gurulearn 1.0.18

library for MLModelAnalysis and multi image model(bug fix 2.0)

###### MLModelAnalysis ######

MLModelAnalysis is a flexible, reusable class for training, evaluating, and making predictions with various machine learning regression models. This tool allows easy switching between models, consistent preprocessing, model evaluation, and prediction for a variety of machine learning tasks.

Supported Models Linear Regression (linear_regression) Decision Tree Regressor (decision_tree) Random Forest Regressor (random_forest) Support Vector Machine (svm) Gradient Boosting Regressor (gradient_boosting) K-Nearest Neighbors (knn) AdaBoost Regressor (ada_boost) Neural Network (MLP Regressor) (mlp) XGBoost Regressor (xgboost) Usage

1. Initializing the Model Initialize the MLModelAnalysis class by specifying the model_type parameter, which defines the machine learning model you want to use. Below are examples of initializing with various models:

python Copy code from sklearn.linear_model import LinearRegression from sklearn.tree import DecisionTreeRegressor from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, AdaBoostRegressor from sklearn.svm import SVR from sklearn.neighbors import KNeighborsRegressor from sklearn.neural_network import MLPRegressor

Initialize with Linear Regression

analysis = MLModelAnalysis(model_type='linear_regression')

Initialize with Random Forest

analysis = MLModelAnalysis(model_type='random_forest')

Initialize with XGBoost

analysis = MLModelAnalysis(model_type='xgboost') 2. Training and Evaluating the Model The train_and_evaluate method preprocesses the data, trains the model, and displays evaluation metrics. Optionally, it saves the trained model, scaler, and encoders for later use.

Parameters: csv_file: Path to the CSV file containing the dataset. x_elements: List of feature columns. y_element: Name of the target column. model_save_path (Optional): Path to save the trained model, scaler, and encoders. Example:

Set the parameters

csv_file = 'data.csv' # Path to the data file x_elements = ['feature1', 'feature2'] # List of feature columns y_element = 'target' # Target column name

Initialize the model

analysis = MLModelAnalysis(model_type='random_forest')

Train and evaluate the model

analysis.train_and_evaluate(csv_file=csv_file, x_elements=x_elements, y_element=y_element, model_save_path='random_forest_model.pkl') After running this code, the model will display R-squared and Mean Squared Error (MSE) metrics for both training and test sets. If model_save_path is specified, it saves the model for future predictions.

3. Loading the Model and Making Predictions The load_model_and_predict method loads a saved model and makes predictions on new input data.

Parameters: model_path: Path to the saved model file (created in the previous step). input_data: Dictionary of feature names and values representing a single input instance for prediction. Example:

Define input data for prediction

input_data = { 'feature1': 5.1, 'feature2': 2.3 }

Load the model and make a prediction

prediction = analysis.load_model_and_predict(model_path='random_forest_model.pkl', input_data=input_data) print(f'Prediction: {prediction}') 4. Visualization When using a linear_regression or svm model with only one feature, the train_and_evaluate method will automatically generate a Plotly plot of actual vs. predicted values for simple visualization.

Example Use Cases Regression Analysis with Random Forest:

analysis = MLModelAnalysis(model_type='random_forest') analysis.train_and_evaluate(csv_file='data.csv', x_elements=['feature1', 'feature2'], y_element='target', model_save_path='random_forest_model.pkl') Quick Prediction with a Pre-trained Model:

prediction = analysis.load_model_and_predict(model_path='random_forest_model.pkl', input_data={'feature1': 5.1, 'feature2': 2.3}) print(f'Prediction: {prediction}') Switching Models Effortlessly:

Simply specify a new model type to use a different algorithm

analysis = MLModelAnalysis(model_type='xgboost') Additional Notes Plotting: Visualizations are supported for linear models and SVM with single-feature datasets. Model Saving: The model_save_path in train_and_evaluate stores the model, scaler, and encoders, allowing consistent predictions when reloading the model later. Dependencies: Ensure required libraries (scikit-learn, pandas, numpy, plotly, and xgboost) are installed.

###### image_classify: ######

example usages:

image_classifier = ImageClassifier() image_classifier.img_train( train_dir='train', test_dir='test', # Optional, can be None to split training data epochs=1, device='cpu', force='vgg16',  finetune=True )

image_classifier = ImageClassifier() image_classifier.img_train( train_dir='train', test_dir='test', # Optional, can be None to split training data epochs=1, device='cpu',  force='vgg16' )

Using directory data

image_classifier = ImageClassifier() image_classifier.img_train( train_dir='path/to/train', test_dir='path/to/test', epochs=10, device='cuda', force='resnet50', finetune=True )

Using CSV data

image_classifier.img_train( csv_file='path/to/data.csv', img_column='image_path', # Column name in CSV containing image paths label_column='label', # Column name in CSV containing labels epochs=10, device='cuda', force='resnet50',  finetune=True )

consist of 20 cnn architecture excluding finetuning

Instantiate and train the classifier with a specific model and fine-tuning enabled

image_classifier = ImageClassifier() image_classifier.img_train("path_to_train_dir", "path_to_test_dir", epochs=10, device="cuda", force="efficientnet", finetune=True)