使用AWS Sagemaker部署的终端节点进行推荐预测的常用场景

#select 100020528 as a random user
for k in df[df['UserId']=='100020528'].index:
     print(df_movie_all_db[df_movie_all_db['MovieID']==str(df.at[k,'MovieID'])].Title.values[0], "         ", df.at[k,'Rate'])

#revise the loadDataset() to accept the userID as an input.
def createInferenceForUser(UserId, lines, columns):
    user_number = 53902
    X = scipy.sparse.lil_matrix((lines, columns)).astype('float32')
    line = 0
    for line in range(0, lines):
        X[line, u_Dict[UserId]] = 1
        X[line, user_number+line] = 1
        line=line+1

    print(X.shape)
    return X

user_inference = createInferenceForUser('100020528', movie_number, user_number+movie_number)
print(user_inference.shape)

#invoke endpoint
client = boto3.client('sagemaker-runtime')

#Make inference
import json
inf_start = 0
step = 10
response = []
while inf_start <= user_inference.shape[0]+1:
    raw_response = client.invoke_endpoint(
                EndpointName = 'factorization-machines-2018-12-18-05-47-26-108',
                Body = fm_serializer(user_inference[inf_start:inf_start+step].toarray()),
                ContentType='application/json')
    result = json.loads(raw_response['Body'].read())
    for counter, p in enumerate(result['predictions']):
        p['MovieID'] = df_m_Dict[df_m_Dict.movie_index == (inf_start + int(counter))].MovieID.values[0]
        response.append(p)
    inf_start = inf_start+step
    print(inf_start)
print(len(response))

df_response_like = df_response[df_response.predicted_label == 1.0]
final = df_response_like.sort_values('score', ascending=False).head(10)
for final_index in final.index:
    print(df_movie_all_db[df_movie_all_db['MovieID']==str(final.at[final_index,'MovieID'])].Title.values[0],'\t',df_movie_all_db[df_movie_all_db['MovieID']==str(final.at[final_index,'MovieID'])].Rate.values[0],'\t',final.at[final_index,'score'])

def createNewUserInference(lines, columns):
    user_number = 53902
    X = scipy.sparse.lil_matrix((lines, columns)).astype('float32')
    line = 0
    for line in range(0, lines):
        X[line, user_number+line] = 1
        line=line+1
    print(X.shape)
    return X

def callEndpointInference(query):
    import json
    inf_start = 0
    step = 10
    response = []
    while inf_start <= query.shape[0]:
        raw_response = client.invoke_endpoint(
                    EndpointName = 'factorization-machines-2018-12-18-05-47-26-108',
                    Body = fm_serializer(query[inf_start:min(inf_start+step, query.shape[0]+1)].toarray()),
                    ContentType='application/json')
        result = json.loads(raw_response['Body'].read())
        for counter, p in enumerate(result['predictions']):
            p['MovieID'] = df_m_Dict[df_m_Dict.movie_index == (inf_start + int(counter))].MovieID.values[0]
            response.append(p)
        inf_start = inf_start+step
        print(inf_start)
    return response

def showResult(response):
    df_response = pd.DataFrame(response)
    df_response_like = df_response[df_response.predicted_label == 1.0]
    final = df_response_like.sort_values('score', ascending=False).head(10)
    for final_index in final.index:
        print(df_movie_all_db[df_movie_all_db['MovieID']==str(final.at[final_index,'MovieID'])].Title.values[0],'\t',df_movie_all_db[df_movie_all_db['MovieID']==str(final.at[final_index,'MovieID'])].Rate.values[0],'\t',final.at[final_index,'score'])

new_inference = createNewUserInference(movie_number, user_number+movie_number)

%%time
new_response = callEndpointInference(new_inference)

showResult(new_response)

df_movie_preference = df_movie_all_db[df_movie_all_db['MovieID'].isin(movie_rated_by_user)].copy()
df_movie_preference.groupby('Tags').count()

df_movie_all_db[df_movie_all_db.Tags.isin(['惊悚','恐怖'])].count()

#Check whether all 1625 movies is in m_Dict, and clean the movies that are not in m_Dict
for m_h in horror_and_terrified_movie:
    if m_h in m_Dict.keys():
        continue
    else:
        print(m_h)
        horror_and_terrified_movie.remove(m_h)
print(len(horror_and_terrified_movie))

m_rated = []
for el_index in df_movie_preference.index:
    m_rated.append(df_movie_preference.at[el_index, 'MovieID'])

for m_h in horror_and_terrified_movie:
    if m_h in m_rated:
        print(m_h)
        horror_and_terrified_movie.remove(m_h)
print(len(horror_and_terrified_movie))

filtered_user_inference = createInferenceForUserPreference('100020528', horror_and_terrified_movie, user_number+movie_number)
print(filtered_user_inference.shape)

%%time
filtered_response = callEndpointInference_noid(filtered_user_inference)

showResultfromDataFrame(df_filtered_counter)

def slicedInferenceUserPreference(userid, step, movielist):
    import json
    #total_line = 26004 #movie_number
    
    start = 0
    response = []
    line = 0
    response = []
    
    while start <= len(movielist)+1:
        js={'instances': []}
        for line in range(0, min(int(step), len(movielist)-start)):
            js['instances'].append({'data':{'features': {
                'keys':[int(u_Dict[userid]), 53902+m_Dict[movielist[line+int(start)]]],
                "shape":[79906],
                "values":[1,1]}}})
        
        #print(json.dumps(js))
        raw_response = client.invoke_endpoint(
                EndpointName = 'factorization-machines-2018-12-18-05-47-26-108',
                Body = json.dumps(js),
                ContentType='application/json')
        result = json.loads(raw_response['Body'].read())
        for counter, p in enumerate(result['predictions']):
            p['MovieID'] = movielist[counter]
            response.append(p)
        print(start+line)
        start = start+step    
    return(response)

%%time
test_user_preference_response = slicedInferenceUserPreference('100020528', 1000, horror_and_terrified_movie)

showResultfromDataFrame(preference_response)