STEPDISC LDA - alcool dataset#
[1]:
#disable warnings
from warnings import simplefilter, filterwarnings
simplefilter(action='ignore', category=FutureWarning)
filterwarnings("ignore")
alcools dataset#
[2]:
#vins dataset
from discrimintools.datasets import load_alcools
D = load_alcools("train")
print(D.info())
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 52 entries, 0 to 51
Data columns (total 9 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 TYPE 52 non-null object
1 MEOH 52 non-null float64
2 ACET 52 non-null float64
3 BU1 52 non-null float64
4 BU2 52 non-null float64
5 ISOP 52 non-null int64
6 MEPR 52 non-null float64
7 PRO1 52 non-null float64
8 ACAL 52 non-null float64
dtypes: float64(7), int64(1), object(1)
memory usage: 3.8+ KB
None
instanciation and training#
[3]:
from discrimintools import DISCRIM, STEPDISC
#split into X and y
y, X = D["TYPE"], D.drop(columns=["TYPE"])
clf = DISCRIM().fit(X,y)
clf2 = STEPDISC(method="forward",alpha=0.01,verbose=True)
clf2.fit(clf)
====================== Step 1 forward selection results =======================
Wilks' Lambda Partial R-Square F Value Num DF Den DF Pr>F
MEOH 0.282629 0.717371 62.186129 2 49 3.587597e-14
ACET 0.971855 0.028145 0.709531 2 49 4.968583e-01
BU1 0.286173 0.713827 61.112585 2 49 4.868527e-14
BU2 0.914588 0.085412 2.288014 2 49 1.122087e-01
ISOP 0.887731 0.112269 3.098457 2 49 5.406192e-02
MEPR 0.691854 0.308146 10.912106 2 49 1.203236e-04
PRO1 0.835465 0.164535 4.824978 2 49 1.222491e-02
ACAL 0.979642 0.020358 0.509127 2 49 6.041644e-01
Variable MEOH will enter
====================== Step 2 forward selection results =======================
Wilks' Lambda Partial R-Square F Value Num DF Den DF Pr>F
ACET 0.253614 0.102660 2.745708 2 48 0.074297
BU1 0.192547 0.318729 11.228252 2 48 0.000100
BU2 0.244101 0.136320 3.788072 2 48 0.029680
ISOP 0.264061 0.065697 1.687604 2 48 0.195751
MEPR 0.221217 0.217287 6.662572 2 48 0.002796
PRO1 0.255676 0.095365 2.530037 2 48 0.090232
ACAL 0.235697 0.166054 4.778852 2 48 0.012803
Variable BU1 will enter
====================== Step 3 forward selection results =======================
Wilks' Lambda Partial R-Square F Value Num DF Den DF Pr>F
ACET 0.178725 0.071786 1.817445 2 47 0.173671
BU2 0.170291 0.115585 3.071236 2 47 0.055772
ISOP 0.174351 0.094502 2.452583 2 47 0.097018
MEPR 0.147786 0.232468 7.117602 2 47 0.001994
PRO1 0.176100 0.085419 2.194821 2 47 0.122666
ACAL 0.173496 0.098943 2.580493 2 47 0.086432
Variable MEPR will enter
====================== Step 4 forward selection results =======================
Wilks' Lambda Partial R-Square F Value Num DF Den DF Pr>F
ACET 0.138022 0.066069 1.627088 2 46 0.207606
BU2 0.131479 0.110340 2.852582 2 46 0.067944
ISOP 0.129820 0.121570 3.183082 2 46 0.050730
PRO1 0.136572 0.075879 1.888507 2 46 0.162842
ACAL 0.127365 0.138180 3.687719 2 46 0.032702
No variable can enter
[3]:
STEPDISC()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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Parameters
| method | 'forward' | |
| alpha | 0.01 | |
| lambda_init | None | |
| verbose | True |
Selected variables#
[4]:
#selected variables
print(clf2.summary_.selected)
['MEOH', 'BU1', 'MEPR']
summary#
[5]:
from discrimintools import summarySTEPDISC
summarySTEPDISC(clf2,detailed=True)
Stepwise Discriminant Analysis - Results
====================== Before forward selection =======================
Discriminant Analysis - Results
Summary Information:
Infos Value DF DF value
0 Total Sample Size 52 DF Total 51
1 Variables 8 DF Within Classes 49
2 Classes 3 DF Between Classes 2
Class Level Information:
Frequency Proportion Prior Probability
KIRSCH 17 0.3269 0.3269
MIRAB 15 0.2885 0.2885
POIRE 20 0.3846 0.3846
Pooled Covariance Matrix Information:
Rank Natural Log of the Determinant
Pooled 8 58.3267
Linear Discriminant Function for TYPE:
KIRSCH MIRAB POIRE
Constant -5.0165 -18.8407 -24.7649
MEOH 0.0034 0.0290 0.0334
ACET 0.0064 0.0164 0.0075
BU1 -0.0637 0.4054 0.3180
BU2 -0.0009 0.0714 0.1150
ISOP 0.0231 0.0298 -0.0085
MEPR 0.0375 -0.1289 0.0618
PRO1 0.0020 -0.0054 -0.0083
ACAL 0.0662 -0.2264 -0.1303
Classification Summary for Calibration Data:
Observation Profile:
Read Used
Number of Observations 52 52
Number of Observations Classified into TYPE:
prediction KIRSCH MIRAB POIRE Total
TYPE
KIRSCH 17 0 0 17
MIRAB 0 14 1 15
POIRE 0 2 18 20
Total 17 16 19 52
Percent Classified into TYPE:
prediction KIRSCH MIRAB POIRE Total
TYPE
KIRSCH 100.0000 0.0000 0.0000 100.0
MIRAB 0.0000 93.3333 6.6667 100.0
POIRE 0.0000 10.0000 90.0000 100.0
Total 32.6923 30.7692 36.5385 100.0
Priors 0.3269 0.2885 0.3846 NaN
Error Count Estimates for TYPE:
KIRSCH MIRAB POIRE Total
Rate 0.0000 0.0667 0.1000 0.0577
Priors 0.3269 0.2885 0.3846 NaN
Classification Report for TYPE:
precision recall f1-score support
KIRSCH 1.0000 1.0000 1.0000 17.0000
MIRAB 0.8750 0.9333 0.9032 15.0000
POIRE 0.9474 0.9000 0.9231 20.0000
accuracy 0.9423 0.9423 0.9423 0.9423
macro avg 0.9408 0.9444 0.9421 52.0000
weighted avg 0.9437 0.9423 0.9425 52.0000
====================== After forward selection =======================
Discriminant Analysis - Results
Summary Information:
Infos Value DF DF value
0 Total Sample Size 52 DF Total 51
1 Variables 3 DF Within Classes 49
2 Classes 3 DF Between Classes 2
Class Level Information:
Frequency Proportion Prior Probability
KIRSCH 17 0.3269 0.3269
MIRAB 15 0.2885 0.2885
POIRE 20 0.3846 0.3846
Pooled Covariance Matrix Information:
Rank Natural Log of the Determinant
Pooled 3 19.4106
Linear Discriminant Function for TYPE:
KIRSCH MIRAB POIRE
Constant -3.6107 -14.7754 -18.3711
MEOH 0.0069 0.0213 0.0226
BU1 -0.0766 0.4010 0.3735
MEPR 0.0867 -0.0325 0.0467
Classification Summary for Calibration Data:
Observation Profile:
Read Used
Number of Observations 52 52
Number of Observations Classified into TYPE:
prediction KIRSCH MIRAB POIRE Total
TYPE
KIRSCH 17 0 0 17
MIRAB 0 12 3 15
POIRE 0 4 16 20
Total 17 16 19 52
Percent Classified into TYPE:
prediction KIRSCH MIRAB POIRE Total
TYPE
KIRSCH 100.0000 0.0000 0.0000 100.0
MIRAB 0.0000 80.0000 20.0000 100.0
POIRE 0.0000 20.0000 80.0000 100.0
Total 32.6923 30.7692 36.5385 100.0
Priors 0.3269 0.2885 0.3846 NaN
Error Count Estimates for TYPE:
KIRSCH MIRAB POIRE Total
Rate 0.0000 0.2000 0.2000 0.1346
Priors 0.3269 0.2885 0.3846 NaN
Classification Report for TYPE:
precision recall f1-score support
KIRSCH 1.0000 1.0000 1.0000 17.0000
MIRAB 0.7500 0.8000 0.7742 15.0000
POIRE 0.8421 0.8000 0.8205 20.0000
accuracy 0.8654 0.8654 0.8654 0.8654
macro avg 0.8640 0.8667 0.8649 52.0000
weighted avg 0.8672 0.8654 0.8658 52.0000
Evaluation of prediction on testing dataset#
Testing data#
[6]:
#testining data
DTest = load_alcools("test")
DTest.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 50 entries, 0 to 49
Data columns (total 9 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 TYPE 50 non-null object
1 MEOH 50 non-null int64
2 ACET 50 non-null int64
3 BU1 50 non-null float64
4 BU2 50 non-null float64
5 ISOP 50 non-null int64
6 MEPR 50 non-null int64
7 PRO1 50 non-null int64
8 ACAL 50 non-null float64
dtypes: float64(3), int64(5), object(1)
memory usage: 3.6+ KB
[7]:
#split into X and y
yTest, XTest = DTest["TYPE"], DTest.drop(columns=["TYPE"])
eval_test = clf2.eval_predict(XTest,yTest,verbose=True)
Observation Profile:
Read Used
Number of Observations 50 50
Number of Observations Classified into TYPE:
prediction KIRSCH MIRAB POIRE Total
TYPE
KIRSCH 14 0 0 14
MIRAB 0 12 5 17
POIRE 2 8 9 19
Total 16 20 14 50
Percent Classified into TYPE:
prediction KIRSCH MIRAB POIRE Total
TYPE
KIRSCH 100.000000 0.000000 0.000000 100.0
MIRAB 0.000000 70.588235 29.411765 100.0
POIRE 10.526316 42.105263 47.368421 100.0
Total 32.000000 40.000000 28.000000 100.0
Priors 0.326923 0.288462 0.384615 NaN
Error Count Estimates for TYPE:
KIRSCH MIRAB POIRE Total
Rate 0.000000 0.294118 0.526316 0.287271
Priors 0.326923 0.288462 0.384615 NaN
Classification Report for TYPE:
precision recall f1-score support
KIRSCH 0.875000 1.000000 0.933333 14.0
MIRAB 0.600000 0.705882 0.648649 17.0
POIRE 0.642857 0.473684 0.545455 19.0
accuracy 0.700000 0.700000 0.700000 0.7
macro avg 0.705952 0.726522 0.709146 50.0
weighted avg 0.693286 0.700000 0.689147 50.0