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Created November 13, 2018 22:56
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---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-20-273b888818ab> in <module>()
7
8 # perform tuning
----> 9 hps, _, _ = optunity.maximize(svm_auc, num_evals=200, logC=[-5, 2], logGamma=[-5, 1])
10
11
/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/optunity/api.py in maximize(f, num_evals, solver_name, pmap, **kwargs)
179 solver = make_solver(**suggestion)
180 solution, details = optimize(solver, f, maximize=True, max_evals=num_evals,
--> 181 pmap=pmap)
182 return solution, details, suggestion
183
/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/optunity/api.py in optimize(solver, func, maximize, max_evals, pmap, decoder)
243 time = timeit.default_timer()
244 try:
--> 245 solution, report = solver.optimize(f, maximize, pmap=pmap)
246 except fun.MaximumEvaluationsException:
247 # early stopping because maximum number of evaluations is reached
/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/optunity/solvers/ParticleSwarm.py in optimize(self, f, maximize, pmap)
269 for g in range(self.num_generations):
270 fitnesses = pmap(evaluate, list(map(self.particle2dict, pop)))
--> 271 for part, fitness in zip(pop, fitnesses):
272 part.fitness = fit * util.score(fitness)
273 if not part.best or part.best_fitness < part.fitness:
/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/optunity/solvers/ParticleSwarm.py in evaluate(d)
257 @functools.wraps(f)
258 def evaluate(d):
--> 259 return f(**d)
260
261 if maximize:
/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/optunity/functions.py in wrapped_f(*args, **kwargs)
299 value = wrapped_f.call_log.get(*args, **kwargs)
300 if value is None:
--> 301 value = f(*args, **kwargs)
302 wrapped_f.call_log.insert(value, *args, **kwargs)
303 return value
/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/optunity/functions.py in wrapped_f(*args, **kwargs)
354 else:
355 wrapped_f.num_evals += 1
--> 356 return f(*args, **kwargs)
357 wrapped_f.num_evals = 0
358 return wrapped_f
/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/optunity/constraints.py in wrapped_f(*args, **kwargs)
149 def wrapped_f(*args, **kwargs):
150 try:
--> 151 return f(*args, **kwargs)
152 except ConstraintViolation:
153 return default
/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/optunity/constraints.py in wrapped_f(*args, **kwargs)
127 if violations:
128 raise ConstraintViolation(violations, *args, **kwargs)
--> 129 return f(*args, **kwargs)
130 wrapped_f.constraints = constraints
131 return wrapped_f
/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/optunity/constraints.py in func(*args, **kwargs)
264 @functions.wraps(f)
265 def func(*args, **kwargs):
--> 266 return f(*args, **kwargs)
267 return func
268
/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/optunity/cross_validation.py in __call__(self, *args, **kwargs)
401 kwargs['y_train'] = select(self.y, rows_train)
402 kwargs['y_test'] = select(self.y, rows_test)
--> 403 scores.append(self.f(**kwargs))
404 return self.reduce(scores)
405
<ipython-input-20-273b888818ab> in svm_auc(x_train, y_train, x_test, y_test, logC, logGamma)
4 model = sklearn.svm.SVC(C=10 ** logC, gamma=10 ** logGamma).fit(x_train, y_train)
5 decision_values = model.decision_function(x_test)
----> 6 return optunity.metrics.roc_auc(y_test, decision_values)
7
8 # perform tuning
/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/optunity/metrics.py in roc_auc(ys, yhat, positive, presorted, return_curve)
408
409 """
--> 410 curve = compute_curve(ys, yhat, _fpr, _recall, positive)
411 if return_curve:
412 return auc(curve), curve
/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/optunity/metrics.py in compute_curve(ys, decision_values, xfun, yfun, positive, presorted)
130 """
131 curve = []
--> 132 tables, _ = contingency_tables(ys, decision_values, positive, presorted)
133 curve = list(map(lambda t: (xfun(t), yfun(t)), tables))
134 return curve
/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/optunity/metrics.py in contingency_tables(ys, decision_values, positive, presorted)
71 # sort decision values
72 ind, srt = zip(*sorted(enumerate(decision_values), reverse=True,
---> 73 key=op.itemgetter(1)))
74
75 # resort y
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
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