一、FM回归任务

1. 导库

import numpy as np

from sklearn.feature_extraction import DictVectorizer

from pyfm import pylibfm

DictVectorizer：它是可以将非结构化的数据转成array格式，这里将字典数据转成数组，一般情况下使用字典是因为在推荐系统中的矩阵一般是稀疏的，所以采用字典存储数据高效，可以不用存储大量无用的0

它转成数组的原理和OneHot差不多，对于数值型数据，它是保留原值，而对于非数值型则会将其利用OneHot进行编码，形成一个稀疏矩阵，每列表示同个特征不同值的选择门

2. 加载数据

"""加载数据"""

def loadData(filename, path="data/ml-100k/"):

   data = []

   y= []

   

   users = set()

   items = set()

   

   with open(path + filename) as f:

       for line in f:

           (user, movie, rating, ts) = line.split('\t')

           data.append({"user_id":str(user), "movie_id":str(movie)})

           y.append(float(rating))

           users.add(user)

           items.add(movie)

   

   return (data, np.array(y), users, items)

3. 获取数据

"""获取数据"""

(train_data, y_train, train_users, train_items) = loadData("ua.base")

(test_data, y_test, test_users, test_items) = loadData("ua.test")

4. 定义编码器

vec = DictVectorizer() # 将字典数据进行编码

X_train = vec.fit_transform(train_data)

X_test = vec.fit_transform(test_data)

5. 构建模型

fm = pylibfm.FM(num_factors = 10, # 交互特征维度

              num_iter = 10, # 迭代次数

              verbose = True, # 是否打印日志

              task = "regression", # 模式

              initial_learning_rate = 0.001, # 学习率

              learning_rate_schedule = "optimal")

fm.fit(X_train, y_train)

6. 衡量误差

pred = fm.predict(X_test)

from sklearn.metrics import mean_squared_error

print("FM MSE: %.4f" % mean_squared_error(y_test, pred)) # 均方误差

二、FM分类任务

1. 导库

import numpy as np

from sklearn.feature_extraction import DictVectorizer

from sklearn.model_selection import train_test_split

from sklearn.datasets import make_classification

from pyfm import pylibfm

2. 构造数据

"""加载数据"""

X, y = make_classification(n_samples=1000,n_features=100, n_clusters_per_class=1)

"""将数据转成字典类型"""

data = [ {v: k for k, v in dict(zip(i, range(len(i)))).items()}  for i in X]

3. 划分数据集

"""划分数据集"""

X_train, X_test, y_train, y_test = train_test_split(data, y, test_size = 0.1, random_state = 2021)

4. 定义编码器

vec = DictVectorizer()

X_train = vec.fit_transform(X_train)

X_test = vec.fit_transform(X_test)

5. 构建模型

"""构建模型"""

fm = pylibfm.FM(num_factors = 2,

              num_iter = 10,

              verbose = True,

              task = "classification",

              initial_learning_rate = 0.0001,

              learning_rate_schedule = "optimal")

fm.fit(X_train, y_train)

6. 衡量误差

"""衡量误差"""

from sklearn.metrics import log_loss

print("Validation log loss: %.4f" % log_loss(y_test,fm.predict(X_test)))