pip install git+https://github.com/coreylynch/pyFM

from pyfm import pylibfm
from sklearn.feature_extraction import DictVectorizer
import numpy as np
train = [
	{"user": "1", "item": "5", "age": 19},
	{"user": "2", "item": "43", "age": 33},
	{"user": "3", "item": "20", "age": 55},
	{"user": "4", "item": "10", "age": 20},
]
v = DictVectorizer()
X = v.fit_transform(train)
print(X.toarray())
[[ 19.   0.   0.   0.   1.   1.   0.   0.   0.]
 [ 33.   0.   0.   1.   0.   0.   1.   0.   0.]
 [ 55.   0.   1.   0.   0.   0.   0.   1.   0.]
 [ 20.   1.   0.   0.   0.   0.   0.   0.   1.]]
y = np.repeat(1.0,X.shape[0])
fm = pylibfm.FM()
fm.fit(X,y)
fm.predict(v.transform({"user": "1", "item": "10", "age": 24}))

import numpy as np
from sklearn.feature_extraction import DictVectorizer
from pyfm import pylibfm

# Read in data
def loadData(filename,path="ml-100k/"):
    data = []
    y = []
    users=set()
    items=set()
    with open(path+filename) as f:
        for line in f:
            (user,movieid,rating,ts)=line.split('\t')
            data.append({ "user_id": str(user), "movie_id": str(movieid)})
            y.append(float(rating))
            users.add(user)
            items.add(movieid)

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

(train_data, y_train, train_users, train_items) = loadData("ua.base")
(test_data, y_test, test_users, test_items) = loadData("ua.test")
v = DictVectorizer()
X_train = v.fit_transform(train_data)
X_test = v.transform(test_data)

# Build and train a Factorization Machine
fm = pylibfm.FM(num_factors=10, num_iter=100, verbose=True, task="regression", initial_learning_rate=0.001, learning_rate_schedule="optimal")

fm.fit(X_train,y_train)
Creating validation dataset of 0.01 of training for adaptive regularization
-- Epoch 1
Training MSE: 0.59477
-- Epoch 2
Training MSE: 0.51841
-- Epoch 3
Training MSE: 0.49125
-- Epoch 4
Training MSE: 0.47589
-- Epoch 5
Training MSE: 0.46571
-- Epoch 6
Training MSE: 0.45852
-- Epoch 7
Training MSE: 0.45322
-- Epoch 8
Training MSE: 0.44908
-- Epoch 9
Training MSE: 0.44557
-- Epoch 10
Training MSE: 0.44278
...
-- Epoch 98
Training MSE: 0.41863
-- Epoch 99
Training MSE: 0.41865
-- Epoch 100
Training MSE: 0.41874

# Evaluate
preds = fm.predict(X_test)
from sklearn.metrics import mean_squared_error
print("FM MSE: %.4f" % mean_squared_error(y_test,preds))
FM MSE: 0.9227

import numpy as np
from sklearn.feature_extraction import DictVectorizer
from sklearn.cross_validation import train_test_split
from pyfm import pylibfm

from sklearn.datasets import make_classification

X, y = make_classification(n_samples=1000,n_features=100, n_clusters_per_class=1)
data = [ {v: k for k, v in dict(zip(i, range(len(i)))).items()}  for i in X]

X_train, X_test, y_train, y_test = train_test_split(data, y, test_size=0.1, random_state=42)

v = DictVectorizer()
X_train = v.fit_transform(X_train)
X_test = v.transform(X_test)

fm = pylibfm.FM(num_factors=50, num_iter=10, verbose=True, task="classification", initial_learning_rate=0.0001, learning_rate_schedule="optimal")

fm.fit(X_train,y_train)

Creating validation dataset of 0.01 of training for adaptive regularization
-- Epoch 1
Training log loss: 1.91885
-- Epoch 2
Training log loss: 1.62022
-- Epoch 3
Training log loss: 1.36736
-- Epoch 4
Training log loss: 1.15562
-- Epoch 5
Training log loss: 0.97961
-- Epoch 6
Training log loss: 0.83356
-- Epoch 7
Training log loss: 0.71208
-- Epoch 8
Training log loss: 0.61108
-- Epoch 9
Training log loss: 0.52705
-- Epoch 10
Training log loss: 0.45685

# Evaluate
from sklearn.metrics import log_loss
print "Validation log loss: %.4f" % log_loss(y_test,fm.predict(X_test))
Validation log loss: 1.5025