Visualizing embeddings in 2D

Visualizing the embeddings in 2D¶

We will use t-SNE to reduce the dimensionality of the embeddings from 1536 to 2. Once the embeddings are reduced to two dimensions, we can plot them in a 2D scatter plot. The dataset is created in the Obtain_dataset Notebook.

1. Reduce dimensionality¶

We reduce the dimensionality to 2 dimensions using t-SNE decomposition.

In [1]:

import pandas as pd
from sklearn.manifold import TSNE
import numpy as np

# Load the embeddings
datafile_path = "data/fine_food_reviews_with_embeddings_1k.csv"
df = pd.read_csv(datafile_path)

# Convert to a list of lists of floats
matrix = np.array(df.embedding.apply(eval).to_list())

# Create a t-SNE model and transform the data
tsne = TSNE(n_components=2, perplexity=15, random_state=42, init='random', learning_rate=200)
vis_dims = tsne.fit_transform(matrix)
vis_dims.shape

import pandas as pd from sklearn.manifold import TSNE import numpy as np # Load the embeddings datafile_path = "data/fine_food_reviews_with_embeddings_1k.csv" df = pd.read_csv(datafile_path) # Convert to a list of lists of floats matrix = np.array(df.embedding.apply(eval).to_list()) # Create a t-SNE model and transform the data tsne = TSNE(n_components=2, perplexity=15, random_state=42, init='random', learning_rate=200) vis_dims = tsne.fit_transform(matrix) vis_dims.shape

Out[1]:

(1000, 2)

2. Plotting the embeddings¶

We colour each review by its star rating, ranging from red to green.

We can observe a decent data separation even in the reduced 2 dimensions.

In [2]:

import matplotlib.pyplot as plt
import matplotlib
import numpy as np

colors = ["red", "darkorange", "gold", "turquoise", "darkgreen"]
x = [x for x,y in vis_dims]
y = [y for x,y in vis_dims]
color_indices = df.Score.values - 1

colormap = matplotlib.colors.ListedColormap(colors)
plt.scatter(x, y, c=color_indices, cmap=colormap, alpha=0.3)
for score in [0,1,2,3,4]:
    avg_x = np.array(x)[df.Score-1==score].mean()
    avg_y = np.array(y)[df.Score-1==score].mean()
    color = colors[score]
    plt.scatter(avg_x, avg_y, marker='x', color=color, s=100)

plt.title("Amazon ratings visualized in language using t-SNE")

import matplotlib.pyplot as plt import matplotlib import numpy as np colors = ["red", "darkorange", "gold", "turquoise", "darkgreen"] x = [x for x,y in vis_dims] y = [y for x,y in vis_dims] color_indices = df.Score.values - 1 colormap = matplotlib.colors.ListedColormap(colors) plt.scatter(x, y, c=color_indices, cmap=colormap, alpha=0.3) for score in [0,1,2,3,4]: avg_x = np.array(x)[df.Score-1==score].mean() avg_y = np.array(y)[df.Score-1==score].mean() color = colors[score] plt.scatter(avg_x, avg_y, marker='x', color=color, s=100) plt.title("Amazon ratings visualized in language using t-SNE")

Out[2]:

Text(0.5, 1.0, 'Amazon ratings visualized in language using t-SNE')