wehub-resource-sync
115 lines
3.3 KiB
Python
115 lines
3.3 KiB
Python
from sklearn.metrics import roc_auc_score
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from mla.ensemble import RandomForestClassifier
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from mla.ensemble.gbm import GradientBoostingClassifier
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from mla.knn import KNNClassifier
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from mla.linear_models import LogisticRegression
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from mla.metrics import accuracy
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from mla.naive_bayes import NaiveBayesClassifier
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from mla.neuralnet import NeuralNet
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from mla.neuralnet.constraints import MaxNorm
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from mla.neuralnet.layers import Activation, Dense, Dropout
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from mla.neuralnet.optimizers import Adadelta
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from mla.neuralnet.parameters import Parameters
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from mla.neuralnet.regularizers import L2
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from mla.svm.kernerls import RBF, Linear
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from mla.svm.svm import SVM
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from mla.utils import one_hot
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try:
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from sklearn.model_selection import train_test_split
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except ImportError:
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from sklearn.cross_validation import train_test_split
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from sklearn.datasets import make_classification
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# Generate a random regression problem
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X, y = make_classification(
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n_samples=750,
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n_features=10,
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n_informative=8,
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random_state=1111,
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n_classes=2,
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class_sep=2.5,
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n_redundant=0,
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)
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X_train, X_test, y_train, y_test = train_test_split(
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X, y, test_size=0.12, random_state=1111
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)
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# All classifiers except convnet, RNN, LSTM.
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def test_linear_model():
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model = LogisticRegression(lr=0.01, max_iters=500, penalty="l1", C=0.01)
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model.fit(X_train, y_train)
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predictions = model.predict(X_test)
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assert roc_auc_score(y_test, predictions) >= 0.95
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def test_random_forest():
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model = RandomForestClassifier(n_estimators=10, max_depth=4)
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model.fit(X_train, y_train)
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predictions = model.predict(X_test)[:, 1]
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assert roc_auc_score(y_test, predictions) >= 0.95
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def test_svm_classification():
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y_signed_train = (y_train * 2) - 1
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y_signed_test = (y_test * 2) - 1
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for kernel in [RBF(gamma=0.05), Linear()]:
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model = SVM(max_iter=500, kernel=kernel)
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model.fit(X_train, y_signed_train)
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predictions = model.predict(X_test)
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assert accuracy(y_signed_test, predictions) >= 0.8
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def test_mlp():
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y_train_onehot = one_hot(y_train)
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y_test_onehot = one_hot(y_test)
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model = NeuralNet(
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layers=[
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Dense(256, Parameters(init="uniform", regularizers={"W": L2(0.05)})),
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Activation("relu"),
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Dropout(0.5),
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Dense(128, Parameters(init="normal", constraints={"W": MaxNorm()})),
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Activation("relu"),
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Dense(2),
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Activation("softmax"),
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],
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loss="categorical_crossentropy",
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optimizer=Adadelta(),
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metric="accuracy",
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batch_size=64,
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max_epochs=25,
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)
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model.fit(X_train, y_train_onehot)
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predictions = model.predict(X_test)
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assert roc_auc_score(y_test_onehot[:, 0], predictions[:, 0]) >= 0.95
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def test_gbm():
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model = GradientBoostingClassifier(
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n_estimators=25, max_depth=3, max_features=5, learning_rate=0.1
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)
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model.fit(X_train, y_train)
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predictions = model.predict(X_test)
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assert roc_auc_score(y_test, predictions) >= 0.95
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def test_naive_bayes():
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model = NaiveBayesClassifier()
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model.fit(X_train, y_train)
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predictions = model.predict(X_test)[:, 1]
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assert roc_auc_score(y_test, predictions) >= 0.95
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def test_knn():
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clf = KNNClassifier(k=5)
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clf.fit(X_train, y_train)
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predictions = clf.predict(X_test)
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assert accuracy(y_test, predictions) >= 0.95
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