def __init__(self, config):
self.config = config
self.activities_df = pandas.read_csv(get_path("../../activities.csv"),
index_col="id")
# todo: read from config
self.JWT_ALGORITHM = "HS256"
self.JWT_SECRET = "0" # secrets.token_bytes(16)
self.EXPIRED_TOKENS = set()
async def fetch_activity(self, request):
if request.id == None:
raise errors.Unauthorized("A valid token is required")
activity_id = int(request.rel_url.query["itemid"])
if activity_id >= len(self.activities_df):
raise errors.UserError("this id is too big")
with open(get_path("../../interactions.csv"), "a") as output_csv:
writer = csv.writer(output_csv)
writer.writerow([request.id, activity_id, 1, int(time.time())])
output = self.activities_df.iloc[activity_id].to_json()
return web.Response(text=output)
def GET(self, id): path = "/%s" % storage.get_path(int(id)) raise web.seeother(path)
def download(file_hash):
file = storage.get_path(file_hash)
return web.FileResponse(file)
def GET(self, id):
path = "/%s" % storage.get_path(int(id))
raise web.seeother(path)
import pandas as pd
import numpy as np
from storage import get_path
import torch
from torch.autograd import Variable
import torch.nn.functional as Functional
from tqdm import tqdm
import matplotlib.pyplot as plt
from storage import get_path
activites = pd.read_csv(get_path("../../activities.csv"))
activites.head()
# Read the CSV of interactions and sort it by timestamp
data = pd.read_csv(get_path("../../interactions.csv"))
data_sorted = data.sort_values("timestamp")
data_sorted.head()
# Cut the data off at 75% in order to save some data for the test process
CUTOFF = 0.70
cutoff_idx = int(len(data_sorted) * CUTOFF)
# Generate the train and test data
data_train = data_sorted.iloc[0:cutoff_idx]
data_test = data_sorted.iloc[cutoff_idx:]
def get_intersection_test_and_train(field):
"""
Get interactions between the test and train datasets
for the given field