Package: pasla
Pasla FrameworkA powerful and flexible framework for deep learning and machine learning projects.Pasla is a Python framework that provides various functionalities for processing data, building machine learning models, and tensor operations. This framework is designed to facilitate the development and implementation of data-driven solutions.Extended DescriptionPasla is a versatile and powerful framework designed to streamline the development of deep learning and machine learning projects. With a focus on simplicity and flexibility, Pasla offers a range of features that cater to both novice and experienced users.Key FeaturesTensor Operations:Efficiently manipulate and preprocess data using fundamental tensor operations with thetensor_operations.pymodule.Model Training:Train machine learning models effortlessly, with support for random forest classifiers and neural networks using themodel_training.pymodule.Data Pre-processing:Normalize and preprocess data for machine learning models using standard techniques.Random Forest Classifierimportpasla.model_trainingasmt# Load datadata=mt.load_data("my_dataset.csv")features,labels=mt.extract_features_labels(data)# Train a random forest classifierrf_model,accuracy=mt.train_random_forest_classifier(features,labels)# Save the trained modelmt.save_model(rf_model,"random_forest_model.pkl")## InstallationToinstallPasla,runthefollowingcommand:```partyw/mebashpipinstallpasla## Table of Contents-[AbouttheProject](#about-the-project)-[TensorOperations](#tensor-operations)-[ModelTraining](#model-training)-[Usage](#usage)-[Features](#features)-[Requirements](#requirements)-[Installation](#installation)-[Contributing](#contributing)-[License](#license)## About the ProjectPaslaisanopen-sourceframeworkdesignedtofacilitatedeeplearningandmachinelearningtasks.Itprovidesmodulesfortensoroperations,modeltraining,andotheressentialfunctionalities.## Tensor OperationsThe`tensor_operations.py`moduleincludesfunctionsforfundamentaltensoroperationsrequiredfordatamanipulationandpreprocessinginmachinelearning.## Model TrainingThe`model_training.py`moduleisdedicatedtotrainingmachinelearningmodelsandincludesfunctionalitiesforbothrandomforestclassifiersandneuralnetworks.Italsoprovidesevaluationmetricsandvisualizationtools.### Random Forest Classifier#### Training```pythonimportpasla.model_trainingasmt# Load datadata=mt.load_data("my_dataset.csv")features,labels=mt.extract_features_labels(data)# Train a random forest classifierrf_model,accuracy=mt.train_random_forest_classifier(features,labels)# Save the trained modelmt.save_model(rf_model,"random_forest_model.pkl")# Load the trained modelloaded_rf_model=mt.load_model("random_forest_model.pkl")# Evaluate the model on test datatest_data=mt.load_data("test_dataset.csv")test_features,test_labels=mt.extract_features_labels(test_data)test_accuracy=mt.evaluate_model(loaded_rf_model,test_features,test_labels)print(f"Random Forest Model Accuracy:{test_accuracy}")# Train a neural networknn_model,history=mt.train_neural_network(features,labels,epochs=10)# Save the trained model weightsmt.save_model_weights(nn_model,"neural_network_weights.h5")# Save training history to a CSV filemt.save_history(history,"neural_network_training_history.csv")# Load the trained neural network modelloaded_nn_model=mt.load_neural_network_model("neural_network_weights.h5")# Evaluate the model on test datann_test_accuracy,nn_auc_score=mt.evaluate_neural_network(loaded_nn_model,test_features,test_labels)print(f"Neural Network Model Accuracy:{nn_test_accuracy}")print(f"Neural Network Model AUC Score:{nn_auc_score}")# Plot ROC curve for the neural networkmt.plot_roc_curve(loaded_nn_model,test_features,test_labels)# Plot loss curve from training historymt.plot_loss_curve(history)# Plot confusion matrix for the neural networkmt.plot_confusion_matrix(loaded_nn_model,test_features,test_labels)# Generate and plot feature importance for a random forest modelmt.plot_feature_importance(rf_model,features.columns)# Generate feature importance tablefeature_importance_df=mt.feature_importance_table(rf_model,features.columns)print(feature_importance_df)### Example Usage```pythonimportpasla.tensor_operationsastoimportpasla.model_trainingasmt# Load datadata=mt.load_data("my_dataset.csv")features,labels=mt.extract_features_labels(data)# Train a random forest classifierrf_model,accuracy=mt.train_random_forest_classifier(features,labels)# Save the trained modelmt.save_model(rf_model,"random_forest_model.pkl")# Load the trained modelloaded_rf_model=mt.load_model("random_forest_model.pkl")# Evaluate the model on test datatest_data=mt.load_data("test_dataset.csv")test_features,test_labels=mt.extract_features_labels(test_data)test_accuracy=mt.evaluate_model(loaded_rf_model,test_features,test_labels)print(f"Random Forest Model Accuracy:{test_accuracy}")importpasla.tensor_operationsasto# Create a tensortensor_data=to.create_tensor([1,2,3,4,5])# Perform tensor operations (contoh: mean)tensor_mean=to.mean(tensor_data)print(f"Mean of Tensor:{tensor_mean}")# Example tensor operationsdata=to.load_data("my_dataset.csv")features,labels=to.extract_features_labels(data)tnormalized_data=to.normalize_data(features)# core/tensor_operations.pyThe`tensor_operations.py`moduleprovidesfunctionalityfortensoroperationsanddatapre-processingbeforemachinelearningmodels.Followingisthefunctionalityprovided:## Data Pre-processing### `preprocess_data(data)`Thisfunctionpre-processesthedatatomodelmachinelearningbynormalizingitusing`StandardScaler`.UsageExample:```pythonimportpasla.tensor_operationsas# Loading datadata=to.load_data("my_dataset.csv")# Preprocess datascaled_data=to.preprocess_data(data)importpasla.tensor_operationsasto# Create a tensortensor_data=to.create_tensor([1,2,3,4,5])