```python
import networkx as nx
from matplotlib import pyplot as plt
import numpy as np
from tqdm import tqdm
np.random.seed(23) 
from scipy import stats
from pathlib import Path
import json
from sympy.geometry import Point, Circle 
import math

```


```python
label_graph = nx.DiGraph()
label_graph.add_edge('B','A')
label_graph.add_edge('J','B')
label_graph.add_edge('C','B')
label_graph.add_edge('D','C')
label_graph.add_edge('E','D')
label_graph.add_edge('F','D')
label_graph.add_edge('G','C')
label_graph.add_edge('H','G')
label_graph.add_edge('I','G')
label_graph.add_edge('N','M')
label_graph.add_edge('P','N')
label_graph.add_edge('Q','N')
label_graph.add_edge('O','M')
label_graph.add_nodes_from(['K', 'L'])
nx.draw_networkx(label_graph, arrows=True)
```


```python
roots = []
for n in label_graph.nodes():
    if label_graph.out_degree(n) == 0:
        roots.append(n)
roots
```




    ['A', 'M', 'K', 'L']




```python
list(label_graph.predecessors('P'))
```




    []




```python
def plot_circles_matplotlib(labels, centres, radii):
    print('Plot Circles Matplotlib...')
    plt.figure(figsize=(20,20))
    for i in range(len(labels)):
        circle=plt.Circle(centres[i],radii[i], fill=False)
        plt.gcf().gca().add_artist(circle)
        plt.gca().annotate(labels[i], xy=(centres[i][0], centres[i][1]+radii[i]), fontsize=20)
    plt.xlim(-100,100)
    plt.ylim(-100,100)
    plt.show()

```


```python
def plot_circles(labels, circles):
    print('Plot circles...')
    centres = []
    radii = []
    for i, circ in enumerate(circles):
        centres.append(circ.center.coordinates)
        radii.append(circ.radius)
    plot_circles_matplotlib(labels, centres, radii)
```


```python
def divide_circle_into_x_circles(c: Circle, n: int, overlap=False):
    if n==1:
        return [Circle(c.center, 0.75*c.radius)]
    if not overlap:
        r = float((1+((n-2)/10 if n<=10 else 0))*c.radius/n)
        rprime = float(c.radius/n)*(n-2) if n>3 else float(c.radius/n)*(n-1)
    else:
        r = float((1+((n-2)/10 if n<=10 else 0))*c.radius/n)
        p = n-2.5 if n<=4 else ((2+(n-4)/n) if n<20  else 5)
        if n==4:
            p = 1
        rprime = float(c.radius/n)*p 
    theta = 360/n
    centres = []
    for i in range(n):
        angle = math.radians((2*i+1)*theta/2)
        y = c.center.coordinates[1]+ (rprime* math.cos(angle))
        x = c.center.coordinates[0]+ (rprime* math.sin(angle))
        centres.append((x,y))
    circles = []
    for i in range(n):
        circles.append(Circle(Point(centres[i]), r))
    return circles

main_c = Circle(Point(0,0), 100)
n=4
circles = divide_circle_into_x_circles(main_c, n, overlap=True) 
plot_circles(['main']+[i for i in range(n)], [main_c]+circles)
```


```python
overlap_count=3 #3 for disjoint root and 2 for joint root
count=0
space_dict = {'main': Circle(Point(0,0), 100)}
q = roots.copy()
overlap_flag = False
while len(q)>0:
    if len(space_dict.keys()) == 1:
        circles = divide_circle_into_x_circles(space_dict['main'], len(q), overlap=False)
        for i, label in enumerate(q):
            space_dict[label] = circles[i]
    else:
        n = q.pop(0)
        n_circle = space_dict[n]
        pred = list(label_graph.predecessors(n))
        if len(pred)>0:
            overlap_flag=True if count<overlap_count else False
            circles = divide_circle_into_x_circles(n_circle, len(pred), overlap=overlap_flag)
            count+=1
            for i,p in enumerate(pred):
                q.append(p)
                space_dict[p]=circles[i]
        
            
plot_circles(list(space_dict.keys()), list(space_dict.values()))
```


```python
data_samples = np.random.uniform(-75, 75, size=(20000, 2))
```


```python
labels = [i for i in space_dict.keys() if i!='main' ]
print(len(labels), labels)
```

    17 ['A', 'M', 'K', 'L', 'B', 'N', 'O', 'J', 'C', 'P', 'Q', 'D', 'G', 'E', 'F', 'H', 'I']



```python
data = []
for x,y in tqdm(data_samples):
    d = {'x': [x,y], 'labels': []}
    for l in labels:
        x_c, y_c = space_dict[l].center.coordinates
        if (x-x_c)**2+(y-y_c)**2<=space_dict[l].radius**2:
            d['labels'].append(l)
    if len(d['labels']) == 0:
        d['labels'] = ['S']
    data.append(d)

```

    100%|██████████| 20000/20000 [01:12<00:00, 276.70it/s]



```python
data
```




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     {'x': [-50.26260312367401, -34.36355938096586], 'labels': ['S']},
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     {'x': [62.78232327119713, -27.363965053872505], 'labels': ['S']},
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     {'x': [9.499331975655522, -19.255761067945507],
      'labels': ['M', 'K', 'N', 'Q']},
     {'x': [-64.43136053755816, -20.495022713248403], 'labels': ['S']},
     {'x': [71.29152749302742, -59.466536387055925], 'labels': ['S']},
     {'x': [-37.74976199607376, 74.2571949532703], 'labels': ['S']},
     {'x': [55.33906291235874, 74.09272220694166], 'labels': ['S']},
     {'x': [-71.47161799558937, -68.5127595682281], 'labels': ['S']},
     {'x': [28.41277789383848, 47.20946010356356], 'labels': ['S']},
     {'x': [-1.174687877176268, -72.99045432739868], 'labels': ['S']},
     {'x': [1.0581550408800666, -42.611890628455726], 'labels': ['M']},
     {'x': [-33.13746899801613, 29.640490708991493], 'labels': ['L']},
     {'x': [-5.359381352636319, -40.507698923685375], 'labels': ['K']},
     {'x': [12.7325347514046, 5.7696565126986314], 'labels': ['A', 'M', 'B']},
     {'x': [-26.924811124036992, -32.35506812347022], 'labels': ['K']},
     {'x': [-41.363702690163116, 40.499993482416784], 'labels': ['S']},
     {'x': [52.06455905454651, -20.22557197227443], 'labels': ['S']},
     {'x': [-52.926409661439806, -6.257527509414771], 'labels': ['S']},
     {'x': [14.397567839896027, -64.40029258125034], 'labels': ['S']},
     {'x': [-70.17703504969948, -34.634204615356914], 'labels': ['S']},
     {'x': [-28.643104782011164, 19.23206630164718], 'labels': ['L']},
     {'x': [70.0499869816588, 41.740916269593384], 'labels': ['S']},
     {'x': [44.49808170160658, -55.824820991051794], 'labels': ['S']},
     {'x': [-74.25454528962285, -74.29277027010887], 'labels': ['S']},
     {'x': [2.998988471367099, -45.377788404056204], 'labels': ['S']},
     {'x': [64.82339861432257, -24.442435356284605], 'labels': ['S']},
     {'x': [-5.87036302555245, 68.45103509084063], 'labels': ['S']},
     {'x': [73.50472784568683, 15.995083049255129], 'labels': ['S']},
     {'x': [72.27274964510477, 49.733124087986695], 'labels': ['S']},
     {'x': [51.77857968598177, -45.036310799462356], 'labels': ['S']},
     {'x': [-33.47949236562944, -18.22744617219223], 'labels': ['K']},
     {'x': [20.481169655525633, -35.02801663334319], 'labels': ['M']},
     {'x': [10.429057706416643, -12.581288130223982], 'labels': ['M', 'K', 'N']},
     {'x': [-11.178961407216676, -55.94114615020576], 'labels': ['S']},
     {'x': [41.01104008180674, 58.72837102499631], 'labels': ['S']},
     {'x': [46.5739222390485, -29.300258463525452], 'labels': ['S']},
     {'x': [73.68067383036424, -63.92122393868904], 'labels': ['S']},
     {'x': [29.199532418739466, -52.65298429109863], 'labels': ['S']},
     {'x': [20.08029660642842, -6.6907455816010355],
      'labels': ['A', 'M', 'N', 'O']},
     {'x': [70.18742669379108, -1.231754734221127], 'labels': ['S']},
     {'x': [-56.751881298765916, -43.62748018607852], 'labels': ['S']},
     {'x': [-51.62085070625507, -68.12937561730963], 'labels': ['S']},
     {'x': [-35.38680522986061, 6.870350994561974], 'labels': ['L']},
     {'x': [32.160165849229415, 40.93617661401555], 'labels': ['A']},
     {'x': [55.86556486621271, 31.30581365901392], 'labels': ['S']},
     {'x': [69.13169931395313, -30.770857960045532], 'labels': ['S']},
     {'x': [-14.443117474515262, 27.35715399428574], 'labels': ['L']},
     {'x': [-5.251992732947244, -62.64561568355532], 'labels': ['S']},
     {'x': [-5.373643056214789, 9.042831922643558], 'labels': ['A', 'K', 'L']},
     {'x': [63.53864091457305, -22.295831217911775], 'labels': ['S']},
     {'x': [57.588997350494225, 29.01410454855693], 'labels': ['S']},
     {'x': [-62.47644632468949, -50.8490480575517], 'labels': ['S']},
     {'x': [-13.66446917152647, 17.6482005411372], 'labels': ['L']},
     {'x': [-43.7699226574499, -72.69046662374005], 'labels': ['S']},
     {'x': [63.856625625955616, 43.49066735382954], 'labels': ['S']},
     {'x': [-27.73735837236709, -24.05164365289241], 'labels': ['K']},
     {'x': [-3.3186730814151133, -14.818921363993802],
      'labels': ['M', 'K', 'N', 'Q']},
     {'x': [-69.54709816058524, -68.74500667042305], 'labels': ['S']},
     {'x': [27.950962353772056, -45.99799599250408], 'labels': ['S']},
     {'x': [36.253464749040575, 38.21396614278041], 'labels': ['A']},
     {'x': [29.491370899311917, -51.1463309713789], 'labels': ['S']},
     {'x': [-50.17999390042975, -49.41077441944446], 'labels': ['S']},
     {'x': [11.73330892189135, 44.96999215541037], 'labels': ['A']},
     {'x': [40.374580091903326, -35.65553554750921], 'labels': ['M']},
     {'x': [20.058282795077645, 33.47828521520482], 'labels': ['A', 'B']},
     {'x': [52.813975040220285, 51.414335287658346], 'labels': ['S']},
     {'x': [48.37234137047575, 41.56399298682895], 'labels': ['S']},
     {'x': [69.01734603635501, 67.97048405849898], 'labels': ['S']},
     {'x': [54.43556262559275, 27.47229608834283], 'labels': ['S']},
     {'x': [-55.92857372935929, 8.115959944174506], 'labels': ['S']},
     {'x': [64.97174034646673, -24.79893584556188], 'labels': ['S']},
     {'x': [-50.705815883923115, 40.721134318950845], 'labels': ['S']},
     {'x': [-31.262954570299506, -23.04856555417377], 'labels': ['K']},
     {'x': [6.1416038904363575, -45.36356027862428], 'labels': ['M']},
     {'x': [-25.935353348748833, -8.093051758210507], 'labels': ['K', 'L']},
     {'x': [-72.40817240250021, -68.30729847860067], 'labels': ['S']},
     {'x': [-25.67115032603523, -72.19760130484745], 'labels': ['S']},
     {'x': [69.72960404793713, 35.06524740645551], 'labels': ['S']},
     {'x': [0.2500295914571353, -0.7026068670930101],
      'labels': ['A', 'M', 'K', 'L']},
     {'x': [-13.829181865913696, -10.619317341019297], 'labels': ['K', 'L']},
     {'x': [61.35925534465966, -54.586376100078404], 'labels': ['S']},
     {'x': [70.32468996643479, -46.310233414055716], 'labels': ['S']},
     {'x': [64.66845846266332, -36.78844321765529], 'labels': ['S']},
     {'x': [-16.44999341966421, -31.229905230011852], 'labels': ['K']},
     {'x': [45.47183850361296, 2.372293862133219], 'labels': ['S']},
     {'x': [-35.194074579588104, 72.39864603180794], 'labels': ['S']},
     {'x': [25.33516064560814, 11.450784741420904], 'labels': ['A', 'B', 'J']},
     {'x': [-42.96795706742799, -70.2736531780199], 'labels': ['S']},
     {'x': [-39.21744131170885, -59.29973364550234], 'labels': ['S']},
     {'x': [-54.40759236703967, 67.37826814212366], 'labels': ['S']},
     {'x': [-38.74360937924784, -30.09868730150268], 'labels': ['K']},
     {'x': [-33.76656052289327, -52.637779666927926], 'labels': ['S']},
     {'x': [-19.222684090680715, -59.843859188404366], 'labels': ['S']},
     {'x': [11.44274851195189, -9.937633440532622], 'labels': ['A', 'M', 'N']},
     {'x': [-46.13448027492642, 16.709977376717518], 'labels': ['L']},
     {'x': [-8.55854557140195, -38.515822510229], 'labels': ['K']},
     {'x': [8.088693511642205, 24.141905415934275],
      'labels': ['A', 'L', 'B', 'C']},
     ...]




```python
np.random.shuffle(data)
```


```python
data_new = []
for d in data:
    d['x'] = list(np.array(d['x'])/75)
    data_new.append(d)
```


```python
train_len = int(0.5*len(data_new))
dev_len = int(0.5*(len(data_new)-train_len))
train = data_new[:train_len]
dev = data_new[train_len:train_len+dev_len]
test = data_new[train_len+dev_len:]
print(len(train), len(dev), len(test))
```

    10000 5000 5000



```python
data_dir = Path('../.data/toy_data_multiple_paths')
with open(data_dir/'train.json', 'w') as outfile:
    json.dump(train, outfile)
with open(data_dir/'dev.json', 'w') as outfile:
    json.dump(dev, outfile)
with open(data_dir/'test.json', 'w') as outfile:
    json.dump(test, outfile)
```


```python
with open(data_dir/'hierarchy.edgelist', 'wb') as f:
    nx.write_edgelist(label_graph, f)
```


```python
# modifying training data to have noise: drop random samples
example_count_with_more_labels = 0
for d in train:
    if len(d['labels'])>1:
        example_count_with_more_labels+=1
example_count_with_more_labels
```




    1735




```python
train_cop = train.copy()
drop_count = 0.3*example_count_with_more_labels
c = 0
train_drop_random_labels = []
for i in range(len(train)):
    d = train[i]
    if c<drop_count and len(d['labels'])>1:
        d['labels'] = list(np.random.choice(d['labels'], size=len(d['labels'])-1, replace=False))
        c+=1
    train_drop_random_labels.append(d)
```


```python
train_drop_random_labels
# modifying training data to have noise: drop random samples
example_count_with_more_labels = 0
for d in train_drop_random_labels:
    if len(d['labels'])>1:
        example_count_with_more_labels+=1
example_count_with_more_labels
```




    1489




```python
# with open(data_dir/'train_noise_drop_labels.json', 'w') as outfile:
#     json.dump(train_drop_random_labels, outfile)
```


```python
with open(data_dir/'train.json', 'r') as outfile:
    train = json.load(outfile)
train_cop = train.copy()
add_count = 0.3*len(train)

```


```python
train_add_random_labels = []
c=0
for i in range(len(train)):
    d = train[i]
    if c<add_count and len(d['labels'])==1:
        label_choices = labels.copy()
        try: 
            label_choices.remove(d['labels'][0])
        except:
            print('.')
        print(d['labels'])
        d['labels'] = d['labels']+ list(np.random.choice(label_choices, size=1, replace=False))
        print('After', d['labels'])
        c+=1
    train_add_random_labels.append(d)
print(c)
```

    .
    ['S']
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    3000



```python
# with open(data_dir/'train_noise_add_labels.json', 'w') as outfile:
#     json.dump(train_add_random_labels, outfile)
```


```python
#Comparing noise percentages
with open(data_dir/'train.json', 'r') as outfile:
    train = json.load(outfile)
with open(data_dir/'train_noise_add_labels.json', 'r') as outfile:
    train_add_random_labels=json.load(outfile)
with open(data_dir/'train_noise_drop_labels.json', 'r') as outfile:
    train_drop_random_labels=json.load(outfile)
```


```python
drop_count = 0
add_count = 0
for i in range(len(train)):
    if set(train[i]['labels'])!=set(train_drop_random_labels[i]['labels']):
        drop_count+=1
    if set(train[i]['labels'])!=set(train_add_random_labels[i]['labels']):
        add_count+=1
print(drop_count/len(train), add_count/len(train))
```

    0.0521 0.3


51aeaeb183d81ed7456bf62eb94f32922a01b653
ipynb
Jupyter Notebook
data_preprocessing/toy-data-automatic-generation.ipynb
iesl/box-mlc
15439b7e46885458d0c45d530c17f1deac0398f8
MIT
data_preprocessing/toy-data-automatic-generation.ipynb
iesl/box-mlc
15439b7e46885458d0c45d530c17f1deac0398f8
MIT
data_preprocessing/toy-data-automatic-generation.ipynb
iesl/box-mlc
15439b7e46885458d0c45d530c17f1deac0398f8
MIT
Qwen/Qwen-72B
1. YES
2. YES