Case study: Racine’s early and late tragedies [1]¶

This tutorial describes two stages of an analysis of a collection of French tragedies: (1) exploratory data analysis and (2) a closer quantitative reading of an aspect of interest in the corpus: Jean Racine’s early and late plays.

Corpus: sixty tragedies¶

The corpus being explored is a collection of 60 French tragedies gathered from Paul Fièvre’s „Théâtre classique” collection of French seventeenth and eighteenth-century plays (available in Datasets).

Following the maxim “know your data”, we first want to gain a rough sense of the length (in words) of the texts in a corpus. Are we dealing with 100,000 word tomes or 1,000 word newspaper articles? Even though we know we are dealing with French tragedies inspecting text lengths is always worthwhile. For example, checking the length of the texts in a corpus is an easy way to spot problems such as incomplete texts. In the present case, we might also consider checking the number of lines (alexandrines) as this is the standard measure for compositions in verse. Regardless of what unit is used, the easiest way to visualize the distribution of lengths in a corpus is a histogram. In order to create a histogram we need first to calculate the length of all the texts in our corpus. If we have a document-term matrix available, the length in words may be calculated by summing the rows of the document-term matrix, as each row corresponds to one document.

In [1]: import os

In [2]: import numpy as np

In [3]: import sklearn.feature_extraction.text as text

In [4]: data_dir = 'data/french-tragedy/'

In [5]: filenames = np.array(sorted(os.listdir(data_dir)))

In [6]: filenames_with_path = [os.path.join(data_dir, fn) for fn in filenames]

# check the first few filenames
In [7]: filenames_with_path[0:4]
Out[7]:
['data/french-tragedy/Crebillon_TR-V-1703-Idomenee.txt',
'data/french-tragedy/Crebillon_TR-V-1707-Atree.txt',
'data/french-tragedy/Crebillon_TR-V-1708-Electre.txt',

In [8]: vectorizer = text.CountVectorizer(input='filename')

In [9]: dtm = vectorizer.fit_transform(filenames_with_path)

In [10]: dtm = dtm.toarray()

In [11]: vocab = np.array(vectorizer.get_feature_names())

# sum over rows to calculate lengths
In [12]: lengths = np.sum(dtm, axis=1)


Now that we have the texts’ lengths stored in the array lengths we can create a histogram with the matplotlib function hist.

In [13]: import matplotlib.pyplot as plt

In [14]: plt.hist(lengths)
Out[14]:
(array([  1.,   2.,   2.,   2.,   8.,  12.,  13.,  15.,   3.,   1.]),
array([  6150. ,   7360.3,   8570.6,   9780.9,  10991.2,  12201.5,
13411.8,  14622.1,  15832.4,  17042.7,  18253. ]),
<a list of 10 Patch objects>)

In [15]: plt.title("Play length in words")
Out[15]: <matplotlib.text.Text at 0x2b96c03418d0>

# find the longest and the shortest texts
In [16]: (np.max(lengths), filenames[np.argmax(lengths)])
Out[16]: (18253, 'PCorneille_TR-V-1661-Toisondor.txt')

In [17]: (np.min(lengths), filenames[np.argmin(lengths)])
Out[17]: (6150, 'Voltaire_TR-V-1751-DucDAlencon.txt')


Turning to the contents of the corpus, one way to explore a collection is to plot the distances between texts. We have already seen how to do this in Working with text. As we have not normalized our texts by play length—and the histogram of lengths shows us that there is considerable variation—cosine distance is an appropriate choice for a measure of distance.

In [18]: from sklearn.manifold import MDS

In [19]: from sklearn.metrics.pairwise import cosine_similarity

# we need distance, not similarity
In [20]: dist = 1 - cosine_similarity(dtm)

In [21]: mds = MDS(n_components=2, dissimilarity="precomputed", random_state=1)

In [22]: pos = mds.fit_transform(dist)  # shape (n_components, n_samples)

# create very short names for plotting
# filenames have form: Voltaire_TR-V-1724-Mariamne.txt
In [23]: names = []

In [24]: authors = []

In [25]: for fn in filenames:
....:     author = fn.split('_')[0]
....:     year = fn.split('-')[2]
....:     authors.append(author)
....:     names.append(author + year)
....:

In [26]: plt.figure(figsize=(11.3, 7))  # use a bigger canvas than usual
Out[26]: <matplotlib.figure.Figure at 0x2b96c0370198>

In [27]: xs, ys = pos[:, 0], pos[:, 1]

In [28]: authors_unique = sorted(set(authors))

In [29]: colors = [authors_unique.index(a) for a in authors]

In [30]: plt.scatter(xs, ys, c=colors, cmap='spring')
Out[30]: <matplotlib.collections.PathCollection at 0x2b96c041f860>

In [31]: for x, y, name in zip(xs, ys, names):
....:     plt.text(x, y, name, alpha=0.5, fontsize=10)
....:

In [32]: plt.tight_layout()


This kind of plot can get overwhelming. A dendrogram plot offers an alternative means of representing the same information (i.e., the distance between texts). It is important, however, not to take the implied hierarchy too seriously. While two texts paired together in the dendrogram are indeed nearest neighbors in terms of distance, there are a variety of methods of hierarchical clustering each often yielding different hierarchies. Right now we are interested in the dendrogram as a convenient summary of the multi-dimensional scaling plot shown above.

In [33]: from scipy.cluster.hierarchy import ward, dendrogram

In [35]: plt.figure(figsize=(11.3, 11.3))  # we need a tall figure
Out[35]: <matplotlib.figure.Figure at 0x2b96c0440a90>

# match dendrogram to that returned by R's hclust()
In [36]: dendrogram(linkage_matrix, orientation="right", labels=names, leaf_font_size=5);

In [37]: plt.tight_layout()  # fixes margins


It should come as no surprise that texts by the same author tend to be adjacent in the dendrogram. It is well documented that authors frequently leave stylistic “signatures” that are detectable at the level of word frequency. [2] There are, however, a number of plays that do not follow the rule and are paired with texts by other writers. A number of these plays are attributed to Racine and it is to these plays we will turn our attention.

Racine’s atypical plays¶

Racine’s atypical plays are easiest to detect on the dendrogram. They include:

Considering these outliers in the context of the chronology of Racine’s works as a whole is helpful. These plays include the first and the final three plays written by Racine. To display this chronology visually we may use a raster graph, coloring the outliers a distinct color. (This visualization has the added benefit of showing the nearly ten year gap between plays in the 1680s.)

In [38]: outliers = [1664, 1677, 1689, 1691]

In [39]: racine_years = []

In [40]: for fn in filenames:
....:     author = fn.split('_')[0]
....:     year = int(fn.split('-')[2])
....:     if author == "Racine":
....:         racine_years.append(year)
....:

In [41]: racine_years = np.array(racine_years)

In [42]: colors = []

In [43]: for year in racine_years:
....:     colors.append('orange' if year in outliers else 'cyan')
....:

In [44]: plt.vlines(racine_years, 0, 1, linewidth=3, color=colors)
Out[44]: <matplotlib.collections.LineCollection at 0x2b96c0497d68>

In [45]: plt.title("Year of publication of Racine's plays")
Out[45]: <matplotlib.text.Text at 0x2b96c049bc50>

# gca() stands for get current axes. Axes are a matplotlib primitive.
# See http://matplotlib.org/users/pyplot_tutorial.html#working-with-multiple-figures-and-axes
In [46]: ax = plt.gca()

In [47]: ax.yaxis.set_visible(False)


A provisional explanation for why the late plays stand out might draw on Racine’s religious turn. In 1679 he married Catherine de Romanet and his Jansenism grew more pronounced. The title Esther refers to the biblical book of the same name and Athalie, Racine’s final play, stages events from the Bible.

Features¶

A useful way to explore the atypical plays further is to directly compare the outliers with a fictitious “average” Racine tragedy. To do this we will first decompose our documents into an ersatz “topic model” using non-negative matrix factorization and then we will average the topic shares of the “normal” Racine plays and compare those shares with the shares of the atypical plays.

We will fit the NMF model using the corpus of tragedies split into approximately 1,000-word sections. Recall that before feeding your document-term matrix into NMF it is helpful to normalize each document by length. Here we will normalize and, additionally, use tf-idf weighting as the invocation is simple: we use TfidfVectorizer instead of CountVectorizer.

In [48]: data_dir = 'data/french-tragedy-split/'

In [49]: filenames = np.array(sorted(os.listdir(data_dir)))

In [50]: filenames_with_path = [os.path.join(data_dir, fn) for fn in filenames]

# check the first few filenames
In [51]: filenames_with_path[0:4]
Out[51]:
['data/french-tragedy-split/Crebillon_TR-V-1703-Idomenee0000.txt',
'data/french-tragedy-split/Crebillon_TR-V-1703-Idomenee0001.txt',
'data/french-tragedy-split/Crebillon_TR-V-1703-Idomenee0002.txt',
'data/french-tragedy-split/Crebillon_TR-V-1703-Idomenee0003.txt']

In [52]: vectorizer = text.TfidfVectorizer(input='filename', min_df=15)

In [53]: dtm = vectorizer.fit_transform(filenames_with_path)

In [54]: dtm = dtm.toarray()

In [55]: vocab = np.array(vectorizer.get_feature_names())

# fit NMF model
In [56]: from sklearn import decomposition

In [57]: num_topics = 15

In [58]: clf = decomposition.NMF(n_components=num_topics, random_state=1)

# this next step may take some time

doctopic_chunks = clf.fit_transform(dtm)


In order to interpret and visualize the NMF components in a manner analogous to LDA topic proportions, we will scale the document-component matrix such that the component values associated with each document sum to one.

# to avoid division by 0 (not a problem with LDA) we add a tiny value to each cell.
In [59]: doctopic_chunks += 1e-6  # 1e-6 is the same as 0.000001

In [60]: doctopic_chunks = doctopic_chunks / np.sum(doctopic_chunks, axis=1, keepdims=True)


As we did in previous sections, we will aggregate the text sections associated with a single play together and average their topic proportions.

In [61]: import itertools

In [62]: import re

In [63]: import operator

# Play sections have filenames like: Racine_TR-V-1677-Phedre0000.txt. We can split
# the last part "0000.txt" off using string slicing since we know that the part of
# the filename we do not want is always 8 characters in width. For example,
In [64]: 'Racine_TR-V-1677-Phedre0000.txt'[:-8]
Out[64]: 'Racine_TR-V-1677-Phedre'

# alternatively, we could use a regular expression:
In [65]: import re

In [66]: re.sub(r'[0-9]+\.txt\$','', 'Racine_TR-V-1677-Phedre0000.txt')
Out[66]: 'Racine_TR-V-1677-Phedre'

In [67]: play_names_chunks = []

In [68]: for fn in filenames:
....:     play_names_chunks.append(fn[:-8])
....:

In [69]: num_plays = len(set(play_names_chunks))

In [70]: doctopic = np.zeros((num_plays, num_topics))

In [71]: play_row_pairs = zip(play_names_chunks, doctopic_chunks)

In [72]: play_names = []

In [73]: for i, (name, pairs) in enumerate(itertools.groupby(play_row_pairs, key=operator.itemgetter(0))):
....:     rows = [row for _, row in pairs]
....:     doctopic[i, :] = sum(rows) / len(rows)
....:     play_names.append(name)
....:


While we have used all the other plays to fit the NMF model—in deriving the topic components and the word-topic associations—we care principally about Racine’s atypical plays and the synthetic “average” play that will serve as a proxy for a “normal” Racine play. We will construct the average play by averaging the shares of the typical plays (i.e., all those that are not atypical):

In [74]: racine_plays = [name for name in play_names if name.startswith('Racine')]

In [75]: racine_atypical = ['Racine_TR-V-1664-Thebaide', 'Racine_TR-V-1677-Phedre', 'Racine_TR-V-1689-Esther', 'Racine_TR-V-1691-Athalie']

In [76]: racine_typical = [name for name in racine_plays if name not in racine_atypical]

# alternatively, an opportunity to use set difference
# racine_typical = list(set(racine_plays) - set(racine_atypical))
# examine the list of typical plays, making sure we have the right ones
In [77]: racine_typical
Out[77]:
['Racine_TR-V-1666-Alexandre',
'Racine_TR-V-1668-Andromaque',
'Racine_TR-V-1670-Britannicus',
'Racine_TR-V-1671-Berenice',
'Racine_TR-V-1672-Bajazet',
'Racine_TR-V-1673-Mithridate',
'Racine_TR-V-1674-Iphigenie']

In [78]: doctopic_racine_typical = np.mean(doctopic[np.in1d(play_names, racine_typical)], axis=0)

In [79]: doctopic_racine_atypical = doctopic[np.in1d(play_names, racine_atypical)]

# stack the typical and the atypical plays by row
In [80]: doctopic_of_interest = np.row_stack([doctopic_racine_typical, doctopic_racine_atypical])

# as a last and final step we need to keep track of the names
# note that some of the manipulation of names and rows is fragile and relies on the names
# being sorted alphabetically. If this were a concern we might use a pandas DataFrame
# instead, as row and column names can be explicitly assigned
In [81]: play_names_of_interest = ['Racine-1666-1674-AVERAGE'] + racine_atypical


Now that we have our matrix of document-topic proportions for the atypical plays and the composite Racine play, we can visualize the topic shares using a heatmap, a procedure which should be familiar from Visualizing topic models.

In [82]: plt.pcolor(doctopic_of_interest, norm=None, cmap='Blues')
Out[82]: <matplotlib.collections.PolyCollection at 0x2b96c064fcf8>

In [83]: topic_labels = ['Topic #{}'.format(k) for k in range(num_topics)]

In [84]: plt.xticks(np.arange(doctopic_of_interest.shape[1]) + 0.5, topic_labels);

In [85]: plt.yticks(np.arange(doctopic_of_interest.shape[0]) + 0.5, play_names_of_interest);

# flip the y-axis so the texts are in the order we anticipate
In [86]: plt.gca().invert_yaxis()

# rotate the ticks on the x-axis
In [87]: plt.xticks(rotation=90)
Out[87]:
(array([  0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,
9.5,  10.5,  11.5,  12.5,  13.5,  14.5]),
<a list of 15 Text xticklabel objects>)

In [88]: plt.colorbar(cmap='Blues')
Out[88]: <matplotlib.colorbar.Colorbar at 0x2b96c044c080>

In [89]: plt.tight_layout()  # fixes margins


Looking at this heatmap, a number of topics stand out as ones which we might wish to examine. In this case there is no harm in visually identifying the topics that vary the most (using our eyes). However, were we confronted with a greater number of topics (say, 100 or 200 topics), such a procedure would be tedious and error prone. We may as well come up with a systematic way of identifying topics that vary substantially across texts of interest. One way of doing this would be to calculate the standard deviation of the document-topic shares across the topics. (Calculating the entropy for topic-document associations would also be a useful measure.)

# examine topics of interest by ranking them by standard deviation
# reminder: NumPy's standard deviation differs from R's standard deviation. If you
# want them to return identical results include the argument ddof=1.
# Essentially,  NumPy's standard deviation divides the variance by n whereas R
# uses n-1 (which is preferable as it gives an unbiased estimate of the variance).
# Using ddof=1 makes NumPy use n-1.
In [90]: topics_by_std = np.argsort(np.std(doctopic_of_interest, axis=0, ddof=1))[::-1]

In [91]: topics_by_std[0:10]
Out[91]: array([ 5, 10,  8,  9,  4,  2, 14,  3, 12,  0])

# First we gather the words most associated with each topic
In [92]: num_top_words = 17

In [93]: topic_words = []

In [94]: for topic in clf.components_:
....:     word_idx = np.argsort(topic)[::-1][0:num_top_words]
....:     topic_words.append([vocab[i] for i in word_idx])
....:

# Now we examine the topic-word distributions for the topics that vary the most
In [95]: for t in topics_by_std[0:5]:
....:     topic_words_str = ' '.join(str(t) for t in topic_words[t])
....:     print("Topic {}: {}".format(t, topic_words_str))
....:
Topic 5: dieu le et temple un la qui des du david de chrétiens séide mahomet sur enfant est
Topic 10: je que me mon ai ne suis et ma mes puis moi veux sais vois dois mais
Topic 8: et le son ses sa il la les dans lui de en roi même vu un du
Topic 9: et un en pour si le que la qui qu autre faire plus amour ou une est
Topic 4: vous votre vos seigneur avez que de madame ne si êtes voulez pas allez point savez pouvez


As our ranking indicates, most of the mystery about the atypical plays is resolved by inspecting topics #5 and #9. (Only Phèdre (1677) needs additional scrutiny.) Given what we know about Racine’s biography, topic #5 (dieu, temple, chrétiens) does not require a great deal of additional explanation. Topic #9 is more strongly associated with Thébaïde (1664) than with any other play. Inspecting the words associated with topic #9 we see it features words such as “et” and “un”. If we read the text of the play it appears that these words do indeed appear comparatively frequently. While we will leave it to Racine scholars to provide a detailed account of the difference, we may venture two provisional explanations: first, this was Racine’s first play and his style had yet to mature, and second, there is strong evidence that Molière contributed to the editing of the play and this fact may have something to do with the stylistic difference.

To verify that the Topic #9 does indeed capture a salient difference, we may compare the rates of the words “et” and “un” across all Racine plays. The rate of “et” in Thébaïde does indeed stand out:

# reassemble the document-term matrix
In [96]: data_dir = 'data/french-tragedy/'

In [97]: filenames = np.array(sorted(os.listdir(data_dir)))

In [98]: filenames_with_path = [os.path.join(data_dir, fn) for fn in filenames]

In [99]: vectorizer = text.CountVectorizer(input='filename')

In [100]: dtm = vectorizer.fit_transform(filenames_with_path)

In [101]: dtm = dtm.toarray()

In [102]: vocab = np.array(vectorizer.get_feature_names())

In [103]: authors = np.array([fn.split('_')[0] for fn in filenames])

# convert to rates per 1000 words as this is easier to interpret
In [104]: dtm = 1000 * dtm / np.sum(dtm, axis=1, keepdims=True)

In [105]: for word in ['et', 'un']:
.....:     print("Rate per 1,000 words of {}".format(word))
.....:     filenames_racine = filenames[authors == 'Racine']
.....:     rates_racine = dtm[authors == 'Racine', vocab == word]
.....:     for filename, rate in zip(filenames_racine, rates_racine):
.....:         print("{:>40s}: {:.1f}".format(filename, rate))
.....:
Rate per 1,000 words of et
Racine_TR-V-1664-Thebaide.txt: 32.8
Racine_TR-V-1666-Alexandre.txt: 24.1
Racine_TR-V-1668-Andromaque.txt: 22.8
Racine_TR-V-1670-Britannicus.txt: 20.5
Racine_TR-V-1671-Berenice.txt: 21.3
Racine_TR-V-1672-Bajazet.txt: 24.9
Racine_TR-V-1673-Mithridate.txt: 29.2
Racine_TR-V-1674-Iphigenie.txt: 26.9
Racine_TR-V-1677-Phedre.txt: 23.1
Racine_TR-V-1689-Esther.txt: 24.6
Racine_TR-V-1691-Athalie.txt: 25.7
Rate per 1,000 words of un
Racine_TR-V-1664-Thebaide.txt: 13.2
Racine_TR-V-1666-Alexandre.txt: 15.2
Racine_TR-V-1668-Andromaque.txt: 13.7
Racine_TR-V-1670-Britannicus.txt: 10.9
Racine_TR-V-1671-Berenice.txt: 11.4
Racine_TR-V-1672-Bajazet.txt: 12.2
Racine_TR-V-1673-Mithridate.txt: 15.6
Racine_TR-V-1674-Iphigenie.txt: 13.8
Racine_TR-V-1677-Phedre.txt: 17.3
Racine_TR-V-1689-Esther.txt: 14.5
Racine_TR-V-1691-Athalie.txt: 13.1


In addition to the Christian vocabulary associated with topic #5, Esther and Athalie also distinguish themselves through an absence of topic #10. Looking at the words associated with these topics a pattern emerges: the words are associated with narration or dialogue. Topic #10 includes the first person singular pronouns “je” and “me” along with the first person singular forms of the verbs “être” and “pouvoir” (“suis” and “puis”). Do Racine’s final plays perhaps feature dialogue to a lesser degree than Racine’s other plays?

Again, to validate the suspicion that the words “je” and “me” do indeed appear more frequently in the final plays we will look directly at their word rates. The low rates of “je” and “me” in the final two plays certainly do stand out.

In [106]: for word in ['je', 'me']:
.....:     print("Rate per 1,000 words of {}".format(word))
.....:     filenames_racine = filenames[authors == 'Racine']
.....:     rates_racine = dtm[authors == 'Racine', vocab == word]
.....:     for filename, rate in zip(filenames_racine, rates_racine):
.....:         print("{:>40s}: {:.1f}".format(filename, rate))
.....:     print()  # print a blank line
.....:
Rate per 1,000 words of je
Racine_TR-V-1664-Thebaide.txt: 19.6
Racine_TR-V-1666-Alexandre.txt: 18.4
Racine_TR-V-1668-Andromaque.txt: 28.8
Racine_TR-V-1670-Britannicus.txt: 22.4
Racine_TR-V-1671-Berenice.txt: 31.8
Racine_TR-V-1672-Bajazet.txt: 25.8
Racine_TR-V-1673-Mithridate.txt: 26.0
Racine_TR-V-1674-Iphigenie.txt: 21.7
Racine_TR-V-1677-Phedre.txt: 24.3
Racine_TR-V-1689-Esther.txt: 11.1
Racine_TR-V-1691-Athalie.txt: 12.6

Rate per 1,000 words of me
Racine_TR-V-1664-Thebaide.txt: 7.0
Racine_TR-V-1666-Alexandre.txt: 7.0
Racine_TR-V-1668-Andromaque.txt: 9.6
Racine_TR-V-1670-Britannicus.txt: 8.0
Racine_TR-V-1671-Berenice.txt: 10.9
Racine_TR-V-1672-Bajazet.txt: 8.8
Racine_TR-V-1673-Mithridate.txt: 10.3
Racine_TR-V-1674-Iphigenie.txt: 8.9
Racine_TR-V-1677-Phedre.txt: 8.2
Racine_TR-V-1689-Esther.txt: 4.5
Racine_TR-V-1691-Athalie.txt: 3.4


Finally, we turn back to Phèdre (1677). In terms of topic proportions, Phèdre (1677) looks similar to the composite “average” Racine play. Inspecting the dendrogram and the multidimensional scaling plot, we observe that the play is, in fact, not so different from Racine’s other plays; it stands out not because it is as atypical as those discussed above but because it happens to be similar (in terms of cosine distance) to several of Voltaire’s plays. Investigating why the works of a radical Enlightenment figure like Voltaire should so strongly resemble Racine’s is left as an exercise for the reader.

 [1] This tutorial was written by Allen Riddell. and Christof Schöch.
 [2] Such signatures do not always appear. They can be eliminated with some modest effort on the part of the writer [BAG11]. There are also many instances of writers changing their style over time—Henry James is an excellent example [Hoo07].)