Quick Start¶
This quick start guide shows the basics of how to use the Pyconstruct library to solve a constrained structured prediction problem. A structured prediction problem is a machine learning task in which we want to predict some structured object, e.g. a sequence, a tree or a graph. Structured prediction problems arise often when dealing with images, text and other types of structured data. If you have at least some idea of what structured prediction is go ahead reading, otherwise check out our introduction first.
Using Pyconstruct we can represent
arbitrary structured objects as key-value assignments (i.e. dictionaries) with
integer, float or list values. For instance, a sequence object can be
represented as a dictionary like {'sequence': [1, 2, 3, 4]}
.
In order to make predictions in the structured-output setting, we need a method that is able to find the object maximizing some function (the learned model). To do so, in Pyconstruct, we use the MiniZinc constraint programming language, together with some compatible constraint solver. Check-out the installation guide for the details on how to install this additional software.
OCR Equations prediction¶
In the following guide we will implement a simple model to perform OCR (Optical
Character Recognition) over handwritten equations. For this purpose, we will use
that equations
dataset available in Pyconstruct, which contains sequences of
images encoding equations of the type . The
numbers , , are positive integers and the equations
are all valid. The following is an example of the kind of equations contained in
this dataset:
which contains the equation . The label associated to
this sequence of images is a list: [4, 6, 2, 10, 3, 8, 4, 11, 8, 4, 6]
(here
and represent the and signs
respectively).
First of all, lets check out the data. Pyconstruct has a module for retrieving some predefined datasets:
from pyconstruct import datasets
eq = datasets.load('equations')
The first time the dataset is loaded, it will be fetched from the web and stored locally on your machine. You can now see the description of the dataset:
print(eq.DESCR)
The standard format to represent structured objects in Pyconstruct is by python
dictionaries. Each objects as several “attributes”, identified by some string.
Each attribute value may be a basic Python data type (integers, floats, list) or
Numpy arrays. In the equations
dataset, for instance, inputs (contained in
the data
member) are represented as dictionaries containing two attributes:
an integer encoding the length
of the equation; a list of 9x9 matrices
(numpy.ndarray
) containing the bitmap images of each symbol in the equation.
The target
(i.e. the labels) also contains dictionaries with a single
attribute sequence
, a list of integers representing the symbols associated
to each image in the equation. For instance:
print(eq.data[0])
print(eq.target[0])
We now have to represent our data into a MiniZinc program, which is then going to be used by Pyconstruct to generate predictions. Before coding our problem, it is first useful to look at how MiniZinc can be used for this purpose.
Intermezzo: MiniZinc for Pyconstruct¶
Here we provide a brief overview to catch the intuition behind how to use MiniZinc for structured prediction with Pyconstruct. For a detailed guide on MiniZinc, check out their webpage.
MiniZinc allows us to easily specify the structure of the objects that we want to predict, the input information we receive prior to the prediction, as well as some constraints that the output objects have to satisfy. In the case of our handwritten equations, we receive as input both the length of the sequence (an integer) as well as the list of images (Numpy array). The output is a list of integers of the same length of the input representing the symbols.
Inputs in MiniZinc are specified through “dzn” parameter variables, while the outputs are the optimiziation variables. In the equations case we would have:
int: length;
array[1 .. length, 1 .. 9, 1 .. 9] of int: images;
array[1 .. length] of var int: sequence;
The parameters length
and images
match the attributes provided in the
dataset. The input dictionaries will be passed by Pyconstruct to MiniZinc upon
making a prediction.
MiniZinc also allows us to easily add constraints into the mix. For example, we know for a fact that the output sequence should contain exactly one (associated to the symbol ) and exactly one (associated to the symbol ), which can be easily stated in MiniZinc like:
constraint count(sequence, 10, 1) /\ count(sequence, 11, 1);
Pyconstruct actually employs a dialect of MiniZinc defined by the PyMzn library, which it uses underneath to execute the MiniZinc compiler and the solvers. The PyMzn code is simply MiniZinc with some templating bits following the Jinja2 format. This templating language allows us to do things like conditionally decide what solving method use and which function to optimize, e.g.:
{% if problem == 'map' %}
solve maximize {{ model }};
{% else %}
solve satisfy;
{% endif %}
When Pyconstruct wants to solve a MAP problem (finding the object maximizing the
model), it passes to Jinja the value 'map'
for the parameter problem
and
some function for the parameter model
and the resulting MiniZinc solve
statement will be formatted accordingly.
Perhaps the most useful feature that templating with Jinja adds to Pyconstruct
is the ability to define packages containing reusable macros. Pyconstruct
provides a shared templating library with
quite a few modules containing reusable macros that can be used to easily define
structured prediction problems with MiniZinc. For instance, the above piece of
code generating a solve statement conditionally to the value of the problem
parameter is similar to what is done by the solve
macro in the Pyconstruct
shared templating library:
{% from 'globals.pmzn' import solve %}
{{ solve(problem, model) }}
Fleshing out the domain¶
In Pyconstruct, we use the term domain to refer to the collection of input and output variables and the constraints over them. The domain essentially represent the feasible space of the objects. A domain is also equipped with an inference procedure, which given a model, returns some prediction. MiniZinc is the default method for implementing a domain in Pyconstruct.
Let’s now start coding out our OCR equations problem. Let’s create our PyMzn
file called equations.pmzn
. This file will contain our domain, as well as
the method to solve sevaral inference problems. The structure of the file will
be the following:
{% from 'globals.pmzn' import domain, solve %}
{% from 'linear.pmzn' import linear_model %}
% Declare here some constants ...
{% call domain(problem) %}
% Declare here input/output variables and constraints ...
{% endcall %}
{% set n_features %}
% Declare here the number of features ...
{% endset %}
{% call linear_model(problem, params, n_features) %}
% Declare here the features ...
{% endcall %}
{% set loss %}
% Declare here the structured loss ...
{% endset %}
{{ solve(problem, loss=loss) }}
For now, we have not yet written a single MiniZinc line. The above code simply
contain some Jinja macros imported from the shared templating library of
Pyconstruct. In particular, we are going to use: the domain
macro, which
will contain the actual input and output variables, as well as the constraints;
the linear_model
macro, which takes care of declaring a linear model of the
type , where is a vector of learned
parameters passed by Pyconstruct and is a vector of features
that we are going to declare; the solve
macro, which takes care of selecting
the right MiniZinc solve statement conditionally to the inference problem to
solve.
Let’s fill in the gaps. First of all, it is useful to add an explicit declaration of some contants that we have in this problem:
int: MAX_HEIGHT = 9;
int: MAX_WIDTH = 9;
set of int: HEIGHT = 1 .. MAX_HEIGHT;
set of int: WIDTH = 1 .. MAX_WIDTH;
% Set of symbols (labels). Digits are encoded as themselves.
% Assume '+' and '=' are encoded respectively with 10 and 11.
int: PLUS = 10;
int: EQUAL = 11;
int: N_SYMBOLS = 12;
set of int: SYMBOLS = 0 .. N_SYMBOLS - 1;
We just declared the constants encoding the maximum height and width of the
images, the number and set of symbols, and the constants encoding the +
and
=
signs in the sequence. These constants will turn useful later on when we
will have to declare variables and constraints. Next, we need to declare the
input and output variables. These go inside the call to the domain
macro:
% Input: Length of the sequence and images
int: length;
set of int: SEQUENCE = 1 .. length;
array[SEQUENCE, HEIGHT, WIDTH] of {0, 1}: images;
% Output: Sequence of symbols
array[SEQUENCE] of var SYMBOLS: sequence;
The above code is similiar to what we have seen before, but here we used the
constants to declare the variables. Here we also imposed that the sequence has
to take values in the SYMBOLS
set, the simplest form of constraint.
We now need to declare some features for the model. A standard choice when handling sequences is to use unary and pairwise features, like those used in a chain CRF. Unary features, also called emission features, correlate the input “attributes” of the elements of the sequence with the output labels of the sequence. For each attribute and each label , the emission features sum the values of the attribute for the active elements of the sequence, i.e. elements of the sequence that are being assigned the label . In the case of the handwritten equations, the input attributes correspond to the pixels of the images, while the labels are the output symbols. On the other hand, pairwise features correlate pairs of labels , counting the number of times in the sequence the label is followed by the label .
Pyconstruct provides utilities for handling this kind of features out of the
box. These are included in the chain.pmzn
file. What we need to do first is
to flatten the images into a two-dimensional vector of attributes:
% Constants
int: N_PIXELS = MAX_HEIGHT * MAX_WIDTH;
set of int: PIXELS = 1 .. N_PIXELS;
% Domain
array[SEQUENCE, PIXELS] of {0, 1}: pixels = array2d(SEQUENCE, PIXELS, [
images[s, i, j] | s in SEQUENCE, i in HEIGHT, j in WIDTH
]);
After defining the attribute vector, we can use the macros in the chain.pmzn
library to define the features for our linear model:
{% from 'chain.pmzn' import
n_emission_features, emission_features,
n_transition_features, transition_features
%}
{% set n_features %}
{{
n_emission_features(n_attributes='N_PIXELS', n_labels='N_SYMBOLS')
}}
+
{{
n_transition_features(n_labels='N_SYMBOLS')
}}
{% endset %}
{% call linear_model(problem, params, n_features) %}
{{
emission_features(
attributes_var='pixels', attributes_set='PIXELS',
sequence_var='sequence', sequence_set='SEQUENCE',
labels_set='SYMBOLS'
)
}}
++
{{
transition_features(
sequence_var='sequence', sequence_set='SEQUENCE',
labels_set='SYMBOLS'
)
}}
{% endcall %}
The above code will compile into something like:
int: N_FEATURES = N_PIXELS * N_SYMBOLS + N_SYMBOLS * N_SYMBOLS;
set of int: FEATURES = 1 .. N_FEATURES;
array[FEATURES] of var float: phi = [
sum(e in SEQUENCE)(pixels[e, a] * sequence[e] == l)) | a in PIXELS, l in SYMBOLS
]
++
[
sum(e1, e2 in SEQUENCE where e1 < e2)(sequence[e1] == l1 /\ sequence[e2] == l2) | l1, l2 in SYMBOLS
];
array[FEATURES] of float: w = [
% weights found in the model paramenters
];
var float: score = sum(e in SEQUENCE)(w[e] * phi[e]);
Notice that we used the variables problem
and params
: these are standard
global variables passed to all domains by Pyconstruct. The params
variable,
in particular, is a dictioray containing the parameters of the model, which can
be used to define the model directly into the MiniZinc file. These are the same
parameters that are returned by the params
property of a Model
instance.
The last piece we need to add is the structured loss function. When learning a
large margin model (Structured SVM), the inference oracle has to repeatedly
solve the so-called loss-augmented inference problem, which is simply an
optimization problem of the type for some input-output pair in the training set. The
function is the structured loss, a problem-dependent
metric that evaluates the goodness of the prediction agaist the true
label . When Pyconstruct needs to solve a loss-augmented inference,
passes the value loss_augmented_map
to the problem
global variable and
the solve
macro takes care of the rest. The only information it requires is
the actual definition of the loss. When solving a loss_augmented_map
inference, Pyconstruct also passes a variable y_true
, which contains the
true label to be used for the loss-augmented inference. In order to access this
variable in the MiniZinc code we need to add the following code to the domain:
{% if problem == 'loss_aumented_map' %}
array[SEQUENCE] of int: true_sequence = {{ y_true['sequence']|dzn }};
{% endif %}
In the above code we declared an array called true_sequence
conditionally to
the problem being a loss_augmented_map
. In the above code we used the a
Jinja filter dzn
provided by the PyMzn library, which takes any python
object and serializes it into dzn format.
We now have to declare the loss. A standard loss for sequence prediction problem
is the (normalized) Hamming loss. The Pyconstruct shared templating library
provides an utility macro to compute the hamming loss of two sequences. We just
need to import the macro hamming
from the library metrics.pmzn
:
{% from 'metrics.pmzn' import hamming %}
{% set loss %}
{{
hamming(
sequence_set='SEQUENCE', sequence='sequence',
true_sequence='true_sequence'
)
}}
{% endset %}
The final model should look like this:
{% from 'globals.pmzn' import domain, solve %}
{% from 'linear.pmzn' import linear_model %}
{% from 'chain.pmzn' import
n_emission_features, emission_features,
n_transition_features, transition_features
%}
{% from 'metrics.pmzn' import hamming %}
int: MAX_HEIGHT = 9;
int: MAX_WIDTH = 9;
set of int: HEIGHT = 1 .. MAX_HEIGHT;
set of int: WIDTH = 1 .. MAX_WIDTH;
% Set of symbols (labels). Digits are encoded as themselves.
% Assume '+' and '=' are encoded respectively with 10 and 11.
int: PLUS = 10;
int: EQUAL = 11;
int: N_SYMBOLS = 12;
set of int: SYMBOLS = 0 .. N_SYMBOLS - 1;
% Constants
int: N_PIXELS = MAX_HEIGHT * MAX_WIDTH;
set of int: PIXELS = 1 .. N_PIXELS;
{% call domain(problem) %}
% Input: Length of the sequence and images
int: length;
set of int: SEQUENCE = 1 .. length;
array[SEQUENCE, HEIGHT, WIDTH] of {0, 1}: images;
% Output: Sequence of symbols
array[SEQUENCE] of var SYMBOLS: sequence;
{% if problem == 'loss_aumented_map' %}
array[SEQUENCE] of int: true_sequence = {{ y_true['sequence']|dzn }};
{% endif %}
array[SEQUENCE, PIXELS] of {0, 1}: pixels = array2d(SEQUENCE, PIXELS, [
images[s, i, j] | s in SEQUENCE, i in HEIGHT, j in WIDTH
]);
{% endcall %}
{% set n_features %}
{{
n_emission_features(n_attributes='N_PIXELS', n_labels='N_SYMBOLS')
}}
+
{{
n_transition_features(n_labels='N_SYMBOLS')
}}
{% endset %}
{% call linear_model(problem, params, n_features) %}
{{
emission_features(
attributes_var='pixels', attributes_set='PIXELS',
sequence_var='sequence', sequence_set='SEQUENCE',
labels_set='SYMBOLS'
)
}}
++
{{
transition_features(
sequence_var='sequence', sequence_set='SEQUENCE',
labels_set='SYMBOLS'
)
}}
{% endcall %}
{% set loss %}
{{
hamming(
sequence_set='SEQUENCE', sequence='sequence',
true_sequence='true_sequence'
)
}}
{% endset %}
{{ solve(problem, loss=loss) }}
We are now ready to launch a learning algorithm over our domain. However, we have not really done with the domain yet. We still have not covered one of the main perks of using MiniZinc as a domain modeller: adding background knowledge as constraints. Adding constraints can speed-up learning and can drastically reduce the number of examples needed to learn an accurate model. In the OCR equations setting, we actually know several facts about our problem and the data, e.g. the numbers are positive integers, the plus always comes before the equal sign, and the equations are always valid.
If you want to add constraints right away, check out the following section. If instead you want to go strait to learning a model with the model we have written so far, jump to the dedicated section further down.
Adding background knowledge as constraints¶
We are now going to add some constraints to the domain based on the facts we know about the OCR equations problem. Recall that the examples in the dataset are all valid equations of the form , with positive , and . We are now going to encode this knowledge into the domain step-by-step.
Let’s start by the fact that no matter how many digits the numbers have, there
are always going to be exaclty one sign and exaclty one
sign. We can use the minizinc global constraint count
for this. In order to
use the global constraints we need to include the library globals.mzn
first:
% At the top
include "globals.mzn";
% In the domain
constraint count(sequence, PLUS, 1) /\ count(sequence, EQUAL, 1);
About the operators, we also know that the plus sign always comes before the equal sign. To encode this constraint it is convenient to extract the indices of the two operators from the sequence:
% Indices of the two operators
array[1 .. 2] of var 2 .. length - 1: opr;
constraint sequence[opr[1]] == PLUS /\ sequence[opr[2]] == EQUAL;
constraint increasing(opr);
constraint opr[1] + 1 < opr[2];
The first of the above constraints enforces the variables opr[1]
and
opr[2]
to be indices corresponding to the sequence values PLUS
and
EQUAL
. Together with the uniqueness contraint defined earlier, this ensures
that the two variables are indeed the two indices of the two operators. The
second constraint is a MiniZinc global constraint the operator indice to be
ordered increasingly. Together with the previous constraint, this means that the
PLUS
value must come before the EQUAL
value. The third constraint
forces the number inbetween the two operators to have at least one digit.
We want now to impose the validity of the equations. To do so, we need to extract the actual numeric values encoded in the sequence of digits. This process is going to require a few advanced constructs from MiniZinc, so make sure you are confortable with them.
To extract the values, we are going to sum the digits multiplied by the
corresponding power of ten. We first need to make an assumption: we need to set
the maximum number of digits in the numbers (can be arbitrary large). In the OCR
equation dataset, the maximum is three digits per number. We then need to put
the digits into a matrix of three zero-padded vectors of length MAX_DIGITS
:
int: MAX_DIGITS = 3;
array[1 .. 4] of var 0 .. length+1: ext = [0, opr[1], opr[2], length+1];
constraint forall(i in 1 .. 3)(ext[i+1] - ext[i] <= MAX_DIGITS + 1);
array[1 .. 3, 1 .. MAX_DIGITS] of var 0 .. 9: num_matrix = array2d(1 .. 3, 1 .. MAX_DIGITS, [
if ext[i] + MAX_DIGITS - k < ext[i+1] then
sequence[ext[i+1] - MAX_DIGITS + k]
else
0
endif
| i in 1 .. 3, k in 0 .. MAX_DIGITS-1
]);
In the above code we declared an array ext
of the extremes of each number.
The two following constraints enforce the length of each number to be lower than
MAX_DIGITS
. Then, for each two consecutive extremes we extracted one vector
containing the zero-padded numbers, iterating over k
. The conditional
statement makes sure the arrays are populated as we expect. For instance, for
the sequence 34 + 56 = 90
we get:
num_matrix = [| 0, 3, 4
| 0, 5, 6
| 0, 9, 0 |];
We can then extract the three numbers by summing powers of ten:
int: MAX_NUM = pow(10, MAX_DIGITS + 1);
array[1 .. 3] of var 0 .. MAX_NUM: num = [
sum(j in 1 .. MAX_DIGITS)(
pow(10, MAX_DIGITS - j) * num_matrix[i, j]
) | i in 1 .. 3
];
Finally, we can impose the validity of the equation simply by adding the constraint:
constraint num[1] + num[2] == num[3];
This type of constraints is especially illustrative of the expressive power of MiniZinc. We just extracted the actual semantic meaning of the sequence of symbols and we reasoned over it. This is something that standard models (even structured ones) cannot do. This is especially useful in settings where there and very few examples to learn from and rich semantic knowledge. While this OCR equations setting is a very toy example, it is easy to see the potential in the Pyconstruct library for modelling very complex tasks like product configuration, planning, dialogue management and more.
Learning and evaulating a model¶
Whether or not you added the constraints to the domain, you now have your domain
fully encoded in the file equations.pmzn
and that is all it is needed to
learn a predictive structured model. Pyconstruct can is pretty flexible, it
allows to use the following code for learning with different domains. This also
means that if you want to add some constraints to an existing model you only
need to modify the MiniZinc file, without worrying about the python code for
learning.
Let’s go ahead and fit a model with the OCR equations data over the domain we
just defined. In python we need to instantiate a Domain, passing the path to
our equations.pmzn
file:
from pyconstruct import Domain
eq_dom = Domain('equations.pmzn')
Now, let’s instantiate a Learner. A learner is a Pyconstruct object that is able
to learn a model using some learning algorithm. Pyconstruct learners work
exaclty like Scikit-learn estimators: we first need to instantiate the learner
with the hyper-parameters we want to use, and then call the fit
function
passing the data. One of the state-of-the-art learning algorithms for learning
structured SVMs is Stochastic Subgradient Descent, also known as SSG. Let us use
the SSG
learner for estimating a linear model over the OCR equation domain:
from pyconstruct import SSG
ssg = SSG(eq_dom)
If you need to set some configuration for PyMzn, now is the time. For instance, if you want to set Gurobi as the solver used by Pyconstruct, you can do so by setting the default solver in PyMzn:
import pymzn
pymzn.config.set('solver', pymzn.gurobi)
At this point we are ready to start the fitting process:
ssg.fit(eq.data, eq.target)
That is pretty much it. We passed the domain to the SSG constructor and then we
called the fit
method, which will pass through the entire dataset and learn
a model calling MiniZinc several times to make inference. After learning we can
pass some batch of examples X
to the predict
function to get
predictions:
Y = ssg.predict(X)
This process will take some time depending on your machine and the solver used.
To speed-up things a bit, you can make inference over a batch of data in
parallel. This can be done by setting the parameter n_jobs
in the domain
instance:
eq_dom = Domain('equations.pmzn', n_jobs=4)
Also, we can parallelize the computation of the gradient steps in the SSG
learner:
ssg = SSG(eq_dom, n_jobs=4)
Another common strategy is to use approximate inference. A very simple way to have approximate inference is to set a timeout to the solver and get the best-so-far solution. This can be done by setting the timeout in the domain constructor:
eq_dom = Domain('equations.pmzn', n_jobs=4, timeout=5) # timeout 5 seconds
Pyconstruct will automatically increase the timeout until a solution is found for each inference problem. Now the training should be smoother even on low-end machines.
While the above code should cover many of the typical cases, Pyconstruct also let’s you have more control over the learning process. First of all, you would probably want to test the model after it is trained. Therefore we probably want to split the data into training and test set. We can use the utility from Scikit-learn for this:
from sklearn.model_selection import train_test_split
X_train, X_test, Y_train, Y_test = train_test_split(eq.data, eq.target, test_size=0.2)
Another thing that you probably want to do is to evaluate the model on the
training set while it is training. Most of Pyconstruct learners are online
learners, so they can learn step-by-step from mini-batches of data. Online
learners implement the partial_fit
method, which takes a mini-batch of data
and updates the current parameters of the model. To split the dataset into
batches and iterate over them, we can use the batches
Pyconstruct utility:
from pyconstruct.utils import batches
for X_b, Y_b in batches(X_train, Y_train, batch_size=8):
ssg.partial_fit(X_b, Y_b)
We can now evaluate our model on the training set right before making a learning step on the batch. To evaluate our sequences we can use again the Hamming loss:
from pyconstruct.metrics import hamming
def loss(Y_pred, Y_true):
return hamming(Y_pred, Y_true, key='sequence')
The above function computes the hamming loss over the sequence
key of all
the objects in the Y_pred
and Y_true
batches and returns a Numpy vector
with all the values.
We can now compute the losses on the training batches and print the average:
for X_b, Y_b in batches(X_train, Y_train, batch_size=10):
Y_pred = ssg.predict(X_b)
avg_loss = loss(Y_b, Y_pred).mean()
print('Training loss {}'.format(avg_loss))
ssg.partial_fit(X_b, Y_b)
Finally we can test the model learned by SSG
on the test set:
Y_pred = ssg.predict(X_test)
avg_loss = loss(Y_test, Y_pred).mean()
print('Test loss {}'.format(avg_loss))
This covers the basics of out to use Pyconstruct. Check out the reference manual to learn more about all the components and the ways you can tweak Pyconstruct to solve your structured prediction problem.


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The proffer
package profiles R code to find bottlenecks. Visit https://r-prof.github.io/proffer for documentation. https://r-prof.github.io/proffer/reference/index.html has a complete list of available functions in the package.
This data processing code is slow.
system.time({
n <- 1e5
x <- data.frame(x = rnorm(n), y = rnorm(n))
for (i in seq_len(n)) {
x[i, ] <- x[i, ] + 1
}
x
})
#> user system elapsed
#> 82.060 28.440 110.582
Why exactly does it take so long? Is it because for
loops are slow as a general rule? Let’s find out empirically.
library(proffer)
px <- pprof({
n <- 1e5
x <- data.frame(x = rnorm(n), y = rnorm(n))
for (i in seq_len(n)) {
x[i, ] <- x[i, ] + 1
}
x
})
#> http://localhost:64610
When we navigate to http://localhost:64610 and look at the flame graph, we see [<-.data.frame()
(i.e. x[i, ] <- x[i, ] + 1
) is taking most of the runtime.
So we refactor the code to avoid data frame row assignment. Much faster, even with a for
loop!
system.time({
n <- 1e5
x <- rnorm(n)
y <- rnorm(n)
for (i in seq_len(n)) {
x[i] <- x[i] + 1
y[i] <- y[i] + 1
}
x <- data.frame(x = x, y = y)
})
#> user system elapsed
#> 0.050 0.003 0.054
Moral of the story: before you optimize, throw away your assumptions and run your code through a profiler. That way, you can spend your time optimizing where it counts!
Sometimes, your pprof
server may not work right away. If that happens, take a look at the error logs.
px <- pprof({
n <- 1e4
x <- data.frame(x = rnorm(n), y = rnorm(n))
for (i in seq_len(n)) {
x[i, ] <- x[i, ] + 1
}
x
})
#> http://localhost:50195
px # How is my background process doing?
#> PROCESS 'R', finished.
px$is_alive()
# [1] FALSE
px$read_error() # Why did it quit soon?
#> [1] "sh: /user/local/bin/pprof: No such file or directory\nWarning message:\nIn system2(Sys.getenv(\"pprof_path\"), args) : error in running command\n"
# Oh, I must have set the wrong path to the pprof executable.
# Let me find out where I actually installed pprof.
system("which", "pprof")
#> "/home/landau/go/bin/pprof"
# I can put a line in my .Rprofile or .Renviron file
# to automatically tell new sessions where pprof lives.
Sys.setenv(pprof_path = "/home/landau/go/bin/pprof")
# Now, pprof should work.
px <- pprof({
n <- 1e4
x <- data.frame(x = rnorm(n), y = rnorm(n))
for (i in seq_len(n)) {
x[i, ] <- x[i, ] + 1
}
x
})
#> http://localhost:64610
px
#> PROCESS 'R', running, pid 12361.
px$is_alive()
# [1] TRUE
# Now a web browser should be able to open http://localhost:64610.
It is best to take down the pprof
server when you are done with it.
px
is the handle of a callr
background process. To learn more about how to manage the process, have a look at the callr
documentation, particularly the function r_bg()
.
The latest release of proffer
is available on CRAN.
Alternatively, you can install the development version from GitHub.
To use functions pprof()
and serve_pprof()
, you need to install pprof
. Installing pprof
is hard, so if you have trouble, please do not hesitate to open an issue and ask for help. And if you cannot install pprof
, then profvis
is an excellent alternative.
RProtoBuf
package. On Linux, you also need to install the supporting protocol buffer libraries, e.g. sudo apt-get install protobuf-compiler libprotobuf-dev libprotoc-dev
on Ubuntu.PATH
environment variable (directions here).go version
. If you get an error, you may need to set the PATH
environment variable as described here for Linux and here for Windows
GOPATH
environment variables on your system. Type go env GOPATH
in in a new terminal session verify that you set it correctly.go get -u github.com/google/pprof
in your terminal to install pprof
pprof
executable. It is usually in the bin
subdirectory of GOPATH
, e.g. /home/landau/go/bin/pprof
..Renviron
file to set the pprof_path
environment variable, e.g. pprof_path=/home/landau/go/bin/pprof
. This variable tells proffer
how to find pprof
.We encourage participation through issues and pull requests. proffer
has a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
Profilers identify bottlenecks, but the do not offer solutions. It helps to learn about fast code in general so you can think of efficient alternatives to try.
The profvis
is much easier to install than proffer
and equally easy to invoke.
library(profvis)
profvis({
n <- 1e5
x <- data.frame(x = rnorm(n), y = rnorm(n))
for (i in seq_len(n)) {
x[i, ] <- x[i, ] + 1
}
x
})
However, profvis
-generated flame graphs can be difficult to read and slow to respond to mouse clicks.
proffer
uses pprof
to create friendlier, faster visualizations.


This is dummy copy.
Only for show.
This is dummy copy. It is not meant to be read. It has been placed here solely to demonstrate the look and feel of finished, typeset text. Only for show. He who searches for meaning here will be sorely disappointed. These words are here to provide the reader with a basic impression of how actual text will appear in its final presentation. Think of them merely as actors on a paper stage, in a performance devoid of content yet rich in form. That being the case, there is really no point in your continuing to read them. After all, you have many other things you should be doing. Who's paying you to waste this time, anyway?
It's Greek to you. Unless, of course, you're Greek
This is dummy copy. It's Greek to you. Unless, of course, you're Greek, in which case, it really makes no sense. Why, you can't even read it! It is strictly for mock-ups. You may mock it up as strictly as you wish.
Meaningless mock-up
Meaningless mock-up, mock turtle soup spilled on a mock turtle neck. Mach I Convertible copy. To kill a mockingbird, you need only force it to read this copy.
This is Meaningless filler. (Elvis movies.) It is not meant to be a forum for value judgments nor a scholarly diatribe on how virtue should be measured. The whole point here (if such a claim can be made in an admittedly pointless paragraph) is that this is dummy copy.
Real bullets explode with destructive intensity.
Real bullets explode with destructive intensity. Such is not the case with dummy bullets. In fact, they don't explode at all. Duds. Dull thuds. Dudley do-wrongs. And do-wrongs don't make a right. Why on earth are you still reading this? Haven't you realized it's just dummy copy? How many times must you be reminded that it's really not meant to be read? You're only wasting precious time. But be that as it may, you've got to throw in a short paragraph from time to time. Here's a short paragraph.
It breaks up the intimidating blocks
It breaks up the intimidating blocks of text and makes the page more inviting to read, which is again ironic, considering this is dummy copy. It is not meant to be read. Someday this space will be occupied by real copy that is meant to be read. What other form of filler would dare be so politically incorrect? This is dummy copy. It is not meant to be read. Good thing.
It is not meant to be read.
This is dummy copy. It is not meant to be read. Accordingly, it is difficult to figure out when to end it. If this were real copy, it would have ended long ago, because—as we all know—no one reads body copy, and even fewer read body copy this long. But then, this is dummy copy. It is not meant to be read. Period.


Hanzi is a learning application based on the first 500 characters learned in MIT's Chinese 1-4 classes. Characters are composed of "sub-characters" known as radicals, and they usually have a main radical that defines its pronunciation or meaning - while also serving as a learning tool.
I made Hanzi as the final project for my Information and Visualization Design Studio class (4.032) at MIT.
I have been studying Mandarin Chinese for around 5 years, and the topics I've learned have ranged from ordering food to visiting friends. One of the hardest aspects of learning Chinese is memorizing all the unique characters in the language, so with Hanzi, I aimed to build a review tool to assist in this learning process.
The context of my project is a non-Chinese student learning a new language: Mandarin Chinese. My audience is current Mandarin Chinese language learners, professors, and anyone else interested in the language or language education.
My source data comes from my Basic Written Chinese (Chinese 1 & 2) and Intermediate Written Chinese (Chinese 3 & 4) books. As I was unable to obtain digital versions of the books to parse the data from, I had to manually type the Chinese character data from the books.


It
Everything in the collection: Earthling Peter quill (Star-Lord), Drax the silent thunder, the green assassin Gamora, the living tree Groot and
talking raccoon. The characters are not
change yourself and punctually keep ending up in an impossible situation disentangle from them with
almost no damage (or even use) for
surrounding. This time they have to reveal one of the most important secrets in the Galaxy: who is actually
the father of Peter quill?


I created this interactive election map for my Skillcrush JavaScript class class. The map itself is from map.js. The voting model mimics the Electoral College. The number of votes for each candidate is predefined, and my JavaScript code determines the winner. The winner of each state is revealed when you hover over it, shown in both the color of the state and the box with vote count at the bottom.
I made this project my own by making it an election for 2 of my favorite comedians, and adding a fun parallax effect to the photo.


Dialogs
Alert Dialog
Confirm Dialog
Prompt Dialog
Alert Dialog(object)
Confirm Dialog(object)
Prompt Dialog(object)
Logs
Standard Log
Success Log
Error Log


City in China Shanghai, on China’s central coast, is the country's biggest city and a global financial hub. Its heart is the Bund, a famed waterfront promenade lined with colonial-era buildings. Across the Huangpu River rises
the Pudong district’s futuristic skyline, including 632m Shanghai Tower and the Oriental Pearl TV Tower, with distinctive pink spheres. Sprawling Yu Garden has traditional pavilions, towers and ponds.
Weather: 9°C, Wind NE at 11 km/h, 79% Humidity
Local time: Thursday 9:58 PM
Population: 24.15 million (2015)
Moscow
Capital of Russia Moscow, on the Moskva River in western Russia, is the nation’s cosmopolitan capital. In its historic core is the Kremlin, a complex that’s home to the president and tsarist treasures in the Armoury. Outside
its walls is Red Square, Russia's symbolic center. It's home to Lenin’s Mausoleum, the State Historical Museum's comprehensive collection and St. Basil’s Cathedral, known for its colorful, onion-shaped domes.
Weather: 9°C, Wind W at 26 km/h, 47% Humidity
Local time: Thursday 5:13 PM
Population: 11.92 million (2012)
London
Capital of England London, the capital of England and the United Kingdom, is a 21st-century city with history stretching back to Roman times. At its centre stand the imposing Houses of Parliament, the iconic ‘Big Ben’ clock
tower and Westminster Abbey, site of British monarch coronations. Across the Thames River, the London Eye observation wheel provides panoramic views of the South Bank cultural complex, and the entire city.
Weather: 11°C, Wind NE at 31 km/h, 57% Humidity
Local time: Thursday 2:22 PM
Population: 8.674 million (2015)
Image cards
Istanbul
City in Turkey Istanbul is a major city in Turkey that straddles Europe and Asia across the Bosphorus Strait. Its Old City reflects cultural influences of the many empires that once ruled here. In the Sultanahmet district, the
open-air, Roman-era Hippodrome was for centuries the site of chariot races, and Egyptian obelisks also remain. The iconic Byzantine Hagia Sophia features a soaring 6th-century dome and rare Christian mosaics.
Weather: 12°C, Wind S at 13 km/h, 71% Humidity
Local time: Friday 4:00 PM
Population: 14.8 million (Dec 31, 2016)
Tokyo
Capital of Japan Tokyo, officially Tokyo Metropolis, is the capital of Japan and one of its 47 prefectures. The Greater Tokyo Area is the most populous metropolitan area in the world. It is the seat of the Emperor of Japan and
the Japanese government.
Wikipedia
Weather: 5°C, Wind NW at 26 km/h, 54% Humidity
Local time: Friday 10:04 PM
Population: 13.62 million (Jul 31, 2016)
São Paulo
Municipality in Brazil São Paulo, Brazil’s vibrant financial center, is among the world's most populous cities, with numerous cultural institutions and a rich architectural tradition. Its iconic buildings range from its neo-Gothic
cathedral and the 1929 Martinelli skyscraper to modernist architect Oscar Niemeyer’s curvy Edifício Copan. The colonial-style Pátio do Colégio church marks where Jesuit priests founded the city in 1554.
Weather: 24°C, Wind E at 10 km/h, 62% Humidity
Local time: Friday 10:05 AM
Population: 12.04 million (2016)


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Last Sunday we added another person to our family. Amber (Dana’s niece) will be staying with the family until early August, leaving a few days before I leave. She is from Colorado and just graduated from high school. So far, Amber has been mostly tolerating my annoying behavior...
We also added another member to the family – Fila. Current and past pets of my family have been named Adidas, Reebok, Jordan and Nike. Fila is a 2-month old Yorkshire Terrier that Dana got for her birthday. The dog is very small right now and is extremely cute.
-PJ


rzip(1) rzip(1) NAME rzip - a large-file compression program SYNOPSIS rzip [OPTIONS] <files...> DESCRIPTION rzip is a file compression program designed to do particularly well on very large files containing long distance redundancy. OPTIONS SUMMARY Here is a summary of the options to rzip. -0, --worst fastest (worst) compression -6 default compression level -9, --best slowest (best) compression -d, --decompress decompress -f, --force force overwrite of any existing files -o, --output FILENAME specify the output file name -k, --keep keep existing files -L, --level LEVEL set compression level -P, --progress show compression progress -S, --suffix SUFFIX specify compressed suffix (default '.rz') -v, --verbose increase verbosity -V, --version show version OPTIONS -h --help Print an options summary page -V --version Print the rzip version number -0 (or --worst) to -9 (or --best) --level Set the compression level from 0 to 9. The default is to use level 6, which is a reasonable compromise between speed and compression. The compression level is also strongly related to how much memory rzip uses, so if you are running rzip on a machine with limited amounts of memory then you will probably want to choose a smaller level. -d --decompress Decompress. If this option is not used then rzip looks at the name used to launch the program. If it contains the string 'runzip' then the -d option is automatically set. -o --output Set the output file name. If this option is not set then the out‐ put file name is chosen based on the input name and the suffix. The -o option cannot be used if more than one file name is speci‐ fied on the command line. -S --suffix Set the compression suffix. The default is '.rz'. -f --force If this option is not specified then rzip will not overwrite any existing files. If you set this option then rzip will silently overwrite any files as needed. -k --keep If this option is not specified then rzip will delete the source file after successful compression or decompression. When this option is specified then the source files are not deleted. -P --progress If this option is specified then rzip will show the percentage progress while compressing. INSTALLATION Just install rzip in your search path. COMPRESSION ALGORITHM rzip operates in two stages. The first stage finds and encodes large chunks of duplicated data over potentially very long distances (up to nearly a gigabyte) in the input file. The second stage is to use a stan‐ dard compression algorithm (bzip2) to compress the output of the first stage. The key difference between rzip and other well known compression algo‐ rithms is its ability to take advantage of very long distance redundency. The well known deflate algorithm used in gzip uses a maximum history buf‐ fer of 32k. The block sorting algorithm used in bzip2 is limited to 900k of history. The history buffer in rzip can be up to 900MB long, several orders of magnitude larger than gzip or bzip2. It is quite common these days to need to compress files that contain long distance redundancies. For example, when compressing a set of home direc‐ tories several users might have copies of the same file, or of quite sim‐ ilar files. It is also common to have a single file that contains large duplicated chunks over long distances, such as pdf files containing repeated copies of the same image. Most compression programs won't be able to take advantage of this redundency, and thus might achieve a much lower compression ratio than rzip can achieve. HISTORY The ideas behind rzip were first implemented in 1998 while I was working on rsync. That version was too slow to be practical, and was replaced by this version in 2003. BUGS Unlike most Unix compression programs, rzip cannot compress or decompress to or from standard input or standard output. This is due to the nature of the algorithm that rzip uses and cannot easily be fixed. CREDITS Thanks to the following people for their contributions to rzip o Paul Russell for many suggestions and the debian packaging o The authors of bzlib for an excellent library AUTHOR rzip was written by Andrew Tridgell http://samba.org/~tridge/ If you wish to report a problem or make a suggestion then please email<EMAIL_ADDRESS>rzip is released under the GNU General Public License version 2 or later. Please see the file COPYING for license details. October 2003 rzip(1)


{% extends 'base.html' %}
{% block title %} | User Dashboard {% endblock %}
{% block content %}
Manage your BDSG account Here are the blood donation requests listings that you have created an appointment for You have not made any inquiriesUser Dashboard
Welcome {{ user.first_name }}
{% if contacts %}
{% for contact in contacts %}
#
Blood donation request
Donation Center
Email
Phone
Date
Status
Action
{% endfor %}
{{ contact.contact.id }}
{{ contact.contact.listing }}
{{ contact.donation_request_appointment.donation_request.donation_center.name }}
{{ contact.contact.email }}
{{ contact.contact.phone }}
{{ contact.donation_request_appointment.appointment_date }}
{{ contact.donation_request_appointment.appointment_status }}
View donation request


| goog.math.Box |
|
top
:
Top.
|
|
right
:
Right.
|
|
bottom
:
Bottom.
|
|
left
:
Left.
|
|
clone()
⇒
Creates a copy of the box with the same dimensions.
Returns:
A clone of this Box.
|
code » | |||||
|
contains(other)
⇒
Returns whether the box contains a coordinate or another box.
Arguments:
Returns:
Whether the box contains the coordinate or other box.
|
code » | |||||
|
expand(top, opt_right, opt_bottom, opt_left)
⇒
Expands box with the given margins.
Arguments:
Returns:
A reference to this Box.
|
code » | |||||
|
toString()
⇒
Returns a nice string representing the box.
Returns:
In the form (50t, 73r, 24b, 13l).
|
code » |
|
Creates a Box by bounding a collection of goog.math.Coordinate objects
Arguments:
Returns:
A Box containing all the specified Coordinates.
|
code » | |||
|
Returns whether a box contains a coordinate.
Arguments:
Returns:
Whether the box contains the coordinate or other box.
|
code » | |||
|
Returns the distance between a coordinate and the nearest corner/side of a
box. Returns zero if the coordinate is inside the box.
Arguments:
Returns:
The distance between
coord and the nearest corner/side of box , or zero if coord is inside box .
|
code » | |||
|
Compares boxes for equality.
Arguments:
Returns:
True iff the boxes are equal, or if both are null.
|
code » |


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Carton Erector
This machine is used to automatically erect case and seal the bottom of the case with pressure-sensitive adhesive tape like PVC and BOPP. It is used to shape the case of regular shape. Our company can customize case erector according to customers’ requirements to satisfy different needs
Tapping System
The machine is highly automated with PLC controlled Carton Sealing and Strapping in single go Can synchronized with auto packaging line
Check Weigher
Applied to automatic online package weighing and sorting Strong stainless steel structure; Water proof design. High precision digital load cell; adopt high-speed digital fitering processing technology Automatic zero tracking
Material Conveying System
SinCos Material conveying systems for bulk material containers and packaging are critical to the effective transfer of material during production operations
Palletizing system-
This machine combines high speed bag handling efficiency with floor level operation, for a versatile and economical palletizing solution. It runs industrial-sized bags of agricultural.


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I am a cognitive neuroscientist studying adaptive memory in humans.
My research examines how changes in our internal states influence the ways in which we search for, process, and encode new information.
Currently, I am a Postdoctoral Associate working with Dr. Alison Adcock at Duke University.