def test_weibull_aft_plot_partial_effects_on_outcome(self, block):
df = load_rossi()
aft = WeibullAFTFitter()
aft.fit(df, "week", "arrest")
aft.plot_partial_effects_on_outcome("age", [10, 50, 80])
self.plt.tight_layout()
self.plt.title("test_weibull_aft_plot_partial_effects_on_outcome")
self.plt.show(block=block)
def test_weibull_aft_plot_partial_effects_on_outcome_with_multiple_columns(self, block):
df = load_rossi()
aft = WeibullAFTFitter()
aft.fit(df, "week", "arrest")
aft.plot_partial_effects_on_outcome(["age", "prio"], [[10, 0], [50, 10], [80, 50]])
self.plt.tight_layout()
self.plt.title("test_weibull_aft_plot_partial_effects_on_outcome_with_multiple_columns")
self.plt.show(block=block)
def test_weibull_aft_plotting_with_subset_of_columns(self, block):
df = load_regression_dataset()
aft = WeibullAFTFitter()
aft.fit(df, "T", "E")
aft.plot(columns=["var1", "var2"])
self.plt.tight_layout()
self.plt.title("test_weibull_aft_plotting_with_subset_of_columns")
self.plt.show(block=block)
def test_weibull_aft_plotting(self, block):
df = load_regression_dataset()
aft = WeibullAFTFitter()
aft.fit(df, "T", "E")
aft.plot()
self.plt.tight_layout()
self.plt.title("test_weibull_aft_plotting")
self.plt.show(block=block)
def test_aft_plot_partial_effects_on_outcome_with_categorical(self, block):
df = load_rossi()
df["cat"] = np.random.choice(["a", "b", "c"], size=df.shape[0])
aft = WeibullAFTFitter()
aft.fit(df, "week", "arrest", formula="cat + age + fin")
aft.plot_partial_effects_on_outcome("cat", values=["a", "b", "c"])
self.plt.title("test_aft_plot_partial_effects_on_outcome_with_categorical")
self.plt.show(block=block)
def test_weibull_aft_plot_covariate_groups(self, block):
df = load_rossi()
aft = WeibullAFTFitter()
aft.fit(df, "week", "arrest")
aft.plot_covariate_groups("age", [10, 50, 80])
self.plt.tight_layout()
self.plt.title("test_weibull_aft_plot_covariate_groups")
self.plt.show(block=block)
def fit_aft_model(data, formula_, yvar_="mainline_vol", event_var="failure"):
aft = WeibullAFTFitter()
aft.fit(
data,
duration_col=yvar_,
event_col=event_var,
formula=formula_,
)
return aft
# -*- coding: utf-8 -*-
# weibull aft
if __name__ == "__main__":
import pandas as pd
import time
from lifelines import WeibullAFTFitter
from lifelines.datasets import load_rossi
df = load_rossi()
df = pd.concat([df] * 1)
# df = df.reset_index()
# df['week'] = np.random.exponential(1, size=df.shape[0])
wp = WeibullAFTFitter()
start_time = time.time()
wp.fit(df, duration_col="week", event_col="arrest")
print("--- %s seconds ---" % (time.time() - start_time))
wp.print_summary()
class XGBSEStackedWeibull(XGBSEBaseEstimator):
"""
Perform stacking of a XGBoost survival model with a Weibull AFT parametric model.
The XGBoost fits the data and then predicts a value that is interpreted as a risk metric.
This risk metric is fed to the Weibull regression which uses it as its only independent variable.
Thus, we can get the benefit of XGBoost discrimination power alongside the Weibull AFT
statistical rigor (e.g. calibrated survival curves).
!!! Note
* As we're stacking XGBoost with a single, one-variable parametric model
(as opposed to `XGBSEDebiasedBCE`), the model can be much faster (especially in training).
* We also have better extrapolation capabilities, as opposed to the cure fraction
problem in `XGBSEKaplanNeighbors` and `XGBSEKaplanTree`.
* However, we also have stronger assumptions about the shape of the survival curve.
Read more in [How XGBSE works](https://loft-br.github.io/xgboost-survival-embeddings/how_xgbse_works.html).
"""
def __init__(
self,
xgb_params=None,
weibull_params=None,
):
"""
Args:
xgb_params (Dict, None): Parameters for XGBoost model.
If not passed, the following default parameters will be used:
```
DEFAULT_PARAMS = {
"objective": "survival:aft",
"eval_metric": "aft-nloglik",
"aft_loss_distribution": "normal",
"aft_loss_distribution_scale": 1,
"tree_method": "hist",
"learning_rate": 5e-2,
"max_depth": 8,
"booster": "dart",
"subsample": 0.5,
"min_child_weight": 50,
"colsample_bynode": 0.5,
}
```
Check <https://xgboost.readthedocs.io/en/latest/parameter.html> for more options.
weibull_params (Dict): Parameters for Weibull Regerssion model.
If not passed, will use the default parameters as shown in the Lifelines documentation.
Check <https://lifelines.readthedocs.io/en/latest/fitters/regression/WeibullAFTFitter.html>
for more options.
"""
if xgb_params is None:
xgb_params = DEFAULT_PARAMS
if weibull_params is None:
weibull_params = DEFAULT_PARAMS_WEIBULL
self.xgb_params = xgb_params
self.weibull_params = weibull_params
self.persist_train = False
def fit(
self,
X,
y,
num_boost_round=1000,
validation_data=None,
early_stopping_rounds=None,
verbose_eval=0,
persist_train=False,
index_id=None,
time_bins=None,
):
"""
Fit XGBoost model to predict a value that is interpreted as a risk metric.
Fit Weibull Regression model using risk metric as only independent variable.
Args:
X ([pd.DataFrame, np.array]): Features to be used while fitting XGBoost model
y (structured array(numpy.bool_, numpy.number)): Binary event indicator as first field,
and time of event or time of censoring as second field.
num_boost_round (Int): Number of boosting iterations.
validation_data (Tuple): Validation data in the format of a list of tuples [(X, y)]
if user desires to use early stopping
early_stopping_rounds (Int): Activates early stopping.
Validation metric needs to improve at least once
in every **early_stopping_rounds** round(s) to continue training.
See xgboost.train documentation.
verbose_eval ([Bool, Int]): Level of verbosity. See xgboost.train documentation.
persist_train (Bool): Whether or not to persist training data to use explainability
through prototypes
index_id (pd.Index): User defined index if intended to use explainability
through prototypes
time_bins (np.array): Specified time windows to use when making survival predictions
Returns:
XGBSEStackedWeibull: Trained XGBSEStackedWeibull instance
"""
E_train, T_train = convert_y(y)
if time_bins is None:
time_bins = get_time_bins(T_train, E_train)
self.time_bins = time_bins
# converting data to xgb format
dtrain = convert_data_to_xgb_format(X, y, self.xgb_params["objective"])
# converting validation data to xgb format
evals = ()
if validation_data:
X_val, y_val = validation_data
dvalid = convert_data_to_xgb_format(X_val, y_val,
self.xgb_params["objective"])
evals = [(dvalid, "validation")]
# training XGB
self.bst = xgb.train(
self.xgb_params,
dtrain,
num_boost_round=num_boost_round,
early_stopping_rounds=early_stopping_rounds,
evals=evals,
verbose_eval=verbose_eval,
)
# predicting risk from XGBoost
train_risk = self.bst.predict(dtrain)
# replacing 0 by minimum positive value in df
# so Weibull can be fitted
min_positive_value = T_train[T_train > 0].min()
T_train = np.clip(T_train, min_positive_value, None)
# creating df to use lifelines API
weibull_train_df = pd.DataFrame({
"risk": train_risk,
"duration": T_train,
"event": E_train
})
# fitting weibull aft
self.weibull_aft = WeibullAFTFitter(**self.weibull_params)
self.weibull_aft.fit(weibull_train_df,
"duration",
"event",
ancillary=True)
if persist_train:
self.persist_train = True
if index_id is None:
index_id = X.index.copy()
index_leaves = self.bst.predict(dtrain, pred_leaf=True)
self.tree = BallTree(index_leaves, metric="hamming")
self.index_id = index_id
return self
def predict(self, X, return_interval_probs=False):
"""
Predicts survival probabilities using the XGBoost + Weibull AFT stacking pipeline.
Args:
X (pd.DataFrame): Dataframe of features to be used as input for the
XGBoost model.
return_interval_probs (Bool): Boolean indicating if interval probabilities are
supposed to be returned. If False the cumulative survival is returned.
Default is False.
Returns:
pd.DataFrame: A dataframe of survival probabilities
for all times (columns), from a time_bins array, for all samples of X
(rows). If return_interval_probs is True, the interval probabilities are returned
instead of the cumulative survival probabilities.
"""
# converting to xgb format
d_matrix = xgb.DMatrix(X)
# getting leaves and extracting neighbors
risk = self.bst.predict(d_matrix)
weibull_score_df = pd.DataFrame({"risk": risk})
# predicting from logistic regression artifacts
preds_df = self.weibull_aft.predict_survival_function(
weibull_score_df, self.time_bins).T
if return_interval_probs:
preds_df = calculate_interval_failures(preds_df)
return preds_df
)
fig = px.histogram(
prebreakdown_merge_len_acc_1500_model_df_one_hot,
x="mainline_vol",
color="failure",
)
prebreakdown_merge_len_acc_1500_model_df_one_hot_no_censor = prebreakdown_merge_len_acc_1500_model_df_one_hot.query(
"failure==1")
aft = WeibullAFTFitter()
aft.fit(
prebreakdown_merge_len_acc_1500_model_df_one_hot,
duration_col="mainline_vol",
event_col="failure",
formula=
"ramp_metering+length_of_acceleration_lane+ffs_cap_df+number_of_mainline_lane_downstream+simple_merge",
)
aft.print_summary()
aft.plot()
aft.median_survival_time_
aft.mean_survival_time_
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(5, 4))
aft.plot_partial_effects_on_outcome(
"ramp_metering",
[0, 1],
cmap="coolwarm",
ax=ax,
