2018 iT 邦幫忙鐵人賽

AI & Machine Learning

玩轉資料與機器學習-以自然語言處理為例系列第 31 篇

周末特別節目-預測文章頭十天的瀏覽人次(限iThome鐵人文章)

2018鐵人賽

王選仲(GoatWang)

2018-01-06 13:04:30

3488 瀏覽

分享至

想預測自己玩張的瀏覽人次，請前往https://ithome-predict-browse-count.herokuapp.com/index/<你的文章的網頁連結>，舉例來說https://ithome-predict-browse-count.herokuapp.com/index/https://ithelp.ithome.com.tw/articles/10195825/。

續

雖然30天已經寫完，不過之前承諾過大家要預測ithome鐵人文章的瀏覽人次，因此又多了這篇讓大家玩玩。

程式碼-Train

Import

import os
import pandas as pd
import jieba
jieba.set_dictionary('dict.txt.big')
with open('stops.txt', 'r', encoding='utf8') as f:
    stops = f.read().split('\n')
    
import numpy as np
import json
import matplotlib.pyplot as plt
from sklearn.preprocessing import MultiLabelBinarizer, LabelEncoder

from datetime import datetime
starttime= datetime.now()

LoadData

我已經連續爬了15天的所有貼文並存成csv檔。

files = [os.path.join('articles', i) for i in os.listdir("articles")]

df = pd.DataFrame()
for f in files:
    df_part = pd.read_csv(f)
    df = pd.concat([df, df_part], ignore_index=True)

df["publish_datetime"] = df["publish_datetime"].apply(pd.to_datetime)
df["crawled_date"] = df["crawled_date"].apply(pd.to_datetime)

for h in ['h1', 'h2', 'h3', 'h4', 'h5', 'h6']:
    df.loc[pd.notnull(df[h]), h] = df.loc[pd.notnull(df[h]), h].apply(eval)

print(len(df))
print(datetime.now()-starttime)

Preprocess

LabelEncoder可以幫你把類別資料轉成數值型類別。如果每一筆資料有多個標籤，MultiLabelBinarizer則會幫你自動將其Onehot Encoding，這邊把一個詞當作一個標籤，不過因為這邊詞量太大，如果用Onehot可能會有記憶體不足的狀況，因此這邊先使用索引值編碼就好，這也就是為甚麼要寫getidxs function。另外，我把發文後到被我爬下來的時間長轉成小時。

le_group = LabelEncoder()
le_group.fit(df['group'])

le_corpus_day = LabelEncoder()
max_corpus_day = df['corpus_day'].max()
le_corpus_day.fit(df['corpus_day'])

mlb = MultiLabelBinarizer()
term_idx_mapping = {}

def preprocess(df, train=True):
    df.fillna('None', inplace=True)
    df[df['corpus_day'] > max_corpus_day] = max_corpus_day  ## for testing purpose

    def preprocess_applyfun(row):
        # combine all headers
        header = ""
        for h in ['h1', 'h2', 'h3', 'h4', 'h5', 'h6']:
            if row[h] != "None":
                header += " ".join(row[h]) + "\n"

        # Tokenize
        row['article_title'] = [w for w in jieba.cut(row['article_title'], cut_all=True)]
        row['corpus_title'] = [w for w in jieba.cut(row['corpus_title'], cut_all=True)]
        row['header'] = [w for w in jieba.cut(header, cut_all=True)]
        row['text_content'] = [w for w in jieba.cut(row['text_content'], cut_all=True) if w not in stops]
        # cal_publish_hours
        timedelta = row['crawled_date'] - row['publish_datetime']
        row['publish_hours'] = timedelta.days * 24 + timedelta.seconds // 3600
        # group and corpus_day categorilize
        row['group'] = le_group.transform([row['group']])[0]
        row['corpus_day'] = le_corpus_day.transform([row['corpus_day']])[0]

        return row
    df = df.apply(preprocess_applyfun, axis=1)

    if train:
        mlb.fit(np.hstack([df['article_title'], df['corpus_title'], df['header'], df['text_content']]))
        for idx, term in enumerate(mlb.classes_):
            term_idx_mapping[term] = idx
            
    # Serialize tokens
    def getidxs(terms):
        idxs = []
        for term in terms:
            if term in term_idx_mapping.keys():
                idx = term_idx_mapping.get(term)
                idxs.append(idx)
        return idxs
    df['article_title'] = df['article_title'].apply(getidxs)
    df['corpus_title'] = df['corpus_title'].apply(getidxs)
    df['header'] = df['header'].apply(getidxs)
    df['text_content'] = df['text_content'].apply(getidxs)
    return df

df = preprocess(df)
print(datetime.now()-starttime)
df[['group', 'corpus_title', 'corpus_day', 'article_title', 'browse_count',
     'text_content', 'header', 'publish_hours']]

切分測試資料集

#EXTRACT DEVELOPTMENT TEST
from sklearn.model_selection import train_test_split
dtrain, dvalid = train_test_split(df, random_state=233, train_size=0.90)
print(dtrain.shape)
print(dvalid.shape)

# (38325, 20)
# (4259, 20)

EMBEDDINGS MAX VALUE

article_title, corpus_title, header, text_content這四個欄位已經被轉換成索引值，但是長度並不一，因此要填上零，讓他們等長，下面pad_sequence會用到。至於MAX_TEXT, MAX_GROUP, MAX_CORPUS_DAY則是在Keras Input時告訴它總共分成幾類。

#EMBEDDINGS MAX VALUE
# print(df['article_title'].apply(len).max())
# print(df['corpus_title'].apply(len).max())
# print(df['header'].apply(len).max())
# print(df['text_content'].apply(len).max())

MAX_ARTICLE_TITLE_SEQ = 60 #60
MAX_CORPUS_TITLE_SEQ = 20 #22
MAX_HEADER_SEQ = 250 #260
MAX_TEXT_CONTENT_SEQ = 500 #1195

MAX_TEXT = len(term_idx_mapping) +1
MAX_GROUP = len(le_group.classes_) 
MAX_CORPUS_DAY = len(le_corpus_day.classes_)

print(MAX_ARTICLE_TITLE_SEQ)  # 60
print(MAX_CORPUS_TITLE_SEQ)  # 20
print(MAX_HEADER_SEQ)  # 250
print(MAX_TEXT_CONTENT_SEQ)  # 500
print(MAX_TEXT)  # 67647
print(MAX_GROUP)  # 7
print(MAX_CORPUS_DAY)  # 34
print(datetime.now() - starttime)  # 0:16:17.879698

準備Keras Input Data

#KERAS DATA DEFINITION
from keras.preprocessing.sequence import pad_sequences

def get_keras_data(dataset):
    X = {
        "seq_article_title":pad_sequences(dataset['article_title'], maxlen=MAX_ARTICLE_TITLE_SEQ),
        "seq_corpus_title":pad_sequences(dataset['corpus_title'], maxlen=MAX_CORPUS_TITLE_SEQ),
        "seq_header":pad_sequences(dataset['header'], maxlen=MAX_HEADER_SEQ),
        "seq_text_content":pad_sequences(dataset['text_content'], maxlen=MAX_TEXT_CONTENT_SEQ),
        'group': np.array(dataset['group']),
        'corpus_day': np.array(dataset['corpus_day']),
        'publish_hours': np.array(dataset['publish_hours']),
    }
    return X

X_train = get_keras_data(dtrain)
X_valid = get_keras_data(dvalid)

print(datetime.now() - starttime)

建立Keras Model

這裡有用到Keras的MultiInput，其中跟文字相關的都使用RNN。

#KERAS MODEL DEFINITION
from keras.layers import Input, Dropout, Dense, BatchNormalization, \
    Activation, concatenate, GRU, Embedding, Flatten
from keras.models import Model
from keras.callbacks import ModelCheckpoint, Callback, EarlyStopping#, TensorBoard
from keras import backend as K
from keras import optimizers
from keras import initializers
def rmsle_cust(y_true, y_pred):
    first_log = K.log(K.clip(y_pred, K.epsilon(), None) + 1.)
    second_log = K.log(K.clip(y_true, K.epsilon(), None) + 1.)
    return K.sqrt(K.mean(K.square(first_log - second_log), axis=-1))

def get_model():
    #params
    dr = 0.20
    
    #Inputs
    seq_corpus_title = Input(shape=[X_train["seq_corpus_title"].shape[1]], name="seq_corpus_title")
    seq_article_title = Input(shape=[X_train["seq_article_title"].shape[1]], name="seq_article_title")
    seq_header = Input(shape=[X_train["seq_header"].shape[1]], name="seq_header")
    seq_text_content = Input(shape=[X_train["seq_text_content"].shape[1]], name="seq_text_content")
    group = Input(shape=[1], name="group")
    corpus_day = Input(shape=[1], name="corpus_day")
    publish_hours = Input(shape=[1], name="publish_hours")
    
    #Embeddings layers
    emb_corpus_title = Embedding(MAX_TEXT, 10)(seq_corpus_title)
    emb_article_title = Embedding(MAX_TEXT, 10)(seq_article_title)
    emb_header = Embedding(MAX_TEXT, 10)(seq_header)
    emb_text_content = Embedding(MAX_TEXT, 100)(seq_text_content)
    
    emb_group = Embedding(MAX_GROUP, 5)(group)
    emb_corpus_day = Embedding(MAX_CORPUS_DAY, 10)(corpus_day)
    
    rnn_layer1 = GRU(8) (emb_corpus_title)
    rnn_layer2 = GRU(8) (emb_article_title)
    rnn_layer3 = GRU(8) (emb_header)
    rnn_layer4 = GRU(16) (emb_text_content)
    
    #main layer
    main_l = concatenate([
        rnn_layer1,
        rnn_layer2,
        rnn_layer3,
        rnn_layer4,
        Flatten() (emb_group),
        Flatten() (emb_corpus_day),
        publish_hours
    ])
    main_l = Dropout(dr)(Dense(512,activation='relu') (main_l))
    main_l = Dropout(dr)(Dense(64,activation='relu') (main_l))
    main_l = Dropout(dr)(Dense(32,activation='relu') (main_l))
    
    #output
    output = Dense(1, activation="linear") (main_l)
    
    #model
    model = Model([ seq_corpus_title, seq_article_title, seq_header, 
                   seq_text_content, group, corpus_day, publish_hours], output)
    
    #optimizer = optimizers.RMSprop()
    optimizer = optimizers.Adam()
    model.compile(loss="mse", optimizer=optimizer, metrics=["mae"])
    return model

model = get_model()
model.summary()

_______________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                  
===============================================================================================
seq_corpus_title (InputLayer)   (None, 20)           0                                         
_______________________________________________________________________________________________
seq_article_title (InputLayer)  (None, 60)           0                                         
_______________________________________________________________________________________________
seq_header (InputLayer)         (None, 250)          0                                         
_______________________________________________________________________________________________
seq_text_content (InputLayer)   (None, 500)          0                                         
_______________________________________________________________________________________________
group (InputLayer)              (None, 1)            0                                         
_______________________________________________________________________________________________
corpus_day (InputLayer)         (None, 1)            0                                         
_______________________________________________________________________________________________
embedding_1 (Embedding)         (None, 20, 10)       676470      seq_corpus_title[0][0]        
_______________________________________________________________________________________________
embedding_2 (Embedding)         (None, 60, 10)       676470      seq_article_title[0][0]       
_______________________________________________________________________________________________
embedding_3 (Embedding)         (None, 250, 10)      676470      seq_header[0][0]              
_______________________________________________________________________________________________
embedding_4 (Embedding)         (None, 500, 100)     6764700     seq_text_content[0][0]        
_______________________________________________________________________________________________
embedding_5 (Embedding)         (None, 1, 5)         35          group[0][0]                   
_______________________________________________________________________________________________
embedding_6 (Embedding)         (None, 1, 10)        340         corpus_day[0][0]              
_______________________________________________________________________________________________
gru_1 (GRU)                     (None, 8)            456         embedding_1[0][0]             
_______________________________________________________________________________________________
gru_2 (GRU)                     (None, 8)            456         embedding_2[0][0]             
_______________________________________________________________________________________________
gru_3 (GRU)                     (None, 8)            456         embedding_3[0][0]             
_______________________________________________________________________________________________
gru_4 (GRU)                     (None, 16)           5616        embedding_4[0][0]             
_______________________________________________________________________________________________
flatten_1 (Flatten)             (None, 5)            0           embedding_5[0][0]             
_______________________________________________________________________________________________
flatten_2 (Flatten)             (None, 10)           0           embedding_6[0][0]             
_______________________________________________________________________________________________
publish_hours (InputLayer)      (None, 1)            0                                         
_______________________________________________________________________________________________
concatenate_1 (Concatenate)     (None, 56)           0           gru_1[0][0]                   
                                                                 gru_2[0][0]                   
                                                                 gru_3[0][0]                   
                                                                 gru_4[0][0]                   
                                                                 flatten_1[0][0]               
                                                                 flatten_2[0][0]               
                                                                 publish_hours[0][0]           
_______________________________________________________________________________________________
dense_1 (Dense)                 (None, 512)          29184       concatenate_1[0][0]           
_______________________________________________________________________________________________
dropout_1 (Dropout)             (None, 512)          0           dense_1[0][0]                 
_______________________________________________________________________________________________
dense_2 (Dense)                 (None, 64)           32832       dropout_1[0][0]               
_______________________________________________________________________________________________
dropout_2 (Dropout)             (None, 64)           0           dense_2[0][0]                 
_______________________________________________________________________________________________
dense_3 (Dense)                 (None, 32)           2080        dropout_2[0][0]               
_______________________________________________________________________________________________
dropout_3 (Dropout)             (None, 32)           0           dense_3[0][0]                 
_______________________________________________________________________________________________
dense_4 (Dense)                 (None, 1)            33          dropout_3[0][0]               
===============================================================================================
Total params: 8,865,
Trainable params: 8,865,
Non-trainable params
_______________________________________________________________________________________________

訓練模型

gc用來節省記憶體空間，然後就用Model去fit。

import gc
gc.collect()
#FITTING THE MODEL
epochs = 5
BATCH_SIZE = 512 * 3
steps = int(len(X_train)/BATCH_SIZE) * epochs
lr_init, lr_fin = 0.017, 0.009
exp_decay = lambda init, fin, steps: (init/fin)**(1/(steps-1)) - 1
lr_decay = exp_decay(lr_init, lr_fin, steps)

model = get_model()
K.set_value(model.optimizer.lr, lr_init)
K.set_value(model.optimizer.decay, lr_decay)

history = model.fit(X_train, dtrain.browse_count
                    , epochs=epochs
                    , batch_size=BATCH_SIZE
#                     , validation_split=0.1
                    , validation_data=(X_valid, dvalid.browse_count)
                    , verbose=1
                    )

print(datetime.now() - starttime)

Train on 38325 samples, validate on 4259 samples
Epoch 1/5
38325/38325 [===========] - 209s 5ms/step - loss: 1026696.9060 - mean_absolute_error: 220.6634 - val_loss: 566747.7857 - val_mean_absolute_error: 162.5117
Epoch 2/5
38325/38325 [===========] - 182s 5ms/step - loss: 790294.7851 - mean_absolute_error: 189.1313 - val_loss: 133049.8886 - val_mean_absolute_error: 159.8171
Epoch 3/5
38325/38325 [===========] - 203s 5ms/step - loss: 549999.5994 - mean_absolute_error: 204.5251 - val_loss: 262894.7622 - val_mean_absolute_error: 143.3210
Epoch 4/5
38325/38325 [===========] - 230s 6ms/step - loss: 431161.4882 - mean_absolute_error: 173.1293 - val_loss: 123154.7756 - val_mean_absolute_error: 150.0321
Epoch 5/5
38325/38325 [===========] - 226s 6ms/step - loss: 234846.9607 - mean_absolute_error: 161.3210 - val_loss: 85303.8106 - val_mean_absolute_error: 121.6386
1:04:58.245270

查看訓練結果

from sklearn.metrics import mean_squared_error
def getdiff(model, valid=True):
    df = pd.DataFrame(dvalid['browse_count'].values, columns=['browse_count_true'])
    df['browse_count_pred'] = (model.predict(X_valid))
    return df

df_diff = getdiff(model)
mse =  mean_squared_error(df_diff['browse_count_true'].values, df_diff['browse_count_pred'].values)
print(mse)

# Plot outputs
mpl.rcParams['figure.figsize'] = 10, 5
df_diff_sorted = df_diff.sort_values('browse_count_true')
plt.scatter(range(len(df_diff_sorted)), df_diff_sorted['browse_count_true'].values, color='black', s=0.5)
plt.scatter(range(len(df_diff_sorted)), df_diff_sorted['browse_count_pred'].values, color='red', s=0.5)

plt.show()
df_diff

紅色的部分是預測值，黑色的部分是實際值。在比較低點的時候普遍來說不會差距太大，不過也有比例尺因素在裡面啦(笑)，在瀏覽人次比較高的地方的時候，預測的誤差就相對就多了，不過還是會有著比低點來的高的預測瀏覽量。

idx	browse_count_true	browse_count_pred
0	271	272.936371
1	256	259.646454
2	849	720.042664
3	305	291.747192
4	208	259.776764
5	222	237.322327
6	209	217.769241
7	342	271.587341
8	67	37.776466
9	254	249.315750
10	583	345.432556
11	630	456.542908
12	694	467.314087
13	249	327.678284
14	822	755.098511
15	229	180.495590
16	316	361.333923
17	409	305.247406
18	571	415.976074
19	390	302.599060
20	113	76.910370

程式碼-Prediction

Import Data

import requests
from bs4 import BeautifulSoup
import re

import os
import pandas as pd
import jieba
jieba.set_dictionary('dict.txt.big')
with open('stops.txt', 'r', encoding='utf8') as f:
    stops = f.read().split('\n')
    
import numpy as np
import json
import matplotlib.pyplot as plt
import matplotlib
zhfont1 = matplotlib.font_manager.FontProperties(fname='simsun.ttf')  ## plt中文字
from sklearn.preprocessing import MultiLabelBinarizer, LabelEncoder

from datetime import datetime

爬蟲

def get_article_data(url):
    if not url.startswith("https://ithelp.ithome.com.tw/articles/"):
        assert "請給ithome文章的網址"
        
    row = {}
    res = requests.get(url)
    soup = BeautifulSoup(res.text, 'lxml')

    ## group
    group = soup.select(".qa-header")[0].find_all('a')[0].text.replace(' ', '').replace('\n', '')

    ## linke count
    like_count = int(soup.select('.likeGroup__num')[0].text)  ## 定位讚的次數

    ## article header
    header = soup.select('.qa-header')[0]
    corpusinfo = header.select('h3')[0].text.replace(' ', '').replace('\n', '')
    corpus_title = corpusinfo.split('第')[0]  ## 定位文章集的的主題
    corpus_day = int(re.findall(r'第[\d]+篇', corpusinfo)[0].replace('第', '').replace('篇', ''))  ## 定位參賽第幾天

    article_title = header.select('h2')[0].text.replace(' ', '').replace('\n', '')  ## 定位文章的title
    writer_name = header.select('.ir-article-info__name')[0].text.replace(' ', '').replace('\n', '')  ## 定位作者名稱
    writer_url = header.select('.ir-article-info__name')[0]['href']  ## 定位作者的個人資訊業面

    publish_date_str = header.select('.qa-header__info-time')[0]['title']  ## 定位發文日期，為了讓日期的格式被讀成python的datetime，所以做了下面很瑣碎的事
    date_items = pd.Series(publish_date_str.split(' ')[0].split('-') + publish_date_str.split(' ')[1].split(':')).astype(int)
    publish_datetime = datetime(date_items[0], date_items[1], date_items[2], date_items[3], date_items[4], date_items[5])

    browse_count = int(re.findall(r'[\d]+', header.select('.ir-article-info__view')[0].text)[0])  ## 定位瀏覽次數

    ## markdown_html
    markdown_html = soup.select('.markdown__style')[0]
    text_content = "\n".join([p.text for p in markdown_html.select('p')])  ## 定位所有文章的段落，這邊我懶得爬讀片跟程式碼了
    h1 = [h1.text for h1 in markdown_html.select('h1')]  ## 定位文章的標題們
    h2 = [h2.text for h2 in markdown_html.select('h2')] 
    h3 = [h3.text for h3 in markdown_html.select('h3')]
    h4 = [h4.text for h4 in markdown_html.select('h4')]
    h5 = [h5.text for h5 in markdown_html.select('h5')]
    h6 = [h6.text for h6 in markdown_html.select('h6')]

    row['group'] = group
    row['like_count'] = like_count
    row['corpus_title'] = corpus_title
    row['corpus_day'] = corpus_day
    row['article_title'] = article_title
    row['writer_name'] = writer_name
    row['writer_url'] = writer_url
    row['publish_datetime'] = publish_datetime
    row['browse_count'] = browse_count

    row['text_content'] = text_content
    row['h1'] = h1 if h1 != [] else None
    row['h2'] = h2 if h2 != [] else None
    row['h3'] = h3 if h3 != [] else None
    row['h4'] = h4 if h4 != [] else None
    row['h5'] = h5 if h5 != [] else None
    row['h6'] = h6 if h6 != [] else None

    row['crawled_date'] = datetime.now()
    
    return row

Preprocess

這邊要手動輸入成預測頭十天的瀏覽量，所以不需要publish_hours這個欄位。

le_group = LabelEncoder()
le_group.classes_ = np.load('le_group.npy')
le_corpus_day = LabelEncoder()
le_corpus_day.classes_ = np.load('le_corpus_day.npy')    
with open('term_idx_mapping.json', 'r', encoding='utf8') as f:
    term_idx_mapping = json.load(f)
with open('max_corpus_day', 'r', encoding='utf8') as f:
    max_corpus_day = int(f.read())
def preprocess(df):
    df.fillna('None', inplace=True)
    df[df['corpus_day'] > max_corpus_day] = max_corpus_day  ## for testing purpose

    def preprocess_applyfun(row):
        # combine all headers
        header = ""
        for h in ['h1', 'h2', 'h3', 'h4', 'h5', 'h6']:
            if row[h] != "None":
                header += " ".join(row[h]) + "\n"

        # Tokenize
        row['article_title'] = [w for w in jieba.cut(row['article_title'], cut_all=True)]
        row['corpus_title'] = [w for w in jieba.cut(row['corpus_title'], cut_all=True)]
        row['header'] = [w for w in jieba.cut(header, cut_all=True)]
        row['text_content'] = [w for w in jieba.cut(row['text_content'], cut_all=True) if w not in stops]
        # cal_publish_hours
#         timedelta = row['crawled_date'] - row['publish_datetime']
#         row['publish_hours'] = timedelta.days * 24 + timedelta.seconds // 3600
        # group and corpus_day categorilize
        row['group'] = le_group.transform([row['group']])[0]
        row['corpus_day'] = le_corpus_day.transform([row['corpus_day']])[0]

        return row
    df = df.apply(preprocess_applyfun, axis=1)
            
    # Serialize tokens
    def getidxs(terms):
        idxs = []
        for term in terms:
            if term in term_idx_mapping.keys():
                idx = term_idx_mapping.get(term)
                idxs.append(idx)
        return idxs
    df['article_title'] = df['article_title'].apply(getidxs)
    df['corpus_title'] = df['corpus_title'].apply(getidxs)
    df['header'] = df['header'].apply(getidxs)
    df['text_content'] = df['text_content'].apply(getidxs)
    return df

預測前準備資料

這邊大家可以自行更改url以預測你想預測那篇文章的瀏覽數。

from keras.models import load_model
model = load_model("20180105 1248.model")

MAX_ARTICLE_TITLE_SEQ = 60 #60
MAX_CORPUS_TITLE_SEQ = 20 #22
MAX_HEADER_SEQ = 250 #260
MAX_TEXT_CONTENT_SEQ = 500 #1195

url = "https://ithelp.ithome.com.tw/articles/10195707"
data = get_article_data(url)
df_test = pd.DataFrame([data] * 10)
for i in range(len(df_test)):
    df_test.loc[i, 'publish_hours'] = 24 * (i+1)
df_test = preprocess(df_test)

df_test[['group', 'corpus_title', 'corpus_day', 'article_title', 'browse_count',
     'text_content', 'header', 'publish_hours']]

準備Keras Input Data

#KERAS DATA DEFINITION
from keras.preprocessing.sequence import pad_sequences
def get_keras_data(dataset):
    X = {
        "seq_article_title":pad_sequences(dataset['article_title'], maxlen=MAX_ARTICLE_TITLE_SEQ),
        "seq_corpus_title":pad_sequences(dataset['corpus_title'], maxlen=MAX_CORPUS_TITLE_SEQ),
        "seq_header":pad_sequences(dataset['header'], maxlen=MAX_HEADER_SEQ),
        "seq_text_content":pad_sequences(dataset['text_content'], maxlen=MAX_TEXT_CONTENT_SEQ),
        'group': np.array(dataset['group']),
        'corpus_day': np.array(dataset['corpus_day']),
        'publish_hours': np.array(dataset['publish_hours']),
    }
    return X

X_test = get_keras_data(df_test)

預測結果

predict_result = model.predict(X_test)
mpl.rcParams['figure.figsize'] = 10, 5
df_pred = pd.DataFrame(predict_result, index=['day'+str(i)+'\n('+ str(i*24) +'hours)' for i in range(1,11)], columns=[data['article_title']])
ax = df_pred.plot(kind='line', legend=False, figsize=(10, 5), grid=True)
plt.show()
df_pred.astype(int).T