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knn_classifier.py
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knn_classifier.py
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import numpy as np
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import GridSearchCV
from platform import processor
if "x86" in processor():
from sklearnex import patch_sklearn
patch_sklearn()
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import GridSearchCV
def k_nn(mailData):
np.random.shuffle(mailData)
k_nn_scores = cross_val_score(
KNeighborsClassifier(n_neighbors=5, p=2, metric="euclidean", n_jobs=-1),
mailData[:, :54],
mailData[:, 57],
cv=10,
)
return k_nn_scores
def k_nn_with_grid_search(mailData):
np.random.shuffle(mailData)
param_grid = {
"weights": ["uniform", "distance"],
"n_neighbors": [5],
"metric": ["euclidean"],
"p": [2],
}
grid_res = GridSearchCV(
KNeighborsClassifier(), param_grid, refit=True, cv=10, n_jobs=-1
)
fitted = grid_res.fit(mailData[:, :54], mailData[:, 57])
k_nn_scores = cross_val_score(
grid_res.best_estimator_, mailData[:, :54], mailData[:, 57], cv=10, n_jobs=-1
)
return k_nn_scores