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Lower left panel | pu.PlotFrequency(s4gdata.logfgas, ii_barred_limited1_m8_5, ii_unbarred_limited1_m8_5, -3,2,0.5, noErase=False, fmt='ro', ms=9, label=ss1m)
pu.PlotFrequency(s4gdata.logfgas, ii_barred_limited2_m9, ii_unbarred_limited2_m9, -3,2,0.5, offset=0.03, noErase=True, fmt='ro', mfc='None', mec='r', ms=9, label=ss2m)
plt.xlabel(xtfgas);plt.ylabel('Bar fraction')
plt.ylim(0,1);plt.xlim(-3,1)
legend(fontsize=9, loc='lower left', framealpha=0.5)
# push bottom of plot upwards so that x-axis label isn't clipped in PDF output
plt.subplots_adjust(bottom=0.14)
if savePlots: plt.savefig(plotDir+"fbar-vs-fgas_2sample.pdf") | s4gbars_main.ipynb | perwin/s4g_barfractions | bsd-3-clause |
Lower right panel | pu.PlotFrequency(s4gdata.logfgas, ii_SB_limited1_m8_5, ii_nonSB_limited1_m8_5, -3,2,0.5, fmt='ko', ms=8, label="SB ("+ss1m+")")
pu.PlotFrequency(s4gdata.logfgas, ii_SB_limited2_m9, ii_nonSB_limited2_m9, -3,2,0.5, noErase=True, ms=8, fmt='ko', mfc='None', mec='k', offset=0.03, label="SB ("+ss2m+")")
pu.PlotFrequency(s4gdata.logfgas, ii_SAB_limited1_m8_5, ii_nonSAB_limited1_m8_5, -3,2,0.5, noErase=True, fmt='co', ms=8, label="SAB ("+ss1m+")")
pu.PlotFrequency(s4gdata.logfgas, ii_SAB_limited2_m9, ii_nonSAB_limited2_m9, -3,2,0.5, noErase=True, ms=8, fmt='co', mfc='None', mec='c', offset=0.03, label="SAB ("+ss2m+")")
plt.legend(loc='upper left', ncol=2, fontsize=10)
plt.ylim(0,1);xlim(-3,1)
plt.xlabel(xtfgas)
plt.ylabel('Bar fraction')
# push bottom of plot upwards so that x-axis label isn't clipped in PDF output
plt.subplots_adjust(bottom=0.14)
if savePlots: plt.savefig(plotDir+"fSB-fSAB-vs-fgas_2sample.pdf") | s4gbars_main.ipynb | perwin/s4g_barfractions | bsd-3-clause |
Figure B2 | ii_all_limited1_S0 = [i for i in range(nDisksTotal) if s4gdata.dist[i] <= 25 and s4gdata.t_s4g[i] <= -0.5]
ii_barred_limited1_with_S0 = [i for i in range(nDisksTotal) if i in ii_barred and s4gdata.dist[i] <= 25]
ii_unbarred_limited1_with_S0 = [i for i in range(nDisksTotal) if i in ii_unbarred and s4gdata.dist[i] <= 25]
ii_barred_limited1_S0 = [i for i in ii_all_limited1_S0 if i in ii_barred]
ii_unbarred_limited1_S0 = [i for i in ii_all_limited1_S0 if i in ii_unbarred]
fig,axs = plt.subplots(1,2, figsize=(15,5))
axs[0].plot([8.0,11.5], [0,1], color='None')
pu.PlotFrequencyWithWeights(s4gdata.logmstar, s4gdata.w25, ii_barred_limited1, ii_unbarred_limited1, 8.0, 11.3, 0.25, noErase=True, axisObj=axs[0], fmt='ro', ms=9, label=ss1 + ", spirals")
txt2 = ss1 + ", S0s + spirals"
pu.PlotFrequencyWithWeights(s4gdata.logmstar, s4gdata.w25, ii_barred_limited1_with_S0, ii_unbarred_limited1_with_S0, 8.0, 11.3, 0.25, axisObj=axs[0], offset=-0.03, fmt='o', color='orange', mew=1.3, mfc='None', mec='orange', ms=7,noErase=True, label=txt2)
txt3 = ss1 + ", S0s only"
pu.PlotFrequencyWithWeights(s4gdata.logmstar, s4gdata.w25, ii_barred_limited1_S0, ii_unbarred_limited1_S0, 8.0, 11.3, 0.25, axisObj=axs[0], offset=0.04, fmt='D', mfc='None', mec='0.5', ecolor='0.5', ms=7, noErase=True, label=txt3)
axs[0].set_ylim(0,1)
axs[0].set_xlabel(xtmstar)
axs[0].set_ylabel('Bar fraction')
axs[0].legend(loc='upper left', fontsize=10)
plt.subplots_adjust(bottom=0.14)
bins = np.arange(8,11.5, 0.25)
axs[1].hist(s4gdata.logmstar[ii_all_limited1], bins=bins, label='Spirals')
axs[1].hist(s4gdata.logmstar[ii_all_limited1_S0], bins=bins, color='r', label='S0')
axs[1].set_ylim(0,100)
axs[1].set_xlabel(xtmstar);axs[1].set_ylabel(r"$N$")
axs[1].legend(loc='upper right', fontsize=10)
plt.subplots_adjust(bottom=0.14)
if savePlots: savefig(plotDir+"fbar-spirals+S0-vs-mstar-with-mstar-hist.pdf") | s4gbars_main.ipynb | perwin/s4g_barfractions | bsd-3-clause |
Create Two Vectors | # Create two vectors
vector_a = np.array([1,2,3])
vector_b = np.array([4,5,6]) | machine-learning/calculate_dot_product_of_two_vectors.ipynb | tpin3694/tpin3694.github.io | mit |
Calculate Dot Product (Method 1) | # Calculate dot product
np.dot(vector_a, vector_b) | machine-learning/calculate_dot_product_of_two_vectors.ipynb | tpin3694/tpin3694.github.io | mit |
Calculate Dot Product (Method 2) | # Calculate dot product
vector_a @ vector_b | machine-learning/calculate_dot_product_of_two_vectors.ipynb | tpin3694/tpin3694.github.io | mit |
Data science friday tales:
Using Fréchet Bounds for Bandwidth selection in MV Kernel Methods.
Lia Silva-Lopez
Tuesday, 19/03/2019
This story starts with a reading accident
One moment you are reading a book...
<img src="img/reading_accident.png">
...And the next there are bounds for everything.
Bounds for distributions* in terms of its marginals?
That makes perfect sense!
Why aren't these bounds more mainstream?
Is it because it's hard to pronounce 'Fréchet'?
*with distributions we mean CDFs, not densities
Allright, let's point the fingers at ourselves:
What would I do with Fréchet bounds?
'Cause the whole point of bounds is to try not to break them. Right?
<img src="https://media3.giphy.com/media/145lrrvcdNq43m/source.gif" style="width: 280px;">
An idea: Let's use them for Bandwidth selection in MV KDEs.
MV kernel estimation is expensive, slow and often hard to check with d>2.
Which is why Kernel methods are recommended for smoothing (mostly).
However, working with multivariate is not easy.
So a lot of people turn to KDEs in the lack of better information.
Or, throw samples at a blackbox and hope for the best.
How can we use Fréchet bounds here?
There are different methods for BW selection, many based on some kind of optimization.
To prune the search space on any iterative method:
(Naive) Removing bws that lead to estimates violating the bounds.
(Less naive, but less parsinomic) prune using thresholds.
To construct functions to optimize over:
(Cheap, uninformative) Counting the number of violations of the bound?
(Cheap, informative) Summing all diffs between each violation and the bound point?
Other questions to answer:
Are we breaking Fréchet bounds when estimating CDFs with Kernels?
And if we break them,
How badly are they usually broken?
Do they get broken often?
What are the consequences of selecting BWs that lead to breaking Fréchet?
<img src="https://memegenerator.net/img/instances/63332969/do-all-the-things.jpg" style="width: 70%;">
What's in Python for this?
Scikit-Learn, Scipy, StatsModels are the usual suspects.
Only StatsModels has a convenience method to estimate CDFs with use Kernels.
Based solely on making my life easy, I chose StatsModels to hack MV KDE methods and insert Fréchet bounds in BW estimation.
StatsModels KDEMultivariate Package
Wrapper code for MV KDE here
Base code for bandwith selection methods here.
Let's have a quick overview for how this normally works.
First we generate some data to call the methods.
We will use some betas. | n=1000
distr=spst.beta #<-- From SciPy
smpl=np.linspace(0,1,num=n)
params={'horns':(0.5,0.5),'horns1':(0.5,0.55),
'shower':(5.,2.),'grower':(2.,5.)}
v_type=f'{"c"*len(params)}' #<-- Statsmodels wants to know if data is
# continuous (c)
# discrete ordered (o)
# discrete unordered (u)
fig, ax = plt.subplots(1,2,figsize=(10,5))
list(map(lambda x: ax[0].plot(distr.cdf(smpl,*x),smpl) , params.values()))
list(map(lambda x: ax[1].plot(smpl,distr.pdf(smpl,*x)) , params.values()))
ax[0].legend(list(params.keys()));ax[1].legend(list(params.keys()))
ax[0].grid(); ax[1].grid()
fig.suptitle(f'CDFs & PDFs for different marginals (Beta distributed)')
plt.show() | mv_kecdf_frechet.ipynb | lia-statsletters/notebooks | gpl-3.0 |
Kernels and BW Selection Methods
Kernel selection depends on "v_type". For "c" -> Gaussian Kernel.
This is a list of the kernel functions available in the package
kernel_func = dict(
wangryzin=kernels.wang_ryzin,
aitchisonaitken=kernels.aitchison_aitken,
gaussian=kernels.gaussian,
aitchison_aitken_reg = kernels.aitchison_aitken_reg,
wangryzin_reg = kernels.wang_ryzin_reg,
gauss_convolution=kernels.gaussian_convolution,
wangryzin_convolution=kernels.wang_ryzin_convolution,
aitchisonaitken_convolution=kernels.aitchison_aitken_convolution,
gaussian_cdf=kernels.gaussian_cdf,
aitchisonaitken_cdf=kernels.aitchison_aitken_cdf,
wangryzin_cdf=kernels.wang_ryzin_cdf,
d_gaussian=kernels.d_gaussian)
Different kernels are selected for different reasons: variable features and if they want to fit pdfs or cdfs.
(probably)
Bandwidth selection methods
We have a choice of 3 BW selection methods:
1. normal_reference: normal reference rule of thumb (default)
BW from this method is the starting point of the other two algorithms
Silverman's rule for MV case
Quick, but too smooth.
2. cv_ml: cross validation maximum likelihood
Not quick, but reasonable estimates in reasonable time (within seconds to few minutes).
Uses the bandwidth estimate that maximizes the leave-out-out likelihood.
Implemented in method "_cv_ml(self)" of "class GenericKDE(object)" in "statsmodels.nonparametric._kernel_base"
The leave-one-out log likelihood function is:
$$\ln L=\sum_{i=1}^{n}\ln f_{-i}(X_{i})$$
The leave-one-out kernel estimator of $f_{-i}$ is:
$$f_{-i}(X_{i})=\frac{1}{(n-1)h} \sum_{j=1,j\neq i}K_{h}(X_{i},X_{j})$$
where $K_{h}$ represents the Generalized product kernel estimator:
$$ K_{h}(X_{i},X_{j})=\prod_{s=1}^{q}h_{s}^{-1}k\left(\frac{X_{is}-X_{js}}{h_{s}}\right) $$
The Generalized product Kernel Estimator is also a method of GenericKDE(object).
3. cv_ls: cross validation least squares
Very, very slow (>8x times slower than ml)
Returns the value of the bandwidth that maximizes the integrated mean square error between the estimated and actual distribution.
Implemented in method "_cv_ls(self)" of "class GenericKDE(object)" in "statsmodels.nonparametric._kernel_base"
The integrated mean square error (IMSE) is given by:
$$ \int\left[\hat{f}(x)-f(x)\right]^{2}dx $$
Comparing times and bandwidth choices
Let's compare times and values for bandwidth selection for each method available in StatsModels, considering 4 dimensions and 1000 samples.
Rule-of-thumb:
3 loops, best of 3: 109 µs per loop
bw with reference [0.15803504 0.15817752 0.07058083 0.07048409]
CV-LOO ML:
3 loops, best of 3: 1min 39s per loop
bw with maximum likelihood [0.04915534 0.03477012 0.09889865 0.09816758]
CV-LS:
3 loops, best of 3: 12min 30s per loop
bw with least squares [1.12156416e-01 1.00000000e-10 1.03594669e-01 9.11747124e-02]
But, check out the bandwidths sizes! | import statsmodels.api as sm
#Generate some independent data for each parameter set
mvdata={k:distr.rvs(*params[k],size=n) for k in params}
rd=np.array(list(mvdata.values()))
%timeit -n3 sm.nonparametric.KDEMultivariate(data=rd,var_type=v_type, bw='normal_reference')
dens_u_rot = sm.nonparametric.KDEMultivariate(data=rd,var_type=v_type, bw='normal_reference')
print('bw with reference',dens_u_rot.bw, '(only available for gaussian kernels)')
%timeit -n3 sm.nonparametric.KDEMultivariate(data=rd,var_type=v_type, bw='cv_ml')
dens_u_ml = sm.nonparametric.KDEMultivariate(data=rd,var_type=v_type, bw='cv_ml')
print('bw with maximum likelihood',dens_u_ml.bw)
# BW with least squares takes >8x more than with ml
%timeit -n3 sm.nonparametric.KDEMultivariate(data=rd,var_type=v_type, bw='cv_ls')
dens_u_ls = sm.nonparametric.KDEMultivariate(data=rd,var_type=v_type, bw='cv_ls')
print('bw with least squares',dens_u_ls.bw) | mv_kecdf_frechet.ipynb | lia-statsletters/notebooks | gpl-3.0 |
Now the fun part: modifying the package to do our bidding
All we need is in two classes: class KDEMultivariate(GenericKDE) and its parent, class GenericKDE(object).
When we call the constructor for the KDEMultivariate object, this happens:
Data checks & reshaping, internal stuff settings.
Bandwidth selection function method is chosen and bandwidth is calculated by doing a call to a hidden parent method (self._compute_bw(bw) or self._compute_efficient(bw))
At the parent method, either one of this methods of the parent called:
_normal_reference() <- Silverman's rule
_cv_ml() <- Cross validation maximum likelihood
_cv_ls() <- Cross validation least squares
How do the BW calculation methods work?
_cv_ml() and _cv_ls() are almost the same method except for:
| _cv_ml() | _cv_ls() |
|---|---|
| h0 = self._normal_reference() | h0 = self._normal_reference() |
|bw = optimize.fmin(self.loo_likelihood, | bw = optimize.fmin(self.imse,|
|x0=h0, args=(np.log, ),| x0=h0,|
|maxiter=1e3, | maxiter=1e3 |
|maxfun=1e3, | maxfun=1e3, |
|disp=0, | disp=0,|
|xtol=1e-3) | xtol=1e-3) |
A bummer: no direct way to feed ranges of hyperparameters in order to constrain the search space!
They simply call scipy.optimize.fmin underneath.
optimize.fmin comes from scipy.optimize
So everything is passed to an optimization function!
Pretty much.
Doesn't mean we can't do something about it :).
Let's look inside loo_likelihood, and see where can we intervene: | def loo_likelihood(self, bw, func=lambda x: x):
LOO = LeaveOneOut(self.data) #<- iterator for a leave-one-out over the data
L = 0
for i, X_not_i in enumerate(LOO): #<- per leave-one-out of the data (ouch!)
f_i = gpke(bw, #<- provided by the optimization algorithm
data=-X_not_i, #<- dataset minus one sample as given by LOO
data_predict=-self.data[i, :], #<- REAL dataset, ith point
var_type=self.var_type) #<- 'cccc' or something similar
L += func(f_i) #<- _cv_ml() passed np.log, so its log-likelihood
# of gkpe at ith point.
return -L | mv_kecdf_frechet.ipynb | lia-statsletters/notebooks | gpl-3.0 |
What happens inside gkpe?
Both the CDF and PDF are estimated with a gpke. They just use a different kernel.
All the kernel implementations are here. | def gpke(bw, data, data_predict, var_type, ckertype='gaussian',
okertype='wangryzin', ukertype='aitchisonaitken', tosum=True):
kertypes = dict(c=ckertype, o=okertype, u=ukertype) #<- kernel selection
Kval = np.empty(data.shape)
for ii, vtype in enumerate(var_type): #per ii dimension
func = kernel_func[kertypes[vtype]]
Kval[:, ii] = func(bw[ii], data[:, ii], data_predict[ii])
iscontinuous = np.array([c == 'c' for c in var_type])
dens = Kval.prod(axis=1) / np.prod(bw[iscontinuous]) #<- Ta-da, kernel products.
if tosum:
return dens.sum(axis=0)
else:
return dens | mv_kecdf_frechet.ipynb | lia-statsletters/notebooks | gpl-3.0 |
What did I do?
Groundwork:
Methods for:
Estimating the Fréchet bounds for a dataset.
Visualizing the bounds (2d datasets) see here
Counting how many violations of the bound were made by a CDF.
Measuring the size of the violation at each point (diff between the point of the CDF in which the violation happened, and the bound that was broken)
Generating experiments
Massaging outputs of the profiler
Then...
Estimated a percentage of bound breaking for winning bandwidths in different methods.
It was not zero!!!
Tried using the violations as a way to prune "unpromising" bandwidths before applying the gpke thru the whole LOO iteration.
It made the optimization algorithm go coo-coo
Because scipy.optimize.fmin was expecting a number out of that function.
To return something "proportionally punishing", I probably should keeping track of the previous estimates.
That would require more work.
Basically also hacking the code for the optimization.
Future work!
Then more...
Hijacked loo_likelihood() to make my own method in which violations are used to guide the optimization algorithm.
Tried feeding number of violations to the algorithm.
The algorithm got lost.
Maybe too little information?
Tried feeding the sum of the size of all violations.
It kinda worked but the final steps of the algorithm were unstable.
Can we make it a bit more informative?
And then, some more.
Tried feeding a weighted sum of the size of all violations.
The weights were the size of the bound at each violation point.
The rationale is that a violation at a narrow point should be punished more than a violation at an already wide point.
It still takes between 20% to 200% more time than cv_ml when it should be at least an order of magnitude faster (cdf estimation is faster than leave-one-out)
Gee, I wonder if I have a bug somewhere?
Yup, I actually had a bug.
What was the bug?
While making this presentation I realized I had a bug.
My method for estimating bounds should be called with THE CDFs of each dimension.
I was calling it with the data directly (!!!).
No wonder I was getting horrible results.
So the actual results of this hack will have to wait :P.
All my tests of the weekend are now useless.
Will keep them somewhere in my hard-drive as mementos...
;D I will repeat the tests with the right call and show the results for the next presentation.
<img src="https://i.kym-cdn.com/photos/images/newsfeed/000/187/324/allthethings.png" style="width: 70%;">
Ok, is not like everything is wrong
Let's do some quick counts here for Fréchet violations. | def get_frechets(dvars):
d=len(dvars)
n=len(dvars[0])
dimx=np.array(range(d))
un=np.ones(d,dtype=int)
bottom_frechet = np.array([max( np.sum( dvars[dimx,un*i] ) +1-d, 0 )
for i in range(n) ])
top_frechet = np.array([min([y[i] for y in dvars]) for i in range(n)])
return {'top': top_frechet, 'bottom': bottom_frechet}
cdfs={fname :distr.cdf(smpl,*params[fname]) for fname in params}
frechets=get_frechets(np.array(list(cdfs.values()))) | mv_kecdf_frechet.ipynb | lia-statsletters/notebooks | gpl-3.0 |
Calculating number of violations | def check_frechet_fails(guinea_cdf,frechets):
fails={'top':[], 'bottom':[]}
for n in range(len(guinea_cdf)):
#n_hyper_point=np.array([x[n] for x in rd])
if guinea_cdf[n]>frechets['top'][n]:
fails['top'].append(True)
else:
fails['top'].append(False)
if guinea_cdf[n]<frechets['bottom'][n]:
fails['bottom'].append(True)
else:
fails['bottom'].append(False)
return {'top':np.array(fails['top']),
'bottom':np.array(fails['bottom'])} | mv_kecdf_frechet.ipynb | lia-statsletters/notebooks | gpl-3.0 |
Given 4 dimensions and 1000 samples, we got:
For Silverman: 58.8% violations
For cv_ml: 58.0% violations
For cv_ls: 57.0% violations | # For Silverman
violations_silverman=check_frechet_fails(dens_u_rot.cdf(),frechets)
violations_silverman=np.sum(violations_silverman['top'])+ np.sum(violations_silverman['bottom'])
print(f'violations:{violations_silverman} ({100.*violations_silverman/len(smpl)}%)')
# For cv_ml
violations_cv_ml=check_frechet_fails(dens_u_ml.cdf(),frechets)
violations_cv_ml=np.sum(violations_cv_ml['top'])+ np.sum(violations_cv_ml['bottom'])
print(f'violations:{violations_cv_ml} ({100.*violations_cv_ml/len(smpl)}%)')
# For cv_ls
violations_cv_ls=check_frechet_fails(dens_u_ls.cdf(),frechets)
violations_cv_ls=np.sum(violations_cv_ls['top'])+ np.sum(violations_cv_ls['bottom'])
print(f'violations:{violations_cv_ls} ({100.*violations_cv_ls/len(smpl)}%)') | mv_kecdf_frechet.ipynb | lia-statsletters/notebooks | gpl-3.0 |
What more?
Quite a lot of sweat went into generating code for comparing my approaches with cv_ml
I may as well show it to you, and point where the bug was :(. | def generate_experiments(reps,n,params, distr, dims):
bws_frechet={f'bw_{x}':[] for x in params}
bws_cv_ml={f'bw_{x}':[] for x in params}
for iteration in range(reps):
mvdata = {k: distr.rvs(*params[k], size=n) for k in params}
rd = np.array(list(mvdata.values())) #<---- THIS IS NOT A CDF!!!!!
# get frechets and thresholds
frechets = get_frechets(rd) #<------- THEREFORE THIS IS A BUG !!!!!
bw_frechets, bw_cv_ml=profile_run(rd, frechets,iteration)
for ix,x in enumerate(params):
bws_frechet[f'bw_{x}'].append(bw_frechets[ix])
bws_cv_ml[f'bw_{x}'].append(bw_cv_ml[ix])
pd.DataFrame(bws_frechet).to_csv(f'/home/lia/liaProjects/outs/bws_frechet_d{dims}-n{n}-iter{reps}.csv')
pd.DataFrame(bws_cv_ml).to_csv(f'/home/lia/liaProjects/outs/bws_cv_ml_d{dims}-n{n}-iter{reps}.csv') | mv_kecdf_frechet.ipynb | lia-statsletters/notebooks | gpl-3.0 |
And this is how the functions that make the calculations look underneath. | def get_bw(datapfft, var_type, reference, frech_bounds=None):
# Using leave-one-out likelihood
# the initial value for the optimization is the normal_reference
# h0 = normal_reference()
data = adjust_shape(datapfft, len(var_type))
if not frech_bounds:
fmin =lambda bw, funcx: loo_likelihood(bw, data, var_type, func=funcx)
argsx=(np.log,)
else:
fmin = lambda bw, funcx: frechet_likelihood(bw, data, var_type,
frech_bounds, func=funcx)
argsx=(None,) #second element of tuple is if debug mode
h0 = reference
bw = optimize.fmin(fmin, x0=h0, args=argsx, #feeding logarithm for loo
maxiter=1e3, maxfun=1e3, disp=0, xtol=1e-3)
# bw = self._set_bw_bounds(bw) # bound bw if necessary
return bw | mv_kecdf_frechet.ipynb | lia-statsletters/notebooks | gpl-3.0 |
And this was my frechet_likelihood method | def frechet_likelihood(bww, datax, var_type, frech_bounds, func=None, debug_mode=False,):
cdf_est = cdf(datax, bww, var_type) # <- calls gpke underneath, but is a short call
d_violations = calc_frechet_fails(cdf_est, frech_bounds)
width_bound = frech_bounds['top'] - frech_bounds['bottom']
viols=(d_violations['top']+d_violations['bottom'])/width_bound
L= np.sum(viols)
return L | mv_kecdf_frechet.ipynb | lia-statsletters/notebooks | gpl-3.0 |
And this is how profiling info was collected
The python profiler is a bit unfriendly, so maybe this code could be useful as a snippet?
Or, getting a professional license of pycharm ;) (Thanks boss!) | def profile_run(rd,frechets,iterx):
dims=len(rd)
n=len(rd[0])
v_type = f'{"c"*dims}'
# threshold: number of violations by the cheapest method.
dens_u_rot = sm.nonparametric.KDEMultivariate(data=rd, var_type=v_type, bw='normal_reference')
cdf_dens_u_rot = dens_u_rot.cdf()
violations_rot = count_frechet_fails(cdf_dens_u_rot, frechets)
#profile frechets
pr = cProfile.Profile()
pr.enable()
bw_frechets = get_bw(rd, v_type, dens_u_rot.bw, frech_bounds=frechets)
pr.disable()
s = io.StringIO()
ps = pstats.Stats(pr, stream=s).sort_stats('cumtime')
ps.print_stats()
s = s.getvalue()
with open(f'/home/lia/liaProjects/outs/frechet-profile-d{dims}-n{n}-iter{iterx}.txt', 'w+') as f:
f.write(s)
#profile cv_ml
pr = cProfile.Profile()
pr.enable()
bw_cv_ml = get_bw(rd, v_type, dens_u_rot.bw)
pr.disable()
s = io.StringIO()
ps = pstats.Stats(pr, stream=s).sort_stats('cumtime')
ps.print_stats()
s = s.getvalue()
with open(f'/home/lia/liaProjects/outs/loo-ml-profile-d{dims}-n{n}-iter{iterx}.txt', 'w+') as f:
f.write(s)
return bw_frechets,bw_cv_ml | mv_kecdf_frechet.ipynb | lia-statsletters/notebooks | gpl-3.0 |
Elif
Let's say you want to check a different condition before just saying, "The first condition was false, let's do the else statement." We could just use a second if statement, but instead we have the else-if statement, elif. It allows us to check a second condition after the first one fails. Let us concrete this idea with an example. | #I love food, let's take a look in my fridge
fridge = ['bananas', 'apples', 'water', 'tortillas', 'cheese']
#I want some pizza, but if I don't have any I will settle for a quesadilla which requires tortillas and cheese
if('pizza' in fridge):
print('Patrick ate pizza and was happy')
elif('tortillas' in fridge and 'cheese' in fridge):
print('Patrick didn\'t get his pizza, but he did get a quesadilla and is still happy!')
else:
print('Patrick is still hungry')
| Python Workshop/Logic.ipynb | CalPolyPat/Python-Workshop | mit |
Let's revamp that example, but this time, I went out and bought a pizza. | #I love food, let's take a look in my fridge
fridge = ['bananas', 'apples', 'water', 'tortillas', 'cheese', 'pizza']
#I want some pizza, but if I don't have any I will settle for a quesadilla which requires tortillas and cheese
if('pizza' in fridge):
print('Patrick ate pizza and was happy')
elif('tortillas' in fridge and 'cheese' in fridge):
print('Patrick didn\'t get his pizza, but he did get a quesadilla and is still happy!')
else:
print('Patrick is still hungry')
| Python Workshop/Logic.ipynb | CalPolyPat/Python-Workshop | mit |
Notice that, although I had the fixings for a quesadilla in my fridge, I had pizza so I never needed to check for a tortilla and cheese. This illustrates the fact that elif wont run unless the if statements before it fails. Further, you can stack elif statements forever. Let's see that. | #I love food, let's take a look in my fridge
fridge = ['bananas', 'apples', 'water', 'tortillas', 'beer']
#I want some pizza, but if I don't have any I will settle for a quesadilla which requires tortillas and cheese
if('pizza' in fridge):
print('Patrick ate pizza and was happy')
elif('tortillas' in fridge and 'cheese' in fridge):
print('Patrick didn\'t get his pizza, but he did get a quesadilla and is still happy!')
elif('beer' in fridge):
print('Patrick is still hungry, but he has beer so he is happy')
else:
print('Patrick is still hungry')
| Python Workshop/Logic.ipynb | CalPolyPat/Python-Workshop | mit |
Exercises
Write some "dummy" if, if else, and if elif else statements that will print out exactly what you expect until you feel comfortable with them.
What will be the output of the following code sample:
if(2<4):
if(len([1,2,3])<=len(set([1,1,1,2,2,3,3,3]))):
print("This will certainly print")
elif(2>1):
print("Or will this print?")
else:
print("It's gotta be this one...")
else:
print("This won't print...or will it.")
Loops
"I feel like I'm doing this over and over again" -Your computer on loops. Wanna do something 10, 100, n times? Loops are your best friend! Want to loop through a list containing all of your data? Loops are your bestest friend! We will look at two different types of loops, while loops and for loops.
While Loops
While loops will continue to loop until some condition is false. While loops follow the format:
while (some condition):
#some code here
While loops can go on forever if the condition is never false. This is not the end of the world and you won't crash your computer. To stop a cell that is running, you can click on the stop button in the Jupyter toolbar. Let's see what we can do with this. | t=15
while(t>0):
print("t-minus " + str(t))
t-=1 | Python Workshop/Logic.ipynb | CalPolyPat/Python-Workshop | mit |
While loops are really good if you want to do something over and over again. Let's generate some fake data with this. I introduce here the range() function. This generates a list of numbers. Let's see briefly how it works. | # Let's make a list of numbers starting at zero and going to 99. range() by default uses a step size of 1
#so this will yield integers from 0 to 99
x = range(0,100)
print(x)
# Unfortunately range does some strange things and doesn't return a list, if you want a list, you already know how to convert it.
print(list(x))
y = []
x = 1
while(x<100):
y.append(x**5-27*x**2-2300*x**-1+x%(x+1))
x+=1
print(y) | Python Workshop/Logic.ipynb | CalPolyPat/Python-Workshop | mit |
So that was a cute example of how we can generate some data based on some equation. Later on, however, we will want to graph our data and this requires a second list for our x values. The while loop is cumbersome in the respect and so we now introduce the for loop.
For Loops
A for loop will loop through any container element by element and conveniently place each element in a special new variable. The format of a for loop is as follows:
for (special variable name) in (container we are looping through):
#do some stuff with, or without, that special variable
The advantage of for loops is that you get each element of some list handed to you on a platter...er, in a variable. Our previous example of generating data now allows us to make a list for our x data and loop through that. Let's see that in action. | x = range(1,100) #Remember that this makes a list of integers from 1 to 99
y = []
for val in x: #val is our special variable here, it will take on the value of every element in x
print(val)
y.append(val**2+3*val)
print(y) | Python Workshop/Logic.ipynb | CalPolyPat/Python-Workshop | mit |
Again, a neat little example. The true power of for loops comes when we have lists that are not numerical. Let's make every string in a list uppercase. | words = ['i', 'am', 'sorry', 'dave', 'i', 'can\'t', 'do', 'that']
upperwords = []
for word in words: #remember that word will take on the value of every element of words
print(word)
upperwords.append(word.upper()) # to make a string uppercase, you can use the .upper() function.
print(upperwords) | Python Workshop/Logic.ipynb | CalPolyPat/Python-Workshop | mit |
We have one more special type of loop to cover. List comprehensions; a quick way to make a list in one line.
List Comprehensions
A list comprehension is essentially a for loop sandwiched into a list. The syntax for a list comprehension is as follows:
X = [(expression involving special variable) for (special variable) in (some list)]
For example, we want a list containing x^2 for x in [0,1,2,3,4,5,6,7,8,9,10], we can create this list by using:
Y = [x**2 for x in range(0,11)]
Does this actually work? | y = [x**2 for x in range(0,11)]
print(y) | Python Workshop/Logic.ipynb | CalPolyPat/Python-Workshop | mit |
What about something wierder? | print(words)
wordslength = [len(word) for word in words]
print(wordslength) | Python Workshop/Logic.ipynb | CalPolyPat/Python-Workshop | mit |
My god, it worked! Think of the possibilities! With these new tools we can do 90% of all programming we will ever do. Pretty neat huh. I would like to show you one more example of list comprehensions. | # I only want words with length less than 3
newwords = [word for word in words if len(word)<3]
print(newwords) | Python Workshop/Logic.ipynb | CalPolyPat/Python-Workshop | mit |
Problem statement
Tuning the hyper-parameters of a machine learning model is often carried out using an exhaustive exploration of (a subset of) the space all hyper-parameter configurations (e.g., using sklearn.model_selection.GridSearchCV), which often results in a very time consuming operation.
In this notebook, we illustrate how skopt can be used to tune hyper-parameters using sequential model-based optimisation, hopefully resulting in equivalent or better solutions, but within less evaluations.
Objective
The first step is to define the objective function we want to minimize, in this case the cross-validation mean absolute error of a gradient boosting regressor over the Boston dataset, as a function of its hyper-parameters: | from sklearn.datasets import load_boston
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.model_selection import cross_val_score
boston = load_boston()
X, y = boston.data, boston.target
reg = GradientBoostingRegressor(n_estimators=50, random_state=0)
def objective(params):
max_depth, learning_rate, max_features, min_samples_split, min_samples_leaf = params
reg.set_params(max_depth=max_depth,
learning_rate=learning_rate,
max_features=max_features,
min_samples_split=min_samples_split,
min_samples_leaf=min_samples_leaf)
return -np.mean(cross_val_score(reg, X, y, cv=5, n_jobs=-1, scoring="mean_absolute_error")) | examples/hyperparameter-optimization.ipynb | glouppe/scikit-optimize | bsd-3-clause |
Next, we need to define the bounds of the dimensions of the search space we want to explore, and (optionally) the starting point: | space = [(1, 5), # max_depth
(10**-5, 10**-1, "log-uniform"), # learning_rate
(1, X.shape[1]), # max_features
(2, 30), # min_samples_split
(1, 30)] # min_samples_leaf
x0 = [3, 0.01, 6, 2, 1] | examples/hyperparameter-optimization.ipynb | glouppe/scikit-optimize | bsd-3-clause |
Optimize all the things!
With these two pieces, we are now ready for sequential model-based optimisation. Here we compare gaussian process-based optimisation versus forest-based optimisation. | from skopt import gp_minimize
res_gp = gp_minimize(objective, space, x0=x0, n_calls=50, random_state=0)
"Best score=%.4f" % res_gp.fun
print("""Best parameters:
- max_depth=%d
- learning_rate=%.6f
- max_features=%d
- min_samples_split=%d
- min_samples_leaf=%d""" % (res_gp.x[0], res_gp.x[1],
res_gp.x[2], res_gp.x[3],
res_gp.x[4]))
from skopt import forest_minimize
res_forest = forest_minimize(objective, space, x0=x0, n_calls=50, random_state=0)
"Best score=%.4f" % res_forest.fun
print("""Best parameters:
- max_depth=%d
- learning_rate=%.6f
- max_features=%d
- min_samples_split=%d
- min_samples_leaf=%d""" % (res_forest.x[0], res_forest.x[1],
res_forest.x[2], res_forest.x[3],
res_forest.x[4])) | examples/hyperparameter-optimization.ipynb | glouppe/scikit-optimize | bsd-3-clause |
As a baseline, let us also compare with random search in the space of hyper-parameters, which is equivalent to sklearn.model_selection.RandomizedSearchCV. | from skopt import dummy_minimize
res_dummy = dummy_minimize(objective, space, x0=x0, n_calls=50, random_state=0)
"Best score=%.4f" % res_dummy.fun
print("""Best parameters:
- max_depth=%d
- learning_rate=%.4f
- max_features=%d
- min_samples_split=%d
- min_samples_leaf=%d""" % (res_dummy.x[0], res_dummy.x[1],
res_dummy.x[2], res_dummy.x[3],
res_dummy.x[4])) | examples/hyperparameter-optimization.ipynb | glouppe/scikit-optimize | bsd-3-clause |
Convergence plot | from skopt.plots import plot_convergence
plot_convergence(("gp_optimize", res_gp),
("forest_optimize", res_forest),
("dummy_optimize", res_dummy)) | examples/hyperparameter-optimization.ipynb | glouppe/scikit-optimize | bsd-3-clause |
Prepare the pipeline
(str) filepath: Give the csv file
(str) y_col: The column to predict
(bool) regression: Regression or Classification ?
(bool) process: (WARNING) apply some preprocessing on your data (tune this preprocess with params below)
(char) sep: delimiter
(list) col_to_drop: which columns you don't want to use in your prediction
(bool) derivate: for all features combination apply, n1 * n2, n1 / n2 ...
(bool) transform: for all features apply, log(n), sqrt(n), square(n)
(bool) scaled: scale the data ?
(bool) infer_datetime: for all columns check the type and build new columns from them (day, month, year, time) if they are date type
(str) encoding: data encoding
(bool) dummify: apply dummies on your categoric variables
The data files have been generated by sklearn.dataset.make_regression | cls = Baboulinet(filepath="toto2.csv", y_col="predict", regression=True) | mozinor/example/Mozinor example Reg.ipynb | Jwuthri/Mozinor | mit |
Open the file located in the path directory, one line at a time, and store it in a list called records. | records = [json.loads(line) for line in open(path,'r')]
type(records)
records[0] | chapter 02/List-dict-defaultdict-Counter.ipynb | harishkrao/Python-for-Data-Analysis | mit |
Calling a specific key within the list | records[0]['tz'] | chapter 02/List-dict-defaultdict-Counter.ipynb | harishkrao/Python-for-Data-Analysis | mit |
Printing all time zone values in the records list.
Here we search for the string 'tz' in each element of the records list.
If the search returns a string, then we print the corresponding value of the key 'tz' for that element. | time_zones = [rec['tz'] for rec in records if 'tz' in rec]
time_zones[:10] | chapter 02/List-dict-defaultdict-Counter.ipynb | harishkrao/Python-for-Data-Analysis | mit |
Counting the frequency of each time zone's occurrence in the list using a dict type in Python | counts = {}
for x in time_zones:
if x in counts:
counts[x] = counts.get(x,0) + 1
else:
counts[x] = 1
print(counts)
from collections import defaultdict
counts = defaultdict(int)
for x in time_zones:
counts[x] += 1
print(counts)
counts['America/New_York']
len(time_zones) | chapter 02/List-dict-defaultdict-Counter.ipynb | harishkrao/Python-for-Data-Analysis | mit |
To list the top n time zone occurrences | def top_counts(count_dict, n):
value_key_pairs = [(count, tz) for tz, count in count_dict.items()]
value_key_pairs.sort()
return value_key_pairs[-n:]
top_counts(counts,10)
from collections import Counter
counts = Counter(time_zones)
counts.most_common(10) | chapter 02/List-dict-defaultdict-Counter.ipynb | harishkrao/Python-for-Data-Analysis | mit |
ReportLab
import the necessary functions one by one | from markdown import markdown as md_markdown
from xml.etree.ElementTree import fromstring as et_fromstring
from xml.etree.ElementTree import tostring as et_tostring
from reportlab.platypus import BaseDocTemplate as plat_BaseDocTemplate
from reportlab.platypus import Frame as plat_Frame
from reportlab.platypus import Paragraph as plat_Paragraph
from reportlab.platypus import PageTemplate as plat_PageTemplate
from reportlab.lib.styles import getSampleStyleSheet as sty_getSampleStyleSheet
from reportlab.lib.pagesizes import A4 as ps_A4
from reportlab.lib.pagesizes import A5 as ps_A5
from reportlab.lib.pagesizes import landscape as ps_landscape
from reportlab.lib.pagesizes import portrait as ps_portrait
from reportlab.lib.units import inch as un_inch | iPython/Reportlab2-FromMarkdown.ipynb | oditorium/blog | agpl-3.0 |
The ReportFactory class creates a ReportLab document / report object; the idea is that all style information as well as page layouts are collected in this object, so that when a different factory is passed to the writer object the report looks different. | class ReportFactory():
"""create a Reportlab report object using BaseDocTemplate
the report creation is a two-step process
1. instantiate a ReportFactory object
2. retrieve the report using the report() method
note: as it currently stands the report object is remembered in the
factory object, so another call to report() return the _same_ object;
this means that changing the paramters after report() has been called
for the first time will not have an impact
"""
def __init__(self, filename=None):
if filename == None: filename = 'report_x1.pdf'
# f = open (filename,'wb') -> reports can take a file handle!
self.filename = filename
self.pagesize = ps_portrait(ps_A4)
self.showboundary = 0
#PAGE_HEIGHT=defaultPageSize[1]; PAGE_WIDTH=defaultPageSize[0]
self.styles=sty_getSampleStyleSheet()
self.bullet = "\u2022"
self._report = None
@staticmethod
def static_page(canvas,doc):
"""template for report page
this template defines how the standard page looks (header, footer, background
objects; it does _not_ define the flow objects though, as those are separately
passed to the PageTemplate() function)
"""
canvas.saveState()
canvas.setFont('Times-Roman',9)
canvas.drawString(un_inch, 0.75 * un_inch, "Report - Page %d" % doc.page)
canvas.restoreState()
def refresh_styles(self):
"""refresh all styles
derived ReportLab styles need to be refreshed in case the parent style
has been modified; this does not really work though - it seems that the
styles are simply flattened....
"""
style_names = self.styles.__dict__['byName'].keys()
for name in style_names:
self.styles[name].refresh()
def report(self):
"""initialise a report object
this function initialised and returns a report object, based on the properties
set on the factory object at this point (note: the report object is only generated
_once_ and subsequent calls return the same object;this implies that most property
changes after this function has been called are not taken into account)
"""
if self._report == None:
rp = plat_BaseDocTemplate(self.filename,showBoundary=self.showboundary, pagesize=self.pagesize)
frame_page = plat_Frame(rp.leftMargin, rp.bottomMargin, rp.width, rp.height, id='main')
pagetemplates = [
plat_PageTemplate(id='Page',frames=frame_page,onPage=self.static_page),
]
rp.addPageTemplates(pagetemplates)
self._report = rp
return self._report
| iPython/Reportlab2-FromMarkdown.ipynb | oditorium/blog | agpl-3.0 |
The ReportWriter object executes the conversion from markdown to pdf. It is currently very simplistic - for example there is no entry hook for starting the conversion at the html level rather than at markdown, and only a few basic tags are implemented. | class ReportWriter():
def __init__(self, report_factory):
self._simple_tags = {
'h1' : 'Heading1',
'h2' : 'Heading2',
'h3' : 'Heading3',
'h4' : 'Heading4',
'h5' : 'Heading5',
'p' : 'BodyText',
}
self.rf = report_factory
self.report = report_factory.report();
def _render_simple_tag(self, el, story):
style_name = self._simple_tags[el.tag]
el.tag = 'para'
text = et_tostring(el)
story.append(plat_Paragraph(text,self.rf.styles[style_name]))
def _render_ol(self, el, story):
return self._render_error(el, story)
def _render_ul(self, ul_el, story):
for li_el in ul_el:
li_el.tag = 'para'
text = et_tostring(li_el)
story.append(plat_Paragraph(text,self.rf.styles['Bullet'], bulletText=self.rf.bullet))
def _render_error(self, el, story):
story.append(plat_Paragraph(
"<para fg='#ff0000' bg='#ffff00'>cannot render '%s' tag</para>" % el.tag,self.rf.styles['Normal']))
@staticmethod
def html_from_markdown(mdown, remove_newline=True, wrap=True):
"""convert markdown to html
mdown - the markdown to be converted
remove_newline - if True, all \n characters are removed after conversion
wrap - if True, the whole html is wrapped in an <html> tag
"""
html = md_markdown(mdown)
if remove_newline: html = html.replace("\n", "")
if wrap: html = "<html>"+html+"</html>"
return html
@staticmethod
def dom_from_html(html, wrap=False):
"""convert html into a dom tree
html - the html to be converted
wrap - if True, the whole html is wrapped in an <html> tag
"""
if wrap: html = "<html>"+html+"</html>"
dom = et_fromstring(html)
return (dom)
@staticmethod
def dom_from_markdown(mdown):
"""convert markdown into a dom tree
mdown - the markdown to be converted
wrap - if True, the whole html is wrapped in an <html> tag
"""
html = ReportWriter.html_from_markdown(mdown, remove_newline=True, wrap=True)
dom = ReportWriter.dom_from_html(html, wrap=False)
return (dom)
def create_report(self, mdown):
"""create report and write it do disk
mdown - markdown source of the report
"""
dom = self.dom_from_markdown(mdown)
story = []
for el in dom:
if el.tag in self._simple_tags:
self._render_simple_tag(el, story)
elif el.tag == 'ul':
self._render_ul(el, story)
elif el.tag == 'ol':
self._render_ol(el, story)
else:
self._render_error(el, story)
self.report.build(story) | iPython/Reportlab2-FromMarkdown.ipynb | oditorium/blog | agpl-3.0 |
create a standard report (A4, black text etc) | rfa4 = ReportFactory('report_a4.pdf')
pdfw = ReportWriter(rfa4)
pdfw.create_report(markdown_text*10) | iPython/Reportlab2-FromMarkdown.ipynb | oditorium/blog | agpl-3.0 |
create a second report with different parameters (A5, changed colors etc; the __dict__ method shows all the options that can be modified for changing styles) | #rfa5.styles['Normal'].__dict__
rfa5 = ReportFactory('report_a5.pdf')
rfa5.pagesize = ps_portrait(ps_A5)
#rfa5.styles['Normal'].textColor = '#664422'
#rfa5.refresh_styles()
rfa5.styles['BodyText'].textColor = '#666666'
rfa5.styles['Bullet'].textColor = '#666666'
rfa5.styles['Heading1'].textColor = '#000066'
rfa5.styles['Heading2'].textColor = '#000066'
rfa5.styles['Heading3'].textColor = '#000066'
pdfw = ReportWriter(rfa5)
pdfw.create_report(markdown_text*10) | iPython/Reportlab2-FromMarkdown.ipynb | oditorium/blog | agpl-3.0 |
Note that 12288 comes from $64 \times 64 \times 3$. Each image is square, 64 by 64 pixels, and 3 is for the RGB colors. Please make sure all these shapes make sense to you before continuing.
Your goal is to build an algorithm capable of recognizing a sign with high accuracy. To do so, you are going to build a tensorflow model that is almost the same as one you have previously built in numpy for cat recognition (but now using a softmax output). It is a great occasion to compare your numpy implementation to the tensorflow one.
The model is LINEAR -> RELU -> LINEAR -> RELU -> LINEAR -> SOFTMAX. The SIGMOID output layer has been converted to a SOFTMAX. A SOFTMAX layer generalizes SIGMOID to when there are more than two classes.
2.1 - Create placeholders
Your first task is to create placeholders for X and Y. This will allow you to later pass your training data in when you run your session.
Exercise: Implement the function below to create the placeholders in tensorflow. | # GRADED FUNCTION: create_placeholders
def create_placeholders(n_x, n_y):
"""
Creates the placeholders for the tensorflow session.
Arguments:
n_x -- scalar, size of an image vector (num_px * num_px = 64 * 64 * 3 = 12288)
n_y -- scalar, number of classes (from 0 to 5, so -> 6)
Returns:
X -- placeholder for the data input, of shape [n_x, None] and dtype "float"
Y -- placeholder for the input labels, of shape [n_y, None] and dtype "float"
Tips:
- You will use None because it let's us be flexible on the number of examples you will for the placeholders.
In fact, the number of examples during test/train is different.
"""
### START CODE HERE ### (approx. 2 lines)
X = tf.placeholder(tf.float32, shape = [n_x, None], name = "X")
Y = tf.placeholder(tf.float32, shape = [n_y, None], name = "Y")
### END CODE HERE ###
return X, Y
X, Y = create_placeholders(12288, 6)
print ("X = " + str(X))
print ("Y = " + str(Y)) | archive/MOOC/Deeplearning_AI/ImprovingDeepNeuralNetworks/HyperparameterTuning/Tensorflow+Tutorial.ipynb | KrisCheng/ML-Learning | mit |
Expected Output:
<table>
<tr>
<td>
**Z3**
</td>
<td>
Tensor("Add_2:0", shape=(6, ?), dtype=float32)
</td>
</tr>
</table>
You may have noticed that the forward propagation doesn't output any cache. You will understand why below, when we get to brackpropagation.
2.4 Compute cost
As seen before, it is very easy to compute the cost using:
python
tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = ..., labels = ...))
Question: Implement the cost function below.
- It is important to know that the "logits" and "labels" inputs of tf.nn.softmax_cross_entropy_with_logits are expected to be of shape (number of examples, num_classes). We have thus transposed Z3 and Y for you.
- Besides, tf.reduce_mean basically does the summation over the examples. | # GRADED FUNCTION: compute_cost
def compute_cost(Z3, Y):
"""
Computes the cost
Arguments:
Z3 -- output of forward propagation (output of the last LINEAR unit), of shape (6, number of examples)
Y -- "true" labels vector placeholder, same shape as Z3
Returns:
cost - Tensor of the cost function
"""
# to fit the tensorflow requirement for tf.nn.softmax_cross_entropy_with_logits(...,...)
logits = tf.transpose(Z3)
labels = tf.transpose(Y)
### START CODE HERE ### (1 line of code)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = Z3, labels = Y))
### END CODE HERE ###
return cost
tf.reset_default_graph()
with tf.Session() as sess:
X, Y = create_placeholders(12288, 6)
parameters = initialize_parameters()
Z3 = forward_propagation(X, parameters)
cost = compute_cost(Z3, Y)
print("cost = " + str(cost)) | archive/MOOC/Deeplearning_AI/ImprovingDeepNeuralNetworks/HyperparameterTuning/Tensorflow+Tutorial.ipynb | KrisCheng/ML-Learning | mit |
<a id='sec3.2'></a>
3.2 Compute POI Info
Compute POI (Longitude, Latitude) as the average coordinates of the assigned photos. | poi_coords = traj[['poiID', 'photoLon', 'photoLat']].groupby('poiID').agg(np.mean)
poi_coords.reset_index(inplace=True)
poi_coords.rename(columns={'photoLon':'poiLon', 'photoLat':'poiLat'}, inplace=True)
poi_coords.head() | tour/ijcai15.ipynb | charmasaur/digbeta | gpl-3.0 |
<a id='sec3.3'></a>
3.3 Construct Travelling Sequences | seq_all = traj[['userID', 'seqID', 'poiID', 'dateTaken']].copy()\
.groupby(['userID', 'seqID', 'poiID']).agg([np.min, np.max])
seq_all.head()
seq_all.columns = seq_all.columns.droplevel()
seq_all.head()
seq_all.reset_index(inplace=True)
seq_all.head()
seq_all.rename(columns={'amin':'arrivalTime', 'amax':'departureTime'}, inplace=True)
seq_all['poiDuration(sec)'] = seq_all['departureTime'] - seq_all['arrivalTime']
seq_all.head()
#tseq = seq_all[['poiID', 'poiDuration(sec)']].copy().groupby('poiID').agg(np.mean)
#tseq
seq_user = seq_all[['seqID', 'userID']].copy()
seq_user = seq_user.groupby('seqID').first()
seq_user.head()
seq_len = seq_all[['userID', 'seqID', 'poiID']].copy()
seq_len = seq_len.groupby(['userID', 'seqID']).agg(np.size)
seq_len.reset_index(inplace=True)
seq_len.rename(columns={'poiID':'seqLen'}, inplace=True)
#seq_len.head()
ax = seq_len['seqLen'].hist(bins=20)
ax.set_yscale('log') | tour/ijcai15.ipynb | charmasaur/digbeta | gpl-3.0 |
<a id='sec3.4'></a>
3.4 Transition Matrix
3.4.1 Transition Matrix for Time at POI | users = seq_all['userID'].unique()
transmat_time = pd.DataFrame(np.zeros((len(users), poi_all.index.shape[0]), dtype=np.float64), \
index=users, columns=poi_all.index)
poi_time = seq_all[['userID', 'poiID', 'poiDuration(sec)']].copy().groupby(['userID', 'poiID']).agg(np.sum)
poi_time.head()
for idx in poi_time.index:
transmat_time.loc[idx[0], idx[1]] += poi_time.loc[idx].iloc[0]
print(transmat_time.shape)
transmat_time.head()
# add 1 (sec) to each cell as a smooth factor
log10_transmat_time = np.log10(transmat_time.copy() + 1)
print(log10_transmat_time.shape)
log10_transmat_time.head() | tour/ijcai15.ipynb | charmasaur/digbeta | gpl-3.0 |
3.4.2 Transition Matrix for POI Category | poi_cats = traj['poiTheme'].unique().tolist()
poi_cats.sort()
poi_cats
ncats = len(poi_cats)
transmat_cat = pd.DataFrame(data=np.zeros((ncats, ncats), dtype=np.float64), index=poi_cats, columns=poi_cats)
for seqid in seq_all['seqID'].unique().tolist():
seqi = seq_all[seq_all['seqID'] == seqid].copy()
seqi.sort(columns=['arrivalTime'], ascending=True, inplace=True)
for j in range(len(seqi.index)-1):
idx1 = seqi.index[j]
idx2 = seqi.index[j+1]
poi1 = seqi.loc[idx1, 'poiID']
poi2 = seqi.loc[idx2, 'poiID']
cat1 = poi_all.loc[poi1, 'poiTheme']
cat2 = poi_all.loc[poi2, 'poiTheme']
transmat_cat.loc[cat1, cat2] += 1
transmat_cat | tour/ijcai15.ipynb | charmasaur/digbeta | gpl-3.0 |
Normalise each row to get an estimate of transition probabilities (MLE). | for r in transmat_cat.index:
rowsum = transmat_cat.ix[r].sum()
if rowsum == 0: continue # deal with lack of data
transmat_cat.loc[r] /= rowsum
transmat_cat | tour/ijcai15.ipynb | charmasaur/digbeta | gpl-3.0 |
Compute the log of transition probabilities with smooth factor $\epsilon=10^{-12}$. | log10_transmat_cat = np.log10(transmat_cat.copy() + 1e-12)
log10_transmat_cat | tour/ijcai15.ipynb | charmasaur/digbeta | gpl-3.0 |
<a id='sec4'></a>
4. Trajectory Recommendation -- Approach I
A different leave-one-out cross-validation approach:
- For each user, choose one trajectory (with length >= 3) uniformly at random from all of his/her trajectories
as the validation trajectory
- Use all other trajectories (of all users) to 'train' (i.e. compute metrics for the ILP formulation)
<a id='sec4.1'></a>
4.1 Choose Cross Validation Sequences | cv_seqs = seq_all[['userID', 'seqID', 'poiID']].copy().groupby(['userID', 'seqID']).agg(np.size)
cv_seqs.rename(columns={'poiID':'seqLen'}, inplace=True)
cv_seqs = cv_seqs[cv_seqs['seqLen'] > 2]
cv_seqs.reset_index(inplace=True)
print(cv_seqs.shape)
cv_seqs.head()
cv_seq_set = []
# choose one sequence for each user in cv_seqs uniformly at random
for user in cv_seqs['userID'].unique():
seqlist = cv_seqs[cv_seqs['userID'] == user]['seqID'].tolist()
seqid = random.choice(seqlist)
cv_seq_set.append(seqid)
len(cv_seq_set) | tour/ijcai15.ipynb | charmasaur/digbeta | gpl-3.0 |
<a id='sec4.2'></a>
4.2 Recommendation by Solving ILPs | def calc_poi_info(seqid_set, seq_all, poi_all):
poi_info = seq_all[seq_all['seqID'].isin(seqid_set)][['poiID', 'poiDuration(sec)']].copy()
poi_info = poi_info.groupby('poiID').agg([np.mean, np.size])
poi_info.columns = poi_info.columns.droplevel()
poi_info.reset_index(inplace=True)
poi_info.rename(columns={'mean':'avgDuration(sec)', 'size':'popularity'}, inplace=True)
poi_info.set_index('poiID', inplace=True)
poi_info['poiTheme'] = poi_all.loc[poi_info.index, 'poiTheme']
poi_info['poiLon'] = poi_all.loc[poi_info.index, 'poiLon']
poi_info['poiLat'] = poi_all.loc[poi_info.index, 'poiLat']
return poi_info.copy()
def calc_user_interest(seqid_set, seq_all, poi_all, poi_info):
user_interest = seq_all[seq_all['seqID'].isin(seqid_set)][['userID', 'poiID', 'poiDuration(sec)']].copy()
user_interest['timeRatio'] = [poi_info.loc[x, 'avgDuration(sec)'] for x in user_interest['poiID']]
user_interest['timeRatio'] = user_interest['poiDuration(sec)'] / user_interest['timeRatio']
user_interest['poiTheme'] = [poi_all.loc[x, 'poiTheme'] for x in user_interest['poiID']]
user_interest.drop(['poiID', 'poiDuration(sec)'], axis=1, inplace=True)
user_interest = user_interest.groupby(['userID', 'poiTheme']).agg([np.sum, np.size]) # the sum
user_interest.columns = user_interest.columns.droplevel()
user_interest.rename(columns={'sum':'timeBased', 'size':'freqBased'}, inplace=True)
user_interest.reset_index(inplace=True)
user_interest.set_index(['userID', 'poiTheme'], inplace=True)
return user_interest.copy()
def calc_dist(longitude1, latitude1, longitude2, latitude2):
"""Calculate the distance (unit: km) between two places on earth"""
# convert degrees to radians
lon1 = math.radians(longitude1)
lat1 = math.radians(latitude1)
lon2 = math.radians(longitude2)
lat2 = math.radians(latitude2)
radius = 6371.009 # mean earth radius is 6371.009km, en.wikipedia.org/wiki/Earth_radius#Mean_radius
# The haversine formula, en.wikipedia.org/wiki/Great-circle_distance
dlon = math.fabs(lon1 - lon2)
dlat = math.fabs(lat1 - lat2)
return 2 * radius * math.asin(math.sqrt(\
(math.sin(0.5*dlat))**2 + math.cos(lat1) * math.cos(lat2) * (math.sin(0.5*dlon))**2 ))
def calc_dist_mat(poi_info):
poi_dist_mat = pd.DataFrame(data=np.zeros((poi_info.shape[0], poi_info.shape[0]), dtype=np.float64), \
index=poi_info.index, columns=poi_info.index)
for i in range(poi_info.index.shape[0]):
for j in range(i+1, poi_info.index.shape[0]):
r = poi_info.index[i]
c = poi_info.index[j]
dist = calc_dist(poi_info.loc[r, 'poiLon'], poi_info.loc[r, 'poiLat'], \
poi_info.loc[c, 'poiLon'], poi_info.loc[c, 'poiLat'])
assert(dist > 0.)
poi_dist_mat.loc[r, c] = dist
poi_dist_mat.loc[c, r] = dist
return poi_dist_mat
def calc_seq_budget(user, seq, poi_info, poi_dist_mat, user_interest):
"""Calculate the travel budget for the given travelling sequence"""
assert(len(seq) > 1)
budget = 0. # travel budget
for i in range(len(seq)-1):
px = seq[i]
py = seq[i+1]
assert(px in poi_info.index)
assert(py in poi_info.index)
budget += 60 * 60 * poi_dist_mat.loc[px, py] / speed # travel time (seconds)
caty = poi_info.loc[py, 'poiTheme']
avgtime = poi_info.loc[py, 'avgDuration(sec)']
userint = 0
if (user, caty) in user_interest.index: userint = user_interest.loc[user, caty] # for testing set
budget += userint * avgtime # expected visit duration
return budget
def recommend_ILP(user, budget, startPoi, endPoi, poi_info, poi_dist_mat, eta, speed, user_interest):
assert(0 <= eta <= 1); assert(budget > 0)
p0 = str(startPoi); pN = str(endPoi); N = poi_info.index.shape[0]
# REF: pythonhosted.org/PuLP/index.html
pois = [str(p) for p in poi_info.index] # create a string list for each POI
prob = pulp.LpProblem('TourRecommendation', pulp.LpMaximize) # create problem
# visit_i_j = 1 means POI i and j are visited in sequence
visit_vars = pulp.LpVariable.dicts('visit', (pois, pois), 0, 1, pulp.LpInteger)
# a dictionary contains all dummy variables
dummy_vars = pulp.LpVariable.dicts('u', [x for x in pois if x != p0], 2, N, pulp.LpInteger)
# add objective
objlist = []
for pi in [x for x in pois if x not in {p0, pN}]:
for pj in [y for y in pois if y != p0]:
cati = poi_info.loc[int(pi), 'poiTheme']
userint = 0; poipop = 0
if (user, cati) in user_interest.index: userint = user_interest.loc[user, cati]
if int(pi) in poi_info.index: poipop = poi_info.loc[int(pi), 'popularity']
objlist.append(visit_vars[pi][pj] * (eta * userint + (1.-eta) * poipop))
prob += pulp.lpSum(objlist), 'Objective'
# add constraints, each constraint should be in ONE line
prob += pulp.lpSum([visit_vars[p0][pj] for pj in pois if pj != p0]) == 1, 'StartAtp0'
prob += pulp.lpSum([visit_vars[pi][pN] for pi in pois if pi != pN]) == 1, 'EndAtpN'
for pk in [x for x in pois if x not in {p0, pN}]:
prob += pulp.lpSum([visit_vars[pi][pk] for pi in pois if pi != pN]) == \
pulp.lpSum([visit_vars[pk][pj] for pj in pois if pj != p0]), 'Connected_' + pk
prob += pulp.lpSum([visit_vars[pi][pk] for pi in pois if pi != pN]) <= 1, 'LeaveAtMostOnce_' + pk
prob += pulp.lpSum([visit_vars[pk][pj] for pj in pois if pj != p0]) <= 1, 'EnterAtMostOnce_' + pk
costlist = []
for pi in [x for x in pois if x != pN]:
for pj in [y for y in pois if y != p0]:
catj = poi_info.loc[int(pj), 'poiTheme']
traveltime = 60 * 60 * poi_dist_mat.loc[int(pi), int(pj)] / speed # seconds
userint = 0; avgtime = 0
if (user, catj) in user_interest.index: userint = user_interest.loc[user, catj]
if int(pj) in poi_info.index: avgtime = poi_info.loc[int(pj), 'avgDuration(sec)']
costlist.append(visit_vars[pi][pj] * (traveltime + userint * avgtime))
prob += pulp.lpSum(costlist) <= budget, 'WithinBudget'
for pi in [x for x in pois if x != p0]:
for pj in [y for y in pois if y != p0]:
prob += dummy_vars[pi] - dummy_vars[pj] + 1 <= (N - 1) * (1 - visit_vars[pi][pj]), \
'SubTourElimination_' + str(pi) + '_' + str(pj)
# solve problem
#prob.solve() # using PuLP's default solver
#prob.solve(pulp.PULP_CBC_CMD(options=['-threads', '8', '-strategy', '1', '-maxIt', '2000000'])) # CBC
#prob.solve(pulp.GLPK_CMD()) # GLPK
gurobi_options = [('TimeLimit', '7200'), ('Threads', '18'), ('NodefileStart', '0.9'), ('Cuts', '2')]
prob.solve(pulp.GUROBI_CMD(options=gurobi_options)) # GUROBI
print('status:', pulp.LpStatus[prob.status]) # print the status of the solution
#print('obj:', pulp.value(prob.objective)) # print the optimised objective function value
#for v in prob.variables(): # print each variable with it's resolved optimum value
# print(v.name, '=', v.varValue)
# if v.varValue != 0: print(v.name, '=', v.varValue)
visit_mat = pd.DataFrame(data=np.zeros((len(pois), len(pois)), dtype=np.float), index=pois, columns=pois)
for pi in pois:
for pj in pois: visit_mat.loc[pi, pj] = visit_vars[pi][pj].varValue
# build the recommended trajectory
recseq = [p0]
while True:
pi = recseq[-1]
pj = visit_mat.loc[pi].idxmax()
assert(round(visit_mat.loc[pi, pj]) == 1)
recseq.append(pj);
#print(recseq); sys.stdout.flush()
if pj == pN: return [int(x) for x in recseq]
cv_seq_dict = dict()
rec_seq_dict = dict()
for seqid in cv_seq_set:
seqi = seq_all[seq_all['seqID'] == seqid].copy()
seqi.sort(columns=['arrivalTime'], ascending=True, inplace=True)
cv_seq_dict[seqid] = seqi['poiID'].tolist()
eta = 0.5
time_based = True
doCompute = True
if os.path.exists(frecseq):
seq_dict = pickle.load(open(frecseq, 'rb'))
if (np.array(sorted(cv_seq_dict.keys())) == np.array(sorted(seq_dict.keys()))).all():
rec_seq_dict = seq_dict
doCompute = False
if doCompute:
n = 1
print('#sequences', len(cv_seq_set))
for seqid, seq in cv_seq_dict.items():
train_set = [x for x in seq_all['seqID'].unique() if x != seqid]
poi_info = calc_poi_info(train_set, seq_all, poi_all)
user_interest = calc_user_interest(train_set, seq_all, poi_all, poi_info)
poi_dist_mat = calc_dist_mat(poi_info)
user = seq_user.loc[seqid].iloc[0]
the_user_interest = None
if time_based == True: the_user_interest = user_interest['timeBased'].copy()
else: the_user_interest = user_interest['freqBased'].copy()
budget = calc_seq_budget(user, seq, poi_info, poi_dist_mat, the_user_interest)
print(n, 'sequence', seq, ', user', user, ', budget', budget); sys.stdout.flush()
recseq = recommend_ILP(user, budget, seq[0], seq[-1], poi_info, poi_dist_mat, eta, speed, the_user_interest)
rec_seq_dict[seqid] = recseq
print('->', recseq, '\n'); sys.stdout.flush()
n += 1
pickle.dump(rec_seq_dict, open(frecseq, 'wb')) | tour/ijcai15.ipynb | charmasaur/digbeta | gpl-3.0 |
<a id='sec4.3'></a>
4.3 Evaluation
Results from paper (Toronto data, time-based uesr interest, eta=0.5):
- Recall: 0.779±0.10
- Precision: 0.706±0.013
- F1-score: 0.732±0.012 | def calc_recall_precision_F1score(seq_act, seq_rec):
assert(len(seq_act) > 0)
assert(len(seq_rec) > 0)
actset = set(seq_act)
recset = set(seq_rec)
intersect = actset & recset
recall = len(intersect) / len(seq_act)
precision = len(intersect) / len(seq_rec)
F1score = 2. * precision * recall / (precision + recall)
return recall, precision, F1score
recall = []
precision = []
F1score = []
for seqid in rec_seq_dict.keys():
assert(seqid in cv_seq_dict)
seq = cv_seq_dict[seqid]
recseq = rec_seq_dict[seqid]
r, p, F1 = calc_recall_precision_F1score(seq, recseq)
recall.append(r)
precision.append(p)
F1score.append(F1)
print('Recall:', np.mean(recall), np.std(recall))
print('Precision:', np.mean(precision), np.std(precision))
print('F1-score:', np.mean(F1score), np.std(F1score)) | tour/ijcai15.ipynb | charmasaur/digbeta | gpl-3.0 |
<a id='sec5'></a>
5. Trajectory Recommendation -- Approach II
The paper stated "We evaluate PERSTOUR and the baselines using leave-one-out cross-validation [Kohavi,1995] (i.e., when evaluating a specific travel sequence of a user, we use this user's other travel sequences for training our algorithms"
While it's not clear if this means when evaluate a travel sequence for a user,
- all other sequences of this user (except the one for validation) as well as all sequences of other users are used for training, (i.e. the approach in the section above) or
- use leave-one-out for each user to construct a testing set (the approach in this section)
<a id='sec5.1'></a>
5.1 Choose Travelling Sequences for Training and Testing
Trajectories with length greater than 3 are used in the paper. | seq_ge3 = seq_len[seq_len['seqLen'] >= 3]
seq_ge3['seqLen'].hist(bins=20) | tour/ijcai15.ipynb | charmasaur/digbeta | gpl-3.0 |
Split travelling sequences into training set and testing set using leave-one-out for each user.
For testing purpose, users with less than two travelling sequences are not considered in this experiment. | train_set = []
test_set = []
user_seqs = seq_ge3[['userID', 'seqID']].groupby('userID')
for user, indices in user_seqs.groups.items():
if len(indices) < 2: continue
idx = random.choice(indices)
test_set.append(seq_ge3.loc[idx, 'seqID'])
train_set.extend([seq_ge3.loc[x, 'seqID'] for x in indices if x != idx])
print('#seq in trainset:', len(train_set))
print('#seq in testset:', len(test_set))
seq_ge3[seq_ge3['seqID'].isin(train_set)]['seqLen'].hist(bins=20)
#data = np.array(seqs1['seqLen'])
#hist, bins = np.histogram(data, bins=3)
#print(hist) | tour/ijcai15.ipynb | charmasaur/digbeta | gpl-3.0 |
Sanity check: the total number of travelling sequences used in training and testing | seq_exp = seq_ge3[['userID', 'seqID']].copy()
seq_exp = seq_exp.groupby('userID').agg(np.size)
seq_exp.reset_index(inplace=True)
seq_exp.rename(columns={'seqID':'#seq'}, inplace=True)
seq_exp = seq_exp[seq_exp['#seq'] > 1] # user with more than 1 sequences
print('total #seq for experiment:', seq_exp['#seq'].sum())
#seq_exp.head() | tour/ijcai15.ipynb | charmasaur/digbeta | gpl-3.0 |
<a id='sec5.2'></a>
5.2 Compute POI popularity and user interest using training set
Compute average POI visit duration, POI popularity as defined at the top of the notebook. | poi_info = seq_all[seq_all['seqID'].isin(train_set)]
poi_info = poi_info[['poiID', 'poiDuration(sec)']].copy()
poi_info = poi_info.groupby('poiID').agg([np.mean, np.size])
poi_info.columns = poi_info.columns.droplevel()
poi_info.reset_index(inplace=True)
poi_info.rename(columns={'mean':'avgDuration(sec)', 'size':'popularity'}, inplace=True)
poi_info.set_index('poiID', inplace=True)
print('#poi:', poi_info.shape[0])
if poi_info.shape[0] < poi_all.shape[0]:
extra_index = list(set(poi_all.index) - set(poi_info.index))
extra_poi = pd.DataFrame(data=np.zeros((len(extra_index), 2), dtype=np.float64), \
index=extra_index, columns=['avgDuration(sec)', 'popularity'])
poi_info = poi_info.append(extra_poi)
print('#poi after extension:', poi_info.shape[0])
poi_info['poiTheme'] = poi_all.loc[poi_info.index, 'poiTheme']
poi_info['poiLon'] = poi_all.loc[poi_info.index, 'poiLon']
poi_info['poiLat'] = poi_all.loc[poi_info.index, 'poiLat']
poi_info.head() | tour/ijcai15.ipynb | charmasaur/digbeta | gpl-3.0 |
Compute time/frequency based user interest as defined at the
top of the notebook. | user_interest = seq_all[seq_all['seqID'].isin(train_set)]
user_interest = user_interest[['userID', 'poiID', 'poiDuration(sec)']].copy()
user_interest['timeRatio'] = [poi_info.loc[x, 'avgDuration(sec)'] for x in user_interest['poiID']]
#user_interest[user_interest['poiID'].isin({9, 10, 12, 18, 20, 26})]
#user_interest[user_interest['timeRatio'] < 1]
user_interest.head()
user_interest['timeRatio'] = user_interest['poiDuration(sec)'] / user_interest['timeRatio']
user_interest.head()
user_interest['poiTheme'] = [poi_all.loc[x, 'poiTheme'] for x in user_interest['poiID']]
user_interest.drop(['poiID', 'poiDuration(sec)'], axis=1, inplace=True) | tour/ijcai15.ipynb | charmasaur/digbeta | gpl-3.0 |
<a id='switch'></a>
Sum defined in paper, but sum of (time ratio) * (avg duration) will become extremely large in some cases, which is unrealistic, switch between the two to have a look at the effects. | #user_interest = user_interest.groupby(['userID', 'poiTheme']).agg([np.sum, np.size]) # the sum
user_interest = user_interest.groupby(['userID', 'poiTheme']).agg([np.mean, np.size]) # try the mean value
user_interest.columns = user_interest.columns.droplevel()
#user_interest.rename(columns={'sum':'timeBased', 'size':'freqBased'}, inplace=True)
user_interest.rename(columns={'mean':'timeBased', 'size':'freqBased'}, inplace=True)
user_interest.reset_index(inplace=True)
user_interest.set_index(['userID', 'poiTheme'], inplace=True)
user_interest.head()
#user_interest.columns.shape[0] | tour/ijcai15.ipynb | charmasaur/digbeta | gpl-3.0 |
<a id='sec5.3'></a>
5.3 Generate ILP | poi_dist_mat = pd.DataFrame(data=np.zeros((poi_info.shape[0], poi_info.shape[0]), dtype=np.float64), \
index=poi_info.index, columns=poi_info.index)
for i in range(poi_info.index.shape[0]):
for j in range(i+1, poi_info.index.shape[0]):
r = poi_info.index[i]
c = poi_info.index[j]
dist = calc_dist(poi_info.loc[r, 'poiLon'], poi_info.loc[r, 'poiLat'], \
poi_info.loc[c, 'poiLon'], poi_info.loc[c, 'poiLat'])
assert(dist > 0.)
poi_dist_mat.loc[r, c] = dist
poi_dist_mat.loc[c, r] = dist
def generate_ILP(lpFilename, user, budget, startPoi, endPoi, poi_info, poi_dist_mat, eta, speed, user_interest):
"""Recommend a trajectory given an existing travel sequence S_N,
the first/last POI and travel budget calculated based on S_N
"""
assert(0 <= eta <= 1)
assert(budget > 0)
p0 = str(startPoi)
pN = str(endPoi)
N = poi_info.index.shape[0]
# The MIP problem
# REF: pythonhosted.org/PuLP/index.html
# create a string list for each POI
pois = [str(p) for p in poi_info.index]
# create problem
prob = pulp.LpProblem('TourRecommendation', pulp.LpMaximize)
# visit_i_j = 1 means POI i and j are visited in sequence
visit_vars = pulp.LpVariable.dicts('visit', (pois, pois), 0, 1, pulp.LpInteger)
# a dictionary contains all dummy variables
dummy_vars = pulp.LpVariable.dicts('u', [x for x in pois if x != p0], 2, N, pulp.LpInteger)
# add objective
objlist = []
for pi in [x for x in pois if x not in {p0, pN}]:
for pj in [y for y in pois if y != p0]:
cati = poi_info.loc[int(pi), 'poiTheme']
userint = 0
if (user, cati) in user_interest.index: userint = user_interest.loc[user, cati]
objlist.append(visit_vars[pi][pj] * (eta * userint + (1.-eta) * poi_info.loc[int(pi), 'popularity']))
prob += pulp.lpSum(objlist), 'Objective'
# add constraints
# each constraint should be in ONE line
prob += pulp.lpSum([visit_vars[p0][pj] for pj in pois if pj != p0]) == 1, 'StartAtp0' # starts at the first POI
prob += pulp.lpSum([visit_vars[pi][pN] for pi in pois if pi != pN]) == 1, 'EndAtpN' # ends at the last POI
for pk in [x for x in pois if x not in {p0, pN}]:
prob += pulp.lpSum([visit_vars[pi][pk] for pi in pois if pi != pN]) == \
pulp.lpSum([visit_vars[pk][pj] for pj in pois if pj != p0]), \
'Connected_' + pk # the itinerary is connected
prob += pulp.lpSum([visit_vars[pi][pk] for pi in pois if pi != pN]) <= 1, \
'LeaveAtMostOnce_' + pk # LEAVE POIk at most once
prob += pulp.lpSum([visit_vars[pk][pj] for pj in pois if pj != p0]) <= 1, \
'EnterAtMostOnce_' + pk # ENTER POIk at most once
# travel cost within budget
costlist = []
for pi in [x for x in pois if x != pN]:
for pj in [y for y in pois if y != p0]:
catj = poi_info.loc[int(pj), 'poiTheme']
traveltime = 60 * 60 * poi_dist_mat.loc[int(pi), int(pj)] / speed # seconds
userint = 0
if (user, catj) in user_interest.index: userint = user_interest.loc[user, catj]
costlist.append(visit_vars[pi][pj] * (traveltime + userint * poi_info.loc[int(pj), 'avgDuration(sec)']))
prob += pulp.lpSum(costlist) <= budget, 'WithinBudget'
for pi in [x for x in pois if x != p0]:
for pj in [y for y in pois if y != p0]:
prob += dummy_vars[pi] - dummy_vars[pj] + 1 <= \
(N - 1) * (1 - visit_vars[pi][pj]), \
'SubTourElimination_' + str(pi) + '_' + str(pj) # TSP sub-tour elimination
# write problem data to an .lp file
prob.writeLP(lpFilename) | tour/ijcai15.ipynb | charmasaur/digbeta | gpl-3.0 |
5.3.1 Generate ILPs for training set | def extract_seq(seqid_set, seq_all):
"""Extract the actual sequences (i.e. a list of POI) from a set of sequence ID"""
seq_dict = dict()
for seqid in seqid_set:
seqi = seq_all[seq_all['seqID'] == seqid].copy()
seqi.sort(columns=['arrivalTime'], ascending=True, inplace=True)
seq_dict[seqid] = seqi['poiID'].tolist()
return seq_dict
train_seqs = extract_seq(train_set, seq_all)
lpDir = os.path.join(data_dir, 'lp_' + suffix)
if not os.path.exists(lpDir):
print('Please create directory "' + lpDir + '"')
eta = 0.5
#eta = 1
time_based = True
for seqid in sorted(train_seqs.keys()):
if not os.path.exists(lpDir):
print('Please create directory "' + lpDir + '"')
break
seq = train_seqs[seqid]
lpFile = os.path.join(lpDir, str(seqid) + '.lp')
user = seq_user.loc[seqid].iloc[0]
the_user_interest = None
if time_based == True:
the_user_interest = user_interest['timeBased'].copy()
else:
the_user_interest = user_interest['freqBased'].copy()
budget = calc_seq_budget(user, seq, poi_info, poi_dist_mat, the_user_interest)
print('generating ILP', lpFile, 'for user', user, 'sequence', seq, 'budget', round(budget, 2))
generate_ILP(lpFile, user, budget, seq[0], seq[-1], poi_info, poi_dist_mat, eta, speed, the_user_interest) | tour/ijcai15.ipynb | charmasaur/digbeta | gpl-3.0 |
5.3.2 Generate ILPs for testing set | test_seqs = extract_seq(test_set, seq_all)
for seqid in sorted(test_seqs.keys()):
if not os.path.exists(lpDir):
print('Please create directory "' + lpDir + '"')
break
seq = test_seqs[seqid]
lpFile = os.path.join(lpDir, str(seqid) + '.lp')
user = seq_user.loc[seqid].iloc[0]
the_user_interest = None
if time_based == True:
the_user_interest = user_interest['timeBased'].copy()
else:
the_user_interest = user_interest['freqBased'].copy()
budget = calc_seq_budget(user, seq, poi_info, poi_dist_mat, the_user_interest)
print('generating ILP', lpFile, 'for user', user, 'sequence', seq, 'budget', round(budget, 2))
generate_ILP(lpFile, user, budget, seq[0], seq[-1], poi_info, poi_dist_mat, eta, speed, the_user_interest) | tour/ijcai15.ipynb | charmasaur/digbeta | gpl-3.0 |
<a id='sec5.4'></a>
5.4 Evaluation | def load_solution_gurobi(fsol, startPoi, endPoi):
"""Load recommended itinerary from MIP solution file by GUROBI"""
seqterm = []
with open(fsol, 'r') as f:
for line in f:
if re.search('^visit_', line): # e.g. visit_0_7 1\n
item = line.strip().split(' ') # visit_21_16 1.56406801399038e-09\n
if round(float(item[1])) == 1:
fromto = item[0].split('_')
seqterm.append((int(fromto[1]), int(fromto[2])))
p0 = startPoi
pN = endPoi
recseq = [p0]
while True:
px = recseq[-1]
for term in seqterm:
if term[0] == px:
recseq.append(term[1])
if term[1] == pN:
return recseq
else:
seqterm.remove(term)
break | tour/ijcai15.ipynb | charmasaur/digbeta | gpl-3.0 |
5.4.1 Evaluation on training set | train_seqs_rec = dict()
solDir = os.path.join(data_dir, os.path.join('lp_' + suffix, 'eta05_time'))
#solDir = os.path.join(data_dir, os.path.join('lp_' + suffix, 'eta10_time'))
if not os.path.exists(solDir):
print('Directory for solution files', solDir, 'does not exist.')
for seqid in sorted(train_seqs.keys()):
if not os.path.exists(solDir):
print('Directory for solution files', solDir, 'does not exist.')
break
seq = train_seqs[seqid]
solFile = os.path.join(solDir, str(seqid) + '.lp.sol')
recseq = load_solution_gurobi(solFile, seq[0], seq[-1])
train_seqs_rec[seqid] = recseq
print('Sequence', seqid, 'Actual:', seq, ', Recommended:', recseq)
recall = []
precision = []
F1score = []
for seqid in train_seqs.keys():
r, p, F1 = calc_recall_precision_F1score(train_seqs[seqid], train_seqs_rec[seqid])
recall.append(r)
precision.append(p)
F1score.append(F1)
print('Recall:', round(np.mean(recall), 2), ',', round(np.std(recall), 2))
print('Precision:', round(np.mean(precision), 2), ',', round(np.std(recall), 2))
print('F1-score:', round(np.mean(F1score), 2), ',', round(np.std(recall), 2)) | tour/ijcai15.ipynb | charmasaur/digbeta | gpl-3.0 |
5.4.2 Evaluation on testing set
Results from paper (Toronto data, time-based uesr interest, eta=0.5):
- Recall: 0.779±0.10
- Precision: 0.706±0.013
- F1-score: 0.732±0.012 | test_seqs_rec = dict()
solDirTest = os.path.join(data_dir, os.path.join('lp_' + suffix, 'eta05_time.test'))
if not os.path.exists(solDirTest):
print('Directory for solution files', solDirTest, 'does not exist.')
for seqid in sorted(test_seqs.keys()):
if not os.path.exists(solDirTest):
print('Directory for solution files', solDirTest, 'does not exist.')
break
seq = test_seqs[seqid]
solFile = os.path.join(solDirTest, str(seqid) + '.lp.sol')
recseq = load_solution_gurobi(solFile, seq[0], seq[-1])
test_seqs_rec[seqid] = recseq
print('Sequence', seqid, 'Actual:', seq, ', Recommended:', recseq)
recallT = []
precisionT = []
F1scoreT = []
for seqid in test_seqs.keys():
r, p, F1 = calc_recall_precision_F1score(test_seqs[seqid], test_seqs_rec[seqid])
recallT.append(r)
precisionT.append(p)
F1scoreT.append(F1)
print('Recall:', round(np.mean(recallT), 2), ',', round(np.std(recallT), 2))
print('Precision:', round(np.mean(precisionT), 2), ',', round(np.std(recallT), 2))
print('F1-score:', round(np.mean(F1scoreT), 2), ',', round(np.std(recallT), 2)) | tour/ijcai15.ipynb | charmasaur/digbeta | gpl-3.0 |
The Pearson's test
Exercise: See the similarities
The above example shows you how two number sequences can be compared with nothing more complicated than by using the dot product. This works as long as the sequences comprise of the same numbers but in a shuffled order. To compare different sequences with the original we normalise by the magnitude of the vectors. To include this step. We use a more complicated equation:
<img src="eqn_full.gif">
https://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient
https://en.wikipedia.org/wiki/Cross-correlation
Hopefully you can see the top of this equation is very similar to the dot-product, except that it is centered on zero (subtraction of the mu, the mean) and the variance is normalised (division by standard deviation).
Because the equation is normalised, a perfectly correlated sequence yeilds a rho value of 1.0. A perfectly random comparison yields 0 and two anti-correlated sequences will yield a value of -1.0. | #The cross-correlation algorithm is another name for the Pearson's test.
#Here it is written in code form and utilising the builtin functions:
c = [0,1,2]
d = [3,4,5]
rho = np.average((c-np.average(c))*(d-np.average(d)))/(np.std(c)*np.std(d))
print('rho',np.round(rho,3))
#equally you can write
rho = np.dot(c-np.average(c),d-np.average(d))/sqrt(((np.dot(c-np.average(c),c-np.average(c)))*np.dot(d-np.average(d),d-np.average(d))))
print('rho',round(rho,3))
#Why is the rho for c and d, 1.0?
#Edit the variables c and d and find the pearson's value for 'a' and 'b'.
#What happens when you correlate 'a' with 'a'?
#Here is an image from the Fiji practical
from tifffile import imread as imreadtiff
im = imreadtiff('neuron.tif')
print('image dimensions',im.shape, ' im dtype:',im.dtype)
subplot(2,2,1)
imshow(im[0,:,:],cmap='Blues_r')
subplot(2,2,2)
imshow(im[1,:,:],cmap='Greens_r')
subplot(2,2,3)
imshow(im[2,:,:],cmap='Greys_r')
subplot(2,2,4)
imshow(im[3,:,:],cmap='Reds_r') | day2_colocalisation/2015 Correlation and Colocalisation practical.ipynb | dwaithe/ONBI_image_analysis | gpl-2.0 |
Pearson's comparison of microscopy derived images | a = im[0,:,:].reshape(-1)
b = im[3,:,:].reshape(-1)
#Calculate the pearson's coefficent (rho) for the image channel 0, 3.
#You should hopefully obtain a value 0.829
#from tifffile import imread as imreadtiff
im = imreadtiff('composite.tif')
#The organisation of this file is not simple. It is also a 16-bit image.
print("shape of im: ",im.shape,"bit-depth: ",im.dtype)
#We can assess the image data like so.
CH0 = im[0,0,:,:]
CH1 = im[1,0,:,:]
#Single channels visualisation can handle 16-bit
subplot(2,2,1)
imshow(CH0,cmap='Reds_r')
subplot(2,2,2)
imshow(CH1,cmap='Greens_r')
subplot(2,2,3)
#RGB data have to range between 0 and 255 in each channel and be int (8-bit).
imRGB = np.zeros((CH0.shape[0],CH0.shape[1],3))
imRGB[:,:,0] = CH0/255.0
imRGB[:,:,1] = CH1/255.0
imshow((imRGB.astype(np.uint8)))
#What is the current Pearson's value for this image?
| day2_colocalisation/2015 Correlation and Colocalisation practical.ipynb | dwaithe/ONBI_image_analysis | gpl-2.0 |
Maybe remove so not to clash with Mark's.
Last challenge
Exercise: The above image is not registered. Can you devise a way of registering this image using the Pearson's test, as a measure for the similarity of the image in different positions. hint you will need to move one of the images relative to the other and measure the colocalisation in this position. The best localisation will have the highest rho value. Produce an image of your fully registered image. | np.max(imRGB/256.0)
rho_max = 0
#This moves one of your images with respect to the other.
for c in range(1,40):
for r in range(1,40):
#We need to dynamically sample our image.
temp = CH0[c:-40+c,r:-40+r].reshape(-1);
#The -40 makes sure they are the same size.
ref = CH1[:-40,:-40].reshape(-1);
rho = np.dot(temp-np.average(temp),ref-np.average(ref))/sqrt(((np.dot(temp-np.average(temp),temp-np.average(temp)))*np.dot(ref-np.average(ref),ref-np.average(ref))))
#You will need to work out the highest rho value is recorded.
#You will then need to find the coordinates of this high rho.
#You will then need to provide a visualisation with the image translated.
np.max(imRGB)
imshow?
whos | day2_colocalisation/2015 Correlation and Colocalisation practical.ipynb | dwaithe/ONBI_image_analysis | gpl-2.0 |
Exercise: Read the documentation of scipy.interpolate.interp1d. Pass a keyword argument to interpolate to specify one of the other kinds of interpolation, and run the code again to see what it looks like. | # Solution goes here | notebooks/chap17.ipynb | AllenDowney/ModSimPy | mit |
Exercise: Interpolate the glucose data and generate a plot, similar to the previous one, that shows the data points and the interpolated curve evaluated at the time values in ts. | # Solution goes here | notebooks/chap17.ipynb | AllenDowney/ModSimPy | mit |
将 tf.summary 用法迁移到 TF 2.0
<table class="tfo-notebook-buttons" align="left">
<td><a target="_blank" href="https://tensorflow.google.cn/tensorboard/migrate"><img src="https://tensorflow.google.cn/images/tf_logo_32px.png">在 TensorFlow.org 上查看 </a></td>
<td><a target="_blank" href="https://colab.research.google.com/github/tensorflow/docs-l10n/blob/master/site/zh-cn/tensorboard/migrate.ipynb"><img src="https://tensorflow.google.cn/images/colab_logo_32px.png">在 Google Colab 中运行 </a></td>
<td><a target="_blank" href="https://github.com/tensorflow/docs-l10n/blob/master/site/zh-cn/tensorboard/migrate.ipynb"><img src="https://tensorflow.google.cn/images/GitHub-Mark-32px.png">在 GitHub 中查看源代码</a></td>
</table>
注：本文档面向已经熟悉 TensorFlow 1.x TensorBoard 并希望将大型 TensorFlow 代码库从 TensorFlow 1.x 迁移至 2.0 的用户。如果您是 TensorBoard 的新用户，另请参阅入门文档。如果您使用 tf.keras，那么可能无需执行任何操作即可升级到 TensorFlow 2.0。 | import tensorflow as tf | site/zh-cn/tensorboard/migrate.ipynb | tensorflow/docs-l10n | apache-2.0 |
TensorFlow 2.0 包含对 tf.summary API（用于写入摘要数据以在 TensorBoard 中进行可视化）的重大变更。
变更
将 tf.summary API 视为两个子 API 非常实用：
一组用于记录各个摘要（summary.scalar()、summary.histogram()、summary.image()、summary.audio() 和 summary.text()）的运算，从您的模型代码内嵌调用。
写入逻辑，用于收集各个摘要并将其写入到特殊格式化的日志文件中（TensorBoard 随后会读取该文件以生成可视化效果）。
在 TF 1.x 中
上述二者必须手动关联在一起，方法是通过 Session.run() 获取摘要运算输出，并调用 FileWriter.add_summary(output, step)。v1.summary.merge_all() 运算通过使用计算图集合汇总所有摘要运算输出使这个操作更轻松，但是这种方式对 Eager Execution 和控制流的效果仍不尽人意，因此特别不适用于 TF 2.0。
在 TF 2.X 中
上述二者紧密集成。现在，单独的 tf.summary 运算在执行时可立即写入其数据。在您的模型代码中使用 API 的方式与以往类似，但是现在对 Eager Execution 更加友好，同时也保留了与计算图模式的兼容性。两个子 API 的集成意味着 summary.FileWriter 现已成为 TensorFlow 执行上下文的一部分，可直接通过 tf.summary 运算访问，因此配置写入器将是主要的差异。
Eager Execution 的示例用法（TF 2.0 中默认）： | writer = tf.summary.create_file_writer("/tmp/mylogs/eager")
with writer.as_default():
for step in range(100):
# other model code would go here
tf.summary.scalar("my_metric", 0.5, step=step)
writer.flush()
ls /tmp/mylogs/eager | site/zh-cn/tensorboard/migrate.ipynb | tensorflow/docs-l10n | apache-2.0 |
tf.function 计算图执行的示例用法： | writer = tf.summary.create_file_writer("/tmp/mylogs/tf_function")
@tf.function
def my_func(step):
with writer.as_default():
# other model code would go here
tf.summary.scalar("my_metric", 0.5, step=step)
for step in tf.range(100, dtype=tf.int64):
my_func(step)
writer.flush()
ls /tmp/mylogs/tf_function | site/zh-cn/tensorboard/migrate.ipynb | tensorflow/docs-l10n | apache-2.0 |
旧 TF 1.x 计算图执行的示例用法： | g = tf.compat.v1.Graph()
with g.as_default():
step = tf.Variable(0, dtype=tf.int64)
step_update = step.assign_add(1)
writer = tf.summary.create_file_writer("/tmp/mylogs/session")
with writer.as_default():
tf.summary.scalar("my_metric", 0.5, step=step)
all_summary_ops = tf.compat.v1.summary.all_v2_summary_ops()
writer_flush = writer.flush()
with tf.compat.v1.Session(graph=g) as sess:
sess.run([writer.init(), step.initializer])
for i in range(100):
sess.run(all_summary_ops)
sess.run(step_update)
sess.run(writer_flush)
ls /tmp/mylogs/session | site/zh-cn/tensorboard/migrate.ipynb | tensorflow/docs-l10n | apache-2.0 |
The figure below shows the input data-matrix, and the current batch batchX_placeholder
is in the dashed rectangle. As we will see later, this “batch window” is slided truncated_backprop_length
steps to the right at each run, hence the arrow. In our example below batch_size = 3, truncated_backprop_length = 3,
and total_series_length = 36. Note that these numbers are just for visualization purposes, the values are different in the code.
The series order index is shown as numbers in a few of the data-points. | Image(url= "https://cdn-images-1.medium.com/max/1600/1*n45uYnAfTDrBvG87J-poCA.jpeg")
#Now it’s time to build the part of the graph that resembles the actual RNN computation,
#first we want to split the batch data into adjacent time-steps.
# Unpack columns
#Unpacks the given dimension of a rank-R tensor into rank-(R-1) tensors.
#so a bunch of arrays, 1 batch per time step
# Change to unstack for new version of TF
inputs_series = tf.unstack(batchX_placeholder, axis=1)
labels_series = tf.unstack(batchY_placeholder, axis=1) | How-to-Use-Tensorflow-for-Time-Series-Live--master/demo_full_notes.ipynb | swirlingsand/deep-learning-foundations | mit |
Project 3D electrodes to a 2D snapshot
Because we have the 3D location of each electrode, we can use the
:func:mne.viz.snapshot_brain_montage function to return a 2D image along
with the electrode positions on that image. We use this in conjunction with
:func:mne.viz.plot_alignment, which visualizes electrode positions. | fig = plot_alignment(info, subject='sample', subjects_dir=subjects_dir,
surfaces=['pial'], meg=False)
mlab.view(200, 70)
xy, im = snapshot_brain_montage(fig, mon)
# Convert from a dictionary to array to plot
xy_pts = np.vstack([xy[ch] for ch in info['ch_names']])
# Define an arbitrary "activity" pattern for viz
activity = np.linspace(100, 200, xy_pts.shape[0])
# This allows us to use matplotlib to create arbitrary 2d scatterplots
fig2, ax = plt.subplots(figsize=(10, 10))
ax.imshow(im)
ax.scatter(*xy_pts.T, c=activity, s=200, cmap='coolwarm')
ax.set_axis_off()
# fig2.savefig('./brain.png', bbox_inches='tight') # For ClickableImage | 0.18/_downloads/66fec418bceb5ce89704fb8b44930330/plot_3d_to_2d.ipynb | mne-tools/mne-tools.github.io | bsd-3-clause |
Custom STIX Content
Custom Properties
Attempting to create a STIX object with properties not defined by the specification will result in an error. Try creating an Identity object with a custom x_foo property: | from stix2 import Identity
Identity(name="John Smith",
identity_class="individual",
x_foo="bar") | docs/guide/custom.ipynb | oasis-open/cti-python-stix2 | bsd-3-clause |
To create a STIX object with one or more custom properties, pass them in as a dictionary parameter called custom_properties: | identity = Identity(name="John Smith",
identity_class="individual",
custom_properties={
"x_foo": "bar"
})
print(identity.serialize(pretty=True)) | docs/guide/custom.ipynb | oasis-open/cti-python-stix2 | bsd-3-clause |
Alternatively, setting allow_custom to True will allow custom properties without requiring a custom_properties dictionary. | identity2 = Identity(name="John Smith",
identity_class="individual",
x_foo="bar",
allow_custom=True)
print(identity2.serialize(pretty=True)) | docs/guide/custom.ipynb | oasis-open/cti-python-stix2 | bsd-3-clause |
Likewise, when parsing STIX content with custom properties, pass allow_custom=True to parse(): | from stix2 import parse
input_string = """{
"type": "identity",
"spec_version": "2.1",
"id": "identity--311b2d2d-f010-4473-83ec-1edf84858f4c",
"created": "2015-12-21T19:59:11Z",
"modified": "2015-12-21T19:59:11Z",
"name": "John Smith",
"identity_class": "individual",
"x_foo": "bar"
}"""
identity3 = parse(input_string, allow_custom=True)
print(identity3.x_foo) | docs/guide/custom.ipynb | oasis-open/cti-python-stix2 | bsd-3-clause |
To remove a custom properties, use new_version() and set that property to None. | identity4 = identity3.new_version(x_foo=None)
print(identity4.serialize(pretty=True)) | docs/guide/custom.ipynb | oasis-open/cti-python-stix2 | bsd-3-clause |
Custom STIX Object Types
To create a custom STIX object type, define a class with the @CustomObject decorator. It takes the type name and a list of property tuples, each tuple consisting of the property name and a property instance. Any special validation of the properties can be added by supplying an __init__ function.
Let's say zoo animals have become a serious cyber threat and we want to model them in STIX using a custom object type. Let's use a species property to store the kind of animal, and make that property required. We also want a property to store the class of animal, such as "mammal" or "bird" but only want to allow specific values in it. We can add some logic to validate this property in __init__. | from stix2 import CustomObject, properties
@CustomObject('x-animal', [
('species', properties.StringProperty(required=True)),
('animal_class', properties.StringProperty()),
])
class Animal(object):
def __init__(self, animal_class=None, **kwargs):
if animal_class and animal_class not in ['mammal', 'bird', 'fish', 'reptile']:
raise ValueError("'%s' is not a recognized class of animal." % animal_class) | docs/guide/custom.ipynb | oasis-open/cti-python-stix2 | bsd-3-clause |
Now we can create an instance of our custom Animal type. | animal = Animal(species="lion",
animal_class="mammal")
print(animal.serialize(pretty=True)) | docs/guide/custom.ipynb | oasis-open/cti-python-stix2 | bsd-3-clause |
Trying to create an Animal instance with an animal_class that's not in the list will result in an error: | Animal(species="xenomorph",
animal_class="alien") | docs/guide/custom.ipynb | oasis-open/cti-python-stix2 | bsd-3-clause |
Parsing custom object types that you have already defined is simple and no different from parsing any other STIX object. | input_string2 = """{
"type": "x-animal",
"id": "x-animal--941f1471-6815-456b-89b8-7051ddf13e4b",
"created": "2015-12-21T19:59:11Z",
"modified": "2015-12-21T19:59:11Z",
"spec_version": "2.1",
"species": "shark",
"animal_class": "fish"
}"""
animal2 = parse(input_string2)
print(animal2.species) | docs/guide/custom.ipynb | oasis-open/cti-python-stix2 | bsd-3-clause |
However, parsing custom object types which you have not defined will result in an error: | input_string3 = """{
"type": "x-foobar",
"id": "x-foobar--d362beb5-a04e-4e6b-a030-b6935122c3f9",
"created": "2015-12-21T19:59:11Z",
"modified": "2015-12-21T19:59:11Z",
"bar": 1,
"baz": "frob"
}"""
parse(input_string3) | docs/guide/custom.ipynb | oasis-open/cti-python-stix2 | bsd-3-clause |
Custom Cyber Observable Types
Similar to custom STIX object types, use a decorator to create custom Cyber Observable types. Just as before, __init__() can hold additional validation, but it is not necessary. | from stix2 import CustomObservable
@CustomObservable('x-new-observable', [
('a_property', properties.StringProperty(required=True)),
('property_2', properties.IntegerProperty()),
])
class NewObservable():
pass
new_observable = NewObservable(a_property="something",
property_2=10)
print(new_observable.serialize(pretty=True)) | docs/guide/custom.ipynb | oasis-open/cti-python-stix2 | bsd-3-clause |
Likewise, after the custom Cyber Observable type has been defined, it can be parsed. | from stix2 import ObservedData
input_string4 = """{
"type": "observed-data",
"id": "observed-data--b67d30ff-02ac-498a-92f9-32f845f448cf",
"spec_version": "2.1",
"created_by_ref": "identity--f431f809-377b-45e0-aa1c-6a4751cae5ff",
"created": "2016-04-06T19:58:16.000Z",
"modified": "2016-04-06T19:58:16.000Z",
"first_observed": "2015-12-21T19:00:00Z",
"last_observed": "2015-12-21T19:00:00Z",
"number_observed": 50,
"objects": {
"0": {
"type": "x-new-observable",
"a_property": "foobaz",
"property_2": 5
}
}
}"""
obs_data = parse(input_string4)
print(obs_data.objects["0"].a_property)
print(obs_data.objects["0"].property_2) | docs/guide/custom.ipynb | oasis-open/cti-python-stix2 | bsd-3-clause |
ID-Contributing Properties for Custom Cyber Observables
STIX 2.1 Cyber Observables (SCOs) have deterministic IDs, meaning that the ID of a SCO is based on the values of some of its properties. Thus, if multiple cyber observables of the same type have the same values for their ID-contributing properties, then these SCOs will have the same ID. UUIDv5 is used for the deterministic IDs, using the namespace "00abedb4-aa42-466c-9c01-fed23315a9b7". A SCO's ID-contributing properties may consist of a combination of required properties and optional properties.
If a SCO type does not have any ID contributing properties defined, or all of the ID-contributing properties are not present on the object, then the SCO uses a randomly-generated UUIDv4. Thus, you can optionally define which of your custom SCO's properties should be ID-contributing properties. Similar to standard SCOs, your custom SCO's ID-contributing properties can be any combination of the SCO's required and optional properties.
You define the ID-contributing properties when defining your custom SCO with the CustomObservable decorator. After the list of properties, you can optionally define the list of id-contributing properties. If you do not want to specify any id-contributing properties for your custom SCO, then you do not need to do anything additional.
See the example below: | from stix2 import CustomObservable
@CustomObservable('x-new-observable-2', [
('a_property', properties.StringProperty(required=True)),
('property_2', properties.IntegerProperty()),
], [
'a_property'
])
class NewObservable2():
pass
new_observable_a = NewObservable2(a_property="A property", property_2=2000)
print(new_observable_a.serialize(pretty=True))
new_observable_b = NewObservable2(a_property="A property", property_2=3000)
print(new_observable_b.serialize(pretty=True))
new_observable_c = NewObservable2(a_property="A different property", property_2=3000)
print(new_observable_c.serialize(pretty=True)) | docs/guide/custom.ipynb | oasis-open/cti-python-stix2 | bsd-3-clause |
In this example, a_property is the only id-contributing property. Notice that the ID for new_observable_a and new_observable_b is the same since they have the same value for the id-contributing a_property property.
Custom Cyber Observable Extensions
Finally, custom extensions to existing Cyber Observable types can also be created. Just use the @CustomExtension decorator. Note that you must provide the Cyber Observable class to which the extension applies. Again, any extra validation of the properties can be implemented by providing an __init__() but it is not required. Let's say we want to make an extension to the File Cyber Observable Object: | from stix2 import CustomExtension
@CustomExtension('x-new-ext', [
('property1', properties.StringProperty(required=True)),
('property2', properties.IntegerProperty()),
])
class NewExtension():
pass
new_ext = NewExtension(property1="something",
property2=10)
print(new_ext.serialize(pretty=True)) | docs/guide/custom.ipynb | oasis-open/cti-python-stix2 | bsd-3-clause |
Once the custom Cyber Observable extension has been defined, it can be parsed. | input_string5 = """{
"type": "observed-data",
"id": "observed-data--b67d30ff-02ac-498a-92f9-32f845f448cf",
"spec_version": "2.1",
"created_by_ref": "identity--f431f809-377b-45e0-aa1c-6a4751cae5ff",
"created": "2016-04-06T19:58:16.000Z",
"modified": "2016-04-06T19:58:16.000Z",
"first_observed": "2015-12-21T19:00:00Z",
"last_observed": "2015-12-21T19:00:00Z",
"number_observed": 50,
"objects": {
"0": {
"type": "file",
"name": "foo.bar",
"hashes": {
"SHA-256": "35a01331e9ad96f751278b891b6ea09699806faedfa237d40513d92ad1b7100f"
},
"extensions": {
"x-new-ext": {
"property1": "bla",
"property2": 50
}
}
}
}
}"""
obs_data2 = parse(input_string5)
print(obs_data2.objects["0"].extensions["x-new-ext"].property1)
print(obs_data2.objects["0"].extensions["x-new-ext"].property2) | docs/guide/custom.ipynb | oasis-open/cti-python-stix2 | bsd-3-clause |
<table class="tfo-notebook-buttons" align="left">
<td><a target="_blank" href="https://tensorflow.google.cn/io/tutorials/genome"><img src="https://tensorflow.google.cn/images/tf_logo_32px.png">在 TensorFlow.org 上查看 </a></td>
<td><a target="_blank" href="https://colab.research.google.com/github/tensorflow/docs-l10n/blob/master/site/zh-cn/io/tutorials/genome.ipynb"><img src="https://tensorflow.google.cn/images/colab_logo_32px.png">在 Google Colab 中运行 </a></td>
<td><a target="_blank" href="https://github.com/tensorflow/docs-l10n/blob/master/site/zh-cn/io/tutorials/genome.ipynb"><img src="https://tensorflow.google.cn/images/GitHub-Mark-32px.png">在 GitHub 中查看源代码</a></td>
<td><a href="https://storage.googleapis.com/tensorflow_docs/io/docs/tutorials/genome.ipynb">{img1下载笔记本</a></td>
</table>
概述
本教程将演示 tfio.genome 软件包，其中提供了常用的基因组学 IO 功能，即读取多种基因组学文件格式，以及提供一些用于准备数据（例如，独热编码或将 Phred 质量解析为概率）的常用运算。
此软件包使用 Google Nucleus 库来提供一些核心功能。
设置 | try:
%tensorflow_version 2.x
except Exception:
pass
!pip install tensorflow-io
import tensorflow_io as tfio
import tensorflow as tf | site/zh-cn/io/tutorials/genome.ipynb | tensorflow/docs-l10n | apache-2.0 |
FASTQ 数据
FASTQ 是一种常见的基因组学文件格式，除了基本的质量信息外，还存储序列信息。
首先，让我们下载一个样本 fastq 文件。 | # Download some sample data:
!curl -OL https://raw.githubusercontent.com/tensorflow/io/master/tests/test_genome/test.fastq | site/zh-cn/io/tutorials/genome.ipynb | tensorflow/docs-l10n | apache-2.0 |
读取 FASTQ 数据
现在，让我们使用 tfio.genome.read_fastq 读取此文件（请注意，tf.data API 即将发布）。 | fastq_data = tfio.genome.read_fastq(filename="test.fastq")
print(fastq_data.sequences)
print(fastq_data.raw_quality) | site/zh-cn/io/tutorials/genome.ipynb | tensorflow/docs-l10n | apache-2.0 |
如您所见，返回的 fastq_data 具有 fastq_data.sequences，后者是 fastq 文件中所有序列的字符串张量（大小可以不同）；并具有 fastq_data.raw_quality，其中包含与在序列中读取的每个碱基的质量有关的 Phred 编码质量信息。
质量
如有兴趣，您可以使用辅助运算将此质量信息转换为概率。 | quality = tfio.genome.phred_sequences_to_probability(fastq_data.raw_quality)
print(quality.shape)
print(quality.row_lengths().numpy())
print(quality) | site/zh-cn/io/tutorials/genome.ipynb | tensorflow/docs-l10n | apache-2.0 |
独热编码
您可能还需要使用独热编码器对基因组序列数据（由 A T C G 碱基组成）进行编码。有一项内置运算可以帮助编码。 | print(tfio.genome.sequences_to_onehot.__doc__)
print(tfio.genome.sequences_to_onehot.__doc__) | site/zh-cn/io/tutorials/genome.ipynb | tensorflow/docs-l10n | apache-2.0 |
We will often define functions to take optional keyword arguments, like this: | def hello(name, loud=False):
if loud:
print ('HELLO, %s' % name.upper())
else:
print ('Hello, %s!' % name)
hello('Bob')
loud = True
hello('Fred', True) | python-tutorial.ipynb | w4zir/ml17s | mit |
KNN Classifier | # read X and y
# cols = ['pclass','sex','age','fare']
cols = ['pclass','sex','age']
X = dframe[cols]
y = dframe[["survived"]]
dframe.head()
# Use scikit-learn KNN classifier to predit survival probability
from sklearn.neighbors import KNeighborsClassifier
neigh = KNeighborsClassifier(n_neighbors=3)
neigh.fit(X, y)
# check accuracy
neigh.score(X,y)
# define a passenger
passenger = [1,1,29]
# predict survial label
print(neigh.predict([passenger]))
# predict survial probability
print(neigh.predict_proba([passenger]))
# find k-nearest neighbors
neigh.kneighbors(passenger,3)
# Let's create some data for DiCaprio and Winslet and you
import numpy as np
colsidx = [0,2,3];
dicaprio = np.array([3, 'Jack Dawson', 0, 19, 0, 0, 'N/A', 5.0000])
winslet = np.array([1, 'Rose DeWitt Bukater', 1, 17, 1, 2, 'N/A', 100.0000])
you = np.array([1, 'user', 1, 21, 0, 2, 'N/A', 50.0000])
# Preprocess data
dicaprio = dicaprio[colsidx]
winslet = winslet[colsidx]
you = you[colsidx]
# # Predict surviving chances (class 1 results)
pred = neigh.predict([dicaprio, winslet, you])
prob = neigh.predict_proba([dicaprio, winslet, you])
print("DiCaprio Surviving:", pred[0], " with probability", prob[0])
print("Winslet Surviving Rate:", pred[1], " with probability", prob[2])
print("user Surviving Rate:", pred[2], " with probability", prob[2]) | python-tutorial.ipynb | w4zir/ml17s | mit |
Get Movielens-1M data
this will download movielens-1m dataset from http://grouplens.org/datasets/movielens/: | data, genres = get_movielens_data(get_genres=True)
data.head()
data.info()
genres.head()
%matplotlib inline | polara_intro.ipynb | Evfro/RecSys_ISP2017 | mit |
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