58 lines
2.3 KiB
Python
58 lines
2.3 KiB
Python
import pandas as pd
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from sklearn.linear_model import LinearRegression
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import joblib
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import os
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from agents.agent import Agent
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from agents.specialist_agent import SpecialistAgent
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from agents.frontier_agent import FrontierAgent
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from agents.random_forest_agent import RandomForestAgent
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class EnsembleAgent(Agent):
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name = "Ensemble Agent"
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color = Agent.YELLOW
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def __init__(self, collection):
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"""
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Create an instance of Ensemble, by creating each of the models
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And loading the weights of the Ensemble
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"""
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self.log("Initializing Ensemble Agent")
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self.specialist = SpecialistAgent()
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self.frontier = FrontierAgent(collection)
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self.random_forest = RandomForestAgent()
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# Resolve model path: prefer local contribution folder copy, fallback to week8 root
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candidate_paths = [
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os.path.join(os.path.dirname(os.path.dirname(__file__)), 'ensemble_model.pkl'), # ../../ensemble_model.pkl
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os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(__file__))), 'ensemble_model.pkl'), # ../../../ensemble_model.pkl (week8 root)
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'ensemble_model.pkl',
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]
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model_path = next((p for p in candidate_paths if os.path.exists(p)), candidate_paths[-1])
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self.model = joblib.load(model_path)
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self.log("Ensemble Agent is ready")
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def price(self, description: str) -> float:
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"""
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Run this ensemble model
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Ask each of the models to price the product
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Then use the Linear Regression model to return the weighted price
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:param description: the description of a product
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:return: an estimate of its price
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"""
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self.log("Running Ensemble Agent - collaborating with specialist, frontier and random forest agents")
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specialist = self.specialist.price(description)
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frontier = self.frontier.price(description)
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random_forest = self.random_forest.price(description)
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X = pd.DataFrame({
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'Specialist': [specialist],
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'Frontier': [frontier],
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'RandomForest': [random_forest],
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'Min': [min(specialist, frontier, random_forest)],
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'Max': [max(specialist, frontier, random_forest)],
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})
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y = max(0, self.model.predict(X)[0])
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self.log(f"Ensemble Agent complete - returning ${y:.2f}")
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return y
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