Charles Darwin is one of the few universal figures of science. His most renowned work is without a doubt his "On the Origin of Species" published in 1859 which introduced the concept of natural selection. But Darwin wrote many other books on a wide range of topics, including geology, plants or his personal life. In this notebook, we will automatically detect how closely related his books are to each other.
To this purpose, we will develop the bases of a content-based book recommendation system, which will determine which books are close to each other based on how similar the discussed topics are. The methods we will use are commonly used in text- or documents-heavy industries such as legal, tech or customer support to perform some common task such as text classification or handling search engine queries.
Let's take a look at the books we'll use in our recommendation system.
In [2]:
# Import library
import glob
# The books files are contained in this folder
folder = "datasets/"
# List all the .txt files and sort them alphabetically
files = glob.glob(folder + '*.txt')
# ... YOUR CODE FOR TASK 1 ...
files.sort()
As a first step, we need to load the content of these books into Python and do some basic pre-processing to facilitate the downstream analyses. We call such a collection of texts a corpus. We will also store the titles for these books for future reference and print their respective length to get a gauge for their contents.
In [4]:
# Import libraries
import re, os
# Initialize the object that will contain the texts and titles
txts = []
titles = []
for n in files:
# Open each file
f = open(n, encoding='utf-8-sig')
# Remove all non-alpha-numeric characters
# ... YOUR CODE FOR TASK 2 ...
# Store the texts and titles of the books in two separate lists
txts.append(re.sub('[\W_]+', ' ', f.read()))
titles.append(os.path.basename(n).replace(".txt", ""))
# Print the length, in characters, of each book
[len(t) for t in txts]
Out[4]:
[123231,
496068,
1776539,
617088,
913713,
624232,
335920,
523021,
797401,
901406,
1047518,
1010643,
767492,
1660866,
298319,
916267,
1093567,
1043499,
341447,
1149574]
In [6]:
# Browse the list containing all the titles
for i in range(len(titles)):
# Store the index if the title is "OriginofSpecies"
# ... YOUR CODE FOR TASK 3 ...
if titles[i] == 'OriginofSpecies':
ori = i
# Print the stored index
# ... YOUR CODE FOR TASK 3 ...
print(ori)
15
As a next step, we need to transform the corpus into a format that is easier to deal with for the downstream analyses. We will tokenize our corpus, i.e., transform each text into a list of the individual words (called tokens) it is made of. To check the output of our process, we will print the first 20 tokens of "On the Origin of Species".
In [8]:
# Define a list of stop words
stoplist = set('for a of the and to in to be which some is at that we i who whom show via may my our might as well'.split())
# Convert the text to lower case
txts_lower_case = [txt.lower() for txt in txts]
# Transform the text into tokens
txts_split = [txt.split() for txt in txts_lower_case]
# Remove tokens which are part of the list of stop words
texts = [[word for word in txt if word not in stoplist] for txt in txts_split]
# Print the first 20 tokens for the "On the Origin of Species" book
# ... YOUR CODE FOR TASK 4 ...
print(texts[ori][:20])
['on', 'origin', 'species', 'but', 'with', 'regard', 'material', 'world', 'can', 'least', 'go', 'so', 'far', 'this', 'can', 'perceive', 'events', 'are', 'brought', 'about']
If you have read On the Origin of Species, you will have noticed that Charles Darwin can use different words to refer to a similar concept. For example, the concept of selection can be described by words such as selection, selective, select or selects. This will dilute the weight given to this concept in the book and potentially bias the results of the analysis.
To solve this issue, it is a common practice to use a stemming process, which will group together the inflected forms of a word so they can be analysed as a single item: the stem. In our On the Origin of Species example, the words related to the concept of selection would be gathered under the select stem.
As we are analysing 20 full books, the stemming algorithm can take several minutes to run and, in order to make the process faster, we will directly load the final results from a pickle file and review the method used to generate it.
In [10]:
import pickle
# Load the stemmed tokens list from the pregenerated pickle file
texts_stem = pickle.load( open( 'datasets/texts_stem.p', 'rb' ) )
# Print the 20 first stemmed tokens from the "On the Origin of Species" book
# ... YOUR CODE FOR TASK 5 ...
print(texts_stem[ori][:20])
['on', 'origin', 'speci', 'but', 'with', 'regard', 'materi', 'world', 'can', 'least', 'go', 'so', 'far', 'thi', 'can', 'perceiv', 'event', 'are', 'brought', 'about']
Now that we have transformed the texts into stemmed tokens, we need to build models that will be useable by downstream algorithms.
First, we need to will create a universe of all words contained in our corpus of Charles Darwin's books, which we call a dictionary. Then, using the stemmed tokens and the dictionary, we will create bag-of-words models (BoW) of each of our texts. The BoW models will represent our books as a list of all uniques tokens they contain associated with their respective number of occurrences.
To better understand the structure of such a model, we will print the five first elements of one of the "On the Origin of Species" BoW model.
In [12]:
# Load the functions allowing to create and use dictionaries
from gensim import corpora
# Create a dictionary from the stemmed tokens
dictionary = corpora.Dictionary(texts_stem)
# Create a bag-of-words model for each book, using the previously generated dictionary
bows = [dictionary.doc2bow(txt) for txt in texts_stem]
# Print the first five elements of the On the Origin of species' BoW model
# ... YOUR CODE FOR TASK 6 ...
print(bows[ori][:5])
[(0, 11), (5, 51), (6, 1), (8, 2), (21, 1)]
The results returned by the bag-of-words model is certainly easy to use for a computer but hard to interpret for a human. It is not straightforward to understand which stemmed tokens are present in a given book from Charles Darwin, and how many occurrences we can find.
In order to better understand how the model has been generated and visualize its content, we will transform it into a DataFrame and display the 10 most common stems for the book "On the Origin of Species".
In [14]:
# Import pandas to create and manipulate DataFrames
import pandas as pd
# Convert the BoW model for "On the Origin of Species" into a DataFrame
df_bow_origin = pd.DataFrame(bows[ori], columns=['index', 'occurrences'])
# Add a column containing the token corresponding to the dictionary index
# ... YOUR CODE FOR TASK 7 ...
df_bow_origin['token'] = df_bow_origin['index'].apply(lambda i: texts_stem[ori][i])
# Sort the DataFrame by descending number of occurrences and print the first 10 values
# ... YOUR CODE FOR TASK 7 ...
df_bow_origin.sort_values('occurrences', ascending=False).head(10)
Out[14]:
index
occurrences
token
748
1168
2023
natur
1119
1736
1558
accumul
1489
2288
1543
uniform
892
1366
1480
prevent
239
393
1362
intercross
1128
1747
1201
variabl
125
218
1140
domest
665
1043
1137
speci
1774
2703
1000
not
1609
2452
962
remark
If it wasn't for the presence of the stem "speci", we would have a hard time to guess this BoW model comes from the On the Origin of Species book. The most recurring words are, apart from few exceptions, very common and unlikely to carry any information peculiar to the given book. We need to use an additional step in order to determine which tokens are the most specific to a book.
To do so, we will use a tf-idf model (term frequency–inverse document frequency). This model defines the importance of each word depending on how frequent it is in this text and how infrequent it is in all the other documents. As a result, a high tf-idf score for a word will indicate that this word is specific to this text.
After computing those scores, we will print the 10 words most specific to the "On the Origin of Species" book (i.e., the 10 words with the highest tf-idf score).
In [16]:
# Load the gensim functions that will allow us to generate tf-idf models
from gensim.models import TfidfModel
# Generate the tf-idf model
model = TfidfModel(bows)
# Print the model for "On the Origin of Species"
# ... YOUR CODE FOR TASK 8 ...
print(model[bows[ori]])
[(8, 0.00020383224047642202), (21, 0.0005716037746542094), (23, 0.0017118699041370883), (27, 0.0006458270601429994), (28, 0.0025678048562056324), (31, 0.0008559349520685442), (35, 0.00101497410751472), (36, 0.00101497410751472), (51, 0.000886740665721021), (54, 0.00202994821502944), (56, 0.0023757190244598344), (57, 0.00010191612023821101), (63, 0.0027544680933525786), (64, 0.000509580601191055), (66, 0.00020383224047642202), (67, 0.0023757190244598344), (68, 0.00202994821502944), (75, 0.0013772340466762893), (76, 0.0004433703328605105), (78, 0.004171843479607349), (80, 0.0020859217398036746), (83, 0.00857405661981314), (84, 0.000509580601191055), (88, 0.002445986885717064), (89, 0.0033632319678609636), (90, 0.000886740665721021), (91, 0.0016747506839411234), (94, 0.000886740665721021), (95, 0.0004433703328605105), (96, 0.003546962662884084), (97, 0.0016306579238113761), (102, 0.037686478293143394), (104, 0.000917245082143899), (106, 0.001417375386254771), (108, 0.0035434384656369273), 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0.005952306519565668), (24395, 0.011904613039131337), (24396, 0.011904613039131337), (24397, 0.023809226078262673), (24398, 0.011904613039131337), (24399, 0.004575072472889379), (24400, 0.011904613039131337), (24401, 0.018300289891557516), (24402, 0.004575072472889379), (24403, 0.011904613039131337), (24404, 0.07320115956623006), (24405, 0.005952306519565668), (24406, 0.005952306519565668), (24407, 0.005952306519565668), (24408, 0.005952306519565668), (24409, 0.011904613039131337), (24410, 0.017856919558697004), (24411, 0.005952306519565668), (24412, 0.011904613039131337), (24413, 0.005952306519565668), (24414, 0.011904613039131337), (24415, 0.009150144945778758), (24416, 0.005952306519565668), (24417, 0.005952306519565668), (24418, 0.005952306519565668), (24419, 0.005952306519565668), (24420, 0.011904613039131337), (24421, 0.004575072472889379), (24422, 0.005952306519565668), (24423, 0.005952306519565668), (24424, 0.004575072472889379), (24425, 0.005952306519565668), (24426, 0.011308326602601896), (24427, 0.004575072472889379), (24428, 0.005952306519565668), (24429, 0.011904613039131337), (24430, 0.005952306519565668), (24431, 0.005952306519565668), (24432, 0.011904613039131337), (24433, 0.004575072472889379), (24434, 0.005952306519565668), (24435, 0.004575072472889379), (24436, 0.004575072472889379), (24437, 0.011904613039131337), (24438, 0.005952306519565668), (24439, 0.005952306519565668), (24440, 0.02976153259782834), (24441, 0.018300289891557516), (24442, 0.004575072472889379), (24443, 0.005952306519565668), (24444, 0.017856919558697004), (24445, 0.08333229127391935), (24446, 0.009150144945778758), (24447, 0.017856919558697004), (24448, 0.004575072472889379), (24449, 0.005952306519565668), (24450, 0.005952306519565668), (24451, 0.005952306519565668), (24452, 0.011904613039131337), (24453, 0.007538884401734597), (24454, 0.005952306519565668), (24455, 0.005952306519565668), (24456, 0.005952306519565668), (24457, 0.005952306519565668), (24458, 0.011904613039131337), (24459, 0.005952306519565668), (24460, 0.005952306519565668), (24461, 0.004575072472889379), (24462, 0.005952306519565668), (24463, 0.005952306519565668), (24464, 0.005952306519565668), (24465, 0.009150144945778758), (24466, 0.005952306519565668), (24467, 0.005952306519565668), (24468, 0.04761845215652535), (24469, 0.011904613039131337), (24470, 0.005952306519565668), (24471, 0.011904613039131337), (24472, 0.023809226078262673), (24473, 0.005952306519565668), (24474, 0.005952306519565668), (24475, 0.011904613039131337), (24476, 0.005952306519565668), (24477, 0.017856919558697004), (24478, 0.004575072472889379), (24479, 0.017856919558697004), (24480, 0.004575072472889379), (24481, 0.011904613039131337), (24482, 0.004575072472889379), (24483, 0.005952306519565668), (24484, 0.009150144945778758), (24485, 0.03571383911739401), (24486, 0.005952306519565668), (24487, 0.005952306519565668), (24488, 0.004575072472889379), (24489, 0.005952306519565668), (24490, 0.005952306519565668), (24491, 0.004575072472889379), (24492, 0.009150144945778758), (24493, 0.011904613039131337), (24494, 0.005952306519565668), (24495, 0.004575072472889379), (24496, 0.0037694422008672985), (24497, 0.005952306519565668)]
In [18]:
# Convert the tf-idf model for "On the Origin of Species" into a DataFrame
df_tfidf = pd.DataFrame(model[bows[ori]], columns=['id', 'score'])
# Add the tokens corresponding to the numerical indices for better readability
# ... YOUR CODE FOR TASK 9 ...
df_tfidf['token'] = df_tfidf['id'].apply(lambda i: texts_stem[ori][i])
# Sort the DataFrame by descending tf-idf score and print the first 10 rows.
# ... YOUR CODE FOR TASK 9 ...
df_tfidf.sort_values('score', ascending=False).head(10)
Out[18]:
id
score
token
878
2164
0.327414
chapter
3106
10108
0.203908
doubt
128
369
0.197721
it
2988
9396
0.167496
further
945
2325
0.148186
sever
284
752
0.145990
on
503
1255
0.128272
trust
370
966
0.127534
411
3889
16210
0.124392
lie
3540
12715
0.121197
differ
The results of the tf-idf algorithm now return stemmed tokens which are specific to each book. We can, for example, see that topics such as selection, breeding or domestication are defining "On the Origin of Species" (and yes, in this book, Charles Darwin talks quite a lot about pigeons too). Now that we have a model associating tokens to how specific they are to each book, we can measure how related to books are between each other.
To this purpose, we will use a measure of similarity called cosine similarity and we will visualize the results as a distance matrix, i.e., a matrix showing all pairwise distances between Darwin's books.
In [20]:
# Load the library allowing similarity computations
from gensim import similarities
# Compute the similarity matrix (pairwise distance between all texts)
sims = similarities.MatrixSimilarity(model[bows])
# Transform the resulting list into a dataframe
sim_df = pd.DataFrame(list(sims))
# Add the titles of the books as columns and index of the dataframe
# ... YOUR CODE FOR TASK 10 ...
sim_df.columns = titles
sim_df.index = titles
# Print the resulting matrix
# ... YOUR CODE FOR TASK 10 ...
print(sim_df)
Autobiography CoralReefs DescentofMan \
Autobiography 1.000000 0.049467 0.080428
CoralReefs 0.049467 1.000000 0.009480
DescentofMan 0.080428 0.009480 1.000000
DifferentFormsofFlowers 0.066482 0.001952 0.072761
EffectsCrossSelfFertilization 0.077184 0.001923 0.029968
ExpressionofEmotionManAnimals 0.088723 0.004999 0.148670
FormationVegetableMould 0.040678 0.029432 0.027055
FoundationsOriginofSpecies 0.059271 0.022096 0.135775
GeologicalObservationsSouthAmerica 0.030562 0.061027 0.009698
InsectivorousPlants 0.014878 0.002276 0.009404
LifeandLettersVol1 0.396709 0.030965 0.059684
LifeandLettersVol2 0.217129 0.017558 0.080314
MonographCirripedia 0.005686 0.006324 0.053506
MonographCirripediaVol2 0.008483 0.010579 0.043275
MovementClimbingPlants 0.022856 0.001518 0.005146
OriginofSpecies 0.099991 0.039089 0.267554
PowerMovementPlants 0.016247 0.002707 0.011357
VariationPlantsAnimalsDomestication 0.049018 0.011586 0.232841
VolcanicIslands 0.038556 0.057514 0.007882
VoyageBeagle 0.183507 0.267749 0.123917
DifferentFormsofFlowers \
Autobiography 0.066482
CoralReefs 0.001952
DescentofMan 0.072761
DifferentFormsofFlowers 1.000000
EffectsCrossSelfFertilization 0.391834
ExpressionofEmotionManAnimals 0.006474
FormationVegetableMould 0.010585
FoundationsOriginofSpecies 0.040104
GeologicalObservationsSouthAmerica 0.002846
InsectivorousPlants 0.007502
LifeandLettersVol1 0.015933
LifeandLettersVol2 0.046523
MonographCirripedia 0.009405
MonographCirripediaVol2 0.005484
MovementClimbingPlants 0.008151
OriginofSpecies 0.128909
PowerMovementPlants 0.018964
VariationPlantsAnimalsDomestication 0.050023
VolcanicIslands 0.002611
VoyageBeagle 0.013124
EffectsCrossSelfFertilization \
Autobiography 0.077184
CoralReefs 0.001923
DescentofMan 0.029968
DifferentFormsofFlowers 0.391834
EffectsCrossSelfFertilization 1.000000
ExpressionofEmotionManAnimals 0.006844
FormationVegetableMould 0.032262
FoundationsOriginofSpecies 0.040288
GeologicalObservationsSouthAmerica 0.002246
InsectivorousPlants 0.006777
LifeandLettersVol1 0.019504
LifeandLettersVol2 0.046504
MonographCirripedia 0.003212
MonographCirripediaVol2 0.002962
MovementClimbingPlants 0.014932
OriginofSpecies 0.146441
PowerMovementPlants 0.039770
VariationPlantsAnimalsDomestication 0.055132
VolcanicIslands 0.002178
VoyageBeagle 0.017140
ExpressionofEmotionManAnimals \
Autobiography 0.088723
CoralReefs 0.004999
DescentofMan 0.148670
DifferentFormsofFlowers 0.006474
EffectsCrossSelfFertilization 0.006844
ExpressionofEmotionManAnimals 1.000000
FormationVegetableMould 0.020985
FoundationsOriginofSpecies 0.047202
GeologicalObservationsSouthAmerica 0.005217
InsectivorousPlants 0.011475
LifeandLettersVol1 0.064873
LifeandLettersVol2 0.048886
MonographCirripedia 0.016825
MonographCirripediaVol2 0.029897
MovementClimbingPlants 0.005913
OriginofSpecies 0.062979
PowerMovementPlants 0.011317
VariationPlantsAnimalsDomestication 0.083847
VolcanicIslands 0.005561
VoyageBeagle 0.098961
FormationVegetableMould \
Autobiography 0.040678
CoralReefs 0.029432
DescentofMan 0.027055
DifferentFormsofFlowers 0.010585
EffectsCrossSelfFertilization 0.032262
ExpressionofEmotionManAnimals 0.020985
FormationVegetableMould 1.000000
FoundationsOriginofSpecies 0.021470
GeologicalObservationsSouthAmerica 0.067989
InsectivorousPlants 0.035589
LifeandLettersVol1 0.027916
LifeandLettersVol2 0.023620
MonographCirripedia 0.019866
MonographCirripediaVol2 0.023984
MovementClimbingPlants 0.038820
OriginofSpecies 0.049259
PowerMovementPlants 0.040182
VariationPlantsAnimalsDomestication 0.033147
VolcanicIslands 0.059407
VoyageBeagle 0.097908
FoundationsOriginofSpecies \
Autobiography 0.059271
CoralReefs 0.022096
DescentofMan 0.135775
DifferentFormsofFlowers 0.040104
EffectsCrossSelfFertilization 0.040288
ExpressionofEmotionManAnimals 0.047202
FormationVegetableMould 0.021470
FoundationsOriginofSpecies 1.000000
GeologicalObservationsSouthAmerica 0.028028
InsectivorousPlants 0.006023
LifeandLettersVol1 0.057820
LifeandLettersVol2 0.054782
MonographCirripedia 0.007618
MonographCirripediaVol2 0.010883
MovementClimbingPlants 0.003973
OriginofSpecies 0.322405
PowerMovementPlants 0.008788
VariationPlantsAnimalsDomestication 0.194533
VolcanicIslands 0.017590
VoyageBeagle 0.089132
GeologicalObservationsSouthAmerica \
Autobiography 0.030562
CoralReefs 0.061027
DescentofMan 0.009698
DifferentFormsofFlowers 0.002846
EffectsCrossSelfFertilization 0.002246
ExpressionofEmotionManAnimals 0.005217
FormationVegetableMould 0.067989
FoundationsOriginofSpecies 0.028028
GeologicalObservationsSouthAmerica 1.000000
InsectivorousPlants 0.006879
LifeandLettersVol1 0.028551
LifeandLettersVol2 0.012104
MonographCirripedia 0.009687
MonographCirripediaVol2 0.024738
MovementClimbingPlants 0.002043
OriginofSpecies 0.058046
PowerMovementPlants 0.003491
VariationPlantsAnimalsDomestication 0.014389
VolcanicIslands 0.373249
VoyageBeagle 0.260141
InsectivorousPlants LifeandLettersVol1 \
Autobiography 0.014878 0.396709
CoralReefs 0.002276 0.030965
DescentofMan 0.009404 0.059684
DifferentFormsofFlowers 0.007502 0.015933
EffectsCrossSelfFertilization 0.006777 0.019504
ExpressionofEmotionManAnimals 0.011475 0.064873
FormationVegetableMould 0.035589 0.027916
FoundationsOriginofSpecies 0.006023 0.057820
GeologicalObservationsSouthAmerica 0.006879 0.028551
InsectivorousPlants 1.000000 0.005967
LifeandLettersVol1 0.005967 1.000000
LifeandLettersVol2 0.016518 0.885828
MonographCirripedia 0.019214 0.005752
MonographCirripediaVol2 0.020023 0.012772
MovementClimbingPlants 0.249814 0.005388
OriginofSpecies 0.014961 0.097457
PowerMovementPlants 0.023056 0.009505
VariationPlantsAnimalsDomestication 0.010522 0.055259
VolcanicIslands 0.008544 0.026374
VoyageBeagle 0.014776 0.171708
LifeandLettersVol2 MonographCirripedia \
Autobiography 0.217129 0.005686
CoralReefs 0.017558 0.006324
DescentofMan 0.080314 0.053506
DifferentFormsofFlowers 0.046523 0.009405
EffectsCrossSelfFertilization 0.046504 0.003212
ExpressionofEmotionManAnimals 0.048886 0.016825
FormationVegetableMould 0.023620 0.019866
FoundationsOriginofSpecies 0.054782 0.007618
GeologicalObservationsSouthAmerica 0.012104 0.009687
InsectivorousPlants 0.016518 0.019214
LifeandLettersVol1 0.885828 0.005752
LifeandLettersVol2 1.000000 0.004967
MonographCirripedia 0.004967 1.000000
MonographCirripediaVol2 0.010843 0.522273
MovementClimbingPlants 0.017565 0.012441
OriginofSpecies 0.096955 0.029902
PowerMovementPlants 0.012099 0.018694
VariationPlantsAnimalsDomestication 0.050764 0.023460
VolcanicIslands 0.011806 0.010754
VoyageBeagle 0.089947 0.014342
MonographCirripediaVol2 \
Autobiography 0.008483
CoralReefs 0.010579
DescentofMan 0.043275
DifferentFormsofFlowers 0.005484
EffectsCrossSelfFertilization 0.002962
ExpressionofEmotionManAnimals 0.029897
FormationVegetableMould 0.023984
FoundationsOriginofSpecies 0.010883
GeologicalObservationsSouthAmerica 0.024738
InsectivorousPlants 0.020023
LifeandLettersVol1 0.012772
LifeandLettersVol2 0.010843
MonographCirripedia 0.522273
MonographCirripediaVol2 1.000000
MovementClimbingPlants 0.006802
OriginofSpecies 0.036755
PowerMovementPlants 0.022376
VariationPlantsAnimalsDomestication 0.030669
VolcanicIslands 0.017952
VoyageBeagle 0.025047
MovementClimbingPlants OriginofSpecies \
Autobiography 0.022856 0.099991
CoralReefs 0.001518 0.039089
DescentofMan 0.005146 0.267554
DifferentFormsofFlowers 0.008151 0.128909
EffectsCrossSelfFertilization 0.014932 0.146441
ExpressionofEmotionManAnimals 0.005913 0.062979
FormationVegetableMould 0.038820 0.049259
FoundationsOriginofSpecies 0.003973 0.322405
GeologicalObservationsSouthAmerica 0.002043 0.058046
InsectivorousPlants 0.249814 0.014961
LifeandLettersVol1 0.005388 0.097457
LifeandLettersVol2 0.017565 0.096955
MonographCirripedia 0.012441 0.029902
MonographCirripediaVol2 0.006802 0.036755
MovementClimbingPlants 1.000000 0.008802
OriginofSpecies 0.008802 1.000000
PowerMovementPlants 0.104966 0.018266
VariationPlantsAnimalsDomestication 0.011530 0.405333
VolcanicIslands 0.002832 0.036014
VoyageBeagle 0.012282 0.164661
PowerMovementPlants \
Autobiography 0.016247
CoralReefs 0.002707
DescentofMan 0.011357
DifferentFormsofFlowers 0.018964
EffectsCrossSelfFertilization 0.039770
ExpressionofEmotionManAnimals 0.011317
FormationVegetableMould 0.040182
FoundationsOriginofSpecies 0.008788
GeologicalObservationsSouthAmerica 0.003491
InsectivorousPlants 0.023056
LifeandLettersVol1 0.009505
LifeandLettersVol2 0.012099
MonographCirripedia 0.018694
MonographCirripediaVol2 0.022376
MovementClimbingPlants 0.104966
OriginofSpecies 0.018266
PowerMovementPlants 1.000000
VariationPlantsAnimalsDomestication 0.020589
VolcanicIslands 0.003819
VoyageBeagle 0.024149
VariationPlantsAnimalsDomestication \
Autobiography 0.049018
CoralReefs 0.011586
DescentofMan 0.232841
DifferentFormsofFlowers 0.050023
EffectsCrossSelfFertilization 0.055132
ExpressionofEmotionManAnimals 0.083847
FormationVegetableMould 0.033147
FoundationsOriginofSpecies 0.194533
GeologicalObservationsSouthAmerica 0.014389
InsectivorousPlants 0.010522
LifeandLettersVol1 0.055259
LifeandLettersVol2 0.050764
MonographCirripedia 0.023460
MonographCirripediaVol2 0.030669
MovementClimbingPlants 0.011530
OriginofSpecies 0.405333
PowerMovementPlants 0.020589
VariationPlantsAnimalsDomestication 1.000000
VolcanicIslands 0.012620
VoyageBeagle 0.114134
VolcanicIslands VoyageBeagle
Autobiography 0.038556 0.183507
CoralReefs 0.057514 0.267749
DescentofMan 0.007882 0.123917
DifferentFormsofFlowers 0.002611 0.013124
EffectsCrossSelfFertilization 0.002178 0.017140
ExpressionofEmotionManAnimals 0.005561 0.098961
FormationVegetableMould 0.059407 0.097908
FoundationsOriginofSpecies 0.017590 0.089132
GeologicalObservationsSouthAmerica 0.373249 0.260141
InsectivorousPlants 0.008544 0.014776
LifeandLettersVol1 0.026374 0.171708
LifeandLettersVol2 0.011806 0.089947
MonographCirripedia 0.010754 0.014342
MonographCirripediaVol2 0.017952 0.025047
MovementClimbingPlants 0.002832 0.012282
OriginofSpecies 0.036014 0.164661
PowerMovementPlants 0.003819 0.024149
VariationPlantsAnimalsDomestication 0.012620 0.114134
VolcanicIslands 1.000000 0.138323
VoyageBeagle 0.138323 1.000000
We now have a matrix containing all the similarity measures between any pair of books from Charles Darwin! We can now use this matrix to quickly extract the information we need, i.e., the distance between one book and one or several others.
As a first step, we will display which books are the most similar to "On the Origin of Species," more specifically we will produce a bar chart showing all books ranked by how similar they are to Darwin's landmark work.
In [22]:
# This is needed to display plots in a notebook
%matplotlib inline
# Import libraries
import matplotlib.pyplot as plt
# Select the column corresponding to "On the Origin of Species" and
v = sim_df.OriginofSpecies
# Sort by ascending scores
v_sorted = v.sort_values()
# v_sorted = v_sorted[:-1]
# Plot this data has a horizontal bar plot
# ... YOUR CODE FOR TASK 11 ...
plt.barh(range(len(v_sorted)), v_sorted.values)
# Modify the axes labels and plot title for a better readability
# ... YOUR CODE FOR TASK 11 ...
plt.xlabel('Similarity')
plt.ylabel('Books')
plt.yticks(range(len(v_sorted)), v_sorted.index)
plt.xlim((0, 1))
plt.title('Books most similar to the "Origin of Species"')
plt.show()
This turns out to be extremely useful if we want to determine a given book's most similar work. For example, we have just seen that if you enjoyed "On the Origin of Species," you can read books discussing similar concepts such as "The Variation of Animals and Plants under Domestication" or "The Descent of Man, and Selection in Relation to Sex." If you are familiar with Darwin's work, these suggestions will likely seem natural to you. Indeed, On the Origin of Species has a whole chapter about domestication and The Descent of Man, and Selection in Relation to Sex applies the theory of natural selection to human evolution. Hence, the results make sense.
However, we now want to have a better understanding of the big picture and see how Darwin's books are generally related to each other (in terms of topics discussed). To this purpose, we will represent the whole similarity matrix as a dendrogram, which is a standard tool to display such data. This last approach will display all the information about book similarities at once. For example, we can find a book's closest relative but, also, we can visualize which groups of books have similar topics (e.g., the cluster about Charles Darwin personal life with his autobiography and letters). If you are familiar with Darwin's bibliography, the results should not surprise you too much, which indicates the method gives good results. Otherwise, next time you read one of the author's book, you will know which other books to read next in order to learn more about the topics it addressed.
In [24]:
# Import libraries
from scipy.cluster import hierarchy
# Compute the clusters from the similarity matrix,
# using the Ward variance minimization algorithm
Z = hierarchy.linkage(sim_df, method='ward')
# Display this result as a horizontal dendrogram
# ... YOUR CODE FOR TASK 12 ...
a = hierarchy.dendrogram(
Z,
leaf_font_size=8,
labels=sim_df.index,
orientation="left"
)
Content source: xR86/ml-stuff
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