ML Week01 Lab

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Lesson slides

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ML Week 1 Lab

Fundamentals of Machine Learning, then hands-on NumPy, Pandas & Matplotlib

Mansoura University

First Semester- 2023-2024
Department of Computer Science
Faculty of Computers and Information
ML week1
Mariam Essam

Fundamentals of Machine Learning

Initial Term plan

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Fundamentals of Machine Learning
Linear Regression and Logistic Regression
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K-Nearest Neighbours Classification
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Model Evaluation & Selection
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Support Vector Machines
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Decision Trees
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Naive Bayes Classifiers
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Neural networks
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Traditional programming
Machine learning programming

What is Machine Learning (ML)?

The study of computer programs (algorithms) that can learn by example
ML algorithms can generalize from existing examples of a task
e.g. after seeing a training set of labeled images, an image classifier can figure out how to apply labels accurately to new, previously unseen images
Machine Learning brings together statistics, computer science, and more..
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ML

Algorithms learn rules from labelled examples (training data)
The learned rules should also be able to generalize to correctly recognize or predict new examples not in the training set.
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Machine Learning vs. applied machiine learning

Pure ML is mostly concerned with algorithm behavior as a whole
ML
Applied ML is concerned with behavior on specific real life data sets.
Applied ML

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Machine Learning for fraud detection
Take a guess :What type of ML is fraud detection?

Types of ML problems

Supervised learning :
Learn to predict target values from labelled data
Classification (target values are discrete classes)
Fraud detection
Email spam detection
Diagnostics
Image classification
Regression (target values are continuous values)
Risk assessment
Score prediction
Unsupervised learning :
Find structure in unlabeled data
Clustring
Find groups of similar instances in the data
Outlier detection
Finding unusual patterns
Reinforcement learning :
Some algorithms can deal with partially labeled training data, usually a lot of unlabeled data and a little bit of labeled data.

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ML
e.g. image pixels, with k-nearest neighbor classifier
e.g. % correct predictions on test set
e.g. try a range of values for
"k" parameter in k-nearest neighbor classifier

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Feature Representations

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Python
Was invented in the 80s
Python is an evolution of ABC programming language and influenced from C, Icon, and Modula -3 programming language.
uses indentation as a syntactic feature
Python encourages modularity and reuse of code .

Python – why?

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Readable and Maintainable Code
readable and clean code base will help you to maintain and update the software without putting extra time and effort
Compatible with Major Platforms and Systems
At present, Python is supports many operating systems. You can even use Python interpreters to run the code on specific platforms and tools. Also, Python is an interpreted programming language. It allows you to run the same code on multiple platforms without recompilation
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Multiple Programming Paradigms
It supports object oriented and structured programming fully. Also, its language features support various concepts in functional and aspect-oriented programming. At the same time, Python also features a dynamic type system and automatic memory management.
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Many Open Source Frameworks and Tools
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Python – why?

Simplify Complex Software Development
Python is a general purpose programming language. Hence, you can use the programming language for developing both desktop and web applications. Also, you can use Python for developing complex scientific and numeric applications. Python is designed with features to facilitate data analysis and visualization. You can take advantage of the data analysis features of Python to create custom big data solutions without putting extra time and effort. At the same time,
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Python has a powerful libraries which very important like numpy , Pandas and Matplotlib ..
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Anaconda is an easy-to-install free package manager, environment manager, Python distribution, and collection of over 720 open-source packages offering free community support. Anaconda can be downloaded for free from https://docs.continuum.io/anaconda/

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So in conclusion python
Uses Dynamic Typing (an interpreter assigns a type to all the variables at run-time)
Simple Syntax
Interpreted
Object orientation ( in earlier python versions it didn’t support encapsulation but know it does )
_ protected variable
__ private variable

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Modules and packages
modules | Packages
a single Python file that can be imported into another module | a collection of modules organized into a directory hierarchy
the module can be directly installed using the import keyword followed by the module name.
import module_name | A package is installed using the import keyword followed by the package name, and you can access the module and sub-packages within the package name using dot notation.
import package_name.module_name
math, random, os, datetime, csv | Numpy, Pandas, Matplotlib

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scikit-learn | Python Machine Learning Library
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
SciPy Library: Scientific Computing Tools | Provides a variety of useful scientific computing tools, including statistical distributions, and a variety of specialized mathematical functions.
• With scikit-learn, it provides support for sparse matrices, a way to store large tables that consist mostly of zeros.
import scipy as sp
NumPy: Scientific Computing Library | Provides fundamental data structures used by scikit-learn, particularly multi-dimensional arrays.
• Typically, data that is input to scikit-learn will be in the form of a NumPy array.
import numpy as np
Pandas: Data Manipulation and Analysis | Provides key data structures like DataFrame
• Also, support for reading/writing data in different formats
import pandas as pd
matplotlib : visualization | import matplotlib.pyplot as plt

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Code example : Object recognition system
Dataset : The fruit dataset
https://github.com/susanli2016/Machine-Learning-with-Python/blob/22c63e943461dab8acdd02bac45710959a90ef65/fruit_data_with_colors.txt

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31 Any Question ?

Next ….
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Lab_1 Content:

Jupyter notebooks
Colab notebooks
Numpy library
Pandas library
Matplotlib library

Numpy

import numpy as np

Creating Arrays

my_list = [0,1,2,3,4]

arr = np.array(my_list)

arr

array([0, 1, 2, 3, 4])

np.arange(0,10)

array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

np.arange(0,10,3)

array([0, 3, 6, 9])

np.zeros((5,5))

array([[0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0.]])

np.ones((2,4))

array([[1., 1., 1., 1.],
       [1., 1., 1., 1.]])

np.random.randint(0,10)

np.random.randint(0,100,(3,3))

array([[83,  8, 29],
       [59, 34, 44],
       [72, 19, 10]])

np.linspace(0,10,6)

array([ 0.,  2.,  4.,  6.,  8., 10.])

np.linspace(0,10,101)

array([ 0. ,  0.1,  0.2,  0.3,  0.4,  0.5,  0.6,  0.7,  0.8,  0.9,  1. ,
        1.1,  1.2,  1.3,  1.4,  1.5,  1.6,  1.7,  1.8,  1.9,  2. ,  2.1,
        2.2,  2.3,  2.4,  2.5,  2.6,  2.7,  2.8,  2.9,  3. ,  3.1,  3.2,
        3.3,  3.4,  3.5,  3.6,  3.7,  3.8,  3.9,  4. ,  4.1,  4.2,  4.3,
        4.4,  4.5,  4.6,  4.7,  4.8,  4.9,  5. ,  5.1,  5.2,  5.3,  5.4,
        5.5,  5.6,  5.7,  5.8,  5.9,  6. ,  6.1,  6.2,  6.3,  6.4,  6.5,
        6.6,  6.7,  6.8,  6.9,  7. ,  7.1,  7.2,  7.3,  7.4,  7.5,  7.6,
        7.7,  7.8,  7.9,  8. ,  8.1,  8.2,  8.3,  8.4,  8.5,  8.6,  8.7,
        8.8,  8.9,  9. ,  9.1,  9.2,  9.3,  9.4,  9.5,  9.6,  9.7,  9.8,
        9.9, 10. ])

Operations

np.random.seed(101)
arr2 = np.random.randint(0,100,10)

arr2

array([95, 11, 81, 70, 63, 87, 75,  9, 77, 40])

print( np.random.randint(0,100,10))

[49 66 58 87 32 40 42 45 33 32]

arr2.max()

arr2.min()

arr2.mean()

60.8

arr2.argmin()

arr2.argmax()

arr2.reshape(2,5)

array([[95, 11, 81, 70, 63],
       [87, 75,  9, 77, 40]])

arr3=np.random.randint(0,100,9)

arr3.reshape(3,3)

array([[ 4, 63, 40],
       [60, 92, 64],
       [ 5, 12, 93]])

Indexing

mat = np.arange(0,100).reshape(10,10)

mat

array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9],
       [10, 11, 12, 13, 14, 15, 16, 17, 18, 19],
       [20, 21, 22, 23, 24, 25, 26, 27, 28, 29],
       [30, 31, 32, 33, 34, 35, 36, 37, 38, 39],
       [40, 41, 42, 43, 44, 45, 46, 47, 48, 49],
       [50, 51, 52, 53, 54, 55, 56, 57, 58, 59],
       [60, 61, 62, 63, 64, 65, 66, 67, 68, 69],
       [70, 71, 72, 73, 74, 75, 76, 77, 78, 79],
       [80, 81, 82, 83, 84, 85, 86, 87, 88, 89],
       [90, 91, 92, 93, 94, 95, 96, 97, 98, 99]])

row = 0
col = 1
mat[row,col]

# With Slices
mat[:,0]

array([ 0, 10, 20, 30, 40, 50, 60, 70, 80, 90])

mat[0,:]

array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

mat[0:3,0:3]

array([[ 0,  1,  2],
       [10, 11, 12],
       [20, 21, 22]])

mat[0:3,-3:-1]

array([[ 7,  8],
       [17, 18],
       [27, 28]])

Pandas

Pandas library

Library for computation with tabular data

import pandas as pd

step_data = [3620, 7891, 9761, 3907, 4338, 5373]
print(step_data)
step_counts = pd.Series(step_data, name='steps')
print(step_counts)

[3620, 7891, 9761, 3907, 4338, 5373]
0    3620
1    7891
2    9761
3    3907
4    4338
5    5373
Name: steps, dtype: int64

step_counts.index = pd.date_range('20240329',periods=6)
print(step_counts)

2024-03-29    3620
2024-03-30    7891
2024-03-31    9761
2024-04-01    3907
2024-04-02    4338
2024-04-03    5373
Freq: D, Name: steps, dtype: int64

# Just like a dictionary
print(step_counts['2024-04-01'])

# Or by index position--like an array
print(step_counts[3])

# View the data type
print(step_counts.dtypes)

int64

cycling_data = [10.7, 0, None, 2.4, 15.3,10.9, 0, None]
# Create a tuple of data
joined_data = list(zip(step_data, cycling_data))
print(joined_data)
# The dataframe
activity_df = pd.DataFrame(joined_data)
print(activity_df)

[(3620, 10.7), (7891, 0), (9761, None), (3907, 2.4), (4338, 15.3), (5373, 10.9)]
      0     1
0  3620  10.7
1  7891   0.0
2  9761   NaN
3  3907   2.4
4  4338  15.3
5  5373  10.9

activity_df = pd.DataFrame(joined_data,
                            index=pd.date_range('20240329',
                            periods=6),
                            columns=['Walking','Cycling'])
print(activity_df)

            Walking  Cycling
2024-03-29     3620     10.7
2024-03-30     7891      0.0
2024-03-31     9761      NaN
2024-04-01     3907      2.4
2024-04-02     4338     15.3
2024-04-03     5373     10.9

print(activity_df['Walking'])

2024-03-29    3620
2024-03-30    7891
2024-03-31    9761
2024-04-01    3907
2024-04-02    4338
2024-04-03    5373
Freq: D, Name: Walking, dtype: int64

print(activity_df.loc['2024-04-01'])

Walking    3907.0
Cycling       2.4
Name: 2024-04-01 00:00:00, dtype: float64

print(activity_df.loc['2024-04-01':'2024-04-03'])

            Walking  Cycling
2024-04-01     3907      2.4
2024-04-02     4338     15.3
2024-04-03     5373     10.9

print(activity_df.loc['2024-04-01':'2024-04-03',['Walking']])

            Walking
2024-04-01     3907
2024-04-02     4338
2024-04-03     5373

print(activity_df.iloc[0,:])
print(activity_df.iloc[:2,0])

Walking    3620.0
Cycling      10.7
Name: 2024-03-29 00:00:00, dtype: float64
2024-03-29    3620
2024-03-30    7891
Freq: D, Name: Walking, dtype: int64

data = pd.read_csv('D:\Deeplearning\LABS\Lab_01\data\Iris_Data.csv')

data.head()

   sepal_length  sepal_width  petal_length  petal_width      species
0           5.1          3.5           1.4          0.2  Iris-setosa
1           4.9          3.0           1.4          0.2  Iris-setosa
2           4.7          3.2           1.3          0.2  Iris-setosa
3           4.6          3.1           1.5          0.2  Iris-setosa
4           5.0          3.6           1.4          0.2  Iris-setosa

print(data.iloc[:5])

   sepal_length  sepal_width  petal_length  petal_width      species
0           5.1          3.5           1.4          0.2  Iris-setosa
1           4.9          3.0           1.4          0.2  Iris-setosa
2           4.7          3.2           1.3          0.2  Iris-setosa
3           4.6          3.1           1.5          0.2  Iris-setosa
4           5.0          3.6           1.4          0.2  Iris-setosa

#access dataset commands or from file mount drive
 
#from google.colab import drive
#drive.mount('/content/drive')

#data = pd.read_csv('/content/drive/MyDrive/Iris_Data.csv')

data.head()

#num of rows
data.shape[1]

print(data['sepal_width'])
#print(data.sepal_width)

0      3.5
1      3.0
2      3.2
3      3.1
4      3.6
      ... 
145    3.0
146    2.5
147    3.0
148    3.4
149    3.0
Name: sepal_width, Length: 150, dtype: float64

Loc and iloc are two functions in Pandas that are used to slice a data set in a Pandas DataFrame. The function .loc is typically used for label indexing and can access multiple columns, while .iloc is used for integer indexing.
The loc() function is label based data selecting method which means that we have to pass the name of the row or column which we want to select. This method includes the last element of the range passed in it, unlike iloc(). loc() can accept the boolean data unlike iloc(). Many operations can be performed using the loc() method https://www.geeksforgeeks.org/difference-between-loc-and-iloc-in-pandas-dataframe/

print(data.loc[2],'\n')
print(data.loc[2,['sepal_length']])

sepal_length            4.7
sepal_width             3.2
petal_length            1.3
petal_width             0.2
species         Iris-setosa
Name: 2, dtype: object 
 
sepal_length    4.7
Name: 2, dtype: object

print(data.iloc[:5,:])

   sepal_length  sepal_width  petal_length  petal_width      species
0           5.1          3.5           1.4          0.2  Iris-setosa
1           4.9          3.0           1.4          0.2  Iris-setosa
2           4.7          3.2           1.3          0.2  Iris-setosa
3           4.6          3.1           1.5          0.2  Iris-setosa
4           5.0          3.6           1.4          0.2  Iris-setosa

print(data.iloc[0,:])
print(data.iloc[:,0])

sepal_length            5.1
sepal_width             3.5
petal_length            1.4
petal_width             0.2
species         Iris-setosa
Name: 0, dtype: object
0      5.1
1      4.9
2      4.7
3      4.6
4      5.0
      ... 
145    6.7
146    6.3
147    6.5
148    6.2
149    5.9
Name: sepal_length, Length: 150, dtype: float64

data['species'] = data.species.str.replace('Iris-', '')
print(data.loc[(data.sepal_width>3.5)])

     sepal_length  sepal_width  petal_length  petal_width    species  \
4             5.0          3.6           1.4          0.2     setosa   
5             5.4          3.9           1.7          0.4     setosa   
10            5.4          3.7           1.5          0.2     setosa   
14            5.8          4.0           1.2          0.2     setosa   
15            5.7          4.4           1.5          0.4     setosa   
16            5.4          3.9           1.3          0.4     setosa   
18            5.7          3.8           1.7          0.3     setosa   
19            5.1          3.8           1.5          0.3     setosa   
21            5.1          3.7           1.5          0.4     setosa   
22            4.6          3.6           1.0          0.2     setosa   
32            5.2          4.1           1.5          0.1     setosa   
33            5.5          4.2           1.4          0.2     setosa   
44            5.1          3.8           1.9          0.4     setosa   
46            5.1          3.8           1.6          0.2     setosa   
48            5.3          3.7           1.5          0.2     setosa   
109           7.2          3.6           6.1          2.5  virginica   
117           7.7          3.8           6.7          2.2  virginica   
131           7.9          3.8           6.4          2.0  virginica   
 
     sepal_area  
4         18.00  
5         21.06  
10        19.98  
14        23.20  
15        25.08  
16        21.06  
18        21.66  
19        19.38  
21        18.87  
22        16.56  
32        21.32  
33        23.10  
44        19.38  
46        19.38  
48        19.61  
109       25.92  
117       29.26  
131       30.02

# Create a new column that is a product
# of both measurements
data['sepal_area'] = data.sepal_length *data.sepal_width
# Print a few rows and columns
print(data.iloc[:5, -3:])

   petal_width species  sepal_area
0          0.2  setosa       17.85
1          0.2  setosa       14.70
2          0.2  setosa       15.04
3          0.2  setosa       14.26
4          0.2  setosa       18.00

# Use the size method with a
# DataFrame to get count For a Series, use the .value_counts method
group_sizes = (data.groupby('species').size())
print(group_sizes)

species
Iris-setosa        50
Iris-versicolor    50
Iris-virginica     50
dtype: int64

In Machine Learning (and in mathematics) there are often three values that interests us:

Mean - The average value
Median - The mid point value
Mode - The most common value

https://www.w3schools.com/python/python_ml_mean_median_mode.asp

print(data.sepal_width.mean())

3.0540000000000003

print(data.petal_length.median())

4.35

print(data.petal_length.mode())

Standard deviation is a number that describes how spread out the values are. A low standard deviation means that most of the numbers are close to the mean (average) value.

A high standard deviation means that the values are spread out over a wider range Standard Deviation is calculated as the square root of the variance

A variance is the average of the squared differences from the mean. To figure out the variance, calculate the difference between each point within the data set and the mean. Once you figure that out, square and average the results.

Standard error of the mean (SEM) measures how far the sample mean (average) of the data is likely to be from the true population mean. The SEM is always smaller than the SD. SEM is calculated simply by taking the standard deviation and dividing it by the square root of the sample size

 
print(data.petal_length.std())
print(data.petal_length.var())
print(data.petal_length.sem())

1.7644204199522617
3.1131794183445156
0.1440643240210085

Matplotlib

import matplotlib.pyplot as plt

ax = plt.axes()
ax.hist(data.petal_length, bins=25);
 
ax.set(xlabel='Petal Length (cm)',
       ylabel='Frequency',
       title='Distribution of Petal Lengths');

plt.plot(data.sepal_length,
              data.sepal_width,
               marker='o')
#plt.xlim(3,9)
#plt.ylim(1,20)
plt.title("Distribution of Petal Lengths")
plt.xlabel("Petal Length (cm)")
plt.ylabel("sepal_width")

Text(0, 0.5, 'sepal_width')

plt.hist(data.sepal_length, bins=25)
plt.title("Histogram")
plt.xlabel("Petal Length (cm)")
plt.ylabel("Frequency")