Update Python demos with tests. (#5651)

* Remove GPU memory usage demo.
* Add tests for demos.
* Remove `silent`.
* Remove shebang as it's not portable.

demo/aft_survival/aft_survival_demo.py

@@ -1,6 +1,7 @@
 """
 Demo for survival analysis (regression) using Accelerated Failure Time (AFT) model
 """
+import os
 from sklearn.model_selection import ShuffleSplit
 import pandas as pd
 import numpy as np
@@ -8,7 +9,8 @@ import xgboost as xgb
 
 # The Veterans' Administration Lung Cancer Trial
 # The Statistical Analysis of Failure Time Data by Kalbfleisch J. and Prentice R (1980)
-df = pd.read_csv('../data/veterans_lung_cancer.csv')
+CURRENT_DIR = os.path.dirname(__file__)
+df = pd.read_csv(os.path.join(CURRENT_DIR, '../data/veterans_lung_cancer.csv'))
 print('Training data:')
 print(df)
 

demo/gpu_acceleration/README.md

@@ -1,5 +1,3 @@
 # GPU Acceleration Demo
 
 `cover_type.py` shows how to train a model on the [forest cover type](https://archive.ics.uci.edu/ml/datasets/covertype) dataset using GPU acceleration. The forest cover type dataset has 581,012 rows and 54 features, making it time consuming to process. We compare the run-time and accuracy of the GPU and CPU histogram algorithms.
-
-`memory.py` shows how to repeatedly train xgboost models while freeing memory between iterations.

demo/gpu_acceleration/cover_type.py

@@ -1,5 +1,4 @@
 import xgboost as xgb
-import numpy as np
 from sklearn.datasets import fetch_covtype
 from sklearn.model_selection import train_test_split
 import time

demo/gpu_acceleration/memory.py

@@ -1,51 +0,0 @@
-import xgboost as xgb
-import numpy as np
-import time
-import pickle
-import GPUtil
-
-n = 10000
-m = 1000
-X = np.random.random((n, m))
-y = np.random.random(n)
-
-param = {'objective': 'binary:logistic',
-         'tree_method': 'gpu_hist'
-         }
-iterations = 5
-dtrain = xgb.DMatrix(X, label=y)
-
-# High memory usage
-# active bst objects with device memory persist across iterations
-boosters = []
-for i in range(iterations):
-    bst = xgb.train(param, dtrain)
-    boosters.append(bst)
-
-print("Example 1")
-GPUtil.showUtilization()
-del boosters
-
-# Better memory usage
-# The bst object can be destroyed by the python gc, freeing device memory
-# The gc may not immediately free the object, so more than one booster can be allocated at a time
-boosters = []
-for i in range(iterations):
-    bst = xgb.train(param, dtrain)
-    boosters.append(pickle.dumps(bst))
-
-print("Example 2")
-GPUtil.showUtilization()
-del boosters
-
-# Best memory usage
-# The gc explicitly frees the booster before starting the next iteration
-boosters = []
-for i in range(iterations):
-    bst = xgb.train(param, dtrain)
-    boosters.append(pickle.dumps(bst))
-    del bst
-
-print("Example 3")
-GPUtil.showUtilization()
-del boosters

demo/guide-python/basic_walkthrough.py

@@ -1,4 +1,3 @@
-#!/usr/bin/env python
 import numpy as np
 import scipy.sparse
 import pickle

demo/guide-python/boost_from_prediction.py

@@ -1,15 +1,17 @@
-#!/usr/bin/python
+import os
 import xgboost as xgb
 
-dtrain = xgb.DMatrix('../data/agaricus.txt.train')
+
-dtest = xgb.DMatrix('../data/agaricus.txt.test')
+CURRENT_DIR = os.path.dirname(__file__)
+dtrain = xgb.DMatrix(os.path.join(CURRENT_DIR, '../data/agaricus.txt.train'))
+dtest = xgb.DMatrix(os.path.join(CURRENT_DIR, '../data/agaricus.txt.test'))
 watchlist = [(dtest, 'eval'), (dtrain, 'train')]
 ###
 # advanced: start from a initial base prediction
 #
 print('start running example to start from a initial prediction')
 # specify parameters via map, definition are same as c++ version
-param = {'max_depth': 2, 'eta': 1, 'silent': 1, 'objective': 'binary:logistic'}
+param = {'max_depth': 2, 'eta': 1, 'objective': 'binary:logistic'}
 # train xgboost for 1 round
 bst = xgb.train(param, dtrain, 1, watchlist)
 # Note: we need the margin value instead of transformed prediction in

demo/guide-python/cross_validation.py

@@ -1,10 +1,11 @@
-#!/usr/bin/python
+import os
 import numpy as np
 import xgboost as xgb
 
-### load data in do training
+# load data in do training
-dtrain = xgb.DMatrix('../data/agaricus.txt.train')
+CURRENT_DIR = os.path.dirname(__file__)
-param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic'}
+dtrain = xgb.DMatrix(os.path.join(CURRENT_DIR, '../data/agaricus.txt.train'))
+param = {'max_depth':2, 'eta':1, 'objective':'binary:logistic'}
 num_round = 2
 
 print('running cross validation')
@@ -56,7 +57,7 @@ def evalerror(preds, dtrain):
     labels = dtrain.get_label()
     return 'error', float(sum(labels != (preds > 0.0))) / len(labels)
 
-param = {'max_depth':2, 'eta':1, 'silent':1}
+param = {'max_depth':2, 'eta':1}
 # train with customized objective
 xgb.cv(param, dtrain, num_round, nfold=5, seed=0,
        obj=logregobj, feval=evalerror)

demo/guide-python/custom_objective.py

@@ -1,4 +1,4 @@
-#!/usr/bin/python
+import os
 import numpy as np
 import xgboost as xgb
 ###
@@ -6,13 +6,14 @@ import xgboost as xgb
 #
 print('start running example to used customized objective function')
 
-dtrain = xgb.DMatrix('../data/agaricus.txt.train')
+CURRENT_DIR = os.path.dirname(__file__)
-dtest = xgb.DMatrix('../data/agaricus.txt.test')
+dtrain = xgb.DMatrix(os.path.join(CURRENT_DIR, '../data/agaricus.txt.train'))
+dtest = xgb.DMatrix(os.path.join(CURRENT_DIR, '../data/agaricus.txt.test'))
 
 # note: for customized objective function, we leave objective as default
 # note: what we are getting is margin value in prediction
 # you must know what you are doing
-param = {'max_depth': 2, 'eta': 1, 'silent': 1}
+param = {'max_depth': 2, 'eta': 1}
 watchlist = [(dtest, 'eval'), (dtrain, 'train')]
 num_round = 2
 

demo/guide-python/evals_result.py

@@ -1,10 +1,12 @@
 ##
 #  This script demonstrate how to access the eval metrics in xgboost
 ##
-
+import os
 import xgboost as xgb
-dtrain = xgb.DMatrix('../data/agaricus.txt.train', silent=True)
+
-dtest = xgb.DMatrix('../data/agaricus.txt.test', silent=True)
+CURRENT_DIR = os.path.dirname(__file__)
+dtrain = xgb.DMatrix(os.path.join(CURRENT_DIR, '../data/agaricus.txt.train'))
+dtest = xgb.DMatrix(os.path.join(CURRENT_DIR, '../data/agaricus.txt.test'))
 
 param = [('max_depth', 2), ('objective', 'binary:logistic'), ('eval_metric', 'logloss'), ('eval_metric', 'error')]
 

demo/guide-python/external_memory.py

@@ -1,6 +1,4 @@
-#!/usr/bin/python
+import os
-import numpy as np
-import scipy.sparse
 import xgboost as xgb
 
 ### simple example for using external memory version
@@ -8,11 +6,12 @@ import xgboost as xgb
 # this is the only difference, add a # followed by a cache prefix name
 # several cache file with the prefix will be generated
 # currently only support convert from libsvm file
-dtrain = xgb.DMatrix('../data/agaricus.txt.train#dtrain.cache')
+CURRENT_DIR = os.path.dirname(__file__)
-dtest = xgb.DMatrix('../data/agaricus.txt.test#dtest.cache')
+dtrain = xgb.DMatrix(os.path.join(CURRENT_DIR, '../data/agaricus.txt.train'))
+dtest = xgb.DMatrix(os.path.join(CURRENT_DIR, '../data/agaricus.txt.test'))
 
 # specify validations set to watch performance
-param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic'}
+param = {'max_depth':2, 'eta':1, 'objective':'binary:logistic'}
 
 # performance notice: set nthread to be the number of your real cpu
 # some cpu offer two threads per core, for example, a 4 core cpu with 8 threads, in such case set nthread=4
@@ -21,5 +20,3 @@ param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic'}
 watchlist = [(dtest, 'eval'), (dtrain, 'train')]
 num_round = 2
 bst = xgb.train(param, dtrain, num_round, watchlist)
-
-

demo/guide-python/gamma_regression.py

@@ -1,4 +1,3 @@
-#!/usr/bin/python
 import xgboost as xgb
 import numpy as np
 
@@ -12,7 +11,7 @@ dtest = xgb.DMatrix(data[4741:6773, 0:34], data[4741:6773, 34])
 
 # for gamma regression, we need to set the objective to 'reg:gamma', it also suggests
 # to set the base_score to a value between 1 to 5 if the number of iteration is small
-param = {'silent':1, 'objective':'reg:gamma', 'booster':'gbtree', 'base_score':3}
+param = {'objective':'reg:gamma', 'booster':'gbtree', 'base_score':3}
 
 # the rest of settings are the same
 watchlist = [(dtest, 'eval'), (dtrain, 'train')]

demo/guide-python/generalized_linear_model.py

@@ -1,16 +1,17 @@
-#!/usr/bin/python
+import os
 import xgboost as xgb
 ##
 #  this script demonstrate how to fit generalized linear model in xgboost
 #  basically, we are using linear model, instead of tree for our boosters
 ##
-dtrain = xgb.DMatrix('../data/agaricus.txt.train')
+CURRENT_DIR = os.path.dirname(__file__)
-dtest = xgb.DMatrix('../data/agaricus.txt.test')
+dtrain = xgb.DMatrix(os.path.join(CURRENT_DIR, '../data/agaricus.txt.train'))
+dtest = xgb.DMatrix(os.path.join(CURRENT_DIR, '../data/agaricus.txt.test'))
 # change booster to gblinear, so that we are fitting a linear model
 # alpha is the L1 regularizer
 # lambda is the L2 regularizer
 # you can also set lambda_bias which is L2 regularizer on the bias term
-param = {'silent':1, 'objective':'binary:logistic', 'booster':'gblinear',
+param = {'objective':'binary:logistic', 'booster':'gblinear',
          'alpha': 0.0001, 'lambda': 1}
 
 # normally, you do not need to set eta (step_size)

demo/guide-python/predict_first_ntree.py

@@ -1,17 +1,18 @@
-#!/usr/bin/python
+import os
 import numpy as np
 import xgboost as xgb
 
-### load data in do training
+# load data in do training
-dtrain = xgb.DMatrix('../data/agaricus.txt.train')
+CURRENT_DIR = os.path.dirname(__file__)
-dtest = xgb.DMatrix('../data/agaricus.txt.test')
+dtrain = xgb.DMatrix(os.path.join(CURRENT_DIR, '../data/agaricus.txt.train'))
-param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic'}
+dtest = xgb.DMatrix(os.path.join(CURRENT_DIR, '../data/agaricus.txt.test'))
+param = {'max_depth': 2, 'eta': 1, 'objective': 'binary:logistic'}
 watchlist = [(dtest, 'eval'), (dtrain, 'train')]
 num_round = 3
 bst = xgb.train(param, dtrain, num_round, watchlist)
 
 print('start testing prediction from first n trees')
-### predict using first 1 tree
+# predict using first 1 tree
 label = dtest.get_label()
 ypred1 = bst.predict(dtest, ntree_limit=1)
 # by default, we predict using all the trees

demo/guide-python/predict_leaf_indices.py

@@ -1,19 +1,20 @@
-#!/usr/bin/python
+import os
 import xgboost as xgb
 
-### load data in do training
+# load data in do training
-dtrain = xgb.DMatrix('../data/agaricus.txt.train')
+CURRENT_DIR = os.path.dirname(__file__)
-dtest = xgb.DMatrix('../data/agaricus.txt.test')
+dtrain = xgb.DMatrix(os.path.join(CURRENT_DIR, '../data/agaricus.txt.train'))
-param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic'}
+dtest = xgb.DMatrix(os.path.join(CURRENT_DIR, '../data/agaricus.txt.test'))
+param = {'max_depth': 2, 'eta': 1, 'objective': 'binary:logistic'}
 watchlist = [(dtest, 'eval'), (dtrain, 'train')]
 num_round = 3
 bst = xgb.train(param, dtrain, num_round, watchlist)
 
 print('start testing predict the leaf indices')
-### predict using first 2 tree
+# predict using first 2 tree
 leafindex = bst.predict(dtest, ntree_limit=2, pred_leaf=True)
 print(leafindex.shape)
 print(leafindex)
-### predict all trees
+# predict all trees
 leafindex = bst.predict(dtest, pred_leaf=True)
 print(leafindex.shape)

demo/guide-python/runall.sh

@@ -1,13 +0,0 @@
-#!/bin/bash
-export PYTHONPATH=$PYTHONPATH:../../python-package
-python basic_walkthrough.py
-python custom_objective.py
-python boost_from_prediction.py
-python predict_first_ntree.py
-python generalized_linear_model.py
-python cross_validation.py
-python predict_leaf_indices.py
-python sklearn_examples.py
-python sklearn_parallel.py
-python external_memory.py
-rm -rf *~ *.model *.buffer

demo/guide-python/sklearn_examples.py

@@ -1,4 +1,3 @@
-#!/usr/bin/python
 '''
 Created on 1 Apr 2015
 
@@ -73,4 +72,3 @@ X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
 clf = xgb.XGBClassifier()
 clf.fit(X_train, y_train, early_stopping_rounds=10, eval_metric="auc",
         eval_set=[(X_test, y_test)])
-

demo/guide-python/sklearn_parallel.py

@@ -1,29 +1,11 @@
-import os
-
-if __name__ == "__main__":
-    # NOTE: on posix systems, this *has* to be here and in the
-    # `__name__ == "__main__"` clause to run XGBoost in parallel processes
-    # using fork, if XGBoost was built with OpenMP support. Otherwise, if you
-    # build XGBoost without OpenMP support, you can use fork, which is the
-    # default backend for joblib, and omit this.
-    try:
-        from multiprocessing import set_start_method
-    except ImportError:
-        raise ImportError("Unable to import multiprocessing.set_start_method."
-                          " This example only runs on Python 3.4")
-    set_start_method("forkserver")
-
-    import numpy as np
 from sklearn.model_selection import GridSearchCV
 from sklearn.datasets import load_boston
 import xgboost as xgb
 
-    rng = np.random.RandomState(31337)
+if __name__ == "__main__":
-
     print("Parallel Parameter optimization")
     boston = load_boston()
 
-    os.environ["OMP_NUM_THREADS"] = "2"  # or to whatever you want
     y = boston['target']
     X = boston['data']
     xgb_model = xgb.XGBRegressor()

demo/kaggle-higgs/higgs-cv.py

@@ -8,7 +8,7 @@ label  = train[:,32]
 data   = train[:,1:31]
 weight = train[:,31]
 dtrain = xgb.DMatrix( data, label=label, missing = -999.0, weight=weight )
-param = {'max_depth':6, 'eta':0.1, 'silent':1, 'objective':'binary:logitraw', 'nthread':4}
+param = {'max_depth':6, 'eta':0.1, 'objective':'binary:logitraw', 'nthread':4}
 num_round = 120
 
 print ('running cross validation, with preprocessing function')

demo/kaggle-higgs/higgs-numpy.py

@@ -37,7 +37,6 @@ param['scale_pos_weight'] = sum_wneg/sum_wpos
 param['eta'] = 0.1
 param['max_depth'] = 6
 param['eval_metric'] = 'auc'
-param['silent'] = 1
 param['nthread'] = 16
 
 # you can directly throw param in, though we want to watch multiple metrics here

demo/kaggle-higgs/higgs-pred.py

@@ -45,6 +45,3 @@ for k, v in res:
 fo.close()
 
 print ('finished writing into prediction file')
-
-
-

demo/kaggle-higgs/speedtest.py

@@ -36,7 +36,6 @@ param['scale_pos_weight'] = sum_wneg/sum_wpos
 param['bst:eta'] = 0.1
 param['bst:max_depth'] = 6
 param['eval_metric'] = 'auc'
-param['silent'] = 1
 param['nthread'] = 4
 
 plst = param.items()+[('eval_metric', 'ams@0.15')]

demo/multiclass_classification/train.py

@@ -28,7 +28,6 @@ param['objective'] = 'multi:softmax'
 # scale weight of positive examples
 param['eta'] = 0.1
 param['max_depth'] = 6
-param['silent'] = 1
 param['nthread'] = 4
 param['num_class'] = 6
 

tests/python/test_demos.py

@@ -2,15 +2,17 @@ import os
 import subprocess
 import sys
 import pytest
+import testing as tm
 
 
 CURRENT_DIR = os.path.dirname(__file__)
 ROOT_DIR = os.path.dirname(os.path.dirname(CURRENT_DIR))
-DEMO_DIR = os.path.join(ROOT_DIR, 'demo', 'guide-python')
+DEMO_DIR = os.path.join(ROOT_DIR, 'demo')
+PYTHON_DEMO_DIR = os.path.join(DEMO_DIR, 'guide-python')
 
 
 def test_basic_walkthrough():
-    script = os.path.join(DEMO_DIR, 'basic_walkthrough.py')
+    script = os.path.join(PYTHON_DEMO_DIR, 'basic_walkthrough.py')
     cmd = ['python', script]
     subprocess.check_call(cmd)
     os.remove('dump.nice.txt')
@@ -18,7 +20,7 @@ def test_basic_walkthrough():
 
 
 def test_custom_multiclass_objective():
-    script = os.path.join(DEMO_DIR, 'custom_softmax.py')
+    script = os.path.join(PYTHON_DEMO_DIR, 'custom_softmax.py')
     cmd = ['python', script, '--plot=0']
     subprocess.check_call(cmd)
 
@@ -27,6 +29,91 @@ def test_custom_rmsle_objective():
     major, minor = sys.version_info[:2]
     if minor < 6:
         pytest.skip('Skipping RMLSE test due to Python version being too low.')
-    script = os.path.join(DEMO_DIR, 'custom_rmsle.py')
+    script = os.path.join(PYTHON_DEMO_DIR, 'custom_rmsle.py')
     cmd = ['python', script, '--plot=0']
     subprocess.check_call(cmd)
+
+
+@pytest.mark.skipif(**tm.no_sklearn())
+def test_sklearn_demo():
+    script = os.path.join(PYTHON_DEMO_DIR, 'sklearn_examples.py')
+    cmd = ['python', script]
+    subprocess.check_call(cmd)
+    assert os.path.exists('best_boston.pkl')
+    os.remove('best_boston.pkl')
+
+
+@pytest.mark.skipif(**tm.no_sklearn())
+def test_sklearn_parallel_demo():
+    script = os.path.join(PYTHON_DEMO_DIR, 'sklearn_parallel.py')
+    cmd = ['python', script]
+    subprocess.check_call(cmd)
+
+
+@pytest.mark.skipif(**tm.no_sklearn())
+def test_sklearn_evals_result_demo():
+    script = os.path.join(PYTHON_DEMO_DIR, 'sklearn_evals_result.py')
+    cmd = ['python', script]
+    subprocess.check_call(cmd)
+
+
+def test_boost_from_prediction_demo():
+    script = os.path.join(PYTHON_DEMO_DIR, 'boost_from_prediction.py')
+    cmd = ['python', script]
+    subprocess.check_call(cmd)
+
+
+def test_predict_first_ntree_demo():
+    script = os.path.join(PYTHON_DEMO_DIR, 'predict_first_ntree.py')
+    cmd = ['python', script]
+    subprocess.check_call(cmd)
+
+
+def test_predict_leaf_indices_demo():
+    script = os.path.join(PYTHON_DEMO_DIR, 'predict_leaf_indices.py')
+    cmd = ['python', script]
+    subprocess.check_call(cmd)
+
+
+def test_generalized_linear_model_demo():
+    script = os.path.join(PYTHON_DEMO_DIR, 'generalized_linear_model.py')
+    cmd = ['python', script]
+    subprocess.check_call(cmd)
+
+
+def test_custom_objective_demo():
+    script = os.path.join(PYTHON_DEMO_DIR, 'custom_objective.py')
+    cmd = ['python', script]
+    subprocess.check_call(cmd)
+
+
+def test_cross_validation_demo():
+    script = os.path.join(PYTHON_DEMO_DIR, 'cross_validation.py')
+    cmd = ['python', script]
+    subprocess.check_call(cmd)
+
+
+def test_external_memory_demo():
+    script = os.path.join(PYTHON_DEMO_DIR, 'external_memory.py')
+    cmd = ['python', script]
+    subprocess.check_call(cmd)
+
+
+def test_evals_result_demo():
+    script = os.path.join(PYTHON_DEMO_DIR, 'evals_result.py')
+    cmd = ['python', script]
+    subprocess.check_call(cmd)
+
+
+def test_aft_demo():
+    script = os.path.join(DEMO_DIR, 'aft_survival', 'aft_survival_demo.py')
+    cmd = ['python', script]
+    subprocess.check_call(cmd)
+    assert os.path.exists('aft_model.json')
+    os.remove('aft_model.json')
+
+
+# gpu_acceleration is not tested due to covertype dataset is being too huge.
+# gamma regression is not tested as it requires running a R script first.
+# aft viz is not tested due to ploting is not controled
+# aft tunning is not tested due to extra dependency.