From the uDemy course on LLM engineering.
https://www.udemy.com/course/llm-engineering-master-ai-and-large-language-models
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75 lines
2.5 KiB
75 lines
2.5 KiB
import math |
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import matplotlib.pyplot as plt |
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GREEN = "\033[92m" |
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YELLOW = "\033[93m" |
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RED = "\033[91m" |
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RESET = "\033[0m" |
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COLOR_MAP = {"red":RED, "orange": YELLOW, "green": GREEN} |
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class Tester: |
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def __init__(self, predictor, data, title=None, size=250): |
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self.predictor = predictor |
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self.data = data |
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self.title = title or predictor.__name__.replace("_", " ").title() |
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self.size = size |
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self.guesses = [] |
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self.truths = [] |
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self.errors = [] |
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self.sles = [] |
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self.colors = [] |
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def color_for(self, error, truth): |
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if error<40 or error/truth < 0.2: |
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return "green" |
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elif error<80 or error/truth < 0.4: |
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return "orange" |
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else: |
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return "red" |
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def run_datapoint(self, i): |
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datapoint = self.data[i] |
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guess = self.predictor(datapoint) |
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truth = datapoint.price |
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error = abs(guess - truth) |
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log_error = math.log(truth+1) - math.log(guess+1) |
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sle = log_error ** 2 |
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color = self.color_for(error, truth) |
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title = datapoint.title if len(datapoint.title) <= 40 else datapoint.title[:40]+"..." |
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self.guesses.append(guess) |
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self.truths.append(truth) |
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self.errors.append(error) |
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self.sles.append(sle) |
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self.colors.append(color) |
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print(f"{COLOR_MAP[color]}{i+1}: Guess: ${guess:,.2f} Truth: ${truth:,.2f} Error: ${error:,.2f} SLE: {sle:,.2f} Item: {title}{RESET}") |
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def chart(self, title): |
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max_error = max(self.errors) |
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plt.figure(figsize=(12, 8)) |
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max_val = max(max(self.truths), max(self.guesses)) |
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plt.plot([0, max_val], [0, max_val], color='deepskyblue', lw=2, alpha=0.6) |
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plt.scatter(self.truths, self.guesses, s=3, c=self.colors) |
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plt.xlabel('Ground Truth') |
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plt.ylabel('Model Estimate') |
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plt.xlim(0, max_val) |
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plt.ylim(0, max_val) |
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plt.title(title) |
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plt.show() |
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def report(self): |
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average_error = sum(self.errors) / self.size |
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rmsle = math.sqrt(sum(self.sles) / self.size) |
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hits = sum(1 for color in self.colors if color=="green") |
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title = f"{self.title} Error=${average_error:,.2f} RMSLE={rmsle:,.2f} Hits={hits/self.size*100:.1f}%" |
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self.chart(title) |
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def run(self): |
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self.error = 0 |
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for i in range(self.size): |
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self.run_datapoint(i) |
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self.report() |
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@classmethod |
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def test(cls, function, data): |
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cls(function, data).run() |