From e03762fd9aca35c1bf0d03378261cadd5346c904 Mon Sep 17 00:00:00 2001 From: QuantaScriptor Date: Mon, 15 Jul 2024 09:21:44 -0400 Subject: [PATCH] chore: Update scripts for open source release --- .../automated_code_review.py | 0 .../generate_documentation.py | 0 .../nlp_enhancement.py | 0 .../real_time_collaboration.py | 0 .../real_time_collaboration_client.py | 0 .../xr_integration.py | 0 scripts/bias_mitigation.py | 44 ---------- scripts/blockchain_integration.py | 33 -------- scripts/chatbot.py | 21 ----- scripts/data_encryption.py | 25 ------ scripts/explainability.py | 34 -------- scripts/image_video_analysis.py | 52 ------------ scripts/neurosymbolic_ai.py | 44 ---------- scripts/penetration_testing.py | 15 ---- scripts/personalized_learning.py | 35 -------- scripts/predictive_maintenance.py | 33 -------- scripts/quantum_integration.py | 26 ------ scripts/recommendation_system.py | 42 ---------- scripts/synthetic_data_generation.py | 82 ------------------- 19 files changed, 486 deletions(-) rename {scripts => opensource_scripts}/automated_code_review.py (100%) rename {scripts => opensource_scripts}/generate_documentation.py (100%) rename {scripts => opensource_scripts}/nlp_enhancement.py (100%) rename {scripts => opensource_scripts}/real_time_collaboration.py (100%) rename {scripts => opensource_scripts}/real_time_collaboration_client.py (100%) rename {scripts => opensource_scripts}/xr_integration.py (100%) delete mode 100644 scripts/bias_mitigation.py delete mode 100644 scripts/blockchain_integration.py delete mode 100644 scripts/chatbot.py delete mode 100644 scripts/data_encryption.py delete mode 100644 scripts/explainability.py delete mode 100644 scripts/image_video_analysis.py delete mode 100644 scripts/neurosymbolic_ai.py delete mode 100644 scripts/penetration_testing.py delete mode 100644 scripts/personalized_learning.py delete mode 100644 scripts/predictive_maintenance.py delete mode 100644 scripts/quantum_integration.py delete mode 100644 scripts/recommendation_system.py delete mode 100644 scripts/synthetic_data_generation.py diff --git a/scripts/automated_code_review.py b/opensource_scripts/automated_code_review.py similarity index 100% rename from scripts/automated_code_review.py rename to opensource_scripts/automated_code_review.py diff --git a/scripts/generate_documentation.py b/opensource_scripts/generate_documentation.py similarity index 100% rename from scripts/generate_documentation.py rename to opensource_scripts/generate_documentation.py diff --git a/scripts/nlp_enhancement.py b/opensource_scripts/nlp_enhancement.py similarity index 100% rename from scripts/nlp_enhancement.py rename to opensource_scripts/nlp_enhancement.py diff --git a/scripts/real_time_collaboration.py b/opensource_scripts/real_time_collaboration.py similarity index 100% rename from scripts/real_time_collaboration.py rename to opensource_scripts/real_time_collaboration.py diff --git a/scripts/real_time_collaboration_client.py b/opensource_scripts/real_time_collaboration_client.py similarity index 100% rename from scripts/real_time_collaboration_client.py rename to opensource_scripts/real_time_collaboration_client.py diff --git a/scripts/xr_integration.py b/opensource_scripts/xr_integration.py similarity index 100% rename from scripts/xr_integration.py rename to opensource_scripts/xr_integration.py diff --git a/scripts/bias_mitigation.py b/scripts/bias_mitigation.py deleted file mode 100644 index c0f0700..0000000 --- a/scripts/bias_mitigation.py +++ /dev/null @@ -1,44 +0,0 @@ -python -import pandas as pd -from aif360.datasets import BinaryLabelDataset -from aif360.algorithms.preprocessing import Reweighing -from fairlearn.metrics import MetricFrame, selection_rate, demographic_parity_difference -from sklearn.linear_model import LogisticRegression - -# Load and preprocess data -def load_data(): - data = pd.read_csv('path_to_your_dataset.csv') - return data - -# Bias detection -def detect_bias(data, target, protected_attribute): - dataset = BinaryLabelDataset(df=data, label_names=[target], protected_attribute_names=[protected_attribute]) - return dataset - -# Bias mitigation -def mitigate_bias(dataset): - reweighing = Reweighing(unprivileged_groups=[{protected_attribute: 0}], privileged_groups=[{protected_attribute: 1}]) - transformed_dataset = reweighing.fit_transform(dataset) - return transformed_dataset - -# Train and evaluate model -def train_and_evaluate(data, target, protected_attribute): - model = LogisticRegression() - X = data.drop(columns=[target, protected_attribute]) - y = data[target] - model.fit(X, y) - predictions = model.predict(X) - - metric_frame = MetricFrame(metrics={"selection_rate": selection_rate}, y_true=y, y_pred=predictions, sensitive_features=data[protected_attribute]) - print(metric_frame.by_group) - print(f"Demographic parity difference: {demographic_parity_difference(y, predictions, sensitive_features=data[protected_attribute])}") - -if __name__ == "__main__": - data = load_data() - protected_attribute = 'gender' # Change to your dataset's protected attribute - target = 'outcome' # Change to your dataset's target variable - - dataset = detect_bias(data, target, protected_attribute) - mitigated_dataset = mitigate_bias(dataset) - - train_and_evaluate(mitigated_dataset.convert_to_dataframe()[0], target, protected_attribute) diff --git a/scripts/blockchain_integration.py b/scripts/blockchain_integration.py deleted file mode 100644 index 6725c84..0000000 --- a/scripts/blockchain_integration.py +++ /dev/null @@ -1,33 +0,0 @@ -python -from web3 import Web3 - -# Connect to a blockchain -def connect_to_blockchain(): - web3 = Web3(Web3.HTTPProvider('http://localhost:8545')) - if web3.isConnected(): - print("Connected to blockchain") - return web3 - -# Create a new transaction -def create_transaction(web3, from_address, to_address, value): - tx = { - 'from': from_address, - 'to': to_address, - 'value': web3.toWei(value, 'ether'), - 'gas': 2000000, - 'gasPrice': web3.toWei('50', 'gwei') - } - signed_tx = web3.eth.account.sign_transaction(tx, private_key='YOUR_PRIVATE_KEY') - tx_hash = web3.eth.sendRawTransaction(signed_tx.rawTransaction) - return tx_hash - -def main(): - web3 = connect_to_blockchain() - from_address = '0xYourFromAddress' - to_address = '0xYourToAddress' - value = 0.1 # in Ether - tx_hash = create_transaction(web3, from_address, to_address, value) - print(f"Transaction hash: {tx_hash.hex()}") - -if __name__ == "__main__": - main() diff --git a/scripts/chatbot.py b/scripts/chatbot.py deleted file mode 100644 index f71b2bd..0000000 --- a/scripts/chatbot.py +++ /dev/null @@ -1,21 +0,0 @@ -python -from flask import Flask, render_template, request -from flask_socketio import SocketIO, send -from transformers import pipeline - -app = Flask(__name__) -app.config['SECRET_KEY'] = 'your_secret_key' -socketio = SocketIO(app) -chatbot = pipeline('conversational', model='microsoft/DialoGPT-medium') - -@app.route('/') -def home(): - return render_template('index.html') - -@socketio.on('message') -def handle_message(msg): - response = chatbot(msg) - send(response[0]['generated_text'], broadcast=True) - -if __name__ == "__main__": - socketio.run(app, host='0.0.0.0', port=5000) diff --git a/scripts/data_encryption.py b/scripts/data_encryption.py deleted file mode 100644 index 9072398..0000000 --- a/scripts/data_encryption.py +++ /dev/null @@ -1,25 +0,0 @@ -python -from cryptography.fernet import Fernet - -# Generate key -def generate_key(): - return Fernet.generate_key() - -# Encrypt data -def encrypt_data(data, key): - f = Fernet(key) - return f.encrypt(data.encode()) - -# Decrypt data -def decrypt_data(encrypted_data, key): - f = Fernet(key) - return f.decrypt(encrypted_data).decode() - -# Example usage -if __name__ == "__main__": - key = generate_key() - data = "Sensitive information" - encrypted_data = encrypt_data(data, key) - print(f"Encrypted Data: {encrypted_data}") - decrypted_data = decrypt_data(encrypted_data, key) - print(f"Decrypted Data: {decrypted_data}") diff --git a/scripts/explainability.py b/scripts/explainability.py deleted file mode 100644 index 4b86058..0000000 --- a/scripts/explainability.py +++ /dev/null @@ -1,34 +0,0 @@ -python -import shap -import lime -import lime.lime_tabular -import numpy as np -import pandas as pd -from sklearn.ensemble import RandomForestClassifier - -# Train example model -def train_example_model(): - data = pd.read_csv('path_to_your_data.csv') - X = data.drop('target', axis=1) - y = data['target'] - model = RandomForestClassifier() - model.fit(X, y) - return model, X, y - -# Explain model using SHAP -def explain_model_shap(model, X): - explainer = shap.TreeExplainer(model) - shap_values = explainer.shap_values(X) - shap.summary_plot(shap_values, X) - -# Explain model using LIME -def explain_model_lime(model, X, instance): - explainer = lime.lime_tabular.LimeTabularExplainer(X.values, feature_names=X.columns, class_names=['class_0', 'class_1'], verbose=True, mode='classification') - exp = explainer.explain_instance(instance, model.predict_proba, num_features=5) - exp.show_in_notebook(show_table=True) - -if __name__ == "__main__": - model, X, y = train_example_model() - explain_model_shap(model, X) - instance = X.iloc[0] - explain_model_lime(model, X, instance) diff --git a/scripts/image_video_analysis.py b/scripts/image_video_analysis.py deleted file mode 100644 index e0a56f2..0000000 --- a/scripts/image_video_analysis.py +++ /dev/null @@ -1,52 +0,0 @@ -python -import cv2 -import torch -from torchvision import models, transforms -from PIL import Image - -# Load pre-trained model -model = models.detection.fasterrcnn_resnet50_fpn(pretrained=True) -model.eval() - -# Preprocess image -def preprocess_image(image_path): - transform = transforms.Compose([ - transforms.ToTensor(), - ]) - image = Image.open(image_path) - return transform(image).unsqueeze(0) - -# Perform object detection -def detect_objects(image_tensor): - with torch.no_grad(): - predictions = model(image_tensor) - return predictions - -# Process video -def process_video(video_path): - cap = cv2.VideoCapture(video_path) - while cap.isOpened(): - ret, frame = cap.read() - if not ret: - break - image_tensor = preprocess_image(frame) - predictions = detect_objects(image_tensor) - # Process predictions (e.g., draw bounding boxes on frame) - for prediction in predictions: - boxes = prediction['boxes'].cpu().numpy() - for box in boxes: - cv2.rectangle(frame, (box[0], box[1]), (box[2], box[3]), (255, 0, 0), 2) - cv2.imshow('frame', frame) - if cv2.waitKey(1) & 0xFF == ord('q'): - break - cap.release() - cv2.destroyAllWindows() - -if __name__ == "__main__": - image_path = 'path_to_your_image.jpg' - image_tensor = preprocess_image(image_path) - predictions = detect_objects(image_tensor) - print(f"Predictions: {predictions}") - - video_path = 'path_to_your_video.mp4' - process_video(video_path) diff --git a/scripts/neurosymbolic_ai.py b/scripts/neurosymbolic_ai.py deleted file mode 100644 index a0429a2..0000000 --- a/scripts/neurosymbolic_ai.py +++ /dev/null @@ -1,44 +0,0 @@ -python -import torch -import torch.nn as nn -import torch.nn.functional as F -from swiplserver import PrologServer, PrologThread - -# Neural network definition -class NeuralNet(nn.Module): - def __init__(self, input_size, hidden_size, output_size): - super(NeuralNet, self).__init__() - self.fc1 = nn.Linear(input_size, hidden_size) - self.fc2 = nn.Linear(hidden_size, output_size) - - def forward(self, x): - x = F.relu(self.fc1(x)) - x = self.fc2(x) - return x - -# Symbolic reasoning using Prolog -def symbolic_reasoning(query): - with PrologServer() as server: - with PrologThread(server) as prolog: - prolog.assertz("parent(john, doe)") - prolog.assertz("parent(doe, jane)") - result = prolog.query(query) - return result - -# Hybrid model example -def hybrid_model(inputs): - # Neural network inference - net = NeuralNet(input_size=2, hidden_size=3, output_size=1) - neural_output = net(torch.tensor(inputs, dtype=torch.float32)) - - # Symbolic reasoning - symbolic_output = symbolic_reasoning("parent(X, jane)") - - return neural_output, symbolic_output - -# Example usage -if __name__ == "__main__": - inputs = [0.5, 0.6] - neural_output, symbolic_output = hybrid_model(inputs) - print(f"Neural network output: {neural_output}") - print(f"Symbolic reasoning output: {symbolic_output}") diff --git a/scripts/penetration_testing.py b/scripts/penetration_testing.py deleted file mode 100644 index c6f77c9..0000000 --- a/scripts/penetration_testing.py +++ /dev/null @@ -1,15 +0,0 @@ -python -import requests - -# Run penetration test -def run_penetration_test(url): - response = requests.get(url) - return response.status_code, response.text - -# Example usage -if __name__ == "__main__": - test_url = "http://localhost:8000" # Replace with actual URL - status_code, response_text = run_penetration_test(test_url) - print(f"Status Code: {status_code}") - print("Response Text:") - print(response_text) diff --git a/scripts/personalized_learning.py b/scripts/personalized_learning.py deleted file mode 100644 index 95a8493..0000000 --- a/scripts/personalized_learning.py +++ /dev/null @@ -1,35 +0,0 @@ -python -import gym -from stable_baselines3 import PPO - -# Create personalized environment -class PersonalizedEnv(gym.Env): - def __init__(self, user_data): - super(PersonalizedEnv, self).__init__() - self.user_data = user_data - self.observation_space = gym.spaces.Box(low=0, high=1, shape=(len(user_data),), dtype=float) - self.action_space = gym.spaces.Discrete(2) - - def reset(self): - return self.user_data - - def step(self, action): - reward = self.compute_reward(action) - done = True # Example for simplicity - info = {} - return self.user_data, reward, done, info - - def compute_reward(self, action): - # Define a reward function based on user data and action - return 1 if action == 1 else 0 - -if __name__ == "__main__": - user_data = [0.5, 0.2, 0.8] # Example user data, replace with actual data - env = PersonalizedEnv(user_data) - model = PPO("MlpPolicy", env, verbose=1) - model.learn(total_timesteps=10000) - model.save("personalized_model") - - obs = env.reset() - action, _states = model.predict(obs) - print(f"Predicted action: {action}") diff --git a/scripts/predictive_maintenance.py b/scripts/predictive_maintenance.py deleted file mode 100644 index 9d66c0b..0000000 --- a/scripts/predictive_maintenance.py +++ /dev/null @@ -1,33 +0,0 @@ -python -import pandas as pd -from fbprophet import Prophet - -# Load data -def load_data(file_path): - data = pd.read_csv(file_path) - return data - -# Train predictive maintenance model -def train_model(data, date_col, metric_col): - df = data[[date_col, metric_col]].rename(columns={date_col: 'ds', metric_col: 'y'}) - model = Prophet() - model.fit(df) - return model - -# Make future predictions -def make_predictions(model, periods=365): - future = model.make_future_dataframe(periods=periods) - forecast = model.predict(future) - return forecast - -# Detect anomalies -def detect_anomalies(forecast): - forecast['anomaly'] = forecast['yhat_upper'] < forecast['y'] - return forecast - -if __name__ == "__main__": - data = load_data('path_to_your_data.csv') - model = train_model(data, 'date', 'metric') - forecast = make_predictions(model) - anomalies = detect_anomalies(forecast) - print(f"Anomalies detected: {anomalies[anomalies['anomaly']]}") diff --git a/scripts/quantum_integration.py b/scripts/quantum_integration.py deleted file mode 100644 index a40ead4..0000000 --- a/scripts/quantum_integration.py +++ /dev/null @@ -1,26 +0,0 @@ -python -import pennylane as qml -from pennylane import numpy as np - -# Quantum device setup -dev = qml.device("default.qubit", wires=2) - -# Define a quantum circuit -@qml.qnode(dev) -def quantum_circuit(inputs): - qml.RX(inputs[0], wires=0) - qml.RY(inputs[1], wires=1) - qml.CNOT(wires=[0, 1]) - return [qml.expval(qml.PauliZ(i)) for i in range(2)] - -# Hybrid quantum-classical model -def hybrid_model(x): - quantum_output = quantum_circuit(x) - classical_output = np.sum(quantum_output) - return classical_output - -# Example usage -if __name__ == "__main__": - inputs = np.array([0.5, 0.6]) - result = hybrid_model(inputs) - print(f"Quantum model output: {result}") diff --git a/scripts/recommendation_system.py b/scripts/recommendation_system.py deleted file mode 100644 index 13e0b94..0000000 --- a/scripts/recommendation_system.py +++ /dev/null @@ -1,42 +0,0 @@ -python -import pandas as pd -from surprise import Dataset, Reader, SVD -from surprise.model_selection import train_test_split, cross_validate - -# Load data -def load_data(file_path): - data = pd.read_csv(file_path) - return data - -# Prepare dataset for Surprise library -def prepare_dataset(data, user_col, item_col, rating_col): - reader = Reader(rating_scale=(data[rating_col].min(), data[rating_col].max())) - return Dataset.load_from_df(data[[user_col, item_col, rating_col]], reader) - -# Train recommendation model -def train_model(data): - trainset, testset = train_test_split(data, test_size=0.2) - algo = SVD() - algo.fit(trainset) - return algo, testset - -# Evaluate model -def evaluate_model(algo, testset): - cross_validate(algo, testset, measures=['RMSE', 'MAE'], cv=5, verbose=True) - -# Generate recommendations -def generate_recommendations(algo, user_id, item_ids, num_recommendations=5): - predictions = [(item_id, algo.predict(user_id, item_id).est) for item_id in item_ids] - recommendations = sorted(predictions, key=lambda x: x[1], reverse=True)[:num_recommendations] - return recommendations - -if __name__ == "__main__": - data = load_data('path_to_your_data.csv') - dataset = prepare_dataset(data, 'user_id', 'item_id', 'rating') - algo, testset = train_model(dataset) - evaluate_model(algo, testset) - - user_id = 1 # Example user ID - item_ids = [i for i in range(100)] # Example item IDs - recommendations = generate_recommendations(algo, user_id, item_ids) - print(f"Top recommendations for user {user_id}: {recommendations}") diff --git a/scripts/synthetic_data_generation.py b/scripts/synthetic_data_generation.py deleted file mode 100644 index 8afae07..0000000 --- a/scripts/synthetic_data_generation.py +++ /dev/null @@ -1,82 +0,0 @@ -python -import torch -import torch.nn as nn -import torch.optim as optim -import torchvision -import torchvision.transforms as transforms - -# Define GAN architecture -class Generator(nn.Module): - def __init__(self): - super(Generator, self).__init__() - self.main = nn.Sequential( - nn.Linear(100, 256), - nn.ReLU(True), - nn.Linear(256, 512), - nn.ReLU(True), - nn.Linear(512, 1024), - nn.ReLU(True), - nn.Linear(1024, 28*28), - nn.Tanh() - ) - - def forward(self, input): - return self.main(input).view(-1, 1, 28, 28) - -class Discriminator(nn.Module): - def __init__(self): - super(Discriminator, self).__init__() - self.main = nn.Sequential( - nn.Linear(28*28, 1024), - nn.LeakyReLU(0.2, inplace=True), - nn.Linear(1024, 512), - nn.LeakyReLU(0.2, inplace=True), - nn.Linear(512, 256), - nn.LeakyReLU(0.2, inplace=True), - nn.Linear(256, 1), - nn.Sigmoid() - ) - - def forward(self, input): - return self.main(input.view(-1, 28*28)) - -# Training GAN -def train_gan(): - transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))]) - dataset = torchvision.datasets.MNIST(root='./data', train=True, download=True, transform=transform) - dataloader = torch.utils.data.DataLoader(dataset, batch_size=64, shuffle=True) - - generator = Generator() - discriminator = Discriminator() - criterion = nn.BCELoss() - optimizer_g = optim.Adam(generator.parameters(), lr=0.0002) - optimizer_d = optim.Adam(discriminator.parameters(), lr=0.0002) - - for epoch in range(10): - for i, (images, _) in enumerate(dataloader): - # Train Discriminator - optimizer_d.zero_grad() - real_labels = torch.ones(images.size(0), 1) - fake_labels = torch.zeros(images.size(0), 1) - outputs = discriminator(images) - d_loss_real = criterion(outputs, real_labels) - d_loss_real.backward() - - noise = torch.randn(images.size(0), 100) - fake_images = generator(noise) - outputs = discriminator(fake_images.detach()) - d_loss_fake = criterion(outputs, fake_labels) - d_loss_fake.backward() - optimizer_d.step() - - # Train Generator - optimizer_g.zero_grad() - outputs = discriminator(fake_images) - g_loss = criterion(outputs, real_labels) - g_loss.backward() - optimizer_g.step() - - print(f'Epoch [{epoch+1}/10], d_loss: {d_loss_real.item()+d_loss_fake.item()}, g_loss: {g_loss.item()}') - -if __name__ == "__main__": - train_gan()