tapilab / ctrosset Goto Github PK

I have some issues with my dedicated server.
You said that you could provide one to me is it still possible ?

Thank you.

• what are the highest weighted accounts for male/female labels. See clf.coef_

• See here

Matplotlib is good for this.

• Average of y values

Hopefully, we're doing better than this.

X = [company x follower proportions]

Y = [company x demographic breakdown]

E.g.

Here's a matrix of two companies, with three follower proportions each.

X = [[0.2, 0.1, 0.7],
       [0.5, 0.4, 0.1]]

Here's a matrix of their demographic breakdown (%male, %female)

Y = [[45.4, 54.6], 
       [46.7, 53.3]]

• Sort all predictions by error (truth - predicted)
• For top 10 errors:
  • report the company name
  • report top 5 features weighted by (abs(feature value * model coefficient))

.1, .3, .5, .7, 1

• Please keep track of each stage of processing and document it in README.md.

That is, enumerate step-by-step how to go from the raw data to the output of your analysis.

I.e., remove columns that are non-zero for fewer than 3 rows.

E.g., person X follows company Y. Collect all Z's that X follows.

Try thresholds of 50, 25, 10, 5, 3 and compute cross-validation MSE and number of remaining columns for each.

We expect threshold of 50 to have many fewer columns (~10,000?)

http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lasso.html

• income
• age

• Introduction
  • what is the problem
  • why is it important
  • roughly what is our solution
  • how have previous people done this?
• Data collection
  • what data did we collect and how
  • rough statistics about the data
• methods
  • how did we compute X matrix
  • what models did we use (ridge regression)
  • how did we pick best alpha
• results
  • cross-validation MSE for each task
  • top features for each output variable (e.g., male, female, old, young) (~10)
• conclusions
  • how well did this work
  • what are next steps

I have the following error on my loadMatrices.py script.

('Coefficients: \n', array([[ 0. , 0.03644562, 0.09987471, ..., 0.00901544,
0.02207366, -0.0079762 ],
[ 0. , -0.03586746, -0.09994756, ..., -0.00893835,
-0.02191355, 0.00792444]]))
Traceback (most recent call last):
File "loadMatrices.py", line 88, in
% np.mean((regr.predict(X) - Y) ** 2))
File "/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python/numpy/matrixlib/defmatrix.py", line 343, in pow
return matrix_power(self, other)
File "/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python/numpy/matrixlib/defmatrix.py", line 160, in matrix_power
raise ValueError("input must be a square array")
ValueError: input must be a square array

I don't know where it could come from.