working algo for clusters
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This commit is contained in:
698
backend/src/sandbox/colmar_data.json
Normal file
698
backend/src/sandbox/colmar_data.json
Normal file
@@ -0,0 +1,698 @@
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|
||||
"name": "Caroll",
|
||||
"shop": "clothes"
|
||||
},
|
||||
"geometry": {
|
||||
"type": "Point",
|
||||
"coordinates": [
|
||||
7.3579354,
|
||||
48.0779291
|
||||
]
|
||||
},
|
||||
"id": "node/11150959880"
|
||||
},
|
||||
{
|
||||
"type": "Feature",
|
||||
"properties": {
|
||||
"@id": "node/11302242094",
|
||||
"branch": "Wintzenheim",
|
||||
"name": "Label Fripe",
|
||||
"opening_hours": "Mo-Sa 09:00-18:45",
|
||||
"phone": "+33 3 89 27 39 25",
|
||||
"second_hand": "only",
|
||||
"shop": "clothes",
|
||||
"website": "https://labelfripe.fr/label-fripe-wintzenheim/"
|
||||
},
|
||||
"geometry": {
|
||||
"type": "Point",
|
||||
"coordinates": [
|
||||
7.3109899,
|
||||
48.0850362
|
||||
]
|
||||
},
|
||||
"id": "node/11302242094"
|
||||
},
|
||||
{
|
||||
"type": "Feature",
|
||||
"properties": {
|
||||
"@id": "node/11392247003",
|
||||
"name": "Lingerie Sipp",
|
||||
"shop": "clothes"
|
||||
},
|
||||
"geometry": {
|
||||
"type": "Point",
|
||||
"coordinates": [
|
||||
7.3111507,
|
||||
48.0841835
|
||||
]
|
||||
},
|
||||
"id": "node/11392247003"
|
||||
},
|
||||
{
|
||||
"type": "Feature",
|
||||
"properties": {
|
||||
"@id": "node/11778819781",
|
||||
"addr:city": "Colmar",
|
||||
"addr:housenumber": "10",
|
||||
"addr:postcode": "68000",
|
||||
"addr:street": "Rue des Têtes",
|
||||
"clothes": "suits;hats;men",
|
||||
"name": "Phillipe",
|
||||
"phone": "0389411983",
|
||||
"shop": "clothes"
|
||||
},
|
||||
"geometry": {
|
||||
"type": "Point",
|
||||
"coordinates": [
|
||||
7.3559389,
|
||||
48.0789064
|
||||
]
|
||||
},
|
||||
"id": "node/11778819781"
|
||||
},
|
||||
{
|
||||
"type": "Feature",
|
||||
"properties": {
|
||||
"@id": "node/11799215969",
|
||||
"brand": "Petit Bateau",
|
||||
"brand:wikidata": "Q3377090",
|
||||
"name": "Petit Bateau",
|
||||
"opening_hours": "Mo-Sa 10:00-19:00; Su 10:00-18:00",
|
||||
"phone": "+33 3 89 24 97 85",
|
||||
"shop": "clothes",
|
||||
"website": "https://stores.petit-bateau.com/france/colmar/9-rue-des-boulangers"
|
||||
},
|
||||
"geometry": {
|
||||
"type": "Point",
|
||||
"coordinates": [
|
||||
7.355149,
|
||||
48.0780213
|
||||
]
|
||||
},
|
||||
"id": "node/11799215969"
|
||||
},
|
||||
{
|
||||
"type": "Feature",
|
||||
"properties": {
|
||||
"@id": "node/11816704669",
|
||||
"addr:housenumber": "10",
|
||||
"addr:street": "Rue des Boulangers",
|
||||
"name": "des petits hauts",
|
||||
"shop": "clothes"
|
||||
},
|
||||
"geometry": {
|
||||
"type": "Point",
|
||||
"coordinates": [
|
||||
7.3555001,
|
||||
48.0780768
|
||||
]
|
||||
},
|
||||
"id": "node/11816704669"
|
||||
},
|
||||
{
|
||||
"type": "Feature",
|
||||
"properties": {
|
||||
"@id": "node/12320343534",
|
||||
"addr:city": "Colmar",
|
||||
"addr:housenumber": "44",
|
||||
"addr:postcode": "68000",
|
||||
"addr:street": "Rue des Clefs",
|
||||
"brand": "Un Jour Ailleurs",
|
||||
"brand:wikidata": "Q105106211",
|
||||
"clothes": "women",
|
||||
"name": "Un Jour Ailleurs",
|
||||
"opening_hours": "Mo-Fr 10:00-19:00; Sa 10:00-18:30",
|
||||
"phone": "+33368318572",
|
||||
"shop": "clothes",
|
||||
"website": "https://boutique.unjourailleurs.com/fr/mode-femme/boutique-colmar-76"
|
||||
},
|
||||
"geometry": {
|
||||
"type": "Point",
|
||||
"coordinates": [
|
||||
7.35897,
|
||||
48.0789807
|
||||
]
|
||||
},
|
||||
"id": "node/12320343534"
|
||||
},
|
||||
{
|
||||
"type": "Feature",
|
||||
"properties": {
|
||||
"@id": "node/12320343536",
|
||||
"addr:city": "Colmar",
|
||||
"addr:housenumber": "38",
|
||||
"addr:postcode": "68000",
|
||||
"addr:street": "Rue des Clefs",
|
||||
"brand": "Timberland",
|
||||
"brand:wikidata": "Q1539185",
|
||||
"name": "Timberland",
|
||||
"opening_hours": "Mo-Sa 10:00-19:00",
|
||||
"phone": "+33389298650",
|
||||
"shop": "clothes"
|
||||
},
|
||||
"geometry": {
|
||||
"type": "Point",
|
||||
"coordinates": [
|
||||
7.3592409,
|
||||
48.0788785
|
||||
]
|
||||
},
|
||||
"id": "node/12320343536"
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -1,4 +1,210 @@
|
||||
import numpy as np
|
||||
import json
|
||||
import os
|
||||
from typing import Optional, Literal
|
||||
from sklearn.cluster import DBSCAN
|
||||
from sklearn.linear_model import RANSACRegressor
|
||||
from sklearn.decomposition import PCA
|
||||
import matplotlib.pyplot as plt
|
||||
from pydantic import BaseModel
|
||||
|
||||
|
||||
|
||||
class ShoppingLocation(BaseModel):
|
||||
type: Literal['street', 'area']
|
||||
importance: int
|
||||
centroid: tuple
|
||||
start: Optional[list] = None
|
||||
end: Optional[list] = None
|
||||
end: Optional[tuple] = None
|
||||
|
||||
|
||||
|
||||
def extract_points(filestr: str) :
|
||||
"""
|
||||
Extract points from geojson file.
|
||||
|
||||
Returns :
|
||||
np.array containing the points
|
||||
"""
|
||||
points = []
|
||||
|
||||
with open(os.path.dirname(__file__) + '/' + filestr, 'r') as f:
|
||||
geojson = json.load(f)
|
||||
|
||||
for feature in geojson['features']:
|
||||
if feature['geometry']['type'] == 'Point':
|
||||
centroid = feature['geometry']['coordinates']
|
||||
points.append(centroid)
|
||||
|
||||
elif feature['geometry']['type'] == 'Polygon':
|
||||
centroid = np.array(feature['geometry']['coordinates'][0][0])
|
||||
points.append(centroid)
|
||||
|
||||
# Convert the list of points to a NumPy array
|
||||
return np.array(points)
|
||||
|
||||
|
||||
def filter_clusters(cluster_points, cluster_labels):
|
||||
"""
|
||||
Remove clusters of less importance.
|
||||
"""
|
||||
label_counts = np.bincount(cluster_labels)
|
||||
|
||||
# Step 3: Get the indices (labels) of the 5 largest clusters
|
||||
top_5_labels = np.argsort(label_counts)[-5:] # Get the largest 5 clusters
|
||||
|
||||
# Step 4: Filter points to keep only the points in the top 5 clusters
|
||||
filtered_cluster_points = []
|
||||
filtered_cluster_labels = []
|
||||
|
||||
for label in top_5_labels:
|
||||
filtered_cluster_points.append(cluster_points[cluster_labels == label])
|
||||
filtered_cluster_labels.append(np.full((label_counts[label],), label)) # Replicate the label
|
||||
|
||||
# Concatenate filtered clusters into a single array
|
||||
return np.vstack(filtered_cluster_points), np.concatenate(filtered_cluster_labels)
|
||||
|
||||
|
||||
def fit_lines(points, labels):
|
||||
"""
|
||||
Fit lines to identified clusters.
|
||||
"""
|
||||
all_x = []
|
||||
all_y = []
|
||||
lines = []
|
||||
locations = []
|
||||
|
||||
for label in set(labels):
|
||||
cluster_points = points[labels == label]
|
||||
|
||||
# If there's not enough points, skip
|
||||
if len(cluster_points) < 2:
|
||||
continue
|
||||
|
||||
# Apply PCA to find the principal component (i.e., the line of best fit)
|
||||
pca = PCA(n_components=1)
|
||||
pca.fit(cluster_points)
|
||||
|
||||
direction = pca.components_[0]
|
||||
centroid = pca.mean_
|
||||
|
||||
# Project the cluster points onto the principal direction (line direction)
|
||||
projections = np.dot(cluster_points - centroid, direction)
|
||||
|
||||
# Get the range of the projections to find the approximate length of the cluster
|
||||
cluster_length = projections.max() - projections.min()
|
||||
|
||||
# Now adjust `t` so that it scales with the cluster length
|
||||
t = np.linspace(-cluster_length / 2.75, cluster_length / 2.75, 10)
|
||||
|
||||
# Calculate the start and end of the line based on min/max projections
|
||||
start_point = centroid[0] + t*direction[0]
|
||||
end_point = centroid[1] + t*direction[1]
|
||||
|
||||
# Store the line
|
||||
lines.append((start_point, end_point))
|
||||
|
||||
# For visualization, store the points
|
||||
all_x.append(min(start_point))
|
||||
all_x.append(max(start_point))
|
||||
all_y.append(min(end_point))
|
||||
all_y.append(max(end_point))
|
||||
|
||||
if np.linalg.norm(t) <= 0.0045 :
|
||||
loc = ShoppingLocation(
|
||||
type='area',
|
||||
centroid=tuple(centroid),
|
||||
importance = len(cluster_points)
|
||||
)
|
||||
else :
|
||||
loc = ShoppingLocation(
|
||||
type='street',
|
||||
centroid=tuple(centroid),
|
||||
start=start_point,
|
||||
end=end_point,
|
||||
importance = len(cluster_points)
|
||||
)
|
||||
|
||||
locations.append(loc)
|
||||
|
||||
xmin = min(all_x)
|
||||
xmax = max(all_x)
|
||||
ymin = min(all_y)
|
||||
ymax = max(all_y)
|
||||
corners = (xmin, xmax, ymin, ymax)
|
||||
|
||||
return corners, locations
|
||||
|
||||
|
||||
|
||||
# Extract points
|
||||
points = extract_points('strasbourg_data.json')
|
||||
|
||||
# Create a figure with 1 row and 3 columns for side-by-side plots
|
||||
fig, axes = plt.subplots(1, 3, figsize=(15, 5))
|
||||
|
||||
# Plot 0: Raw data points
|
||||
axes[0].set_title('Raw Data')
|
||||
axes[0].scatter(points[:, 0], points[:, 1], color='blue', s=20)
|
||||
|
||||
# Apply DBSCAN to find clusters. Choose different settings for different cities.
|
||||
if len(points) > 400 :
|
||||
dbscan = DBSCAN(eps=0.00118, min_samples=15, algorithm='kd_tree') # for large cities
|
||||
else :
|
||||
dbscan = DBSCAN(eps=0.00075, min_samples=10, algorithm='kd_tree') # for small cities
|
||||
|
||||
labels = dbscan.fit_predict(points)
|
||||
|
||||
# Separate clustered points and noise points
|
||||
clustered_points = points[labels != -1]
|
||||
clustered_labels = labels[labels != -1]
|
||||
noise_points = points[labels == -1]
|
||||
|
||||
# Plot n°1: DBSCAN Clustering Results
|
||||
axes[1].set_title('DBSCAN Clusters')
|
||||
axes[1].scatter(clustered_points[:, 0], clustered_points[:, 1], c=clustered_labels, cmap='rainbow', s=20)
|
||||
axes[1].scatter(noise_points[:, 0], noise_points[:, 1], c='blue', s=7, label='Noise')
|
||||
|
||||
clustered_points, clustered_labels = filter_clusters(clustered_points, clustered_labels)
|
||||
|
||||
|
||||
|
||||
# Fit lines
|
||||
corners, locations = fit_lines(clustered_points, clustered_labels)
|
||||
(xmin, xmax, ymin, ymax) = corners
|
||||
|
||||
|
||||
# Plot clustered points in normal size and noise points separately
|
||||
axes[2].scatter(clustered_points[:, 0], clustered_points[:, 1], c=clustered_labels, cmap='rainbow', s=30)
|
||||
# axes[2].scatter(noise_points[:, 0], noise_points[:, 1], c='blue', s=10, label='Noise')
|
||||
|
||||
# Step 4: Plot the detected lines in the final plot
|
||||
for loc in locations:
|
||||
|
||||
if loc.type == 'street' :
|
||||
line_x = loc.start
|
||||
line_y = loc.end
|
||||
axes[2].plot(line_x, line_y, color='lime', linewidth=3)
|
||||
else :
|
||||
axes[2].scatter(loc.centroid[0], loc.centroid[1], color='None', edgecolors='lime', s=200, linewidth=3)
|
||||
# print(8)
|
||||
|
||||
axes[2].set_title('PCA Fitted Lines on Clusters')
|
||||
|
||||
# print(all_x)
|
||||
|
||||
# Adjust the axis limit for previous plots
|
||||
axes[0].set_xlim(xmin-0.01, xmax+0.01)
|
||||
axes[0].set_ylim(ymin-0.01, ymax+0.01)
|
||||
|
||||
axes[1].set_xlim(xmin-0.01, xmax+0.01)
|
||||
axes[1].set_ylim(ymin-0.01, ymax+0.01)
|
||||
|
||||
axes[2].set_xlim(xmin-0.01, xmax+0.01)
|
||||
axes[2].set_ylim(ymin-0.01, ymax+0.01)
|
||||
|
||||
# Adjust layout for better spacing
|
||||
plt.tight_layout()
|
||||
|
||||
# Show the plots
|
||||
plt.show()
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
4947
backend/src/sandbox/strasbourg_data.json
Normal file
4947
backend/src/sandbox/strasbourg_data.json
Normal file
File diff suppressed because it is too large
Load Diff
23140
backend/src/sandbox/vienna_data.json
Normal file
23140
backend/src/sandbox/vienna_data.json
Normal file
File diff suppressed because it is too large
Load Diff
2844
backend/src/sandbox/winterthur_data.json
Normal file
2844
backend/src/sandbox/winterthur_data.json
Normal file
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@@ -33,19 +33,19 @@ def invalid_client():
|
||||
([91, 181], {"sightseeing": {"type": "nature", "score": 5},
|
||||
"nature": {"type": "nature", "score": 5},
|
||||
"shopping": {"type": "shopping", "score": 5},
|
||||
}, 423),
|
||||
}, 422),
|
||||
([-91, 181], {"sightseeing": {"type": "nature", "score": 5},
|
||||
"nature": {"type": "nature", "score": 5},
|
||||
"shopping": {"type": "shopping", "score": 5},
|
||||
}, 423),
|
||||
}, 422),
|
||||
([91, -181], {"sightseeing": {"type": "nature", "score": 5},
|
||||
"nature": {"type": "nature", "score": 5},
|
||||
"shopping": {"type": "shopping", "score": 5},
|
||||
}, 423),
|
||||
}, 422),
|
||||
([-91, -181], {"sightseeing": {"type": "nature", "score": 5},
|
||||
"nature": {"type": "nature", "score": 5},
|
||||
"shopping": {"type": "shopping", "score": 5},
|
||||
}, 423),
|
||||
}, 422),
|
||||
]
|
||||
)
|
||||
def test_input(invalid_client, start, preferences, status_code): # pylint: disable=redefined-outer-name
|
||||
|
||||
Reference in New Issue
Block a user