Sample code with dimensionality reduction and binary classification

dimred.py

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

# arguments
dim_red_type = 'pca'
classifier = 'svc'
n_comp = 10

# dataset
X, y = make_classification(n_samples=1000, n_features=30,
                           n_informative=15, n_redundant=15, 
                           random_state=42)

# data split
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=n_comp)
  elif dim_red_type == 'lle':
    dim_red = LocallyLinearEmbedding(n_components=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)
train('lr',X_train,y_train,X_test,y_test)