Format using ruff

Author: rushter

examples/gbm.py

@@ -18,11 +18,21 @@
 def classification():
     # Generate a random binary classification problem.
     X, y = make_classification(
-        n_samples=350, n_features=15, n_informative=10, random_state=1111, n_classes=2, class_sep=1.0, n_redundant=0
+        n_samples=350,
+        n_features=15,
+        n_informative=10,
+        random_state=1111,
+        n_classes=2,
+        class_sep=1.0,
+        n_redundant=0,
+    )
+    X_train, X_test, y_train, y_test = train_test_split(
+        X, y, test_size=0.15, random_state=1111
     )
-    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15, random_state=1111)
 
-    model = GradientBoostingClassifier(n_estimators=50, max_depth=4, max_features=8, learning_rate=0.1)
+    model = GradientBoostingClassifier(
+        n_estimators=50, max_depth=4, max_features=8, learning_rate=0.1
+    )
     model.fit(X_train, y_train)
     predictions = model.predict(X_test)
     print(predictions)
@@ -34,14 +44,25 @@ def classification():
 def regression():
     # Generate a random regression problem
     X, y = make_regression(
-        n_samples=500, n_features=5, n_informative=5, n_targets=1, noise=0.05, random_state=1111, bias=0.5
+        n_samples=500,
+        n_features=5,
+        n_informative=5,
+        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=0.1, random_state=1111
     )
-    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=1111)
 
     model = GradientBoostingRegressor(n_estimators=25, max_depth=5, max_features=3)
     model.fit(X_train, y_train)
     predictions = model.predict(X_test)
-    print("regression, mse: %s" % mean_squared_error(y_test.flatten(), predictions.flatten()))
+    print(
+        "regression, mse: %s"
+        % mean_squared_error(y_test.flatten(), predictions.flatten())
+    )
 
 
 if __name__ == "__main__":

examples/kmeans.py

@@ -5,7 +5,9 @@
 
 
 def kmeans_example(plot=False):
-    X, y = make_blobs(centers=4, n_samples=500, n_features=2, shuffle=True, random_state=42)
+    X, y = make_blobs(
+        centers=4, n_samples=500, n_features=2, shuffle=True, random_state=42
+    )
     clusters = len(np.unique(y))
     k = KMeans(K=clusters, max_iters=150, init="++")
     k.fit(X)

examples/linear_models.py

@@ -17,9 +17,17 @@
 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
+        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
     )
-    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)
@@ -30,9 +38,16 @@ def regression():
 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
+        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
     )
-    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)

examples/naive_bayes.py

@@ -8,9 +8,17 @@
 def classification():
     # Generate a random binary classification problem.
     X, y = make_classification(
-        n_samples=1000, n_features=10, n_informative=10, random_state=1111, n_classes=2, class_sep=2.5, n_redundant=0
+        n_samples=1000,
+        n_features=10,
+        n_informative=10,
+        random_state=1111,
+        n_classes=2,
+        class_sep=2.5,
+        n_redundant=0,
+    )
+    X_train, X_test, y_train, y_test = train_test_split(
+        X, y, test_size=0.1, random_state=1111
     )
-    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=1111)
 
     model = NaiveBayesClassifier()
     model.fit(X_train, y_train)

examples/nearest_neighbors.py

@@ -13,9 +13,17 @@
 def regression():
     # Generate a random regression problem
     X, y = make_regression(
-        n_samples=500, n_features=5, n_informative=5, n_targets=1, noise=0.05, random_state=1111, bias=0.5
+        n_samples=500,
+        n_features=5,
+        n_informative=5,
+        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
     )
-    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=1111)
 
     model = knn.KNNRegressor(k=5, distance_func=distance.euclidean)
     model.fit(X_train, y_train)
@@ -35,7 +43,9 @@ def classification():
         class_sep=1.5,
     )
 
-    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=1111)
+    X_train, X_test, y_train, y_test = train_test_split(
+        X, y, test_size=0.1, random_state=1111
+    )
 
     clf = knn.KNNClassifier(k=5, distance_func=distance.euclidean)
 

examples/nnet_convnet_mnist.py

@@ -3,7 +3,14 @@
 from mla.datasets import load_mnist
 from mla.metrics import accuracy
 from mla.neuralnet import NeuralNet
-from mla.neuralnet.layers import Activation, Convolution, MaxPooling, Flatten, Dropout, Parameters
+from mla.neuralnet.layers import (
+    Activation,
+    Convolution,
+    MaxPooling,
+    Flatten,
+    Dropout,
+    Parameters,
+)
 from mla.neuralnet.layers import Dense
 from mla.neuralnet.optimizers import Adadelta
 from mla.utils import one_hot

examples/nnet_mlp.py

@@ -23,10 +23,17 @@
 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
+        n_samples=1000,
+        n_features=100,
+        n_informative=75,
+        random_state=1111,
+        n_classes=2,
+        class_sep=2.5,
     )
     y = one_hot(y)
-    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15, random_state=1111)
+    X_train, X_test, y_train, y_test = train_test_split(
+        X, y, test_size=0.15, random_state=1111
+    )
 
     model = NeuralNet(
         layers=[
@@ -51,9 +58,18 @@ def classification():
 
 def regression():
     # Generate a random regression problem
-    X, y = make_regression(n_samples=5000, n_features=25, n_informative=25, n_targets=1, random_state=100, noise=0.05)
+    X, y = make_regression(
+        n_samples=5000,
+        n_features=25,
+        n_informative=25,
+        n_targets=1,
+        random_state=100,
+        noise=0.05,
+    )
     y *= 0.01
-    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=1111)
+    X_train, X_test, y_train, y_test = train_test_split(
+        X, y, test_size=0.1, random_state=1111
+    )
 
     model = NeuralNet(
         layers=[

examples/nnet_rnn_binary_add.py

@@ -38,7 +38,9 @@ def addition_dataset(dim=10, n_samples=10000, batch_size=64):
         # Generate target variable (a+b)
         y[i, :, 0] = list(reversed([int(x) for x in binary_format.format(a + b)]))
 
-    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1111)
+    X_train, X_test, y_train, y_test = train_test_split(
+        X, y, test_size=0.2, random_state=1111
+    )
 
     # Round number of examples for batch processing
     train_b = (X_train.shape[0] // batch_size) * batch_size

examples/pca.py

@@ -12,11 +12,18 @@
 
 # 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
+    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)
+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)

examples/random_forest.py

@@ -19,32 +19,51 @@
 def classification():
     # Generate a random binary classification problem.
     X, y = make_classification(
-        n_samples=500, n_features=10, n_informative=10, random_state=1111, n_classes=2, class_sep=2.5, n_redundant=0
+        n_samples=500,
+        n_features=10,
+        n_informative=10,
+        random_state=1111,
+        n_classes=2,
+        class_sep=2.5,
+        n_redundant=0,
     )
 
-    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15, random_state=1111)
+    X_train, X_test, y_train, y_test = train_test_split(
+        X, y, test_size=0.15, random_state=1111
+    )
 
     model = RandomForestClassifier(n_estimators=10, max_depth=4)
     model.fit(X_train, y_train)
 
     predictions_prob = model.predict(X_test)[:, 1]
     predictions = np.argmax(model.predict(X_test), axis=1)
-    #print(predictions.shape)
+    # print(predictions.shape)
     print("classification, roc auc score: %s" % roc_auc_score(y_test, predictions_prob))
     print("classification, accuracy score: %s" % accuracy_score(y_test, predictions))
 
 
 def regression():
     # Generate a random regression problem
     X, y = make_regression(
-        n_samples=500, n_features=5, n_informative=5, n_targets=1, noise=0.05, random_state=1111, bias=0.5
+        n_samples=500,
+        n_features=5,
+        n_informative=5,
+        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=0.1, random_state=1111
     )
-    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=1111)
 
     model = RandomForestRegressor(n_estimators=50, max_depth=10, max_features=3)
     model.fit(X_train, y_train)
     predictions = model.predict(X_test)
-    print("regression, mse: %s" % mean_squared_error(y_test.flatten(), predictions.flatten()))
+    print(
+        "regression, mse: %s"
+        % mean_squared_error(y_test.flatten(), predictions.flatten())
+    )
 
 
 if __name__ == "__main__":