[PER57] - Perceptron Model 1957

Example of use of a Perceptron, with sklearn and IRIS dataset of 1936 !

Objectives :

• Implement a historical linear classifier with a historical dataset !
• The objective is to predict the type of Iris from the size of the leaves.
• Identifying its limitations

The IRIS dataset is probably one of the oldest datasets, dating back to 1936 .

What we're going to do :

• Retrieve the dataset, via scikit learn
• training and classifying

Step 1 - Import and init

import numpy as np
from sklearn.datasets     import load_iris
from sklearn.linear_model import Perceptron
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib

import os,sys

import fidle

# Init Fidle environment
run_id, run_dir, datasets_dir = fidle.init('PER57')

FIDLE - Environment initialization

Version              : 2.3.0
Run id               : PER57
Run dir              : ./run/PER57
Datasets dir         : /gpfswork/rech/mlh/uja62cb/fidle-project/datasets-fidle
Start time           : 03/03/24 21:03:20
Hostname             : r3i6n3 (Linux)
Tensorflow log level : Info + Warning + Error  (=0)
Update keras cache   : False
Update torch cache   : False
Save figs            : ./run/PER57/figs (True)
numpy                : 1.24.4
sklearn              : 1.3.2
yaml                 : 6.0.1
matplotlib           : 3.8.2
pandas               : 2.1.3

Step 2 - Prepare IRIS Dataset

Retrieve a dataset : http://scikit-learn.org/stable/modules/classes.html#module-sklearn.datasets
About the datesets : https://scikit-learn.org/stable/datasets.html#datasets

Data fields (X) :

• 0 : sepal length in cm
• 1 : sepal width in cm
• 2 : petal length in cm
• 3 : petal width in cm

Class (y) :

• 0 : class 0=Iris-Setosa, 1=Iris-Versicolour, 2=Iris-Virginica

2.1 - Get dataset

x0,y0 = load_iris(return_X_y=True)

x = x0[:, (2,3)]     # We only keep fields 2 and 3
y = y0.copy()

y[ y0==0 ] = 1       # 1 = Iris setosa
y[ y0>=1 ] = 0       # 0 = not iris setosa

df=pd.DataFrame.from_dict({'Length (x1)':x[:,0], 'Width (x2)':x[:,1], 'Setosa {0,1} (y)':y})
display(df)

print(f'x shape : {x.shape}')
print(f'y shape : {y.shape}')

	Length (x1)	Width (x2)	Setosa {0,1} (y)
0	1.4	0.2	1
1	1.4	0.2	1
2	1.3	0.2	1
3	1.5	0.2	1
4	1.4	0.2	1
...	...	...	...
145	5.2	2.3	0
146	5.0	1.9	0
147	5.2	2.0	0
148	5.4	2.3	0
149	5.1	1.8	0

150 rows × 3 columns

x shape : (150, 2)
y shape : (150,)

2.2 - Train and test sets

x,y = fidle.utils.shuffle_np_dataset(x, y)
    
n=int(len(x)*0.8)
x_train = x[:n]
y_train = y[:n]
x_test  = x[n:]
y_test  = y[n:]

print(f'x_train shape : {x_train.shape}')
print(f'y_train shape : {y_train.shape}')
print(f'x_test shape  : {x_test.shape}')
print(f'y_test shape  : {y_test.shape}')

Datasets have been shuffled.
x_train shape : (120, 2)
y_train shape : (120,)
x_test shape  : (30, 2)
y_test shape  : (30,)

Step 3 - Get a perceptron, and train it

pct = Perceptron(max_iter=100, random_state=82, tol=0.01, verbose=1)
pct.fit(x_train, y_train)

-- Epoch 1
Norm: 1.56, NNZs: 2, Bias: 3.000000, T: 120, Avg. loss: 0.205917
Total training time: 0.00 seconds.
-- Epoch 2
Norm: 1.56, NNZs: 2, Bias: 3.000000, T: 240, Avg. loss: 0.000000
Total training time: 0.00 seconds.
-- Epoch 3
Norm: 1.56, NNZs: 2, Bias: 3.000000, T: 360, Avg. loss: 0.000000
Total training time: 0.00 seconds.
-- Epoch 4
Norm: 1.56, NNZs: 2, Bias: 3.000000, T: 480, Avg. loss: 0.000000
Total training time: 0.00 seconds.
-- Epoch 5
Norm: 1.56, NNZs: 2, Bias: 3.000000, T: 600, Avg. loss: 0.000000
Total training time: 0.00 seconds.
-- Epoch 6
Norm: 1.56, NNZs: 2, Bias: 3.000000, T: 720, Avg. loss: 0.000000
Total training time: 0.00 seconds.
-- Epoch 7
Norm: 1.56, NNZs: 2, Bias: 3.000000, T: 840, Avg. loss: 0.000000
Total training time: 0.00 seconds.
Convergence after 7 epochs took 0.00 seconds

Perceptron(max_iter=100, random_state=82, tol=0.01, verbose=1)

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Step 4 - Prédictions

y_pred = pct.predict(x_test) 

df=pd.DataFrame.from_dict({'Length (x1)':x_test[:,0], 'Width (x2)':x_test[:,1], 'y_test':y_test, 'y_pred':y_pred})
display(df[:15])

	Length (x1)	Width (x2)	y_test	y_pred
0	6.3	1.8	0	0
1	6.1	2.3	0	0
2	1.3	0.3	1	1
3	1.7	0.4	1	1
4	4.5	1.5	0	0
5	5.6	1.4	0	0
6	1.3	0.3	1	1
7	1.2	0.2	1	1
8	4.5	1.5	0	0
9	1.0	0.2	1	1
10	4.8	1.8	0	0
11	1.4	0.2	1	1
12	6.1	2.5	0	0
13	1.4	0.2	1	1
14	5.9	2.3	0	0

Step 5 - Visualisation

def plot_perceptron(x_train,y_train,x_test,y_test):
    a = -pct.coef_[0][0] / pct.coef_[0][1]
    b = -pct.intercept_ / pct.coef_[0][1]
    box=[x.min(axis=0)[0],x.max(axis=0)[0],x.min(axis=0)[1],x.max(axis=0)[1]]
    mx=(box[1]-box[0])/20
    my=(box[3]-box[2])/20
    box=[box[0]-mx,box[1]+mx,box[2]-my,box[3]+my]

    fig, axs = plt.subplots(1, 1)
    fig.set_size_inches(10,6)
 
    axs.plot(x_train[y_train==1, 0], x_train[y_train==1, 1], "o", color='tomato', label="Iris-Setosa")
    axs.plot(x_train[y_train==0, 0], x_train[y_train==0, 1], "o", color='steelblue',label="Autres")
    
    axs.plot(x_test[y_pred==1, 0],   x_test[y_pred==1, 1],   "o", color='lightsalmon', label="Iris-Setosa (pred)")
    axs.plot(x_test[y_pred==0, 0],   x_test[y_pred==0, 1],   "o", color='lightblue',   label="Autres (pred)")
    
    axs.plot([box[0], box[1]], [a*box[0]+b, a*box[1]+b], "k--", linewidth=2)
    axs.set_xlabel("Petal length (cm)", labelpad=15) #, fontsize=14)
    axs.set_ylabel("Petal width (cm)",  labelpad=15) #, fontsize=14)
    axs.legend(loc="lower right", fontsize=14)
    axs.set_xlim(box[0],box[1])
    axs.set_ylim(box[2],box[3])
    fidle.scrawler.save_fig('01-perceptron-iris')
    plt.show()
    
plot_perceptron(x_train,y_train, x_test,y_test)

Saved: ./run/PER57/figs/01-perceptron-iris

fidle.end()

End time : 03/03/24 21:03:21
Duration : 00:00:01 876ms
This notebook ends here :-)
https://fidle.cnrs.fr

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