---
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backend/app/__pycache__
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backend/datasets/*
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backend/deploy.sh
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backend/env
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backend/.env.production
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backend/project/__pycache__
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frontend/.env
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This is free and unencumbered software released into the public domain.
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||||||
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Anyone is free to copy, modify, publish, use, compile, sell, or
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distribute this software, either in source code form or as a compiled
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binary, for any purpose, commercial or non-commercial, and by any
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means.
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In jurisdictions that recognize copyright laws, the author or authors
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of this software dedicate any and all copyright interest in the
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software to the public domain. We make this dedication for the benefit
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of the public at large and to the detriment of our heirs and
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successors. We intend this dedication to be an overt act of
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relinquishment in perpetuity of all present and future rights to this
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software under copyright law.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
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EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
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MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
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IN NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR
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OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,
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ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
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||||||
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OTHER DEALINGS IN THE SOFTWARE.
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||||||
|
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||||||
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For more information, please refer to <http://unlicense.org/>
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# HSL Bike Helper [](https://github.com/ElliotAtHelsinki/data-science-project/blob/main/LICENSE.md)  
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HSL Bike Helper is an app that predicts the number of bikes at stations in [`Helsinki`](https://hel.fi/) and [`Espoo`](https://espoo.fi/) at any hour using the [`SARIMA`](https://en.wikipedia.org/wiki/Autoregressive_integrated_moving_average) model.
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### Repository
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https://gitea.elliot-at-zuri.ch/root/DATA11001-Introduction-to-Data-Science
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### Installation
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The application can be directly accessed via a web browser at the following addresses, without requiring any installation:
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\- **Frontend**: https://hsl-frontend.elliot-at-zuri.ch
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\- **Backend**: https://hsl-backend.elliot-at-zuri.ch/app/predict
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Optionally, however, the application can be installed as a mobile or desktop app, since it is a PWA.
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### Development
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To run the `Django` backend locally, navigate to the `backend` folder and:
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\- Source the `env`: `source env/bin/activate`
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\- Create an empty `.env` file: `touch .env`
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\- Install the dependencies: `pip install -r requirements.txt`
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\- Run the first two code blocks of the `main.ipynb` file
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\- Start the server: `python manage.py runserver`
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To run the `Next.js` frontend locally, navigate to the `frontend` folder and:
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\- Install the dependencies: `npm install`
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\- Start the application: `npm run dev`
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env
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datasets/20*
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datasets/full_bike_data.csv
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main.ipynb
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deploy.sh
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.env
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FROM python:3.13.0-bookworm
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WORKDIR /usr/src/app
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COPY . .
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COPY .env.production .env
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RUN pip install -r requirements.txt
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EXPOSE 9000
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ENV DJANGO_ENV=production
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CMD ["python", "manage.py", "runserver", "0.0.0.0:9000", "--noreload"]
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## This is the Django backend for our Data Science project.
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from django.contrib import admin
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# Register your models here.
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from django.apps import AppConfig
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class AppConfig(AppConfig):
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default_auto_field = 'django.db.models.BigAutoField'
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name = 'app'
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from django.db import models
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# Create your models here.
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from django.test import TestCase
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# Create your tests here.
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from django.urls import path
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from . import views
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urlpatterns = [
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path('', views.index, name='index'),
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path('predict', views.predict, name='predict'),
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]
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from django.http import HttpResponse, JsonResponse
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from datetime import datetime
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import pandas as pd
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import statsmodels.api as sm
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stations = ['Kamppi (M)', 'Rautatientori - itä']
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station_dict = {}
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for station in stations:
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departure_data = pd.read_csv('datasets/' + station + '_hourly_aggregate.csv')
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return_data = pd.read_csv('datasets/' + station + '_return_hourly_aggregate.csv')
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departure_data['Departure'] = pd.to_datetime(departure_data['Departure'], format='mixed')
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return_data['Return'] = pd.to_datetime(return_data['Return'], format='mixed')
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departure_data.set_index(departure_data['Departure'], inplace=True)
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return_data.set_index(return_data['Return'], inplace=True)
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departure_data['trip'] = pd.to_numeric(departure_data['trip'], errors='coerce')
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return_data['trip'] = pd.to_numeric(return_data['trip'], errors='coerce')
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departure_data = departure_data.dropna(axis=1)
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return_data = return_data.dropna(axis=1)
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departure_mod = sm.tsa.statespace.SARIMAX(departure_data['trip'], order=(1, 1, 1), seasonal_order=(0, 1, 0, 24), freq='h').fit(disp=False, low_memory=True)
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return_mod = sm.tsa.statespace.SARIMAX(return_data['trip'], order=(1, 1, 1), seasonal_order=(0, 1, 0, 24), freq='h').fit(disp=False, low_memory=True)
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station_dict[station] = {}
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station_dict[station]['departure_mod'] = departure_mod
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station_dict[station]['return_mod'] = return_mod
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def index(request):
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return HttpResponse('You\'re at the app index.')
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def predict(request):
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if request.method == 'GET':
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current = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
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timestamp = request.GET.get('timestamp', current)
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station = request.GET.get('station', 'Kamppi (M)')
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ts = timestamp
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defaultBikeCount = 28
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departingCount = -1
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if departure_data.index.max() >= pd.to_datetime(timestamp):
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ls = departure_data[departure_data['Departure'] == ts]['trip'].tolist()
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if len(ls) != 0:
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departingCount = ls[0]
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else:
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pass
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else:
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departure_mod = station_dict[station]['departure_mod']
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departForecast = departure_mod.forecast(timestamp)
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departingCount = round(departForecast[-1])
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returningCount = -1
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if return_data.index.max() >= pd.to_datetime(timestamp):
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ls = return_data[return_data['Return'] == ts]['trip'].tolist()
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if len(ls) != 0:
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returningCount = ls[0]
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else:
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pass
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else:
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return_mod = station_dict[station]['return_mod']
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returnForecast = return_mod.forecast(timestamp)
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returningCount = round(returnForecast[-1])
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bikeAtStationCount = defaultBikeCount - departingCount + returningCount
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result = {
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'timestamp': timestamp,
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'station': station,
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'departingCount': departingCount,
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'returningCount': returningCount,
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'bikeAtStationCount': bikeAtStationCount,
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'increasing': returningCount > departingCount
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}
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return JsonResponse(result)
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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|
"id": "cd21c67a-e679-43e3-85a4-471a35522d42",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"\"\\nparam_mse = list()\\nfor param in parameter_space:\\n model = sm.tsa.statespace.SARIMAX(train_data['trip'], order=param, seasonal_order=(0, 1, 0, 24), freq='h').fit()\\n forecast = model.forecast(datetime(year=2023,month=10,day=31,hour=17))\\n forecast = forecast[datetime(year=2023,month=4,day=1):]\\n #test_data.index = pd.DatetimeIndex(forecast.index)\\n \\n final = pd.concat([forecast,test_data], axis=1)\\n errors = final['trip'] - final['predicted_mean']\\n mse = np.mean(errors**2)\\n\\n param_mse.append((mse, param))\\n\""
|
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|
]
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|
},
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|
"execution_count": 2,
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||||||
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"metadata": {},
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||||||
|
"output_type": "execute_result"
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}
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||||||
|
],
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"source": [
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|
"import pandas as pd\n",
|
||||||
|
"import numpy as np\n",
|
||||||
|
"from sklearn.model_selection import TimeSeriesSplit\n",
|
||||||
|
"from datetime import datetime\n",
|
||||||
|
"import statsmodels.api as sm\n",
|
||||||
|
"\n",
|
||||||
|
"# Use Kamppi as an example, could be generalized to all stations\n",
|
||||||
|
"df = pd.read_csv(\"datasets/Jämeräntaival_hourly_aggregate.csv\")\n",
|
||||||
|
"df['Departure'] = pd.to_datetime(df['Departure'], format='mixed')\n",
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||||||
|
"df.set_index('Departure', inplace=True)\n",
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||||||
|
"\n",
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|
"train_end = datetime(year=2022, month=10, day=31)\n",
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"train_start = datetime(year=2018,month=4,day=1)\n",
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"train_data = df[:train_end]\n",
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"test_end = datetime(year=2023, month=10, day = 31)\n",
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|
"test_data = df[datetime(year=2023,month=4,day=1):]\n",
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"\n",
|
||||||
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"parameter_space = list()\n",
|
||||||
|
"\n",
|
||||||
|
"for i in range(0,3):\n",
|
||||||
|
" for j in range(0,3):\n",
|
||||||
|
" for k in range(0,3):\n",
|
||||||
|
" parameter_space.append((i,j,k))\n",
|
||||||
|
"\n",
|
||||||
|
"#parameter_space = [x for x in parameter_space if x[1] == 0]\n",
|
||||||
|
"parameter_space = [x for x in parameter_space if x != (0,0,0)]\n",
|
||||||
|
"'''\n",
|
||||||
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"param_mse = list()\n",
|
||||||
|
"for param in parameter_space:\n",
|
||||||
|
" model = sm.tsa.statespace.SARIMAX(train_data['trip'], order=param, seasonal_order=(0, 1, 0, 24), freq='h').fit()\n",
|
||||||
|
" forecast = model.forecast(datetime(year=2023,month=10,day=31,hour=17))\n",
|
||||||
|
" forecast = forecast[datetime(year=2023,month=4,day=1):]\n",
|
||||||
|
" #test_data.index = pd.DatetimeIndex(forecast.index)\n",
|
||||||
|
" \n",
|
||||||
|
" final = pd.concat([forecast,test_data], axis=1)\n",
|
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|
" errors = final['trip'] - final['predicted_mean']\n",
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||||||
|
" mse = np.mean(errors**2)\n",
|
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|
"\n",
|
||||||
|
" param_mse.append((mse, param))\n",
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|
"'''\n",
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|
"\n",
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||||||
|
"\n",
|
||||||
|
"\n",
|
||||||
|
" \n"
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|
]
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|
},
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|
{
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|
"cell_type": "code",
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|
"execution_count": 23,
|
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|
"id": "db9591dd-7bfd-46d3-aa50-8a757776aeb0",
|
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|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"9.043665073451029 9.056463656418249\n",
|
||||||
|
"9.029642126494705 9.043665073451029\n",
|
||||||
|
"9.029317575211266 9.029642126494705\n",
|
||||||
|
"(np.float64(9.029317575211266), (1, 0, 2))\n"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"min_mse = param_mse[0][0]\n",
|
||||||
|
"\n",
|
||||||
|
"index = 0\n",
|
||||||
|
"for i in range(0,len(param_mse)):\n",
|
||||||
|
" if param_mse[i][0] < min_mse:\n",
|
||||||
|
" print(param_mse[i][0], min_mse)\n",
|
||||||
|
" min_mse = param_mse[i][0]\n",
|
||||||
|
" index = i\n",
|
||||||
|
"\n",
|
||||||
|
"\n",
|
||||||
|
"print(param_mse[index])\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 5,
|
||||||
|
"id": "5b01e8b2-19f2-4809-bee7-0e0589489841",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stderr",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"/home/aleksi/venv/lib/python3.12/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency h will be used.\n",
|
||||||
|
" self._init_dates(dates, freq)\n",
|
||||||
|
" This problem is unconstrained.\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"RUNNING THE L-BFGS-B CODE\n",
|
||||||
|
"\n",
|
||||||
|
" * * *\n",
|
||||||
|
"\n",
|
||||||
|
"Machine precision = 2.220D-16\n",
|
||||||
|
" N = 3 M = 10\n",
|
||||||
|
"\n",
|
||||||
|
"At X0 0 variables are exactly at the bounds\n",
|
||||||
|
"\n",
|
||||||
|
"At iterate 0 f= 2.51523D+00 |proj g|= 8.46816D-02\n",
|
||||||
|
"\n",
|
||||||
|
"At iterate 5 f= 2.46808D+00 |proj g|= 9.28777D-03\n",
|
||||||
|
"\n",
|
||||||
|
"At iterate 10 f= 2.45763D+00 |proj g|= 8.55309D-03\n",
|
||||||
|
"\n",
|
||||||
|
"At iterate 15 f= 2.45509D+00 |proj g|= 5.19865D-03\n",
|
||||||
|
"\n",
|
||||||
|
"At iterate 20 f= 2.45471D+00 |proj g|= 1.64676D-03\n",
|
||||||
|
"\n",
|
||||||
|
"At iterate 25 f= 2.45467D+00 |proj g|= 4.36219D-04\n",
|
||||||
|
"\n",
|
||||||
|
"At iterate 30 f= 2.45467D+00 |proj g|= 2.78346D-06\n",
|
||||||
|
"\n",
|
||||||
|
" * * *\n",
|
||||||
|
"\n",
|
||||||
|
"Tit = total number of iterations\n",
|
||||||
|
"Tnf = total number of function evaluations\n",
|
||||||
|
"Tnint = total number of segments explored during Cauchy searches\n",
|
||||||
|
"Skip = number of BFGS updates skipped\n",
|
||||||
|
"Nact = number of active bounds at final generalized Cauchy point\n",
|
||||||
|
"Projg = norm of the final projected gradient\n",
|
||||||
|
"F = final function value\n",
|
||||||
|
"\n",
|
||||||
|
" * * *\n",
|
||||||
|
"\n",
|
||||||
|
" N Tit Tnf Tnint Skip Nact Projg F\n",
|
||||||
|
" 3 30 36 1 0 0 2.783D-06 2.455D+00\n",
|
||||||
|
" F = 2.4546707052577292 \n",
|
||||||
|
"\n",
|
||||||
|
"CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL \n",
|
||||||
|
"10.388099588499935\n"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"model = sm.tsa.statespace.SARIMAX(train_data['trip'], order=(1,1,1), seasonal_order=(0,1,0,24), freq='h').fit()\n",
|
||||||
|
"forecast = model.forecast(datetime(year=2023,month=10,day=31,hour=17))\n",
|
||||||
|
"forecast = forecast[datetime(year=2023,month=4,day=1):]\n",
|
||||||
|
"#test_data.index = pd.DatetimeIndex(forecast.index)\n",
|
||||||
|
"\n",
|
||||||
|
"final = pd.concat([forecast,test_data], axis=1)\n",
|
||||||
|
"errors = final['trip'] - final['predicted_mean']\n",
|
||||||
|
"mse = np.mean(errors**2)\n",
|
||||||
|
"\n",
|
||||||
|
"print(mse)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"id": "726acc6f-ea29-4bb6-8252-a6556ae10ac4",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": []
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"metadata": {
|
||||||
|
"kernelspec": {
|
||||||
|
"display_name": "Python 3 (ipykernel)",
|
||||||
|
"language": "python",
|
||||||
|
"name": "python3"
|
||||||
|
},
|
||||||
|
"language_info": {
|
||||||
|
"codemirror_mode": {
|
||||||
|
"name": "ipython",
|
||||||
|
"version": 3
|
||||||
|
},
|
||||||
|
"file_extension": ".py",
|
||||||
|
"mimetype": "text/x-python",
|
||||||
|
"name": "python",
|
||||||
|
"nbconvert_exporter": "python",
|
||||||
|
"pygments_lexer": "ipython3",
|
||||||
|
"version": "3.12.3"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"nbformat": 4,
|
||||||
|
"nbformat_minor": 5
|
||||||
|
}
|
||||||
Binary file not shown.
@@ -0,0 +1 @@
|
|||||||
|
[(np.float64(3.3274909833208963), 'no exogenous data'), (np.float64(3.158263780804383), 'just weather'), (np.float64(3.3220296790264277), 'just weekdays and hours'), (np.float64(3.1363872336562717), 'all exogenous')]
|
||||||
@@ -0,0 +1,301 @@
|
|||||||
|
{
|
||||||
|
"cells": [
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 56,
|
||||||
|
"id": "cd21c67a-e679-43e3-85a4-471a35522d42",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"asd\n",
|
||||||
|
"asd2\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"name": "stderr",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"/Users/otto/.pyenv/versions/3.11.2/lib/python3.11/site-packages/statsmodels/tsa/statespace/sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
|
||||||
|
" warn('Non-stationary starting autoregressive parameters'\n",
|
||||||
|
"/Users/otto/.pyenv/versions/3.11.2/lib/python3.11/site-packages/statsmodels/tsa/statespace/sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
|
||||||
|
" warn('Non-invertible starting MA parameters found.'\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"RUNNING THE L-BFGS-B CODE\n",
|
||||||
|
"\n",
|
||||||
|
" * * *\n",
|
||||||
|
"\n",
|
||||||
|
"Machine precision = 2.220D-16\n",
|
||||||
|
" N = 8 M = 10\n",
|
||||||
|
"\n",
|
||||||
|
"At X0 0 variables are exactly at the bounds\n",
|
||||||
|
"\n",
|
||||||
|
"At iterate 0 f= 2.52687D+00 |proj g|= 3.92148D-01\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"name": "stderr",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
" This problem is unconstrained.\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"\n",
|
||||||
|
"At iterate 5 f= 2.24021D+00 |proj g|= 1.15085D-01\n",
|
||||||
|
"\n",
|
||||||
|
"At iterate 10 f= 2.19412D+00 |proj g|= 5.75993D-03\n",
|
||||||
|
"\n",
|
||||||
|
"At iterate 15 f= 2.19337D+00 |proj g|= 8.22902D-04\n",
|
||||||
|
"\n",
|
||||||
|
"At iterate 20 f= 2.19327D+00 |proj g|= 4.98625D-03\n",
|
||||||
|
"\n",
|
||||||
|
"At iterate 25 f= 2.19325D+00 |proj g|= 3.48694D-05\n",
|
||||||
|
"\n",
|
||||||
|
" * * *\n",
|
||||||
|
"\n",
|
||||||
|
"Tit = total number of iterations\n",
|
||||||
|
"Tnf = total number of function evaluations\n",
|
||||||
|
"Tnint = total number of segments explored during Cauchy searches\n",
|
||||||
|
"Skip = number of BFGS updates skipped\n",
|
||||||
|
"Nact = number of active bounds at final generalized Cauchy point\n",
|
||||||
|
"Projg = norm of the final projected gradient\n",
|
||||||
|
"F = final function value\n",
|
||||||
|
"\n",
|
||||||
|
" * * *\n",
|
||||||
|
"\n",
|
||||||
|
" N Tit Tnf Tnint Skip Nact Projg F\n",
|
||||||
|
" 8 25 26 1 0 0 3.487D-05 2.193D+00\n",
|
||||||
|
" F = 2.1932501470633761 \n",
|
||||||
|
"\n",
|
||||||
|
"CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH \n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"name": "stderr",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"/Users/otto/.pyenv/versions/3.11.2/lib/python3.11/site-packages/statsmodels/tsa/statespace/sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
|
||||||
|
" warn('Non-stationary starting autoregressive parameters'\n",
|
||||||
|
"/Users/otto/.pyenv/versions/3.11.2/lib/python3.11/site-packages/statsmodels/tsa/statespace/sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
|
||||||
|
" warn('Non-invertible starting MA parameters found.'\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"RUNNING THE L-BFGS-B CODE\n",
|
||||||
|
"\n",
|
||||||
|
" * * *\n",
|
||||||
|
"\n",
|
||||||
|
"Machine precision = 2.220D-16\n",
|
||||||
|
" N = 10 M = 10\n",
|
||||||
|
"\n",
|
||||||
|
"At X0 0 variables are exactly at the bounds\n",
|
||||||
|
"\n",
|
||||||
|
"At iterate 0 f= 2.52689D+00 |proj g|= 3.92567D-01\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"name": "stderr",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
" This problem is unconstrained.\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"\n",
|
||||||
|
"At iterate 5 f= 2.23987D+00 |proj g|= 1.15352D-01\n",
|
||||||
|
"\n",
|
||||||
|
"At iterate 10 f= 2.19343D+00 |proj g|= 5.82758D-03\n",
|
||||||
|
"\n",
|
||||||
|
"At iterate 15 f= 2.19242D+00 |proj g|= 1.41481D-03\n",
|
||||||
|
"\n",
|
||||||
|
"At iterate 20 f= 2.19228D+00 |proj g|= 1.02376D-03\n",
|
||||||
|
"\n",
|
||||||
|
"At iterate 25 f= 2.19225D+00 |proj g|= 8.93884D-04\n",
|
||||||
|
"\n",
|
||||||
|
"At iterate 30 f= 2.19224D+00 |proj g|= 6.02699D-05\n",
|
||||||
|
"\n",
|
||||||
|
"At iterate 35 f= 2.19224D+00 |proj g|= 4.09553D-04\n",
|
||||||
|
"\n",
|
||||||
|
"At iterate 40 f= 2.19223D+00 |proj g|= 4.81752D-04\n",
|
||||||
|
"\n",
|
||||||
|
"At iterate 45 f= 2.19220D+00 |proj g|= 9.39974D-04\n",
|
||||||
|
"\n",
|
||||||
|
"At iterate 50 f= 2.19205D+00 |proj g|= 5.16353D-03\n",
|
||||||
|
"\n",
|
||||||
|
" * * *\n",
|
||||||
|
"\n",
|
||||||
|
"Tit = total number of iterations\n",
|
||||||
|
"Tnf = total number of function evaluations\n",
|
||||||
|
"Tnint = total number of segments explored during Cauchy searches\n",
|
||||||
|
"Skip = number of BFGS updates skipped\n",
|
||||||
|
"Nact = number of active bounds at final generalized Cauchy point\n",
|
||||||
|
"Projg = norm of the final projected gradient\n",
|
||||||
|
"F = final function value\n",
|
||||||
|
"\n",
|
||||||
|
" * * *\n",
|
||||||
|
"\n",
|
||||||
|
" N Tit Tnf Tnint Skip Nact Projg F\n",
|
||||||
|
" 10 50 54 1 0 0 5.164D-03 2.192D+00\n",
|
||||||
|
" F = 2.1920465431369465 \n",
|
||||||
|
"\n",
|
||||||
|
"STOP: TOTAL NO. of ITERATIONS REACHED LIMIT \n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"name": "stderr",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"/Users/otto/.pyenv/versions/3.11.2/lib/python3.11/site-packages/statsmodels/base/model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
|
||||||
|
" warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"import pandas as pd\n",
|
||||||
|
"import numpy as np\n",
|
||||||
|
"from sklearn.model_selection import TimeSeriesSplit\n",
|
||||||
|
"from datetime import datetime\n",
|
||||||
|
"import statsmodels.api as sm\n",
|
||||||
|
"\n",
|
||||||
|
"stations = ['Jämeräntaival']\n",
|
||||||
|
"data = pd.read_csv('datasets/' + stations[0] + '_hourly_aggregate.csv')\n",
|
||||||
|
"data['Departure'] = pd.to_datetime(data['Departure'], format='mixed')\n",
|
||||||
|
"\n",
|
||||||
|
"results = []\n",
|
||||||
|
"\n",
|
||||||
|
"weather_df = pd.read_csv('datasets/weather_hourly_helsinki.csv')\n",
|
||||||
|
"weather_df = weather_df.loc[1:, :]\n",
|
||||||
|
"weather_df.columns = weather_df.iloc[0]\n",
|
||||||
|
"weather_df = weather_df.loc[2:, :]\n",
|
||||||
|
"weather_df['time'] = pd.to_datetime(weather_df['time'], format='mixed')\n",
|
||||||
|
"\n",
|
||||||
|
"data = pd.merge(weather_df, data, how='inner', left_on='time', right_on='Departure')\n",
|
||||||
|
"data = data.drop(['time'], axis=1)\n",
|
||||||
|
"\n",
|
||||||
|
"data['temperature_2m (°C)'] = pd.to_numeric(data['temperature_2m (°C)'], errors='coerce')\n",
|
||||||
|
"data['rain (mm)'] = pd.to_numeric(data['rain (mm)'], errors='coerce')\n",
|
||||||
|
"data['trip'] = pd.to_numeric(data['trip'], errors='coerce')\n",
|
||||||
|
"\n",
|
||||||
|
"\n",
|
||||||
|
"# generation of weekday & hour series\n",
|
||||||
|
"datedata = pd.DataFrame()\n",
|
||||||
|
"datedata['date'] = data['Departure']\n",
|
||||||
|
"datedata['weekday'] = data['Departure'].dt.weekday\n",
|
||||||
|
"days = ['mon', 'tue', 'wed', 'thu', 'fri', 'sat']\n",
|
||||||
|
"for day in days:\n",
|
||||||
|
" datedata[day] = 0\n",
|
||||||
|
" datedata.loc[datedata['date'].dt.weekday == 0, day] = 1\n",
|
||||||
|
"for i in range(0, 23):\n",
|
||||||
|
" asd = 'hour' + str(i)\n",
|
||||||
|
" days.append(asd)\n",
|
||||||
|
" datedata[asd] = 0\n",
|
||||||
|
" datedata.loc[datedata['date'].dt.hour == i, asd] = 1\n",
|
||||||
|
"\n",
|
||||||
|
"data = pd.merge(datedata, data, how='inner', left_on='date', right_on='Departure')\n",
|
||||||
|
"data.set_index(data['Departure'], inplace=True)\n",
|
||||||
|
"\n",
|
||||||
|
"\n",
|
||||||
|
"\n",
|
||||||
|
"train_end = datetime(year=2022, month=10, day=31)\n",
|
||||||
|
"train_start = datetime(year=2018,month=4,day=1)\n",
|
||||||
|
"train_data = data[:train_end]\n",
|
||||||
|
"test_end = datetime(year=2023, month=10, day = 31)\n",
|
||||||
|
"test_data = data[datetime(year=2023,month=4,day=1):]\n",
|
||||||
|
"\n",
|
||||||
|
"\n",
|
||||||
|
"def MASE(y_true, y_pred, y_train):\n",
|
||||||
|
" forecast = y_pred.reset_index(drop=True)\n",
|
||||||
|
" outsample = y_true[:].iloc[:len(y_pred)]\n",
|
||||||
|
" insample = y_train.reset_index(drop=True).to_numpy()\n",
|
||||||
|
" frequency=1\n",
|
||||||
|
" return np.mean(np.abs(forecast - outsample)) / np.mean(np.abs(insample[:-frequency] - insample[frequency:]))\n",
|
||||||
|
"\n",
|
||||||
|
"\n",
|
||||||
|
"# reset indexes as they do funky things\n",
|
||||||
|
"test_data.reset_index(drop=True, inplace=True)\n",
|
||||||
|
"train_data.reset_index(drop=True, inplace=True)\n",
|
||||||
|
"\n",
|
||||||
|
"print('')\n",
|
||||||
|
"\n",
|
||||||
|
"# no exogenous variables at all\n",
|
||||||
|
"model = sm.tsa.statespace.SARIMAX(train_data['trip'], order=(3,1,2), seasonal_order=(1, 1, 1, 24)).fit()\n",
|
||||||
|
"forecast = model.forecast(steps=24*30*7)\n",
|
||||||
|
"forecast = forecast[:]\n",
|
||||||
|
"results.append((MASE(test_data['trip'], forecast, train_data['trip']), 'no exog'))\n",
|
||||||
|
"\n",
|
||||||
|
"#just the weather as exogenous data\n",
|
||||||
|
"exogenous = ['rain (mm)', 'temperature_2m (°C)']\n",
|
||||||
|
"model = sm.tsa.statespace.SARIMAX(train_data['trip'], order=(3,1,2), seasonal_order=(1, 1, 1, 24), exog=train_data[exogenous]).fit()\n",
|
||||||
|
"forecast = model.forecast(steps=24*30*7, exog=test_data[exogenous].iloc[:(24*30*7)])\n",
|
||||||
|
"forecast = forecast[:]\n",
|
||||||
|
"results.append((MASE(test_data['trip'], forecast, train_data['trip']), 'just weather'))\n",
|
||||||
|
"\n",
|
||||||
|
"#just weekday + time of the day as exogenous\n",
|
||||||
|
"model = sm.tsa.statespace.SARIMAX(train_data['trip'], order=(3,1,2), seasonal_order=(1, 1, 1, 24), exog=train_data[days]).fit()\n",
|
||||||
|
"forecast = model.forecast(steps=24*30*7, exog=test_data[days].iloc[:(24*30*7)])\n",
|
||||||
|
"forecast = forecast[:]\n",
|
||||||
|
"results.append((MASE(test_data['trip'], forecast, train_data['trip']), 'just timely stuff'))\n",
|
||||||
|
"\n",
|
||||||
|
"\n",
|
||||||
|
"#all exogenous variables\n",
|
||||||
|
"for asd in days:\n",
|
||||||
|
" exogenous.append(asd)\n",
|
||||||
|
"model = sm.tsa.statespace.SARIMAX(train_data['trip'], order=(3,1,2), seasonal_order=(1, 1, 1, 24), exog=train_data[exogenous]).fit()\n",
|
||||||
|
"forecast = model.forecast(steps=24*30*7, exog=test_data[exogenous].iloc[:(24*30*7)])\n",
|
||||||
|
"forecast = forecast[:]\n",
|
||||||
|
"results.append((MASE(test_data['trip'], forecast, train_data['trip']), 'all exogs'))\n",
|
||||||
|
"\n",
|
||||||
|
"\n",
|
||||||
|
"print(results)\n",
|
||||||
|
" \n",
|
||||||
|
"\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"id": "5b01e8b2-19f2-4809-bee7-0e0589489841",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": []
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"metadata": {
|
||||||
|
"kernelspec": {
|
||||||
|
"display_name": "Python 3 (ipykernel)",
|
||||||
|
"language": "python",
|
||||||
|
"name": "python3"
|
||||||
|
},
|
||||||
|
"language_info": {
|
||||||
|
"codemirror_mode": {
|
||||||
|
"name": "ipython",
|
||||||
|
"version": 3
|
||||||
|
},
|
||||||
|
"file_extension": ".py",
|
||||||
|
"mimetype": "text/x-python",
|
||||||
|
"name": "python",
|
||||||
|
"nbconvert_exporter": "python",
|
||||||
|
"pygments_lexer": "ipython3",
|
||||||
|
"version": "3.11.2"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"nbformat": 4,
|
||||||
|
"nbformat_minor": 5
|
||||||
|
}
|
||||||
@@ -0,0 +1,108 @@
|
|||||||
|
{
|
||||||
|
"cells": [
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 65,
|
||||||
|
"id": "edadd89f-0fc1-4e07-a249-2a0d4052258c",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
" SARIMAX Results \n",
|
||||||
|
"==============================================================================\n",
|
||||||
|
"Dep. Variable: Trip count No. Observations: 7999\n",
|
||||||
|
"Model: ARIMA(2, 1, 2) Log Likelihood -14325.727\n",
|
||||||
|
"Date: Thu, 26 Sep 2024 AIC 28661.454\n",
|
||||||
|
"Time: 17:35:05 BIC 28696.389\n",
|
||||||
|
"Sample: 04-01-2023 HQIC 28673.412\n",
|
||||||
|
" - 10-31-2023 \n",
|
||||||
|
"Covariance Type: opg \n",
|
||||||
|
"==============================================================================\n",
|
||||||
|
" coef std err z P>|z| [0.025 0.975]\n",
|
||||||
|
"------------------------------------------------------------------------------\n",
|
||||||
|
"ar.L1 0.4432 0.034 12.890 0.000 0.376 0.511\n",
|
||||||
|
"ar.L2 0.2343 0.009 26.703 0.000 0.217 0.251\n",
|
||||||
|
"ma.L1 -1.5914 0.035 -45.151 0.000 -1.660 -1.522\n",
|
||||||
|
"ma.L2 0.5926 0.035 16.913 0.000 0.524 0.661\n",
|
||||||
|
"sigma2 2.1040 0.015 136.783 0.000 2.074 2.134\n",
|
||||||
|
"===================================================================================\n",
|
||||||
|
"Ljung-Box (L1) (Q): 0.14 Jarque-Bera (JB): 29710.16\n",
|
||||||
|
"Prob(Q): 0.71 Prob(JB): 0.00\n",
|
||||||
|
"Heteroskedasticity (H): 0.77 Skew: 2.39\n",
|
||||||
|
"Prob(H) (two-sided): 0.00 Kurtosis: 11.15\n",
|
||||||
|
"===================================================================================\n",
|
||||||
|
"\n",
|
||||||
|
"Warnings:\n",
|
||||||
|
"[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
|
||||||
|
"2024-02-28 07:00 0.438484\n",
|
||||||
|
"Freq: h, dtype: float64\n"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"import pandas as pd\n",
|
||||||
|
"import statsmodels.api as sm\n",
|
||||||
|
"from statsmodels.tsa.arima.model import ARIMA\n",
|
||||||
|
"\n",
|
||||||
|
"import matplotlib.pyplot as plt\n",
|
||||||
|
"df = pd.read_csv('datasets/aggregated_2023_Designmuseo.csv')\n",
|
||||||
|
"df['Departure'] = pd.to_datetime(df['Departure'], format='mixed')\n",
|
||||||
|
"df['Trip count'] = pd.to_numeric(df['Trip count'])\n",
|
||||||
|
"\n",
|
||||||
|
"start_date = '2023-01-04 00:00'\n",
|
||||||
|
"end_date = '2023-31-10 23:00'\n",
|
||||||
|
"\n",
|
||||||
|
"start = pd.to_datetime(start_date, format = '%Y-%d-%m %H:%M')\n",
|
||||||
|
"end_date = pd.to_datetime(end_date, format = '%Y-%d-%m %H:%M')\n",
|
||||||
|
"dates = pd.DataFrame({'Departure':pd.date_range(start, end_date, freq='h')})\n",
|
||||||
|
"\n",
|
||||||
|
"\n",
|
||||||
|
"df3 = pd.concat([df, dates])\n",
|
||||||
|
"values = {'Trip count' : 0, 'Departure station id' : 7.0, 'Departure station name' : 'Designmuseo'}\n",
|
||||||
|
"df3.fillna(value=values, inplace = True)\n",
|
||||||
|
"df3 = df3.sort_values(by='Departure')\n",
|
||||||
|
"\n",
|
||||||
|
"df3.set_index('Departure', inplace=True)\n",
|
||||||
|
"df3.index = pd.DatetimeIndex(df3.index).to_period('h')\n",
|
||||||
|
"\n",
|
||||||
|
"model = ARIMA(df3['Trip count'], order=(2,1,2))\n",
|
||||||
|
"model_fit = model.fit()\n",
|
||||||
|
"# summary of fit model\n",
|
||||||
|
"print(model_fit.summary())\n",
|
||||||
|
"'''\n",
|
||||||
|
"print(df3.sort_values(by='Departure'))\n",
|
||||||
|
"plt.acorr(df3['Trip count'], maxlags = 1000)\n",
|
||||||
|
"plt.grid(True)\n",
|
||||||
|
"'''\n",
|
||||||
|
"'''\n",
|
||||||
|
"sm.graphics.tsa.plot_pacf(df3['Trip count'], lags =24, method='ywm')\n",
|
||||||
|
"plt.show()\n",
|
||||||
|
"'''\n",
|
||||||
|
"print(model_fit.forecast(1))\n"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"metadata": {
|
||||||
|
"kernelspec": {
|
||||||
|
"display_name": "Python 3 (ipykernel)",
|
||||||
|
"language": "python",
|
||||||
|
"name": "python3"
|
||||||
|
},
|
||||||
|
"language_info": {
|
||||||
|
"codemirror_mode": {
|
||||||
|
"name": "ipython",
|
||||||
|
"version": 3
|
||||||
|
},
|
||||||
|
"file_extension": ".py",
|
||||||
|
"mimetype": "text/x-python",
|
||||||
|
"name": "python",
|
||||||
|
"nbconvert_exporter": "python",
|
||||||
|
"pygments_lexer": "ipython3",
|
||||||
|
"version": "3.12.3"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"nbformat": 4,
|
||||||
|
"nbformat_minor": 5
|
||||||
|
}
|
||||||
+1305
File diff suppressed because one or more lines are too long
Executable
+22
@@ -0,0 +1,22 @@
|
|||||||
|
#!/usr/bin/env python
|
||||||
|
"""Django's command-line utility for administrative tasks."""
|
||||||
|
import os
|
||||||
|
import sys
|
||||||
|
|
||||||
|
|
||||||
|
def main():
|
||||||
|
"""Run administrative tasks."""
|
||||||
|
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'project.settings')
|
||||||
|
try:
|
||||||
|
from django.core.management import execute_from_command_line
|
||||||
|
except ImportError as exc:
|
||||||
|
raise ImportError(
|
||||||
|
"Couldn't import Django. Are you sure it's installed and "
|
||||||
|
"available on your PYTHONPATH environment variable? Did you "
|
||||||
|
"forget to activate a virtual environment?"
|
||||||
|
) from exc
|
||||||
|
execute_from_command_line(sys.argv)
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
main()
|
||||||
@@ -0,0 +1,16 @@
|
|||||||
|
"""
|
||||||
|
ASGI config for project project.
|
||||||
|
|
||||||
|
It exposes the ASGI callable as a module-level variable named ``application``.
|
||||||
|
|
||||||
|
For more information on this file, see
|
||||||
|
https://docs.djangoproject.com/en/5.1/howto/deployment/asgi/
|
||||||
|
"""
|
||||||
|
|
||||||
|
import os
|
||||||
|
|
||||||
|
from django.core.asgi import get_asgi_application
|
||||||
|
|
||||||
|
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'project.settings')
|
||||||
|
|
||||||
|
application = get_asgi_application()
|
||||||
@@ -0,0 +1,122 @@
|
|||||||
|
"""
|
||||||
|
Django settings for project project.
|
||||||
|
|
||||||
|
Generated by 'django-admin startproject' using Django 5.1.1.
|
||||||
|
|
||||||
|
For more information on this file, see
|
||||||
|
https://docs.djangoproject.com/en/5.1/topics/settings/
|
||||||
|
|
||||||
|
For the full list of settings and their values, see
|
||||||
|
https://docs.djangoproject.com/en/5.1/ref/settings/
|
||||||
|
"""
|
||||||
|
|
||||||
|
from pathlib import Path
|
||||||
|
import os
|
||||||
|
from dotenv import load_dotenv
|
||||||
|
|
||||||
|
load_dotenv()
|
||||||
|
DJANGO_ENV = os.getenv('DJANGO_ENV', 'development')
|
||||||
|
__prod__ = DJANGO_ENV == 'production'
|
||||||
|
|
||||||
|
# Build paths inside the project like this: BASE_DIR / 'subdir'.
|
||||||
|
BASE_DIR = Path(__file__).resolve().parent.parent
|
||||||
|
|
||||||
|
# Quick-start development settings - unsuitable for production
|
||||||
|
# See https://docs.djangoproject.com/en/5.1/howto/deployment/checklist/
|
||||||
|
|
||||||
|
# SECURITY WARNING: keep the secret key used in production secret!
|
||||||
|
SECRET_KEY=os.getenv('SECRET_KEY') if __prod__ else 'django-insecure-tu6x-0wmq^rlhrlcd=k$p2ytx7jr7y0jp+*xhq8$(2g5(+ensv'
|
||||||
|
|
||||||
|
# SECURITY WARNING: don't run with debug turned on in production!
|
||||||
|
# DEBUG = False if __prod__ else True
|
||||||
|
DEBUG = True
|
||||||
|
|
||||||
|
# ALLOWED_HOSTS = [os.getenv('BACKEND_ORIGIN')] if __prod__ else ['.localhost', '127.0.0.1', '[::1]', '127.0.0.1:3000', 'localhost:3000']
|
||||||
|
ALLOWED_HOSTS = ['*']
|
||||||
|
|
||||||
|
# Application definition
|
||||||
|
|
||||||
|
INSTALLED_APPS = ['django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'corsheaders']
|
||||||
|
|
||||||
|
MIDDLEWARE = [
|
||||||
|
'django.middleware.security.SecurityMiddleware',
|
||||||
|
'django.contrib.sessions.middleware.SessionMiddleware',
|
||||||
|
'django.middleware.common.CommonMiddleware',
|
||||||
|
'django.middleware.csrf.CsrfViewMiddleware',
|
||||||
|
'django.contrib.auth.middleware.AuthenticationMiddleware',
|
||||||
|
'django.contrib.messages.middleware.MessageMiddleware',
|
||||||
|
'django.middleware.clickjacking.XFrameOptionsMiddleware',
|
||||||
|
'corsheaders.middleware.CorsMiddleware',
|
||||||
|
'django.middleware.common.CommonMiddleware',
|
||||||
|
]
|
||||||
|
|
||||||
|
ROOT_URLCONF = 'project.urls'
|
||||||
|
|
||||||
|
TEMPLATES = [
|
||||||
|
{
|
||||||
|
'BACKEND': 'django.template.backends.django.DjangoTemplates',
|
||||||
|
'DIRS': [],
|
||||||
|
'APP_DIRS': True,
|
||||||
|
'OPTIONS': {
|
||||||
|
'context_processors': [
|
||||||
|
'django.template.context_processors.debug',
|
||||||
|
'django.template.context_processors.request',
|
||||||
|
'django.contrib.auth.context_processors.auth',
|
||||||
|
'django.contrib.messages.context_processors.messages',
|
||||||
|
],
|
||||||
|
},
|
||||||
|
},
|
||||||
|
]
|
||||||
|
|
||||||
|
WSGI_APPLICATION = 'project.wsgi.application'
|
||||||
|
|
||||||
|
# Database
|
||||||
|
# https://docs.djangoproject.com/en/5.1/ref/settings/#databases
|
||||||
|
|
||||||
|
DATABASES = {
|
||||||
|
'default': {
|
||||||
|
'ENGINE': 'django.db.backends.sqlite3',
|
||||||
|
'NAME': BASE_DIR / 'db.sqlite3',
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
# Password validation
|
||||||
|
# https://docs.djangoproject.com/en/5.1/ref/settings/#auth-password-validators
|
||||||
|
|
||||||
|
AUTH_PASSWORD_VALIDATORS = [
|
||||||
|
{
|
||||||
|
'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',
|
||||||
|
},
|
||||||
|
{
|
||||||
|
'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',
|
||||||
|
},
|
||||||
|
{
|
||||||
|
'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',
|
||||||
|
},
|
||||||
|
{
|
||||||
|
'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',
|
||||||
|
},
|
||||||
|
]
|
||||||
|
|
||||||
|
# Internationalization
|
||||||
|
# https://docs.djangoproject.com/en/5.1/topics/i18n/
|
||||||
|
|
||||||
|
LANGUAGE_CODE = 'en-us'
|
||||||
|
|
||||||
|
TIME_ZONE = 'UTC'
|
||||||
|
|
||||||
|
USE_I18N = True
|
||||||
|
|
||||||
|
USE_TZ = True
|
||||||
|
|
||||||
|
# Static files (CSS, JavaScript, Images)
|
||||||
|
# https://docs.djangoproject.com/en/5.1/howto/static-files/
|
||||||
|
|
||||||
|
STATIC_URL = 'static/'
|
||||||
|
|
||||||
|
# Default primary key field type
|
||||||
|
# https://docs.djangoproject.com/en/5.1/ref/settings/#default-auto-field
|
||||||
|
|
||||||
|
DEFAULT_AUTO_FIELD = 'django.db.models.BigAutoField'
|
||||||
|
|
||||||
|
CORS_ALLOW_ALL_ORIGINS = True
|
||||||
@@ -0,0 +1,23 @@
|
|||||||
|
"""
|
||||||
|
URL configuration for project project.
|
||||||
|
|
||||||
|
The `urlpatterns` list routes URLs to views. For more information please see:
|
||||||
|
https://docs.djangoproject.com/en/5.1/topics/http/urls/
|
||||||
|
Examples:
|
||||||
|
Function views
|
||||||
|
1. Add an import: from my_app import views
|
||||||
|
2. Add a URL to urlpatterns: path('', views.home, name='home')
|
||||||
|
Class-based views
|
||||||
|
1. Add an import: from other_app.views import Home
|
||||||
|
2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')
|
||||||
|
Including another URLconf
|
||||||
|
1. Import the include() function: from django.urls import include, path
|
||||||
|
2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))
|
||||||
|
"""
|
||||||
|
from django.contrib import admin
|
||||||
|
from django.urls import include, path
|
||||||
|
|
||||||
|
urlpatterns = [
|
||||||
|
path('admin/', admin.site.urls),
|
||||||
|
path('app/', include('app.urls'))
|
||||||
|
]
|
||||||
@@ -0,0 +1,16 @@
|
|||||||
|
"""
|
||||||
|
WSGI config for project project.
|
||||||
|
|
||||||
|
It exposes the WSGI callable as a module-level variable named ``application``.
|
||||||
|
|
||||||
|
For more information on this file, see
|
||||||
|
https://docs.djangoproject.com/en/5.1/howto/deployment/wsgi/
|
||||||
|
"""
|
||||||
|
|
||||||
|
import os
|
||||||
|
|
||||||
|
from django.core.wsgi import get_wsgi_application
|
||||||
|
|
||||||
|
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'project.settings')
|
||||||
|
|
||||||
|
application = get_wsgi_application()
|
||||||
@@ -0,0 +1,8 @@
|
|||||||
|
pandas
|
||||||
|
numpy
|
||||||
|
matplotlib
|
||||||
|
seaborn
|
||||||
|
statsmodels
|
||||||
|
datetime
|
||||||
|
django-cors-headers
|
||||||
|
python-dotenv
|
||||||
@@ -0,0 +1 @@
|
|||||||
|
NEXT_PUBLIC_BACKEND_ORIGIN=
|
||||||
@@ -0,0 +1,36 @@
|
|||||||
|
# See https://help.github.com/articles/ignoring-files/ for more about ignoring files.
|
||||||
|
|
||||||
|
# dependencies
|
||||||
|
/node_modules
|
||||||
|
/.pnp
|
||||||
|
.pnp.js
|
||||||
|
.yarn/install-state.gz
|
||||||
|
|
||||||
|
# testing
|
||||||
|
/coverage
|
||||||
|
|
||||||
|
# next.js
|
||||||
|
/.next/
|
||||||
|
/out/
|
||||||
|
|
||||||
|
# production
|
||||||
|
/build
|
||||||
|
|
||||||
|
# misc
|
||||||
|
.DS_Store
|
||||||
|
*.pem
|
||||||
|
|
||||||
|
# debug
|
||||||
|
npm-debug.log*
|
||||||
|
yarn-debug.log*
|
||||||
|
yarn-error.log*
|
||||||
|
|
||||||
|
# local env files
|
||||||
|
.env*.local
|
||||||
|
|
||||||
|
# vercel
|
||||||
|
.vercel
|
||||||
|
|
||||||
|
# typescript
|
||||||
|
*.tsbuildinfo
|
||||||
|
next-env.d.ts
|
||||||
@@ -0,0 +1 @@
|
|||||||
|
## This is the Next.js frontend for our Data Science project.
|
||||||
Binary file not shown.
|
After Width: | Height: | Size: 31 KiB |
@@ -0,0 +1,23 @@
|
|||||||
|
import type { Metadata } from 'next'
|
||||||
|
import { Providers } from './providers'
|
||||||
|
import { Flex } from '@chakra-ui/react'
|
||||||
|
|
||||||
|
export const metadata: Metadata = {
|
||||||
|
title: 'HSL Bike Helper',
|
||||||
|
description: 'An app that makes prediction based on Open Data from the Helsinki Bike System.',
|
||||||
|
}
|
||||||
|
|
||||||
|
const RootLayout = ({ children, }: Readonly<{ children: React.ReactNode }>) => {
|
||||||
|
return (
|
||||||
|
<html lang='en'>
|
||||||
|
<body>
|
||||||
|
<Providers>
|
||||||
|
<Flex justifyContent='center' width='100%' padding={4} minH='calc(100vh - 72px)'>
|
||||||
|
{children}
|
||||||
|
</Flex>
|
||||||
|
</Providers>
|
||||||
|
</body>
|
||||||
|
</html>
|
||||||
|
)
|
||||||
|
}
|
||||||
|
export default RootLayout
|
||||||
@@ -0,0 +1,26 @@
|
|||||||
|
import type { MetadataRoute } from 'next'
|
||||||
|
|
||||||
|
const manifest = (): MetadataRoute.Manifest => {
|
||||||
|
return {
|
||||||
|
name: 'HSL Bike Helper',
|
||||||
|
short_name: 'HSL',
|
||||||
|
description: 'An app that makes prediction based on Open Data from the Helsinki Bike System.',
|
||||||
|
start_url: '/',
|
||||||
|
display: 'standalone',
|
||||||
|
background_color: '#ffffff',
|
||||||
|
theme_color: '#000000',
|
||||||
|
icons: [
|
||||||
|
{
|
||||||
|
src: '/icon-192x192.png',
|
||||||
|
sizes: '192x192',
|
||||||
|
type: 'image/png',
|
||||||
|
},
|
||||||
|
{
|
||||||
|
src: '/icon-512x512.png',
|
||||||
|
sizes: '512x512',
|
||||||
|
type: 'image/png',
|
||||||
|
},
|
||||||
|
],
|
||||||
|
}
|
||||||
|
}
|
||||||
|
export default manifest
|
||||||
@@ -0,0 +1,78 @@
|
|||||||
|
'use client'
|
||||||
|
|
||||||
|
import { InputField, Wrapper } from '@/components'
|
||||||
|
import { Box, Button, Flex, FormLabel, Select, Text } from '@chakra-ui/react'
|
||||||
|
import { Field, Form, Formik } from 'formik'
|
||||||
|
import { useState } from 'react'
|
||||||
|
|
||||||
|
interface Response {
|
||||||
|
timestamp: string
|
||||||
|
station: string
|
||||||
|
departingCount: number
|
||||||
|
returningCount: number
|
||||||
|
bikeAtStationCount: number
|
||||||
|
increasing: boolean
|
||||||
|
}
|
||||||
|
|
||||||
|
const Home: React.FC = () => {
|
||||||
|
const [predictionData, setPredictionData] = useState<Response>()
|
||||||
|
|
||||||
|
return (
|
||||||
|
<Box w='100%'>
|
||||||
|
<Wrapper>
|
||||||
|
<Formik
|
||||||
|
initialValues={{ station: 'Kamppi (M)', timestamp: '' }}
|
||||||
|
onSubmit={async ({ station, timestamp }, { setErrors }) => {
|
||||||
|
const response: Response = await (await fetch(`${process.env.NEXT_PUBLIC_BACKEND_ORIGIN}/app/predict?timestamp=${timestamp}&station=${station}`)).json()
|
||||||
|
setPredictionData(response)
|
||||||
|
}}
|
||||||
|
>
|
||||||
|
{({ isSubmitting, values }) => (
|
||||||
|
<Form>
|
||||||
|
<Flex flexDir='column' alignItems='center' mt={4}>
|
||||||
|
<Field name='station'>
|
||||||
|
{/* @ts-ignore */}
|
||||||
|
{({ field }) => (
|
||||||
|
<>
|
||||||
|
<FormLabel htmlFor='station'>Station</FormLabel>
|
||||||
|
<Select id='station' placeholder='' textAlign='center' {...field} >
|
||||||
|
<option value='Kamppi (M)'>Kamppi (M)</option>
|
||||||
|
<option value='Rautatientori - itä'>Rautatientori - itä</option>
|
||||||
|
</Select>
|
||||||
|
</>
|
||||||
|
)}
|
||||||
|
</Field>
|
||||||
|
|
||||||
|
<FormLabel htmlFor='timestamp' mt={4} mb={0}>Timestamp</FormLabel>
|
||||||
|
<InputField name='timestamp' label='' placeholder='YYYY-MM-DD HH:MM:SS' textAlign='center' />
|
||||||
|
|
||||||
|
<Button type='submit' mt={4} disabled={isSubmitting} isLoading={isSubmitting}>Predict</Button>
|
||||||
|
{
|
||||||
|
predictionData &&
|
||||||
|
<Box mt={4}>
|
||||||
|
<Text>Timestamp: {predictionData.timestamp}</Text>
|
||||||
|
<Text>Station: {predictionData.station}</Text>
|
||||||
|
<Text>Predicted Number of Bikes Arriving at Station: {predictionData.returningCount}</Text>
|
||||||
|
<Text>Predicted Number of Bikes Departing from Station: {predictionData.departingCount}</Text>
|
||||||
|
<Text>Predicted Number of Bikes at Station: {predictionData.bikeAtStationCount}</Text>
|
||||||
|
<Text>Predicted Bike Count Status: {predictionData.increasing ? 'Decreasing' : 'Increasing'}</Text>
|
||||||
|
<Text>
|
||||||
|
In need of more bikes:
|
||||||
|
{
|
||||||
|
predictionData.bikeAtStationCount > 10 ? 'No' :
|
||||||
|
predictionData.bikeAtStationCount > 5 ? 'Yes, but not urgently.' :
|
||||||
|
'Yes, urgently.'
|
||||||
|
}
|
||||||
|
</Text>
|
||||||
|
</Box>
|
||||||
|
}
|
||||||
|
|
||||||
|
</Flex>
|
||||||
|
</Form>
|
||||||
|
)}
|
||||||
|
</Formik>
|
||||||
|
</Wrapper>
|
||||||
|
</Box>
|
||||||
|
)
|
||||||
|
}
|
||||||
|
export default Home
|
||||||
@@ -0,0 +1,9 @@
|
|||||||
|
'use client'
|
||||||
|
|
||||||
|
import { ChakraProvider } from '@chakra-ui/react'
|
||||||
|
|
||||||
|
export const Providers = ({ children }: { children: React.ReactNode }) => {
|
||||||
|
return (
|
||||||
|
<ChakraProvider>{children}</ChakraProvider>
|
||||||
|
)
|
||||||
|
}
|
||||||
@@ -0,0 +1,18 @@
|
|||||||
|
'use client'
|
||||||
|
import { FormControl, FormErrorMessage, FormLabel, Input, InputProps } from '@chakra-ui/react'
|
||||||
|
import { useField } from 'formik'
|
||||||
|
|
||||||
|
type Props = React.InputHTMLAttributes<HTMLInputElement> & { name: string, label: string, isTextArea?: boolean } & InputProps
|
||||||
|
|
||||||
|
export const InputField: React.FC<Props> = ({size: _, isTextArea=false, ...props}) => {
|
||||||
|
const [field, { error }] = useField(props)
|
||||||
|
const { label, placeholder } = props
|
||||||
|
|
||||||
|
return (
|
||||||
|
<FormControl isInvalid={!!error}>
|
||||||
|
<FormLabel htmlFor={field.name}>{label}</FormLabel>
|
||||||
|
<Input {...field} {...props} id={field.name} placeholder={placeholder} />
|
||||||
|
{error ? <FormErrorMessage>{error}</FormErrorMessage> : null}
|
||||||
|
</FormControl>
|
||||||
|
)
|
||||||
|
}
|
||||||
@@ -0,0 +1,36 @@
|
|||||||
|
'use client'
|
||||||
|
import { FormControl, FormErrorMessage, FormLabel, Textarea, TextareaProps } from '@chakra-ui/react'
|
||||||
|
import autosize from 'autosize'
|
||||||
|
import { useField } from 'formik'
|
||||||
|
import { useRef, useEffect } from 'react'
|
||||||
|
|
||||||
|
type Props = React.TextareaHTMLAttributes<HTMLTextAreaElement> & { name: string, label: string } & TextareaProps
|
||||||
|
|
||||||
|
export const TextareaField: React.FC<Props> = (props): JSX.Element => {
|
||||||
|
const [field, { error }] = useField(props)
|
||||||
|
const { label, placeholder } = props
|
||||||
|
|
||||||
|
// https://github.com/chakra-ui/chakra-ui/issues/670
|
||||||
|
const ref: any = useRef()
|
||||||
|
useEffect(() => {
|
||||||
|
const current = ref.current
|
||||||
|
autosize(current)
|
||||||
|
return () => {
|
||||||
|
autosize.destroy(current)
|
||||||
|
}
|
||||||
|
}, [])
|
||||||
|
|
||||||
|
return (
|
||||||
|
<FormControl isInvalid={!!error}>
|
||||||
|
<FormLabel htmlFor={field.name}>{label}</FormLabel>
|
||||||
|
<Textarea
|
||||||
|
{...field}
|
||||||
|
{...props}
|
||||||
|
ref={ref}
|
||||||
|
id={field.name}
|
||||||
|
placeholder={placeholder}
|
||||||
|
/>
|
||||||
|
{error ? <FormErrorMessage>{error}</FormErrorMessage> : null}
|
||||||
|
</FormControl>
|
||||||
|
)
|
||||||
|
}
|
||||||
@@ -0,0 +1,75 @@
|
|||||||
|
'use client'
|
||||||
|
// ChatGPT-generated
|
||||||
|
import { Line } from 'react-chartjs-2'
|
||||||
|
import { Chart as ChartJS, TimeScale, LinearScale, PointElement, LineElement, Tooltip, Legend } from 'chart.js'
|
||||||
|
import 'chartjs-adapter-date-fns'
|
||||||
|
import { Entry } from '@/constants'
|
||||||
|
|
||||||
|
// Register required chart components
|
||||||
|
ChartJS.register(TimeScale, LinearScale, PointElement, LineElement, Tooltip, Legend)
|
||||||
|
|
||||||
|
export const TripCountChart = ({ data }: { data: Entry[] }) => {
|
||||||
|
const chartData = {
|
||||||
|
labels: data.map((entry) => entry.time),
|
||||||
|
datasets: [
|
||||||
|
{
|
||||||
|
label: 'Trip Count',
|
||||||
|
data: data.map((entry) => entry.trip_count),
|
||||||
|
fill: false,
|
||||||
|
borderColor: 'rgba(75,192,192,1)',
|
||||||
|
tension: 0.1,
|
||||||
|
segment: {
|
||||||
|
borderColor: (ctx: any) => {
|
||||||
|
const dataIndex = ctx.p0DataIndex // Starting index of the segment
|
||||||
|
const totalPoints = data.length
|
||||||
|
const threeQuarterIndex = Math.floor(totalPoints * 0.75)
|
||||||
|
|
||||||
|
// Use a different color after 3/4 of the x-axis
|
||||||
|
return dataIndex >= threeQuarterIndex
|
||||||
|
? 'rgba(255,99,132,1)' // Different color after 3/4
|
||||||
|
: 'rgba(75,192,192,1)' // Default color for the first 3/4
|
||||||
|
},
|
||||||
|
},
|
||||||
|
},
|
||||||
|
],
|
||||||
|
}
|
||||||
|
|
||||||
|
const options = {
|
||||||
|
scales: {
|
||||||
|
x: {
|
||||||
|
type: 'time',
|
||||||
|
time: {
|
||||||
|
unit: 'hour',
|
||||||
|
tooltipFormat: 'PPpp',
|
||||||
|
displayFormats: {
|
||||||
|
hour: 'MMM d, yyyy HH:mm',
|
||||||
|
},
|
||||||
|
},
|
||||||
|
title: {
|
||||||
|
display: true,
|
||||||
|
text: 'Time (Hour)',
|
||||||
|
},
|
||||||
|
},
|
||||||
|
y: {
|
||||||
|
beginAtZero: true,
|
||||||
|
title: {
|
||||||
|
display: true,
|
||||||
|
text: 'Trip Count',
|
||||||
|
},
|
||||||
|
},
|
||||||
|
},
|
||||||
|
plugins: {
|
||||||
|
tooltip: {
|
||||||
|
callbacks: {
|
||||||
|
label: function (context: any) {
|
||||||
|
return `Trip Count: ${context.raw}`
|
||||||
|
},
|
||||||
|
},
|
||||||
|
},
|
||||||
|
},
|
||||||
|
}
|
||||||
|
|
||||||
|
return <Line data={chartData} options={options as any} />
|
||||||
|
}
|
||||||
|
|
||||||
|
export default TripCountChart
|
||||||
@@ -0,0 +1,14 @@
|
|||||||
|
import { Box } from '@chakra-ui/react'
|
||||||
|
|
||||||
|
interface Props {
|
||||||
|
children?: React.ReactNode
|
||||||
|
variant?: 'small' | 'regular'
|
||||||
|
}
|
||||||
|
|
||||||
|
export const Wrapper: React.FC<Props> = ({ children, variant = 'regular' }) => {
|
||||||
|
return (
|
||||||
|
<Box w='100%' maxW={variant == 'small' ? 400 : 800} mt={8} mx='auto'>
|
||||||
|
{children}
|
||||||
|
</Box>
|
||||||
|
)
|
||||||
|
}
|
||||||
@@ -0,0 +1,4 @@
|
|||||||
|
export * from './Wrapper'
|
||||||
|
export * from './TripCountChart'
|
||||||
|
export * from './InputField'
|
||||||
|
export * from './TextareaField'
|
||||||
@@ -0,0 +1 @@
|
|||||||
|
export * from './sampleTripCountData'
|
||||||
File diff suppressed because it is too large
Load Diff
Vendored
+9
@@ -0,0 +1,9 @@
|
|||||||
|
declare global {
|
||||||
|
namespace NodeJS {
|
||||||
|
interface ProcessEnv {
|
||||||
|
NEXT_PUBLIC_BACKEND_ORIGIN: string;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
export {}
|
||||||
@@ -0,0 +1,4 @@
|
|||||||
|
/** @type {import('next').NextConfig} */
|
||||||
|
const nextConfig = {}
|
||||||
|
|
||||||
|
export default nextConfig
|
||||||
Generated
+2603
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,36 @@
|
|||||||
|
{
|
||||||
|
"name": "hsl-frontend",
|
||||||
|
"version": "1.0.0",
|
||||||
|
"private": true,
|
||||||
|
"scripts": {
|
||||||
|
"env:generate": "gen-env-types .env -o env.d.ts -e .",
|
||||||
|
"dev": "next dev",
|
||||||
|
"build": "next build",
|
||||||
|
"start": "next start",
|
||||||
|
"lint": "next lint"
|
||||||
|
},
|
||||||
|
"dependencies": {
|
||||||
|
"@chakra-ui/next-js": "^2.2.0",
|
||||||
|
"@chakra-ui/react": "^2.8.2",
|
||||||
|
"@emotion/react": "^11.13.3",
|
||||||
|
"@emotion/styled": "^11.13.0",
|
||||||
|
"autosize": "^6.0.1",
|
||||||
|
"chart.js": "^4.4.4",
|
||||||
|
"chartjs-adapter-date-fns": "^3.0.0",
|
||||||
|
"date-fns": "^4.1.0",
|
||||||
|
"formik": "^2.4.6",
|
||||||
|
"framer-motion": "^11.5.4",
|
||||||
|
"next": "14.2.9",
|
||||||
|
"react": "^18",
|
||||||
|
"react-chartjs-2": "^5.2.0",
|
||||||
|
"react-dom": "^18"
|
||||||
|
},
|
||||||
|
"devDependencies": {
|
||||||
|
"@types/autosize": "^4.0.3",
|
||||||
|
"@types/node": "^20",
|
||||||
|
"@types/react": "^18",
|
||||||
|
"@types/react-dom": "^18",
|
||||||
|
"gen-env-types": "^1.3.4",
|
||||||
|
"typescript": "^5"
|
||||||
|
}
|
||||||
|
}
|
||||||
Binary file not shown.
|
After Width: | Height: | Size: 32 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 134 KiB |
@@ -0,0 +1,39 @@
|
|||||||
|
{
|
||||||
|
"compilerOptions": {
|
||||||
|
"lib": [
|
||||||
|
"dom",
|
||||||
|
"dom.iterable",
|
||||||
|
"esnext"
|
||||||
|
],
|
||||||
|
"allowJs": true,
|
||||||
|
"skipLibCheck": true,
|
||||||
|
"strict": true,
|
||||||
|
"noEmit": true,
|
||||||
|
"esModuleInterop": true,
|
||||||
|
"module": "esnext",
|
||||||
|
"moduleResolution": "bundler",
|
||||||
|
"resolveJsonModule": true,
|
||||||
|
"isolatedModules": true,
|
||||||
|
"jsx": "preserve",
|
||||||
|
"incremental": true,
|
||||||
|
"plugins": [
|
||||||
|
{
|
||||||
|
"name": "next"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"paths": {
|
||||||
|
"@/*": [
|
||||||
|
"./*"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"include": [
|
||||||
|
"next-env.d.ts",
|
||||||
|
"**/*.ts",
|
||||||
|
"**/*.tsx",
|
||||||
|
".next/types/**/*.ts"
|
||||||
|
],
|
||||||
|
"exclude": [
|
||||||
|
"node_modules"
|
||||||
|
]
|
||||||
|
}
|
||||||
Reference in New Issue
Block a user