evp package

Submodules

evp.evp module

class evp.evp.EstimatedValuePredictor(data: DataFrame, train_size=0.8, val_size=0.1, test_size=0.1, model_type: Predictors = Predictors.RandomForest, model_name: str | None = None, limit_classes: bool = False, selected_features: list | None = None, **model_args)

Bases: object

lead_classifier: Classifier

predict(X) → list[MerchantSizeByDPV]

save_model() → None

train(epochs=1, batch_size=None) → None

evp.predictors module

class evp.predictors.AdaBoost(model_name: str | None = None, n_estimators=100, class_weight=None, random_state=42)

Bases: Classifier

predict(X) → MerchantSizeByDPV

train(X_train, y_train, X_test, y_test, epochs=1, batch_size=None) → None

class evp.predictors.Classifier(model_name: str | None = None, *args, **kwargs)

Bases: ABC

load(model_name: str) → None

abstract predict(X) → list[MerchantSizeByDPV]

save(num_classes: int = 5) → None

abstract train(X_train, y_train, X_test, y_test, epochs=1, batch_size=None) → None

class evp.predictors.KNNClassifier(model_name: str | None = None, random_state=42, n_neighbors=10, weights='distance')

Bases: Classifier

predict(X) → list[MerchantSizeByDPV]

train(X_train, y_train, X_test, y_test, epochs=1, batch_size=None) → None

class evp.predictors.LightGBM(model_name: str | None = None, num_leaves=1000, random_state=42)

Bases: Classifier

predict(X) → MerchantSizeByDPV

train(X_train, y_train, X_test, y_test, epochs=1, batch_size=None) → None

class evp.predictors.MerchantSizeByDPV(value)

Bases: Enum

An enumeration.

Invalid = -1

L = 3

M = 2

S = 1

XL = 4

XS = 0

class evp.predictors.NaiveBayesClassifier(model_name: str | None = None, random_state=42)

Bases: Classifier

predict(X) → list[MerchantSizeByDPV]

train(X_train, y_train, X_test, y_test, epochs=1, batch_size=None) → None

class evp.predictors.Predictors(value)

Bases: Enum

An enumeration.

AdaBoost = 'AdaBoost'

KNN = 'KNN Classifier'

LightGBM = 'LightGBM'

NaiveBayes = 'Naive Bayes'

RandomForest = 'Random Forest'

XGBoost = 'XGBoost'

class evp.predictors.RandomForest(model_name: str | None = None, n_estimators=100, class_weight=None, random_state=42)

Bases: Classifier

predict(X) → MerchantSizeByDPV

train(X_train, y_train, X_test, y_test, epochs=1, batch_size=None) → None

class evp.predictors.XGB(model_name: str | None = None, num_rounds=2000, random_state=42)

Bases: Classifier

predict(X) → MerchantSizeByDPV

train(X_train, y_train, X_test, y_test, epochs=1, batch_size=None) → None

Module contents