PLSDA - breast dataset

#disable warnings
from warnings import simplefilter, filterwarnings
simplefilter(action='ignore', category=FutureWarning)
filterwarnings("ignore")

breast dataset

#alcools dataset
from discrimintools.datasets import load_dataset
D = load_dataset("breast")
D.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 699 entries, 0 to 698
Data columns (total 4 columns):
 #   Column     Non-Null Count  Dtype
---  ------     --------------  -----
 0   ucellsize  699 non-null    int64
 1   normnucl   699 non-null    int64
 2   mitoses    699 non-null    int64
 3   Class      699 non-null    object
dtypes: int64(3), object(1)
memory usage: 22.0+ KB

#split into X and y
y, X = D["Class"], D.drop(columns=["Class"])

Instanciation and training

#instanciation and training
from discrimintools import PLSDA
clf = PLSDA()
clf.fit(X,y)

PLSDA()

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Coefficients

#coefficients
print(clf.coef_)

           negative  positive
Constant   1.080102 -0.080102
ucellsize -0.085323  0.085323
normnucl  -0.053251  0.053251
mitoses   -0.003001  0.003001

Summary

#summary
from discrimintools import summaryPLSDA
summaryPLSDA(clf,detailed=True)

                     Partial Least Squares Discriminant Analysis - Results

Class Level Information:
          Frequency  Proportion  Prior Probability
negative        458      0.6552             0.6552
positive        241      0.3448             0.3448

Importance of PLS components:
      Proportion (%)  Cumulative (%)
Can1         69.1520         69.1520
Can2         20.1981         89.3501

Classification functions coefficients:
           negative  positive     VIP
Constant     1.0801   -0.0801     NaN
ucellsize   -0.0853    0.0853  1.2038
normnucl    -0.0533    0.0533  1.0346
mitoses     -0.0030    0.0030  0.6933

Classification Summary for Calibration Data:

Observation Profile:
                        Read  Used
Number of Observations   699   699

Number of Observations Classified into Class:
prediction  negative  positive  Total
Class
negative         448        10    458
positive          56       185    241
Total            504       195    699

Percent Classified into Class:
prediction  negative  positive  Total
Class
negative     97.8166    2.1834  100.0
positive     23.2365   76.7635  100.0
Total        72.1030   27.8970  100.0
Priors        0.6552    0.3448    NaN

Error Count Estimates for Class:
        negative  positive   Total
Rate      0.0218    0.2324  0.0944
Priors    0.6552    0.3448     NaN

Classification Report for Class:
              precision  recall  f1-score   support
negative         0.8889  0.9782    0.9314  458.0000
positive         0.9487  0.7676    0.8486  241.0000
accuracy         0.9056  0.9056    0.9056    0.9056
macro avg        0.9188  0.8729    0.8900  699.0000
weighted avg     0.9095  0.9056    0.9029  699.0000

Plotting

#plotting
from discrimintools import fviz_plsr

Graph of individuals

#graph of individuals
p = fviz_plsr(clf,element="ind",repel=False)
p.show()

Graph of variables

#graph of variables
p = fviz_plsr(clf,element="var",repel=True)
p.show()

Distance between barycenter

#distance between barycenter
p = fviz_plsr(clf,element="dist",repel=False,y_lim=(-0.2,0.15))
p.show()