from sklearn.datasets import make_classification创建分类数据集

        make_classification创建用于分类的数据集，官方文档

例子：

### 创建模型
def create_model():
    
    # 生成数据
    from sklearn.datasets import make_classification
    X, y = make_classification(n_samples=10000,        # 样本个数
                               n_features=25,          # 特征个数
                               n_informative=3,        # 有效特征个数
                               n_redundant=2,          # 冗余特征个数（有效特征的随机组合）
                               n_repeated=0,           # 重复特征个数（有效特征和冗余特征的随机组合）
                               n_classes=3,            # 样本类别
                               n_clusters_per_class=1, # 簇的个数
                               random_state=0)
    
    print("原始特征维度",X.shape)
    
    # 读取数据
    print("读取数据")
    #import pandas as pd
    #data = pd.read_csv(datapath)
    
    # 数据划分
    print("数据划分")
    from sklearn.model_selection import train_test_split
    global x_train,x_valid,x_test,y_train,y_valid,y_test
    x_train,x_test,y_train,y_test = train_test_split(X,y,random_state = 33,test_size = 0.25)
    x_train,x_valid,y_train,y_valid = train_test_split(x_train,y_train,random_state = 33,test_size = 0.25)

    # 创建模型
    print("创建模型")
    from sklearn.linear_model import LogisticRegression
    global model 
    model = LogisticRegression(penalty = 'l2').fit(x_train,y_train)

### 保存模型    
def save_model():
    print("保存模型")
    from sklearn.externals import joblib
    joblib.dump(model,'model.pkl')

### 模型验证   
def validate_model():
    print("模型验证")
    print(model.score(x_valid,y_valid))  
    
### 模型预测
def predict_model():
    print("模型预测")
    global pred
    pred = model.predict_proba(x_test)
    print(pred)
    
if __name__ == "__main__":
    create_model()
    save_model()
    validate_model()
    predict_model()