seanbenhur

import torch

class SAM(torch.optim.Optimizer):
    def __init__(self, params, base_optimizer, rho=0.05, **kwargs):
        assert rho >= 0.0, f"Invalid rho, should be non-negative: {rho}"

        defaults = dict(rho=rho, **kwargs)
        super(SAM, self).__init__(params, defaults)

        self.base_optimizer = base_optimizer(self.param_groups, **kwargs)

seanbenhur

from sam import SAM
...

model = YourModel()
base_optimizer = torch.optim.SGD  # define an optimizer for the "sharpness-aware" update
optimizer = SAM(model.parameters(), base_optimizer, lr=0.1, momentum=0.9)
...

for input, output in data:

seanbenhur

seanbenhur

seanbenhur

from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import make_pipeline
from sklearn.metrics import classification_report
from imblearn.under_sampling import RandomUnderSampler
from imblearn.pipeline import make_pipeline as make_pipeline_imb
from collections import Counter

categories = [

seanbenhur

model = make_pipeline(TfidfVectorizer(), MultinomialNB())
model.fit(X_train, y_train)
y_pred = model.predict(X_test)

print(classification_report(y_test,y_pred))
 
  #precision    recall  f1-score   support

  #         0       0.67      0.94      0.79       319
  #         1       0.96      0.92      0.94       389

seanbenhur

model = make_pipeline_imb(TfidfVectorizer(), RandomUnderSampler(), MultinomialNB())

model.fit(X_train, y_train)
y_pred = model.predict(X_test)

print(classification_report(y_test,y_pred))

#            precision    recall  f1-score   support
#
#          0       0.73      0.87      0.79       319

seanbenhur

import numpy as np
import matplotlib.pyplot as plt
import smote_variants as sv
import imbalanced_databases as imbd

# loading the dataset
dataset= imbd.load_iris0()
features, target= dataset['data'], dataset['target']

seanbenhur

seanbenhur

# printing the number of samples before smote

print('majority class: %d' % np.sum(y == 0))
print('minority class: %d' % np.sum(y == 1))

#majority class: 100
#minority class: 50

#The oversampling is carried out by instantiating any oversampler implemented in the package and calling the sample function.
oversampler= sv.distance_SMOTE()