class_docstr: This module helps in fitting regression models that are available in Scikit-learn
  Parameters
  ----------
  verbose : int, optional (default=0)
      For the liblinear and lbfgs solvers set verbose to any positive
      number for verbosity.
  ignore_warnings : bool, optional (default=True)
      When set to True, the warning related to algorigms that are not able to run are ignored.
  custom_metric : function, optional (default=None)
      When function is provided, models are evaluated based on the custom evaluation metric provided.
  prediction : bool, optional (default=False)
      When set to True, the predictions of all the models models are returned as dataframe.
  regressors : list, optional (default="all")
      When function is provided, trains the chosen regressor(s).
  
  Examples
  --------
  >>> from lazypredict.Supervised import LazyRegressor
  >>> from sklearn import datasets
  >>> from sklearn.utils import shuffle
  >>> import numpy as np
  
  >>> boston = datasets.load_boston()
  >>> X, y = shuffle(boston.data, boston.target, random_state=13)
  >>> X = X.astype(np.float32)
  
  >>> offset = int(X.shape[0] * 0.9)
  >>> X_train, y_train = X[:offset], y[:offset]
  >>> X_test, y_test = X[offset:], y[offset:]
  
  >>> reg = LazyRegressor(verbose=0, ignore_warnings=False, custom_metric=None)
  >>> models, predictions = reg.fit(X_train, X_test, y_train, y_test)
  >>> model_dictionary = reg.provide_models(X_train, X_test, y_train, y_test)
  >>> models
  | Model                         | Adjusted R-Squared | R-Squared |  RMSE | Time Taken |
  |:------------------------------|-------------------:|----------:|------:|-----------:|
  | SVR                           |               0.83 |      0.88 |  2.62 |       0.01 |
  | BaggingRegressor              |               0.83 |      0.88 |  2.63 |       0.03 |
  | NuSVR                         |               0.82 |      0.86 |  2.76 |       0.03 |
  | RandomForestRegressor         |               0.81 |      0.86 |  2.78 |       0.21 |
  | XGBRegressor                  |               0.81 |      0.86 |  2.79 |       0.06 |
  | GradientBoostingRegressor     |               0.81 |      0.86 |  2.84 |       0.11 |
  | ExtraTreesRegressor           |               0.79 |      0.84 |  2.98 |       0.12 |
  | AdaBoostRegressor             |               0.78 |      0.83 |  3.04 |       0.07 |
  | HistGradientBoostingRegressor |               0.77 |      0.83 |  3.06 |       0.17 |
  | PoissonRegressor              |               0.77 |      0.83 |  3.11 |       0.01 |
  | LGBMRegressor                 |               0.77 |      0.83 |  3.11 |       0.07 |
  | KNeighborsRegressor           |               0.77 |      0.83 |  3.12 |       0.01 |
  | DecisionTreeRegressor         |               0.65 |      0.74 |  3.79 |       0.01 |
  | MLPRegressor                  |               0.65 |      0.74 |  3.80 |       1.63 |
  | HuberRegressor                |               0.64 |      0.74 |  3.84 |       0.01 |
  | GammaRegressor                |               0.64 |      0.73 |  3.88 |       0.01 |
  | LinearSVR                     |               0.62 |      0.72 |  3.96 |       0.01 |
  | RidgeCV                       |               0.62 |      0.72 |  3.97 |       0.01 |
  | BayesianRidge                 |               0.62 |      0.72 |  3.97 |       0.01 |
  | Ridge                         |               0.62 |      0.72 |  3.97 |       0.01 |
  | TransformedTargetRegressor    |               0.62 |      0.72 |  3.97 |       0.01 |
  | LinearRegression              |               0.62 |      0.72 |  3.97 |       0.01 |
  | ElasticNetCV                  |               0.62 |      0.72 |  3.98 |       0.04 |
  | LassoCV                       |               0.62 |      0.72 |  3.98 |       0.06 |
  | LassoLarsIC                   |               0.62 |      0.72 |  3.98 |       0.01 |
  | LassoLarsCV                   |               0.62 |      0.72 |  3.98 |       0.02 |
  | Lars                          |               0.61 |      0.72 |  3.99 |       0.01 |
  | LarsCV                        |               0.61 |      0.71 |  4.02 |       0.04 |
  | SGDRegressor                  |               0.60 |      0.70 |  4.07 |       0.01 |
  | TweedieRegressor              |               0.59 |      0.70 |  4.12 |       0.01 |
  | GeneralizedLinearRegressor    |               0.59 |      0.70 |  4.12 |       0.01 |
  | ElasticNet                    |               0.58 |      0.69 |  4.16 |       0.01 |
  | Lasso                         |               0.54 |      0.66 |  4.35 |       0.02 |
  | RANSACRegressor               |               0.53 |      0.65 |  4.41 |       0.04 |
  | OrthogonalMatchingPursuitCV   |               0.45 |      0.59 |  4.78 |       0.02 |
  | PassiveAggressiveRegressor    |               0.37 |      0.54 |  5.09 |       0.01 |
  | GaussianProcessRegressor      |               0.23 |      0.43 |  5.65 |       0.03 |
  | OrthogonalMatchingPursuit     |               0.16 |      0.38 |  5.89 |       0.01 |
  | ExtraTreeRegressor            |               0.08 |      0.32 |  6.17 |       0.01 |
  | DummyRegressor                |              -0.38 |     -0.02 |  7.56 |       0.01 |
  | LassoLars                     |              -0.38 |     -0.02 |  7.56 |       0.01 |
  | KernelRidge                   |             -11.50 |     -8.25 | 22.74 |       0.01 |
class_docstr_tok: ["This","module","helps","in","fitting","regression","models","that","are","available","in","Scikit-learn","Parameters","--","--","--","--","--","verbose",":","int",",","optional","(","default=0",")","For","the","liblinear","and","lbfgs","solvers","set","verbose","to","any","positive","number","for","verbosity",".","ignore_warnings",":","bool",",","optional","(","default=True",")","When","set","to","True",",","the","warning","related","to","algorigms","that","are","not","able","to","run","are","ignored",".","custom_metric",":","function",",","optional","(","default=None",")","When","function","is","provided",",","models","are","evaluated","based","on","the","custom","evaluation","metric","provided",".","prediction",":","bool",",","optional","(","default=False",")","When","set","to","True",",","the","predictions","of","all","the","models","models","are","returned","as","dataframe",".","regressors",":","list",",","optional","(","default=","''","all","''",")","When","function","is","provided",",","trains","the","chosen","regressor","(","s",")",".","Examples","--","--","--","--",">",">",">","from","lazypredict.Supervised","import","LazyRegressor",">",">",">","from","sklearn","import","datasets",">",">",">","from","sklearn.utils","import","shuffle",">",">",">","import","numpy","as","np",">",">",">","boston","=","datasets.load_boston","(",")",">",">",">","X",",","y","=","shuffle","(","boston.data",",","boston.target",",","random_state=13",")",">",">",">","X","=","X.astype","(","np.float32",")",">",">",">","offset","=","int","(","X.shape","[","0","]","*","0.9",")",">",">",">","X_train",",","y_train","=","X","[",":","offset","]",",","y","[",":","offset","]",">",">",">","X_test",",","y_test","=","X","[","offset",":","]",",","y","[","offset",":","]",">",">",">","reg","=","LazyRegressor","(","verbose=0",",","ignore_warnings=False",",","custom_metric=None",")",">",">",">","models",",","predictions","=","reg.fit","(","X_train",",","X_test",",","y_train",",","y_test",")",">",">",">","model_dictionary","=","reg.provide_models","(","X_train",",","X_test",",","y_train",",","y_test",")",">",">",">","models","|","Model","|","Adjusted","R-Squared","|","R-Squared","|","RMSE","|","Time","Taken","|","|",":","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","|","--","--","--","--","--","--","--","--","--","-",":","|","--","--","--","--","--",":","|","--","--","--",":","|","--","--","--","--","--","-",":","|","|","SVR","|","0.83","|","0.88","|","2.62","|","0.01","|","|","BaggingRegressor","|","0.83","|","0.88","|","2.63","|","0.03","|","|","NuSVR","|","0.82","|","0.86","|","2.76","|","0.03","|","|","RandomForestRegressor","|","0.81","|","0.86","|","2.78","|","0.21","|","|","XGBRegressor","|","0.81","|","0.86","|","2.79","|","0.06","|","|","GradientBoostingRegressor","|","0.81","|","0.86","|","2.84","|","0.11","|","|","ExtraTreesRegressor","|","0.79","|","0.84","|","2.98","|","0.12","|","|","AdaBoostRegressor","|","0.78","|","0.83","|","3.04","|","0.07","|","|","HistGradientBoostingRegressor","|","0.77","|","0.83","|","3.06","|","0.17","|","|","PoissonRegressor","|","0.77","|","0.83","|","3.11","|","0.01","|","|","LGBMRegressor","|","0.77","|","0.83","|","3.11","|","0.07","|","|","KNeighborsRegressor","|","0.77","|","0.83","|","3.12","|","0.01","|","|","DecisionTreeRegressor","|","0.65","|","0.74","|","3.79","|","0.01","|","|","MLPRegressor","|","0.65","|","0.74","|","3.80","|","1.63","|","|","HuberRegressor","|","0.64","|","0.74","|","3.84","|","0.01","|","|","GammaRegressor","|","0.64","|","0.73","|","3.88","|","0.01","|","|","LinearSVR","|","0.62","|","0.72","|","3.96","|","0.01","|","|","RidgeCV","|","0.62","|","0.72","|","3.97","|","0.01","|","|","BayesianRidge","|","0.62","|","0.72","|","3.97","|","0.01","|","|","Ridge","|","0.62","|","0.72","|","3.97","|","0.01","|","|","TransformedTargetRegressor","|","0.62","|","0.72","|","3.97","|","0.01","|","|","LinearRegression","|","0.62","|","0.72","|","3.97","|","0.01","|","|","ElasticNetCV","|","0.62","|","0.72","|","3.98","|","0.04","|","|","LassoCV","|","0.62","|","0.72","|","3.98","|","0.06","|","|","LassoLarsIC","|","0.62","|","0.72","|","3.98","|","0.01","|","|","LassoLarsCV","|","0.62","|","0.72","|","3.98","|","0.02","|","|","Lars","|","0.61","|","0.72","|","3.99","|","0.01","|","|","LarsCV","|","0.61","|","0.71","|","4.02","|","0.04","|","|","SGDRegressor","|","0.60","|","0.70","|","4.07","|","0.01","|","|","TweedieRegressor","|","0.59","|","0.70","|","4.12","|","0.01","|","|","GeneralizedLinearRegressor","|","0.59","|","0.70","|","4.12","|","0.01","|","|","ElasticNet","|","0.58","|","0.69","|","4.16","|","0.01","|","|","Lasso","|","0.54","|","0.66","|","4.35","|","0.02","|","|","RANSACRegressor","|","0.53","|","0.65","|","4.41","|","0.04","|","|","OrthogonalMatchingPursuitCV","|","0.45","|","0.59","|","4.78","|","0.02","|","|","PassiveAggressiveRegressor","|","0.37","|","0.54","|","5.09","|","0.01","|","|","GaussianProcessRegressor","|","0.23","|","0.43","|","5.65","|","0.03","|","|","OrthogonalMatchingPursuit","|","0.16","|","0.38","|","5.89","|","0.01","|","|","ExtraTreeRegressor","|","0.08","|","0.32","|","6.17","|","0.01","|","|","DummyRegressor","|","-0.38","|","-0.02","|","7.56","|","0.01","|","|","LassoLars","|","-0.38","|","-0.02","|","7.56","|","0.01","|","|","KernelRidge","|","-11.50","|","-8.25","|","22.74","|","0.01","|"]
code: def __init__(
      self,
      verbose=0,
      ignore_warnings=True,
      custom_metric=None,
      predictions=False,
      random_state=42,
      regressors="all",
  ):
      self.verbose = verbose
      self.ignore_warnings = ignore_warnings
      self.custom_metric = custom_metric
      self.predictions = predictions
      self.models = {}
      self.random_state = random_state
      self.regressors = regressors
  
imports: import numpy
  import pandas
  from tqdm import tqdm
  import datetime
  import time
  from sklearn.pipeline import Pipeline
  from sklearn.impute import SimpleImputer, MissingIndicator
  from sklearn.preprocessing import StandardScaler, OneHotEncoder, OrdinalEncoder
  from sklearn.compose import ColumnTransformer
  from sklearn.utils import all_estimators
  from sklearn.base import RegressorMixin
  from sklearn.base import ClassifierMixin
  from sklearn.metrics import accuracy_score, balanced_accuracy_score, roc_auc_score, f1_score, r2_score, mean_squared_error
  import warnings
  import xgboost
  import lightgbm
label_desc: Use image node_id 1 to create a new LazyRegressor object  with example: obj = LazyRegressor(verbose, ignore_warnings, custom_metric, predictions, random_state, regressors)