logging basicconfiglevellogging debug generate a random binary classification problem fit pca with training data not entire dataset logging basicconfig level logging debug generate a random binary classification problem fit pca with training data not entire dataset
try:
    from sklearn.model_selection import train_test_split
except ImportError:
    from sklearn.cross_validation import train_test_split
from sklearn.datasets import make_classification

from mla.linear_models import LogisticRegression
from mla.metrics import accuracy
from mla.pca import PCA




X, y = make_classification(
    n_samples=1000, n_features=100, n_informative=75, random_state=1111, n_classes=2, class_sep=2.5
)


X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=1111)

for s in ["svd", "eigen"]:
    p = PCA(15, solver=s)

    
    p.fit(X_train)
    X_train_reduced = p.transform(X_train)
    X_test_reduced = p.transform(X_test)

    model = LogisticRegression(lr=0.001, max_iters=2500)
    model.fit(X_train_reduced, y_train)
    predictions = model.predict(X_test_reduced)
    print("Classification accuracy for %s PCA: %s" % (s, accuracy(y_test, predictions)))