I am currently working on a decision support system for Licensure Examination Performance using Python but I stumbled with this problem. I want to determine which AREA should an examinee should concentrate on to be able to pass or at least get a Rating of 75.
Suppose I have the following dataframe in Python
Age Sex_M HS_GWA Col_GWA Major Passed_P Rating
21 1 85 90 1 1 85
23 0 87 88 3 1 75
19 0 91 92 2 1 77
20 0 86 85 0 1 80
20 1 76 86 1 0 65
22 1 88 75 2 0 70
I have SUCCESSFULLY implemented the classification (when the target is Passed_P) and regression (target variable is Rating) algorithms of SKLearn in creating prediction models and used them for prediction.
THE CHALLENGE:
What if HS_GWA is now the TARGET variable while Rating becomes constant at 75 and become part of the independent variables. How can we use the independent variables to determine/forecast the minimum value of the DEPENDENT variable?
What if HS_GWA is now the TARGET variable while Passed_P becomes constant at 1 and become part of the independent variables. How can we use the independent variables to determine/forecast the minimum value of the DEPENDENT variable?
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This might sound bonkers, but looking to see if there are any ideas on how to do this.
I have N categories (say 7) where a set number of people (say 1000) have to be allocated. I know from historical data the minimum and maximum for each category (there is limited historical data, say 15 samples, so I have data that looks like this - if I had a larger sample, I would try to generate a distribution for each category from all the samples, but there isn't.
-Year 1: [78 97 300 358 132 35 0]
-Year 2: [24 74 346 300 148 84 22]
-.
-.
-Year 15:[25 85 382 302 146 52 8]
The min and max for each category over these 15 years of data is:
Min: [25 74 252 278 112 27 0 ]
Max: [132 141 382 360 177 84 22]
I am trying to scale this using simulation - by allocating 1000 to each category within the min and max limits, and repeating it. The only condition is that the sum of the allocation across the seven categories in each simulation has to sum to 1000.
Any ideas would be greatly appreciated!
The distribution you want is called the multinomial distribution. You can use the RandMultinomial function in SAS/IML to produce random samples from the multinomial distribution. To use the multinomial distribution, you need to know the probability of an individual in each category. If this probability has not changed over time, the best estimate of this probability is to take the average proportion in each category.
Thus, I would recommend using ALL the data to estimate the probability, not just max and min:
proc iml;
X = {...}; /* X is a 15 x 7 matrix of counts, each row is a year */
mean = mean(X);
p = mean / sum(mean);
/* simulate new counts by using the multinomial distribution */
numSamples = 10;
SampleSize = 1000;
Y = randmultinomial(numSamples, SampleSize, p);
print Y;
Now, if you insist on using the max/min, you could use the midrange to estimate the most likely value and use that to estimate the probabilty, as follows:
Min = {25 74 252 278 112 27 0};
Max = {132 141 382 360 177 84 22};
/* use midrange to estimate probabilities */
midrange = (Min + Max)/2;
p = midrange / sum(midrange);
/* now use RandMultinomial, as before */
If you use the second method, there is no guarantee that the simulated values will not exceed the Min/Max values, although in practice many of the samples will obey that criterion.
Personally, I advocate the first method, which uses the average count. Or you can use a time-weighted count, if you think recent observations are more relevant than observations from 15 years ago.
I'm writing a bingo game in python. So far I can generate a bingo card and print it.
My problem is after I've randomly generated a number to call out, I don't know how to 'cross out' that number on the card to note that it's been called out.
This is the ouput, it's a randomly generated card:
B 11 13 14 2 1
I 23 28 26 27 22
N 42 45 40 33 44
G 57 48 59 56 55
O 66 62 75 63 67
I was thinking to use random.pop to generate a number to call out (in bingo the numbers go from 1 to 75)
random_draw_list = random.sample(range(1, 76), 75)
number_drawn = random_draw_list.pop()
How can I write a funtion that will 'cross out' a number on the card after its been called.
So for example if number_drawn results in 11, it should replace 11 on the card with an x or a zero.
I have several multiple linear regressions to carry out, I am wondering if there is a VBA solution for getting the VIF of regression outputs for different equations.
My current data format:
i=1
Year DependantVariable Variable2 Variable3 Variable4 Variable5 ....
2009 100 10 20 -
2010 110 15 25 -
2011 115 20 30 -
2012 125 25 35 -
2013 130 25 40 -
I have the above table, with the value of i determining the value of the variables (essentially, different regression input tables in place for every value of i)
I am looking for a VBA that will check every value of i (stored in a column), calculate the VIF for every value of i and output something like below
ivalue variable1VIF variable2VIF ...
1 1.1 1.3
2 1.2 10.1
this is my data and im trying to get the formula of it, how can i do it ( it dosent have to be using excell only but i dont know how to do it )
0 2 4 6 8
0 100 90 80 70 60
2 85 64.49 53.5 48.15 50
4 70 48.9 38.43 35.03 40
6 55 38.78 30.39 27.07 30
8 40 35 30 25 20
and this is the graphic that i obtain
but when i try to do an adjustment of the data i cant find the option as in a 2d graph
ok i didnt find how to get the equation for those values but this problem its solved by the bilinear interpolation
i used this video https://www.youtube.com/watch?v=va8vFViss90
and this calculator to make sure that i didnt messed it up https://www.ajdesigner.com/phpinterpolation/bilinear_interpolation_equation.php#ajscroll
I have a text file consisting of data that is separated by tab-delimited columns. There are many ways to read data in from the file into python, but I am specifically trying to use a method similar to one outlined below. When using a context manager like with open(...) as ..., I've seen that the general concept is to have all of the subsequent code indented within the with statement. Yet when defining a function, the return statement is usually placed at the same indentation as the first line of code within the function (excluding cases with awkward if-else loops). In this case, both approaches work. Is one method considered correct or generally preferred over the other?
def read_in(fpath, contents=[], row_limit=np.inf):
"""
fpath is filelocation + filename + '.txt'
contents is the initial data that the file data will be appeneded to
row_limit is the maximum number of rows to be read (in case one would like to not read in every row).
"""
nrows = 0
with open(fpath, 'r') as f:
for row in f:
if nrows < row_limit:
contents.append(row.split())
nrows += 1
else:
break
# return contents
return contents
Below is a snippet of the text-file I am using for this example.
1996 02 08 05 17 49 263 70 184 247 126 0 -6.0 1.6e+14 2.7e+28 249
1996 02 12 05 47 26 91 53 160 100 211 236 2.0 1.3e+15 1.6e+29 92
1996 02 17 02 06 31 279 73 317 257 378 532 9.9 3.3e+14 1.6e+29 274
1996 02 17 05 18 59 86 36 171 64 279 819 27.9 NaN NaN 88
1996 02 19 05 15 48 98 30 266 129 403 946 36.7 NaN NaN 94
1996 03 02 04 11 53 88 36 108 95 120 177 1.0 1.5e+14 8.7e+27 86
1996 03 03 04 12 30 99 26 186 141 232 215 2.3 1.6e+14 2.8e+28 99
And below is a sample call.
fpath = "/Users/.../sample_data.txt"
data_in = read_in(fpath)
for i in range(len(data_in)):
print(data_in[i])
(I realize that it's better to use chunks of pre-defined sizes to read in data, but the number of characters per row of data varies. So I'm instead trying to give user control over the number of rows read in; one could read in a subset of the rows at a time and append them into contents, continually passing them into read_in - possibly in a loop - if the file size is large enough. That said, I'd love to know if I'm wrong about this approach as well, though this isn't my main question.)
If your function needs to do some other things after writing to the file, you usually do it outside the with block. So essentially you need to return outside the with block too.
However if the purpose of your function is just to read in a file, you can return within the with block, or outside it. I believe none of the methods are preferred in this case.
I don't really understand your second question.
You can put return also withing with context.
By exiting context, the cleanup are done. This is the power of with, not to need to check all possible exit paths. Note: also with exception inside with the exit context is called.
But if file is empty (as an example), you should still return something. So in such case your code is clear, and follow the principle: one exit path. But if you should handle end of file without finding something important, I would putting normal return within with context, and handle the special case after it.