promptlearn 0.1.1

LLM-powered estimators for scikit-learn pipelines

⚡️ promptlearn

promptlearn brings large language models into your scikit-learn workflow.
It replaces traditional estimators with language-native reasoning systems that learn, adapt, and describe patterns using natural language as the model substrate.

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📊 Outperforming Traditional Models with Built-In Knowledge

promptlearn allows LLMs to internalize both structure and semantics during training. As a result, the models often exceed the capabilities of classical estimators when the task requires reasoning, real-world knowledge, or symbolic understanding.

Consider a simple binary classification task: predicting whether an animal is a mammal based on its name, weight, and lifespan.

Traditional models depend solely on the input features. But promptlearn models can use their internal understanding of zoology to form highly accurate rules. Even when a label like "Whale" is never seen during training, the model knows it belongs to the mammal class.

Model	Accuracy
promptlearn-o4-mini	1.00
promptlearn-gpt-4o	0.97
logistic_regression	0.60
random_forest	0.46
dummy	0.34

This type of semantic generalization is a powerful advantage for LLM-backed models.

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Now compare performance on a regression task where the data contains samples of objects falling from different heights, under different gravity. This is a classic physics problem, with a well-known equation:

fall_time_s = sqrt((2 * height_m) / gravity_mps2)

Recent promptlearn estimators are able to recover this exact formula and use it to generate near-perfect predictions:

Model	MSE
promptlearn-o4-mini	0.00006
promptlearn-gpt-4o	0.00006
gradient_boosting	0.035
linear_regression	0.498
dummy	5.27
promptlearn-gpt-4	43.17

No feature engineering was performed. No physics constants were added. The model discovered the rule and applied it directly. Classical regressors, by contrast, approximated a curve but missed the exact structure.

These results highlight the practical benefit of reasoning models: they learn compact, expressive heuristics and can outperform traditional systems when symbolic insight or background knowledge is essential.

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🤖 Estimators Powered by Language

promptlearn provides scikit-learn-compatible estimators that use LLMs as the modeling engine:

• PromptClassifier – for predicting classes through generalized reasoning
• PromptRegressor – for modeling numeric relationships in data

These estimators follow the same API as other scikit-learn models (fit, predict, score) but operate via dynamic prompt construction and few-shot abstraction.

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📘 What it Learns: The Heuristic

When you call .fit(), the LLM reviews your data and writes out an internal heuristic — a compact representation of what it has inferred. This heuristic might describe:

• A relationship between age, hours worked, and income
• How education, gender, and occupation relate to survival rates
• Why one row differs from another

The result is a plain-text model. It is readable, portable, and expressive. This is stored in .heuristic_, and it powers all predictions.

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🧠 Language-Aware Reasoning

Because the models are backed by LLMs, they can reason across both structure and semantics:

• Names of columns matter
• Missing data can be explained or inferred
• World knowledge is available by default

A trained model might use context like:

“Bachelors” typically correlates with medium income
“Private” workclass often means lower capital gain
Rows with missing native-country likely default to “United States”

This allows reasoning across incomplete, skewed, or lightly structured data without hand-tuning features.

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🧬 Background Knowledge Included

The LLM brings its internal knowledge graph to the modeling task. For instance:

Input: country = "Norway"
Output: has_blue_in_flag = 1

Even if there is no signal in the data, the model may still predict correctly by referencing background information. This creates a kind of ambient “web join” during training and inference.

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🕳 Zero-Example Learning

If you call .fit() with no rows — just column names — promptlearn will still return a working model.

This is possible because the LLM can hallucinate a plausible mapping based on:

• Column names
• Prior knowledge
• Type hints or value patterns

This makes rapid prototyping and conceptual modeling trivial.

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🧠 Scaling with Chunked Training

To support large datasets, promptlearn uses a sliding window training mechanism.

During .fit():

• The dataset is processed in batches (“chunks”)
• The current heuristic is passed forward like a scratchpad
• Each chunk contributes feedback and refinement
• The model evolves with each window

This allows training on limitless rows using a fixed memory budget. The process is transparent. If the dataset is large, chunked training activates automatically.

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🧪 Native .sample() Support

You can generate synthetic rows directly from any trained model using .sample(n):

>>> model.sample(3)
fruit    is_citrus
Lime     1
Banana   0
Orange   1

This is useful for:

• Understanding what the model believes
• Creating test sets or bootstrapped data
• Building readable examples from internal logic

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💾 Save and Reload with joblib

Like any scikit-learn model, promptlearn estimators can be serialized:

import joblib

joblib.dump(model, "model.joblib")
model = joblib.load("model.joblib")

The LLM client is excluded from the saved file and re-initialized on load. The heuristic remains intact, interpretable, and ready to use.

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📚 Related Work

Scikit-LLM

Scikit-LLM provides zero- and few-shot classification through template-based prompting.
It is lightweight and NLP-focused.

promptlearn offers a broader modeling philosophy:

Capability	Scikit-LLM	promptlearn
Prompt generated during fit	❌ No	✅ Yes
Regression support	❌ No	✅ Yes
Produces textual heuristics	❌ No	✅ Yes
Works on tabular data	✅ Partial	✅ Full
Generates sample rows	❌ No	✅ .sample()

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📁 License

MIT © 2025 Fredrik Linaker