Python db_connect示例

示例#1

def get_records():
    '''
    get records from database
    '''
    conn = module_io.db_connect()
    query = module_io.load_sql("../sql/query-features-train.sql")
    records = module_io.db_query(conn, query)
    return records

示例#2

文件： develop.py 项目： SnakeHunt2012/kdd-cup

def get_records():
    '''
    get records from database
    '''
    conn = module_io.db_connect()
    query = module_io.load_sql("../sql/query-features-train.sql")
    records = module_io.db_query(conn, query)
    return records

示例#3

文件： test.py 项目： SnakeHunt2012/kdd-cup

import module_io
from sklearn.ensemble import RandomForestClassifier

if __name__=="__main__":

    # look backwared & look forward
    look_backward = 29
    look_forward = 4
    
    # get records from database
    print "getting features from database"
    conn = module_io.db_connect()
    query = module_io.load_sql("../sql/query-features-test.sql")
    records = module_io.db_query(conn, query)

    # load model
    classifier = module_io.load_model("../model/benchmark.pickle")

    # clean features
    features = []
    for record in records:
        vector_feature = list(record[1:])
        for index in range(len(vector_feature)):
            if vector_feature[index] is None:
                vector_feature[index] = 0
        features.append(vector_feature)

    # get features
    range_test = features[look_backward:(-look_forward)]
    features_test = []
    for index in range(look_backward, len(features) - look_forward):

示例#4

文件： test.py 项目： Keesiu/meta-kaggle

import module_io
from sklearn.ensemble import RandomForestClassifier

if __name__ == "__main__":

    # look backwared & look forward
    look_backward = 29
    look_forward = 4

    # get records from database
    print "getting features from database"
    conn = module_io.db_connect()
    query = module_io.load_sql("../sql/query-features-test.sql")
    records = module_io.db_query(conn, query)

    # load model
    classifier = module_io.load_model("../model/benchmark.pickle")

    # clean features
    features = []
    for record in records:
        vector_feature = list(record[1:])
        for index in range(len(vector_feature)):
            if vector_feature[index] is None:
                vector_feature[index] = 0
        features.append(vector_feature)

    # get features
    range_test = features[look_backward:(-look_forward)]
    features_test = []
    for index in range(look_backward, len(features) - look_forward):