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feat: working auto mode

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This commit is contained in:
Krzysztof Rudnicki 2023-06-08 19:32:30 +02:00
parent e82ae526cb
commit e7f73f6221
1 changed files with 50 additions and 15 deletions
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65
final/code/main.py
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@ -2,10 +2,12 @@
Code for preprocessing data and creating model that predicts and
recomends anime based on another anime entered by user
"""
import math
import argparse
import pandas as pd
import numpy as np
from sklearn.neighbors import NearestNeighbors
from sklearn.neighbors import VALID_METRICS_SPARSE
from scipy.sparse import csr_matrix
@ -191,12 +193,12 @@ def predict(prediction_model, pivot_table, seed=42, anime="RANDOM", recommendati
def calculate_neighbors(rows_number, neighbors=5):
neighbor_value = {
"default": 5,
"sqrt": sqrt(rows_number),
"half": rows_number / 2,
"log": log(rows_number),
"sqrt": math.floor(math.sqrt(rows_number)),
"half": math.floor(rows_number / 2),
"log": math.floor(math.log(rows_number)),
"n-1": rows_number - 1
}
if type(neighbors) == string:
if isinstance(neighbors, str):
return neighbor_value[neighbors]
return neighbors
@ -269,20 +271,51 @@ def handle_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():
def simulate_different_data_size():
data_spread: [27306186, 54612373, -1]
metric_spread: ["cosine", "mahalanobis", "euclidean"]
algorithm_spread: ['auto', 'ball_tree', 'kd_tree', 'brute']
neighbor_spread: [5, "sqrt", "half", "log", "n-1"]
user_threshold_spread: [0, 500, 1000]
anime_threshold_spread: [0, 200, 500]
for data_size in data_spread:
starting_rating_data, starting_anime_contact_data, starting_rows_number = get_data()
preprocess_model_predict(
starting_rating_data, starting_anime_contact_data, starting_rows_number, data_limit=data_size)
def preprocess_model_predict(data_limit, db, debug, user_threshold, anime_threshold, metric, algorithm, neighbors, seed, anime, recommendation_amount):
RATING_DATA, ANIME_CONTACT_DATA, ROWS_NUMBER = get_data(data_limit, db)
def auto_mode():
print("Started auto mode")
metric_spread = ["cosine", "euclidean"]
algorithm_spread = ['ball_tree', 'kd_tree', 'brute']
neighbor_spread = [5, "sqrt", "half", "log", "n-1"]
user_threshold_spread = [500]
anime_threshold_spread = [200]
# 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()
print("automode, metric spread")
for metric in metric_spread:
preprocess_model_predict(
starting_rating_data, starting_anime_contact_data, starting_rows_number, metric=metric)
for algorithm in algorithm_spread:
print("automode, algorithm_spread")
for metric in sorted(VALID_METRICS_SPARSE[algorithm]):
preprocess_model_predict(
starting_rating_data, starting_anime_contact_data, starting_rows_number, algorithm=algorithm)
for neighbor_amount in neighbor_spread:
print("automode, neighbor_spread")
preprocess_model_predict(starting_rating_data, starting_anime_contact_data,
starting_rows_number, neighbors=neighbor_amount)
for user_threshold in user_threshold_spread:
print("automode, user_threshold_spread")
preprocess_model_predict(starting_rating_data, starting_anime_contact_data,
starting_rows_number, user_threshold=user_threshold)
for anime_threshold in anime_threshold_spread:
print("automode, anime_threshold_spread")
preprocess_model_predict(starting_rating_data, starting_anime_contact_data,
starting_rows_number, anime_threshold=anime_threshold)
# simulate_different_data_size()
def preprocess_model_predict(rating_data, anime_contact_data, rows_number, 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):
PIVOT_TABLE = preprocessing(
RATING_DATA, ANIME_CONTACT_DATA, debug, user_threshold, anime_threshold)
MODEL = create_model(PIVOT_TABLE, ROWS_NUMBER,
rating_data, anime_contact_data, debug, user_threshold, anime_threshold)
MODEL = create_model(PIVOT_TABLE, rows_number,
metric, algorithm, neighbors)
predict(MODEL, PIVOT_TABLE, seed, anime, recommendation_amount)
@ -290,7 +323,9 @@ def preprocess_model_predict(data_limit, db, debug, user_threshold, anime_thresh
if __name__ == "__main__":
SEED, DEBUG, DATA_LIMIT, DB, METRIC, ALGORITHM, ANIME, NEIGHBORS, USER_THRESHOLD, ANIME_THRESHOLD, RECOMMENDATION_AMOUNT, AUTO = handle_arguments()
if not AUTO:
preprocess_model_predict(DATA_LIMIT, DB, DEBUG, USER_THRESHOLD, ANIME_THRESHOLD,
starting_rating_data, starting_anime_contact_data, starting_rows_number = get_data()
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)
if AUTO:
auto_mode()