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You are to perform hierarchical clustering on publicly available Pokemon stats. Each
Pokemon is defined by a row in the data set. Because there are various ways to
characterize how strong a Pokemon is, we summarize the stats into a shorter feature
vector. For this assignment, you must represent a Pokemon’s quality by 6 numbers:
Attack, Sp. Atk, Speed, Defense, Sp. Def, and HP
After each Pokemon is represented as a 6-dimensional feature vector (x1, . . . , x6), you
need to cluster the first n Pokemon with hierarchical agglomerative clustering (HAC).
Your function should work similarly to scipy.cluster.hierarchy.linkage() with
Program Overview
The data in CSV format can be found in the file Pokemon.csv. Note, there is no starter
code for this assignment. If you feel stuck, refer to the starter code from past
assignments. You will have to write a few python functions for this assignment. Here is a
high level description of each for reference:
1. load_data(filepath) — takes in a string with a path to a CSV file, and returns the
data points as a list of dicts. Section 0.1
2. calc_features(row) — takes in one row dict from the data loaded from the previous
function then calculates the corresponding feature vector for that Pokemon as
specified above, and returns it as a numpy array of shape (6,). The dtype of this
array should be int64. Section 0.2
3. hac(features) — performs complete linkage hierarchical agglomerative clustering
on the Pokemon with the (x1, . . . , x6) feature representation, and returns a numpy
array representing the clustering. Section 0.3
4. imshow_hac(Z, names) — visualizes the hierarchical agglomerative clustering on
the Pokemon’s feature representation. Section 0.4
You may implement other helper functions as necessary, but these are the functions we
are testing. In particular, your final python file is just a suite of functions, you should not
have code that runs outside of the functions. To test your code, you may want a "main"
method to put it all together. Make sure, you either delete any testing code running
outside functions or wrap it in a if __name__=="__main__":. This is discussed more in
Section 0.5.
Program Details
0.1 load_data(filepath)
Summary. [20pts]
• Input: string, the path to a file to be read.
• Output: list, where each element is a dict representing one row of the file read.
1. Read in the file specified in the argument, filepath. Note, the DictReader from
Python’s csv module is useful but, depending on your python version, this might
return OrderedDicts instead of normal dicts. Make sure you convert to dict as
appropriate if you choose to use this function.
2. Return a list of dictionaries, where each row in the dataset is a dictionary with the
column headers as keys and the row elements as values.
You may assume the file exists and is a properly formatted CSV.
0.2 calc_features(row)
Summary. [20pts]
• Input: dict representing one Pokemon.
• Output: numpy array of shape (6,) and dtype int64. The first element is x1 and so on
with the sixth element being x6.
Details. This function takes as input the dict representing one Pokemon, and computes
the feature representation (x1, . . . , x6). Specifically,
1. x1 = Attack
2. x2 = Sp. Attack
3. x3 = Speed
4. x4 = Defense
5. x5 = Sp. Def
6. x6 = HP
Note, these stats in the dict may not be int. Make sure to convert each relevant stat to int
when computing each xi. Return a numpy array having each xi in order: x1, . . . , x6. The
shape of this array should be (6,). The dtype of this array should be int64. Remember,
this function works for only one Pokemon at a time, not all of the ones that you loaded in
load_data simultaneously. Make sure you are outputting the exact data structures with
appropriate types as specified or you risk a major reduction in points.
0.3 hac(features)
Summary. [50pts]
• Input: list of numpy arrays of shape (6,), where each array is an (x1, . . . , x6) feature
representation as computed in Section 0.2. The total number of feature vectors,
i.e. the length of the input list, is n. Note, we test your code on different n’s as
stated in Section 0.5).
• Output: numpy array of shape (n−1) × 4. For any i, Z[i, 0] and Z[i, 1] represent the
indices of the two clusters that were merged in the ith iteration of the clustering
algorithm. Then, Z[i, 2] = d(Z[i, 0], Z[i, 1]) is the complete linkage distance between
the two clusters that were merged in the ith iteration (this will be a real value, not
integer like the other quantities). Lastly, Z[i, 3] is the size of the new cluster formed
by the merge, i.e. the total number of Pokemon in this cluster. Note, the original
Pokemon are considered clusters indexed by 0,...,n−1, and the cluster
constructed in the ith iteration (i≥1) of the algorithm has cluster index (n−1) + i.
Also, there is a tie-breaking rule specified below that must be followed.
Details. For this function, we would like you to mimic the behavior of SciPy’s HAC
function, linkage(). You may not use this function in your implementation, but we
strongly recommend using it to verify your results! This is how you can test your code.
Distance. Using complete linkage, perform the hierarchical agglomerative clustering
algorithm as detailed in lecture. Use the standard Euclidean distance function for
calculating the distance between two points. You may implement your own distance
function or use numpy.linalg.norm(). Other distance functions might not work as
expected so check it works on the CSL machines first! You are liable for any reductions
in points you might get for using a package distance function.
Outline. Here is one possible path you could follow to implement hac()
1. Number each of your starting data points from 0 to n −1. These are their original
cluster numbers.
2. Create an (n −1) × 4 array or list. Iterate through this array/list row by row. For
each row,
(a) Determine which two clusters you should merge and put their numbers into
the first and second elements of the row, Z[i, 0] and Z[i, 1]. The first element
listed, Z[i, 0] should be the smaller of the two cluster indexes.
(b) The complete-linkage distance between the two clusters goes into the third
element of the row, Z[i, 2]
(c) The total number of Pokemon in the cluster goes into the fourth element, Z[i, 3]
If you merge a cluster containing more than one Pokemon, its index (for the first or
second element of the row) is given by n + the row index in which the cluster was
3. Before returning the data structure, convert it into a NumPy array if it isn’t one
For this method to run efficiently when n is large you should maintain a distance matrix
throughout the process to avoid having to recalculate the distances between points or
clusters. You should be able to run hac efficiently on all 800 Pokemon. To create the
distance matrix we will leverage the following:
Suppose we have a set of n vectors x1, x2, ..., xn in d-dimensional space. The square
distance between two vectors xi and xj can be represented as:
𝑥𝑖−𝑥𝑗||||2= 𝑥𝑖||||2+𝑥𝑗||||2−2(𝑥𝑖•𝑥𝑗)
To simplify the computation, we can use the Gramian matrix G which is defined as the
matrix whose entries are the dot products of all pairs of vectors xi and xj . The diagonal
elements of G form a vector g, which represents the squares of the lengths of the
vectors. By combining these terms, we can write the square distance matrix D² as the
sum of a vector of ones and the transpose of the vector g minus twice the Gramian
matrix G. In other words, D² is obtained by adding the outer product of the vector g and
the transpose of a vector of ones, and subtracting twice the Gramian matrix G.To get
the distance matrix we need to take the square root of D², do not forget this step.
𝐷2= 𝑥1𝑖||||2


For our problem you will need to start by converting your feature input into a numpy
array of shape (n,6) lets call it X. We will have three components:
comp1 = , comp2 = , comp3 =𝑥1𝑖||||2


Comp1 and comp2 are created by taking the sum of X². Comp1 should have shape
(n,1) and comp2 should have shape (n,). Comp3 just has the original X as X1 and X2.
For example, if you have a NumPy array called distance_matrix then
distance_matrix[3,4] is equal to the euclidean distance between the Pokemon at index 3
and 4 in the features input. You can compare your output with
scipy.spatial.distance_matrix, please note we are using Euclidean distance for this
Tie Breaking. When choosing the next two clusters to merge, we pick the pair having the
smallest complete-linkage distance. In the case that multiple pairs have the same
distance, we need additional criteria to pick between them. We do this with a
tie-breaking rule on indices as follows: Suppose (i1, j1), . . . ,(ih, jh) are pairs of cluster
indices with equal distance, i.e., d(i1, j1) = · · · = d(ih, jh), and assume that it < jt for all t (so
each pair is sorted). We tie-break by picking the pair with the smallest first index, i. If
there are multiple pairs having first index i, we need to further distinguish between them.
Say these pairs are (i, t1),(i, t2), . . . and so on. To tie-break between these pairs, we pick
the pair with the smallest second index, i.e., the smallest t value in these pairs. Be
aware that this tie-breaking strategy may not produce identical results to linkage().
0.4 imshow_hac(Z, names)
Summary. [10pts]
• Input: numpy array Z output from hac, and list of string names corresponding to
Pokemon names with size n by 1.
• Output: None, simply a graph that visualizes the hierarchical clustering.
You should use dendrogram in the scipy module with labels set to names and
leaf_rotation = ??.
Details. Your plot will likely cut off the x labels. For the graph to look like the below
examples you will need to create a subplot before the dendrogram and call tight_layout()
on the figure before
Here are some examples of successful visualizations for different sized lists of Pokemon
N = 15 N = 10
0.5 Testing
To test your code, try running the following lines in a main method or in a jupyter
notebook for various choices of n:
features_and_names = [(calc_features(row), row[‘Name’]) for row in load_data(‘Pokemon.csv’)[:n]]
Z = hac([row[0] for row in features_and_names])
names = [row[1] for row in features_and_names]
For the hac function we will test on both small and large values of n up to 800, the entire
Pokeman set. We will test on n <= 30 for imshow_hac. You can then compare your
clustering to what linkage() would give you (remember, set method = 'complete'), and
look at the different clustering visualizations.
Submission Details
• Please submit your files in a zip file named hw4_<netid>.zip
• Inside your zip file, there should be only one file named:
• All code should be contained in functions or under a if __name__=="__main__":
• Be sure to remove all debugging output before submission.

What I have tried:

import numpy as np
import pandas as pd
from scipy.cluster.hierarchy import linkage, dendrogram
import matplotlib.pyplot as plt
import csv

def load_data(filepath):
    This function takes in the path to a CSV file and returns a list of dictionaries,
    where each dictionary represents a row of the CSV file.
    data = []
    with open(filepath) as f:
        reader = csv.DictReader(f)
        for row in reader:
    return data

def calc_features(row):
    This function takes in a dictionary representing one Pokemon and returns
    a numpy array of shape (6,) and dtype int64 containing the six specified features.
    x1 = int(row['Attack'])
    x2 = int(row['Sp. Atk'])
    x3 = int(row['Speed'])
    x4 = int(row['Defense'])
    x5 = int(row['Sp. Def'])
    x6 = int(row['HP'])
    features = np.array([x1, x2, x3, x4, x5, x6], dtype=np.int64)
    return features

def hac(features):
    This function takes in a list of numpy arrays of shape (6,), where each array is
    an (x1, . . . , x6) feature representation as computed in the calc_features method.
    It performs complete linkage hierarchical agglomerative clustering and returns a
    numpy array representing the clustering.
    Z = linkage(features, method='complete')
    return Z

def imshow_hac(Z, names):
    This function takes in a numpy array representing the clustering returned by the
    hac method, and a list of names corresponding to the original data points. It
    visualizes the hierarchical agglomerative clustering on the Pokemon’s feature
    plt.figure(figsize=(10, 5))
    dendrogram(Z, labels=names)
Updated 22-Feb-23 21:40pm
Richard MacCutchan 23-Feb-23 4:39am    
"Need it fast"
Then you need to get working ...

1 solution

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