katipogluMustafa

class NetflixDataset(Dataset):
    def load_movies(movies_path, movies_col_names=('item_id', 'year', 'title')):
        movies = pd.read_csv(movies_path, encoding='ISO-8859-1', header=None, names=movies_col_names).set_index('item_id')
        movies['year'].replace([np.inf, -np.inf, np.nan], 0, inplace=True)
        movies['year'] = movies['year'].astype(int)
        movies = movies.reindex(columns=['title', 'year'])
        
        # From the netflix prize dataset, I will only be using the first part which contains 4449 unique movies. 
        # That is why I will be truncating the other movies but if you load all the netflix data, remove this line.
        movies = movies[:4499]    # Keep only the first 4499 movies of the dataset

katipogluMustafa

class NetflixDataset(Dataset):
  def load_movies(self):
    movies = self.__read_movies_data_from_file()
    movies = NetflixDataset.__replace_invalid_years_with_zero(movies)
    movies = NetflixDataset.__convert_movies_year_format_to_int(movies)
    movies = NetflixDataset.__interchange_movies_title_and_year(movies)
    return NetflixDataset.__get_only_first_quarter_of_all_movies_for_performance(movies)

  def __init__(self, movies_file_path):
    self.movies_file_path    = movies_file_path 

katipogluMustafa

class TimeConstraint:
    """
    TimeConstraint is a constraint on the timestamp of the movie ratings.
    We classify a TimeConstraint as either max_time_constraint or time_bin_constraint.
    max_time_constraint is used to simulate real life in which we do not know the future but all the data up until one point in time.
    time_bin_constraint is used to grab a portion of a time interval where starting and ending points are strictly defined and data is well known.
    """
    
    def __init__(self, end_dt, start_dt=None):
        """

katipogluMustafa

from constraints.interval import Interval, TimebinInterval, MaxLimitInterval

class TimeConstraint:
  def is_valid_timebin(interval: Interval):
    return TimeConstraint.__is_expected_class(interval, TimebinInterval) and interval.is_valid()
  
  def is_valid_max_limit(interval: Interval):
    return TimeConstraint.__is_expected_class(interval, MaxLimitInterval) and interval.is_valid()
  
  def __is_expected_class(interval: Interval, target_class: type):

katipogluMustafa

class Interval:
  def __init__(self, interval_beginning_datetime=None, interval_end_datetime=None):
    self.__interval_beginning = interval_beginning_datetime
    self.__interval_end = interval_end_datetime

  def get_interval(self):
    return self.__interval_beginning, self.__interval_end

  def is_valid(self):
    return False

katipogluMustafa

class NetflixDataset(Dataset):
    def load_ratings(ratings_path, ratings_col_names):    
        if not os.path.isfile(ratings_path) or not ratings_col_names:
            return None
        
        ratings_raw = pd.read_csv(ratings_path, header=None, names=['user_id', 'rating', 'timestamp'], usecols=[0, 1, 2])
        ratings_raw['rating'] = ratings_raw['rating'].astype(float)
        # Find empty rows to slice dataframe for each movie
        temp_movies = ratings_raw[ratings_raw['rating'].isna()]['user_id'].reset_index()
        movie_indexes = [[index, int(movie[:-1])] for index, movie in temp_movies.values]

katipogluMustafa

class NetflixDataset(Dataset):
  def load_ratings(self):
    if not Dataset.is_valid_input_file(self.ratings_file_path):
      raise InvalidDatasetInputFilePath
    unstructured_ratings = self.__read_ratings_data_from_file()
    ratings = NetflixDataset.__structure_ratings_dataframe(unstructured_ratings)
    ratings = NetflixDataset.__reduce_dataset_size_by_removing_low_active_user_data(ratings)
    Dataset.sort_ratings_by_timestamp(ratings)
    return ratings

katipogluMustafa

# Source: https://github.com/NicolasHug/Surprise/blob/master/surprise/prediction_algorithms/matrix_factorization.pyx
"""
the :mod:`matrix_factorization` module includes some algorithms using matrix
factorization.
"""

from __future__ import (absolute_import, division, print_function,
                        unicode_literals)

cimport numpy as np  # noqa

katipogluMustafa

<?xml version="1.0" encoding="utf-8"?>
<manifest xmlns:android="http://schemas.android.com/apk/res/android"
    package="com.teknoarktik.android.greenhouse">
  
    <!-- Put the following two uses permissions statemens for remote db connection -->
  
    <uses-permission android:name="android.permission.INTERNET" />
    <uses-permission android:name="android.permission.ACCESS_NETWORK_STATE" />

    <application

katipogluMustafa

import java.sql.Connection;
import java.sql.DriverManager;

public class Database {

    private Connection connection;
    
    // For Amazon Postgresql
    // private final String host = "ssprojectinstance.csv2nbvvgbcb.us-east-2.rds.amazonaws.com"