Improvements from Cass project #164
There are no files selected for viewing
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
|
@@ -6,11 +6,54 @@ include "configs/prediction_models/common/MLCommon.gin" | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| # Train params | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| train_common.model = @LGBMClassifier | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| # Hyperparameter tuning configuration | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| model/hyperparameter.class_to_tune = @LGBMClassifier | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| # Core tree parameters | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| model/hyperparameter.n_estimators = (500, 2000, 5000, 10000, 100000) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| model/hyperparameter.max_depth = (3, 5, 7, 10, 15) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| model/hyperparameter.num_leaves = (8, 16, 31, 64, 128, 256, "log", 2) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| model/hyperparameter.min_child_samples = (10, 20, 50, 100, 500, 1000) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| model/hyperparameter.min_child_weight = (1e-3, 10.0, "log") | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| # Learning rate and regularization | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| model/hyperparameter.learning_rate = (0.01, 0.3, "log") | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| model/hyperparameter.reg_alpha = (1e-6, 1.0, "log") | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| model/hyperparameter.reg_lambda = (1e-6, 1.0, "log") | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| # Sampling parameters | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| model/hyperparameter.subsample = (0.4, 1.0) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| model/hyperparameter.subsample_freq = (0, 1, 5, 10) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| model/hyperparameter.colsample_bytree = (0.4, 1.0) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| model/hyperparameter.colsample_bynode = (0.4, 1.0) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Comment on lines
+24
to
+29
There was a problem hiding this comment. 🛠️ Refactor suggestion Remove LightGBM/XGBoost alias duplication and an invalid parameter name.
Apply this diff to remove the aliases here and manage sampling via the canonical keys in the Performance block (Lines 42-47): -# Sampling parameters
-model/hyperparameter.subsample = (0.4, 1.0)
-model/hyperparameter.subsample_freq = (0, 1, 5, 10)
-model/hyperparameter.colsample_bytree = (0.4, 1.0)
-model/hyperparameter.colsample_bynode = (0.4, 1.0)Then, add the per-node variant where the canonical feature/bagging params are declared (see the suggested change on Lines 42-47). 📝 Committable suggestion
Suggested change
🤖 Prompt for AI Agents |
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| # Boosting parameters | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| model/hyperparameter.boosting_type = ["gbdt", "dart"] | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| # Advanced DART parameters (active when boosting_type="dart") | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| model/hyperparameter.drop_rate = (0.1, 0.5) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| model/hyperparameter.max_drop = (10, 50) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| model/hyperparameter.skip_drop = (0.1, 0.9) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| # GOSS parameters (active when boosting_type="goss") | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| model/hyperparameter.top_rate = (0.1, 0.5) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| model/hyperparameter.other_rate = (0.05, 0.2) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Comment on lines
+30
to
+41
There was a problem hiding this comment. 🛠️ Refactor suggestion GOSS parameters present without enabling GOSS; make the search space consistent. You’re tuning Apply one of the following: Option A — enable GOSS: -model/hyperparameter.boosting_type = ["gbdt", "dart"]
+model/hyperparameter.boosting_type = ["gbdt", "dart", "goss"]Option B — keep only GBDT/DART (remove GOSS-only params): -# GOSS parameters (active when boosting_type="goss")
-model/hyperparameter.top_rate = (0.1, 0.5)
-model/hyperparameter.other_rate = (0.05, 0.2)Note: When using GOSS, ensure 📝 Committable suggestion
Suggested change
Suggested change
🤖 Prompt for AI Agents |
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| # Performance and stability | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| model/hyperparameter.feature_fraction = (0.4, 1.0) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| model/hyperparameter.bagging_fraction = (0.4, 1.0) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| model/hyperparameter.bagging_freq = (0, 1, 5, 10) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| model/hyperparameter.min_split_gain = (1e-6, 1.0, "log") | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Comment on lines
+42
to
+47
There was a problem hiding this comment. 🛠️ Refactor suggestion Consolidate sampling on canonical LightGBM params and add by-node feature sampling. Given aliases were removed above, keep tuning here via canonical names. If you want per-node feature sampling, add Apply this diff to extend the canonical block: model/hyperparameter.feature_fraction = (0.4, 1.0)
model/hyperparameter.bagging_fraction = (0.4, 1.0)
model/hyperparameter.bagging_freq = (0, 1, 5, 10)
model/hyperparameter.min_split_gain = (1e-6, 1.0, "log")
+model/hyperparameter.feature_fraction_bynode = (0.4, 1.0)📝 Committable suggestion
Suggested change
🤖 Prompt for AI Agents |
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| # Categorical handling | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| model/hyperparameter.cat_smooth = (1.0, 100.0, "log") | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| model/hyperparameter.cat_l2 = (1.0, 100.0, "log") | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| # Early stopping and validation | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| model/hyperparameter.early_stopping_rounds = 100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| model/hyperparameter.eval_metric = ["binary_logloss", "auc", "binary_error"] | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Comment on lines
+52
to
+55
There was a problem hiding this comment. 💡 Verification agent 🧩 Analysis chainConfirm fit-time plumbing for early stopping and evaluation metric.
Run the following script to verify that your training pipeline passes these through to LightGBM’s Optional: For stability, consider dropping Apply if desired: -model/hyperparameter.eval_metric = ["binary_logloss", "auc", "binary_error"]
+model/hyperparameter.eval_metric = ["binary_logloss", "auc"]🏁 Script executed: #!/bin/bash
set -euo pipefail
# Inspect how tuning params are materialized and passed to LGBM
rg -n -C3 -e 'early_stopping_rounds|eval_metric|class_to_tune|LGBMClassifier|fit\('
# Common places:
# - Tuning utilities that map gin "model/hyperparameter.*" into estimator kwargs
# - Trainer code that calls estimator.fit(..., eval_set=..., eval_metric=..., early_stopping_rounds=...)Length of output: 45667 🏁 Script executed: #!/bin/bash
# Display the beginning of the LGBM wrapper to inspect how fit() is implemented
sed -n '1,200p' icu_benchmarks/models/ml_models/lgbm.pyLength of output: 1753 🏁 Script executed: #!/bin/bash
# Locate the MLWrapper class to inspect how hyperparameters are mapped
rg -C5 -n 'class MLWrapper' icu_benchmarks/models/wrappers.pyLength of output: 485 🏁 Script executed: #!/bin/bash
# Inspect how model hyperparameters (including eval_metric, early_stopping_rounds) are applied during model instantiation
rg -C5 -n 'def set_model_args' icu_benchmarks/models/wrappers.pyLength of output: 654 🏁 Script executed: #!/bin/bash
python3 - << 'EOF'
import lightgbm as lgb
import inspect
print(inspect.signature(lgb.LGBMClassifier.__init__))
EOFLength of output: 132 🏁 Script executed: #!/bin/bash
sed -n '630,730p' icu_benchmarks/models/wrappers.pyLength of output: 4091 Update LGBMWrapper.fit_model to wire through Gin-configured early stopping and eval metrics Your current implementation in
As a result, neither --- a/icu_benchmarks/models/ml_models/lgbm.py
+++ b/icu_benchmarks/models/ml_models/lgbm.py
@@ class LGBMWrapper(MLWrapper):
- # Early stopping and logging (hard-coded)
- callbacks = [
- lgbm.early_stopping(self.hparams.patience, verbose=True),
- lgbm.log_evaluation(period=-1)
- ]
-
- self.model = self.model.fit(
- train_data,
- train_labels,
- eval_set=(val_data, val_labels),
- callbacks=callbacks,
- )
+ # Wire through Gin hyperparameters for early stopping and evaluation metrics
+ callbacks = []
+ # Use Gin’s early_stopping_rounds if set
+ if getattr(self.hparams, "early_stopping_rounds", None):
+ callbacks.append(lgbm.early_stopping(self.hparams.early_stopping_rounds, verbose=True))
+
+ # Override the eval_metric on the model if configured
+ if getattr(self.hparams, "eval_metric", None):
+ # scikit-learn wrapper will pass this metric to the Booster
+ self.model.set_params(eval_metric=self.hparams.eval_metric)
+
+ self.model = self.model.fit(
+ train_data,
+ train_labels,
+ eval_set=(val_data, val_labels),
+ callbacks=callbacks,
+ )This change ensures that:
References: 📝 Committable suggestion
Suggested change
🤖 Prompt for AI Agents |
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| # Class imbalance handling | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| model/hyperparameter.is_unbalance = [True, False] | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| model/hyperparameter.scale_pos_weight = (0.1, 10.0, "log") | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Comment on lines
+56
to
+59
There was a problem hiding this comment. 🛠️ Refactor suggestion Avoid combining is_unbalance with scale_pos_weight; prefer one strategy. LightGBM treats these as alternative approaches to class imbalance. Searching both simultaneously can produce ambiguous or counteracting settings. Prefer tuning Apply this diff to prefer -# Class imbalance handling
-model/hyperparameter.is_unbalance = [True, False]
-model/hyperparameter.scale_pos_weight = (0.1, 10.0, "log")
+# Class imbalance handling (prefer explicit weighting)
+model/hyperparameter.is_unbalance = [False]
+model/hyperparameter.scale_pos_weight = (0.1, 10.0, "log")If you want to compare strategies, introduce a higher-level toggle (e.g., 📝 Committable suggestion
Suggested change
🤖 Prompt for AI Agents |
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change | ||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| @@ -1,14 +1,28 @@ | ||||||||||||||||||||||||
| # Settings for ImbLearn RUSBoost Classifier (Random Under-sampling with Boosting) | ||||||||||||||||||||||||
|
|
||||||||||||||||||||||||
| # Common settings for ML models | ||||||||||||||||||||||||
| include "configs/prediction_models/common/MLCommon.gin" | ||||||||||||||||||||||||
|
|
||||||||||||||||||||||||
| # Train params | ||||||||||||||||||||||||
| train_common.model = @RUSBClassifier | ||||||||||||||||||||||||
|
|
||||||||||||||||||||||||
| # Hyperparameter tuning configuration | ||||||||||||||||||||||||
| model/hyperparameter.class_to_tune = @RUSBClassifier | ||||||||||||||||||||||||
|
|
||||||||||||||||||||||||
|
Comment on lines
7
to
11
There was a problem hiding this comment. 💡 Verification agent 🧩 Analysis chainAlign model name with public API choice (RUSB vs XGBEnsemble) You removed RUSBClassifier from the public API but this config still binds
If migrating: -train_common.model = @RUSBClassifier
-model/hyperparameter.class_to_tune = @RUSBClassifier
+train_common.model = @XGBEnsembleClassifier
+model/hyperparameter.class_to_tune = @XGBEnsembleClassifier🏁 Script executed: #!/usr/bin/env bash
set -e
echo "Finding RUSBClassifier class definitions..."
rg -nP "class\s+RUSBClassifier\b" .
echo "Searching for RUSBClassifier exports in __init__.py..."
rg -n "RUSBClassifier" -g "**/__init__.py" -n
echo "Finding XGBEnsembleClassifier class definitions..."
rg -nP "class\s+XGBEnsembleClassifier\b" .
echo "Searching for XGBEnsembleClassifier references..."
rg -n "XGBEnsembleClassifier" -g "**/*.py" -nLength of output: 143 🏁 Script executed: #!/usr/bin/env bash
set -e
echo "Searching for any occurrences of RUSBClassifier..."
rg -n "RUSBClassifier" .
echo "Searching for any occurrences of XGBEnsembleClassifier..."
rg -n "XGBEnsembleClassifier" .
echo "Searching for filenames containing RUSBClassifier or XGBEnsembleClassifier..."
fd --hidden --no-ignore RUSBClassifier
fd --hidden --no-ignore XGBEnsembleClassifierLength of output: 2022 Align config with public API—migrate or re-export RUSBClassifier If migrating: -train_common.model = @RUSBClassifier
-model/hyperparameter.class_to_tune = @RUSBClassifier
+train_common.model = @XGBEnsembleClassifier
+model/hyperparameter.class_to_tune = @XGBEnsembleClassifier📝 Committable suggestion
Suggested change
🤖 Prompt for AI Agents |
||||||||||||||||||||||||
| # Number of estimators (boosting rounds) | ||||||||||||||||||||||||
| model/hyperparameter.n_estimators = (50, 100, 200, 300, 500) | ||||||||||||||||||||||||
|
|
||||||||||||||||||||||||
| # Learning rate for boosting | ||||||||||||||||||||||||
| model/hyperparameter.learning_rate = (0.01, 2.0, "log") | ||||||||||||||||||||||||
|
|
||||||||||||||||||||||||
| # Sampling strategy for random under-sampling | ||||||||||||||||||||||||
| model/hyperparameter.sampling_strategy = ["auto", "majority", "not minority"] | ||||||||||||||||||||||||
|
|
||||||||||||||||||||||||
| # Base estimator parameters (typically DecisionTreeClassifier) | ||||||||||||||||||||||||
| model/hyperparameter.base_estimator__max_depth = [1, 2, 3, 4, 5, 6] | ||||||||||||||||||||||||
| model/hyperparameter.base_estimator__min_samples_split = [2, 5, 10, 20] | ||||||||||||||||||||||||
| model/hyperparameter.base_estimator__min_samples_leaf = [1, 2, 5, 10] | ||||||||||||||||||||||||
|
|
||||||||||||||||||||||||
| # Replacement strategy for under-sampling | ||||||||||||||||||||||||
| model/hyperparameter.replacement = [True, False] | ||||||||||||||||||||||||
|
|
||||||||||||||||||||||||
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,21 @@ | ||
| # Settings for XGBoost classifier. | ||
|
|
||
| # Common settings for ML models | ||
| include "configs/prediction_models/common/MLCommon.gin" | ||
|
|
||
| # Train params | ||
| train_common.model = @XGBClassifierGPU | ||
|
|
||
| model/hyperparameter.class_to_tune = @XGBClassifierGPU | ||
| model/hyperparameter.learning_rate = (0.01, 0.1, "log") | ||
| model/hyperparameter.n_estimators = [50, 100, 250, 500, 750, 1000,1500,2000, 2500, 3000, 3500, 4000, 4500, 5000] | ||
| model/hyperparameter.max_depth = [3, 5, 10, 15] | ||
| model/hyperparameter.scale_pos_weight = [1, 5, 10, 15, 20, 25, 30, 35, 40, 50, 75, 99, 100, 1000] | ||
| model/hyperparameter.min_child_weight = [0.1, 0.5, 1, 2, 5, 10] | ||
| model/hyperparameter.max_delta_step = [0, 1, 2, 3, 4, 5, 10] | ||
| model/hyperparameter.colsample_bytree = [0.1, 0.25, 0.5, 0.75, 1.0] | ||
| # model/hyperparameter.eval_metric = "aucpr" | ||
| model/hyperparameter.gamma = [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 2.0] | ||
| # model/hyperparameter.early_stopping_rounds = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100] | ||
| model/hyperparameter.reg_lambda = [0, 0.01, 0.1, 1, 10, 100] | ||
| model/hyperparameter.reg_alpha = [0, 0.01, 0.1, 1, 10, 100] |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,5 +1,6 @@ | ||
| # Hyperparameter tuner settings for classical Machine Learning. | ||
| tune_hyperparameters.scopes = ["model"] | ||
| tune_hyperparameters.n_initial_points = 5 | ||
| tune_hyperparameters.n_calls = 100 | ||
| tune_hyperparameters.folds_to_tune_on = 1 | ||
| tune_hyperparameters.repetitions_to_tune_on = 5 | ||
rvandewater marked this conversation as resolved.
Show resolved
Hide resolved
|
||
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,3 @@ | ||
| incl_n,excl_n_total,excl_n | ||
| 125,10,7 | ||
| 99,34,26 |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,20 @@ | ||
| # Minimal makefile for Sphinx documentation | ||
| # | ||
|
|
||
| # You can set these variables from the command line, and also | ||
| # from the environment for the first two. | ||
| SPHINXOPTS ?= | ||
| SPHINXBUILD ?= sphinx-build | ||
| SOURCEDIR = source | ||
| BUILDDIR = build | ||
|
|
||
| # Put it first so that "make" without argument is like "make help". | ||
| help: | ||
| @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) | ||
|
|
||
| .PHONY: help Makefile | ||
|
|
||
| # Catch-all target: route all unknown targets to Sphinx using the new | ||
| # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). | ||
| %: Makefile | ||
| @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,35 @@ | ||
| @ECHO OFF | ||
|
|
||
| pushd %~dp0 | ||
|
|
||
| REM Command file for Sphinx documentation | ||
|
|
||
| if "%SPHINXBUILD%" == "" ( | ||
| set SPHINXBUILD=sphinx-build | ||
| ) | ||
| set SOURCEDIR=source | ||
| set BUILDDIR=build | ||
|
|
||
| %SPHINXBUILD% >NUL 2>NUL | ||
| if errorlevel 9009 ( | ||
| echo. | ||
| echo.The 'sphinx-build' command was not found. Make sure you have Sphinx | ||
| echo.installed, then set the SPHINXBUILD environment variable to point | ||
| echo.to the full path of the 'sphinx-build' executable. Alternatively you | ||
| echo.may add the Sphinx directory to PATH. | ||
| echo. | ||
| echo.If you don't have Sphinx installed, grab it from | ||
| echo.https://www.sphinx-doc.org/ | ||
| exit /b 1 | ||
| ) | ||
|
|
||
| if "%1" == "" goto help | ||
|
|
||
| %SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% | ||
| goto end | ||
|
|
||
| :help | ||
| %SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% | ||
|
|
||
| :end | ||
| popd |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,4 @@ | ||
| sphinx | ||
| sphinx-rtd-theme | ||
| sphinx-autoapi | ||
| sphinx-autobuild |
Oops, something went wrong.
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
🛠️ Refactor suggestion
Fix malformed num_leaves spec and consider simplifying n_estimators.
num_leavestuple mixes discrete values with"log", 2. This is likely to break your parser (or yield invalid values). Prefer either a discrete list or a proper range tuple.n_estimatorsadds search noise/cost. A log-range or a single high ceiling with early stopping is more efficient.Apply this diff to make both entries unambiguous:
Notes:
[15, 31, 63, 127, 255]instead.max_depth = [-1, 3, 5, 7, 10, 15]so the unconstrained depth case is explored.📝 Committable suggestion
🤖 Prompt for AI Agents