GradientDescent 0.0.2

Makes learning gradient descent easy

This code has two classes

1. Preprocess - For preprocessing and normalizing the data
2. Descent - For performing the gradient descent

Descent Class

Constructor

def __init__(self,X:list,y:list,epoch:int,method='linear',theta=None,alpha=0.01,decimals=5) -> None:
# Usage 
# Goal - to construct a linear regression for specified hyperparameters
gd = Descent(X=X_norm, y=y_norm, epoch=5000,alpha=0.01)

Constructs the Descent instance with the specified hyperparameters
Parameters:

1. X (list): The independent variables
2. y (list): The dependent variable
3. epoch (int): The number of iterations to be performed during regression
4. Method (str, optional): The method by which you would like to solve the gradient descent problem. Defaults to 'linear'
5. theta (list): The initialized weights/thetas for regression. Defaults to None
6. alpha (float): The learning rate for the regression. Defaults to 0.01
7. decimals (int, optional): The number of decimal places till which the early stopping can be implpemented. Defaults to 5.

Fit function

def fit(self)->tuple:
# Goal - to calibrate the linear regression model 
pastCost,pastTheta,stopEpoch = gd.fit()

Calibrates the coefficient for gradient descent
Returns:

1. pastCost - The history of costs incurred by the model while performing the iterations
2. pastTheta - The history of thetas calculated by the model while performing the iterations
3. stopEpoch - The epoch at which the gradient descent model converged for the specified decimal places

Predict function

def predict(self, values:list)->tuple:
# Goal - to predict the coefficients for the test set 
bestTheta, preds = gd.predict(X_test)

Predicts the output for the specified values Parameters

1. values - The values for which you would like to perform the predictions Returns
   
2. bestTheta - The optimised value for theta after gradient descent
3. predictions - The predictions for those values

Attributes

@property
def summary(self)->pd.DataFrame:

Prints the summary of the regression along with the necessary descriptive statistics
Returns the following metrics

1. Skewness of residuals
2. Kurtosis of residuals
3. Jarque Bera Coefficient
4. Jarque Bera p-value

If the model is linear, it also returns

• R Squared
• Adjusted R Squared
• Mean Squared Error

If the model is logistic, it also returns

• Precision
• Recall
• F1 Score

Preprocess class

Constructor

def __init__(self,data):
# Goal - to create a preprocess instance for x
X_scaler = Preprocess(X)

Stores the mean and standard deviation of the data for future transformations

transform function

def transform(self,values=None,add_ones=False)->np.array:
# Goal - to normalize the data and concatenate ones in the begining 
X1 = X_scaler.transform(add_ones=True)

Normalizes the inputs by using the formula x_norm = (x-mean(x))/std(x)
Arguments:

1. values - The values on which you want to apply the transformation, if not given then it transforms the data passed to it in the constructor
   
2. add_ones (bool, optional): Whether you want to add ones for intercept or not. Defaults to True.
   Returns the normalized data

inverse_transform function

def inverse_transform(self,inp):
#goal - to invert the transformation on the data 
x_rescaled = X_scaler.inverse_transform()

Reverses the normalization by using the formula x = (x_norm*std(x))+mean(x)
Arguments

1. inp (np.array or pd.Series or pd.DataFrame): The normalized data which you would like to convert to original data

Returns the converted data