def test_random_train_test_split(test_percentage):
data = fetch_movielens()["train"]
train, test = random_train_test_split(data, test_percentage=test_percentage)
assert test.nnz / float(data.nnz) == test_percentage
_assert_disjoint(train, test)
def test_basic_fetching():
data = fetch_movielens()
assert isinstance(data['train'], sp.coo_matrix)
assert isinstance(data['test'], sp.coo_matrix)
assert data['train'].shape == data['test'].shape
assert data['train'].shape == (943, 1682)
assert (data['train'].getnnz() + data['test'].getnnz()) == 100000
assert data['item_features'].shape == (1682, 1682)
assert len(data['item_feature_labels']) == 1682
assert data['item_feature_labels'] is data['item_labels']
data = fetch_movielens(genre_features=True)
assert data['item_features'].shape == (1682, len(data['item_feature_labels']))
assert data['item_feature_labels'] is not data['item_labels']
with pytest.raises(ValueError):
data = fetch_movielens(indicator_features=False, genre_features=False)
def test_movielens_genre_accuracy():
item_features = fetch_movielens(indicator_features=False, genre_features=True)[
"item_features"
]
assert item_features.shape[1] < item_features.shape[0]
model = LightFM(random_state=SEED)
model.fit_partial(train, item_features=item_features, epochs=10)
train_predictions = model.predict(train.row, train.col, item_features=item_features)
test_predictions = model.predict(test.row, test.col, item_features=item_features)
assert roc_auc_score(train.data, train_predictions) > 0.75
assert roc_auc_score(test.data, test_predictions) > 0.69
def test_movielens_both_accuracy():
"""
Accuracy with both genre metadata and item-specific
features shoul be no worse than with just item-specific
features (though more training may be necessary).
"""
item_features = fetch_movielens(indicator_features=True, genre_features=True)[
"item_features"
]
model = LightFM(random_state=SEED)
model.fit_partial(train, item_features=item_features, epochs=15)
train_predictions = model.predict(train.row, train.col, item_features=item_features)
test_predictions = model.predict(test.row, test.col, item_features=item_features)
assert roc_auc_score(train.data, train_predictions) > 0.84
assert roc_auc_score(test.data, test_predictions) > 0.75
def get_movielens_100k(min_positive_score=4, negative_value=0):
movielens_100k_dict = datasets.fetch_movielens(indicator_features=True, genre_features=True)
def flip_ratings(ratings_matrix):
ratings_matrix.data = np.array([1 if rating >= min_positive_score else negative_value
for rating in ratings_matrix.data])
return ratings_matrix
test_interactions = flip_ratings(movielens_100k_dict['test'])
train_interactions = flip_ratings(movielens_100k_dict['train'])
# Create indicator features for all users
num_users = train_interactions.shape[0]
user_features = sp.identity(num_users)
# Movie titles
titles = movielens_100k_dict['item_labels']
return train_interactions, test_interactions, user_features, movielens_100k_dict['item_features'], titles
dtype=np.int32).tocsr()
return (user_features.tocsr(),
item_features.tocsr())
def _binarize(dataset):
positives = dataset.data >= 4.0
dataset.data[positives] = 1.0
dataset.data[np.logical_not(positives)] = -1.0
return dataset
movielens = fetch_movielens()
train, test = _binarize(movielens['train']), _binarize(movielens['test'])
(train_user_features,
train_item_features) = _get_feature_matrices(train)
(test_user_features,
test_item_features) = _get_feature_matrices(test)
def test_movielens_accuracy():
model = LightFM(random_state=SEED)
model.fit_partial(train,
epochs=10)
import numpy as np from lightfm.datasets import fetch_movielens from lightfm import LightFM import scipy.sparse as sp i = 0 data = fetch_movielens(indicator_features=True, genre_features=True) print data ''' model = LightFM(loss='warp') #train model model.fit(data['train'], epochs=30, num_threads=2) pos_values_for_a_user = data["item_labels"][data["train"].tocsr()[942].indices] #print data["train"].tocsr()[24].shape scores = model.predict(942,np.arange(data["train"].shape[0])) top_items = data["item_labels"][np.argsort(-scores)] top_N = [str(x) for x in top_items[:5]] print top_N for x in top_items[:5] : print x for i in range(len(top_items)) :
"""
DOCSTRING
"""
import lightfm
import lightfm.datasets as datasets
import numpy
data = datasets.fetch_movielens(min_rating=4.0)
print (repr(data['train']))
print (repr(data['test']))
model = lightfm.LightFM(loss='warp')
model.fit(data['train'], epochs=30, num_threads=2)
def sample_recommendation(model, data, user_ids):
n_users, n_items = data['train'].shape
for user_id in user_ids:
known_positives = data['item_labels'][data['train'].tocsr()[user_id].indices]
scores = model.predict(user_id, numpy.arange(n_items))
top_items = data['item_labels'][numpy.argsort(-scores)]
print ("User %s" % user_id)
print (" Known positives:")
for x in known_positives[:3]:
print (" %s" % x)
print (" Recommended:")
for x in top_items[:3]:
print (" %s" % x)
sample_recommendation(model, data, [3, 25, 450])
# -*- coding: utf-8 -*-
"""Untitled1.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/17_ZWfnM3s4qxUmCUEKNWKjZMaZlAuybl
"""
# importing the dependencies
import numpy as np
from lightfm.datasets import fetch_movielens # movie dataset
from lightfm import LightFM # library for various recommondation models
data = fetch_movielens(
min_rating=4) # using only movies with 4 or higher rating
model = LightFM(loss='warp') # defining the loss function
model.fit(data['train'], epochs=50, num_threads=4) # training the model
# created a function to recommend movies to the user
def my_rec(model, data,
user_ids): # trained model, our dataset, and the userid
n_users, n_item = data[
'train'].shape # calculating the number of user and movies
for id in user_ids:
# extrating the known positive movie means movies already liked by user
known_positive = data['item_labels'][data['train'].tocsr()
[user_ids].indices]
score = model.predict(
#!/usr/bin/env python
# coding: utf-8
# In[ ]:
# Import libaries
import numpy as np
from lightfm.datasets import fetch_movielens # movielens is big csv file containing 100K movies ratings from 1K users on 1700 movies
from lightfm import lightFM
# Fetch and format data
data = fetch_movielens(
min_rating=4.0
) # creat a variable called "data" to store data set in; min_rating equals 4.0 means we only include the movies with a rating of 4 or higher
# print training and testing data
print(repr(data['train']))
print(repr(data['test']))
# creat and initialize model
model = LightFM(
loss='warp'
) # choosing a Weighted Approximate-Rank Pairwise loss function called warp, which will help us creat recommendations for each users by looking at the existing users rating pairs and predicting rankings for each. It uses the gradient descent algorithm to iteratively find the weights that improve our prediction over time. This model takes into account book the user's past rating history content and similar users ratings collaborative
# train the model
model.fit(data['train'], epochs=30, num_threads=2)
# define a sample recommendation function
def sampel_recommendation(model, data, user_ids):
n_users, n_items = data[
from personal.MaurizioFramework.MatrixFactorization.Cython.MF_BPR_Cython import MF_BPR_Cython
# from utils.datareader import Datareader
from recommenders.similarity.similarity import *
import numpy as np
import scipy.sparse as sps
from tqdm import tqdm
import gc
from sklearn.model_selection import train_test_split
import lightfm.datasets as ld
import sys
# sys.stdout = open("mf_prova.txt", "w")
data = ld.fetch_movielens(min_rating=1.0)
train = sp.csr_matrix(data["train"])
test = sp.csr_matrix(data["test"])
train.data = np.ones(len(train.data))
test.data = np.ones(len(test.data))
print(train.shape, train.nnz)
print(test.shape, test.nnz)
print(len(train.data), np.sum(train.data))
print(len(test.data), np.sum(test.data))
print(train[100])
print(test[100])
epochs = 500
factors = 42
learning = 0.001
import numpy as np
from lightfm.datasets import fetch_movielens
from lightfm import LightFM
data = fetch_movielens(
min_rating=4.0
) #data holds all the fetched and formated data with a minimum rating of 4.0
print(repr(data['train'])) #print out the size of training and test data
print(repr(data['test']))
#create model
model = LightFM(
loss='warp') # choose a loss = weighted approximate-rank pairwise
#train the model
model.fit(data['train'], epochs=30, num_threads=2)
def sample_recommendation(model, data, user_ids):
#number of users and items
n_users, n_items = data['train'].shape
#go through every user and assigne a recommendation
for user_id in user_ids:
#store the movies they already like
liked_movies = data['item_labels'][data['train'].tocsr()
[user_id].indices]
#our prediction
score = model.predict(user_id, np.arange(n_items))
import numpy as np
from flask import Flask, request, render_template
from models.simple_recommender_model import lfm_model
from lightfm.datasets import fetch_movielens
model = lfm_model() # import model
data = fetch_movielens(min_rating=4.0) # import data
app = Flask(__name__)
@app.route('/')
def my_form():
return render_template('my-form.html')
@app.route("/predict", methods=['POST'])
def sample_recommendation(model=model, data=data):
result = request.form['number']
input_user_ids = np.fromstring(result, dtype=int,
sep=' ') # User input data
n_users, n_items = data['train'].shape
x_list = []
i_list = []
for user_id in input_user_ids:
known_positives = data['item_labels'][data['train'].tocsr()
[user_id].indices]
from lightfm import LightFM from lightfm.datasets import fetch_movielens from lightfm.evaluation import precision_at_k data = fetch_movielens(data_home='../../_dataset/recommend', min_rating=5.0) # Instantiate and train the model model = LightFM(loss='warp') model.fit(data['train'], epochs=30, num_threads=2) # Evaluate the trained model test_precision = precision_at_k(model, data['test'], k=5).mean() print(test_precision) print(data['train'])
def main():
args = get_arguments()
data = fetch_movielens(min_rating=4.0)
model = train_model(data['train'])
sample_recommendations(model, data['train'], data['item_labels'], args.users.split(','))
def fetch_data(): return fetch_movielens(min_rating=4.0)
import numpy as np from lightfm.datasets import fetch_movielens #fetch_movielens is a method from lightfm import LightFM #importing lightfm class to create a model later from lightfm.evaluation import precision_at_k #fetch the data and format it data = fetch_movielens(min_rating=4.0) #limiting movies below rating 4.0 #This method creates an interaction matrix from the data. #The data has movie names and ratings of it from ALL users #print(repr(data['train'])) #print(repr(data['test'])) #Create model using the lightfm model, pass the value 'warp' to the loss parameter #Loss means the loss function and it measures the diff between the model's prediction and the actual output #warp means Weighted Approximate-Rank Pairwise. It helps us create recommendations #for each user by looking at the existing user-rating pairs and predicting #rankings for each. It uses the gradient descent algorithm to iteratively #find the weights that improve our prediction over time. It takes into #account a user's past ratings and similar user's ratings for the same title #thus Content+Collaborative model_1 = LightFM(loss='warp') model_1.fit(data['train'], epochs=30, num_threads=2) test_precision_1 = precision_at_k(model_1, data['test'], k=3).mean() model_2 = LightFM(loss='warp-kos', n=10, k=5) model_2.fit(data['train'], epochs=30, num_threads=2)
#importing all libraries
import numpy as np
import pandas as pd
from lightfm.datasets import fetch_movielens
from lightfm import LightFM
#fetching of movies having rating 5
m_u_data = fetch_movielens(min_rating=5.0)
print(m_u_data)
print(repr(m_u_data['train']))
print(repr(m_u_data['test']))
print(m_u_data['train'])
print(m_u_data['item_labels'])
#model creation
model = LightFM(loss='warp')
#fiting of a model with epoch 40
model.fit(m_u_data['train'], epochs=40, num_threads=2)
def recommendation(model, m_u_data, uids):
n_users = m_u_data['train'].shape[0]
n_items = m_u_data['train'].shape[1]
for uid in uids:
known_positive = m_u_data['item_labels'][m_u_data['train'].tocsr()
[uid].indices]
scores = model.predict(uid, np.arange(n_items))
top_items = m_u_data['item_labels'][np.argsort(-scores)]
print("User :"******"known movies :")
import numpy as np
from lightfm.datasets import fetch_movielens
from lightfm import LightFM
data = fetch_movielens(data_home='.', min_rating=4.0)
#print training and testing data
print(repr(data['train']))
print(repr(data['test']))
model = LightFM(loss='warp')
model.fit(data['train'], epochs=30, num_threads=2)
def sample_recommendation(model, data, user_ids):
n_users, n_items = data['train'].shape
for user_id in user_ids:
known_positives = data['item_labels'][data['train'].tocsr()
[user_id].indices]
scores = model.predict(user_id, np.arange(n_items))
top_items = data['item_labels'][np.argsort(-scores)]
#print out the results
print("User %s" % user_id)
print(" --Known Movies:")
for x in known_positives[:3]:
print(" %s" % x)
print(" --Recommended Movies:")
import numpy as np
from lightfm.datasets import fetch_movielens
from lightfm import LightFM # Will we helpfull in creating a Model.
# fetch data and format it.
data = fetch_movielens(
min_rating=4.0
) # only collecting the movies with the rating of 4.0 or higher
# this method will create a interaction matrix from CSV file and store it in our "data" variable as a dictionary.
print(repr(data['train'])) # 90%
print(repr(data['test'])) # 10%
'''
# Store our model in a variable named "model"
# Initialize a Lightfm class using a single parameter called "lose"
# Lose means a Loss function and it measures the difference between the model prediction and desired output
# We want to minimize it during training so that our model gets more accurate over time in its prediction.
# Here we are using a Loss called WARP = Weighted Approximate-Rate PairWise.
'''
model = LightFM(loss='warp')
'''
# Wrap help us create recommendation for each users by looking at the existing user rating pairs,
# and predicting ratings for each.
# It uses the gradiebt descent algorithm to iteratively find the weights
# that imporve our prediction overtime. Using users past rating history and similar users rating.
# Content + Collaborative = Hybrid System
'''
# Use fit method to trian our model
# fit takes 3 parameters
import numpy as np
from lightfm.datasets import fetch_movielens
from lightfm import LightFM
# fetch data and format it
data = fetch_movielens(
min_rating=4.0) # only collect the movies with a rating of 4 or higher
# the structure of data
#first: train
# first: test
# first: item_features
# first: item_feature_labels
# first: item_labels
# print training and testing data
# print(data['train'])
# for single_data in data['train']:
# print("first:",single_data[0],"second",data[1])
# print("first:",)
# print(repr(data['train']))
# print(repr(data['test']))
'''repr()函数将对象转化为供解释器读取的形式'''
# 下面是例子，相当于加了括号
# >>> dict = {'runoob': 'runoob.com', 'google': 'google.com'};
# >>> repr(dict)
# "{'google': 'google.com', 'runoob': 'runoob.com'}"
# m1=data['train'].tocsr()
# [rows,cols]=m1.shape
# for i in range(5):
# for j in range(5):
import numpy as np
from lightfm.datasets import fetch_movielens
from lightfm import LightFM
#fetch date and format it
data = fetch_movielens(min_rating=4.0) #Data is a CSV file that contens movies
#print training and testing data
print(repr(data['train']))
print(repr(data['test']))
#Create model
model = LightFM(loss='warp')
#Train model
model.fit(data['train'], epochs=30, num_threads=2)
def sample_recommendation(model, data, user_ids):
#Number of users and movies in traing data
n_users, n_items = data['train'].shape
#Generate recommendations for each user we input
for user_id in user_ids:
#Movies they already like
known_positives = data['item_labels'][data['train'].tocsr()[user_id].indices]
#Movies our model predicts they will like
scores = model.predict(user_id, np.arange(n_items))
#Rank them in order of most liked to least
top_items = data['item_labels'][np.argsort(-scores)]
#Print Out the results
print("Users {}".format(user_id))
import numpy as np
from lightfm.datasets import fetch_movielens
from lightfm import LightFM
# fetch data and format it
data = fetch_movielens(min_rating=0.4)
#print training and testing data
print(repr(data['train']))
print(repr(data['test']))
# create model
model = LightFM(loss='warp')
model.fit(data['train'], epochs=30, num_threads=2)
def sample_recommendation(model, data, user_ids):
# n. of users and movies in training data
n_users, n_items = data['train'].shape
#generate recommendations for each user we input
for user_id in user_ids:
# movies they already liked
known_positives = data['item_labels'][data['train'].tocsr()
[user_id].indices]
# movies our model predict they will like
scores = model.predict(user_id, np.arange(n_items))
# 案例
from lightfm import LightFM
from lightfm.datasets import fetch_movielens
from lightfm.evaluation import precision_at_k
import numpy as np
# Load the MovieLens 100k dataset. Only five
# star ratings are treated as positive.
data = fetch_movielens(data_home='./data', min_rating=5.0)
print(data['train'])
# Instantiate and train the model
model = LightFM(loss='warp')
model.fit(data['train'], epochs=30, num_threads=2)
# Evaluate the trained model
test_precision = precision_at_k(model, data['test'], k=5).mean()
print("Train precision: %.2f" % precision_at_k(model, data['train'], k=5).mean())
print("Test precision: %.2f" % precision_at_k(model, data['test'], k=5).mean())
def sample_recommendation(model, data, user_ids):
n_users, n_items = data['train'].shape
for user_id in user_ids:
known_positives = data['item_labels'][data['train'].tocsr()[user_id].indices]
print(data['train'].tocsr())
print(data['train'].tocsr()[user_id])
print(data['train'].tocsr()[user_id].indices)
# Uses a predefined data set of users and movies to generate movie recommendations for a particular user.
# Uses the 100k MovieLens database and the LightFM package
import numpy as np
from lightfm.datasets import fetch_movielens
from lightfm import LightFM
#fetches and formats the data from dataset
data = fetch_movielens()
#training data - data['train']
#testing data - data['test']
#create a model for recommendations
model = LightFM(loss='warp')
#train the model with the training data
model.fit(data['train'], epochs=30, num_threads=2)
def recommendation(model, data, user_ids):
#no. of users and movies in the training data
num_users, num_movies = data['train'].shape
#generate recommendation for each user in the user_ids array
for user in user_ids:
#movies they like
liked_movies = data['item_labels'][data['train'].tocsr()[user].indices]
#predicted movies
predicted = model.predict(user, np.arange(num_movies))
#rank them from most liked to least
from lightfm.datasets import fetch_movielens
from lightfm import LightFM
from lightfm.evaluation import precision_at_k
from lightfm.evaluation import recall_at_k
from lightfm.evaluation import auc_score
EPOCHS = 10
ALPHA = 1e-3
NUM_THREADS = 4
# fetch data
movielens = fetch_movielens(data_home='.')
for key, value in movielens.items():
print(key, type(value), value.shape)
train = movielens['train']
test = movielens['test']
# BPR model
model = LightFM(learning_rate=0.05, loss='bpr')
model = model.fit(train,
epochs=EPOCHS,
num_threads=NUM_THREADS)
train_precision = precision_at_k(model, train, k=10).mean()
test_precision = precision_at_k(model, test, k=10).mean()
train_auc = auc_score(model, train).mean()
test_auc = auc_score(model, test).mean()
print 'BPR model'
from flask import Flask, jsonify
import numpy as np
from lightfm.datasets import fetch_movielens
from lightfm import LightFM
import json
app = Flask(__name__)
data = fetch_movielens(min_rating=3.5)
model = LightFM(loss='warp')
model.fit(data['train'], epochs=30, num_threads=2)
def recommendation(model, data, user_ids):
n_users, n_items = data['train'].shape
for user_id in user_ids:
known_positives = data['item_labels'][data['train'].tocsr()[user_id].indices]
scores = model.predict(user_id, np.arange(n_items))
top_items = data['item_labels'][np.argsort(-scores)]
movies_to_send = []
for x in top_items[:10]:
movies_to_send.append(x)
return movies_to_send
@app.route('/')
def index():
to_display = recommendation(model, data, [1])
return jsonify(to_display)
def fetch_data(rating=4.0):
# fetch data and format it
print("Fetching Movie data with ratings: {0} and above.".format(rating))
data = fetch_movielens(min_rating=rating)
# len(data)
return data
import numpy as np
from lightfm.datasets import fetch_movielens #A large csv dataset that has 100K movie ratings from 1K users on 1700 movies
from lightfm import LightFM
#fetch and format data
data = fetch_movielens(
min_rating=4.0) #Only fetches movies rated 4.0 stars or higher
#print training and testing data we use repr to see just the shape of the data rather than every single piece
print(repr(data['train']))
print(repr(data['test']))
#create model
model = LightFM(loss='warp')
#train model
model.fit(data['train'], epochs=30, num_threads=2)
def sample_recommendation(model, data, user_ids):
#number of users and movies in training data
n_users, n_items = data['train'].shape
#generate recommendations for each inputted user
for user_id in user_ids:
#movies they already like
known_positives = data['item_labels'][data['train'].tocsr()
[user_id].indices]
#movies our model predicts they will like
scores = model.predict(user_id, np.arange(n_items))
import numpy as np
from lightfm.datasets import fetch_movielens
from lightfm import LightFM
dt = fetch_movielens(min_rating=5.0)
print("train - " + repr(dt['train']))
print("test - " + repr(dt['test']))
model = LightFM(loss='warp')
#train model
model.fit(dt['train'], epochs=30, num_threads=2)
#Modify this function so that it parses your dataset correctly to retrieve
#the necessary variables (products, songs, tv shows, etc.)
#then print out the recommended results
def sample_recommendation(model, data, user_ids):
#number of users and movies in training data
n_users, n_items = data['train'].shape
#generate recommendations for each user we input
for user_id in user_ids:
#movies they already like
known_positives = data['item_labels'][data['train'].tocsr()
[user_id].indices]
#movies our model predicts they will like
import numpy as np
from lightfm.datasets import fetch_movielens
from lightfm import LightFM
# fetch data and format it
data = fetch_movielens(min_rating=4.0)
# print training and testing data
print(repr(data['train']))
print(repr(data['test']))
# create model
model = LightFM(loss='warp')
#train model
model.fit(data['train'], epochs=30, num_threads=2)
def sample_recommendation(model, data, user_ids):
# number of users and movies in training datasets
n_users, n_items = data['train'].shape
# generate recommendations for each user we input
for user_id in user_ids:
# movies they already like
known_positives = data['item_labels'][data['train'].tocsr()[user_id].indices]
# movies our model predicts they will like
scores = model.predict(user_id, np.arange(n_items))
import numpy as np
from lightfm.datasets import fetch_movielens
from lightfm import LightFM
#fetch data and format it
#movies with rating 4.0 or higher
data = fetch_movielens(
min_rating=4.0
) # 100k movie rating from 1k users on 1700 movies - each user rated at least 20 movies 1-5
#print training and testing data
print(repr(data['train']))
print(repr(data['test']))
#create model
model = LightFM(
loss='warp'
) # warp = Weighted Approximate-Rank Pairwise - https://medium.com/@gabrieltseng/intro-to-warp-loss-automatic-differentiation-and-pytorch-b6aa5083187a
#train model
model.fit(data['train'], epochs=30)
def sample_recommendation(model, data, user_ids):
#number of users and movies in training data
n_users, n_items = data['train'].shape
#generate recommendations for each user we input
for user_id in user_ids:
#movies they already like
known_positives = data['item_labels'][data['train'].tocsr()
#collaborative systems and content based systems
import numpy as np
from lightfm.datasets import fetch_movielens
from lightfm import LightFM
#fetch data and format it
data = fetch_movielens(min_rating=4.0)
#print training and testing data
print(repr(data['train']))
print(repr(data['test']))
#create model
model = LightFM(loss='warp')
#train model
model.fit(data['train'], epochs=30, num_threads=2)
def sample_recommendation(model, data, user_ids):
#number of users and movies in training data
n_users, n_items = data['train'].shape
#generate recommendations for each user we input
for user_id in user_ids:
#movies they already like
known_positives = data['item_labels'][data['train'].tocsr()
[user_id].indices]
# instalar numpy, scipy e lightfm
import numpy as np
from lightfm.datasets import fetch_movielens
from lightfm import LightFM
dados = fetch_movielens(min_rating=4.0)
#Listando teste e treino dos dados
print(repr(dados['train']))
print(repr(dados['test']))
#Criando o model
# weighted approximate rank pairwise, ajuda a criar recomendaçõeos
model = LightFM(loss='warp')
# Treinando os modelos
model.fit(dados['train'], epochs=30, num_threads=2)
#Função de recomendação
def recomendacao(model, dados, user_ids):
n_users, n_items = dados['train'].shape
# Gerar recomendação de acordo com cada usuário inserido
