| from sklearn.manifold import TSNE | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| # Latent Feature Cluster for Training Data using T-SNE | |
| def TSNE_reduction(latent_points, perplexity=30, learning_rate=20): | |
| latent_dimensionality = len(latent_points[0]) | |
| model = TSNE(n_components=2, random_state=0, perplexity=perplexity, | |
| learning_rate=learning_rate) # Perplexity(5-50) | learning_rate(10-1000) | |
| embedding = model | |
| # configuring the parameters | |
| # the number of components = dimension of the embedded space | |
| # default perplexity = 30 " Perplexity balances the attention t-SNE gives to local and global aspects of the data. | |
| # It is roughly a guess of the number of close neighbors each point has. ..a denser dataset ... requires higher perplexity value" | |
| # default learning rate = 200 "If the learning rate is too high, the data may look like a ‘ball’ with any point | |
| # approximately equidistant from its nearest neighbours. If the learning rate is too low, | |
| # most points may look compressed in a dense cloud with few outliers." | |
| tsne_data = model.fit_transform( | |
| latent_points) # When there are more data points, trainX should be the first couple hundred points so TSNE doesn't take too long | |
| x = tsne_data[:, 0] | |
| y = tsne_data[:, 1] | |
| title = ("T-SNE of Data") | |
| return x, y, title, embedding | |
| ######################################################################################################################## | |
| import pandas as pd | |
| import json | |
| df = pd.read_csv('2D_Lattice.csv') | |
| row = 0 | |
| box = df.iloc[row,1] | |
| array = np.array(json.loads(box)) | |
| """ | |
| # For plotting CSV data | |
| # define a function to flatten a box | |
| def flatten_box(box_str): | |
| box = json.loads(box_str) | |
| return np.array(box).flatten() | |
| # apply the flatten_box function to each row of the dataframe and create a list of flattened arrays | |
| flattened_arrays = df['Array'].apply(flatten_box).tolist() | |
| x, y, title, embedding = TSNE_reduction(flattened_arrays) | |
| plt.scatter(x,y) | |
| plt.title(title) | |
| plt.show() | |
| """ | |
| # def plot_dimensionality_reduction(x, y, label_set, title): | |
| # plt.title(title) | |
| # if label_set[0].dtype == float: | |
| # plt.scatter(x, y, c=label_set) | |
| # plt.colorbar() | |
| # print("using scatter") | |
| # else: | |
| # for label in set(label_set): | |
| # cond = np.where(np.array(label_set) == str(label)) | |
| # plt.plot(x[cond], y[cond], marker='o', linestyle='none', label=label) | |
| # | |
| # plt.legend(numpoints=1) | |
| # | |
| # plt.show() | |
| # plt.close() |