victorkohler

#-------------
# HYPERPARAMS
#-------------

num_neg = 6
latent_features = 8
epochs = 20
batch_size = 256
learning_rate = 0.001

victorkohler

#-------------
# HYPERPARAMS
#-------------

num_neg = 4
latent_features = 8
epochs = 20
batch_size = 256
learning_rate = 0.001

victorkohler

for epoch in range(epochs):

    # Get our training input.
    user_input, item_input, labels = get_train_instances()

    # Generate a list of minibatches.
    minibatches = random_mini_batches(user_input, item_input, labels)

    # This has noting to do with tensorflow but gives

victorkohler

import tensorflow as tf
import numpy as np
import pandas as pd
import math
import heapq
from tqdm import tqdm

# Load and prepare our data.
uids, iids, df_train, df_test, df_neg, users, items, item_lookup = load_dataset()

victorkohler

def get_train_instances():
     """Samples a number of negative user-item interactions for each
     user-item pair in our testing data.

     Returns:
         user_input (list): A list of all users for each item
         item_input (list): A list of all items for every user,
             both positive and negative interactions.
         labels (list): A list of all labels. 0 or 1.
     """

victorkohler

def get_negatives(uids, iids, items, df_test):
    """Returns a pandas dataframe of 100 negative interactions
    based for each user in df_test.

    Args:
        uids (np.array): Numpy array of all user ids.
        iids (np.array): Numpy array of all item ids.
        items (list): List of all unique items.
        df_test (dataframe): Our test set.

victorkohler

import pandas as pd
import numpy as np
import pickle

def load_dataset():
    """
    Loads the lastfm dataset from a pickle file into a pandas dataframe
    and transforms it into the format we need. 
    
    We then split it into a training and a test set.

victorkohler

#---------------------
# MAKE RECOMMENDATION
#---------------------

def make_recommendation(user_id=None, num_items=10):
    """Recommend items for a given user given a trained model

    Args:
        user_id (int): The id of the user we want to create recommendations for.

victorkohler

#-----------------------
# FIND SIMILAR ARTISTS
#-----------------------

def find_similar_artists(artist=None, num_items=10):
    """Find artists similar to an artist.

    Args:
        artist (str): The name of the artist we want to find similar artists for

victorkohler

#------------------
# GRAPH EXECUTION
#------------------

# Run the session. 
session = tf.Session(config=None, graph=graph)
session.run(init)

# This has noting to do with tensorflow but gives