c024c42dce
* Use sphinx gallery to integrate examples. * Remove mock objects. * Add dask doc inventory.
96 lines
3.1 KiB
Python
96 lines
3.1 KiB
Python
"""
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Getting started with XGBoost
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============================
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"""
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import numpy as np
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import scipy.sparse
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import pickle
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import xgboost as xgb
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import os
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# Make sure the demo knows where to load the data.
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CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
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XGBOOST_ROOT_DIR = os.path.dirname(os.path.dirname(CURRENT_DIR))
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DEMO_DIR = os.path.join(XGBOOST_ROOT_DIR, 'demo')
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# simple example
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# load file from text file, also binary buffer generated by xgboost
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dtrain = xgb.DMatrix(os.path.join(DEMO_DIR, 'data', 'agaricus.txt.train?indexing_mode=1'))
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dtest = xgb.DMatrix(os.path.join(DEMO_DIR, 'data', 'agaricus.txt.test?indexing_mode=1'))
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# specify parameters via map, definition are same as c++ version
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param = {'max_depth': 2, 'eta': 1, 'objective': 'binary:logistic'}
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# specify validations set to watch performance
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watchlist = [(dtest, 'eval'), (dtrain, 'train')]
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num_round = 2
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bst = xgb.train(param, dtrain, num_round, watchlist)
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# this is prediction
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preds = bst.predict(dtest)
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labels = dtest.get_label()
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print('error=%f' %
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(sum(1 for i in range(len(preds)) if int(preds[i] > 0.5) != labels[i]) /
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float(len(preds))))
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bst.save_model('0001.model')
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# dump model
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bst.dump_model('dump.raw.txt')
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# dump model with feature map
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bst.dump_model('dump.nice.txt', os.path.join(DEMO_DIR, 'data/featmap.txt'))
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# save dmatrix into binary buffer
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dtest.save_binary('dtest.buffer')
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# save model
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bst.save_model('xgb.model')
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# load model and data in
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bst2 = xgb.Booster(model_file='xgb.model')
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dtest2 = xgb.DMatrix('dtest.buffer')
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preds2 = bst2.predict(dtest2)
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# assert they are the same
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assert np.sum(np.abs(preds2 - preds)) == 0
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# alternatively, you can pickle the booster
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pks = pickle.dumps(bst2)
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# load model and data in
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bst3 = pickle.loads(pks)
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preds3 = bst3.predict(dtest2)
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# assert they are the same
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assert np.sum(np.abs(preds3 - preds)) == 0
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###
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# build dmatrix from scipy.sparse
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print('start running example of build DMatrix from scipy.sparse CSR Matrix')
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labels = []
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row = []
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col = []
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dat = []
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i = 0
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for l in open(os.path.join(DEMO_DIR, 'data', 'agaricus.txt.train')):
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arr = l.split()
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labels.append(int(arr[0]))
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for it in arr[1:]:
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k, v = it.split(':')
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row.append(i)
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col.append(int(k))
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dat.append(float(v))
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i += 1
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csr = scipy.sparse.csr_matrix((dat, (row, col)))
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dtrain = xgb.DMatrix(csr, label=labels)
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watchlist = [(dtest, 'eval'), (dtrain, 'train')]
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bst = xgb.train(param, dtrain, num_round, watchlist)
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print('start running example of build DMatrix from scipy.sparse CSC Matrix')
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# we can also construct from csc matrix
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csc = scipy.sparse.csc_matrix((dat, (row, col)))
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dtrain = xgb.DMatrix(csc, label=labels)
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watchlist = [(dtest, 'eval'), (dtrain, 'train')]
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bst = xgb.train(param, dtrain, num_round, watchlist)
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print('start running example of build DMatrix from numpy array')
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# NOTE: npymat is numpy array, we will convert it into scipy.sparse.csr_matrix
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# in internal implementation then convert to DMatrix
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npymat = csr.todense()
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dtrain = xgb.DMatrix(npymat, label=labels)
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watchlist = [(dtest, 'eval'), (dtrain, 'train')]
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bst = xgb.train(param, dtrain, num_round, watchlist)
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