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[Quy Anh] «Elliot» Nguyen
2026-06-24 16:52:08 +02:00
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backend/app/__pycache__
backend/datasets/*
backend/deploy.sh
backend/env
backend/.env.production
backend/project/__pycache__
frontend/.env
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LICENSE.md
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This is free and unencumbered software released into the public domain.
Anyone is free to copy, modify, publish, use, compile, sell, or
distribute this software, either in source code form or as a compiled
binary, for any purpose, commercial or non-commercial, and by any
means.
In jurisdictions that recognize copyright laws, the author or authors
of this software dedicate any and all copyright interest in the
software to the public domain. We make this dedication for the benefit
of the public at large and to the detriment of our heirs and
successors. We intend this dedication to be an overt act of
relinquishment in perpetuity of all present and future rights to this
software under copyright law.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
IN NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR
OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,
ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
OTHER DEALINGS IN THE SOFTWARE.
For more information, please refer to <http://unlicense.org/>
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README.md
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# HSL Bike Helper [![Unlicense](https://img.shields.io/badge/License-Unlicense-2ea44f)](https://github.com/ElliotAtHelsinki/data-science-project/blob/main/LICENSE.md) ![v - 1.0.0](https://img.shields.io/badge/v-1.0.0-blue) ![PRs Welcome](https://img.shields.io/badge/PRs-welcome-green.svg)
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.
### Repository
https://gitea.elliot-at-zuri.ch/root/DATA11001-Introduction-to-Data-Science
### Installation
The application can be directly accessed via a web browser at the following addresses, without requiring any installation:
\- **Frontend**: https://hsl-frontend.elliot-at-zuri.ch
\- **Backend**: https://hsl-backend.elliot-at-zuri.ch/app/predict
Optionally, however, the application can be installed as a mobile or desktop app, since it is a PWA.
### Development
To run the `Django` backend locally, navigate to the `backend` folder and:
\- Source the `env`: `source env/bin/activate`
\- Create an empty `.env` file: `touch .env`
\- Install the dependencies: `pip install -r requirements.txt`
\- Run the first two code blocks of the `main.ipynb` file
\- Start the server: `python manage.py runserver`
To run the `Next.js` frontend locally, navigate to the `frontend` folder and:
\- Install the dependencies: `npm install`
\- Start the application: `npm run dev`
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env
datasets/20*
datasets/full_bike_data.csv
main.ipynb
deploy.sh
.env
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FROM python:3.13.0-bookworm
WORKDIR /usr/src/app
COPY . .
COPY .env.production .env
RUN pip install -r requirements.txt
EXPOSE 9000
ENV DJANGO_ENV=production
CMD ["python", "manage.py", "runserver", "0.0.0.0:9000", "--noreload"]
backend/README.md
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## This is the Django backend for our Data Science project.
backend/app/__init__.py
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backend/app/admin.py
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from django.contrib import admin
# Register your models here.
backend/app/apps.py
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from django.apps import AppConfig
class AppConfig(AppConfig):
default_auto_field = 'django.db.models.BigAutoField'
name = 'app'
backend/app/migrations/__init__.py
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backend/app/models.py
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from django.db import models
# Create your models here.
backend/app/tests.py
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from django.test import TestCase
# Create your tests here.
backend/app/urls.py
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from django.urls import path
from . import views
urlpatterns = [
path('', views.index, name='index'),
path('predict', views.predict, name='predict'),
]
backend/app/views.py
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from django.http import HttpResponse, JsonResponse
from datetime import datetime
import pandas as pd
import statsmodels.api as sm
stations = ['Kamppi (M)', 'Rautatientori - itä']
station_dict = {}
for station in stations:
departure_data = pd.read_csv('datasets/' + station + '_hourly_aggregate.csv')
return_data = pd.read_csv('datasets/' + station + '_return_hourly_aggregate.csv')
departure_data['Departure'] = pd.to_datetime(departure_data['Departure'], format='mixed')
return_data['Return'] = pd.to_datetime(return_data['Return'], format='mixed')
departure_data.set_index(departure_data['Departure'], inplace=True)
return_data.set_index(return_data['Return'], inplace=True)
departure_data['trip'] = pd.to_numeric(departure_data['trip'], errors='coerce')
return_data['trip'] = pd.to_numeric(return_data['trip'], errors='coerce')
departure_data = departure_data.dropna(axis=1)
return_data = return_data.dropna(axis=1)
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)
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)
station_dict[station] = {}
station_dict[station]['departure_mod'] = departure_mod
station_dict[station]['return_mod'] = return_mod
def index(request):
return HttpResponse('You\'re at the app index.')
def predict(request):
if request.method == 'GET':
current = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
timestamp = request.GET.get('timestamp', current)
station = request.GET.get('station', 'Kamppi (M)')
ts = timestamp
defaultBikeCount = 28
departingCount = -1
if departure_data.index.max() >= pd.to_datetime(timestamp):
ls = departure_data[departure_data['Departure'] == ts]['trip'].tolist()
if len(ls) != 0:
departingCount = ls[0]
else:
pass
else:
departure_mod = station_dict[station]['departure_mod']
departForecast = departure_mod.forecast(timestamp)
departingCount = round(departForecast[-1])
returningCount = -1
if return_data.index.max() >= pd.to_datetime(timestamp):
ls = return_data[return_data['Return'] == ts]['trip'].tolist()
if len(ls) != 0:
returningCount = ls[0]
else:
pass
else:
return_mod = station_dict[station]['return_mod']
returnForecast = return_mod.forecast(timestamp)
returningCount = round(returnForecast[-1])
bikeAtStationCount = defaultBikeCount - departingCount + returningCount
result = {
'timestamp': timestamp,
'station': station,
'departingCount': departingCount,
'returningCount': returningCount,
'bikeAtStationCount': bikeAtStationCount,
'increasing': returningCount > departingCount
}
return JsonResponse(result)
backend/cross_validation_parameters.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"id": "cd21c67a-e679-43e3-85a4-471a35522d42",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\"\\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\""
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"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",
"# 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",
"df.set_index('Departure', inplace=True)\n",
"\n",
"train_end = datetime(year=2022, month=10, day=31)\n",
"train_start = datetime(year=2018,month=4,day=1)\n",
"train_data = df[:train_end]\n",
"test_end = datetime(year=2023, month=10, day = 31)\n",
"test_data = df[datetime(year=2023,month=4,day=1):]\n",
"\n",
"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",
"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",
" errors = final['trip'] - final['predicted_mean']\n",
" mse = np.mean(errors**2)\n",
"\n",
" param_mse.append((mse, param))\n",
"'''\n",
"\n",
"\n",
"\n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "db9591dd-7bfd-46d3-aa50-8a757776aeb0",
"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
}
backend/db.sqlite3
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backend/error_metric_calculations_results.txt
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[(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')]
backend/forecast_error_metric_calculations.ipynb
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{
"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
}
backend/initial_model.ipynb
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{
"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
}
backend/main.ipynb
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backend/manage.py
Executable
+22
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#!/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()
backend/project/__init__.py
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backend/project/asgi.py
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"""
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()
backend/project/settings.py
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"""
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
backend/project/urls.py
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"""
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'))
]
backend/project/wsgi.py
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"""
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()
backend/requirements.txt
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pandas
numpy
matplotlib
seaborn
statsmodels
datetime
django-cors-headers
python-dotenv
frontend/.env.example
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NEXT_PUBLIC_BACKEND_ORIGIN=
frontend/.gitignore
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# 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
frontend/README.md
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## This is the Next.js frontend for our Data Science project.
frontend/app/favicon.ico
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frontend/app/layout.tsx
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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
frontend/app/manifest.ts
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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
frontend/app/page.tsx
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'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: &nbsp;
{
predictionData.bikeAtStationCount > 10 ? 'No' :
predictionData.bikeAtStationCount > 5 ? 'Yes, but not urgently.' :
'Yes, urgently.'
}
</Text>
</Box>
}
</Flex>
</Form>
)}
</Formik>
</Wrapper>
</Box>
)
}
export default Home
frontend/app/providers.tsx
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'use client'
import { ChakraProvider } from '@chakra-ui/react'
export const Providers = ({ children }: { children: React.ReactNode }) => {
return (
<ChakraProvider>{children}</ChakraProvider>
)
}
frontend/components/InputField.tsx
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'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>
)
}
frontend/components/TextareaField.tsx
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'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>
)
}
frontend/components/TripCountChart.tsx
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'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
frontend/components/Wrapper.tsx
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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>
)
}
frontend/components/index.ts
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export * from './Wrapper'
export * from './TripCountChart'
export * from './InputField'
export * from './TextareaField'
frontend/constants/index.ts
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export * from './sampleTripCountData'
frontend/constants/sampleTripCountData.ts
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frontend/env.d.ts
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declare global {
namespace NodeJS {
interface ProcessEnv {
NEXT_PUBLIC_BACKEND_ORIGIN: string;
}
}
}
export {}
frontend/next.config.mjs
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/** @type {import('next').NextConfig} */
const nextConfig = {}
export default nextConfig
frontend/package-lock.json
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frontend/package.json
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{
"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"
}
}
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frontend/tsconfig.json
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{
"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"
]
}