notebook.community

Edit and run



In [58]:

    
# a) Import packages declared in ueb01 excercise 4
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn.cross_validation import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
from sklearn.feature_selection import RFECV



In [59]:

    
# task a load breast cancer wisconsin dataset as csv after recognizing csv format ;-) See ueb01 exercise 01
# Replace header with numerical values for slicing
data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.data', header=None)



In [60]:

    
# examining data
#data.tail(15)
data.head(15)









    Out[60]:






  
    
      
      0
      1
      2
      3
      4
      5
      6
      7
      8
      9
      ...
      22
      23
      24
      25
      26
      27
      28
      29
      30
      31
    
  
  
    
      0
      842302
      M
      17.99
      10.38
      122.80
      1001.0
      0.11840
      0.27760
      0.30010
      0.14710
      ...
      25.38
      17.33
      184.60
      2019.0
      0.1622
      0.6656
      0.7119
      0.26540
      0.4601
      0.11890
    
    
      1
      842517
      M
      20.57
      17.77
      132.90
      1326.0
      0.08474
      0.07864
      0.08690
      0.07017
      ...
      24.99
      23.41
      158.80
      1956.0
      0.1238
      0.1866
      0.2416
      0.18600
      0.2750
      0.08902
    
    
      2
      84300903
      M
      19.69
      21.25
      130.00
      1203.0
      0.10960
      0.15990
      0.19740
      0.12790
      ...
      23.57
      25.53
      152.50
      1709.0
      0.1444
      0.4245
      0.4504
      0.24300
      0.3613
      0.08758
    
    
      3
      84348301
      M
      11.42
      20.38
      77.58
      386.1
      0.14250
      0.28390
      0.24140
      0.10520
      ...
      14.91
      26.50
      98.87
      567.7
      0.2098
      0.8663
      0.6869
      0.25750
      0.6638
      0.17300
    
    
      4
      84358402
      M
      20.29
      14.34
      135.10
      1297.0
      0.10030
      0.13280
      0.19800
      0.10430
      ...
      22.54
      16.67
      152.20
      1575.0
      0.1374
      0.2050
      0.4000
      0.16250
      0.2364
      0.07678
    
    
      5
      843786
      M
      12.45
      15.70
      82.57
      477.1
      0.12780
      0.17000
      0.15780
      0.08089
      ...
      15.47
      23.75
      103.40
      741.6
      0.1791
      0.5249
      0.5355
      0.17410
      0.3985
      0.12440
    
    
      6
      844359
      M
      18.25
      19.98
      119.60
      1040.0
      0.09463
      0.10900
      0.11270
      0.07400
      ...
      22.88
      27.66
      153.20
      1606.0
      0.1442
      0.2576
      0.3784
      0.19320
      0.3063
      0.08368
    
    
      7
      84458202
      M
      13.71
      20.83
      90.20
      577.9
      0.11890
      0.16450
      0.09366
      0.05985
      ...
      17.06
      28.14
      110.60
      897.0
      0.1654
      0.3682
      0.2678
      0.15560
      0.3196
      0.11510
    
    
      8
      844981
      M
      13.00
      21.82
      87.50
      519.8
      0.12730
      0.19320
      0.18590
      0.09353
      ...
      15.49
      30.73
      106.20
      739.3
      0.1703
      0.5401
      0.5390
      0.20600
      0.4378
      0.10720
    
    
      9
      84501001
      M
      12.46
      24.04
      83.97
      475.9
      0.11860
      0.23960
      0.22730
      0.08543
      ...
      15.09
      40.68
      97.65
      711.4
      0.1853
      1.0580
      1.1050
      0.22100
      0.4366
      0.20750
    
    
      10
      845636
      M
      16.02
      23.24
      102.70
      797.8
      0.08206
      0.06669
      0.03299
      0.03323
      ...
      19.19
      33.88
      123.80
      1150.0
      0.1181
      0.1551
      0.1459
      0.09975
      0.2948
      0.08452
    
    
      11
      84610002
      M
      15.78
      17.89
      103.60
      781.0
      0.09710
      0.12920
      0.09954
      0.06606
      ...
      20.42
      27.28
      136.50
      1299.0
      0.1396
      0.5609
      0.3965
      0.18100
      0.3792
      0.10480
    
    
      12
      846226
      M
      19.17
      24.80
      132.40
      1123.0
      0.09740
      0.24580
      0.20650
      0.11180
      ...
      20.96
      29.94
      151.70
      1332.0
      0.1037
      0.3903
      0.3639
      0.17670
      0.3176
      0.10230
    
    
      13
      846381
      M
      15.85
      23.95
      103.70
      782.7
      0.08401
      0.10020
      0.09938
      0.05364
      ...
      16.84
      27.66
      112.00
      876.5
      0.1131
      0.1924
      0.2322
      0.11190
      0.2809
      0.06287
    
    
      14
      84667401
      M
      13.73
      22.61
      93.60
      578.3
      0.11310
      0.22930
      0.21280
      0.08025
      ...
      15.03
      32.01
      108.80
      697.7
      0.1651
      0.7725
      0.6943
      0.22080
      0.3596
      0.14310
    
  

15 rows × 32 columns



In [61]:

    
# Examining shape of data
data.shape









    Out[61]:





(569, 32)



In [62]:

    
# Instantiating LabelEncoder and slicing of data into values and classification
label_encoder = LabelEncoder()
X = data.loc[:, 2:].values
Y = data.loc[:, 1].values

# b) encoding Malign (M) and Bengin (B) into 1 and 0
Y = label_encoder.fit_transform(Y)



In [63]:

    
# Check encoding
Y









    Out[63]:





array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,
       0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0,
       0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1,
       0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0,
       0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0,
       1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
       1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0,
       1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0,
       1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1,
       1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0,
       0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
       0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1,
       0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0,
       0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0,
       1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0,
       0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
       1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0,
       0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
       1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
       0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0,
       0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0])



In [64]:

    
# c) Divide dataset into training and test data (80% training  and 20% test data)
test_size = 0.20
rand_state = 1
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size, random_state=rand_state)



In [65]:

    
# d) + e) Building pipeline of transformer and estimators to calculate accuracy
standard_scaler = ('ss',StandardScaler())
pca = ('pcs', PCA(n_components=2))
logistic_regression = ('lr', LogisticRegression(random_state=rand_state))
pipeline = Pipeline([standard_scaler, pca, logistic_regression])
pipeline.fit(X_train, Y_train)
print('Accuracy: %.3f' % pipeline.score(X_test, Y_test))









    



Accuracy: 0.947



In [69]:

    
# f) Switching PCA for RFECV to determine valuable feature selections and estimating max accuracy instead of the PCA step
# Using formerly intruduces LogisticRegression classificator as estimator
lr = logistic_regression[1]
selector = RFECV(lr, step=1)
selector.fit(X_train, Y_train)









    Out[69]:





RFECV(cv=None,
   estimator=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='l2', random_state=1, solver='liblinear', tol=0.0001,
          verbose=0, warm_start=False),
   estimator_params=None, scoring=None, step=1, verbose=0)



In [70]:

    
selector.support_









    Out[70]:





array([ True, False, False, False, False, False,  True,  True,  True,
       False,  True, False,  True, False, False, False, False, False,
       False, False,  True, False,  True, False, False, False,  True,
        True, False, False], dtype=bool)



In [71]:

    
selector.ranking_









    Out[71]:





array([ 1,  5,  9, 20, 11, 13,  1,  1,  1, 19,  1,  3,  1,  2, 18, 17, 12,
       15, 14, 21,  1,  4,  1, 16, 10,  6,  1,  1,  7,  8])



In [73]:

    
standard_scaler = ('ss',StandardScaler())
# pca = ('pcs', PCA(n_components=2))
logistic_regression = ('lr', LogisticRegression(random_state=rand_state))
selector = ('sel', RFECV(logistic_regression[1], step=1))
pipeline = Pipeline([standard_scaler, selector, logistic_regression])
pipeline.fit(X_train, Y_train)
print('Accuracy: %.3f' % pipeline.score(X_test, Y_test))









    



Accuracy: 0.974



In [ ]:

	0	1	2	3	4	5	6	7	8	9	...	22	23	24	25	26	27	28	29	30	31
0	842302	M	17.99	10.38	122.80	1001.0	0.11840	0.27760	0.30010	0.14710	...	25.38	17.33	184.60	2019.0	0.1622	0.6656	0.7119	0.26540	0.4601	0.11890
1	842517	M	20.57	17.77	132.90	1326.0	0.08474	0.07864	0.08690	0.07017	...	24.99	23.41	158.80	1956.0	0.1238	0.1866	0.2416	0.18600	0.2750	0.08902
2	84300903	M	19.69	21.25	130.00	1203.0	0.10960	0.15990	0.19740	0.12790	...	23.57	25.53	152.50	1709.0	0.1444	0.4245	0.4504	0.24300	0.3613	0.08758
3	84348301	M	11.42	20.38	77.58	386.1	0.14250	0.28390	0.24140	0.10520	...	14.91	26.50	98.87	567.7	0.2098	0.8663	0.6869	0.25750	0.6638	0.17300
4	84358402	M	20.29	14.34	135.10	1297.0	0.10030	0.13280	0.19800	0.10430	...	22.54	16.67	152.20	1575.0	0.1374	0.2050	0.4000	0.16250	0.2364	0.07678
5	843786	M	12.45	15.70	82.57	477.1	0.12780	0.17000	0.15780	0.08089	...	15.47	23.75	103.40	741.6	0.1791	0.5249	0.5355	0.17410	0.3985	0.12440
6	844359	M	18.25	19.98	119.60	1040.0	0.09463	0.10900	0.11270	0.07400	...	22.88	27.66	153.20	1606.0	0.1442	0.2576	0.3784	0.19320	0.3063	0.08368
7	84458202	M	13.71	20.83	90.20	577.9	0.11890	0.16450	0.09366	0.05985	...	17.06	28.14	110.60	897.0	0.1654	0.3682	0.2678	0.15560	0.3196	0.11510
8	844981	M	13.00	21.82	87.50	519.8	0.12730	0.19320	0.18590	0.09353	...	15.49	30.73	106.20	739.3	0.1703	0.5401	0.5390	0.20600	0.4378	0.10720
9	84501001	M	12.46	24.04	83.97	475.9	0.11860	0.23960	0.22730	0.08543	...	15.09	40.68	97.65	711.4	0.1853	1.0580	1.1050	0.22100	0.4366	0.20750
10	845636	M	16.02	23.24	102.70	797.8	0.08206	0.06669	0.03299	0.03323	...	19.19	33.88	123.80	1150.0	0.1181	0.1551	0.1459	0.09975	0.2948	0.08452
11	84610002	M	15.78	17.89	103.60	781.0	0.09710	0.12920	0.09954	0.06606	...	20.42	27.28	136.50	1299.0	0.1396	0.5609	0.3965	0.18100	0.3792	0.10480
12	846226	M	19.17	24.80	132.40	1123.0	0.09740	0.24580	0.20650	0.11180	...	20.96	29.94	151.70	1332.0	0.1037	0.3903	0.3639	0.17670	0.3176	0.10230
13	846381	M	15.85	23.95	103.70	782.7	0.08401	0.10020	0.09938	0.05364	...	16.84	27.66	112.00	876.5	0.1131	0.1924	0.2322	0.11190	0.2809	0.06287
14	84667401	M	13.73	22.61	93.60	578.3	0.11310	0.22930	0.21280	0.08025	...	15.03	32.01	108.80	697.7	0.1651	0.7725	0.6943	0.22080	0.3596	0.14310