Merge branch 'main' of https://github.com/TobiasSchaeuble-EH/LunchHeroes

README.md

@@ -1,3 +1,8 @@
+[![Project Status: proof-of-concept – repository is only intended to be a limited example, demo, or proof-of-concept.](https://www.repostatus.org/badges/latest/concept.svg)](https://www.repostatus.org/#concept)
+[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/charliermarsh/ruff/main/assets/badge/v2.json)](https://github.com/charliermarsh/ruff)
+[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
+
+
 # LunchHeroes
 
 Lunch Heros Basel Hack Event Repo

backend/main.py

@@ -1,5 +1,6 @@
-from src.routes.users.getUserWithId import getUserWithId
-from src.routes.users.getAllUsers import getAllUsers
+
+
+from src.routes.users.userFunctions import getAllUsers, getUserWithId
 from fastapi import FastAPI
 from supabase_client import supabase_client
 
@@ -12,7 +13,7 @@ def read_root():
     return {"Hello": "World", "data" : data}
 
 @app.get("/users")
-def read_root():
+def load_all_users():
     allUsers = getAllUsers()
     return { "allUsers" : allUsers}
 

backend/src/lunchheros/distance/__init__.py

@@ -1,4 +1,7 @@
 """Subpackage related to calculation of distances between users."""
 
+from lunchheros.distance._distances import EuclideanDistance, MahattanDistance
 
-__all__ = ["EuclideanDistances"]
+from lunchheros.distance._utils import calculate_distance_matrix
+
+__all__ = ["EuclideanDistance", "MahattanDistance", "calculate_distance_matrix"]

backend/src/lunchheros/distance/_distances.py

@@ -22,3 +22,22 @@ def is_symmetric(self) -> bool:
             ``False`` should be returned.
         """
         return True
+
+class MahattanDistance(interface_distance.Distance):
+    """Manhattan distance between two interests."""
+
+    def calculate(self, /, interst_vec1, interst_vec2) -> float:
+        """Calculates distance between ``interst_val1`` and ``interst_val2``."""
+
+        return metrics.pairwise.manhattan_distances([interst_vec1], [interst_vec2])
+
+    def is_symmetric(self) -> bool:
+        """Returns ``True`` if the similarity function is symmetric,
+        i.e., :math:`s(t_1, t_2) = s(t_2, t_1)` for all pairs of users.
+
+        Note:
+            If it is not known whether the similarity function is symmetric,
+            ``False`` should be returned.
+        """
+        return True
+

backend/src/lunchheros/distance/_utils.py

@@ -0,0 +1,40 @@
+"""Temporarily existing module with utility functions.
+
+When the structure of the subpackage starts to appear,
+we will move the functions here to the right packages.
+
+Note:
+    Treat this submodule as a part of the *private* API.
+"""
+
+from lunchheros.distance._interface import Distance
+
+import numpy as np
+
+
+def calculate_distance_matrix(
+    users: list[list],
+    *,
+    distance: Distance,
+) -> np.ndarray:
+    """Calculates a cross-distance matrix
+    ``d[i, j] = distance(trees1[i], trees2[j])``
+
+    Args:
+        vec1: sequence of trees in one set, length m
+        vec1: sequence of trees in the second set, length n
+        distance: distance or similarity function
+
+    Returns:
+        distance matrix, shape (m, n)
+    """
+    m, n = len(users), len(users)
+
+    result = np.zeros((m, n))
+
+    for i, vec1 in enumerate(users):
+        for j, vec2 in enumerate(users):
+            result[i, j] = distance().calculate(vec1, vec2)
+        # print(f"Distance between user {i} and {j} is {result[i, j]}")
+
+    return result

backend/src/routes/users/userFunctions.py

@@ -0,0 +1,13 @@
+from supabase_client import supabase_client
+
+
+users_select = "*, company:companies(*)"
+
+def getUserWithId(userId):
+    return supabase_client.table("users").select(users_select).eq("id", userId).single().execute()
+
+from supabase_client import supabase_client
+
+def getAllUsers():
+    return  supabase_client.table("users").select(users_select).execute()
+

backend/tests/distance/test_distances.py

@@ -2,7 +2,7 @@
 
 import numpy as np
 
-from lunchheros.distance import EuclideanDistance
+from lunchheros.distance import EuclideanDistance, MahattanDistance
 
 
 def test_euclidean_distance():
@@ -17,3 +17,47 @@ def test_euclidean_distance():
     dist_func = EuclideanDistance().calculate(a, b)
 
     assert dist == dist_func
+
+
+def test_euclidean_distance_bool():
+    """Tests for the euclidean distance."""
+
+    # make a vector of 3 dimensions
+    a = np.array([1, 0, 1])
+    b = np.array([1, 0, 0])
+    # calculate the euclidean distance
+    dist = np.linalg.norm(a - b)
+    # calculate the euclidean distance using the function
+    dist_func = EuclideanDistance().calculate(a, b)
+
+    assert dist == dist_func
+
+
+
+def test_mahattan_distance():
+    """Tests for the euclidean distance."""
+
+    # make a vector of 3 dimensions
+    a = np.array([1, 2, 3])
+    b = np.array([4, 5, 6])
+    # calculate the euclidean distance
+    dist = np.sum(np.abs(a - b))
+    # calculate the euclidean distance using the function
+    dist_func = MahattanDistance().calculate(a, b)
+
+    assert dist == dist_func
+
+
+def test_mahattan_distance_bool():
+    """Tests for the euclidean distance."""
+
+    # make a vector of 3 dimensions
+    a = np.array([1, 0, 1])
+    b = np.array([1, 0, 0])
+    # calculate the euclidean distance
+    dist = np.sum(np.abs(a - b))
+    # calculate the euclidean distance using the function
+    dist_func = MahattanDistance().calculate(a, b)
+
+    assert dist == dist_func
+

backend/tests/distance/test_utils.py

@@ -0,0 +1,27 @@
+"""Tests for the distance module."""
+
+import numpy as np
+
+from lunchheros.distance import MahattanDistance, calculate_distance_matrix
+
+
+def test_calculate_distance_matrix():
+    """Tests for the calculate_distance_matrix function."""
+
+    # make 3 vectors of length 4
+    a = np.array([1, 2, 3, 4])
+    b = np.array([4, 5, 6, 7])
+    c = np.array([7, 8, 9, 10])
+    # make a list of the vectors
+    users = [a, b, c]
+    # calculate the euclidean distance for all pairs of vectors
+    dist = np.zeros((3, 3))
+    for i, vec1 in enumerate(users):
+        for j, vec2 in enumerate(users):
+            dist[i, j] = np.sum(np.abs(vec1 - vec2))
+    # calculate the manhattan distance using the function
+    dist2 = calculate_distance_matrix(users, distance=MahattanDistance)
+
+    print(dist)
+    print(dist2)
+    assert np.allclose(dist, dist2)