markdown stringlengths 0 1.02M | code stringlengths 0 832k | output stringlengths 0 1.02M | license stringlengths 3 36 | path stringlengths 6 265 | repo_name stringlengths 6 127 |
|---|---|---|---|---|---|
Flow Control Control flow statements help you to structure the code and direct it towards your convenience and introduce loops and so on. If statements | price = -5;
if price <0:
print("Price is negative!")
elif price <1:
print("Price is too small!")
else:
print("Price is suitable.") | Price is negative!
| MIT | Week0/Week0-notes-python-fundamentals.ipynb | Magica-Chen/WebSNA-notes |
Especially in text mining, comparing strings is very important: | #Comparing strings
name1 = "edinburgh"
name2 = "Edinburgh"
if name1 == name2:
print("Equal")
else:
print("Not equal")
if name1.lower() == name2.lower():
print("Equal")
else:
print("Not equal") | Not equal
Equal
| MIT | Week0/Week0-notes-python-fundamentals.ipynb | Magica-Chen/WebSNA-notes |
Using multiple conditions: | number = 9
if number > 1 and not number > 9:
print("Number is between 1 and 10")
number = 9
name = 'johannes'
if number < 5 or 'j' in name:
print("Number is lower than 5 or the name contains a 'j'") | Number is between 1 and 10
Number is lower than 5 or the name contains a 'j'
| MIT | Week0/Week0-notes-python-fundamentals.ipynb | Magica-Chen/WebSNA-notes |
While loops | number = 4
while number > 1:
print(number)
number = number -1 | 4
3
2
| MIT | Week0/Week0-notes-python-fundamentals.ipynb | Magica-Chen/WebSNA-notes |
For loops For loops allow you to iteratre over elements in a certain collection, for example a list: | # We'll look into lists in a minute
number_list = [1, 2, 3, 4]
for item in number_list:
print(item)
list = ['a', 'b', 'c']
for item in list:
print(item) | a
b
c
| MIT | Week0/Week0-notes-python-fundamentals.ipynb | Magica-Chen/WebSNA-notes |
Ranges are also useful. Note that the upper element is not included and we can adjust the step size: | for i in range(1,4):
print(i)
for i in range(30,100, 10):
print(i) | 30
40
50
60
70
80
90
| MIT | Week0/Week0-notes-python-fundamentals.ipynb | Magica-Chen/WebSNA-notes |
Indentation Please be very careful with indentation | number_1 = 3
number_2 = 5
print('No indent (no tabs used)')
if number_1 > 1:
print('\tNumber 1 higher than 1.')
if number_2 > 5:
print('\t\tnumber 2 higher than 5')
print('\tnumber 2 higher than 5')
number_1 = 3
number_2 = 6
print('No indent (no tabs used)')
if number_1 > 1:
print('\tNumber 1... | No indent (no tabs used)
Number 1 higher than 1.
number 2 higher than 5
No indent (no tabs used)
Number 1 higher than 1.
number 2 higher than 5
number 2 higher than 5
| MIT | Week0/Week0-notes-python-fundamentals.ipynb | Magica-Chen/WebSNA-notes |
List & Tuple Lists Lists are great for collecting anything. They can contain objects of different types. For example: | names = [5, "Giovanni", "Rose", "Yongzhe", "Luciana", "Imani"] | _____no_output_____ | MIT | Week0/Week0-notes-python-fundamentals.ipynb | Magica-Chen/WebSNA-notes |
Although that is not best practice. Let's start with a list of names: | names = ["Johannes", "Giovanni", "Rose", "Yongzhe", "Luciana", "Imani"]
# Loop names
for name in names:
print('Name: '+name)
# Get 'Giovanni' from list
# Lists start counting at 0
giovanni = names[1]
print(giovanni.upper())
# Get last item
name = names[-1]
print(name.upper())
# Get second to last item
name = nam... | Name: Johannes
Name: Giovanni
Name: Rose
Name: Yongzhe
Name: Luciana
Name: Imani
GIOVANNI
IMANI
LUCIANA
First three: ['Johannes', 'Giovanni', 'Rose']
First four: ['Johannes', 'Giovanni', 'Rose', 'Yongzhe']
Up until the second to last one: ['Johannes', 'Giovanni', 'Rose', 'Yongzhe']
Last two: ['Luciana', 'Imani']
| MIT | Week0/Week0-notes-python-fundamentals.ipynb | Magica-Chen/WebSNA-notes |
Enumeration We can enumerate collections/lists that adds an index to every element: | for index, name in enumerate(names):
print(str(index) , " " , name, " is in the list.") | 0 Johannes is in the list.
1 Giovanni is in the list.
2 Rose is in the list.
3 Yongzhe is in the list.
4 Luciana is in the list.
5 Imani is in the list.
| MIT | Week0/Week0-notes-python-fundamentals.ipynb | Magica-Chen/WebSNA-notes |
Searching and editing | names = ["Johannes", "Giovanni", "Rose", "Yongzhe", "Luciana", "Imani"]
# Finding an element
print(names.index("Johannes"))
# Adding an element
names.append("Kumiko")
# Adding an element at a specific location
names.insert(2, "Roberta")
print(names)
#Removal
fruits = ["apple","orange","pear"]
del fruits[0]
fruits.... | 0
['Johannes', 'Giovanni', 'Roberta', 'Rose', 'Yongzhe', 'Luciana', 'Imani', 'Kumiko']
Fruits: ['orange']
['Johannes', 'Giovanni', 'Roberta', 'Rose', 'Yongzhe', 'Tom', 'Imani', 'Kumiko']
True
Length of the list: 8
| MIT | Week0/Week0-notes-python-fundamentals.ipynb | Magica-Chen/WebSNA-notes |
Python starts at 0!!! Sorting and copying | # Temporary sorting:
print(sorted(names))
print(names)
# Make changes permanent
names.sort()
print("Sorted names: " + str(names))
names.sort(reverse=True)
print("Reverse sorted names: " + str(names))
# Copying list (a shallow copy just duplicates the pointer to the memory address)
namez = names
namez.remove("Johannes"... | ['Yongzhe', 'Tom', 'Rose', 'Roberta', 'Kumiko', 'Imani', 'Giovanni']
['Yongzhe', 'Tom', 'Rose', 'Roberta', 'Kumiko', 'Imani', 'Giovanni']
After deep copy
['Yongzhe', 'Tom', 'Rose', 'Roberta', 'Kumiko', 'Imani']
['Yongzhe', 'Tom', 'Rose', 'Roberta', 'Kumiko', 'Imani', 'Giovanni']
['Yongzhe', 'Tom', 'Rose', 'Roberta', 'K... | MIT | Week0/Week0-notes-python-fundamentals.ipynb | Magica-Chen/WebSNA-notes |
Strings as lists Strings can be manipulated and used just like lists. This is especially handy in text mining: | course = "Predictive analytics"
print("Last nine letters: "+course[-9:])
print("Analytics in course title? " + str("analytics" in course))
print("Start location of 'analytics': " + str(course.find("analytics")))
print(course.replace("analytics","analysis"))
list_of_words = course.split(" ")
for index, word in enumerate... | Last nine letters: analytics
Analytics in course title? True
Start location of 'analytics': 11
Predictive analysis
Word 0 : Predictive
Word 1 : analytics
| MIT | Week0/Week0-notes-python-fundamentals.ipynb | Magica-Chen/WebSNA-notes |
Sets Sets only contain unique elements. They have to be declared upfront using set() and allow for operations such as intersection(): | name_set = set(names)
print(name_set)
# Add an element
name_set.add("Galina")
print(name_set)
# Discard an element
name_set.discard("Johannes")
print(name_set)
name_set2 = set(["Rose", "Tom"])
# Difference and intersection
difference = name_set - name_set2
print(difference)
intersection = name_set.intersection(name_... | {'Yongzhe', 'Kumiko', 'Roberta', 'Giovanni', 'Tom', 'Imani', 'Rose'}
{'Yongzhe', 'Kumiko', 'Roberta', 'Giovanni', 'Tom', 'Imani', 'Galina', 'Rose'}
{'Yongzhe', 'Kumiko', 'Roberta', 'Giovanni', 'Tom', 'Imani', 'Galina', 'Rose'}
{'Yongzhe', 'Kumiko', 'Roberta', 'Giovanni', 'Imani', 'Galina'}
{'Tom', 'Rose'}
| MIT | Week0/Week0-notes-python-fundamentals.ipynb | Magica-Chen/WebSNA-notes |
Dictionary & Function Dictionaries Dictionaries are a great way to store particular data as key-value pairs, which mimics the basic structure of a simple database. | courses = {"Johannes" : "Predictive analytics", "Kumiko" : "Prescriptive analytics", "Luciana" : "Descriptive analytics"}
for organizer in courses:
print(organizer + " teaches " + courses[organizer]) | Johannes teaches Predictive analytics
Kumiko teaches Prescriptive analytics
Luciana teaches Descriptive analytics
| MIT | Week0/Week0-notes-python-fundamentals.ipynb | Magica-Chen/WebSNA-notes |
We can also write: | for organizer, course in courses.items():
print(organizer + " teaches " + course)
# Adding items
courses["Imani"] = "Other analytics"
print(courses)
# Overwrite
courses["Johannes"] = "Business analytics"
print(courses)
# Remove
del courses["Johannes"]
print(courses)
# Looping values
for course in courses.values():... | Imani teaches Other analytics
Kumiko teaches Prescriptive analytics
Luciana teaches Descriptive analytics
| MIT | Week0/Week0-notes-python-fundamentals.ipynb | Magica-Chen/WebSNA-notes |
Functions Functions form the backbone of all code. You have already used some, like print(). They can be easily defined by yourself as well. | def my_function(a, b):
a = a.title()
b = b.upper()
print(a+ " "+b)
def my_function2(a, b):
a = a.title()
b = b.upper()
return a + " " + b
my_function("johannes","de smedt")
output = my_function2("johannes","de smedt")
print(output) | Johannes DE SMEDT
Johannes DE SMEDT
| MIT | Week0/Week0-notes-python-fundamentals.ipynb | Magica-Chen/WebSNA-notes |
Notice how the first function already prints, while the second returns a string we have to print ourselves. Python is weakly-typed, so a function can produce different results, like in this example: | # Different output type
def calculate_mean(a, b):
if (a>0):
return (a+b)/2
else:
return "a is negative"
output = calculate_mean(1,2)
print(output)
output = calculate_mean(0,1)
print(output) | 1.5
a is negative
| MIT | Week0/Week0-notes-python-fundamentals.ipynb | Magica-Chen/WebSNA-notes |
Comprehensions Comprehensions allow you to quickly/efficiently write lists/dictionaries: | # Finding even numbers
evens = [i for i in range(1,11) if i % 2 ==0]
print(evens) | [2, 4, 6, 8, 10]
| MIT | Week0/Week0-notes-python-fundamentals.ipynb | Magica-Chen/WebSNA-notes |
In Python, you can easily make tuples such as pairs, like here: | # Double fun
pairs = [(x,y) for x in range(1,11) for y in range(5,11) if x>y]
print(pairs) | [(6, 5), (7, 5), (7, 6), (8, 5), (8, 6), (8, 7), (9, 5), (9, 6), (9, 7), (9, 8), (10, 5), (10, 6), (10, 7), (10, 8), (10, 9)]
| MIT | Week0/Week0-notes-python-fundamentals.ipynb | Magica-Chen/WebSNA-notes |
They are also useful to perform some pre-processing, e.g., on strings: | # Operations
names = ["jamal", "maurizio", "johannes"]
titled_names = [name.title() for name in names]
print(titled_names)
j_s = [name.title() for name in names if name.lower()[0] == 'j']
print(j_s) | ['Jamal', 'Maurizio', 'Johannes']
['Jamal', 'Johannes']
| MIT | Week0/Week0-notes-python-fundamentals.ipynb | Magica-Chen/WebSNA-notes |
IO & Library | # Download some datasets
# If you are using git, then you don't need to run the following.
!wget -q https://raw.githubusercontent.com/Magica-Chen/WebSNA-notes/main/Week0/data/DM_1.csv
!wget -q https://raw.githubusercontent.com/Magica-Chen/WebSNA-notes/main/Week0/data/DM_2.csv
!wget -q https://raw.githubusercontent.com/... | _____no_output_____ | MIT | Week0/Week0-notes-python-fundamentals.ipynb | Magica-Chen/WebSNA-notes |
Reading files In Python, we can easily open any file type. Naturally, it is most suitable for plainly-structured formats such as .txt., .csv., as so on. You can also open Excel files with appropriate packages, such as pandas (more on this later). Let's read in a .csv file: | # Open a file for reading ('r')
file = open('data/DM_1.csv','r')
for line in file:
print(line) | Name,Email,City,Salary
Brent Hopkins,Cum.sociis.natoque@aodiosemper.edu,Mount Pearl,38363
Colt Bender,Vivamus.non.lorem@Proin.org,Castle Douglas,21506
Arthur Hammond,nisl.Maecenas@sed.net,Biloxi,27511
Sean Warner,enim.nisl.elementum@Vivamus.edu,Moere,25201
Tate Greene,velit.justo.nec@aliquetlobortisnisi.edu,Ipswic... | MIT | Week0/Week0-notes-python-fundamentals.ipynb | Magica-Chen/WebSNA-notes |
We can store this information in objects and start using it: | # File is looped now, hence, reread file
file = open('data/DM_1.csv','r')
# ignore the header
next(file)
# Store names with amount (i.e. columns 1 & 2)
amount_per_person = {}
for line in file:
cells = line.split(",")
amount_per_person[cells[0]] = int(cells[3])
for person, amount in sorted(amount_per_person.it... | _____no_output_____ | MIT | Week0/Week0-notes-python-fundamentals.ipynb | Magica-Chen/WebSNA-notes |
Libraries Libraries are imported by using `import`: | import numpy
import pandas
import sklearn | _____no_output_____ | MIT | Week0/Week0-notes-python-fundamentals.ipynb | Magica-Chen/WebSNA-notes |
If you haven't installed sklearn, please install it by: | !pip install sklearn | Collecting sklearn
Downloading sklearn-0.0.tar.gz (1.1 kB)
Requirement already satisfied: scikit-learn in c:\users\zchen112\anaconda3\lib\site-packages (from sklearn) (0.24.1)
Requirement already satisfied: joblib>=0.11 in c:\users\zchen112\anaconda3\lib\site-packages (from scikit-learn->sklearn) (1.0.1)
Requirement ... | MIT | Week0/Week0-notes-python-fundamentals.ipynb | Magica-Chen/WebSNA-notes |
We can import just a few bits using `from`, or create aliases using `as`: | import math as m
from math import pi
print(numpy.add(1, 2))
print(pi)
print(m.sin(1)) | 3
3.141592653589793
0.8414709848078965
| MIT | Week0/Week0-notes-python-fundamentals.ipynb | Magica-Chen/WebSNA-notes |
In the next part, some basic procedures that exist in NumPy, pandas, and scikit-learn are covered. This only scratches the surface of the possibilities, and many other functions and code will be used later on. Make sure to search around for the possiblities that exist yourself, and get a grasp of how the modules are ca... | import numpy as np
import pandas as pd
import sklearn | _____no_output_____ | MIT | Week0/Week0-notes-python-fundamentals.ipynb | Magica-Chen/WebSNA-notes |
Numpy | # Create empty arrays/matrices
empty_array = np.zeros(5)
empty_matrix = np.zeros((5,2))
print('Empty array: \n',empty_array)
print('Empty matrix: \n',empty_matrix)
# Create matrices
mat = np.array([[1,2,3],[4,5,6]])
print('Matrix: \n', mat)
print('Transpose: \n', mat.T)
print('Item 2,2: ', mat[1,1])
print('Item 2,3: ... | Matrix:
[[1 2 3]
[4 5 6]]
Transpose:
[[1 4]
[2 5]
[3 6]]
Item 2,2: 5
Item 2,3: 6
rows and columns: (2, 3)
Sum total matrix: 21
Sum row 1: 6
Sum row 2: 15
Sum column 2: 9
| MIT | Week0/Week0-notes-python-fundamentals.ipynb | Magica-Chen/WebSNA-notes |
pandas Creating dataframes pandas is great for reading and creating datasets, as well as performing basic operations on them. | # Creating a matrix with three rows of data
data = [['johannes',10], ['giovanni',2], ['john',3]]
# Creating and printing a pandas DataFrame object from the matrix
df = pd.DataFrame(data)
print(df)
# Adding columns to the DataFrame object
df.columns = ['names', 'years']
print(df)
df_2 = pd.DataFrame(data = data, column... | names years
0 johannes 10
1 giovanni 2
2 john 3
3 giovanni 2
4 john 3
names years
5 giovanni 2
6 john 3
7 giovanni 2
8 john 3
9 johannes 10
| MIT | Week0/Week0-notes-python-fundamentals.ipynb | Magica-Chen/WebSNA-notes |
Reading files You can read files: | dataset = pd.read_csv('data/DM_1.csv')
print(dataset.head()) | Name Email City \
0 Brent Hopkins Cum.sociis.natoque@aodiosemper.edu Mount Pearl
1 Colt Bender Vivamus.non.lorem@Proin.org Castle Douglas
2 Arthur Hammond nisl.Maecenas@sed.net Biloxi
3 Se... | MIT | Week0/Week0-notes-python-fundamentals.ipynb | Magica-Chen/WebSNA-notes |
Using dataframes | # Print all unique values of the column names
print(df['names'].unique())
# Print all values and their frequency:
print(df['names'].value_counts())
print(df['years'].value_counts())
# Add a column names 'code' with all zeros
df['code'] = np.zeros(10)
print(df) | names years code
0 johannes 10 0.0
1 giovanni 2 0.0
2 john 3 0.0
3 giovanni 2 0.0
4 john 3 0.0
5 giovanni 2 0.0
6 john 3 0.0
7 giovanni 2 0.0
8 john 3 0.0
9 johannes 10 0.0
| MIT | Week0/Week0-notes-python-fundamentals.ipynb | Magica-Chen/WebSNA-notes |
You can also easily find things in a DataFrame use `.loc`: | # Rows 2 to 5 and all columns:
print(df.loc[2:5, :])
# Looping columns
for variable in df.columns:
print(df[variable])
# Looping columns and obtaining the values (which returns an array)
for variable in df.columns:
print(df[variable].values) | ['johannes' 'giovanni' 'john' 'giovanni' 'john' 'giovanni' 'john'
'giovanni' 'john' 'johannes']
[10 2 3 2 3 2 3 2 3 10]
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
| MIT | Week0/Week0-notes-python-fundamentals.ipynb | Magica-Chen/WebSNA-notes |
preparing datasets | dataset_1 = pd.read_csv('data/DM_1.csv', encoding='latin1')
dataset_2 = pd.read_csv('data/DM_2.csv', encoding='latin1')
dataset_1
dataset_2
dataset_2.columns = ['First name', 'Last name', 'Days active']
dataset_2 | _____no_output_____ | MIT | Week0/Week0-notes-python-fundamentals.ipynb | Magica-Chen/WebSNA-notes |
We can convert the second dataset to only have 1 column for names: | # .title() can be used to only make the first letter a capital
names = [dataset_2.loc[i,'First name'] + " " + dataset_2.loc[i,'Last name'].title() for i in range(0, len(dataset_2))]
# Make a new column for the name
dataset_2['Name'] = names
# Remove the old columns
dataset_2 = dataset_2.drop(['First name', 'Last name... | _____no_output_____ | MIT | Week0/Week0-notes-python-fundamentals.ipynb | Magica-Chen/WebSNA-notes |
Bringing together the datasets Now the datasets are made compatible, we can merge them in a few different ways. | # A left join starts from the left dataset, in this case dataset_1, and for every row matches the value in the
# column used for joining. As you will see, the result has 22 rows since some names appear multiple times in
# the second dataset dataset_2.
both = pd.merge(dataset_1, dataset_2, on='Name', how='left')
both... | _____no_output_____ | MIT | Week0/Week0-notes-python-fundamentals.ipynb | Magica-Chen/WebSNA-notes |
Notice how observation 12 is missing, as there is no corresponding value in `dataset_1`. | both = pd.merge(dataset_1, dataset_2, on='Name', how='outer')
both | _____no_output_____ | MIT | Week0/Week0-notes-python-fundamentals.ipynb | Magica-Chen/WebSNA-notes |
In the last table, we have 23 rows, as both matching and non-matching values are returned.Merging datasets can be really helpful. This code should give you ample ideas on how to do this quickly yourself. As always, there are a number of ways of achieving the same result. Don't hold back to explore other solutions that ... | from sklearn import datasets as ds
# Load the Boston Housing dataset
dataset = ds.load_boston()
# It is a dictionary, see the keys for details:
print(dataset.keys())
# The 'DESCR' key holds a description text for the whole dataset
print(dataset['DESCR'])
# The data (independent variables) are stored under the 'data' k... | 0.7406426641094095
| MIT | Week0/Week0-notes-python-fundamentals.ipynb | Magica-Chen/WebSNA-notes |
Very often, we need to perform an operation on a single observation. In that case, we have to reshape the data using numpy: | # Consider a single observation
so = df.loc[2, :]
print(so)
# Just the values of the observation without meta data
print(so.values)
# Reshaping yields a new matrix with one row with as many columns as the original observation (indicated by the -1)
print(np.reshape(so.values, (1, -1)))
# For two observations:
so_2 = ... | [[2.7290e-02 0.0000e+00 7.0700e+00 0.0000e+00 4.6900e-01 7.1850e+00
6.1100e+01 4.9671e+00 2.0000e+00 2.4200e+02 1.7800e+01 3.9283e+02
4.0300e+00]
[3.2370e-02 0.0000e+00 2.1800e+00 0.0000e+00 4.5800e-01 6.9980e+00
4.5800e+01 6.0622e+00 3.0000e+00 2.2200e+02 1.8700e+01 3.9463e+02
2.9400e+00]]
| MIT | Week0/Week0-notes-python-fundamentals.ipynb | Magica-Chen/WebSNA-notes |
This concludes our quick run-through of some basic functionality of the modules. Later on, we will use more and more specialized functions and objects, but for now this allows you to play around with data already. Visualisation The visualisations often require a bit of tricks and extra lines of code to make things loo... | # First, we need to import our packages
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd | _____no_output_____ | MIT | Week0/Week0-notes-python-fundamentals.ipynb | Magica-Chen/WebSNA-notes |
Pie and bar chart | # Data to plot
labels = 'classification', 'regression', 'time series'
sizes = [10, 22, 2]
colors = ['lightblue', 'lightgreen', 'pink']
# Allows us to highlight a certain piece of the pie chart
explode = (0.1, 0, 0)
# Plot a pie chart with the pie() function. Notice how various parameters are given for coloring, l... | _____no_output_____ | MIT | Week0/Week0-notes-python-fundamentals.ipynb | Magica-Chen/WebSNA-notes |
Adding a legend: | patches, texts = plt.pie(sizes, colors=colors, shadow=True, startangle=90)
plt.legend(patches, labels, loc="best")
plt.axis('equal')
plt.title("Pie chart of modelling techniques")
plt.show()
# Bar charts are relatively similar. Here we use the bar() function
plt.bar(labels, sizes, align='center')
plt.xticks(labels)
plt... | _____no_output_____ | MIT | Week0/Week0-notes-python-fundamentals.ipynb | Magica-Chen/WebSNA-notes |
Histogram | # This function plots a diagram with the 'data' object providing the data
# bins are calculated automatically, as indicated by the 'auto' option, which makes them relatively balanced and
# sets appropriate boundaries
# color sets the color of the bars
# the rwidth sets the bars to somewhat slightly less wide than the b... | _____no_output_____ | MIT | Week0/Week0-notes-python-fundamentals.ipynb | Magica-Chen/WebSNA-notes |
See how we cut the tail off the distribution. | # Now, let's build a histogram with radomly generated data that follows a normal distribution
# Mean = 10, stddev = 15, sample size = 1,000
# More on random numbers will follow in module 2
s = np.random.normal(10, 15, 1000)
plt.hist(x=s, bins='auto', color='#008000', rwidth=0.85)
plt.grid(axis='y')
plt.xlabel('Value')... | _____no_output_____ | MIT | Week0/Week0-notes-python-fundamentals.ipynb | Magica-Chen/WebSNA-notes |
Boxplot | # Boxplots are even easier. We can just use the boxplot() function without many parameters
# We use the implementation of Pandas, which relies on Matplotlib in the background
# We now use subplots.
data = [3,8,3,4,1,7,5,3,8,2,7,3,1,6,10,10,3,6,5,10]
# Subplot with 1 row, 2 columns, here we add figure 1 of 2 (first row,... | _____no_output_____ | MIT | Week0/Week0-notes-python-fundamentals.ipynb | Magica-Chen/WebSNA-notes |
Boxplot for multiple variables: | # Generate 4 columns with 10 observations
df = pd.DataFrame(data = np.random.random(size=(10,3)), columns = ['class.','reg.','time series'])
print(df)
boxplot = df.boxplot()
plt.title('Triple boxplot')
plt.show()
df = pd.DataFrame(data = np.random.random(size=(10,3)), columns = ['class.','reg.','time series'])
df['nu... | class. reg. time series
0 0.402362 0.348025 0.893360
1 0.496534 0.454527 0.631422
2 0.268591 0.815153 0.371747
3 0.596372 0.121358 0.591864
4 0.575830 0.964928 0.908575
5 0.380839 0.435604 0.488436
6 0.788519 0.562830 0.303210
7 0.424057 0.888664 0.476388
8 0.... | MIT | Week0/Week0-notes-python-fundamentals.ipynb | Magica-Chen/WebSNA-notes |
Scatterplot | # We load the data gain
x = [3,8,3,4,1,7,5,3,8,2,7,3,1,6,10,10,3,6,5,10]
y = [10,7,2,7,5,4,2,3,4,1,5,7,8,4,10,2,3,4,5,6]
# Here, we build a simple scatterplot of the two variables
plt.scatter(x,y)
plt.xlabel('x')
plt.ylabel('y')
plt.title('Simple scatterplot')
plt.show() | _____no_output_____ | MIT | Week0/Week0-notes-python-fundamentals.ipynb | Magica-Chen/WebSNA-notes |
Hard to tell which variable is what, but it gives an overall impression of the data. | # A simple line plot
# We use the plot function for this. 'o-' indicates we want to use circles for markers and connect them with lines
plt.plot(x,'o-',color='blue',)
# Here we use 'x--' for cross-shaped markers connected with intermittent lines
plt.plot(y,'x--',color='red')
plt.xlabel('Time')
plt.ylabel('Value')
plt... | _____no_output_____ | MIT | Week0/Week0-notes-python-fundamentals.ipynb | Magica-Chen/WebSNA-notes |
---_You are currently looking at **version 1.0** of this notebook. To download notebooks and datafiles, as well as get help on Jupyter notebooks in the Coursera platform, visit the [Jupyter Notebook FAQ](https://www.coursera.org/learn/python-text-mining/resources/d9pwm) course resource._--- Assignment 4 - Document Sim... | import numpy as np
import nltk
nltk.download('punkt')
nltk.download('averaged_perceptron_tagger')
nltk.download('wordnet')
from nltk.corpus import wordnet as wn
import pandas as pd
def convert_tag(tag):
"""Convert the tag given by nltk.pos_tag to the tag used by wordnet.synsets"""
tag_dict = {'N': 'n',... | [nltk_data] Downloading package punkt to /home/jovyan/nltk_data...
[nltk_data] Package punkt is already up-to-date!
[nltk_data] Downloading package averaged_perceptron_tagger to
[nltk_data] /home/jovyan/nltk_data...
[nltk_data] Package averaged_perceptron_tagger is already up-to-
[nltk_data] date!
[nltk_d... | MIT | 4-5 Applied Text Mining in Python/Assignment 4.ipynb | MLunov/Applied-Data-Science-with-Python-Specialization-Michigan |
test_document_path_similarityUse this function to check if doc_to_synsets and similarity_score are correct.*This function should return the similarity score as a float.* | def test_document_path_similarity():
doc1 = 'This is a function to test document_path_similarity.'
doc2 = 'Use this function to see if your code in doc_to_synsets \
and similarity_score is correct!'
return document_path_similarity(doc1, doc2)
test_document_path_similarity() | _____no_output_____ | MIT | 4-5 Applied Text Mining in Python/Assignment 4.ipynb | MLunov/Applied-Data-Science-with-Python-Specialization-Michigan |
___`paraphrases` is a DataFrame which contains the following columns: `Quality`, `D1`, and `D2`.`Quality` is an indicator variable which indicates if the two documents `D1` and `D2` are paraphrases of one another (1 for paraphrase, 0 for not paraphrase). | # Use this dataframe for questions most_similar_docs and label_accuracy
paraphrases = pd.read_csv('paraphrases.csv')
paraphrases.head() | _____no_output_____ | MIT | 4-5 Applied Text Mining in Python/Assignment 4.ipynb | MLunov/Applied-Data-Science-with-Python-Specialization-Michigan |
___ most_similar_docsUsing `document_path_similarity`, find the pair of documents in paraphrases which has the maximum similarity score.*This function should return a tuple `(D1, D2, similarity_score)`* | def most_similar_docs():
# Your Code Here
return max(map(document_path_similarity, paraphrases['D1'], paraphrases['D2'])) # Your Answer Here
most_similar_docs() | _____no_output_____ | MIT | 4-5 Applied Text Mining in Python/Assignment 4.ipynb | MLunov/Applied-Data-Science-with-Python-Specialization-Michigan |
label_accuracyProvide labels for the twenty pairs of documents by computing the similarity for each pair using `document_path_similarity`. Let the classifier rule be that if the score is greater than 0.75, label is paraphrase (1), else label is not paraphrase (0). Report accuracy of the classifier using scikit-learn's... | def label_accuracy():
from sklearn.metrics import accuracy_score
paraphrases['labels'] = [1 if i > 0.75 else 0 for i in map(document_path_similarity, paraphrases['D1'], paraphrases['D2'])] # Your Code Here
return accuracy_score(paraphrases['Quality'], paraphrases['labels']) # Your Answer Here
label_ac... | _____no_output_____ | MIT | 4-5 Applied Text Mining in Python/Assignment 4.ipynb | MLunov/Applied-Data-Science-with-Python-Specialization-Michigan |
Part 2 - Topic ModellingFor the second part of this assignment, you will use Gensim's LDA (Latent Dirichlet Allocation) model to model topics in `newsgroup_data`. You will first need to finish the code in the cell below by using gensim.models.ldamodel.LdaModel constructor to estimate LDA model parameters on the corpus... | import pickle
import gensim
from sklearn.feature_extraction.text import CountVectorizer
# Load the list of documents
with open('newsgroups', 'rb') as f:
newsgroup_data = pickle.load(f)
# Use CountVectorizor to find three letter tokens, remove stop_words,
# remove tokens that don't appear in at least 20 documents... | _____no_output_____ | MIT | 4-5 Applied Text Mining in Python/Assignment 4.ipynb | MLunov/Applied-Data-Science-with-Python-Specialization-Michigan |
lda_topicsUsing `ldamodel`, find a list of the 10 topics and the most significant 10 words in each topic. This should be structured as a list of 10 tuples where each tuple takes on the form:`(9, '0.068*"space" + 0.036*"nasa" + 0.021*"science" + 0.020*"edu" + 0.019*"data" + 0.017*"shuttle" + 0.015*"launch" + 0.015*"ava... | def lda_topics():
# Your Code Here
return ldamodel.print_topics(num_topics=10, num_words=10) # Your Answer Here
lda_topics() | _____no_output_____ | MIT | 4-5 Applied Text Mining in Python/Assignment 4.ipynb | MLunov/Applied-Data-Science-with-Python-Specialization-Michigan |
topic_distributionFor the new document `new_doc`, find the topic distribution. Remember to use vect.transform on the the new doc, and Sparse2Corpus to convert the sparse matrix to gensim corpus.*This function should return a list of tuples, where each tuple is `(topic, probability)`* | new_doc = ["\n\nIt's my understanding that the freezing will start to occur because \
of the\ngrowing distance of Pluto and Charon from the Sun, due to it's\nelliptical orbit. \
It is not due to shadowing effects. \n\n\nPluto can shadow Charon, and vice-versa.\n\nGeorge \
Krumins\n-- "]
def topic_distribution():
... | _____no_output_____ | MIT | 4-5 Applied Text Mining in Python/Assignment 4.ipynb | MLunov/Applied-Data-Science-with-Python-Specialization-Michigan |
topic_namesFrom the list of the following given topics, assign topic names to the topics you found. If none of these names best matches the topics you found, create a new 1-3 word "title" for the topic.Topics: Health, Science, Automobiles, Politics, Government, Travel, Computers & IT, Sports, Business, Society & Lifes... | def topic_names():
# Your Code Here
return ['Automobiles', 'Health', 'Science',
'Politics',
'Sports',
'Business', 'Society & Lifestyle',
'Religion', 'Education', 'Computers & IT'] # Your Answer Here
topic_names() | _____no_output_____ | MIT | 4-5 Applied Text Mining in Python/Assignment 4.ipynb | MLunov/Applied-Data-Science-with-Python-Specialization-Michigan |
NOAA extreme weather eventsThe [National Oceanic and Atmospheric Administration](https://en.wikipedia.org/wiki/National_Oceanic_and_Atmospheric_Administration) has a database of extreme weather events that contains lots of detail for every year ([Link](https://www.climate.gov/maps-data/dataset/severe-storms-and-extrem... | import pandas as pd
import numpy as np
import random
import geopandas
import matplotlib.pyplot as plt
pd.set_option('display.max_columns', None) # Unlimited columns
# Custom function for displaying the shape and head of a dataframe
def display(df, n=5):
print(df.shape)
return df.head(n) | _____no_output_____ | MIT | notebooks/DMA8 - NOAA weather events by coords.ipynb | KimDuclos/liveSafe-data |
Get map of US counties | # Import a shape file with all the counties in the US.
# Note how it doesn't include all the same territories as the
# quake contour map.
counties = geopandas.read_file('../data_input/1_USCounties/')
# Turn state codes from strings to integers
for col in ['STATE_FIPS', 'CNTY_FIPS', 'FIPS']:
counties[col] = counti... | _____no_output_____ | MIT | notebooks/DMA8 - NOAA weather events by coords.ipynb | KimDuclos/liveSafe-data |
Process NOAA data for one year onlyAs a starting point that I'll generalize later. | # Get NOAA extreme weather event data for one year
df1 = pd.read_csv('../data_local/NOAA/StormEvents_details-ftp_v1.0_d2018_c20190422.csv')
print(df1.shape)
print(df1.columns)
df1.head(2)
# Extract only a few useful columns
df2 = df1[['TOR_F_SCALE','EVENT_TYPE','BEGIN_LAT','BEGIN_LON']].copy()
# Remove any rows with n... | _____no_output_____ | MIT | notebooks/DMA8 - NOAA weather events by coords.ipynb | KimDuclos/liveSafe-data |
NOAA file processing functionGeneralize the previous operations so they can apply to the data for any year | def process_noaa(filepath):
"""
Process one year of NOAA Extreme weather events. Requires
the list of official counties and the list of official weather
event types.
Inputs
------
filepath (string) : file path for the list of events from one year.
Outputs
-------
r... | _____no_output_____ | MIT | notebooks/DMA8 - NOAA weather events by coords.ipynb | KimDuclos/liveSafe-data |
Process all the available data | import glob
import os
# Read the CSV files for each year going back to 1996 (the first year
# when many of these event types started being recorded)
path = '../data_local/NOAA/'
filenames = sorted(glob.glob(os.path.join(path, '*.csv')))
layers = []
# Aggregate the dataframes in a list
for name in filenames:
year... | _____no_output_____ | MIT | notebooks/DMA8 - NOAA weather events by coords.ipynb | KimDuclos/liveSafe-data |
Process tornado dataIn 2007, the National Weather Service (NWS) switched their scale for measuring tornado intensity, from the Fujita (F) scale to the Enhanced Fujita (EF) scale. I will lump them together here and just make a note for the user that the scale means something slightly different before and after 2007. ... | # Tornadoes by magnitude, using the NWS's original labels.
# Notice the two different scales and also a label for 'unknown'
tornadoes.TOR_F_SCALE.value_counts()
# Function that extracts the scale level and sets unkwnown to zero.
def process_fujita(x):
if x[-1] == 'U':
return 0
else:
return int(x... | _____no_output_____ | MIT | notebooks/DMA8 - NOAA weather events by coords.ipynb | KimDuclos/liveSafe-data |
Visualizing the data | # Sample of 2000 storms in the Lower48
fig, ax = plt.subplots(figsize=(20,20))
counties.plot(ax=ax, color='white', edgecolor='black');
storms.sample(2000).plot(ax=ax, marker='o')
ax.set_xlim(-125.0011,-66.9326)
ax.set_ylim(24.9493, 49.5904)
plt.show() | _____no_output_____ | MIT | notebooks/DMA8 - NOAA weather events by coords.ipynb | KimDuclos/liveSafe-data |
Floods and tornadoes show basically the same distribution, so I won't plot them separately. For reference, this is what the dataframes that we're about to export look like. | display(storms)
display(floods)
display(tornadoes) | (30898, 3)
| MIT | notebooks/DMA8 - NOAA weather events by coords.ipynb | KimDuclos/liveSafe-data |
Export! | storms.to_file("../data_output/5__NOAA/storms.geojson",
driver='GeoJSON')
floods.to_file("../data_output/5__NOAA/floods.geojson",
driver='GeoJSON')
tornadoes.to_file("../data_output/5__NOAA/tornadoes.geojson",
driver='GeoJSON') | _____no_output_____ | MIT | notebooks/DMA8 - NOAA weather events by coords.ipynb | KimDuclos/liveSafe-data |
**1D Convolutional Neural Networks**"A Convolutional Neural Network (ConvNet/CNN) is a Deep Learning algorithm which can take in an input image, assign importance (learnable weights and biases) to various aspects/objects in the image and be able to differentiate one from the other. The pre-processing required in a Co... | pip install tensorflow
!pip install fsspec
# cnn model
from numpy import mean
from numpy import std
from numpy import dstack
from pandas import read_csv
from matplotlib import pyplot
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Flatten
from keras.layers import Dropout
from... | (7352, 128, 9) (7352, 1)
(2947, 128, 9) (2947, 1)
(7352, 128, 9) (7352, 6) (2947, 128, 9) (2947, 6)
>#1: 90.363
>#2: 88.157
>#3: 92.467
>#4: 90.601
>#5: 90.227
>#6: 90.058
>#7: 91.992
>#8: 90.363
>#9: 89.786
>#10: 91.211
[90.3630793094635, 88.15745115280151, 92.46691465377808, 90.60060977935791, 90.22734761238098, 90.0... | MIT | code/clustering_and_classification/1D_CNN.ipynb | iotanalytics/IoTTutorial |
Pymaceuticals Inc.--- Analysis* Overall, it is clear that Capomulin is a viable drug regimen to reduce tumor growth.* Capomulin had the most number of mice complete the study, with the exception of Remicane, all other regimens observed a number of mice deaths across the duration of the study. * There is a strong corre... | # Dependencies and Setup
import matplotlib.pyplot as plt
import pandas as pd
import scipy.stats as st
# Study data files
mouse_metadata_path = "data/Mouse_metadata.csv"
study_results_path = "data/Study_results.csv"
# Read the mouse data and the study results
mouse_metadata = pd.read_csv(mouse_metadata_path)
study_res... | _____no_output_____ | ADSL | pymaceuticals_starter_with_plots.ipynb | vaideheeshah13/MatPlotLib |
Summary Statistics | # Generate a summary statistics table of mean, median, variance, standard deviation, and SEM of the tumor volume for each regimen
# Use groupby and summary statistical methods to calculate the following properties of each drug regimen:
# mean, median, variance, standard deviation, and SEM of the tumor volume.
# Asse... | _____no_output_____ | ADSL | pymaceuticals_starter_with_plots.ipynb | vaideheeshah13/MatPlotLib |
Bar and Pie Charts | # Generate a bar plot showing the total number of measurements taken on each drug regimen using pandas.
# Generate a bar plot showing the total number of measurements taken on each drug regimen using using pyplot.
# Generate a pie plot showing the distribution of female versus male mice using pandas
# Generate a pie... | _____no_output_____ | ADSL | pymaceuticals_starter_with_plots.ipynb | vaideheeshah13/MatPlotLib |
Quartiles, Outliers and Boxplots | # Calculate the final tumor volume of each mouse across four of the treatment regimens:
# Capomulin, Ramicane, Infubinol, and Ceftamin
# Start by getting the last (greatest) timepoint for each mouse
# Merge this group df with the original dataframe to get the tumor volume at the last timepoint
# Put treatments in... | _____no_output_____ | ADSL | pymaceuticals_starter_with_plots.ipynb | vaideheeshah13/MatPlotLib |
Line and Scatter Plots | # Generate a line plot of tumor volume vs. time point for a mouse treated with Capomulin
# Generate a scatter plot of average tumor volume vs. mouse weight for the Capomulin regimen
| _____no_output_____ | ADSL | pymaceuticals_starter_with_plots.ipynb | vaideheeshah13/MatPlotLib |
Correlation and Regression | # Calculate the correlation coefficient and linear regression model
# for mouse weight and average tumor volume for the Capomulin regimen
| The correlation between mouse weight and the average tumor volume is 0.84
| ADSL | pymaceuticals_starter_with_plots.ipynb | vaideheeshah13/MatPlotLib |
Microsoft Insights Module Example Notebook | %run /OEA_py
%run /NEW_Insights_py
# 0) Initialize the OEA framework and Insights module class notebook.
oea = OEA()
insights = Insights()
insights.ingest() | _____no_output_____ | CC-BY-4.0 | modules/Microsoft_Data/Microsoft_Education_Insights_Premium/notebook/Insights_module_ingestion.ipynb | ahalabi/OpenEduAnalytics |
WeatherPy---- Note* Instructions have been included for each segment. You do not have to follow them exactly, but they are included to help you think through the steps. | w_api = 'f85af5acc7275a9eb032d03a3cca5913'
# Dependencies and Setup
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import requests
import time
from scipy.stats import linregress
# Import API key
# from api_keys import weather_api_key
# Incorporated citipy to determine city based on latitude a... | _____no_output_____ | ADSL | starter_code/old/WeatherPy.ipynb | rbvancleave/python-api-challenge |
Generate Cities List | # List for holding lat_lngs and cities
lat_lngs = []
cities = []
# Create a set of random lat and lng combinations
lats = np.random.uniform(lat_range[0], lat_range[1], size=1500)
lngs = np.random.uniform(lng_range[0], lng_range[1], size=1500)
lat_lngs = zip(lats, lngs)
# Identify nearest city for each lat, lng combin... | _____no_output_____ | ADSL | starter_code/old/WeatherPy.ipynb | rbvancleave/python-api-challenge |
Perform API Calls* Perform a weather check on each city using a series of successive API calls.* Include a print log of each city as it'sbeing processed (with the city number and city name). Convert Raw Data to DataFrame* Export the city data into a .csv.* Display the DataFrame Inspect the data and remove the cities... | # Get the indices of cities that have humidity over 100%.
# Make a new DataFrame equal to the city data to drop all humidity outliers by index.
# Passing "inplace=False" will make a copy of the city_data DataFrame, which we call "clean_city_data".
| _____no_output_____ | ADSL | starter_code/old/WeatherPy.ipynb | rbvancleave/python-api-challenge |
Matplotlib Applied **Aim: SWBAT create a figure with 4 subplots of varying graph types.** | import matplotlib.pyplot as plt
import numpy as np
from numpy.random import seed, randint
seed(100)
# Create Figure and Subplots
fig, axes = plt.subplots(2,2, figsize=(10,6), sharex=True, sharey=True, dpi=100)
# Define the colors and markers to use
colors = {0:'g', 1:'b', 2:'r', 3:'y'}
markers = {0:'o', 1:'x', 2:'*',... | _____no_output_____ | MIT | Phase_1/ds-data_visualization-main/Matplotlib_Applied.ipynb | BenJMcCarty/ds-east-042621-lectures |
Go through and play with the code above to try answer the questions below:- What do you think `sharex` and `sharey` do?- What does the `dpi` argument control?- What does `numpy.ravel()` do, and why do they call it here?- What does `yaxis.set_ticks_position()` do?- How do they use the `colors` and `markers` dictionari... | from numpy.random import seed, randint
seed(100)
x = sorted(randint(0,10,10))
x2 = sorted(randint(0,20,10))
y = sorted(randint(0,10,10))
y2 = sorted(randint(0,20,10)) | _____no_output_____ | MIT | Phase_1/ds-data_visualization-main/Matplotlib_Applied.ipynb | BenJMcCarty/ds-east-042621-lectures |
Great tutorial on matplotlibhttps://www.machinelearningplus.com/plots/matplotlib-tutorial-complete-guide-python-plot-examples/ | fig | _____no_output_____ | MIT | Phase_1/ds-data_visualization-main/Matplotlib_Applied.ipynb | BenJMcCarty/ds-east-042621-lectures |
Now You Code 2: Paint PricingHouse Depot, a big-box hardware retailer, has contracted you to create an app to calculate paint prices. The price of paint is determined by the following factors:- Everyday quality paint is `$19.99` per gallon.- Select quality paint is `$24.99` per gallon.- Premium quality paint is `$32.9... | # Step 2: Write code here
choices = ["everyday", "select", "premium"]
colorChoices = ["y", "n"]
quality = input("which paint quality would you like? ["everyday", "select", "premium"]")
if quality in choices
if quality == "everyday":
quality =19.99
elif quality == "select":
quality = 24.99
e... | _____no_output_____ | MIT | content/lessons/04/Now-You-Code/NYC2-Paint-Matching.ipynb | MahopacHS/spring2019-rizzenM |
Kaggle ML and Data Science Survey Analysis Data 512, Final Project Plan - Zicong Liang Project MotivationThis project an analysis for a survey about Machine Learning and Data Science. Recently, lots of people are talking about machine learning and Data Science. In addition, more and more companies hire data science t... | import pandas as pd
my_data = pd.read_csv("multipleChoiceResponses.csv", encoding='ISO-8859-1', delimiter=',', low_memory=False)
my_data.head()
my_data.shape | _____no_output_____ | MIT | Final Project Plan.ipynb | lzctony/data-512-finalproject |
Continuous training pipeline with Kubeflow Pipeline and AI Platform **Learning Objectives:**1. Learn how to use Kubeflow Pipeline(KFP) pre-build components (BiqQuery, AI Platform training and predictions)1. Learn how to use KFP lightweight python components1. Learn how to build a KFP with these components1. Learn how ... | #!grep 'BASE_IMAGE =' -A 5 pipeline/covertype_training_pipeline.py
!pip list | grep kfp | kfp 1.0.0
kfp-pipeline-spec 0.1.7
kfp-server-api 1.5.0
| Apache-2.0 | on_demand/kfp-caip-sklearn/lab-02-kfp-pipeline/lab-02.ipynb | bharathraja23/mlops-on-gcp |
The pipeline uses a mix of custom and pre-build components.- Pre-build components. The pipeline uses the following pre-build components that are included with the KFP distribution: - [BigQuery query component](https://github.com/kubeflow/pipelines/tree/0.2.5/components/gcp/bigquery/query) - [AI Platform Training ... | %%writefile ./pipeline/covertype_training_pipeline.py
# Copyright 2019 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unles... | _____no_output_____ | Apache-2.0 | on_demand/kfp-caip-sklearn/lab-02-kfp-pipeline/lab-02.ipynb | bharathraja23/mlops-on-gcp |
The custom components execute in a container image defined in `base_image/Dockerfile`. | !cat base_image/Dockerfile | _____no_output_____ | Apache-2.0 | on_demand/kfp-caip-sklearn/lab-02-kfp-pipeline/lab-02.ipynb | bharathraja23/mlops-on-gcp |
The training step in the pipeline employes the AI Platform Training component to schedule a AI Platform Training job in a custom training container. The custom training image is defined in `trainer_image/Dockerfile`. | !cat trainer_image/Dockerfile | _____no_output_____ | Apache-2.0 | on_demand/kfp-caip-sklearn/lab-02-kfp-pipeline/lab-02.ipynb | bharathraja23/mlops-on-gcp |
Building and deploying the pipelineBefore deploying to AI Platform Pipelines, the pipeline DSL has to be compiled into a pipeline runtime format, also refered to as a pipeline package. The runtime format is based on [Argo Workflow](https://github.com/argoproj/argo), which is expressed in YAML. Configure environment... | !gsutil ls | _____no_output_____ | Apache-2.0 | on_demand/kfp-caip-sklearn/lab-02-kfp-pipeline/lab-02.ipynb | bharathraja23/mlops-on-gcp |
**HINT:** For **ENDPOINT**, use the value of the `host` variable in the **Connect to this Kubeflow Pipelines instance from a Python client via Kubeflow Pipelines SDK** section of the **SETTINGS** window.For **ARTIFACT_STORE_URI**, copy the bucket name which starts with the qwiklabs-gcp-xx-xxxxxxx-kubeflowpipelines-defa... | REGION = 'us-central1'
ENDPOINT = '627be4a1d4049ed3-dot-us-central1.pipelines.googleusercontent.com' # TO DO: REPLACE WITH YOUR ENDPOINT
ARTIFACT_STORE_URI = 'gs://dna-gcp-data-kubeflowpipelines-default' # TO DO: REPLACE WITH YOUR ARTIFACT_STORE NAME
PROJECT_ID = !(gcloud config get-value core/project)
PROJECT_ID = P... | _____no_output_____ | Apache-2.0 | on_demand/kfp-caip-sklearn/lab-02-kfp-pipeline/lab-02.ipynb | bharathraja23/mlops-on-gcp |
Build the trainer image | IMAGE_NAME='trainer_image'
TAG='test'
TRAINER_IMAGE='gcr.io/{}/{}:{}'.format(PROJECT_ID, IMAGE_NAME, TAG) | _____no_output_____ | Apache-2.0 | on_demand/kfp-caip-sklearn/lab-02-kfp-pipeline/lab-02.ipynb | bharathraja23/mlops-on-gcp |
**Note**: Please ignore any **incompatibility ERROR** that may appear for the packages visions as it will not affect the lab's functionality. | !gcloud builds submit --timeout 15m --tag $TRAINER_IMAGE trainer_image | _____no_output_____ | Apache-2.0 | on_demand/kfp-caip-sklearn/lab-02-kfp-pipeline/lab-02.ipynb | bharathraja23/mlops-on-gcp |
Build the base image for custom components | IMAGE_NAME='base_image'
TAG='test2'
BASE_IMAGE='gcr.io/{}/{}:{}'.format(PROJECT_ID, IMAGE_NAME, TAG)
!pwd
!gcloud builds submit --timeout 15m --tag $BASE_IMAGE base_image | Creating temporary tarball archive of 2 file(s) totalling 290 bytes before compression.
Uploading tarball of [base_image] to [gs://dna-gcp-data_cloudbuild/source/1621581960.433286-cef9441cb3234402ad8faeccf31ce5fe.tgz]
Created [https://cloudbuild.googleapis.com/v1/projects/dna-gcp-data/locations/global/builds/d2e1016b-5... | Apache-2.0 | on_demand/kfp-caip-sklearn/lab-02-kfp-pipeline/lab-02.ipynb | bharathraja23/mlops-on-gcp |
Compile the pipelineYou can compile the DSL using an API from the **KFP SDK** or using the **KFP** compiler.To compile the pipeline DSL using the **KFP** compiler. Set the pipeline's compile time settingsThe pipeline can run using a security context of the GKE default node pool's service account or the service accoun... | USE_KFP_SA = False
COMPONENT_URL_SEARCH_PREFIX = 'https://raw.githubusercontent.com/kubeflow/pipelines/0.2.5/components/gcp/'
RUNTIME_VERSION = '1.15'
PYTHON_VERSION = '3.7'
ENDPOINT='https://627be4a1d4049ed3-dot-us-central1.pipelines.googleusercontent.com'
%env USE_KFP_SA={USE_KFP_SA}
%env BASE_IMAGE={BASE_IMAGE}
%e... | env: USE_KFP_SA=False
env: BASE_IMAGE=gcr.io/dna-gcp-data/base_image:test2
env: TRAINER_IMAGE=gcr.io/dna-gcp-data/trainer_image:test
env: COMPONENT_URL_SEARCH_PREFIX=https://raw.githubusercontent.com/kubeflow/pipelines/0.2.5/components/gcp/
env: RUNTIME_VERSION=1.15
env: PYTHON_VERSION=3.7
env: ENDPOINT=https://627be4a... | Apache-2.0 | on_demand/kfp-caip-sklearn/lab-02-kfp-pipeline/lab-02.ipynb | bharathraja23/mlops-on-gcp |
Use the CLI compiler to compile the pipeline | !dsl-compile --py pipeline/covertype_training_pipeline.py --output covertype_training_pipeline.yaml | _____no_output_____ | Apache-2.0 | on_demand/kfp-caip-sklearn/lab-02-kfp-pipeline/lab-02.ipynb | bharathraja23/mlops-on-gcp |
The result is the `covertype_training_pipeline.yaml` file. | !head covertype_training_pipeline.yaml | _____no_output_____ | Apache-2.0 | on_demand/kfp-caip-sklearn/lab-02-kfp-pipeline/lab-02.ipynb | bharathraja23/mlops-on-gcp |
Deploy the pipeline package | PIPELINE_NAME='covertype_continuous_training'
!kfp --endpoint $ENDPOINT pipeline upload \
-p $PIPELINE_NAME \
covertype_training_pipeline.yaml | Pipeline 7eda6268-681e-41eb-8f65-a9c853030888 has been submitted
Pipeline Details
------------------
ID 7eda6268-681e-41eb-8f65-a9c853030888
Name covertype_continuous_training
Description
Uploaded at 2021-05-21T08:50:00+00:00
+--------------------------+----------------------------------------------... | Apache-2.0 | on_demand/kfp-caip-sklearn/lab-02-kfp-pipeline/lab-02.ipynb | bharathraja23/mlops-on-gcp |
Submitting pipeline runsYou can trigger pipeline runs using an API from the KFP SDK or using KFP CLI. To submit the run using KFP CLI, execute the following commands. Notice how the pipeline's parameters are passed to the pipeline run. List the pipelines in AI Platform Pipelines | !kfp --endpoint $ENDPOINT experiment list | +--------------------------------------+-------------------------------+---------------------------+
| Experiment ID | Name | Created at |
+======================================+===============================+===========================+
| 889c1532-fee9-4... | Apache-2.0 | on_demand/kfp-caip-sklearn/lab-02-kfp-pipeline/lab-02.ipynb | bharathraja23/mlops-on-gcp |
Submit a runFind the ID of the `covertype_continuous_training` pipeline you uploaded in the previous step and update the value of `PIPELINE_ID` . | PIPELINE_ID='7eda6268-681e-41eb-8f65-a9c853030888' # TO DO: REPLACE WITH YOUR PIPELINE ID
EXPERIMENT_NAME = 'Covertype_Classifier_Training'
RUN_ID = 'Run_001'
SOURCE_TABLE = 'covertype_dataset.covertype'
DATASET_ID = 'covertype_dataset'
EVALUATION_METRIC = 'accuracy'
MODEL_ID = 'covertype_classifier'
VERSION_ID = 'v01... | _____no_output_____ | Apache-2.0 | on_demand/kfp-caip-sklearn/lab-02-kfp-pipeline/lab-02.ipynb | bharathraja23/mlops-on-gcp |
Run the pipeline using the `kfp` command line by retrieving the variables from the environment to pass to the pipeline where:- EXPERIMENT_NAME is set to the experiment used to run the pipeline. You can choose any name you want. If the experiment does not exist it will be created by the command- RUN_ID is the name of th... | !kfp --endpoint $ENDPOINT run submit \
-e $EXPERIMENT_NAME \
-r $RUN_ID \
-p $PIPELINE_ID \
project_id=$PROJECT_ID \
gcs_root=$GCS_STAGING_PATH \
region=$REGION \
source_table_name=$SOURCE_TABLE \
dataset_id=$DATASET_ID \
evaluation_metric_name=$EVALUATION_METRIC \
model_id=$MODEL_ID \
version_id=$VERSION_ID \
replace_... | _____no_output_____ | Apache-2.0 | on_demand/kfp-caip-sklearn/lab-02-kfp-pipeline/lab-02.ipynb | bharathraja23/mlops-on-gcp |
Feature: TF-IDF Distances Create TF-IDF vectors from question texts and compute vector distances between them. Imports This utility package imports `numpy`, `pandas`, `matplotlib` and a helper `kg` module into the root namespace. | from pygoose import *
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_distances, euclidean_distances | _____no_output_____ | MIT | notebooks/feature-tfidf.ipynb | MinuteswithMetrics/kaggle-quora-question-pairs |
Config Automatically discover the paths to various data folders and compose the project structure. | project = kg.Project.discover() | _____no_output_____ | MIT | notebooks/feature-tfidf.ipynb | MinuteswithMetrics/kaggle-quora-question-pairs |
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