jeanmidevacc

for idx, row in dfp_archetypes.iterrows():
    print("ARCHETYPE:", row["userid"])
    inventory_positive = dfp_inventory_positive.loc[row["userid"]]
    
    # Get the candidates
    buffer = []
    for contentid in inventory_positive:
        closest_contentids = get_closest_neighbors(model_retriever_items, contentid, 10, type_="item")
        buffer.extend(closest_contentids)
    sp_count_contentids = pd.Series(dict(Counter(buffer))).sort_values(ascending=False)

jeanmidevacc

def get_closest_neighbors(model, entityid, k, type_="item"):
    if type_ == "item":
        inner_entity_id = model.trainset.to_inner_iid(entityid)
    else:
        inner_entity_id = model.trainset.to_inner_uid(entityid)
        
    closest_entity_id = model.get_neighbors(inner_entity_id, k)
    
    if type_ == "item":
        return [model.trainset.to_raw_iid(id_) for id_ in closest_entity_id]

jeanmidevacc

def build_recommendations(model, userid, inventory, dfp_items, k=5):
    dfp_recommendations = dfp_items[["title", "category", "year", "contentid"]]
    dfp_recommendations["contentid"] = dfp_recommendations["contentid"].astype(str)

    dfp_recommendations["rating_predicted"] = dfp_recommendations["contentid"].apply(lambda contentid: compute_ranking(model, str(userid), str(contentid)))
    dfp_recommendations.sort_values("rating_predicted", ascending=False, inplace=True)
    dfp_recommendations = dfp_recommendations.loc[dfp_recommendations["contentid"].isin(inventory) == False]
    return dfp_recommendations.head(k).reset_index(drop=True)

jeanmidevacc

from time import time
from hyperopt import fmin, tpe, hp, anneal, Trials
import mlflow
from sklearn.metrics import mean_squared_error
import surprise

def evaluate_model(model, dfp_ratings_test):
    dfp_evaluation = dfp_ratings_test.copy()
    dfp_evaluation["rating_predicted"] = dfp_evaluation.apply(lambda row: compute_ranking(model, str(row["userid"]), str(row["contentid"])), axis=1)
    return mean_squared_error(dfp_evaluation["rating"].tolist(), dfp_evaluation["rating_predicted"].tolist(), squared=False)

jeanmidevacc

import pandas as pd
from kats import models

# Selection your base model
def build_model(model, kts):
    if model == "prophet":
        return models.prophet.ProphetModel(kts, params=models.prophet.ProphetParams())
    elif model == "theta":
        return models.theta.ThetaModel(kts, params=models.theta.ThetaParams())
    elif model == "holtwinters":

jeanmidevacc

from kats.consts import TimeSeriesData

def build_kats_timeserie(dfp, column_time = "time", column_value = "value"):
    return TimeSeriesData(time=dfp[column_time], value=dfp[column_value])

kts_test = build_kats_timeserie(dfp_test,"date","value")

jeanmidevacc

from neo4j import GraphDatabase

# Declare the connector to the graph database
uri = "bolt://localhost:7687"
driver = GraphDatabase.driver(uri, auth=("neo4j", "pwd"), encrypted=False)

# Build the query to build the relationship
query  = f'''
MATCH (u:User), (s:Song) 
WHERE u.id = "id of the user" AND s.id = "id of the song"

jeanmidevacc

from neo4j import GraphDatabase

# Declare the connector to the graph database
uri = "bolt://localhost:7687"
driver = GraphDatabase.driver(uri, auth=("neo4j", "pwd"), encrypted=False)

# Define the node properties
node = {
    'url': 'https://bsaber.com/members/jeanmidev/',
    'picture': 'https://bsaber.com/wp-content/uploads/avatars/89910/5e99a50d31e27-bpfull.png',

jeanmidevacc

%%time
def get_recommendations_jaccard(user_id):
    # Based on the code in https://neo4j.com/docs/graph-data-science/current/alpha-algorithms/jaccard/
    query = f"""
    MATCH (u1:User)-[:BOOKMARKED]->(song1)
    WITH u1, collect(id(song1)) AS u1Song
    WHERE u1.id = '{user_id}'
    MATCH (u2:User)-[:BOOKMARKED]->(song2) WHERE u1 <> u2
    WITH u1, u1Song, u2, collect(id(song2)) AS u2Song
    RETURN u2.id AS user_id,

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def get_recommendations_fof(user_id, relationship = 'IS_FRIEND_WITH'):
    query = f"""
    MATCH (u:User)-[:{relationship}]->(u1:User)-[:{relationship}]->(u2:User)
    WHERE u.id = '{user_id}'
    RETURN collect(DISTINCT u2.id) AS recommendations
    """
    with driver.session() as session:
        result = session.run(query)

    return result.values()[0][0]