CANDISC - oliveoil dataset#
[1]:
#disable warnings
from warnings import simplefilter, filterwarnings
simplefilter(action='ignore', category=FutureWarning)
filterwarnings("ignore")
oliveoil dataset#
[2]:
#vins dataset
from discrimintools.datasets import load_oliveoil
D = load_oliveoil()
print(D.info())
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 569 entries, 0 to 568
Data columns (total 9 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 CLASSE 569 non-null object
1 palmitic 569 non-null int64
2 palmitoleic 569 non-null int64
3 stearic 569 non-null int64
4 oleic 569 non-null int64
5 linoleic 569 non-null int64
6 linolenic 569 non-null int64
7 arachidic 569 non-null int64
8 eicosenoic 569 non-null int64
dtypes: int64(8), object(1)
memory usage: 40.1+ KB
None
[3]:
#split into X and y
y, X = D["CLASSE"], D.drop(columns=["CLASSE"])
instanciation and training#
[4]:
from discrimintools import CANDISC
clf = CANDISC(n_components=2)
clf.fit(X,y)
[4]:
CANDISC()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
Parameters
| n_components | 2 | |
| classes | None | |
| warn_message | True |
eval_predict function#
[5]:
#eval_predict function
eval_train = clf.eval_predict(X,y,verbose=True)
Observation Profile:
Read Used
Number of Observations 569 569
Number of Observations Classified into CLASSE:
prediction Centre_North Sardinia South Total
CLASSE
Centre_North 146 4 0 150
Sardinia 0 97 0 97
South 1 0 321 322
Total 147 101 321 569
Percent Classified into CLASSE:
prediction Centre_North Sardinia South Total
CLASSE
Centre_North 97.333333 2.666667 0.000000 100.0
Sardinia 0.000000 100.000000 0.000000 100.0
South 0.310559 0.000000 99.689441 100.0
Total 25.834798 17.750439 56.414763 100.0
Priors 0.263620 0.170475 0.565905 NaN
Error Count Estimates for CLASSE:
Centre_North Sardinia South Total
Rate 0.026667 0.000000 0.003106 0.008787
Priors 0.263620 0.170475 0.565905 NaN
Classification Report for CLASSE:
precision recall f1-score support
Centre_North 0.993197 0.973333 0.983165 150.000000
Sardinia 0.960396 1.000000 0.979798 97.000000
South 1.000000 0.996894 0.998445 322.000000
accuracy 0.991213 0.991213 0.991213 0.991213
macro avg 0.984531 0.990076 0.987136 569.000000
weighted avg 0.991455 0.991213 0.991238 569.000000
score function#
[6]:
#score function
print("Accuracy : {}%".format(100*round(clf.score(X,y),2)))
Accuracy : 99.0%
[7]:
#error rate
print("Error rate : {}%".format(100-100*round(clf.score(X,y),2)))
Error rate : 1.0%
summary#
[8]:
from discrimintools import summaryCANDISC
Simple summary#
[9]:
#simple summary
summaryCANDISC(clf)
Canonical Discriminant Analysis - Results
Summary Information:
infos Value DF DF value
0 Total Sample Size 569 DF Total 568
1 Variables 8 DF Within Classes 566
2 Classes 3 DF Between Classes 2
Class Level Information:
Frequency Proportion Prior Probability
Centre_North 150 0.2636 0.2636
Sardinia 97 0.1705 0.1705
South 322 0.5659 0.5659
Total-Sample Class Means:
Centre_North Sardinia South
palmitic 1094.8333 1112.0619 1332.3696
palmitoleic 83.8933 96.3505 154.8882
stearic 231.0400 226.3505 228.7081
oleic 7791.9733 7266.9072 7099.5311
linoleic 727.8800 1197.3608 1034.0093
linolenic 21.7467 27.0103 38.0373
arachidic 37.5467 73.0000 63.1025
eicosenoic 1.9733 1.9278 27.3323
Importance of components:
Eigenvalue Difference Proportion Cumulative
Can1 8.4718 6.1684 78.6232 78.6232
Can2 2.3034 NaN 21.3768 100.0000
Raw Canonical and Classification Functions Coefficients:
Can1 Can2 Centre_North Sardinia South
Constant -13.0646 -56.9169 -70.0899 194.6549 -37.1812
palmitic 0.0028 0.0089 0.0072 -0.0344 0.0070
palmitoleic 0.0131 0.0184 -0.0095 -0.0959 0.0333
stearic -0.0028 0.0043 0.0171 -0.0029 -0.0071
oleic 0.0006 0.0062 0.0094 -0.0199 0.0016
linoleic 0.0011 -0.0013 -0.0061 -0.0001 0.0029
linolenic 0.0411 0.0058 -0.1257 -0.1523 0.1045
arachidic -0.0173 -0.0347 -0.0063 0.1565 -0.0442
eicosenoic 0.1631 0.0101 -0.5231 -0.5673 0.4146
Detailed summary#
[10]:
#detailed summary
summaryCANDISC(clf,detailed=True)
Canonical Discriminant Analysis - Results
Summary Information:
infos Value DF DF value
0 Total Sample Size 569 DF Total 568
1 Variables 8 DF Within Classes 566
2 Classes 3 DF Between Classes 2
Class Level Information:
Frequency Proportion Prior Probability
Centre_North 150 0.2636 0.2636
Sardinia 97 0.1705 0.1705
South 322 0.5659 0.5659
Total-Sample Class Means:
Centre_North Sardinia South
palmitic 1094.8333 1112.0619 1332.3696
palmitoleic 83.8933 96.3505 154.8882
stearic 231.0400 226.3505 228.7081
oleic 7791.9733 7266.9072 7099.5311
linoleic 727.8800 1197.3608 1034.0093
linolenic 21.7467 27.0103 38.0373
arachidic 37.5467 73.0000 63.1025
eicosenoic 1.9733 1.9278 27.3323
Importance of components:
Eigenvalue Difference Proportion Cumulative
Can1 8.4718 6.1684 78.6232 78.6232
Can2 2.3034 NaN 21.3768 100.0000
Raw Canonical and Classification Functions Coefficients:
Can1 Can2 Centre_North Sardinia South
Constant -13.0646 -56.9169 -70.0899 194.6549 -37.1812
palmitic 0.0028 0.0089 0.0072 -0.0344 0.0070
palmitoleic 0.0131 0.0184 -0.0095 -0.0959 0.0333
stearic -0.0028 0.0043 0.0171 -0.0029 -0.0071
oleic 0.0006 0.0062 0.0094 -0.0199 0.0016
linoleic 0.0011 -0.0013 -0.0061 -0.0001 0.0029
linolenic 0.0411 0.0058 -0.1257 -0.1523 0.1045
arachidic -0.0173 -0.0347 -0.0063 0.1565 -0.0442
eicosenoic 0.1631 0.0101 -0.5231 -0.5673 0.4146
Test of H0: The canonical correlations in the current row and all that follow are zero
Canonical Correlation Squared Canonical Correlation Likelihood Ratio \
0 0.9457 0.8944 0.0320
1 0.8350 0.6973 0.3027
Approximate F value Num DF Den DF Pr>F Chi-Square DF Pr>Chi2
0 320.9837 16 1118 0.0 1936.8430 16 0.0
1 184.2721 7 560 0.0 672.1608 7 0.0
Classification Summary for Calibration Data:
Observation Profile:
Read Used
Number of Observations 569 569
Number of Observations Classified into CLASSE:
prediction Centre_North Sardinia South Total
CLASSE
Centre_North 146 4 0 150
Sardinia 0 97 0 97
South 1 0 321 322
Total 147 101 321 569
Percent Classified into CLASSE:
prediction Centre_North Sardinia South Total
CLASSE
Centre_North 97.3333 2.6667 0.0000 100.0
Sardinia 0.0000 100.0000 0.0000 100.0
South 0.3106 0.0000 99.6894 100.0
Total 25.8348 17.7504 56.4148 100.0
Priors 0.2636 0.1705 0.5659 NaN
Error Count Estimates for CLASSE:
Centre_North Sardinia South Total
Rate 0.0267 0.0000 0.0031 0.0088
Priors 0.2636 0.1705 0.5659 NaN
Classification Report for CLASSE:
precision recall f1-score support
Centre_North 0.9932 0.9733 0.9832 150.0000
Sardinia 0.9604 1.0000 0.9798 97.0000
South 1.0000 0.9969 0.9984 322.0000
accuracy 0.9912 0.9912 0.9912 0.9912
macro avg 0.9845 0.9901 0.9871 569.0000
weighted avg 0.9915 0.9912 0.9912 569.0000
Evaluation of prediction on testing dataset#
Testing data#
[11]:
#testining data
DTest = load_oliveoil("test")
print(DTest.info())
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3 entries, 0 to 2
Data columns (total 9 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 CLASSE 3 non-null object
1 palmitic 3 non-null int64
2 palmitoleic 3 non-null int64
3 stearic 3 non-null int64
4 oleic 3 non-null int64
5 linoleic 3 non-null int64
6 linolenic 3 non-null int64
7 arachidic 3 non-null int64
8 eicosenoic 3 non-null int64
dtypes: int64(8), object(1)
memory usage: 344.0+ bytes
None
[12]:
#display
print(DTest)
CLASSE palmitic palmitoleic stearic oleic linoleic linolenic \
0 Sardinia 1042 135 210 7376 1116 35
1 Centre_North 1090 60 195 7955 600 28
2 South 1306 127 250 7254 869 47
arachidic eicosenoic
0 90 3
1 42 2
2 68 24
[13]:
#display into X and y
yTest, XTest = DTest["CLASSE"], DTest.drop(columns=["CLASSE"])
#eval_predict function
eval_test = clf.eval_predict(XTest,yTest,verbose=True)
Observation Profile:
Read Used
Number of Observations 3 3
Number of Observations Classified into CLASSE:
prediction Centre_North Sardinia South Total
CLASSE
Centre_North 1 0 0 1
Sardinia 0 1 0 1
South 0 0 1 1
Total 1 1 1 3
Percent Classified into CLASSE:
prediction Centre_North Sardinia South Total
CLASSE
Centre_North 100.000000 0.000000 0.000000 100.0
Sardinia 0.000000 100.000000 0.000000 100.0
South 0.000000 0.000000 100.000000 100.0
Total 33.333333 33.333333 33.333333 100.0
Priors 0.263620 0.170475 0.565905 NaN
Error Count Estimates for CLASSE:
Centre_North Sardinia South Total
Rate 0.00000 0.000000 0.000000 0.0
Priors 0.26362 0.170475 0.565905 NaN
Classification Report for CLASSE:
precision recall f1-score support
Centre_North 1.0 1.0 1.0 1.0
Sardinia 1.0 1.0 1.0 1.0
South 1.0 1.0 1.0 1.0
accuracy 1.0 1.0 1.0 1.0
macro avg 1.0 1.0 1.0 3.0
weighted avg 1.0 1.0 1.0 3.0
Plotting functions#
[14]:
#plotting
from discrimintools import fviz_candisc
Graph of individuals#
[15]:
#graph of individuals
p = fviz_candisc(clf,element="ind",repel=True)
p.show()
We add supplementary individuals to initial plot.
[16]:
#with supplementary individuals
from discrimintools import add_scatter
p = add_scatter(p,clf.transform(XTest),color="blue",repel=True)
p.show()
Graph of variables#
[17]:
#graph of variables
fviz_candisc(clf,element="var",repel=True).show()
Biplot of individuals and variables#
[18]:
#biplot of individuals and variables
p = fviz_candisc(clf,element="biplot",repel=True)
#add supplementary individuals
p = add_scatter(p,clf.transform(XTest),color="blue",repel=True)
p.show()
Distance between barycenter#
[19]:
#distance between barycenter
fviz_candisc(clf,element="dist",repel=True).show()
