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regression.py
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def setup(data,
target,
train_size=0.7,
sampling=True,
sample_estimator = None,
session_id = None):
"""
Description:
------------
This function initialize the environment in pycaret. setup() must called before
executing any other function in pycaret. It takes two mandatory parameters i.e.
dataframe {array-like, sparse matrix} and name of the target column.
All other parameters are optional.
Example
-------
experiment_name = setup(data, 'target')
data is a pandas DataFrame and 'target' is the name of the column in dataframe.
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 training set. By default 70% of the data will be used for training and
validation.
sampling: bool, default = True
When sample size exceed 25,000 samples, pycaret creates base estimator at various
sample level of the original dataset. This will return the performance plot of
R2 at various sample level, that will help you decide sample size for modeling.
You are then required to enter the desired sample size that will be considered
for training and validation in the pycaret environment. 1 - sample size
will be discarded and not be used any further.
sample_estimator: object, default = None
If None, Linear Regression is used by default.
Returns:
--------
info grid: Information grid is printed.
-----------
environment: This function returns various outputs that are stored in variable
----------- as tuple. They are being 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.')
#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 + 2
else:
max = 2
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
#ignore warnings
import warnings
warnings.filterwarnings('ignore')
#declaring global variables to be accessed by other functions
global X, y, X_train, X_test, y_train, y_test, seed, experiment__
#generate seed to be used globally
if session_id is None:
seed = random.randint(150,9000)
else:
seed = session_id
#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]
#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] > 20000:
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)
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 Size' : split_percent, 'Metric' : metric_results, 'Metric Name': metric_name})
#fig, ax = plt.subplots(figsize=(8, 5))
plt.figure(figsize=(8, 5))
plt.grid(True, which='both')
plt.xlim(0,1)
plt.ylim(-1,1)
plt.tick_params(axis='both', which='major', bottom=False)
#plt.majorticks_on()
sns.lineplot(x="Sample Size", y="Metric", hue="Metric Name", data=model_results, color='blue', lw=2).set_title('Metric of ' + model_name + ' at Different Sample Size', fontsize=15).set_style("normal")
#sns.set_style("whitegrid")
print(' ')
plt.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(' ')
print('Setup Succesfully Completed!')
functions = pd.DataFrame ( [ ['session_id', seed ],
['Original Data',X.shape ],
['Sampled Data',X.shape ],
['Sample %',X.shape[0] / X.shape[0]],
['Training Set', X_train.shape ],
['Testing Set',X_test.shape ],
], columns = ['Description', 'Value'] )
functions_ = functions.style.hide_index()
display(functions_)
'''
Final display Ends
'''
#log into experiment
experiment__.append(('Info', 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))
return X, y, X_train, X_test, y_train, y_test, seed, 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(' ')
print('Setup Succesfully Completed!')
functions = pd.DataFrame ( [ ['session_id', seed ],
['Original Data',X.shape ],
['Sampled Data',X_selected.shape ],
['Sample %',X_selected.shape[0] / X.shape[0]],
['Training Set', X_train.shape ],
['Testing Set',X_test.shape ],
], columns = ['Description', 'Value'] )
functions_ = functions.style.hide_index()
display(functions_)
'''
Final display Ends
'''
#log into experiment
experiment__.append(('Info', 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))
return X, y, X_train, X_test, y_train, y_test, seed, 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(' ')
print('Setup Succesfully Completed!')
functions = pd.DataFrame ( [ ['session_id', seed ],
['Original Data',X.shape ],
['Sampled Data',X.shape ],
['Sample %',X.shape[0] / X.shape[0]],
['Training Set', X_train.shape ],
['Testing Set',X_test.shape ],
], columns = ['Description', 'Value'] )
functions_ = functions.style.hide_index()
display(functions_)
'''
Final display Ends
'''
#log into experiment
experiment__.append(('Info', 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))
return X, y, X_train, X_test, y_train, y_test, seed, 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 the score grid that shows MAE, MSE,
RMSE, R2 and Max Error (ME).
Function also returns a trained model object that can be used for further
processing in pycaret or can be used to call any method available in sklearn.
setup() function must be called before using create_model()
Example
-------
lr = create_model('lr')
This will return trained Linear Regression.
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
ensemble: Boolean, default = False
True would result in ensemble of estimator using the method parameter defined (see below).
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 metrics in 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 and ME. Mean and
standard deviation of the scores across the folds is 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']
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
#progress bar
progress = ipw.IntProgress(value=0, min=0, max=fold+3, step=1 , description='Processing: ')
master_display = pd.DataFrame(columns=['MAE','MSE','RMSE', 'R2', 'ME'])
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
data_y = y_train
#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_r2 =np.empty((0,0))
score_max_error =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_max_error =np.empty((0,0))
'''
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(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'
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, 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)
mae = metrics.mean_absolute_error(ytest,pred_)
mse = metrics.mean_squared_error(ytest,pred_)
rmse = np.sqrt(mse)
r2 = metrics.r2_score(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_r2 =np.append(score_r2,r2)
score_max_error = np.append(score_max_error,max_error_)
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], 'ME': [max_error_] }).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_r2=np.mean(score_r2)
mean_max_error=np.mean(score_max_error)
std_mae=np.std(score_mae)
std_mse=np.std(score_mse)
std_rmse=np.std(score_rmse)
std_r2=np.std(score_r2)
std_max_error=np.std(score_max_error)
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_r2 = np.append(avgs_r2, mean_r2)
avgs_r2 = np.append(avgs_r2, std_r2)
avgs_max_error = np.append(avgs_max_error, mean_max_error)
avgs_max_error = np.append(avgs_max_error, std_max_error)
progress.value += 1
model_results = pd.DataFrame({'MAE': score_mae, 'MSE': score_mse, 'RMSE' : score_rmse, 'R2' : score_r2,
'ME' : score_max_error})
model_avgs = pd.DataFrame({'MAE': avgs_mae, 'MSE': avgs_mse, 'RMSE' : avgs_rmse, 'R2' : avgs_r2 ,
'ME' : avgs_max_error},index=['Mean', 'SD'])
model_results = model_results.append(model_avgs)
model_results = model_results.round(round)
#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 ensemble the trained base estimator using method defined in 'method'
param (by default method = 'Bagging'). The output prints the score grid that shows
MAE, MSE, RMSE, R2 and Max Error (ME) by fold (default CV = 10 Folds).
Function also returns a trained model object that can be used for further
processing in pycaret or can be used to call any method available in sklearn.
Model must be created using create_model() or tune_model() in pycaret or using any
other package that returns sklearn object.
Example:
--------
ensembled_lr = ensemble_model(lr)
This will return ensembled Linear Regression.
variable 'lr' is created used lr = create_model('lr')
Using ensemble = True and method = 'Bagging' in create_model() is equivalent
to using ensemble_model(lr) where lr is created using create_model().
Parameters
----------
estimator : object, default = None
method: String, default = 'Bagging'
Bagging implementation is based on sklearn.ensemble.BaggingRegressor
Boosting implementation is based on sklearn.ensemble.AdaBoostRegressor
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 metrics in score grid will be rounded to.
n_estimators: integer, default = 10
The number of base estimators in the ensemble.
In case of perfect fit, the learning procedure is stopped early.
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 and ME. Mean and
standard deviation of the scores across the folds is 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+3, step=1 , description='Processing: ')
master_display = pd.DataFrame(columns=['MAE','MSE','RMSE', 'R2', 'ME'])
display(progress)
#display monitor
timestampStr = datetime.datetime.now().strftime("%H:%M:%S")
monitor = pd.DataFrame( [ ['Initiated' , '. . . . . . . . . . . . . . . . . .', timestampStr ],
['Status' , '. . . . . . . . . . . . . . . . . .' , 'Loading Dependencies' ],