Skip to content

Instantly share code, notes, and snippets.

@rohithteja
Last active May 20, 2022 14:31
Show Gist options
  • Star 1 You must be signed in to star a gist
  • Fork 1 You must be signed in to fork a gist
  • Save rohithteja/717a2a67da7273b2dcab90e612568a02 to your computer and use it in GitHub Desktop.
Save rohithteja/717a2a67da7273b2dcab90e612568a02 to your computer and use it in GitHub Desktop.
Gooey Example
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser
from sklearn.datasets import make_classification
from sklearn.decomposition import PCA
from sklearn.manifold import LocallyLinearEmbedding
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from gooey import Gooey
@Gooey # The Decorator goes here
def parse_args():
parser = ArgumentParser(formatter_class=ArgumentDefaultsHelpFormatter,
conflict_handler='resolve')
parser.add_argument('--dim_red_type', default='PCA', choices=[
'PCA','LLE'], help='The dim red. types')
parser.add_argument('--n_comp', default=10, type=int, choices=[
5,10], help='output dimensions')
parser.add_argument('--classifier', default='LR', choices=[
'LR','SVC','RF'], help='Classifiers')
args = parser.parse_args()
return args
def main(args):
X, y = make_classification(n_samples=1000, n_features=30,
n_informative=15, n_redundant=15,
random_state=42)
X_train,X_test,y_train,y_test = train_test_split(X, y,stratify=y,
test_size=0.3,
random_state=42)
# dimensionality reduction
def dim_reduction(X_train,X_test,dim_red_type,n_comp):
if dim_red_type == 'pca':
dim_red = PCA(n_components=int(n_comp))
elif dim_red_type == 'lle':
dim_red = LocallyLinearEmbedding(n_components=int(n_comp))
dim_red.fit(X_train)
X_train_dim = dim_red.transform(X_train)
X_test_dim = dim_red.transform(X_test)
return X_train_dim, X_test_dim
# model training and eval
def train(classifier,X_train,y_train,X_test,y_test):
if classifier == 'lr':
clf = LogisticRegression()
elif classifier == 'svc':
clf = SVC()
elif classifier == 'rf':
clf = RandomForestClassifier()
clf.fit(X_train,y_train)
y_pred = clf.predict(X_test)
acc_score = accuracy_score(y_test,y_pred).round(3)
return acc_score * 100
X_train, X_test = dim_reduction(X_train,X_test,'lle',2)
acc_score = train('lr',X_train,y_train,X_test,y_test)
print(f'Dimensionality reduction: {args.dim_red_type}')
print(f'Number of components: {args.n_comp}')
print(f'Classifier: {args.classifier}')
print('Accuracy Score: ',acc_score)
print('*'*10)
def more_main():
args = parse_args()
main(parse_args())
if __name__ == "__main__":
more_main()
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment