File size: 2,012 Bytes
2f3c093 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | 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):
# Placeholder for integration logic with new components
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):
# Placeholder for compatibility logic
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
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