How to use the datatable.DataTable function in datatable
To help you get started, we’ve selected a few datatable examples, based on popular ways it is used in public projects.
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h2oai / h2o4gpu / tests / python / open_data / gbm / test_xgboost_dtinput.py View on Github 
dtest = xgb.DMatrix(dtdata_X_test, dtdata_y_test, nthread=-1)
print ("dt->DMatrix2 Time: %s seconds" % (str(time.time() - tmp)))
print("Training with '%s'" % param['tree_method'])
tmp = time.time()
res_tmp = {}
xgb.train(param, dtrain, iterations, evals=[(dtrain, "train"),(dtest, "test")], evals_result=res_tmp)
res['2'] = res_tmp['train']['error']
print ("Train Time: %s seconds" % (str(time.time() - tmp)))
if HAVE_DT and do_dt_likeDAI:
# convert to column-major contiguous in memory to mimic persistent column-major state
# do_cccont = True leads to prepare2 time of about 1.4s for 1000000 rows * 50 columns
# do_cccont = False leads to prepare2 time of about 0.000548 for 1000000 rows * 50 columns
tmp = time.time()
dtdata_X_train = dt.DataTable(X_train_cc)
dtdata_X_test = dt.DataTable(X_test_cc)
dtdata_y_train = dt.DataTable(y_train_cc)
dtdata_y_test = dt.DataTable(y_test_cc)
print ("dt prepare2 Time: %s seconds" % (str(time.time() - tmp)))
#test = dtdata_X_train.tonumpy()
#print(test)
print ("dt->DMatrix Start")
# omp way
tmp = time.time()
dtrain = xgb.DMatrix(dtdata_X_train.tonumpy(), dtdata_y_train.tonumpy(), nthread=-1)
print ("dt->DMatrix1 Time: %s seconds" % (str(time.time() - tmp)))
tmp = time.time()
dtest = xgb.DMatrix(dtdata_X_test.tonumpy(), dtdata_y_test.tonumpy(), nthread=-1)
print ("dt->DMatrix2 Time: %s seconds" % (str(time.time() - tmp)))print ("np->DMatrix2 Time: %s seconds" % (str(time.time() - tmp)))
print("Training with '%s'" % param['tree_method'])
tmp = time.time()
res_tmp = {}
xgb.train(param, dtrain, iterations, evals=[(dtrain, "train"),(dtest, "test")], evals_result=res_tmp)
res['1'] = res_tmp['train']['error']
print("Train Time: %s seconds" % (str(time.time() - tmp)))
if HAVE_DT and do_dt:
# convert to column-major contiguous in memory to mimic persistent column-major state
# do_cccont = True leads to prepare2 time of about 1.4s for 1000000 rows * 50 columns
# do_cccont = False leads to prepare2 time of about 0.000548 for 1000000 rows * 50 columns
tmp = time.time()
dtdata_X_train = dt.DataTable(X_train_cc)
dtdata_X_test = dt.DataTable(X_test_cc)
dtdata_y_train = dt.DataTable(y_train_cc)
dtdata_y_test = dt.DataTable(y_test_cc)
print ("dt prepare2 Time: %s seconds" % (str(time.time() - tmp)))
#test = dtdata_X_train.tonumpy()
#print(test)
print ("dt->DMatrix Start")
# omp way
tmp = time.time()
# below takes about 0.47s - 0.53s independent of do_ccont
dtrain = xgb.DMatrix(dtdata_X_train, dtdata_y_train, nthread=-1)
print ("dt->DMatrix1 Time: %s seconds" % (str(time.time() - tmp)))
tmp = time.time()
dtest = xgb.DMatrix(dtdata_X_test, dtdata_y_test, nthread=-1)
print ("dt->DMatrix2 Time: %s seconds" % (str(time.time() - tmp)))print("Training with '%s'" % param['tree_method'])
tmp = time.time()
res_tmp = {}
xgb.train(param, dtrain, iterations, evals=[(dtrain, "train"),(dtest, "test")], evals_result=res_tmp)
res['2'] = res_tmp['train']['error']
print ("Train Time: %s seconds" % (str(time.time() - tmp)))
if HAVE_DT and do_dt_likeDAI:
# convert to column-major contiguous in memory to mimic persistent column-major state
# do_cccont = True leads to prepare2 time of about 1.4s for 1000000 rows * 50 columns
# do_cccont = False leads to prepare2 time of about 0.000548 for 1000000 rows * 50 columns
tmp = time.time()
dtdata_X_train = dt.DataTable(X_train_cc)
dtdata_X_test = dt.DataTable(X_test_cc)
dtdata_y_train = dt.DataTable(y_train_cc)
dtdata_y_test = dt.DataTable(y_test_cc)
print ("dt prepare2 Time: %s seconds" % (str(time.time() - tmp)))
#test = dtdata_X_train.tonumpy()
#print(test)
print ("dt->DMatrix Start")
# omp way
tmp = time.time()
dtrain = xgb.DMatrix(dtdata_X_train.tonumpy(), dtdata_y_train.tonumpy(), nthread=-1)
print ("dt->DMatrix1 Time: %s seconds" % (str(time.time() - tmp)))
tmp = time.time()
dtest = xgb.DMatrix(dtdata_X_test.tonumpy(), dtdata_y_test.tonumpy(), nthread=-1)
print ("dt->DMatrix2 Time: %s seconds" % (str(time.time() - tmp)))
print("Training with '%s'" % param['tree_method'])print("Training with '%s'" % param['tree_method'])
tmp = time.time()
res_tmp = {}
xgb.train(param, dtrain, iterations, evals=[(dtrain, "train"),(dtest, "test")], evals_result=res_tmp)
res['1'] = res_tmp['train']['error']
print("Train Time: %s seconds" % (str(time.time() - tmp)))
if HAVE_DT and do_dt:
# convert to column-major contiguous in memory to mimic persistent column-major state
# do_cccont = True leads to prepare2 time of about 1.4s for 1000000 rows * 50 columns
# do_cccont = False leads to prepare2 time of about 0.000548 for 1000000 rows * 50 columns
tmp = time.time()
dtdata_X_train = dt.DataTable(X_train_cc)
dtdata_X_test = dt.DataTable(X_test_cc)
dtdata_y_train = dt.DataTable(y_train_cc)
dtdata_y_test = dt.DataTable(y_test_cc)
print ("dt prepare2 Time: %s seconds" % (str(time.time() - tmp)))
#test = dtdata_X_train.tonumpy()
#print(test)
print ("dt->DMatrix Start")
# omp way
tmp = time.time()
# below takes about 0.47s - 0.53s independent of do_ccont
dtrain = xgb.DMatrix(dtdata_X_train, dtdata_y_train, nthread=-1)
print ("dt->DMatrix1 Time: %s seconds" % (str(time.time() - tmp)))
tmp = time.time()
dtest = xgb.DMatrix(dtdata_X_test, dtdata_y_test, nthread=-1)
print ("dt->DMatrix2 Time: %s seconds" % (str(time.time() - tmp)))print ("dt->DMatrix2 Time: %s seconds" % (str(time.time() - tmp)))
print("Training with '%s'" % param['tree_method'])
tmp = time.time()
res_tmp = {}
xgb.train(param, dtrain, iterations, evals=[(dtrain, "train"),(dtest, "test")], evals_result=res_tmp)
res['2'] = res_tmp['train']['error']
print ("Train Time: %s seconds" % (str(time.time() - tmp)))
if HAVE_DT and do_dt_likeDAI:
# convert to column-major contiguous in memory to mimic persistent column-major state
# do_cccont = True leads to prepare2 time of about 1.4s for 1000000 rows * 50 columns
# do_cccont = False leads to prepare2 time of about 0.000548 for 1000000 rows * 50 columns
tmp = time.time()
dtdata_X_train = dt.DataTable(X_train_cc)
dtdata_X_test = dt.DataTable(X_test_cc)
dtdata_y_train = dt.DataTable(y_train_cc)
dtdata_y_test = dt.DataTable(y_test_cc)
print ("dt prepare2 Time: %s seconds" % (str(time.time() - tmp)))
#test = dtdata_X_train.tonumpy()
#print(test)
print ("dt->DMatrix Start")
# omp way
tmp = time.time()
dtrain = xgb.DMatrix(dtdata_X_train.tonumpy(), dtdata_y_train.tonumpy(), nthread=-1)
print ("dt->DMatrix1 Time: %s seconds" % (str(time.time() - tmp)))
tmp = time.time()
dtest = xgb.DMatrix(dtdata_X_test.tonumpy(), dtdata_y_test.tonumpy(), nthread=-1)
print ("dt->DMatrix2 Time: %s seconds" % (str(time.time() - tmp)))print("Training with '%s'" % param['tree_method'])
tmp = time.time()
res_tmp = {}
xgb.train(param, dtrain, iterations, evals=[(dtrain, "train"),(dtest, "test")], evals_result=res_tmp)
res['2'] = res_tmp['train']['error']
print ("Train Time: %s seconds" % (str(time.time() - tmp)))
if HAVE_DT and do_dt_likeDAI:
# convert to column-major contiguous in memory to mimic persistent column-major state
# do_cccont = True leads to prepare2 time of about 1.4s for 1000000 rows * 50 columns
# do_cccont = False leads to prepare2 time of about 0.000548 for 1000000 rows * 50 columns
tmp = time.time()
dtdata_X_train = dt.DataTable(X_train_cc)
dtdata_X_test = dt.DataTable(X_test_cc)
dtdata_y_train = dt.DataTable(y_train_cc)
dtdata_y_test = dt.DataTable(y_test_cc)
print ("dt prepare2 Time: %s seconds" % (str(time.time() - tmp)))
#test = dtdata_X_train.tonumpy()
#print(test)
print ("dt->DMatrix Start")
# omp way
tmp = time.time()
dtrain = xgb.DMatrix(dtdata_X_train.tonumpy(), dtdata_y_train.tonumpy(), nthread=-1)
print ("dt->DMatrix1 Time: %s seconds" % (str(time.time() - tmp)))
tmp = time.time()
dtest = xgb.DMatrix(dtdata_X_test.tonumpy(), dtdata_y_test.tonumpy(), nthread=-1)
print ("dt->DMatrix2 Time: %s seconds" % (str(time.time() - tmp)))
print("Training with '%s'" % param['tree_method'])
tmp = time.time()else:
dataTable = dataTableOrFileName
self._handle = SPLINTER.call(SPLINTER.getHandle().bspline_init, dataTable.getHandle(), degree)
if __name__ == "__main__":
import SPLINTER
SPLINTER.load("/home/anders/SPLINTER/build/release/libsplinter-matlab-1-4.so")
from datatable import DataTable
def f(x):
return x[0]*x[1]
d = DataTable()
for i in range(10):
for j in range(10):
d.addSample([i,j], f([i,j]))
b = BSpline(d, 1)
for i in range(10):
for j in range(10):
print(str(b.eval([0.9*i,0.9*j])) + " == " + str(0.81*i*j))
print(b.evalJacobian([3,3]))
print(b.evalHessian([3,3]))
b.save("test.bspline")
b2 = BSpline("test.bspline")
print(b.eval([2,3]))def transform(self, X: dt.Frame):
transformed_X = X[:, :, dt.join(self._group_means)][:, -1]
return dt.DataTable(transformed_X.to_pandas().fillna(self.dataset_mean))datatable
Python library for fast multi-threaded data manipulation and munging.
Package Health Score
68 / 100Popular datatable functions
- datatable.by
- datatable.cbind
- datatable.count
- datatable.DataTable
- datatable.dt.Frame
- datatable.expr.expr.Expr
- datatable.f
- datatable.f.A
- datatable.Frame
- datatable.fread
- datatable.internal.frame_integrity_check
- datatable.isna
- datatable.join
- datatable.lib.core
- datatable.ltype
- datatable.models.Ftrl
- datatable.stype
- datatable.stype.str32
- datatable.sum
- datatable.utils.terminal.term.color