From bcce17e68841221f8cb29a74160d59bfc70d0263 Mon Sep 17 00:00:00 2001 From: Jiaming Yuan Date: Fri, 1 Apr 2022 00:59:42 +0800 Subject: [PATCH] Remove text loading in basic walk through demo. (#7753) --- demo/guide-python/basic_walkthrough.py | 92 ++++++++++---------------- doc/python/python_intro.rst | 1 + python-package/xgboost/core.py | 12 ++-- 3 files changed, 43 insertions(+), 62 deletions(-) diff --git a/demo/guide-python/basic_walkthrough.py b/demo/guide-python/basic_walkthrough.py index e35a1e27c..06c9fac60 100644 --- a/demo/guide-python/basic_walkthrough.py +++ b/demo/guide-python/basic_walkthrough.py @@ -1,50 +1,65 @@ """ Getting started with XGBoost ============================ + +This is a simple example of using the native XGBoost interface, there are other +interfaces in the Python package like scikit-learn interface and Dask interface. + + +See :doc:`/python/python_intro` and :doc:`/tutorials/index` for other references. + """ import numpy as np -import scipy.sparse import pickle import xgboost as xgb import os +from sklearn.datasets import load_svmlight_file + # Make sure the demo knows where to load the data. CURRENT_DIR = os.path.dirname(os.path.abspath(__file__)) XGBOOST_ROOT_DIR = os.path.dirname(os.path.dirname(CURRENT_DIR)) -DEMO_DIR = os.path.join(XGBOOST_ROOT_DIR, 'demo') +DEMO_DIR = os.path.join(XGBOOST_ROOT_DIR, "demo") -# simple example -# load file from text file, also binary buffer generated by xgboost -dtrain = xgb.DMatrix(os.path.join(DEMO_DIR, 'data', 'agaricus.txt.train?indexing_mode=1')) -dtest = xgb.DMatrix(os.path.join(DEMO_DIR, 'data', 'agaricus.txt.test?indexing_mode=1')) +# X is a scipy csr matrix, XGBoost supports many other input types, +X, y = load_svmlight_file(os.path.join(DEMO_DIR, "data", "agaricus.txt.train")) +dtrain = xgb.DMatrix(X, y) +# validation set +X_test, y_test = load_svmlight_file(os.path.join(DEMO_DIR, "data", "agaricus.txt.test")) +dtest = xgb.DMatrix(X_test, y_test) # specify parameters via map, definition are same as c++ version -param = {'max_depth': 2, 'eta': 1, 'objective': 'binary:logistic'} +param = {"max_depth": 2, "eta": 1, "objective": "binary:logistic"} # specify validations set to watch performance -watchlist = [(dtest, 'eval'), (dtrain, 'train')] +watchlist = [(dtest, "eval"), (dtrain, "train")] +# number of boosting rounds num_round = 2 -bst = xgb.train(param, dtrain, num_round, watchlist) +bst = xgb.train(param, dtrain, num_boost_round=num_round, evals=watchlist) -# this is prediction +# run prediction preds = bst.predict(dtest) labels = dtest.get_label() -print('error=%f' % - (sum(1 for i in range(len(preds)) if int(preds[i] > 0.5) != labels[i]) / - float(len(preds)))) -bst.save_model('0001.model') +print( + "error=%f" + % ( + sum(1 for i in range(len(preds)) if int(preds[i] > 0.5) != labels[i]) + / float(len(preds)) + ) +) +bst.save_model("model-0.json") # dump model -bst.dump_model('dump.raw.txt') +bst.dump_model("dump.raw.txt") # dump model with feature map -bst.dump_model('dump.nice.txt', os.path.join(DEMO_DIR, 'data/featmap.txt')) +bst.dump_model("dump.nice.txt", os.path.join(DEMO_DIR, "data/featmap.txt")) # save dmatrix into binary buffer -dtest.save_binary('dtest.buffer') +dtest.save_binary("dtest.dmatrix") # save model -bst.save_model('xgb.model') +bst.save_model("model-1.json") # load model and data in -bst2 = xgb.Booster(model_file='xgb.model') -dtest2 = xgb.DMatrix('dtest.buffer') +bst2 = xgb.Booster(model_file="model-1.json") +dtest2 = xgb.DMatrix("dtest.dmatrix") preds2 = bst2.predict(dtest2) # assert they are the same assert np.sum(np.abs(preds2 - preds)) == 0 @@ -56,40 +71,3 @@ bst3 = pickle.loads(pks) preds3 = bst3.predict(dtest2) # assert they are the same assert np.sum(np.abs(preds3 - preds)) == 0 - -### -# build dmatrix from scipy.sparse -print('start running example of build DMatrix from scipy.sparse CSR Matrix') -labels = [] -row = [] -col = [] -dat = [] -i = 0 -for l in open(os.path.join(DEMO_DIR, 'data', 'agaricus.txt.train')): - arr = l.split() - labels.append(int(arr[0])) - for it in arr[1:]: - k, v = it.split(':') - row.append(i) - col.append(int(k)) - dat.append(float(v)) - i += 1 -csr = scipy.sparse.csr_matrix((dat, (row, col))) -dtrain = xgb.DMatrix(csr, label=labels) -watchlist = [(dtest, 'eval'), (dtrain, 'train')] -bst = xgb.train(param, dtrain, num_round, watchlist) - -print('start running example of build DMatrix from scipy.sparse CSC Matrix') -# we can also construct from csc matrix -csc = scipy.sparse.csc_matrix((dat, (row, col))) -dtrain = xgb.DMatrix(csc, label=labels) -watchlist = [(dtest, 'eval'), (dtrain, 'train')] -bst = xgb.train(param, dtrain, num_round, watchlist) - -print('start running example of build DMatrix from numpy array') -# NOTE: npymat is numpy array, we will convert it into scipy.sparse.csr_matrix -# in internal implementation then convert to DMatrix -npymat = csr.todense() -dtrain = xgb.DMatrix(npymat, label=labels) -watchlist = [(dtest, 'eval'), (dtrain, 'train')] -bst = xgb.train(param, dtrain, num_round, watchlist) diff --git a/doc/python/python_intro.rst b/doc/python/python_intro.rst index 054598873..7dc7ef9da 100644 --- a/doc/python/python_intro.rst +++ b/doc/python/python_intro.rst @@ -45,6 +45,7 @@ including: - XGBoost binary buffer file. - LIBSVM text format file - Comma-separated values (CSV) file +- Arrow table. (See :doc:`/tutorials/input_format` for detailed description of text input format.) diff --git a/python-package/xgboost/core.py b/python-package/xgboost/core.py index 36548d813..3321e2f08 100644 --- a/python-package/xgboost/core.py +++ b/python-package/xgboost/core.py @@ -565,12 +565,14 @@ class DMatrix: # pylint: disable=too-many-instance-attributes """Parameters ---------- data : os.PathLike/string/numpy.array/scipy.sparse/pd.DataFrame/ - dt.Frame/cudf.DataFrame/cupy.array/dlpack + dt.Frame/cudf.DataFrame/cupy.array/dlpack/arrow.Table + Data source of DMatrix. - When data is string or os.PathLike type, it represents the path - libsvm format txt file, csv file (by specifying uri parameter - 'path_to_csv?format=csv'), or binary file that xgboost can read - from. + + When data is string or os.PathLike type, it represents the path libsvm + format txt file, csv file (by specifying uri parameter + 'path_to_csv?format=csv'), or binary file that xgboost can read from. + label : array_like Label of the training data. weight : array_like