Added ensemble agent updated with xg_boost agent to community-contributions

week8/community_contributions/ensemble-updated/ensemble_agent.py

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+import pandas as pd
+from sklearn.linear_model import LinearRegression
+import joblib
+
+from agents.agent import Agent
+from agents.specialist_agent import SpecialistAgent
+from agents.frontier_agent import FrontierAgent
+from agents.random_forest_agent import RandomForestAgent
+from agents.xg_boost_agent import XGBoostAgent
+
+class EnsembleAgent(Agent):
+
+    name = "Ensemble Agent"
+    color = Agent.YELLOW
+    
+    def __init__(self, collection):
+        """
+        Create an instance of Ensemble, by creating each of the models
+        And loading the weights of the Ensemble
+        """
+        self.log("Initializing Ensemble Agent")
+        self.specialist = SpecialistAgent()
+        self.frontier = FrontierAgent(collection)
+        self.random_forest = RandomForestAgent()
+        self.xg_boost = XGBoostAgent()
+        self.model = joblib.load('ensemble_model.pkl')
+        self.log("Ensemble Agent is ready")
+
+    def price(self, description: str) -> float:
+        """
+        Run this ensemble model
+        Ask each of the models to price the product
+        Then use the Linear Regression model to return the weighted price
+        :param description: the description of a product
+        :return: an estimate of its price
+        """
+        self.log("Running Ensemble Agent - collaborating with specialist, frontier, xg boost and random forest agents")
+        specialist = self.specialist.price(description)
+        frontier = self.frontier.price(description)
+        random_forest = self.random_forest.price(description)
+        xg_boost = self.xg_boost.price(description)
+        X = pd.DataFrame({
+            'Specialist': [specialist],
+            'Frontier': [frontier],
+            'RandomForest': [random_forest],
+            'XGBoost' : [xg_boost], 
+            'Min': [min(specialist, frontier, random_forest, xg_boost)],
+            'Max': [max(specialist, frontier, random_forest, xg_boost)],
+        })
+        y = max(0, self.model.predict(X)[0])
+        self.log(f"Ensemble Agent complete - returning ${y:.2f}")
+        return y

week8/community_contributions/ensemble-updated/xg_boost_agent.py

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+# imports
+
+import os
+import re
+from typing import List
+from sentence_transformers import SentenceTransformer
+import joblib
+from agents.agent import Agent
+import xgboost as xgb
+
+
+
+
+class XGBoostAgent(Agent):
+
+    name = "XG Boost Agent"
+    color = Agent.BRIGHT_MAGENTA
+
+    def __init__(self):
+        """
+        Initialize this object by loading in the saved model weights
+        and the SentenceTransformer vector encoding model
+        """
+        self.log("XG Boost Agent is initializing")
+        self.vectorizer = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
+        self.model = joblib.load('xg_boost_model.pkl')
+        self.log("XG Boost Agent is ready")
+
+    def price(self, description: str) -> float:
+        """
+        Use an XG Boost model to estimate the price of the described item
+        :param description: the product to be estimated
+        :return: the price as a float
+        """        
+        self.log("XG Boost Agent is starting a prediction")
+        vector = self.vectorizer.encode([description])
+        vector = vector.reshape(1, -1)
+        # Convert the vector to DMatrix
+        dmatrix = xgb.DMatrix(vector)
+        # Predict the price using the model
+        result = max(0, self.model.predict(dmatrix)[0])
+        self.log(f"XG Boost Agent completed - predicting ${result:.2f}")
+        return result
+
+
+