| import numpy as np |
| from sklearn.ensemble import RandomForestClassifier |
| from sklearn.model_selection import train_test_split |
| from sklearn.metrics import accuracy_score |
|
|
| class PredictiveAnalytics: |
| def __init__(self): |
| self.model = RandomForestClassifier(n_estimators=100) |
| self.data = None |
| self.labels = None |
|
|
| def load_data(self, data, labels): |
| self.data = data |
| self.labels = labels |
|
|
| def train_model(self): |
| if self.data is None or self.labels is None: |
| raise ValueError("Data and labels must be loaded before training the model.") |
| |
| X_train, X_test, y_train, y_test = train_test_split(self.data, self.labels, test_size=0.2, random_state=42) |
| self.model.fit(X_train, y_train) |
| predictions = self.model.predict(X_test) |
| accuracy = accuracy_score(y_test, predictions) |
| return accuracy |
|
|
| def predict(self, new_data): |
| if self.model is None: |
| raise ValueError("Model must be trained before making predictions.") |
| |
| return self.model.predict(new_data) |
|
|
| def render(self): |
| return "Predictive Analytics Module: Ready to predict potential threats and vulnerabilities." |
|
|
| def integrate_with_new_components(self, new_component_data): |
| |
| integrated_data = { |
| "new_component_data": new_component_data.get("data", {}), |
| "new_component_labels": new_component_data.get("labels", {}) |
| } |
| return integrated_data |
|
|
| def ensure_compatibility(self, existing_data, new_component_data): |
| |
| compatible_data = { |
| "existing_data": existing_data.get("data", {}), |
| "existing_labels": existing_data.get("labels", {}), |
| "new_component_data": new_component_data.get("data", {}), |
| "new_component_labels": new_component_data.get("labels", {}) |
| } |
| return compatible_data |
|
|
