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/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
-dtrain = xgb.DMatrix('../data/agaricus.txt.train')
-param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic'}
+# load data in do training
+CURRENT_DIR = os.path.dirname(__file__)
+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')
-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'))
 
 # 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

@@ -1,13 +1,15 @@
 ##
 #  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')]
- 
+
 num_round = 2
 watchlist = [(dtest,'eval'), (dtrain,'train')]
 

demo/guide-python/external_memory.py

@@ -1,6 +1,4 @@
-#!/usr/bin/python
-import numpy as np
-import scipy.sparse
+import os
 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')
-dtest = xgb.DMatrix('../data/agaricus.txt.test#dtest.cache')
+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'))
 
 # 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')
-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'))
 # 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
-dtrain = xgb.DMatrix('../data/agaricus.txt.train')
-dtest = xgb.DMatrix('../data/agaricus.txt.test')
-param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic'}
+# load data in do training
+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, '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
-dtrain = xgb.DMatrix('../data/agaricus.txt.train')
-dtest = xgb.DMatrix('../data/agaricus.txt.test')
-param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic'}
+# load data in do training
+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, '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
+print('start testing predict the leaf indices')
+# 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_evals_result.py

@@ -20,7 +20,7 @@ clf = xgb.XGBModel(**param_dist)
 # Or you can use: clf = xgb.XGBClassifier(**param_dist)
 
 clf.fit(X_train, y_train,
-        eval_set=[(X_train, y_train), (X_test, y_test)], 
+        eval_set=[(X_train, y_train), (X_test, y_test)],
         eval_metric='logloss',
         verbose=True)
 
@@ -37,7 +37,7 @@ for e_name, e_mtrs in evals_result.items():
     for e_mtr_name, e_mtr_vals in e_mtrs.items():
         print('   - {}'.format(e_mtr_name))
         print('      - {}'.format(e_mtr_vals))
- 
+
 print('')
 print('Access complete dict:')
 print(evals_result)

demo/guide-python/sklearn_examples.py

@@ -1,4 +1,3 @@
-#!/usr/bin/python
 '''
 Created on 1 Apr 2015
 
@@ -52,9 +51,9 @@ y = boston['target']
 X = boston['data']
 xgb_model = xgb.XGBRegressor()
 clf = GridSearchCV(xgb_model,
-                   {'max_depth': [2,4,6],
-                    'n_estimators': [50,100,200]}, verbose=1)
-clf.fit(X,y)
+                   {'max_depth': [2, 4, 6],
+                    'n_estimators': [50, 100, 200]}, verbose=1)
+clf.fit(X, y)
 print(clf.best_score_)
 print(clf.best_params_)
 
@@ -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
+from sklearn.model_selection import GridSearchCV
+from sklearn.datasets import load_boston
+import xgboost as xgb
 
 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)
-
     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()