regression.py

# Module: Regression
# Author: Moez Ali <moez.ali@queensu.ca>
# License: MIT



def setup(data, 
          target, 
          train_size=0.7,
          sampling=True,
          sample_estimator = None,
          categorical_features = None,
          categorical_imputation = 'constant',
          ordinal_features = None,
          numeric_features = None,
          numeric_imputation = 'mean',
          date_features = None,
          ignore_features = None,
          normalize = False,
          normalize_method = 'zscore',
          transformation = False,
          transformation_method = 'yeo-johnson',
          handle_unknown_categorical = True, #new              #create docstring and exception
          unknown_categorical_method = 'least_frequent', #new  #create docstring and exception
          pca = False, #new
          pca_method = 'linear', #new
          pca_components = None, #new
          ignore_low_variance = False, #new
          combine_rare_levels = False, #new
          rare_level_threshold = 0.10, #new
          bin_numeric_features = None, #new
          remove_outliers = False, #new
          outliers_threshold = 0.05, #new
          remove_multicollinearity = False, #new
          multicollinearity_threshold = 0.9, #new
          create_clusters = False, #new
          cluster_iter = 20, #new
          polynomial_features = False, #new                  #create checking exceptions and docstring
          polynomial_degree = 2, #new                        #create checking exceptions and docstring
          trigonometry_features = False, #new                #create checking exceptions and docstring
          polynomial_threshold = 0.1, #new                   #create checking exceptions and docstring
          group_features = None, #new                        #create checking exceptions and docstring
          group_names = None, #new                           #create checking exceptions and docstring
          feature_selection = False, #new                    #create checking exceptions and docstring
          feature_selection_threshold = 0.8, #new            #create checking exceptions and docstring
          feature_interaction = False, #new                  #create checking exceptions and docstring
          feature_ratio = False, #new                        #create checking exceptions and docstring
          interaction_threshold = 0.01,    #new              #create checking exceptions and docstring
          transform_target = False, #new
          transform_target_method = 'box-cox', #new
          session_id = None,
          silent = False,
          profile = False):
    
    """
        
    Description:
    ------------    
    This function initializes the environment in pycaret and creates the transformation
    pipeline to prepare the data for modeling and deployment. setup() must called before
    executing any other function in pycaret. It takes two mandatory parameters:
    dataframe {array-like, sparse matrix} and name of the target column. 
    
    All other parameters are optional.

        Example
        -------
        from pycaret.datasets import get_data
        boston = get_data('boston')
        
        experiment_name = setup(data = boston,  target = 'medv')

        'boston' is a pandas DataFrame and 'medv' is the name of target column.

    Parameters
    ----------
    data : {array-like, sparse matrix}, shape (n_samples, n_features) where n_samples 
    is the number of samples and n_features is the number of features.

    target: string
    Name of target column to be passed in as string. 
    
    train_size: float, default = 0.7
    Size of the training set. By default, 70% of the data will be used for training 
    and validation. The remaining data will be used for test / hold-out set.

    sampling: bool, default = True
    When the sample size exceeds 25,000 samples, pycaret will build a base estimator
    at various sample sizes from the original dataset. This will return a performance 
    plot of R2 values at various sample levels, that will assist in deciding the 
    preferred sample size for modeling.  The desired sample size must then be entered 
    for training and validation in the  pycaret environment. When sample_size entered 
    is less than 1, the remaining dataset (1 - sample) is used for fitting the model 
    only when finalize_model() is called.
    
    sample_estimator: object, default = None
    If None, Linear Regression is used by default.
    
    categorical_features: string, default = None
    If the inferred data types are not correct, categorical_features can be used to
    overwrite the inferred type. If when running setup the type of 'column1' is
    inferred as numeric instead of categorical, then this parameter can be used 
    to overwrite the type by passing categorical_features = ['column1'].
    
    categorical_imputation: string, default = 'constant'
    If missing values are found in categorical features, they will be imputed with
    a constant 'not_available' value. The other available option is 'mode' which 
    imputes the missing value using most frequent value in the training dataset. 
    
    ordinal_features: dictionary, default = None
    When the data contains ordinal features, they must be encoded differently using 
    the ordinal_features param. If the data has a categorical variable with values
    of 'low', 'medium', 'high' and it is known that low < medium < high, then it can 
    be passed as ordinal_features = { 'column_name' : ['low', 'medium', 'high'] }. 
    The list sequence must be in increasing order from lowest to highest.
    
    numeric_features: string, default = None
    If the inferred data types are not correct, numeric_features can be used to
    overwrite the inferred type. If when running setup the type of 'column1' is 
    inferred as a categorical instead of numeric, then this parameter can be used 
    to overwrite by passing numeric_features = ['column1'].    

    numeric_imputation: string, default = 'mean'
    If missing values are found in numeric features, they will be imputed with the 
    mean value of the feature. The other available option is 'median' which imputes 
    the value using the median value in the training dataset. 
    
    date_features: string, default = None
    If the data has a DateTime column that is not automatically detected when running
    setup, this parameter can be used by passing date_features = 'date_column_name'. 
    It can work with multiple date columns. Date columns are not used in modeling. 
    Instead, feature extraction is performed and date columns are dropped from the 
    dataset. If the date column includes a time stamp, features related to time will 
    also be extracted.
    
    ignore_features: string, default = None
    If any feature should be ignored for modeling, it can be passed to the param
    ignore_features. The ID and DateTime columns when inferred, are automatically 
    set to ignore for modeling. 
    
    normalize: bool, default = False
    When set to True, the feature space is transformed using the normalized_method
    param. Generally, linear algorithms perform better with normalized data however, 
    the results may vary and it is advised to run multiple experiments to evaluate
    the benefit of normalization.
    
    normalize_method: string, default = 'zscore'
    Defines the method to be used for normalization. By default, normalize method
    is set to 'zscore'. The standard zscore is calculated as z = (x - u) / s. The
    other available options are:
    
    'minmax'    : scales and translates each feature individually such that it is in 
                  the range of 0 - 1.
    
    'maxabs'    : scales and translates each feature individually such that the maximal 
                  absolute value of each feature will be 1.0. It does not shift/center 
                  the data, and thus does not destroy any sparsity.
    
    'robust'    : scales and translates each feature according to the Interquartile range.
                  When the dataset contains outliers, robust scaler often gives better
                  results.
    
    transformation: bool, default = False
    When set to True, a power transformation is applied to make the data more normal /
    Gaussian-like. This is useful for modeling issues related to heteroscedasticity or 
    other situations where normality is desired. The optimal parameter for stabilizing 
    variance and minimizing skewness is estimated through maximum likelihood.
    
    transformation_method: string, default = 'yeo-johnson'
    Defines the method for transformation. By default, the transformation method is set
    to 'yeo-johnson'. The other available option is 'quantile' transformation. Both 
    the transformation transforms the feature set to follow a Gaussian-like or normal
    distribution. Note that the quantile transformer is non-linear and may distort linear 
    correlations between variables measured at the same scale.
    
    handle_unknown_categorical: bool, default = True
    When set to True, unknown categorical levels in new / unseen data are replaced by
    the most or least frequent level as learned in the training data. The method is 
    defined under the unknown_categorical_method param.
    
    unknown_categorical_method: string, default = 'least_frequent'
    Method used to replace unknown categorical levels in unseen data. Method can be
    set to 'least_frequent' or 'most_frequent'.
    
    pca: bool, default = False
    When set to True, dimensionality reduction is applied to project the data into 
    a lower dimensional space using the method defined in pca_method param. In 
    supervised learning pca is generally performed when dealing with high feature
    space and memory is a constraint. Note that not all datasets can be decomposed
    efficiently using a linear PCA technique and that applying PCA may result in loss 
    of information. As such, it is advised to run multiple experiments with different 
    pca_methods to evaluate the impact. 

    pca_method: string, default = 'linear'
    The 'linear' method performs Linear dimensionality reduction using Singular Value 
    Decomposition. The other available options are:
    
    kernel      : dimensionality reduction through the use of RVF kernel.  
    
    incremental : replacement for 'linear' pca when the dataset to be decomposed is 
                  too large to fit in memory
    
    pca_components: int/float, default = 0.99
    Number of components to keep. if pca_components is a float, it is treated as a 
    target percentage for information retention. When pca_components is an integer
    it is treated as the number of features to be kept. pca_components must be strictly
    less than the original number of features in the dataset.
    
    ignore_low_variance: bool, default = False
    When set to True, all categorical features with statistically insignificant variances 
    are removed from the dataset. The variance is calculated using the ratio of unique 
    values to the number of samples, and the ratio of the most common value to the 
    frequency of the second most common value.
    
    combine_rare_levels: bool, default = False
    When set to True, all levels in categorical features below the threshold defined 
    in rare_level_threshold param are combined together as a single level. There must be 
    atleast two levels under the threshold for this to take effect. rare_level_threshold
    represents the percentile distribution of level frequency. Generally, this technique 
    is applied to limit a sparse matrix caused by high numbers of levels in categorical 
    features. 
    
    rare_level_threshold: float, default = 0.1
    Percentile distribution below which rare categories are combined. Only comes into
    effect when combine_rare_levels is set to True.
    
    bin_numeric_features: list, default = None
    When a list of numeric features is passed they are transformed into categorical
    features using KMeans, where values in each bin have the same nearest center of a 
    1D k-means cluster. The number of clusters are determined based on the 'sturges' 
    method. It is only optimal for gaussian data and underestimates the number of bins 
    for large non-gaussian datasets.
    
    remove_outliers: bool, default = False
    When set to True, outliers from the training data are removed using PCA linear
    dimensionality reduction using the Singular Value Decomposition technique.
    
    outliers_threshold: float, default = 0.05
    The percentage / proportion of outliers in the dataset can be defined using
    the outliers_threshold param. By default, 0.05 is used which means 0.025 of the 
    values on each side of the distribution's tail are dropped from training data.
    
    remove_multicollinearity: bool, default = False
    When set to True, the variables with inter-correlations higher than the threshold
    defined under the multicollinearity_threshold param are dropped. When two features
    are highly correlated with each other, the feature that is less correlated with 
    the target variable is dropped. 
    
    multicollinearity_threshold: float, default = 0.9
    Threshold used for dropping the correlated features. Only comes into effect when 
    remove_multicollinearity is set to True.
    
    create_clusters: bool, default = False
    When set to True, an additional feature is created where each instance is assigned
    to a cluster. The number of clusters is determined using a combination of 
    Calinski-Harabasz and Silhouette criterion. 
    
    cluster_iter: int, default = 20
    Number of iterations used to create a cluster. Each iteration represents cluster 
    size. Only comes into effect when create_clusters param is set to True.
    
    polynomial_features: bool, default = False
    When set to True, new features are created based on all polynomial combinations 
    that exist within the numeric features in a dataset to the degree defined in 
    polynomial_degree param. 
    
    polynomial_degree: int, default = 2
    Degree of polynomial features. For example, if an input sample is two dimensional 
    and of the form [a, b], the polynomial features with degree = 2 are: 
    [1, a, b, a^2, ab, b^2].
    
    trigonometry_features: bool, default = False
    When set to True, new features are created based on all trigonometric combinations 
    that exist within the numeric features in a dataset to the degree defined in the
    polynomial_degree param.
    
    polynomial_threshold: float, default = 0.1
    This is used to compress a sparse matrix of polynomial and trigonometric features.
    Polynomial and trigonometric features whose feature importance based on the 
    combination of Random Forest, AdaBoost and Linear correlation falls within the 
    percentile of the defined threshold are kept in the dataset. Remaining features 
    are dropped before further processing.
    
    group_features: list or list of list, default = None
    When a dataset contains features that have related characteristics, the group_features
    param can be used for statistical feature extraction. For example, if a dataset has 
    numeric features that are related with each other (i.e 'Col1', 'Col2', 'Col3'), a list 
    containing the column names can be passed under group_features to extract statistical 
    information such as the mean, median, mode and standard deviation.
    
    group_names: list, default = None
    When group_features is passed, a name of the group can be passed into the group_names 
    param as a list containing strings. The length of a group_names list must equal to the 
    length  of group_features. When the length doesn't match or the name is not passed, new 
    features are sequentially named such as group_1, group_2 etc.
    
    feature_selection: bool, default = False
    When set to True, a subset of features are selected using a combination of various
    permutation importance techniques including Random Forest, Adaboost and Linear 
    correlation with target variable. The size of the subset is dependent on the 
    feature_selection_param. Generally, this is used to constrain the feature space 
    in order to improve efficiency in modeling. When polynomial_features and 
    feature_interaction  are used, it is highly recommended to define the 
    feature_selection_threshold param with a lower value.

    feature_selection_threshold: float, default = 0.8
    Threshold used for feature selection (including newly created polynomial features).
    A higher value will result in a higher feature space. It is recommended to do multiple
    trials with different values of feature_selection_threshold specially in cases where 
    polynomial_features and feature_interaction are used. Setting a very low value may be 
    efficient but could result in under-fitting.
    
    feature_interaction: bool, default = False 
    When set to True, it will create new features by interacting (a * b) for all numeric 
    variables in the dataset including polynomial and trigonometric features (if created). 
    This feature is not scalable and may not work as expected on datasets with large 
    feature space.
    
    feature_ratio: bool, default = False
    When set to True, it will create new features by calculating the ratios (a / b) of all 
    numeric variables in the dataset. This feature is not scalable and may not work as 
    expected on datasets with large feature space.
    
    interaction_threshold: bool, default = 0.01
    Similar to polynomial_threshold, It is used to compress a sparse matrix of newly 
    created features through interaction. Features whose importance based on the 
    combination  of  Random Forest, AdaBoost and Linear correlation falls within the 
    percentile of the  defined threshold are kept in the dataset. Remaining features 
    are dropped before further processing.
    
    transform_target: bool, default = False
    When set to True, target variable is transformed using the method defined in
    transform_target_method param. Target transformation is applied separately from 
    feature transformations. 
    
    transform_target_method: string, default = 'box-cox'
    'Box-cox' and 'yeo-johnson' methods are supported. Box-Cox requires input data to 
    be strictly positive, while Yeo-Johnson supports both positive or negative data.
    When transform_target_method is 'box-cox' and target variable contains negative
    values, method is internally forced to 'yeo-johnson' to avoid exceptions.
    
    session_id: int, default = None
    If None, a random seed is generated and returned in the Information grid. The 
    unique number is then distributed as a seed in all functions used during the 
    experiment. This can be used for later reproducibility of the entire experiment.
    
    silent: bool, default = False
    When set to True, confirmation of data types is not required. All preprocessing will 
    be performed assuming automatically inferred data types. Not recommended for direct use 
    except for established pipelines.
    
    profile: bool, default = False
    If set to true, a data profile for Exploratory Data Analysis will be displayed 
    in an interactive HTML report. 
    
    Returns:
    --------

    info grid:    Information grid is printed.
    -----------      

    environment:  This function returns various outputs that are stored in variable
    -----------   as tuple. They are used by other functions in pycaret.

    Warnings:
    ---------
    None
      
      
    """
    
    #exception checking   
    import sys
    
    #checking train size parameter
    if type(train_size) is not float:
        sys.exit('(Type Error): train_size parameter only accepts float value.')
    
    #checking sampling parameter
    if type(sampling) is not bool:
        sys.exit('(Type Error): sampling parameter only accepts True or False.')
        
    #checking sampling parameter
    if target not in data.columns:
        sys.exit('(Value Error): Target parameter doesnt exist in the data provided.')   

    #checking session_id
    if session_id is not None:
        if type(session_id) is not int:
            sys.exit('(Type Error): session_id parameter must be an integer.')   
    
    #checking sampling parameter
    if type(profile) is not bool:
        sys.exit('(Type Error): profile parameter only accepts True or False.')
      
    #checking normalize parameter
    if type(normalize) is not bool:
        sys.exit('(Type Error): normalize parameter only accepts True or False.')
        
    #checking transformation parameter
    if type(transformation) is not bool:
        sys.exit('(Type Error): transformation parameter only accepts True or False.')
        
    #checking categorical imputation
    allowed_categorical_imputation = ['constant', 'mode']
    if categorical_imputation not in allowed_categorical_imputation:
        sys.exit("(Value Error): categorical_imputation param only accepts 'constant' or 'mode' ")
    
    #ordinal_features
    if ordinal_features is not None:
        if type(ordinal_features) is not dict:
            sys.exit("(Type Error): ordinal_features must be of type dictionary with column name as key and ordered values as list. ")
    
    #ordinal features check
    if ordinal_features is not None:
        data_cols = data.columns
        data_cols = data_cols.drop(target)
        ord_keys = ordinal_features.keys()
        
        for i in ord_keys:
            if i not in data_cols:
                sys.exit("(Value Error) Column name passed as a key in ordinal_features param doesnt exist. ")
                
        for k in ord_keys:
            if len(data[k].unique()) != len(ordinal_features.get(k)):
                sys.exit("(Value Error) Levels passed in ordinal_features param doesnt match with levels in data. ")

        for i in ord_keys:
            value_in_keys = ordinal_features.get(i)
            value_in_data = list(data[i].unique())
            for j in value_in_keys:
                if j not in value_in_data:
                    text =  "Column name '" + str(i) + "' doesnt contain any level named '" + str(j) + "'."
                    sys.exit(text)
                    
    #checking numeric imputation
    allowed_numeric_imputation = ['mean', 'median']
    if numeric_imputation not in allowed_numeric_imputation:
        sys.exit("(Value Error): numeric_imputation param only accepts 'mean' or 'median' ")
        
    #checking normalize method
    allowed_normalize_method = ['zscore', 'minmax', 'maxabs', 'robust']
    if normalize_method not in allowed_normalize_method:
        sys.exit("(Value Error): normalize_method param only accepts 'zscore', 'minxmax', 'maxabs' or 'robust'. ")      
    
    #checking transformation method
    allowed_transformation_method = ['yeo-johnson', 'quantile']
    if transformation_method not in allowed_transformation_method:
        sys.exit("(Value Error): transformation_method param only accepts 'yeo-johnson' or 'quantile' ")        
    
    #handle unknown categorical
    if type(handle_unknown_categorical) is not bool:
        sys.exit('(Type Error): handle_unknown_categorical parameter only accepts True or False.')
        
    #unknown categorical method
    unknown_categorical_method_available = ['least_frequent', 'most_frequent']
    
    if unknown_categorical_method not in unknown_categorical_method_available:
        sys.exit("(Type Error): unknown_categorical_method only accepts 'least_frequent' or 'most_frequent'.")
    
    #check pca
    if type(pca) is not bool:
        sys.exit('(Type Error): PCA parameter only accepts True or False.')
        
    #pca method check
    allowed_pca_methods = ['linear', 'kernel', 'incremental',]
    if pca_method not in allowed_pca_methods:
        sys.exit("(Value Error): pca method param only accepts 'linear', 'kernel', or 'incremental'. ")    
    
    #pca components check
    if pca is True:
        if pca_method is not 'linear':
            if pca_components is not None:
                if(type(pca_components)) is not int:
                    sys.exit("(Type Error): pca_components parameter must be integer when pca_method is not 'linear'. ")

    #pca components check 2
    if pca is True:
        if pca_method is not 'linear':
            if pca_components is not None:
                if pca_components > len(data.columns)-1:
                    sys.exit("(Type Error): pca_components parameter cannot be greater than original features space.")                
 
    #pca components check 3
    if pca is True:
        if pca_method is 'linear':
            if pca_components is not None:
                if type(pca_components) is not float:
                    if pca_components > len(data.columns)-1: 
                        sys.exit("(Type Error): pca_components parameter cannot be greater than original features space or float between 0 - 1.")      
    
    #check ignore_low_variance
    if type(ignore_low_variance) is not bool:
        sys.exit('(Type Error): ignore_low_variance parameter only accepts True or False.')
        
    #check ignore_low_variance
    if type(combine_rare_levels) is not bool:
        sys.exit('(Type Error): combine_rare_levels parameter only accepts True or False.')
        
    #check rare_level_threshold
    if type(rare_level_threshold) is not float:
        sys.exit('(Type Error): rare_level_threshold must be a float between 0 and 1. ')
    
    #bin numeric features
    if bin_numeric_features is not None:
        all_cols = list(data.columns)
        all_cols.remove(target)
        
        for i in bin_numeric_features:
            if i not in all_cols:
                sys.exit("(Value Error): Column type forced is either target column or doesn't exist in the dataset.")
    
    #check transform_target
    if type(transform_target) is not bool:
        sys.exit('(Type Error): transform_target parameter only accepts True or False.')
        
    #transform_target_method
    allowed_transform_target_method = ['box-cox', 'yeo-johnson']
    if transform_target_method not in allowed_transform_target_method:
        sys.exit("(Value Error): transform_target_method param only accepts 'box-cox' or 'yeo-johnson'. ") 
    
    #remove_outliers
    if type(remove_outliers) is not bool:
        sys.exit('(Type Error): remove_outliers parameter only accepts True or False.')    
    
    #outliers_threshold
    if type(outliers_threshold) is not float:
        sys.exit('(Type Error): outliers_threshold must be a float between 0 and 1. ')   
        
    #remove_multicollinearity
    if type(remove_multicollinearity) is not bool:
        sys.exit('(Type Error): remove_multicollinearity parameter only accepts True or False.')
        
    #multicollinearity_threshold
    if type(multicollinearity_threshold) is not float:
        sys.exit('(Type Error): multicollinearity_threshold must be a float between 0 and 1. ')  
        
    #create_clusters
    if type(create_clusters) is not bool:
        sys.exit('(Type Error): create_clusters parameter only accepts True or False.')
        
    #cluster_iter
    if type(cluster_iter) is not int:
        sys.exit('(Type Error): cluster_iter must be a integer greater than 1. ') 
    
    #polynomial_features
    if type(polynomial_features) is not bool:
        sys.exit('(Type Error): polynomial_features only accepts True or False. ')   
    
    #polynomial_degree
    if type(polynomial_degree) is not int:
        sys.exit('(Type Error): polynomial_degree must be an integer. ')
        
    #polynomial_features
    if type(trigonometry_features) is not bool:
        sys.exit('(Type Error): trigonometry_features only accepts True or False. ')    
        
    #polynomial threshold
    if type(polynomial_threshold) is not float:
        sys.exit('(Type Error): polynomial_threshold must be a float between 0 and 1. ')      
        
    #group features
    if group_features is not None:
        if type(group_features) is not list:
            sys.exit('(Type Error): group_features must be of type list. ')     
    
    if group_names is not None:
        if type(group_names) is not list:
            sys.exit('(Type Error): group_names must be of type list. ')         
    
    #cannot drop target
    if ignore_features is not None:
        if target in ignore_features:
            sys.exit("(Value Error): cannot drop target column. ")  
                
    #feature_selection
    if type(feature_selection) is not bool:
        sys.exit('(Type Error): feature_selection only accepts True or False. ')   
        
    #feature_selection_threshold
    if type(feature_selection_threshold) is not float:
        sys.exit('(Type Error): feature_selection_threshold must be a float between 0 and 1. ')  
        
    #feature_interaction
    if type(feature_interaction) is not bool:
        sys.exit('(Type Error): feature_interaction only accepts True or False. ')  
        
    #feature_ratio
    if type(feature_ratio) is not bool:
        sys.exit('(Type Error): feature_ratio only accepts True or False. ')     
        
    #interaction_threshold
    if type(interaction_threshold) is not float:
        sys.exit('(Type Error): interaction_threshold must be a float between 0 and 1. ')      


    #cannot drop target
    if ignore_features is not None:
        if target in ignore_features:
            sys.exit("(Value Error): cannot drop target column. ")  
        
    #forced type check
    all_cols = list(data.columns)
    all_cols.remove(target)
    
    #categorical
    if categorical_features is not None:
        for i in categorical_features:
            if i not in all_cols:
                sys.exit("(Value Error): Column type forced is either target column or doesn't exist in the dataset.")
        
    #numeric
    if numeric_features is not None:
        for i in numeric_features:
            if i not in all_cols:
                sys.exit("(Value Error): Column type forced is either target column or doesn't exist in the dataset.")    
    
    #date features
    if date_features is not None:
        for i in date_features:
            if i not in all_cols:
                sys.exit("(Value Error): Column type forced is either target column or doesn't exist in the dataset.")      
    
    #drop features
    if ignore_features is not None:
        for i in ignore_features:
            if i not in all_cols:
                sys.exit("(Value Error): Feature ignored is either target column or doesn't exist in the dataset.") 
     
    #silent
    if type(silent) is not bool:
        sys.exit("(Type Error): silent parameter only accepts True or False. ")
        
    #pre-load libraries
    import pandas as pd
    import ipywidgets as ipw
    from IPython.display import display, HTML, clear_output, update_display
    import datetime, time
   
    #progress bar
    if sampling:
        max = 10 + 3
    else:
        max = 3
        
    progress = ipw.IntProgress(value=0, min=0, max=max, step=1 , description='Processing: ')
    display(progress)
    
    timestampStr = datetime.datetime.now().strftime("%H:%M:%S")
    monitor = pd.DataFrame( [ ['Initiated' , '. . . . . . . . . . . . . . . . . .', timestampStr ], 
                             ['Status' , '. . . . . . . . . . . . . . . . . .' , 'Loading Dependencies' ],
                             ['ETC' , '. . . . . . . . . . . . . . . . . .',  'Calculating ETC'] ],
                              columns=['', ' ', '   ']).set_index('')
    
    display(monitor, display_id = 'monitor')
    
    #general dependencies
    import numpy as np
    from sklearn.linear_model import LinearRegression
    from sklearn.model_selection import train_test_split
    from sklearn import metrics
    import random
    import seaborn as sns
    import matplotlib.pyplot as plt
    import plotly.express as px
    
    #define highlight function for function grid to display
    def highlight_max(s):
        is_max = s == True
        return ['background-color: lightgreen' if v else '' for v in is_max]
    
    #cufflinks
    import cufflinks as cf
    cf.go_offline()
    cf.set_config_file(offline=False, world_readable=True)
    
    #ignore warnings
    import warnings
    warnings.filterwarnings('ignore') 
    
    #copy original data for pandas profiler
    data_before_preprocess = data.copy()
    
    #declaring global variables to be accessed by other functions
    global X, y, X_train, X_test, y_train, y_test, seed, prep_pipe, target_inverse_transformer, experiment__, preprocess
    
    #generate seed to be used globally
    if session_id is None:
        seed = random.randint(150,9000)
    else:
        seed = session_id
    

    """
    preprocessing starts here
    """
    
    monitor.iloc[1,1:] = 'Preparing Data for Modeling'
    update_display(monitor, display_id = 'monitor')
            
    #define parameters for preprocessor
    
    #categorical features
    if categorical_features is None:
        cat_features_pass = []
    else:
        cat_features_pass = categorical_features
    
    #numeric features
    if numeric_features is None:
        numeric_features_pass = []
    else:
        numeric_features_pass = numeric_features
     
    #drop features
    if ignore_features is None:
        ignore_features_pass = []
    else:
        ignore_features_pass = ignore_features
     
    #date features
    if date_features is None:
        date_features_pass = []
    else:
        date_features_pass = date_features
        
    #categorical imputation strategy
    if categorical_imputation == 'constant':
        categorical_imputation_pass = 'not_available'
    elif categorical_imputation == 'mode':
        categorical_imputation_pass = 'most frequent'
    
    #transformation method strategy
    if transformation_method == 'yeo-johnson':
        trans_method_pass = 'yj'
    elif transformation_method == 'quantile':
        trans_method_pass = 'quantile'
    
    #pass method
    if pca_method == 'linear':
        pca_method_pass = 'pca_liner'
            
    elif pca_method == 'kernel':
        pca_method_pass = 'pca_kernal'
            
    elif pca_method == 'incremental':
        pca_method_pass = 'incremental'
            
    elif pca_method == 'pls':
        pca_method_pass = 'pls'
        
    #pca components
    if pca is True:
        if pca_components is None:
            if pca_method == 'linear':
                pca_components_pass = 0.99
            else:
                pca_components_pass = int((len(data.columns)-1)*0.5)
                
        else:
            pca_components_pass = pca_components
            
    else:
        pca_components_pass = 0.99
        
    if bin_numeric_features is None:
        apply_binning_pass = False
        features_to_bin_pass = []
    
    else:
        apply_binning_pass = True
        features_to_bin_pass = bin_numeric_features
    
    #trignometry
    if trigonometry_features is False:
        trigonometry_features_pass = []
    else:
        trigonometry_features_pass = ['sin', 'cos', 'tan']
    
    #group features
    #=============#
    
    #apply grouping
    if group_features is not None:
        apply_grouping_pass = True
    else:
        apply_grouping_pass = False
    
    #group features listing
    if apply_grouping_pass is True:
        
        if type(group_features[0]) is str:
            group_features_pass = []
            group_features_pass.append(group_features)
        else:
            group_features_pass = group_features
            
    else:
        
        group_features_pass = [[]]
    
    #group names
    if apply_grouping_pass is True:

        if (group_names is None) or (len(group_names) != len(group_features_pass)):
            group_names_pass = list(np.arange(len(group_features_pass)))
            group_names_pass = ['group_' + str(i) for i in group_names_pass]

        else:
            group_names_pass = group_names
            
    else:
        group_names_pass = []
    
    #feature interactions
    
    if feature_interaction or feature_ratio:
        apply_feature_interactions_pass = True
    else:
        apply_feature_interactions_pass = False
    
    interactions_to_apply_pass = []
    
    if feature_interaction:
        interactions_to_apply_pass.append('multiply')
    
    if feature_ratio:
        interactions_to_apply_pass.append('divide')
    
    #unknown categorical
    if unknown_categorical_method == 'least_frequent':
        unknown_categorical_method_pass = 'least frequent'
    elif unknown_categorical_method == 'most_frequent':
        unknown_categorical_method_pass = 'most frequent'

    #ordinal_features
    if ordinal_features is not None:
        apply_ordinal_encoding_pass = True
    else:
        apply_ordinal_encoding_pass = False
        
    if apply_ordinal_encoding_pass is True:
        ordinal_columns_and_categories_pass = ordinal_features
    else:
        ordinal_columns_and_categories_pass = {}
    
    if silent:
        display_dtypes_pass = False
    else:
        display_dtypes_pass = True
        
    #transform target method
    if transform_target_method == 'box-cox':
        transform_target_method_pass = 'bc'
    elif transform_target_method == 'yeo-johnson':
        transform_target_method_pass = 'yj'
        
    #import library
    from pycaret import preprocess
    
    data = preprocess.Preprocess_Path_One(train_data = data, 
                                          target_variable = target,
                                          categorical_features = cat_features_pass,
                                          apply_ordinal_encoding = apply_ordinal_encoding_pass, #new
                                          ordinal_columns_and_categories = ordinal_columns_and_categories_pass, #new
                                          numerical_features = numeric_features_pass,
                                          time_features = date_features_pass,
                                          features_todrop = ignore_features_pass,
                                          numeric_imputation_strategy = numeric_imputation,
                                          categorical_imputation_strategy = categorical_imputation_pass,
                                          scale_data = normalize,
                                          scaling_method = normalize_method,
                                          Power_transform_data = transformation,
                                          Power_transform_method = trans_method_pass,
                                          apply_untrained_levels_treatment= handle_unknown_categorical, #new
                                          untrained_levels_treatment_method = unknown_categorical_method_pass, #new
                                          apply_pca = pca, #new
                                          pca_method = pca_method_pass, #new
                                          pca_variance_retained_or_number_of_components = pca_components_pass, #new
                                          apply_zero_nearZero_variance = ignore_low_variance, #new
                                          club_rare_levels = combine_rare_levels, #new
                                          rara_level_threshold_percentage = rare_level_threshold, #new
                                          apply_binning = apply_binning_pass, #new
                                          features_to_binn = features_to_bin_pass, #new
                                          remove_outliers = remove_outliers, #new
                                          outlier_contamination_percentage = outliers_threshold, #new
                                          outlier_methods = ['pca'], #pca hardcoded
                                          remove_multicollinearity = remove_multicollinearity, #new
                                          maximum_correlation_between_features = multicollinearity_threshold, #new
                                          cluster_entire_data = create_clusters, #new
                                          range_of_clusters_to_try = cluster_iter, #new
                                          apply_polynomial_trigonometry_features = polynomial_features, #new
                                          max_polynomial = polynomial_degree, #new
                                          trigonometry_calculations = trigonometry_features_pass, #new
                                          top_poly_trig_features_to_select_percentage = polynomial_threshold, #new
                                          apply_grouping = apply_grouping_pass, #new
                                          features_to_group_ListofList = group_features_pass, #new
                                          group_name = group_names_pass, #new
                                          apply_feature_selection = feature_selection, #new
                                          feature_selection_top_features_percentage = feature_selection_threshold, #new
                                          apply_feature_interactions = apply_feature_interactions_pass, #new
                                          feature_interactions_to_apply = interactions_to_apply_pass, #new
                                          feature_interactions_top_features_to_select_percentage=interaction_threshold, #new
                                          display_types = display_dtypes_pass, #new #to be parameterized in setup later.
                                          target_transformation = transform_target, #new
                                          target_transformation_method = transform_target_method_pass, #new
                                          random_state = seed)

    progress.value += 1

    if hasattr(preprocess.dtypes, 'replacement'):
            label_encoded = preprocess.dtypes.replacement
            label_encoded = str(label_encoded).replace("'", '')
            label_encoded = str(label_encoded).replace("{", '')
            label_encoded = str(label_encoded).replace("}", '')

    else:
        label_encoded = 'None'

    try:
        res_type = ['quit','Quit','exit','EXIT','q','Q','e','E','QUIT','Exit']
        res = preprocess.dtypes.response
        if res in res_type:
            sys.exit("(Process Exit): setup has been interupted with user command 'quit'. setup must rerun." )
    except:
        pass
    
    #save prep pipe
    prep_pipe = preprocess.pipe
    
    #save target inverse transformer
    try:
        target_inverse_transformer = preprocess.pt_target.p_transform_target
    except:
        target_inverse_transformer = None
    
    #generate values for grid show
    missing_values = data_before_preprocess.isna().sum().sum()
    if missing_values > 0:
        missing_flag = True
    else:
        missing_flag = False
    
    if normalize is True:
        normalize_grid = normalize_method
    else:
        normalize_grid = 'None'
        
    if transformation is True:
        transformation_grid = transformation_method
    else:
        transformation_grid = 'None'
    
    if pca is True:
        pca_method_grid = pca_method
    else:
        pca_method_grid = 'None'
   
    if pca is True:
        pca_components_grid = pca_components_pass
    else:
        pca_components_grid = 'None'
    
    if combine_rare_levels:
        rare_level_threshold_grid = rare_level_threshold
    else:
        rare_level_threshold_grid = 'None'
    
    if bin_numeric_features is None:
        numeric_bin_grid = False
    else:
        numeric_bin_grid = True
    
    if remove_outliers is False:
        outliers_threshold_grid = None
    else:
        outliers_threshold_grid = outliers_threshold
    
    if remove_multicollinearity is False:
        multicollinearity_threshold_grid = None
    else:
        multicollinearity_threshold_grid = multicollinearity_threshold
    
    if create_clusters is False:
        cluster_iter_grid = None
    else:
        cluster_iter_grid = cluster_iter
    
    if polynomial_features:
        polynomial_degree_grid = polynomial_degree
    else:
        polynomial_degree_grid = None
    
    if polynomial_features or trigonometry_features:
        polynomial_threshold_grid = polynomial_threshold
    else:
        polynomial_threshold_grid = None
    
    if feature_selection:
        feature_selection_threshold_grid = feature_selection_threshold
    else:
        feature_selection_threshold_grid = None
    
    if feature_interaction or feature_ratio:
        interaction_threshold_grid = interaction_threshold
    else:
        interaction_threshold_grid = None
        
    if ordinal_features is not None:
        ordinal_features_grid = True
    else:
        ordinal_features_grid = False
        
    if handle_unknown_categorical:
        unknown_categorical_method_grid = unknown_categorical_method
    else:
        unknown_categorical_method_grid = None
       
    if group_features is not None:
        group_features_grid = True
    else:
        group_features_grid = False
        
    learned_types = preprocess.dtypes.learent_dtypes
    learned_types.drop(target, inplace=True)

    float_type = 0 
    cat_type = 0

    for i in preprocess.dtypes.learent_dtypes:
        if 'float' in str(i):
            float_type += 1
        elif 'object' in str(i):
            cat_type += 1
        elif 'int' in str(i):
            float_type += 1
    
    #target transformation method
    if transform_target is False:
        transform_target_method_grid = None
    else:
        transform_target_method_grid = preprocess.pt_target.function_to_apply
    
    """
    preprocessing ends here
    """
    
    #reset pandas option
    #pd.reset_option("display.max_rows") #switch back on 
    #pd.reset_option("display.max_columns")
    
    #create an empty list for pickling later.
    experiment__ = []
    
    #sample estimator
    if sample_estimator is None:
        model = LinearRegression()
    else:
        model = sample_estimator
        
    model_name = str(model).split("(")[0]
    
    if 'CatBoostRegressor' in model_name:
        model_name = 'CatBoostRegressor'
        
    #creating variables to be used later in the function
    X = data.drop(target,axis=1)
    y = data[target]
    
    progress.value += 1
    
    if sampling is True and data.shape[0] > 25000: #change back to 25000
    
        split_perc = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,0.99]
        split_perc_text = ['10%','20%','30%','40%','50%','60%', '70%', '80%', '90%', '100%']
        split_perc_tt = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,0.99]
        split_perc_tt_total = []
        split_percent = []

        metric_results = []
        metric_name = []
        
        counter = 0
        
        for i in split_perc:
            
            progress.value += 1
            
            t0 = time.time()
            
            '''
            MONITOR UPDATE STARTS
            '''
            
            perc_text = split_perc_text[counter]
            monitor.iloc[1,1:] = 'Fitting Model on ' + perc_text + ' sample'
            update_display(monitor, display_id = 'monitor')

            '''
            MONITOR UPDATE ENDS
            '''
    
            X_, X__, y_, y__ = train_test_split(X, y, test_size=1-i, random_state=seed)
            X_train, X_test, y_train, y_test = train_test_split(X_, y_, test_size=0.3, random_state=seed)
            model.fit(X_train,y_train)
            pred_ = model.predict(X_test)
            
            r2 = metrics.r2_score(y_test,pred_)
            metric_results.append(r2)
            metric_name.append('R2')
            split_percent.append(i)
            
            t1 = time.time()
                       
            '''
            Time calculation begins
            '''
          
            tt = t1 - t0
            total_tt = tt / i
            split_perc_tt.pop(0)
            
            for remain in split_perc_tt:
                ss = total_tt * remain
                split_perc_tt_total.append(ss)
                
            ttt = sum(split_perc_tt_total) / 60
            ttt = np.around(ttt, 2)
        
            if ttt < 1:
                ttt = str(np.around((ttt * 60), 2))
                ETC = ttt + ' Seconds Remaining'

            else:
                ttt = str (ttt)
                ETC = ttt + ' Minutes Remaining'
                
            monitor.iloc[2,1:] = ETC
            update_display(monitor, display_id = 'monitor')
            
            
            '''
            Time calculation Ends
            '''
            
            split_perc_tt_total = []
            counter += 1

        #model_results = pd.DataFrame({'Sample %' : split_percent, 'Metric' : metric_results, 'Metric Name': metric_name})

        model_results = pd.DataFrame({'Sample %' : split_percent, 'Metric' : metric_results, 'Metric Name': metric_name})
        fig = px.line(model_results, x='Sample %', y='Metric', color='Metric Name', line_shape='linear', range_y = [0,1])
        fig.update_layout(plot_bgcolor='rgb(245,245,245)')
        title= str(model_name) + ' Metric and Sample %'
        fig.update_layout(title={'text': title, 'y':0.95,'x':0.45,'xanchor': 'center','yanchor': 'top'})
        fig.show()
        
        monitor.iloc[1,1:] = 'Waiting for input'
        update_display(monitor, display_id = 'monitor')
        
        
        print('Please Enter the sample % of data you would like to use for modeling. Example: Enter 0.3 for 30%.')
        print('Press Enter if you would like to use 100% of the data.')
        
        print(' ')
        
        sample_size = input("Sample Size: ")
        
        if sample_size == '' or sample_size == '1':
            
            X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=1-train_size, random_state=seed)
            
            '''
            Final display Starts
            '''
            clear_output()
            print(' ')

            if profile:
                print('Setup Succesfully Completed! Loading Profile Now... Please Wait!')
            else:
                print('Setup Succesfully Completed!')    
        
            functions = pd.DataFrame ( [ ['session_id', seed ],
                                         ['Transform Target ', transform_target],
                                         ['Transform Target Method', transform_target_method_grid],
                                         ['Original Data', data_before_preprocess.shape ],
                                         ['Missing Values ', missing_flag],
                                         ['Numeric Features ', float_type ],
                                         ['Categorical Features ', cat_type ],
                                         ['Ordinal Features ', ordinal_features_grid], #new
                                         ['Sampled Data', '(' + str(X_train.shape[0] + X_test.shape[0]) + ', ' + str(data_before_preprocess.shape[1]) + ')' ], 
                                         ['Transformed Train Set', X_train.shape ], 
                                         ['Transformed Test Set',X_test.shape ],
                                         ['Numeric Imputer ', numeric_imputation],
                                         ['Categorical Imputer ', categorical_imputation],
                                         ['Normalize ', normalize ],
                                         ['Normalize Method ', normalize_grid ],
                                         ['Transformation ', transformation ],
                                         ['Transformation Method ', transformation_grid ],
                                         ['PCA ', pca],
                                         ['PCA Method ', pca_method_grid],
                                         ['PCA Components ', pca_components_grid],
                                         ['Ignore Low Variance ', ignore_low_variance],
                                         ['Combine Rare Levels ', combine_rare_levels],
                                         ['Rare Level Threshold ', rare_level_threshold_grid],
                                         ['Numeric Binning ', numeric_bin_grid],
                                         ['Remove Outliers ', remove_outliers],
                                         ['Outliers Threshold ', outliers_threshold_grid],
                                         ['Remove Multicollinearity ', remove_multicollinearity],
                                         ['Multicollinearity Threshold ', multicollinearity_threshold_grid],
                                         ['Clustering ', create_clusters],
                                         ['Clustering Iteration ', cluster_iter_grid],
                                         ['Polynomial Features ', polynomial_features], #new
                                         ['Polynomial Degree ', polynomial_degree_grid], #new
                                         ['Trignometry Features ', trigonometry_features], #new
                                         ['Polynomial Threshold ', polynomial_threshold_grid], #new
                                         ['Group Features ', group_features_grid], #new
                                         ['Feature Selection ', feature_selection], #new
                                         ['Features Selection Threshold ', feature_selection_threshold_grid], #new
                                         ['Feature Interaction ', feature_interaction], #new
                                         ['Feature Ratio ', feature_ratio], #new
                                         ['Interaction Threshold ', interaction_threshold_grid], #new
                                       ], columns = ['Description', 'Value'] )

            #functions_ = functions.style.hide_index()
            functions_ = functions.style.apply(highlight_max)
            display(functions_)
            
            if profile:
                try:
                    import pandas_profiling
                    pf = pandas_profiling.ProfileReport(data_before_preprocess)
                    clear_output()
                    display(pf)
                except:
                    print('Data Profiler Failed. No output to show, please continue with Modeling.')
            
            '''
            Final display Ends
            '''   
            
            #log into experiment
            experiment__.append(('Regression Setup Config', functions))
            experiment__.append(('X_training Set', X_train))
            experiment__.append(('y_training Set', y_train))
            experiment__.append(('X_test Set', X_test))
            experiment__.append(('y_test Set', y_test))
            experiment__.append(('Transformation Pipeline', prep_pipe))
            try:
                experiment__.append(('Target Inverse Transformer', target_inverse_transformer))
            except:
                pass
            
            return X, y, X_train, X_test, y_train, y_test, seed, prep_pipe, target_inverse_transformer, experiment__
        
        else:
            
            sample_n = float(sample_size)
            X_selected, X_discard, y_selected, y_discard = train_test_split(X, y, test_size=1-sample_n,  
                                                                random_state=seed)
            
            X_train, X_test, y_train, y_test = train_test_split(X_selected, y_selected, test_size=1-train_size, 
                                                                random_state=seed)
            clear_output()
            
            
            '''
            Final display Starts
            '''
            
            clear_output()
            print(' ')
            
            if profile:
                print('Setup Succesfully Completed! Loading Profile Now... Please Wait!')
            else:
                print('Setup Succesfully Completed!')
            
            functions = pd.DataFrame ( [ ['session_id', seed ],
                                         ['Transform Target ', transform_target],
                                         ['Transform Target Method', transform_target_method_grid],
                                         ['Original Data', data_before_preprocess.shape ],
                                         ['Missing Values ', missing_flag],
                                         ['Numeric Features ', float_type ],
                                         ['Categorical Features ', cat_type ],
                                         ['Ordinal Features ', ordinal_features_grid], #new
                                         ['Sampled Data', '(' + str(X_train.shape[0] + X_test.shape[0]) + ', ' + str(data_before_preprocess.shape[1]) + ')' ], 
                                         ['Transformed Train Set', X_train.shape ], 
                                         ['Transformed Test Set',X_test.shape ],
                                         ['Numeric Imputer ', numeric_imputation],
                                         ['Categorical Imputer ', categorical_imputation],
                                         ['Normalize ', normalize ],
                                         ['Normalize Method ', normalize_grid ],
                                         ['Transformation ', transformation ],
                                         ['Transformation Method ', transformation_grid ],
                                         ['PCA ', pca],
                                         ['PCA Method ', pca_method_grid],
                                         ['PCA Components ', pca_components_grid],
                                         ['Ignore Low Variance ', ignore_low_variance],
                                         ['Combine Rare Levels ', combine_rare_levels],
                                         ['Rare Level Threshold ', rare_level_threshold_grid],
                                         ['Numeric Binning ', numeric_bin_grid],
                                         ['Remove Outliers ', remove_outliers],
                                         ['Outliers Threshold ', outliers_threshold_grid],
                                         ['Remove Multicollinearity ', remove_multicollinearity],
                                         ['Multicollinearity Threshold ', multicollinearity_threshold_grid],
                                         ['Clustering ', create_clusters],
                                         ['Clustering Iteration ', cluster_iter_grid],
                                         ['Polynomial Features ', polynomial_features], #new
                                         ['Polynomial Degree ', polynomial_degree_grid], #new
                                         ['Trignometry Features ', trigonometry_features], #new
                                         ['Polynomial Threshold ', polynomial_threshold_grid], #new
                                         ['Group Features ', group_features_grid], #new
                                         ['Feature Selection ', feature_selection], #new
                                         ['Features Selection Threshold ', feature_selection_threshold_grid], #new
                                         ['Feature Interaction ', feature_interaction], #new
                                         ['Feature Ratio ', feature_ratio], #new
                                         ['Interaction Threshold ', interaction_threshold_grid], #new
                                       ], columns = ['Description', 'Value'] )
            
            #functions_ = functions.style.hide_index()
            functions_ = functions.style.apply(highlight_max)
            display(functions_)
            
            if profile:
                try:
                    import pandas_profiling
                    pf = pandas_profiling.ProfileReport(data_before_preprocess)
                    clear_output()
                    display(pf)
                except:
                    print('Data Profiler Failed. No output to show, please continue with Modeling.')
            
            '''
            Final display Ends
            ''' 
            
            #log into experiment
            experiment__.append(('Regression Setup Config', functions))
            experiment__.append(('X_training Set', X_train))
            experiment__.append(('y_training Set', y_train))
            experiment__.append(('X_test Set', X_test))
            experiment__.append(('y_test Set', y_test))
            experiment__.append(('Transformation Pipeline', prep_pipe))
            try:
                experiment__.append(('Target Inverse Transformer', target_inverse_transformer))
            except:
                pass
            
            return X, y, X_train, X_test, y_train, y_test, seed, prep_pipe, target_inverse_transformer, experiment__

    else:
        
        monitor.iloc[1,1:] = 'Splitting Data'
        update_display(monitor, display_id = 'monitor')
        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=1-train_size, random_state=seed)
        progress.value += 1
        
        clear_output()
        
        '''
        Final display Starts
        '''
        clear_output()
        print(' ')
        if profile:
            print('Setup Succesfully Completed! Loading Profile Now... Please Wait!')
        else:
            print('Setup Succesfully Completed!')
        functions = pd.DataFrame ( [ ['session_id', seed ],
                                     ['Transform Target ', transform_target],
                                     ['Transform Target Method', transform_target_method_grid],
                                     ['Original Data', data_before_preprocess.shape ],
                                     ['Missing Values ', missing_flag],
                                     ['Numeric Features ', float_type ],
                                     ['Categorical Features ', cat_type ],
                                     ['Ordinal Features ', ordinal_features_grid], #new
                                     ['Sampled Data', '(' + str(X_train.shape[0] + X_test.shape[0]) + ', ' + str(data_before_preprocess.shape[1]) + ')' ], 
                                     ['Transformed Train Set', X_train.shape ], 
                                     ['Transformed Test Set',X_test.shape ],
                                     ['Numeric Imputer ', numeric_imputation],
                                     ['Categorical Imputer ', categorical_imputation],
                                     ['Normalize ', normalize ],
                                     ['Normalize Method ', normalize_grid ],
                                     ['Transformation ', transformation ],
                                     ['Transformation Method ', transformation_grid ],
                                     ['PCA ', pca],
                                     ['PCA Method ', pca_method_grid],
                                     ['PCA Components ', pca_components_grid],
                                     ['Ignore Low Variance ', ignore_low_variance],
                                     ['Combine Rare Levels ', combine_rare_levels],
                                     ['Rare Level Threshold ', rare_level_threshold_grid],
                                     ['Numeric Binning ', numeric_bin_grid],
                                     ['Remove Outliers ', remove_outliers],
                                     ['Outliers Threshold ', outliers_threshold_grid],
                                     ['Remove Multicollinearity ', remove_multicollinearity],
                                     ['Multicollinearity Threshold ', multicollinearity_threshold_grid],
                                     ['Clustering ', create_clusters],
                                     ['Clustering Iteration ', cluster_iter_grid],
                                     ['Polynomial Features ', polynomial_features], #new
                                     ['Polynomial Degree ', polynomial_degree_grid], #new
                                     ['Trignometry Features ', trigonometry_features], #new
                                     ['Polynomial Threshold ', polynomial_threshold_grid], #new
                                     ['Group Features ', group_features_grid], #new
                                     ['Feature Selection ', feature_selection], #new
                                     ['Features Selection Threshold ', feature_selection_threshold_grid], #new
                                     ['Feature Interaction ', feature_interaction], #new
                                     ['Feature Ratio ', feature_ratio], #new
                                     ['Interaction Threshold ', interaction_threshold_grid], #new
                                   ], columns = ['Description', 'Value'] )
        
        #functions_ = functions.style.hide_index()
        functions_ = functions.style.apply(highlight_max)
        display(functions_)
            
        if profile:
            try:
                import pandas_profiling
                pf = pandas_profiling.ProfileReport(data_before_preprocess)
                clear_output()
                display(pf)
            except:
                print('Data Profiler Failed. No output to show, please continue with Modeling.')
            
        '''
        Final display Ends
        '''   
        
        #log into experiment
        experiment__.append(('Regression Setup Config', functions))
        experiment__.append(('X_training Set', X_train))
        experiment__.append(('y_training Set', y_train))
        experiment__.append(('X_test Set', X_test))
        experiment__.append(('y_test Set', y_test))
        experiment__.append(('Transformation Pipeline', prep_pipe))
        try:
            experiment__.append(('Target Inverse Transformer', target_inverse_transformer))
        except:
            pass
        
        return X, y, X_train, X_test, y_train, y_test, seed, prep_pipe, target_inverse_transformer, experiment__






def create_model(estimator = None, 
                 ensemble = False, 
                 method = None, 
                 fold = 10, 
                 round = 4,  
                 verbose = True):
    
     
    """  
     
    Description:
    ------------
    This function creates a model and scores it using Kfold Cross Validation. 
    (default = 10 Fold). The output prints a score grid that shows MAE, MSE, 
    RMSE, RMSLE, R2 and MAPE.

    This function returns a trained model object. 

    setup() function must be called before using create_model()

        Example
        -------
        from pycaret.datasets import get_data
        boston = get_data('boston')
        experiment_name = setup(data = boston,  target = 'medv')
        
        lr = create_model('lr')

        This will create a trained Linear Regression model.

    Parameters
    ----------
    estimator : string, default = None

    Enter abbreviated string of the estimator class. List of estimators supported:

    Estimator                     Abbreviated String     Original Implementation 
    ---------                     ------------------     -----------------------
    Linear Regression             'lr'                   linear_model.LinearRegression
    Lasso Regression              'lasso'                linear_model.Lasso
    Ridge Regression              'ridge'                linear_model.Ridge
    Elastic Net                   'en'                   linear_model.ElasticNet
    Least Angle Regression        'lar'                  linear_model.Lars
    Lasso Least Angle Regression  'llar'                 linear_model.LassoLars
    Orthogonal Matching Pursuit   'omp'                  linear_model.OMP
    Bayesian Ridge                'br'                   linear_model.BayesianRidge
    Automatic Relevance Determ.   'ard'                  linear_model.ARDRegression
    Passive Aggressive Regressor  'par'                  linear_model.PAR
    Random Sample Consensus       'ransac'               linear_model.RANSACRegressor
    TheilSen Regressor            'tr'                   linear_model.TheilSenRegressor
    Huber Regressor               'huber'                linear_model.HuberRegressor 
    Kernel Ridge                  'kr'                   kernel_ridge.KernelRidge
    Support Vector Machine        'svm'                  svm.SVR
    K Neighbors Regressor         'knn'                  neighbors.KNeighborsRegressor 
    Decision Tree                 'dt'                   tree.DecisionTreeRegressor
    Random Forest                 'rf'                   ensemble.RandomForestRegressor
    Extra Trees Regressor         'et'                   ensemble.ExtraTreesRegressor
    AdaBoost Regressor            'ada'                  ensemble.AdaBoostRegressor
    Gradient Boosting             'gbr'                  ensemble.GradientBoostingRegressor 
    Multi Level Perceptron        'mlp'                  neural_network.MLPRegressor
    Extreme Gradient Boosting     'xgboost'              xgboost.readthedocs.io
    Light Gradient Boosting       'lightgbm'             github.com/microsoft/LightGBM
    CatBoost Regressor            'catboost'             https://catboost.ai

    ensemble: Boolean, default = False
    True would result in an ensemble of estimator using the method parameter defined. 

    method: String, 'Bagging' or 'Boosting', default = None.
    method must be defined when ensemble is set to True. Default method is set to None. 

    fold: integer, default = 10
    Number of folds to be used in Kfold CV. Must be at least 2. 

    round: integer, default = 4
    Number of decimal places the metrics in the score grid will be rounded to. 

    verbose: Boolean, default = True
    Score grid is not printed when verbose is set to False.

    Returns:
    --------

    score grid:   A table containing the scores of the model across the kfolds. 
    -----------   Scoring metrics used are MAE, MSE, RMSE, RMSLE, R2 and MAPE. 
                  Mean and standard deviation of the scores across the folds are 
                  also returned.

    model:        trained model object
    -----------

    Warnings:
    ---------
    None
      
    
  
    """


    '''
    
    ERROR HANDLING STARTS HERE
    
    '''
    
    #exception checking   
    import sys
    
    #checking error for estimator (string)
    available_estimators = ['lr', 'lasso', 'ridge', 'en', 'lar', 'llar', 'omp', 'br', 'ard', 'par', 
                            'ransac', 'tr', 'huber', 'kr', 'svm', 'knn', 'dt', 'rf', 'et', 'ada', 'gbr', 
                            'mlp', 'xgboost', 'lightgbm', 'catboost']
    
    if estimator not in available_estimators:
        sys.exit('(Value Error): Estimator Not Available. Please see docstring for list of available estimators.')
        
    #checking error for ensemble:
    if type(ensemble) is not bool:
        sys.exit('(Type Error): Ensemble parameter can only take argument as True or False.') 
    
    #checking error for method:
    
    #1 Check When method given and ensemble is not set to True.
    if ensemble is False and method is not None:
        sys.exit('(Type Error): Method parameter only accepts value when ensemble is set to True.')

    #2 Check when ensemble is set to True and method is not passed.
    if ensemble is True and method is None:
        sys.exit("(Type Error): Method parameter missing. Pass method = 'Bagging' or 'Boosting'.")
        
    #3 Check when ensemble is set to True and method is passed but not allowed.
    available_method = ['Bagging', 'Boosting']
    if ensemble is True and method not in available_method:
        sys.exit("(Value Error): Method parameter only accepts two values 'Bagging' or 'Boosting'.")
        
    #checking fold parameter
    if type(fold) is not int:
        sys.exit('(Type Error): Fold parameter only accepts integer value.')
    
    #checking round parameter
    if type(round) is not int:
        sys.exit('(Type Error): Round parameter only accepts integer value.')
 
    #checking verbose parameter
    if type(verbose) is not bool:
        sys.exit('(Type Error): Verbose parameter can only take argument as True or False.') 
    
    
    '''
    
    ERROR HANDLING ENDS HERE
    
    '''
    
    #pre-load libraries
    import pandas as pd
    import ipywidgets as ipw
    from IPython.display import display, HTML, clear_output, update_display
    import datetime, time
    
    #pd.set_option("display.colheader_justify","centre")
    
    #progress bar
    progress = ipw.IntProgress(value=0, min=0, max=fold+4, step=1 , description='Processing: ')
    master_display = pd.DataFrame(columns=['MAE','MSE','RMSE', 'R2', 'RMSLE', 'MAPE'])
    display(progress)
    
    #display monitor
    timestampStr = datetime.datetime.now().strftime("%H:%M:%S")
    monitor = pd.DataFrame( [ ['Initiated' , '. . . . . . . . . . . . . . . . . .', timestampStr ], 
                             ['Status' , '. . . . . . . . . . . . . . . . . .' , 'Loading Dependencies' ],
                             ['ETC' , '. . . . . . . . . . . . . . . . . .',  'Calculating ETC'] ],
                              columns=['', ' ', '   ']).set_index('')
    
    display(monitor, display_id = 'monitor')
    
    if verbose:
        display_ = display(master_display, display_id=True)
        display_id = display_.display_id
    
    #ignore warnings
    import warnings
    warnings.filterwarnings('ignore') 

    #Storing X_train and y_train in data_X and data_y parameter
    data_X = X_train.copy()
    data_y = y_train.copy()
    
    #reset index
    data_X.reset_index(drop=True, inplace=True)
    data_y.reset_index(drop=True, inplace=True)
    
    #general dependencies
    import numpy as np
    from sklearn import metrics
    from sklearn.model_selection import KFold
    
    progress.value += 1
    
    #cross validation setup starts here
    kf = KFold(fold, random_state=seed)
    
    score_mae =np.empty((0,0))
    score_mse =np.empty((0,0))
    score_rmse =np.empty((0,0))
    score_rmsle =np.empty((0,0))
    score_r2 =np.empty((0,0))
    score_mape =np.empty((0,0))
    avgs_mae =np.empty((0,0))
    avgs_mse =np.empty((0,0))
    avgs_rmse =np.empty((0,0))
    avgs_r2 =np.empty((0,0))
    avgs_mape =np.empty((0,0)) 
    avgs_rmsle =np.empty((0,0))
    
    def calculate_mape(actual, prediction):
        mask = actual != 0
        return (np.fabs(actual - prediction)/actual)[mask].mean()
  
    '''
    MONITOR UPDATE STARTS
    '''
    
    monitor.iloc[1,1:] = 'Selecting Estimator'
    update_display(monitor, display_id = 'monitor')
    
    '''
    MONITOR UPDATE ENDS
    '''
        
    if estimator == 'lr':
        
        from sklearn.linear_model import LinearRegression
        model = LinearRegression()
        full_name = 'Linear Regression'
        
    elif estimator == 'lasso':
        
        from sklearn.linear_model import Lasso
        model = Lasso(random_state=seed)
        full_name = 'Lasso Regression'
        
    elif estimator == 'ridge':
        
        from sklearn.linear_model import Ridge
        model = Ridge(random_state=seed)
        full_name = 'Ridge Regression'
        
    elif estimator == 'en':
        
        from sklearn.linear_model import ElasticNet
        model = ElasticNet(random_state=seed)
        full_name = 'Elastic Net'
        
    elif estimator == 'lar':
        
        from sklearn.linear_model import Lars
        model = Lars()
        full_name = 'Least Angle Regression'
        
    elif estimator == 'llar':
        
        from sklearn.linear_model import LassoLars
        model = LassoLars()
        full_name = 'Lasso Least Angle Regression'
        
    elif estimator == 'omp':
        
        from sklearn.linear_model import OrthogonalMatchingPursuit
        model = OrthogonalMatchingPursuit()
        full_name = 'Orthogonal Matching Pursuit'
        
    elif estimator == 'br':
        from sklearn.linear_model import BayesianRidge
        model = BayesianRidge()
        full_name = 'Bayesian Ridge Regression' 
        
    elif estimator == 'ard':
        
        from sklearn.linear_model import ARDRegression
        model = ARDRegression()
        full_name = 'Automatic Relevance Determination'        
        
    elif estimator == 'par':
        
        from sklearn.linear_model import PassiveAggressiveRegressor
        model = PassiveAggressiveRegressor(random_state=seed)
        full_name = 'Passive Aggressive Regressor'    
        
    elif estimator == 'ransac':
        
        from sklearn.linear_model import RANSACRegressor
        model = RANSACRegressor(min_samples=0.5, random_state=seed)
        full_name = 'Random Sample Consensus'   
        
    elif estimator == 'tr':
        
        from sklearn.linear_model import TheilSenRegressor
        model = TheilSenRegressor(random_state=seed)
        full_name = 'TheilSen Regressor'     
        
    elif estimator == 'huber':
        
        from sklearn.linear_model import HuberRegressor
        model = HuberRegressor()
        full_name = 'Huber Regressor'   
        
    elif estimator == 'kr':
        
        from sklearn.kernel_ridge import KernelRidge
        model = KernelRidge()
        full_name = 'Kernel Ridge'
        
    elif estimator == 'svm':
        
        from sklearn.svm import SVR
        model = SVR()
        full_name = 'Support Vector Regression'  
        
    elif estimator == 'knn':
        
        from sklearn.neighbors import KNeighborsRegressor
        model = KNeighborsRegressor()
        full_name = 'Nearest Neighbors Regression' 
        
    elif estimator == 'dt':
        
        from sklearn.tree import DecisionTreeRegressor
        model = DecisionTreeRegressor(random_state=seed)
        full_name = 'Decision Tree Regressor'
        
    elif estimator == 'rf':
        
        from sklearn.ensemble import RandomForestRegressor
        model = RandomForestRegressor(random_state=seed)
        full_name = 'Random Forest Regressor'
        
    elif estimator == 'et':
        
        from sklearn.ensemble import ExtraTreesRegressor
        model = ExtraTreesRegressor(random_state=seed)
        full_name = 'Extra Trees Regressor'    
        
    elif estimator == 'ada':
        
        from sklearn.ensemble import AdaBoostRegressor
        model = AdaBoostRegressor(random_state=seed)
        full_name = 'AdaBoost Regressor'   
        
    elif estimator == 'gbr':
        
        from sklearn.ensemble import GradientBoostingRegressor
        model = GradientBoostingRegressor(random_state=seed)
        full_name = 'Gradient Boosting Regressor'       
        
    elif estimator == 'mlp':
        
        from sklearn.neural_network import MLPRegressor
        model = MLPRegressor(random_state=seed)
        full_name = 'MLP Regressor'
        
    elif estimator == 'xgboost':
        
        from xgboost import XGBRegressor
        model = XGBRegressor(random_state=seed, n_jobs=-1, verbosity=0)
        full_name = 'Extreme Gradient Boosting Regressor'
        
    elif estimator == 'lightgbm':
        
        import lightgbm as lgb
        model = lgb.LGBMRegressor(random_state=seed)
        full_name = 'Light Gradient Boosting Machine'
        
    elif estimator == 'catboost':
        from catboost import CatBoostRegressor
        model = CatBoostRegressor(random_state=seed, silent = True)
        full_name = 'CatBoost Regressor'
        
    else:
        model = estimator
        full_name = str(model).split("(")[0]
    
    progress.value += 1
    
    #checking method when ensemble is set to True. 

    if method == 'Bagging':
        
        from sklearn.ensemble import BaggingRegressor
        model = BaggingRegressor(model,bootstrap=True,n_estimators=10, random_state=seed)

    elif method == 'Boosting':
        
        from sklearn.ensemble import AdaBoostRegressor
        model = AdaBoostRegressor(model, n_estimators=10, random_state=seed)
    
    
    '''
    MONITOR UPDATE STARTS
    '''
    
    monitor.iloc[1,1:] = 'Initializing CV'
    update_display(monitor, display_id = 'monitor')
    
    '''
    MONITOR UPDATE ENDS
    '''
    
    
    fold_num = 1
    
    for train_i , test_i in kf.split(data_X,data_y):
        
        t0 = time.time()
        
        '''
        MONITOR UPDATE STARTS
        '''
    
        monitor.iloc[1,1:] = 'Fitting Fold ' + str(fold_num) + ' of ' + str(fold)
        update_display(monitor, display_id = 'monitor')

        '''
        MONITOR UPDATE ENDS
        '''
        
        Xtrain,Xtest = data_X.iloc[train_i], data_X.iloc[test_i]
        ytrain,ytest = data_y.iloc[train_i], data_y.iloc[test_i]  
        model.fit(Xtrain,ytrain)
        pred_ = model.predict(Xtest)
        
        try:
            pred_ = target_inverse_transformer.inverse_transform(np.array(pred_).reshape(-1,1))
            ytest = target_inverse_transformer.inverse_transform(np.array(ytest).reshape(-1,1))
            pred_ = np.nan_to_num(pred_)
            ytest = np.nan_to_num(ytest)
            
        except:
            pass
        
        mae = metrics.mean_absolute_error(ytest,pred_)
        mse = metrics.mean_squared_error(ytest,pred_)
        rmse = np.sqrt(mse)
        rmsle = np.sqrt(np.mean(np.power(np.log(np.array(abs(pred_))+1) - np.log(np.array(abs(ytest))+1), 2)))
        r2 = metrics.r2_score(ytest,pred_)
        mape = calculate_mape(ytest,pred_)
        #max_error_ = metrics.max_error(ytest,pred_)
        score_mae = np.append(score_mae,mae)
        score_mse = np.append(score_mse,mse)
        score_rmse = np.append(score_rmse,rmse)
        score_rmsle = np.append(score_rmsle,rmsle)
        score_r2 =np.append(score_r2,r2)
        score_mape = np.append(score_mape,mape)
       
        progress.value += 1
        
        
        '''
        
        This section handles time calculation and is created to update_display() as code loops through 
        the fold defined.
        
        '''
        
        fold_results = pd.DataFrame({'MAE':[mae], 'MSE': [mse], 'RMSE': [rmse], 'R2': [r2],
                                     'RMSLE' : [rmsle], 'MAPE': [mape] }).round(round)
        master_display = pd.concat([master_display, fold_results],ignore_index=True)
        fold_results = []
        
        '''
        TIME CALCULATION SUB-SECTION STARTS HERE
        '''
        t1 = time.time()
        
        tt = (t1 - t0) * (fold-fold_num) / 60
        tt = np.around(tt, 2)
        
        if tt < 1:
            tt = str(np.around((tt * 60), 2))
            ETC = tt + ' Seconds Remaining'
                
        else:
            tt = str (tt)
            ETC = tt + ' Minutes Remaining'
            
        '''
        MONITOR UPDATE STARTS
        '''

        monitor.iloc[2,1:] = ETC
        update_display(monitor, display_id = 'monitor')

        '''
        MONITOR UPDATE ENDS
        '''
            
        fold_num += 1
        
        '''
        TIME CALCULATION ENDS HERE
        '''
        
        if verbose:
            update_display(master_display, display_id = display_id)
            
        
        '''
        
        Update_display() ends here
        
        '''

    mean_mae=np.mean(score_mae)
    mean_mse=np.mean(score_mse)
    mean_rmse=np.mean(score_rmse)
    mean_rmsle=np.mean(score_rmsle)
    mean_r2=np.mean(score_r2)
    mean_mape=np.mean(score_mape)
    std_mae=np.std(score_mae)
    std_mse=np.std(score_mse)
    std_rmse=np.std(score_rmse)
    std_rmsle=np.std(score_rmsle)
    std_r2=np.std(score_r2)
    std_mape=np.std(score_mape)

    avgs_mae = np.append(avgs_mae, mean_mae)
    avgs_mae = np.append(avgs_mae, std_mae) 
    avgs_mse = np.append(avgs_mse, mean_mse)
    avgs_mse = np.append(avgs_mse, std_mse)
    avgs_rmse = np.append(avgs_rmse, mean_rmse)
    avgs_rmse = np.append(avgs_rmse, std_rmse)
    avgs_rmsle = np.append(avgs_rmsle, mean_rmsle)
    avgs_rmsle = np.append(avgs_rmsle, std_rmsle)
    avgs_r2 = np.append(avgs_r2, mean_r2)
    avgs_r2 = np.append(avgs_r2, std_r2)
    avgs_mape = np.append(avgs_mape, mean_mape)
    avgs_mape = np.append(avgs_mape, std_mape)
    
    progress.value += 1
    
    model_results = pd.DataFrame({'MAE': score_mae, 'MSE': score_mse, 'RMSE' : score_rmse, 'R2' : score_r2,
                                  'RMSLE' : score_rmsle, 'MAPE' : score_mape})
    model_avgs = pd.DataFrame({'MAE': avgs_mae, 'MSE': avgs_mse, 'RMSE' : avgs_rmse, 'R2' : avgs_r2,
                                'RMSLE' : avgs_rmsle, 'MAPE' : avgs_mape},index=['Mean', 'SD'])

    model_results = model_results.append(model_avgs)
    model_results = model_results.round(round)
    
    #refitting the model on complete X_train, y_train
    monitor.iloc[1,1:] = 'Compiling Final Model'
    update_display(monitor, display_id = 'monitor')
    
    model.fit(data_X, data_y)
    
    progress.value += 1
    
    #storing into experiment
    tup = (full_name,model)
    experiment__.append(tup)
    nam = str(full_name) + ' Score Grid'
    tup = (nam, model_results)
    experiment__.append(tup)
    
    if verbose:
        clear_output()
        display(model_results)
        return model
    else:
        clear_output()
        return model


def ensemble_model(estimator,
                   method = 'Bagging', 
                   fold = 10,
                   n_estimators = 10,
                   round = 4,  
                   verbose = True):
    """
    
    Description:
    ------------
    This function ensembles the trained base estimator using the method defined 
    in 'method' param (default = 'Bagging'). The output prints a score grid that 
    shows MAE, MSE, RMSE, R2, RMSLE and MAPE by fold (default CV = 10 Folds).

    This function returns a trained model object.  

    Model must be created using create_model() or tune_model().

        Example:
        --------
        from pycaret.datasets import get_data
        boston = get_data('boston')
        experiment_name = setup(data = boston,  target = 'medv')
        dt = create_model('dt')
        
        ensembled_dt = ensemble_model(dt)

        This will return an ensembled Decision Tree model using 'Bagging'.

    Parameters
    ----------
    estimator : object, default = None

    method: String, default = 'Bagging'
    Bagging method will create an ensemble meta-estimator that fits base 
    regressor each on random subsets of the original dataset. The other
    available method is 'Boosting' that fits a regressor on the original 
    dataset and then fits additional copies of the regressor on the same 
    dataset but where the weights of instances are adjusted according to 
    the error of the current prediction. As such, subsequent regressors 
    focus more on difficult cases.
    
    fold: integer, default = 10
    Number of folds to be used in Kfold CV. Must be at least 2.
    
    n_estimators: integer, default = 10
    The number of base estimators in the ensemble.
    In case of perfect fit, the learning procedure is stopped early.

    round: integer, default = 4
    Number of decimal places the metrics in the score grid will be rounded to.

    verbose: Boolean, default = True
    Score grid is not printed when verbose is set to False.


    Returns:
    --------

    score grid:   A table containing the scores of the model across the kfolds. 
    -----------   Scoring metrics used are MAE, MSE, RMSE, R2, RMSLE and MAPE.
                  Mean and standard deviation of the scores across the folds are 
                  also returned.

    model:        trained ensembled model object
    -----------

    Warnings:
    ---------
    None
      
        
    
    """
    
    
    '''
    
    ERROR HANDLING STARTS HERE
    
    '''
    
    #exception checking   
    import sys
        
    #Check for allowed method
    available_method = ['Bagging', 'Boosting']
    if method not in available_method:
        sys.exit("(Value Error): Method parameter only accepts two values 'Bagging' or 'Boosting'.")
    
    #checking fold parameter
    if type(fold) is not int:
        sys.exit('(Type Error): Fold parameter only accepts integer value.')
    
    #checking n_estimators parameter
    if type(n_estimators) is not int:
        sys.exit('(Type Error): n_estimators parameter only accepts integer value.')
    
    #checking round parameter
    if type(round) is not int:
        sys.exit('(Type Error): Round parameter only accepts integer value.')
 
    #checking verbose parameter
    if type(verbose) is not bool:
        sys.exit('(Type Error): Verbose parameter can only take argument as True or False.') 
    
    '''
    
    ERROR HANDLING ENDS HERE
    
    '''    
    
    #pre-load libraries
    import pandas as pd
    import datetime, time
    import ipywidgets as ipw
    from IPython.display import display, HTML, clear_output, update_display
    
    #progress bar
    progress = ipw.IntProgress(value=0, min=0, max=fold+4, step=1 , description='Processing: ')
    master_display = pd.DataFrame(columns=['MAE','MSE','RMSE', 'R2', 'RMSLE', 'MAPE'])
    display(progress)
    
    #display monitor
    timestampStr = datetime.datetime.now().strftime("%H:%M:%S")
    monitor = pd.DataFrame( [ ['Initiated' , '. . . . . . . . . . . . . . . . . .', timestampStr ], 
                             ['Status' , '. . . . . . . . . . . . . . . . . .' , 'Loading Dependencies' ],
                             ['ETC' , '. . . . . . . . . . . . . . . . . .',  'Calculating ETC'] ],
                              columns=['', ' ', '   ']).set_index('')
    
    display(monitor, display_id = 'monitor')
    
    if verbose:
        display_ = display(master_display, display_id=True)
        display_id = display_.display_id
        
    #dependencies
    import numpy as np
    from sklearn import metrics
    from sklearn.model_selection import KFold   
    
    #ignore warnings
    import warnings
    warnings.filterwarnings('ignore')    
    
    #Storing X_train and y_train in data_X and data_y parameter
    data_X = X_train.copy()
    data_y = y_train.copy()
    
    #reset index
    data_X.reset_index(drop=True, inplace=True)
    data_y.reset_index(drop=True, inplace=True)
      
    progress.value += 1
    
    #defining estimator as model
    model = estimator
    
    '''
    MONITOR UPDATE STARTS
    '''
    
    monitor.iloc[1,1:] = 'Selecting Estimator'
    update_display(monitor, display_id = 'monitor')
    
    '''
    MONITOR UPDATE ENDS
    '''
    
    if method == 'Bagging':
        
        from sklearn.ensemble import BaggingRegressor
        model = BaggingRegressor(model,bootstrap=True,n_estimators=n_estimators, random_state=seed)
         
    else:
        
        from sklearn.ensemble import AdaBoostRegressor
        model = AdaBoostRegressor(model, n_estimators=n_estimators, random_state=seed)
    
    progress.value += 1
    
    '''
    MONITOR UPDATE STARTS
    '''
    
    monitor.iloc[1,1:] = 'Initializing CV'
    update_display(monitor, display_id = 'monitor')
    
    '''
    MONITOR UPDATE ENDS
    '''
    
    kf = KFold(fold, random_state=seed)
    
    score_mae =np.empty((0,0))
    score_mse =np.empty((0,0))
    score_rmse =np.empty((0,0))
    score_rmsle =np.empty((0,0))
    score_r2 =np.empty((0,0))
    score_mape =np.empty((0,0))
    avgs_mae =np.empty((0,0))
    avgs_mse =np.empty((0,0))
    avgs_rmse =np.empty((0,0))
    avgs_rmsle =np.empty((0,0))
    avgs_r2 =np.empty((0,0))
    avgs_mape =np.empty((0,0))
    
    def calculate_mape(actual, prediction):
        mask = actual != 0
        return (np.fabs(actual - prediction)/actual)[mask].mean()
    
    fold_num = 1 
    
    for train_i , test_i in kf.split(data_X,data_y):
        
        t0 = time.time()
        
        '''
        MONITOR UPDATE STARTS
        '''
    
        monitor.iloc[1,1:] = 'Fitting Fold ' + str(fold_num) + ' of ' + str(fold)
        update_display(monitor, display_id = 'monitor')

        '''
        MONITOR UPDATE ENDS
        '''
        
        Xtrain,Xtest = data_X.iloc[train_i], data_X.iloc[test_i]
        ytrain,ytest = data_y.iloc[train_i], data_y.iloc[test_i]
        model.fit(Xtrain,ytrain)
        pred_ = model.predict(Xtest)
        
        try:
            pred_ = target_inverse_transformer.inverse_transform(np.array(pred_).reshape(-1,1))
            ytest = target_inverse_transformer.inverse_transform(np.array(ytest).reshape(-1,1))
            pred_ = np.nan_to_num(pred_)
            ytest = np.nan_to_num(ytest)
            
        except:
            pass
        
        mae = metrics.mean_absolute_error(ytest,pred_)
        mse = metrics.mean_squared_error(ytest,pred_)
        rmse = np.sqrt(mse)
        rmsle = np.sqrt(np.mean(np.power(np.log(np.array(abs(pred_))+1) - np.log(np.array(abs(ytest))+1), 2)))
        r2 = metrics.r2_score(ytest,pred_)
        mape = calculate_mape(ytest,pred_)
        score_mae = np.append(score_mae,mae)
        score_mse = np.append(score_mse,mse)
        score_rmse = np.append(score_rmse,rmse)
        score_rmsle = np.append(score_rmsle,rmsle)
        score_r2 =np.append(score_r2,r2)
        score_mape = np.append(score_mape,mape)
        
        progress.value += 1
        
                
        '''
        
        This section is created to update_display() as code loops through the fold defined.
        
        '''
        
        fold_results = pd.DataFrame({'MAE':[mae], 'MSE': [mse], 'RMSE': [rmse], 
                                     'R2': [r2], 'RMSLE': [rmsle], 'MAPE': [mape]}).round(round)
        master_display = pd.concat([master_display, fold_results],ignore_index=True)
        fold_results = []
        
        '''
        
        TIME CALCULATION SUB-SECTION STARTS HERE
        
        '''
        t1 = time.time()
        
        tt = (t1 - t0) * (fold-fold_num) / 60
        tt = np.around(tt, 2)
        
        if tt < 1:
            tt = str(np.around((tt * 60), 2))
            ETC = tt + ' Seconds Remaining'
                
        else:
            tt = str (tt)
            ETC = tt + ' Minutes Remaining'
            
        update_display(ETC, display_id = 'ETC')
            
        fold_num += 1
        
        
        '''
        MONITOR UPDATE STARTS
        '''

        monitor.iloc[2,1:] = ETC
        update_display(monitor, display_id = 'monitor')

        '''
        MONITOR UPDATE ENDS
        '''
        
        '''
        
        TIME CALCULATION ENDS HERE
        
        '''

        if verbose:
            update_display(master_display, display_id = display_id)
        
        '''
        
        Update_display() ends here
        
        '''
        
    mean_mae=np.mean(score_mae)
    mean_mse=np.mean(score_mse)
    mean_rmse=np.mean(score_rmse)
    mean_rmsle=np.mean(score_rmsle)
    mean_r2=np.mean(score_r2)
    mean_mape=np.mean(score_mape)
    std_mae=np.std(score_mae)
    std_mse=np.std(score_mse)
    std_rmse=np.std(score_rmse)
    std_rmsle=np.std(score_rmsle)
    std_r2=np.std(score_r2)
    std_mape=np.std(score_mape)

    avgs_mae = np.append(avgs_mae, mean_mae)
    avgs_mae = np.append(avgs_mae, std_mae) 
    avgs_mse = np.append(avgs_mse, mean_mse)
    avgs_mse = np.append(avgs_mse, std_mse)
    avgs_rmse = np.append(avgs_rmse, mean_rmse)
    avgs_rmse = np.append(avgs_rmse, std_rmse)
    avgs_rmsle = np.append(avgs_rmsle, mean_rmsle)
    avgs_rmsle = np.append(avgs_rmsle, std_rmsle)
    avgs_r2 = np.append(avgs_r2, mean_r2)
    avgs_r2 = np.append(avgs_r2, std_r2)
    avgs_mape = np.append(avgs_mape, mean_mape)
    avgs_mape = np.append(avgs_mape, std_mape)

    model_results = pd.DataFrame({'MAE': score_mae, 'MSE': score_mse, 'RMSE' : score_rmse, 'R2' : score_r2 , 
                                 'RMSLE' : score_rmsle, 'MAPE' : score_mape})
    model_avgs = pd.DataFrame({'MAE': avgs_mae, 'MSE': avgs_mse, 'RMSE' : avgs_rmse, 'R2' : avgs_r2 , 
                               'RMSLE' : avgs_rmsle, 'MAPE' : avgs_mape},index=['Mean', 'SD'])

    model_results = model_results.append(model_avgs)
    model_results = model_results.round(round)  
    
    progress.value += 1
    
    #refitting the model on complete X_train, y_train
    monitor.iloc[1,1:] = 'Compiling Final Model'
    update_display(monitor, display_id = 'monitor')
    
    model.fit(data_X, data_y)
    
    progress.value += 1
    
    #storing into experiment
    model_name = str(model).split("(")[0]
    tup = (model_name,model)
    experiment__.append(tup)
    
    nam = str(model_name) + ' Score Grid'
    tup = (nam, model_results)
    experiment__.append(tup)
    
    if verbose:
        clear_output()
        display(model_results)
        return model
    else:
        clear_output()
        return model



def compare_models(blacklist = None,
                   fold = 10, 
                   round = 4, 
                   sort = 'R2',
                   turbo = True):
    
    """
       
   
    Description:
    ------------
    This function uses all models in the model library and scores them using  
    Kfold Cross Validation. The output prints a score grid that shows MAE, MSE, 
    RMSE, R2, RMSLE and MAPE by fold (default CV = 10 Folds) of all the available
    models in model library.
    
    When turbo is set to True ('kr', 'ard' and 'mlp') are excluded due to longer
    training times. By default turbo param is set to True.

    List of models in Model Library

    Estimator                     Abbreviated String     Original Implementation 
    ---------                     ------------------     -----------------------
    Linear Regression             'lr'                   linear_model.LinearRegression
    Lasso Regression              'lasso'                linear_model.Lasso
    Ridge Regression              'ridge'                linear_model.Ridge
    Elastic Net                   'en'                   linear_model.ElasticNet
    Least Angle Regression        'lar'                  linear_model.Lars
    Lasso Least Angle Regression  'llar'                 linear_model.LassoLars
    Orthogonal Matching Pursuit   'omp'                  linear_model.OMP
    Bayesian Ridge                'br'                   linear_model.BayesianRidge
    Automatic Relevance Determ.   'ard'                  linear_model.ARDRegression
    Passive Aggressive Regressor  'par'                  linear_model.PAR
    Random Sample Consensus       'ransac'               linear_model.RANSACRegressor
    TheilSen Regressor            'tr'                   linear_model.TheilSenRegressor
    Huber Regressor               'huber'                linear_model.HuberRegressor 
    Kernel Ridge                  'kr'                   kernel_ridge.KernelRidge
    Support Vector Machine        'svm'                  svm.SVR
    K Neighbors Regressor         'knn'                  neighbors.KNeighborsRegressor 
    Decision Tree                 'dt'                   tree.DecisionTreeRegressor
    Random Forest                 'rf'                   ensemble.RandomForestRegressor
    Extra Trees Regressor         'et'                   ensemble.ExtraTreesRegressor
    AdaBoost Regressor            'ada'                  ensemble.AdaBoostRegressor
    Gradient Boosting             'gbr'                  ensemble.GradientBoostingRegressor 
    Multi Level Perceptron        'mlp'                  neural_network.MLPRegressor
    Extreme Gradient Boosting     'xgboost'              xgboost.readthedocs.io
    Light Gradient Boosting       'lightgbm'             github.com/microsoft/LightGBM
    CatBoost Regressor            'catboost'             https://catboost.ai

        Example:
        --------
        from pycaret.datasets import get_data
        boston = get_data('boston')
        experiment_name = setup(data = boston,  target = 'medv')

        compare_models() 

        This will return the averaged score grid of all models except 'kr', 'ard' 
        and 'mlp'. When turbo param is set to False, all models including 'kr',
        'ard' and 'mlp' are used, but this may result in longer training times.
        
        compare_models(blacklist = ['knn','gbr'], turbo = False) 

        This will return a comparison of all models except K Nearest Neighbour and
        Gradient Boosting Regressor.
        
        compare_models(blacklist = ['knn','gbr'] , turbo = True) 

        This will return a comparison of all models except K Nearest Neighbour, 
        Gradient Boosting Regressor, Kernel Ridge Regressor, Automatic Relevance
        Determinant and Multi Level Perceptron.
        
    Parameters
    ----------
    blacklist: string, default = None
    In order to omit certain models from the comparison, the abbreviation string 
    (see above list) can be passed as list in blacklist param. This is normally
    done to be more efficient with time. 

    fold: integer, default = 10
    Number of folds to be used in Kfold CV. Must be at least 2. 

    round: integer, default = 4
    Number of decimal places the metrics in the score grid will be rounded to.
  
    sort: string, default = 'MAE'
    The scoring measure specified is used for sorting the average score grid
    Other options are 'MAE', 'MSE', 'RMSE', 'R2', 'RMSLE' and 'MAPE'.

    turbo: Boolean, default = True
    When turbo is set to True, it blacklists estimators that have longer
    training times.
    
    Returns:
    --------

    score grid:   A table containing the scores of the model across the kfolds. 
    -----------   Scoring metrics used are MAE, MSE, RMSE, R2, RMSLE and MAPE
                  Mean and standard deviation of the scores across the folds is
                  also returned.

    Warnings:
    ---------
    - compare_models() though attractive, might be time consuming with large 
      datasets. By default turbo is set to True, which blacklists models that
      have longer training times. Changing turbo parameter to False may result 
      in very high training times with datasets where number of samples exceed 
      10,000.

    - This function doesn't return model object.
      
               
    
    """
    
    '''
    
    ERROR HANDLING STARTS HERE
    
    '''
    
    #exception checking   
    import sys
    
    #checking error for blacklist (string)
    available_estimators = ['lr', 'lasso', 'ridge', 'en', 'lar', 'llar', 'omp', 'br', 'ard', 'par', 
                            'ransac', 'tr', 'huber', 'kr', 'svm', 'knn', 'dt', 'rf', 'et', 'ada', 'gbr', 
                            'mlp', 'xgboost', 'lightgbm', 'catboost']

    if blacklist != None:
        for i in blacklist:
            if i not in available_estimators:
                sys.exit('(Value Error): Estimator Not Available. Please see docstring for list of available estimators.')
        
    #checking fold parameter
    if type(fold) is not int:
        sys.exit('(Type Error): Fold parameter only accepts integer value.')
    
    #checking round parameter
    if type(round) is not int:
        sys.exit('(Type Error): Round parameter only accepts integer value.')
 
    #checking sort parameter
    allowed_sort = ['MAE', 'MSE', 'RMSE', 'R2', 'RMSLE', 'MAPE']
    if sort not in allowed_sort:
        sys.exit('(Value Error): Sort method not supported. See docstring for list of available parameters.')
    
    
    '''
    
    ERROR HANDLING ENDS HERE
    
    '''
    
    #pre-load libraries
    import pandas as pd
    import time, datetime
    import ipywidgets as ipw
    from IPython.display import display, HTML, clear_output, update_display
    
    #progress bar
    if blacklist is None:
        len_of_blacklist = 0
    else:
        len_of_blacklist = len(blacklist)
        
    if turbo:
        len_mod = 22 - len_of_blacklist
    else:
        len_mod = 25 - len_of_blacklist
        
    progress = ipw.IntProgress(value=0, min=0, max=(fold*len_mod)+25, step=1 , description='Processing: ')
    master_display = pd.DataFrame(columns=['Model', 'MAE','MSE','RMSE', 'R2', 'RMSLE', 'MAPE'])
    display(progress)
    
    #display monitor
    timestampStr = datetime.datetime.now().strftime("%H:%M:%S")
    monitor = pd.DataFrame( [ ['Initiated' , '. . . . . . . . . . . . . . . . . .', timestampStr ], 
                             ['Status' , '. . . . . . . . . . . . . . . . . .' , 'Loading Dependencies' ],
                             ['Estimator' , '. . . . . . . . . . . . . . . . . .' , 'Compiling Library' ],
                             ['ETC' , '. . . . . . . . . . . . . . . . . .',  'Calculating ETC'] ],
                              columns=['', ' ', '   ']).set_index('')
    
    display(monitor, display_id = 'monitor')
    
    display_ = display(master_display, display_id=True)
    display_id = display_.display_id
    
    
    #ignore warnings
    import warnings
    warnings.filterwarnings('ignore') 
    
    #general dependencies
    import numpy as np
    import random
    from sklearn import metrics
    from sklearn.model_selection import KFold
    import pandas.io.formats.style
    
    #Storing X_train and y_train in data_X and data_y parameter
    data_X = X_train.copy()
    data_y = y_train.copy()
    
    #reset index
    data_X.reset_index(drop=True, inplace=True)
    data_y.reset_index(drop=True, inplace=True)
    
    progress.value += 1
    
    #import sklearn dependencies
    from sklearn.linear_model import LinearRegression
    from sklearn.linear_model import Ridge
    from sklearn.linear_model import Lasso
    from sklearn.linear_model import ElasticNet
    from sklearn.linear_model import Lars
    from sklearn.linear_model import LassoLars
    from sklearn.linear_model import OrthogonalMatchingPursuit
    from sklearn.linear_model import BayesianRidge
    from sklearn.linear_model import ARDRegression
    from sklearn.linear_model import PassiveAggressiveRegressor
    from sklearn.linear_model import RANSACRegressor
    from sklearn.linear_model import TheilSenRegressor
    from sklearn.linear_model import HuberRegressor
    from sklearn.kernel_ridge import KernelRidge
    from sklearn.svm import SVR
    from sklearn.neighbors import KNeighborsRegressor
    from sklearn.tree import DecisionTreeRegressor
    from sklearn.ensemble import RandomForestRegressor
    from sklearn.ensemble import ExtraTreesRegressor
    from sklearn.ensemble import AdaBoostRegressor
    from sklearn.ensemble import GradientBoostingRegressor
    from sklearn.neural_network import MLPRegressor
    from xgboost import XGBRegressor
    from catboost import CatBoostRegressor
    try:
        import lightgbm as lgb
    except:
        pass
   
    progress.value += 1

    
    '''
    MONITOR UPDATE STARTS
    '''
    
    monitor.iloc[1,1:] = 'Loading Estimator'
    update_display(monitor, display_id = 'monitor')
    
    '''
    MONITOR UPDATE ENDS
    '''
    
    #creating model object
    lr = LinearRegression()
    lasso = Lasso(random_state=seed)
    ridge = Ridge(random_state=seed)
    en = ElasticNet(random_state=seed)
    lar = Lars()
    llar = LassoLars()
    omp = OrthogonalMatchingPursuit()
    br = BayesianRidge()
    ard = ARDRegression()
    par = PassiveAggressiveRegressor(random_state=seed)
    ransac = RANSACRegressor(min_samples=0.5, random_state=seed)
    tr = TheilSenRegressor(random_state=seed)
    huber = HuberRegressor()
    kr = KernelRidge()
    svm = SVR()
    knn = KNeighborsRegressor()
    dt = DecisionTreeRegressor(random_state=seed)
    rf = RandomForestRegressor(random_state=seed)
    et = ExtraTreesRegressor(random_state=seed)
    ada = AdaBoostRegressor(random_state=seed)
    gbr = GradientBoostingRegressor(random_state=seed)
    mlp = MLPRegressor(random_state=seed)
    xgboost = XGBRegressor(random_state=seed, n_jobs=-1, verbosity=0)
    lightgbm = lgb.LGBMRegressor(random_state=seed)
    catboost = CatBoostRegressor(random_state=seed, silent = True)
    
    progress.value += 1
    
    model_library = [lr, lasso, ridge, en, lar, llar, omp, br, ard, par, ransac, tr, huber, kr, 
                     svm, knn, dt, rf, et, ada, gbr, mlp, xgboost, lightgbm, catboost]
    
    model_names = ['Linear Regression',
                   'Lasso Regression',
                   'Ridge Regression',
                   'Elastic Net',
                   'Least Angle Regression',
                   'Lasso Least Angle Regression',
                   'Orthogonal Matching Pursuit',
                   'Bayesian Ridge',
                   'Automatic Relevance Determination',
                   'Passive Aggressive Regressor',
                   'Random Sample Consensus',
                   'TheilSen Regressor',
                   'Huber Regressor',
                   'Kernel Ridge',
                   'Support Vector Machine',
                   'K Neighbors Regressor',
                   'Decision Tree',
                   'Random Forest',
                   'Extra Trees Regressor',
                   'AdaBoost Regressor',
                   'Gradient Boosting Regressor',
                   'Multi Level Perceptron',
                   'Extreme Gradient Boosting',
                   'Light Gradient Boosting Machine',
                   'CatBoost Regressor']
    
    
    #checking for blacklist models
    
    model_library_str = ['lr', 'lasso', 'ridge', 'en', 'lar', 'llar', 'omp', 'br', 'ard',
                         'par', 'ransac', 'tr', 'huber', 'kr', 'svm', 'knn', 'dt', 'rf', 
                         'et', 'ada', 'gbr', 'mlp', 'xgboost', 'lightgbm', 'catboost']
    
    model_library_str_ = ['lr', 'lasso', 'ridge', 'en', 'lar', 'llar', 'omp', 'br', 'ard',
                         'par', 'ransac', 'tr', 'huber', 'kr', 'svm', 'knn', 'dt', 'rf', 
                         'et', 'ada', 'gbr', 'mlp', 'xgboost', 'lightgbm', 'catboost']
    
    if blacklist is not None:
        
        if turbo:
            internal_blacklist = ['kr', 'ard', 'mlp']
            compiled_blacklist = blacklist + internal_blacklist
            blacklist = list(set(compiled_blacklist))
            
        else:
            blacklist = blacklist
        
        for i in blacklist:
            model_library_str_.remove(i)
        
        si = []
        
        for i in model_library_str_:
            s = model_library_str.index(i)
            si.append(s)
        
        model_library_ = []
        model_names_= []
        for i in si:
            model_library_.append(model_library[i])
            model_names_.append(model_names[i])
            
        model_library = model_library_
        model_names = model_names_
        
        
    if blacklist is None and turbo is True:
        
        model_library = [lr, lasso, ridge, en, lar, llar, omp, br, par, ransac, tr, huber, 
                         svm, knn, dt, rf, et, ada, gbr, xgboost, lightgbm, catboost]
    
        model_names = ['Linear Regression',
                       'Lasso Regression',
                       'Ridge Regression',
                       'Elastic Net',
                       'Least Angle Regression',
                       'Lasso Least Angle Regression',
                       'Orthogonal Matching Pursuit',
                       'Bayesian Ridge',
                       'Passive Aggressive Regressor',
                       'Random Sample Consensus',
                       'TheilSen Regressor',
                       'Huber Regressor',
                       'Support Vector Machine',
                       'K Neighbors Regressor',
                       'Decision Tree',
                       'Random Forest',
                       'Extra Trees Regressor',
                       'AdaBoost Regressor',
                       'Gradient Boosting Regressor',
                       'Extreme Gradient Boosting',
                       'Light Gradient Boosting Machine',
                       'CatBoost Regressor']
    
        
            
    progress.value += 1

    
    '''
    MONITOR UPDATE STARTS
    '''
    
    monitor.iloc[1,1:] = 'Initializing CV'
    update_display(monitor, display_id = 'monitor')
    
    '''
    MONITOR UPDATE ENDS
    '''
    
    #cross validation setup starts here
    kf = KFold(fold, random_state=seed)

    score_mae =np.empty((0,0))
    score_mse =np.empty((0,0))
    score_rmse =np.empty((0,0))
    score_rmsle =np.empty((0,0))
    score_r2 =np.empty((0,0))
    score_mape =np.empty((0,0))
    avgs_mae =np.empty((0,0))
    avgs_mse =np.empty((0,0))
    avgs_rmse =np.empty((0,0))
    avgs_rmsle =np.empty((0,0))
    avgs_r2 =np.empty((0,0))
    avgs_mape =np.empty((0,0))  
    
    def calculate_mape(actual, prediction):
        mask = actual != 0
        return (np.fabs(actual - prediction)/actual)[mask].mean()
    
    name_counter = 0
      
    for model in model_library:
        
        progress.value += 1
        
        '''
        MONITOR UPDATE STARTS
        '''
        monitor.iloc[2,1:] = model_names[name_counter]
        monitor.iloc[3,1:] = 'Calculating ETC'
        update_display(monitor, display_id = 'monitor')

        '''
        MONITOR UPDATE ENDS
        '''
        
        fold_num = 1
        
        for train_i , test_i in kf.split(data_X,data_y):
        
            progress.value += 1
            
            t0 = time.time()
            
            '''
            MONITOR UPDATE STARTS
            '''
                
            monitor.iloc[1,1:] = 'Fitting Fold ' + str(fold_num) + ' of ' + str(fold)
            update_display(monitor, display_id = 'monitor')
            
            '''
            MONITOR UPDATE ENDS
            '''            
     
            Xtrain,Xtest = data_X.iloc[train_i], data_X.iloc[test_i]
            ytrain,ytest = data_y.iloc[train_i], data_y.iloc[test_i]
            model.fit(Xtrain,ytrain)
            pred_ = model.predict(Xtest)
            
            try:
                pred_ = target_inverse_transformer.inverse_transform(np.array(pred_).reshape(-1,1))
                ytest = target_inverse_transformer.inverse_transform(np.array(ytest).reshape(-1,1))
                pred_ = np.nan_to_num(pred_)
                ytest = np.nan_to_num(ytest)

            except:
                pass
        
            mae = metrics.mean_absolute_error(ytest,pred_)
            mse = metrics.mean_squared_error(ytest,pred_)
            rmse = np.sqrt(mse)
            r2 = metrics.r2_score(ytest,pred_)
            rmsle = np.sqrt(np.mean(np.power(np.log(np.array(abs(pred_))+1) - np.log(np.array(abs(ytest))+1), 2)))
            mape = calculate_mape(ytest,pred_)
            #max_error_ = metrics.max_error(ytest,pred_)
            score_mae = np.append(score_mae,mae)
            score_mse = np.append(score_mse,mse)
            score_rmse = np.append(score_rmse,rmse)
            score_rmsle = np.append(score_rmsle,rmsle)
            score_r2 =np.append(score_r2,r2)
            score_mape = np.append(score_mape,mape)            
                
                
            '''
            TIME CALCULATION SUB-SECTION STARTS HERE
            '''
            t1 = time.time()
        
            tt = (t1 - t0) * (fold-fold_num) / 60
            tt = np.around(tt, 2)
        
            if tt < 1:
                tt = str(np.around((tt * 60), 2))
                ETC = tt + ' Seconds Remaining'
                
            else:
                tt = str (tt)
                ETC = tt + ' Minutes Remaining'
            
            fold_num += 1
            
            '''
            MONITOR UPDATE STARTS
            '''

            monitor.iloc[3,1:] = ETC
            update_display(monitor, display_id = 'monitor')

            '''
            MONITOR UPDATE ENDS
            '''
        
        avgs_mae = np.append(avgs_mae,np.mean(score_mae))
        avgs_mse = np.append(avgs_mse,np.mean(score_mse))
        avgs_rmse = np.append(avgs_rmse,np.mean(score_rmse))
        avgs_rmsle = np.append(avgs_rmsle,np.mean(score_rmsle))
        avgs_r2 = np.append(avgs_r2,np.mean(score_r2))
        avgs_mape = np.append(avgs_mape,np.mean(score_mape))
        
        compare_models_ = pd.DataFrame({'Model':model_names[name_counter], 'MAE':avgs_mae, 'MSE':avgs_mse, 
                           'RMSE':avgs_rmse, 'R2':avgs_r2, 'RMSLE':avgs_rmsle, 'MAPE':avgs_mape})
        master_display = pd.concat([master_display, compare_models_],ignore_index=True)
        master_display = master_display.round(round)
        
        if sort == 'R2':
            master_display = master_display.sort_values(by=sort,ascending=False)
        else:
            master_display = master_display.sort_values(by=sort,ascending=True)
        
        master_display.reset_index(drop=True, inplace=True)
        
        update_display(master_display, display_id = display_id)
        
        score_mae =np.empty((0,0))
        score_mse =np.empty((0,0))
        score_rmse =np.empty((0,0))
        score_rmsle =np.empty((0,0))
        score_r2 =np.empty((0,0))
        score_mape =np.empty((0,0))
        
        avgs_mae = np.empty((0,0))
        avgs_mse = np.empty((0,0))
        avgs_rmse = np.empty((0,0))
        avgs_rmsle = np.empty((0,0))
        avgs_r2 = np.empty((0,0))
        avgs_mape = np.empty((0,0))
        
        name_counter += 1
  
    progress.value += 1
    
    #storing into experiment
    model_name = 'Compare Models Score Grid'
    tup = (model_name,master_display)
    experiment__.append(tup)
    
    def highlight_min(s):
        is_min = s == s.min()
        return ['background-color: yellow' if v else '' for v in is_min]

    compare_models_ = master_display.style.apply(highlight_min,subset=['MAE','MSE','RMSE','RMSLE','MAPE' ])
    compare_models_ = compare_models_.set_properties(**{'text-align': 'left'})
    compare_models_ = compare_models_.set_table_styles([dict(selector='th', props=[('text-align', 'left')])])
    
    progress.value += 1
    
    clear_output()

    return compare_models_



def blend_models(estimator_list = 'All', 
                 fold = 10, 
                 round = 4, 
                 turbo = True,
                 verbose = True):
    
    """
        
    Description:
    ------------
    This function creates an ensemble meta-estimator that fits a base regressor on 
    the whole dataset. It then averages the predictions to form a final prediction. 
    By default, this function will use all estimators in the model library (excl. 
    the few estimators when turbo is True) or a specific trained estimator passed 
    as a list in estimator_list param. It scores it using Kfold Cross Validation. 
    The output prints the score grid that shows MAE, MSE, RMSE, R2, RMSLE and MAPE 
    by fold (default = 10 Fold). 

    This function returns a trained model object.  

        Example:
        --------
        from pycaret.datasets import get_data
        boston = get_data('boston')
        experiment_name = setup(data = boston,  target = 'medv')
        
        blend_all = blend_models() 

        This will result in VotingRegressor for all models in the library except 'ard',
        'kr' and 'mlp'.
        
        For specific models, you can use:

        lr = create_model('lr')
        rf = create_model('rf')
        knn = create_model('knn')

        blend_three = blend_models(estimator_list = [lr,rf,knn])
    
        This will create a VotingRegressor of lr, rf and knn.

    Parameters
    ----------
    estimator_list : string ('All') or list of object, default = 'All'

    fold: integer, default = 10
    Number of folds to be used in Kfold CV. Must be at least 2. 

    round: integer, default = 4
    Number of decimal places the metrics in the score grid will be rounded to.

    turbo: Boolean, default = True
    When turbo is set to True, it blacklists estimator that uses Radial Kernel.

    verbose: Boolean, default = True
    Score grid is not printed when verbose is set to False.

    Returns:
    --------

    score grid:   A table containing the scores of the model across the kfolds. 
    -----------   Scoring metrics used are MAE, MSE, RMSE, R2, RMSLE and MAPE. 
                  Mean and standard deviation of the scores across the folds are 
                  also returned.

    model:        trained Voting Regressor model object. 
    -----------

    Warnings:
    ---------
    None
      
       
       
  
    """
    
    
    '''
    
    ERROR HANDLING STARTS HERE
    
    '''
    
    #testing
    #global model_names
    
    #exception checking   
    import sys
    
    #checking error for estimator_list (string)
    
    if estimator_list != 'All':
        for i in estimator_list:
            if 'sklearn' not in str(type(i)) and 'CatBoostRegressor' not in str(type(i)):
                sys.exit("(Value Error): estimator_list parameter only accepts 'All' as string or trained model object")
   
    #checking fold parameter
    if type(fold) is not int:
        sys.exit('(Type Error): Fold parameter only accepts integer value.')
    
    #checking round parameter
    if type(round) is not int:
        sys.exit('(Type Error): Round parameter only accepts integer value.')
    
    #checking verbose parameter
    if type(verbose) is not bool:
        sys.exit('(Type Error): Verbose parameter can only take argument as True or False.') 
        
    '''
    
    ERROR HANDLING ENDS HERE
    
    '''
    

    
    #pre-load libraries
    import pandas as pd
    import time, datetime
    import ipywidgets as ipw
    from IPython.display import display, HTML, clear_output, update_display
    
    #progress bar
    progress = ipw.IntProgress(value=0, min=0, max=fold+4, step=1 , description='Processing: ')
    master_display = pd.DataFrame(columns=['MAE','MSE','RMSE', 'R2', 'RMSLE', 'MAPE'])
    display(progress)
    
    #display monitor
    timestampStr = datetime.datetime.now().strftime("%H:%M:%S")
    monitor = pd.DataFrame( [ ['Initiated' , '. . . . . . . . . . . . . . . . . .', timestampStr ], 
                             ['Status' , '. . . . . . . . . . . . . . . . . .' , 'Loading Dependencies' ],
                             ['ETC' , '. . . . . . . . . . . . . . . . . .',  'Calculating ETC'] ],
                              columns=['', ' ', '   ']).set_index('')
    
    display(monitor, display_id = 'monitor')
    
    if verbose:
        display_ = display(master_display, display_id=True)
        display_id = display_.display_id
        
    #ignore warnings
    import warnings
    warnings.filterwarnings('ignore') 
    
    #general dependencies
    import numpy as np
    from sklearn import metrics
    from sklearn.model_selection import KFold  
    from sklearn.ensemble import VotingRegressor
    import re
    
    #Storing X_train and y_train in data_X and data_y parameter
    data_X = X_train.copy()
    data_y = y_train.copy()
    
    #reset index
    data_X.reset_index(drop=True, inplace=True)
    data_y.reset_index(drop=True, inplace=True)
    
    progress.value += 1
    
    score_mae =np.empty((0,0))
    score_mse =np.empty((0,0))
    score_rmse =np.empty((0,0))
    score_rmsle =np.empty((0,0))
    score_r2 =np.empty((0,0))
    score_mape =np.empty((0,0))
    avgs_mae =np.empty((0,0))
    avgs_mse =np.empty((0,0))
    avgs_rmse =np.empty((0,0))
    avgs_rmsle =np.empty((0,0))
    avgs_r2 =np.empty((0,0))
    avgs_mape =np.empty((0,0))
    
    def calculate_mape(actual, prediction):
        mask = actual != 0
        return (np.fabs(actual - prediction)/actual)[mask].mean()

    kf = KFold(fold, random_state=seed)
    
    '''
    MONITOR UPDATE STARTS
    '''
    
    monitor.iloc[1,1:] = 'Compiling Estimators'
    update_display(monitor, display_id = 'monitor')
    
    '''
    MONITOR UPDATE ENDS
    '''
    
    if estimator_list == 'All':

        from sklearn.linear_model import LinearRegression
        from sklearn.linear_model import Ridge
        from sklearn.linear_model import Lasso
        from sklearn.linear_model import ElasticNet
        from sklearn.linear_model import Lars
        from sklearn.linear_model import LassoLars
        from sklearn.linear_model import OrthogonalMatchingPursuit
        from sklearn.linear_model import BayesianRidge
        from sklearn.linear_model import ARDRegression
        from sklearn.linear_model import PassiveAggressiveRegressor
        from sklearn.linear_model import RANSACRegressor
        from sklearn.linear_model import TheilSenRegressor
        from sklearn.linear_model import HuberRegressor
        from sklearn.kernel_ridge import KernelRidge
        from sklearn.svm import SVR
        from sklearn.neighbors import KNeighborsRegressor
        from sklearn.tree import DecisionTreeRegressor
        from sklearn.ensemble import RandomForestRegressor
        from sklearn.ensemble import ExtraTreesRegressor
        from sklearn.ensemble import AdaBoostRegressor
        from sklearn.ensemble import GradientBoostingRegressor
        from sklearn.neural_network import MLPRegressor
        from xgboost import XGBRegressor
        import lightgbm as lgb
        from catboost import CatBoostRegressor

        lr = LinearRegression()
        lasso = Lasso(random_state=seed)
        ridge = Ridge(random_state=seed)
        en = ElasticNet(random_state=seed)
        lar = Lars()
        llar = LassoLars()
        omp = OrthogonalMatchingPursuit()
        br = BayesianRidge()
        ard = ARDRegression()
        par = PassiveAggressiveRegressor(random_state=seed)
        ransac = RANSACRegressor(min_samples=0.5, random_state=seed)
        tr = TheilSenRegressor(random_state=seed)
        huber = HuberRegressor()
        kr = KernelRidge()
        svm = SVR()
        knn = KNeighborsRegressor()
        dt = DecisionTreeRegressor(random_state=seed)
        rf = RandomForestRegressor(random_state=seed)
        et = ExtraTreesRegressor(random_state=seed)
        ada = AdaBoostRegressor(random_state=seed)
        gbr = GradientBoostingRegressor(random_state=seed)
        mlp = MLPRegressor(random_state=seed)
        xgboost = XGBRegressor(random_state=seed, n_jobs=-1, verbosity=0)
        lightgbm = lgb.LGBMRegressor(random_state=seed)
        catboost = CatBoostRegressor(random_state=seed, silent = True)

        progress.value += 1
        
        if turbo:
            
            estimator_list = [lr, lasso, ridge, en, lar, llar, omp, br, par, ransac, tr, huber, 
                             svm, knn, dt, rf, et, ada, gbr, xgboost, lightgbm, catboost]

        else:
            
            estimator_list = [lr, lasso, ridge, en, lar, llar, omp, br, ard, par, ransac, tr, huber, kr, 
                             svm, knn, dt, rf, et, ada, gbr, mlp, xgboost, lightgbm, catboost]
            

    else:

        estimator_list = estimator_list
        
    model_names = []

    for names in estimator_list:

        model_names = np.append(model_names, str(names).split("(")[0])
        
    model_names_fixed = []
    
    for i in model_names:
        if 'CatBoostRegressor' in i:
            model_names_fixed.append('CatBoost Regressor')
        else:
            model_names_fixed.append(i)
        
    model_names = model_names_fixed

    def putSpace(input):
        words = re.findall('[A-Z][a-z]*', input)
        words = ' '.join(words)
        return words  

    model_names_modified = []
    
    for i in model_names:
        
        model_names_modified.append(putSpace(i))
        model_names = model_names_modified
    
    model_names_final = []
  
    for j in model_names_modified:

        if j == 'A R D Regression':
            model_names_final.append('Automatic Relevance Determination')

        elif j == 'M L P Regressor':
            model_names_final.append('MLP Regressor')

        elif j == 'R A N S A C Regressor':
            model_names_final.append('RANSAC Regressor')

        elif j == 'S V R':
            model_names_final.append('Support Vector Regressor')
            
        elif j == 'Lars':
            model_names_final.append('Least Angle Regression')
            
        elif j == 'X G B Regressor':
            model_names_final.append('Extreme Gradient Boosting Regressor')

        elif j == 'L G B M Regressor':
            model_names_final.append('Light Gradient Boosting Machine')
            
        elif j == 'Cat Boost Regressor':
            model_names_final.append('CatBoost Regressor')        
            
        else: 
            model_names_final.append(j)
            
    model_names = model_names_final

    model_names_n = []
    counter = 0
    
    for i in model_names:
        mn = str(i) + '_' + str(counter)
        model_names_n.append(mn)
        counter += 1
        
    model_names = model_names_n

    estimator_list = estimator_list
    
    estimator_list_ = zip(model_names, estimator_list)
    #estimator_list_ = set(estimator_list_) #in order to accomodate catboost set is switched off
    estimator_list_ = list(estimator_list_)

    try:
        model = VotingRegressor(estimators=estimator_list_, n_jobs=-1)
        model.fit(Xtrain,ytrain)
    except:
        model = VotingRegressor(estimators=estimator_list_)
    
    progress.value += 1
    
    '''
    MONITOR UPDATE STARTS
    '''
    
    monitor.iloc[1,1:] = 'Initializing CV'
    update_display(monitor, display_id = 'monitor')
    
    '''
    MONITOR UPDATE ENDS
    '''
    
    fold_num = 1
    
    for train_i , test_i in kf.split(data_X,data_y):
        
        progress.value += 1
        
        t0 = time.time()
        
        '''
        MONITOR UPDATE STARTS
        '''
    
        monitor.iloc[1,1:] = 'Fitting Fold ' + str(fold_num) + ' of ' + str(fold)
        update_display(monitor, display_id = 'monitor')

        '''
        MONITOR UPDATE ENDS
        '''
    
        Xtrain,Xtest = data_X.iloc[train_i], data_X.iloc[test_i]
        ytrain,ytest = data_y.iloc[train_i], data_y.iloc[test_i]      
        model.fit(Xtrain,ytrain)
        pred_ = model.predict(Xtest)
        
        try:
            pred_ = target_inverse_transformer.inverse_transform(np.array(pred_).reshape(-1,1))
            ytest = target_inverse_transformer.inverse_transform(np.array(ytest).reshape(-1,1))
            pred_ = np.nan_to_num(pred_)
            ytest = np.nan_to_num(ytest)
            
        except:
            pass
        
        mae = metrics.mean_absolute_error(ytest,pred_)
        mse = metrics.mean_squared_error(ytest,pred_)
        rmse = np.sqrt(mse)
        rmsle = np.sqrt(np.mean(np.power(np.log(np.array(abs(pred_))+1) - np.log(np.array(abs(ytest))+1), 2)))
        r2 = metrics.r2_score(ytest,pred_)
        mape = calculate_mape(ytest,pred_)
        #max_error_ = metrics.max_error(ytest,pred_)
        score_mae = np.append(score_mae,mae)
        score_mse = np.append(score_mse,mse)
        score_rmse = np.append(score_rmse,rmse)
        score_rmsle = np.append(score_rmsle,rmsle)
        score_r2 =np.append(score_r2,r2)
        score_mape = np.append(score_mape,mape)
    
        '''
        
        This section handles time calculation and is created to update_display() as code loops through 
        the fold defined.
        
        '''
        
        fold_results = pd.DataFrame({'MAE':[mae], 'MSE': [mse], 'RMSE': [rmse], 
                                     'R2': [r2], 'RMSLE': [rmsle], 'MAPE': [mape]}).round(round)
        master_display = pd.concat([master_display, fold_results],ignore_index=True)
        fold_results = []
        
        '''
        TIME CALCULATION SUB-SECTION STARTS HERE
        '''
        t1 = time.time()
        
        tt = (t1 - t0) * (fold-fold_num) / 60
        tt = np.around(tt, 2)
        
        if tt < 1:
            tt = str(np.around((tt * 60), 2))
            ETC = tt + ' Seconds Remaining'
                
        else:
            tt = str (tt)
            ETC = tt + ' Minutes Remaining'
            
        fold_num += 1
        
        '''
        MONITOR UPDATE STARTS
        '''

        monitor.iloc[2,1:] = ETC
        update_display(monitor, display_id = 'monitor')

        '''
        MONITOR UPDATE ENDS
        '''
        
        '''
        TIME CALCULATION ENDS HERE
        '''
        
        if verbose:
            update_display(master_display, display_id = display_id)
            
        
        '''
        
        Update_display() ends here
        
        '''
    
    mean_mae=np.mean(score_mae)
    mean_mse=np.mean(score_mse)
    mean_rmse=np.mean(score_rmse)
    mean_rmsle=np.mean(score_rmsle)
    mean_r2=np.mean(score_r2)
    mean_mape=np.mean(score_mape)
    std_mae=np.std(score_mae)
    std_mse=np.std(score_mse)
    std_rmse=np.std(score_rmse)
    std_rmsle=np.std(score_rmsle)
    std_r2=np.std(score_r2)
    std_mape=np.std(score_mape)
    
    avgs_mae = np.append(avgs_mae, mean_mae)
    avgs_mae = np.append(avgs_mae, std_mae) 
    avgs_mse = np.append(avgs_mse, mean_mse)
    avgs_mse = np.append(avgs_mse, std_mse)
    avgs_rmse = np.append(avgs_rmse, mean_rmse)
    avgs_rmse = np.append(avgs_rmse, std_rmse)
    avgs_rmsle = np.append(avgs_rmsle, mean_rmsle)
    avgs_rmsle = np.append(avgs_rmsle, std_rmsle)
    avgs_r2 = np.append(avgs_r2, mean_r2)
    avgs_r2 = np.append(avgs_r2, std_r2)
    avgs_mape = np.append(avgs_mape, mean_mape)
    avgs_mape = np.append(avgs_mape, std_mape)
    
    
    progress.value += 1
    
    model_results = pd.DataFrame({'MAE': score_mae, 'MSE': score_mse, 'RMSE' : score_rmse, 'R2' : score_r2, 
                                  'RMSLE' : score_rmsle, 'MAPE' : score_mape})
    model_avgs = pd.DataFrame({'MAE': avgs_mae, 'MSE': avgs_mse, 'RMSE' : avgs_rmse, 'R2' : avgs_r2, 
                                'RMSLE' : avgs_rmsle, 'MAPE' : avgs_mape},index=['Mean', 'SD'])

    model_results = model_results.append(model_avgs)
    model_results = model_results.round(round)
    
    progress.value += 1
    
    #refitting the model on complete X_train, y_train
    monitor.iloc[1,1:] = 'Compiling Final Model'
    update_display(monitor, display_id = 'monitor')
    
    model.fit(data_X, data_y)
    
    progress.value += 1
    
    
    #storing into experiment
    model_name = 'Voting Regressor'
    tup = (model_name,model)
    experiment__.append(tup)
    nam = str(model_name) + ' Score Grid'
    tup = (nam, model_results)
    experiment__.append(tup)
    
    if verbose:
        clear_output()
        display(model_results)
        return model
    
    else:
        clear_output()
        return model




def tune_model(estimator = None, 
               fold = 10, 
               round = 4, 
               n_iter = 10, 
               optimize = 'r2',
               ensemble = False, 
               method = None,
               verbose = True):
    
      
    """
        
    Description:
    ------------
    This function tunes the hyperparameters of a model and scores it using Kfold 
    Cross Validation. The output prints the score grid that shows MAE, MSE, RMSE, 
    R2, RMSLE and MAPE by fold (by default = 10 Folds).

    This function returns a trained model object.  

    tune_model() only accepts a string parameter for estimator.

        Example
        -------
        from pycaret.datasets import get_data
        boston = get_data('boston')
        experiment_name = setup(data = boston,  target = 'medv')
        
        tuned_xgboost = tune_model('xgboost') 

        This will tune the hyperparameters of Extreme Gradient Boosting Regressor.

    Parameters
    ----------
    estimator : string, default = None

    Enter abbreviated name of the estimator class. List of estimators supported:

    Estimator                     Abbreviated String    Original Implementation 
    ---------                     ------------------    -----------------------
    Linear Regression             'lr'                  linear_model.LinearRegression
    Lasso Regression              'lasso'               linear_model.Lasso
    Ridge Regression              'ridge'               linear_model.Ridge
    Elastic Net                   'en'                  linear_model.ElasticNet
    Least Angle Regression        'lar'                 linear_model.Lars
    Lasso Least Angle Regression  'llar'                linear_model.LassoLars
    Orthogonal Matching Pursuit   'omp'                 linear_model.OMP
    Bayesian Ridge                'br'                  linear_model.BayesianRidge
    Automatic Relevance Determ.   'ard'                 linear_model.ARDRegression
    Passive Aggressive Regressor  'par'                 linear_model.PAR
    Random Sample Consensus       'ransac'              linear_model.RANSACRegressor
    TheilSen Regressor            'tr'                  linear_model.TheilSenRegressor
    Huber Regressor               'huber'               linear_model.HuberRegressor 
    Kernel Ridge                  'kr'                  kernel_ridge.KernelRidge
    Support Vector Machine        'svm'                 svm.SVR
    K Neighbors Regressor         'knn'                 neighbors.KNeighborsRegressor 
    Decision Tree                 'dt'                  tree.DecisionTreeRegressor
    Random Forest                 'rf'                  ensemble.RandomForestRegressor
    Extra Trees Regressor         'et'                  ensemble.ExtraTreesRegressor
    AdaBoost Regressor            'ada'                 ensemble.AdaBoostRegressor
    Gradient Boosting             'gbr'                 ensemble.GradientBoostingRegressor 
    Multi Level Perceptron        'mlp'                 neural_network.MLPRegressor
    Extreme Gradient Boosting     'xgboost'             xgboost.readthedocs.io
    Light Gradient Boosting       'lightgbm'            github.com/microsoft/LightGBM
    CatBoost Regressor            'catboost'            https://catboost.ai

    fold: integer, default = 10
    Number of folds to be used in Kfold CV. Must be at least 2. 

    round: integer, default = 4
    Number of decimal places the metrics in the score grid will be rounded to. 

    n_iter: integer, default = 10
    Number of iterations within the Random Grid Search. For every iteration, 
    the model randomly selects one value from the pre-defined grid of hyperparameters.

    optimize: string, default = 'r2'
    Measure used to select the best model through hyperparameter tuning.
    The default scoring measure is 'r2'. Other measures include 'mae', 'mse'.

    ensemble: Boolean, default = None
    True enables ensembling of the model through the method defined in 'method' param.

    method: String, 'Bagging' or 'Boosting', default = None
    method comes into effect only when ensemble = True. Default is set to None. 

    verbose: Boolean, default = True
    Score grid is not printed when verbose is set to False.

    Returns:
    --------

    score grid:   A table containing the scores of the model across the kfolds. 
    -----------   Scoring metrics used are MAE, MSE, RMSE, R2, RMSLE and MAPE.
                  Mean and standard deviation of the scores across the folds are 
                  also returned.

    model:        trained model object
    -----------

    Warnings:
    ---------
    - estimator parameter takes an abbreviated string. Passing a trained model object
      returns an error. The tune_model() function internally calls create_model() 
      before tuning the hyperparameters.
        
         
  """
 


    '''
    
    ERROR HANDLING STARTS HERE
    
    '''
    
    #exception checking   
    import sys
    
    #checking error for estimator (string)
    available_estimators = ['lr', 'lasso', 'ridge', 'en', 'lar', 'llar', 'omp', 'br', 'ard', 'par', 
                            'ransac', 'tr', 'huber', 'kr', 'svm', 'knn', 'dt', 'rf', 'et', 'ada', 'gbr', 
                            'mlp', 'xgboost', 'lightgbm', 'catboost']
    
    if estimator not in available_estimators:
        sys.exit('(Value Error): Estimator Not Available. Please see docstring for list of available estimators.')
        
    #checking error for ensemble:
    if type(ensemble) is not bool:
        sys.exit('(Type Error): Ensemble parameter can only take argument as True or False.') 
    
    #checking error for method:
    
    #1 Check When method given and ensemble is not set to True.
    if ensemble is False and method is not None:
        sys.exit('(Type Error): Method parameter only accepts value when ensemble is set to True.')

    #2 Check when ensemble is set to True and method is not passed.
    if ensemble is True and method is None:
        sys.exit("(Type Error): Method parameter missing. Pass method = 'Bagging' or 'Boosting'.")
        
    #3 Check when ensemble is set to True and method is passed but not allowed.
    available_method = ['Bagging', 'Boosting']
    if ensemble is True and method not in available_method:
        sys.exit("(Value Error): Method parameter only accepts two values 'Bagging' or 'Boosting'.")
        
    #checking fold parameter
    if type(fold) is not int:
        sys.exit('(Type Error): Fold parameter only accepts integer value.')
    
    #checking round parameter
    if type(round) is not int:
        sys.exit('(Type Error): Round parameter only accepts integer value.')
 
    #checking n_iter parameter
    if type(n_iter) is not int:
        sys.exit('(Type Error): n_iter parameter only accepts integer value.')

    #checking optimize parameter
    allowed_optimize = ['mae', 'mse', 'r2']
    if optimize not in allowed_optimize:
        sys.exit('(Value Error): Optimization method not supported. See docstring for list of available parameters.')
    
    if type(n_iter) is not int:
        sys.exit('(Type Error): n_iter parameter only accepts integer value.')
        
    #checking verbose parameter
    if type(verbose) is not bool:
        sys.exit('(Type Error): Verbose parameter can only take argument as True or False.') 
    
    
    '''
    
    ERROR HANDLING ENDS HERE
    
    '''
    
    
    #pre-load libraries
    import pandas as pd
    import time, datetime
    import ipywidgets as ipw
    from IPython.display import display, HTML, clear_output, update_display
    
    #progress bar
    progress = ipw.IntProgress(value=0, min=0, max=fold+6, step=1 , description='Processing: ')
    master_display = pd.DataFrame(columns=['MAE','MSE','RMSE', 'R2', 'RMSLE', 'MAPE'])
    display(progress)    
    
    #display monitor
    timestampStr = datetime.datetime.now().strftime("%H:%M:%S")
    monitor = pd.DataFrame( [ ['Initiated' , '. . . . . . . . . . . . . . . . . .', timestampStr ], 
                             ['Status' , '. . . . . . . . . . . . . . . . . .' , 'Loading Dependencies' ],
                             ['ETC' , '. . . . . . . . . . . . . . . . . .',  'Calculating ETC'] ],
                              columns=['', ' ', '   ']).set_index('')
    
    display(monitor, display_id = 'monitor')
    
    if verbose:
        display_ = display(master_display, display_id=True)
        display_id = display_.display_id
    
    #ignore warnings
    import warnings
    warnings.filterwarnings('ignore') 
    
    #ignore warnings
    import warnings
    warnings.filterwarnings('ignore')    

    #Storing X_train and y_train in data_X and data_y parameter
    data_X = X_train.copy()
    data_y = y_train.copy()
    
    #reset index
    data_X.reset_index(drop=True, inplace=True)
    data_y.reset_index(drop=True, inplace=True)

    #define optimizer
      #defining optimizer
    if optimize == 'mae':
        optimize = 'neg_mean_absolute_error'
    elif optimize == 'mse':
        optimize = 'neg_mean_squared_error'
    elif optimize == 'me':
        optimize = 'max_error'
    elif optimize == 'r2':
        optimize = 'r2'
    
    progress.value += 1
    
    
    #general dependencies
    import random
    import numpy as np
    from sklearn import metrics
    from sklearn.model_selection import KFold
    from sklearn.model_selection import RandomizedSearchCV
    
    progress.value += 1
    
    kf = KFold(fold, random_state=seed)

    score_mae =np.empty((0,0))
    score_mse =np.empty((0,0))
    score_rmse =np.empty((0,0))
    score_rmsle =np.empty((0,0))
    score_r2 =np.empty((0,0))
    score_mape =np.empty((0,0))
    avgs_mae =np.empty((0,0))
    avgs_mse =np.empty((0,0))
    avgs_rmse =np.empty((0,0))
    avgs_rmsle =np.empty((0,0))
    avgs_r2 =np.empty((0,0))
    avgs_mape =np.empty((0,0))
    
    def calculate_mape(actual, prediction):
        mask = actual != 0
        return (np.fabs(actual - prediction)/actual)[mask].mean()
    
    '''
    MONITOR UPDATE STARTS
    '''
    
    monitor.iloc[1,1:] = 'Tuning Hyperparameters'
    update_display(monitor, display_id = 'monitor')
    
    '''
    MONITOR UPDATE ENDS
    '''
    
    #setting turbo parameters
    cv = 3
    
    if estimator == 'lr':

        from sklearn.linear_model import LinearRegression
        param_grid = {'fit_intercept': [True, False],
                     'normalize' : [True, False]
                    }        
        model_grid = RandomizedSearchCV(estimator=LinearRegression(), param_distributions=param_grid, 
                                        scoring=optimize, n_iter=n_iter, cv=cv, random_state=seed,
                                        n_jobs=-1, iid=False)

        model_grid.fit(X_train,y_train)
        model = model_grid.best_estimator_
        best_model = model_grid.best_estimator_
        best_model_param = model_grid.best_params_

    elif estimator == 'lasso':

        from sklearn.linear_model import Lasso

        param_grid = {'alpha': [0.0001, 0.001, 0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
                      'fit_intercept': [True, False],
                      'normalize' : [True, False],
                     }
        model_grid = RandomizedSearchCV(estimator=Lasso(random_state=seed), 
                                        param_distributions=param_grid, scoring=optimize, n_iter=n_iter, cv=cv, 
                                        random_state=seed, iid=False,n_jobs=-1)
        
        model_grid.fit(X_train,y_train)
        model = model_grid.best_estimator_
        best_model = model_grid.best_estimator_
        best_model_param = model_grid.best_params_

    elif estimator == 'ridge':

        from sklearn.linear_model import Ridge

        param_grid = {"alpha": [0.0001, 0.001, 0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
                      "fit_intercept": [True, False],
                      "normalize": [True, False],
                      }

        model_grid = RandomizedSearchCV(estimator=Ridge(random_state=seed), param_distributions=param_grid,
                                       scoring=optimize, n_iter=n_iter, cv=cv, random_state=seed,
                                       iid=False, n_jobs=-1)

        model_grid.fit(X_train,y_train)
        model = model_grid.best_estimator_
        best_model = model_grid.best_estimator_
        best_model_param = model_grid.best_params_

    elif estimator == 'en':

        from sklearn.linear_model import ElasticNet

        param_grid = {'alpha': [0.0001,0.001,0.1,0.15,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9],
                      'l1_ratio' : [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1],
                      'fit_intercept': [True, False],
                      'normalize': [True, False]
                     } 

        model_grid = RandomizedSearchCV(estimator=ElasticNet(random_state=seed), 
                                        param_distributions=param_grid, scoring=optimize, n_iter=n_iter, cv=cv, 
                                        random_state=seed, iid=False, n_jobs=-1)

        model_grid.fit(X_train,y_train)
        model = model_grid.best_estimator_
        best_model = model_grid.best_estimator_
        best_model_param = model_grid.best_params_

    elif estimator == 'lar':

        from sklearn.linear_model import Lars

        param_grid = {'fit_intercept':[True, False],
                     'normalize' : [True, False],
                     'eps': [0.00001, 0.0001, 0.001, 0.01, 0.05, 0.0005, 0.005, 0.00005, 0.02, 0.007]}

        model_grid = RandomizedSearchCV(estimator=Lars(), param_distributions=param_grid,
                                       scoring=optimize, n_iter=n_iter, cv=cv, random_state=seed,
                                       n_jobs=-1)

        model_grid.fit(X_train,y_train)
        model = model_grid.best_estimator_
        best_model = model_grid.best_estimator_
        best_model_param = model_grid.best_params_  

    elif estimator == 'llar':

        from sklearn.linear_model import LassoLars

        param_grid = {'alpha': [0.0001,0.001,0.1,0.15,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9],
                     'fit_intercept':[True, False],
                     'normalize' : [True, False],
                     'eps': [0.00001, 0.0001, 0.001, 0.01, 0.05, 0.0005, 0.005, 0.00005, 0.02, 0.007]}

        model_grid = RandomizedSearchCV(estimator=LassoLars(), param_distributions=param_grid,
                                       scoring=optimize, n_iter=n_iter, cv=cv, random_state=seed,
                                       n_jobs=-1)

        model_grid.fit(X_train,y_train)
        model = model_grid.best_estimator_
        best_model = model_grid.best_estimator_
        best_model_param = model_grid.best_params_    

    elif estimator == 'omp':

        from sklearn.linear_model import OrthogonalMatchingPursuit
        import random

        param_grid = {'n_nonzero_coefs': range(1,len(X_train.columns)+1),
                      'fit_intercept' : [True, False],
                      'normalize': [True, False]}

        model_grid = RandomizedSearchCV(estimator=OrthogonalMatchingPursuit(), 
                                        param_distributions=param_grid, scoring=optimize, n_iter=n_iter, 
                                        cv=cv, random_state=seed, n_jobs=-1)

        model_grid.fit(X_train,y_train)
        model = model_grid.best_estimator_
        best_model = model_grid.best_estimator_
        best_model_param = model_grid.best_params_        

    elif estimator == 'br':

        from sklearn.linear_model import BayesianRidge

        param_grid = {'alpha_1': [0.0000001, 0.000001, 0.0001, 0.001, 0.01, 0.0005, 0.005, 0.05, 0.1, 0.15, 0.2, 0.3],
                      'alpha_2': [0.0000001, 0.000001, 0.0001, 0.001, 0.01, 0.0005, 0.005, 0.05, 0.1, 0.15, 0.2, 0.3],
                      'lambda_1': [0.0000001, 0.000001, 0.0001, 0.001, 0.01, 0.0005, 0.005, 0.05, 0.1, 0.15, 0.2, 0.3],
                      'lambda_2': [0.0000001, 0.000001, 0.0001, 0.001, 0.01, 0.0005, 0.005, 0.05, 0.1, 0.15, 0.2, 0.3],
                      'compute_score': [True, False],
                      'fit_intercept': [True, False],
                      'normalize': [True, False]
                     }    

        model_grid = RandomizedSearchCV(estimator=BayesianRidge(), 
                                        param_distributions=param_grid, scoring=optimize, n_iter=n_iter, 
                                        cv=cv, random_state=seed, n_jobs=-1)

        model_grid.fit(X_train,y_train)
        model = model_grid.best_estimator_
        best_model = model_grid.best_estimator_
        best_model_param = model_grid.best_params_    

    elif estimator == 'ard':

        from sklearn.linear_model import ARDRegression

        param_grid = {'alpha_1': [0.0000001, 0.000001, 0.0001, 0.001, 0.01, 0.0005, 0.005, 0.05, 0.1, 0.15, 0.2, 0.3],
                      'alpha_2': [0.0000001, 0.000001, 0.0001, 0.001, 0.01, 0.0005, 0.005, 0.05, 0.1, 0.15, 0.2, 0.3],
                      'lambda_1': [0.0000001, 0.000001, 0.0001, 0.001, 0.01, 0.0005, 0.005, 0.05, 0.1, 0.15, 0.2, 0.3],
                      'lambda_2': [0.0000001, 0.000001, 0.0001, 0.001, 0.01, 0.0005, 0.005, 0.05, 0.1, 0.15, 0.2, 0.3],
                      'threshold_lambda' : [5000,10000,15000,20000,25000,30000,35000,40000,45000,50000,55000,60000],
                      'compute_score': [True, False],
                      'fit_intercept': [True, False],
                      'normalize': [True, False]
                     }    

        model_grid = RandomizedSearchCV(estimator=ARDRegression(), 
                                        param_distributions=param_grid, scoring=optimize, n_iter=n_iter, 
                                        cv=cv, random_state=seed, n_jobs=-1)

        model_grid.fit(X_train,y_train)
        model = model_grid.best_estimator_
        best_model = model_grid.best_estimator_
        best_model_param = model_grid.best_params_       

    elif estimator == 'par':

        from sklearn.linear_model import PassiveAggressiveRegressor

        param_grid = {'C': [0.01, 0.005, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1],
                      'fit_intercept': [True, False],
                      'early_stopping' : [True, False],
                      #'validation_fraction': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1],
                      'loss' : ['epsilon_insensitive', 'squared_epsilon_insensitive'],
                      'epsilon' : [0.1, 0.2, 0.3, 0.4, 0.5, 0.6],
                      'shuffle' : [True, False]
                     }    

        model_grid = RandomizedSearchCV(estimator=PassiveAggressiveRegressor(random_state=seed), 
                                        param_distributions=param_grid, scoring=optimize, n_iter=n_iter, 
                                        cv=cv, random_state=seed, n_jobs=-1)

        model_grid.fit(X_train,y_train)
        model = model_grid.best_estimator_
        best_model = model_grid.best_estimator_
        best_model_param = model_grid.best_params_         

    elif estimator == 'ransac':

        from sklearn.linear_model import RANSACRegressor

        param_grid = {'min_samples': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1],
                      'max_trials': [1,2,3,4,5,6,7,8,9,10],
                      'max_skips': [1,2,3,4,5,6,7,8,9,10],
                      'stop_n_inliers': [1,2,3,4,5,6,7,8,9,10],
                      'stop_probability': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1],
                      'loss' : ['absolute_loss', 'squared_loss'],
                     }    

        model_grid = RandomizedSearchCV(estimator=RANSACRegressor(random_state=seed), 
                                        param_distributions=param_grid, scoring=optimize, n_iter=n_iter, 
                                        cv=cv, random_state=seed, n_jobs=-1)

        model_grid.fit(X_train,y_train)
        model = model_grid.best_estimator_
        best_model = model_grid.best_estimator_
        best_model_param = model_grid.best_params_         

    elif estimator == 'tr':

        from sklearn.linear_model import TheilSenRegressor

        param_grid = {'fit_intercept': [True, False],
                      'max_subpopulation': [5000, 10000, 15000, 20000, 25000, 30000, 40000, 50000]
                     }    

        model_grid = RandomizedSearchCV(estimator=TheilSenRegressor(random_state=seed), 
                                        param_distributions=param_grid, scoring=optimize, n_iter=n_iter, 
                                        cv=cv, random_state=seed, n_jobs=-1)

        model_grid.fit(X_train,y_train)
        model = model_grid.best_estimator_
        best_model = model_grid.best_estimator_
        best_model_param = model_grid.best_params_    

    elif estimator == 'huber':

        from sklearn.linear_model import HuberRegressor

        param_grid = {'epsilon': [1.1, 1.2, 1.3, 1.35, 1.4, 1.5, 1.55, 1.6, 1.7, 1.8, 1.9],
                      'alpha': [0.00001, 0.0001, 0.0003, 0.005, 0.05, 0.1, 0.0005, 0.15],
                      'fit_intercept' : [True, False]
                     }    

        model_grid = RandomizedSearchCV(estimator=HuberRegressor(), 
                                        param_distributions=param_grid, scoring=optimize, n_iter=n_iter, 
                                        cv=cv, random_state=seed, n_jobs=-1)

        model_grid.fit(X_train,y_train)
        model = model_grid.best_estimator_
        best_model = model_grid.best_estimator_
        best_model_param = model_grid.best_params_        

    elif estimator == 'kr':

        from sklearn.kernel_ridge import KernelRidge

        param_grid = {'alpha': [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1] }    

        model_grid = RandomizedSearchCV(estimator=KernelRidge(), 
                                        param_distributions=param_grid, scoring=optimize, n_iter=n_iter, 
                                        cv=cv, random_state=seed, n_jobs=-1)

        model_grid.fit(X_train,y_train)
        model = model_grid.best_estimator_
        best_model = model_grid.best_estimator_
        best_model_param = model_grid.best_params_       

    elif estimator == 'svm':

        from sklearn.svm import SVR

        param_grid = {#'kernel': ['linear', 'poly', 'rbf', 'sigmoid', 'precomputed'],
                      #'float' : [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1],
                      'C' : [0.01, 0.005, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1],
                      'epsilon' : [1.1, 1.2, 1.3, 1.35, 1.4, 1.5, 1.55, 1.6, 1.7, 1.8, 1.9],
                      'shrinking': [True, False]
                     }    

        model_grid = RandomizedSearchCV(estimator=SVR(), 
                                        param_distributions=param_grid, scoring=optimize, n_iter=n_iter, 
                                        cv=cv, random_state=seed, n_jobs=-1)

        model_grid.fit(X_train,y_train)
        model = model_grid.best_estimator_
        best_model = model_grid.best_estimator_
        best_model_param = model_grid.best_params_     

    elif estimator == 'knn':

        from sklearn.neighbors import KNeighborsRegressor

        param_grid = {'n_neighbors': range(1,51),
                     'weights' :  ['uniform', 'distance'],
                     'algorithm': ['ball_tree', 'kd_tree', 'brute'],
                     'leaf_size': [10,20,30,40,50,60,70,80,90]
                     } 

        model_grid = RandomizedSearchCV(estimator=KNeighborsRegressor(), 
                                        param_distributions=param_grid, scoring=optimize, n_iter=n_iter, 
                                        cv=cv, random_state=seed, n_jobs=-1)

        model_grid.fit(X_train,y_train)
        model = model_grid.best_estimator_
        best_model = model_grid.best_estimator_
        best_model_param = model_grid.best_params_         

    elif estimator == 'dt':

        from sklearn.tree import DecisionTreeRegressor

        param_grid = {"max_depth": np.random.randint(1, (len(X_train.columns)*.85),4),
                      "max_features": np.random.randint(3, len(X_train.columns),4),
                      "min_samples_leaf": [0.1,0.2,0.3,0.4,0.5],
                      "min_samples_split" : [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9],
                      "min_weight_fraction_leaf" : [0.1,0.2,0.3,0.4,0.5],
                      "min_impurity_decrease" : [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9],
                      "criterion": ["mse", "mae", "friedman_mse"],
                      #"max_leaf_nodes" : [1,2,3,4,5,6,7,8,9,10,None]
                     } 

        model_grid = RandomizedSearchCV(estimator=DecisionTreeRegressor(random_state=seed), 
                                        param_distributions=param_grid, scoring=optimize, n_iter=n_iter, 
                                        cv=cv, random_state=seed, n_jobs=-1)

        model_grid.fit(X_train,y_train)
        model = model_grid.best_estimator_
        best_model = model_grid.best_estimator_
        best_model_param = model_grid.best_params_         

    elif estimator == 'rf':

        from sklearn.ensemble import RandomForestRegressor


        param_grid = {'n_estimators': [10, 20, 30, 40, 50, 60, 70, 80, 90, 100],
                      'criterion': ['mse', 'mae'],
                      'max_depth': [int(x) for x in np.linspace(10, 110, num = 11)],
                      'min_samples_split': [2, 5, 7, 9, 10],
                      'min_samples_leaf' : [1, 2, 4],
                      'max_features' : ['auto', 'sqrt', 'log2'],
                      'bootstrap': [True, False]
                      }

        model_grid = RandomizedSearchCV(estimator=RandomForestRegressor(random_state=seed), 
                                        param_distributions=param_grid, scoring=optimize, n_iter=n_iter, 
                                        cv=cv, random_state=seed, n_jobs=-1)

        model_grid.fit(X_train,y_train)
        model = model_grid.best_estimator_
        best_model = model_grid.best_estimator_
        best_model_param = model_grid.best_params_       


    elif estimator == 'et':

        from sklearn.ensemble import ExtraTreesRegressor

        param_grid = {'n_estimators': [10, 20, 30, 40, 50, 60, 70, 80, 90, 100],
                      'criterion': ['mse', 'mae'],
                      'max_depth': [int(x) for x in np.linspace(10, 110, num = 11)],
                      'min_samples_split': [2, 5, 7, 9, 10],
                      'min_samples_leaf' : [1, 2, 4],
                      'max_features' : ['auto', 'sqrt', 'log2'],
                      'bootstrap': [True, False]
                      }  

        model_grid = RandomizedSearchCV(estimator=ExtraTreesRegressor(random_state=seed), 
                                        param_distributions=param_grid, scoring=optimize, n_iter=n_iter, 
                                        cv=cv, random_state=seed, n_jobs=-1)

        model_grid.fit(X_train,y_train)
        model = model_grid.best_estimator_
        best_model = model_grid.best_estimator_
        best_model_param = model_grid.best_params_       

    elif estimator == 'ada':

        from sklearn.ensemble import AdaBoostRegressor

        param_grid = {'n_estimators': [10, 40, 70, 80, 90, 100, 120, 140, 150],
                      'learning_rate': [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1],
                      'loss' : ["linear", "square", "exponential"]
                     }    

        model_grid = RandomizedSearchCV(estimator=AdaBoostRegressor(random_state=seed), 
                                        param_distributions=param_grid, scoring=optimize, n_iter=n_iter, 
                                        cv=cv, random_state=seed, n_jobs=-1)

        model_grid.fit(X_train,y_train)
        model = model_grid.best_estimator_
        best_model = model_grid.best_estimator_
        best_model_param = model_grid.best_params_ 

    elif estimator == 'gbr':

        from sklearn.ensemble import GradientBoostingRegressor

        param_grid = {'loss': ['ls', 'lad', 'huber', 'quantile'],
                      'n_estimators': [10, 40, 70, 80, 90, 100, 120, 140, 150],
                      'learning_rate': [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1],
                      'subsample' : [0.1,0.3,0.5,0.7,0.9,1],
                      'criterion' : ['friedman_mse', 'mse', 'mae'],
                      'min_samples_split' : [2,4,5,7,9,10],
                      'min_samples_leaf' : [1,2,3,4,5],
                      'max_depth': [int(x) for x in np.linspace(10, 110, num = 11)],
                      'max_features' : ['auto', 'sqrt', 'log2']
                     }     

        model_grid = RandomizedSearchCV(estimator=GradientBoostingRegressor(random_state=seed), 
                                        param_distributions=param_grid, scoring=optimize, n_iter=n_iter, 
                                        cv=cv, random_state=seed, n_jobs=-1)

        model_grid.fit(X_train,y_train)
        model = model_grid.best_estimator_
        best_model = model_grid.best_estimator_
        best_model_param = model_grid.best_params_         

    elif estimator == 'mlp':

        from sklearn.neural_network import MLPRegressor

        param_grid = {'learning_rate': ['constant', 'invscaling', 'adaptive'],
                      'solver' : ['lbfgs', 'adam'],
                      'alpha': [0.0001, 0.001, 0.01, 0.00001, 0.003, 0.0003, 0.0005, 0.005, 0.05],
                      'hidden_layer_sizes': np.random.randint(50,150,10),
                      'activation': ["tanh", "identity", "logistic","relu"]
                      }    

        model_grid = RandomizedSearchCV(estimator=MLPRegressor(random_state=seed), 
                                        param_distributions=param_grid, scoring=optimize, n_iter=n_iter, 
                                        cv=cv, random_state=seed, n_jobs=-1)    

        model_grid.fit(X_train,y_train)
        model = model_grid.best_estimator_
        best_model = model_grid.best_estimator_
        best_model_param = model_grid.best_params_   
        
        
    elif estimator == 'xgboost':
        
        from xgboost import XGBRegressor
        
        param_grid = {'learning_rate': [0.01, 0.02, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1], 
                      'n_estimators':[10, 30, 50, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000], 
                      'subsample': [0.1, 0.2, 0.3, 0.5, 0.7, 0.9, 1],
                      'max_depth': [int(x) for x in np.linspace(10, 110, num = 11)], 
                      'colsample_bytree': [0.5, 0.7, 0.9, 1],
                      'min_child_weight': [1, 2, 3, 4]
                     }

        model_grid = RandomizedSearchCV(estimator=XGBRegressor(random_state=seed, n_jobs=-1, verbosity=0), 
                                        param_distributions=param_grid, scoring=optimize, n_iter=n_iter, 
                                        cv=cv, random_state=seed, n_jobs=-1)

        model_grid.fit(X_train,y_train)
        model = model_grid.best_estimator_
        best_model = model_grid.best_estimator_
        best_model_param = model_grid.best_params_   
        
        
    elif estimator == 'lightgbm':
        
        import lightgbm as lgb
        
        param_grid = {#'boosting_type' : ['gbdt', 'dart', 'goss', 'rf'],
                      'num_leaves': [10,20,30,40,50,60,70,80,90,100,150,200],
                      'max_depth': [int(x) for x in np.linspace(10, 110, num = 11)],
                      'learning_rate': [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1],
                      'n_estimators': [10, 30, 50, 70, 90, 100, 120, 150, 170, 200], 
                      'min_split_gain' : [0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9],
                      'reg_alpha': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
                      'reg_lambda': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
                     }
            
        model_grid = RandomizedSearchCV(estimator=lgb.LGBMRegressor(random_state=seed), 
                                        param_distributions=param_grid, scoring=optimize, n_iter=n_iter, 
                                        cv=cv, random_state=seed, n_jobs=-1)

        model_grid.fit(X_train,y_train)
        model = model_grid.best_estimator_
        best_model = model_grid.best_estimator_
        best_model_param = model_grid.best_params_   

    elif estimator == 'catboost':
        
        from catboost import CatBoostRegressor
        
        param_grid = {'depth':[3,1,2,6,4,5,7,8,9,10],
                      'iterations':[250,100,500,1000], 
                      'learning_rate':[0.03,0.001,0.01,0.1,0.2,0.3], 
                      'l2_leaf_reg':[3,1,5,10,100], 
                      'border_count':[32,5,10,20,50,100,200], 
                      #'ctr_border_count':[50,5,10,20,100,200]
                      }
            
        model_grid = RandomizedSearchCV(estimator=CatBoostRegressor(random_state=seed, silent=True), 
                                        param_distributions=param_grid, scoring=optimize, n_iter=n_iter, 
                                        cv=cv, random_state=seed, n_jobs=-1)

        model_grid.fit(X_train,y_train)
        model = model_grid.best_estimator_
        best_model = model_grid.best_estimator_
        best_model_param = model_grid.best_params_ 
    
    progress.value += 1
    
    '''
    MONITOR UPDATE STARTS
    '''
    
    monitor.iloc[1,1:] = 'Tuning Hyperparameters of Ensemble'
    update_display(monitor, display_id = 'monitor')
    
    '''
    MONITOR UPDATE ENDS
    '''
    
    if estimator == 'dt' and ensemble == True and method == 'Bagging':
    
    #when using normal BaggingRegressor() DT estimator raise's an exception for max_features parameter. Hence a separate 
    #call has been made for estimator='dt' and method = 'Bagging' where max_features has been removed from param_grid_dt.
    
        from sklearn.tree import DecisionTreeRegressor
        from sklearn.ensemble import BaggingRegressor

        param_grid = {'n_estimators': [10,15,20,25,30],
                     'max_samples': [0.3,0.5,0.6,0.7,0.8,0.9],
                     'max_features':[0.3,0.5,0.6,0.7,0.8,0.9],
                     'bootstrap': [True, False],
                     'bootstrap_features': [True, False],
                     }

        param_grid_dt = {"max_depth": np.random.randint(3, (len(X_train.columns)*.85),4),
                         "min_samples_leaf": [2,3,4],
                         "min_samples_leaf": [0.1,0.2,0.3,0.4,0.5],
                         "min_samples_split" : [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9],
                         "min_weight_fraction_leaf" : [0.1,0.2,0.3,0.4,0.5],
                         "min_impurity_decrease" : [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9],
                         "criterion": ["mse", "mae", "friedman_mse"]}


        model_grid = RandomizedSearchCV(estimator=DecisionTreeRegressor(random_state=seed), param_distributions=param_grid_dt,
                                       scoring=optimize, n_iter=n_iter, cv=fold, random_state=seed,
                                       iid=False, n_jobs=-1)

        model_grid.fit(X_train,y_train)
        model = model_grid.best_estimator_
        best_model = model_grid.best_estimator_
        best_model_param = model_grid.best_params_

        best_model = BaggingRegressor(best_model, random_state=seed)

        model_grid = RandomizedSearchCV(estimator=best_model, 
                                        param_distributions=param_grid, n_iter=n_iter, 
                                        cv=fold, random_state=seed, iid=False, n_jobs=-1)

        model_grid.fit(X_train,y_train)
        model = model_grid.best_estimator_
        best_model = model_grid.best_estimator_
        best_model_param = model_grid.best_params_    
  
    elif ensemble and method == 'Bagging':
    
        from sklearn.ensemble import BaggingRegressor

        param_grid = {'n_estimators': [10,15,20,25,30],
                     'max_samples': [0.3,0.5,0.6,0.7,0.8,0.9],
                     'max_features':[0.3,0.5,0.6,0.7,0.8,0.9],
                     'bootstrap': [True, False],
                     'bootstrap_features': [True, False],
                     }

        best_model = BaggingRegressor(best_model, random_state=seed)

        model_grid = RandomizedSearchCV(estimator=best_model, 
                                        param_distributions=param_grid, scoring=optimize, n_iter=n_iter, 
                                        cv=fold, random_state=seed, iid=False, n_jobs=-1)

        model_grid.fit(X_train,y_train)
        model = model_grid.best_estimator_
        best_model = model_grid.best_estimator_
        best_model_param = model_grid.best_params_    
  
      
    elif ensemble and method =='Boosting':
    
        from sklearn.ensemble import AdaBoostRegressor

        param_grid = {'n_estimators': [10, 40, 70, 80, 90, 100, 120, 140, 150],
                      'learning_rate': [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1],
                      'loss' : ["linear", "square", "exponential"]
                     }          

        best_model = AdaBoostRegressor(best_model, random_state=seed)

        model_grid = RandomizedSearchCV(estimator=best_model, 
                                        param_distributions=param_grid, scoring=optimize, n_iter=n_iter, 
                                        cv=fold, random_state=seed, iid=False, n_jobs=-1)
    
    
   
    progress.value += 1

    
    '''
    MONITOR UPDATE STARTS
    '''
    
    monitor.iloc[1,1:] = 'Initializing CV'
    update_display(monitor, display_id = 'monitor')
    
    '''
    MONITOR UPDATE ENDS
    '''
    
    fold_num = 1
    
    for train_i , test_i in kf.split(data_X,data_y):
        
        t0 = time.time()
        
        
        '''
        MONITOR UPDATE STARTS
        '''
    
        monitor.iloc[1,1:] = 'Fitting Fold ' + str(fold_num) + ' of ' + str(fold)
        update_display(monitor, display_id = 'monitor')

        '''
        MONITOR UPDATE ENDS
        '''
        
        Xtrain,Xtest = data_X.iloc[train_i], data_X.iloc[test_i]
        ytrain,ytest = data_y.iloc[train_i], data_y.iloc[test_i]  
        model.fit(Xtrain,ytrain)
        pred_ = model.predict(Xtest)
        
        try:
            pred_ = target_inverse_transformer.inverse_transform(np.array(pred_).reshape(-1,1))
            ytest = target_inverse_transformer.inverse_transform(np.array(ytest).reshape(-1,1))
            pred_ = np.nan_to_num(pred_)
            ytest = np.nan_to_num(ytest)
            
        except:
            pass
        
        mae = metrics.mean_absolute_error(ytest,pred_)
        mse = metrics.mean_squared_error(ytest,pred_)
        rmse = np.sqrt(mse)
        r2 = metrics.r2_score(ytest,pred_)
        rmsle = np.sqrt(np.mean(np.power(np.log(np.array(abs(pred_))+1) - np.log(np.array(abs(ytest))+1), 2)))
        mape = calculate_mape(ytest,pred_)
        #max_error_ = metrics.max_error(ytest,pred_)
        score_mae = np.append(score_mae,mae)
        score_mse = np.append(score_mse,mse)
        score_rmse = np.append(score_rmse,rmse)
        score_rmsle = np.append(score_rmsle,rmsle)
        score_r2 =np.append(score_r2,r2)
        score_mape = np.append(score_mape,mape)
            
        progress.value += 1
            
            
        '''
        
        This section is created to update_display() as code loops through the fold defined.
        
        '''
        
        fold_results = pd.DataFrame({'MAE':[mae], 'MSE': [mse], 'RMSE': [rmse], 
                                     'R2': [r2], 'RMSLE': [rmsle], 'MAPE': [mape]}).round(round)
        master_display = pd.concat([master_display, fold_results],ignore_index=True)
        fold_results = []
        
        '''
        
        TIME CALCULATION SUB-SECTION STARTS HERE
        
        '''
        
        t1 = time.time()
        
        tt = (t1 - t0) * (fold-fold_num) / 60
        tt = np.around(tt, 2)
        
        if tt < 1:
            tt = str(np.around((tt * 60), 2))
            ETC = tt + ' Seconds Remaining'
                
        else:
            tt = str (tt)
            ETC = tt + ' Minutes Remaining'
            
        update_display(ETC, display_id = 'ETC')
            
        fold_num += 1
        
        '''
        MONITOR UPDATE STARTS
        '''

        monitor.iloc[2,1:] = ETC
        update_display(monitor, display_id = 'monitor')

        '''
        MONITOR UPDATE ENDS
        '''
       
        '''
        
        TIME CALCULATION ENDS HERE
        
        '''
        
        if verbose:
            update_display(master_display, display_id = display_id)
        
        '''
        
        Update_display() ends here
        
        '''
        
    progress.value += 1
    
    mean_mae=np.mean(score_mae)
    mean_mse=np.mean(score_mse)
    mean_rmse=np.mean(score_rmse)
    mean_rmsle=np.mean(score_rmsle)
    mean_r2=np.mean(score_r2)
    mean_mape=np.mean(score_mape)
    std_mae=np.std(score_mae)
    std_mse=np.std(score_mse)
    std_rmse=np.std(score_rmse)
    std_rmsle=np.std(score_rmsle)
    std_r2=np.std(score_r2)
    std_mape=np.std(score_mape)

    avgs_mae = np.append(avgs_mae, mean_mae)
    avgs_mae = np.append(avgs_mae, std_mae) 
    avgs_mse = np.append(avgs_mse, mean_mse)
    avgs_mse = np.append(avgs_mse, std_mse)
    avgs_rmse = np.append(avgs_rmse, mean_rmse)
    avgs_rmse = np.append(avgs_rmse, std_rmse)
    avgs_rmsle = np.append(avgs_rmsle, mean_rmsle)
    avgs_rmsle = np.append(avgs_rmsle, std_rmsle)
    avgs_r2 = np.append(avgs_r2, mean_r2)
    avgs_r2 = np.append(avgs_r2, std_r2)
    avgs_mape = np.append(avgs_mape, mean_mape)
    avgs_mape = np.append(avgs_mape, std_mape)
    

    progress.value += 1
    
    model_results = pd.DataFrame({'MAE': score_mae, 'MSE': score_mse, 'RMSE' : score_rmse, 'R2' : score_r2 , 
                                  'RMSLE' : score_rmsle, 'MAPE' : score_mape})
    model_avgs = pd.DataFrame({'MAE': avgs_mae, 'MSE': avgs_mse, 'RMSE' : avgs_rmse, 'R2' : avgs_r2, 
                                'RMSLE' : avgs_rmsle, 'MAPE' : avgs_mape},index=['Mean', 'SD'])

    model_results = model_results.append(model_avgs)
    model_results = model_results.round(round)

    progress.value += 1
    
    #refitting the model on complete X_train, y_train
    monitor.iloc[1,1:] = 'Compiling Final Model'
    update_display(monitor, display_id = 'monitor')
    
    best_model.fit(data_X, data_y)
    
    progress.value += 1
    
    
    #storing into experiment
    model_name = 'Tuned ' + str(model).split("(")[0]
    tup = (model_name,best_model)
    experiment__.append(tup)
    nam = str(model_name) + ' Score Grid'
    tup = (nam, model_results)
    experiment__.append(tup)
    
    
    if verbose:
        clear_output()
        display(model_results)
        return best_model
    else:
        clear_output()
        return best_model




def stack_models(estimator_list, 
                 meta_model = None, 
                 fold = 10,
                 round = 4, 
                 restack = False, 
                 plot = False,
                 finalize = False,
                 verbose = True):
    
    """
      
            
    Description:
    ------------
    This function creates a meta model and scores it using Kfold Cross Validation.
    The predictions from the base level models as passed in the estimator_list param 
    are used as input features for the meta model. The restacking parameter controls
    the ability to expose raw features to the meta model when set to True
    (default = False).

    The output prints a score grid that shows MAE, MSE, RMSE, R2, RMSLE and MAPE by 
    fold (default = 10 Folds).
    
    This function returns a container which is the list of all models in stacking. 

        Example:
        --------
        from pycaret.datasets import get_data
        boston = get_data('boston')
        experiment_name = setup(data = boston,  target = 'medv')
        dt = create_model('dt')
        rf = create_model('rf')
        ada = create_model('ada')
        ridge = create_model('ridge')
        knn = create_model('knn')

        stacked_models = stack_models(estimator_list=[dt,rf,ada,ridge,knn])

        This will create a meta model that will use the predictions of all the 
        models provided in estimator_list param. By default, the meta model is 
        Linear Regression but can be changed with meta_model param.

    Parameters
    ----------
    estimator_list : list of object

    meta_model : object, default = None
    if set to None, Linear Regression is used as a meta model.

    fold: integer, default = 10
    Number of folds to be used in Kfold CV. Must be at least 2. 

    round: integer, default = 4
    Number of decimal places the metrics in the score grid will be rounded to.

    restack: Boolean, default = False
    When restack is set to True, raw data will be exposed to meta model when
    making predictions, otherwise when False, only the predicted label is passed 
    to meta model when making final predictions.

    plot: Boolean, default = False
    When plot is set to True, it will return the correlation plot of prediction
    from all base models provided in estimator_list.
    
    finalize: Boolean, default = False
    When finalize is set to True, it will fit the stacker on entire dataset
    including the hold-out sample created during the setup() stage. It is not 
    recommended to set this to True here, If you would like to fit the stacker 
    on the entire dataset including the hold-out, use finalize_model().
    
    verbose: Boolean, default = True
    Score grid is not printed when verbose is set to False.

    Returns:
    --------

    score grid:   A table containing the scores of the model across the kfolds. 
    -----------   Scoring metrics used are MAE, MSE, RMSE, R2, RMSLE and MAPE.
                  Mean and standard deviation of the scores across the folds are 
                  also returned.

    container:    list of all the models where last element is meta model.
    ----------

    Warnings:
    ---------
    None
      
             
          
    """
    
    '''
    
    ERROR HANDLING STARTS HERE
    
    '''
    
    #exception checking   
    import sys
    
    #checking error for estimator_list
    for i in estimator_list:
        if 'sklearn' not in str(type(i)) and 'CatBoostRegressor' not in str(type(i)):
            sys.exit("(Value Error): estimator_list parameter only trained model object")
            
    #checking meta model
    if meta_model is not None:
        if 'sklearn' not in str(type(meta_model)) and 'CatBoostRegressor' not in str(type(meta_model)):
            sys.exit("(Value Error): estimator_list parameter only trained model object")
    
    #checking fold parameter
    if type(fold) is not int:
        sys.exit('(Type Error): Fold parameter only accepts integer value.')
    
    #checking round parameter
    if type(round) is not int:
        sys.exit('(Type Error): Round parameter only accepts integer value.')

    #checking restack parameter
    if type(restack) is not bool:
        sys.exit('(Type Error): Restack parameter can only take argument as True or False.')    
    
    #checking plot parameter
    if type(restack) is not bool:
        sys.exit('(Type Error): Plot parameter can only take argument as True or False.')  
        
    #checking verbose parameter
    if type(verbose) is not bool:
        sys.exit('(Type Error): Verbose parameter can only take argument as True or False.') 
        
    '''
    
    ERROR HANDLING ENDS HERE
    
    '''
    #testing
    #no active tests
    
    #pre-load libraries
    import pandas as pd
    import ipywidgets as ipw
    from IPython.display import display, HTML, clear_output, update_display
    import time, datetime
    from copy import deepcopy
    
    #copy estimator_list
    estimator_list = deepcopy(estimator_list)
    
    #Defining meta model. Linear Regression hardcoded for now
    if meta_model == None:
        from sklearn.linear_model import LinearRegression
        meta_model = LinearRegression()
    else:
        meta_model = deepcopy(meta_model) 
    
    clear_output()
    
    #progress bar
    max_progress = len(estimator_list) + fold + 4
    progress = ipw.IntProgress(value=0, min=0, max=max_progress, step=1 , description='Processing: ')
    master_display = pd.DataFrame(columns=['MAE','MSE','RMSE', 'R2', 'RMSLE', 'MAPE'])
    display(progress)
    
    #display monitor
    timestampStr = datetime.datetime.now().strftime("%H:%M:%S")
    monitor = pd.DataFrame( [ ['Initiated' , '. . . . . . . . . . . . . . . . . .', timestampStr ], 
                             ['Status' , '. . . . . . . . . . . . . . . . . .' , 'Loading Dependencies' ],
                             ['ETC' , '. . . . . . . . . . . . . . . . . .',  'Calculating ETC'] ],
                              columns=['', ' ', '   ']).set_index('')
    
    display(monitor, display_id = 'monitor')
    
    if verbose:
        display_ = display(master_display, display_id=True)
        display_id = display_.display_id
        
    #ignore warnings
    import warnings
    warnings.filterwarnings('ignore') 
    
    #dependencies
    import numpy as np
    from sklearn import metrics
    from sklearn.model_selection import KFold
    from sklearn.model_selection import cross_val_predict
    import seaborn as sns
    import matplotlib.pyplot as plt
    
    progress.value += 1

    
    #defining data_X and data_y
    if finalize:
        data_X = X.copy()
        data_y = y.copy()
    else:       
        data_X = X_train.copy()
        data_y = y_train.copy()

    #reset index
    data_X.reset_index(drop=True,inplace=True)
    data_y.reset_index(drop=True,inplace=True)
    
    #models_ for appending
    models_ = []
    
    #defining model_library model names
    model_names = np.zeros(0)
    for item in estimator_list:
        model_names = np.append(model_names, str(item).split("(")[0])
    
    model_names_fixed = []
    
    for i in model_names:
        if 'CatBoostRegressor' in i:
            a = 'CatBoostRegressor'
            model_names_fixed.append(a)
        else:
            model_names_fixed.append(i)
            
    model_names = model_names_fixed
    
    model_names_fixed = []
    
    counter = 0
    for i in model_names:
        s = str(i) + '_' + str(counter)
        model_names_fixed.append(s)
        counter += 1
    
    base_array = np.zeros((0,0))
    base_prediction = pd.DataFrame(data_y) #changed to data_y
    base_prediction = base_prediction.reset_index(drop=True)
    
    counter = 0
    
    for model in estimator_list:
        
        '''
        MONITOR UPDATE STARTS
        '''

        monitor.iloc[1,1:] = 'Evaluating ' + model_names[counter]
        update_display(monitor, display_id = 'monitor')

        '''
        MONITOR UPDATE ENDS
        '''
        
        #fitting and appending
        model.fit(data_X, data_y)
        models_.append(model)
        
        progress.value += 1
        
        base_array = cross_val_predict(model,data_X,data_y,cv=fold, method='predict')
        base_array_df = pd.DataFrame(base_array)
        base_prediction = pd.concat([base_prediction,base_array_df],axis=1)
        base_array = np.empty((0,0))
        
        counter += 1
    
    #defining column names now
    target_col_name = np.array(base_prediction.columns[0])
    model_names = np.append(target_col_name, model_names_fixed) #adding fixed column names now
    base_prediction.columns = model_names #defining colum names now
    
    #defining data_X and data_y dataframe to be used in next stage.
    
    #drop column from base_prediction
    base_prediction.drop(base_prediction.columns[0],axis=1,inplace=True)
    
    if restack:
        data_X = pd.concat([data_X, base_prediction], axis=1)
        
    else:
        data_X = base_prediction
        
    #data_y = base_prediction[base_prediction.columns[0]]
    
    #Correlation matrix of base_prediction
    #base_prediction_cor = base_prediction.drop(base_prediction.columns[0],axis=1)
    base_prediction_cor = base_prediction.corr()
    
    #Meta Modeling Starts Here
    
    model = meta_model #this defines model to be used below as model = meta_model (as captured above)
    
    #appending in models
    model.fit(data_X, data_y)
    models_.append(model)
    
    kf = KFold(fold, random_state=seed) #capturing fold requested by user

    score_mae =np.empty((0,0))
    score_mse =np.empty((0,0))
    score_rmse =np.empty((0,0))
    score_rmsle =np.empty((0,0))
    score_r2 =np.empty((0,0))
    score_mape =np.empty((0,0))
    avgs_mae =np.empty((0,0))
    avgs_mse =np.empty((0,0))
    avgs_rmse =np.empty((0,0))
    avgs_rmsle =np.empty((0,0))
    avgs_r2 =np.empty((0,0))
    avgs_mape =np.empty((0,0))  
    
    def calculate_mape(actual, prediction):
        mask = actual != 0
        return (np.fabs(actual - prediction)/actual)[mask].mean()

    
    progress.value += 1
    
    fold_num = 1
    
    for train_i , test_i in kf.split(data_X,data_y):
        
        t0 = time.time()
        
        '''
        MONITOR UPDATE STARTS
        '''
    
        monitor.iloc[1,1:] = 'Fitting Meta Model Fold ' + str(fold_num) + ' of ' + str(fold)
        update_display(monitor, display_id = 'monitor')

        '''
        MONITOR UPDATE ENDS
        '''
        
        progress.value += 1
        
        Xtrain,Xtest = data_X.iloc[train_i], data_X.iloc[test_i]
        ytrain,ytest = data_y.iloc[train_i], data_y.iloc[test_i]

        model.fit(Xtrain,ytrain)
        pred_ = model.predict(Xtest)
        
        try:
            pred_ = target_inverse_transformer.inverse_transform(np.array(pred_).reshape(-1,1))
            ytest = target_inverse_transformer.inverse_transform(np.array(ytest).reshape(-1,1))
            pred_ = np.nan_to_num(pred_)
            ytest = np.nan_to_num(ytest)
            
        except:
            pass
        
        mae = metrics.mean_absolute_error(ytest,pred_)
        mse = metrics.mean_squared_error(ytest,pred_)
        rmse = np.sqrt(mse)
        r2 = metrics.r2_score(ytest,pred_)
        rmsle = np.sqrt(np.mean(np.power(np.log(np.array(abs(pred_))+1) - np.log(np.array(abs(ytest))+1), 2)))
        mape = calculate_mape(ytest,pred_)
        #max_error_ = metrics.max_error(ytest,pred_)
        score_mae = np.append(score_mae,mae)
        score_mse = np.append(score_mse,mse)
        score_rmse = np.append(score_rmse,rmse)
        score_rmsle = np.append(score_rmsle,rmsle)
        score_r2 =np.append(score_r2,r2)
        score_mape = np.append(score_mape,mape)
        
        
        '''
        
        This section handles time calculation and is created to update_display() as code loops through 
        the fold defined.
        
        '''
        
        fold_results = pd.DataFrame({'MAE':[mae], 'MSE': [mse], 'RMSE': [rmse], 
                                     'R2': [r2], 'RMSLE': [rmsle], 'MAPE': [mape]}).round(round)
        master_display = pd.concat([master_display, fold_results],ignore_index=True)
        fold_results = []
        
        
        '''
        
        TIME CALCULATION SUB-SECTION STARTS HERE
        
        '''
        
        t1 = time.time()
        
        tt = (t1 - t0) * (fold-fold_num) / 60
        tt = np.around(tt, 2)
        
        if tt < 1:
            tt = str(np.around((tt * 60), 2))
            ETC = tt + ' Seconds Remaining'
                
        else:
            tt = str (tt)
            ETC = tt + ' Minutes Remaining'
        
        '''
        MONITOR UPDATE STARTS
        '''

        monitor.iloc[2,1:] = ETC
        
        update_display(monitor, display_id = 'monitor')

        '''
        MONITOR UPDATE ENDS
        '''
        
        #update_display(ETC, display_id = 'ETC')
            
        fold_num += 1
        
        
        '''
        
        TIME CALCULATION ENDS HERE
        
        '''
        
        if verbose:
            update_display(master_display, display_id = display_id)
            
        
        '''
        
        Update_display() ends here
        
        '''
     
    mean_mae=np.mean(score_mae)
    mean_mse=np.mean(score_mse)
    mean_rmse=np.mean(score_rmse)
    mean_rmsle=np.mean(score_rmsle)
    mean_r2=np.mean(score_r2)
    mean_mape=np.mean(score_mape)
    std_mae=np.std(score_mae)
    std_mse=np.std(score_mse)
    std_rmse=np.std(score_rmse)
    std_rmsle=np.std(score_rmsle)
    std_r2=np.std(score_r2)
    std_mape=np.std(score_mape)
    
    avgs_mae = np.append(avgs_mae, mean_mae)
    avgs_mae = np.append(avgs_mae, std_mae) 
    avgs_mse = np.append(avgs_mse, mean_mse)
    avgs_mse = np.append(avgs_mse, std_mse)
    avgs_rmse = np.append(avgs_rmse, mean_rmse)
    avgs_rmse = np.append(avgs_rmse, std_rmse)
    avgs_rmsle = np.append(avgs_rmsle, mean_rmsle)
    avgs_rmsle = np.append(avgs_rmsle, std_rmsle)
    avgs_r2 = np.append(avgs_r2, mean_r2)
    avgs_r2 = np.append(avgs_r2, std_r2)
    avgs_mape = np.append(avgs_mape, mean_mape)
    avgs_mape = np.append(avgs_mape, std_mape)
      
    model_results = pd.DataFrame({'MAE': score_mae, 'MSE': score_mse, 'RMSE' : score_rmse, 'R2' : score_r2 , 
                                 'RMSLE' : score_rmsle, 'MAPE' : score_mape})
    model_avgs = pd.DataFrame({'MAE': avgs_mae, 'MSE': avgs_mse, 'RMSE' : avgs_rmse, 'R2' : avgs_r2, 
                               'RMSLE' : avgs_rmsle, 'MAPE' : avgs_mape},index=['Mean', 'SD'])
  
    model_results = model_results.append(model_avgs)
    model_results = model_results.round(round)  
    
    progress.value += 1
    
    #appending method into models_
    models_.append(restack)
    
    #storing into experiment
    model_name = 'Stacking Regressor (Single Layer)'
    tup = (model_name,models_)
    experiment__.append(tup)
    nam = str(model_name) + ' Score Grid'
    tup = (nam, model_results)
    experiment__.append(tup)
    
    if plot:
        clear_output()
        plt.subplots(figsize=(15,7))
        ax = sns.heatmap(base_prediction_cor, vmin=0.2, vmax=1, center=0,cmap='magma', square=True, annot=True, 
                         linewidths=1)
        ax.set_ylim(sorted(ax.get_xlim(), reverse=True))
    
    if verbose:
        clear_output()
        display(model_results)
        return models_
    else:
        clear_output()
        return models_




def create_stacknet(estimator_list,
                    meta_model = None,
                    fold = 10,
                    round = 4,
                    restack = False,
                    finalize = False,
                    verbose = True):
    
    """
         
    Description:
    ------------
    This function creates a sequential stack net using cross validated predictions 
    at each layer. The final score grid contains predictions from the meta model 
    using Kfold Cross Validation. Base level models can be passed as estimator_list
    param, the layers can be organized as a sub list within the estimator_list object.  
    Restacking param controls the ability to expose raw features to meta model.

        Example:
        --------
        from pycaret.datasets import get_data
        boston = get_data('boston')
        experiment_name = setup(data = boston,  target = 'medv')
        dt = create_model('dt')
        rf = create_model('rf')
        ada = create_model('ada')
        ridge = create_model('ridge')
        knn = create_model('knn')

        stacknet = create_stacknet(estimator_list =[[dt,rf],[ada,ridge,knn]])

        This will result in the stacking of models in multiple layers. The first layer 
        contains dt and rf, the predictions of which are used by models in the second 
        layer to generate predictions which are then used by the meta model to generate
        final predictions. By default, the meta model is Linear Regression but can be 
        changed with meta_model param.

    Parameters
    ----------
    estimator_list : nested list of objects

    meta_model : object, default = None
    if set to None, Linear Regression is used as a meta model.

    fold: integer, default = 10
    Number of folds to be used in Kfold CV. Must be at least 2. 

    round: integer, default = 4
    Number of decimal places the metrics in the score grid will be rounded to.
  
    restack: Boolean, default = False
    When restack is set to True, raw data and prediction of all layers will be 
    exposed to the meta model when making predictions. When set to False, only 
    the predicted label of last layer is passed to meta model when making final 
    predictions.
    
    finalize: Boolean, default = False
    When finalize is set to True, it will fit the stacker on entire dataset
    including the hold-out sample created during the setup() stage. It is not 
    recommended to set this to True here, if you would like to fit the stacker 
    on the entire dataset including the hold-out, use finalize_model().
    
    verbose: Boolean, default = True
    Score grid is not printed when verbose is set to False.

    Returns:
    --------

    score grid:   A table containing the scores of the model across the kfolds. 
    -----------   Scoring metrics used are MAE, MSE, RMSE, R2, RMSLE and MAPE.
                  Mean and standard deviation of the scores across the folds are 
                  also returned.

    container:    list of all models where the last element is the meta model.
    ----------

    Warnings:
    ---------    
    None
      
      
    
    """

    
    
    '''
    
    ERROR HANDLING STARTS HERE
    
    '''
    
    #for checking only
    #No active test
    
    #exception checking   
    import sys
    
    #checking estimator_list
    if type(estimator_list[0]) is not list:
        sys.exit("(Type Error): estimator_list parameter must be list of list. ")

    #checking error for estimator_list
    for i in estimator_list:
        for j in i:
            if 'sklearn' not in str(type(j)) and 'CatBoostRegressor' not in str(type(j)):
                sys.exit("(Value Error): estimator_list parameter only trained model object")
            
    #checking meta model
    if meta_model is not None:
        if 'sklearn' not in str(type(meta_model)) and 'CatBoostRegressor' not in str(type(meta_model)):
            sys.exit("(Value Error): estimator_list parameter only trained model object")
    
    #checking fold parameter
    if type(fold) is not int:
        sys.exit('(Type Error): Fold parameter only accepts integer value.')
    
    #checking round parameter
    if type(round) is not int:
        sys.exit('(Type Error): Round parameter only accepts integer value.')
 
    #checking restack parameter
    if type(restack) is not bool:
        sys.exit('(Type Error): Restack parameter can only take argument as True or False.')    
    
    #checking verbose parameter
    if type(verbose) is not bool:
        sys.exit('(Type Error): Verbose parameter can only take argument as True or False.') 
        
    '''
    
    ERROR HANDLING ENDS HERE
    
    '''
    
    #pre-load libraries
    import pandas as pd
    import ipywidgets as ipw
    from IPython.display import display, HTML, clear_output, update_display
    import time, datetime
    from copy import deepcopy
    
    #copy estimator_list
    estimator_list = deepcopy(estimator_list)
    
    #defining meta model
    if meta_model == None:
        from sklearn.linear_model import LinearRegression
        meta_model = LinearRegression()
    else:
        meta_model = deepcopy(meta_model)
        
    clear_output()
    
    #progress bar
    max_progress = len(estimator_list) + fold + 4
    progress = ipw.IntProgress(value=0, min=0, max=max_progress, step=1 , description='Processing: ')
    display(progress)
    
    #display monitor
    timestampStr = datetime.datetime.now().strftime("%H:%M:%S")
    monitor = pd.DataFrame( [ ['Initiated' , '. . . . . . . . . . . . . . . . . .', timestampStr ], 
                             ['Status' , '. . . . . . . . . . . . . . . . . .' , 'Loading Dependencies' ],
                             ['ETC' , '. . . . . . . . . . . . . . . . . .',  'Calculating ETC'] ],
                              columns=['', ' ', '   ']).set_index('')
    
    display(monitor, display_id = 'monitor')
    
    if verbose:
        master_display = pd.DataFrame(columns=['MAE','MSE','RMSE', 'R2', 'RMSLE', 'MAPE'])
        display_ = display(master_display, display_id=True)
        display_id = display_.display_id
    
    #ignore warnings
    import warnings
    warnings.filterwarnings('ignore') 
    
    #general dependencies
    import numpy as np
    from sklearn import metrics
    from sklearn.model_selection import KFold
    from sklearn.model_selection import cross_val_predict
    
    #models_ list
    models_ = []

    progress.value += 1
    
    
    base_level = estimator_list[0]
    base_level_names = []
    
    #defining base_level_names
    for item in base_level:
            base_level_names = np.append(base_level_names, str(item).split("(")[0])
    
    
    base_level_fixed = []
    
    for i in base_level_names:
        if 'CatBoostRegressor' in i:
            a = 'CatBoostRegressor'
            base_level_fixed.append(a)
        else:
            base_level_fixed.append(i)
        
    base_level_names = base_level_fixed
        
    base_level_fixed_2 = []
    
    counter = 0
    for i in base_level_names:
        s = str(i) + '_' + 'BaseLevel_' + str(counter)
        base_level_fixed_2.append(s)
        counter += 1
    
    base_level_fixed = base_level_fixed_2
    
    inter_level = estimator_list[1:]
    inter_level_names = []
    
    #defining inter_level names
    for item in inter_level:
        for m in item:
            inter_level_names = np.append(inter_level_names, str(m).split("(")[0])    
            
    #defining inter_level names
    for item in inter_level:
        for m in item:
            inter_level_names = np.append(inter_level_names, str(m).split("(")[0])  
            
    #defining data_X and data_y
    if finalize:
        data_X = X.copy()
        data_y = y.copy()
    else:       
        data_X = X_train.copy()
        data_y = y_train.copy()
        
    #reset index
    data_X.reset_index(drop=True, inplace=True)
    data_y.reset_index(drop=True, inplace=True)

        
    base_array = np.zeros((0,0))
    base_array_df = pd.DataFrame()
    base_prediction = pd.DataFrame(data_y) #changed to data_y
    base_prediction = base_prediction.reset_index(drop=True)
    
    base_counter = 0
    
    base_models_ = []

    for model in base_level:
        
        base_models_.append(model.fit(data_X,data_y)) #changed to data_X and data_y
        
        '''
        MONITOR UPDATE STARTS
        '''

        monitor.iloc[1,1:] = 'Evaluating ' + base_level_names[base_counter]
        update_display(monitor, display_id = 'monitor')

        '''
        MONITOR UPDATE ENDS
        '''
        
        progress.value += 1
                     
        base_array = cross_val_predict(model,data_X,data_y,cv=fold, method='predict')
        base_array = base_array
        base_array = pd.DataFrame(base_array)
        base_array_df = pd.concat([base_array_df, base_array], axis=1)
        base_array = np.empty((0,0))
        
        base_counter += 1
        
    base_array_df.columns = base_level_fixed
    
    if restack:
        base_array_df = pd.concat([data_X,base_array_df], axis=1)
        
    early_break = base_array_df.copy()
    
    models_.append(base_models_)
    
    inter_counter = 0
    
    for level in inter_level:
        inter_inner = []
        model_counter = 0
        inter_array_df = pd.DataFrame()
        
        for model in level:
            
            '''
            MONITOR UPDATE STARTS
            '''

            monitor.iloc[1,1:] = 'Evaluating ' + inter_level_names[inter_counter]
            update_display(monitor, display_id = 'monitor')

            '''
            MONITOR UPDATE ENDS
            '''
            
            model = model.fit(X = base_array_df, y = data_y) #changed to data_y
            inter_inner.append(model)
            
            base_array = cross_val_predict(model,X = base_array_df, y = data_y,cv=fold, method='predict')
            base_array = pd.DataFrame(base_array)
            
            """
            defining columns
            """
            
            col = str(model).split("(")[0]
            if 'CatBoostRegressor' in col:
                col = 'CatBoostRegressor'
            col = col + '_InterLevel_' + str(inter_counter) + '_' + str(model_counter)
            base_array.columns = [col]
            
            """
            defining columns end here
            """
            
            inter_array_df = pd.concat([inter_array_df, base_array], axis=1)
            base_array = np.empty((0,0))
            
            model_counter += 1
    
        base_array_df = pd.concat([base_array_df,inter_array_df], axis=1)
            
        models_.append(inter_inner)
    
        if restack == False:
            i = base_array_df.shape[1] - len(level)
            base_array_df = base_array_df.iloc[:,i:]
        
        inter_counter += 1
    
    model = meta_model
    
    #redefine data_X and data_y
    data_X = base_array_df.copy()
    
    
    meta_model_ = model.fit(data_X,data_y)
    
    kf = KFold(fold, random_state=seed) #capturing fold requested by user

    score_mae =np.empty((0,0))
    score_mse =np.empty((0,0))
    score_rmse =np.empty((0,0))
    score_rmsle =np.empty((0,0))
    score_r2 =np.empty((0,0))
    score_mape =np.empty((0,0))
    avgs_mae =np.empty((0,0))
    avgs_mse =np.empty((0,0))
    avgs_rmse =np.empty((0,0))
    avgs_rmsle =np.empty((0,0))
    avgs_r2 =np.empty((0,0))
    avgs_mape =np.empty((0,0))  
    
    def calculate_mape(actual, prediction):
        mask = actual != 0
        return (np.fabs(actual - prediction)/actual)[mask].mean()
    
    
    fold_num = 1
    
    for train_i , test_i in kf.split(data_X,data_y):
        
        t0 = time.time()
        
        '''
        MONITOR UPDATE STARTS
        '''
    
        monitor.iloc[1,1:] = 'Fitting Meta Model Fold ' + str(fold_num) + ' of ' + str(fold)
        update_display(monitor, display_id = 'monitor')

        '''
        MONITOR UPDATE ENDS
        '''
        
        Xtrain,Xtest = data_X.iloc[train_i], data_X.iloc[test_i]
        ytrain,ytest = data_y.iloc[train_i], data_y.iloc[test_i]
        
        model.fit(Xtrain,ytrain)
        pred_ = model.predict(Xtest)
        
        try:
            pred_ = target_inverse_transformer.inverse_transform(np.array(pred_).reshape(-1,1))
            ytest = target_inverse_transformer.inverse_transform(np.array(ytest).reshape(-1,1))
            pred_ = np.nan_to_num(pred_)
            ytest = np.nan_to_num(ytest)
            
        except:
            pass
        
        mae = metrics.mean_absolute_error(ytest,pred_)
        mse = metrics.mean_squared_error(ytest,pred_)
        rmse = np.sqrt(mse)
        r2 = metrics.r2_score(ytest,pred_)
        rmsle = np.sqrt(np.mean(np.power(np.log(np.array(abs(pred_))+1) - np.log(np.array(abs(ytest))+1), 2)))
        mape = calculate_mape(ytest,pred_)
        #max_error_ = metrics.max_error(ytest,pred_)
        score_mae = np.append(score_mae,mae)
        score_mse = np.append(score_mse,mse)
        score_rmse = np.append(score_rmse,rmse)
        score_rmsle = np.append(score_rmsle,rmsle)
        score_r2 =np.append(score_r2,r2)
        score_mape = np.append(score_mape,mape)

        progress.value += 1
        
        '''
        
        This section handles time calculation and is created to update_display() as code loops through 
        the fold defined.
        
        '''
        
        fold_results = pd.DataFrame({'MAE':[mae], 'MSE': [mse], 'RMSE': [rmse], 
                                     'R2': [r2], 'RMSLE' : [rmsle], 'MAPE': [mape]}).round(round)
        
        if verbose:
            master_display = pd.concat([master_display, fold_results],ignore_index=True)
        
        fold_results = []
        
        '''
        TIME CALCULATION SUB-SECTION STARTS HERE
        '''
        t1 = time.time()
        
        tt = (t1 - t0) * (fold-fold_num) / 60
        tt = np.around(tt, 2)
        
        if tt < 1:
            tt = str(np.around((tt * 60), 2))
            ETC = tt + ' Seconds Remaining'
                
        else:
            tt = str (tt)
            ETC = tt + ' Minutes Remaining'
            
        fold_num += 1
        
        '''
        MONITOR UPDATE STARTS
        '''

        monitor.iloc[2,1:] = ETC
        update_display(monitor, display_id = 'monitor')

        '''
        MONITOR UPDATE ENDS
        '''
        
        '''
        TIME CALCULATION ENDS HERE
        '''
        
        if verbose:
            update_display(master_display, display_id = display_id)
            
        
        '''
        
        Update_display() ends here
        
        '''
    
    mean_mae=np.mean(score_mae)
    mean_mse=np.mean(score_mse)
    mean_rmse=np.mean(score_rmse)
    mean_rmsle=np.mean(score_rmsle)
    mean_r2=np.mean(score_r2)
    mean_mape=np.mean(score_mape)
    std_mae=np.std(score_mae)
    std_mse=np.std(score_mse)
    std_rmse=np.std(score_rmse)
    std_rmsle=np.std(score_rmsle)
    std_r2=np.std(score_r2)
    std_mape=np.std(score_mape)
    
    avgs_mae = np.append(avgs_mae, mean_mae)
    avgs_mae = np.append(avgs_mae, std_mae) 
    avgs_mse = np.append(avgs_mse, mean_mse)
    avgs_mse = np.append(avgs_mse, std_mse)
    avgs_rmse = np.append(avgs_rmse, mean_rmse)
    avgs_rmse = np.append(avgs_rmse, std_rmse)
    avgs_rmsle = np.append(avgs_rmsle, mean_rmsle)
    avgs_rmsle = np.append(avgs_rmsle, std_rmsle)
    avgs_r2 = np.append(avgs_r2, mean_r2)
    avgs_r2 = np.append(avgs_r2, std_r2)
    avgs_mape = np.append(avgs_mape, mean_mape)
    avgs_mape = np.append(avgs_mape, std_mape)
      
    model_results = pd.DataFrame({'MAE': score_mae, 'MSE': score_mse, 'RMSE' : score_rmse, 'R2' : score_r2 , 
                                  'RMSLE' : score_rmsle, 'MAPE' : score_mape})
    model_avgs = pd.DataFrame({'MAE': avgs_mae, 'MSE': avgs_mse, 'RMSE' : avgs_rmse, 'R2' : avgs_r2, 
                                'RMSLE' : avgs_rmsle, 'MAPE' : avgs_mape},index=['Mean', 'SD'])
  
    model_results = model_results.append(model_avgs)
    model_results = model_results.round(round)      
    
    progress.value += 1
        
    #appending meta_model into models_
    models_.append(meta_model_)
    
    #appending restack param
    models_.append(restack)
    
    #storing into experiment
    model_name = 'Stacking Regressor (Multi Layer)'
    tup = (model_name,models_)
    experiment__.append(tup)
    nam = str(model_name) + ' Score Grid'
    tup = (nam, model_results)
    experiment__.append(tup)
    
    if verbose:
        clear_output()
        display(model_results)
        return models_
    
    else:
        clear_output()
        return models_ 



def plot_model(estimator, 
               plot = 'residuals'): 
    
    
    """
          
    Description:
    ------------
    This function takes a trained model object and returns a plot based on the
    test / hold-out set. The process may require the model to be re-trained in
    certain cases. See list of plots supported below. 
    
    Model must be created using create_model() or tune_model().

        Example:
        --------
        from pycaret.datasets import get_data
        boston = get_data('boston')
        experiment_name = setup(data = boston,  target = 'medv')
        lr = create_model('lr')
        
        plot_model(lr)

        This will return an residuals plot of a trained Linear Regression model.

    Parameters
    ----------
    estimator : object, default = none
    A trained model object should be passed as an estimator. 
   
    plot : string, default = residual
    Enter abbreviation of type of plot. The current list of plots supported are:

    Name                        Abbreviated String     Original Implementation 
    ---------                   ------------------     -----------------------
    Residuals Plot               'residuals'           .. / residuals.html
    Prediction Error Plot        'error'               .. / peplot.html
    Cooks Distance Plot          'cooks'               .. / influence.html
    Recursive Feat. Selection    'rfe'                 .. / rfecv.html
    Learning Curve               'learning'            .. / learning_curve.html
    Validation Curve             'vc'                  .. / validation_curve.html
    Manifold Learning            'manifold'            .. / manifold.html
    Feature Importance           'feature'                   N/A 
    Model Hyperparameter         'parameter'                 N/A 

    ** https://www.scikit-yb.org/en/latest/api/regressor/<reference>

    Returns:
    --------

    Visual Plot:  Prints the visual plot. 
    ------------

    Warnings:
    ---------
    None
      
                
    """  
    
    
    '''
    
    ERROR HANDLING STARTS HERE
    
    '''
    
    #for testing
    #No active testing
    
    #exception checking   
    import sys
    
    #checking plots (string)
    available_plots = ['residuals', 'error', 'cooks', 'feature', 'parameter', 'rfe', 'learning', 'manifold', 'vc']
    
    if plot not in available_plots:
        sys.exit('(Value Error): Plot Not Available. Please see docstring for list of available Plots.')

    #exception for CatBoost
    if 'CatBoostRegressor' in str(type(estimator)):
        sys.exit('(Estimator Error): CatBoost estimator is not compatible with plot_model function, try using Catboost with interpret_model instead.')
        
    #checking for feature plot
    if not ( hasattr(estimator, 'coef_') or hasattr(estimator,'feature_importances_') ) and (plot == 'feature' or plot == 'rfe'):
        sys.exit('(Type Error): Feature Importance plot not available for estimators with coef_ attribute.')
    
    '''
    
    ERROR HANDLING ENDS HERE
    
    '''
    
    #pre-load libraries
    import pandas as pd
    import ipywidgets as ipw
    from IPython.display import display, HTML, clear_output, update_display
    
    #progress bar
    progress = ipw.IntProgress(value=0, min=0, max=5, step=1 , description='Processing: ')
    display(progress)
    
    #ignore warnings
    import warnings
    warnings.filterwarnings('ignore') 
    
    #general dependencies
    import matplotlib.pyplot as plt
    import numpy as np
    import pandas as pd
    
    progress.value += 1
    
    #defining estimator as model locally
    model = estimator
    
    progress.value += 1
    
    if plot == 'residuals':
        
        from yellowbrick.regressor import ResidualsPlot
        progress.value += 1
        visualizer = ResidualsPlot(model)
        progress.value += 1
        visualizer.score(X_test, y_test)  # Evaluate the model on the test data
        progress.value += 1
        clear_output()
        visualizer.show()
        
        
    elif plot == 'error':
        from yellowbrick.regressor import PredictionError
        progress.value += 1
        visualizer = PredictionError(model)
        progress.value += 1
        visualizer.score(X_test, y_test)
        progress.value += 1
        clear_output()
        visualizer.show()
        
    elif plot == 'cooks':
        from yellowbrick.regressor import CooksDistance
        progress.value += 1
        visualizer = CooksDistance()
        progress.value += 1
        visualizer.fit(X, y)
        progress.value += 1
        clear_output()
        visualizer.show() 
        
    elif plot == 'rfe':
        
        from yellowbrick.model_selection import RFECV 
        progress.value += 1
        visualizer = RFECV(model, cv=10)
        progress.value += 1
        visualizer.fit(X_train, y_train)
        progress.value += 1
        clear_output()
        visualizer.poof()
        
    elif plot == 'learning':
        
        from yellowbrick.model_selection import LearningCurve
        progress.value += 1
        sizes = np.linspace(0.3, 1.0, 10)  
        visualizer = LearningCurve(model, cv=10, train_sizes=sizes, n_jobs=1, random_state=seed)
        progress.value += 1
        visualizer.fit(X_train, y_train)
        progress.value += 1
        clear_output()
        visualizer.poof()
    
    elif plot == 'manifold':
        
        from yellowbrick.features import Manifold
        
        progress.value += 1
        X_train_transformed = X_train.select_dtypes(include='float64') 
        visualizer = Manifold(manifold='tsne', random_state = seed)
        progress.value += 1
        visualizer.fit_transform(X_train_transformed, y_train)
        progress.value += 1
        clear_output()
        visualizer.poof()

    elif plot == 'vc':
        
        model_name = str(model).split("(")[0]
        
        not_allowed = ['LinearRegression', 'PassiveAggressiveRegressor']
        
        if model_name in not_allowed:
            clear_output()
            sys.exit('(Value Error): Estimator not supported in Validation Curve Plot.')
        
        elif model_name == 'GradientBoostingRegressor':
            param_name='alpha'
            param_range = np.arange(0.1,1,0.1)
        
        #lasso/ridge/en/llar/huber/kr/mlp/br/ard
        elif hasattr(model, 'alpha'):
            param_name='alpha'
            param_range = np.arange(0,1,0.1)
            
        elif hasattr(model, 'alpha_1'):
            param_name='alpha_1'
            param_range = np.arange(0,1,0.1)
            
        #par/svm
        elif hasattr(model, 'C'):
            param_name='C'
            param_range = np.arange(1,11)
            
        #tree based models (dt/rf/et)
        elif hasattr(model, 'max_depth'):
            param_name='max_depth'
            param_range = np.arange(1,11)
        
        #knn
        elif hasattr(model, 'n_neighbors'):
            param_name='n_neighbors'
            param_range = np.arange(1,11)         
            
        #Bagging / Boosting (ada/gbr)
        elif hasattr(model, 'n_estimators'):
            param_name='n_estimators'
            param_range = np.arange(1,100,10)   

        #Bagging / Boosting (ada/gbr)
        elif hasattr(model, 'n_nonzero_coefs'):
            param_name='n_nonzero_coefs'
            if len(X_train.columns) >= 10:
                param_max = 11
            else:
                param_max = len(X_train.columns)+1
            param_range = np.arange(1,param_max,1) 
            
        elif hasattr(model, 'eps'):
            param_name='eps'
            param_range = np.arange(0,1,0.1)   
            
        elif hasattr(model, 'max_subpopulation'):
            param_name='max_subpopulation'
            param_range = np.arange(1000,20000,2000)   

        elif hasattr(model, 'min_samples'):
            param_name='min_samples'
            param_range = np.arange(0.01,1,0.1)  
            
        else: 
            clear_output()
            sys.exit('(Value Error): Estimator not supported in Validation Curve Plot.')
        
            
        progress.value += 1
            
        from yellowbrick.model_selection import ValidationCurve
        viz = ValidationCurve(model, param_name=param_name, param_range=param_range,cv=10, 
                              random_state=seed)
        viz.fit(X_train, y_train)
        progress.value += 1
        clear_output()
        viz.poof()
        
    elif plot == 'feature':
        if hasattr(estimator, 'coef_'):
            variables = abs(model.coef_)
        else:
            variables = abs(model.feature_importances_)
        col_names = np.array(X_train.columns)
        coef_df = pd.DataFrame({'Variable': X_train.columns, 'Value': variables})
        progress.value += 1
        sorted_df = coef_df.sort_values(by='Value', ascending=False)
        sorted_df = sorted_df.head(10)
        sorted_df = sorted_df.sort_values(by='Value')
        my_range=range(1,len(sorted_df.index)+1)
        plt.figure(figsize=(8,5))
        plt.hlines(y=my_range, xmin=0, xmax=sorted_df['Value'], color='skyblue')
        plt.plot(sorted_df['Value'], my_range, "o")
        plt.yticks(my_range, sorted_df['Variable'])
        progress.value += 1
        plt.title("Feature Importance Plot")
        plt.xlabel('Variable Importance')
        plt.ylabel('Features') 
        progress.value += 1
        clear_output()
   
    elif plot == 'parameter':
        
        clear_output()
        param_df = pd.DataFrame.from_dict(estimator.get_params(estimator), orient='index', columns=['Parameters'])
        display(param_df)



def interpret_model(estimator,
                   plot = 'summary',
                   feature = None, 
                   observation = None):
    
    
    """
          
    Description:
    ------------
    This function takes a trained model object and returns an interpretation plot 
    based on the test / hold-out set. It only supports tree based algorithms. 

    This function is implemented based on the SHAP (SHapley Additive exPlanations),
    which is a unified approach to explain the output of any machine learning model. 
    SHAP connects game theory with local explanations.

    For more information : https://shap.readthedocs.io/en/latest/

        Example:
        --------
        from pycaret.datasets import get_data
        boston = get_data('boston')
        experiment_name = setup(data = boston,  target = 'medv')
        dt = create_model('dt')
        
        interpret_model(dt)

        This will return a summary interpretation plot of Decision Tree model.

    Parameters
    ----------
    estimator : object, default = none
    A trained tree based model object should be passed as an estimator. 

    plot : string, default = 'summary'
    other available options are 'correlation' and 'reason'.

    feature: string, default = None
    This parameter is only needed when plot = 'correlation'. By default feature is 
    set to None which means the first column of the dataset will be used as a variable. 
    A feature parameter must be passed to change this.

    observation: integer, default = None
    This parameter only comes into effect when plot is set to 'reason'. If no observation
    number is provided, it will return an analysis of all observations with the option
    to select the feature on x and y axes through drop down interactivity. For analysis at
    the sample level, an observation parameter must be passed with the index value of the
    observation in test / hold-out set. 

    Returns:
    --------

    Visual Plot:  Returns the visual plot.
    -----------   Returns the interactive JS plot when plot = 'reason'.

    Warnings:
    ---------
    None
     
         
         
    """
    
    
    
    '''
    Error Checking starts here
    
    '''
    
    import sys
    
    #allowed models
    allowed_models = ['RandomForestRegressor',
                      'DecisionTreeRegressor',
                      'ExtraTreesRegressor',
                      'GradientBoostingRegressor',
                      'XGBRegressor',
                      'LGBMRegressor',
                      'CatBoostRegressor']
    
    model_name = str(estimator).split("(")[0]

    #Statement to find CatBoost and change name :
    if model_name.find("catboost.core.CatBoostRegressor") != -1:
        model_name = 'CatBoostRegressor'
    
    if model_name not in allowed_models:
        sys.exit('(Type Error): This function only supports tree based models.')
        
    #plot type
    allowed_types = ['summary', 'correlation', 'reason']
    if plot not in allowed_types:
        sys.exit("(Value Error): type parameter only accepts 'summary', 'correlation' or 'reason'.")   
           
    
    '''
    Error Checking Ends here
    
    '''
        
    
    #general dependencies
    import numpy as np
    import pandas as pd
    import shap
    
    #storing estimator in model variable
    model = estimator
    
    if plot == 'summary':
        
        explainer = shap.TreeExplainer(model)
        shap_values = explainer.shap_values(X_test)
        shap.summary_plot(shap_values, X_test)
                              
    elif plot == 'correlation':
        
        if feature == None:
            
            dependence = X_test.columns[0]
            
        else:
            
            dependence = feature
        
        explainer = shap.TreeExplainer(model)
        shap_values = explainer.shap_values(X_test) 
        shap.dependence_plot(dependence, shap_values, X_test)
        
    elif plot == 'reason':
     
        if observation is None:

            explainer = shap.TreeExplainer(model)
            shap_values = explainer.shap_values(X_test)
            shap.initjs()
            return shap.force_plot(explainer.expected_value, shap_values, X_test)

        else:

            row_to_show = observation
            data_for_prediction = X_test.iloc[row_to_show]
            explainer = shap.TreeExplainer(model)
            shap_values = explainer.shap_values(X_test)
            shap.initjs()
            return shap.force_plot(explainer.expected_value, shap_values[row_to_show,:], X_test.iloc[row_to_show,:])



def evaluate_model(estimator):
    
    
    """
          
    Description:
    ------------
    This function displays a user interface for all of the available plots for 
    a given estimator. It internally uses the plot_model() function. 
    
        Example:
        --------
        from pycaret.datasets import get_data
        boston = get_data('boston')
        experiment_name = setup(data = boston,  target = 'medv')
        lr = create_model('lr')
        
        evaluate_model(lr)
        
        This will display the User Interface for all of the plots for a given
        estimator.

    Parameters
    ----------
    estimator : object, default = none
    A trained model object should be passed as an estimator. 

    Returns:
    --------

    User Interface:  Displays the user interface for plotting.
    --------------

    Warnings:
    ---------
    None
      
         
         
    """
        
        
    from ipywidgets import widgets
    from ipywidgets.widgets import interact, fixed, interact_manual

    a = widgets.ToggleButtons(
                            options=[('Hyperparameters', 'parameter'),
                                     ('Residuals Plot', 'residuals'), 
                                     ('Prediction Error Plot', 'error'), 
                                     ('Cooks Distance Plot', 'cooks'),
                                     ('Recursive Feature Selection', 'rfe'),
                                     ('Learning Curve', 'learning'),
                                     ('Validation Curve', 'vc'),
                                     ('Manifold Learning', 'manifold'),
                                     ('Feature Importance', 'feature')
                                    ],

                            description='Plot Type:',

                            disabled=False,

                            button_style='', # 'success', 'info', 'warning', 'danger' or ''

                            icons=['']
    )
    
  
    d = interact(plot_model, estimator = fixed(estimator), plot = a)




def finalize_model(estimator):
    
    """
          
    Description:
    ------------
    This function fits the estimator onto the complete dataset passed during the
    setup() stage. The purpose of this function is to prepare for final model
    deployment after experimentation. 
    
        Example:
        --------
        from pycaret.datasets import get_data
        boston = get_data('boston')
        experiment_name = setup(data = boston,  target = 'medv')
        lr = create_model('lr')
        
        final_lr = finalize_model(lr)
        
        This will return the final model object fitted to complete dataset. 

    Parameters
    ----------
    estimator : object, default = none
    A trained model object should be passed as an estimator. 

    Returns:
    --------

    Model:  Trained model object fitted on complete dataset.
    ------   

    Warnings:
    ---------
    - If the model returned by finalize_model(), is used on predict_model() without 
      passing a new unseen dataset, then the information grid printed is misleading 
      as the model is trained on the complete dataset including test / hold-out sample. 
      Once finalize_model() is used, the model is considered ready for deployment and
      should be used on new unseens dataset only.
       
         
    """
    
    #ignore warnings
    import warnings
    warnings.filterwarnings('ignore') 
    
    #import depedencies
    from copy import deepcopy
    
    if type(estimator) is list:
        
        if type(estimator[0]) is not list:
            
            """
            Single Layer Stacker
            """
            
            stacker_final = deepcopy(estimator)
            stack_restack = stacker_final.pop()
            stack_meta_final = stacker_final.pop()
            
            model_final = stack_models(estimator_list = stacker_final, 
                                       meta_model = stack_meta_final, 
                                       restack = stack_restack,
                                       finalize=True, 
                                       verbose=False)
            
        else:
            
            """
            multiple layer stacknet
            """
            
            stacker_final = deepcopy(estimator)
            stack_restack = stacker_final.pop()
            stack_meta_final = stacker_final.pop()
            
            model_final = create_stacknet(estimator_list = stacker_final,
                                          meta_model = stack_meta_final,
                                          restack = stack_restack,
                                          finalize = True,
                                          verbose = False)

    else:
        model_final = deepcopy(estimator)
        model_final.fit(X,y)
    
    #storing into experiment
    model_name = str(estimator).split("(")[0]
    model_name = 'Final ' + model_name
    tup = (model_name,model_final)
    experiment__.append(tup)
    
    return model_final



def save_model(model, model_name, verbose=True):
    
    """
          
    Description:
    ------------
    This function saves the transformation pipeline and trained model object 
    into the current active directory as a pickle file for later use. 
    
        Example:
        --------
        from pycaret.datasets import get_data
        boston = get_data('boston')
        experiment_name = setup(data = boston,  target = 'medv')
        lr = create_model('lr')
        
        save_model(lr, 'lr_model_23122019')
        
        This will save the transformation pipeline and model as a binary pickle
        file in the current directory. 

    Parameters
    ----------
    model : object, default = none
    A trained model object should be passed as an estimator. 
    
    model_name : string, default = none
    Name of pickle file to be passed as a string.
    
    verbose: Boolean, default = True
    Success message is not printed when verbose is set to False.

    Returns:
    --------    
    Success Message
    
    Warnings:
    ---------
    None
      
            
    """
    
    #ignore warnings
    import warnings
    warnings.filterwarnings('ignore') 
    
    model_ = []
    model_.append(prep_pipe)
    model_.append(model)
    model_.append(target_inverse_transformer)
    
    import joblib
    model_name = model_name + '.pkl'
    joblib.dump(model_, model_name)
    if verbose:
        print('Transformation Pipeline and Model Succesfully Saved')



def load_model(model_name, 
               platform = None,
               authentication = None,
               verbose=True):
    
    """
          
    Description:
    ------------
    This function loads a previously saved transformation pipeline and model 
    from the current active directory into the current python environment. 
    Load object must be a pickle file.
    
        Example:
        --------
        saved_lr = load_model('lr_model_23122019')
        
        This will load the previously saved model in saved_lr variable. The file 
        must be in the current directory.

    Parameters
    ----------
    model_name : string, default = none
    Name of pickle file to be passed as a string.
    
    platform: string, default = None
    Name of platform, if loading model from cloud. Current available options are:
    'aws'.
    
    authentication : dict
    dictionary of applicable authentication tokens. 
    
     When platform = 'aws': 
     {'bucket' : 'Name of Bucket on S3'}
    
    verbose: Boolean, default = True
    Success message is not printed when verbose is set to False.

    Returns:
    --------    
    Success Message
    
    Warnings:
    ---------
    None
      
        
    """
    
    #ignore warnings
    import warnings
    warnings.filterwarnings('ignore') 
    
    #exception checking
    import sys
    
    if platform is not None:
        if authentication is None:
            sys.exit("(Value Error): Authentication is missing.")
            
        
    #cloud provider
    if platform == 'aws':
        
        import boto3
        bucketname = authentication.get('bucket')
        filename = str(model_name) + '.pkl'
        s3 = boto3.resource('s3')
        s3.Bucket(bucketname).download_file(filename, filename)
        filename = str(model_name)
        model = load_model(filename, verbose=False)
        
        if verbose:
            print('Transformation Pipeline and Model Sucessfully Loaded')
            
        return model
    
    import joblib
    model_name = model_name + '.pkl'
    if verbose:
        print('Transformation Pipeline and Model Sucessfully Loaded')
    return joblib.load(model_name)



def save_experiment(experiment_name=None):
    
        
    """
          
    Description:
    ------------
    This function saves the entire experiment into the current active directory. 
    All outputs using pycaret are internally saved into a binary list which is
    pickilized when save_experiment() is used. 
    
        Example:
        --------
        save_experiment()
        
        This will save the entire experiment into the current active directory. By 
        default, the name of the experiment will use the session_id generated during 
        setup(). To use a custom name, a string must be passed to the experiment_name 
        param. For example:
        
        save_experiment('experiment_23122019')

    Parameters
    ----------
    experiment_name : string, default = none
    Name of pickle file to be passed as a string.

    Returns:
    --------    
    Success Message
    
    Warnings:
    ---------
    None
      
         
         
    """
    
    #ignore warnings
    import warnings
    warnings.filterwarnings('ignore') 
    
    #general dependencies
    import joblib
    global experiment__
    
    #defining experiment name
    if experiment_name is None:
        experiment_name = 'experiment_' + str(seed)
        
    else:
        experiment_name = experiment_name  
        
    experiment_name = experiment_name + '.pkl'
    joblib.dump(experiment__, experiment_name)
    
    print('Experiment Succesfully Saved')



def load_experiment(experiment_name):
    
    """
          
    Description:
    ------------
    This function loads a previously saved experiment from the current active 
    directory into current python environment. Load object must be a pickle file.
    
        Example:
        --------
        saved_experiment = load_experiment('experiment_23122019')
        
        This will load the entire experiment pipeline into the object saved_experiment.
        The experiment file must be in current directory.
        
    Parameters
    ----------
    experiment_name : string, default = none
    Name of pickle file to be passed as a string.

    Returns:
    --------    
    Information Grid containing details of saved objects in experiment pipeline.
    
    Warnings:
    ---------
    None
      
          
    """
    
    #ignore warnings
    import warnings
    warnings.filterwarnings('ignore') 
    
    #general dependencies
    import joblib
    import pandas as pd
    
    experiment_name = experiment_name + '.pkl'
    temp = joblib.load(experiment_name)
    
    name = []
    exp = []

    for i in temp:
        name.append(i[0])
        exp.append(i[-1])

    ind = pd.DataFrame(name, columns=['Object'])
    display(ind)

    return exp



def predict_model(estimator, 
                  data=None,
                  platform=None,
                  authentication=None,
                  round=4):
    
    """
       
    Description:
    ------------
    This function is used to predict new data using a trained estimator. It accepts
    an estimator created using one of the function in pycaret that returns a trained 
    model object or a list of trained model objects created using stack_models() or 
    create_stacknet(). New unseen data can be passed to data param as pandas Dataframe. 
    If data is not passed, the test / hold-out set separated at the time of setup() is
    used to generate predictions. 
    
        Example:
        --------
        from pycaret.datasets import get_data
        boston = get_data('boston')
        experiment_name = setup(data = boston,  target = 'medv')
        lr = create_model('lr')
        
        lr_predictions_holdout = predict_model(lr)
        
    Parameters
    ----------
    estimator : object or list of objects / string,  default = None
    When estimator is passed as string, load_model() is called internally to load the
    pickle file from active directory or cloud platform when platform param is passed.
    
    data : {array-like, sparse matrix}, shape (n_samples, n_features) where n_samples 
    is the number of samples and n_features is the number of features. All features 
    used during training must be present in the new dataset.
    
    platform: string, default = None
    Name of platform, if loading model from cloud. Current available options are:
    'aws'.
    
    authentication : dict
    dictionary of applicable authentication tokens. 
    
     When platform = 'aws': 
     {'bucket' : 'Name of Bucket on S3'}
     
    round: integer, default = 4
    Number of decimal places the predicted labels will be rounded to.
    
    Returns:
    --------

    info grid:    Information grid is printed when data is None.
    ----------      
    
    Warnings:
    ---------
    - if the estimator passed is created using finalize_model() then the metrics 
      printed in the information grid maybe misleading as the model is trained on
      the complete dataset including the test / hold-out set. Once finalize_model() 
      is used, the model is considered ready for deployment and should be used on new 
      unseen datasets only.
      
    
    
    """
    
    #ignore warnings
    import warnings
    warnings.filterwarnings('ignore') 
    
    #testing
    #global pred_, target_transformer
    
    #general dependencies
    import sys
    import numpy as np
    import pandas as pd
    import re
    from sklearn import metrics
    from copy import deepcopy
    from IPython.display import clear_output, update_display
    
    def calculate_mape(actual, prediction):
        mask = actual != 0
        return (np.fabs(actual - prediction)/actual)[mask].mean()
    
    estimator = deepcopy(estimator)
    clear_output()
    
    if type(estimator) is str:
        if platform == 'aws':
            estimator_ = load_model(str(estimator), platform='aws', 
                                   authentication={'bucket': authentication.get('bucket')},
                                   verbose=False)
            
        else:
            estimator_ = load_model(str(estimator), verbose=False)
            
    else:
        
        estimator_ = estimator
            
    if type(estimator_) is list:

        if 'sklearn.pipeline.Pipeline' in str(type(estimator_[0])):

            prep_pipe_transformer = estimator_.pop(0)
            model = estimator_[0]
            estimator = estimator_[0]
            target_transformer = estimator_[1]

        else:
            
            try:

                prep_pipe_transformer = prep_pipe
                target_transformer = target_inverse_transformer
                model = estimator
                estimator = estimator
                
            except:
                
                sys.exit("(Type Error): Transformation Pipeline Missing. ")
            
    else:

        try:

            prep_pipe_transformer = prep_pipe
            target_transformer = target_inverse_transformer
            model = estimator
            estimator = estimator
            
        except:
            
            sys.exit("(Type Error): Transformation Pipeline Missing. ")
            
    #dataset
    if data is None:
        
        Xtest = X_test.copy()
        ytest = y_test.copy()
        X_test_ = X_test.copy()
        y_test_ = y_test.copy()
        
        Xtest.reset_index(drop=True, inplace=True)
        ytest.reset_index(drop=True, inplace=True)
        X_test_.reset_index(drop=True, inplace=True)
        y_test_.reset_index(drop=True, inplace=True)
        
        model = estimator
        estimator_ = estimator
        
    else:
        
        Xtest = prep_pipe_transformer.transform(data)                     
        X_test_ = data.copy() #original concater
        
        Xtest.reset_index(drop=True, inplace=True)
        X_test_.reset_index(drop=True, inplace=True)
        
        estimator_ = estimator

    #try:
    #    model = finalize_model(estimator)
    #except:
    #    model = estimator

    if type(estimator) is list:
        
        if type(estimator[0]) is list:
        
            """
            Multiple Layer Stacking
            """
            
            #utility
            stacker = model
            restack = stacker.pop()
            #stacker_method = stacker.pop()
            #stacker_method = stacker_method[0]
            stacker_meta = stacker.pop()
            stacker_base = stacker.pop(0)

            #base model names
            base_model_names = []

            #defining base_level_names
            for i in stacker_base:
                b = str(i).split("(")[0]
                base_model_names.append(b)

            base_level_fixed = []

            for i in base_model_names:
                if 'CatBoostRegressor' in i:
                    a = 'CatBoostRegressor'
                    base_level_fixed.append(a)
                else:
                    base_level_fixed.append(i)

            base_level_fixed_2 = []

            counter = 0
            for i in base_level_fixed:
                s = str(i) + '_' + 'BaseLevel_' + str(counter)
                base_level_fixed_2.append(s)
                counter += 1

            base_level_fixed = base_level_fixed_2

            """
            base level predictions
            """
            base_pred = []
            for i in stacker_base:
                a = i.predict(Xtest) #change
                base_pred.append(a)

            base_pred_df = pd.DataFrame()
            for i in base_pred:
                a = pd.DataFrame(i)
                base_pred_df = pd.concat([base_pred_df, a], axis=1)

            base_pred_df.columns = base_level_fixed
            
            base_pred_df_no_restack = base_pred_df.copy()
            base_pred_df = pd.concat([Xtest,base_pred_df], axis=1)
            

            """
            inter level predictions
            """

            inter_pred = []
            combined_df = pd.DataFrame(base_pred_df)

            inter_counter = 0

            for level in stacker:

                inter_pred_df = pd.DataFrame()

                model_counter = 0 

                for model in level:
                    try:
                        if inter_counter == 0:
                            try:
                                p = model.predict(base_pred_df)
                            except:
                                p = model.predict(base_pred_df_no_restack)
                            
                        else:
                            p = model.predict(last_level_df)
            
                    except:
                        p = model.predict(combined_df)

                    p = pd.DataFrame(p)

                    col = str(model).split("(")[0]
                    if 'CatBoostRegressor' in col:
                        col = 'CatBoostRegressor'
                    col = col + '_InterLevel_' + str(inter_counter) + '_' + str(model_counter)
                    p.columns = [col]

                    inter_pred_df = pd.concat([inter_pred_df, p], axis=1)

                    model_counter += 1

                last_level_df = inter_pred_df.copy()

                inter_counter += 1

                combined_df = pd.concat([combined_df,inter_pred_df], axis=1)

            """
            meta final predictions
            """

            #final meta predictions
            try:
                pred_ = stacker_meta.predict(combined_df)
            except:
                pred_ = stacker_meta.predict(inter_pred_df)
            
            try:
                pred_ = target_transformer.inverse_transform(np.array(pred_).reshape(-1,1))
                pred_ = np.nan_to_num(pred_)
                
            except:
                pred_ = np.nan_to_num(pred_)
                
            if data is None:
                
                try:
                    ytest = target_transformer.inverse_transform(np.array(ytest).reshape(-1,1))
                    ytest = pd.DataFrame(np.nan_to_num(ytest))
                    
                except:
                    pass
                
                mae = metrics.mean_absolute_error(ytest,pred_)
                mse = metrics.mean_squared_error(ytest,pred_)
                rmse = np.sqrt(mse)
                rmsle = np.sqrt(np.mean(np.power(np.log(np.array(abs(pred_))+1) - np.log(np.array(abs(ytest))+1), 2)))
                r2 = metrics.r2_score(ytest,pred_)
                mape = calculate_mape(ytest,pred_)
                #max_error_ = metrics.max_error(ytest,pred_)


                df_score = pd.DataFrame( {'Model' : 'Stacking Regressor', 'MAE' : [mae], 'MSE' : [mse], 'RMSE' : [rmse], 
                                          'R2' : [r2], 'RMSLE' : [rmsle], 'MAPE' : mape})
                df_score = df_score.round(round)
                display(df_score)
        
            label = pd.DataFrame(pred_)
            label = label.round(round)
            label.columns = ['Label']
            label['Label']=label['Label']

            if data is None:
                X_test_ = pd.concat([Xtest,ytest,label], axis=1)
            else:
                X_test_ = pd.concat([X_test_,label], axis=1)

        else:
            
            """
            Single Layer Stacking
            """
            
            #copy
            stacker = model
            
            #restack
            restack = stacker.pop()

            #separate metamodel
            meta_model = stacker.pop()

            model_names = []
            for i in stacker:
                model_names = np.append(model_names, str(i).split("(")[0])

            model_names_fixed = []

            for i in model_names:
                if 'CatBoostRegressor' in i:
                    a = 'CatBoostRegressor'
                    model_names_fixed.append(a)
                else:
                    model_names_fixed.append(i)

            model_names = model_names_fixed

            model_names_fixed = []
            counter = 0

            for i in model_names:
                s = str(i) + '_' + str(counter)
                model_names_fixed.append(s)
                counter += 1

            model_names = model_names_fixed

            base_pred = []

            for i in stacker:
                p = i.predict(Xtest) #change
                base_pred.append(p)

            df = pd.DataFrame()
            for i in base_pred:
                i = pd.DataFrame(i)
                df = pd.concat([df,i], axis=1)

            df.columns = model_names
            
            df_restack = pd.concat([Xtest,df], axis=1) #change

            #ytest = y_test

            #meta predictions starts here

            #restacking check
            try:
                pred_ = meta_model.predict(df)
            except:
                pred_ = meta_model.predict(df_restack) 
                
            try:
                pred_ = target_transformer.inverse_transform(np.array(pred_).reshape(-1,1))
                pred_ = np.nan_to_num(pred_)
                
            except:
                pred_ = np.nan_to_num(pred_)
            
            if data is None:
                
                try:
                    ytest = target_transformer.inverse_transform(np.array(ytest).reshape(-1,1))
                    ytest = pd.DataFrame(np.nan_to_num(ytest))
                    
                except:
                    pass
                
                global moez, mehreen
                moez = pred_.copy()
                mehreen = ytest.copy()
                
                mae = metrics.mean_absolute_error(ytest,pred_)
                mse = metrics.mean_squared_error(ytest,pred_)
                rmse = np.sqrt(mse)
                rmsle = np.sqrt(np.mean(np.power(np.log(np.array(abs(pred_))+1) - np.log(np.array(abs(ytest))+1), 2)))
                r2 = metrics.r2_score(ytest,pred_)
                mape = calculate_mape(ytest,pred_)
                #max_error_ = metrics.max_error(ytest,pred_)


                df_score = pd.DataFrame( {'Model' : 'Stacking Regressor', 'MAE' : [mae], 'MSE' : [mse], 'RMSE' : [rmse], 
                                          'R2' : [r2], 'RMSLE' : [rmsle], 'MAPE' : mape})
                df_score = df_score.round(round)
                display(df_score)
                
            label = pd.DataFrame(pred_)
            label = label.round(round)
            label.columns = ['Label']
            label['Label']=label['Label']

            if data is None:
                X_test_ = pd.concat([Xtest,ytest,label], axis=1)
            else:
                X_test_ = pd.concat([X_test_,label], axis=1)


    else:
        
        #model name
        full_name = str(model).split("(")[0]
        def putSpace(input):
            words = re.findall('[A-Z][a-z]*', input)
            words = ' '.join(words)
            return words  
        full_name = putSpace(full_name)

        if full_name == 'A R D Regression':
            full_name = 'Automatic Relevance Determination'

        elif full_name == 'M L P Regressor':
            full_name = 'MLP Regressor'

        elif full_name == 'R A N S A C Regressor':
            full_name = 'RANSAC Regressor'

        elif full_name == 'S V R':
            full_name = 'Support Vector Regressor'
            
        elif full_name == 'Lars':
            full_name = 'Least Angle Regression'
            
        elif full_name == 'X G B Regressor':
            full_name = 'Extreme Gradient Boosting Regressor'

        elif full_name == 'L G B M Regressor':
            full_name = 'Light Gradient Boosting Machine'

        elif 'Cat Boost Regressor' in full_name:
            full_name = 'CatBoost Regressor'

        #prediction starts here
        pred_ = model.predict(Xtest)
        
        try:
            pred_ = target_transformer.inverse_transform(np.array(pred_).reshape(-1,1))
            pred_ = np.nan_to_num(pred_)
        
        except:
            pred_ = np.nan_to_num(pred_)
            
        if data is None:
            
            try:
                ytest = target_transformer.inverse_transform(np.array(ytest).reshape(-1,1))
                ytest = pd.DataFrame(np.nan_to_num(ytest))

            except:
                pass
                
            mae = metrics.mean_absolute_error(ytest,pred_)
            mse = metrics.mean_squared_error(ytest,pred_)
            rmse = np.sqrt(mse)
            rmsle = np.sqrt(np.mean(np.power(np.log(np.array(abs(pred_))+1) - np.log(np.array(abs(ytest))+1), 2)))
            r2 = metrics.r2_score(ytest,pred_)
            mape = calculate_mape(ytest,pred_)
            
            #max_error_ = metrics.max_error(ytest,pred_)

            
            df_score = pd.DataFrame( {'Model' : [full_name], 'MAE' : [mae], 'MSE' : [mse], 'RMSE' : [rmse], 
                                      'R2' : [r2], 'RMSLE' : [rmsle], 'MAPE' : mape })
            df_score = df_score.round(4)
            display(df_score)
        
            label = pd.DataFrame(pred_)
            label = label.round(round)
            label.columns = ['Label']
            label['Label']=label['Label']

        label = pd.DataFrame(pred_)
        label = label.round(round)
        label.columns = ['Label']
        label['Label']=label['Label']
        
        if data is None:
            X_test_ = pd.concat([Xtest,ytest,label], axis=1)
        else:
            X_test_ = pd.concat([X_test_,label], axis=1)

    return X_test_



def deploy_model(model, 
                 model_name, 
                 authentication,
                 platform = 'aws'):
    
    """
       
    Description:
    ------------
    (In Preview)
    
    This function deploys the transformation pipeline and trained model object for
    production use. The platform of deployment can be defined under the platform
    param along with the applicable authentication tokens which are passed as a
    dictionary to the authentication param.
    
        Example:
        --------
        from pycaret.datasets import get_data
        boston = get_data('boston')
        experiment_name = setup(data = boston,  target = 'medv')
        lr = create_model('lr')
        
        deploy_model(model = lr, model_name = 'deploy_lr', platform = 'aws', 
                     authentication = {'bucket' : 'pycaret-test'})
        
        This will deploy the model on AWS S3 account under bucket 'pycaret-test'
        
        For AWS users:
        --------------
        Before deploying a model to an AWS S3 ('aws'), environment variables must be 
        configured using the command line interface. To configure AWS env. variables, 
        type aws configure in your python command line. The following information is
        required which can be generated using the Identity and Access Management (IAM) 
        portal of your amazon console account:
    
           - AWS Access Key ID
           - AWS Secret Key Access
           - Default Region Name (can be seen under Global settings on your AWS console)
           - Default output format (must be left blank)

    Parameters
    ----------
    model : object
    A trained model object should be passed as an estimator. 
    
    model_name : string
    Name of model to be passed as a string.
    
    authentication : dict
    dictionary of applicable authentication tokens. 
      
     When platform = 'aws': 
     {'bucket' : 'Name of Bucket on S3'}
    
    platform: string, default = 'aws'
    Name of platform for deployment. Current available options are: 'aws'.

    Returns:
    --------    
    Success Message
    
    Warnings:
    ---------
    - This function uses file storage services to deploy the model on cloud platform. 
      As such, this is efficient for batch-use. Where the production objective is to 
      obtain prediction at an instance level, this may not be the efficient choice as 
      it transmits the binary pickle file between your local python environment and
      the platform. 
        
      
    """
    
    #ignore warnings
    import warnings
    warnings.filterwarnings('ignore') 
    
    #general dependencies
    import ipywidgets as ipw
    import pandas as pd
    from IPython.display import clear_output, update_display
        
    try:
        model = finalize_model(model)
    except:
        pass
    
    if platform == 'aws':
        
        import boto3
        
        save_model(model, model_name = model_name, verbose=False)
        
        #initiaze s3
        s3 = boto3.client('s3')
        filename = str(model_name)+'.pkl'
        key = str(model_name)+'.pkl'
        bucket_name = authentication.get('bucket')
        s3.upload_file(filename,bucket_name,key)
        clear_output()
        print("Model Succesfully Deployed on AWS S3")