I am trying to make a button that takes in the state a person lives in and their race and outputs their life expectancy. I have a long list of data for all the states and various races. I also have my button set up, but need to find a way to use this data in an efficient way that allows the button to take in the two pieces of information and output one number, the life expectancy that correctly corresponds.
Thanks!
Here is what I have so far-
def life_expectancy_race(b):
'''This will tell you your life expectancy based on the information plugged into the boxes.'''
#number = print(text_input1.value) and print(text_input2.value)
display.clear_output()
display.display(display.Latex("Your life expectancy is {} y ears".format(life)))
text_input1= widgets.Text(description='Enter the state you live in here')
text_input2= widgets.Text(description='Enter your race here (White, Native American, Latino, Asian American, African American )')
button = widgets.Button(description='What is my life expectancy?')
button.on_click(life_expectancy_race)
display.display(text_input1)
display.display(text_input2)
display.display(button)
Really not sure what your asking are you asking but would something like this work? I'm not saying that specifically, just whipped that up :/ but something in that general thought process. Sorry if I misunderstood your question.
white = 30
America = 40
Print (Text1 + text2)
Related
I have spent a few days exploring the excellent FARM library and its modular approach to building models. The default output (result) however is very verbose, including a multiplicity of texts, values and ASCII artwork. For my research I only require the predicted labels from my NLP text classification model, together with the individual probabilities. How do I do that? I have been experimenting with nested lists/dictionaries but am unable to neatly produce a simple list of output labels and probabilities.
enter code here
# Test your model on a sample (Inference)
from farm.infer import Inferencer
from pprint import PrettyPrinter
infer_model = Inferencer(processor=processor, model=model, task_type="text_classification", gpu=True)
basic_texts = [
# a snippet or two from Dickens
{"text": "Mr Dombey had remained in his own apartment since the death of his wife, absorbed in visions of the youth, education, and destination of his baby son. Something lay at the bottom of his cool heart, colder and heavier than its ordinary load; but it was more a sense of the child’s loss than his own, awakening within him an almost angry sorrow."},
{"text": "Soon after seven o'clock we went down to dinner, carefully, by Mrs. Jellyby's advice, for the stair-carpets, besides being very deficient in stair-wires, were so torn as to be absolute traps."},
{"text": "Walter passed out at the door, and was about to close it after him, when, hearing the voices of the brothers again, and also the mention of his own name, he stood irresolutely, with his hand upon the lock, and the door ajar, uncertain whether to return or go away."},
# from Lewis Carroll
{"text": "I have kept one for many years, and have found it of the greatest possible service, in many ways: it secures my _answering_ Letters, however long they have to wait; it enables me to refer, for my own guidance, to the details of previous correspondence, though the actual Letters may have been destroyed long ago;"},
{"text": "The Queen gasped, and sat down: the rapid journey through the air had quite taken away her breath and for a minute or two she could do nothing but hug the little Lily in silence."},
{"text": "Rub as she could, she could make nothing more of it: she was in a little dark shop, leaning with her elbows on the counter, and opposite to her was an old Sheep, sitting in an arm-chair knitting, and every now and then leaving off to look at her through a great pair of spectacles."},
# G K Chesterton
{"text": "Basil and I walked rapidly to the window which looked out on the garden. It was a small and somewhat smug suburban garden; the flower beds a little too neat and like the pattern of a coloured carpet; but on this shining and opulent summer day even they had the exuberance of something natural, I had almost said tropical. "},
{"text": "This is the whole danger of our time. There is a difference between the oppression which has been too common in the past and the oppression which seems only too probable in the future."},
{"text": "But whatever else the worst doctrine of depravity may have been, it was a product of spiritual conviction; it had nothing to do with remote physical origins. Men thought mankind wicked because they felt wicked themselves. "},
]
result = infer_model.inference_from_dicts(dicts=basic_texts)
PrettyPrinter().pprint(result)
#print(result)
All logging (incl. the ASCII artwork) is done in FARM via Python's logging framework. You can simply disable the logs up to a certain level like this at the beginning of your script:
import logging
logging.disable(logging.ERROR)
Is that what you are looking for or do you rather want to adjust the output format of the model predictions? If you only need label and probability, you could do something like this:
...
basic_texts = [
{"text": "Stackoverflow is a great community"},
{"text": "It's snowing"},
]
infer_model = Inferencer(processor=processor, model=model, task_type="text_classification", gpu=True)
result = infer_model.inference_from_dicts(dicts=basic_texts)
minimal_results = []
for sample in result:
# Only extract the top 1 prediction per sample
top_pred = sample["predictions"][0]
minimal_results.append({"label": top_pred["label"], "probability": top_pred["probability"]})
PrettyPrinter().pprint(minimal_results)
infer_model.close_multiprocessing_pool()
(I left out the initial model loading etc. - see this example for more details)
I'm not sure if this is a really dumb question, but here goes.
text_corpus = '''Insurance bosses plead guilty\n\nAnother three US insurance executives have pleaded guilty to fraud charges stemming from an ongoing investigation into industry malpractice.\n\nTwo executives from American International Group (AIG) and one from Marsh & McLennan were the latest. The investigation by New York attorney general Eliot Spitzer has now obtained nine guilty pleas. The highest ranking executive pleading guilty on Tuesday was former Marsh senior vice president Joshua Bewlay.\n\nHe admitted one felony count of scheming to defraud and faces up to four years in prison. A Marsh spokeswoman said Mr Bewlay was no longer with the company. Mr Spitzer\'s investigation of the US insurance industry looked at whether companies rigged bids and fixed prices. Last month Marsh agreed to pay $850m (£415m) to settle a lawsuit filed by Mr Spitzer, but under the settlement it "neither admits nor denies the allegations".\n'''
def get_entities(document_text, model):
analyzed_doc = model(document_text)
entities = [entity for entity in analyzed_doc.ents if entity.label_ in ["PER", "ORG", "LOC", "GPE"]]
return entities
model = spacy.load("en_core_web_sm")
entities_1 = get_entities(text_corpus, model)
entities_2 = get_entities(text_corpus, model)
but when it run the following,
entities_1[0] in entities_2
The output is False.
Why is that? The objects in both the entity lists are the same. Yet an item from one list is not in the other one. That's extremely odd. Can someone please explain why that is so to me?
This is due to the way ents's are represented in spaCy. They are classes with specific implementations so even entities_2[0] == entities_1[0] will evaluate to False. By the looks of it, the Span class does not have an implementation of __eq__ which, at first glance at least, is the simple reason why.
If you print out the value of entities_2[0] it will give you US but this is simply because the span class has a __repr__ method implemented in the same file. If you want to do a boolean comparison, one way would be to use the text property of Span and do something like:
entities_1[0].text in [e.text for e in entities_2]
edit:
As #abb pointed out, Span implements __richcmp__, however this is applicable to the same instance of Span since it checks the position of the token itself.
I am trying to print a large list like a sequence of charterers or subtitle in TV. I have attached the following photo to understand more:
And as you can see the input text should rotate from right to left.
To arrive at this point I wrote the following code:
from __future__ import print_function
import os
from time import sleep
text = """A company which supplied lingerie to the Queen has lost its royal warrant over a book which revealed details of royal bra fittings.Rigby & Peller,
a luxury underwear firm founded in London, had held the royal warrant since 1960. It was withdrawn after June Kenton, who fitted bras for the Quee
n, released a book called 'Storm in a D-Cup'.Mrs Kenton said there was "nothing" in the book to "be upset about", adding that it was an "unbelieva
ble" decision.Buckingham Palace said it did not comment on individual companies"""
whole_Str = []
i = 0
while(i < len(text)):
rest = text[i:i+50]
i+=1
whole_Str.append(rest)
for sequence in whole_Str:
print (sequence, end ='\r')
sleep(0.1)
However, there are two problems:
First, python editors do not allow me to over write different parts of this text on the same place.
Second and most important, I guess sleep() is not a reasonable solution to create a delay since for a large scale text, system should not stop to print just subtitles.
Any kind of help would be greatly appreciated
maybe your first question is solved by this:
import sys
from time import sleep
text = """dummy text"""
whole_Str = text
for i in range(len(text)-50):
sys.stdout.write("\r" + text[i:i+50])
sys.stdout.flush()
sleep(0.1)
Not sure though what you mean with the second one.
I was able to get it to work with the curses library. the code looks like this.
import time
import curses
text = """A company which supplied lingerie to the Queen has lost its royal warrant over a book which revealed details of royal bra fittings.Rigby & Peller,
a luxury underwear firm founded in London, had held the royal warrant since 1960. It was withdrawn after June Kenton, who fitted bras for the Quee
n, released a book called 'Storm in a D-Cup'.Mrs Kenton said there was "nothing" in the book to "be upset about", adding that it was an "unbelieva
ble" decision.Buckingham Palace said it did not comment on individual companies"""
def pbar(window):
for i in text:
#add a character to the string
window.addstr(i)
#refresh the window so the new character appears
window.refresh()
#sleep to get the scroll effect
time.sleep(0.5)
#start the printing
curses.wrapper(pbar)
This is give the effect of a horizontally scrolling text that scrolls from left to right.
Hi~ I am having a problem while I am trying to tokenize facebook comments which are in CSV format. I have my CSV data ready, and I completed reading the file.
I am using Anaconda3; Python 3.5. (My CSV data has about 20k in rows and 1 in cols)
The codes are,
import csv
from nltk import sent_tokenize, word_tokenize as sent_tokenize, word_tokenize
with open('facebook_comments_samsung.csv', 'r') as f:
reader = csv.reader(f)
your_list = list(reader) #list(reader)
print (your_list)
What comes, as a result, is something like this:
[['comment_message'], ['b"Yet again been told a pack of lies by Samsung Customer services who have lost my daughters phone and couldn\'t care less. ANYONE WHO PURCHASES ANYTHING FROM THIS COMPANY NEEDS THEIR HEAD TESTED"'], ["b'You cannot really blame an entire brand worldwide for a problem caused by a branch. It is a problem yes, but address your local problem branch'"], ["b'Haha!! Sorry if they lost your daughters phone but I will always buy Samsung products no matter what.'"], ["b'Salim Gaji BEST REPLIE EVER \\xf0\\x9f\\x98\\x8e'"], ["b'<3 Bewafa zarge <3 \\r\\n\\n \\xe2\\x80\\x94\\xe2\\x80\\x94\\xe2\\x80\\x94\\xe2\\x80\\x94\\xe2\\x80\\x94\\xe2\\x80\\x94\\xe2\\x80\\x94\\xe2\\x80\\x94\\xe2\\x80\\x94\\xe2\\x80\\x94\\xe2\\x80\\x94\\xe2\\x80\\x94\\xe2\\x80\\x94\\xe2\\x80\\x94\\xe2\\x80\\x94\\xe2\\x80\\x94\\xe2\\x80\\x94\\xe2\\x80\\x93\\r\\n\\xf0\\x9f\\x8e\\xad\\xf0\\x9f\\x91\\x89 AQIB-BOT.ML \\xf0\\x9f\\x91\\x88\\xf0\\x9f\\x8e\\xadMANUAL\\xe2\\x99\\xaaKing.Bot\\xe2\\x84\\xa2 \\r\\n\\xe2\\x80\\x94\\xe2\\x80\\x94\\xe2\\x80\\x94\\xe2\\x80\\x94\\xe2\\x80\\x94\\xe2\\x80\\x94\\xe2\\x80\\x94\\xe2\\x80\\x94\\xe2\\x80\\x94\\xe2\\x80\\x94\\xe2\\x80\\x94\\xe2\\x80\\x94\\xe2\\x80\\x94\\xe2\\x80\\x94\\xe2\\x80\\x94\\xe2\\x80\\x94\\xe2\\x80\\x93\\xe2\\x80\\x94'"], ["b'\\xf0\\x9f\\x8c\\x90 LATIF.ML \\xf0\\x9f\\x8c\\x90'"], ['b"I\'m just waiting here patiently for you guys to say that you\'ll be releasing the s8 and s8+ a week early, for those who pre-ordered. Wishful thinking \\xf0\\x9f\\x98\\x86. Can\'t wait!"'], ['b"That\'s some good positive thinking there sir."'], ["b'(y) #NextIsNow #DoWhatYouCant'"], ["b'looking good'"], ['b"I\'ve always thought that when I first set eyes on my first born that I\'d like it to be on the screen of a cameraphone at arms length rather than eye-to-eye while holding my child. Thank you Samsung for improving our species."'], ["b'cool story'"], ["b'I believe so!'"], ["b'superb'"], ["b'Nice'"], ["b'thanks for the share'"], ["b'awesome'"], ["b'How can I talk to Samsung'"], ["b'Wow'"], ["b'#DoWhatYouCant siempre grandes innovadores Samsung Mobile'"], ["b'I had a problem with my s7 edge when I first got it all fixed now. However when I went to the Samsung shop they were useless and rude they refused to help and said there is nothing they could do no wonder the shop was dead quiet'"], ["b'Zeeshan Khan Masti Khel'"], ["b'I dnt had any problem wd my phn'"], ["b'I have maybe just had a bad phone to start with until it got fixed eventually. I had to go to carphone warehouse they were very helpful'"], ["b'awesome'"], ["b'Ch Shuja Uddin'"], ["b'akhheeerrr'"], ["b'superb'"], ["b'nice story'"], ["b'thanks for the share'"], ["b'superb'"], ["b'thanks for the share'"], ['b"On February 18th 2017 I sent my phone away to with a screen issue. The lower part of the screen was flickering bright white. The phone had zero physical damage to the screen\\n\\nI receive an email from Samsung Quotations with a picture of my SIM tray. Upon phoning I was told my SIM tray was stuck inside the phone and was handed a \\xc2\\xa392.14 repair bill. There is no way that my SIM tray was stuck in the phone as I removed my SIM and memory card before sending the phone away.\\n\\nAfter numerous calls I finally gave in and agreed to pay the \\xc2\\xa392.14 on the understanding that my screen repair would also be covered in this cost. This was confirmed to me by the person on the phone.\\n\\nOn
Sorry for your inconvenience in reading the result. My bad.
To continue, I added,
tokens = [word_tokenize(i) for i in your_list]
for i in tokens:
print (i)
print (tokens)
This is the part where I get the following error:
C:\Program Files\Anaconda3\lib\site-packages\nltk\tokenize\punkt.py in _slices_from_text(self, text) in line 1278 TypeError: expected string or bytes-like object
What I want to do next is,
import nltk
en = nltk.Text(tokens)
print(len(en.tokens))
print(len(set(en.tokens)))
en.vocab()
en.plot(50)
en.count('galaxy s8')
And finally, I want to draw a wordcloud based on the data.
Being aware of the fact that every seconds of your time is precious, I am terribly sorry to ask for your help. I have been working this for a couple of days, and cannot find the right solution for my problem. Thank you for reading.
The error you're getting is because your CSV file is turned into a list of lists-- one for each row in the file. The file only contains one column, so each of these lists has one element: The string containing the message you want to tokenize. To get past the error, unpack the sublists by using this line instead:
tokens = [word_tokenize(row[0]) for row in your_list]
After that, you'll need to learn some more python and learn how to examine your program and your variables.
I am new to python, and am wondering if anyone can help me with some file loading.
Situation is I have some text files and i'm trying to do sentiment analysis. Here's the text file. It is split into three category: <department>, <user>, <review>
Here are some sample data:
men peter123 the pants are too tight for my liking!
kids georgel i really like this toy, it keeps my kid entertained for days! It is affordable and comes on time, i strongly recommend it
health kksd1 the health pills is drowsy by nature, please take care and do not drive after you eat the pills
office ty7d1 the printer came on time, the only problem with it is with the duplex function which i suspect its not really working
I want to make into this
<category> <user> <review>
I have 50k lines of these data.
I have tried to load directly into numpy, but it says its an empty separator error. I looked up stackoverflow, but i couldn't find a situation where it applies to different number of delimiters. For instance, i will never get to know how many spaces are there in the data set that i have.
My biggest problem is, how do you count the number of delimiters and give them column. Is there a way that I can make into three categories <department>, <user>, <review>. Bear in mind that the review data can contain random commas and spaces which i can't control. So the system must be smart enough to pick up!
Any ideas? Is there a way that i can tell python that after you read the user data, then everything behind falls under review?
With data like this I'd just use split() with the maxplit argument:
If maxsplit is given, at most maxsplit splits are done (thus, the list will have at most maxsplit+1 elements).
Example:
from StringIO import StringIO
s = StringIO("""men peter123 the pants are too tight for my liking!
kids georgel i really like this toy, it keeps my kid entertained for days! It is affordable and comes on time, i strongly recommend it
health kksd1 the health pills is drowsy by nature, please take care and do not drive after you eat the pills
office ty7d1 the printer came on time, the only problem with it is with the duplex function which i suspect its not really working""")
for line in s:
category, user, review = line.split(None, 2)
print ("category: {} - user: {} - review: '{}'".format(category,
user,
review.strip()))
The output is:
category: men - user: peter123 - review: 'the pants are too tight for my liking!'
category: kids - user: georgel - review: 'i really like this toy, it keeps my kid entertained for days! It is affordable and comes on time, i strongly recommend it'
category: health - user: kksd1 - review: 'the health pills is drowsy by nature, please take care and do not drive after you eat the pills'
category: office - user: ty7d1 - review: 'the printer came on time, the only problem with it is with the duplex function which i suspect its not really working'
For reference:
https://docs.python.org/2/library/stdtypes.html#str.split
What about doing it sorta manually:
data = []
for line in input_data:
tmp_split = line.split(" ")
#Get the first part (dept)
dept = tmp_split[0]
#get the 2nd part
user = tmp_split[1]
#everything after is the review - put spaces inbetween each piece
review = " ".join(tmp_split[2:])
data.append([dept, user, review])