import math import matplotlib.pyplot as plt GREEN = "\033[92m" YELLOW = "\033[93m" RED = "\033[91m" RESET = "\033[0m" COLOR_MAP = {"red":RED, "orange": YELLOW, "green": GREEN} class Tester: def __init__(self, predictor, data, title=None, size=250): self.predictor = predictor self.data = data self.title = title or predictor.__name__.replace("_", " ").title() self.size = size self.guesses = [] self.truths = [] self.errors = [] self.colors = [] def color_for(self, error, truth): if error == truth: return "green" else: return "red" def run_datapoint(self, i): datapoint = self.data[i] guess = self.predictor(datapoint) truth = datapoint.price error = guess == truth color = self.color_for(error, truth) title = datapoint.title if len(datapoint.title) <= 40 else datapoint.title[:40]+"..." self.guesses.append(guess) self.truths.append(truth) self.errors.append(error) self.colors.append(color) print(f"{COLOR_MAP[color]}{i+1}: Guess: ${guess:,.2f} Truth: ${truth:,.2f} Error: ${error:,.2f} SLE: {sle:,.2f} Item: {title}{RESET}") def chart(self, title): actual = self.truths predicted = self.guesses # Get unique classes classes = list(set(actual + predicted)) # Union of unique classes in actual and predicted # Initialize the confusion matrix as a dictionary confusion_matrix = {true: {pred: 0 for pred in classes} for true in classes} # Populate the confusion matrix for a, p in zip(actual, predicted): confusion_matrix[a][p] += 1 # Convert the confusion matrix into a 2D list for visualization matrix = [[confusion_matrix[true][pred] for pred in classes] for true in classes] # Plot the confusion matrix plt.figure(figsize=(8, 6)) plt.imshow(matrix, interpolation='nearest', cmap=plt.cm.Blues) plt.title(title) plt.colorbar() # Add labels tick_marks = range(len(classes)) plt.xticks(tick_marks, classes) plt.yticks(tick_marks, classes) plt.ylabel('Actual Label') plt.xlabel('Predicted Label') # Add text annotations for i in range(len(classes)): for j in range(len(classes)): plt.text(j, i, matrix[i][j], horizontalalignment="center", color="white" if matrix[i][j] > max(max(row) for row in matrix) / 2 else "black") plt.tight_layout() plt.show() def report(self): average_error = sum(self.errors) / self.size rmsle = math.sqrt(sum(self.sles) / self.size) hits = sum(1 for color in self.colors if color=="green") title = f"{self.title} Error=${average_error:,.2f} RMSLE={rmsle:,.2f} Hits={hits/self.size*100:.1f}%" self.chart(title) def run(self): self.error = 0 for i in range(self.size): self.run_datapoint(i) self.report() @classmethod def test(cls, function, data): cls(function, data).run()