EazyPredict

EazyPredict serves as a quick way to try out multiple prediction algorithms on data while writing as few lines as possible. It also provides the possibility to create an ensemble of the top models

The 'EazyPredict' module was heavily inspired by LazyPredict. This module varies in terms of its functionality and intended use, as outlined in the following ways:

• The 'EazyPredict' module utilizes a limited number of prediction algorithms (around 9) in order to minimize memory usage and prevent potential issues on platforms such as Kaggle.

• Users have the option to input a custom list of prediction algorithms (as demonstrated in the example provided) in order to perform personalized comparisons.

• The models can be saved to an output folder at the user's discretion and are returned as a dictionary, allowing for easy addition of custom hyperparameters.

• The top 5 models are selected to create an ensemble using a voting classifier.

Installation

pip install eazypredict

Usage

For classification

from eazypredict.EazyClassifier import EazyClassifier

from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split

data = load_breast_cancer()
X = data.data
y = data.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.5,random_state=123)

clf = EazyClassifier()

model_list, prediction_list, model_results = clf.fit(X_train, X_test, y_train, y_test)

print(model_results)

OUTPUT

                        Accuracy  f1 score  ROC AUC score
XGBClassifier           0.978947  0.978990       0.979302
LGBMClassifier          0.971930  0.971930       0.969594
RandomForestClassifier  0.968421  0.968516       0.968953
RidgeClassifier         0.964912  0.964670       0.955671
MLPClassifier           0.961404  0.961185       0.952923
GaussianNB              0.957895  0.957707       0.950176
DecisionTreeClassifier  0.936842  0.937093       0.935800
KNeighborsClassifier    0.936842  0.936407       0.925264
SVC                     0.919298  0.917726       0.896778
SGDClassifier           0.831579  0.834856       0.861811

For regression

from eazypredict.EazyRegressor import EazyRegressor

from sklearn.datasets import fetch_california_housing
from sklearn.preprocessing import RobustScaler
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle
import numpy as np

california_housing = fetch_california_housing(as_frame=True)
X, y = california_housing.data, california_housing.target

scaler = RobustScaler()
X_norm = pd.DataFrame(scaler.fit_transform(X), columns=X.columns)

X_train, X_test, y_train, y_test = train_test_split(X_norm, y, test_size= 0.2)

reg = EazyRegressor()
models, predictions = reg.fit(X_train, X_test, y_train, y_test)

print(models)

OUTPUT

                           RMSE       R Squared
LGBMRegressor              0.468025	  0.838435
XGBRegressor               0.470601	  0.836651
RandomForestRegressor      0.490834	  0.822303
MLPRegressor               0.590093	  0.743167
KNeighborsRegressor        0.646152	  0.692051
NuSVR                      0.656935	  0.681687
DecisionTreeRegressor      0.704143	  0.634294
LinearRegression           0.736127	  0.600318
Ridge                      0.736145	  0.600297
GaussianProcessRegressor   10.893585	-86.528957

Creating an ensemble model

reg = EazyRegressor()

model_dict, prediction_list, model_results = reg.fit(X_train, y_train, X_test, y_test)

ensemble_reg, ensemble_results = reg.fitVotingEnsemble(model_dict, model_results)
print(ensemble_results)

OUTPUT

                                                            RMSE        R Squared
LGBMRegressor XGBRegressor RandomForestRegress...           0.48638   0.825514

Custom Estimators

Get more estimators from sklearn.

custom_list = [
  "LinearSVC",
  "NearestCentroid",
  "ExtraTreeClassifier",
  "LinearDiscriminantAnalysis",
  "AdaBoostClassifier"
]

clf = EazyClassifier(classififers=custom_list)
model_list, prediction_list, model_results = clf.fit(X_train, y_train, X_test, y_test)

print(model_results)

OUTPUT

                            Accuracy  f1 score  ROC AUC score
AdaBoostClassifier          0.961404  0.961444       0.959245
LinearDiscriminantAnalysis  0.961404  0.961089       0.950816
ExtraTreeClassifier         0.908772  0.909134       0.905393
NearestCentroid             0.898246  0.894875       0.865545
LinearSVC                   0.838596  0.841756       0.867305

Future Plans subject to improvisaton

• Hyperparameter Tuning Feature
• Parallel computation of training