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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()