import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import itertools
import json
import os
np.random.seed(42)
[docs]
def make_spirals(n_samples=5000, n_classes=2, noise=0.3, dim=3):
"""Generates an N-dimensional dataset of spirals."""
X = []
y = []
for i in range(n_classes):
t = np.linspace(0, 4 * np.pi, n_samples // n_classes)
x = t * np.cos(t + i * np.pi) + np.random.normal(0, noise, n_samples // n_classes)
y_ = t * np.sin(t + i * np.pi) + np.random.normal(0, noise, n_samples // n_classes)
z = t + np.random.normal(0, noise, n_samples // n_classes)
if dim==3:
X.append(np.column_stack([x, y_, z])) # any new dimensions need to be added to this list
# to add more dimensions, apparently you would just keep adding 't' variable from above, to each new dimension,
# as seen below. The question is, how can we iteratively do this while maintaining the binary classification
# that this for loop is creating?
# nesting a loop iterating over the number of dimensions doesn't really work from what I'm seeing. so far
# However, manually adding repeats of the same 3Ds, does work, as seen below -- is this correct?
# for j in range(dim-3): # for anything above the first 3D
if dim==6:
new_d1 = t * np.cos(t + i * np.pi) + np.random.normal(0, noise, n_samples // n_classes)
new_d2 = t * np.sin(t + i * np.pi) + np.random.normal(0, noise, n_samples // n_classes)
new_d3 = t + np.random.normal(0, noise, n_samples // n_classes)
X.append(np.column_stack([x, y_, z, new_d1, new_d2, new_d3])) # any new dimensions need to be added to this list
if dim==9:
new_d1 = t * np.cos(t + i * np.pi) + np.random.normal(0, noise, n_samples // n_classes)
new_d2 = t * np.sin(t + i * np.pi) + np.random.normal(0, noise, n_samples // n_classes)
new_d3 = t + np.random.normal(0, noise, n_samples // n_classes)
new_d4 = t * np.cos(t + i * np.pi) + np.random.normal(0, noise, n_samples // n_classes)
new_d5 = t * np.sin(t + i * np.pi) + np.random.normal(0, noise, n_samples // n_classes)
new_d6 = t + np.random.normal(0, noise, n_samples // n_classes)
X.append(np.column_stack([x, y_, z, new_d1, new_d2, new_d3, new_d4, new_d5, new_d6])) # any new dimensions need to be added to this list
if dim==12:
new_d1 = t * np.cos(t + i * np.pi) + np.random.normal(0, noise, n_samples // n_classes)
new_d2 = t * np.sin(t + i * np.pi) + np.random.normal(0, noise, n_samples // n_classes)
new_d3 = t + np.random.normal(0, noise, n_samples // n_classes)
new_d4 = t * np.cos(t + i * np.pi) + np.random.normal(0, noise, n_samples // n_classes)
new_d5 = t * np.sin(t + i * np.pi) + np.random.normal(0, noise, n_samples // n_classes)
new_d6 = t + np.random.normal(0, noise, n_samples // n_classes)
new_d7 = t * np.cos(t + i * np.pi) + np.random.normal(0, noise, n_samples // n_classes)
new_d8 = t * np.sin(t + i * np.pi) + np.random.normal(0, noise, n_samples // n_classes)
new_d9 = t + np.random.normal(0, noise, n_samples // n_classes)
X.append(np.column_stack([x, y_, z, new_d1, new_d2, new_d3, new_d4, new_d5, new_d6, new_d7, new_d8, new_d9]))
y.extend([i] * (n_samples // n_classes))
return np.vstack(X), np.array(y)
# parameters to vary across the configurations
N_SAMPLES = list(range(100, 300, 50))
N_CLASSES = [2]
NOISE = [0.3, 0.6, 0.9]
DIM = [3, 6, 9, 12]
[docs]
def my_make_spirals(
n_s=N_SAMPLES,
n_c=N_CLASSES,
n_n=NOISE,
n_d=DIM,
save_path=None,
):
print("Generating spirals dataset...")
if not os.path.exists(save_path):
os.makedirs(save_path)
# enumerate all possible combinations of parameters based on ranges above
configurations = list(itertools.product(*[n_s, n_c, n_n, n_d]))
# print(configurations)
# print(len(configurations))
count_configs = 1
dataset_config = {}
# populate all the configs with the corresponding argument values
for n_s, n_c, n_n, n_d in configurations:
config = "samples={}, classes={}, noise={}, dimensions={}".format(
n_s, n_c, n_n, n_d
)
# print(count_configs)
X, y = make_spirals(
n_samples=n_s,
n_classes=n_c,
noise=n_n,
dim=n_d
)
# print("Configuration {}/{}: {}".format(count_configs, len(configurations), config))
dataset = pd.DataFrame(X)
dataset['class'] = y
with open( os.path.join( save_path, 'dataset_config.json' ), 'w') as outfile:
dataset_config.update({'spirals_data-{}.csv'.format(count_configs):
{'n_samples': n_s,
'noise': n_n,
'dimensions': n_d
}})
json.dump(dataset_config, outfile, indent=4)
new_dataset = dataset.to_csv( os.path.join( save_path, 'spirals_data-{}.csv'.format(count_configs)), index=False)
count_configs += 1
# plot the last 3 dimensions in each case
# fig = plt.figure()
# ax = fig.add_subplot(111, projection='3d')
# ax.scatter(X[:, n_d-3], X[:, n_d-2],X[:, n_d-1], c=y, cmap='viridis')
# plt.savefig('spirals_data/spirals_data-{}.png'.format(count_configs))
#print(X.shape)
#print(y.shape)
return
