PLSLOGIT - breast dataset# 

[1]:

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

breast dataset# 

[2]:

#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

[3]:

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

Instanciation and training# 

[4]:

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

Optimization terminated successfully.
         Current function value: 0.181262
         Iterations 8

[4]:

PLSLOGIT()
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Canonical coefficients# 

[5]:

#canonical coefficients
cancoef = clf.cancoef_
cancoef._fields

[5]:

('standardized', 'raw')

Standardized canonical coefficients# 

[6]:

#standardized canonical coefficients
print(cancoef.standardized)

               Can1      Can2
ucellsize  0.640964 -0.388665
normnucl   0.623278 -0.121004
mitoses    0.463204  0.954876

Raw canonical coefficients# 

[7]:

#raw canonical coefficients
print(cancoef.raw)

               Can1      Can2
Constant  -1.672841 -0.372066
ucellsize  0.210051 -0.127370
normnucl   0.204110 -0.039626
mitoses    0.270077  0.556754

Coefficients# 

[8]:

#coefficients
coef = clf.coef_
coef._fields

[8]:

('standardized', 'raw')

Standardized coefficients# 

[9]:

#standardized coefficients
print(coef.standardized.to_frame())

           positive
const     -0.335260
ucellsize  2.319484
normnucl   2.058202
mitoses    0.727368

Raw coefficients# 

[10]:

#raw coefficients
print(coef.raw)

Constant    -5.324298
ucellsize    0.760123
normnucl     0.674017
mitoses      0.424102
Name: positive, dtype: float64

Summary# 

[11]:

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

                     Partial Least Squares Logistic Regression - 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

Raw Canonical Coefficients:
             Can1    Can2
Constant  -1.6728 -0.3721
ucellsize  0.2101 -0.1274
normnucl   0.2041 -0.0396
mitoses    0.2701  0.5568

PLS Logistic Regression Coefficients:
           positive
Constant    -5.3243
ucellsize    0.7601
normnucl     0.6740
mitoses      0.4241

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         441        17    458
positive          25       216    241
Total            466       233    699

Percent Classified into Class:
prediction  negative  positive  Total
Class
negative     96.2882    3.7118  100.0
positive     10.3734   89.6266  100.0
Total        66.6667   33.3333  100.0
Priors        0.6552    0.3448    NaN

Error Count Estimates for Class:
        negative  positive   Total
Rate      0.0371    0.1037  0.0601
Priors    0.6552    0.3448     NaN

Classification Report for Class:
              precision  recall  f1-score   support
negative         0.9464  0.9629    0.9545  458.0000
positive         0.9270  0.8963    0.9114  241.0000
accuracy         0.9399  0.9399    0.9399    0.9399
macro avg        0.9367  0.9296    0.9330  699.0000
weighted avg     0.9397  0.9399    0.9397  699.0000

Plotting# 

[12]:

#plotting
from discrimintools import fviz_plsr

Graph of individuals# 

[13]:

#graph of individuals
p = fviz_plsr(clf,element="ind",repel=False)
p.show()
../../_images/source_examples_20_plslogit_breast_24_0.png

Graph of variables# 

[14]:

#graph of variables
p = fviz_plsr(clf,element="var",repel=True)
p.show()
../../_images/source_examples_20_plslogit_breast_26_0.png

Distance between barycenter# 

[15]:

#distance between barycenter
p = fviz_plsr(clf,element="dist",repel=False,y_lim=(-0.2,0.15))
p.show()
../../_images/source_examples_20_plslogit_breast_28_0.png