You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
116 lines
3.1 KiB
116 lines
3.1 KiB
|
|
from sklearn.linear_model import LinearRegression
|
|
import pandas as pd
|
|
import numpy as np
|
|
import requests
|
|
import logging
|
|
import json
|
|
from datetime import datetime
|
|
|
|
# --------------- Fonction ----------------- #
|
|
def generateJsonModel(model:LinearRegression):
|
|
listCoef = []
|
|
listIntercept = []
|
|
for i in range(0,len(model.coef_)):
|
|
listCoef.append(model.coef_[i][0])
|
|
listIntercept.append(model.intercept_[i])
|
|
json = {"coef":listCoef,"intercept":listIntercept}
|
|
return json
|
|
|
|
def generateModele(dataJson):
|
|
# -- Préparation des données
|
|
arrayBpm = []
|
|
arrayStartTime = []
|
|
arrayTimeOfActivity = []
|
|
|
|
for data in dataJson:
|
|
|
|
info = json.loads(data["json"])
|
|
|
|
arrayBpm.append(int(info["bpmAvg"]))
|
|
arrayTimeOfActivity.append(float(info["timeOfActivity"]))
|
|
|
|
# Convertir la chaîne en objet datetime
|
|
dt_object = datetime.strptime(info["startTime"], "%Y-%m-%dT%H:%M:%S.%f")
|
|
# Convertir l'objet datetime en millisecondes depuis l'époque
|
|
milliseconds_since_epoch = int(dt_object.timestamp() * 1000)
|
|
arrayStartTime.append(milliseconds_since_epoch)
|
|
# -- DataFrame
|
|
data = pd.DataFrame({
|
|
"Bpm": arrayBpm,
|
|
"TimeOfActivity": arrayTimeOfActivity
|
|
})
|
|
# -- Régression linéaire
|
|
model = LinearRegression()
|
|
model.fit(np.array(arrayStartTime).reshape(-1,1),data)
|
|
return model
|
|
|
|
|
|
def getUserWithData(url:str):
|
|
response = requests.get(urlGetAllData)
|
|
if ( response.status_code != 200):
|
|
print('problème lors de l extraction des données avec l api !! -> "getUserWithData" (status_code != 200)')
|
|
exit()
|
|
return response.json()
|
|
|
|
def sendJsonToApi(url,json):
|
|
header = {"Content-type": "application/json"}
|
|
response = requests.post(url,json,headers=header)
|
|
if ( response.status_code != 200):
|
|
print('Problème lors de l envoi des données avec l api !! -> "sendJsonToApi" (status_code != 200)')
|
|
exit()
|
|
return
|
|
|
|
# ---------------- Main ------------------- #
|
|
logging.error("RUNNNNNNNN !")
|
|
|
|
urlGetAllData = "https://codefirst.iut.uca.fr/containers/SmartFit-smartfit_api/ai/data"
|
|
|
|
# --- Call Api
|
|
dataUser = getUserWithData(url=urlGetAllData)
|
|
'''
|
|
dataUser = [{
|
|
"uuid": "xxxx",
|
|
"categories": [
|
|
{
|
|
"name": "walking",
|
|
"infos": [
|
|
{
|
|
"json": {"BpmAvg":100,"TimeOfActivity":225,"StartTime":1234}
|
|
}
|
|
]
|
|
},
|
|
{
|
|
"name": "cycling",
|
|
"infos": [
|
|
{
|
|
"json": {"BpmAvg":110,"TimeOfActivity":225,"StartTime":12345}
|
|
}
|
|
]
|
|
}
|
|
]
|
|
}
|
|
]'''
|
|
|
|
logging.error("Nombre de User : "+str(len(dataUser)))
|
|
i = 0
|
|
|
|
for user in dataUser:
|
|
|
|
userUUID = user["uuid"]
|
|
|
|
for category in user["categories"]:
|
|
jsonTmp = {}
|
|
#Mettre la condition longueur ici
|
|
|
|
model = generateModele(category["infos"])
|
|
|
|
jsonTmp["uuid"] = userUUID
|
|
jsonTmp["category"] = category["name"]
|
|
jsonTmp["model"] = json.dumps(generateJsonModel(model))
|
|
|
|
sendJsonToApi(urlGetAllData,json.dumps(jsonTmp))
|
|
i+=1
|
|
logging.error("User nb "+str(i)+" finis")
|
|
|
|
logging.error("Exec Fini") |