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#!/usr/bin/env python3
"""
Test script for softmax-based word selection in thematic word generator.
"""
import os
import sys
# Set environment variables for testing
os.environ['SIMILARITY_TEMPERATURE'] = '0.7'
os.environ['USE_SOFTMAX_SELECTION'] = 'true'
# Test the configuration loading
def test_config_loading():
from thematic_word_generator import UnifiedThematicWordGenerator
print("🧪 Testing configuration loading...")
# Test default values
generator = UnifiedThematicWordGenerator()
print(f" Similarity Temperature: {generator.similarity_temperature}")
print(f" Use Softmax Selection: {generator.use_softmax_selection}")
# Test environment variable override
os.environ['SIMILARITY_TEMPERATURE'] = '0.3'
os.environ['USE_SOFTMAX_SELECTION'] = 'false'
generator2 = UnifiedThematicWordGenerator()
print(f" After env change - Temperature: {generator2.similarity_temperature}")
print(f" After env change - Use Softmax: {generator2.use_softmax_selection}")
print("✅ Configuration test passed!")
def test_softmax_logic():
"""Test just the softmax logic without full initialization"""
import numpy as np
print("\n🧪 Testing softmax selection logic...")
# Mock data - candidates with (word, similarity, tier)
candidates = [
("elephant", 0.85, "tier_5_common"),
("tiger", 0.75, "tier_6_moderately_common"),
("dog", 0.65, "tier_4_highly_common"),
("cat", 0.55, "tier_3_very_common"),
("fish", 0.45, "tier_5_common"),
("bird", 0.35, "tier_4_highly_common"),
("ant", 0.25, "tier_7_somewhat_uncommon"),
]
# Test multiple runs to see randomness
print(" Testing selection variability (temperature=0.7):")
class MockGenerator:
def __init__(self):
self.similarity_temperature = 0.7
def _softmax_with_temperature(self, scores, temperature=1.0):
if temperature <= 0:
temperature = 0.01
scaled_scores = scores / temperature
max_score = np.max(scaled_scores)
exp_scores = np.exp(scaled_scores - max_score)
probabilities = exp_scores / np.sum(exp_scores)
return probabilities
def _softmax_weighted_selection(self, candidates, num_words, temperature=None):
if len(candidates) <= num_words:
return candidates
if temperature is None:
temperature = self.similarity_temperature
similarities = np.array([score for _, score, _ in candidates])
probabilities = self._softmax_with_temperature(similarities, temperature)
selected_indices = np.random.choice(
len(candidates),
size=min(num_words, len(candidates)),
replace=False,
p=probabilities
)
return [candidates[i] for i in selected_indices]
generator = MockGenerator()
# Run selection multiple times to show variety
for run in range(3):
selected = generator._softmax_weighted_selection(candidates, 4)
selected.sort(key=lambda x: x[1], reverse=True) # Sort by similarity for display
words = [f"{word}({sim:.2f})" for word, sim, _ in selected]
print(f" Run {run+1}: {', '.join(words)}")
print("✅ Softmax selection logic test passed!")
if __name__ == "__main__":
test_config_loading()
test_softmax_logic()
print("\n🎉 All tests completed successfully!") |