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fix: manual mode

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This commit is contained in:
Krzysztof Rudnicki 2023-06-11 23:16:25 +02:00
parent 5f2a5c8836
commit e05fef37a2
1 changed files with 13 additions and 10 deletions
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23
final/code/main.py
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@ -187,10 +187,10 @@ def predict(prediction_model, pivot_table, seed=42, anime="RANDOM", recommendati
query) query)
if debug: if debug:
print("prediction model, distance: ", distance) print("prediction model, distance: ", distance)
for i in range(0, 1): for i in range(0, 2):
if i == 0 and not auto and not debug: if i == 0:
print(f"Recommendations for {chosen_anime_name}:\n") print(f"Recommendations for {chosen_anime_name}:\n")
elif not auto and not debug: else:
print( print(
f"""{i}: {pivot_table.index[suggestions.flatten()[i]]}, f"""{i}: {pivot_table.index[suggestions.flatten()[i]]},
with distance of {distance.flatten()[i]}:""" with distance of {distance.flatten()[i]}:"""
@ -198,11 +198,11 @@ def predict(prediction_model, pivot_table, seed=42, anime="RANDOM", recommendati
average_distance = np.mean(distance.flatten()) average_distance = np.mean(distance.flatten())
closest_anime_name = pivot_table.index[suggestions.flatten()[1]] closest_anime_name = pivot_table.index[suggestions.flatten()[1]]
closest_anime_distance = distance.flatten()[1] closest_anime_distance = distance.flatten()[1]
average_minus_closest_distance = closest_anime_distance - average_distance average_minus_closest_distance = average_distance - closest_anime_distance
print( print(
f"Average distance: {average_distance}, average_minus_closest_distance: {average_minus_closest_distance}") f"Average distance: {average_distance}, average_minus_closest_distance: {average_minus_closest_distance}")
return chosen_anime, suggestions.flatten()[1:recommendation_number+1], distance.flatten()[1:recommendation_number+1] return chosen_anime, suggestions.flatten()[1:recommendation_number+1], distance.flatten()[1:recommendation_number+1], f"{closest_anime_distance}_{average_distance}_{average_minus_closest_distance}"
# return f"{chosen_anime_name}_{closest_anime_name}_{closest_anime_distance}_{average_distance}_{average_minus_closest_distance}" # return f"{chosen_anime_name}_{closest_anime_name}_{closest_anime_distance}_{average_distance}_{average_minus_closest_distance}"
@ -298,7 +298,7 @@ def handle_arguments():
def auto_mode(data_limit=-1, seed=42, anime="RANDOM"): def auto_mode(data_limit=-1, seed=42, anime="RANDOM"):
print("Started auto mode") print("Started auto mode")
algorithm_spread = ['auto', 'ball_tree', 'kd_tree', 'brute'] algorithm_spread = ['auto', 'brute']
neighbor_spread = [5, "sqrt", "half", "log", "n-1"] neighbor_spread = [5, "sqrt", "half", "log", "n-1"]
# No reason to access and waste computational power every time we run the simulation # No reason to access and waste computational power every time we run the simulation
starting_rating_data, starting_anime_contact_data, starting_rows_number = get_data( starting_rating_data, starting_anime_contact_data, starting_rows_number = get_data(
@ -351,8 +351,8 @@ def preprocess_model_predict(rating_data, anime_contact_data, rows_number, pivot
metric, algorithm, neighbors) metric, algorithm, neighbors)
result = "" result = ""
if MODEL != "Error!": if MODEL != "Error!":
chosen_anime, suggestions, distance = predict(MODEL, pivot_table, seed, chosen_anime, suggestions, distance, distance_data = predict(MODEL, pivot_table, seed,
anime, recommendation_amount) anime, recommendation_amount)
chosen_anime_name = pivot_table.index[chosen_anime] chosen_anime_name = pivot_table.index[chosen_anime]
# average_distance = np.mean(distance) # average_distance = np.mean(distance)
@ -365,7 +365,8 @@ def preprocess_model_predict(rating_data, anime_contact_data, rows_number, pivot
result = f"{chosen_anime_name}:\n" result = f"{chosen_anime_name}:\n"
for i in range(len(suggestions)): for i in range(len(suggestions)):
result += f"{pivot_table.index[suggestions[i]]}; Distance: {distance[i]}\n" result += f"{pivot_table.index[suggestions[i]]}; Distance: {distance[i]}\n"
result += f"Precision: {precision*100}%" result += f"Precision: {precision*100}%\n"
result += "Smallest distance, average distance, Average - Smallest distance: " + distance_data
# result = f"{chosen_anime_name}_{closest_anime_name}_{closest_anime_distance}_{average_distance}_{average_minus_closest_distance}" # result = f"{chosen_anime_name}_{closest_anime_name}_{closest_anime_distance}_{average_distance}_{average_minus_closest_distance}"
write_test_results( write_test_results(
f"dl={rows_number}&s={seed}&m={metric}&a={algorithm}&ut={user_threshold}&at={anime_threshold}&n={neighbors}", result) f"dl={rows_number}&s={seed}&m={metric}&a={algorithm}&ut={user_threshold}&at={anime_threshold}&n={neighbors}", result)
@ -374,7 +375,9 @@ def preprocess_model_predict(rating_data, anime_contact_data, rows_number, pivot
if __name__ == "__main__": if __name__ == "__main__":
SEED, DEBUG, DATA_LIMIT, DB, METRIC, ALGORITHM, ANIME, NEIGHBORS, USER_THRESHOLD, ANIME_THRESHOLD, RECOMMENDATION_AMOUNT, AUTO = handle_arguments() SEED, DEBUG, DATA_LIMIT, DB, METRIC, ALGORITHM, ANIME, NEIGHBORS, USER_THRESHOLD, ANIME_THRESHOLD, RECOMMENDATION_AMOUNT, AUTO = handle_arguments()
if not AUTO: if not AUTO:
starting_rating_data, starting_anime_contact_data, starting_rows_number = get_data() print("Entered not auto mode")
starting_rating_data, starting_anime_contact_data, starting_rows_number = get_data(
limit_data=DATA_LIMIT, data_folder_path=DB)
pivot_table = preprocessing( pivot_table = preprocessing(
starting_rating_data, starting_anime_contact_data, USER_THRESHOLD, ANIME_THRESHOLD) starting_rating_data, starting_anime_contact_data, USER_THRESHOLD, ANIME_THRESHOLD)
preprocess_model_predict(starting_rating_data, starting_anime_contact_data, starting_rows_number, preprocess_model_predict(starting_rating_data, starting_anime_contact_data, starting_rows_number,