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@@ -54,21 +54,26 @@ def _get_best_gpu_model(X_train, y_train):
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gammas = [0.001, 0.01, 0.1, 5, 10, 100]
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bestModel = None
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- modelScore = 0.
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+ bestScore = 0.
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+
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+ n_eval = 1
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for c in Cs:
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for g in gammas:
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- print('C:', c, ', gamma:', g)
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svc = SVC(probability=True, class_weight='balanced', kernel='rbf', gamma=g, C=c)
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svc.fit(X_train, y_train)
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score = svc.score(X_train, y_train)
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- if score > modelScore:
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- modelScore = score
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+ # keep track of best model
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+ if score > bestScore:
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+ bestScore = score
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bestModel = svc
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+ print('Eval n° {} [C: {}, gamma: {}] => [score: {}, bestScore:{}]'.format(n_eval, c, g, score, bestScore))
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+ n_eval += 1
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+
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return bestModel
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def svm_gpu(X_train, y_train):
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