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[Doc] Fix typos in AFT tutorial (#5716)

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Philip Hyunsu Cho 2020-05-28 14:04:34 -07:00 committed by GitHub
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doc/tutorials/aft_survival_analysis.rst
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@ -35,7 +35,7 @@ There are four kinds of censoring:
* **Uncensored**: the label is not censored and given as a single number.
* **Right-censored**: the label is of form :math:`[a, +\infty)`, where :math:`a` is the lower bound.
* **Left-censored**: the label is of form :math:`(-\infty, b]`, where :math:`b` is the upper bound.
* **Left-censored**: the label is of form :math:`[0, b]`, where :math:`b` is the upper bound.
* **Interval-censored**: the label is of form :math:`[a, b]`, where :math:`a` and :math:`b` are the lower and upper bounds, respectively.
Right-censoring is the most commonly used.
@ -83,7 +83,7 @@ Censoring type Interval form Lower bound finite? Upper bound finite?
================= ==================== =================== ===================
Uncensored :math:`[a, a]` |tick| |tick|
Right-censored :math:`[a, +\infty)` |tick| |cross|
Left-censored :math:`(-\infty, b]` |cross| |tick|
Left-censored :math:`[0, b]` |tick| |tick|
Interval-censored :math:`[a, b]` |tick| |tick|
================= ==================== =================== ===================
@ -102,7 +102,7 @@ Collect the lower bound numbers in one array (let's call it ``y_lower_bound``) a
# Associate ranged labels with the data matrix.
# This example shows each kind of censored labels.
# uncensored right left interval
y_lower_bound = np.array([ 2.0, 3.0, -np.inf, 4.0])
y_lower_bound = np.array([ 2.0, 3.0, 0.0, 4.0])
y_upper_bound = np.array([ 2.0, +np.inf, 4.0, 5.0])
dtrain.set_float_info('label_lower_bound', y_lower_bound)
dtrain.set_float_info('label_upper_bound', y_upper_bound)
@ -120,7 +120,7 @@ Collect the lower bound numbers in one array (let's call it ``y_lower_bound``) a
# Associate ranged labels with the data matrix.
# This example shows each kind of censored labels.
# uncensored right left interval
y_lower_bound <- c( 2., 3., -Inf, 4.)
y_lower_bound <- c( 2., 3., 0., 4.)
y_upper_bound <- c( 2., +Inf, 4., 5.)
setinfo(dtrain, 'label_lower_bound', y_lower_bound)
setinfo(dtrain, 'label_upper_bound', y_upper_bound)
@ -136,7 +136,7 @@ Now we are ready to invoke the training API:
'aft_loss_distribution_scale': 1.20,
'tree_method': 'hist', 'learning_rate': 0.05, 'max_depth': 2}
bst = xgb.train(params, dtrain, num_boost_round=5,
evals=[(dtrain, 'train'), (dvalid, 'valid')])
evals=[(dtrain, 'train')])
.. code-block:: r
:caption: R
@ -165,4 +165,4 @@ Currently, you can choose from three probability distributions for ``aft_loss_di
``extreme`` :math:`e^z e^{-\exp{z}}`
========================= ===========================================
Note that it is not yet possible to set the ranged label using the scikit-learn interface (e.g. :class:`xgboost.XGBRegressor`). For now, you should use :class:`xgboost.train` with :class:`xgboost.DMatrix`.
Note that it is not yet possible to set the ranged label using the scikit-learn interface (e.g. :class:`xgboost.XGBRegressor`). For now, you should use :class:`xgboost.train` with :class:`xgboost.DMatrix`.