Khalil micaleel

NCF

There are difference between GMF, MLP AND NeuMF

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	Explore how embeddings are created with Autoencoders
	* https://blog.eto.ai/vector-similarity-search-with-duckdb-44dec043532a
	* https://medium.com/kirey-group/autoembedder-training-embedding-layers-on-unsupervised-tasks-fc364c0f6eec
	* https://www.kaggle.com/code/marlesson/autoencoder-embedding-for-food/notebook

	regs = regs or [0, 0]
	user_input = Input(shape=(1,), dtype="int32", name="user_input")
	item_input = Input(shape=(1,), dtype="int32", name="item_input")

	user_embeds = Embedding(
	input_dim=num_users,
	output_dim=embed_size,
	embeddings_initializer="normal",
	embeddings_regularizer=l2(regs[0]),
	input_length=1,

	% solvers for DEs in ode.m

	% TODO Insert relevant values into `range` and `initial_conditions`
	% NOTE initial_conditions is a vector (ordered collection) of input values that will
	% unpacked in ode.m file into the X, L, S, N, and H variables respectively.
	range = [1:50]
	initial_conditions = [600, 3, 0, 3, 3]

	# Use ode45 solver
	[time_solution, var_solution] = ode45(@ode_sys, range, initial_conditions)

	% General constants obtained from Section 3.1
	mu_X_max = 0, 275
	K_XS = 0.0624
	K_iXS = 12.40
	K_XN = 0.0812
	K_iXN = 0.6250
	K_I = 45.50
	K_iI = 297.75
	A_0X = 0.6175
	E_aX = 25.9243

	from typing import List, Tuple
	import numpy as np
	import os
	import pandas as pd


	DATA_DIR = ...

	data = pd.read_csv(
	os.path.join(DATA_DIR, "ml-100k", "u.data"),

	"""
	Yelp review processor.

	The module clusters Yelp features based on their sentence similarities.


	This module takes a set of reviews as inputs, then generates two files:
	- items.csv - item and their properties.
	- extractions.csv - features and sentiments mined from item reviews.
	"""

	ratings_df = load_data(ML100K, implicit=IMPLICIT, n_users=NUM_USERS)
	train_df, test_df = train_test_split(ratings_df, loo=True)
	sampler = NegativeSampler(ratings_df['item_id'].unique()).fit(train=train_df, test=test_df)
	train_df = sampler.transform(train_df, train=True, size=50)
	test_df = sampler.transform(test_df, train=False, size=10)


	gmf = GMF(n_users=ratings_df.user_id.nunique(), n_items=ratings_df.item_id.nunique(), implicit=IMPLICIT)
	reco = Recommender(keras_model=gmf, loss='binary_crossentropy', optimizer='adam', )
	reco.fit(data=train_df, verbose=True, epochs=10)