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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# import algorithms for classification
from sklearn.linear_model import LogisticRegression, SGDClassifier, RidgeClassifier
from sklearn.ensemble import RandomForestClassifier,AdaBoostClassifier,GradientBoostingClassifier,HistGradientBoostingClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import SVC
from xgboost import XGBClassifier,XGBRFClassifier
from sklearn.neural_network import MLPClassifier
from lightgbm import LGBMClassifier
from sklearn.naive_bayes import MultinomialNB,CategoricalNB
# import algorithms for regression
from sklearn.linear_model import LinearRegression, SGDRegressor, Ridge, Lasso, ElasticNet
from sklearn.ensemble import RandomForestRegressor,AdaBoostRegressor,GradientBoostingRegressor,HistGradientBoostingRegressor
from sklearn.neighbors import KNeighborsRegressor
from sklearn.tree import DecisionTreeRegressor
from sklearn.svm import SVR
from xgboost import XGBRegressor, XGBRFRegressor
from sklearn.neural_network import MLPRegressor
from lightgbm import LGBMRegressor
from sklearn.naive_bayes import GaussianNB
# dictionary where keys are name of algorithm and values are algorithm for classifier
algos_class = {
"Logistic Regression": LogisticRegression(),
"SGD Classifier": SGDClassifier(),
"Ridge Classifier": RidgeClassifier(),
"Random Forest Classifier": RandomForestClassifier(),
"AdaBoost Classifier": AdaBoostClassifier(),
"Gradient Boosting Classifier": GradientBoostingClassifier(),
"Hist Gradient Boosting Classifier": HistGradientBoostingClassifier(),
"K Neighbors Classifier": KNeighborsClassifier(),
"Decision Tree Classifier": DecisionTreeClassifier(),
"SVC": SVC(),
"XGB Classifier": XGBClassifier(),
"XGBRF Classifier": XGBRFClassifier(),
"MLP Classifier": MLPClassifier(),
"LGBM Classifier": LGBMClassifier(),
"Multinomial Naive Bayes": MultinomialNB(),
"Categorical Naive Bayes": CategoricalNB()}
# dictionary where keys are name of algorithm and values are algorithm for regression
algos_reg = {
"Linear Regression": LinearRegression(),
"SGD Regressor": SGDRegressor(),
"Ridge Regressor": Ridge(),
"Lasso Regressor": Lasso(),
"ElasticNet Regressor": ElasticNet(),
"Random Forest Regressor": RandomForestRegressor(),
"AdaBoost Regressor": AdaBoostRegressor(),
"Gradient Boosting Regressor": GradientBoostingRegressor(),
"Hist Gradient Boosting Regressor": HistGradientBoostingRegressor(),
"K Neighbors Regressor": KNeighborsRegressor(),
"Decision Tree Regressor": DecisionTreeRegressor(),
"SVR": SVR(),
"XGB Regressor": XGBRegressor(),
"XGBRF Regressor": XGBRFRegressor(),
"MLP Regressor": MLPRegressor(),
"LGBM Regressor": LGBMRegressor(),
"Gaussian Naive Bayes": GaussianNB()}
# dataframe where index are name of algorithm as "algorithm name" , column is algorithm as "algorithm"
Classification_models = pd.DataFrame(data=algos_class.values(), index=algos_class.keys())
Regression_models = pd.DataFrame(data=algos_reg.values(), index=algos_reg.keys())
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