treemind

treemind is designed for analyzing gradient boosting models. It simplifies understanding how features influence predictions within specific intervals and provides powerful tools for analyzing individual features and their interactions.

A formal research paper detailing the theoretical foundation and implementation of treemind is forthcoming.

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Algorithm

The treemind algorithm analyzes feature contributions and interactions in tree-based models, focusing on specific feature intervals to evaluate their impact on predictions.

Algorithm Overview

Performance

• Fast and effective for exploratory analysis.
• Highly efficient, even for large datasets.

Performance Experiments

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Installation

To install treemind, use the following pip command:

pip install treemind

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Key Features

1. Feature Analysis: Provides statistical analysis on how features behave across different decision splits.

2. Interaction Analysis: Identifies complex relationships between features by analyzing how they work together to influence predictions. The algorithm can analyze interactions up to n features, depending on memory constraints and time limitations.

3. High Performance: Optimized with Cython for fast execution, even on large models and datasets.

4. Advanced Visualization: Offers user-friendly plots to visually explain the model's decision-making process and feature interactions.

5. Compatibility with Popular Frameworks: Fully compatible with xgboost, lightgbm and catboost, supporting regression and binary classification tasks.

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Usage

This example demonstrates how to set up and use the Explainer with a basic lightgbm model trained on the Breast Cancer dataset.

For detailed information, please refer to the API Reference.

Setup Code

from lightgbm import LGBMClassifier
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split

from treemind import Explainer
from treemind.plot import (
    feature_plot,
    interaction_plot,
    interaction_scatter_plot,
)

# Load the dataset
X, y = load_breast_cancer(return_X_y=True, as_frame=True)

# Train the model
model = LGBMClassifier(verbose=-1)
model.fit(X, y)

Once the model is trained, it is ready to be analyzed with the Explainer.

Initializing the Explainer

After training the model, initialize the Explainer by calling it with the model object:

explainer = Explainer()
explainer(model)

Counting Feature Appearances

The count_node function analyzes how often individual features or pairs of features appear in decision splits across the model's trees. This analysis can help identify the most influential features or feature interactions in the model's decision-making process.

To count individual feature appearances in splits:

explainer.count_node(order=1)

| column_index | count |
|--------------|-------|
| 21           | 1739  |
| 27           | 1469  |
| 22           | 1422  |
| 23           | 1323  |
| 1            | 1129  |

To count feature-pair interactions in splits:

explainer.count_node(order=2)

| column1_index | column2_index | count |
|---------------|---------------|-------|
| 21            | 22            | 927   |
| 21            | 23            | 876   |
| 21            | 27            | 852   |
| 1             | 27            | 792   |
| 23            | 27            | 734   |

Analyzing Specific Feature

The analyze_feature function calculates statistical metrics for a specific feature based on its split points across the model's trees. This analysis helps in understanding the distribution and impact of a single feature across different split points.

To analyze a specific feature by its index (e.g., 21), use:

feature_df = explainer.analyze_feature(21)

| worst_texture_lb | Worst_texture_ub |   value   |   std    |  count  |
|------------------|------------------|-----------|----------|---------|
| -inf             | 18.460           | 3.185128  | 8.479232 | 402.24  |
| 18.460           | 19.300           | 3.160656  | 8.519873 | 402.39  |
| 19.300           | 19.415           | 3.119814  | 8.489262 | 401.85  |
| 19.415           | 20.225           | 3.101601  | 8.490439 | 402.55  |
| 20.225           | 20.360           | 2.772929  | 8.711773 | 433.16  |

To visualize feature statistics calculated by analyze_feature using feature_plot:

feature_plot(feature_df)

Analyzing Feature Interactions

The analyze_feature function given multiple indices calculates the dependency between two or more features by examining their split points across the model’s trees.

To analyze an interaction between two features (e.g., feature indices 21 and 22), use:

df = explainer.analyze_feature([21, 22])

Example output:

| worst_texture_lb | worst_texture_ub | worst_concave_points_lb | worst_concave_points_ub |   value   |   std    |  count  |
|------------------|------------------|--------------------------|------------------------|-----------|----------|---------|
| -inf             | 18.46            | -inf                     | 0.058860               | 4.929324  | 7.679424 | 355.40  |
| -inf             | 18.46            | 0.058860                 | 0.059630               | 4.928594  | 7.679772 | 355.34  |
| -inf             | 18.46            | 0.059630                 | 0.065540               | 4.923128  | 7.679783 | 355.03  |
| -inf             | 18.46            | 0.065540                 | 0.069320               | 4.912888  | 7.682064 | 354.70  |
| -inf             | 18.46            | 0.069320                 | 0.069775               | 4.912888  | 7.682064 | 354.70  |

To visualize interactions between two features calculated by analyze_interaction using interaction_plot:

interaction_plot(df)

To visualize interactions between two features on given data by analyze_interaction using interaction_scatter_plot:

interaction_scatter_plot(X, df, 21, 22)

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Contributing

Contributions are welcome! If you'd like to improve treemind or suggest new features, feel free to fork the repository and submit a pull request.

License

treemind is released under the BSD 3-Clause License. See the LICENSE file for more details.