Neurix 0.1.0

EasyMl is a simple ML utility library with preprocessing, regression, classification, and evaluation tools

NEURIX

Neurix is a custom-built Python machine learning library. It contains modules for classification, regression, data preprocessing, statistical relationships, and evaluation metrics.

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Directory Structure

├── Classification
│   ├── LogisticRegression.py
│   └── __init__.py
├── preprocessing
│   ├── MasAbsScaler.py
│   ├── MeanScaler.py
│   ├── MinMaxScaler.py
│   ├── SimpleImputer.py
│   ├── StandardScaler.py
│   └── __init__.py
├── Regression
│   ├── GDRidgeRegression.py
│   ├── LinearRegression.py
│   ├── OLSRidgeRegression.py
│   └── __init__.py
├── relation
│   ├── covariance.py
│   ├── Pearson_corr.py
│   └── __init__.py
├── score
│   ├── score.py
│   └── __init__.py
└── __init__.py

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Modules Overview

Classification

A custom implementation of multiclass logistic regression using softmax. Supports early stopping and one-hot encoding internally.

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Class: LogisticRegression

Constructor

LogisticRegression(epochs=1000, learning_rate=0.1, patience=20, min_del=1e-4)

Parameters:

• epochs (int): Number of iterations for training.
• learning_rate (float): Step size for weight updates.
• patience (int): Number of epochs to wait for improvement before early stopping.
• min_del (float): Minimum decrease in loss to be considered an improvement.

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Methods

🔹 fit(X, y)

Fits the logistic regression model to the training data.

Parameters:

• X (numpy.ndarray): Feature matrix of shape (m, n) where m is the number of samples and n is the number of features.
• y (numpy.ndarray): Target vector of shape (m,) containing class labels.

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🔹 predict(X)

Predicts class labels for given input data.

Parameters:

• X (numpy.ndarray): Feature matrix of shape (m, n).

Returns:

• numpy.ndarray: Predicted class labels of shape (m,).

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🔹 predict_prob(X)

Returns the class probabilities (softmax scores) for each sample.

Parameters:

• X (numpy.ndarray): Feature matrix.

Returns:

• numpy.ndarray: Probability matrix of shape (m, k), where k is the number of classes.

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⚙️ Internal Methods

🔸 softmax(z)

Applies the softmax activation function.

🔸 one_hot(y)

Converts labels to one-hot encoded format.

🔸 compute_loss(y_true, y_pred)

Computes the cross-entropy loss.

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Early Stopping

Stops training if the loss does not improve by min_del for patience consecutive epochs.

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📌 Example

from Neurix.Classification import LogisticRegression
import numpy as np

# Sample data
X = np.random.rand(100, 4)
y = np.random.randint(0, 3, size=(100,))

model = LogisticRegression(epochs=500, learning_rate=0.05)
model.fit(X, y)

predictions = model.predict(X)
probs = model.predict_prob(X)

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Regression

LinearRegression

A custom implementation of simple linear regression using the closed-form (Normal Equation) solution. This class supports fitting and predicting with NumPy arrays.

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Class: LinearRegression

Constructor

LinearRegression()

Creates an instance of the linear regression model. No parameters are required during initialization.

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Methods

🔹 fit(X_train, y_train)

Fits the linear regression model using the closed-form solution.

Parameters:

• X_train (numpy.ndarray): Training data of shape (m, n) where m is the number of samples and n is the number of features.
• y_train (numpy.ndarray): Target values of shape (m,).

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🔹 predict(X_test)

Predicts continuous target values for the input features.

Parameters:

• X_test (numpy.ndarray): Feature matrix of shape (m, n).

Returns:

• numpy.ndarray: Predicted values of shape (m,).

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⚙️ Attributes

• coef (numpy.ndarray): Coefficients of the trained linear model, shape (n,).
• intercept (float): Intercept term (bias).

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📌 Example

from Neurix.Regression import LinearRegression
import numpy as np

# Example dataset
X = np.array([[1], [2], [3], [4]])
y = np.array([2.5, 4.9, 7.4, 9.8])

# Model training
model = LinearRegression()
model.fit(X, y)

# Predictions
X_test = np.array([[5], [6]])
preds = model.predict(X_test)
print(preds)

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GDRidge

A custom implementation of Ridge Regression using Gradient Descent. This model is used for linear regression with L2 regularization to prevent overfitting.

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Class: GDRidge

Constructor

GDRidge(epochs, learning_rate, alpha)

Parameters:

• epochs (int): Number of iterations to run gradient descent.

• learning_rate (float): Step size for updating weights.

• alpha (float): Regularization strength (L2 penalty).

Methods

🔹 fit(X_train, y_train)

Trains the ridge regression model using gradient descent.

Parameters:

• X_train (ndarray): Training features of shape (m, n) where m = number of samples, n = number of features.

• y_train (ndarray): Target values of shape (m,).

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🔹predict(X_test)

Predicts target values using the trained model.

Parameters:

• X_test (ndarray): Test features of shape (m, n).

Returns:

• ndarray: Predicted target values of shape (m,).

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Attributes:

• coef (ndarray): Weights learned for each feature. Shape: (n,)

• intercept (float): Bias term of the model.

from GDRidgeRegression import GDRidge
import numpy as np

# Example dataset
X = np.array([[1], [2], [3]])
y = np.array([2, 4, 6])

# Create and train model
model = GDRidge(epochs=1000, learning_rate=0.01, alpha=0.1)
model.fit(X, y)

# Predict on new data
X_test = np.array([[4], [5]])
predictions = model.predict(X_test)
print(predictions)

OLSRidge

A custom implementation of Ridge Regression using the Closed-Form (Normal Equation) solution. This model includes L2 regularization and solves the optimization problem analytically.

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Class: OLSRidge

Constructor

OLSRidge(alpha=0.1)

Parameters:

• alpha (float): L2 regularization strength. Defaults to 0.1.

Methods:

🔹 fit(X_train, y_train)

Parameters:

• X_train (ndarray): Training data of shape (m, n) — m samples, n features.

• y_train (ndarray): Target values of shape (m,).

🔹 predict(X_test)

Predicts continuous values for the test data using the learned weights.

Parameters:

• X_test (ndarray): Input features of shape (m, n).

Returns:

• ndarray: Predicted values of shape (m,).

Attributes

• coef (ndarray): Weights for each feature (shape (n,)).

• intercept (float): Bias term of the model.

from Neurix.Regression import OLSRidge
import numpy as np

# Sample training data
X = np.array([[1], [2], [3]])
y = np.array([3, 6, 9])

# Train the model
model = OLSRidge(alpha=0.1)
model.fit(X, y)

# Make predictions
X_test = np.array([[4], [5]])
predictions = model.predict(X_test)
print(predictions)

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Preprocessing

SimpleImputer

A custom implementation of a simple data imputer for handling missing values (NaNs) in numeric arrays. Supports replacing missing values using mean, median, mode, or a specific constant.

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Class: SimpleImputer

Constructor

SimpleImputer(fill_value='mean')

Parameters:

• fill_value (str or float or int): Strategy to fill missing values.

Supported options:

• 'mean': Replace missing values with the mean.

• 'median': Replace with the median.

• 'mode': Replace with the most frequent value.

• numeric: Replace with a constant numeric value.

Methods

• fill(X)

Fills missing values in the input array using the specified strategy.

Parameters:

• X (array-like): Input 1D array or list with possible np.nan values.

Returns:

• ndarray: A NumPy array with all missing values filled.

Raises:

• ValueError: If fill_value is not one of 'mean', 'median', 'mode', or a number.

from Neurix.preprocessing import SimpleImputer
import numpy as np

X = [1, 2, np.nan, 4, np.nan, 5]

# Using mean
imputer = SimpleImputer(fill_value='mean')
filled = imputer.fill(X)
print("Mean-filled:", filled)

# Using mode
imputer = SimpleImputer(fill_value='mode')
filled = imputer.fill(X)
print("Mode-filled:", filled)

# Using a constant value
imputer = SimpleImputer(fill_value=0)
filled = imputer.fill(X)
print("Constant-filled:", filled)

Notes

• Input array is internally converted to NumPy with dtype=float to handle np.nan.

• Only supports 1D arrays or lists.

• Does not support categorical/factor variables.

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StandardScaler

A custom implementation of the StandardScaler, which standardizes features by removing the mean and scaling to unit variance.

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Class: StandardScaler

Constructor

StandardScaler()

Initializes the scaler. No parameters are required.

Methods:

🔹 fit(X_train)

Computes the mean and standard deviation for each feature from the training data.

Parameters:

• X_train (ndarray): Training data of shape (m, n).

Returns:

self: Returns the scaler instance itself.

🔹 fit_transform(X_train)

Fits to the data and then transforms it.

Parameters:

• X_train (ndarray): Training data of shape (m, n).

Returns:

• ndarray: Standardized training data of shape (m, n).

🔹 transform(X_test)

Standardizes new data using the previously computed mean and standard deviation.

Parameters:

• X_test (ndarray): Data to transform, shape (m, n).

Returns:

• ndarray: Transformed data of shape (m, n).

Attributes

• mean (ndarray): Mean of each feature in the training data.

• std (ndarray): Standard deviation of each feature in the training data. Zero std values are replaced with 1 to avoid division by zero.

from Neurix.preprocessing import StandardScaler
import numpy as np

X_train = np.array([[1, 2], [3, 4], [5, 6]])
X_test = np.array([[7, 8]])

scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

print("Train scaled:\n", X_train_scaled)
print("Test scaled:\n", X_test_scaled)

Notes

• This scaler is safe against zero standard deviation (division by 0) — such values are treated as 1.

• Assumes input is numeric and NumPy-compatible.

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MinMaxScaler

A custom implementation of the Min-Max Scaler that rescales features to a specified range [min, max]. Default range is [0, 1].

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Class: MinMaxScaler

Constructor

MinMaxScaler(min=0, max=1)

Parameters:

• min (float): Minimum value of the transformed data. Default is 0.

• max (float): Maximum value of the transformed data. Default is 1.

Raises Exception: If min > max.

Methods

🔹 fit(x)

Computes the min_val and max_val for each feature from the training data.

Parameters:

• x (ndarray): Training data of shape (m, n).

Returns:

• self: The fitted scaler instance.

🔹 fit_transform(x)

Fits the scaler to the training data, then transforms it.

Parameters:

• x (ndarray): Training data of shape (m, n).

Returns:

• ndarray: Scaled data of shape (m, n).

🔹 transform(x)

Transforms new data using the previously computed min_val and max_val.

Parameters:

• x (ndarray): Data to transform, shape (m, n).

Returns:

• ndarray: Scaled data of shape (m, n).

Attributes

• min_val (ndarray): Minimum value of each feature in the training data.

• max_val (ndarray): Maximum value of each feature in the training data.

• scale_ (ndarray): Feature-wise scaling factors. If a feature has zero range, it is scaled by 1.

from Neurix.preprocessing import MinMaxScaler
import numpy as np

X_train = np.array([[1, 2], [3, 4], [5, 6]])
X_test = np.array([[7, 8]])

scaler = MinMaxScaler(min=0, max=1)
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

print("Train scaled:\n", X_train_scaled)
print("Test scaled:\n", X_test_scaled)

Notes

• The scaler handles zero-range features by replacing zero denominators with 1.

• Assumes input is numeric and compatible with NumPy operations.

MeanScaler

A simple custom scaler that centers data by subtracting the mean of each feature. This transformation results in zero-mean features, but does not scale the variance.

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Class: MeanScaler

Constructor

MeanScaler()

Initializes the scaler. No parameters are required.

Methods

🔹 fit(X_train)

Computes the mean of each feature from the training data.

Parameters:

• X_train (ndarray): Training data of shape (m, n).

Returns:

self: The fitted scaler instance.

🔹 fit_transform(X_train)

Fits the scaler to the training data, then transforms it.

Parameters:

• X_train (ndarray): Training data of shape (m, n).

Returns:

• ndarray: Centered data of shape (m, n).

🔹 transform(X_test)

Centers new data using the previously computed mean.

Parameters:

• X_test (ndarray): Test data to transform, shape (m, n).

Returns:

• ndarray: Centered data of shape (m, n).

Attribute:

• mean (ndarray): Mean of each feature in the training data.

from Nuerix.preprocessing import MeanScaler
import numpy as np

X_train = np.array([[1, 2], [3, 4], [5, 6]])
X_test = np.array([[7, 8]])

scaler = MeanScaler()
X_train_centered = scaler.fit_transform(X_train)
X_test_centered = scaler.transform(X_test)

print("Train centered:\n", X_train_centered)
print("Test centered:\n", X_test_centered)

Notes:

• This scaler only performs mean centering; it does not scale variance.

• Assumes input is numeric and NumPy-compatible.

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MaxAbsScaler

A custom implementation of the Max Absolute Scaler, which scales each feature by its maximum absolute value, transforming data to the range [-1, 1]. It is especially useful for sparse data and when preserving the sign of the data is important.

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Class: MaxAbsScaler

Constructor

MaxAbsScaler()

Initializes the scaler. No parameters are required.

Methods

🔹 fit(x)

Computes the maximum absolute value for each feature in the training data.

Parameters:

• x (ndarray): Training data of shape (m, n).

Returns:

• self: The fitted scaler instance.

🔹 fit_transform(x)

Fits the scaler to the training data, then transforms it.

Parameters:

• x (ndarray): Training data of shape (m, n).

Returns:

• ndarray: Scaled data of shape (m, n).

🔹 transform(x)

Transforms the input data using the previously computed maximum absolute values.

Parameters:

• x (ndarray): Data to transform, shape (m, n).

Returns:

• ndarray: Scaled data of shape (m, n).

Attributes

• Absmax (ndarray): Maximum absolute value for each feature.

• scale_ (ndarray): Scaling factors for each feature. Features with 0 max absolute value are scaled using 1 to avoid division by zero.

from Neurix.preprocessing import MaxAbsScaler
import numpy as np

X_train = np.array([[1, -2], [-3, 4], [5, -6]])
X_test = np.array([[7, -8]])

scaler = MaxAbsScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

print("Train scaled:\n", X_train_scaled)
print("Test scaled:\n", X_test_scaled)

Notes:

• This scaler does not center the data (mean remains unchanged).

• Useful when you want to scale data between -1 and 1 while preserving sparsity.

• Automatically handles zero-valued features.

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relation

covariance

A simple implementation of the covariance calculation between two numerical features. Covariance measures the degree to which two variables vary together.

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Class: covariance

Constructor

covariance()

Initializes the object and internal variables for computing covariance.

Methods

🔹 cov(x, y)

Computes the sample covariance between two 1D arrays.

Parameters:

• x (ndarray): 1D NumPy array of shape (n,).

• y (ndarray): 1D NumPy array of shape (n,).

Returns:

• float: Covariance between x and y.

Raises Exception: If the input arrays x and y do not have the same length.

Attributes

• x_mean (float): Mean of array x.

• y_mean (float): Mean of array y.

• feat_len (int): Number of samples in the input arrays.

from Neurix.relation import covariance
import numpy as np

x = np.array([1, 2, 3, 4])
y = np.array([5, 6, 7, 8])

cov_calc = covariance()
result = cov_calc.cov(x, y)

print("Covariance:", result)

Notes

• This implementation uses the sample covariance formula (denominator is n - 1).

• Assumes both x and y are numeric and 1D.

• Does not support missing values (NaN).

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Pearson_corr

A custom implementation of the Pearson Correlation Coefficient, which measures the linear relationship between two variables. Values range from -1 (perfect negative correlation) to +1 (perfect positive correlation).

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Class: Pearson_corr

Constructor

Pearson_corr()

Initializes internal variables required for computing correlation.

Methods

🔹 corr(x, y)

Computes the Pearson correlation coefficient between two 1D arrays.

Parameters:

• x (ndarray): 1D NumPy array of shape (n,).

• y (ndarray): 1D NumPy array of shape (n,).

Returns:

• float: Pearson correlation coefficient between x and y.

Raises Exception: If the lengths of x and y do not match.

Attributes

• x_mean (float): Mean of input array x.

• y_mean (float): Mean of input array y.

• x_std (float): Sample standard deviation of x.

• y_std (float): Sample standard deviation of y.

• feat_len (int): Number of elements in input arrays.

from relation.Pearson_corr import Pearson_corr
import numpy as np

x = np.array([10, 20, 30, 40])
y = np.array([15, 25, 35, 45])

corr_calc = Pearson_corr()
result = corr_calc.corr(x, y)

print("Pearson Correlation Coefficient:", result)

Notes:

• Uses sample standard deviation (ddof=1) in the denominator.

• Assumes both inputs are numeric, 1D arrays of equal length.

• Pearson correlation is undefined if either standard deviation is zero.

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score

A custom class to compute multiple regression evaluation metrics such as R², Adjusted R², MSE, RMSE, and MAE.

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Class: score

Constructor

score(y_test, y_pred)

Initializes the score object with actual and predicted values.

Parameters:

• y_test (ndarray): Actual target values of shape (n,).

• y_pred (ndarray): Predicted values of shape (n,).

Raises ValueError: If y_test and y_pred have different lengths.

Methods:

🔹 r2_score()

Computes the coefficient of determination ( R^2 ).

Returns:

• float: R² score (between 0 and 1).

Raises ValueError: If the total variance is zero (undefined R²).

🔹 adjusted_r2_score(n_features_in)

Computes the Adjusted R² score, which adjusts R² for the number of features.

Parameters:

• n_features_in (int): Number of input features used during prediction.

Returns:

• float: Adjusted R² score.

🔹 mse()

Computes the Mean Squared Error.

Returns:

• float: MSE value.

🔹 rmse()

Computes the Root Mean Squared Error.

Returns:

• float: RMSE value.

🔹 mae()

Computes the Mean Absolute Error.

Returns:

• float: MAE value.

from Neurix.score import score
import numpy as np

y_test = np.array([3.0, -0.5, 2.0, 7.0])
y_pred = np.array([2.5, 0.0, 2.0, 8.0])

sc = score(y_test, y_pred)

print("R² Score:", sc.r2_score())
print("Adjusted R² Score:", sc.adjusted_r2_score(n_features_in=1))
print("MSE:", sc.mse())
print("RMSE:", sc.rmse())
print("MAE:", sc.mae())

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📦 Installation

pip install Neurix

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👨‍💻 Author

Avatanshu Gupta

• GitHub- https://github.com/AvatanshuGupta
• LinkedIn- https://www.linkedin.com/in/avatanshu-gupta-073699310/