final: codes

final/src/final.sh

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+#!/usr/bin/env bash
+
+if [ $# != 3 ]; then
+ echo "Usage: bash final.sh [training set values] [training set labels] [testing set values]";
+fi
+
+wget www.csie.ntu.edu.tw/~b03502040/8275.zip
+unzip 8275.zip
+
+python3 train.py --train $1 --label $2 --test $3 --model depth23/*

final/src/requirement.txt

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+numpy
+scipy
+pandas
+xgboost

final/src/train.py

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+import os
+import logging
+import argparse
+import numpy as np
+import pandas as pd
+import xgboost as xgb
+from scipy.stats import mode
+from utils import DataProcessor
+
+
+def parse_args():
+ parser = argparse.ArgumentParser('Pump it up.')
+ parser.add_argument('--train', required=True)
+ parser.add_argument('--label', required=True)
+ parser.add_argument('--test', required=True)
+ parser.add_argument('--cv', type=int, default=4)
+ parser.add_argument('--eta', type=float, default=0.025)
+ parser.add_argument('--depth', type=int, default=23)
+ parser.add_argument('--seed', nargs=2, type=int, default=[60, 73])
+ parser.add_argument('--model', nargs='*')
+ return parser.parse_args()
+
+
+def main(args):
+
+ logger = logging.getLogger()
+ handler = logging.StreamHandler()
+ formatter = logging.Formatter('%(asctime)s %(message)s')
+ handler.setFormatter(formatter)
+ logger.addHandler(handler)
+ logger.setLevel(logging.INFO)
+
+ data = DataProcessor()
+
+ logger.info('Read csvs')
+ data.read_data(args.train, args.test, args.label)
+
+ logger.info('Preprocess data')
+ data.preprocess()
+
+ train_dmatrix = xgb.DMatrix(data=data.train, label=data.labels, missing=np.nan)
+ test_dmatrix = xgb.DMatrix(data=data.test, missing=np.nan)
+
+ if args.model is None:
+ depth_dir = 'depth{}'.format(args.depth)
+ if not os.path.exists(depth_dir):
+ os.mkdir(depth_dir)
+
+ param = {
+ 'booster': 'gbtree',
+ 'obective': 'multi:softmax',
+ 'eta': args.eta,
+ 'max_depth': args.depth,
+ 'colsample_bytree': 0.4,
+ 'silent': 1,
+ 'eval_metric': 'merror',
+ 'num_class': 4
+ }
+
+ logger.info('Start training from seed {} to {}'.format(args.seed[0], args.seed[1]-1))
+ for i in range(args.seed[0], args.seed[1]):
+ logger.info('Cross validate with seed {}, depth {}, {}-fold'.format(i, args.depth, args.cv))
+
+ param['seed'] = i
+ #res = xgb.cv(param, dtrain=train_dmatrix, seed=i, num_boost_round=500, 
+ # nfold=args.cv, early_stopping_rounds=30, maximize=False, verbose_eval=True)
+ #num_boost_round = res['test-merror-mean'].argmin()
+ num_boost_round = 210
+ logger.info('Train xgboost tree with seed {}, depth {}, num_boost_round {}'.format(i, args.depth, num_boost_round))
+
+ clf = xgb.train(param, dtrain=train_dmatrix, num_boost_round=num_boost_round, maximize=False)
+
+ save_path = os.path.join(depth_dir, 'xgb-model-seed-{}'.format(i))
+
+ clf.save_model(save_path)
+
+ logger.info('Save xgboost tree at {}'.format(save_path))
+
+ logger.info('End of training. All models are saved at {}'.format(depth_dir))
+
+ else:
+ pred_overall = []
+ for mfile in args.model:
+ logger.info('Load xgboost tree model {}'.format(mfile))
+ clf = xgb.Booster()
+ clf.load_model(mfile)
+
+ pred = clf.predict(data=test_dmatrix).astype(int)
+ pred_overall.append(pred)
+
+ pred_overall = mode(pred_overall, axis=0)[0].squeeze()
+ data.write_data('output.csv', pred_overall)
+
+
+if __name__ == '__main__':
+ args = parse_args()
+ main(args)

final/src/utils.py

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+import numpy as np
+import pandas as pd
+
+class DataProcessor():
+
+ def __init__(self):
+ pass
+
+ def read_data(self, train_f, test_f, label_f):
+ self.raw_train_features = pd.read_csv(train_f)
+ self.raw_test_features = pd.read_csv(test_f)
+ self.raw_labels = pd.read_csv(label_f)
+
+ def preprocess(self):
+ train = self.raw_train_features
+ test = self.raw_test_features
+ labels = self.raw_labels
+
+ train['test'] = 0
+ test['test'] = 1
+
+ data = pd.concat([train, test])
+
+ data['date_recorded'] = pd.to_datetime(data['date_recorded'])
+ data['date_recorded'] = (data['date_recorded'] - data['date_recorded'].min()) / np.timedelta64(1, 'D')
+
+ data['construction_year'] = data['construction_year'] - 1960
+ data['construction_year'][data['construction_year'] < 0] = data['construction_year'][data['construction_year'] >= 0].median()
+
+ data['gps_height'][data['gps_height'] == 0] = data['gps_height'][data['gps_height'] > 0].median()
+
+ data.drop(['num_private', 'recorded_by', 'wpt_name', 'extraction_type_group', 'extraction_type', 'payment_type',
+ 'water_quality', 'scheme_management', 'district_code', 'region', 'region_code', 'subvillage', 'ward',
+ 'waterpoint_type_group', 'quantity_group', 'installer'], axis=1, inplace=True)
+
+ columns = list(data.select_dtypes(include=['object']).columns)
+
+ data = pd.get_dummies(data, columns=columns)
+
+ train = data.loc[data['test'] == 0]
+ test = data.loc[data['test'] == 1]
+
+ self.id = test['id']
+
+ train.drop(['id', 'test'], axis=1, inplace=True)
+ test.drop(['id', 'test'], axis=1, inplace=True)
+
+ labels.drop(['id'], axis=1, inplace=True)
+ labels = labels['status_group'].astype('category')
+
+ labels.cat.reorder_categories(['non functional', 'functional needs repair', 'functional'], inplace=True)
+
+ self.train = train.values
+ self.test = test.values
+ self.labels = labels.cat.codes.values
+ self.classes = labels.cat.categories
+
+ def write_data(self, filename, pred): 
+ pred = [self.classes[i] for i in pred]
+
+ output = pd.DataFrame({'id': self.id, 'status_group': pred}, columns=['id', 'status_group'])
+ output.to_csv(filename, index=False, columns=('id', 'status_group'))