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\" }