42 lines
1.1 KiB
Python
42 lines
1.1 KiB
Python
import numpy as np
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import pandas as pd
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import xgboost as xgb
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import time
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import random
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from sklearn.model_selection import StratifiedKFold
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#For sampling rows from input file
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random_seed = 9
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subset = 0.4
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n_rows = 1183747;
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train_rows = int(n_rows * subset)
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random.seed(random_seed)
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skip = sorted(random.sample(range(1,n_rows + 1),n_rows-train_rows))
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data = pd.read_csv("../data/train_numeric.csv", index_col=0, dtype=np.float32, skiprows=skip)
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y = data['Response'].values
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del data['Response']
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X = data.values
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param = {}
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param['objective'] = 'binary:logistic'
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param['eval_metric'] = 'auc'
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param['max_depth'] = 5
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param['eta'] = 0.3
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param['silent'] = 0
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param['tree_method'] = 'gpu_exact'
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num_round = 20
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skf = StratifiedKFold(n_splits=5)
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for i, (train, test) in enumerate(skf.split(X, y)):
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dtrain = xgb.DMatrix(X[train], label=y[train])
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tmp = time.time()
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bst = xgb.train(param, dtrain, num_round)
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boost_time = time.time() - tmp
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res = bst.eval(xgb.DMatrix(X[test], label=y[test]))
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print("Fold {}: {}, Boost Time {}".format(i, res, str(boost_time)))
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del bst
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