Scikit-learn：模型选择Model selection之pipline和交叉验证_typeerror: estimator should be an estimator implem

http://blog.csdn.net/pipisorry/article/details/52250983

选择合适的estimator

通常机器学习最难的一部分是选择合适的estimator，不同的estimator适用于不同的数据集和问题。

sklearn官方文档提供了一个图[flowchart]，可以快速地根据你的数据和问题选择合适的estimator，单击相应的区域还可以获得更具体的内容。

代码中我一般这么写

def gen_estimators():
    '''
    List of the different estimators.
    '''
    estimators = [
        # ('Lasso regression', linear_model.Lasso(alpha=0.1), True),
        ('Ridge regression', linear_model.Ridge(alpha=0.1), True),
        # ('Hinge regression', linear_model.Hinge(), True),
        # ('LassoLars regression', linear_model.LassoLars(alpha=0.1), True),
        ('OrthogonalMatchingPursuitCV regression', linear_model.OrthogonalMatchingPursuitCV(), True),
        ('BayesianRidge regression', linear_model.BayesianRidge(), True),
        ('PassiveAggressiveRegressor regression', linear_model.PassiveAggressiveRegressor(), True),
        ('HuberRegressor regression', linear_model.HuberRegressor(), True),
        # ('LogisticRegression regression', linear_model.LogisticRegression(), True),
    ]
    return estimators

然后如下遍历算法

def cross_validate():
    for name, clf, flag in gen_estimators():
        x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.4, random_state=0)
        clf.fit(x_train, y_train)
        print(name, '\n', clf.coef_)
        # scores = cross_val_score(clf, x, y, cv=5, scoring='roc_auc')
        y_score = clf.predict(x_test)
        y_score = np.select([y_score < 0.0, y_score > 1.0, True], [0.0, 1.0, y_score])
        scores = metrics.roc_auc_score(y_true=[1.0 if _ > 0.0 else 0.0 for _ in y_test], y_score=y_score)
        print(scores)

自己写的模型也可以，但是写的estimator类必须有的方法是有：get_params, set_params(**params), fit(x,y), predict(new_samples), score(x, y_true)。其中有的可以直接从from sklearn.base import BaseEstimator中继承。如：

class DriveRiskEstimator(BaseEstimator):
    def __init__(self):
        pass
 
    def get_params(self, deep=True):
        return {}
 
    def set_params(self, **params):
        return self
 
    def fit(self, x, y):
        '''
        模型训练
        '''
        return self
 
    def predict(self, new_samples):
        '''
        模型预测
        '''
        result = np.zeros(len(new_samples))
        return result
 
    def score(self, x, y_true):
      y_pred = self.predict(x)
      score = metrics.***(y_true, y_pred)
      return score

[Scikit-learn：模型选择之调参grid search]

皮皮blog

Scikit-klean训练集和测试集分割

sklearn.model_selection.train_test_split(*arrays, test_size=None, train_size=None, random_state=None, shuffle=True, stratify=None)

参数

*arrays    sequence of indexables with same length / shape[0]
Allowed inputs are lists, numpy arrays, scipy-sparse matrices or pandas dataframes.

test_size    float or int, default=None
If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the train split. If int, represents the absolute number of train samples. If None, the value is automatically set to the complement of the test size.

random_state int, RandomState instance or None, default=None
Controls the shuffling applied to the data before applying the split. Pass an int for reproducible output across multiple function calls. 设置成一个定值后续可以复现。

示例

示例：从文件读取后分割

from sklearn import model_selection
 
with open('***', encoding='utf-8') as f:
    lines = f.readlines()
train_data, eval_data = model_selection.train_test_split(lines, test_size=0.2, shuffle=True, random_state=0)
train_data, test_data = model_selection.train_test_split(train_data, test_size=0.1, shuffle=True, random_state=0)

这里train_data类型是list。

示例：分割dataframe

直接对dataframe分割，如果df存在头部，则分割后的每个部分都会自带头部：

data_df = readCsv(path_train)
train_data, test_data = model_selection.train_test_split(data_df, test_size=0.3)

示例：对ndarray类型的x\y分割

X_train, X_test, y_train, y_test = model_selection.train_test_split(x, y, test_size=0.4, random_state=0)
X_train.shape, y_train.shape
((90, 4), (90,))
X_test.shape, y_test.shape
((60, 4), (60,))
 
clf = svm.SVC(kernel='linear', C=1).fit(X_train, y_train)
clf.score(X_test, y_test)                           
0.96...

X_train类型应该是ndarray 

[train_test_split]

Sklearn交叉验证

scores = cross_val_score(clf, x, y, cv=10, scoring=rocAucScorer)

也可以不指定scoring函数，此时自动调用定义的Estimator类中的score函数score(self, x, y_true)：

estimator = Estimator().fit(x, y)
scores = cross_val_score(estimator, x, y, cv=cv)

自定义CV策略

（cv是整数的话默认使用KFold）：

>>> n_samples = iris.data.shape[0]
>>> cv = cross_validation.ShuffleSplit(n_samples, n_iter=3, test_size=0.3, random_state=0)
>>> cross_validation.cross_val_score(clf, iris.data, iris.target, cv=cv)          
array([ 0.97...,  0.97...,  1.        ])

另一个接口cross_val_predict ，可以返回每个元素作为test set时的确切预测值（只有在CV的条件下数据集中每个元素都有唯一预测值时才不会出现异常），进而评估estimator：
>>> predicted = cross_validation.cross_val_predict(clf, iris.data, iris.target, cv=10)
>>> metrics.accuracy_score(iris.target, predicted)
0.966...

[scikit-klean交叉验证]

皮皮blog

from: Scikit-learn：模型选择Model selection之pipline和交叉验证-CSDN博客

ref: [scikit-learn User Guide]

[Model selection and evaluation]

[3.1. Cross-validation: evaluating estimator performance]*

[3.4. Model persistence]

[Sample pipeline for text feature extraction and evaluation*]