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Simple demo script for running Xgboost
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# Ieva Zarina, 2016, licensed under the Apache 2.0 licnese | |
import numpy as np | |
import xgboost as xgb | |
from sklearn import datasets | |
from sklearn.cross_validation import train_test_split | |
from sklearn.datasets import dump_svmlight_file | |
from sklearn.externals import joblib | |
from sklearn.metrics import precision_score | |
iris = datasets.load_iris() | |
X = iris.data | |
y = iris.target | |
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) | |
# use DMatrix for xgbosot | |
dtrain = xgb.DMatrix(X_train, label=y_train) | |
dtest = xgb.DMatrix(X_test, label=y_test) | |
# use svmlight file for xgboost | |
dump_svmlight_file(X_train, y_train, 'dtrain.svm', zero_based=True) | |
dump_svmlight_file(X_test, y_test, 'dtest.svm', zero_based=True) | |
dtrain_svm = xgb.DMatrix('dtrain.svm') | |
dtest_svm = xgb.DMatrix('dtest.svm') | |
# set xgboost params | |
param = { | |
'max_depth': 3, # the maximum depth of each tree | |
'eta': 0.3, # the training step for each iteration | |
'silent': 1, # logging mode - quiet | |
'objective': 'multi:softprob', # error evaluation for multiclass training | |
'num_class': 3} # the number of classes that exist in this datset | |
num_round = 20 # the number of training iterations | |
#------------- numpy array ------------------ | |
# training and testing - numpy matrices | |
bst = xgb.train(param, dtrain, num_round) | |
preds = bst.predict(dtest) | |
# extracting most confident predictions | |
best_preds = np.asarray([np.argmax(line) for line in preds]) | |
print "Numpy array precision:", precision_score(y_test, best_preds, average='macro') | |
# ------------- svm file --------------------- | |
# training and testing - svm file | |
bst_svm = xgb.train(param, dtrain_svm, num_round) | |
preds = bst.predict(dtest_svm) | |
# extracting most confident predictions | |
best_preds_svm = [np.argmax(line) for line in preds] | |
print "Svm file precision:",precision_score(y_test, best_preds_svm, average='macro') | |
# -------------------------------------------- | |
# dump the models | |
bst.dump_model('dump.raw.txt') | |
bst_svm.dump_model('dump_svm.raw.txt') | |
# save the models for later | |
joblib.dump(bst, 'bst_model.pkl', compress=True) | |
joblib.dump(bst_svm, 'bst_svm_model.pkl', compress=True) |
Thanks for the sample. I get the following error at line 6.
ImportError: No module named cross_validation
any ideas what I am doing wrong?
change this line from sklearn.cross_validation import train_test_split to from sklearn.model_selection import train_test_split
dtrain_svm = xgb.DMatrix('dtrain.svm')
dtest_svm = xgb.DMatrix('dtest.svm')
dtrain_svm = xgb.DMatrix('dtrain.svm?format=libsvm')
dtest_svm = xgb.DMatrix('dtest.svm?format=libsvm')
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Thanks for the sample. I get the following error at line 6.
ImportError: No module named cross_validation
any ideas what I am doing wrong?