import myfm 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()) # print # [[ 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.asarray([0, 1, 1, 0]) fm = myfm.MyFMClassifier(rank=4) fm.fit(X,y) fm.predict(v.transform({"user": "1", "item": "10", "age": 24}))

import numpy as np from sklearn.preprocessing import OneHotEncoder from sklearn import metrics import myfm from myfm.utils.benchmark_data import MovieLens100kDataManager data_manager = MovieLens100kDataManager() df_train, df_test = data_manager.load_rating_predefined_split( fold=3 ) # Note the dependence on the fold def test_myfm(df_train, df_test, rank=8, grouping=None, n_iter=100, samples=95): explanation_columns = ["user_id", "movie_id"] ohe = OneHotEncoder(handle_unknown="ignore") X_train = ohe.fit_transform(df_train[explanation_columns]) X_test = ohe.transform(df_test[explanation_columns]) y_train = df_train.rating.values y_test = df_test.rating.values fm = myfm.MyFMRegressor(rank=rank, random_seed=114514) if grouping: # specify how columns of X_train are grouped group_shapes = [len(category) for category in ohe.categories_] assert sum(group_shapes) == X_train.shape[1] else: group_shapes = None fm.fit( X_train, y_train, group_shapes=group_shapes, n_iter=n_iter, n_kept_samples=samples, ) prediction = fm.predict(X_test) rmse = ((y_test - prediction) ** 2).mean() ** 0.5 mae = np.abs(y_test - prediction).mean() print("rmse={rmse}, mae={mae}".format(rmse=rmse, mae=mae)) return fm # basic regression test_myfm(df_train, df_test, rank=8) # rmse=0.90321, mae=0.71164 # with grouping fm = test_myfm(df_train, df_test, rank=8, grouping=True) # rmse=0.89594, mae=0.70481

import pandas as pd import numpy as np from myfm import MyFMRegressor, RelationBlock from sklearn.preprocessing import OneHotEncoder users = pd.DataFrame([ {'user_id': 1, 'age': '20s', 'married': False}, {'user_id': 2, 'age': '30s', 'married': False}, {'user_id': 3, 'age': '40s', 'married': True} ]).set_index('user_id') movies = pd.DataFrame([ {'movie_id': 1, 'comedy': True, 'action': False }, {'movie_id': 2, 'comedy': False, 'action': True }, {'movie_id': 3, 'comedy': True, 'action': True} ]).set_index('movie_id') ratings = pd.DataFrame([ {'user_id': 1, 'movie_id': 1, 'rating': 2}, {'user_id': 1, 'movie_id': 2, 'rating': 5}, {'user_id': 2, 'movie_id': 2, 'rating': 4}, {'user_id': 2, 'movie_id': 3, 'rating': 3}, {'user_id': 3, 'movie_id': 3, 'rating': 3}, ]) user_ids, user_indices = np.unique(ratings.user_id, return_inverse=True) movie_ids, movie_indices = np.unique(ratings.movie_id, return_inverse=True) user_ohe = OneHotEncoder(handle_unknown='ignore').fit(users.reset_index()) # include user id as feature movie_ohe = OneHotEncoder(handle_unknown='ignore').fit(movies.reset_index()) X_user = user_ohe.transform( users.reindex(user_ids).reset_index() ) X_movie = movie_ohe.transform( movies.reindex(movie_ids).reset_index() ) block_user = RelationBlock(user_indices, X_user) block_movie = RelationBlock(movie_indices, X_movie) fm = MyFMRegressor(rank=2).fit(None, ratings.rating.values, X_rel=[block_user, block_movie]) prediction_df = pd.DataFrame([ dict(user_id=user_id,movie_id=movie_id, user_index=user_index, movie_index=movie_index) for user_index, user_id in enumerate(user_ids) for movie_index, movie_id in enumerate(movie_ids) ]) predicted_rating = fm.predict(None, [ RelationBlock(prediction_df.user_index, X_user), RelationBlock(prediction_df.movie_index, X_movie) ]) prediction_df['prediction'] = predicted_rating print( prediction_df.merge(ratings.rename(columns={'rating':'ground_truth'}), how='left') )