chore: import upstream snapshot with attribution

examples/linear_models.py

@@ -0,0 +1,60 @@
+import logging
+
+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 sklearn.datasets import make_regression
+
+from mla.linear_models import LinearRegression, LogisticRegression
+from mla.metrics.metrics import mean_squared_error, accuracy
+
+# Change to DEBUG to see convergence
+logging.basicConfig(level=logging.ERROR)
+
+
+def regression():
+    # Generate a random regression problem
+    X, y = make_regression(
+        n_samples=10000,
+        n_features=100,
+        n_informative=75,
+        n_targets=1,
+        noise=0.05,
+        random_state=1111,
+        bias=0.5,
+    )
+    X_train, X_test, y_train, y_test = train_test_split(
+        X, y, test_size=0.25, random_state=1111
+    )
+
+    model = LinearRegression(lr=0.01, max_iters=2000, penalty="l2", C=0.03)
+    model.fit(X_train, y_train)
+    predictions = model.predict(X_test)
+    print("regression mse", mean_squared_error(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))
+
+
+if __name__ == "__main__":
+    regression()
+    classification()