AttributeError: module 'surprise.dataset' has no attribute 'load_from_df' about surprise HOT 12 CLOSED

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from surprise import KNNWithMeans
from surprise import Dataset
from surprise import evaluate, print_perf
from surprise import Reader

anime_rating = pd.read_csv('rating.csv') # the data
anime_rating = anime_rating[anime_rating.rating > 0] # keep ratings between 1-10
anime_rating['user_anime_count'] = anime_rating.groupby('user_id')['user_id'].transform(np.count_nonzero)
anime_rating = anime_rating[anime_rating.user_anime_count > 19] # keep users that have seen at least 20 anime
anime_rating = anime_rating.drop('user_anime_count', axis=1) # having served its purpose, drop the user count column

#loading from pandas dataframe
reader = Reader(line_format='user item rating', rating_scale=(1, 10))
data = Dataset.load_from_df(anime_rating[['user_id', 'anime_id', 'rating']], reader)

#testing the knnwithmeans
sim_options = {'name': 'pearson', # compute Pearson correlation coefficient between all pairs of items
               'user_based': False  # compute similarities between items
               }
algo = KNNWithMeans(k=20, min_k=11, sim_options=sim_options)
perf = evaluate(algo, data, measures=['RMSE','MAE'])

Evaluating RMSE, MAE of algorithm KNNWithMeans.

------------
Fold 1
Computing the pearson similarity matrix...
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-14-017eb59430bf> in <module>()
      3                }
      4 algo = KNNWithMeans(k=20, min_k=11, sim_options=sim_options)
----> 5 perf = evaluate(algo, data, measures=['RMSE','MAE'])

c:\users\mike\appdata\local\programs\python\python35\lib\site-packages\surprise\evaluate.py in evaluate(algo, data, measures, with_dump, dump_dir, verbose)
     63 
     64         # train and test algorithm. Keep all rating predictions in a list
---> 65         algo.train(trainset)
     66         predictions = algo.test(testset, verbose=(verbose == 2))
     67 

c:\users\mike\appdata\local\programs\python\python35\lib\site-packages\surprise\prediction_algorithms\knns.py in train(self, trainset)
    166 
    167         SymmetricAlgo.train(self, trainset)
--> 168         self.sim = self.compute_similarities()
    169 
    170         self.means = np.zeros(self.n_x)

c:\users\mike\appdata\local\programs\python\python35\lib\site-packages\surprise\prediction_algorithms\algo_base.py in compute_similarities(self)
    229         try:
    230             print('Computing the {0} similarity matrix...'.format(name))
--> 231             sim = construction_func[name](*args)
    232             print('Done computing similarity matrix.')
    233             return sim

c:\users\mike\appdata\local\programs\python\python35\lib\site-packages\surprise\similarities.pyx in surprise.similarities.pearson (surprise/similarities.c:6558)()

AttributeError: 'int' object has no attribute 'sqrt'

import surprise
import pandas as pd

df = pd.DataFrame.from_csv('rating.csv')
df.reset_index(level=0, inplace=True)  # so that columns are user_id, anime_id and rating
df = df[df.rating>0]

# I'm only loading the first 1000 rows to save time
reader = surprise.dataset.Reader(rating_scale=(1, 10))
data = surprise.Dataset.load_from_df(df.head(1000)[['user_id', 'anime_id', 'rating']], reader)

sim_options = {'name': 'cosine', 'user_based': False}
algo = surprise.KNNWithMeans(k=20, min_k=11, sim_options=sim_options)
surprise.evaluate(algo, data, measures=['RMSE', 'MAE'])