7700, 'tbs': 7701, 'persolvo': 7702, 'forå£38': 7703, 'kath': 7704, 'manchester': 7705, 'youåõre': 7706, 'burden': 7707, 'noworriesloans': 7708, '08717111821': 7709, 'harder': 7710, 'nbme': 7711, 'sickness': 7712, 'villa': 7713, 'gam': 7714, 'smash': 7715, 'religiously': 7716, 'heroes': 7717, 'tips': 7718, '07973788240': 7719, '08715203649': 7720, 'muhommad': 7721, 'penny': 7722, 'fiting': 7723, 'load': 7724, 'mj': 7725, 'unconvinced': 7726, 'elaborate': 7727, 'willpower': 7728, 'absence': 7729, 'answerin': 7730, 'åè10': 7731, 'evey': 7732, 'prin': 7733, 'gsoh': 7734, 'spam': 7735, 'gigolo': 7736, 'mens': 7737, 'oncall': 7738, 'mjzgroup': 7739, '08714342399': 7740, '50rcvd': 7741, 'ashwini': 7742, '08707500020': 7743, 'ukp': 7744, '09061790125': 7745, 'thet': 7746, 'skinny': 7747, 'casting': 7748, 'elections': 7749, 'shouldn\x89û÷t': 7750, '116': 7751, 'hlday': 7752, 'camp': 7753, 'amrca': 7754, 'serena': 7755, 'prescribed': 7756, 'meatballs': 7757, 'approve': 7758, 'panalam': 7759, 'spjanuary': 7760, 'fortune': 7761, 'allday': 7762, 'perf': 7763, 'outsider': 7764, 'receipts\x89ûówell': 7765, 'what\x89û÷s': 7766, '98321561': 7767, 'familiar': 7768, 'depression': 7769, 'infact': 7770, 'simpsons': 7771, 'band': 7772, 'canåõt': 7773, 'isnåõt': 7774, 'shite': 7775, 'kip': 7776, 'hont': 7777, 'amanda': 7778, 'regard': 7779, 'renewing': 7780, 'upgrading': 7781, 'subject': 7782, 'nannys': 7783, 'puts': 7784, 'perspective': 7785, 'conveying': 7786, 'debating': 7787, 'wtlp': 7788, 'jb': 7789, "joke's": 7790, 'florida': 7791, 'hidden': 7792, 'teams': 7793, 'swhrt': 7794, '0906346330': 7795, '47': 7796, 'po19': 7797, '2ez': 7798, 'general': 7799, 'jetton': 7800, 'cmon': 7801, 'replies': 7802, 'dealer': 7803, 'lunsford': 7804, 'enjoying': 7805, '0796xxxxxx': 7806, 'prizeawaiting': 7807, 'kfc': 7808, 'meals': 7809, 'gravy': 7810, '07008009200': 7811, 'attended': 7812, 'mw': 7813, 'tuth': 7814, "mine's": 7815, 'eviction': 7816, 'spiral': 7817, 'michael': 7818, 'riddance': 7819, 'suffers': 7820, 'raglan': 7821, 'edward': 7822, 'cricket': 7823, 'closeby': 7824, 'skye': 7825, 'bookedthe': 7826, 'hut': 7827, 'drastic': 7828, '3750': 7829, 'garments': 7830, 'sez': 7831, 'arab': 7832, 'evry1': 7833, 'eshxxxxxxxxxxx': 7834, 'lay': 7835, 'bimbo': 7836, "ugo's": 7837, '3lions': 7838, 'portege': 7839, 'm100': 7840, 'semiobscure': 7841, 'gprs': 7842, 'loosu': 7843, 'careless': 7844, 'freaking': 7845, 'myspace': 7846, 'logged': 7847, "partner's": 7848, 'method': 7849, 'jewelry': 7850, 'breaker': 7851, 'deluxe': 7852, 'features': 7853, 'graphics': 7854, 'bbdeluxe': 7855, 'fumbling': 7856, 'stone': 7857, 'weekdays': 7858, 'nails': 7859, "nobody's": 7860, 'asia': 7861, 'greatest': 7862, 'courage': 7863, 'bear': 7864, 'defeat': 7865, 'stil': 7866, 'tobed': 7867, '430': 7868, 'natalja': 7869, 'nat27081980': 7870, 'asthma': 7871, 'attack': 7872, 'ball': 7873, 'spin': 7874, 'haiyoh': 7875, 'million': 7876, "aunty's": 7877, 'prsn': 7878, 'saves': 7879, 'audiitions': 7880, 'relocate': 7881, 'pocked': 7882, 'motivating': 7883, 'brison': 7884, 'spelled': 7885, 'caps': 7886, 'bullshit': 7887, 'motherfucker': 7888, 'kit': 7889, 'strip': 7890, '1013': 7891, 'ig11': 7892, 'oja': 7893, '08712402578': 7894, 'thesmszone': 7895, 'anonymous': 7896, 'masked': 7897, 'abuse': 7898, 'woodland': 7899, 'avenue': 7900, 'parish': 7901, 'magazine': 7902, 'billy': 7903, 'awww': 7904, 'useless': 7905, 'loo': 7906, 'ed': 7907, 'shelf': 7908, 'swollen': 7909, 'glands': 7910, 'bcaz': 7911, 'stu': 7912, 'truble': 7913, 'evone': 7914, 'hates': 7915, 'view': 7916, 'gays': 7917, 'dual': 7918, 'hostile': 7919, 'haircut': 7920, 'breezy': 7921, '09061744553': 7922, 'polyh': 7923, '1apple': 7924, '1tulsi': 7925, 'leaf': 7926, '1lemon': 7927, '1cup': 7928, 'problms': 7929, 'litres': 7930, 'watr': 7931, 'diseases': 7932, 'snd': 7933, 'lavender': 7934, 'manky': 7935, 'scouse': 7936, 'travelling': 7937, 'inmind': 7938, 'recreation': 7939, 'mesages': 7940, 'judgemental': 7941, 'fridays': 7942, 'waheeda': 7943, 'bot': 7944, 'notes': 7945, 'eventually': 7946, 'tolerance': 7947, 'hits': 7948, '0789xxxxxxx': 7949, 'hellogorgeous': 7950, 'nitw': 7951, 'texd': 7952, 'hopeu': 7953, '4ward': 7954, 'jaz': 7955, '09058091870': 7956, 'exorcism': 7957, 'emily': 7958, 'emotion': 7959, 'prayrs': 7960, 'othrwise': 7961, 'ujhhhhhhh': 7962, 'sandiago': 7963, 'parantella': 7964, 'hugging': 7965, 'sweater': 7966, 'mango': 7967, 'involved': 7968, 'å£600': 7969, 'landmark': 7970, 'bob': 7971, 'barry': 7972, '83738': 7973, 'absolutly': 7974, 'consent': 7975, 'tonexs': 7976, 'renewed': 7977, 'clubzed': 7978, 'billing': 7979, 'can\x89û÷t': 7980, 'mathews': 7981, 'tait': 7982, 'edwards': 7983, 'anderson': 7984, 'haunt': 7985, 'promoting': 7986, 'hex': 7987, 'crowd': 7988, '8000930705': 7989, 'snowboarding': 7990, 'christmassy': 7991, 'recpt': 7992, 'baaaaaaaabe': 7993, 'ignoring': 7994, 'shola': 7995, 'sagamu': 7996, 'lautech': 7997, 'vital': 7998, 'completes': 7999, 'education': 8000, 'zealand': 8001, 'qet': 8002, 'browser': 8003, 'surf': 8004, 'subscribers': 8005, 'conversations': 8006, 'senses': 8007, 'overemphasise': 8008, 'headset': 8009, 'adp': 8010, 'internal': 8011, 'extract': 8012, 'immed': 8013, 'skint': 8014, 'fancied': 8015, 'bevies': 8016, 'waz': 8017, 'othrs': 8018, 'spoon': 8019, 'watchng': 8020, 'comfey': 8021, 'quitting': 8022, 'least5times': 8023, "wudn't": 8024, "ìï'll": 8025, "i'ma": 8026, 'frequently': 8027, 'cupboard': 8028, 'route': 8029, '2mro': 8030, 'floppy': 8031, 'snappy': 8032, 'risk': 8033, 'grasp': 8034, 'flavour': 8035, 'laready': 8036, 'denying': 8037, 'dom': 8038, 'ffffuuuuuuu': 8039, 'julianaland': 8040, 'oblivious': 8041, 'dehydrated': 8042, 'mapquest': 8043, 'dogwood': 8044, 'archive': 8045, '08719839835': 8046, 'mgs': 8047, '89123': 8048, 'lengths': 8049, 'behalf': 8050, 'stunning': 8051, 'visa': 8052, 'gucci': 8053, "shit's": 8054, 'sozi': 8055, 'culdnt': 8056, 'talkbut': 8057, 'wannatell': 8058, 'wenwecan': 8059, 'smsing': 8060, 'efficient': 8061, '15pm': 8062, 'erutupalam': 8063, 'thandiyachu': 8064, 'invention': 8065, 'lyrics': 8066, 'nevr': 8067, 'unrecognized': 8068, 'somone': 8069, 'valuing': 8070, 'definitly': 8071, 'undrstnd': 8072, 'ger': 8073, 'toking': 8074, 'syd': 8075, 'khelate': 8076, 'kintu': 8077, 'opponenter': 8078, 'dhorte': 8079, 'lage': 8080, 'fried': 8081, 'spares': 8082, 'looovvve': 8083, 'warwick': 8084, 'tmw': 8085, 'canceled': 8086, "havn't": 8087, 'tops': 8088, 'grandma': 8089, 'parade': 8090, 'proze': 8091, 'norcorp': 8092, 'posting': 8093, '7cfca1a': 8094, 'grumble': 8095, 'linear': 8096, 'algebra': 8097, 'decorating': 8098, 'wining': 8099, '946': 8100, 'roomate': 8101, 'graduated': 8102, 'adjustable': 8103, 'cooperative': 8104, 'allows': 8105, 'nottingham': 8106, '63miles': 8107, '40mph': 8108, 'mornin': 8109, 'thanku': 8110, 'guessed': 8111, "hubby's": 8112, '89938': 8113, 'strings': 8114, '50ea': 8115, 'otbox': 8116, '731': 8117, 'la1': 8118, '7ws': 8119, 'beside': 8120, 'brisk': 8121, 'walks': 8122, 'sexiest': 8123, 'dirtiest': 8124, 'tellmiss': 8125, "party's": 8126, 'contribute': 8127, 'greatly': 8128, 'urgh': 8129, 'coach': 8130, 'smells': 8131, 'duvet': 8132, 'predictive': 8133, 'w8in': 8134, '4utxt': 8135, '24th': 8136, 'beverage': 8137, 'pist': 8138, 'surrender': 8139, 'symptoms': 8140, 'rdy': 8141, 'backwards': 8142, 'abstract': 8143, 'africa': 8144, 'avin': 8145, 'chit': 8146, 'logon': 8147, '8883': 8148, '4217': 8149, 'w1a': 8150, '6zf': 8151, '118p': 8152, 'quiteamuzing': 8153, 'thatåõscool': 8154, 'å£1000call': 8155, '09071512432': 8156, '300603t': 8157, 'callcost150ppmmobilesvary': 8158, 'rows': 8159, 'engagement': 8160, 'fixd': 8161, 'njan': 8162, 'vilikkam': 8163, 'sudn': 8164, 'maths': 8165, 'chapter': 8166, 'chop': 8167, 'noooooooo': 8168, '08718727870150ppm': 8169, 'firsg': 8170, 'split': 8171, 'wasnt': 8172, 'heat': 8173, 'applyed': 8174, 'sumfing': 8175, 'hiphop': 8176, 'oxygen': 8177, 'resort': 8178, 'roller': 8179, 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'snuggles': 8241, 'contented': 8242, 'whispers': 8243, 'healthy': 8244, '2bold': 8245, 'brother\x89û÷s': 8246, 'scraped': 8247, 'barrel': 8248, 'misfits': 8249, 'sections': 8250, 'clearer': 8251, 'peach': 8252, 'tasts': 8253, 'rayman': 8254, 'golf': 8255, 'activ8': 8256, 'termsapply': 8257, "there'll": 8258, 'shindig': 8259, 'phonebook': 8260, 'rocking': 8261, 'ashes': 8262, "xin's": 8263, 'shijutta': 8264, 'offense': 8265, 'dvg': 8266, 'vinobanagar': 8267, 'ovulate': 8268, '3wks': 8269, 'woah': 8270, 'realising': 8271, 'orh': 8272, 'hides': 8273, 'secrets': 8274, 'n8': 8275, 'axel': 8276, 'akon': 8277, 'eyed': 8278, 'canteen': 8279, 'stressfull': 8280, 'adds': 8281, 'continued': 8282, 'president': 8283, '140': 8284, 'ìä': 8285, '180': 8286, 'pleasured': 8287, 'providing': 8288, 'assistance': 8289, 'whens': 8290, '1172': 8291, 'watever': 8292, 'built': 8293, 'lonlines': 8294, 'lotz': 8295, 'memories': 8296, 'gailxx': 8297, 'complacent': 8298, 'mina': 8299, 'miwa': 8300, '09066649731from': 8301, 'opposite': 8302, 'heavily': 8303, 'dolls': 8304, 'patrick': 8305, 'swayze': 8306, '09077818151': 8307, 'calls1': 8308, '50ppm': 8309, '30s': 8310, 'santacalling': 8311, 'quarter': 8312, 'fired': 8313, 'limping': 8314, 'aa': 8315, '078498': 8316, '08719180219': 8317, 'oga': 8318, 'poorly': 8319, 'punishment': 8320, 'brb': 8321, 'kill': 8322, 'predicte': 8323, 'situations': 8324, 'loosing': 8325, 'smaller': 8326, 'capacity': 8327, 'videos': 8328, 'shsex': 8329, 'netun': 8330, 'fgkslpopw': 8331, 'fgkslpo': 8332, '0871277810710p': 8333, 'defer': 8334, 'admission': 8335, 'checkmate': 8336, 'chess': 8337, 'persian': 8338, 'phrase': 8339, 'shah': 8340, 'maat': 8341, 'rats': 8342, 'themes': 8343, 'photoshop': 8344, 'manageable': 8345, '08715203652': 8346, '42810': 8347, 'ashley': 8348, 'increase': 8349, 'north': 8350, 'carolina': 8351, 'texas': 8352, 'gre': 8353, 'bomb': 8354, 'breathing': 8355, 'powerful': 8356, 'weapon': 8357, "'heart'": 8358, 'lovly': 8359, 'msgrcvd': 8360, 'customercare': 8361, 'clas': 8362, 'lit': 8363, 'loooooool': 8364, 'couch': 8365, 'rents': 8366, 'swashbuckling': 8367, 'terror': 8368, 'cruel': 8369, 'decent': 8370, 'joker': 8371, "dip's": 8372, 'gek1510': 8373, 'lyricalladie': 8374, 'hmmross': 8375, "world's": 8376, 'happiest': 8377, 'characters': 8378, 'differences': 8379, 'lists': 8380, "tyler's": 8381, 'antibiotic': 8382, 'abdomen': 8383, 'gynae': 8384, '6times': 8385, 'exposed': 8386, 'chastity': 8387, 'device': 8388, 'beatings': 8389, 'uses': 8390, 'gut': 8391, 'wrenching': 8392, "fuck's": 8393, 'tallahassee': 8394, 'ou': 8395, 'taka': 8396, 'pobox202': 8397, 'nr31': 8398, '7zs': 8399, '450pw': 8400, 'ritten': 8401, 'fold': 8402, '83118': 8403, 'colin': 8404, 'farrell': 8405, 'swat': 8406, 'mre': 8407, 'solihull': 8408, 'nhs': 8409, '2b': 8410, 'terminated': 8411, 'inconvenience': 8412, 'dentists': 8413, 'yards': 8414, 'bergkamp': 8415, 'margin': 8416, '78': 8417, "it'snot": 8418, "child's": 8419, 'unintentional': 8420, 'nonetheless': 8421, 'hooch': 8422, 'toaday': 8423, 'splat': 8424, 'grazed': 8425, 'knees': 8426, 'deny': 8427, 'hearin': 8428, 'yah': 8429, 'torture': 8430, 'hopeing': 8431, 'wasn\x89û÷t': 8432, 'sisters': 8433, 'sexychat': 8434, 'lips': 8435, 'congratulation': 8436, 'court': 8437, 'chapel': 8438, 'frontierville': 8439, 'mountain': 8440, 'deer': 8441, 'mailed': 8442, 'varma': 8443, 'secure': 8444, 'parties': 8445, 'farting': 8446, 'ortxt': 8447, 'trained': 8448, 'advisors': 8449, 'dialling': 8450, '402': 8451, 'stuffing': 8452, 'ahhhh': 8453, 'experiencehttp': 8454, 'vouch4me': 8455, 'etlp': 8456, 'kaila': 8457, '09058094507': 8458, "unicef's": 8459, 'asian': 8460, 'tsunami': 8461, 'disaster': 8462, 'fund': 8463, '864233': 8464, 'hos': 8465, 'collapsed': 8466, 'cumming': 8467, 'jade': 8468, 'paul': 8469, 'barmed': 8470, 'thinkthis': 8471, 'dangerous': 8472, 'rushing': 8473, 'coulda': 8474, 'phony': 8475, 'okday': 8476, 'hmph': 8477, 'baller': 8478, 'punto': 8479, 'ayo': 8480, 'travelled': 8481, 'å£125': 8482, 'freeentry': 8483, 'xt': 8484, 'toyota': 8485, 'camry': 8486, "olayiwola's": 8487, 'mileage': 8488, 'landing': 8489, 'clover': 8490, 'achan': 8491, 'amma': 8492, "'rencontre'": 8493, 'mountains': 8494, '08714712412': 8495, 'nìâte': 8496, 'puppy': 8497, 'noise': 8498, 'meg': 8499, '08715203685': 8500, '4xx26': 8501, 'crossing': 8502, 'deepest': 8503, 'darkest': 8504, '09094646631': 8505, 'inconvenient': 8506, 'adsense': 8507, 'approved': 8508, 'dudette': 8509, 'perumbavoor': 8510, 'stage': 8511, 'clarify': 8512, 'preponed': 8513, 'natalie': 8514, 'natalie2k9': 8515, 'younger': 8516, '08701213186': 8517, 'liver': 8518, 'opener': 8519, 'guides': 8520, 'watched': 8521, 'loneliness': 8522, 'skyving': 8523, "dad's": 8524, 'onwords': 8525, 'mtnl': 8526, 'mumbai': 8527, '83039': 8528, '62735': 8529, 'å£450': 8530, 'accommodationvouchers': 8531, 'mustprovide': 8532, '15541': 8533, 'rajitha': 8534, 'ranju': 8535, 'styles': 8536, 'tscs08714740323': 8537, '1winawk': 8538, '50perweeksub': 8539, 'slp': 8540, 'muah': 8541, '09066361921': 8542, 'disagreeable': 8543, 'afterwards': 8544, 'uawake': 8545, 'feellikw': 8546, 'justfound': 8547, 'aletter': 8548, 'thatmum': 8549, 'gotmarried': 8550, '4thnov': 8551, 'ourbacks': 8552, 'fuckinnice': 8553, 'rearrange': 8554, 'dormitory': 8555, 'astronomer': 8556, 'starer': 8557, 'election': 8558, 'recount': 8559, 'hitler': 8560, 'eleven': 8561, 'worms': 8562, 'suffering': 8563, 'dysentry': 8564, 'andre': 8565, "virgil's": 8566, 'gokila': 8567, 'shanil': 8568, 'exchanged': 8569, 'uncut': 8570, 'diamond': 8571, 'dino': 8572, 'kotees': 8573, 'panther': 8574, 'sugababes': 8575, 'zebra': 8576, 'animation': 8577, 'badass': 8578, 'hoody': 8579, 'resent': 8580, 'queries': 8581, 'customersqueries': 8582, 'netvision': 8583, 'hassling': 8584, 'andres': 8585, 'haughaighgtujhyguj': 8586, 'fassyole': 8587, 'blacko': 8588, 'londn': 8589, 'responsibilities': 8590, '08715205273': 8591, 'vco': 8592, 'humanities': 8593, 'reassurance': 8594, 'aslamalaikkum': 8595, 'tohar': 8596, 'beeen': 8597, 'muht': 8598, 'albi': 8599, 'mufti': 8600, 'mahfuuz': 8601, '078': 8602, 'enufcredeit': 8603, 'tocall': 8604, 'ileave': 8605, 'treats': 8606, 'okors': 8607, 'ibored': 8608, 'adding': 8609, 'zeros': 8610, 'savings': 8611, 'goigng': 8612, 'perfume': 8613, 'sday': 8614, 'grocers': 8615, 'pubs': 8616, 'frankie': 8617, 'bennys': 8618, 'changing': 8619, 'diapers': 8620, 'owed': 8621, 'unlike': 8622, 'patients': 8623, 'turkeys': 8624, 'helens': 8625, 'princes': 8626, 'unintentionally': 8627, 'garden': 8628, 'bulbs': 8629, 'seeds': 8630, 'scotsman': 8631, 'go2': 8632, 'notxt': 8633, 'wenever': 8634, 'stability': 8635, 'tranquility': 8636, 'vibrant': 8637, 'colourful': 8638, 'bawling': 8639, 'failure': 8640, 'failing': 8641, 'velusamy': 8642, "sir's": 8643, 'facilities': 8644, 'karnan': 8645, 'bluray': 8646, 'i\x89û÷ve': 8647, 'salt': 8648, 'wounds': 8649, 'logging': 8650, 'geoenvironmental': 8651, 'implications': 8652, 'fuuuuck': 8653, 'salmon': 8654, 'uploaded': 8655, 'wrkin': 8656, 'ree': 8657, 'compensation': 8658, 'awkward': 8659, 'splash': 8660, 'leg': 8661, 'musta': 8662, 'overdid': 8663, 'telediscount': 8664, 'gastroenteritis': 8665, 'replace': 8666, 'reduce': 8667, 'limiting': 8668, 'illness': 8669, 'foned': 8670, 'chuck': 8671, 'port': 8672, 'stuffs': 8673, 'juswoke': 8674, 'boatin': 8675, 'docks': 8676, 'spinout': 8677, '08715203656': 8678, '42049': 8679, 'uworld': 8680, 'qbank': 8681, 'assessment': 8682, 'someonone': 8683, '09064015307': 8684, 'tke': 8685, 'temales': 8686, 'finishd': 8687, 'dull': 8688, 'studies': 8689, 'anyones': 8690, 'treadmill': 8691, 'craigslist': 8692, 'absolutely': 8693, 'swan': 8694, 'lamp': 8695, 'foward': 8696, '09061790126': 8697, 'misundrstud': 8698, '2u2': 8699, 'genes': 8700, 'com1win150ppmx3age16subscription': 8701, 'resuming': 8702, 'reapply': 8703, "treatin'": 8704, 'treacle': 8705, 'mumhas': 8706, 'beendropping': 8707, 'theplace': 8708, 'adress': 8709, 'oyster': 8710, 'sashimi': 8711, 'rumbling': 8712, 'marandratha': 8713, 'correctly': 8714, 'alaikkum': 8715, 'heaven': 8716, 'pisces': 8717, 'aquarius': 8718, '2yrs': 8719, 'steyn': 8720, 'wicket': 8721, 'sterm': 8722, 'resolved': 8723, 'jam': 8724, 'hannaford': 8725, 'wheat': 8726, 'chex': 8727, 'grownup': 8728, 'costume': 8729, 'jerk': 8730, 'stink': 8731, 'follows': 8732, 'subsequent': 8733, 'openings': 8734, 'upcharge': 8735, 'guai': 8736, 'astrology': 8737, "ryan's": 8738, 'slacking': 8739, 'mentor': 8740, 'percent': 8741, '09095350301': 8742, 'erotic': 8743, 'ecstacy': 8744, 'dept': 8745, '08717507382': 8746, 'coincidence': 8747, 'sane': 8748, 'helping': 8749, 'leading': 8750, '151': 8751, 'pause': 8752, '8800': 8753, 'psp': 8754, 'spacebucks': 8755, "weather's": 8756, '083': 8757, '6089': 8758, 'squeezed': 8759, 'maintaining': 8760, 'dreading': 8761, 'thou': 8762, 'suggestion': 8763, 'lands': 8764, 'helps': 8765, 'forgt': 8766, 'ajith': 8767, 'ooooooh': 8768, 'yoville': 8769, 'asda': 8770, 'counts': 8771, 'officer': 8772, 'bffs': 8773, 'carly': 8774, 'seperated': 8775, '\x8eö´\x89ó': 8776, '\x8bû¬ud': 8777, 'brolly': 8778, 'franxx': 8779, 'prometazine': 8780, 'syrup': 8781, '5mls': 8782, 'feed': 8783, 'singapore': 8784, 'victoria': 8785, 'pocay': 8786, 'wocay': 8787, '2morrowxxxx': 8788, 'broth': 8789, 'ramen': 8790, 'fowler': 8791, 'tats': 8792, 'flew': 8793, '09058094583': 8794, 'attention': 8795, 'tix': 8796, "biola's": 8797, 'fne': 8798, 'youdoing': 8799, 'worc': 8800, 'foregate': 8801, 'shrub': 8802, 'get4an18th': 8803, '32000': 8804, 'legitimat': 8805, 'efreefone': 8806, 'receipts': 8807, 'pendent': 8808, 'toilet': 8809, 'stolen': 8810, 'cops': 8811, 'hu': 8812, 'navigate': 8813, 'choosing': 8814, 'require': 8815, 'guidance': 8816, 'chick': 8817, 'boobs': 8818, 'revealing': 8819, 'sparkling': 8820, 'breaks': 8821, '45': 8822, '0121': 8823, '2025050': 8824, 'shortbreaks': 8825, 'org': 8826, 'gyno': 8827, 'belong': 8828, 'gamb': 8829, 'treasure': 8830, '820554ad0a1705572711': 8831, 'trueåác': 8832, 'ringtoneåá': 8833, '09050000332': 8834, "mummy's": 8835, 'positive': 8836, 'negative': 8837, 'hmmmm': 8838, 'command': 8839, 'stressful': 8840, 'holby': 8841, '09064017295': 8842, 'li': 8843, 'lecturer': 8844, 'repeating': 8845, 'yeovil': 8846, 'motor': 8847, 'max': 8848, 'rhode': 8849, 'bong': 8850, 'ofcourse': 8851, '08448350055': 8852, 'planettalkinstant': 8853, 'marvel': 8854, 'ultimate': 8855, '83338': 8856, '8ball': 8857, 'tamilnadu': 8858, 'tip': 8859, '07808247860': 8860, '08719899229': 8861, '40411': 8862, 'identification': 8863, 'limit': 8864, 'boundaries': 8865, 'endless': 8866, 'reassuring': 8867, 'young': 8868, 'referin': 8869, "mei's": 8870, 'saibaba': 8871, 'colany': 8872, 'chic': 8873, 'declare': 8874, '49557': 8875, 'disappointment': 8876, 'irritation': 8877, "tantrum's": 8878, 'compliments': 8879, 'adventuring': 8880, 'chief': 8881, 'gsex': 8882, '2667': 8883, 'wc1n': 8884, '3xx': 8885, '3mobile': 8886, 'chatlines': 8887, 'inclu': 8888, 'servs': 8889, 'l8er': 8890, 'bailiff': 8891, 'mouse': 8892, 'desk': 8893, 'childporn': 8894, 'jumpers': 8895, 'hat': 8896, 'belt': 8897, 'cribbs': 8898, 'spiritual': 8899, 'barring': 8900, 'sudden': 8901, 'influx': 8902, 'kane': 8903, 'shud': 8904, 'pshew': 8905, 'units': 8906, 'accent': 8907, '4years': 8908, 'dental': 8909, 'nmde': 8910, 'dump': 8911, 'heap': 8912, 'lowes': 8913, 'salesman': 8914, 'å£750': 8915, '087187272008': 8916, 'now1': 8917, 'pity': 8918, 'suggestions': 8919, 'bitching': 8920}
vocab_size : 8921
메일의 최대 길이 : 189
메일의 평균 길이 : 15.610370
[[   0    0    0    0    0    0    0    0    0    0    0    0    0    0
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     0    0    0    0    0    0    0    0    0    0    0    0    0    0
     0   47  433 4013  780  705  662   64    8 1202   94  121  434 1203
   142 2712 1204   68   57 4014  137]]
훈련 데이터의 크기(shape) : (5169, 189)
train 수 : 4135
test 수 : 1034
(4135, 189) (4135,) (1034, 189) (1034,)
2022-12-19 11:04:56.222854: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 embedding (Embedding)       (None, None, 32)          285472    
                                                                 
 lstm (LSTM)                 (None, 32)                8320      
                                                                 
 dense (Dense)               (None, 32)                1056      
                                                                 
 dropout (Dropout)           (None, 32)                0         
                                                                 
 dense_1 (Dense)             (None, 1)                 33        
                                                                 
=================================================================
Total params: 294,881
Trainable params: 294,881
Non-trainable params: 0
_________________________________________________________________
None
Epoch 1/5
52/52 - 3s - loss: 0.4272 - acc: 0.8615 - val_loss: 0.2155 - val_acc: 0.8730 - 3s/epoch - 55ms/step
Epoch 2/5
52/52 - 1s - loss: 0.1320 - acc: 0.9643 - val_loss: 0.0939 - val_acc: 0.9722 - 1s/epoch - 24ms/step
Epoch 3/5
52/52 - 1s - loss: 0.0418 - acc: 0.9909 - val_loss: 0.0503 - val_acc: 0.9855 - 1s/epoch - 24ms/step
Epoch 4/5
52/52 - 1s - loss: 0.0163 - acc: 0.9961 - val_loss: 0.0524 - val_acc: 0.9855 - 1s/epoch - 23ms/step
Epoch 5/5
52/52 - 1s - loss: 0.0058 - acc: 0.9988 - val_loss: 0.0788 - val_acc: 0.9819 - 1s/epoch - 24ms/step

 1/33 [..............................] - ETA: 0s - loss: 2.1198e-04 - acc: 1.0000
10/33 [========>.....................] - ETA: 0s - loss: 0.0759 - acc: 0.9781    
19/33 [================>.............] - ETA: 0s - loss: 0.0582 - acc: 0.9852
28/33 [========================>.....] - ETA: 0s - loss: 0.0560 - acc: 0.9866
33/33 [==============================] - 0s 6ms/step - loss: 0.0666 - acc: 0.9855
evaluate : [0.0666288211941719, 0.9854932427406311]

1/1 [==============================] - ETA: 0s
1/1 [==============================] - 0s 247ms/step
예측값 : [0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0]
실제값 : [0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0]

loss, val_loss 시각화

acc, val_acc 시각

TensorFlow 기초 40 - 자소 단위로 분리한 후 텍스트 생성 모델

코딩탕탕 — Fri, 16 Dec 2022 10:22:26 +0900

# 자소 단위로 분리한 후 텍스트 생성 모델
# !pip install jamotools
# !pip --use-deprecated=legacy-resolver install  모듈명  # 라이브러리를 install 할 때 현재 버전에 안 맞을 때 사용 (낮은 버전의 파이썬에서 임의 모듈 설치치)

import jamotools
import tensorflow as tf
import numpy as np


path_to_file = tf.keras.utils.get_file("toji.txt", "https://raw.githubusercontent.com/pykwon/etc/master/rnn_short_toji.txt")

train_text = open(path_to_file, 'rb').read().decode(encoding='utf-8')
s = train_text[:100]
print(s)

s_split = jamotools.split_syllables(s)
print(s_split)

s2 = jamotools.join_jamos(s_split)
print(s2)
print(s == s2)

# train_text로 자모단위 분리
train_text_x = jamotools.split_syllables(train_text)
vocab = sorted(set(train_text_x))
vocab.append('UNK') # 사전에 정의되지 않은 기호가 있는 경우 'UNK'로 사전에 등록
print(len(vocab)) # 136

char2idx = {u:i for i, u in enumerate(vocab)}
print(char2idx) # 인덱싱이 된다.

idx2char = np.array(vocab)
print(idx2char)
text_as_int = np.array([char2idx[c] for c in train_text_x])
print(text_as_int)

print(train_text_x[:20])
print(text_as_int[:20])

# 학습 데이터 생성
seq_length = 80
exam_per_epoch = len(text_as_int) // seq_length
print(exam_per_epoch) # 8636
char_dataset = tf.data.Dataset.from_tensor_slices(text_as_int)

char_dataset = char_dataset.batch(seq_length + 1, drop_remainder=True) # 처음 80개 자소와 그 뒤에 나올 정답이 될 한 단어를 합쳐서 반환

for item in char_dataset.take(1):
    print(idx2char[item.numpy()])
    print(item.numpy())
    
def split_input_target2(chunk):
    return [chunk[:-1], chunk[-1]]

train_dataset = char_dataset.map(split_input_target2)

for x, y in train_dataset.take(1):
    print(idx2char[x.numpy()])
    print(x.numpy())
    print(idx2char[y.numpy()])
    print(y.numpy())
    
# model
BATCH_SIZE = 64
steps_per_epoch = exam_per_epoch // BATCH_SIZE
print('steps_per_epoch :', steps_per_epoch)
train_dataset = train_dataset.shuffle(buffer_size=5000).batch(BATCH_SIZE, drop_remainder=True)

total_chars = len(vocab)

model = tf.keras.Sequential([
    tf.keras.layers.Embedding(total_chars, 100, input_length=seq_length),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.LSTM(256, activation='tanh'),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.Dense(256, activation='relu'),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.Dense(total_chars, activation='softmax')
])

model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
print(model.summary())

from keras.utils import pad_sequences

def testmodel(epoch, logs):
    if epoch % 5 != 0 and epoch != 49:
        return
    
    # 5의 배수 또는 49면 작업을 진행
    test_sentence = train_text[:48]
    test_sentence = jamotools.split_syllables(test_sentence)
    next_chars = 300
    for _ in range(next_chars):
        test_text_x = test_sentence[-seq_length:]
        test_text_x = np.array([char2idx[c] if c in char2idx else char2idx['UNK'] for c in test_text_x])
        test_text_x = pad_sequences([test_text_x], maxlen=seq_length, padding='pre', value=char2idx['UNK'])
        output_idx = np.argmax(model.predict(test_text_x), axis = -1)
        test_sentence += idx2char[output_idx[0]]
    print()
    print(jamotools.join_jamos(test_sentence))

# 모델을 학습시키며 모델이 생성한 결과물을 확인하기 위한 용도
testmodelcb = tf.keras.callbacks.LambdaCallback(on_epoch_end=testmodel)  # 에폭이 끝날 때 마다 testmodel 함수를 호출

# repeat() : input을 무한반복. 한번의 에폭의 끝과 다음 에폭의 시작에 상관없이 인자 만큼 반복
history = model.fit(train_dataset.repeat(), epochs=50, steps_per_epoch=steps_per_epoch, callbacks=[testmodelcb], verbose=2)

model.save('nlp14.hdf5')

# 임의의 문장을 사용해 학습된 모델로 새로운 글 생성
test_sentence = '최참판댁 사랑은 무인지경처럼 적막하다.'

test_sentence = jamotools.split_syllables(test_sentence)
print(test_sentence)

# 앞에서 작성한 for문 복붙
next_chars = 500
for _ in range(next_chars):
    test_text_x = test_sentence[-seq_length:]
    test_text_x = np.array([char2idx[c] if c in char2idx else char2idx['UNK'] for c in test_text_x])
    test_text_x = pad_sequences([test_text_x], maxlen=seq_length, padding='pre', value=char2idx['UNK'])
    output_idx = np.argmax(model.predict(test_text_x), axis = -1)
    test_sentence += idx2char[output_idx[0]]

print('글 생성 결과 ----------')
print(jamotools.join_jamos(test_sentence))

# 시각화
import matplotlib.pyplot as plt
plt.plot(history.history['loss'], c='r', label='loss')
plt.legend()
plt.show()

plt.plot(history.history['accuracy'], c='b', label='accuracy')
plt.legend()
plt.show()


    
<console>
귀녀의 모습을 한번 쳐다보고 떠나려 했다. 집안을 이리저리 기웃거리던 강표수는 윤씨부

인에게 인사를 올리고 중문을 나서는  치수 뒷모습을 보았다. 실망에  얼굴이 일그러지면서 

ㄱㅟㄴㅕㅇㅢ ㅁㅗㅅㅡㅂㅇㅡㄹ ㅎㅏㄴㅂㅓㄴ ㅊㅕㄷㅏㅂㅗㄱㅗ ㄸㅓㄴㅏㄹㅕ ㅎㅐㅆㄷㅏ. ㅈㅣㅂㅇㅏㄴㅇㅡㄹ ㅇㅣㄹㅣㅈㅓㄹㅣ ㄱㅣㅇㅜㅅㄱㅓㄹㅣㄷㅓㄴ ㄱㅏㅇㅍㅛㅅㅜㄴㅡㄴ ㅇㅠㄴㅆㅣㅂㅜ

ㅇㅣㄴㅇㅔㄱㅔ ㅇㅣㄴㅅㅏㄹㅡㄹ ㅇㅗㄹㄹㅣㄱㅗ ㅈㅜㅇㅁㅜㄴㅇㅡㄹ ㄴㅏㅅㅓㄴㅡㄴ  ㅊㅣㅅㅜ ㄷㅟㅅㅁㅗㅅㅡㅂㅇㅡㄹ ㅂㅗㅇㅏㅆㄷㅏ. ㅅㅣㄹㅁㅏㅇㅇㅔ  ㅇㅓㄹㄱㅜㄹㅇㅣ ㅇㅣㄹㄱㅡㄹㅓㅈㅣㅁㅕㄴㅅㅓ 

귀녀의 모습을 한번 쳐다보고 떠나려 했다. 집안을 이리저리 기웃거리던 강표수는 윤씨부

인에게 인사를 올리고 중문을 나서는  치수 뒷모습을 보았다. 실망에  얼굴이 일그러지면서 

True
136
{'\n': 0, '\r': 1, ' ': 2, '!': 3, '"': 4, "'": 5, '(': 6, ')': 7, ',': 8, '-': 9, '.': 10, '0': 11, '1': 12, '2': 13, '3': 14, '4': 15, '5': 16, '6': 17, '7': 18, '8': 19, '9': 20, ':': 21, '?': 22, 'a': 23, 'd': 24, 'f': 25, 'l': 26, 'n': 27, 'p': 28, '‘': 29, '’': 30, '“': 31, '”': 32, '…': 33, '\u3000': 34, 'ㄱ': 35, 'ㄲ': 36, 'ㄳ': 37, 'ㄴ': 38, 'ㄵ': 39, 'ㄶ': 40, 'ㄷ': 41, 'ㄸ': 42, 'ㄹ': 43, 'ㄺ': 44, 'ㄻ': 45, 'ㄼ': 46, 'ㄽ': 47, 'ㄾ': 48, 'ㅀ': 49, 'ㅁ': 50, 'ㅂ': 51, 'ㅃ': 52, 'ㅄ': 53, 'ㅅ': 54, 'ㅆ': 55, 'ㅇ': 56, 'ㅈ': 57, 'ㅉ': 58, 'ㅊ': 59, 'ㅋ': 60, 'ㅌ': 61, 'ㅍ': 62, 'ㅎ': 63, 'ㅏ': 64, 'ㅐ': 65, 'ㅑ': 66, 'ㅒ': 67, 'ㅓ': 68, 'ㅔ': 69, 'ㅕ': 70, 'ㅖ': 71, 'ㅗ': 72, 'ㅘ': 73, 'ㅙ': 74, 'ㅚ': 75, 'ㅛ': 76, 'ㅜ': 77, 'ㅝ': 78, 'ㅞ': 79, 'ㅟ': 80, 'ㅠ': 81, 'ㅡ': 82, 'ㅢ': 83, 'ㅣ': 84, '主': 85, '事': 86, '亡': 87, '佛': 88, '刑': 89, '割': 90, '化': 91, '匠': 92, '善': 93, '地': 94, '壁': 95, '妄': 96, '婚': 97, '子': 98, '寺': 99, '工': 100, '常': 101, '役': 102, '情': 103, '惡': 104, '意': 105, '日': 106, '杖': 107, '水': 108, '池': 109, '無': 110, '燈': 111, '眞': 112, '祈': 113, '祭': 114, '私': 115, '童': 116, '籍': 117, '絶': 118, '置': 119, '者': 120, '衣': 121, '谷': 122, '身': 123, '迷': 124, '造': 125, '銀': 126, '錫': 127, '長': 128, '陷': 129, '電': 130, '食': 131, '金': 132, '落': 133, '來': 134, 'UNK': 135}
['\n' '\r' ' ' '!' '"' "'" '(' ')' ',' '-' '.' '0' '1' '2' '3' '4' '5' '6'
 '7' '8' '9' ':' '?' 'a' 'd' 'f' 'l' 'n' 'p' '‘' '’' '“' '”' '…' '\u3000'
 'ㄱ' 'ㄲ' 'ㄳ' 'ㄴ' 'ㄵ' 'ㄶ' 'ㄷ' 'ㄸ' 'ㄹ' 'ㄺ' 'ㄻ' 'ㄼ' 'ㄽ' 'ㄾ' 'ㅀ' 'ㅁ' 'ㅂ' 'ㅃ'
 'ㅄ' 'ㅅ' 'ㅆ' 'ㅇ' 'ㅈ' 'ㅉ' 'ㅊ' 'ㅋ' 'ㅌ' 'ㅍ' 'ㅎ' 'ㅏ' 'ㅐ' 'ㅑ' 'ㅒ' 'ㅓ' 'ㅔ' 'ㅕ'
 'ㅖ' 'ㅗ' 'ㅘ' 'ㅙ' 'ㅚ' 'ㅛ' 'ㅜ' 'ㅝ' 'ㅞ' 'ㅟ' 'ㅠ' 'ㅡ' 'ㅢ' 'ㅣ' '主' '事' '亡' '佛'
 '刑' '割' '化' '匠' '善' '地' '壁' '妄' '婚' '子' '寺' '工' '常' '役' '情' '惡' '意' '日'
 '杖' '水' '池' '無' '燈' '眞' '祈' '祭' '私' '童' '籍' '絶' '置' '者' '衣' '谷' '身' '迷'
 '造' '銀' '錫' '長' '陷' '電' '食' '金' '落' '來' 'UNK']
[35 80 38 ... 10  2  0]
ㄱㅟㄴㅕㅇㅢ ㅁㅗㅅㅡㅂㅇㅡㄹ ㅎㅏㄴㅂ
[35 80 38 70 56 83  2 50 72 54 82 51 56 82 43  2 63 64 38 51]
8636
2022-12-16 11:56:56.659825: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
['ㄱ' 'ㅟ' 'ㄴ' 'ㅕ' 'ㅇ' 'ㅢ' ' ' 'ㅁ' 'ㅗ' 'ㅅ' 'ㅡ' 'ㅂ' 'ㅇ' 'ㅡ' 'ㄹ' ' ' 'ㅎ' 'ㅏ'
 'ㄴ' 'ㅂ' 'ㅓ' 'ㄴ' ' ' 'ㅊ' 'ㅕ' 'ㄷ' 'ㅏ' 'ㅂ' 'ㅗ' 'ㄱ' 'ㅗ' ' ' 'ㄸ' 'ㅓ' 'ㄴ' 'ㅏ'
 'ㄹ' 'ㅕ' ' ' 'ㅎ' 'ㅐ' 'ㅆ' 'ㄷ' 'ㅏ' '.' ' ' 'ㅈ' 'ㅣ' 'ㅂ' 'ㅇ' 'ㅏ' 'ㄴ' 'ㅇ' 'ㅡ'
 'ㄹ' ' ' 'ㅇ' 'ㅣ' 'ㄹ' 'ㅣ' 'ㅈ' 'ㅓ' 'ㄹ' 'ㅣ' ' ' 'ㄱ' 'ㅣ' 'ㅇ' 'ㅜ' 'ㅅ' 'ㄱ' 'ㅓ'
 'ㄹ' 'ㅣ' 'ㄷ' 'ㅓ' 'ㄴ' ' ' 'ㄱ' 'ㅏ' 'ㅇ']
[35 80 38 70 56 83  2 50 72 54 82 51 56 82 43  2 63 64 38 51 68 38  2 59
 70 41 64 51 72 35 72  2 42 68 38 64 43 70  2 63 65 55 41 64 10  2 57 84
 51 56 64 38 56 82 43  2 56 84 43 84 57 68 43 84  2 35 84 56 77 54 35 68
 43 84 41 68 38  2 35 64 56]
['ㄱ' 'ㅟ' 'ㄴ' 'ㅕ' 'ㅇ' 'ㅢ' ' ' 'ㅁ' 'ㅗ' 'ㅅ' 'ㅡ' 'ㅂ' 'ㅇ' 'ㅡ' 'ㄹ' ' ' 'ㅎ' 'ㅏ'
 'ㄴ' 'ㅂ' 'ㅓ' 'ㄴ' ' ' 'ㅊ' 'ㅕ' 'ㄷ' 'ㅏ' 'ㅂ' 'ㅗ' 'ㄱ' 'ㅗ' ' ' 'ㄸ' 'ㅓ' 'ㄴ' 'ㅏ'
 'ㄹ' 'ㅕ' ' ' 'ㅎ' 'ㅐ' 'ㅆ' 'ㄷ' 'ㅏ' '.' ' ' 'ㅈ' 'ㅣ' 'ㅂ' 'ㅇ' 'ㅏ' 'ㄴ' 'ㅇ' 'ㅡ'
 'ㄹ' ' ' 'ㅇ' 'ㅣ' 'ㄹ' 'ㅣ' 'ㅈ' 'ㅓ' 'ㄹ' 'ㅣ' ' ' 'ㄱ' 'ㅣ' 'ㅇ' 'ㅜ' 'ㅅ' 'ㄱ' 'ㅓ'
 'ㄹ' 'ㅣ' 'ㄷ' 'ㅓ' 'ㄴ' ' ' 'ㄱ' 'ㅏ']
[35 80 38 70 56 83  2 50 72 54 82 51 56 82 43  2 63 64 38 51 68 38  2 59
 70 41 64 51 72 35 72  2 42 68 38 64 43 70  2 63 65 55 41 64 10  2 57 84
 51 56 64 38 56 82 43  2 56 84 43 84 57 68 43 84  2 35 84 56 77 54 35 68
 43 84 41 68 38  2 35 64]
ㅇ
56
steps_per_epoch : 134
Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 embedding (Embedding)       (None, 80, 100)           13600     
                                                                 
 dropout (Dropout)           (None, 80, 100)           0         
                                                                 
 lstm (LSTM)                 (None, 256)               365568    
                                                                 
 dropout_1 (Dropout)         (None, 256)               0         
                                                                 
 dense (Dense)               (None, 256)               65792     
                                                                 
 dropout_2 (Dropout)         (None, 256)               0         
                                                                 
 dense_1 (Dense)             (None, 136)               34952     
                                                                 
=================================================================
Total params: 479,912
Trainable params: 479,912
Non-trainable params: 0
_________________________________________________________________
None

최참판댁 사랑은 무인지경처럼 적막하다.
 "아아우, 기러서보, 알, 알래하......나가. 촛사솟한 시조. '요애해힌데 길하가 살을 흘려 들어자."
""느나?"....ㄴ데 출서빈 아니다. 강포수를 밟토 들어다.
 "기었다. 그 줌글 설고 있었다. 예김화는 깃서바.""" "허허헜자."
""제 줌 사장에 했다. 서엉이 둠어자렀다.  "아아우!" 하지, 어리 기우? 하집해서 갗겨섰다.  "요아우!" 하잡하들 춧하고 있었다. 예기라  부추서 있는 인이 일이 일ㄹㄹㄹㄹㄹㄹㄹㄹㄹㄱㄱㄱ고  우었다.
 "귀운화 서엇다 그서 이 기래하  북겁더서 술에 출렀다.

라이브러리를 install 할 때 현재 버전에 안 맞을 때 사용 (낮은 버전의 파이썬에서 임의 모듈 설치) 참고 사이

pip multiple versions of dependency resolver problem

INFO: pip is looking at multiple versions of to determine which version is compatible with other requirements. This could take a while. INFO: This is taking longer than usual. You might need to provide the dependency resolver with stricter constraints to r

uiandwe.tistory.com

뉴욕타임즈 뉴스 기사 중 헤드라인을 읽어 텍스트 생성 연습(LSTM)

코딩탕탕 — Wed, 14 Dec 2022 13:10:23 +0900

# 뉴욕타임즈 뉴스 기사 중 헤드라인을 읽어 텍스트 생성 연습
import pandas as pd

df = pd.read_csv('https://raw.githubusercontent.com/pykwon/python/master/testdata_utf8/articlesapril.csv')
# print(df.head(1))
print(df.columns, len(df.columns))
print()
print(df['headline'].head(2))
print()
print(df.headline.values)
print(df['headline'].isnull().values.any()) # False

headline = []
headline.extend(list(df.headline.values))
print(headline[:10])
print(len(headline)) # 1324

# 'Unknown'(noise) 자료 제거
headline = [n for n in headline if n != 'Unknown']
print(headline[:10])
print(len(headline)) # 1214

# ascii 코드 문자, 구두점 제거, 단어 소문자화
from string import punctuation

# print('He하이llo 가a나다.'.encode('ascii', errors='ignore').decode()) # 영어만 출력
# print(' pytion.'.strip(punctuation))
# print(' pytion.'.strip(punctuation + ' '))
def replace_func(s):
    s = s.encode('utf8').decode('ascii', 'ignore') # ascii 코드로 변환 시 ascii 코드를 지원하지 않으면 제외 시킨다.
    return ''.join(c for c in s if c not in punctuation).lower() # 구두점 제거, 단어 소문자화

sss = ['abc123, 하하GOOD']
print([replace_func(x) for x in sss])

text = [replace_func(x) for x in headline]
print(text[:5])
# 'Former N.F.L. Cheerleaders’ Settlement Offer: $1 and a Meeting With Goodell'
# 'former nfl cheerleaders settlement offer 1 and a meeting with goodell'

# vocabulary
from keras.preprocessing.text import Tokenizer

tok = Tokenizer()
tok.fit_on_texts(text) # list가 들어와야 된다.(위에서 list 타입으로 해 놓음)
vocab_size = len(tok.word_index) + 1
print('vocab_size :', vocab_size) # 3494

sequences = list()
for line in text:
    enc = tok.texts_to_sequences([line])[0] # 각 샘플에 대한 인덱싱 처리(정수 인코딩)
    for i in range(1, len(enc)):
        se = enc[:i + 1]
        sequences.append(se)
        
# print(sequences) # [[99, 269], [99, 269, 371], [99, 269, 371, 1115], ...
print(tok.word_index) # {'the': 1, 'a': 2, 'to': 3, 'of': 4, 'in': 5, 'for': 6, ...
print(sequences[:11])
# 'former nfl cheerleaders settlement offer 1 and a meeting with goodell'
# [[99, 269], [99, 269, 371], [99, 269, 371, 1115], ...

print('dict items :', tok.word_index.items()) # [('the', 1), ('a', 2), ('to', 3), ... ==> {1:'the', 2:'a', ...}이렇게 만들기
index_to_word = {}
for key, value in tok.word_index.items():
    # print('key :', key)
    # print('value :', value)
    index_to_word[value] = key
    
print(index_to_word[100])

max_len = max(len(i) for i in sequences)
print('max_len :', max_len) # 24

from keras.utils import pad_sequences
psequences = pad_sequences(sequences, maxlen=max_len, padding='pre')
print(psequences[:3])

import numpy as np
psequences = np.array(psequences)
x = psequences[:, :-1] # feature
y = psequences[:, -1]  # label

print(x[:3])
print(y[:3])

from keras.utils import to_categorical
y = to_categorical(y, num_classes=vocab_size)
print(y[:1])

# model
from keras.models import Sequential
from keras.layers import Embedding, Flatten, Dense, LSTM

model = Sequential()
model.add(Embedding(vocab_size, 32, input_length=max_len - 1))
model.add(LSTM(128, activation='tanh'))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(64, activation='relu'))
model.add(Dense(vocab_size, activation='softmax'))
print(model.summary())

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(x, y, epochs=200, verbose=0)
print('model evaluate :', model.evaluate(x, y))

# 문자열 생성 함수
def seq_gen_text_func(model, t, current_word, n):
    init_word = current_word # 처음 들어온 단어도 마지막 함께 출력할 계획
    sentence = ''
    for _ in range(n):
        encoded = t.texts_to_sequences([current_word])[0]
        encoded = pad_sequences([encoded], maxlen=max_len - 1, padding='pre')
        result = np.argmax(model.predict(encoded, verbose=0), axis = -1)
        # 예측 단어 찾기
        for word, index in t.word_index.items():
            # print(word, ' :', index)
            if index == result: # 예측한 단어, 그리고 인덱스와 동일한 단어가 있다면 해당 단어는 예측단어 이므로 break 
                break
        current_word = current_word + ' ' + word
        sentence = sentence + ' ' + word
    sentence = init_word + sentence
    return sentence

print(seq_gen_text_func(model, tok, 'defense', 10))
print(seq_gen_text_func(model, tok, 'tuesday', 10))
print(seq_gen_text_func(model, tok, 'beginning', 10))


<console>
Index(['articleID', 'articleWordCount', 'byline', 'documentType', 'headline',
       'keywords', 'multimedia', 'newDesk', 'printPage', 'pubDate',
       'sectionName', 'snippet', 'source', 'typeOfMaterial', 'webURL'],
      dtype='object') 15

0    Former N.F.L. Cheerleaders’ Settlement Offer: ...
1    E.P.A. to Unveil a New Rule. Its Effect: Less ...
Name: headline, dtype: object

['Former N.F.L. Cheerleaders’ Settlement Offer: $1 and a Meeting With Goodell'
 'E.P.A. to Unveil a New Rule. Its Effect: Less Science in Policymaking.'
 'The New Noma, Explained' ...
 'Gen. Michael Hayden Has One Regret: Russia'
 'There Is Nothin’ Like a Tune' 'Unknown']
False
['Former N.F.L. Cheerleaders’ Settlement Offer: $1 and a Meeting With Goodell', 'E.P.A. to Unveil a New Rule. Its Effect: Less Science in Policymaking.', 'The New Noma, Explained', 'Unknown', 'Unknown', 'Unknown', 'Unknown', 'Unknown', 'How a Bag of Texas Dirt  Became a Times Tradition', 'Is School a Place for Self-Expression?']
1324
['Former N.F.L. Cheerleaders’ Settlement Offer: $1 and a Meeting With Goodell', 'E.P.A. to Unveil a New Rule. Its Effect: Less Science in Policymaking.', 'The New Noma, Explained', 'How a Bag of Texas Dirt  Became a Times Tradition', 'Is School a Place for Self-Expression?', 'Commuter Reprogramming', 'Ford Changed Leaders, Looking for a Lift. It’s Still Looking.', 'Romney Failed to Win at Utah Convention, But Few Believe He’s Doomed', 'Chain Reaction', 'He Forced the Vatican to Investigate Sex Abuse. Now He’s Meeting With Pope Francis.']
1214
['abc123 good']
['former nfl cheerleaders settlement offer 1 and a meeting with goodell', 'epa to unveil a new rule its effect less science in policymaking', 'the new noma explained', 'how a bag of texas dirt  became a times tradition', 'is school a place for selfexpression']

{'the': 1, 'a': 2, 'to': 3, 'of': 4, 'in': 5, 'for': 6, 'and': 7, 'is': 8, 'on': 9, 'with': 10, 'trump': 11, 'as': 12, 'at': 13, 'new': 14, 'how': 15, 'from': 16, 'it': 17, 'an': 18, 'that': 19, 'be': 20, 'season': 21, 'us': 22, 'you': 23, 'its': 24, 'what': 25, 'episode': 26, 'can': 27, 'your': 28, 'not': 29, 'he': 30, 'now': 31, 'his': 32, 'are': 33, 'teaching': 34, 'war': 35, 'out': 36, 'no': 37, 'was': 38, 'by': 39, 'trumps': 40, 'has': 41, 'over': 42, 'may': 43, 'into': 44, 'why': 45, 'more': 46, 'we': 47, 'who': 48, 'about': 49, 'recap': 50, 'activities': 51, '1': 52, 'just': 53, 'do': 54, 'women': 55, 'when': 56, 'syria': 57, 'trade': 58, 'i': 59, '2': 60, 'or': 61, 'will': 62, 'this': 63, 'have': 64, 'president': 65, 'but': 66, 'home': 67, 'up': 68, 'long': 69, 'one': 70, 'off': 71, 'facebook': 72, 'house': 73, 'gop': 74, 'our': 75, 'case': 76, 'they': 77, 'life': 78, 'end': 79, 'right': 80, 'some': 81, 'big': 82, 'dead': 83, 'power': 84, 'say': 85, 'white': 86, 'after': 87, 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234, 'gaza': 235, 'vs': 236, 'senate': 237, 'heart': 238, 'justice': 239, 'killing': 240, 'cia': 241, 'come': 242, 'shows': 243, 'turning': 244, 'schools': 245, 'parents': 246, 'law': 247, 'legal': 248, 'economy': 249, 'strike': 250, 'him': 251, 'threat': 252, 'before': 253, 'takes': 254, 'night': 255, 'mueller': 256, 'close': 257, 'cut': 258, 'dream': 259, 'face': 260, 'friends': 261, 'does': 262, 'game': 263, 'dear': 264, 'boss': 265, 'baby': 266, 'kings': 267, 'jail': 268, 'nfl': 269, 'jimmy': 270, 'word': 271, 'hot': 272, 'turns': 273, 'gap': 274, 'got': 275, 'hope': 276, 'making': 277, 'overlooked': 278, 'push': 279, 'high': 280, 'deal': 281, 'heck': 282, 'live': 283, 'families': 284, 'wrong': 285, '2018': 286, 'fix': 287, 'lawyers': 288, 'koreas': 289, 'choose': 290, 'public': 291, 'cuomo': 292, 'steel': 293, 'open': 294, 'scott': 295, 'beyond': 296, 'variety': 297, 'ethics': 298, 'files': 299, 'dept': 300, 'let': 301, 'inside': 302, 'director': 303, 'far': 304, 'side': 305, 'rupauls': 306, 'drag': 307, 'fence': 308, 'sanctions': 309, 'want': 310, 'global': 311, 'great': 312, 'fish': 313, 'market': 314, 'team': 315, 'eat': 316, 'problem': 317, 'miracle': 318, 'tied': 319, 'hours': 320, 'working': 321, 'other': 322, 'yet': 323, 'call': 324, 'allies': 325, 'privacy': 326, 'data': 327, 'experts': 328, 'go': 329, 'met': 330, 'brooklyn': 331, 'didnt': 332, 'russian': 333, 'trial': 334, 'apply': 335, 'college': 336, 'many': 337, '4': 338, 'security': 339, 'year': 340, 'tale': 341, 'social': 342, 'control': 343, 'been': 344, 'hit': 345, 'early': 346, 'behind': 347, 'match': 348, 'ok': 349, 'fears': 350, 'isis': 351, 'walking': 352, 'national': 353, 'presidency': 354, 'student': 355, 'second': 356, 'limits': 357, 'care': 358, 'era': 359, 'south': 360, 'guard': 361, 'rise': 362, 'edge': 363, 'hero': 364, 'tech': 365, 'secret': 366, 'storm': 367, 'gets': 368, 'watch': 369, 'weapons': 370, 'cheerleaders': 371, 'meeting': 372, 'less': 373, 'science': 374, 'dirt': 375, 'times': 376, 'looking': 377, 'win': 378, 'pope': 379, 'stuff': 380, 'wants': 381, 'ruling': 382, 'town': 383, 'cold': 384, 'behavior': 385, 'guns': 386, 'stand': 387, 'human': 388, 'economic': 389, 'tragedy': 390, 'paul': 391, 'them': 392, 'key': 393, 'down': 394, 'given': 395, 'urge': 396, 'kids': 397, 'westworld': 398, 'picture': 399, 'april': 400, '23': 401, 'subway': 402, 'lets': 403, 'impeachment': 404, 'feel': 405, 'told': 406, 'smile': 407, 'meet': 408, 'wild': 409, 'guide': 410, 'business': 411, 'republicans': 412, 'starbucks': 413, 'door': 414, 'heres': 415, 'punch': 416, 'air': 417, 'caution': 418, 'democratic': 419, 'seen': 420, 'west': 421, 'once': 422, 'marriage': 423, 'politics': 424, 'mission': 425, 'support': 426, 'extra': 427, 'stephen': 428, 'colbert': 429, 'doesnt': 430, 'marijuana': 431, 'reading': 432, 'flight': 433, 'navy': 434, 'veteran': 435, 'ban': 436, 'atlanta': 437, 'finally': 438, 'ready': 439, 'todays': 440, 'puzzle': 441, 'deputy': 442, 'gave': 443, 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'quit': 650, '70': 651, 'conspiracy': 652, 'pick': 653, 'wells': 654, 'fargo': 655, 'mayhem': 656, 'fields': 657, '50': 658, 'lack': 659, 'cynthia': 660, 'nixon': 661, 'along': 662, 'promise': 663, 'testing': 664, 'candidates': 665, 'pruitts': 666, 'reforms': 667, 'selling': 668, 'know': 669, 'runners': 670, 'tarot': 671, 'card': 672, 'dress': 673, 'pilot': 674, 'nerves': 675, 'letting': 676, 'al': 677, 'equal': 678, 'returns': 679, 'snake': 680, 'oil': 681, 'betrayed': 682, 'nation': 683, 'fashion': 684, 'lay': 685, 'fine': 686, 'fraud': 687, 'again': 688, 'exfbi': 689, 'sent': 690, 'name': 691, 'sea': 692, 'stands': 693, 'spur': 694, 'means': 695, 'progressive': 696, 'standardized': 697, 'tests': 698, 'antibias': 699, 'goal': 700, 'gentrification': 701, 'shine': 702, 'road': 703, 'deportation': 704, 'voice': 705, 'crazy': 706, 'warming': 707, 'interview': 708, 'launches': 709, 'allout': 710, 'barbara': 711, 'ill': 712, 'treatment': 713, 'blasts': 714, 'slippery': 715, 'around': 716, 'office': 717, 'todo': 718, 'list': 719, 'housing': 720, 'leaves': 721, 'pinch': 722, 'took': 723, 'assad': 724, 'library': 725, 'payment': 726, 'snarl': 727, 'makes': 728, '87': 729, 'comedy': 730, 'rising': 731, 'stars': 732, 'alike': 733, 'coal': 734, 'lobbyist': 735, 'crossword': 736, 'animals': 737, 'whos': 738, 'pentagon': 739, 'signs': 740, 'childhood': 741, 'courts': 742, 'shift': 743, 'saying': 744, 'scandal': 745, 'sticker': 746, 'shock': 747, 'teacher': 748, 'walkouts': 749, 'grip': 750, 'red': 751, 'speaker': 752, 'leave': 753, 'british': 754, 'mexico': 755, 'wish': 756, 'alive': 757, 'longer': 758, 'moscow': 759, 'believes': 760, 'beef': 761, 'stew': 762, 'chinese': 763, 'partner': 764, 'tabloid': 765, 'catches': 766, 'facebooks': 767, 'lot': 768, 'health': 769, 'putin': 770, 'india': 771, 'these': 772, 'begin': 773, 'protest': 774, 'florida': 775, 'colorado': 776, 'thats': 777, 'hopes': 778, 'access': 779, 'hollywood': 780, 'tape': 781, 'focus': 782, 'fbi': 783, 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849, 'calls': 850, 'polar': 851, 'bears': 852, 'online': 853, 'bust': 854, 'bars': 855, 'private': 856, 'fans': 857, 'remains': 858, 'funny': 859, 'daughter': 860, 'offered': 861, 'massacre': 862, 'prison': 863, 'votes': 864, 'very': 865, 'outsiders': 866, 'gender': 867, 'scrutiny': 868, 'trail': 869, 'nightmare': 870, 'species': 871, 'fuels': 872, 'jacket': 873, 'hair': 874, '81': 875, 'foreign': 876, '9': 877, 'arabian': 878, 'official': 879, 'afghan': 880, 'enters': 881, 'journalism': 882, 'later': 883, 'cry': 884, 'vows': 885, 'price': 886, 'raising': 887, 'song': 888, 'gallery': 889, 'hockey': 890, 'fox': 891, 'fiery': 892, 'chemical': 893, 'hasty': 894, 'viral': 895, 'voters': 896, 'arlee': 897, 'arent': 898, 'friday': 899, 'something': 900, 'every': 901, 'buying': 902, 'ad': 903, 'asparagus': 904, 'blame': 905, 'democracy': 906, 'bracing': 907, 'blood': 908, 'estate': 909, 'jobs': 910, 'raised': 911, 'islam': 912, 'yorks': 913, 'give': 914, 'blast': 915, 'small': 916, 'forces': 917, 'tight': 918, 'labor': 919, 'manhattan': 920, 'intellectual': 921, 'tiny': 922, 'haunting': 923, 'thoughts': 924, 'misconduct': 925, 'usual': 926, 'lisa': 927, 'evil': 928, 'springs': 929, 'stress': 930, 'monkeys': 931, 'gay': 932, 'safety': 933, 'crashes': 934, 'queen': 935, 'indexes': 936, 'indian': 937, 'dreams': 938, 'role': 939, 'macrons': 940, 'locals': 941, 'shot': 942, 'whims': 943, 'sees': 944, 'makers': 945, 'pipe': 946, 'slow': 947, 'quiet': 948, 'ohio': 949, 'primary': 950, 'readers': 951, 'universe': 952, 'older': 953, 'well': 954, 'rents': 955, 'saving': 956, 'worship': 957, 'detail': 958, 'project': 959, 'dissent': 960, 'kennedy': 961, 'assassination': 962, 'edges': 963, 'brazil': 964, 'sports': 965, 'dad': 966, 'amazon': 967, 'trauma': 968, 'anxiety': 969, '100': 970, 'endless': 971, 'video': 972, 'legacy': 973, 'candidate': 974, 'shooting': 975, 'silicon': 976, 'valley': 977, 'light': 978, 'chose': 979, 'heated': 980, 'leads': 981, 'israelis': 982, 'lone': 983, 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'students': 1047, 'genre': 1048, 'instagram': 1049, 'survey': 1050, 'wouldnt': 1051, 'phone': 1052, 'standing': 1053, 'act': 1054, 'would': 1055, 'pulitzer': 1056, 'prize': 1057, 'treat': 1058, 'tea': 1059, 'mother': 1060, 'peace': 1061, 'virtual': 1062, 'currency': 1063, 'journalists': 1064, 'finds': 1065, 'lawman': 1066, 'wins': 1067, 'itself': 1068, 'abroad': 1069, 'mercy': 1070, 'suspects': 1071, 'doctors': 1072, 'letter': 1073, 'comeys': 1074, 'spoil': 1075, 'child': 1076, 'inch': 1077, 'site': 1078, 'finale': 1079, 'immune': 1080, 'therapy': 1081, 'japanese': 1082, 'conquering': 1083, 'graft': 1084, 'really': 1085, 'immigrant': 1086, 'spf': 1087, 'overdue': 1088, 'presidential': 1089, 'dreamers': 1090, 'officer': 1091, 'judge': 1092, 'water': 1093, 'im': 1094, 'workers': 1095, 'n0': 1096, 'guilty': 1097, 'killer': 1098, 'pain': 1099, 'same': 1100, 'led': 1101, 'wave': 1102, 'inquiry': 1103, 'least': 1104, 'jury': 1105, 'bill': 1106, 'street': 1107, 'reckoning': 1108, 'birthday': 1109, 'ride': 1110, 'board': 1111, 'meddling': 1112, 'sets': 1113, 'telecom': 1114, 'settlement': 1115, 'goodell': 1116, 'unveil': 1117, 'effect': 1118, 'policymaking': 1119, 'noma': 1120, 'explained': 1121, 'became': 1122, 'selfexpression': 1123, 'commuter': 1124, 'reprogramming': 1125, 'ford': 1126, 'changed': 1127, 'lift': 1128, 'romney': 1129, 'convention': 1130, 'doomed': 1131, 'chain': 1132, 'reaction': 1133, 'vatican': 1134, 'investigate': 1135, 'francis': 1136, 'berlin': 1137, 'knows': 1138, 'reignite': 1139, 'churchstate': 1140, 'separation': 1141, 'procrastinating': 1142, 'dilatory': 1143, 'bout': 1144, 'e': 1145, 'coli': 1146, 'poisoning': 1147, 'brexit': 1148, 'yearned': 1149, 'seafaring': 1150, 'muddied': 1151, 'quote': 1152, 'disproved': 1153, 'bizarre': 1154, 'groups': 1155, 'equality': 1156, 'nashville': 1157, 'education': 1158, 'relents': 1159, 'approved': 1160, 'buses': 1161, 'turnaround': 1162, 'dinner': 1163, 'nods': 1164, 'traditions': 1165, 'pouring': 1166, 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'pigeons': 1464, 'salute': 1465, 'signature': 1466, 'signing': 1467, 'expressions': 1468, 'affection': 1469, 'republic': 1470, 'targets': 1471, 'pull': 1472, 'yourself': 1473, 'together': 1474, 'henry': 1475, 'higgins': 1476, 'pictures': 1477, 'viewing': 1478, 'through': 1479, 'resignation': 1480, 'ahead': 1481, 'spurn': 1482, 'endorse': 1483, 'rival': 1484, 'burgundy': 1485, 'iconoclast': 1486, 'laurent': 1487, 'ponsot': 1488, 'looks': 1489, 'projects': 1490, 'trading': 1491, 'mitzi': 1492, 'shore': 1493, 'club': 1494, 'owner': 1495, 'fostered': 1496, 'university': 1497, 'rice': 1498, 'balls': 1499, 'subtle': 1500, 'showy': 1501, 'omusubi': 1502, 'gonbei': 1503, 'insider': 1504, 'addressing': 1505, 'areas': 1506, 'punched': 1507, 'clock': 1508, 'issue': 1509, 'rises': 1510, 'fore': 1511, 'vindicating': 1512, 'zoo': 1513, 'performer': 1514, 'staring': 1515, 'urges': 1516, 'greater': 1517, 'pill': 1518, 'sarms': 1519, 'armageddon': 1520, 'revert': 1521, 'queens': 1522, 'appointing': 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'overpass': 1642, 'cograndparent': 1643, 'am': 1644, 'paying': 1645, 'nap': 1646, 'tethered': 1647, 'raging': 1648, 'buffoon': 1649, 'hammers': 1650, 'congress': 1651, 'dependence': 1652, 'cash': 1653, 'berkshires': 1654, 'solved': 1655, 'dorm': 1656, 'mans': 1657, 'obsession': 1658, 'uncertainty': 1659, 'missouri': 1660, 'maze': 1661, 'lost': 1662, 'syrias': 1663, 'sustainable': 1664, 'environment': 1665, 'bank': 1666, 'account': 1667, 'paltry': 1668, 'neighbor': 1669, 'chasing': 1670, 'thief': 1671, 'spideys': 1672, 'creator': 1673, 'web': 1674, 'strife': 1675, 'upends': 1676, 'flower': 1677, 'shop': 1678, 'ecclesiastic': 1679, 'serious': 1680, 'rupocalypse': 1681, '7': 1682, 'drake': 1683, 'points': 1684, 'inevitable': 1685, 'pardon': 1686, 'convicted': 1687, 'visceral': 1688, 'grim': 1689, 'memoir': 1690, 'works': 1691, 'hill': 1692, 'cheer': 1693, 'favors': 1694, 'flimflam': 1695, 'renaissance': 1696, 'perfume': 1697, 'mincing': 1698, 'sheriff': 1699, 'indulged': 1700, 'mounted': 1701, 'overdose': 1702, 'accessible': 1703, 'falters': 1704, 'shifting': 1705, 'baffle': 1706, 'chinas': 1707, 'masculinity': 1708, 'age': 1709, 'photo': 1710, 'twice': 1711, 'major': 1712, 'lifts': 1713, 'renewable': 1714, 'sources': 1715, 'historians': 1716, 'versus': 1717, 'genealogists': 1718, 'weighs': 1719, 'rejoining': 1720, 'accord': 1721, 'pacific': 1722, 'rim': 1723, 'aboutface': 1724, 'brisbane': 1725, 'gauge': 1726, 'apart': 1727, 'imitators': 1728, 'medallion': 1729, 'builtin': 1730, 'advantage': 1731, 'majority': 1732, 'receding': 1733, 'camdens': 1734, 'improve': 1735, 'stateappointed': 1736, 'superintendent': 1737, 'steps': 1738, 'aside': 1739, 'abortion': 1740, 'roe': 1741, 'unwanted': 1742, 'advances': 1743, 'missouris': 1744, 'italy': 1745, 'deleted': 1746, 'ago': 1747, 'musicians': 1748, 'concert': 1749, 'promises': 1750, 'promote': 1751, 'alone': 1752, 'weight': 1753, 'fictional': 1754, 'pence': 1755, 'plane': 1756, 'algerias': 1757, 'worst': 1758, '257': 1759, 'giant': 1760, 'collects': 1761, 'uses': 1762, 'except': 1763, 'pedestrian': 1764, 'reddest': 1765, 'rural': 1766, 'districts': 1767, 'nra': 1768, 'hidden': 1769, 'answering': 1770, 'seth': 1771, 'meyers': 1772, 'wonders': 1773, 'spells': 1774, 'doom': 1775, 'animal': 1776, 'welfare': 1777, 'suffering': 1778, 'brassai': 1779, 'paris': 1780, 'python': 1781, 'pet': 1782, 'snatched': 1783, 'smells': 1784, 'memories': 1785, 'classic': 1786, 'else': 1787, 'bite': 1788, 'ignored': 1789, 'clown': 1790, 'bleached': 1791, 'angry': 1792, 'subpoenas': 1793, 'nearly': 1794, 'december': 1795, 'traumatic': 1796, 'injuries': 1797, 'dementia': 1798, 'salt': 1799, 'pepper': 1800, 'already': 1801, 'gory': 1802, 'el': 1803, 'chapo': 1804, 'draining': 1805, 'three': 1806, 'investigates': 1807, 'admissions': 1808, 'tiki': 1809, 'bar': 1810, 'lessthantropical': 1811, 'raids': 1812, 'hush': 1813, 'fumes': 1814, 'trembling': 1815, 'screaming': 1816, 'cream': 1817, 'homeland': 1818, 'bolton': 1819, 'settles': 1820, 'filipinos': 1821, 'tatters': 1822, 'trip': 1823, 'latin': 1824, 'citing': 1825, 'climate': 1826, 'skeptics': 1827, 'beg': 1828, 'differ': 1829, 'speak': 1830, 'accountable': 1831, 'misunderstands': 1832, 'imagines': 1833, 'went': 1834, 'diving': 1835, 'renovation': 1836, 'gusto': 1837, 'rhodesias': 1838, 'supremacists': 1839, 'cambridge': 1840, 'analytica': 1841, 'course': 1842, 'escapes': 1843, 'riots': 1844, 'beatings': 1845, 'prisons': 1846, 'soups': 1847, 'beautiful': 1848, 'bogot': 1849, 'travelers': 1850, 'starter': 1851, 'kit': 1852, 'groped': 1853, 'pro': 1854, 'bosses': 1855, 'disapprove': 1856, 'vouchers': 1857, 'mayors': 1858, 'streetcar': 1859, 'markdowns': 1860, 'poem': 1861, 'cure': 1862, 'pages': 1863, 'exspy': 1864, 'hospital': 1865, 'popup': 1866, 'classes': 1867, 'reporters': 1868, 'recorded': 1869, 'myanmar': 1870, 'authority': 1871, 'months': 1872, 'resigning': 1873, 'event': 1874, 'revulsion': 1875, 'eroded': 1876, 'obamacares': 1877, 'stable': 1878, 'failures': 1879, 'antitrumpism': 1880, 'bannon': 1881, 'beliefs': 1882, 'parent': 1883, 'allay': 1884, '911': 1885, 'born': 1886, 'zuckerberg': 1887, 'center': 1888, 'reshape': 1889, 'tokens': 1890, 'please': 1891, 'widens': 1892, 'onenight': 1893, 'temptation': 1894, 'handlers': 1895, 'leeches': 1896, 'trackers': 1897, 'poised': 1898, 'balloon': 1899, 'trillion': 1900, 'deficit': 1901, 'grilling': 1902, 'tie': 1903, 'shirt': 1904, 'mets': 1905, 'excited': 1906, 'authentic': 1907, 'retaliation': 1908, 'abou': 1909, 'sizzle': 1910, 'grill': 1911, 'tense': 1912, 'fossilized': 1913, 'finger': 1914, 'bone': 1915, 'earliest': 1916, 'peninsula': 1917, 'advisers': 1918, 'head': 1919, 'actions': 1920, 'commander': 1921, 'returning': 1922, 'battleground': 1923, '20': 1924, 'rallying': 1925, 'reactions': 1926, 'shingles': 1927, 'vaccine': 1928, 'reasons': 1929, 'masked': 1930, 'wrestling': 1931, 'priest': 1932, 'prospect': 1933, 'missile': 1934, 'hub': 1935, 'contemplation': 1936, 'challenge': 1937, 'books': 1938, 'teach': 1939, 'management': 1940, 'cleaning': 1941, 'wit': 1942, 'tina': 1943, 'fey': 1944, 'splintered': 1945, 'hearts': 1946, 'horror': 1947, 'governed': 1948, 'amp': 1949, 'powderkeg': 1950, 'bon': 1951, 'vivant': 1952, 'tower': 1953, 'couldnt': 1954, 'sell': 1955, 'affected': 1956, 'users': 1957, 'carnage': 1958, 'muddles': 1959, 'exit': 1960, 'sweeping': 1961, 'election': 1962, 'victory': 1963, 'hungarys': 1964, 'ruler': 1965, 'constitution': 1966, 'antioch': 1967, 'initials': 1968, 'stroller': 1969, 'scrawls': 1970, 'credit': 1971, 'trusts': 1972, 'complain': 1973, 'triple': 1974, 'spoonerisms': 1975, 'murky': 1976, 'perils': 1977, 'quitting': 1978, 'antidepressants': 1979, 'landlord': 1980, 'refund': 1981, 'fee': 1982, 'amenities': 1983, 'available': 1984, 'renegades': 1985, 'sri': 1986, 'lanka': 1987, 'bombast': 1988, 'samantha': 1989, 'germs': 1990, 'diet': 1991, 'soda': 1992, 'dvd': 1993, 'staff': 1994, 'advised': 1995, 'resistant': 1996, 'epas': 1997, 'current': 1998, 'tb': 1999, 'insufficient': 2000, 'princes': 2001, 'tour': 2002, 'murdoch': 2003, 'gates': 2004, 'oprah': 2005, 'anonymous': 2006, 'earned': 2007, 'divine': 2008, 'status': 2009, 'frustrated': 2010, 'yorkers': 2011, 'catering': 2012, 'fliers': 2013, '30000': 2014, 'feet': 2015, 'enemy': 2016, 'cecil': 2017, 'taylor': 2018, 'pianist': 2019, 'defied': 2020, 'jazz': 2021, 'orthodoxy': 2022, '89': 2023, 'pressure': 2024, 'miscarriage': 2025, 'dynasty': 2026, 'passing': 2027, 'torch': 2028, 'disappointing': 2029, 'alarms': 2030, 'nominees': 2031, 'views': 2032, '80s': 2033, 'kors': 2034, 'dismantle': 2035, 'obamas': 2036, 'hatewatching': 2037, 'hgtv': 2038, 'putins': 2039, 'inner': 2040, 'circle': 2041, 'gendered': 2042, 'emasculator': 2043, 'until': 2044, 'arrived': 2045, 'chappaquiddick': 2046, 'theory': 2047, 'march': 2048, 'structure': 2049, 'denies': 2050, 'knowledge': 2051, 'porn': 2052, 'flail': 2053, 'concerns': 2054, 'ousted': 2055, 'script': 2056, 'restaurateur': 2057, 'starr': 2058, 'keith': 2059, 'mcnally': 2060, 'reopen': 2061, 'pastis': 2062, 'liberal': 2063, 'nirvana': 2064, 'sometimes': 2065, 'room': 2066, 'protecting': 2067, 'outposts': 2068, 'pulling': 2069, 'lawn': 2070, 'chairs': 2071, 'someones': 2072, 'prom': 2073, 'include': 2074, 'squeezes': 2075, 'restaurants': 2076, 'growing': 2077, 'threatens': 2078, 'aides': 2079, 'exits': 2080, 'confident': 2081, 'buzz': 2082, 'v': 2083, 'sale': 2084, 'harder': 2085, 'disabilities': 2086, 'contract': 2087, 'authoritarianism': 2088, 'driven': 2089, 'elevator': 2090, 'oh': 2091, 'accuse': 2092, 'architect': 2093, 'finding': 2094, 'decent': 2095, 'wades': 2096, 'deeper': 2097, 'detainee': 2098, 'operations': 2099, 'kurds': 2100, 'recovery': 2101, 'weakness': 2102, 'strength': 2103, 'commerce': 2104, 'murkowski': 2105, 'mastered': 2106, 'myerss': 2107, 'fallon': 2108, 'homework': 2109, 'therapist': 2110, 'japans': 2111, 'popular': 2112, 'bathing': 2113, 'temple': 2114, 'elephants': 2115, 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2175, 'officers': 2176, 'keep': 2177, 'crossing': 2178, 'undeterred': 2179, 'washingtons': 2180, 'agreement': 2181, 'nafta': 2182, 'must': 2183, '45000': 2184, 'bribes': 2185, 'luxuries': 2186, 'prosecutors': 2187, 'escalates': 2188, 'tariff': 2189, 'mistake': 2190, 'worries': 2191, 'sending': 2192, 'pointing': 2193, 'believed': 2194, 'infrastructure': 2195, 'fund': 2196, 'hype': 2197, 'shhhh': 2198, 'silence': 2199, 'mostly': 2200, 'enchanted': 2201, 'aims': 2202, 'transparency': 2203, 'defeated': 2204, 'circus': 2205, 'deploy': 2206, 'condoning': 2207, 'illiteracy': 2208, 'footprints': 2209, 'scottish': 2210, 'isle': 2211, 'playground': 2212, 'dinosaurs': 2213, 'lovely': 2214, 'tempts': 2215, 'kasich': 2216, 'hearing': 2217, 'multiple': 2218, 'channels': 2219, 'balancing': 2220, 'chaos': 2221, 'mighty': 2222, 'yes': 2223, 'wiser': 2224, 'homelessness': 2225, 'follows': 2226, 'minnesota': 2227, 'miners': 2228, 'houses': 2229, 'astounding': 2230, 'pulls': 2231, 'total': 2232, 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3158), ('classical', 3159), ('low', 3160), ('inflation', 3161), ('rates', 3162), ('copenhagen', 3163), ('surges', 3164), ('fed', 3165), ('karate', 3166), ('kid', 3167), ('grudge', 3168), ('rematch', 3169), ('lifes', 3170), ('chance', 3171), ('revolution', 3172), ('uprising', 3173), ('different', 3174), ('41', 3175), ('lgbt', 3176), ('reject', 3177), ('names', 3178), ('mommy', 3179), ('daddy', 3180), ('isolated', 3181), ('hid', 3182), ('buyers', 3183), ('terraces', 3184), ('linger', 3185), ('merkel', 3186), ('visit', 3187), ('welcome', 3188), ('mat', 3189), ('distances', 3190), ('savory', 3191), ('salty', 3192), ('carricantes', 3193), ('sicily', 3194), ('lesson', 3195), ('fiano', 3196), ('dumplings', 3197), ('nod', 3198), ('signal', 3199), ('genealogy', 3200), ('front', 3201), ('pop', 3202), ('rebellion', 3203), ('siberia', 3204), ('unlikely', 3205), ('greenpeace', 3206), ('tots', 3207), ('heavy', 3208), ('weights', 3209), ('unreserved', 3210), ('hawk', 3211), ('confirmed', 3212), ('diplomat', 3213), ('spare', 3214), ('italian', 3215), ('subs', 3216), ('australia', 3217), ('games', 3218), ('squash', 3219), ('ceiling', 3220), ('spider', 3221), ('tree', 3222), ('tries', 3223), ('untangle', 3224), ('evolution', 3225), ('webs', 3226), ('codes', 3227), ('smiles', 3228), ('comments', 3229), ('rift', 3230), ('braindamaged', 3231), ('review', 3232), ('searches', 3233), ('misogynists', 3234), ('terrorists', 3235), ('abby', 3236), ('caffeine', 3237), ('overweight', 3238), ('offspring', 3239), ('modernday', 3240), ('myth', 3241), ('verge', 3242), ('shares', 3243), ('prosecutor', 3244), ('seeking', 3245), ('smarts', 3246), ('poor', 3247), ('wrongdoing', 3248), ('killers', 3249), ('barflies', 3250), ('clinging', 3251), ('ends', 3252), ('comics', 3253), ('mona', 3254), ('short', 3255), ('theater', 3256), ('square', 3257), ('unite', 3258), ('banish', 3259), ('jabs', 3260), ('melania', 3261), ('gift', 3262), ('members', 3263), ('airconditioning', 3264), ('learned', 3265), ('corporate', 3266), ('diary', 3267), ('coop', 3268), ('slowed', 3269), ('growth', 3270), ('blip', 3271), ('driving', 3272), ('cliff', 3273), ('hart', 3274), ('stepping', 3275), ('outside', 3276), ('dollhouse', 3277), ('alkaline', 3278), ('owes', 3279), ('build', 3280), ('biden', 3281), ('disease', 3282), ('lawyer', 3283), ('clinton', 3284), ('surefire', 3285), ('winner', 3286), ('certain', 3287), ('debates', 3288), ('verdict', 3289), ('financial', 3290), ('fresh', 3291), ('bright', 3292), ('stirfry', 3293), ('happening', 3294), ('bloody', 3295), ('preview', 3296), ('deluxe', 3297), ('frittata', 3298), ('topped', 3299), ('burrata', 3300), ('merger', 3301), ('tmobile', 3302), ('sprint', 3303), ('popcorn', 3304), ('bosss', 3305), ('youtoo', 3306), ('nicaraguan', 3307), ('daunting', 3308), ('task', 3309), ('tracking', 3310), ('bootlegger', 3311), ('mayor', 3312), ('sells', 3313), ('bridge', 3314), ('yipes', 3315), ('canned', 3316), ('chaplain', 3317), ('scoffs', 3318), ('clues', 3319), ('placed', 3320), ('serial', 3321), ('nike', 3322), ('forcing', 3323), ('populist', 3324), ('tide', 3325), ('remaking', 3326), ('supporters', 3327), ('cartoon', 3328), ('nobel', 3329), ('laureate', 3330), ('alfie', 3331), ('evans', 3332), ('rich', 3333), ('extinction', 3334), ('identity', 3335), ('intelligence', 3336), ('misunabbreviated', 3337), ('hed', 3338), ('pursuit', 3339), ('truce', 3340), ('dinners', 3341), ('merging', 3342), ('tappan', 3343), ('zee', 3344), ('among', 3345), ('fluke', 3346), ('porgies', 3347), ('150', 3348), ('reach', 3349), ('brace', 3350), ('deadline', 3351), ('draws', 3352), ('lessons', 3353), ('trueblue', 3354), ('liberals', 3355), ('2020', 3356), ('proxy', 3357), ('underwear', 3358), ('potatoes', 3359), ('doug', 3360), ('jones', 3361), ('tennessee', 3362), ('gazan', 3363), ('living', 3364), ('thing', 3365), ('sense', 3366), ('transgender', 3367), ('bans', 3368), ('facts', 3369), ('apu', 3370), ('disarmament', 3371), ('deterrents', 3372), ('libya', 3373), ('always', 3374), ('uphill', 3375), ('laws', 3376), ('passed', 3377), ('judgment', 3378), ('eaten', 3379), ('focuses', 3380), ('snowladen', 3381), ('flights', 3382), ('jfk', 3383), ('minutes', 3384), ('deadliest', 3385), ('afghanistan', 3386), ('since', 3387), ('2002', 3388), ('fearing', 3389), ('photographing', 3390), ('rituals', 3391), ('surround', 3392), ('emails', 3393), ('hate', 3394), ('opaque', 3395), ('ownership', 3396), ('llc', 3397), ('worried', 3398), ('risky', 3399), ('teenage', 3400), ('tougher', 3401), ('survived', 3402), ('started', 3403), ('30', 3404), ('happy', 3405), ('karl', 3406), ('marx', 3407), ('incels', 3408), ('jihadists', 3409), ('orphan', 3410), ('frequently', 3411), ('supports', 3412), ('gamify', 3413), ('prepared', 3414), ('cede', 3415), ('pledges', 3416), ('invade', 3417), ('demands', 3418), ('ditch', 3419), ('wheelchair', 3420), ('tokyo', 3421), ('distinctive', 3422), ('austin', 3423), ('crew', 3424), ('gardens', 3425), ('ending', 3426), ('infinity', 3427), ('documenter', 3428), ('fortnite', 3429), ('addict', 3430), ('philosophy', 3431), ('pays', 3432), ('disappearances', 3433), ('highlight', 3434), ('militarys', 3435), ('mexicos', 3436), ('documents', 3437), ('irans', 3438), ('atomic', 3439), ('subterfuge', 3440), ('investment', 3441), ('holds', 3442), ('steady', 3443), ('q', 3444), ('hows', 3445), ('standoff', 3446), ('eases', 3447), ('allow', 3448), ('eight', 3449), ('seek', 3450), ('asylum', 3451), ('assumes', 3452), ('beauty', 3453), ('megamerger', 3454), ('tightens', 3455), ('app', 3456), ('investigation', 3457), ('couple', 3458), ('dividing', 3459), ('delaying', 3460), ('limit', 3461), ('researchers', 3462), ('sheldon', 3463), ('silver', 3464), ('retrial', 3465), ('revisits', 3466), ('gig', 3467), ('dealt', 3468), ('blow', 3469), ('cardinal', 3470), ('faces', 3471), ('charges', 3472), ('context', 3473), ('show', 3474), ('depth', 3475), ('obstacles', 3476), ('competitive', 3477), ('sport', 3478), ('rootslush', 3479), ('historical', 3480), ('rooted', 3481), ('naturenovels', 3482), ('steeped', 3483), ('historyancient', 3484), ('reimaginedjurassic', 3485), ('classics', 3486), ('question', 3487), ('reinforces', 3488), ('anna', 3489), ('llama', 3490), ('hayden', 3491), ('regret', 3492), ('nothin', 3493)])
epa
max_len : 24
[[   0    0    0    0    0    0    0    0    0    0    0    0    0    0
     0    0    0    0    0    0    0    0   99  269]
 [   0    0    0    0    0    0    0    0    0    0    0    0    0    0
     0    0    0    0    0    0    0   99  269  371]
 [   0    0    0    0    0    0    0    0    0    0    0    0    0    0
     0    0    0    0    0    0   99  269  371 1115]]
[[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
    0   0   0   0  99]
 [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
    0   0   0  99 269]
 [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
    0   0  99 269 371]]
[ 269  371 1115]
[[0. 0. 0. ... 0. 0. 0.]]
Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 embedding (Embedding)       (None, 23, 32)            111808    
                                                                 
 lstm (LSTM)                 (None, 128)               82432     
                                                                 
 flatten (Flatten)           (None, 128)               0         
                                                                 
 dense (Dense)               (None, 128)               16512     
                                                                 
 dense_1 (Dense)             (None, 64)                8256      
                                                                 
 dense_2 (Dense)             (None, 3494)              227110    
                                                                 
=================================================================
Total params: 446,118
Trainable params: 446,118
Non-trainable params: 0
_________________________________________________________________
None

defense from people too other state dinner nods to traditions and
tuesday the americans season 6 episode 5 recap the end game
beginning the americans season 6 episode 5 recap the end game

영어의 경우에는 한글과 달리 형태소 분석을 하지 않아도 된다.

TensorFlow 기초 39 - LSTM을 이용한 텍스트 생성, 문맥을 반영하여 다음 단어를 예측하기

코딩탕탕 — Wed, 14 Dec 2022 11:35:24 +0900

# RNN을 이용한 텍스트 생성
# 문맥을 반영하여 다음 단어를 예측하기

import numpy as np
from keras.models import Sequential
from keras.layers import Embedding, Flatten, Dense, LSTM
from keras.preprocessing.text import Tokenizer
from keras.utils import pad_sequences, to_categorical
from anaconda_project.internal.conda_api import result
"""
text = '''
경마장에 있는 말이 뛰고 있다
그의 말이 법이다
가는 말이 고와야 오는 말이 곱다'''
"""

text = '''수도권 개별 단지들의 전세가격 하락세가 두드러지게 나타나고 있다
경기 파주 힐스테이트 운정은 지난 10월 16층이 2억8000만원에 전세 거래됐다
2020년 같은 달 같은 층수는 3억1500만원에 거래됐다
인천 미추홀구 인천SK스카이뷰 역시 지난달 12층이 3억원에 거래되면서 2년 전 가격보다 5500만원 내렸으며,
부개주공3단지는 2년 전보다 4500만원 내린 1억8000만원에 거래됐다'''

tok = Tokenizer()
tok.fit_on_texts([text]) # fit_on_texts의 값으로는 무조건 list type으로 주어야 된다. 단어와 인덱싱을 출력
print(tok.word_index)
encoded = tok.texts_to_sequences([text]) # 단어를 정수로 메트릭스화 해서 출력
print(encoded) # [[2, 3, 1, 4, 5, 6, 1, 7, 8, 1, 9, 10, 1, 11]]

vocab_size = len(tok.word_index) + 1 # Embedding(가능한 토큰 수)
print(vocab_size)

# 훈련 데이터 만들기
sequences = list()
for line in text.split('\n'): # 문장 토큰화
    enco = tok.texts_to_sequences([line])[0]
    # print(enco)
    # 바로 다음 단어를 label로 사용하기 위해 리스트에 벡터 기억
    for i in range(1, len(enco)):
        sequ = enco[:i + 1]
        # print(sequ)
        sequences.append(sequ)
        
print(sequences) # [[2, 3], [2, 3, 1], [2, 3, 1, 4], [2, 3, 1, 4, 5], [6, 1], ...
print('학습 참여 샘플 수 :', len(sequences)) # 11
print(max(len(i) for i in sequences)) # 6

# 가장 긴 벡터의 길이를 기준으로 각 벡터의 크기를 통일
max_len = max(len(i) for i in sequences)

psequences = pad_sequences(sequences, maxlen=max_len, padding='pre') # pre는 왼쪽을 0 으로 채운다. 'post'는 오른쪽을 0으로 채운다.
print(psequences)

# 각 벡터의 마지막 요소(단어)를 label로 사용
x = psequences[:, :-1] # feature, 마지막 숫자(단어) 이외의 것이 feature가 된다.
y = psequences[:, -1]  # label, 마지막 숫자(단어)가 label이 된다.
print(x[:2])
print(y) # [ 3  1  4  5  1  7  1  9 10  1 11]

# 모델의 최종분류 활성화 함수를 softmax로 사용하므로 y를 원핫 처리
y = to_categorical(y, num_classes=vocab_size)
print(y[:2]) # [ 3  1  4  5  1  7  1  9 10  1 11]

# model
model = Sequential()
model.add(Embedding(vocab_size, 32, input_length=max_len - 1))
model.add(LSTM(32, activation='tanh'))
model.add(Flatten())
model.add(Dense(32, activation='relu'))
model.add(Dense(32, activation='relu'))
model.add(Dense(vocab_size, activation='softmax'))
print(model.summary())

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(x, y, epochs=200, verbose=0)
print('model evaluate :', model.evaluate(x, y))

# 문자열 생성 함수
def seq_gen_text_func(model, t, current_word, n):
    init_word = current_word # 처음 들어온 단어도 마지막 함께 출력할 계획
    sentence = ''
    for _ in range(n):
        encoded = t.texts_to_sequences([current_word])[0]
        encoded = pad_sequences([encoded], maxlen=max_len - 1, padding='pre')
        result = np.argmax(model.predict(encoded, verbose=0), axis = -1)
        # 예측 단어 찾기
        for word, index in t.word_index.items():
            # print(word, ' :', index)
            if index == result: # 예측한 단어, 그리고 인덱스와 동일한 단어가 있다면 해당 단어는 예측단어 이므로 break 
                break
        current_word = current_word + ' ' + word
        sentence = sentence + ' ' + word
    sentence = init_word + sentence
    return sentence

'''
print(seq_gen_text_func(model, tok, '경마장', 1))
print(seq_gen_text_func(model, tok, '그의', 2))
print(seq_gen_text_func(model, tok, '가는', 3))
print(seq_gen_text_func(model, tok, '가는', 4))
print(seq_gen_text_func(model, tok, '경마장에', 4))
'''
print(seq_gen_text_func(model, tok, '수도권', 4))
print(seq_gen_text_func(model, tok, '인천', 4))


<console>
{'거래됐다': 1, '같은': 2, '2년': 3, '수도권': 4, '개별': 5, '단지들의': 6, '전세가격': 7, '하락세가': 8, '두드러지게': 9, '나타나고': 10, '있다': 11, '경기': 12, '파주': 13, '힐스테이트': 14, '운정은': 15, '지난': 16, '10월': 17, '16층이': 18, '2억8000만원에': 19, '전세': 20, '2020년': 21, '달': 22, '층수는': 23, '3억1500만원에': 24, '인천': 25, '미추홀구': 26, '인천sk스카이뷰': 27, '역시': 28, '지난달': 29, '12층이': 30, '3억원에': 31, '거래되면서': 32, '전': 33, '가격보다': 34, '5500만원': 35, '내렸으며': 36, '부개주공3단지는': 37, '전보다': 38, '4500만원': 39, '내린': 40, '1억8000만원에': 41}
[[4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 21, 2, 22, 2, 23, 24, 1, 25, 26, 27, 28, 29, 30, 31, 32, 3, 33, 34, 35, 36, 37, 3, 38, 39, 40, 41, 1]]
42
[[4, 5], [4, 5, 6], [4, 5, 6, 7], [4, 5, 6, 7, 8], [4, 5, 6, 7, 8, 9], [4, 5, 6, 7, 8, 9, 10], [4, 5, 6, 7, 8, 9, 10, 11], [12, 13], [12, 13, 14], [12, 13, 14, 15], [12, 13, 14, 15, 16], [12, 13, 14, 15, 16, 17], [12, 13, 14, 15, 16, 17, 18], [12, 13, 14, 15, 16, 17, 18, 19], [12, 13, 14, 15, 16, 17, 18, 19, 20], [12, 13, 14, 15, 16, 17, 18, 19, 20, 1], [21, 2], [21, 2, 22], [21, 2, 22, 2], [21, 2, 22, 2, 23], [21, 2, 22, 2, 23, 24], [21, 2, 22, 2, 23, 24, 1], [25, 26], [25, 26, 27], [25, 26, 27, 28], [25, 26, 27, 28, 29], [25, 26, 27, 28, 29, 30], [25, 26, 27, 28, 29, 30, 31], [25, 26, 27, 28, 29, 30, 31, 32], [25, 26, 27, 28, 29, 30, 31, 32, 3], [25, 26, 27, 28, 29, 30, 31, 32, 3, 33], [25, 26, 27, 28, 29, 30, 31, 32, 3, 33, 34], [25, 26, 27, 28, 29, 30, 31, 32, 3, 33, 34, 35], [25, 26, 27, 28, 29, 30, 31, 32, 3, 33, 34, 35, 36], [37, 3], [37, 3, 38], [37, 3, 38, 39], [37, 3, 38, 39, 40], [37, 3, 38, 39, 40, 41], [37, 3, 38, 39, 40, 41, 1]]
학습 참여 샘플 수 : 40
13
[[ 0  0  0  0  0  0  0  0  0  0  0  4  5]
 [ 0  0  0  0  0  0  0  0  0  0  4  5  6]
 [ 0  0  0  0  0  0  0  0  0  4  5  6  7]
 [ 0  0  0  0  0  0  0  0  4  5  6  7  8]
 [ 0  0  0  0  0  0  0  4  5  6  7  8  9]
 [ 0  0  0  0  0  0  4  5  6  7  8  9 10]
 [ 0  0  0  0  0  4  5  6  7  8  9 10 11]
 [ 0  0  0  0  0  0  0  0  0  0  0 12 13]
 [ 0  0  0  0  0  0  0  0  0  0 12 13 14]
 [ 0  0  0  0  0  0  0  0  0 12 13 14 15]
 [ 0  0  0  0  0  0  0  0 12 13 14 15 16]
 [ 0  0  0  0  0  0  0 12 13 14 15 16 17]
 [ 0  0  0  0  0  0 12 13 14 15 16 17 18]
 [ 0  0  0  0  0 12 13 14 15 16 17 18 19]
 [ 0  0  0  0 12 13 14 15 16 17 18 19 20]
 [ 0  0  0 12 13 14 15 16 17 18 19 20  1]
 [ 0  0  0  0  0  0  0  0  0  0  0 21  2]
 [ 0  0  0  0  0  0  0  0  0  0 21  2 22]
 [ 0  0  0  0  0  0  0  0  0 21  2 22  2]
 [ 0  0  0  0  0  0  0  0 21  2 22  2 23]
 [ 0  0  0  0  0  0  0 21  2 22  2 23 24]
 [ 0  0  0  0  0  0 21  2 22  2 23 24  1]
 [ 0  0  0  0  0  0  0  0  0  0  0 25 26]
 [ 0  0  0  0  0  0  0  0  0  0 25 26 27]
 [ 0  0  0  0  0  0  0  0  0 25 26 27 28]
 [ 0  0  0  0  0  0  0  0 25 26 27 28 29]
 [ 0  0  0  0  0  0  0 25 26 27 28 29 30]
 [ 0  0  0  0  0  0 25 26 27 28 29 30 31]
 [ 0  0  0  0  0 25 26 27 28 29 30 31 32]
 [ 0  0  0  0 25 26 27 28 29 30 31 32  3]
 [ 0  0  0 25 26 27 28 29 30 31 32  3 33]
 [ 0  0 25 26 27 28 29 30 31 32  3 33 34]
 [ 0 25 26 27 28 29 30 31 32  3 33 34 35]
 [25 26 27 28 29 30 31 32  3 33 34 35 36]
 [ 0  0  0  0  0  0  0  0  0  0  0 37  3]
 [ 0  0  0  0  0  0  0  0  0  0 37  3 38]
 [ 0  0  0  0  0  0  0  0  0 37  3 38 39]
 [ 0  0  0  0  0  0  0  0 37  3 38 39 40]
 [ 0  0  0  0  0  0  0 37  3 38 39 40 41]
 [ 0  0  0  0  0  0 37  3 38 39 40 41  1]]
[[0 0 0 0 0 0 0 0 0 0 0 4]
 [0 0 0 0 0 0 0 0 0 0 4 5]]
[ 5  6  7  8  9 10 11 13 14 15 16 17 18 19 20  1  2 22  2 23 24  1 26 27
 28 29 30 31 32  3 33 34 35 36  3 38 39 40 41  1]
[[0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]
2022-12-14 11:38:38.601465: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 embedding (Embedding)       (None, 12, 32)            1344      
                                                                 
 lstm (LSTM)                 (None, 32)                8320      
                                                                 
 flatten (Flatten)           (None, 32)                0         
                                                                 
 dense (Dense)               (None, 32)                1056      
                                                                 
 dense_1 (Dense)             (None, 32)                1056      
                                                                 
 dense_2 (Dense)             (None, 42)                1386      
                                                                 
=================================================================
Total params: 13,162
Trainable params: 13,162
Non-trainable params: 0
_________________________________________________________________
None

1/2 [==============>...............] - ETA: 0s - loss: 0.3459 - accuracy: 0.9688
2/2 [==============================] - 0s 3ms/step - loss: 0.3261 - accuracy: 0.9750
model evaluate : [0.326116144657135, 0.9750000238418579]
수도권 개별 단지들의 전세가격 하락세가
인천 미추홀구 인천sk스카이뷰 역시 지난달

token을 생성하고 나서 fit_on_texts의 값으로는 무조건 list type으로 주어야 된다. 단어와 인덱싱을 출력

texts_to_sequences() 단어를 정수로 메트릭스화 해서 출력한다.

단어를 정수로 바꿔주었기 때문에 predict(x) x에 들어가는 값도 정수로 바꿔서 넣어주어야 된다.

그리고 예측 후 다시 단어로 바꿔준다.

학습 내용에 따라 그것이 적용되므로 text 안의 내용만 바꿔주면 된다.

TensorFlow 기초 38 - 문자열(corpus - 자연어 데이터 집합) 토큰화 + LSTM으로 감성 분류

코딩탕탕 — Tue, 13 Dec 2022 17:30:33 +0900

padding : 서로 다른 길이의 데이터를 가장 긴 데이터의 길이와 같게 만듦

Embedding에 입력될 단어의 수를 지정하는데 가능한 토큰 갯수는 단어 인덱스 최대값 + 1을 부여한다.

# 문자열(corpus - 자연어 데이터 집합) 토큰화 + LSTM으로 감성 분류
# 토큰(Token): text를 단어, 문장, 형태소 별로 나눌 수 있는데 이렇게 나뉜 조각들을 token이라고 한다.
import numpy as np
from keras.preprocessing.text import Tokenizer 
from keras.utils import pad_sequences

docs = ['너무 재밌네요', '최고에요', '참 잘 만든 작품입니다', '추천하고 싶어요', '한 번 더 보고 싶군요', '글쎄요', '별로네요', '생각보다 너무 지루해요', '연기가 어색하더군요', '재미없어요']
labels = np.array([1,1,1,1,1,0,0,0,0,0])

token = Tokenizer()
token.fit_on_texts(docs) # 정수 인코딩
print(token.word_index) # 각 단어에 대한 인덱싱 확인

x = token.texts_to_sequences(docs) # 텍스트를 정수 인덱싱하여 list로 반환
print(x)

# padding : 서로 다른 길이의 데이터를 가장 긴 데이터의 길이와 같게 만듦
padded_x = pad_sequences(x, 5)
print('padded_x :\n', padded_x)

# model
from keras.models import Sequential
from keras.layers import Dense, Flatten, Embedding, LSTM

word_size = len(token.word_index) + 1 # Embedding에 입력될 단어의 수를 지정. 가능한 토큰 갯수는 단어 인덱스 최대값 + 1
model = Sequential()

model.add(Embedding(word_size, 8, input_length=5)) # (가능 토큰 수, 임베딩 차원, 입력 수)
model.add(LSTM(32, activation='tanh')) # 자연어 처리

model.add(Flatten())

model.add(Dense(units=32, activation='relu'))
model.add(Dense(units=1, activation='sigmoid'))
print(model.summary())

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.fit(padded_x, labels, epochs=20, verbose=2)
print('evaluate :', model.evaluate(padded_x, labels))

print('predict :', np.where(model.predict(padded_x) > 0.5, 1, 0).ravel())
print('labels :', labels)


<console>
{'너무': 1, '재밌네요': 2, '최고에요': 3, '참': 4, '잘': 5, '만든': 6, '작품입니다': 7, '추천하고': 8, '싶어요': 9, '한': 10, '번': 11, '더': 12, '보고': 13, '싶군요': 14, '글쎄요': 15, '별로네요': 16, '생각보다': 17, '지루해요': 18, '연기가': 19, '어색하더군요': 20, '재미없어요': 21}
[[1, 2], [3], [4, 5, 6, 7], [8, 9], [10, 11, 12, 13, 14], [15], [16], [17, 1, 18], [19, 20], [21]]
padded_x :
 [[ 0  0  0  1  2]
 [ 0  0  0  0  3]
 [ 0  4  5  6  7]
 [ 0  0  0  8  9]
 [10 11 12 13 14]
 [ 0  0  0  0 15]
 [ 0  0  0  0 16]
 [ 0  0 17  1 18]
 [ 0  0  0 19 20]
 [ 0  0  0  0 21]]

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 embedding (Embedding)       (None, 5, 8)              176       
                                                                 
 lstm (LSTM)                 (None, 32)                5248      
                                                                 
 flatten (Flatten)           (None, 32)                0         
                                                                 
 dense (Dense)               (None, 32)                1056      
                                                                 
 dense_1 (Dense)             (None, 1)                 33        
                                                                 
=================================================================
Total params: 6,513
Trainable params: 6,513
Non-trainable params: 0
_________________________________________________________________
None
Epoch 1/20
1/1 - 1s - loss: 0.6936 - accuracy: 0.3000 - 1s/epoch - 1s/step
Epoch 2/20
1/1 - 0s - loss: 0.6929 - accuracy: 0.6000 - 2ms/epoch - 2ms/step
Epoch 3/20
1/1 - 0s - loss: 0.6924 - accuracy: 0.8000 - 2ms/epoch - 2ms/step
Epoch 4/20
1/1 - 0s - loss: 0.6919 - accuracy: 0.8000 - 2ms/epoch - 2ms/step
Epoch 5/20
1/1 - 0s - loss: 0.6915 - accuracy: 0.8000 - 1ms/epoch - 1ms/step
Epoch 6/20
1/1 - 0s - loss: 0.6911 - accuracy: 0.9000 - 2ms/epoch - 2ms/step
Epoch 7/20
1/1 - 0s - loss: 0.6906 - accuracy: 0.9000 - 3ms/epoch - 3ms/step
Epoch 8/20
1/1 - 0s - loss: 0.6902 - accuracy: 0.9000 - 2ms/epoch - 2ms/step
Epoch 9/20
1/1 - 0s - loss: 0.6897 - accuracy: 0.9000 - 2ms/epoch - 2ms/step
Epoch 10/20
1/1 - 0s - loss: 0.6891 - accuracy: 0.9000 - 997us/epoch - 997us/step
Epoch 11/20
1/1 - 0s - loss: 0.6886 - accuracy: 0.9000 - 996us/epoch - 996us/step
Epoch 12/20
1/1 - 0s - loss: 0.6879 - accuracy: 0.9000 - 997us/epoch - 997us/step
Epoch 13/20
1/1 - 0s - loss: 0.6873 - accuracy: 0.9000 - 997us/epoch - 997us/step
Epoch 14/20
1/1 - 0s - loss: 0.6865 - accuracy: 0.9000 - 3ms/epoch - 3ms/step
Epoch 15/20
1/1 - 0s - loss: 0.6856 - accuracy: 0.9000 - 2ms/epoch - 2ms/step
Epoch 16/20
1/1 - 0s - loss: 0.6847 - accuracy: 0.9000 - 996us/epoch - 996us/step
Epoch 17/20
1/1 - 0s - loss: 0.6837 - accuracy: 0.9000 - 996us/epoch - 996us/step
Epoch 18/20
1/1 - 0s - loss: 0.6826 - accuracy: 0.9000 - 2ms/epoch - 2ms/step
Epoch 19/20
1/1 - 0s - loss: 0.6814 - accuracy: 0.9000 - 2ms/epoch - 2ms/step
Epoch 20/20
1/1 - 0s - loss: 0.6802 - accuracy: 0.9000 - 996us/epoch - 996us/step

1/1 [==============================] - ETA: 0s - loss: 0.6788 - accuracy: 0.9000
1/1 [==============================] - 0s 271ms/step - loss: 0.6788 - accuracy: 0.9000
evaluate : [0.6788328289985657, 0.8999999761581421]

1/1 [==============================] - ETA: 0s
1/1 [==============================] - 0s 301ms/step
predict : [1 0 1 1 1 0 0 0 0 0]
labels : [1 1 1 1 1 0 0 0 0 0]

텍스트를 정수 인덱싱하여 list로 반환하여 출력하였다.

# Embedding 클래스 : 문장 토큰화와 단어 토큰화를 위한 개념 이해용 소스

참고 : 케라스의 Embedding 레이어는 처음에 무작위로 초기화된 상태에서 정수로 오는 word를 정해진 크기의 벡터로 바꿔서 다음 레이어로 넘기고, 학습단계에서는 역전파되는 기울기를 바탕으로 해당 word의 임베딩 값을 조정한다. 즉 주변 문맥을 반영하지 않는다. 그럼 Dense레이어랑 같은거 아닌가 싶지만 차이점은 원핫벡터로 값을 안넣어줘도 되서 덕분에 메모리 절약할 수 있고, 케라스에서 지원하는 masking 기능을 사용할 수 있고, 꼭 문장뿐만아니라 추천 시스템 등에서 User 등을 벡터로 나타낼 때도 사용할 수 있다. 하지만 웬만하면 weights 지정 기능을 이용해서 pre-trained 된 word2vec 등을 지정해야 할 것이다.

출처 : https://cafe.daum.net/flowlife/S2Ul/19

TensorFlow 기초 37 - RNN(Recurrent Neural Network)

코딩탕탕 — Tue, 13 Dec 2022 17:01:06 +0900

RNN(Recurrent Neural Network)은 히든 노드가 방향을 가진 엣지로 연결되어 순환구조를 이루는(directed cycle) 인공신경망의 한 종류입니다.
음성, 문자 등 순차적으로 등장하는 데이터 처리에 적합한 모델로 알려져 있다.
Convolutional Neural Networks(CNN)과 더불어 최근 들어 각광받고 있는 알고리즘이다.
활용 : 텍스트 분류, 품사 태깅, 문서 요약, 문서 작성, 기계 번역, 이미지 캡션... 등 사용

RNN은 sequence data : 입력값에 대해 현재의 state가 다음의 state에 영향을 준다.

# RNN(Recurrent Neural Network)은 히든 노드가 방향을 가진 엣지로 연결되어 순환구조를 이루는(directed cycle) 인공신경망의 한 종류입니다.
# 음성, 문자 등 순차적으로 등장하는 데이터 처리에 적합한 모델로 알려져 있다.
# Convolutional Neural Networks(CNN)과 더불어 최근 들어 각광받고 있는 알고리즘이다.
# 활용 : 텍스트 분류, 품사 태깅, 문서 요약, 문서 작성, 기계 번역, 이미지 캡션... 등 사용
# RNN은 sequence data : 입력값에 대해 현재의 state가 다음의 state에 영향을 준다.

from keras.models import Sequential
from keras.layers import SimpleRNN, LSTM, GRU, Dense

model = Sequential()
# model.add(SimpleRNN(units=3, input_shape=(2, 10)))
# model.add(SimpleRNN(3, input_length=2, input_dim=10)) # 위와 동일
model.add(LSTM(units=3, input_shape=(2, 10))) # Long Short-Term Memory units
print(model.summary())

model = Sequential()
model.add(LSTM(units=3, batch_input_shape=(8, 2, 10))) # batch_size=8, input_length=2, input_dim=10
print(model.summary())

model = Sequential()
model.add(LSTM(units=3, batch_input_shape=(8, 2, 10), return_sequences=True)) # 다음 층으로 모든 은닉상태를 전달
print(model.summary())

# LSTM으로 다음 숫자 예측하기
from numpy import array

x = array([[1,2,3],[2,3,4],[3,4,5],[4,5,6],[5,6,7],[10,20,30],[20,30,40],[30,40,50]])
y = array([4,5,6,7,8,40,50,60])
print(x, x.shape) # (8, 3)
print(y, y.shape) # (8,)

print(x, x.shape) # (8, 3, 1)

# 모델 구성
model = Sequential()
model.add(LSTM(10, activation='tanh', input_shape=(3, 1)))
# model.add(LSTM(10, activation='tanh', input_shape=(3, 1), return_sequences=True)) # many-to-many
model.add(Dense(5, activation='relu'))
model.add(Dense(1))

model.compile(optimizer='adam', loss='mse')
print(model.summary())

from keras.callbacks import EarlyStopping
es = EarlyStopping(monitor='loss', patience=5, mode='auto')
model.fit(x, y, epochs=1000, batch_size=1, verbose=0, callbacks=[es])

pred = model.predict(x)
print('예측값 :', pred.flatten())
print('실제값 :', y)

x_new = array([[3, 5, 7]])
new_pred = model.predict(x_new)
print('새로운 예측값 :', new_pred.flatten())


<console>
2022-12-13 16:29:16.224256: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 lstm (LSTM)                 (None, 3)                 168       
                                                                 
=================================================================
Total params: 168
Trainable params: 168
Non-trainable params: 0
_________________________________________________________________
None
Model: "sequential_1"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 lstm_1 (LSTM)               (8, 3)                    168       
                                                                 
=================================================================
Total params: 168
Trainable params: 168
Non-trainable params: 0
_________________________________________________________________
None
Model: "sequential_2"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 lstm_2 (LSTM)               (8, 2, 3)                 168       
                                                                 
=================================================================
Total params: 168
Trainable params: 168
Non-trainable params: 0
_________________________________________________________________
None
[[ 1  2  3]
 [ 2  3  4]
 [ 3  4  5]
 [ 4  5  6]
 [ 5  6  7]
 [10 20 30]
 [20 30 40]
 [30 40 50]] (8, 3)
[ 4  5  6  7  8 40 50 60] (8,)
[[ 1  2  3]
 [ 2  3  4]
 [ 3  4  5]
 [ 4  5  6]
 [ 5  6  7]
 [10 20 30]
 [20 30 40]
 [30 40 50]] (8, 3)
Model: "sequential_3"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 lstm_3 (LSTM)               (None, 10)                480       
                                                                 
 dense (Dense)               (None, 5)                 55        
                                                                 
 dense_1 (Dense)             (None, 1)                 6         
                                                                 
=================================================================
Total params: 541
Trainable params: 541
Non-trainable params: 0
_________________________________________________________________
None

1/1 [==============================] - ETA: 0s
1/1 [==============================] - 0s 236ms/step
예측값 : [ 4.0012474  4.9936314  5.99559    6.952085   8.063006  39.780865
 49.39792   56.75779  ]
실제값 : [ 4  5  6  7  8 40 50 60]

1/1 [==============================] - ETA: 0s
1/1 [==============================] - 0s 8ms/step
새로운 예측값 : [9.879905]

TensorFlow 기초 36 - 네이버 영화 리뷰 데이터로 word2vec 객체 생성 후 특정 단에 대한 유사도 확인

코딩탕탕 — Tue, 13 Dec 2022 15:07:49 +0900

# 네이버 영화 리뷰 데이터로 word2vec 객체 생성 후 특정 단에 대한 유사도 확인
import pandas as pd
import matplotlib.pyplot as plt
import urllib.request
from gensim.models.word2vec import Word2Vec
from konlpy.tag import Okt

urllib.request.urlretrieve("https://raw.githubusercontent.com/pykwon/python/master/testdata_utf8/ratings.txt", filename='rating.txt')
# print(pd.read_table('rating.txt'))
train_data = pd.read_table('rating.txt')
print(train_data[:3])
print(len(train_data)) # 10295
print(train_data[train_data['label'].isnull()])
print(train_data.isnull().values.any())
train_data = train_data.dropna(how='any')
print(train_data.isnull().values.any())
print(len(train_data))

# 한글 외 문자 제거
train_data['document'] = train_data['document'].str.replace('[^가-힣 ]', '')
print(train_data[:5])

# 불용어 제거
stopwords = ['을', '으로', '은', '는', '들', '와', '에', '게', '해서'] # 매우 주관적이다

# Okt로 토큰 처리
okt = Okt()
token_data = []
for sent in train_data['document']:
    temp = okt.morphs(sent, stem=True)
    temp = [word for word in temp if not word in stopwords] # 불용어 제거
    token_data.append(temp)


import time
time.sleep(5) # 작업이 끝나고 출력될 수 있도록 time.sleep을 주는 것이 좋다.


print(token_data)

print('리뷰의 최대 길이 :', max(len(i) for i in token_data))
print('리뷰의 평균 길이 :', sum(map(len, token_data))/ len(token_data))

plt.hist([len(s) for s in token_data])
plt.xlabel('length of samples')
plt.xlabel('number of samples')
plt.show()

word_model = Word2Vec(sentences=token_data, size = 100, window = 5, min_count=5, sg=0)
print(word_model.wv.vectors.shape) # (2696, 100)
print(word_model.wv.most_similar('주인공'))
print(word_model.wv.most_similar('드라마')) # 드라마와 유사성 있는 단어

# 사전 훈련된 Word2Vec 객체를 사용할 수 있다.
import gensim

model = gensim.models.Word2Vec.load('ko.bin')
result = model.wv.most_similar("프로그램")
print(result)
result = model.wv.most_similar("자바")
print(result)
result = model.wv.most_similar("축구")
print(result)


<console>
        id                                           document  label
0  8112052                                어릴때보고 지금다시봐도 재밌어요ㅋㅋ    1.0
1  8132799  디자인을 배우는 학생으로, 외국디자이너와 그들이 일군 전통을 통해 발전해가는 문화산...    1.0
2  4655635               폴리스스토리 시리즈는 1부터 뉴까지 버릴께 하나도 없음.. 최고.    1.0
10295
           id                                         document  label
3624  2078364                        좋다. 잔잔하고 깊이있게 파고드는 내용.  1    NaN
3632  4166145  영화음악도 나름 괜찮았었죠. 테잎으로 사기까지 했는데...이젠 구하기가...--; 1    NaN
True
False
10293

         id                                           document  label
0   8112052                                  어릴때보고 지금다시봐도 재밌어요    1.0
1   8132799  디자인을 배우는 학생으로 외국디자이너와 그들이 일군 전통을 통해 발전해가는 문화산업...    1.0
2   4655635                   폴리스스토리 시리즈는 부터 뉴까지 버릴께 하나도 없음 최고    1.0
3   9251303   와 연기가 진짜 개쩔구나 지루할거라고 생각했는데 몰입해서 봤다 그래 이런게 진짜 영화지    1.0
4  10067386                         안개 자욱한 밤하늘에 떠 있는 초승달 같은 영화    1.0
<ipython-input-11-3ceeb5958b23>:2: FutureWarning: The default value of regex will change from True to False in a future version.
  train_data['document'] = train_data['document'].str.replace('[^가-힣 ]', '')
  
  
리뷰의 최대 길이 : 68
리뷰의 평균 길이 : 11.994948022928204

(2696, 100)
[('그리고', 0.9998807907104492), ('모든', 0.9998741149902344), ('남자', 0.9998416900634766), ('남', 0.9998341202735901), ('대한', 0.9998313188552856), ('여', 0.9998266100883484), ('여자', 0.9998222589492798), ('훌륭하다', 0.9998208284378052), ('차다', 0.9998202323913574), ('수준', 0.999819815158844)]
[('걸작', 0.9996998310089111), ('명작', 0.9996989965438843), ('한국', 0.9996887445449829), ('년대', 0.999679446220398), ('정말', 0.9996774196624756), ('임', 0.9996671676635742), ('막장', 0.9996431469917297), ('멋지다', 0.9996140003204346), ('팬', 0.9996113181114197), ('급', 0.9996106624603271)]
[('망하다', 0.9995930194854736), ('버리다', 0.9995826482772827), ('전', 0.9995824098587036), ('받다', 0.9995762705802917), ('쓰다', 0.9995754957199097), ('놓다', 0.9995753169059753), ('존나', 0.9995747804641724), ('없이', 0.9995696544647217), ('서', 0.9995642304420471), ('빠지다', 0.9995630979537964)]

[('애플리케이션', 0.6408973932266235), ('시스템', 0.6084378957748413), ('플러그인', 0.6059278249740601), ('자막', 0.5899795889854431), ('포맷', 0.5888468623161316), ('플랫폼', 0.5867334008216858), ('소프트웨어', 0.5808665752410889), ('텔레비전', 0.5772788524627686), ('세션', 0.5750851035118103), ('솔루션', 0.5719414353370667)]
[('스크립트', 0.7378242015838623), ('컴파일러', 0.6451905965805054), ('리눅스', 0.6398512721061707), ('라이브러리', 0.6347814798355103), ('유닉스', 0.6131169199943542), ('리스프', 0.6066397428512573), ('도스', 0.6017436385154724), ('미디', 0.5987685918807983), ('프로그래밍', 0.5934149622917175), ('인터프리터', 0.5905465483665466)]
[('아이스하키', 0.7354738116264343), ('배구', 0.7272718548774719), ('권투', 0.7065301537513733), ('축구팀', 0.6947455406188965), ('농구', 0.6805489659309387), ('야구', 0.6605638265609741), ('럭비', 0.653668224811554), ('미식축구', 0.644683837890625), ('테니스', 0.6392970681190491), ('골프', 0.6258326768875122)]

리뷰의 길이 평균 시각화