我正在附加代码。我试图复制链接link中给出的代码。我在我的数据上尝试此代码。下面是我得到的代码和错误。很抱歉问题的形成不好我试图以正确的方式插入代码，但它似乎不适用于某些命令。
search = {'algorithm': {'k-nn': {'n_neighbors': [1, 5]},
'SVM': {'kernel': {'linear': {'C': [0, 2]},
'rbf': {'gamma': [0, 1], 'C': [0, 10]},
'poly': {'degree': [2, 5], 'C': [0, 50], 'coef0': [0, 1]}
}
},
'naive-bayes': None,
'random-forest': {'n_estimators': [10, 30],
'max_features': [5, 20]}
}
}
@optunity.cross_validated(x=data, y=labels, num_folds=5)
def performance(x_train, y_train, x_test, y_test,
algorithm, n_neighbors=None, n_estimators=None, max_features=None,
kernel=None, C=None, gamma=None, degree=None, coef0=None):
# fit the model
if algorithm == 'k-nn':
model = KNeighborsClassifier(n_neighbors=int(n_neighbors))
model.fit(x_train, y_train)
elif algorithm == 'naive-bayes':
model = GaussianNB()
model.fit(x_train, y_train)
elif algorithm == 'random-forest':
model = RandomForestClassifier(n_estimators=int(n_estimators),
max_features=int(max_features))
model.fit(x_train, y_train)
# predict the test set
if algorithm == 'k-nn':
predictions = model.predict_proba(x_test)[:, 1]
else:
predictions = model.predict_proba(x_test)[:, 1]
return optunity.metrics.roc_auc(y_test, predictions, positive=True)
optimal_configuration, info, _ = optunity.maximize_structured(performance,
search_space=search,
num_evals=300)
这是我收到的错误消息
TypeError Traceback (most recent call last)
C:\ProgramData\Anaconda3\lib\site-packages\optunity\cross_validation.py in select(collection, indices)
76 try:
---> 77 return collection[indices, ...]
78 except IndexError: # caused by scipy.sparse in some versions
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\frame.py in __getitem__(self, key)
1963 else:
-> 1964 return self._getitem_column(key)
1965
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\frame.py in _getitem_column(self, key)
1970 if self.columns.is_unique:
-> 1971 return self._get_item_cache(key)
1972
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\generic.py in _get_item_cache(self, item)
1642 cache = self._item_cache
-> 1643 res = cache.get(item)
1644 if res is None:
TypeError: unhashable type: 'list'
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance)
2441 try:
-> 2442 return self._engine.get_loc(key)
2443 except KeyError:
pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
KeyError: 26770
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
<ipython-input-153-3d5c9d0e2047> in <module>()
1 optimal_configuration, info, _ = optunity.maximize_structured(performance,
2 search_space=search,
----> 3 num_evals=300)
C:\ProgramData\Anaconda3\lib\site-packages\optunity\api.py in maximize_structured(f, search_space, num_evals, pmap)
368 solver = make_solver(**suggestion)
369 solution, details = optimize(solver, f, maximize=True, max_evals=num_evals,
--> 370 pmap=pmap, decoder=tree.decode)
371 return solution, details, suggestion
372
C:\ProgramData\Anaconda3\lib\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
C:\ProgramData\Anaconda3\lib\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:
C:\ProgramData\Anaconda3\lib\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:
C:\ProgramData\Anaconda3\lib\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
C:\ProgramData\Anaconda3\lib\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
C:\ProgramData\Anaconda3\lib\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
C:\ProgramData\Anaconda3\lib\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
C:\ProgramData\Anaconda3\lib\site-packages\optunity\search_spaces.py in wrapped(**kwargs)
324 def wrapped(**kwargs):
325 decoded = self.decode(kwargs)
--> 326 return f(**decoded)
327 return wrapped
328
C:\ProgramData\Anaconda3\lib\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
C:\ProgramData\Anaconda3\lib\site-packages\optunity\cross_validation.py in __call__(self, *args, **kwargs)
396 for i in range(self.num_folds)
397 if not i == fold]))
--> 398 kwargs['x_train'] = select(self.x, rows_train)
399 kwargs['x_test'] = select(self.x, rows_test)
400 if not self.y is None: # dealing with a supervised algorithm
C:\ProgramData\Anaconda3\lib\site-packages\optunity\cross_validation.py in select(collection, indices)
82 indexset = set(indices)
83 return collection.zipWithIndex().filter(lambda x: x[1] in indexset).map(lambda x: x[0])
---> 84 return [collection[i] for i in indices]
85
86
C:\ProgramData\Anaconda3\lib\site-packages\optunity\cross_validation.py in <listcomp>(.0)
82 indexset = set(indices)
83 return collection.zipWithIndex().filter(lambda x: x[1] in indexset).map(lambda x: x[0])
---> 84 return [collection[i] for i in indices]
85
86
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\frame.py in __getitem__(self, key)
1962 return self._getitem_multilevel(key)
1963 else:
-> 1964 return self._getitem_column(key)
1965
1966 def _getitem_column(self, key):
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\frame.py in _getitem_column(self, key)
1969 # get column
1970 if self.columns.is_unique:
-> 1971 return self._get_item_cache(key)
1972
1973 # duplicate columns & possible reduce dimensionality
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\generic.py in _get_item_cache(self, item)
1643 res = cache.get(item)
1644 if res is None:
-> 1645 values = self._data.get(item)
1646 res = self._box_item_values(item, values)
1647 cache[item] = res
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\internals.py in get(self, item, fastpath)
3588
3589 if not isnull(item):
-> 3590 loc = self.items.get_loc(item)
3591 else:
3592 indexer = np.arange(len(self.items))[isnull(self.items)]
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance)
2442 return self._engine.get_loc(key)
2443 except KeyError:
-> 2444 return self._engine.get_loc(self._maybe_cast_indexer(key))
2445
2446 indexer = self.get_indexer([key], method=method, tolerance=tolerance)
pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
KeyError: 26770
答案 0 :(得分：0)
所以我得到了答案。输入数据集的格式需要在数组中，标签作为布尔列表。