bluecast 0.60

A lightweight and fast automl framework

BlueCast

A lightweight and fast auto-ml library. BlueCast focuses on a few model architectures (on default Xgboost only) and a few preprocessing options (only what is needed for Xgboost). This allows for a much faster development cycle and a much more stable codebase while also having as few dependencies as possible for the library. Despite being lightweight in its core BlueCast offers high customization options for advanced users. Find the full documentation here.

• Installation
  • Installation for end users
  • Installation for developers
• General usage
  • Basic usage
  • Advanced usage
    • Explanatory analysis
    • Leakage detection
    • Enable cross-validation
    • Gaining extra performance
    • Use multi-model blended pipeline
    • Categorical encoding
    • Custom training configuration
    • Custom preprocessing
    • Custom feature selection
    • Custom ML model
    • Using the inbuilt ExperientTracker
• Convenience features
• Code quality
• Documentation
• Kaggle competition results and example notebooks
• How to contribute
• Meta

Installation

Installation for end users

From PyPI:

pip install bluecast

Using a fresh environment with Python 3.9 or higher is recommended. We consciously do not support Python 3.8 or lower to prevent the usage of outdated Python versions and issues connected to it.

Installation for developers

• Clone the repository:
• Create a new conda environment with Python 3.9 or higher
• run pip install poetry to install poetry as dependency manager
• run poetry install to install all dependencies

General usage

Basic usage

The module blueprints contains the main functionality of the library. The main entry point is the Blueprint class. It already includes needed preprocessing (including some convenience functionality like feature type detection) and model hyperparameter tuning.

from bluecast.blueprints.cast import BlueCast

automl = BlueCast(
        class_problem="binary",
        target_column="target"
    )

automl.fit(df_train, target_col="target")
y_probs, y_classes = automl.predict(df_val)

BlueCast has simple utilities to save and load your pipeline:

from bluecast.general_utils.general_utils import save_to_production, load_for_production

# save pipeline including tracker
save_to_production(automl, "/kaggle/working/", "bluecast_cv_pipeline")

# in production or for further experiments this can be loaded again
automl = load_for_production("/kaggle/working/", "bluecast_cv_pipeline")

Advanced usage

Explanatory analysis

BlueCast offers a simple way to get a first overview of the data:

from bluecast.eda.analyse import (
    bi_variate_plots,
    correlation_heatmap,
    correlation_to_target,
    plot_pca,
    plot_theil_u_heatmap,
    plot_tsne,
    univariate_plots,
    check_unique_values,
    plot_null_percentage
)

from bluecast.preprocessing.feature_types import FeatureTypeDetector

# Here we automatically detect the numeric columns
feat_type_detector = FeatureTypeDetector()
train_data = feat_type_detector.fit_transform_feature_types(train_data)

# show univariate plots
univariate_plots(
        train_data.loc[:, feat_type_detector.num_columns],  # here the target column EC1 is already included
        "EC1",
    )

# show bi-variate plots
bi_variate_plots(
    train_data.loc[:, feat_type_detector.num_columns],
      "EC1"
      )

# show correlation heatmap
correlation_heatmap(train_data.loc[:, feat_type_detector.num_columns])

# show correlation to target
correlation_to_target(
    train_data.loc[:, feat_type_detector.num_columns],
      "EC1",
      )

# show feature space after principal component analysis
plot_pca(
    train_data.loc[:, feat_type_detector.num_columns],
    "target"
    )

# show feature space after t-SNE
plot_tsne(
    train_data.loc[:, feat_type_detector.num_columns],
    "target",
    perplexity=30,
    random_state=0
    )

# show a heatmap of assocations between categorical variables
theil_matrix = plot_theil_u_heatmap(train_data, feat_type_detector.cat_columns)

# plot the percentage of Nulls for all features
plot_null_percentage(
    train_data.loc[:, feat_type_detector.num_columns],
    )

# detect columns with a very high share of unique values
many_unique_cols = check_unique_values(train_data, feat_type_detector.cat_columns)

Leakage detection

With big data and complex pipelines data leakage can easily sneak in. To detect leakage BlueCast offers two functions:

from bluecast.eda.data_leakage_checks import (
    detect_categorical_leakage,
    detect_leakage_via_correlation,
)


# Detect leakage of numeric columns based on correlation
result = detect_leakage_via_correlation(
        train_data.loc[:, feat_type_detector.num_columns], "target", threshold=0.9
    )

# Detect leakage of categorical columns based on Theil's U
result = detect_categorical_leakage(
        train_data.loc[:, feat_type_detector.cat_columns], "target", threshold=0.9
    )

Enable cross-validation

While the default behaviour of BlueCast is to use a simple train-test-split, cross-validation can be enabled easily:

from bluecast.blueprints.cast import BlueCast
from bluecast.config.training_config import TrainingConfig


# Create a custom training config and adjust general training parameters
train_config = TrainingConfig()
train_config.hypertuning_cv_folds = 5 # default is 1

# Pass the custom configs to the BlueCast class
automl = BlueCast(
        class_problem="binary",
        target_column="target"
        conf_training=train_config,
    )

automl.fit(df_train, target_col="target")
y_probs, y_classes = automl.predict(df_val)

Gaining extra performance

By default BlueCast uses Optuna's Bayesian hyperparameter optimization, however Bayesian methods give an estimate and do not necessarly find the ideal spot, thus BlueCast has an optional GridSearch setting that allows BlueCast to refine some of the parameters Optuna has found. This can be enabled by setting enable_grid_search_fine_tuning to True. This fine-tuning step uses a different random seed than the autotuning routine (seed from the settings + 1000).

from bluecast.blueprints.cast import BlueCast
from bluecast.config.training_config import TrainingConfig


# Create a custom training config and adjust general training parameters
train_config = TrainingConfig()
train_config.hypertuning_cv_folds = 5 # default is 1
train_config.enable_grid_search_fine_tuning = True # default is False
train_config.gridsearch_tuning_max_runtime_secs = 3600 # max runtime in secs
train_config.gridsearch_nb_parameters_per_grid = 5 # increasing this means X^3 trials atm

# Pass the custom configs to the BlueCast class
automl = BlueCast(
        class_problem="binary",
        target_column="target"
        conf_training=train_config,
    )

automl.fit(df_train, target_col="target")
y_probs, y_classes = automl.predict(df_val)

This comes with a tradeoff of longer runtime. This behaviour can be further controlled with two parameters:

• gridsearch_nb_parameters_per_grid: Decides how many steps the grid shall have per parameter
• gridsearch_tuning_max_runtime_secs: Sets the maximum time in seconds the tuning shall run. This will finish the latest trial nd will exceed this limit though.

Use multi-model blended pipeline

By default, BlueCast trains a single model. However, it is possible to train multiple models with one call for extra robustness. BlueCastCV has a fit and a fit_eval method. The fit_eval method trains the models, but also provides out-of-fold validation. Also BlueCastCV allows to pass custom configurations.

from bluecast.blueprints.cast import BlueCastCV
from bluecast.config.training_config import TrainingConfig, XgboostTuneParamsConfig

# Pass the custom configs to the BlueCast class
automl = BlueCastCV(
        class_problem="binary",
        #conf_training=train_config,
        #conf_xgboost=xgboost_param_config,
        #custom_preprocessor=custom_preprocessor, # this takes place right after test_train_split
        #custom_last_mile_computation=custom_last_mile_computation, # last step before model training/prediction
        #custom_feature_selector=custom_feature_selector,
    )

# this class has a train method:
# automl.fit(df_train, target_col="target")

automl.fit_eval(df_train, target_col="target")
y_probs, y_classes = automl.predict(df_val)

Categorical encoding

By default, BlueCast uses target encoding. This behaviour can be changed in the TrainingConfig by setting cat_encoding_via_ml_algorithm to True. This will change the expectations of custom_last_mile_computation though. If cat_encoding_via_ml_algorithm is set to False, custom_last_mile_computation will receive numerical features only as target encoding will apply before. If cat_encoding_via_ml_algorithm is True (default setting) custom_last_mile_computation will receive categorical features as well, because Xgboost's inbuilt categorical encoding will be used.

Custom training configuration

Despite e2eml, BlueCast allows easy customization. Users can adjust the configuration and just pass it to the BlueCast class. Here is an example:

from bluecast.blueprints.cast import BlueCast
from bluecast.config.training_config import TrainingConfig, XgboostTuneParamsConfig

# Create a custom tuning config and adjust hyperparameter search space
xgboost_param_config = XgboostTuneParamsConfig()
xgboost_param_config.steps_max = 100
xgboost_param_config.max_leaves_max = 16
# Create a custom training config and adjust general training parameters
train_config = TrainingConfig()
train_config.hyperparameter_tuning_rounds = 10
train_config.autotune_model = False # we want to run just normal training, no hyperparameter tuning
# We could even just overwrite the final Xgboost params using the XgboostFinalParamConfig class

# Pass the custom configs to the BlueCast class
automl = BlueCast(
        class_problem="binary",
        target_column="target"
        conf_training=train_config,
        conf_xgboost=xgboost_param_config,
    )

automl.fit(df_train, target_col="target")
y_probs, y_classes = automl.predict(df_val)

Custom preprocessing

The BlueCast class also allows for custom preprocessing. This is done by an abstract class that can be inherited and passed into the BlueCast class. BlueCast provides two entry points to inject custom preprocessing. The attribute custom_preprocessor is called right after the train_test_split. The attribute custom_last_mile_computation will be called before the model training or prediction starts (when only numerical features are present anymore) and allows users to execute last computations (i.e. sub sampling or final calculations).

from bluecast.blueprints.cast import BlueCast
from bluecast.preprocessing.custom import CustomPreprocessing

# Create a custom tuning config and adjust hyperparameter search space
xgboost_param_config = XgboostTuneParamsConfig()
xgboost_param_config.steps_max = 100
xgboost_param_config.max_leaves_max = 16
# Create a custom training config and adjust general training parameters
train_config = TrainingConfig()
train_config.hyperparameter_tuning_rounds = 10
train_config.autotune_model = False # we want to run just normal training, no hyperparameter tuning
# We could even just overwrite the final Xgboost params using the XgboostFinalParamConfig class

class MyCustomPreprocessing(CustomPreprocessing):
    def __init__(self):
        self.trained_patterns = {}

    def fit_transform(
        self, df: pd.DataFrame, target: pd.Series
    ) -> Tuple[pd.DataFrame, pd.Series]:
        num_columns = df.drop(['Beta', 'Gamma', 'Delta', 'Alpha', 'EJ'], axis=1).columns
        cat_df = df[['Beta', 'Gamma', 'Delta', 'Alpha', 'EJ']].copy()

        zscores = Zscores()
        zscores.fit_all(df, ['Beta', 'Gamma', 'Delta', 'Alpha', 'EJ'])
        df = zscores.transform_all(df, ['Beta', 'Gamma', 'Delta', 'Alpha', 'EJ'])
        self.trained_patterns["zscores"] = zscores

        imp_mean = SimpleImputer(missing_values=np.nan, strategy='median')
        num_columns = df.drop(['Beta', 'Gamma', 'Delta', 'Alpha', 'EJ'], axis=1).columns
        imp_mean.fit(df.loc[:, num_columns])
        df = imp_mean.transform(df.loc[:, num_columns])
        self.trained_patterns["imputation"] = imp_mean

        df = pd.DataFrame(df, columns=num_columns).merge(cat_df, left_index=True, right_index=True, how="left")

        df = df.drop(['Beta', 'Gamma', 'Delta', 'Alpha'], axis=1)

        return df, target

    def transform(
        self,
        df: pd.DataFrame,
        target: Optional[pd.Series] = None,
        predicton_mode: bool = False,
    ) -> Tuple[pd.DataFrame, Optional[pd.Series]]:
        num_columns = df.drop(['Beta', 'Gamma', 'Delta', 'Alpha', 'EJ'], axis=1).columns
        cat_df = df[['Beta', 'Gamma', 'Delta', 'Alpha', 'EJ']].copy()

        df = self.trained_patterns["zscores"].transform_all(df, ['Beta', 'Gamma', 'Delta', 'Alpha', 'EJ'])

        imp_mean = self.trained_patterns["imputation"]
        num_columns = df.drop(['Beta', 'Gamma', 'Delta', 'Alpha', 'EJ'], axis=1).columns
        df.loc[:, num_columns] = df.loc[:, num_columns].replace([np.inf, -np.inf], np.nan)
        df = imp_mean.transform(df.loc[:, num_columns])

        df = pd.DataFrame(df, columns=num_columns).merge(cat_df, left_index=True, right_index=True, how="left")

        df = df.drop(['Beta', 'Gamma', 'Delta', 'Alpha'], axis=1)

        return df, target

# add custom last mile computation
class MyCustomLastMilePreprocessing(CustomPreprocessing):
    def custom_function(self, df: pd.DataFrame) -> pd.DataFrame:
        df = df / 2
        df["custom_col"] = 5
        return df

    # Please note: The base class enforces that the fit_transform method is implemented
    def fit_transform(
        self, df: pd.DataFrame, target: pd.Series
    ) -> Tuple[pd.DataFrame, pd.Series]:
        df = self.custom_function(df)
        df = df.head(1000)
        target = target.head(1000)
        return df, target

    # Please note: The base class enforces that the fit_transform method is implemented
    def transform(
        self,
        df: pd.DataFrame,
        target: Optional[pd.Series] = None,
        predicton_mode: bool = False,
    ) -> Tuple[pd.DataFrame, Optional[pd.Series]]:
        df = self.custom_function(df)
        if not predicton_mode and isinstance(target, pd.Series):
            df = df.head(100)
            target = target.head(100)
        return df, targe

custom_last_mile_computation = MyCustomLastMilePreprocessing()
custom_preprocessor = MyCustomPreprocessing()

# Pass the custom configs to the BlueCast class
automl = BlueCast(
        class_problem="binary",
        target_column="target"
        conf_training=train_config,
        conf_xgboost=xgboost_param_config,
        custom_preprocessor=custom_preprocessor, # this takes place right after test_train_split
        custom_last_mile_computation=custom_last_mile_computation, # last step before model training/prediction
    )

automl.fit(df_train, target_col="target")
y_probs, y_classes = automl.predict(df_val)

Custom feature selection

BlueCast offers automated feature selection. On default the feature selection is disabled, but BlueCast raises a warning to inform the user about this option. The behaviour can be controlled via the TrainingConfig.

from bluecast.blueprints.cast import BlueCast
from bluecast.preprocessing.custom import CustomPreprocessing
from bluecast.config.training_config import TrainingConfig

# Create a custom training config and adjust general training parameters
train_config = TrainingConfig()
train_config.hyperparameter_tuning_rounds = 10
train_config.autotune_model = False # we want to run just normal training, no hyperparameter tuning
train_config.enable_feature_selection = True

# Pass the custom configs to the BlueCast class
automl = BlueCast(
        class_problem="binary",
        target_column="target"
        conf_training=train_config,
    )

automl.fit(df_train, target_col="target")
y_probs, y_classes = automl.predict(df_val)

Also this step can be customized. The following example shows how to:

from bluecast.config.training_config import TrainingConfig
from bluecast.preprocessing.custom import CustomPreprocessing
from sklearn.feature_selection import RFECV
from sklearn.metrics import make_scorer, matthews_corrcoef
from sklearn.model_selection import StratifiedKFold
from typing import Optional, Tuple


# Create a custom training config and adjust general training parameters
train_config = TrainingConfig()
train_config.enable_feature_selection = True

# add custom feature selection
class RFECVSelector(CustomPreprocessing):
    def __init__(self, random_state: int = 0):
        super().__init__()
        self.selected_features = None
        self.random_state = random_state
        self.selection_strategy: RFECV = RFECV(
            estimator=xgb.XGBClassifier(),
            step=1,
            cv=StratifiedKFold(5, random_state=random_state, shuffle=True),
            min_features_to_select=1,
            scoring=make_scorer(matthews_corrcoef),
            n_jobs=2,
        )

    def fit_transform(self, df: pd.DataFrame, target: pd.Series) -> Tuple[pd.DataFrame, Optional[pd.Series]]:
        self.selection_strategy.fit(df, target)
        self.selected_features = self.selection_strategy.support_
        df = df.loc[:, self.selected_features]
        return df, target

    def transform(self,
                  df: pd.DataFrame,
                  target: Optional[pd.Series] = None,
                  predicton_mode: bool = False) -> Tuple[pd.DataFrame, Optional[pd.Series]]:
        df = df.loc[:, self.selected_features]
        return df, target

custom_feature_selector = RFECVSelector()

# Create an instance of the BlueCast class with the custom model
bluecast = BlueCast(
    class_problem="binary",
    target_column="target",
    conf_feature_selection=custom_feat_sel,
    conf_training=train_config,
    custom_feature_selector=custom_feature_selector,

# Create some sample data for testing
x_train = pd.DataFrame(
    {"feature1": [i for i in range(10)], "feature2": [i for i in range(10)]}
)
y_train = pd.Series([0, 1, 0, 1, 0, 1, 0, 1, 0, 1])
x_test = pd.DataFrame(
    {"feature1": [i for i in range(10)], "feature2": [i for i in range(10)]}

x_train["target"] = y_trai
# Fit the BlueCast model using the custom model
bluecast.fit(x_train, "target"
# Predict on the test data using the custom model
predicted_probas, predicted_classes = bluecast.predict(x_test)

Custom ML model

For some users it might just be convenient to use the BlueCast class to enjoy convenience features (details see below), but use a custom ML model. This is possible by passing a custom model to the BlueCast class. The needed properties are defined via the BaseClassMlModel class. Here is an example:

from bluecast.ml_modelling.base_classes import (
    BaseClassMlModel,
    PredictedClasses,  # just for linting checks
    PredictedProbas,  # just for linting checks
)


class CustomModel(BaseClassMlModel):
    def __init__(self):
        self.model = None

    def fit(
        self,
        x_train: pd.DataFrame,
        x_test: pd.DataFrame,
        y_train: pd.Series,
        y_test: pd.Series,
    ) -> None:
        self.model = LogisticRegression()
        self.model.fit(x_train, y_train)
        # if you wih to track experiments using an own ExperimentTracker add it here
        # or in the fit method itself

    def predict(self, df: pd.DataFrame) -> Tuple[PredictedProbas, PredictedClasses]:
        predicted_probas = self.model.predict_proba(df)
        predicted_classes = self.model.predict(df)
        return predicted_probas, predicted_classes

custom_model = CustomModel()

# Create an instance of the BlueCast class with the custom model
bluecast = BlueCast(
    class_problem="binary",
    target_column="target",
    ml_model=custom_model,

# Create some sample data for testing
x_train = pd.DataFrame(
    {"feature1": [i for i in range(10)], "feature2": [i for i in range(10)]}
)
y_train = pd.Series([0, 1, 0, 1, 0, 1, 0, 1, 0, 1])
x_test = pd.DataFrame(
    {"feature1": [i for i in range(10)], "feature2": [i for i in range(10)]}

x_train["target"] = y_trai
# Fit the BlueCast model using the custom model
bluecast.fit(x_train, "target"
# Predict on the test data using the custom model
predicted_probas, predicted_classes = bluecast.predict(x_test)

Please note that custom ML models require user defined hyperparameter tuning. Pre-defined configurations are not available for custom models. Also note that the calculation of SHAP values only works with tree based models by default. For other model architectures disable SHAP values in the TrainingConfig via:

train_config.calculate_shap_values = True

Just instantiate a new instance of the TrainingConfig, update the param as above and pass the config as an argument to the BlueCast instance during instantiation. Feature importance can be added in the custom model definition.

Using the inbuilt ExperientTracker

For experimentation environments it can be useful to store all variables and results from model runs. BlueCast has an inbuilt experiment tracker to enhance the provided insights. No setup is required. BlueCast will automatically store all necessary data after each hyperparameter tuning trial.

# instantiate and train BlueCast
from bluecast.blueprints.cast import BlueCast

automl = BlueCast(
        class_problem="binary",
        target_column="target"
    )

automl.fit_eval(df_train, df_eval, y_eval, target_col="target")

# access the experiment tracker
tracker = automl.experiment_tracker

# see all stored information as a Pandas DataFrame
tracker_df = tracker.retrieve_results_as_df()

Now from here you could even feed selected columns back into a BlueCast instance and try to predict the eval_score to check the get the feature importance of your experiment data! Maybe you uncover hidden patterns for your model training.

Please note that the number of stored experiments will probably be lower than the number of started hyperparameter tuning trials. The experiment tracker is skipped whenever Optuna prunes a trial. The experiment triggers whenever the fit or fit_eval methods of a BlueCast class instance are called (also within BlueCastCV). This means for custom models the tracker will not trigger automatically and has to be added manually.

Convenience features

Despite being a lightweight library, BlueCast also includes some convenience with the following features:

• automatic feature type detection and casting
• automatic DataFrame schema detection: checks if unseen data has new or missing columns
• categorical feature encoding (target encoding or directly in Xgboost)
• datetime feature encoding
• automated GPU availability check and usage for Xgboost a fit_eval method to fit a model and evaluate it on a validation set to mimic production environment reality
• functions to save and load a trained pipeline
• shapley values
• warnings for potential misconfigurations

The fit_eval method can be used like this:

from bluecast.blueprints.cast import BlueCast

automl = BlueCast(
        class_problem="binary",
        target_column="target"
    )

automl.fit_eval(df_train, df_eval, y_eval, target_col="target")
y_probs, y_classes = automl.predict(df_val)

It is important to note that df_train contains the target column while df_eval does not. The target column is passed separately as y_eval.

Code quality

To ensure code quality, we use the following tools:

• various pre-commit libraries
• strong type hinting in the code base
• unit tests using Pytest

For contributors, it is expected that all pre-commit and unit tests pass. For new features it is expected that unit tests are added.

Documentation

Documentation is provided via Read the Docs

Kaggle competition results and example notebooks

Even though BlueCast has been designed to be a lightweight automl framework, it still offers the possibilities to reach very good performance. We tested BlueCast in Kaggle competitions to show case the libraries capabilities feature- and performance-wise.

• ICR top 20% finish with over 6000 participants (notebook)
• An advanced example covering lots of functionalities (notebook)

How to contribute

Contributions are welcome. Please follow the following steps:

• Create a new branch from develop branch
• Add your feature or fix
• Add unit tests for new features
• Run pre-commit checks and unit tests (using Pytest)
• Adjust the docs/source/index.md file
• Copy paste the content of the docs/source/index.md file into the README.md file
• Push your changes and create a pull request

If library or dev dependencies have to be changed, adjust the pyproject.toml. For readthedocs it is also requited to update the docs/srtd_requirements.txt file. Simply run:

poetry export --with dev -f requirements.txt --output docs/rtd_requirements.txt

If readthedocs will be able to create the documentation can be tested via:

poetry run sphinx-autobuild docs/source docs/build/html

This will show a localhost link containing the documentation.

Meta

Creator: Thomas Meißner – LinkedIn