markdown stringlengths 0 37k | code stringlengths 1 33.3k | path stringlengths 8 215 | repo_name stringlengths 6 77 | license stringclasses 15
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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(xt... | 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(s4g... | 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]... | 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 distribution... | 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 ordere... | 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.ga... | 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... | 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 func... | 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
... | 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
f... | 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,... | 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... | 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 gui... | 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... | 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!... | 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... | 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']
... | 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 = cou... | 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... | #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 'c... | 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 frid... | 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 'che... | 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(... | 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))
... | 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 e... | 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) i... | 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 i... | 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_r... | 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,... | 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],
... | 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],
... | 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 u... | 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 ... | 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
fac... | 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',
}
... | 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.sty... | 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 tensorflo... | # 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:... | 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 brackpropaga... | # 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 c... | 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':'d... | 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(... | 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.sor... | 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... | 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 i... | 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(c... | 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 * recal... | 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 cle... | 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 !... | 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... | 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':'popu... | 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[... | 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':'... | 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.i... | 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[... | 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... | 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(' ') # visi... | 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()):
... | 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... | 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 ... | #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... | 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.
prin... | 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 measu... | 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].r... | 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/git... | 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 Sess... | 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_... | 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 tot... | 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-... | 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" ... | 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"
}"""... | 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... | 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', '... | 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... | 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:... | 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... | 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=2... | 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... | 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(p... | 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... | 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/b... | 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)
... | 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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