:

# Please execute this cell
import sciprog


# Exam - Thu 23, Jan 2020

Scientific Programming - Data Science @ University of Trento

## Introduction

• Taking part to this exam erases any vote you had before

### What to do

1. Download sciprog-ds-2020-01-23-exam.zip and extract it on your desktop. Folder content should be like this:

sciprog-ds-2020-01-23-FIRSTNAME-LASTNAME-ID
data
db.mm
proof.txt

exam-2020-01-23.ipynb
digi_list.py
digi_list_test.py
bin_tree.py
bin_tree_test.py
jupman.py
sciprog.py

1. Rename sciprog-ds-2020-01-23-FIRSTNAME-LASTNAME-ID folder: put your name, lastname an id number, like sciprog-ds-2020-01-23-john-doe-432432

From now on, you will be editing the files in that folder. At the end of the exam, that is what will be evaluated.

1. Edit the files following the instructions in this worksheet for each exercise. Every exercise should take max 25 mins. If it takes longer, leave it and try another exercise.

2. When done:

• if you have unitn login: zip and send to examina.icts.unitn.it/studente

• If you don’t have unitn login: tell instructors and we will download your work manually

## Part A

Open Jupyter and start editing this notebook exam-2020-01-23.ipynb

## Metamath

NOTICE: this part of the exam was ported to softpython website

There you can find a more curated version (notice it may be longer than here)

Metamath is a language that can express theorems, accompanied by proofs that can be verified by a computer program. Its website lets you browse from complex theorems up to the most basic axioms they rely on to be proven .

For this exercise, we have two files to consider, db.mm and proof.txt.

• db.mm contains the description of a simple algebra where you can only add zero to variables

• proof.txt contains the awesome proof that… any variable is equal to itself

The purpose of this exercise is to visualize the steps of the proof as a graph, and visualize statement frequencies.

DISCLAIMER: No panic !

You DO NOT need to understand any of the mathematics which follows. Here we are only interested in parsing the data and visualize it

### Metamath db

First you will load data/db.mm and parse text file into Python, here is the full content:

$( Declare the constant symbols we will use$)
$c 0 + = -> ( ) term wff |-$.
$( Declare the metavariables we will use$)
$v t r s P Q$.
$( Specify properties of the metavariables$)
tt $f term t$.
tr $f term r$.
ts $f term s$.
wp $f wff P$.
wq $f wff Q$.
$( Define "term" and "wff"$)
tze $a term 0$.
tpl $a term ( t + r )$.
weq $a wff t = r$.
wim $a wff ( P -> Q )$.
$( State the axioms$)
a1 $a |- ( t = r -> ( t = s -> r = s ) )$.
a2 $a |- ( t + 0 ) = t$.
$( Define the modus ponens inference rule$)
${ min$e |- P $. maj$e |- ( P -> Q ) $. mp$a |- Q $.$}


Format description:

• Each row is a statement

• Words are separated by spaces. Each word that appears in a statement is called a token

• Tokens starting with dollar $ are called keywords, you may have $(, $), $c, $v, $a,$f,${,$}, $.

• Statements may be identified with a unique arbitrary label, which is placed at the beginning of the row. For example, tt, weq, maj are all labels (in the file there are more):

• tt $f term t$.

• weq $a wff t = r$.

• maj $e |- ( P -> Q )$.

• Some rows have no label, examples:

• $c 0 + = -> ( ) term wff |-$.

• $v t r s P Q$.

• $( State the axioms$)

• ${ • $}

• in each row, after the first dollar keyword, you may have an arbitratry sequence of characters terminated by a dollar followed by a dot $.. You don’t need to care about the sequence meaning! Examples: • tt$f term t $. has sequence term t • weq$a wff t = r $. has sequence wff t = r • $v t r s P Q $. has sequence t r s P Q Now implement function parse_db which scans the file line by line (it is a text file, so you can use line files examples), parses ONLY rows with labels, and RETURN a dictionary mapping labels to remaining data in the row represented as a dictionary, formatted like this (showing here only first three labels): { 'a1': {'keyword': '$a',
'sequence': '|- ( t = r -> ( t = s -> r = s ) )'
},
'a2':  {
'keyword': '$a', 'sequence': '|- ( t + 0 ) = t' }, 'maj': { 'keyword': '$e',
'sequence': '|- ( P -> Q )'
},
.
.
.
}


## A.1 Metamath db

Show solution
:

def parse_db(filepath):
raise Exception('TODO IMPLEMENT ME !')

db_mm = parse_db('data/db.mm')

assert db_mm['tt'] == {'keyword': '$f', 'sequence': 'term t'} assert db_mm['maj'] == {'keyword': '$e', 'sequence': '|- ( P -> Q )'}
# careful 'mp' label shouldn't have spaces inside !
assert 'mp' in db_mm
assert db_mm['mp'] == {'keyword': '$a', 'sequence': '|- Q'} from pprint import pprint #pprint(db_mm)  :  from pprint import pprint print("************ EXPECTED OUTPUT: ****************") pprint(db_mm)  ************ EXPECTED OUTPUT: **************** {'a1': {'keyword': '$a', 'sequence': '|- ( t = r -> ( t = s -> r = s ) )'},
'a2': {'keyword': '$a', 'sequence': '|- ( t + 0 ) = t'}, 'maj': {'keyword': '$e', 'sequence': '|- ( P -> Q )'},
'min': {'keyword': '$e', 'sequence': '|- P'}, 'mp': {'keyword': '$a', 'sequence': '|- Q'},
'tpl': {'keyword': '$a', 'sequence': 'term ( t + r )'}, 'tr': {'keyword': '$f', 'sequence': 'term r'},
'ts': {'keyword': '$f', 'sequence': 'term s'}, 'tt': {'keyword': '$f', 'sequence': 'term t'},
'tze': {'keyword': '$a', 'sequence': 'term 0'}, 'weq': {'keyword': '$a', 'sequence': 'wff t = r'},
'wim': {'keyword': '$a', 'sequence': 'wff ( P -> Q )'}, 'wp': {'keyword': '$f', 'sequence': 'wff P'},
'wq': {'keyword': '$f', 'sequence': 'wff Q'}}  ## A.2 Metamath proof A proof file is made of steps, one per row. Each statement, in order to be proven, needs other steps to be proven until very basic facts called axioms are reached, which need no further proof (typically proofs in Metamath are shown in much shorter format, but here we use a more explicit way) So a proof can be nicely displayed as a tree of the steps it is made of, where the top node is the step to be proven and the axioms are the leaves of the tree. Complete content of data/proof.txt:  1 tt$f term t
2 tze           $a term 0 3 1,2 tpl$a term ( t + 0 )
4 tt            $f term t 5 3,4 weq$a wff ( t + 0 ) = t
6 tt            $f term t 7 tt$f term t
8 6,7 weq       $a wff t = t 9 tt$f term t
10 9 a2          $a |- ( t + 0 ) = t 11 tt$f term t
12 tze           $a term 0 13 11,12 tpl$a term ( t + 0 )
14 tt            $f term t 15 13,14 weq$a wff ( t + 0 ) = t
16 tt            $f term t 17 tze$a term 0
18 16,17 tpl     $a term ( t + 0 ) 19 tt$f term t
20 18,19 weq     $a wff ( t + 0 ) = t 21 tt$f term t
22 tt            $f term t 23 21,22 weq$a wff t = t
24 20,23 wim     $a wff ( ( t + 0 ) = t -> t = t ) 25 tt$f term t
26 25 a2         $a |- ( t + 0 ) = t 27 tt$f term t
28 tze           $a term 0 29 27,28 tpl$a term ( t + 0 )
30 tt            $f term t 31 tt$f term t
32 29,30,31 a1   $a |- ( ( t + 0 ) = t -> ( ( t + 0 ) = t -> t = t ) ) 33 15,24,26,32 mp$a |- ( ( t + 0 ) = t -> t = t )
34 5,8,10,33 mp  $a |- t = t  Each line represents a step of the proof. Last line is the final goal of the proof. Each line contains, in order: • a step number at the beginning, starting from 1 (step_id) • possibly a list of other step_ids, separated by commas, like 29,30,31 - they are references to previous rows • label of the db_mm statement referenced by the step, like tt, tze, weq - that label must have been defined somewhere in db.mm file • statement type: a token starting with a dollar, like $a, $f • a sequence of characters, like (for you they are just characters, don’t care about the meaning !): • term ( t + 0 ) • |- ( ( t + 0 ) = t -> ( ( t + 0 ) = t -> t = t ) ) Implement function parse_proof, which takes a filepath to the proof and RETURN a list of steps expressed as a dictionary, in this format (showing here only first 5 items): NOTE: referenced step_ids are integer numbers and they are the original ones from the file, meaning they start from one. [ {'keyword': '$f',
'label': 'tt',
'sequence': 'term t',
'step_ids': []},
{'keyword': '$a', 'label': 'tze', 'sequence': 'term 0', 'step_ids': []}, {'keyword': '$a',
'label': 'tpl',
'sequence': 'term ( t + 0 )',
'step_ids': [1,2]},
{'keyword': '$f', 'label': 'tt', 'sequence': 'term t', 'step_ids': []}, {'keyword': '$a',
'label': 'weq',
'sequence': 'wff ( t + 0 ) = t',
'step_ids': [3,4]},
.
.
.
]

Show solution
:

def parse_proof(filepath):
raise Exception('TODO IMPLEMENT ME !')

proof = parse_proof('data/proof.txt')

assert proof == {'keyword': '$f', 'label': 'tt', 'sequence': 'term t', 'step_ids': []} assert proof == {'keyword': '$a', 'label': 'tze', 'sequence': 'term 0', 'step_ids': []}
assert proof == {'keyword': '$a', 'label': 'tpl', 'sequence': 'term ( t + 0 )', 'step_ids': [1, 2]} assert proof == {'keyword': '$a',
'label': 'weq',
'sequence': 'wff ( t + 0 ) = t',
'step_ids': [3,4]}
assert proof == { 'keyword': '$a', 'label': 'mp', 'sequence': '|- t = t', 'step_ids': [5, 8, 10, 33]} pprint(proof)  [{'keyword': '$f', 'label': 'tt', 'sequence': 'term t', 'step_ids': []},
{'keyword': '$a', 'label': 'tze', 'sequence': 'term 0', 'step_ids': []}, {'keyword': '$a',
'label': 'tpl',
'sequence': 'term ( t + 0 )',
'step_ids': [1, 2]},
{'keyword': '$f', 'label': 'tt', 'sequence': 'term t', 'step_ids': []}, {'keyword': '$a',
'label': 'weq',
'sequence': 'wff ( t + 0 ) = t',
'step_ids': [3, 4]},
{'keyword': '$f', 'label': 'tt', 'sequence': 'term t', 'step_ids': []}, {'keyword': '$f', 'label': 'tt', 'sequence': 'term t', 'step_ids': []},
{'keyword': '$a', 'label': 'weq', 'sequence': 'wff t = t', 'step_ids': [6, 7]}, {'keyword': '$f', 'label': 'tt', 'sequence': 'term t', 'step_ids': []},
{'keyword': '$a', 'label': 'a2', 'sequence': '|- ( t + 0 ) = t', 'step_ids': }, {'keyword': '$f', 'label': 'tt', 'sequence': 'term t', 'step_ids': []},
{'keyword': '$a', 'label': 'tze', 'sequence': 'term 0', 'step_ids': []}, {'keyword': '$a',
'label': 'tpl',
'sequence': 'term ( t + 0 )',
'step_ids': [11, 12]},
{'keyword': '$f', 'label': 'tt', 'sequence': 'term t', 'step_ids': []}, {'keyword': '$a',
'label': 'weq',
'sequence': 'wff ( t + 0 ) = t',
'step_ids': [13, 14]},
{'keyword': '$f', 'label': 'tt', 'sequence': 'term t', 'step_ids': []}, {'keyword': '$a', 'label': 'tze', 'sequence': 'term 0', 'step_ids': []},
{'keyword': '$a', 'label': 'tpl', 'sequence': 'term ( t + 0 )', 'step_ids': [16, 17]}, {'keyword': '$f', 'label': 'tt', 'sequence': 'term t', 'step_ids': []},
{'keyword': '$a', 'label': 'weq', 'sequence': 'wff ( t + 0 ) = t', 'step_ids': [18, 19]}, {'keyword': '$f', 'label': 'tt', 'sequence': 'term t', 'step_ids': []},
{'keyword': '$f', 'label': 'tt', 'sequence': 'term t', 'step_ids': []}, {'keyword': '$a',
'label': 'weq',
'sequence': 'wff t = t',
'step_ids': [21, 22]},
{'keyword': '$a', 'label': 'wim', 'sequence': 'wff ( ( t + 0 ) = t -> t = t )', 'step_ids': [20, 23]}, {'keyword': '$f', 'label': 'tt', 'sequence': 'term t', 'step_ids': []},
{'keyword': '$a', 'label': 'a2', 'sequence': '|- ( t + 0 ) = t', 'step_ids': }, {'keyword': '$f', 'label': 'tt', 'sequence': 'term t', 'step_ids': []},
{'keyword': '$a', 'label': 'tze', 'sequence': 'term 0', 'step_ids': []}, {'keyword': '$a',
'label': 'tpl',
'sequence': 'term ( t + 0 )',
'step_ids': [27, 28]},
{'keyword': '$f', 'label': 'tt', 'sequence': 'term t', 'step_ids': []}, {'keyword': '$f', 'label': 'tt', 'sequence': 'term t', 'step_ids': []},
{'keyword': '$a', 'label': 'a1', 'sequence': '|- ( ( t + 0 ) = t -> ( ( t + 0 ) = t -> t = t ) )', 'step_ids': [29, 30, 31]}, {'keyword': '$a',
'label': 'mp',
'sequence': '|- ( ( t + 0 ) = t -> t = t )',
'step_ids': [15, 24, 26, 32]},
{'keyword': '\$a',
'label': 'mp',
'sequence': '|- t = t',
'step_ids': [5, 8, 10, 33]}]


### Checking proof

If you’ve done everything properly, by executing following cells you should be be able to see nice graphs.

IMPORTANT: You do not need to implement anything!

Just look if results match expected graphs

#### Overview plot

Here we only show step numbers using function draw_proof defined in sciprog library

:

from sciprog import draw_proof
# uncomment and check
#draw_proof(proof, db_mm, only_ids=True)  # all graph, only numbers

:

print()
print('************************ EXPECTED COMPLETE GRAPH  **********************************')
draw_proof(proof, db_mm, only_ids=True)


************************ EXPECTED COMPLETE GRAPH  ********************************** #### Detail plot

Here we show data from both the proof and the db_mm we calculated earlier. To avoid having a huge graph we only focus on subtree starting from step_id 24.

To understand what is shown, look at node 20: - first line contains statement wff ( t + 0 ) = t taken from line 20 of proof file - second line weq: wff t = r is taken from db_mm, and means rule labeled weq was used to derive the statement in the first line.

:

# uncomment and check
#draw_proof(proof, db_mm, step_id=24)

:

print()
print('************************* EXPECTED DETAIL GRAPH  *******************************')
draw_proof(proof, db_mm, step_id=24)


************************* EXPECTED DETAIL GRAPH  ******************************* ## A.3 Metamath top statements

We can measure the importance of theorems and definitions (in general, statements) by counting how many times they are referenced in proofs.

### A3.1 histogram

Write some code to plot the histogram of statement labels referenced by steps in proof, from most to least frequently referenced.

A label gets a count each time a step references another step with that label.

For example, in the subgraph above:

• tt is referenced 4 times, that is, there are 4 steps referencing other steps which contain the label tt

• weq is referenced 2 times

• tpl and tze are referenced 1 time each

• wim is referenced 0 times (it is only present in the last node, which being the root node cannot be referenced by any step)

NOTE: the previous counts are just for the subgraph example.

In your exercise, you will need to consider all the steps

### A3.2 print list

Below the graph, print the list of labels from most to least frequent, associating them to corresponding statement sequence taken from db_mm

:

# write here

Show solution
: tt :     term t
weq :    wff t = r
tpl :    term ( t + r )
tze :    term 0
a2 :     |- ( t + 0 ) = t
wim :    wff ( P -> Q )
mp :     |- Q
a1 :     |- ( t = r -> ( t = s -> r = s ) )


## B1 Theory

Write the solution in separate theory.txt file

### B1.1 my_fun

Given a list L of n elements, please compute the asymptotic computational complexity of the following function, explaining your reasoning.

def my_fun(L):
n = len(L)
if n <= 1:
return 1
else:
L1 = L[0:n//2]
L2 = L[n//2:]
a = my_fun(L1) + max(L1)
b = my_fun(L2) + max(L2)
return a + b


### B1.2 differences

Briefly describe the main differences between the stack and queue data structures. Please provide an example of where you would use one or the other.

## B2 plus_one

Open a text editor and edit file digi_lists.py

You are given this class:

class DigiList:
"""
This is a stripped down version of the LinkedList as previously seen,
which can only hold integer digits 0-9

NOTE: there is also a _last pointer

"""


Implement this method:

def plus_one(self):
""" MODIFIES the digi list by summing one to the integer number it represents
- you are allowed to perform multiple scans of the linked list
- remember the list has a _last pointer

- MUST execute in O(N) where N is the size of the list
- DO *NOT* create new nodes EXCEPT for special cases:
a. empty list ( [] ->  )
b. all nines ( [9,9,9] -> [1,0,0,0] )
- DO *NOT* convert the digi list to a python int
- DO *NOT* convert the digi list to a python list
- DO *NOT* reverse the digi list
"""


Test: python3 -m unittest digi_list_test.PlusOneTest

Example:

:

from digi_list_sol import *

dl = DigiList()

print(dl)

DigiList: 2,9,3,7,9,9

:

dl.last()

:

9

:

dl.plus_one()

:

print(dl)

DigiList: 2,9,3,8,0,0


Open a text editor and edit file bin_tree.py. Now implement this method:

def add_row(self, elems):
""" Takes as input a list of data and MODIFIES the tree by adding
a row of new leaves, each having as data one element of elems,
in order.

- elems size can be less than 2*|leaves|
- if elems size is more than 2*|leaves|, raises ValueError
- for simplicity, you can assume assume self is a perfect
binary tree, that is a binary tree in which all interior nodes
have two children and all leaves have the same depth
- MUST execute in O(n+|elems|)  where n is the size of the tree
- DO *NOT* use recursion
- implement it with a while and a stack (as a Python list)
"""


Test: python3 -m unittest bin_tree_test.AddRowTest

Example:

:

from bin_tree_sol import *
from bin_tree_test import bt

t = bt('a',
bt('b',
bt('d'),
bt('e')),
bt('c',
bt('f'),
bt('g')))

print(t)

a
├b
│├d
│└e
└c
├f
└g

:

t.add_row(['h','i','j','k','l'])

:

print(t)

a
├b
│├d
││├h
││└i
│└e
│ ├j
│ └k
└c
├f
│├l
│└
└g