2h

.gitignore

@@ -0,0 +1,4 @@
+**/api/migrations
+**/api/data/.ipynb_checkpoints
+**/api/data/*csv
+db*

Pipfile

@@ -0,0 +1,17 @@
+[[source]]
+name = "pypi"
+url = "https://pypi.org/simple"
+verify_ssl = true
+
+[dev-packages]
+
+[packages]
+pandas = "*"
+djangorestframework = "*"
+django = "*"
+sklearn = "*"
+numpy = "*"
+joblib = "*"
+
+[requires]
+python_version = "3.7"

Pipfile.lock

@@ -0,0 +1,190 @@
+{
+    "_meta": {
+        "hash": {
+            "sha256": "b61f243cbc1d68a628203e9bf1210e97d98d5e74516535f2cb22fccf2471043b"
+        },
+        "pipfile-spec": 6,
+        "requires": {
+            "python_version": "3.7"
+        },
+        "sources": [
+            {
+                "name": "pypi",
+                "url": "https://pypi.org/simple",
+                "verify_ssl": true
+            }
+        ]
+    },
+    "default": {
+        "asgiref": {
+            "hashes": [
+                "sha256:7e51911ee147dd685c3c8b805c0ad0cb58d360987b56953878f8c06d2d1c6f1a",
+                "sha256:9fc6fb5d39b8af147ba40765234fa822b39818b12cc80b35ad9b0cef3a476aed"
+            ],
+            "version": "==3.2.10"
+        },
+        "django": {
+            "hashes": [
+                "sha256:31a5fbbea5fc71c99e288ec0b2f00302a0a92c44b13ede80b73a6a4d6d205582",
+                "sha256:5457fc953ec560c5521b41fad9e6734a4668b7ba205832191bbdff40ec61073c"
+            ],
+            "index": "pypi",
+            "version": "==3.0.8"
+        },
+        "djangorestframework": {
+            "hashes": [
+                "sha256:05809fc66e1c997fd9a32ea5730d9f4ba28b109b9da71fccfa5ff241201fd0a4",
+                "sha256:e782087823c47a26826ee5b6fa0c542968219263fb3976ec3c31edab23a4001f"
+            ],
+            "index": "pypi",
+            "version": "==3.11.0"
+        },
+        "joblib": {
+            "hashes": [
+                "sha256:8f52bf24c64b608bf0b2563e0e47d6fcf516abc8cfafe10cfd98ad66d94f92d6",
+                "sha256:d348c5d4ae31496b2aa060d6d9b787864dd204f9480baaa52d18850cb43e9f49"
+            ],
+            "index": "pypi",
+            "version": "==0.16.0"
+        },
+        "numpy": {
+            "hashes": [
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+                "sha256:ef71a1d4fd4858596ae80ad1ec76404ad29701f8ca7cdcebc50300178db14dfc"
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+            "version": "==1.19.1"
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+                "sha256:fc714895b6de6803ac9f661abb316853d0cd657f5d23985222255ad76ccedc25"
+            ],
+            "index": "pypi",
+            "version": "==1.1.0"
+        },
+        "python-dateutil": {
+            "hashes": [
+                "sha256:73ebfe9dbf22e832286dafa60473e4cd239f8592f699aa5adaf10050e6e1823c",
+                "sha256:75bb3f31ea686f1197762692a9ee6a7550b59fc6ca3a1f4b5d7e32fb98e2da2a"
+            ],
+            "version": "==2.8.1"
+        },
+        "pytz": {
+            "hashes": [
+                "sha256:a494d53b6d39c3c6e44c3bec237336e14305e4f29bbf800b599253057fbb79ed",
+                "sha256:c35965d010ce31b23eeb663ed3cc8c906275d6be1a34393a1d73a41febf4a048"
+            ],
+            "version": "==2020.1"
+        },
+        "scikit-learn": {
+            "hashes": [
+                "sha256:04799686060ecbf8992f26a35be1d99e981894c8c7860c1365cda4200f954a16",
+                "sha256:058d213092de4384710137af1300ed0ff030b8c40459a6c6f73c31ccd274cc39",
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+                "sha256:5bcea4d6ee431c814261117281363208408aa4e665633655895feb059021aca6",
+                "sha256:93f56abd316d131645559ec0ab4f45e3391c2ccdd4eadaa4912f4c1e0a6f2c96",
+                "sha256:9e04c0811ea92931ee8490d638171b8cb2f21387efcfff526bbc8c2a3da60f1c",
+                "sha256:bded94236e16774385202cafd26190ce96db18e4dc21e99473848c61e4fdc400",
+                "sha256:c2fa33d20408b513cf432505c80e6eb4bf4d71434f1ae36680765d4a2c2a16ec",
+                "sha256:e3fec1c8831f8f93ad85581ca29ca1bb88e2da377fb097cf8322aa89c21bc9b8",
+                "sha256:e585682e37f2faa81ad6cd4472fff646bf2fd0542147bec93697a905db8e6bd2",
+                "sha256:e9879ba9e64ec3add41bf201e06034162f853652ef4849b361d73b0deb3153ad",
+                "sha256:ebe853e6f318f9d8b3b74dd17e553720d35646eff675a69eeaed12fbbbb07daa"
+            ],
+            "version": "==0.23.1"
+        },
+        "scipy": {
+            "hashes": [
+                "sha256:066c513d90eb3fd7567a9e150828d39111ebd88d3e924cdfc9f8ce19ab6f90c9",
+                "sha256:07e52b316b40a4f001667d1ad4eb5f2318738de34597bd91537851365b6c61f1",
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+                "sha256:8e28e74b97fc8d6aa0454989db3b5d36fc27e69cef39a7ee5eaf8174ca1123cb",
+                "sha256:92eb04041d371fea828858e4fff182453c25ae3eaa8782d9b6c32b25857d23bc",
+                "sha256:a0afbb967fd2c98efad5f4c24439a640d39463282040a88e8e928db647d8ac3d",
+                "sha256:a785409c0fa51764766840185a34f96a0a93527a0ff0230484d33a8ed085c8f8",
+                "sha256:cca9fce15109a36a0a9f9cfc64f870f1c140cb235ddf27fe0328e6afb44dfed0",
+                "sha256:d56b10d8ed72ec1be76bf10508446df60954f08a41c2d40778bc29a3a9ad9bce",
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+                "sha256:fc98f3eac993b9bfdd392e675dfe19850cc8c7246a8fd2b42443e506344be7d9"
+            ],
+            "version": "==1.5.2"
+        },
+        "six": {
+            "hashes": [
+                "sha256:30639c035cdb23534cd4aa2dd52c3bf48f06e5f4a941509c8bafd8ce11080259",
+                "sha256:8b74bedcbbbaca38ff6d7491d76f2b06b3592611af620f8426e82dddb04a5ced"
+            ],
+            "version": "==1.15.0"
+        },
+        "sklearn": {
+            "hashes": [
+                "sha256:e23001573aa194b834122d2b9562459bf5ae494a2d59ca6b8aa22c85a44c0e31"
+            ],
+            "index": "pypi",
+            "version": "==0.0"
+        },
+        "sqlparse": {
+            "hashes": [
+                "sha256:022fb9c87b524d1f7862b3037e541f68597a730a8843245c349fc93e1643dc4e",
+                "sha256:e162203737712307dfe78860cc56c8da8a852ab2ee33750e33aeadf38d12c548"
+            ],
+            "version": "==0.3.1"
+        },
+        "threadpoolctl": {
+            "hashes": [
+                "sha256:38b74ca20ff3bb42caca8b00055111d74159ee95c4370882bbff2b93d24da725",
+                "sha256:ddc57c96a38beb63db45d6c159b5ab07b6bced12c45a1f07b2b92f272aebfa6b"
+            ],
+            "version": "==2.1.0"
+        }
+    },
+    "develop": {}
+}

README.md

@@ -0,0 +1,25 @@
+# Exercice dev python
+
+MeilleureCopro souhaite développer une API de statistiques sur des données d’annonces immobilières pour des besoins internes.
+
+Afin de valider cette demande, votre objectif est de construire en un minimum de temps une API qui répondra aux besoins suivants des utilisateurs internes MeilleureCopro :
+
+- L'utilisateur doit pouvoir connaître les charges de copropriétés moyennes, les quantiles 10%, 90% sur un département, une ville ou code postal
+- L'utilisateur interne doit pouvoir très simplement interroger la base de données via un navigateur
+- L'utilisateur doit pouvoir rentrer les informations suivantes sur un appartement au sein d'une copropriété :
+
+  - une adresse,
+  - la surface de l'appartement,
+  - la présence d'ascenseur,
+  - la présence de chauffage collectif
+
+  et obtenir une estimation des charges de l'appartement. Vous utiliserez un modèle pour faire cette estimation.
+
+Voici le dataset d’annonces immobilières sur lequel vous calculerez les statistiques demandées : [dataset](https://storage.googleapis.com/data.meilleurecopro.com/stage/dataset_annonces.csv.tar.gz).
+
+## Outils choisis
+
+- pandas
+- scikit-learn
+- Django
+- Django REST Framework

api/admin.py

@@ -0,0 +1,3 @@
+from django.contrib import admin
+
+# Register your models here.

api/apps.py

@@ -0,0 +1,5 @@
+from django.apps import AppConfig
+
+
+class ApiConfig(AppConfig):
+    name = 'api'

api/data/prediction.py

@@ -0,0 +1,83 @@
+import numpy as np
+import pandas as pd
+from sklearn.impute import SimpleImputer
+from sklearn.linear_model import LinearRegression
+from sklearn.model_selection import train_test_split
+# TODO: use one hot encoding to handle unknown values in transform step
+from sklearn.preprocessing import LabelEncoder
+
+from .statistics import data_path
+
+
+class LinearRegressor:
+
+    def __init__(self):
+        self.X, self.y = self.preprocessing()
+
+    def preprocessing(self):
+        """Prepare data for modelling."""
+
+        # read csv
+        raw_data = pd.read_csv(
+            data_path, dtype={'DEPT_CODE': str, 'ZIP_CODE': str, 'INSEE_CODE': str})
+
+        # drop missing values from significative columns
+        feature_cols = ['CONDOMINIUM_EXPENSES',
+                        'DEPT_CODE', 'ZIP_CODE', 'CITY', 'SURFACE']
+        data = raw_data.dropna(subset=feature_cols)
+
+        # columns used in modelling
+        cols = ['ZIP_CODE', 'DEPT_CODE', 'CITY', 'INSEE_CODE', 'LATITUDE',
+                'LONGITUDE', 'SURFACE', 'HEATING_MODE', 'ELEVATOR']
+
+        # target
+        y = data['CONDOMINIUM_EXPENSES']
+        # features
+        X = data[cols]
+
+        # surface rows are not correctly typed I do it manually
+        X['SURFACE'] = X['SURFACE'].astype(float)
+
+        # split dataset for training
+        X_train, X_val, y_train, y_val = train_test_split(
+            X, y, train_size=0.7, random_state=0)
+
+        # fill missing values with most frequent values. TODO: suboptimal method
+        train_mode = dict(X_train.mode().iloc[0])
+        X_train = X_train.fillna(train_mode)
+
+        cols = X_train.dtypes == 'object'
+        cat_cols = list(cols[cols].index)
+        cols = X_train.dtypes != 'object'
+        num_cols = list(cols[cols].index)
+
+        # handle missing values on categorical columns
+        imputer = SimpleImputer(strategy='constant')
+        X_train[cat_cols] = imputer.fit_transform(X_train[cat_cols])
+        X_val[cat_cols] = imputer.transform(X_val[cat_cols])
+
+        # encode categorical data
+        encoder = LabelEncoder()
+
+        for col in cat_cols:
+            X_train[col] = encoder.fit_transform(X_train[col])
+
+        # handle numerical missing values
+        num_imputer = SimpleImputer(strategy='mean')
+
+        X_train[num_cols] = num_imputer.fit_transform(X_train[num_cols])
+        X_val[num_cols] = num_imputer.transform(X_val[num_cols])
+
+        return X_train, y_train
+
+    def get_trained_model(self):
+        lin_reg = LinearRegression().fit(self.X, self.y)
+
+        return lin_reg
+
+    def predict_expenses(self, data):
+        """Return condominium expenses as a function of data."""
+
+        model = self.get_trained_model()
+
+        return model.predict(data)

api/data/statistics.py

@@ -0,0 +1,65 @@
+import os
+
+import pandas as pd
+
+from copro.settings import BASE_DIR
+
+# TODO: put in env var and import it
+data_path = os.path.join(BASE_DIR, 'api/data/annonces.csv')
+
+
+def get_data_from_csv(path):
+    """
+    Create dataframe from csv filepath. Keep only needed columns
+
+    `path` is the csv file location.
+    """
+
+    # import data as dataframe
+    raw_data = pd.read_csv(path)
+
+    # keep only useful columns
+    feature_cols = ['CONDOMINIUM_EXPENSES', 'DEPT_CODE', 'ZIP_CODE', 'CITY']
+    data = raw_data.dropna(subset=feature_cols)
+
+    return data
+
+
+def get_condo_expenses_by(col, value):
+    """
+    Return mean, 1st and 9th decile condominium expenses values for the given query type.
+
+    `Col` can either be: `DEPT_CODE`, `CITY` or `ZIP_CODE`.
+
+    `Value` is the actual query parameter.
+    """
+
+    assert col in ('DEPT_CODE', 'CITY',
+                   'ZIP_CODE'), "col must be 'dept', 'city' or 'zip'"
+
+    data = get_data_from_csv(data_path)
+
+    # group data by column and compute statistics
+    group = data.groupby(col, as_index=False)['CONDOMINIUM_EXPENSES']
+    mean = group.mean()
+    first_quantile = group.quantile(0.1)
+    ninth_quantile = group.quantile(0.9)
+
+    # build filtering condition
+    if col == 'DEPT_CODE':
+        condition = mean['DEPT_CODE'] == value
+    elif col == 'CITY':
+        condition = mean['CITY'] == value
+    else:
+        condition = mean['ZIP_CODE'] == value
+
+    # TODO: refactor
+    mean = mean['CONDOMINIUM_EXPENSES'][condition].iloc[0]
+    first_quantile = first_quantile['CONDOMINIUM_EXPENSES'][condition].iloc[0]
+    ninth_quantile = ninth_quantile['CONDOMINIUM_EXPENSES'][condition].iloc[0]
+
+    return {
+        "mean": mean,
+        "1st_quantile": first_quantile,
+        "9th_quantile": ninth_quantile,
+    }

api/models.py

@@ -0,0 +1,18 @@
+from django.db import models
+
+
+class CondominiumExpense(models.Model):
+    mean = models.FloatField()
+    first_quantile = models.FloatField()
+    ninth_quantile = models.FloatField()
+
+
+class CondominiumExpenseQuery(models.Model):
+    QUERY_TYPES = [
+        ('DEPT_CODE', 'Department'),
+        ('ZIP_CODE', 'Postal Code'),
+        ('CITY', 'City'),
+    ]
+    query_type = models.CharField(
+        max_length=20, choices=QUERY_TYPES, default='DEPT_CODE')
+    value = models.CharField(max_length=40, blank=True, default="")

api/serializers.py

@@ -0,0 +1,15 @@
+from rest_framework import serializers
+
+from .models import CondominiumExpense, CondominiumExpenseQuery
+
+
+class CondominiumSerializer(serializers.ModelSerializer):
+    class Meta:
+        model = CondominiumExpense
+        fields = '__all__'
+
+
+class CondominiumQuerySerializer(serializers.ModelSerializer):
+    class Meta:
+        model = CondominiumExpenseQuery
+        fields = '__all__'

api/tests.py

@@ -0,0 +1,3 @@
+from django.test import TestCase
+
+# Create your tests here.

api/urls.py

@@ -0,0 +1,8 @@
+from django.urls import path
+
+from .views import get_condominium_expenses, make_expenses_prediction
+
+urlpatterns = [
+    path('api/v1/', get_condominium_expenses),
+    path('api/v1/predict/', make_expenses_prediction),
+]

api/views.py

@@ -0,0 +1,29 @@
+from rest_framework.decorators import api_view
+from rest_framework.response import Response
+
+from .data.prediction import LinearRegressor
+from .data.statistics import get_condo_expenses_by
+
+
+@api_view(['GET', 'POST'])
+def get_condominium_expenses(request):
+    """TODO: refactor using APIView class"""
+
+    data = {}
+    if request.method == "POST":
+        query_type = request.data["query_type"]
+        query_val = request.data['query_val']
+
+        data = get_condo_expenses_by(query_type, query_val)
+
+    return Response(data)
+
+
+@api_view(['POST'])
+def make_expenses_prediction(request):
+    """Return condominium expenses prediction."""
+
+    algo = LinearRegressor
+    prediction = algo.predict_expenses(request.data)
+
+    return Response(prediction)

copro/asgi.py

@@ -0,0 +1,16 @@
+"""
+ASGI config for copro 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/3.0/howto/deployment/asgi/
+"""
+
+import os
+
+from django.core.asgi import get_asgi_application
+
+os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'copro.settings')
+
+application = get_asgi_application()

copro/settings.py

@@ -0,0 +1,122 @@
+"""
+Django settings for copro project.
+
+Generated by 'django-admin startproject' using Django 3.0.4.
+
+For more information on this file, see
+https://docs.djangoproject.com/en/3.0/topics/settings/
+
+For the full list of settings and their values, see
+https://docs.djangoproject.com/en/3.0/ref/settings/
+"""
+
+import os
+
+# Build paths inside the project like this: os.path.join(BASE_DIR, ...)
+BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+
+# Quick-start development settings - unsuitable for production
+# See https://docs.djangoproject.com/en/3.0/howto/deployment/checklist/
+
+# SECURITY WARNING: keep the secret key used in production secret!
+SECRET_KEY = '6)akoq-jb7%!ydxtji%ss+xk7o&@e-&=eep(4r2%3zz^^&4wzv'
+
+# SECURITY WARNING: don't run with debug turned on in production!
+DEBUG = True
+
+ALLOWED_HOSTS = []
+
+
+# Application definition
+
+INSTALLED_APPS = [
+    'django.contrib.admin',
+    'django.contrib.auth',
+    'django.contrib.contenttypes',
+    'django.contrib.sessions',
+    'django.contrib.messages',
+    'django.contrib.staticfiles',
+    'rest_framework',
+    'api.apps.ApiConfig'
+]
+
+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',
+]
+
+ROOT_URLCONF = 'copro.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 = 'copro.wsgi.application'
+
+
+# Database
+# https://docs.djangoproject.com/en/3.0/ref/settings/#databases
+
+DATABASES = {
+    'default': {
+        'ENGINE': 'django.db.backends.sqlite3',
+        'NAME': os.path.join(BASE_DIR, 'db.sqlite3'),
+    }
+}
+
+
+# Password validation
+# https://docs.djangoproject.com/en/3.0/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/3.0/topics/i18n/
+
+LANGUAGE_CODE = 'en-us'
+
+TIME_ZONE = 'UTC'
+
+USE_I18N = True
+
+USE_L10N = True
+
+USE_TZ = True
+
+
+# Static files (CSS, JavaScript, Images)
+# https://docs.djangoproject.com/en/3.0/howto/static-files/
+
+STATIC_URL = '/static/'

copro/urls.py

@@ -0,0 +1,22 @@
+"""copro URL Configuration
+
+The `urlpatterns` list routes URLs to views. For more information please see:
+    https://docs.djangoproject.com/en/3.0/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('', include('api.urls'))
+]

copro/wsgi.py

@@ -0,0 +1,16 @@
+"""
+WSGI config for copro 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/3.0/howto/deployment/wsgi/
+"""
+
+import os
+
+from django.core.wsgi import get_wsgi_application
+
+os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'copro.settings')
+
+application = get_wsgi_application()

manage.py

@@ -0,0 +1,21 @@
+#!/usr/bin/env python
+"""Django's command-line utility for administrative tasks."""
+import os
+import sys
+
+
+def main():
+    os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'copro.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()