import sys
import matplotlib.pyplot as plt
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
sys.path.append('..')
from mlscratch.models.logistic_regression import LogisticRegression
from mlscratch.metrics import accuracy_score
if __name__ == '__main__':
X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y)
model = LogisticRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
acc = accuracy_score(y_test, y_pred)
print(f'Accuracy: {acc}')
plt.plot(model.train_metric_list)
plt.title('Training BCE')
plt.xlabel('Iterations')
plt.ylabel('Binary Cross Entropy')
plt.show()
import sys
sys.path.append('../')
import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from mlscratch.models.k_neighbor import KNN
from mlscratch.metrics import accuracy_score
if __name__ == '__main__':
X, y = load_iris(return_X_y=True)
X = StandardScaler().fit_transform(X)
X_train, X_valid, y_train, y_valid = train_test_split(X,
y,
shuffle=True,
random_state=27)
model = KNN()
model.fit(X_train, y_train)
y_pred_proba = model.predict(X_valid)
y_pred = np.round(y_pred_proba)
acc = accuracy_score(y_valid, y_pred)
print(f'Accuracy: {acc}')
def test_return(self):
acc = metrics.accuracy_score(self.y_true, self.y_pred)
self.assertAlmostEqual(acc, 0.75)
self.assertIsInstance(acc, np.float64)
import sys
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
sys.path.append('..')
from mlscratch.models.multilayer_perceptron import MLP
from mlscratch.metrics import accuracy_score
if __name__ == '__main__':
X, y = load_iris(return_X_y=True)
encoder = OneHotEncoder()
y = encoder.fit_transform(y.reshape(-1, 1)).toarray()
X_train, X_valid, y_train, y_valid = train_test_split(X,
y,
shuffle=True,
random_state=27)
model = MLP(num_hidden=64)
model.fit(X_train, y_train)
y_pred = model.predict(X_valid)
acc = accuracy_score(y_valid.argmax(axis=1), y_pred.argmax(axis=1))
print(f'Accuracy: {acc}')