`pyqpanda_alg.QARM.qarm`¶

Module Contents¶

Classes¶

QuantumAssociationRulesMining

Association rule mining finds interesting associations or correlations between item sets in

class pyqpanda_alg.QARM.qarm.QuantumAssociationRulesMining(transaction_data, min_support=0.2, min_conf=0.3)¶

Association rule mining finds interesting associations or correlations between item sets in a large amount of data . Mining the implicit relationship between objects from large-scale data is called association analysis or association rule learning, which can reveal the hidden association pattern in data and help people to carry out market operation and decision support. For example, on the same trip to the supermarket, if the customer buys milk, what is the likelihood that he will also buy bread?

Based on the famous classical association rule mining algorithm Apriori algorithm, a quantum association rule mining algorithm is proposed to realize the core task. Specifically, given a quantum black box accessing a trading database, the algorithm first uses the quantum parallel amplitude estimation algorithm to estimate the support of all candidate K term sets in a quantum parallel manner, and store it in a quantum superposition state. Next, the quantum amplitude amplification algorithm is used to search the candidate K term sets that are not less than the predetermined threshold from the superposition quantum states.

Parameters

transaction_data: list

The input datasets,you can prepare TXT documents.There are several lines in the document,each column is separated by a comma. Each element represents an associated thing.

min_support: float

Minimum support, a hyper parameter,the value ranges from 0 to 1. The default minimum support is set to 0.2

min_conf: float

Minimum confidence,a hyper parameter,the value ranges from 0 to 1. The default minimum confidences set to 0.3

run(show=None, file_name='', machine_type='CPU', **kwargs)¶

Parameters:

show: string

Enumeration of the circuit show type,should be one of”None”, “Picture”and”OriginIR”

None : no output;

Picture : Console output the quantum circuit,output default file name;

OriginIR : Console does not output,output the IR of the circuit.

file_name: string

the output file namethat record the circuit information.

machine_type: string

enumeration of QVM type, should be one of “CPU”, and “QCloud”

**kwargs: dict args

Use keywords to pass parameters,the twoparameters areapi_keyand ip_compute.

api_key : API key of the local platform account

ip_compute : The IP address of the local request computing task

Returns:

out: dict

confidence result

Examples

import os
from pyqpanda_alg.QARM.qarm import QuantumAssociationRulesMining
from pyqpanda_alg import QARM


def read(file_path):
    if os.path.exists(file_path):
        trans_data = []
        with open(file_path, 'r', encoding='utf8') as f:
            data_line = f.readlines()
            if data_line:
                for line in data_line:
                    if line:
                        data_list = line.strip().split(',')
                        trans_data.append([data.strip() for data in data_list])
            else:
                raise ValueError("The file {} has no any data!".format(file_path))
    else:
        raise FileNotFoundError('The file {} does not exists!'.format(file_path))
    return trans_data


if __name__ == '__main__':
    data_path = QARM.__path__[0]
    data_file = os.path.join(data_path, 'dataset/data2.txt')
    trans_data = read(data_file)
    support = 0.2
    conf = 0.5
    qarm = QuantumAssociationRulesMining(trans_data, support, conf)
    result = qarm.run()
    print(result)

pyqpanda_alg.QARM.qarm¶

Module Contents¶

Classes¶

`pyqpanda_alg.QARM.qarm`¶