def setUp(self):
self.srv = TestUtil.fork_process("regression", port)
self.cli = regression(host, port)
method = "PA"
self.converter = '{\n"string_filter_types":{}, \n"string_filter_rules":[], \n"num_filter_types":{}, \n"num_filter_rules":[], \n"string_types":{}, \n"string_rules":\n[{"key":"*", "type":"space", \n"sample_weight":"bin", "global_weight":"bin"}\n], \n"num_types":{}, \n"num_rules":[\n{"key":"*", "type":"num"}\n]\n}'
cd = config_data(method, self.converter)
self.cli.set_config("name", cd)
def main():
args = parse_options()
client = regression("127.0.0.1", 9199)
# train
num = 0
if args.traindata:
with open(args.traindata, "r") as traindata:
for data in traindata:
# skip comments
if not len(data) or data.startswith("#"):
continue
num += 1
rent, distance, space, age, stair, aspect = map(str.strip, data.strip().split(","))
string_values = [["aspect", aspect]]
num_values = [
["distance", float(distance)],
["space", float(space)],
["age", float(age)],
["stair", float(stair)],
]
d = datum(string_values, num_values)
train_data = [[float(rent), d]]
# train
client.train("", train_data)
# print train number
print "train ...", num
# anaylze
with open(args.analyzedata, "r") as analyzedata:
myhome = yaml.load(analyzedata)
string_values = [["aspect", str(myhome["aspect"])]]
num_values = [
["distance", float(myhome["distance"])],
["space", float(myhome["space"])],
["age", float(myhome["age"])],
["stair", float(myhome["stair"])],
]
d = datum(string_values, num_values)
analyze_data = [d]
result = client.estimate("", analyze_data)
print "rent ....", round(result[0], 1)
def main():
args = parse_options()
client = regression('127.0.0.1', 9199)
# train
num = 0
if args.traindata:
with open(args.traindata, 'r') as traindata:
for data in traindata:
# skip comments
if not len(data) or data.startswith('#'):
continue
num += 1
rent, distance, space, age, stair, aspect = map(
str.strip,
data.strip().split(','))
string_values = [['aspect', aspect]]
num_values = [['distance', float(distance)],
['space', float(space)], ['age',
float(age)],
['stair', float(stair)]]
d = datum(string_values, num_values)
train_data = [[float(rent), d]]
# train
client.train('', train_data)
# print train number
print 'train ...', num
# anaylze
with open(args.analyzedata, 'r') as analyzedata:
myhome = yaml.load(analyzedata)
string_values = [['aspect', str(myhome['aspect'])]]
num_values = [['distance', float(myhome['distance'])],
['space', float(myhome['space'])],
['age', float(myhome['age'])],
['stair', float(myhome['stair'])]]
d = datum(string_values, num_values)
analyze_data = [d]
result = client.estimate('', analyze_data)
print 'rent ....', round(result[0], 1)
def setUp(self):
self.config = {
"method": "PA",
"converter": {
"string_filter_types": {},
"string_filter_rules": [],
"num_filter_types": {},
"num_filter_rules": [],
"string_types": {},
"string_rules": [{"key": "*", "type": "str", "sample_weight": "bin", "global_weight": "bin"}],
"num_types": {},
"num_rules": [{"key": "*", "type": "num"}]
},
"parameter": {
"sensitivity" : 0.1,
"regularization_weight" : 3.402823e+38
}
}
TestUtil.write_file('config_regression.json', json.dumps(self.config))
self.srv = TestUtil.fork_process('regression', port, 'config_regression.json')
self.cli = regression(host, port)
dest = 'analyzedata'
)
parser.add_argument(
'-t',
help = 'train data file (CSV)',
metavar = 'FILE',
dest = 'traindata'
)
return parser.parse_args()
if __name__ == '__main__':
args = parse_options()
client = regression('localhost', 9199)
method = "PA"
converter = """
{
\"string_filter_types\" : {},
\"string_filter_rules\" : [],
\"num_filter_types\" : {},
\"num_filter_rules\" : [],
\"string_types\" : {},
\"string_rules\" : [
{
\"key\" : \"aspect\",
\"type\" : \"str\",
\"sample_weight\" : \"bin\",
\"global_weight\" : \"bin\"
}
