def test_build_features(): users, items = 10, 100 dataset = Dataset(user_identity_features=False, item_identity_features=False) dataset.fit( range(users), range(items), ["user:{}".format(x) for x in range(users)], ["item:{}".format(x) for x in range(items)], ) # Build from lists user_features = dataset.build_user_features( [ (user_id, ["user:{}".format(x) for x in range(users)]) for user_id in range(users) ] ) assert user_features.getnnz() == users ** 2 item_features = dataset.build_item_features( [ (item_id, ["item:{}".format(x) for x in range(items)]) for item_id in range(items) ] ) assert item_features.getnnz() == items ** 2 # Build from dicts user_features = dataset.build_user_features( [ (user_id, {"user:{}".format(x): float(x) for x in range(users)}) for user_id in range(users) ], normalize=False, ) assert np.all(user_features.todense() == np.array([list(range(users))] * users)) item_features = dataset.build_item_features( [ (item_id, {"item:{}".format(x): float(x) for x in range(items)}) for item_id in range(items) ], normalize=False, ) assert np.all(item_features.todense() == np.array([list(range(items))] * items)) # Test normalization item_features = dataset.build_item_features( [ (item_id, {"item:{}".format(x): float(x) for x in range(items)}) for item_id in range(items) ] ) assert np.all(item_features.sum(1) == 1.0)
def test_build_features(): users, items = 10, 100 dataset = Dataset(user_identity_features=False, item_identity_features=False) dataset.fit( range(users), range(items), ["user:{}".format(x) for x in range(users)], ["item:{}".format(x) for x in range(items)], ) # Build from lists user_features = dataset.build_user_features( [(user_id, ["user:{}".format(x) for x in range(users)]) for user_id in range(users)] ) assert user_features.getnnz() == users ** 2 item_features = dataset.build_item_features( [(item_id, ["item:{}".format(x) for x in range(items)]) for item_id in range(items)] ) assert item_features.getnnz() == items ** 2 # Build from dicts user_features = dataset.build_user_features( [ (user_id, {"user:{}".format(x): float(x) for x in range(users)}) for user_id in range(users) ], normalize=False, ) assert np.all(user_features.todense() == np.array([list(range(users))] * users)) item_features = dataset.build_item_features( [ (item_id, {"item:{}".format(x): float(x) for x in range(items)}) for item_id in range(items) ], normalize=False, ) assert np.all(item_features.todense() == np.array([list(range(items))] * items)) # Test normalization item_features = dataset.build_item_features( [ (item_id, {"item:{}".format(x): float(x) for x in range(items)}) for item_id in range(items) ] ) assert np.all(item_features.sum(1) == 1.0)
def interactions(df): movie_genre = [x.split("|") for x in df["genre"]] all_movie_genre = sorted( list(set(itertools.chain.from_iterable(movie_genre)))) all_occupations = sorted(list(set(df["occupation"]))) dataset = Dataset() dataset.fit( df["userID"], df["itemID"], item_features=all_movie_genre, user_features=all_occupations, ) item_features = dataset.build_item_features( (x, y) for x, y in zip(df.itemID, movie_genre)) user_features = dataset.build_user_features( (x, [y]) for x, y in zip(df.userID, df["occupation"])) (interactions, _) = dataset.build_interactions(df.iloc[:, 0:3].values) train_interactions, test_interactions = cross_validation.random_train_test_split( interactions, test_percentage=TEST_PERCENTAGE, random_state=np.random.RandomState(SEEDNO), ) return train_interactions, test_interactions, item_features, user_features
def prepareData(df, tags): df = df[df.actionCategory == "WebNei clicked"] actionByUsers = df.groupby(["userName", "actionName"]).size() uniqueUsers = df[df.userName.isin( actionByUsers.index.get_level_values( 0).unique().values)].drop_duplicates('userName') uniqueUsers['user_features'] = uniqueUsers[[ 'title', 'team', 'organization', 'department' ]].values.tolist() dataset = Dataset() dataset.fit((list(actionByUsers.index.get_level_values(0))), (list(actionByUsers.index.get_level_values(1)))) rowM, colM = prepareJson(tags) rowU, colU = prepareUserFeatures(uniqueUsers) dataset.fit_partial(items=rowM, item_features=colM, users=rowU, user_features=colU) (interactions, weights) = dataset.build_interactions( zip(list(actionByUsers.index.get_level_values(0)), list(actionByUsers.index.get_level_values(1)))) item_features = dataset.build_item_features(zip(rowM, [colM])) user_features = dataset.build_user_features(zip(rowU, [colU])) return interactions, item_features, user_features
def create_dataset(df, item_features, list_item_features): """ function to create the dataset based on df which stores all the data including features (tags) of each products Args: df(pandas dataframe) - """ ## create a mapping between the user and item ids from our input data #to indices that will be used internally by the model dataset = Dataset(item_identity_features=True) list_user_names = list(df.index) list_items = df.columns.values dataset.fit( (user_name for user_name in list_user_names), (item for item in list_items), item_features=(item_feature for item_feature in list_item_features)) ## Build the interaction matrix # it encodes the interactions betwee users and items. # need (user, item) pair that has 1's in df list_pairs = list(df.stack().index) (interactions, weights) = dataset.build_interactions( (pair for pair in list_pairs)) item_feature_matrix = dataset.build_item_features(item_features) return dataset, interactions, weights, item_feature_matrix
class DataFit: def __init__(self): self.dataset = None def fit(self): book_list = DataPrep.get_book_list() book_feature_list = DataPrep.get_feature_list() user_list = DataPrep.get_user_list() self.dataset = Dataset() self.dataset.fit(users=user_list, items=book_list, item_features=book_feature_list) rating_list = DataPrep.get_rating_list() interactions, weights = self.dataset.build_interactions(rating_list) book_features = DataPrep.create_features() books_features = self.dataset.build_item_features(book_features) return interactions, weights, books_features def create_new_interactions(self, checkpoint): rating_list = DataPrep.get_rating_list_from_checkpoint(checkpoint) interactions, weights = self.dataset.build_interactions(rating_list) return interactions, weights def get_user_mapping(self): user_id_map, user_feature_map, item_id_map, item_feature_map = self.dataset.mapping( ) return user_id_map def get_book_mapping(self): user_id_map, user_feature_map, item_id_map, item_feature_map = self.dataset.mapping( ) return item_id_map @staticmethod def fit_evaluate(test_percentage=0.1): book_list = DataPrep.get_book_list() book_feature_list = DataPrep.get_feature_list() user_list = DataPrep.get_user_list() dataset = Dataset() dataset.fit(users=user_list, items=book_list, item_features=book_feature_list) rating_list = DataPrep.get_rating_list() random.shuffle(rating_list) rating_list_test = rating_list[:int(test_percentage * len(rating_list))] rating_list_train = rating_list[int(test_percentage * len(rating_list)):] interactions_train, weights_train = dataset.build_interactions( rating_list_train) interactions_test, weights_test = dataset.build_interactions( rating_list_test) return interactions_train, weights_train, interactions_test, weights_test
def obtener_matrices(self): """ Método obtener_matrices. Obtiene las matrices necesarias para la creación de los modelos de LightFM. Este método solo se utiliza en la interfaz de texto. """ global train, test, modelo, item_features, user_features # Se obtienen los dataframes Entrada.obtener_datos() ratings_df = Entrada.ratings_df users_df = Entrada.users_df items_df = Entrada.items_df # Se transforman los dataframes en matrices que puedan ser utilzadas por los modelos dataset = Dataset() dataset.fit(users_df[users_df.columns.values[0]], items_df[items_df.columns.values[0]], user_features=users_df[users_df.columns.values[1]], item_features=items_df[items_df.columns.values[1]]) # Si el modelo es colaborativo o híbrido se tienen en cuenta las valoraciones de los usuarios if self.opcion_modelo == 1 or self.opcion_modelo == 2: (interacciones, pesos) = dataset.build_interactions( (row[ratings_df.columns.values[0]], row[ratings_df.columns.values[1]], row[ratings_df.columns.values[2]]) for index, row in ratings_df.iterrows()) else: (interacciones, pesos) = dataset.build_interactions( (row[ratings_df.columns.values[0]], row[ratings_df.columns.values[1]]) for index, row in ratings_df.iterrows()) # Se obtienen las matrices de features y se guardan item_features = dataset.build_item_features( (row[items_df.columns.values[0]], [row[items_df.columns.values[1]]]) for index, row in items_df.iterrows()) user_features = dataset.build_user_features( (row[users_df.columns.values[0]], [row[users_df.columns.values[1]]]) for index, row in users_df.iterrows()) print("Guarda la matriz de item features") guardar_datos_pickle(item_features, 'la matriz de item features') print("Guarda la matriz de user features") guardar_datos_pickle(user_features, 'la matriz de user feautures') # Se dividen las interacciones en conjuntos de entrenamiento y test y se guardan train, test = random_train_test_split(interacciones, test_percentage=0.2) print("Guarda la matriz de entrenamiento") guardar_datos_pickle(train, 'la matriz de entrenamiento') print("Guarda la matriz de test") guardar_datos_pickle(test, 'la matriz de test')
def test_fitting(): users, items = 10, 100 dataset = Dataset() dataset.fit(range(users), range(items)) assert dataset.interactions_shape() == (users, items) assert dataset.user_features_shape() == (users, users) assert dataset.item_features_shape() == (items, items) assert dataset.build_interactions([])[0].shape == (users, items) assert dataset.build_user_features([]).getnnz() == users assert dataset.build_item_features([]).getnnz() == items
def test_fitting_no_identity(): users, items = 10, 100 dataset = Dataset(user_identity_features=False, item_identity_features=False) dataset.fit(range(users), range(items)) assert dataset.interactions_shape() == (users, items) assert dataset.user_features_shape() == (users, 0) assert dataset.item_features_shape() == (items, 0) assert dataset.build_interactions([])[0].shape == (users, items) assert dataset.build_user_features([], normalize=False).getnnz() == 0 assert dataset.build_item_features([], normalize=False).getnnz() == 0
def build_lightfm_dataset(self) -> None: """ Builds final datasets for user-variant and variant-variant recommendations. """ logging.info("Creating LightFM matrices...") lightfm_dataset = LFMDataset() ratings_list = self.interaction_list logging.info('#'*60) lightfm_dataset.fit_partial( (rating['user_id'] for rating in ratings_list), (rating['product_id'] for rating in ratings_list) ) item_feature_names = self.item_df.columns logging.info(f'Logging item_feature_names - with product_id: \n{item_feature_names}') item_feature_names = item_feature_names[~item_feature_names.isin(['product_id'])] logging.info(f'Logging item_feature_names - without product_id: \n{item_feature_names}') for item_feature_name in item_feature_names: lightfm_dataset.fit_partial( items=(item['product_id'] for item in self.item_list), item_features=((item[item_feature_name] for item in self.item_list)), ) item_features_data = [] for item in self.item_list: item_features_data.append( ( item['product_id'], [ item['product_name'], item['aisle'], item['department'] ], ) ) logging.info(f'Logging item_features_data @build_lightfm_dataset: \n{item_features_data}') self.item_features = lightfm_dataset.build_item_features(item_features_data) self.interactions, self.weights = lightfm_dataset.build_interactions( ((rating['user_id'], rating['product_id']) for rating in ratings_list) ) self.n_users, self.n_items = self.interactions.shape logging.info(f'Logging self.interactions @build_lightfm_dataset: \n{self.interactions}') logging.info(f'Logging self.weights @build_lightfm_dataset: \n{self.weights}') logging.info( f'The shape of self.interactions {self.interactions.shape} ' f'and self.weights {self.weights.shape} represent the user-item matrix.')
def fit_data(self, matrix, user_features=None, item_features=None): """ Create datasets for .fit() method. Args: matrix: User-item interactions matrix (weighted) user_features: User-features pandas dataframe which index contains user_ids (crd_no) item_features: Item-features pandas dataframe which index contains good_ids (plu_id) Returns: Model with fitted (mapped) datasets """ matrix.sort_index(inplace=True) matrix.sort_index(inplace=True, axis=1) dataset = Dataset() dataset.fit((x for x in matrix.index), (x for x in matrix.columns)) interactions = pd.melt( matrix.replace(0, np.nan).reset_index(), id_vars='index', value_vars=list(matrix.columns[1:]), var_name='plu_id', value_name='rating').dropna().sort_values('index') interactions.columns = ['crd_no', 'plu_id', 'rating'] self.interactions, self.weights = dataset.build_interactions( [tuple(x) for x in interactions.values]) if user_features is not None: user_features.sort_index(inplace=True) dataset.fit_partial(users=user_features.index, user_features=user_features) self.user_features = dataset.build_user_features( ((index, dict(row)) for index, row in user_features.iterrows())) else: self.user_features = None if item_features is not None: item_features.sort_index(inplace=True) dataset.fit_partial(items=item_features.index, item_features=item_features) self.item_features = dataset.build_item_features( ((index, dict(row)) for index, row in item_features.iterrows())) else: self.item_features = None
def load_parameter(): ratings = get_ratings() books = get_books() users = get_users() books_pd = convert_pd(books) id_users_books = StoreValue() for x in ratings: id_users_books._user_id.append(x[0]) id_users_books._book_id.append(x[1]) # Được tạo ra theo hướng dẫn tại https://making.lyst.com/lightfm/docs/examples/dataset.html dataset_explicit = Dataset() dataset_explicit.fit(id_users_books._user_id, id_users_books._book_id) num_users, num_items = dataset_explicit.interactions_shape() print('Num users: {}, num_items {}.'.format(num_users, num_items)) dataset_explicit.fit_partial(items=(x[0] for x in books), item_features=(x[7] for x in books)) dataset_explicit.fit_partial(users=(x[0] for x in users)) # create ---> mapping # interactions: dưới dạng COO_maxtrix, các tương tác sẽ là user_id và book_id # Trọng số voting (interactions_explicit, weights_explicit) = dataset_explicit.build_interactions((id_users_books._user_id[i], id_users_books._book_id[i]) for i in range(len(ratings))) # Đây là đặc trưng trích xuất từ các items (sách) dựa trên tác giả của cuốn sách được cung cấp item_features = dataset_explicit.build_item_features(((x[0], [x[7]]) for x in books)) # user_features = dataset_explicit.build_user_features(((x[0], [x[1]]) for x in users)) model_explicit_ratings = LightFM_ext(loss='warp') (train, test) = random_train_test_split(interactions=interactions_explicit, test_percentage=0.02) model_explicit_ratings.fit(train, item_features=item_features, epochs=2, num_threads=4) return model_explicit_ratings, dataset_explicit, interactions_explicit, weights_explicit, item_features, books_pd
def create_datasets(cluster_id): events_list = get_events_from_es(cluster_id) dataframe_interactions, dataframe_users_features, dataframe_item_features, user_tuple, item_tuple = create_interactions_and_features(events_list, cluster_id) print(dataframe_interactions, cluster_id, file=sys.stderr) print(dataframe_users_features, cluster_id, file=sys.stderr) print(dataframe_item_features, cluster_id, file=sys.stderr) #print(user_tuple) # print(item_tuple) user_features = format_users_features(dataframe_users_features) #print(user_features) item_features = format_items_features(dataframe_item_features) #print(item_features) dataset = Dataset() dataset.fit( dataframe_interactions['user'].unique(), # all the users dataframe_interactions['item'].unique(), # all the items user_features = user_features, item_features = item_features ) (interactions, weights) = dataset.build_interactions([(x[0], x[1], x[2]) for x in dataframe_interactions.values ]) # print(interactions) # print(weights) final_user_features = dataset.build_user_features(user_tuple, normalize= False) final_item_features = dataset.build_item_features(item_tuple, normalize= False) return dataset, interactions, weights, final_item_features, final_user_features
def predict(user_id: int) -> str: model_file = Path(BASE_DIR).joinpath(MODEL_FILE_NAME) data_file = Path(BASE_DIR).joinpath(DATA_FILE_NAME) if not model_file.exists(): return None if not data_file.exists(): return None model: LightFM = pickle.load(open(model_file, "rb")) data: pd.DataFrame = pd.read_csv(data_file) dataset = Dataset() dataset.fit((cac for cac in data.cac.unique()), (product for product in data.product_code.unique())) features = ['product_code', 'country_code', 'cost_bin'] for product_feature in features: dataset.fit_partial( users=(cac for cac in data.cac.unique()), items=(product for product in data.product_code.unique()), item_features=(feature for feature in data[product_feature].unique())) item_features = dataset.build_item_features(((getattr(row, 'product_code'), [getattr(row, product_feature) for product_feature in features if product_feature != 'product_code']) \ for row in data[features].itertuples())) predicted_products: List[str] = sample_recommendation( model=model, dataset=dataset, raw_data=data, item_features=item_features, user_ids=user_id) return predicted_products
def train_model( df, user_id_col='user_id', item_id_col='business_id', item_name_col='name_business', evaluate=True): """ Train the model using collaborative filtering. Args: df: the input dataframe. user_id_col: user id column. item_id_col: item id column. item_name_col: item name column. evaluate: if evaluate the model performance. Returns: model_full: the trained model. df_interactions: dataframe with user-item interactions. user_dict: user dictionary containing user_id as key and interaction_index as value. item_dict: item dictionary containing item_id as key and item_name as value. user_feature_map: the feature map of users business_feature_map: the feature map of items """ if evaluate: print('Evaluating model...') evaluate_model(df, user_id_col='user_id', item_id_col='business_id') print('Training model...') # build recommendations for known users and known businesses # with collaborative filtering method ds_full = Dataset() # we call fit to supply userid, item id and user/item features user_cols = ['user_id', 'average_stars'] categories = [c for c in df.columns if c[0].isupper()] item_cols = ['business_id', 'state'] for i in df.columns[10:]: item_cols.append(str(i)) user_features = user_cols[1:] item_features = item_cols[2:] ds_full.fit( df[user_id_col].unique(), # all the users df[item_id_col].unique(), # all the items user_features=user_features, # additional user features item_features=item_features ) df_users = df.drop_duplicates(user_id_col) # df_users = df[df.duplicated(user_id_col) == False] users_features = [] for i in range(len(df_users)): users_features.append(get_users_features_tuple(df_users.values[i])) users_features = ds_full.build_user_features( users_features, normalize=False) items = df.drop_duplicates(item_id_col) # items = df[df.duplicated(item_id_col) == False] items_features = [] for i in range(len(items)): items_features.append(get_items_features_tuple( items.values[i], categories)) items_features = ds_full.build_item_features( items_features, normalize=False) (interactions, weights) = ds_full.build_interactions( [(x[0], x[1], x[2]) for x in df.values]) # model model_full = LightFM( no_components=100, learning_rate=0.05, loss='warp', max_sampled=50) model_full.fit( interactions, user_features=users_features, item_features=items_features, sample_weight=weights, epochs=10, num_threads=10) # mapping user_id_map, user_feature_map, business_id_map, business_feature_map = \ ds_full.mapping() # data preparation df_interactions = pd.DataFrame(weights.todense()) df_interactions.index = list(user_id_map.keys()) df_interactions.columns = list(business_id_map.keys()) user_dict = user_id_map item_dict = df.set_index(item_id_col)[item_name_col].to_dict() return model_full, df_interactions, user_dict, \ item_dict, user_feature_map, business_feature_map
def lambda_handler(event, context): try: ## Fetch data from RDS code connection = pymysql.connect( host='fitbookdb.crm91a2epcbi.us-east-1.rds.amazonaws.com', user='******', passwd='postgres', db='fitbookdb', cursorclass=pymysql.cursors.DictCursor) print("Connection successful") except: print("Connection error") # In[3]: #Get Food DataFrame dict_list = [] with connection.cursor() as cur: cur.execute("select * from food_dataset") for row in cur: dict_list.append(row) food_rds_df = pd.DataFrame(dict_list) food_df = food_rds_df.copy() food_df.drop([ 'Portion_Default', 'Portion_Amount', 'Factor', 'Increment', 'Multiplier', 'Portion_Display_Name', 'Food_Code', 'Display_Name' ], axis=1, inplace=True) # food_df.head() print('Food Dataframe imported') # In[4]: # # TODO: Perform Binning # food_30_bins = ['Alcohol', 'Calories', 'Saturated_Fats'] # for each_column in food_30_bins: # bins = np.linspace(food_df[each_column].min(), food_df[each_column].max(), 30) # food_df[each_column+'bin'] = pd.cut(food_df[each_column], bins, labels=np.arange(0,len(bins)-1)) # food_df # In[5]: # for each_column in food_30_bins: # print(food_df[each_column].min()) # In[6]: #Get User Dataframe # user_df = pd.read_csv('user_db_try.csv') # user_df.head() dict_list = [] with connection.cursor() as cur: cur.execute("select * from tblUserData") for row in cur: dict_list.append(row) user_rds_df = pd.DataFrame(dict_list) user_df = user_rds_df.copy() user_df.drop([ 'cognitoAccessToken', 'cognitoIDToken', 'cognitoRefreshToken', 'fitbitAccessToken', 'fitbitUserID', 'userName' ], axis=1, inplace=True) # user_df.head() print('User Dataframe imported') # In[7]: #Get userItem DataFrame # userItem_df = pd.read_csv('userItem_db_try_new.csv') # userItem_df.head() dict_list = [] with connection.cursor() as cur: cur.execute("select * from tblUserRating") for row in cur: dict_list.append(row) userItem_rds_df = pd.DataFrame(dict_list) userItem_df = userItem_rds_df.copy() # userItem_df.head() print('UserItem Dataframe imported') # In[8]: #Make all the feature values unique for column_name in food_df.columns: if column_name != 'food_ID': food_df[column_name] = str( column_name) + ":" + food_df[column_name].astype(str) # food_df.head() # In[9]: #This Dict will be useful while creating tupples food_features_df = food_df.drop(['food_ID'], axis=1).copy() food_features_dict = food_features_df.to_dict('split') # food_features_dict # In[10]: food_feature_values = [] for column_name in food_features_df.columns: food_feature_values.extend(food_features_df[column_name].unique()) # food_feature_values # In[11]: for column_name in user_df.columns: if column_name != 'userID': user_df[column_name] = str( column_name) + ":" + user_df[column_name].astype(str) user_features_df = user_df.drop(['userID'], axis=1).copy() user_features_dict = user_features_df.to_dict('split') # user_features_dict # In[12]: user_feature_values = [] for column_name in user_features_df.columns: user_feature_values.extend(user_features_df[column_name].unique()) # user_feature_values # In[13]: user_tuples = [] food_tuples = [] for index, row in user_df.iterrows(): user_tuples.append((row['userID'], user_features_dict['data'][index])) for index, row in food_df.iterrows(): food_tuples.append((row['food_ID'], food_features_dict['data'][index])) # food_tuples # In[14]: print("Creating LightFm dataset") dataset = Dataset() dataset.fit(users=(user_id for user_id in user_df['userID']), items=(food_id for food_id in food_df['food_ID'])) print("Dataset Created") # In[15]: num_users, num_items = dataset.interactions_shape() print('Num users: {}, num_items {}.'.format(num_users, num_items)) # In[16]: # dataset.fit_partial(items=(food_id for food_id in food_df['Food_Code']), # item_features=((each_feature for each_feature in food_features)for food_features in food_features_dict['data'])) # In[17]: # dataset.fit_partial(items=(food_id for food_id in food_df['Food_Code']), # item_features=((row['Milk'], row['Meats'], row['Alcohol'], row['Calories'])for index,row in food_df.iterrows())) # In[18]: print("fittng item partial features") dataset.fit_partial(items=(food_id for food_id in food_df['food_ID']), item_features=(each_value for each_value in food_feature_values)) # In[19]: # dataset.fit_partial(users=(user_id for user_id in user_df['Id']), # user_features=((each_feature for each_feature in user_features)for user_features in user_features_dict['data'])) # In[20]: print("fittng user partial features") dataset.fit_partial(users=(user_id for user_id in user_df['userID']), user_features=(each_value for each_value in user_feature_values)) # In[21]: # dataset.item_features_shape() # dataset.user_features_shape() # In[22]: print("Building Interactions") (interactions, weights) = dataset.build_interactions( ((x['userID'], x['food_ID'], x['rating']) for y, x in userItem_df.iterrows())) # print(repr(interactions)) # print(weights) # In[23]: # interactions.shape # In[24]: print("Building item features") item_features = dataset.build_item_features(each_tuple for each_tuple in food_tuples) # print(item_features) # In[25]: user_features = dataset.build_user_features(each_tuple for each_tuple in user_tuples) # print(user_features) # In[26]: print("Fitting Model") model = LightFM(loss='warp') model.fit(interactions, item_features=item_features, user_features=user_features) print("Model trained!!") print("Pickle started!!") pickle.dump(model, open("/tmp/model.pkl", 'wb'), protocol=2) bucketName = "fitbook-lambda-packages" Key = "/tmp/model.pkl" outPutname = "model.pkl" print("Uploading to S3") s3 = boto3.client('s3') s3.upload_file(Key, bucketName, outPutname) print("Upload done") os.remove("/tmp/model.pkl") print("Pickle file deleted") print("Successssss!!!!!")
def evaluate_model( df, user_id_col='user_id', item_id_col='business_id', stratify=None): """ Model evaluation. Args: df: the input dataframe. user_id_col: user id column. item_id_col: item id column. stratify: if use stratification. No return value """ # create test and train datasets print('model evaluation') train, test = train_test_split(df, test_size=0.2, stratify=stratify) ds = Dataset() # we call fit to supply userid, item id and user/item features user_cols = ['user_id', 'average_stars'] categories = [c for c in df.columns if c[0].isupper()] item_cols = ['business_id', 'state'] for i in df.columns[10:]: item_cols.append(str(i)) user_features = user_cols[1:] item_features = item_cols[2:] ds.fit( df[user_id_col].unique(), # all the users df[item_id_col].unique(), # all the items user_features=user_features, # additional user features item_features=item_features ) train_users = train.drop_duplicates('user_id') # train_users = train[train.duplicated('user_id') == False] train_user_features = [] for i in range(len(train_users)): train_user_features.append(get_users_features_tuple( train_users.values[i])) train_user_features = ds.build_user_features( train_user_features, normalize=False) test_users = test.drop_duplicates('user_id') # test_users = test[test.duplicated('user_id') == False] test_user1_features = [] for i in range(len(test_users)): test_user1_features.append(get_users_features_tuple( test_users.values[i])) test_user_features = ds.build_user_features( test_user1_features, normalize=False) train_items = train.drop_duplicates('business_id') # train_items = train[train.duplicated('business_id') == False] train_item1_features = [] for i in range(len(train_items)): train_item1_features.append(get_items_features_tuple( train_items.values[i], categories)) train_item_features = ds.build_item_features( train_item1_features, normalize=False) test_items = test.drop_duplicates('business_id') # test_items = test[test.duplicated('business_id') == False] test_item_features = [] for i in range(len(test_items)): test_item_features.append(get_items_features_tuple( test_items.values[i], categories)) test_item_features = ds.build_item_features( test_item_features, normalize=False) # plugging in the interactions and their weights (train_interactions, train_weights) = ds.build_interactions( [(x[0], x[1], x[2]) for x in train.values]) (test_interactions, test_weights) = ds.build_interactions( [(x[0], x[1], x[2]) for x in test.values]) # model model = LightFM( no_components=100, learning_rate=0.05, loss='warp', max_sampled=50) model.fit( train_interactions, user_features=train_user_features, item_features=train_item_features, sample_weight=train_weights, epochs=10, num_threads=10) # auc-roc train_auc = auc_score( model, train_interactions, user_features=train_user_features, item_features=train_item_features, num_threads=20).mean() print('Training set AUC: %s' % train_auc) test_auc = auc_score( model, test_interactions, user_features=test_user_features, item_features=test_item_features, num_threads=20).mean() print('Testing set AUC: %s' % test_auc)
def calc(request): try : stores = Store.objects.all(); reviews = Review.objects.all(); stores = pd.DataFrame(list(stores.values('id', 'store_id','store_name', 'category', 'address','latitude','longitude','average_rating'))) reviews = pd.DataFrame(list(reviews.values('id', 'storeid','userid', 'score','reg_time'))) reviews_source = [(reviews['userid'][i], reviews['storeid'][i]) for i in range(reviews.shape[0])] item_feature_source = [(stores['store_id'][i], [ stores['category'][i],stores['address'][i],stores['latitude'][i],stores['longitude'][i], stores['average_rating'][i]] ) for i in range(stores.shape[0]) ] dataset = Dataset() dataset.fit(users=reviews['userid'].unique(), items=reviews['storeid'].unique(), item_features=stores[stores.columns[1:]].values.flatten()) interactions, weights = dataset.build_interactions(reviews_source) item_features = dataset.build_item_features(item_feature_source) # Split Train, Test data train, test = random_train_test_split(interactions, test_percentage=0.1) train, test = train.tocsr().tocoo(), test.tocsr().tocoo() train_weights = train.multiply(weights).tocoo() # Define Search Space trials = Trials() space = [hp.choice('no_components', range(10, 50, 10)), hp.uniform('learning_rate', 0.01, 0.05)] # Define Objective Function def objective(params): no_components, learning_rate = params global model model = LightFM(no_components=no_components, learning_schedule='adagrad', loss='warp', learning_rate=learning_rate, random_state=0) model.fit(interactions=train, item_features=item_features, sample_weight=train_weights, epochs=3, verbose=False) test_precision = precision_at_k(model, test, k=5, item_features=item_features).mean() print("no_comp: {}, lrn_rate: {:.5f}, precision: {:.5f}".format( no_components, learning_rate, test_precision)) # test_auc = auc_score(model, test, item_features=item_features).mean() output = -test_precision if np.abs(output+1) < 0.01 or output < -1.0: output = 0.0 return output # max_evals가 몇번 반복실행 할껀지. best_params = fmin(fn=objective, space=space, algo=tpe.suggest, max_evals=10, trials=trials) # 아이템피쳐 저장 with open('./saved_models/item_features.pickle', 'wb') as fle: pickle.dump(item_features, fle, protocol=pickle.HIGHEST_PROTOCOL) # 모델 저장해야 됨 with open('./saved_models/model.pickle', 'wb') as fle: pickle.dump(model, fle, protocol=pickle.HIGHEST_PROTOCOL) item_biases, item_embeddings = model.get_item_representations(features=item_features) # item_embeddings 저장하기 with open('./saved_models/item_embeddings.pickle', 'wb') as fle: pickle.dump(item_embeddings, fle, protocol=pickle.HIGHEST_PROTOCOL) return Response({'result': True}) except : return Response({'result': False})
print('Num users : {}, num_items {}.'.format(num_users, num_items)) # add some item feature mappings, and creates a unique feature for each author # NOTE: more item ids are fitted than usual, to make sure our mappings are complete # even if there are items in the features dataset that are not in the interaction set dataset.fit_partial(items=(x['ISBN'] for x in get_book_features()), item_features=(x['Book-Author'] for x in get_book_features())) # build the interaction matrix which is a main input to the LightFM model # it encodes the interactions between the users and the items (interactions, weights) = dataset.build_interactions( ((x['User-ID'], x['ISBN']) for x in get_ratings())) # item_features matrix can also be created item_features = dataset.build_item_features( ((x['ISBN'], [x['Book-Author']]) for x in get_book_features())) # split the current dataset into a training and test dataset train, test = random_train_test_split(interactions, test_percentage=0.01, random_state=None) # build the model using the training dataset, notice the use of item_features as well, # this is a hybrid model model = LightFM(loss='warp', item_alpha=ITEM_ALPHA, no_components=NUM_COMPONENTS) # train the hybrid model on the training dataset model.fit(train, item_features=item_features, epochs=NUM_EPOCHS, num_threads=1)
def run_lightfm(ratings, train, test, k_items, dataset): def create_interaction_matrix(df, user_col, item_col, rating_col, norm=False, threshold=None): ''' Function to create an interaction matrix dataframe from transactional type interactions Required Input - - df = Pandas DataFrame containing user-item interactions - user_col = column name containing user's identifier - item_col = column name containing item's identifier - rating col = column name containing user feedback on interaction with a given item - norm (optional) = True if a normalization of ratings is needed - threshold (required if norm = True) = value above which the rating is favorable Expected output - - Pandas dataframe with user-item interactions ready to be fed in a recommendation algorithm ''' interactions = df.groupby([user_col, item_col])[rating_col] \ .sum().unstack().reset_index(). \ fillna(0).set_index(user_col) if norm: interactions = interactions.applymap(lambda x: 1 if x > threshold else 0) return interactions test_interactions = create_interaction_matrix(df=test, user_col='userId', item_col='movieId', rating_col='rating') budget_l = dataset.budget.unique().tolist() gross_l = dataset.gross.unique().tolist() awards_l = dataset.awards.unique().tolist() nom_l = dataset.nominations.unique().tolist() votes_l = dataset.votes.unique().tolist() item_ids = np.unique(train.movieId.astype(int)) print(f'length dataset: {len(dataset)}') dataset = dataset[dataset.movieId.isin(item_ids)] print(f'length dataset: {len(dataset)}') item_features_list = [f'rating_{f}' for f in range(11)] gen = [ 'Action', 'Adventure', 'Animation', "Children's", 'Comedy', 'Crime', 'Documentary', 'Drama', 'Fantasy', 'Film-Noir', 'Horror', 'IMAX', 'Musical', 'Mystery', 'Romance', 'Sci-Fi', 'Thriller', 'War', 'Western' ] # 'unknown' add unknown for movielens100k item_features_list += gen item_features_list += budget_l item_features_list += gross_l item_features_list += awards_l item_features_list += nom_l item_features_list += votes_l item_features = [] for y, x in dataset.iterrows(): genres = x['genres'] tmp_row = (int(x['movieId']), [ x['rating'], x['budget'], x['gross'], x['awards'], x['nominations'], x['votes'] ]) for g in genres: tmp_row[1].append(g) item_features.append(tmp_row) #item_features = [(int(x['movieId']), [x['rating'], z, x['budget'], x['gross'], x['awards'], x['votes']]) for y, x in dataset.iterrows() for z in x['genres']] #x['nominations'] user_ids = np.unique(train.userId) built_dif = Dataset() built_dif.fit_partial(users=user_ids) built_dif.fit_partial(items=item_ids) built_dif.fit_partial(item_features=item_features_list) dataset_item_features = built_dif.build_item_features(item_features) (interactions, weights) = built_dif.build_interactions( ((int(x['userId']), int(x['movieId'])) for y, x in train.iterrows())) modelx = LightFM(no_components=30, loss='bpr', k=15, random_state=1) modelx.fit(interactions, epochs=30, num_threads=4, item_features=dataset_item_features ) #item_features=dataset_item_features test = sparse.csr_matrix(test_interactions.values) test = test.tocoo() num_users, num_items = built_dif.interactions_shape() print('Num users: {}, num_items {}.'.format(num_users, num_items)) prec_list = dict() rec_list = dict() for num_k in k_items: trainprecision = precision_at_k( modelx, test, k=num_k, item_features=dataset_item_features).mean( ) #item_features=dataset_item_features, print('Hybrid training set precision: %s' % trainprecision) trainrecall = recall_at_k(modelx, test, k=num_k, item_features=dataset_item_features).mean( ) #item_features=dataset_item_features print('Hybrid training set recall: %s' % trainrecall) if num_k in prec_list: prec_list[num_k].append(trainprecision) else: prec_list[num_k] = trainprecision if num_k in rec_list: rec_list[num_k].append(trainrecall) else: rec_list[num_k] = trainrecall return prec_list, rec_list
for rid, row in movies.iterrows(): for m in match_lst: if m.lower() in row[1].lower(): matches.append(row[0]) print(good_ratings.head()) rating_iter = zip(good_ratings['userId'], good_ratings['movieId']) new_iter = ((new_user, x) for x in matches) interactions, weights = dataset.build_interactions(chain( rating_iter, new_iter)) print(repr(interactions)) mov_features = ((row[0], row[2].split('|') + [row[3], row[0]]) for rid, row in movies.iterrows()) # print(mov_features[0]) item_features = dataset.build_item_features(mov_features) model = LightFM(loss='warp', no_components=28, item_alpha=0.0001, learning_rate=0.05) model.fit(interactions, item_features=item_features, num_threads=16) movie2name = {} for rid, row in movies.iterrows(): movie2name[row[0]] = row[1] n_users, n_items = dataset.interactions_shape() # Adjust using base ratings base_mat = model.predict(0, np.arange(n_items), num_threads=16) base_mat = (base_mat + np.min(base_mat))
item_features=range(2048)) # Build Train Interactions (train_interactions, weights) = train.build_interactions( ((row[0], row[1]) for index, row in df_train.iterrows())) # Build Features ## Call build_user/item_features with iterables of (user/item id, [features]) or (user/item id, {feature: feature weight}) to build feature matrices. print('Loading Features...') list_features = {} for image_index, image_feature in enumerate(features): list_features[image_index] = [] for feature_index, feature_weight in enumerate(image_feature): list_features[image_index].append({feature_index: feature_weight}) features_generator = ((item_id, ele) for item_id in list_features.keys() for ele in list_features[item_id]) item_features = train.build_item_features(features_generator, normalize=False) print('End Loading Features.') ### LOAD print('Load Model...') with open(weight_directory + '_step{0}_LFM.pickle'.format(args.epoch), 'rb') as dump: model = pickle.load(dump) print('End Model') # # Evaluation print("Evaluation...") with open( result_directory + '_top{0}_ep{1}_LFM.tsv'.format(args.topk, args.epoch), 'w') as out:
def main(): if request.method == 'POST': global df_movies # global top_trending_ids # print(list(df_movies[df_movies.movie_id_ml.isin(top_trending_ids)].title) ) print(request.form) # Get recommendations! if 'run-mf-model' in request.form: for i, user_rating in enumerate(session['arr']): session['arr'][i] = user_rating[:-2] session['movieIds'] = session['movieIds'][:-2] rated_movies = min(len(session['arr'][0]), len(session['movieIds'])) for i, user_rating in enumerate(session['arr']): session['arr'][i] = user_rating[:rated_movies] session['movieIds'] = session['movieIds'][:rated_movies] pu = recommendation_mf(session['arr'], session['members'], session['movieIds']) session.clear() top_trending_ids = list(df_movies.sort_values(by="trending_score").head(200).sample(15).movie_id_ml) session['counter'] = 0 session['members'] = 0 session['userAges'] = [] session['userGenders'] = [] session['movieIds'] = list(df_movies[df_movies.movie_id_ml.isin(top_trending_ids)].movie_id_ml) session['top15'] = list(df_movies[df_movies.movie_id_ml.isin(top_trending_ids)].title) session['top15_posters'] = list(df_movies[df_movies.movie_id_ml.isin(top_trending_ids)].poster_url) session['arr'] = None return(render_template('main.html', settings = {'friendsInfo':False, 'showVote': False, 'people': 0, 'buttonDisable': False,'chooseRecommendation':False, 'recommendation': pu})) if 'run-siamese-model' in request.form: # global df global friends global ratings global new_friend_id new_ratings = [] for mid, movie_real_id in enumerate(session['movieIds']): avg_mv_rating = np.median(np.array([user_ratings[mid] for user_ratings in session['arr']])) new_ratings.append({'movie_id_ml':movie_real_id, 'rating': avg_mv_rating, 'friend_id': new_friend_id}) new_friend = {'friend_id': new_friend_id, 'friends_age': np.mean(np.array(session['userAges'])), 'friends_gender': np.mean(np.array(session['userGenders']))} friends.append(new_friend) ratings.extend(new_ratings) dataset = LightFMDataset() item_str_for_eval = "x['title'],x['release'], x['unknown'], x['action'], x['adventure'],x['animation'], x['childrens'], x['comedy'], x['crime'], x['documentary'], x['drama'], x['fantasy'], x['noir'], x['horror'], x['musical'],x['mystery'], x['romance'], x['scifi'], x['thriller'], x['war'], x['western'], *soup_movie_features[x['soup_id']]" friend_str_for_eval = "x['friends_age'], x['friends_gender']" dataset.fit(users=(int(x['friend_id']) for x in friends), items=(int(x['movie_id_ml']) for x in movies), item_features=(eval("("+item_str_for_eval+")") for x in movies), user_features=((eval(friend_str_for_eval)) for x in friends)) num_friends, num_items = dataset.interactions_shape() print(f'Num friends: {num_friends}, num_items {num_items}. {datetime.datetime.now()}') (interactions, weights) = dataset.build_interactions(((int(x['friend_id']), int(x['movie_id_ml'])) for x in ratings)) item_features = dataset.build_item_features(((x['movie_id_ml'], [eval("("+item_str_for_eval+")")]) for x in movies) ) user_features = dataset.build_user_features(((x['friend_id'], [eval(friend_str_for_eval)]) for x in friends) ) print(f"Item and User features created {datetime.datetime.now()}") epochs = 50 #150 lr = 0.015 max_sampled = 11 loss_type = "warp" # "bpr" model = LightFM(learning_rate=lr, loss=loss_type, max_sampled=max_sampled) model.fit_partial(interactions, epochs=epochs, user_features=user_features, item_features=item_features) train_precision = precision_at_k(model, interactions, k=10, user_features=user_features, item_features=item_features).mean() train_auc = auc_score(model, interactions, user_features=user_features, item_features=item_features).mean() print(f'Precision: {train_precision}, AUC: {train_auc}, {datetime.datetime.now()}') k = 18 top_movie_ids, scores = predict_top_k_movies(model, new_friend_id, k, num_items, user_features=user_features, item_features=item_features, use_features = False) top_movies = df_movies[df_movies.movie_id_ml.isin(top_movie_ids)] pu = recommendation_siamese(top_movies, scores) return(render_template('main.html', settings = {'friendsInfo':False, 'showVote': False, 'people': 0, 'buttonDisable': False,'chooseRecommendation':False, 'recommendation': pu})) # Collect friends info elif 'person-select-gender-0' in request.form: for i in range(session['members']): session['userAges'].append(int(request.form.get(f'age-{i}'))) session['userGenders'].append(int(request.form.get(f'person-select-gender-{i}'))) return(render_template('main.html', settings = {'friendsInfo':False, 'showVote': True, 'people': session['members'], 'buttonDisable': True,'chooseRecommendation':False, 'recommendation': None})) # Choose number of people in the group elif 'people-select' in request.form: count = int(request.form.get('people-select')) session['members'] = count session['arr'] = [[0 for x in range(15)] for y in range(count)] return(render_template('main.html', settings = {'friendsInfo':True, 'showVote': False, 'people': count, 'buttonDisable': True,'chooseRecommendation':False, 'recommendation': None})) # All people voting elif 'person-select-0' in request.form: for i in range(session['members']): session['arr'][i][session['counter']] = int(request.form.get(f'person-select-{i}')) session['counter'] += 1 if session['counter'] < 15: return(render_template('main.html', settings = {'friendsInfo':False, 'showVote': True, 'people': len(request.form), 'buttonDisable': True,'chooseRecommendation':False, 'recommendation': None})) else: return(render_template('main.html', settings = {'friendsInfo':False, 'showVote': False, 'people': len(request.form), 'buttonDisable': True,'chooseRecommendation':True, 'recommendation': None})) elif request.method == 'GET': session.clear() top_trending_ids = list(df_movies.sort_values(by="trending_score").head(200).sample(15).movie_id_ml) print(top_trending_ids) print(list(df_movies[df_movies.movie_id_ml.isin(top_trending_ids)].title) ) session['counter'] = 0 session['members'] = 0 session['userAges'] = [] session['userGenders'] = [] session['movieIds'] = list(df_movies[df_movies.movie_id_ml.isin(top_trending_ids)].movie_id_ml) session['top15'] = list(df_movies[df_movies.movie_id_ml.isin(top_trending_ids)].title) session['top15_posters'] = list(df_movies[df_movies.movie_id_ml.isin(top_trending_ids)].poster_url) session['arr'] = None return(render_template('main.html', settings = {'showVote': False, 'people': 0, 'buttonDisable': False, 'recommendation': None}))
items = pd.read_csv('items.txt', sep=';', error_bad_lines=False, header=None) users = pd.read_csv('usersDescription.txt', sep=';', header=None) ratings = pd.read_csv('ratings.txt', sep=';', header=None) from lightfm.data import Dataset dataset = Dataset(user_identity_features=True, item_identity_features=True) dataset.fit(users=(users[50].unique()), items=(items[0]), item_features=list(range(2, 10)), user_features=list(range(2, 50))) items_features_raw = list( (item[1], (np.argwhere(np.array(item[3:]) == 1)[0] + 2).tolist()) for item in items.itertuples()) items_features = dataset.build_item_features(items_features_raw) users_features_raw = build_user_dict(users) users_features = dataset.build_user_features(users_features_raw) num_users, num_items = dataset.interactions_shape() print('Num users: {}, num_items {}.'.format(num_users, num_items)) ratings2 = ratings[ratings[2] > 0] ratings2 = ratings2.drop_duplicates(subset=[1, 2, 3]) train, test = train_test_split(ratings2, test_size=0.1) print(train.shape) print(test.shape) (train_interactions, train_weights) = dataset.build_interactions(train[[3, 1]].values) (test_interactions, test_weights) = dataset.build_interactions(test[[3, 1
def obtener_matrices_gui(self, ruta_ratings, sep_ratings, encoding_ratings, ruta_users, sep_users, encoding_users, ruta_items, sep_items, encoding_items): """ Método obtener_matrices_gui. Obtiene las matrices necesarias para la creación de los modelos de LightFM. Este método solo se utiliza en la interfaz web. Parameters ---------- ruta_ratings: str ruta del archivo que contiene las valoraciones. sep_ratings: str separador utilizado en el archivo de valoraiones. encoding_ratings: str encoding utilizado en el archivo de valoraciones. ruta_users: str ruta del archivo que contiene los datos de los usuarios. sep_users: str separador utilizado en el archivo de usuarios. encoding_users: str encoding utilizado en el archivo de usuarios. ruta_items: str ruta del archivo que contiene los datos de los ítems. sep_items: str separador utilizado en el archivo de ítems. encoding_items: str encoding utilizado en el archivo de ítems. """ global train, test, item_features, user_features # Se obtienen los dataframes ratings_df = Entrada.leer_csv(ruta_ratings, sep_ratings, encoding_ratings) ratings_df.sort_values( [ratings_df.columns.values[0], ratings_df.columns.values[1]], inplace=True) users_df = Entrada.leer_csv(ruta_users, sep_users, encoding_users) users_df.sort_values([users_df.columns.values[0]], inplace=True) items_df = Entrada.leer_csv(ruta_items, sep_items, encoding_items) items_df.sort_values([items_df.columns.values[0]], inplace=True) # Se transforman los dataframes en matrices que puedan ser utilzadas por los modelos dataset = Dataset() dataset.fit(users_df[users_df.columns.values[0]], items_df[items_df.columns.values[0]], user_features=users_df[users_df.columns.values[1]], item_features=items_df[items_df.columns.values[1]]) # Si el modelo es colaborativo o híbrido se tienen en cuenta las valoraciones de los usuarios if self.opcion_modelo == 1 or self.opcion_modelo == 2: (interacciones, pesos) = dataset.build_interactions( (row[ratings_df.columns.values[0]], row[ratings_df.columns.values[1]], row[ratings_df.columns.values[2]]) for index, row in ratings_df.iterrows()) else: (interacciones, pesos) = dataset.build_interactions( (row[ratings_df.columns.values[0]], row[ratings_df.columns.values[1]]) for index, row in ratings_df.iterrows()) # Se obtienen las matrices de features y se guardan item_features = dataset.build_item_features( (row[items_df.columns.values[0]], [row[items_df.columns.values[1]]]) for index, row in items_df.iterrows()) user_features = dataset.build_user_features( (row[users_df.columns.values[0]], [row[users_df.columns.values[1]]]) for index, row in users_df.iterrows()) print("Guarda la matriz de item features") guardar_datos_pickle(item_features, 'la matriz de item features') print("Guarda la matriz de user features") guardar_datos_pickle(user_features, 'la matriz de user feautures') # Se dividen las interacciones en conjuntos de entrenamiento y test y se guardan train, test = random_train_test_split(interacciones, test_percentage=0.2) print("Guarda la matriz de entrenamiento") guardar_datos_pickle(train, 'la matriz de entrenamiento') print("Guarda la matriz de test") guardar_datos_pickle(test, 'la matriz de test')
#This will create a feature for every unique author name in the dataset. #(Note that we fit some more item ids: this is to make sure our mappings are complete even if there are items in the features dataset that are not in the interactions set.) ## Building the interactions matrix #Having created the mapping, we build the interaction matrix: (interactions, weights) = dataset.build_interactions( ((x['User-ID'], x['ISBN']) for x in get_ratings())) print(repr(interactions)) #This is main input into a LightFM model: it encodes the interactions betwee users and items. #Since we have item features, we can also create the item features matrix: item_features = dataset.build_item_features( ((x['ISBN'], [x['Book-Author']]) for x in get_book_features())) print(repr(item_features)) ## Building a model #This is all we need to build a LightFM model: model = LightFM(loss='bpr') model.fit(interactions, item_features=item_features) #trying to put own csv files into lightFM dataset def get_own_data():
dataset.fit_partial(item_features=(x[str(fields[i])] for x in winefeatures)) num_users, num_items = dataset.interactions_shape() #building the interaction matrix for training ratings (interactions, weights) = dataset.build_interactions(((x['taster'],x['title']) for x in trainrankings)) #and the corresponding sparse matrices for CV and Test ratings (testinteractions, testweights) = dataset.build_interactions(((x['taster'],x['title']) for x in testrankings)) (cvinteractions, cvweights) = dataset.build_interactions(((x['taster'],x['title']) for x in cvrankings)) #here we need to remove title so our next iterator works properly fields.remove('title') #double list comprehension to build the item features in a smart way, providing each feature in wine features #which is >200 item_features = dataset.build_item_features((x['title'],[x[field] for field in fields[1:]]) for x in winefeatures) #uncomment below to run randomized optimization #yieldlist = list(randomsearch(interactions, cvinteractions, item_features)) #(score, hyperparams, model) = max(yieldlist, key=lambda x: x[0]) #print("Best score {} at {}".format(score, hyperparams)) #print(yieldlist) #Below are the results of our random optimiaztion, hardcoded as parameters now. #Best score 0.9843319654464722 at bestparams = {'no_components': 59, 'learning_schedule': 'adagrad', 'loss': 'warp', 'learning_rate': 0.08565020895037347, 'item_alpha': 7.345729662383957e-10, 'user_alpha': 4.776609106732949e-09, 'max_sampled': 14, 'random_state':69} model = LightFM(**bestparams)
user_features=user_feature_names, item_features=item_feature_names ) # check shape num_users, num_items = dataset.interactions_shape() print('Num users: {}, num_items: {}.'.format(num_users, num_items)) _, num_users_feature = dataset.user_features_shape() _, num_items_feature = dataset.item_features_shape() print('Num users feature: {}, num_items feature: {}.'.format(num_users_feature, num_items_feature)) # build user feature matrix user_feature_matrix = dataset.build_user_features(user_feature_iterable, normalize=True) # build item feature matrix item_feature_matrix = dataset.build_item_features(item_feature_iterable, normalize=True) # build interaction (train_interactions, weights) = dataset.build_interactions(data=((row['userCode'], row['project_id'], row[interaction_col_name])for index, row in train.iterrows() if row['project_id'] not in ignore_project)) from lightfm import LightFM model = LightFM(loss='warp', random_state=44, learning_schedule='adagrad') model.fit(train_interactions, item_features=item_feature_matrix, user_features=user_feature_matrix, ) is_evaluate=0 if is_evaluate: from lightfm.evaluation import precision_at_k