Hello,

thank you for your nice tool. I am using the function LinearTreeRegressor to draw a continuous piecewise linear. It works well, I am wondering, is it possible to show the location (the coordinates) of the breakpoints?

thank you

Thanks for the good library.

When using LinearTreeRegressor, I think that max_depth is often optimized by cross-validation.

This library allows max_depth in the range 1-20. However, depending on the dataset, simple linear regression may be suitable. Even in such a dataset, max_depth is forced to be 1 or more, so Simple Linear Regression cannot be applied properly with LinearTreeRegressor.

• Of course, it is appropriate to use sklearn.linear_model.LinearRegression for such datasets.

My suggestion is to change to a program that uses base_estimator to perform regression when "max_depth = 0". With this change, LinearTreeRegressor can flexibly respond to both segmented regression and simple regression by changing hyperparameters.

I have been using your library for quite a while and am super happy with it. So first, thanks a lot!

Lately, I used my framework (which also uses your library) on modern many core server with many jobs. Worked fine. Now I have updated everything via pip and with 8 jobs on my MacBook, I got the following error.

This error does not occur when using only a single job (I pass the number of jobs to n_jobs).

I cannot nail the down the actual problem, but since it occurred right after the upgrade, I assume this might be the reason?

Am I doing something wrong here?

"""
Traceback (most recent call last):
  File "/Users/martin/opt/anaconda3/lib/python3.7/site-packages/joblib/externals/loky/process_executor.py", line 436, in _process_worker
    r = call_item()
  File "/Users/martin/opt/anaconda3/lib/python3.7/site-packages/joblib/externals/loky/process_executor.py", line 288, in __call__
    return self.fn(*self.args, **self.kwargs)
  File "/Users/martin/opt/anaconda3/lib/python3.7/site-packages/joblib/_parallel_backends.py", line 595, in __call__
    return self.func(*args, **kwargs)
  File "/Users/martin/opt/anaconda3/lib/python3.7/site-packages/joblib/parallel.py", line 263, in __call__
    for func, args, kwargs in self.items]
  File "/Users/martin/opt/anaconda3/lib/python3.7/site-packages/joblib/parallel.py", line 263, in <listcomp>
    for func, args, kwargs in self.items]
  File "/Users/martin/opt/anaconda3/lib/python3.7/site-packages/lineartree/_classes.py", line 56, in __call__
    with config_context(**self.config):
  File "/Users/martin/opt/anaconda3/lib/python3.7/contextlib.py", line 239, in helper
    return _GeneratorContextManager(func, args, kwds)
  File "/Users/martin/opt/anaconda3/lib/python3.7/contextlib.py", line 82, in __init__
    self.gen = func(*args, **kwds)
TypeError: config_context() got an unexpected keyword argument 'target_offload'
"""

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "compression_selection_pipeline.py", line 41, in <module>
    model_pipeline.learn_runtime_models(calibration_result_dir)
  File "/Users/martin/Programming/compression_selection_v3/hyrise_calibration/model_pipeline.py", line 670, in learn_runtime_models
    non_splitting_models("table_scan", table_scans)
  File "/Users/martin/Programming/compression_selection_v3/hyrise_calibration/model_pipeline.py", line 590, in non_splitting_models
    fitted_model = model_dict["model"].fit(X_train, y_train)
  File "/Users/martin/Programming/compression_selection_v3/hyrise_calibration/model_pipeline.py", line 209, in fit
    return self.regression.fit(X, y)
  File "/Users/martin/opt/anaconda3/lib/python3.7/site-packages/lineartree/lineartree.py", line 187, in fit
    self._fit(X, y, sample_weight)
  File "/Users/martin/opt/anaconda3/lib/python3.7/site-packages/lineartree/_classes.py", line 576, in _fit
    self._grow(X, y, sample_weight)
  File "/Users/martin/opt/anaconda3/lib/python3.7/site-packages/lineartree/_classes.py", line 387, in _grow
    loss=loss)
  File "/Users/martin/opt/anaconda3/lib/python3.7/site-packages/lineartree/_classes.py", line 285, in _split
    for feat in split_feat)
  File "/Users/martin/opt/anaconda3/lib/python3.7/site-packages/joblib/parallel.py", line 1056, in __call__
    self.retrieve()
  File "/Users/martin/opt/anaconda3/lib/python3.7/site-packages/joblib/parallel.py", line 935, in retrieve
    self._output.extend(job.get(timeout=self.timeout))
  File "/Users/martin/opt/anaconda3/lib/python3.7/site-packages/joblib/_parallel_backends.py", line 542, in wrap_future_result
    return future.result(timeout=timeout)
  File "/Users/martin/opt/anaconda3/lib/python3.7/concurrent/futures/_base.py", line 435, in result
    return self.__get_result()
  File "/Users/martin/opt/anaconda3/lib/python3.7/concurrent/futures/_base.py", line 384, in __get_result
    raise self._exception
TypeError: config_context() got an unexpected keyword argument 'target_offload'

PS: I have already left a star. :D

Added two additional parameters:

• split_features: Indices of features that can be used for splitting. Default all.
• linear_features: Indices of features that are used by the linear models in the leaves. Default all except for categorical features

This implements a feature requested in https://github.com/cerlymarco/linear-tree/issues/2

Potential performance improvement: Currently the code still computes bins for all features and not only for those used for splitting.

I noticed that the implementations of _parallel_binning_fit and _grow internally round loss values to 5 decimal places. This makes the regression results dependent on the scale of the labels, as data with a lower natural loss value will result in many different splits of the data having the same loss when rounded to 5 decimal places. Is there a reason why this is the case?

This behavior can be observed by fitting a LinearTreeRegressor using the default loss function and multiplying the scale of the labels by a small number (like 1e-9). This will result in the regressor no longer learning any splits.

Great work! I'm new to ML and stuck with this. I'm trying to combine pipeline and GridSearch to search for best possible hyperparameters for a model.

I got the following error:

Kindly help : )

Hey, I have been playing around a lot with your linear trees. Like them very much. Thanks!

Nevertheless, I am somewhat disappointed by the runtime performance. Compared to XGBoost Regressors (I know it's not a fair comparison) or linear regressions (also not fair), the linear tree is reeeeeaally slow. 50k observations, 80 features: 2s for linear regression, 27s for XGBoost, and 300s for the linear tree. Have you seen similar runtimes or might I be using it wrong?

Another aspects that's interesting to me is the question whether is possibe to limit the features which are used for splits. I haven't found it in the code. Any change to see it in the future?

Hi

thanks for writing this great package!

I was trying to display the decision tree with graphviz I get this error

AttributeError: 'LinearTreeRegressor' object has no attribute 'n_features_'

from lineartree import LinearTreeRegressor from sklearn.linear_model import LinearRegression

reg = LinearTreeRegressor(base_estimator=LinearRegression()) reg.fit(train[x_cols], train["y"])

from graphviz import Source from sklearn import tree

graph = Source( tree.export_graphviz(reg, out_file=None,feature_names=train.columns))

/lineartree/_classes.py:338: DeprecationWarning:

the interpolation= argument to quantile was renamed to method=, which has additional options. Users of the modes 'nearest', 'lower', 'higher', or 'midpoint' are encouraged to review the method they. (Deprecated NumPy 1.22)

Seems like a quick update here would get this warning to stop showing up, right? I can always ignore it, but figured I would mention it in case it is actually an error on my side.

Also, sorry, I don't actually what the best open source etiquette is. If I'm supposed to create a pull request with a proposed fix instead of just mentioning it then feel free to correct me.

Hi, I am wondering how to perform a GridsearchCV to find best parameters for the tree and regression model? For now I am able to tune the tree component of my model:

`

 param_grid={
    'n_estimators': [50, 100, 500, 700],
    'max_depth': [10, 20, 30, 50],
    'min_samples_split' : [2, 4, 8, 16, 32],
    'max_features' : ['sqrt', 'log2', None]
}
cv = RepeatedKFold(n_repeats=3,
                   n_splits=3,
                   random_state=1)

model = GridSearchCV(
    LinearForestRegressor(ElasticNet(random_state = 0), random_state=42),
    param_grid=param_grid,
    n_jobs=-1,
    cv=cv,
    scoring='neg_root_mean_squared_error'
    )

`

Hello! Thank you for useful package!

I think I might have found a potential bug in LinearForestClassifier.

I expected 'predict_proba' to use 'self.decision_function', similarly to 'predict' - to include predictions from both estimators (base + forest). Is that a potential bug or am I in wrong here?

https://github.com/cerlymarco/linear-tree/blob/8d5beca8d492cb8c57e6618e3fb770860f28b550/lineartree/lineartree.py#L1560