🔬 Scikit-learn Integration
Bring scikit-learn models into production pipelines with automatic property extraction and 16+ auto-detected attributes.
🔄 Auto-Properties 📋 16+ Attributes 🏷️ Model Registry
Scikit-Learn Integration 🧠
Classic ML pipelines made robust and reproducible.
What you'll learn
How to version and deploy sklearn models with automatic metadata extraction. Turn notebook scripts into production pipelines.
Why Scikit-Learn + flowyml?
- Auto-Extracted Metadata: Hyperparameters, feature importance, coefficients—all captured automatically.
- Pipeline Versioning: Version the entire preprocessing + model chain.
- Model Registry: Promote the best Random Forest to production.
- Easy Serving: Deploy sklearn models as APIs.
🎯 Model.from_sklearn() Convenience Method
The easiest way to create Model assets with full metadata extraction:
from sklearn.ensemble import RandomForestClassifier
from flowyml import Model
# Train your model
rf = RandomForestClassifier(n_estimators=100, max_depth=10)
rf.fit(X_train, y_train)
# 🎯 Use convenience method for full extraction
model_asset = Model.from_sklearn(
rf,
name="random_forest_classifier",
)
# Access auto-extracted properties
print(f"Framework: {model_asset.framework}") # 'sklearn'
print(f"Class: {model_asset.metadata.properties.get('model_class')}") # 'RandomForestClassifier'
print(f"Is Fitted: {model_asset.metadata.properties.get('is_fitted')}") # True
# Hyperparameters auto-extracted!
print(f"Hyperparameters: {model_asset.hyperparameters}")
# {'n_estimators': 100, 'max_depth': 10, 'criterion': 'gini', ...}
# Feature importance (for tree-based models)
print(f"Has Feature Importance: {model_asset.metadata.properties.get('has_feature_importances')}")
print(f"Num Features: {model_asset.metadata.properties.get('num_features')}")
🧠 Pipeline Pattern
Return a sklearn.pipeline.Pipeline object for automatic serialization:
from sklearn.pipeline import Pipeline as SkPipeline
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import StandardScaler
from flowyml import step, Model
@step
def build_pipeline():
return SkPipeline([
('scaler', StandardScaler()),
('rf', RandomForestClassifier(n_estimators=100))
])
@step
def train(pipeline, X, y):
pipeline.fit(X, y)
# Return a Model asset with auto-extracted metadata
return Model.from_sklearn(
pipeline,
name="trained_pipeline",
)
🔧 Auto-Extracted Properties
The following properties are automatically extracted from sklearn models:
| Property | Description |
|---|---|
framework |
Always 'sklearn' |
model_class |
Class name (RandomForestClassifier, etc.) |
architecture |
Same as model_class |
hyperparameters |
All model hyperparameters |
is_fitted |
Whether model has been fitted |
has_feature_importances |
True for tree-based models |
num_features |
Number of features (if fitted) |
n_features_in |
Number of input features |
n_estimators |
For ensemble models |
num_estimators_fitted |
Actual estimators fitted |
max_depth |
For tree-based models |
num_classes |
For classifiers |
classes |
Class labels (for classifiers) |
coef_shape |
Coefficient shape (for linear models) |
intercept |
Intercept (for linear models) |
🌳 Supported Model Types
Full auto-extraction works for all sklearn estimators:
- Classifiers: RandomForest, GradientBoosting, SVM, LogisticRegression, etc.
- Regressors: RandomForest, Ridge, Lasso, ElasticNet, etc.
- Transformers: StandardScaler, PCA, etc.
- Ensembles: VotingClassifier, StackingClassifier, etc.
- Pipelines: sklearn.pipeline.Pipeline
🚀 What's Next?
🔥 PyTorch Integration
Integrate deep learning models with automatic state dict handling and metadata.
🤗 HuggingFace Integration
Use Transformers models and datasets with full tokenizer and model artifact support.