chore: import upstream snapshot with attribution

mla/tests/test_classification_accuracy.py

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+from sklearn.metrics import roc_auc_score
+
+from mla.ensemble import RandomForestClassifier
+from mla.ensemble.gbm import GradientBoostingClassifier
+from mla.knn import KNNClassifier
+from mla.linear_models import LogisticRegression
+from mla.metrics import accuracy
+from mla.naive_bayes import NaiveBayesClassifier
+from mla.neuralnet import NeuralNet
+from mla.neuralnet.constraints import MaxNorm
+from mla.neuralnet.layers import Activation, Dense, Dropout
+from mla.neuralnet.optimizers import Adadelta
+from mla.neuralnet.parameters import Parameters
+from mla.neuralnet.regularizers import L2
+from mla.svm.kernerls import RBF, Linear
+from mla.svm.svm import SVM
+from mla.utils import one_hot
+
+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
+
+# Generate a random regression problem
+X, y = make_classification(
+    n_samples=750,
+    n_features=10,
+    n_informative=8,
+    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=0.12, random_state=1111
+)
+
+
+# All classifiers except convnet, RNN, LSTM.
+
+
+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
+
+
+def test_random_forest():
+    model = RandomForestClassifier(n_estimators=10, max_depth=4)
+    model.fit(X_train, y_train)
+    predictions = model.predict(X_test)[:, 1]
+    assert roc_auc_score(y_test, predictions) >= 0.95
+
+
+def test_svm_classification():
+    y_signed_train = (y_train * 2) - 1
+    y_signed_test = (y_test * 2) - 1
+
+    for kernel in [RBF(gamma=0.05), Linear()]:
+        model = SVM(max_iter=500, kernel=kernel)
+        model.fit(X_train, y_signed_train)
+        predictions = model.predict(X_test)
+        assert accuracy(y_signed_test, predictions) >= 0.8
+
+
+def test_mlp():
+    y_train_onehot = one_hot(y_train)
+    y_test_onehot = one_hot(y_test)
+
+    model = NeuralNet(
+        layers=[
+            Dense(256, Parameters(init="uniform", regularizers={"W": L2(0.05)})),
+            Activation("relu"),
+            Dropout(0.5),
+            Dense(128, Parameters(init="normal", constraints={"W": MaxNorm()})),
+            Activation("relu"),
+            Dense(2),
+            Activation("softmax"),
+        ],
+        loss="categorical_crossentropy",
+        optimizer=Adadelta(),
+        metric="accuracy",
+        batch_size=64,
+        max_epochs=25,
+    )
+    model.fit(X_train, y_train_onehot)
+    predictions = model.predict(X_test)
+    assert roc_auc_score(y_test_onehot[:, 0], predictions[:, 0]) >= 0.95
+
+
+def test_gbm():
+    model = GradientBoostingClassifier(
+        n_estimators=25, max_depth=3, max_features=5, learning_rate=0.1
+    )
+    model.fit(X_train, y_train)
+    predictions = model.predict(X_test)
+    assert roc_auc_score(y_test, predictions) >= 0.95
+
+
+def test_naive_bayes():
+    model = NaiveBayesClassifier()
+    model.fit(X_train, y_train)
+    predictions = model.predict(X_test)[:, 1]
+    assert roc_auc_score(y_test, predictions) >= 0.95
+
+
+def test_knn():
+    clf = KNNClassifier(k=5)
+
+    clf.fit(X_train, y_train)
+    predictions = clf.predict(X_test)
+    assert accuracy(y_test, predictions) >= 0.95