ランダムフォレストを使用している場合、ScickitのValueError：max_featuresが「0、n_features」である必要があります

Question

私は20個のフィーチャと840行のデータセットを持っています クラシファイア（ランダムフォレスト） 100とmax_features = 5 私は各機能の分類をしたいと思っていますが、それぞれの機能で予測精度を知りたいのですが、コードを使用するとエラーになります scikit verを使用しています。 18.

私はこの問題をどのように修正することができますか？だから私は解決するために管理

for name in ["AWA"]: 
    x=sio.loadmat('/home/TrainVal/{}_Fp1.mat'.format(name))['x'] 
    s_y=sio.loadmat('/home/TrainVal/{}_Fp1.mat'.format(name))['y'] 
    y=np.ravel(s_y) 

    print(name, x.shape, y.shape) 
    print("") 


    clf = make_pipeline(preprocessing.RobustScaler(), RandomForestClassifier(n_estimators = 100, 
                     max_features=5, n_jobs=-1)) 
    #########10x10 SSS############## 
    print("10x10") 

    for i in range(x.shape[1]): 
     xA=x[:, i].reshape(-1,1) 

     xSSSmean = [] 
     for j in range(10): 
      sss = StratifiedShuffleSplit(n_splits=10, test_size=0.1, random_state=j) 
      scoresSSS = cross_val_score(clf, xA, y, cv=sss) 
      xSSSmean.append(scoresSSS.mean()) 

     result_list.append(np.mean(xSSSmean)) 
     plt.bar(i, np.mean(xSSSmean)*100, align = 'center')  
     plt.ylabel('Accuracy') 
     plt.xlabel('Features')  
     plt.title('Accuracy per feature: {}_RF_Fp1(20)'.format(name)) 

     xticks=np.arange(i+1) 
     plt.xticks(xticks, rotation = 'vertical') 
    plt.show() 





#THE ERROR 


ValueError        Traceback (most recent call last) 
<ipython-input-2-a5faae7f83a2> in <module>() 
    24 
    25    sss = StratifiedShuffleSplit(n_splits=10, test_size=0.1, random_state=j)#ver18 
---> 26    scoresSSS = cross_val_score(clf, xA, y, cv=sss) 
    27    xSSSmean.append(scoresSSS.mean()) 
    28    #print(scoresSSS) 

/home/karim/anaconda2/envs/scikit18/lib/python3.5/site-packages/sklearn/model_selection/_validation.py in cross_val_score(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch) 
    138            train, test, verbose, None, 
    139            fit_params) 
--> 140      for train, test in cv_iter) 
    141  return np.array(scores)[:, 0] 
    142 

/home/karim/anaconda2/envs/scikit18/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py in __call__(self, iterable) 
    756    # was dispatched. In particular this covers the edge 
    757    # case of Parallel used with an exhausted iterator. 
--> 758    while self.dispatch_one_batch(iterator): 
    759     self._iterating = True 
    760    else: 

/home/karim/anaconda2/envs/scikit18/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py in dispatch_one_batch(self, iterator) 
    606     return False 
    607    else: 
--> 608     self._dispatch(tasks) 
    609     return True 
    610 

/home/karim/anaconda2/envs/scikit18/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py in _dispatch(self, batch) 
    569   dispatch_timestamp = time.time() 
    570   cb = BatchCompletionCallBack(dispatch_timestamp, len(batch), self) 
--> 571   job = self._backend.apply_async(batch, callback=cb) 
    572   self._jobs.append(job) 
    573 

/home/karim/anaconda2/envs/scikit18/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py in apply_async(self, func, callback) 
    107  def apply_async(self, func, callback=None): 
    108   """Schedule a func to be run""" 
--> 109   result = ImmediateResult(func) 
    110   if callback: 
    111    callback(result) 

/home/karim/anaconda2/envs/scikit18/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py in __init__(self, batch) 
    324   # Don't delay the application, to avoid keeping the input 
    325   # arguments in memory 
--> 326   self.results = batch() 
    327 
    328  def get(self): 

/home/karim/anaconda2/envs/scikit18/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py in __call__(self) 
    129 
    130  def __call__(self): 
--> 131   return [func(*args, **kwargs) for func, args, kwargs in self.items] 
    132 
    133  def __len__(self): 

/home/karim/anaconda2/envs/scikit18/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py in <listcomp>(.0) 
    129 
    130  def __call__(self): 
--> 131   return [func(*args, **kwargs) for func, args, kwargs in self.items] 
    132 
    133  def __len__(self): 

/home/karim/anaconda2/envs/scikit18/lib/python3.5/site-packages/sklearn/model_selection/_validation.py in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, error_score) 
    236    estimator.fit(X_train, **fit_params) 
    237   else: 
--> 238    estimator.fit(X_train, y_train, **fit_params) 
    239 
    240  except Exception as e: 

/home/karim/anaconda2/envs/scikit18/lib/python3.5/site-packages/sklearn/pipeline.py in fit(self, X, y, **fit_params) 
    268   Xt, fit_params = self._fit(X, y, **fit_params) 
    269   if self._final_estimator is not None: 
--> 270    self._final_estimator.fit(Xt, y, **fit_params) 
    271   return self 
    272 

/home/karim/anaconda2/envs/scikit18/lib/python3.5/site-packages/sklearn/ensemble/forest.py in fit(self, X, y, sample_weight) 
    324      t, self, X, y, sample_weight, i, len(trees), 
    325      verbose=self.verbose, class_weight=self.class_weight) 
--> 326     for i, t in enumerate(trees)) 
    327 
    328    # Collect newly grown trees 

/home/karim/anaconda2/envs/scikit18/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py in __call__(self, iterable) 
    756    # was dispatched. In particular this covers the edge 
    757    # case of Parallel used with an exhausted iterator. 
--> 758    while self.dispatch_one_batch(iterator): 
    759     self._iterating = True 
    760    else: 

/home/karim/anaconda2/envs/scikit18/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py in dispatch_one_batch(self, iterator) 
    606     return False 
    607    else: 
--> 608     self._dispatch(tasks) 
    609     return True 
    610 

/home/karim/anaconda2/envs/scikit18/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py in _dispatch(self, batch) 
    569   dispatch_timestamp = time.time() 
    570   cb = BatchCompletionCallBack(dispatch_timestamp, len(batch), self) 
--> 571   job = self._backend.apply_async(batch, callback=cb) 
    572   self._jobs.append(job) 
    573 

/home/karim/anaconda2/envs/scikit18/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py in apply_async(self, func, callback) 
    107  def apply_async(self, func, callback=None): 
    108   """Schedule a func to be run""" 
--> 109   result = ImmediateResult(func) 
    110   if callback: 
    111    callback(result) 

/home/karim/anaconda2/envs/scikit18/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py in __init__(self, batch) 
    324   # Don't delay the application, to avoid keeping the input 
    325   # arguments in memory 
--> 326   self.results = batch() 
    327 
    328  def get(self): 

/home/karim/anaconda2/envs/scikit18/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py in __call__(self) 
    129 
    130  def __call__(self): 
--> 131   return [func(*args, **kwargs) for func, args, kwargs in self.items] 
    132 
    133  def __len__(self): 

/home/karim/anaconda2/envs/scikit18/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py in <listcomp>(.0) 
    129 
    130  def __call__(self): 
--> 131   return [func(*args, **kwargs) for func, args, kwargs in self.items] 
    132 
    133  def __len__(self): 

/home/karim/anaconda2/envs/scikit18/lib/python3.5/site-packages/sklearn/ensemble/forest.py in _parallel_build_trees(tree, forest, X, y, sample_weight, tree_idx, n_trees, verbose, class_weight) 
    118    curr_sample_weight *= compute_sample_weight('balanced', y, indices) 
    119 
--> 120   tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False) 
    121  else: 
    122   tree.fit(X, y, sample_weight=sample_weight, check_input=False) 

/home/karim/anaconda2/envs/scikit18/lib/python3.5/site-packages/sklearn/tree/tree.py in fit(self, X, y, sample_weight, check_input, X_idx_sorted) 
    737    sample_weight=sample_weight, 
    738    check_input=check_input, 
--> 739    X_idx_sorted=X_idx_sorted) 
    740   return self 
    741 

/home/karim/anaconda2/envs/scikit18/lib/python3.5/site-packages/sklearn/tree/tree.py in fit(self, X, y, sample_weight, check_input, X_idx_sorted) 
    244    raise ValueError("max_depth must be greater than zero. ") 
    245   if not (0 < max_features <= self.n_features_): 
--> 246    raise ValueError("max_features must be in (0, n_features]") 
    247   if not isinstance(max_leaf_nodes, (numbers.Integral, np.integer)): 
    248    raise ValueError("max_leaf_nodes must be integral number but was " 

ValueError: max_features must be in (0, n_features] 

Answer 1

問題！！！ :) scikit pageで は言う：

*フロート場合、機能は各分割で考慮される割合とINT（max_features * n_features）であるmax_features *

マイ値：。

• リスト項目

n_features = 20。これはintにあります。これは、私のデータセットにあるフィーチャーの数です。

max_features：これは、使用したい機能の数です。しかし、彼らはので、私は、私はscikitである式を使用する必要がフロートにそれを有効にするにはフロート

にそれらを有効にする必要がありint型にあります

int(max_features * n_features) 
int(x * 20)=2 
x=0.1 

我々は仮定しなければなりません私は20

xからわずか2機能を使用することをフロートにおける割合です

max_featuresの値をintからfloatに変更しました。

max_features：

（INT）（フロート）

20 = 1.0

15 = 0.75

10 = 0.5

5 = 0.25

ちょうどこのような

2 = 0.1

例

#Instead of: 
clf = make_pipeline(preprocessing.RobustScaler(), RandomForestClassifier(n_estimators = 100, 
        max_features=5, n_jobs=-1)) 

#I did: 
clf = make_pipeline(preprocessing.RobustScaler(), RandomForestClassifier(n_estimators = 100, 
        max_features=0.25, n_jobs=-1))