In [7]:

    

from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.cross_validation import train_test_split
from textblob import TextBlob,Word
from sklearn.neighbors import KNeighborsClassifier
import pandas as pd


    

    




C:\Anaconda3\lib\site-packages\sklearn\cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.
  "This module will be removed in 0.20.", DeprecationWarning)

In [8]:

    

train = pd.read_csv('files/restaurant_review1.csv', encoding = "ISO-8859-1")


    

In [9]:

    

train=train.dropna(axis=0)


    

In [10]:

    

def stemming_tokenizer(text):
    stopwords = []
    with open('files/new_sw.txt','r+') as f:
        stopword = f.readlines()
        for sw in stopword:
            sw = sw[:-1]
            stopwords.append(sw)
    f.close()
    text = text.lower()
    words = TextBlob(text).correct().words
    words = [Word(w).lemmatize("v") for w in words if not w in stopwords]
    return words


    

In [31]:

    

food_classifier = Pipeline([
        ('vectorizer', CountVectorizer(tokenizer=stemming_tokenizer,ngram_range=(1,3))),
        ('tfidf', TfidfTransformer()),
        ('classifier', KNeighborsClassifier(n_neighbors=5)),
        ])


    

In [36]:

    

X_train, X_test, y_train, y_test = train_test_split(train['review'],train['Food'], test_size=0.25, random_state=33)


    

In [37]:

    

classifier=food_classifier.fit(X_train, y_train)
print("Accuracy: %s" % classifier.score(X_test, y_test))


    

    




Accuracy: 0.697674418605

In [38]:

    

service_classifier = Pipeline([
        ('vectorizer', CountVectorizer(tokenizer=stemming_tokenizer,ngram_range=(1,3))),
        ('tfidf', TfidfTransformer()),
        ('classifier', KNeighborsClassifier(n_neighbors=5)),
        ])

X_train, X_test, y_train, y_test = train_test_split(train['review'],train['Service'], test_size=0.25, random_state=33)

classifier=service_classifier.fit(X_train, y_train)
print("Accuracy: %s" % classifier.score(X_test, y_test))


    

    




Accuracy: 0.697674418605

In [39]:

    

amb_classifier = Pipeline([
        ('vectorizer', CountVectorizer(tokenizer=stemming_tokenizer,ngram_range=(1,3))),
        ('tfidf', TfidfTransformer()),
        ('classifier', KNeighborsClassifier(n_neighbors=5)),
        ])

X_train, X_test, y_train, y_test = train_test_split(train['review'],train['Ambience'], test_size=0.25, random_state=33)

classifier=amb_classifier.fit(X_train, y_train)
print("Accuracy: %s" % classifier.score(X_test, y_test))


    

    




Accuracy: 0.720930232558

In [40]:

    

deal_classifier = Pipeline([
        ('vectorizer', CountVectorizer(tokenizer=stemming_tokenizer,ngram_range=(1,3))),
        ('tfidf', TfidfTransformer()),
        ('classifier', KNeighborsClassifier(n_neighbors=5)),
        ])

X_train, X_test, y_train, y_test = train_test_split(train['review'],train['deal'], test_size=0.25, random_state=33)

classifier=deal_classifier.fit(X_train, y_train)

print("Accuracy: %s" % classifier.score(X_test, y_test))


    

    




Accuracy: 0.860465116279

In [44]:

    

df = pd.read_csv('files/reviews.csv', encoding = "ISO-8859-1")


    

In [119]:

    

df.head()


    

    
Out[119]:







  

    

      

      
funny
      
rating
      
user_id
      
review
      
restaurant_id
      
review_id
      
date
      
cool
      
useful
    
  
  

    

      
0
      
0
      
5
      
24538
      
My wife took me here on my birthday for breakf...
      
3010
      
1
      
2011-01-26
      
2
      
5
    
    

      
1
      
0
      
5
      
40413
      
I have no idea why some people give bad review...
      
1191
      
2
      
2011-07-27
      
0
      
0
    
    

      
2
      
0
      
4
      
36383
      
love the gyro plate. Rice is so good and I als...
      
1989
      
3
      
2012-06-14
      
0
      
1
    
    

      
3
      
1
      
4
      
25790
      
Quiessence is, simply put, beautiful.  Full wi...
      
3566
      
4
      
2007-12-13
      
4
      
3
    
    

      
4
      
4
      
5
      
16256
      
Drop what you're doing and drive here. After I...
      
1019
      
5
      
2010-02-12
      
7
      
7
    
  

In [120]:

    

def getReview(res_id):
    reviews = []
    
    rev = df[(df['restaurant_id']==res_id) & (df['useful']>0)]['review']
    for r in rev:
        reviews.append(r)
    return reviews


    

In [46]:

    

rev = getReview(3)


    

In [53]:

    

pdf= pd.read_csv('files/restaurants.csv',encoding = "ISO-8859-1")


    

In [56]:

    

pred= pdf[['new_id']]


    

In [63]:

    

pred.columns=['restaurant_id']


    

In [64]:

    

pred['food']=-1


    

    




C:\Anaconda3\lib\site-packages\ipykernel\__main__.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  if __name__ == '__main__':

In [65]:

    

pred['service']=-1
pred['ambience']=-1
pred['deals']=-1


    

    




C:\Anaconda3\lib\site-packages\ipykernel\__main__.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  if __name__ == '__main__':
C:\Anaconda3\lib\site-packages\ipykernel\__main__.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  from ipykernel import kernelapp as app
C:\Anaconda3\lib\site-packages\ipykernel\__main__.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  app.launch_new_instance()

In [3]:

    

import pickle


    

In [136]:

    

def save_obj(obj, name ):
    with open( name + '.pkl', 'wb') as f:
        pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)


    

In [144]:

    

save_obj(food_classifier, 'food_classifier')
save_obj(service_classifier, 'service_classifier')
save_obj(amb_classifier, 'amb_classifier')
save_obj(deal_classifier, 'deal_classifier')


    

In [5]:

    

def load_obj(name ):
    with open( name + '.pkl', 'rb') as f:
        return pickle.load(f)


    

In [11]:

    

f= load_obj('food_classifier')
s= load_obj('service_classifier')
a= load_obj('amb_classifier')
d= load_obj('deal_classifier')


    

In [25]:

    

from sklearn.metrics import classification_report
from sklearn.metrics import accuracy_score


    

In [27]:

    

X_train, X_test, y_train, y_test = train_test_split(train['review'],train['Food'], test_size=0.25, random_state=33)
print(classification_report(y_test,f.predict(X_test)))


    

    




             precision    recall  f1-score   support

        0.0       0.75      0.35      0.48        17
        1.0       0.69      0.92      0.79        26

avg / total       0.71      0.70      0.67        43

In [29]:

    

X_train, X_test, y_train, y_test = train_test_split(train['review'],train['Service'], test_size=0.25, random_state=33)
print(classification_report(y_test,s.predict(X_test)))


    

    




             precision    recall  f1-score   support

        0.0       0.76      0.83      0.79        30
        1.0       0.50      0.38      0.43        13

avg / total       0.68      0.70      0.69        43

In [30]:

    

X_train, X_test, y_train, y_test = train_test_split(train['review'],train['Ambience'], test_size=0.25, random_state=33)
print(classification_report(y_test,a.predict(X_test)))


    

    




             precision    recall  f1-score   support

        0.0       0.70      1.00      0.82        28
        1.0       1.00      0.20      0.33        15

avg / total       0.80      0.72      0.65        43

In [31]:

    

X_train, X_test, y_train, y_test = train_test_split(train['review'],train['deal'], test_size=0.25, random_state=33)
print(classification_report(y_test,d.predict(X_test)))


    

    




             precision    recall  f1-score   support

        0.0       0.85      1.00      0.92        35
        1.0       1.00      0.25      0.40         8

avg / total       0.88      0.86      0.82        43

In [132]:

    

def getPrediction(id):
    rev= getReview(id)
    food_count = 0 
    serv_count = 0
    amb_count = 0
    deal_count = 0

    for review in rev:
        prediction = {}
        prediction["food"] = food_classifier.predict([review])[0]
        prediction["service"] = service_classifier.predict([review])[0]
        prediction["amb"] = amb_classifier.predict([review])[0]
        prediction["deals"] = deal_classifier.predict([review])[0]

        if(prediction["food"]==1):
            food_count += 1
        if(prediction["service"]==1):
            serv_count += 1
        if(prediction["amb"]==1):
            amb_count += 1
        if(prediction["deals"]==1):
            deal_count += 1

    #     print(prediction)

    rev_size = len(rev)
    if(rev_size> 0 ):
        food_per = food_count/rev_size*100
        serv_per = serv_count/rev_size*100
        amb_per = amb_count/rev_size*100
        deal_per = deal_count/rev_size*100
    else:
        food_per= 0 
        serv_per=0
        amb_per=0
        deal_per=0 

    # print(rev_size,food_per,serv_per,amb_per,deal_per)
    return (rev_size,food_per,serv_per,amb_per,deal_per)


    

In [127]:

    

for id in pred[pred['food']==-1].restaurant_id:
    i,a,b,c,d= getPrediction(id)
    pred.iloc[id-1,1]= a
    pred.iloc[id-1,2]= b 
    pred.iloc[id-1,3]= c 
    pred.iloc[id-1,4]= d 
    print(i)


    

    




---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-127-afe62aa5d5d9> in <module>()
      1 for id in pred[pred['food']==-1].restaurant_id:
----> 2     i,a,b,c,d= getPrediction(id)
      3     pred.iloc[id-1,1]= a
      4     pred.iloc[id-1,2]= b
      5     pred.iloc[id-1,3]= c

<ipython-input-121-bc461ddd612a> in getPrediction(id)
     10         prediction["food"] = food_classifier.predict([review])[0]
     11         prediction["service"] = service_classifier.predict([review])[0]
---> 12         prediction["amb"] = amb_classifier.predict([review])[0]
     13         prediction["deals"] = deal_classifier.predict([review])[0]
     14 

C:\Anaconda3\lib\site-packages\sklearn\utils\metaestimators.py in <lambda>(*args, **kwargs)
     52 
     53         # lambda, but not partial, allows help() to work with update_wrapper
---> 54         out = lambda *args, **kwargs: self.fn(obj, *args, **kwargs)
     55         # update the docstring of the returned function
     56         update_wrapper(out, self.fn)

C:\Anaconda3\lib\site-packages\sklearn\pipeline.py in predict(self, X)
    324         for name, transform in self.steps[:-1]:
    325             if transform is not None:
--> 326                 Xt = transform.transform(Xt)
    327         return self.steps[-1][-1].predict(Xt)
    328 

C:\Anaconda3\lib\site-packages\sklearn\feature_extraction\text.py in transform(self, raw_documents)
    891 
    892         # use the same matrix-building strategy as fit_transform
--> 893         _, X = self._count_vocab(raw_documents, fixed_vocab=True)
    894         if self.binary:
    895             X.data.fill(1)

C:\Anaconda3\lib\site-packages\sklearn\feature_extraction\text.py in _count_vocab(self, raw_documents, fixed_vocab)
    760         for doc in raw_documents:
    761             feature_counter = {}
--> 762             for feature in analyze(doc):
    763                 try:
    764                     feature_idx = vocabulary[feature]

C:\Anaconda3\lib\site-packages\sklearn\feature_extraction\text.py in <lambda>(doc)
    239 
    240             return lambda doc: self._word_ngrams(
--> 241                 tokenize(preprocess(self.decode(doc))), stop_words)
    242 
    243         else:

<ipython-input-30-3182fd224a51> in stemming_tokenizer(text)
      8     f.close()
      9     text = text.lower()
---> 10     words = TextBlob(text).correct().words
     11     words = [Word(w).lemmatize("v") for w in words if not w in stopwords]
     12     return words

C:\Anaconda3\lib\site-packages\textblob\blob.py in correct(self)
    553         tokens = nltk.tokenize.regexp_tokenize(self.raw, "\w+|[^\w\s]|\s")
    554         corrected = (Word(w).correct() for w in tokens)
--> 555         ret = ''.join(corrected)
    556         return self.__class__(ret)
    557 

C:\Anaconda3\lib\site-packages\textblob\blob.py in <genexpr>(.0)
    552         # regex matches: word or punctuation or whitespace
    553         tokens = nltk.tokenize.regexp_tokenize(self.raw, "\w+|[^\w\s]|\s")
--> 554         corrected = (Word(w).correct() for w in tokens)
    555         ret = ''.join(corrected)
    556         return self.__class__(ret)

C:\Anaconda3\lib\site-packages\textblob\blob.py in correct(self)
    125         .. versionadded:: 0.6.0
    126         '''
--> 127         return Word(self.spellcheck()[0][0])
    128 
    129     @cached_property

C:\Anaconda3\lib\site-packages\textblob\blob.py in spellcheck(self)
    117         .. versionadded:: 0.6.0
    118         '''
--> 119         return suggest(self.string)
    120 
    121     def correct(self):

C:\Anaconda3\lib\site-packages\textblob\en\__init__.py in suggest(w)
    121     """ Returns a list of (word, confidence)-tuples of spelling corrections.
    122     """
--> 123     return spelling.suggest(w)
    124 
    125 def polarity(s, **kwargs):

C:\Anaconda3\lib\site-packages\textblob\_text.py in suggest(self, w)
   1396         candidates = self._known([w]) \
   1397                   or self._known(self._edit1(w)) \
-> 1398                   or self._known(self._edit2(w)) \
   1399                   or [w]
   1400         candidates = [(self.get(c, 0.0), c) for c in candidates]

C:\Anaconda3\lib\site-packages\textblob\_text.py in _edit2(self, w)
   1373         # Of all spelling errors, 99% is covered by edit distance 2.
   1374         # Only keep candidates that are actually known words (20% speedup).
-> 1375         return set(e2 for e1 in self._edit1(w) for e2 in self._edit1(e1) if e2 in self)
   1376 
   1377     def _known(self, words=[]):

C:\Anaconda3\lib\site-packages\textblob\_text.py in <genexpr>(.0)
   1373         # Of all spelling errors, 99% is covered by edit distance 2.
   1374         # Only keep candidates that are actually known words (20% speedup).
-> 1375         return set(e2 for e1 in self._edit1(w) for e2 in self._edit1(e1) if e2 in self)
   1376 
   1377     def _known(self, words=[]):

C:\Anaconda3\lib\site-packages\textblob\_text.py in _edit1(self, w)
   1363             [a + b[1:] for a, b in split if b],
   1364             [a + b[1] + b[0] + b[2:] for a, b in split if len(b) > 1],
-> 1365             [a + c + b[1:] for a, b in split for c in Spelling.ALPHA if b],
   1366             [a + c + b[0:] for a, b in split for c in Spelling.ALPHA]
   1367         )

C:\Anaconda3\lib\site-packages\textblob\_text.py in <listcomp>(.0)
   1363             [a + b[1:] for a, b in split if b],
   1364             [a + b[1] + b[0] + b[2:] for a, b in split if len(b) > 1],
-> 1365             [a + c + b[1:] for a, b in split for c in Spelling.ALPHA if b],
   1366             [a + c + b[0:] for a, b in split for c in Spelling.ALPHA]
   1367         )

KeyboardInterrupt: 

In [125]:

    

rev = getReview(96)


    

In [126]:

    

len(rev)


    

    
Out[126]:





244

In [131]:

    

pred.head()


    

    
Out[131]:







  

    

      

      
restaurant_id
      
food
      
service
      
ambience
      
deals
    
  
  

    

      
0
      
1
      
100.000000
      
23.076923
      
0.000000
      
0.0
    
    

      
1
      
2
      
62.500000
      
25.000000
      
12.500000
      
0.0
    
    

      
2
      
3
      
100.000000
      
25.000000
      
0.000000
      
0.0
    
    

      
3
      
4
      
100.000000
      
4.000000
      
6.000000
      
0.0
    
    

      
4
      
5
      
90.909091
      
31.818182
      
4.545455
      
0.0
    
  

In [130]:

    

pred[(pred['food']==-1)]


    

    
Out[130]:







  

    

      

      
restaurant_id
      
food
      
service
      
ambience
      
deals
    
  
  

    

      
95
      
96
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
96
      
97
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
97
      
98
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
98
      
99
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
99
      
100
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
100
      
101
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
101
      
102
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
102
      
103
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
103
      
104
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
104
      
105
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
105
      
106
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
106
      
107
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
107
      
108
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
108
      
109
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
109
      
110
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
110
      
111
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
111
      
112
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
112
      
113
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
113
      
114
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
114
      
115
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
115
      
116
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
116
      
117
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
117
      
118
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
118
      
119
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
119
      
120
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
120
      
121
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
121
      
122
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
122
      
123
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
123
      
124
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
124
      
125
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
...
      
...
      
...
      
...
      
...
      
...
    
    

      
4473
      
4474
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4474
      
4475
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4475
      
4476
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4476
      
4477
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4477
      
4478
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4478
      
4479
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4479
      
4480
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4480
      
4481
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4481
      
4482
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4482
      
4483
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4483
      
4484
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4484
      
4485
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4485
      
4486
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4486
      
4487
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4487
      
4488
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4488
      
4489
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4489
      
4490
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4490
      
4491
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4491
      
4492
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4492
      
4493
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4493
      
4494
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4494
      
4495
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4495
      
4496
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4496
      
4497
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4497
      
4498
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4498
      
4499
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4499
      
4500
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4500
      
4501
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4501
      
4502
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4502
      
4503
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
  

4408 rows × 5 columns

In [129]:

    

pred.to_csv("prediction.csv", index=False)


    

In [149]:

    

pred[(pred['food']==-1)& (pred['restaurant_id']>=1000)]


    

    
Out[149]:







  

    

      

      
restaurant_id
      
food
      
service
      
ambience
      
deals
    
  
  

    

      
999
      
1000
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
1000
      
1001
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
1001
      
1002
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
1002
      
1003
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
1003
      
1004
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
1004
      
1005
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
1005
      
1006
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
1006
      
1007
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
1007
      
1008
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
1008
      
1009
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
1009
      
1010
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
1010
      
1011
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
1011
      
1012
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
1012
      
1013
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
1013
      
1014
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
1014
      
1015
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
1015
      
1016
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
1016
      
1017
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
1017
      
1018
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
1018
      
1019
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
1019
      
1020
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
1020
      
1021
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
1021
      
1022
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
1022
      
1023
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
1023
      
1024
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
1024
      
1025
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
1025
      
1026
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
1026
      
1027
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
1027
      
1028
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
1028
      
1029
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
...
      
...
      
...
      
...
      
...
      
...
    
    

      
4473
      
4474
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4474
      
4475
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4475
      
4476
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4476
      
4477
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4477
      
4478
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4478
      
4479
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4479
      
4480
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4480
      
4481
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4481
      
4482
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4482
      
4483
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4483
      
4484
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4484
      
4485
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4485
      
4486
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4486
      
4487
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4487
      
4488
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4488
      
4489
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4489
      
4490
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4490
      
4491
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4491
      
4492
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4492
      
4493
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4493
      
4494
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4494
      
4495
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4495
      
4496
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4496
      
4497
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4497
      
4498
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4498
      
4499
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4499
      
4500
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4500
      
4501
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4501
      
4502
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
    

      
4502
      
4503
      
-1.0
      
-1.0
      
-1.0
      
-1.0
    
  

3504 rows × 5 columns

In [ ]:

    




    

In [ ]: