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modifyData.py
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modifyData.py
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import pandas as pd
games_df = pd.read_csv("Datasets/games.csv")
user_df = pd.read_csv("Datasets/user.csv")
create_useful_game_info = False
create_user_df_with_ratings = False
create_game_indexes = False
create_game_indexes_inverted = False
create_user_indexes = False
create_user_indexes_inverted = False
create_similarity_matrix = False
overwrite_data = False
normalize_ratings = False
#####################################################################################
if create_useful_game_info:
useful_game_info = games_df[["name", "popular_tags", "game_details", "genre", "all_reviews"]].copy()
useful_game_info.dropna(inplace = True)
useful_game_info.to_csv("Datasets/useful_game_info.csv")
#####################################################################################
if create_user_df_with_ratings:
purchase_dict = {"purchase": 0, "play":1}
user_df.drop(user_df.columns[4], axis = 1, inplace = True)
user_df.columns = ["userId", "game", "purchase/play", "timePlayed"]
user_df["purchase/play"] = user_df["purchase/play"].map(purchase_dict)
indexes = user_df[user_df["purchase/play"] == 0].index
user_df.drop(indexes, inplace = True)
user_df.drop("purchase/play", axis = 1, inplace = True)
average_time_played = pd.read_csv("Datasets/average_time_play_full.csv")
average_time_played.drop("Unnamed: 0", axis = 1, inplace = True)
average_time_played.drop("Unnamed: 0.1", axis = 1, inplace = True)
user_df = user_df.reset_index()
user_df.drop("index", axis = 1, inplace = True)
row = 0
ratings = []
for user in user_df["userId"]:
game = user_df.iloc[row]["game"]
user_play_time = user_df.iloc[row]["timePlayed"]
average_hours = average_time_played[game][0]
if user_play_time > 1.5 * average_hours:
rating = 5
elif user_play_time > 1.25 * average_hours:
rating = 4
elif user_play_time > average_hours:
rating = 3
elif user_play_time > 0.75 * average_hours:
rating = 2
elif user_play_time > 0.5 * average_hours:
rating = 1
else:
rating = 0
ratings.append(rating)
row += 1
user_df["rating"] = ratings
user_df.to_csv("Datasets/user_df_with_ratings.csv")
#####################################################################################
if create_game_indexes:
index_df = user_df["game"].unique()
index_df = pd.DataFrame(index_df)
index_df = index_df.T
index_df = (index_df.T.reset_index().T.reset_index(drop=True).set_axis([f'{name}' for name in user_df["game"].unique()], axis=1))
index_df.drop(1, inplace=True)
index_df.to_csv("Datasets/game_indexes.csv")
######################################################################################
if create_game_indexes_inverted:
game_indexes = pd.read_csv("Datasets/game_indexes.csv")
game_indexes.drop("Unnamed: 0", axis = 1, inplace=True)
games = game_indexes.columns
indexes = [i for i in range(len(games))]
game_indexes_inverted = pd.DataFrame(games, index=indexes, columns=["game"])
game_indexes_inverted = game_indexes_inverted.T
game_indexes_inverted.to_csv("Datasets/game_indexes_inverted.csv")
#######################################################################################
if create_user_indexes:
user_df = pd.read_csv("Datasets/user_df_with_ratings.csv")
index_df = user_df["userId"].unique()
index_df = pd.DataFrame(index_df)
index_df = index_df.T
index_df = (index_df.T.reset_index().T.reset_index(drop=True).set_axis([f'{name}' for name in user_df["userId"].unique()], axis=1))
index_df.drop(1, inplace=True)
index_df.to_csv("Datasets/user_indexes.csv")
#######################################################################################
if create_user_indexes_inverted:
user_indexes = pd.read_csv("Datasets/user_indexes.csv")
user_indexes.drop("Unnamed: 0", axis = 1, inplace=True)
users = user_indexes.columns
indexes = [i for i in range(len(users))]
user_indexes_inverted = pd.DataFrame(users, index=indexes, columns=["userId"])
user_indexes_inverted = user_indexes_inverted.T
user_indexes_inverted.to_csv("Datasets/user_indexes_inverted.csv")
########################################################################################
if create_similarity_matrix:
import scipy.stats
import numpy as np
ratings_df = pd.read_csv("Datasets/user_df_with_all_ratings.csv")
ratings_df.drop('userId', axis=1, inplace=True)
ratings_df.fillna(99, inplace=True)
def similarity_pearson(x, y):
return scipy.stats.pearsonr(x, y)[0]
users = len(ratings_df)
similarity_matrix = []
for i in range(users):
for j in range(users):
similarity_matrix.append(similarity_pearson(ratings_df.iloc[i,:], ratings_df.iloc[j,:]))
if i%1000 == 0:
print(f"Currently:{(i*users + j) / (users*users) * 100}%")
textfile = open("backup_file.txt", "w")
for element in similarity_matrix:
textfile.write(str(element) + " ")
textfile.close()
similarity_matrix = np.array(similarity_matrix)
similarity_df = pd.DataFrame(data = similarity_matrix.reshape(users, users))
similarity_df.to_csv("Datasets/users_similarity_df.csv")
#########################################################################################
if overwrite_data:
user_df = pd.read_csv("Datasets/user_df_with_ratings.csv")
main_df = pd.read_csv("Datasets/game_indexes.csv")
user_df.drop("Unnamed: 0", axis = 1, inplace=True)
user_df.drop("timePlayed", axis = 1, inplace=True)
main_df.drop("Unnamed: 0", axis = 1, inplace=True)
if normalize_ratings:
user_df = pd.read_csv("Datasets/user_df_with_ratings.csv")
ratings_df = pd.read_csv("Datasets/user_df_with_all_ratings.csv")
ratings_df.drop("Unnamed: 0", axis = 1, inplace=True)
def create_rating_vector(userId, verbose = False):
rating_vector = []
current_user = user_df.loc[user_df["userId"] == userId]
for game in main_df.columns:
if game in list(current_user["game"]):
rating = list(current_user.loc[user_df["game"] == game]["rating"])[0]
rating_vector.append(rating)
if verbose:
print(f"he played {game} and gave it a {rating}")
else:
rating_vector.append(np.NaN)
return rating_vector
if overwrite_data:
for user in user_df["userId"].unique():
main_df_length = len(main_df)
main_df.loc[main_df_length] = create_rating_vector(userId = user)
print(main_df_length / len(user_df["userId"].unique()) * 100, "%")
main_df.to_csv("Datasets/user_df_with_all_ratings.csv")
if normalize_ratings:
ratings_df.drop([0], axis = 0, inplace=True)
ratings_df['userId'] = user_df['userId'].unique()
ratings_df.set_index('userId', inplace=True)
def normalize(dataframe):
dataframe_mean = dataframe.mean(axis=1)
return dataframe.subtract(dataframe_mean, axis = 0)
ratings_df = normalize(ratings_df)
ratings_df.to_csv("Datasets/user_df_with_all_ratings_normalized.csv")