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feat: add precision to auto mode

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This commit is contained in:
Jakub Kliszko 2023-06-11 21:58:41 +02:00
parent 69fd151d93
commit 4a66ce8731
1 changed files with 53 additions and 20 deletions
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73
final/code/main.py
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@ -157,8 +157,8 @@ def preprocessing(rating_data, anime_contact_data,
rating_data, "anime_id", anime_threshold)
rating_data = combine_name_and_ratings(rating_data)
rating_data = rating_data.drop(columns="rating_x")
rating_data = rating_data.rename(columns={"rating_y": "rating"})
rating_data = rating_data.drop(columns="rating_y")
rating_data = rating_data.rename(columns={"rating_x": "rating"})
if debug and not auto:
print(rating_data)
get_data_info(rating_data, True)
@ -171,7 +171,7 @@ def preprocessing(rating_data, anime_contact_data,
return pivot_table
def predict(prediction_model, pivot_table, seed=42, anime="RANDOM", recommendation_number=6, auto=False, debug = False):
def predict(prediction_model, pivot_table, seed=42, anime="RANDOM", recommendation_number=6, auto=False, debug=False):
"""
This will choose a random anime name and our prediction_model will predict similar anime.
"""
@ -187,7 +187,7 @@ def predict(prediction_model, pivot_table, seed=42, anime="RANDOM", recommendati
query)
if debug:
print("prediction model, distance: ", distance)
for i in range(0, 4):
for i in range(recommendation_number):
if i == 0 and not auto and not debug:
print(f"Recommendations for {chosen_anime_name}:\n")
elif not auto and not debug:
@ -199,8 +199,11 @@ def predict(prediction_model, pivot_table, seed=42, anime="RANDOM", recommendati
closest_anime_name = pivot_table.index[suggestions.flatten()[1]]
closest_anime_distance = distance.flatten()[1]
average_minus_closest_distance = closest_anime_distance - average_distance
print(f"Average distance: {average_distance}, average_minus_closest_distance: {average_minus_closest_distance}")
return f"{chosen_anime_name}_{closest_anime_name}_{closest_anime_distance}_{average_distance}_{average_minus_closest_distance}"
print(
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 f"{chosen_anime_name}_{closest_anime_name}_{closest_anime_distance}_{average_distance}_{average_minus_closest_distance}"
def calculate_neighbors(rows_number, neighbors=5):
@ -224,9 +227,10 @@ def create_model(pivot_table, rows_number, metric="cosine", algorithm="brute", n
pivot_table_matrix = csr_matrix(pivot_table.values)
if algorithm == "brute":
model = NearestNeighbors(n_neighbors=neighbors_number,
metric=metric, algorithm=algorithm)
metric=metric, algorithm=algorithm)
else:
model = NearestNeighbors(n_neighbors=neighbors_number, algorithm=algorithm)
model = NearestNeighbors(
n_neighbors=neighbors_number, algorithm=algorithm)
try:
model.fit(pivot_table_matrix)
except:
@ -291,12 +295,14 @@ def handle_arguments():
# Access the values of the arguments
return args.seed, args.debug, args.data_limit, args.database, args.metric, args.algorithm, args.anime, args.neighbors, args.user_threshold, args.anime_threshold, args.recommendation_amount, args.auto
def auto_mode(data_limit = -1, seed = 42, anime="RANDOM"):
def auto_mode(data_limit=-1, seed=42, anime="RANDOM"):
print("Started auto mode")
algorithm_spread = ['auto', 'ball_tree', 'kd_tree', 'brute']
neighbor_spread = [5, "sqrt", "half", "log", "n-1"]
# 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(limit_data=data_limit)
starting_rating_data, starting_anime_contact_data, starting_rows_number = get_data(
limit_data=data_limit)
original_pivot_table = preprocessing(
starting_rating_data, starting_anime_contact_data)
if os.path.exists('test_results'):
@ -311,40 +317,67 @@ def auto_mode(data_limit = -1, seed = 42, anime="RANDOM"):
for metric in possibleMetrics:
print("testing for algorithm, metric: ", algorithm, metric)
for neighbor_amount in neighbor_spread:
print("testing for algorithm, metric, neighbor_amount: ", algorithm, metric, neighbor_amount)
print("testing for algorithm, metric, neighbor_amount: ",
algorithm, metric, neighbor_amount)
preprocess_model_predict(starting_rating_data, starting_anime_contact_data,
starting_rows_number, original_pivot_table, seed=seed, anime=anime, neighbors=neighbor_amount, algorithm=algorithm, metric=metric)
starting_rows_number, original_pivot_table, seed=seed, anime=anime, neighbors=neighbor_amount, algorithm=algorithm, metric=metric)
def write_test_results(title, result = ""):
def write_test_results(title, result=""):
# Create directory if it doesn't already exist
if not os.path.exists('test_results'):
os.makedirs('test_results')
# Generate timestamped filename
timestamp = datetime.datetime.now().strftime('%Y%m%d%H%M%S') # e.g., 20230611235959
timestamp = datetime.datetime.now().strftime(
'%Y%m%d%H%M%S') # e.g., 20230611235959
filename = f"{title}_{timestamp}.txt"
# Create and write to the file
with open(os.path.join('test_results', filename), 'a') as file:
file.write(result)
def calculate_precision(predictions, threshold=8):
ratings = [anime[anime > 0].mean() for anime in predictions]
precision = [1 if r >= threshold else 0 for r in ratings]
return np.mean(precision)
def preprocess_model_predict(rating_data, anime_contact_data, rows_number, pivot_table, data_limit=-1, db="database", debug=False, user_threshold=500, anime_threshold=200, metric="cosine", algorithm="brute", neighbors=5, seed=42, anime="RANDOM", recommendation_amount=5):
MODEL = create_model(pivot_table, rows_number,
metric, algorithm, neighbors)
result = ""
if MODEL != "Error!":
result = predict(MODEL, pivot_table, seed, anime, recommendation_amount)
write_test_results(f"dl:{rows_number}_s:{seed}_m:{metric}_a:{algorithm}_ut:{user_threshold}_at:{anime_threshold}_n:{neighbors}", result)
chosen_anime, suggestions, distance = predict(MODEL, pivot_table, seed,
anime, recommendation_amount)
chosen_anime_name = pivot_table.index[chosen_anime]
# average_distance = np.mean(distance)
# closest_anime_name = pivot_table.index[suggestions[1]]
# closest_anime_distance = distance[1]
# average_minus_closest_distance = closest_anime_distance - average_distance
precision = calculate_precision(
[pivot_table.iloc[s] for s in suggestions])
result = f"{chosen_anime_name}:\n"
for i in range(len(suggestions)):
result += f"{pivot_table.index[suggestions[i]]}; Distance: {distance[i]}\n"
result += f"Precision: {precision*100}%"
# result = f"{chosen_anime_name}_{closest_anime_name}_{closest_anime_distance}_{average_distance}_{average_minus_closest_distance}"
write_test_results(
f"dl={rows_number}&s={seed}&m={metric}&a={algorithm}&ut={user_threshold}&at={anime_threshold}&n={neighbors}", result)
if __name__ == "__main__":
SEED, DEBUG, DATA_LIMIT, DB, METRIC, ALGORITHM, ANIME, NEIGHBORS, USER_THRESHOLD, ANIME_THRESHOLD, RECOMMENDATION_AMOUNT, AUTO = handle_arguments()
if not AUTO:
starting_rating_data, starting_anime_contact_data, starting_rows_number = get_data()
pivot_table = preprocessing(
starting_rating_data, starting_anime_contact_data, USER_THRESHOLD, ANIME_THRESHOLD)
preprocess_model_predict(starting_rating_data, starting_anime_contact_data, starting_rows_number,
DATA_LIMIT, DB, DEBUG, USER_THRESHOLD, ANIME_THRESHOLD,
METRIC, ALGORITHM, NEIGHBORS, SEED, ANIME, RECOMMENDATION_AMOUNT)
pivot_table, data_limit=DATA_LIMIT, db=DB, debug=DEBUG, user_threshold=USER_THRESHOLD, anime_threshold=ANIME_THRESHOLD,
metric=METRIC, algorithm=ALGORITHM, neighbors=NEIGHBORS, seed=SEED, anime=ANIME, recommendation_amount=RECOMMENDATION_AMOUNT)
if AUTO:
auto_mode(DATA_LIMIT, SEED, ANIME)