Welcome to sam-ml-pyβs documentation!ο
sam_ml is a machine-learning library created as an API that shall help to make modelling easier. It has different preprocessing steps, classifiers, regressors, and auto-ml approaches implemented.
Installationο
There are two versions of sam_ml - with and without the SMAC library for hyperparameter-tuning.
with SMACο
If you want to use SMAC, you first have to install swig (see swig installation).
pip install "sam-ml-py[with_swig]"
without SMACο
pip install sam-ml-py
Letβs get startedο
You can start with jumping directly into the Auto-ML classes and utilising the combined power of this library. Or you can start by getting an overview of the modules and their interrelationships in the Package Graph.
I also recommend to read the advanced scoring documentation for classification problems as this is one of the biggest advantages of this library.
As already mentioned, another big advantage of this library are the two auto-ml classes for classification and regression. Here an example use for binary classification with precision optimisation of label 1 (underrepresented):
>>> # load data (replace with own data)
>>> import pandas as pd
>>> from sklearn.datasets import make_classification
>>> from sklearn.model_selection import train_test_split
>>> X, y = make_classification(n_samples=3000, n_features=4, n_classes=2, weights=[0.9], random_state=42)
>>> X, y = pd.DataFrame(X, columns=["col1", "col2", "col3", "col4"]), pd.Series(y)
>>> x_train, x_test, y_train, y_test = train_test_split(X,y, train_size=0.80, random_state=42)
>>>
>>> # start modelling
>>> from sam_ml.models.automl import CTest
>>>
>>> # initialise auot-ml class
>>> ctest = CTest(models = "all", scaler = "standard", selector = None, sampler = "ros")
>>>
>>> # start randomCVsearch with 5 configurations per model type and evaluate the best parameters
>>> ctest.find_best_model_randomCV(x_train,y_train,x_test,y_test, scoring="s_score", avg="binary", pos_label=1, secondary_scoring="precision", strength=3, n_trails=5, cv_num=3)
>>>
>>> # output and sort results
>>> score_df = ctest.output_scores_as_pd(sort_by=["s_score", "train_time"])
randomCVsearch (LogisticRegression (vec=None, scaler=standard, selector=None, sampler=ros)): 100%|ββββββββββ| 5/5 [00:00<00:00, 13.74it/s]
2023-12-08 21:12:57,721 - sam_ml.models.main_auto_ml - INFO - LogisticRegression (vec=None, scaler=standard, selector=None, sampler=ros) - score: 0.8114282933429915 (s_score) - parameters: {'C': 1.0, 'penalty': 'l2', 'solver': 'lbfgs'}
randomCVsearch (QuadraticDiscriminantAnalysis (vec=None, scaler=standard, selector=None, sampler=ros)): 100%|ββββββββββ| 5/5 [00:00<00:00, 19.47it/s]
2023-12-08 21:12:58,010 - sam_ml.models.main_auto_ml - INFO - QuadraticDiscriminantAnalysis (vec=None, scaler=standard, selector=None, sampler=ros) - score: 0.8788135203591323 (s_score) - parameters: {'reg_param': 0.0}
...
accuracy precision recall s_score l_score train_time train_score best_score (rCVs) best_hyperparameters (rCVs)
AdaBoostClassifier (DTC based) (vec=None, scale... 0.983333 0.943396 0.877193 0.984656 0.999998 0:00:02 0.995061 0.985320 {'algorithm': 'SAMME', 'estimator__max_depth':...
AdaBoostClassifier (RFC based) (vec=None, scale... 0.983333 0.943396 0.877193 0.984656 0.999998 0:00:01 0.995061 0.984980 {'algorithm': 'SAMME', 'estimator__max_depth':...
XGBClassifier (vec=None, scaler=standard, selec... 0.981667 0.942308 0.859649 0.983298 0.999995 0:00:00 0.994929 0.985982 {'colsample_bytree': 1.0, 'gamma': 0.0, 'learn...
KNeighborsClassifier (vec=None, scaler=standard... 0.980000 0.909091 0.877193 0.980948 0.999998 0:00:00 0.995061 0.978702 {'leaf_size': 37, 'n_neighbors': 2, 'p': 1, 'w...
...