Ask Ghassem - Recent activity in Programming

Which code has best runtime and why?(the one commented or the other one)

Fri, 02 Sep 2022 14:39:49 +0000

# for key, value in dict.items():
            #     if value >= long:
            #         long = value
            #         long_name = key
            #     if value < short:
            #         short = value
            #         short_name = key
        long = max(dict.values())
        long_name = max(dict, key=dict.get)
        short = min(dict.values())
        short_name = min(dict, key=dict.get)

Answered: how to output f1-score instead of accuracy

Sat, 02 Apr 2022 14:32:22 +0000

from sklearn.metrics import f1_score
f1_score(data_test,target_test)

I cannot get this code to work. please help.

Mon, 21 Mar 2022 05:59:53 +0000

from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from sklearn.model_selection import train_test_split

model = Sequential()
model.add(LSTM( 10, input_shape=(1, 1)))
model.add(Dense(1, activation="linear"))
model.compile(loss="mse", optimizer="adam")

X, y = get_data()

X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=1)
X_train_2, X_val, y_train_2, y_val = train_test_split(X_train, y_train, test_size=0.25, random_state=1)

model.fit(X_train, y_train, epochs=800, validation_data=(X_val, y_val), shuffle=False)

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Answered: What are the best resources for studying SQL?

Mon, 25 Jan 2021 16:06:30 +0000

I recommend this tutorial (https://www.w3schools.com/sql/). It is very useful and basic. I also recommend this website to learn more about SQL (https://www.sololearn.com/learning/1060). All the best.

Recategorized: Looking for program to graph a network with 2 clusters.

Fri, 28 Aug 2020 09:51:47 +0000

I'm a PhD student using qualitative content analysis to look at a number of documents to identify a norm cluster. The structure of the norms I'm trying to look at is that "ideations" link to "behaviors". I've coded the documents so that I know I have a spreadsheet with the coded behaviors in rows and the ideations in columns. The number in a cell then shows the overlap between a behavior and an ideation.

I'm looking for a program that would allow me to graphically portray this so that the ideations appear as bubbles clustered together, size dependent upon the number of times they were coded, with all behaviors clustered together in the same way and lines (size dependent on the strength of the connection) connecting the two. A simple example of this can be found on page 28 of this (problem is constant in my question).

Thanks in advance for any answers.

How to print confusion matrix if I am using stratifiedkfold method?

Thu, 06 Aug 2020 21:41:19 +0000

I am facing the error after importing torch module in python. How can I solve it? The error link is given below

Fri, 31 Jul 2020 14:56:42 +0000

https://ibb.co/MgXvrkL

Edited: Suggestion For Model

Thu, 30 Jul 2020 22:11:03 +0000

How to split into train and test using PKL file?

Thu, 30 Jul 2020 22:08:47 +0000

Answered: Please provide information about Certificate in Data Science at School of Continuing Studies, University of Toronto

Tue, 21 Jan 2020 21:29:33 +0000

I did not get any certificate in data science, all self-taught only.

Answered: How to create def for cross_val_score related to linear regression problem?

Sun, 07 Jul 2019 20:58:51 +0000

It seems you have a problem in defining the function. I do not understand why did you create the following function?! Why did not you use this library?

def cross_val_score(estimator,X,y,scoring,cv):
    scores=cross_val_score
    scores_rmse=np.sqrt(-scores)
    print('Scores: ',scores_rmse)
    print("Mean:", scores_rmse.mean())
    print("Standard deviation:", scores_rmse.std())

Answered: What are some best features of Python3 in comparison to Python2?

Mon, 20 May 2019 16:19:34 +0000

There is a list of great features many do not use yet. Here is the list of some of them:

f-strings (3.6+)
Pathlib (3.4+)
Type hinting (3.5+)
Enumerations (3.4+)
Built-in LRU cache (3.2+)
Extended iterable unpacking (3.0+)
Data classes (3.7+)
Implicit namespace packages (3.3+)

This article shows some examples, and you can take the notebook for your test from this GitHub page.

Answered: What are the best resources for studying Python?

Mon, 20 May 2019 11:57:40 +0000

Hey! There are a ton of good websites that provide both in-depth theoretical knowledge on python, as well as easy to follow practical projects. Here are just a few sites this that:

https://www.learnpython.org/

https://www.codecademy.com/learn/learn-python

Feel free to check out any of these sites!

Hope this helps.

Answered: How to convert equations in documents to LaTeX?

Sat, 13 Apr 2019 00:54:25 +0000

There is a free software called Mathpix that you can take a screenshot from any math equation and convert it to LaTeX.

https://i.imgur.com/piGMkyh.png

Commented: Who can write a recursive Python program for Fibonacci sequence 0,1,1,2,3,5,8,...?

Fri, 22 Mar 2019 19:39:16 +0000

You should correct your code as follows:

sum = fibonacci(n-1) + fibonacci(n-2)

Answered: How to use Watson Assistant V2 in my nodejs code?

Sun, 24 Feb 2019 01:55:40 +0000

You should make sure you already installed the library using npm in your command line:

npm install watson-developer-cloud

Answered: How do I create comments in my code?

Fri, 02 Nov 2018 14:27:35 +0000

Just use # to add comments in your code. Below is an example of how you can add the comments in python

import numpy as np
# importing numpy library

Reshown: If s='Sammy' what is the output of s[2:]?

Wed, 31 Oct 2018 17:48:36 +0000

Retagged: Can you share with us how to download ipynb file as pdf through latex?

Wed, 31 Oct 2018 17:43:24 +0000

Retagged: How to force Jupyter Notebook to print all the variables in each cell, not just the last one?

Wed, 31 Oct 2018 17:43:02 +0000

Answered: What are Decorators?

Mon, 29 Oct 2018 23:00:29 +0000

Technically, decorators are just functions (or classes), that take a callable object as an argument, and return ananalogous object with the decoration.

Answered: What is a Magic class methods ?

Mon, 29 Oct 2018 22:58:07 +0000

Magic class methods (not a complete list) Magic methods (which begin and end with double underscore) add functionality to your classes consistent with the broader language.

Answer selected: Hey, anyone knows how to save workspace in jupyter python? I would like to save the variables and functions, etc.

Wed, 24 Oct 2018 21:28:38 +0000

I think you need to use dill for this purpose.
"dill extends python’s pickle module for serializing and de-serializing python objects to the majority of the built-in python types. Serialization is the process of converting an object to a byte stream, and the inverse of which is converting a byte stream back to on python object hierarchy."

conda install -c anaconda dill

To save the Jupyter Notebook session:

import dill
dill.dump_session('notebook_session.db')

To restore the session:

import dill
dill.load_session('notebook_session.db')

Answered: ploting the xticks to be datatime

Sun, 21 Oct 2018 22:18:58 +0000

Please take a look at the following code. I converted the data to time-series, and then showed how to resample it with a different frequency such 30 minutes. For 4 hours, change the first resample parameter to 4H.

You can run the code here.

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import io

data = """2012-01-01 00:15:00	3.807107	22.759602	00:15:00
2012-01-01 00:30:00	5.076142	22.759602	00:30:00
2012-01-01 00:45:00	3.807107	22.759602	00:45:00
2012-01-01 01:00:00	3.807107	22.759602	01:00:00
2012-01-01 01:15:00	5.076142	22.048364	01:15:00"""

df = pd.read_csv(io.StringIO(data), names =["date","time", "MT1", "MT2", "hour"], sep ="\s+")
df.drop(['hour'], axis = 1, inplace = True) #removing redundant hour


#Creating timestamp column by merging date and time
df["timestamp"] = (df["date"].map(str) + " " + df["time"])
df.index = pd.to_datetime(df["timestamp"])
df.drop(['date','timestamp'], axis = 1, inplace = True)
df2 = df.resample('30Min' ,base=0).last()
print("\ndf\n", df)
print("\ndf2\n",df2)

f, (ax1, ax2) = plt.subplots(1, 2,figsize=(20,5) )
ax1.plot(df['time'], df['MT1'],'ro--')
ax2.plot(df2['time'], df2['MT1'],'ro--')
ax1.plot(df['time'], df['MT2'],'bo--')
ax2.plot(df2['time'], df2['MT2'],'bo--')
ax1.grid(True)
ax2.grid(True)
ax1.legend()
ax2.legend()
plt.setp(ax1.xaxis.get_majorticklabels(), rotation=70, ha="right")
plt.setp(ax2.xaxis.get_majorticklabels(), rotation=70, ha="right")

plt.show()
plt.savefig('graph.png')

Answered: How can you tell a Python library is a good one and useful?

Fri, 19 Oct 2018 03:48:16 +0000

One of the features of GitHub is stars developers give to a library and number of forks (copies). If the number of stars is high, it seems you are seeing a great library.

Commented: plotting categorical radio

Wed, 17 Oct 2018 20:59:44 +0000

Thanks for sharing. Two notes:
1) Please use code section on the toolbar when sharing your codes.
2) That would be great to provide executable version on https://repl.it

Answered: In a dataframe of a size (5,10) with random values, how to have exactly 5 NAN values in each row?

Wed, 17 Oct 2018 20:48:31 +0000

You can create a random DataFrame, then generate random column numbers and replace those columns for each row with NaN. You can run it here.

import pandas as pd
import numpy as np
import random

rows = 5
cols = 10 
nan_n = 5

df = pd.DataFrame(np.random.randint(0,100,size=(rows, cols)))

print(df)
for row in range(0,rows):
    nan_cols = np.asarray(random.sample(range(0, 10), nan_n))
    df.at[row, nan_cols] = np.nan

print(df)

My own transformation for dropping feature doesn't work

Wed, 17 Oct 2018 00:52:29 +0000

I made my own transformation to drop some of the features not required in the dataframe but I can't seem to get it working. Thoughts ?

https://repl.it/@Neo_sauga/HarmfulNearApplicationstack

Answered: How to fetch the data from a url using Python?

Mon, 15 Oct 2018 20:33:57 +0000

Use the following code, and do not forget the space in URLs should be converted to %20. You can run the code here.

import os
import urllib.request

def fetch_credit_card_data(url, directory, file_name):
    if not os.path.isdir(directory):
        os.makedirs(directory)
    urllib.request.urlretrieve(url, directory+'/'+file_name)


url = "https://archive.ics.uci.edu/ml/machine-learning-databases/00350/default%20of%20credit%20card%20clients.xls"
directory = 'directory'
file_name = 'credit_card.xls'

fetch_credit_card_data(url, directory, file_name)

Answered: How to update "conda" version itself?

Wed, 10 Oct 2018 16:58:20 +0000

Please use the following command in your Terminal (Linux/Mac) or Command (Windows):

conda update -n base conda -c anaconda

Answer selected: What are the best tools and resources for learning programming?

Mon, 08 Oct 2018 11:13:11 +0000

One of the best tools I know is SoloLearn. In its app description is written:

"SoloLearn has the largest collection of FREE code learning content, from beginner to pro! Choose from thousands of programming topics to learn coding concepts, brush up your programming knowledge, or stay aligned with the latest coding trends."

You can download the app and the courses on Android or iOS or use the application web-based. You can also download the courses in offline mode and therefore, you can continue studying and solve quizzes even when you are not connected to the internet.

Answered: Could you please introduce resources for learning more about Git and GitHub?

Mon, 08 Oct 2018 03:35:48 +0000

You can start with the following cheat sheets to review what are available in Git. The first two cheat sheets are from GitHub (click to download the pdf version), and the last two cheat sheets are taken from git-tower.com. If you are looking a complete step by step course from the beginning, you can also take a look at Learning Git and GitHub from Lynda.com.

https://i.imgur.com/PtQxdT2.png
https://i.imgur.com/db8NX2S.png
https://i.imgur.com/BR9GeFJ.png
https://i.imgur.com/dRlADeE.png

Answered: Access denied while updating Anaconda?

Sun, 30 Sep 2018 04:54:44 +0000

Do you have administrative privileges to update Anaconda? You can try Run as administrator this time when you run cmd (figure below: Right click on Command Prompt -> Run as administrator) to see if it gives you error again.

Answered: What is the difference between single quotation (' ') and double ("") quotation in Python?

Fri, 28 Sep 2018 14:37:44 +0000

A string as a sequence of characters not intended to have numeric value. In Python, such sequence of characters is included inside single or double quotes. As far as language syntax is concerned, there is no difference in single or double quoted string. Both representations can be used interchangeably. However, if either single or double quote is a part of the string itself, then the string must be placed in double or single quotes respectively.

For example a text Hello “Python” is to be stored as a string, then one should use:

str1='Hello "Python"'

On the other hand, if string should resemble Hello ‘Python’ then it should be declared as follows:

Str2="Hello 'Python'"

source

Answer selected: What do L1-norm and L2-norm mean as it applies to Norm?

Wed, 26 Sep 2018 15:41:08 +0000

This article describes both of them in more details, but in short, we define $|| x ||_p$ as p-norm.

p-norm: Given $x$, a vector with $i$ components, a p-norm is defined as:

$|| x ||_p = \left(\sum_i |x_i|^p\right)^{1/p}$

The simplest norm conceptually is Euclidean distance. This is what we typically think of as distance between two points in space:

2-norm (Euclidean distance ro L2-norm)

$|| x ||_2 = \sqrt{\left(\sum_i x_i^2\right)} = \sqrt{x_1^2 + x_2^2 + \ldots + x_i^2}$

1-norm (Manhattan distance or L1-norm)

$|| x ||_1 = \sum_i |x_i| = |x_1| + |x_2| + \ldots + |x_i|$

Python implementation

If you want to calculate them in Python, numpy.linalg.norm provides functions for it. In addition, you can use Distance computations (scipy.spatial.distance) library. For example, for the Euclidean distance between two vectors, $a$ and $b$, you can use the following code:

from scipy.spatial import distance
distance.euclidean(a, b)

and if you want to calculate the Manhattan distance, you can use the following code:

from scipy.spatial import distance
distance.cityblock(a, b)

How did I know this time it is called cityblock? I read the documentation and looked up for Manhattan distance.

Answered: Can someone please post the video tutorial for Pandas, Numpy and Matplotlib ?

Wed, 19 Sep 2018 18:51:30 +0000

There are many good tutorials out there. For Lynda.com (here you can see you to use it for free), I recommend the following tutorials.

NumPy Data Science Essential Training (4 hours)
Pandas for Data Science (2 hours)
Matplotlib (20 minutes)

You can also take all of them all to together in a shorter series in Data Science Foundations: Python Scientific Stack (3.5 hours) course.

Answer edited: Can someone please post instructions on how to install Anaconda?

Wed, 19 Sep 2018 13:27:50 +0000

I recommend you use the Anaconda with Python 3.6. To avoid conflicts, first, remove any other Python distributions you already installed.

You can take a look at the following video tutorials:

General Introduction from Lynda (here you can see how to use Lynda.com for free)

Anaconda on Mac: Jupyter Notebook Tutorial: Introduction, Setup, and Walkthrough (30 minutes)

Note: If you are installing on Windows, during your installation it asks you if it should be added to your PATH. I highly recommend you add it to the PATH as you can see both of the following boxes should be checked in installation:

If you already installed it and forgot to add it to your path, look at the following video to see how you can add Anaconda to the PATH after installation:

Anaconda on Windows 10: How to set PATH for Anaconda to run Jupyter Notebook from the command line (8 minutes)

Answer selected: What are the best resources for studying R?

Sun, 16 Sep 2018 20:29:50 +0000

I found Swirl a great intuitive way for learning R and its libraries. Just follow their installation instructions and install courses about different topics such as p-value, regression, etc.