

In [354]:

    
# Import the libraries we will be using
import numpy as np
import pandas as pd
import re

from sklearn import preprocessing
from sklearn.tree import DecisionTreeClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.svm import LinearSVC
from sklearn.calibration import CalibratedClassifierCV

from sklearn.feature_selection import RFE
from sklearn.model_selection import GridSearchCV
from sklearn import metrics
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split

#from dstools import data_tools   #if plot decision tree surface

import seaborn as sns
import matplotlib.pylab as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = 10, 8



In [13]:

    
df = pd.read_pickle('../data_processeing/Yelp_Cuisine_Chinese.pkl')
print df.shape
df.head(2)









    



(27314, 98)






    Out[13]:






  
    
      
      address
      attributes
      business_id
      categories
      city
      hours
      is_open
      latitude
      longitude
      name
      ...
      RestaurantsDelivery
      RestaurantsGoodForGroups
      RestaurantsPriceRange2
      RestaurantsReservations
      RestaurantsTableService
      RestaurantsTakeOut
      Smoking
      WheelchairAccessible
      WiFi
      cuisine_Chinese
    
  
  
    
      EDqCEAGXVGCH4FJXgqtjqg
      979 Bloor Street W
      [{u'Alcohol': u'none'}, {u'Ambience': {u'roman...
      EDqCEAGXVGCH4FJXgqtjqg
      [Restaurants, Pizza, Chicken Wings, Italian]
      Toronto
      [Monday 11:0-2:0, Tuesday 11:0-2:0, Wednesday ...
      1
      43.661054
      -79.429089
      Pizza Pizza
      ...
      False
      False
      NaN
      NaN
      NaN
      NaN
      False
      NaN
      NaN
      1
    
    
      GDnbt3isfhd57T1QqU6flg
      11072 No Frank Lloyd Wright
      [{u'Alcohol': u'none'}, {u'Ambience': {u'roman...
      GDnbt3isfhd57T1QqU6flg
      [Tex-Mex, Mexican, Fast Food, Restaurants]
      Scottsdale
      [Monday 10:0-22:0, Tuesday 10:0-22:0, Wednesda...
      1
      33.586710
      -111.835410
      Taco Bell
      ...
      False
      False
      NaN
      NaN
      NaN
      NaN
      False
      NaN
      NaN
      1
    
  

2 rows × 98 columns



In [29]:

    
#df.review_count.unique()



In [159]:

    
## Find and drop HairSpecializesIn
hair_columns = reduce(lambda x,y: x+y, map(lambda att: re.findall('HairSpecializesIn_.*', att), df.columns.values))
df.drop(hair_columns, axis=1, inplace=True)



In [160]:

    
df.shape









    Out[160]:





(27314, 90)

flaten attributes



In [161]:

    
df2 = df.copy()



In [162]:

    
df_att = df2.loc[ :, u'AcceptsInsurance':'WiFi'] # include 'WiFi'
att_type = {}
col_b = []
col_m = []
for i in range(df_att.shape[1]):
    temp = df_att.groupby(df_att.iloc[:,i]).size().keys()
    print temp.name, ': ', temp.values
    if set(temp.values) == set([False, True]) or set(temp.values).issubset(set([False, True])):
        att_type[temp.name] = 'b'
        col_b.append(temp.name)
    else:
        att_type[temp.name] = 'm'
        col_m.append(temp.name)

print len(col_b), len(col_m)
col_all = col_b + col_m









    



AcceptsInsurance :  [False True]
AgesAllowed :  [False True]
Alcohol :  [False True]
Ambience_casual :  [False True]
Ambience_classy :  ['beer_and_wine' 'full_bar' 'none']
Ambience_divey :  [False True]
Ambience_hipster :  ['average' 'loud' 'quiet' 'very_loud']
Ambience_intimate :  [False True]
Ambience_romantic :  ['casual' 'dressy' 'formal']
Ambience_touristy :  [False True]
Ambience_trendy :  [False True]
Ambience_upscale :  [False True]
BYOB :  [False True]
BYOBCorkage :  [False True]
BestNights_friday :  [False]
BestNights_monday :  [False True]
BestNights_saturday :  [False True]
BestNights_sunday :  [False True]
BestNights_thursday :  [False 'yes_corkage' 'yes_free']
BestNights_tuesday :  [False True]
BestNights_wednesday :  [False True]
BikeParking :  [False True]
BusinessAcceptsBitcoin :  [False True]
BusinessAcceptsCreditCards :  [False True]
BusinessParking_garage :  [False True]
BusinessParking_lot :  [False 'free' 'paid']
BusinessParking_street :  [False True]
BusinessParking_valet :  [False True]
BusinessParking_validated :  [False True]
ByAppointmentOnly :  [False True]
Caters :  [False True]
CoatCheck :  [False True]
Corkage :  [False True]
DietaryRestrictions_dairy-free :  [False True]
DietaryRestrictions_gluten-free :  [False True]
DietaryRestrictions_halal :  [False True]
DietaryRestrictions_kosher :  [False True]
DietaryRestrictions_soy-free :  [False True]
DietaryRestrictions_vegan :  [False True]
DietaryRestrictions_vegetarian :  [False True]
DogsAllowed :  [False True]
DriveThru :  [False True]
GoodForDancing :  [False True]
GoodForKids :  [False True]
GoodForMeal_breakfast :  [False True]
GoodForMeal_brunch :  [False True]
GoodForMeal_dessert :  [False True]
GoodForMeal_dinner :  [False True]
GoodForMeal_latenight :  [False True]
GoodForMeal_lunch :  [False True]
HappyHour :  [False True]
HasTV :  [False True]
Music_background_music :  [False True]
Music_dj :  [False True]
Music_jukebox :  [False True]
Music_karaoke :  [False True]
Music_live :  [False True]
Music_no_music :  [False True]
Music_video :  [False True]
NoiseLevel :  [False True]
Open24Hours :  [False True]
OutdoorSeating :  [False True]
RestaurantsAttire :  [False True]
RestaurantsCounterService :  [False True]
RestaurantsDelivery :  [False True]
RestaurantsGoodForGroups :  [False True]
RestaurantsPriceRange2 :  [False True]
RestaurantsReservations :  [False True 'outdoor']
RestaurantsTableService :  [False True]
RestaurantsTakeOut :  [False True]
Smoking :  [False True]
WheelchairAccessible :  [False True]
WiFi :  [False True]
67 6



In [163]:

    
### join label with all the expanded"attributes" and "review count"
df_att_b = df2.loc[:,col_b].join(df[['review_count','cuisine_Chinese']])
df_att_b.head(1)









    Out[163]:






  
    
      
      AcceptsInsurance
      AgesAllowed
      Alcohol
      Ambience_casual
      Ambience_divey
      Ambience_intimate
      Ambience_touristy
      Ambience_trendy
      Ambience_upscale
      BYOB
      ...
      RestaurantsDelivery
      RestaurantsGoodForGroups
      RestaurantsPriceRange2
      RestaurantsTableService
      RestaurantsTakeOut
      Smoking
      WheelchairAccessible
      WiFi
      review_count
      cuisine_Chinese
    
  
  
    
      EDqCEAGXVGCH4FJXgqtjqg
      NaN
      False
      NaN
      NaN
      True
      NaN
      NaN
      NaN
      True
      False
      ...
      False
      False
      NaN
      NaN
      NaN
      False
      NaN
      NaN
      7
      1
    
  

1 rows × 69 columns



In [164]:

    
df_att_m = df2.loc[:,col_m].join(df[['review_count','cuisine_Chinese']])
df_att_m.head(1)









    Out[164]:






  
    
      
      Ambience_classy
      Ambience_hipster
      Ambience_romantic
      BestNights_thursday
      BusinessParking_lot
      RestaurantsReservations
      review_count
      cuisine_Chinese
    
  
  
    
      EDqCEAGXVGCH4FJXgqtjqg
      none
      quiet
      casual
      NaN
      free
      NaN
      7
      1



In [165]:

    
df_att_all = df_att.join(df[['review_count','cuisine_Chinese']])
df_att_all.head(1)









    Out[165]:






  
    
      
      AcceptsInsurance
      AgesAllowed
      Alcohol
      Ambience_casual
      Ambience_classy
      Ambience_divey
      Ambience_hipster
      Ambience_intimate
      Ambience_romantic
      Ambience_touristy
      ...
      RestaurantsGoodForGroups
      RestaurantsPriceRange2
      RestaurantsReservations
      RestaurantsTableService
      RestaurantsTakeOut
      Smoking
      WheelchairAccessible
      WiFi
      review_count
      cuisine_Chinese
    
  
  
    
      EDqCEAGXVGCH4FJXgqtjqg
      NaN
      False
      NaN
      NaN
      none
      True
      quiet
      NaN
      casual
      NaN
      ...
      False
      NaN
      NaN
      NaN
      NaN
      False
      NaN
      NaN
      7
      1
    
  

1 rows × 75 columns



In [166]:

    
df_att_all.shape









    Out[166]:





(27314, 75)



In [167]:

    
df_att_all_filled = df_att_all.fillna(value='none')
df_att_all_filled.head(3)









    Out[167]:






  
    
      
      AcceptsInsurance
      AgesAllowed
      Alcohol
      Ambience_casual
      Ambience_classy
      Ambience_divey
      Ambience_hipster
      Ambience_intimate
      Ambience_romantic
      Ambience_touristy
      ...
      RestaurantsGoodForGroups
      RestaurantsPriceRange2
      RestaurantsReservations
      RestaurantsTableService
      RestaurantsTakeOut
      Smoking
      WheelchairAccessible
      WiFi
      review_count
      cuisine_Chinese
    
  
  
    
      EDqCEAGXVGCH4FJXgqtjqg
      none
      False
      none
      none
      none
      True
      quiet
      none
      casual
      none
      ...
      False
      none
      none
      none
      none
      False
      none
      none
      7
      1
    
    
      GDnbt3isfhd57T1QqU6flg
      none
      False
      none
      none
      none
      False
      quiet
      none
      casual
      none
      ...
      False
      none
      none
      none
      none
      False
      none
      none
      9
      1
    
    
      a1Ba6XeIOP48e64YFD0dMw
      none
      none
      none
      none
      none
      none
      none
      none
      none
      none
      ...
      none
      none
      none
      none
      none
      none
      none
      none
      3
      0
    
  

3 rows × 75 columns



In [168]:

    
## transformed certain columns to BINARY varibales #####
df_chin_bi = pd.get_dummies(df_att_all_filled, \
                            prefix=col_all, prefix_sep='_', dummy_na=False, \
                            columns=col_all, sparse=False, drop_first=False)



In [169]:

    
## change column order
df_chin_bi = df_chin_bi.loc[:,['cuisine_Chinese','review_count'] + list(df_chin_bi.columns.values[2:])]



In [170]:

    
df_chin_bi.head(2)









    Out[170]:






  
    
      
      cuisine_Chinese
      review_count
      AcceptsInsurance_False
      AcceptsInsurance_True
      AcceptsInsurance_none
      AgesAllowed_False
      AgesAllowed_True
      AgesAllowed_none
      Alcohol_False
      Alcohol_True
      ...
      BestNights_thursday_yes_corkage
      BestNights_thursday_yes_free
      BusinessParking_lot_False
      BusinessParking_lot_free
      BusinessParking_lot_none
      BusinessParking_lot_paid
      RestaurantsReservations_False
      RestaurantsReservations_True
      RestaurantsReservations_none
      RestaurantsReservations_outdoor
    
  
  
    
      EDqCEAGXVGCH4FJXgqtjqg
      1
      7
      0
      0
      1
      1
      0
      0
      0
      0
      ...
      0
      0
      0
      1
      0
      0
      0
      0
      1
      0
    
    
      GDnbt3isfhd57T1QqU6flg
      1
      9
      0
      0
      1
      1
      0
      0
      0
      0
      ...
      0
      0
      1
      0
      0
      0
      0
      0
      1
      0
    
  

2 rows × 226 columns



In [171]:

    
df_chin_bi.shape









    Out[171]:





(27314, 226)



In [172]:

    
# Split training(labeled) and test(unlabled) 
df_chin_train = df_chin_bi[df_chin_bi['cuisine_Chinese'] != 0]
df_chin_test = df_chin_bi[df_chin_bi['cuisine_Chinese'] == 0]



In [173]:

    
## Separate X(feature) and Y(traget) for training set
X = df_chin_train.ix[:, 1:]
Y = df_chin_train['cuisine_Chinese'].apply(lambda n: n-1)



In [472]:

    
X_train, X_vali, Y_train, Y_vali = train_test_split(
    X, Y, train_size=0.7, random_state=90) #Use random_state to fix samples

Logistic Regression

Parameter tuning



In [391]:

    
# panalty and C value tuning
penalties = ['l1', 'l2'] 
C_values = sorted([np.exp(a*5) for a in np.linspace(-2,2,15)])
C_values









    Out[391]:





[4.5399929762484854e-05,
 0.00018944182523289392,
 0.00079049032311996614,
 0.0032985057559390941,
 0.01376378673305039,
 0.057432619267617294,
 0.23965103644177568,
 1.0,
 4.1727338835980934,
 17.41170806332763,
 72.654424207165334,
 303.16757768254888,
 1265.03762380433,
 5278.6653568747415,
 22026.465794806718]



In [392]:

    
# pick train test split ratio as 0.7

# LR GridSearch 1

# tuning hyper parameter
tuned_parameters_LR = {'C': C_values,
                       'penalty': penalties} 

lr = LogisticRegression(random_state=99)
gr_lr = GridSearchCV(lr, param_grid=tuned_parameters_LR, cv=5, scoring="roc_auc")
gr_lr.fit(X_train, Y_train)

gr_lr_auc_scores = cross_val_score(gr_lr, X, Y, scoring="roc_auc", cv=5)

print ( "Logistic regression training size(0.7): Mean AUC %.4f\n" % (np.mean(gr_lr_auc_scores)))

print "Best parameters set found:"
print gr_lr.best_params_
print"Gridsearch Scores: "
print gr_lr.score(X_vali, Y_vali)

means = gr_lr.cv_results_['mean_test_score']
stds = gr_lr.cv_results_['std_test_score']
for mean, std, params in zip(means, stds, gr_lr.cv_results_['params']):
    print("%0.3f (+/-%0.03f) for %r"
          % (mean, std * 2, params))









    



Logistic regression training size(0.7): Mean AUC 0.7368

Best parameters set found:
{'penalty': 'l1', 'C': 1.0}
Gridsearch Scores: 
0.730713489409
0.507 (+/-0.036) for {'penalty': 'l1', 'C': 4.5399929762484854e-05}
0.504 (+/-0.045) for {'penalty': 'l2', 'C': 4.5399929762484854e-05}
0.507 (+/-0.036) for {'penalty': 'l1', 'C': 0.00018944182523289392}
0.640 (+/-0.046) for {'penalty': 'l2', 'C': 0.00018944182523289392}
0.507 (+/-0.036) for {'penalty': 'l1', 'C': 0.00079049032311996614}
0.703 (+/-0.028) for {'penalty': 'l2', 'C': 0.00079049032311996614}
0.630 (+/-0.041) for {'penalty': 'l1', 'C': 0.0032985057559390941}
0.720 (+/-0.019) for {'penalty': 'l2', 'C': 0.0032985057559390941}
0.681 (+/-0.023) for {'penalty': 'l1', 'C': 0.01376378673305039}
0.731 (+/-0.018) for {'penalty': 'l2', 'C': 0.01376378673305039}
0.730 (+/-0.012) for {'penalty': 'l1', 'C': 0.057432619267617294}
0.736 (+/-0.018) for {'penalty': 'l2', 'C': 0.057432619267617294}
0.736 (+/-0.019) for {'penalty': 'l1', 'C': 0.23965103644177568}
0.737 (+/-0.017) for {'penalty': 'l2', 'C': 0.23965103644177568}
0.737 (+/-0.018) for {'penalty': 'l1', 'C': 1.0}
0.737 (+/-0.016) for {'penalty': 'l2', 'C': 1.0}
0.737 (+/-0.016) for {'penalty': 'l1', 'C': 4.1727338835980934}
0.737 (+/-0.015) for {'penalty': 'l2', 'C': 4.1727338835980934}
0.736 (+/-0.015) for {'penalty': 'l1', 'C': 17.41170806332763}
0.736 (+/-0.015) for {'penalty': 'l2', 'C': 17.41170806332763}
0.736 (+/-0.015) for {'penalty': 'l1', 'C': 72.654424207165334}
0.736 (+/-0.015) for {'penalty': 'l2', 'C': 72.654424207165334}
0.736 (+/-0.015) for {'penalty': 'l1', 'C': 303.16757768254888}
0.737 (+/-0.015) for {'penalty': 'l2', 'C': 303.16757768254888}
0.736 (+/-0.015) for {'penalty': 'l1', 'C': 1265.03762380433}
0.737 (+/-0.015) for {'penalty': 'l2', 'C': 1265.03762380433}
0.736 (+/-0.015) for {'penalty': 'l1', 'C': 5278.6653568747415}
0.737 (+/-0.015) for {'penalty': 'l2', 'C': 5278.6653568747415}
0.736 (+/-0.015) for {'penalty': 'l1', 'C': 22026.465794806718}
0.737 (+/-0.015) for {'penalty': 'l2', 'C': 22026.465794806718}



In [393]:

    
p_opt = gr_lr.best_params_.values()[0]
c_opt = gr_lr.best_params_.values()[1]

Decision Tree Classifier



In [477]:

    
mdepth_values = sorted([int(a) for a in np.linspace(1, 40, 10)])
print mdepth_values
mss_values = sorted([int(a) for a in np.linspace(100, 400, 10)])
print mss_values
msl_values = sorted([int(a) for a in np.linspace(1, 50, 10)])
print msl_values









    



[1, 5, 9, 14, 18, 22, 27, 31, 35, 40]
[100, 133, 166, 200, 233, 266, 300, 333, 366, 400]
[1, 6, 11, 17, 22, 28, 33, 39, 44, 50]



In [480]:

    
# tuning hyperparameter
tuned_parameters_DT = {'max_depth': mdepth_values,
                       'min_samples_split': mss_values,
                       'min_samples_leaf':msl_values
                       }

dt = DecisionTreeClassifier(criterion='entropy', random_state=99)
gr_dt = GridSearchCV(dt, param_grid=tuned_parameters_DT, cv=5, scoring="roc_auc")
gr_dt.fit(X_train, Y_train)

gr_dt_auc_scores = cross_val_score(gr_dt, X, Y, scoring="roc_auc", cv=5)

print ( "Decision Tree with training size(0.7): Mean AUC %.4f\n" % (np.mean(gr_dt_auc_scores)))

print "Best parameters set found:"
print gr_dt.best_params_
print"Gridsearch Scores: "
print gr_dt.score(X_vali, Y_vali)

means = gr_dt.cv_results_['mean_test_score']
stds = gr_dt.cv_results_['std_test_score']
for mean, std, params in zip(means, stds, gr_dt.cv_results_['params']):
    print("%0.3f (+/-%0.03f) for %r"
          % (mean, std * 2, params))









    



Decision Tree with training size(0.7): Mean AUC 0.7180

Best parameters set found:
{'min_samples_split': 266, 'max_depth': 14, 'min_samples_leaf': 28}
Gridsearch Scores: 
0.719029969178
0.584 (+/-0.042) for {'min_samples_split': 100, 'max_depth': 1, 'min_samples_leaf': 1}
0.584 (+/-0.042) for {'min_samples_split': 133, 'max_depth': 1, 'min_samples_leaf': 1}
0.584 (+/-0.042) for {'min_samples_split': 166, 'max_depth': 1, 'min_samples_leaf': 1}
0.584 (+/-0.042) for {'min_samples_split': 200, 'max_depth': 1, 'min_samples_leaf': 1}
0.584 (+/-0.042) for {'min_samples_split': 233, 'max_depth': 1, 'min_samples_leaf': 1}
0.584 (+/-0.042) for {'min_samples_split': 266, 'max_depth': 1, 'min_samples_leaf': 1}
0.584 (+/-0.042) for {'min_samples_split': 300, 'max_depth': 1, 'min_samples_leaf': 1}
0.584 (+/-0.042) for {'min_samples_split': 333, 'max_depth': 1, 'min_samples_leaf': 1}
0.584 (+/-0.042) for {'min_samples_split': 366, 'max_depth': 1, 'min_samples_leaf': 1}
0.584 (+/-0.042) for {'min_samples_split': 400, 'max_depth': 1, 'min_samples_leaf': 1}
0.584 (+/-0.042) for {'min_samples_split': 100, 'max_depth': 1, 'min_samples_leaf': 6}
0.584 (+/-0.042) for {'min_samples_split': 133, 'max_depth': 1, 'min_samples_leaf': 6}
0.584 (+/-0.042) for {'min_samples_split': 166, 'max_depth': 1, 'min_samples_leaf': 6}
0.584 (+/-0.042) for {'min_samples_split': 200, 'max_depth': 1, 'min_samples_leaf': 6}
0.584 (+/-0.042) for {'min_samples_split': 233, 'max_depth': 1, 'min_samples_leaf': 6}
0.584 (+/-0.042) for {'min_samples_split': 266, 'max_depth': 1, 'min_samples_leaf': 6}
0.584 (+/-0.042) for {'min_samples_split': 300, 'max_depth': 1, 'min_samples_leaf': 6}
0.584 (+/-0.042) for {'min_samples_split': 333, 'max_depth': 1, 'min_samples_leaf': 6}
0.584 (+/-0.042) for {'min_samples_split': 366, 'max_depth': 1, 'min_samples_leaf': 6}
0.584 (+/-0.042) for {'min_samples_split': 400, 'max_depth': 1, 'min_samples_leaf': 6}
0.584 (+/-0.042) for {'min_samples_split': 100, 'max_depth': 1, 'min_samples_leaf': 11}
0.584 (+/-0.042) for {'min_samples_split': 133, 'max_depth': 1, 'min_samples_leaf': 11}
0.584 (+/-0.042) for {'min_samples_split': 166, 'max_depth': 1, 'min_samples_leaf': 11}
0.584 (+/-0.042) for {'min_samples_split': 200, 'max_depth': 1, 'min_samples_leaf': 11}
0.584 (+/-0.042) for {'min_samples_split': 233, 'max_depth': 1, 'min_samples_leaf': 11}
0.584 (+/-0.042) for {'min_samples_split': 266, 'max_depth': 1, 'min_samples_leaf': 11}
0.584 (+/-0.042) for {'min_samples_split': 300, 'max_depth': 1, 'min_samples_leaf': 11}
0.584 (+/-0.042) for {'min_samples_split': 333, 'max_depth': 1, 'min_samples_leaf': 11}
0.584 (+/-0.042) for {'min_samples_split': 366, 'max_depth': 1, 'min_samples_leaf': 11}
0.584 (+/-0.042) for {'min_samples_split': 400, 'max_depth': 1, 'min_samples_leaf': 11}
0.584 (+/-0.042) for {'min_samples_split': 100, 'max_depth': 1, 'min_samples_leaf': 17}
0.584 (+/-0.042) for {'min_samples_split': 133, 'max_depth': 1, 'min_samples_leaf': 17}
0.584 (+/-0.042) for {'min_samples_split': 166, 'max_depth': 1, 'min_samples_leaf': 17}
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0.713 (+/-0.014) for {'min_samples_split': 266, 'max_depth': 40, 'min_samples_leaf': 1}
0.711 (+/-0.011) for {'min_samples_split': 300, 'max_depth': 40, 'min_samples_leaf': 1}
0.713 (+/-0.014) for {'min_samples_split': 333, 'max_depth': 40, 'min_samples_leaf': 1}
0.714 (+/-0.013) for {'min_samples_split': 366, 'max_depth': 40, 'min_samples_leaf': 1}
0.714 (+/-0.012) for {'min_samples_split': 400, 'max_depth': 40, 'min_samples_leaf': 1}
0.690 (+/-0.012) for {'min_samples_split': 100, 'max_depth': 40, 'min_samples_leaf': 6}
0.695 (+/-0.012) for {'min_samples_split': 133, 'max_depth': 40, 'min_samples_leaf': 6}
0.701 (+/-0.015) for {'min_samples_split': 166, 'max_depth': 40, 'min_samples_leaf': 6}
0.705 (+/-0.018) for {'min_samples_split': 200, 'max_depth': 40, 'min_samples_leaf': 6}
0.708 (+/-0.014) for {'min_samples_split': 233, 'max_depth': 40, 'min_samples_leaf': 6}
0.712 (+/-0.017) for {'min_samples_split': 266, 'max_depth': 40, 'min_samples_leaf': 6}
0.711 (+/-0.015) for {'min_samples_split': 300, 'max_depth': 40, 'min_samples_leaf': 6}
0.712 (+/-0.015) for {'min_samples_split': 333, 'max_depth': 40, 'min_samples_leaf': 6}
0.713 (+/-0.014) for {'min_samples_split': 366, 'max_depth': 40, 'min_samples_leaf': 6}
0.713 (+/-0.012) for {'min_samples_split': 400, 'max_depth': 40, 'min_samples_leaf': 6}
0.695 (+/-0.020) for {'min_samples_split': 100, 'max_depth': 40, 'min_samples_leaf': 11}
0.699 (+/-0.012) for {'min_samples_split': 133, 'max_depth': 40, 'min_samples_leaf': 11}
0.705 (+/-0.013) for {'min_samples_split': 166, 'max_depth': 40, 'min_samples_leaf': 11}
0.707 (+/-0.014) for {'min_samples_split': 200, 'max_depth': 40, 'min_samples_leaf': 11}
0.711 (+/-0.015) for {'min_samples_split': 233, 'max_depth': 40, 'min_samples_leaf': 11}
0.714 (+/-0.011) for {'min_samples_split': 266, 'max_depth': 40, 'min_samples_leaf': 11}
0.712 (+/-0.008) for {'min_samples_split': 300, 'max_depth': 40, 'min_samples_leaf': 11}
0.714 (+/-0.007) for {'min_samples_split': 333, 'max_depth': 40, 'min_samples_leaf': 11}
0.715 (+/-0.010) for {'min_samples_split': 366, 'max_depth': 40, 'min_samples_leaf': 11}
0.715 (+/-0.009) for {'min_samples_split': 400, 'max_depth': 40, 'min_samples_leaf': 11}
0.701 (+/-0.012) for {'min_samples_split': 100, 'max_depth': 40, 'min_samples_leaf': 17}
0.705 (+/-0.011) for {'min_samples_split': 133, 'max_depth': 40, 'min_samples_leaf': 17}
0.710 (+/-0.018) for {'min_samples_split': 166, 'max_depth': 40, 'min_samples_leaf': 17}
0.712 (+/-0.020) for {'min_samples_split': 200, 'max_depth': 40, 'min_samples_leaf': 17}
0.715 (+/-0.023) for {'min_samples_split': 233, 'max_depth': 40, 'min_samples_leaf': 17}
0.717 (+/-0.016) for {'min_samples_split': 266, 'max_depth': 40, 'min_samples_leaf': 17}
0.715 (+/-0.013) for {'min_samples_split': 300, 'max_depth': 40, 'min_samples_leaf': 17}
0.716 (+/-0.013) for {'min_samples_split': 333, 'max_depth': 40, 'min_samples_leaf': 17}
0.716 (+/-0.012) for {'min_samples_split': 366, 'max_depth': 40, 'min_samples_leaf': 17}
0.715 (+/-0.012) for {'min_samples_split': 400, 'max_depth': 40, 'min_samples_leaf': 17}
0.700 (+/-0.016) for {'min_samples_split': 100, 'max_depth': 40, 'min_samples_leaf': 22}
0.705 (+/-0.011) for {'min_samples_split': 133, 'max_depth': 40, 'min_samples_leaf': 22}
0.708 (+/-0.016) for {'min_samples_split': 166, 'max_depth': 40, 'min_samples_leaf': 22}
0.713 (+/-0.020) for {'min_samples_split': 200, 'max_depth': 40, 'min_samples_leaf': 22}
0.717 (+/-0.016) for {'min_samples_split': 233, 'max_depth': 40, 'min_samples_leaf': 22}
0.718 (+/-0.013) for {'min_samples_split': 266, 'max_depth': 40, 'min_samples_leaf': 22}
0.715 (+/-0.013) for {'min_samples_split': 300, 'max_depth': 40, 'min_samples_leaf': 22}
0.716 (+/-0.013) for {'min_samples_split': 333, 'max_depth': 40, 'min_samples_leaf': 22}
0.717 (+/-0.012) for {'min_samples_split': 366, 'max_depth': 40, 'min_samples_leaf': 22}
0.714 (+/-0.012) for {'min_samples_split': 400, 'max_depth': 40, 'min_samples_leaf': 22}
0.705 (+/-0.013) for {'min_samples_split': 100, 'max_depth': 40, 'min_samples_leaf': 28}
0.709 (+/-0.011) for {'min_samples_split': 133, 'max_depth': 40, 'min_samples_leaf': 28}
0.714 (+/-0.014) for {'min_samples_split': 166, 'max_depth': 40, 'min_samples_leaf': 28}
0.717 (+/-0.019) for {'min_samples_split': 200, 'max_depth': 40, 'min_samples_leaf': 28}
0.718 (+/-0.022) for {'min_samples_split': 233, 'max_depth': 40, 'min_samples_leaf': 28}
0.719 (+/-0.017) for {'min_samples_split': 266, 'max_depth': 40, 'min_samples_leaf': 28}
0.717 (+/-0.014) for {'min_samples_split': 300, 'max_depth': 40, 'min_samples_leaf': 28}
0.718 (+/-0.016) for {'min_samples_split': 333, 'max_depth': 40, 'min_samples_leaf': 28}
0.719 (+/-0.014) for {'min_samples_split': 366, 'max_depth': 40, 'min_samples_leaf': 28}
0.717 (+/-0.013) for {'min_samples_split': 400, 'max_depth': 40, 'min_samples_leaf': 28}
0.707 (+/-0.015) for {'min_samples_split': 100, 'max_depth': 40, 'min_samples_leaf': 33}
0.709 (+/-0.013) for {'min_samples_split': 133, 'max_depth': 40, 'min_samples_leaf': 33}
0.712 (+/-0.016) for {'min_samples_split': 166, 'max_depth': 40, 'min_samples_leaf': 33}
0.716 (+/-0.019) for {'min_samples_split': 200, 'max_depth': 40, 'min_samples_leaf': 33}
0.718 (+/-0.020) for {'min_samples_split': 233, 'max_depth': 40, 'min_samples_leaf': 33}
0.717 (+/-0.018) for {'min_samples_split': 266, 'max_depth': 40, 'min_samples_leaf': 33}
0.715 (+/-0.015) for {'min_samples_split': 300, 'max_depth': 40, 'min_samples_leaf': 33}
0.717 (+/-0.015) for {'min_samples_split': 333, 'max_depth': 40, 'min_samples_leaf': 33}
0.717 (+/-0.013) for {'min_samples_split': 366, 'max_depth': 40, 'min_samples_leaf': 33}
0.716 (+/-0.013) for {'min_samples_split': 400, 'max_depth': 40, 'min_samples_leaf': 33}
0.702 (+/-0.013) for {'min_samples_split': 100, 'max_depth': 40, 'min_samples_leaf': 39}
0.707 (+/-0.010) for {'min_samples_split': 133, 'max_depth': 40, 'min_samples_leaf': 39}
0.710 (+/-0.017) for {'min_samples_split': 166, 'max_depth': 40, 'min_samples_leaf': 39}
0.713 (+/-0.019) for {'min_samples_split': 200, 'max_depth': 40, 'min_samples_leaf': 39}
0.713 (+/-0.020) for {'min_samples_split': 233, 'max_depth': 40, 'min_samples_leaf': 39}
0.714 (+/-0.017) for {'min_samples_split': 266, 'max_depth': 40, 'min_samples_leaf': 39}
0.713 (+/-0.014) for {'min_samples_split': 300, 'max_depth': 40, 'min_samples_leaf': 39}
0.715 (+/-0.015) for {'min_samples_split': 333, 'max_depth': 40, 'min_samples_leaf': 39}
0.715 (+/-0.013) for {'min_samples_split': 366, 'max_depth': 40, 'min_samples_leaf': 39}
0.715 (+/-0.015) for {'min_samples_split': 400, 'max_depth': 40, 'min_samples_leaf': 39}
0.702 (+/-0.015) for {'min_samples_split': 100, 'max_depth': 40, 'min_samples_leaf': 44}
0.705 (+/-0.008) for {'min_samples_split': 133, 'max_depth': 40, 'min_samples_leaf': 44}
0.710 (+/-0.013) for {'min_samples_split': 166, 'max_depth': 40, 'min_samples_leaf': 44}
0.713 (+/-0.015) for {'min_samples_split': 200, 'max_depth': 40, 'min_samples_leaf': 44}
0.715 (+/-0.016) for {'min_samples_split': 233, 'max_depth': 40, 'min_samples_leaf': 44}
0.713 (+/-0.018) for {'min_samples_split': 266, 'max_depth': 40, 'min_samples_leaf': 44}
0.713 (+/-0.012) for {'min_samples_split': 300, 'max_depth': 40, 'min_samples_leaf': 44}
0.716 (+/-0.014) for {'min_samples_split': 333, 'max_depth': 40, 'min_samples_leaf': 44}
0.716 (+/-0.013) for {'min_samples_split': 366, 'max_depth': 40, 'min_samples_leaf': 44}
0.716 (+/-0.015) for {'min_samples_split': 400, 'max_depth': 40, 'min_samples_leaf': 44}
0.703 (+/-0.011) for {'min_samples_split': 100, 'max_depth': 40, 'min_samples_leaf': 50}
0.704 (+/-0.005) for {'min_samples_split': 133, 'max_depth': 40, 'min_samples_leaf': 50}
0.711 (+/-0.008) for {'min_samples_split': 166, 'max_depth': 40, 'min_samples_leaf': 50}
0.713 (+/-0.008) for {'min_samples_split': 200, 'max_depth': 40, 'min_samples_leaf': 50}
0.713 (+/-0.011) for {'min_samples_split': 233, 'max_depth': 40, 'min_samples_leaf': 50}
0.713 (+/-0.016) for {'min_samples_split': 266, 'max_depth': 40, 'min_samples_leaf': 50}
0.713 (+/-0.011) for {'min_samples_split': 300, 'max_depth': 40, 'min_samples_leaf': 50}
0.714 (+/-0.016) for {'min_samples_split': 333, 'max_depth': 40, 'min_samples_leaf': 50}
0.714 (+/-0.014) for {'min_samples_split': 366, 'max_depth': 40, 'min_samples_leaf': 50}
0.714 (+/-0.015) for {'min_samples_split': 400, 'max_depth': 40, 'min_samples_leaf': 50}



In [481]:

    
md_opt = gr_dt.best_params_.values()[0]
mss_opt = gr_dt.best_params_.values()[1]
msl_opt = gr_dt.best_params_.values()[2]

SVM Classifier



In [222]:

    
X_train.head(2)









    Out[222]:






  
    
      
      review_count
      AcceptsInsurance_False
      AcceptsInsurance_True
      AcceptsInsurance_none
      AgesAllowed_False
      AgesAllowed_True
      AgesAllowed_none
      Alcohol_False
      Alcohol_True
      Alcohol_none
      ...
      BestNights_thursday_yes_corkage
      BestNights_thursday_yes_free
      BusinessParking_lot_False
      BusinessParking_lot_free
      BusinessParking_lot_none
      BusinessParking_lot_paid
      RestaurantsReservations_False
      RestaurantsReservations_True
      RestaurantsReservations_none
      RestaurantsReservations_outdoor
    
  
  
    
      nKeGPmH5dYNVL5AyAItuKg
      106
      0
      0
      1
      1
      0
      0
      1
      0
      0
      ...
      0
      0
      1
      0
      0
      0
      1
      0
      0
      0
    
    
      DKglcPMQnoEksCWeMkv31Q
      16
      0
      0
      1
      1
      0
      0
      0
      0
      1
      ...
      0
      0
      0
      0
      1
      0
      0
      0
      1
      0
    
  

2 rows × 225 columns

Scaling selected columns(feature variables)



In [246]:

    
## Scale transforming
def scale_variables(df_X, columns=None):
    if columns:
        X_unchanged = df_X.loc[:, df_X.columns[~df_X.columns.isin(columns)]]
        
        if len(columns) == 1:
        # scale X
            scaler = preprocessing.StandardScaler().fit(df_X[columns[0]].values.reshape(-1,1))
            X_p = scaler.transform(df_X[columns[0]].values.reshape(-1,1))
            X_std = np.hstack((X_p, X_unchanged))

        else:
            scaler = preprocessing.StandardScaler().fit(df_X.loc[:, columns])
            X_p = scaler.transform(df_X.loc[:, columns])
            X_std = np.hstack((X_p, X_unchanged))
    else:
        scaler = preprocessing.StandardScaler().fit(df_X)
        X_std = scaler.transform(df_X)  
        
    return X_std



In [259]:

    
X_train_std = scale_variables(X_train, columns=['review_count'])
X_vali_std = scale_variables(X_vali, ['review_count'])
X_all_std = scale_variables(X, columns=['review_count'])



In [248]:

    
X_train_std









    Out[248]:





array([[ 0.12075868,  0.        ,  0.        , ...,  0.        ,
         0.        ,  0.        ],
       [-0.41613288,  0.        ,  0.        , ...,  0.        ,
         1.        ,  0.        ],
       [-0.26103087,  0.        ,  0.        , ...,  0.        ,
         1.        ,  0.        ],
       ..., 
       [-0.41016742,  0.        ,  0.        , ...,  0.        ,
         1.        ,  0.        ],
       [-0.4757875 ,  0.        ,  0.        , ...,  0.        ,
         1.        ,  0.        ],
       [-0.3505128 ,  0.        ,  0.        , ...,  0.        ,
         1.        ,  0.        ]])



In [249]:

    
X_train.shape









    Out[249]:





(13199, 225)

### fit model SVC ###### estimator = SVC(kernel="linear", probability=True, random_state=99) ## how to use "rbf" ?? selector = RFE(estimator, 20, step=1) # select 20 features selector = selector.fit(X_train_std, Y_train) # use only the selected feature X_train_reduced = selector.transform(X_train_std) # predict target variable Y with validation set X Y_vali_pred = selector.predict(X_vali_std) print 'Training set accuracy score: {}'.format(selector.score(X_train_std, Y_train)) print 'Validation set accuracy score: {}'.format(selector.score(X_vali_std, Y_vali)) ###########

# selected features feature_selected = X_train.columns[selector.support_]

selector.support_ selector.ranking_ ######################################



In [396]:

    
## Using LinearSVC ##
######################
# tuning hyperparameter
tuned_parameters_SVC = {'C': C_values} 

svc = LinearSVC(random_state=99)
gr_svc = GridSearchCV(svc, param_grid=tuned_parameters_SVC, cv=5, scoring="roc_auc")
gr_svc.fit(X_train_std, Y_train)

gr_svc_auc_scores = cross_val_score(gr_svc, X_all_std, Y, scoring="roc_auc", cv=5)

print ( "SVM Classifier with training size(0.7): Mean AUC %.4f\n" % (np.mean(gr_svc_auc_scores)))

print "Best parameters set found:"
print gr_svc.best_params_
print"Gridsearch Scores: "
print gr_svc.score(X_vali_std, Y_vali)

means = gr_svc.cv_results_['mean_test_score']
stds = gr_svc.cv_results_['std_test_score']

for mean, std, params in zip(means, stds, gr_svc.cv_results_['params']):
    print("%0.3f (+/-%0.03f) for %r"
          % (mean, std * 2, params))









    



SVM Classifier with training size(0.7): Mean AUC 0.7344

Best parameters set found:
{'C': 0.23965103644177568}
Gridsearch Scores: 
0.728751000066
0.694 (+/-0.029) for {'C': 4.5399929762484854e-05}
0.719 (+/-0.018) for {'C': 0.00018944182523289392}
0.730 (+/-0.018) for {'C': 0.00079049032311996614}
0.734 (+/-0.019) for {'C': 0.0032985057559390941}
0.735 (+/-0.019) for {'C': 0.01376378673305039}
0.735 (+/-0.017) for {'C': 0.057432619267617294}
0.735 (+/-0.017) for {'C': 0.23965103644177568}
0.735 (+/-0.016) for {'C': 1.0}
0.726 (+/-0.012) for {'C': 4.1727338835980934}
0.682 (+/-0.049) for {'C': 17.41170806332763}
0.664 (+/-0.062) for {'C': 72.654424207165334}
0.662 (+/-0.038) for {'C': 303.16757768254888}
0.642 (+/-0.057) for {'C': 1265.03762380433}
0.648 (+/-0.068) for {'C': 5278.6653568747415}
0.647 (+/-0.068) for {'C': 22026.465794806718}



In [397]:

    
c_svc_opt = gr_svc.best_params_.values()[0]



In [398]:

    
c_svc_opt









    Out[398]:





0.23965103644177568

Compare Models: LR & DT & SVM

Models with optimal parameters



In [482]:

    
models = []
labels = []

# fit LR model
lr_opt = LogisticRegression(C=c_opt, penalty=p_opt, random_state=99)
lr_opt.fit(X_train, Y_train)
models.append(lr_opt)
labels.append('Logistic Regression')

# fit DT model
dt_opt = DecisionTreeClassifier(criterion='entropy', random_state=99, \
                                max_depth=md_opt, min_samples_split=mss_opt, min_samples_leaf=msl_opt)
dt_opt.fit(X_train, Y_train)
models.append(dt_opt)
labels.append('Decision Tree')

# fit LinearSVM model
svm_opt = LinearSVC(penalty='l2', C=c_svc_opt, random_state=99)  ## how to use "rbf" ??
svm_opt.fit(X_train_std, Y_train)

svm_cali = CalibratedClassifierCV(base_estimator=svm_opt)  # for calculating probability
svm_cali.fit(X_train_std, Y_train)
models.append([svm_opt, svm_cali])
labels.append(['LinearSVC', 'SVM Calibrated Classifier'])



In [483]:

    
models









    Out[483]:





[LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
           intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
           penalty='l1', random_state=99, solver='liblinear', tol=0.0001,
           verbose=0, warm_start=False),
 DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=266,
             max_features=None, max_leaf_nodes=None,
             min_impurity_split=1e-07, min_samples_leaf=28,
             min_samples_split=14, min_weight_fraction_leaf=0.0,
             presort=False, random_state=99, splitter='best'),
 [LinearSVC(C=0.23965103644177568, class_weight=None, dual=True,
       fit_intercept=True, intercept_scaling=1, loss='squared_hinge',
       max_iter=1000, multi_class='ovr', penalty='l2', random_state=99,
       tol=0.0001, verbose=0),
  CalibratedClassifierCV(base_estimator=LinearSVC(C=0.23965103644177568, class_weight=None, dual=True,
       fit_intercept=True, intercept_scaling=1, loss='squared_hinge',
       max_iter=1000, multi_class='ovr', penalty='l2', random_state=99,
       tol=0.0001, verbose=0),
              cv=3, method='sigmoid')]]



In [484]:

    
## Reference: https://medium.com/@aneesha/visualising-top-features-in-linear-svm-with-scikit-learn-and-matplotlib-3454ab18a14d

def plot_coefficients(classifier, label, feature_names, top_features=20):
    try:
        coef = classifier.coef_.ravel()
    except AttributeError:
        coef = classifier.feature_importances_
        pass
        
    top_positive_coefficients = np.argsort(coef)[-top_features:]   
    top_negative_coefficients = np.argsort(coef)[:top_features]
    if coef[top_negative_coefficients].min() != 0:
        top_coefficients = np.hstack([top_negative_coefficients, top_positive_coefficients])
    else:
        top_coefficients = top_positive_coefficients
        top_negative_coefficients = []
    
    # create plot
    plt.figure(figsize=(16, 5))
    colors = ['red' if c < 0 else 'blue' for c in coef[top_coefficients]]
    plt.bar(np.arange(len(top_coefficients)), coef[top_coefficients], color=colors)
    feature_names = np.array(feature_names)
    plt.xticks(np.arange(1, 1 + len(top_coefficients)), feature_names[top_coefficients], rotation=60, ha='right')
    plt.title('Top {} Important Features - {}'.format(top_features, label), size=20)
    plt.show()
    
    if len(top_negative_coefficients) != 0:
        df = pd.DataFrame(np.asarray([feature_names[top_positive_coefficients], coef[top_positive_coefficients],\
                           feature_names[top_negative_coefficients], coef[top_negative_coefficients]]).T, \
                          columns=['top_pos_features', 'top_pos_coef', 'top_neg_features', 'top_neg_coef'])
    else:
        df = pd.DataFrame(np.asarray([feature_names[top_positive_coefficients], coef[top_positive_coefficients]]).T,\
                          columns=['top_pos_features', 'top_pos_coef'])
    
    return label, df



In [485]:

    
# plot top 20 important features
label, df_lr_impfeatures = plot_coefficients(models[0], labels[0], X_train.columns.values)



In [486]:

    
print label
df_lr_impfeatures









    



Logistic Regression






    Out[486]:






  
    
      
      top_pos_features
      top_pos_coef
      top_neg_features
      top_neg_coef
    
  
  
    
      0
      Music_jukebox_True
      0.147195
      BikeParking_True
      -2.02343
    
    
      1
      HasTV_True
      0.183261
      Ambience_intimate_True
      -1.52782
    
    
      2
      BusinessParking_lot_False
      0.184873
      RestaurantsCounterService_True
      -1.12724
    
    
      3
      GoodForMeal_dessert_True
      0.206868
      DietaryRestrictions_vegetarian_True
      -1.08315
    
    
      4
      Music_dj_False
      0.214629
      Alcohol_False
      -1.00358
    
    
      5
      Ambience_divey_False
      0.223113
      AcceptsInsurance_False
      -0.700886
    
    
      6
      Ambience_touristy_True
      0.228778
      DietaryRestrictions_halal_False
      -0.678505
    
    
      7
      WiFi_True
      0.246436
      Music_live_True
      -0.639322
    
    
      8
      AcceptsInsurance_True
      0.259693
      DietaryRestrictions_halal_none
      -0.616209
    
    
      9
      BYOB_none
      0.285319
      DietaryRestrictions_dairy-free_True
      -0.605563
    
    
      10
      Corkage_False
      0.321775
      Ambience_upscale_True
      -0.558956
    
    
      11
      Open24Hours_True
      0.384018
      BestNights_monday_False
      -0.556929
    
    
      12
      HappyHour_True
      0.408885
      Ambience_classy_full_bar
      -0.522173
    
    
      13
      Ambience_classy_none
      0.411701
      BestNights_monday_True
      -0.514093
    
    
      14
      Ambience_intimate_False
      0.459552
      GoodForMeal_dessert_False
      -0.443133
    
    
      15
      BusinessParking_valet_True
      0.535162
      Music_video_False
      -0.418317
    
    
      16
      BYOBCorkage_True
      0.621322
      GoodForMeal_brunch_True
      -0.405736
    
    
      17
      GoodForMeal_breakfast_True
      0.723649
      DietaryRestrictions_soy-free_False
      -0.396902
    
    
      18
      DietaryRestrictions_vegan_True
      1.41059
      RestaurantsReservations_True
      -0.387097
    
    
      19
      BusinessAcceptsBitcoin_True
      1.56792
      Ambience_romantic_dressy
      -0.343712

print '{0}: \n Top positive featues:\n{1}\nCoeff:\n{2}\n\nTop negative featues:\n{3}\nCoeff{4}'\ .format(label, top_pos_f, top_pos_coef, top_neg_f, top_neg_coef)



In [487]:

    
###### Decision Tree #######
label, df_dt_impfeatures = plot_coefficients(models[1], labels[1], X_train.columns.values)



In [488]:

    
print label
df_dt_impfeatures









    



Decision Tree






    Out[488]:






  
    
      
      top_pos_features
      top_pos_coef
    
  
  
    
      0
      Ambience_classy_none
      0.0117302
    
    
      1
      BusinessParking_garage_True
      0.0138583
    
    
      2
      Music_no_music_False
      0.0140212
    
    
      3
      DietaryRestrictions_halal_False
      0.0149907
    
    
      4
      Corkage_False
      0.0170498
    
    
      5
      BusinessAcceptsCreditCards_True
      0.0171505
    
    
      6
      Ambience_divey_True
      0.0172003
    
    
      7
      DietaryRestrictions_soy-free_True
      0.0227405
    
    
      8
      BusinessParking_lot_False
      0.0232321
    
    
      9
      BusinessAcceptsCreditCards_False
      0.0234581
    
    
      10
      BYOB_True
      0.0238137
    
    
      11
      BusinessParking_lot_free
      0.0262042
    
    
      12
      Ambience_divey_False
      0.0263206
    
    
      13
      HasTV_none
      0.0271715
    
    
      14
      RestaurantsCounterService_none
      0.033106
    
    
      15
      GoodForMeal_dessert_True
      0.0479189
    
    
      16
      BikeParking_True
      0.0507656
    
    
      17
      HappyHour_True
      0.0778826
    
    
      18
      Ambience_classy_full_bar
      0.128921
    
    
      19
      review_count
      0.17291



In [489]:

    
###### SVM #########
label, df_svc_impfeatures = plot_coefficients(models[2][0], labels[2][0], X_train.columns.values)



In [490]:

    
print label
df_svc_impfeatures









    



LinearSVC






    Out[490]:






  
    
      
      top_pos_features
      top_pos_coef
      top_neg_features
      top_neg_coef
    
  
  
    
      0
      BusinessParking_validated_False
      0.0877111
      Ambience_casual_none
      -0.328464
    
    
      1
      ByAppointmentOnly_True
      0.0887222
      DietaryRestrictions_vegan_False
      -0.322294
    
    
      2
      DietaryRestrictions_vegetarian_none
      0.0931329
      Ambience_intimate_True
      -0.2563
    
    
      3
      DietaryRestrictions_dairy-free_False
      0.0994974
      GoodForDancing_False
      -0.247681
    
    
      4
      HappyHour_True
      0.1042
      BikeParking_True
      -0.246135
    
    
      5
      DietaryRestrictions_halal_True
      0.104797
      BusinessAcceptsBitcoin_False
      -0.208422
    
    
      6
      GoodForMeal_dessert_True
      0.111688
      GoodForKids_True
      -0.187429
    
    
      7
      BestNights_monday_none
      0.113802
      DogsAllowed_True
      -0.183979
    
    
      8
      DogsAllowed_none
      0.113924
      DietaryRestrictions_vegetarian_True
      -0.166513
    
    
      9
      Ambience_trendy_True
      0.117577
      Music_dj_True
      -0.149369
    
    
      10
      Music_dj_False
      0.121224
      RestaurantsTakeOut_False
      -0.145176
    
    
      11
      Ambience_classy_none
      0.123963
      Ambience_classy_full_bar
      -0.142269
    
    
      12
      RestaurantsTakeOut_True
      0.186631
      RestaurantsCounterService_True
      -0.133461
    
    
      13
      BusinessAcceptsBitcoin_True
      0.195889
      Alcohol_False
      -0.124859
    
    
      14
      BikeParking_False
      0.209143
      DietaryRestrictions_dairy-free_True
      -0.112964
    
    
      15
      GoodForKids_False
      0.228884
      HappyHour_False
      -0.0996927
    
    
      16
      Ambience_intimate_False
      0.243767
      Ambience_upscale_True
      -0.0913189
    
    
      17
      GoodForDancing_True
      0.289136
      BestNights_monday_False
      -0.0903772
    
    
      18
      Ambience_casual_False
      0.307151
      GoodForMeal_dessert_False
      -0.0896872
    
    
      19
      DietaryRestrictions_vegan_True
      0.363749
      RestaurantsReservations_True
      -0.0881104



In [491]:

    
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = 8, 8

#### Plot One model - train & validation sets ####
def plot_ROC(model, Y_train, X_train, Y_vali, X_vali, label):
    print ("AUC on the %s Train data = %.5f" % \
           (label, metrics.roc_auc_score(model.predict(X_train), Y_train)))
    print ("AUC on the %s Validation data = %.5f\n" % \
           (label, metrics.roc_auc_score(model.predict(X_vali), Y_vali)))

    #fpr, tpr, thresholds = metrics.roc_curve(Y_train, model.predict_proba(X_train)[:,1])    
    #plt.plot(fpr, tpr, label='{} Train set'.format(label))
    
    fpr, tpr, thresholds = metrics.roc_curve(Y_vali, model.predict_proba(X_vali)[:,1])    
    plt.plot(fpr, tpr, label='{} Validation set'.format(label))
    
    plt.xlabel("false-positive rate", size=18)
    plt.ylabel("true-positibe rate", size=18)
    plt.title("ROC Curve of {} Model".format(label), size=20)
    plt.legend(loc='best')

    
##### more than one model - validation sets #####
def plot_ROCs(models, Y_trains, X_trains, Y_valis, X_valis, labels):  
    for model, Y_train, X_train, Y_vali, X_vali, label in \
    zip(models, Y_trains, X_trains, Y_valis, X_valis, labels):
        
        print ("AUC on the %s Train data = %.5f" % \
               (label, metrics.roc_auc_score(model.predict(X_train), Y_train)))
        print ("AUC on the %s Validation data = %.5f\n" % \
               (label, metrics.roc_auc_score(model.predict(X_vali), Y_vali)))
        
        fpr, tpr, thresholds = metrics.roc_curve(Y_vali, model.predict_proba(X_vali)[:,1])
        plt.plot(fpr, tpr, label=label)
    
    plt.xlabel("fpr", size=18)
    plt.ylabel("tpr", size=18)
    plt.title("ROC Curves for Models and Validation sets", size=20)
    plt.legend(loc='best')



In [492]:

    
plot_ROC(lr_opt, Y_train, X_train, Y_vali, X_vali, label='LR')









    



AUC on the LR Train data = 0.70919
AUC on the LR Validation data = 0.74669



In [493]:

    
models_roc = [models[0],models[1],models[2][1]]
labels_roc = [labels[0],labels[1],labels[2][1]]



In [494]:

    
labels_roc









    Out[494]:





['Logistic Regression', 'Decision Tree', 'SVM Calibrated Classifier']



In [495]:

    
models_roc









    Out[495]:





[LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
           intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
           penalty='l1', random_state=99, solver='liblinear', tol=0.0001,
           verbose=0, warm_start=False),
 DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=266,
             max_features=None, max_leaf_nodes=None,
             min_impurity_split=1e-07, min_samples_leaf=28,
             min_samples_split=14, min_weight_fraction_leaf=0.0,
             presort=False, random_state=99, splitter='best'),
 CalibratedClassifierCV(base_estimator=LinearSVC(C=0.23965103644177568, class_weight=None, dual=True,
      fit_intercept=True, intercept_scaling=1, loss='squared_hinge',
      max_iter=1000, multi_class='ovr', penalty='l2', random_state=99,
      tol=0.0001, verbose=0),
             cv=3, method='sigmoid')]



In [496]:

    
plot_ROCs(models_roc[:2], [Y_train]*2, [X_train]*2, [Y_vali]*2, [X_vali]*2, labels_roc[:2])









    



AUC on the Logistic Regression Train data = 0.70919
AUC on the Logistic Regression Validation data = 0.74669

AUC on the Decision Tree Train data = 0.73967
AUC on the Decision Tree Validation data = 0.66968



In [497]:

    
# Plot AUC for DT, LR, SVC optimal models
plot_ROCs(models_roc, [Y_train]*3, [X_train]*2+[X_train_std], [Y_vali]*3, [X_vali]*2+[X_vali_std], labels_roc)









    



AUC on the Logistic Regression Train data = 0.70919
AUC on the Logistic Regression Validation data = 0.74669

AUC on the Decision Tree Train data = 0.73967
AUC on the Decision Tree Validation data = 0.66968

AUC on the SVM Calibrated Classifier Train data = 0.72088
AUC on the SVM Calibrated Classifier Validation data = 0.80351



In [ ]:

	address	attributes	business_id	categories	city	hours	is_open	latitude	longitude	name	...	RestaurantsDelivery	RestaurantsGoodForGroups	RestaurantsPriceRange2	RestaurantsReservations	RestaurantsTableService	RestaurantsTakeOut	Smoking	WheelchairAccessible	WiFi	cuisine_Chinese
EDqCEAGXVGCH4FJXgqtjqg	979 Bloor Street W	[{u'Alcohol': u'none'}, {u'Ambience': {u'roman...	EDqCEAGXVGCH4FJXgqtjqg	[Restaurants, Pizza, Chicken Wings, Italian]	Toronto	[Monday 11:0-2:0, Tuesday 11:0-2:0, Wednesday ...	1	43.661054	-79.429089	Pizza Pizza	...	False	False	NaN	NaN	NaN	NaN	False	NaN	NaN	1
GDnbt3isfhd57T1QqU6flg	11072 No Frank Lloyd Wright	[{u'Alcohol': u'none'}, {u'Ambience': {u'roman...	GDnbt3isfhd57T1QqU6flg	[Tex-Mex, Mexican, Fast Food, Restaurants]	Scottsdale	[Monday 10:0-22:0, Tuesday 10:0-22:0, Wednesda...	1	33.586710	-111.835410	Taco Bell	...	False	False	NaN	NaN	NaN	NaN	False	NaN	NaN	1

	AcceptsInsurance	AgesAllowed	Alcohol	Ambience_casual	Ambience_classy	Ambience_divey	Ambience_hipster	Ambience_intimate	Ambience_romantic	Ambience_touristy	...	RestaurantsGoodForGroups	RestaurantsPriceRange2	RestaurantsReservations	RestaurantsTableService	RestaurantsTakeOut	Smoking	WheelchairAccessible	WiFi	review_count	cuisine_Chinese
EDqCEAGXVGCH4FJXgqtjqg	none	False	none	none	none	True	quiet	none	casual	none	...	False	none	none	none	none	False	none	none	7	1
GDnbt3isfhd57T1QqU6flg	none	False	none	none	none	False	quiet	none	casual	none	...	False	none	none	none	none	False	none	none	9	1
a1Ba6XeIOP48e64YFD0dMw	none	none	none	none	none	none	none	none	none	none	...	none	none	none	none	none	none	none	none	3	0

	review_count	AcceptsInsurance_False	AcceptsInsurance_True	AcceptsInsurance_none	AgesAllowed_False	AgesAllowed_True	AgesAllowed_none	Alcohol_False	Alcohol_True	Alcohol_none	...	BestNights_thursday_yes_corkage	BestNights_thursday_yes_free	BusinessParking_lot_False	BusinessParking_lot_free	BusinessParking_lot_none	BusinessParking_lot_paid	RestaurantsReservations_False	RestaurantsReservations_True	RestaurantsReservations_none	RestaurantsReservations_outdoor
nKeGPmH5dYNVL5AyAItuKg	106	0	0	1	1	0	0	1	0	0	...	0	0	1	0	0	0	1	0	0	0
DKglcPMQnoEksCWeMkv31Q	16	0	0	1	1	0	0	0	0	1	...	0	0	0	0	1	0	0	0	1	0

	top_pos_features	top_pos_coef	top_neg_features	top_neg_coef
0	Music_jukebox_True	0.147195	BikeParking_True	-2.02343
1	HasTV_True	0.183261	Ambience_intimate_True	-1.52782
2	BusinessParking_lot_False	0.184873	RestaurantsCounterService_True	-1.12724
3	GoodForMeal_dessert_True	0.206868	DietaryRestrictions_vegetarian_True	-1.08315
4	Music_dj_False	0.214629	Alcohol_False	-1.00358
5	Ambience_divey_False	0.223113	AcceptsInsurance_False	-0.700886
6	Ambience_touristy_True	0.228778	DietaryRestrictions_halal_False	-0.678505
7	WiFi_True	0.246436	Music_live_True	-0.639322
8	AcceptsInsurance_True	0.259693	DietaryRestrictions_halal_none	-0.616209
9	BYOB_none	0.285319	DietaryRestrictions_dairy-free_True	-0.605563
10	Corkage_False	0.321775	Ambience_upscale_True	-0.558956
11	Open24Hours_True	0.384018	BestNights_monday_False	-0.556929
12	HappyHour_True	0.408885	Ambience_classy_full_bar	-0.522173
13	Ambience_classy_none	0.411701	BestNights_monday_True	-0.514093
14	Ambience_intimate_False	0.459552	GoodForMeal_dessert_False	-0.443133
15	BusinessParking_valet_True	0.535162	Music_video_False	-0.418317
16	BYOBCorkage_True	0.621322	GoodForMeal_brunch_True	-0.405736
17	GoodForMeal_breakfast_True	0.723649	DietaryRestrictions_soy-free_False	-0.396902
18	DietaryRestrictions_vegan_True	1.41059	RestaurantsReservations_True	-0.387097
19	BusinessAcceptsBitcoin_True	1.56792	Ambience_romantic_dressy	-0.343712

	top_pos_features	top_pos_coef
0	Ambience_classy_none	0.0117302
1	BusinessParking_garage_True	0.0138583
2	Music_no_music_False	0.0140212
3	DietaryRestrictions_halal_False	0.0149907
4	Corkage_False	0.0170498
5	BusinessAcceptsCreditCards_True	0.0171505
6	Ambience_divey_True	0.0172003
7	DietaryRestrictions_soy-free_True	0.0227405
8	BusinessParking_lot_False	0.0232321
9	BusinessAcceptsCreditCards_False	0.0234581
10	BYOB_True	0.0238137
11	BusinessParking_lot_free	0.0262042
12	Ambience_divey_False	0.0263206
13	HasTV_none	0.0271715
14	RestaurantsCounterService_none	0.033106
15	GoodForMeal_dessert_True	0.0479189
16	BikeParking_True	0.0507656
17	HappyHour_True	0.0778826
18	Ambience_classy_full_bar	0.128921
19	review_count	0.17291

	top_pos_features	top_pos_coef	top_neg_features	top_neg_coef
0	BusinessParking_validated_False	0.0877111	Ambience_casual_none	-0.328464
1	ByAppointmentOnly_True	0.0887222	DietaryRestrictions_vegan_False	-0.322294
2	DietaryRestrictions_vegetarian_none	0.0931329	Ambience_intimate_True	-0.2563
3	DietaryRestrictions_dairy-free_False	0.0994974	GoodForDancing_False	-0.247681
4	HappyHour_True	0.1042	BikeParking_True	-0.246135
5	DietaryRestrictions_halal_True	0.104797	BusinessAcceptsBitcoin_False	-0.208422
6	GoodForMeal_dessert_True	0.111688	GoodForKids_True	-0.187429
7	BestNights_monday_none	0.113802	DogsAllowed_True	-0.183979
8	DogsAllowed_none	0.113924	DietaryRestrictions_vegetarian_True	-0.166513
9	Ambience_trendy_True	0.117577	Music_dj_True	-0.149369
10	Music_dj_False	0.121224	RestaurantsTakeOut_False	-0.145176
11	Ambience_classy_none	0.123963	Ambience_classy_full_bar	-0.142269
12	RestaurantsTakeOut_True	0.186631	RestaurantsCounterService_True	-0.133461
13	BusinessAcceptsBitcoin_True	0.195889	Alcohol_False	-0.124859
14	BikeParking_False	0.209143	DietaryRestrictions_dairy-free_True	-0.112964
15	GoodForKids_False	0.228884	HappyHour_False	-0.0996927
16	Ambience_intimate_False	0.243767	Ambience_upscale_True	-0.0913189
17	GoodForDancing_True	0.289136	BestNights_monday_False	-0.0903772
18	Ambience_casual_False	0.307151	GoodForMeal_dessert_False	-0.0896872
19	DietaryRestrictions_vegan_True	0.363749	RestaurantsReservations_True	-0.0881104