visualize.md

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Let's visualize this data!

What do you notice about the data? Do you think our features will be good at predicting news and romance sentences? Which features do you think will be the most useful? We will be using the matplotlib and pandas libraries to visualize our data, and the latter is built on the former. As mentioned above, a matplotlib visualization is a figure onto which is attached one or more axes. Each axes has a horizontal (x) axis and vertical (y) axis, and the data is encoded using color and glyphs such as markers (for example circles) or lines or polygons (called patches). The figure below annotates these parts of a visualization and was created by Nicolas P. Rougier using matplotlib. The source code can be found in the matplotlib documentation. ^{1 1}https://matplotlib.org/gallery/showcase/anatomy.html#sphx-glr-gallery-showcase-anatomy-py

How do we visualize the table of parts of speech?

We can directly plot any dataframe in pandas using the .plot method, and can do .plot.<type> to denote the type of chart we want to plot. This situation calls for a bar chart because there are two classes of discrete sums of counts. To explore a different grouping, the table can be rotated using .T (transpose).

fig, (ax1,ax2) = plt.subplots(ncols=2,  figsize=(15,5))
_ = df.groupby('label').sum().plot.bar(ax=ax1, rot=0, color=['blue', 'red'])
_ = df.groupby('label').sum().T.plot.bar(ax=ax2, color=['tab:blue','tab:orange'], rot=0)

fig.savefig("images/part_of_speech.png", bbox_inches = 'tight', pad_inches = 0)

Are there patterns in the individual observations?

We can make a scatter plot of our data colored by label to see if the patterns observed in the aggregate are visible in the individual observations.

# seperate our data into the two classes
news = df[df['label']=='news']
romance = df[df['label']=='romance']

fig, ax = plt.subplots(figsize=(15,10))
_ = ax.scatter(news['NN'], news['JJ'], label="news", alpha=.25, edgecolor='k')
_ = ax.scatter(romance['NN'], romance['JJ'], label="romance", alpha=.25, edgecolor='k')
_ = ax.legend(fontsize=14)
_ = ax.set_xlabel("nouns", fontsize=14)
_ = ax.set_ylabel("adjectives", fontsize=14)
_ = ax.set_aspect("equal")
fig.savefig("images/news_scatter.png", bbox_inches = 'tight', pad_inches = 0)

# Let's flip the order
fig, ax = plt.subplots(figsize=(15,10))
_ = ax.scatter(romance['NN'], romance['JJ'], label="romance", color='tab:orange', alpha=.5, edgecolor='k')
_ = ax.scatter(news['NN'], news['JJ'], label="news", color='tab:blue', alpha=.5, edgecolor='k')
_ = ax.legend(fontsize=14)
_ = ax.set_xlabel("nouns", fontsize=14)
_ = ax.set_ylabel("adjectives", fontsize=14)
_ = ax.set_aspect("equal")
fig.savefig("images/romance_scatter.png", bbox_inches = 'tight', pad_inches = 0)

How do we visualize dense, highly overlapping data?

As seen above, when the data is very dense the points can sometimes overlap. One way to visualize this overlapping data is to shift each point by a tiny amount so that the data is no longer at exactly the same coordinate. This technique is called jittering. In the jitter function below, we compute a scale factor by which to multiply random numbers between 0 and 1 so that the jitter is large enough to effect the visualization, but small enough that it does not strongly distort the data.

np.random.seed(42)

def jitter(arr):
    scale = .01*(arr.min() - arr.max())
    return arr + np.random.randn(arr.shape[0]) * scale

fig, ax = plt.subplots(figsize=(15,10))

_ = ax.scatter(jitter(news['NN']), jitter(news['JJ']), label="news", alpha=.25, edgecolor='k')
_ = ax.scatter(jitter(romance['NN']), jitter(romance['JJ']), label="romance", alpha=.25, edgecolor='k')
_ = ax.legend(fontsize=14)
_ = ax.set_xlabel("nouns", fontsize=14)
_ = ax.set_ylabel("adjectives", fontsize=14)
_ = ax.set_aspect("equal")
fig.savefig("images/jitter.png", bbox_inches = 'tight', pad_inches = 0)

Visualize Side By Side

Lets put the scatter plot next to the bar chart aggregated comparisons so that we can see how our overall patterns compare to what is happening in the individual observations.

fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(15,3), gridspec_kw={'width_ratios':[1,5]})

_ = df.groupby('label').sum().T.plot.bar(ax=ax1, color=['tab:blue','tab:orange'], rot=0, 
                                         fontsize=14)
ax1.set_ylabel("Total Count of POS")

_ = ax2.scatter(jitter(news['NN']), jitter(news['JJ']), label="news", alpha=.25, edgecolor='k')
_ = ax2.scatter(jitter(romance['NN']), jitter(romance['JJ']), label="romance", alpha=.25, edgecolor='k')
_ = ax2.legend(fontsize=14)
_ = ax2.set_xlabel("nouns", fontsize=14)
_ = ax2.set_ylabel("adjectives", fontsize=14)
_ = ax2.set_aspect('equal')
fig.savefig("images/side_by_side.png", bbox_inches = 'tight', pad_inches = 0)

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