def classification():
# Generate a random binary classification problem.
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=0.1,
random_state=1111)
model = LogisticRegression(lr=0.01, max_iters=500, penalty='l1', C=0.01)
model.fit(X_train, y_train)
predictions = model.predict(X_test)
print('classification accuracy', accuracy(y_test, predictions))
def classification():
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=0.1,
random_state=1111)
model = LogisticRegression(lr=0.01, max_iters=500, penalty='l1', C=0.01)
model.fit(X_train, y_train)
predictions = model.predict(X_test)
print('classification accuracy', accuracy(y_test, predictions))
def classification():
# Generate a random binary classification problem.
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=0.1,
random_state=1111)
model = LogisticRegression(lr=0.01, max_iters=500, penalty="l1", C=0.01)
model.fit(X_train, y_train)
predictions = model.predict(X_test)
print("classification accuracy", accuracy(y_test, predictions))
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
# logging.basicConfig(level=logging.DEBUG)
# Generate a random binary classification problem.
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)
# fit PCA with training data, not entire dataset
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)))
def test_linear_model_classification():
model = LogisticRegression(lr=0.01, max_iters=500, penalty='l1', C=0.01)
model.fit(X_train, y_train)
predictions = model.predict(X_test)
assert roc_auc_score(y_test, predictions) >= 0.95
def test_linear_model():
model = LogisticRegression(lr=0.01, max_iters=500, penalty='l1', C=0.01)
model.fit(X_train, y_train)
predictions = model.predict(X_test)
assert roc_auc_score(y_test, predictions) >= 0.95
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
# logging.basicConfig(level=logging.DEBUG)
# Generate a random binary classification problem.
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)
# fit PCA with training data, not entire dataset
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)))