Installation of Packages#
First install packages like numpy, scikit-learn, matplotlib
!pip install numpy scikit-learn matplotlib
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Importation of packages#
We import the necessary packages
import numpy as np
from sklearn.neural_network import MLPClassifier
from sklearn import datasets, metrics
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plot
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
Load Dataset#
We load the necessary IRIS dataset.
wine = datasets.load_wine()
Description of the Dataset#
Input features#
wine.feature_names
['alcohol',
'malic_acid',
'ash',
'alcalinity_of_ash',
'magnesium',
'total_phenols',
'flavanoids',
'nonflavanoid_phenols',
'proanthocyanins',
'color_intensity',
'hue',
'od280/od315_of_diluted_wines',
'proline']
Target feature#
wine.target_names
array(['class_0', 'class_1', 'class_2'], dtype='<U7')
Verify number of records#
print(f"Number of Input Records: {len(wine.data)}")
print(f"Number of Target Records: {len(wine.target)}")
Number of Input Records: 178
Number of Target Records: 178
Visulizing the dataset#
x = wine.data
y = wine.target
plot.scatter(x[:, 0], x[:, 1], c=y, cmap=plot.cm.Set1, edgecolor="k")
plot.xlabel(wine.feature_names[0])
plot.ylabel(wine.feature_names[1])
plot.show()
plot.scatter(x[:, 2], x[:, 3], c=y, cmap=plot.cm.Set1, edgecolor="k")
plot.xlabel(wine.feature_names[2])
plot.ylabel(wine.feature_names[3])
plot.show()
fig = plot.figure(figsize=(6, 6))
ax = fig.add_subplot(projection="3d")
ax.scatter(x[:, 1], x[:, 2], x[:, 3], c=y, cmap=plot.cm.Set1, edgecolor="k")
ax.set_xlabel(wine.feature_names[1])
ax.set_ylabel(wine.feature_names[2])
ax.set_zlabel(wine.feature_names[3])
plot.show()
fig = plot.figure(figsize=(6, 6))
ax = fig.add_subplot(projection="3d")
ax.scatter(x[:, 0], x[:, 2], x[:, 3], c=y, cmap=plot.cm.Set1, edgecolor="k")
ax.set_xlabel(wine.feature_names[0])
ax.set_ylabel(wine.feature_names[2])
ax.set_zlabel(wine.feature_names[3])
plot.show()
Training#
x = wine.data
y = wine.target
x_train, x_test, y_train, y_test = train_test_split(
x, y, train_size=0.7, random_state=12, stratify=y
)
print(f"Number of Training Records (input): {len(x_train)}")
print(f"Number of Training Records (target): {len(y_train)}")
print(f"Number of Test Records (input): {len(x_test)}")
print(f"Number of Test Records (input): {len(x_test)}")
Number of Training Records (input): 124
Number of Training Records (target): 124
Number of Test Records (input): 54
Number of Test Records (input): 54
Standardization of features#
sc = StandardScaler()
sc.fit(x_train)
print(f"Mean: {sc.mean_} \nVariance={sc.var_}")
Mean: [1.30047581e+01 2.37379032e+00 2.35193548e+00 1.94088710e+01
9.95161290e+01 2.29951613e+00 2.00870968e+00 3.52903226e-01
1.58637097e+00 4.97782257e+00 9.67870968e-01 2.62653226e+00
7.34419355e+02]
Variance=[6.14365264e-01 1.36032838e+00 7.36639958e-02 1.13516149e+01
2.25798127e+02 3.83523959e-01 9.57067690e-01 1.61738293e-02
3.23856991e-01 5.47239437e+00 5.11881769e-02 4.75337168e-01
8.90159532e+04]
x_train_std = sc.transform(x_train)
x_test_std = sc.transform(x_test)
classifier = MLPClassifier(max_iter=1000, random_state=12)
# training
classifier.fit(x_train_std, y_train)
MLPClassifier(max_iter=1000, random_state=12)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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MLPClassifier(max_iter=1000, random_state=12)
Classification report#
predicted_target = classifier.predict(x_test_std)
# classification report
print(metrics.classification_report(y_test, predicted_target))
precision recall f1-score support
0 1.00 1.00 1.00 18
1 0.95 1.00 0.98 21
2 1.00 0.93 0.97 15
accuracy 0.98 54
macro avg 0.98 0.98 0.98 54
weighted avg 0.98 0.98 0.98 54
Confusion matrix#
cm = confusion_matrix(y_test, predicted_target, normalize="pred")
disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=wine.target_names)
disp.plot(cmap=plot.cm.Blues)
<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f549f569420>
cm = confusion_matrix(y_test, predicted_target, normalize="true")
disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=wine.target_names)
disp.plot(cmap=plot.cm.Blues)