## mlcourse.ai – Open Machine Learning Course¶

Author: Yury Kashnitskiy. Translated and edited by Serge Oreshkov, and Yuanyuan Pao. This material is subject to the terms and conditions of the Creative Commons CC BY-NC-SA 4.0 license. Free use is permitted for any non-commercial purpose.

# Topic 8. Vowpal Wabbit: Learning with Gigabytes of Data

This week, we’ll cover two reasons for Vowpal Wabbit’s exceptional training speed, namely, online learning and hashing trick, in both theory and practice. We will try it out with news, movie reviews, and StackOverflow questions.

# Outline¶

In [1]:
import warnings
import os
import re
import numpy as np
import pandas as pd
from tqdm import tqdm_notebook
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, accuracy_score, log_loss
from sklearn.metrics import roc_auc_score, roc_curve, confusion_matrix
from scipy.sparse import csr_matrix
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns

In [2]:
warnings.filterwarnings('ignore')


## 1. Stochastic gradient descent and online learning¶

Despite the fact that gradient descent is one of the first things learned in machine learning and optimization courses, it is one of its modifications, Stochastic Gradient Descent (SGD), that is hard to top.

Recall that the idea of gradient descent is to minimize some function by making small steps in the direction of the fastest decrease. This method was named due to the following fact from calculus: vector $\nabla f = (\frac{\partial f}{\partial x_1}, \ldots \frac{\partial f}{\partial x_n})^\text{T}$ of partial derivatives of the function $f(x) = f(x_1, \ldots x_n)$ points to the direction of the fastest function growth. It means that, by moving in the opposite direction (antigradient), it is possible to decrease the function value with the fastest rate.

Here is a snowboarder (me) in Sheregesh, Russia's most popular winter resort. (I highly recommended it if you like skiing or snowboarding). In addition to advertising the beautiful landscapes, this picture depicts the idea of gradient descent. If you want to ride as fast as possible, you need to choose the path of steepest descent. Calculating antigradients can be seen as evaluating the slope at various spots.

Example

The paired regression problem can be solved with gradient descent. Let us predict one variable using another: height with weight. Assume that these variables are linearly dependent. We will use the SOCR dataset.

In [3]:
PATH_TO_ALL_DATA = '../../data/'
'weights_heights.csv'))

In [4]:
plt.scatter(data_demo['Weight'], data_demo['Height']);
plt.xlabel('Weight in lb')
plt.ylabel('Height in inches');


Here we have a vector $x$ of dimension $\ell$ (weight of every person i.e. training sample) and $y$, a vector containing the height of every person in the dataset.

The task is the following: find weights $w_0$ and $w_1$ such that predicting height as $y_i = w_0 + w_1 x_i$ (where $y_i$ is $i$-th height value, $x_i$ is $i$-th weight value) minimizes the squared error (as well as mean squared error since $\frac{1}{\ell}$ doesn't make any difference ): $$SE(w_0, w_1) = \frac{1}{2}\sum_{i=1}^\ell(y_i - (w_0 + w_1x_{i}))^2 \rightarrow min_{w_0,w_1}$$

We will use gradient descent, utilizing the partial derivatives of $SE(w_0, w_1)$ over weights $w_0$ and $w_1$. An iterative training procedure is then defined by simple update formulas (we change model weights in small steps, proportional to a small constant $\eta$, towards the antigradient of the function $SE(w_0, w_1)$):

$$\begin{array}{rcl} w_0^{(t+1)} = w_0^{(t)} -\eta \frac{\partial SE}{\partial w_0} |_{t} \\ w_1^{(t+1)} = w_1^{(t)} -\eta \frac{\partial SE}{\partial w_1} |_{t} \end{array}$$

Computing the partial derivatives, we get the following:

$$\begin{array}{rcl} w_0^{(t+1)} = w_0^{(t)} + \eta \sum_{i=1}^{\ell}(y_i - w_0^{(t)} - w_1^{(t)}x_i) \\ w_1^{(t+1)} = w_1^{(t)} + \eta \sum_{i=1}^{\ell}(y_i - w_0^{(t)} - w_1^{(t)}x_i)x_i \end{array}$$

This math works quite well as long as the amount of data is not large (we will not discuss issues with local minima, saddle points, choosing the learning rate, moments and other stuff –- these topics are covered very thoroughly in the Numeric Computation chapter in "Deep Learning"). There is an issue with batch gradient descent -- the gradient evaluation requires the summation of a number of values for every object from the training set. In other words, the algorithm requires a lot of iterations, and every iteration recomputes weights with formula which contains a sum $\sum_{i=1}^\ell$ over the whole training set. What happens when we have billions of training samples?

Hence the motivation for stochastic gradient descent! Simply put, we throw away the summation sign and update the weights only over single training samples (or a small number of them). In our case, we have the following:

$$\begin{array}{rcl} w_0^{(t+1)} = w_0^{(t)} + \eta (y_i - w_0^{(t)} - w_1^{(t)}x_i) \\ w_1^{(t+1)} = w_1^{(t)} + \eta (y_i - w_0^{(t)} - w_1^{(t)}x_i)x_i \end{array}$$

With this approach, there is no guarantee that we will move in best possible direction at every iteration. Therefore, we may need many more iterations, but we get much faster weight updates.

Andrew Ng has a good illustration of this in his machine learning course. Let's take a look.

These are the contour plots for some function, and we want to find the global minimum of this function. The red curve shows weight changes (in this picture, $\theta_0$ and $\theta_1$ correspond to our $w_0$ and $w_1$). According to the properties of a gradient, the direction of change at every point is orthogonal to contour plots. With stochastic gradient descent, weights are changing in a less predictable manner, and it even may seem that some steps are wrong by leading away from minima; however, both procedures converge to the same solution.

### 1.2. Online approach to learning¶

Stochastic gradient descent gives us practical guidance for training both classifiers and regressors with large amounts of data up to hundreds of GBs (depending on computational resources).

Considering the case of paired regression, we can store the training data set $(X,y)$ in HDD without loading it into RAM (where it simply won't fit), read objects one by one, and update the weights of our model:

$$\begin{array}{rcl} w_0^{(t+1)} = w_0^{(t)} + \eta (y_i - w_0^{(t)} - w_1^{(t)}x_i) \\ w_1^{(t+1)} = w_1^{(t)} + \eta (y_i - w_0^{(t)} - w_1^{(t)}x_i)x_i \end{array}$$

After working through the whole training dataset, our loss function (for example, quadratic squared root error in regression or logistic loss in classification) will decrease, but it usually takes dozens of passes over the training set to make the loss small enough.

This approach to learning is called online learning, and this name emerged even before machine learning MOOC-s turned mainstream.

We did not discuss many specifics about SGD here. If you want dive into theory, I highly recommend "Convex Optimization" by Stephen Boyd. Now, we will introduce the Vowpal Wabbit library, which is good for training simple models with huge data sets thanks to stochastic optimization and another trick, feature hashing.

In scikit-learn, classifiers and regressors trained with SGD are named SGDClassifier and SGDRegressor in sklearn.linear_model. These are nice implementations of SGD, but we'll focus on VW since it is more performant than sklearn's SGD models in many aspects.

## 2. Categorical feature processing¶

### 2.1. Label Encoding¶

Many classification and regression algorithms operate in Euclidean or metric space, implying that data is represented with vectors of real numbers. However, in real data, we often have categorical features with discrete values such as yes/no or January/February/.../December. We will see how to process this kind of data, particularly with linear models, and how to deal with many categorical features even when they have many unique values.

Let's explore the UCI bank marketing dataset where most of features are categorical.

In [5]:
df = pd.read_csv(os.path.join(PATH_TO_ALL_DATA, 'bank_train.csv'))
'bank_train_target.csv'),


Out[5]:
age job marital education default housing loan contact month day_of_week duration campaign pdays previous poutcome emp.var.rate cons.price.idx cons.conf.idx euribor3m nr.employed
0 26 student single high.school no no no telephone jun mon 901 1 999 0 nonexistent 1.4 94.465 -41.8 4.961 5228.1
1 46 admin. married university.degree no yes no cellular aug tue 208 2 999 0 nonexistent 1.4 93.444 -36.1 4.963 5228.1
2 49 blue-collar married basic.4y unknown yes yes telephone jun tue 131 5 999 0 nonexistent 1.4 94.465 -41.8 4.864 5228.1
3 31 technician married university.degree no no no cellular jul tue 404 1 999 0 nonexistent -2.9 92.469 -33.6 1.044 5076.2
4 42 housemaid married university.degree no yes no telephone nov mon 85 1 999 0 nonexistent -0.1 93.200 -42.0 4.191 5195.8

We can see that most of features are not represented by numbers. This poses a problem because we cannot use most machine learning methods (at least those implemented in scikit-learn) out-of-the-box.

Let's dive into the "education" feature.

In [6]:
df['education'].value_counts().plot.barh();


The most straightforward solution is to map each value of this feature into a unique number. For example, we can map university.degree to 0, basic.9y to 1, and so on. You can use sklearn.preprocessing.LabelEncoder to perform this mapping.

In [7]:
label_encoder = LabelEncoder()


The fit method of this class finds all unique values and builds the actual mapping between categories and numbers, and the transform method converts the categories into numbers. After fit is executed, label_encoder will have the classes_ attribute with all unique values of the feature. Let us count them to make sure the transformation was correct.

In [8]:
mapped_education = pd.Series(label_encoder.fit_transform(
df['education']))
mapped_education.value_counts().plot.barh()
print(dict(enumerate(label_encoder.classes_)))

{0: 'basic.4y', 1: 'basic.6y', 2: 'basic.9y', 3: 'high.school', 4: 'illiterate', 5: 'professional.course', 6: 'university.degree', 7: 'unknown'}

In [9]:
df['education'] = mapped_education

Out[9]:
age job marital education default housing loan contact month day_of_week duration campaign pdays previous poutcome emp.var.rate cons.price.idx cons.conf.idx euribor3m nr.employed
0 26 student single 3 no no no telephone jun mon 901 1 999 0 nonexistent 1.4 94.465 -41.8 4.961 5228.1
1 46 admin. married 6 no yes no cellular aug tue 208 2 999 0 nonexistent 1.4 93.444 -36.1 4.963 5228.1
2 49 blue-collar married 0 unknown yes yes telephone jun tue 131 5 999 0 nonexistent 1.4 94.465 -41.8 4.864 5228.1
3 31 technician married 6 no no no cellular jul tue 404 1 999 0 nonexistent -2.9 92.469 -33.6 1.044 5076.2
4 42 housemaid married 6 no yes no telephone nov mon 85 1 999 0 nonexistent -0.1 93.200 -42.0 4.191 5195.8

Let's apply the transformation to other columns of type object.

In [10]:
categorical_columns = df.columns[df.dtypes
== 'object'].union(['education'])
for column in categorical_columns:
df[column] = label_encoder.fit_transform(df[column])

Out[10]:
age job marital education default housing loan contact month day_of_week duration campaign pdays previous poutcome emp.var.rate cons.price.idx cons.conf.idx euribor3m nr.employed
0 26 8 2 3 0 0 0 1 4 1 901 1 999 0 1 1.4 94.465 -41.8 4.961 5228.1
1 46 0 1 6 0 2 0 0 1 3 208 2 999 0 1 1.4 93.444 -36.1 4.963 5228.1
2 49 1 1 0 1 2 2 1 4 3 131 5 999 0 1 1.4 94.465 -41.8 4.864 5228.1
3 31 9 1 6 0 0 0 0 3 3 404 1 999 0 1 -2.9 92.469 -33.6 1.044 5076.2
4 42 3 1 6 0 2 0 1 7 1 85 1 999 0 1 -0.1 93.200 -42.0 4.191 5195.8

The main issue with this approach is that we have now introduced some relative ordering where it might not exist.

For example, we implicitly introduced algebra over the values of the job feature where we can now substract the job of client #2 from the job of client #1 :

In [11]:
df.loc[1].job - df.loc[2].job

Out[11]:
-1.0

Does this operation make any sense? Not really. Let's try to train logisitic regression with this feature transformation.

In [12]:
def logistic_regression_accuracy_on(dataframe, labels):
features = dataframe.as_matrix()
train_features, test_features, train_labels, test_labels = \
train_test_split(features, labels)

logit = LogisticRegression()
logit.fit(train_features, train_labels)
return classification_report(test_labels,
logit.predict(test_features))

print(logistic_regression_accuracy_on(df[categorical_columns],
labels))

              precision    recall  f1-score   support

0       0.89      1.00      0.94      6133
1       0.00      0.00      0.00       766

micro avg       0.89      0.89      0.89      6899
macro avg       0.44      0.50      0.47      6899
weighted avg       0.79      0.89      0.84      6899



We can see that logistic regression never predicts class 1. In order to use linear models with categorical features, we will use a different approach: One-Hot Encoding.

### 2.2. One-Hot Encoding¶

Suppose that some feature can have one of 10 unique values. One-hot encoding creates 10 new features corresponding to these unique values, all of them except one are zeros.

In [13]:
one_hot_example = pd.DataFrame([{i: 0 for i in range(10)}])
one_hot_example.loc[0, 6] = 1
one_hot_example

Out[13]:
0 1 2 3 4 5 6 7 8 9
0 0 0 0 0 0 0 1 0 0 0

This idea is implemented in the OneHotEncoder class from sklearn.preprocessing. By default OneHotEncoder transforms data into a sparse matrix to save memory space because most of the values are zeroes and because we do not want to take up more RAM. However, in this particular example, we do not encounter such problems, so we are going to use a "dense" matrix representation.

In [14]:
onehot_encoder = OneHotEncoder(sparse=False)

In [15]:
encoded_categorical_columns = \
pd.DataFrame(onehot_encoder.fit_transform(
df[categorical_columns]))

Out[15]:
0 1 2 3 4 5 6 7 8 9 ... 43 44 45 46 47 48 49 50 51 52
0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 ... 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0
1 1.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0
2 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 ... 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0
3 1.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 ... 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0
4 0.0 1.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0

5 rows × 53 columns

We have 53 columns that correspond to the number of unique values of categorical features in our data set. When transformed with One-Hot Encoding, this data can be used with linear models:

In [16]:
print(logistic_regression_accuracy_on(encoded_categorical_columns, labels))

              precision    recall  f1-score   support

0       0.91      0.99      0.95      6140
1       0.64      0.16      0.26       759

micro avg       0.90      0.90      0.90      6899
macro avg       0.77      0.57      0.60      6899
weighted avg       0.88      0.90      0.87      6899



### 2.3. Hashing trick¶

Real data can be volatile, meaning we cannot guarantee that new values of categorical features will not occur. This issue hampers using a trained model on new data. Besides that, LabelEncoder requires preliminary analysis of the whole dataset and storage of constructed mappings in memory, which makes it difficult to work with large datasets.

There is a simple approach to vectorization of categorical data based on hashing and is known as, not-so-surprisingly, the hashing trick.

Hash functions can help us find unique codes for different feature values, for example:

In [17]:
for s in ('university.degree', 'high.school', 'illiterate'):
print(s, '->', hash(s))

university.degree -> -4819547379497397962
high.school -> 6407817176082389474
illiterate -> -426073671251698964


We will not use negative values or values of high magnitude, so we restrict the range of values for the hash function:

In [18]:
hash_space = 25
for s in ('university.degree', 'high.school', 'illiterate'):
print(s, '->', hash(s) % hash_space)

university.degree -> 13
high.school -> 24
illiterate -> 11


Imagine that our data set contains a single (i.e. not married) student, who received a call on Monday. His feature vectors will be created similarly as in the case of One-Hot Encoding but in the space with fixed range for all features:

In [19]:
hashing_example = pd.DataFrame([{i: 0.0 for i in range(hash_space)}])
for s in ('job=student', 'marital=single', 'day_of_week=mon'):
print(s, '->', hash(s) % hash_space)
hashing_example.loc[0, hash(s) % hash_space] = 1
hashing_example

job=student -> 9
marital=single -> 17
day_of_week=mon -> 15

Out[19]:
0 1 2 3 4 5 6 7 8 9 ... 15 16 17 18 19 20 21 22 23 24
0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 1.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

1 rows × 25 columns

We want to point out that we hash not only feature values but also pairs of feature name + feature value. It is important to do this so that we can distinguish the same values of different features.

In [20]:
assert hash('no') == hash('no')
assert hash('housing=no') != hash('loan=no')


Is it possible to have a collision when using hash codes? Sure, it is possible, but it is a rare case with large enough hashing spaces. Even if collision occurs, regression or classification metrics will not suffer much. In this case, hash collisions work as a form of regularization.

You may be saying "WTF?"; hashing seems counterintuitive. This is true, but these heuristics sometimes are, in fact, the only plausible approach to work with categorical data (what else can you do if you have 30M features?). Moreover, this technique has proven to just work. As you work more with data, you may see this for yourself.

A good analysis of hash collisions, their dependency on feature space and hashing space dimensions and affecting classification/regression performance is done in this article by Booking.com.

## 3. Vowpal Wabbit¶

Vowpal Wabbit (VW) is one of the most widespread machine learning libraries used in industry. It is prominent for its training speed and support of many training modes, especially for online learning with big and high-dimentional data. This is one of the major merits of the library. Also, with the hashing trick implemented, Vowpal Wabbit is a perfect choice for working with text data.

Shell is the main interface for VW.

In [21]:
!vw --help

Num weight bits = 18
learning rate = 0.5
initial_t = 0
power_t = 0.5
using no cache
num sources = 1

VW options:
--ring_size arg                       size of example ring

Update options:
-l [ --learning_rate ] arg            Set learning rate
--power_t arg                         t power value
--decay_learning_rate arg             Set Decay factor for learning_rate
between passes
--initial_t arg                       initial t value
--feature_mask arg                    Use existing regressor to determine
which parameters may be updated.  If no
initial_regressor given, also used for
initial weights.

Weight options:
-i [ --initial_regressor ] arg        Initial regressor(s)
--initial_weight arg                  Set all weights to an initial value of
arg.
--random_weights arg                  make initial weights random
--normal_weights arg                  make initial weights normal
--truncated_normal_weights arg        make initial weights truncated normal
--sparse_weights                      Use a sparse datastructure for weights
--input_feature_regularizer arg       Per feature regularization input file

Parallelization options:
--span_server arg                     Location of server for setting up
spanning tree
--unique_id arg (=0)                  unique id used for cluster parallel
jobs
--total arg (=1)                      total number of nodes used in cluster
parallel job
--node arg (=0)                       node number in cluster parallel job

Diagnostic options:
--version                             Version information
-a [ --audit ]                        print weights of features
-P [ --progress ] arg                 Progress update frequency. int:
--quiet                               Don't output disgnostics and progress
-h [ --help ]                         Look here: http://hunch.net/~vw/ and
click on Tutorial.

Random Seed option:
--random_seed arg                     seed random number generator

Feature options:
--hash arg                            how to hash the features. Available
options: strings, all
--hash_seed arg (=0)                  seed for hash function
--ignore arg                          ignore namespaces beginning with
character <arg>
--ignore_linear arg                   ignore namespaces beginning with
character <arg> for linear terms only
--keep arg                            keep namespaces beginning with
character <arg>
--redefine arg                        redefine namespaces beginning with
characters of string S as namespace N.
<arg> shall be in form 'N:=S' where :=
is operator. Empty N or S are treated
as default namespace. Use ':' as a
wildcard in S.
-b [ --bit_precision ] arg            number of bits in the feature table
--noconstant                          Don't add a constant feature
-C [ --constant ] arg                 Set initial value of constant
--ngram arg                           Generate N grams. To generate N grams
for a single namespace 'foo', arg
should be fN.
--skips arg                           Generate skips in N grams. This in
conjunction with the ngram tag can be
used to generate generalized
n-skip-k-gram. To generate n-skips for
a single namespace 'foo', arg should be
fN.
--feature_limit arg                   limit to N features. To apply to a
single namespace 'foo', arg should be
fN
--affix arg                           generate prefixes/suffixes of features;
argument '+2a,-3b,+1' means generate
2-char prefixes for namespace a, 3-char
suffixes for b and 1 char prefixes for
default namespace
--spelling arg                        compute spelling features for a give
namespace (use '_' for default
namespace)
features (arg either 'x:file' or just
'file')
--dictionary_path arg                 look in this directory for
dictionaries; defaults to current
directory or env{PATH}
--interactions arg                    Create feature interactions of any
level between namespaces.
combinations for feature interactions
of same namespace.
--leave_duplicate_interactions        Don't remove interactions with
duplicate combinations of namespaces.
For ex. this is a duplicate: '-q ab -q
ba' and a lot more in '-q ::'.
--q: arg                              : corresponds to a wildcard for all
printable characters
--cubic arg                           Create and use cubic features

Example options:
-t [ --testonly ]                     Ignore label information and just test
--holdout_off                         no holdout data in multiple passes
--holdout_period arg (=10)            holdout period for test only
--holdout_after arg                   holdout after n training examples,
default off (disables holdout_period)
--early_terminate arg (=3)            Specify the number of passes tolerated
when holdout loss doesn't decrease
before early termination
--passes arg                          Number of Training Passes
--initial_pass_length arg             initial number of examples per pass
--examples arg                        number of examples to parse
--min_prediction arg                  Smallest prediction to output
--max_prediction arg                  Largest prediction to output
--sort_features                       turn this on to disregard order in
which features have been defined. This
will lead to smaller cache sizes
--loss_function arg (=squared)        Specify the loss function to be used,
uses squared by default. Currently
available ones are squared, classic,
hinge, logistic, quantile and poisson.
--quantile_tau arg (=0.5)             Parameter \tau associated with Quantile
loss. Defaults to 0.5
--l1 arg                              l_1 lambda
--l2 arg                              l_2 lambda
--no_bias_regularization arg          no bias in regularization
--named_labels arg                    use names for labels (multiclass, etc.)
rather than integers, argument
specified all possible labels,
comma-sep, eg "--named_labels

Output model:
-f [ --final_regressor ] arg          Final regressor
with numeric features
--invert_hash arg                     Output human-readable final regressor
with feature names.  Computationally
expensive.
--save_resume                         save extra state so learning can be
resumed later with new data
--preserve_performance_counters       reset performance counters when
warmstarting
--save_per_pass                       Save the model after every pass over
data
--output_feature_regularizer_binary arg
Per feature regularization output file
--output_feature_regularizer_text arg Per feature regularization output file,
in text
--id arg                              User supplied ID embedded into the
final regressor

Output options:
-p [ --predictions ] arg              File to output predictions to
-r [ --raw_predictions ] arg          File to output unnormalized predictions
to

Audit Regressor:
--audit_regressor arg                 stores feature names and their
regressor values. Same dataset must be
used for both regressor training and
this mode.

Search options:
--search arg                          Use learning to search,
argument=maximum action id or 0 for LDF
list" to get a list of available tasks)

"--search_metatask list" to get a list
--search_interpolation arg            at what level should interpolation
happen? [*data|policy]
--search_rollout arg                  how should rollouts be executed?
[policy|oracle|*mix_per_state|mix_p
er_roll|none]
--search_rollin arg                   how should past trajectories be
generated? [policy|oracle|*mix_per_stat
e|mix_per_roll]
--search_passes_per_policy arg (=1)   number of passes per policy (only valid
for search_interpolation=policy)
--search_beta arg (=0.5)              interpolation rate for policies (only
valid for search_interpolation=policy)
--search_alpha arg (=1.00000001e-10)  annealed beta = 1-(1-alpha)^t (only
valid for search_interpolation=data)
--search_total_nb_policies arg        if we are going to train the policies
through multiple separate calls to vw,
we need to specify this parameter and
tell vw how many policies are
eventually going to be trained
--search_trained_nb_policies arg      the number of trained policies in a
file
--search_allowed_transitions arg      read file of allowed transitions [def:
all transitions are allowed]
--search_subsample_time arg           instead of training at all timesteps,
use a subset. if value in (0,1), train
on a random v%. if v>=1, train on
precisely v steps per example, if
v<=-1, use active learning
--search_neighbor_features arg        copy features from neighboring lines.
argument looks like: '-1:a,+2' meaning
copy previous line namespace a and next
next line from namespace _unnamed_,
where ',' separates them
--search_rollout_num_steps arg        how many calls of "loss" before we stop
really predicting on rollouts and
switch to oracle (default means
"infinite")
--search_history_length arg (=1)      some tasks allow you to specify how
much history their depend on; specify
that here
--search_no_caching                   turn off the built-in caching ability
(makes things slower, but technically
more safe)
--search_xv                           train two separate policies,
alternating prediction/learning
--search_perturb_oracle arg (=0)      perturb the oracle on rollin with this
probability
--search_linear_ordering              insist on generating examples in linear
order (def: hoopla permutation)
--search_active_verify arg            verify that active learning is doing
the right thing (arg = multiplier,
should be = cost_range * range_c)
--search_save_every_k_runs arg        save model every k runs

Experience Replay:
--replay_c arg                        use experience replay at a specified
level [b=classification/regression,
m=multiclass, c=cost sensitive] with
specified buffer size

--replay_c_count arg (=1)             how many times (in expectation) should
each example be played (default: 1 =
permuting)

Explore evaluation:

--multiplier arg                      Multiplier used to make all rejection
sample probabilities <= 1

Make Multiclass into Contextual Bandit:
--cbify arg                           Convert multiclass on <k> classes into
a contextual bandit problem

--cbify_cs                            consume cost-sensitive classification
--loss0 arg (=0)                      loss for correct label
--loss1 arg (=1)                      loss for incorrect label

Contextual Bandit Exploration with Action Dependent Features:
--cb_explore_adf                      Online explore-exploit for a contextual
bandit problem with multiline action
dependent features

--first arg                           tau-first exploration
--epsilon arg                         epsilon-greedy exploration
--bag arg                             bagging-based exploration
--cover arg                           Online cover based exploration
--psi arg (=1)                        disagreement parameter for cover
--nounif                              do not explore uniformly on
zero-probability actions in cover
--softmax                             softmax exploration
--regcb                               RegCB-elim exploration
--regcbopt                            RegCB optimistic exploration
--mellowness arg (=0.100000001)       RegCB mellowness parameter c_0. Default
0.1
--greedify                            always update first policy once in
bagging
--cb_min_cost arg (=0)                lower bound on cost
--cb_max_cost arg (=1)                upper bound on cost
--first_only                          Only explore the first action in a
tie-breaking event
--lambda arg (=-1)                    parameter for softmax

Contextual Bandit Exploration:
--cb_explore arg                      Online explore-exploit for a <k> action
contextual bandit problem

--first arg                           tau-first exploration
--epsilon arg (=0.0500000007)         epsilon-greedy exploration
--bag arg                             bagging-based exploration
--cover arg                           Online cover based exploration
--psi arg (=1)                        disagreement parameter for cover

Multiworld Testing Options:
--multiworld_test arg                 Evaluate features as a policies

--learn arg                           Do Contextual Bandit learning on <n>
classes.
--exclude_eval                        Discard mwt policy features before
learning

Contextual Bandit with Action Dependent Features:
--cb_adf                              Do Contextual Bandit learning with
multiline action dependent features.

--rank_all                            Return actions sorted by score order
--no_predict                          Do not do a prediction when training
--cb_type arg (=ips)                  contextual bandit method to use in
{ips,dm,dr, mtr}

Contextual Bandit Options:
--cb arg                              Use contextual bandit learning with <k>
costs

--cb_type arg (=dr)                   contextual bandit method to use in
{ips,dm,dr}
--eval                                Evaluate a policy rather than
optimizing.

Cost Sensitive One Against All with Label Dependent Features:
--csoaa_ldf arg                       Use one-against-all multiclass learning
with label dependent features.

--ldf_override arg                    Override singleline or multiline from
csoaa_ldf or wap_ldf, eg if stored in
file
--csoaa_rank                          Return actions sorted by score order
--probabilities                       predict probabilites of all classes

--wap_ldf arg                         Use weighted all-pairs multiclass
learning with label dependent features.
Specify singleline or multiline.

Interact via elementwise multiplication:
--interact arg                        Put weights on feature products from
namespaces <n1> and <n2>

Cost Sensitive One Against All:
--csoaa arg                           One-against-all multiclass with <k>
costs

Cost-sensitive Active Learning:
--cs_active arg                       Cost-sensitive active learning with <k>
costs

--simulation                          cost-sensitive active learning
simulation mode
--baseline                            cost-sensitive active learning baseline
--domination                          cost-sensitive active learning use
domination. Default 1
--mellowness arg (=0.100000001)       mellowness parameter c_0. Default 0.1.
--range_c arg (=0.5)                  parameter controlling the threshold for
per-label cost uncertainty. Default
0.5.
--max_labels arg (=18446744073709551615)
maximum number of label queries.
--min_labels arg (=18446744073709551615)
minimum number of label queries.
--cost_max arg (=1)                   cost upper bound. Default 1.
--cost_min arg (=0)                   cost lower bound. Default 0.
--csa_debug                           print debug stuff for cs_active

Multilabel One Against All:
--multilabel_oaa arg                  One-against-all multilabel with <k>
labels

importance weight classes:
--classweight arg                     importance weight multiplier for class

Recall Tree:
--recall_tree arg                     Use online tree for multiclass

--max_candidates arg                  maximum number of labels per leaf in
the tree
--bern_hyper arg (=1)                 recall tree depth penalty
--max_depth arg                       maximum depth of the tree, default
log_2 (#classes)
--node_only arg (=0)                  only use node features, not full path
features
--randomized_routing arg (=0)         randomized routing

Logarithmic Time Multiclass Tree:
--log_multi arg                       Use online tree for multiclass

--no_progress                         disable progressive validation
--swap_resistance arg (=4)            higher = more resistance to swap,
default=4

Error Correcting Tournament Options:
--ect arg                             Error correcting tournament with <k>
labels

--error arg (=0)                      errors allowed by ECT

Boosting:
--boosting arg                        Online boosting with <N> weak learners

--gamma arg (=0.100000001)            weak learner's edge (=0.1), used only
by online BBM
--alg arg (=BBM)                      specify the boosting algorithm: BBM

One Against All Options:
--oaa arg                             One-against-all multiclass with <k>
labels

--oaa_subsample arg                   subsample this number of negative
examples when learning
--probabilities                       predict probabilites of all classes
--scores                              output raw scores per class

Top K:
--top arg                             top k recommendation

Experience Replay:
--replay_m arg                        use experience replay at a specified
level [b=classification/regression,
m=multiclass, c=cost sensitive] with
specified buffer size

--replay_m_count arg (=1)             how many times (in expectation) should
each example be played (default: 1 =
permuting)

Binary loss:
--binary                              report loss as binary classification on
-1,1

Bootstrap:
--bootstrap arg                       k-way bootstrap by online importance
resampling

--bs_type arg                         prediction type {mean,vote}

scorer options:
logistic, glf1 or poisson

Stagewise polynomial options:
--stage_poly                          use stagewise polynomial feature
learning

--sched_exponent arg (=1)             exponent controlling quantity of
included features
--batch_sz arg (=1000)                multiplier on batch size before
including more features
--batch_sz_no_doubling                batch_sz does not double

--lrqfa arg                           use low rank quadratic features with
field aware weights

--lrq arg                             use low rank quadratic features

--lrqdropout                          use dropout training for low rank

Marginal:
--marginal arg                        substitute marginal label estimates for
ids

--initial_denominator arg (=1)        initial denominator
--initial_numerator arg (=0.5)        initial numerator
--compete                             enable competition with marginal
features
--update_before_learn arg (=0)        update marginal values before learning
--unweighted_marginals arg (=0)       ignore importance weights when
computing marginals
--decay arg (=0)                      decay multiplier per event (1e-3 for
example)

Matrix Factorization Reduction:
--new_mf arg                          rank for reduction-based matrix
factorization

Neural Network:
--nn arg                              Sigmoidal feedforward network with <k>
hidden units

--inpass                              Train or test sigmoidal feedforward
network with input passthrough.
--multitask                           Share hidden layer across all reduced
--dropout                             Train or test sigmoidal feedforward
network using dropout.
--meanfield                           Train or test sigmoidal feedforward
network using mean field.

Confidence:
--confidence                          Get confidence for binary predictions

--confidence_after_training           Confidence after training

Active Learning with Cover:
--active_cover                        enable active learning with cover

--mellowness arg (=8)                 active learning mellowness parameter
c_0. Default 8.
--alpha arg (=1)                      active learning variance upper bound
parameter alpha. Default 1.
--beta_scale arg (=3.1622777)         active learning variance upper bound
parameter beta_scale. Default sqrt(10).
--cover arg (=12)                     cover size. Default 12.
--oracular                            Use Oracular-CAL style query or not.
Default false.

Active Learning:
--active                              enable active learning

--simulation                          active learning simulation mode
--mellowness arg (=8)                 active learning mellowness parameter
c_0. Default 8

Experience Replay:
--replay_b arg                        use experience replay at a specified
level [b=classification/regression,
m=multiclass, c=cost sensitive] with
specified buffer size

--replay_b_count arg (=1)             how many times (in expectation) should
each example be played (default: 1 =
permuting)

Baseline options:
--baseline                            Learn an additive baseline (from
constant features) and a residual
separately in regression.

--lr_multiplier arg                   learning rate multiplier for baseline
model
--global_only                         use separate example with only global
constant for baseline predictions
--check_enabled                       only use baseline when the example
contains enabled flag

OjaNewton options:
--OjaNewton                           Online Newton with Oja's Sketch

--sketch_size arg (=10)               size of sketch
--epoch_size arg (=1)                 size of epoch
--alpha arg (=1)                      mutiplicative constant for indentiy
--alpha_inverse arg                   one over alpha, similar to learning
rate
--learning_rate_cnt arg (=2)          constant for the learning rate 1/t
--normalize arg (=1)                  normalize the features or not
--random_init arg (=1)                randomize initialization of Oja or not

optimization

--bfgs                                use bfgs optimization

--hessian_on                          use second derivative in line search
--mem arg (=15)                       memory in bfgs
--termination arg (=0.00100000005)    Termination threshold

Latent Dirichlet Allocation:
--lda arg                             Run lda with <int> topics

--lda_alpha arg (=0.100000001)        Prior on sparsity of per-document topic
weights
--lda_rho arg (=0.100000001)          Prior on sparsity of topic
distributions
--lda_D arg (=10000)                  Number of documents
--lda_epsilon arg (=0.00100000005)    Loop convergence threshold
--minibatch arg (=1)                  Minibatch size, for LDA
--math-mode arg (=0)                  Math mode: simd, accuracy, fast-approx
--metrics arg (=0)                    Compute metrics

Noop Learner:
--noop                                do no learning

Print psuedolearner:
--print                               print examples

--rank arg                            rank for matrix factorization.

Network sending:
--sendto arg                          send examples to <host>

--svrg                                Streaming Stochastic Variance Reduced

--stage_size arg (=1)                 Number of passes per SVRG stage

--ftrl                                FTRL: Follow the Proximal Regularized

--ftrl_alpha arg (=0.00499999989)     Learning rate for FTRL optimization
--ftrl_beta arg (=0.100000001)        FTRL beta parameter

--pistol                              FTRL: Parameter-free Stochastic
Learning

--ftrl_alpha arg (=1)                 Learning rate for FTRL optimization
--ftrl_beta arg (=0.5)                FTRL beta parameter

Kernel SVM:
--ksvm                                kernel svm

--reprocess arg (=1)                  number of reprocess steps for LASVM
--pool_greedy                         use greedy selection on mini pools
--para_active                         do parallel active learning
--pool_size arg (=1)                  size of pools for active learning
--subsample arg (=1)                  number of items to subsample from the
pool
--kernel arg (=linear)                type of kernel (rbf or linear
(default))
--bandwidth arg (=1)                  bandwidth of rbf kernel
--degree arg (=2)                     degree of poly kernel
--lambda arg                          saving regularization for test time

--sgd                                 use regular stochastic gradient descent
update.
rates.
--normalized                          use per feature normalized updates
--sparse_l2 arg (=0)                  use per feature normalized updates
--l1_state arg (=0)                   use per feature normalized updates
--l2_state arg (=1)                   use per feature normalized updates

Input options:
-d [ --data ] arg                     Example Set
--daemon                              persistent daemon mode on port 26542
--foreground                          in persistent daemon mode, do not run
in the background
--port arg                            port to listen on; use 0 to pick unused
port
--num_children arg                    number of children for persistent
daemon mode
--pid_file arg                        Write pid file in persistent daemon
mode
--port_file arg                       Write port used in persistent daemon
mode
-c [ --cache ]                        Use a cache.  The default is
<data>.cache
--cache_file arg                      The location(s) of cache_file.
--json                                Enable JSON parsing.
--dsjson                              Enable Decision Service JSON parsing.
-k [ --kill_cache ]                   do not reuse existing cache: create a
new one always
--compressed                          use gzip format whenever possible. If a
cache file is being created, this
option creates a compressed cache file.
A mixture of raw-text & compressed
inputs are supported with
autodetection.
--no_stdin                            do not default to reading from stdin


Vowpal Wabbit reads data from files or from standard input stream (stdin) with the following format:

[Label] [Importance] [Tag]|Namespace Features |Namespace Features ... |Namespace Features

Namespace=String[:Value]

Features=(String[:Value] )*

here [] denotes non-mandatory elements, and (...)* means multiple inputs allowed.

• Label is a number. In the case of classification, it is usually 1 and -1; for regression, it is a real float value
• Importance is a number. It denotes the sample weight during training. Setting this helps when working with imbalanced data.
• Tag is a string without spaces. It is the "name" of the sample that VW saves upon prediction. In order to separate Tag from Importance, it is better to start Tag with the ' character.
• Namespace is for creating different feature spaces.
• Features are object features inside a given Namespace. Features have weight 1.0 by default, but it can be changed, for example feature:0.1.

The following string matches the VW format:

1 1.0 |Subject WHAT car is this |Organization University of Maryland:0.5 College Park

Let's check the format by running VW with this training sample:

In [22]:
! echo '1 1.0 |Subject WHAT car is this |Organization University of Maryland:0.5 College Park' | vw

Num weight bits = 18
learning rate = 0.5
initial_t = 0
power_t = 0.5
using no cache
num sources = 1
average  since         example        example  current  current  current
loss     last          counter         weight    label  predict features
1.000000 1.000000            1            1.0   1.0000   0.0000       10

finished run
number of examples = 1
weighted example sum = 1.000000
weighted label sum = 1.000000
average loss = 1.000000
best constant = 1.000000
best constant's loss = 0.000000
total feature number = 10


VW is a wonderful tool for working with text data. We'll illustrate it with the 20newsgroups dataset, which contains letters from 20 different newsletters.

### 3.1. News. Binary classification.¶

In [23]:
# load data with sklearn's function
newsgroups = fetch_20newsgroups(PATH_TO_ALL_DATA)

In [24]:
newsgroups['target_names']

Out[24]:
['alt.atheism',
'comp.graphics',
'comp.os.ms-windows.misc',
'comp.sys.ibm.pc.hardware',
'comp.sys.mac.hardware',
'comp.windows.x',
'misc.forsale',
'rec.autos',
'rec.motorcycles',
'rec.sport.baseball',
'rec.sport.hockey',
'sci.crypt',
'sci.electronics',
'sci.med',
'sci.space',
'soc.religion.christian',
'talk.politics.guns',
'talk.politics.mideast',
'talk.politics.misc',
'talk.religion.misc']

Lets look at the first document in this collection:

In [25]:
text = newsgroups['data'][0]
target = newsgroups['target_names'][newsgroups['target'][0]]

print('-----')
print(target)
print('-----')
print(text.strip())
print('----')

-----
rec.autos
-----
From: [email protected] (where's my thing)
Subject: WHAT car is this!?
Nntp-Posting-Host: rac3.wam.umd.edu
Organization: University of Maryland, College Park
Lines: 15

I was wondering if anyone out there could enlighten me on this car I saw
the other day. It was a 2-door sports car, looked to be from the late 60s/
early 70s. It was called a Bricklin. The doors were really small. In addition,
the front bumper was separate from the rest of the body. This is
all I know. If anyone can tellme a model name, engine specs, years
of production, where this car is made, history, or whatever info you
have on this funky looking car, please e-mail.

Thanks,
- IL
---- brought to you by your neighborhood Lerxst ----
----


Now we convert the data into something Vowpal Wabbit can understand. We will throw away words shorter than 3 symbols. Here, we will skip some important NLP stages such as stemming and lemmatization; however, we will later see that VW solves the problem even without these steps.

In [26]:
def to_vw_format(document, label=None):
return str(label or '') + ' |text ' + ' '.join(re.findall('\w{3,}',
document.lower())) + '\n'

to_vw_format(text, 1 if target == 'rec.autos' else -1)

Out[26]:
'1 |text from lerxst wam umd edu where thing subject what car this nntp posting host rac3 wam umd edu organization university maryland college park lines was wondering anyone out there could enlighten this car saw the other day was door sports car looked from the late 60s early 70s was called bricklin the doors were really small addition the front bumper was separate from the rest the body this all know anyone can tellme model name engine specs years production where this car made history whatever info you have this funky looking car please mail thanks brought you your neighborhood lerxst\n'

We split the dataset into train and test and write these into separate files. We will consider a document as positive if it corresponds to rec.autos. Thus, we are constructing a model which distinguishes articles about cars from other topics:

In [27]:
all_documents = newsgroups['data']
all_targets = [1 if newsgroups['target_names'][target] == 'rec.autos'
else -1 for target in newsgroups['target']]

In [28]:
train_documents, test_documents, train_labels, test_labels = \
train_test_split(all_documents, all_targets, random_state=7)

with open(os.path.join(PATH_TO_ALL_DATA, '20news_train.vw'), 'w') as vw_train_data:
for text, target in zip(train_documents, train_labels):
vw_train_data.write(to_vw_format(text, target))
with open(os.path.join(PATH_TO_ALL_DATA, '20news_test.vw'), 'w') as vw_test_data:
for text in test_documents:
vw_test_data.write(to_vw_format(text))


Now, we pass the created training file to Vowpal Wabbit. We solve the classification problem with a hinge loss function (linear SVM). The trained model will be saved in the 20news_model.vw file:

In [29]:
!vw -d $PATH_TO_ALL_DATA/20news_train.vw \ --loss_function hinge -f$PATH_TO_ALL_DATA/20news_model.vw

final_regressor = ../../data//20news_model.vw
Num weight bits = 18
learning rate = 0.5
initial_t = 0
power_t = 0.5
using no cache
num sources = 1
average  since         example        example  current  current  current
loss     last          counter         weight    label  predict features
1.000000 1.000000            1            1.0  -1.0000   0.0000      157
0.911276 0.822551            2            2.0  -1.0000  -0.1774      159
0.605793 0.300311            4            4.0  -1.0000  -0.3994       92
0.419594 0.233394            8            8.0  -1.0000  -0.8167      129
0.313998 0.208402           16           16.0  -1.0000  -0.6509      108
0.196014 0.078029           32           32.0  -1.0000  -1.0000      115
0.183158 0.170302           64           64.0  -1.0000  -0.7072      114
0.261046 0.338935          128          128.0   1.0000  -0.7900      110
0.262910 0.264774          256          256.0  -1.0000  -0.6425       44
0.216663 0.170415          512          512.0  -1.0000  -1.0000      160
0.176710 0.136757         1024         1024.0  -1.0000  -1.0000      194
0.134541 0.092371         2048         2048.0  -1.0000  -1.0000      438
0.104403 0.074266         4096         4096.0  -1.0000  -1.0000      644
0.081329 0.058255         8192         8192.0  -1.0000  -1.0000      174

finished run
number of examples = 8485
weighted example sum = 8485.000000
weighted label sum = -7555.000000
average loss = 0.079837
best constant = -1.000000
best constant's loss = 0.109605
total feature number = 2048932


VW prints a lot of interesting info while training (one can suppress it with the --quiet parameter). You can see documentation of the diagnostic output on GitHub. Note how average loss drops while training. For loss computation, VW uses samples it has never seen before, so this measure is usually accurate. Now, we apply our trained model to the test set, saving predictions into a file with the -p flag:

In [30]:
!vw -i $PATH_TO_ALL_DATA/20news_model.vw -t -d$PATH_TO_ALL_DATA/20news_test.vw \
-p $PATH_TO_ALL_DATA/20news_test_predictions.txt  only testing predictions = ../../data//20news_test_predictions.txt Num weight bits = 18 learning rate = 0.5 initial_t = 0 power_t = 0.5 using no cache Reading datafile = ../../data//20news_test.vw num sources = 1 average since example example current current current loss last counter weight label predict features n.a. n.a. 1 1.0 unknown 1.0000 349 n.a. n.a. 2 2.0 unknown -1.0000 50 n.a. n.a. 4 4.0 unknown -1.0000 251 n.a. n.a. 8 8.0 unknown -1.0000 237 n.a. n.a. 16 16.0 unknown -0.8978 106 n.a. n.a. 32 32.0 unknown -1.0000 964 n.a. n.a. 64 64.0 unknown -1.0000 261 n.a. n.a. 128 128.0 unknown 0.4621 82 n.a. n.a. 256 256.0 unknown -1.0000 186 n.a. n.a. 512 512.0 unknown -1.0000 162 n.a. n.a. 1024 1024.0 unknown -1.0000 283 n.a. n.a. 2048 2048.0 unknown -1.0000 104 finished run number of examples = 2829 weighted example sum = 2829.000000 weighted label sum = 0.000000 average loss = n.a. total feature number = 642215  Now we load our predictions, compute AUC, and plot the ROC curve: In [31]: with open(os.path.join(PATH_TO_ALL_DATA, '20news_test_predictions.txt')) as pred_file: test_prediction = [float(label) for label in pred_file.readlines()] auc = roc_auc_score(test_labels, test_prediction) roc_curve = roc_curve(test_labels, test_prediction) with plt.xkcd(): plt.plot(roc_curve[0], roc_curve[1]); plt.plot([0,1], [0,1]) plt.xlabel('FPR'); plt.ylabel('TPR'); plt.title('test AUC = %f' % (auc)); plt.axis([-0.05,1.05,-0.05,1.05]);  The AUC value we get shows that we have achieved high classification quality. ### 3.2. News. Multiclass classification¶ We will use the same news dataset, but, this time, we will solve a multiclass classification problem. Vowpal Wabbit is a little picky – it wants labels starting from 1 till K, where K – is the number of classes in the classification task (20 in our case). So we will use LabelEncoder and add 1 afterwards (recall that LabelEncoder maps labels into range from 0 to K-1). In [32]: all_documents = newsgroups['data'] topic_encoder = LabelEncoder() all_targets_mult = topic_encoder.fit_transform(newsgroups['target']) + 1  The data is the same, but we have changed the labels, train_labels_mult and test_labels_mult, into label vectors from 1 to 20. In [33]: train_documents, test_documents, train_labels_mult, test_labels_mult = \ train_test_split(all_documents, all_targets_mult, random_state=7) with open(os.path.join(PATH_TO_ALL_DATA, '20news_train_mult.vw'), 'w') as vw_train_data: for text, target in zip(train_documents, train_labels_mult): vw_train_data.write(to_vw_format(text, target)) with open(os.path.join(PATH_TO_ALL_DATA, '20news_test_mult.vw'), 'w') as vw_test_data: for text in test_documents: vw_test_data.write(to_vw_format(text))  We train Vowpal Wabbit in multiclass classification mode, passing the oaa parameter("one against all") with the number of classes. Also, let's see what parameters our model quality is dependent on (more info can be found in the official Vowpal Wabbit tutorial): • learning rate (-l, 0.5 default) – rate of weight change on every step • learning rate decay (--power_t, 0.5 default) – it is proven in practice, that, if the learning rate drops with the number of steps in stochastic gradient descent, we approach the minimum loss better • loss function (--loss_function) – the entire training algorithm depends on it. See docs for loss functions • Regularization (-l1) – note that VW calculates regularization for every object. That is why we usually set regularization values to about$10^{-20}.$Additionally, we can try automatic Vowpal Wabbit parameter tuning with Hyperopt. In [34]: %%time !vw --oaa 20$PATH_TO_ALL_DATA/20news_train_mult.vw -f $PATH_TO_ALL_DATA/20news_model_mult.vw \ --loss_function=hinge  final_regressor = ../../data//20news_model_mult.vw Num weight bits = 18 learning rate = 0.5 initial_t = 0 power_t = 0.5 using no cache Reading datafile = ../../data//20news_train_mult.vw num sources = 1 average since example example current current current loss last counter weight label predict features 1.000000 1.000000 1 1.0 15 1 157 1.000000 1.000000 2 2.0 2 15 159 1.000000 1.000000 4 4.0 15 10 92 1.000000 1.000000 8 8.0 16 15 129 1.000000 1.000000 16 16.0 13 12 108 0.937500 0.875000 32 32.0 2 9 115 0.906250 0.875000 64 64.0 16 16 114 0.867188 0.828125 128 128.0 8 4 110 0.816406 0.765625 256 256.0 7 15 44 0.646484 0.476562 512 512.0 13 9 160 0.502930 0.359375 1024 1024.0 3 4 194 0.388672 0.274414 2048 2048.0 1 1 438 0.300293 0.211914 4096 4096.0 11 11 644 0.225098 0.149902 8192 8192.0 5 5 174 finished run number of examples = 8485 weighted example sum = 8485.000000 weighted label sum = 0.000000 average loss = 0.222392 total feature number = 2048932 CPU times: user 4.77 ms, sys: 4.45 ms, total: 9.22 ms Wall time: 506 ms  In [35]: %%time !vw -i$PATH_TO_ALL_DATA/20news_model_mult.vw -t -d $PATH_TO_ALL_DATA/20news_test_mult.vw \ -p$PATH_TO_ALL_DATA/20news_test_predictions_mult.txt

only testing
predictions = ../../data//20news_test_predictions_mult.txt
Num weight bits = 18
learning rate = 0.5
initial_t = 0
power_t = 0.5
using no cache
num sources = 1
average  since         example        example  current  current  current
loss     last          counter         weight    label  predict features
n.a.     n.a.            1            1.0  unknown        8      349
n.a.     n.a.            2            2.0  unknown        6       50
n.a.     n.a.            4            4.0  unknown       18      251
n.a.     n.a.            8            8.0  unknown       18      237
n.a.     n.a.           16           16.0  unknown        4      106
n.a.     n.a.           32           32.0  unknown       15      964
n.a.     n.a.           64           64.0  unknown        4      261
n.a.     n.a.          128          128.0  unknown        8       82
n.a.     n.a.          256          256.0  unknown       10      186
n.a.     n.a.          512          512.0  unknown        1      162
n.a.     n.a.         1024         1024.0  unknown       11      283
n.a.     n.a.         2048         2048.0  unknown       14      104

finished run
number of examples = 2829
weighted example sum = 2829.000000
weighted label sum = 0.000000
average loss = n.a.
total feature number = 642215
CPU times: user 6.79 ms, sys: 13.6 ms, total: 20.4 ms
Wall time: 371 ms

In [36]:
with open(os.path.join(PATH_TO_ALL_DATA,
'20news_test_predictions_mult.txt')) as pred_file:
test_prediction_mult = [float(label)

In [37]:
accuracy_score(test_labels_mult, test_prediction_mult)

Out[37]:
0.8734535171438671

Here is how often the model misclassifies atheism with other topics:

In [38]:
M = confusion_matrix(test_labels_mult, test_prediction_mult)
for i in np.where(M[0,:] > 0)[0][1:]:
print(newsgroups['target_names'][i], M[0,i])

rec.autos 1
rec.sport.baseball 1
sci.med 1
soc.religion.christian 3
talk.religion.misc 5


### 3.3. IMDB movie reviews¶

In this part we will do binary classification of IMDB (International Movie DataBase) movie reviews. We will see how fast Vowpal Wabbit performs.

Using the load_files function from sklearn.datasets, we load the movie reviews datasets. It's the same dataset we used in topic04 part4 notebook.

In [39]:
#Download the dataset if not already in place
from io import BytesIO
import requests
import tarfile

url = "http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz"

if os.path.isfile(os.path.join(extract_path, "aclImdb", "README")) and not overwrite:
print("IMDB dataset is already in place.")
return

response = requests.get(url)

tar = tarfile.open(mode= "r:gz", fileobj = BytesIO(response.content))

data = tar.extractall(extract_path)


IMDB dataset is already in place.


In [40]:
PATH_TO_IMDB = "../../data/aclImdb"

categories=['pos', 'neg'])

text_train, y_train = reviews_train.data, reviews_train.target

In [41]:
print("Number of documents in training data: %d" % len(text_train))
print(np.bincount(y_train))

Number of documents in training data: 25000
[12500 12500]


Do the same for the test set.

In [42]:
reviews_test = load_files(os.path.join(PATH_TO_IMDB, "test"),
categories=['pos', 'neg'])
text_test, y_test = reviews_test.data, reviews_test.target

In [43]:
print("Number of documents in test data: %d" % len(text_test))
print(np.bincount(y_test))

Number of documents in test data: 25000
[12500 12500]


Take a look at examples of reviews and their corresponding labels.

In [44]:
text_train[0]

Out[44]:
b"Zero Day leads you to think, even re-think why two boys/young men would do what they did - commit mutual suicide via slaughtering their classmates. It captures what must be beyond a bizarre mode of being for two humans who have decided to withdraw from common civility in order to define their own/mutual world via coupled destruction.<br /><br />It is not a perfect movie but given what money/time the filmmaker and actors had - it is a remarkable product. In terms of explaining the motives and actions of the two young suicide/murderers it is better than 'Elephant' - in terms of being a film that gets under our 'rationalistic' skin it is a far, far better film than almost anything you are likely to see. <br /><br />Flawed but honest with a terrible honesty."
In [45]:
y_train[0] # good review

Out[45]:
1
In [46]:
text_train[1]

Out[46]:
b'Words can\'t describe how bad this movie is. I can\'t explain it by writing only. You have too see it for yourself to get at grip of how horrible a movie really can be. Not that I recommend you to do that. There are so many clich\xc3\xa9s, mistakes (and all other negative things you can imagine) here that will just make you cry. To start with the technical first, there are a LOT of mistakes regarding the airplane. I won\'t list them here, but just mention the coloring of the plane. They didn\'t even manage to show an airliner in the colors of a fictional airline, but instead used a 747 painted in the original Boeing livery. Very bad. The plot is stupid and has been done many times before, only much, much better. There are so many ridiculous moments here that i lost count of it really early. Also, I was on the bad guys\' side all the time in the movie, because the good guys were so stupid. "Executive Decision" should without a doubt be you\'re choice over this one, even the "Turbulence"-movies are better. In fact, every other movie in the world is better than this one.'
In [47]:
y_train[1] # bad review

Out[47]:
0
In [48]:
to_vw_format(str(text_train[1]), 1 if y_train[0] == 1 else -1)

Out[48]:
'1 |text words can describe how bad this movie can explain writing only you have too see for yourself get grip how horrible movie really can not that recommend you that there are many clich xc3 xa9s mistakes and all other negative things you can imagine here that will just make you cry start with the technical first there are lot mistakes regarding the airplane won list them here but just mention the coloring the plane they didn even manage show airliner the colors fictional airline but instead used 747 painted the original boeing livery very bad the plot stupid and has been done many times before only much much better there are many ridiculous moments here that lost count really early also was the bad guys side all the time the movie because the good guys were stupid executive decision should without doubt you choice over this one even the turbulence movies are better fact every other movie the world better than this one\n'

Now, we prepare training (movie_reviews_train.vw), validation (movie_reviews_valid.vw), and test (movie_reviews_test.vw) sets for Vowpal Wabbit. We will use 70% for training, 30% for the hold-out set.

In [49]:
train_share = int(0.7 * len(text_train))
train, valid = text_train[:train_share], text_train[train_share:]
train_labels, valid_labels = y_train[:train_share], y_train[train_share:]

In [50]:
len(train_labels), len(valid_labels)

Out[50]:
(17500, 7500)
In [51]:
with open(os.path.join(PATH_TO_ALL_DATA, 'movie_reviews_train.vw'), 'w') as vw_train_data:
for text, target in zip(train, train_labels):
vw_train_data.write(to_vw_format(str(text), 1 if target == 1 else -1))
with open(os.path.join(PATH_TO_ALL_DATA, 'movie_reviews_valid.vw'), 'w') as vw_train_data:
for text, target in zip(valid, valid_labels):
vw_train_data.write(to_vw_format(str(text), 1 if target == 1 else -1))
with open(os.path.join(PATH_TO_ALL_DATA, 'movie_reviews_test.vw'), 'w') as vw_test_data:
for text in text_test:
vw_test_data.write(to_vw_format(str(text)))

In [52]:
!head -2 $PATH_TO_ALL_DATA/movie_reviews_train.vw  1 |text zero day leads you think even think why two boys young men would what they did commit mutual suicide via slaughtering their classmates captures what must beyond bizarre mode being for two humans who have decided withdraw from common civility order define their own mutual world via coupled destruction not perfect movie but given what money time the filmmaker and actors had remarkable product terms explaining the motives and actions the two young suicide murderers better than elephant terms being film that gets under our rationalistic skin far far better film than almost anything you are likely see flawed but honest with terrible honesty -1 |text words can describe how bad this movie can explain writing only you have too see for yourself get grip how horrible movie really can not that recommend you that there are many clich xc3 xa9s mistakes and all other negative things you can imagine here that will just make you cry start with the technical first there are lot mistakes regarding the airplane won list them here but just mention the coloring the plane they didn even manage show airliner the colors fictional airline but instead used 747 painted the original boeing livery very bad the plot stupid and has been done many times before only much much better there are many ridiculous moments here that lost count really early also was the bad guys side all the time the movie because the good guys were stupid executive decision should without doubt you choice over this one even the turbulence movies are better fact every other movie the world better than this one  In [53]: !head -2$PATH_TO_ALL_DATA/movie_reviews_valid.vw

1 |text matter life and death what can you really say that would properly justice the genius and beauty this film powell and pressburger visual imagination knows bounds every frame filled with fantastically bold compositions the switches between the bold colours the real world the stark black and white heaven ingenious showing visually just how much more vibrant life the final court scene also fantastic the judge and jury descend the stairway heaven hold court over peter david niven operation all the performances are spot roger livesey being standout and the romantic energy the film beautiful never has there been more romantic film than this there has haven seen matter life and death all about the power love and just how important life and jack cardiff cinematography reason enough watch the film alone the way lights kim hunter face makes her all the more beautiful what genius can make simple things such game table tennis look exciting and the sound design also impeccable the way the sound mutes vital points was decision way ahead its time this true classic that can restore anyone faith cinema under appreciated its initial release and today audiences but one all time favourites which why give this film word beautiful
1 |text while this was better movie than 101 dalmations live action not animated version think still fell little short what disney could was well filmed the music was more suited the action and the effects were better done compared 101 the acting was perhaps better but then the human characters were given far more appropriate roles this sequel and glenn close really not missed the first movie she makes shine her poor lackey and the overzealous furrier sidekicks are wonderful characters play off and they add the spectacle disney has given this great family film with little objectionable material and yet remains fun and interesting for adults and children alike bound classic many disney films are here hoping the third will even better still because you know they probably want make one

In [54]:
!head -2 $PATH_TO_ALL_DATA/movie_reviews_test.vw   |text don hate heather graham because she beautiful hate her because she fun watch this movie like the hip clothing and funky surroundings the actors this flick work well together casey affleck hysterical and heather graham literally lights the screen the minor characters goran visnjic sigh and patricia velazquez are talented they are gorgeous congratulations miramax director lisa krueger |text don know how this movie has received many positive comments one can call artistic and beautifully filmed but those things don make for the empty plot that was filled with sexual innuendos wish had not wasted time watch this movie rather than being biographical was poor excuse for promoting strange and lewd behavior was just another hollywood attempt convince that that kind life normal and from the very beginning asked self what was the point this movie and continued watching hoping that would change and was quite disappointed that continued the same vein glad did not spend the money see this theater  Now we launch Vowpal Wabbit with the following arguments: • -d, path to training set (corresponding .vw file) • --loss_function – hinge (feel free to experiment here) • -f – path to the output file (which can also be in the .vw format) In [55]: !vw -d$PATH_TO_ALL_DATA/movie_reviews_train.vw --loss_function hinge \
-f $PATH_TO_ALL_DATA/movie_reviews_model.vw --quiet  Next, make the hold-out prediction with the following VW arguments: • -i –path to the trained model (.vw file) • -d – path to the hold-out set (.vw file) • -p – path to a txt-file where the predictions will be stored • -t - tells VW to ignore labels In [56]: !vw -i$PATH_TO_ALL_DATA/movie_reviews_model.vw -t \
-d $PATH_TO_ALL_DATA/movie_reviews_valid.vw -p$PATH_TO_ALL_DATA/movie_valid_pred.txt --quiet


Read the predictions from the text file and estimate the accuracy and ROC AUC. Note that VW prints probability estimates of the +1 class. These estimates are distributed from -1 to 1, so we can convert these into binary answers, assuming that positive values belong to class 1.

In [57]:
with open(os.path.join(PATH_TO_ALL_DATA, 'movie_valid_pred.txt')) as pred_file:
valid_prediction = [float(label)
print("Accuracy: {}".format(round(accuracy_score(valid_labels,
[int(pred_prob > 0) for pred_prob in valid_prediction]), 3)))
print("AUC: {}".format(round(roc_auc_score(valid_labels, valid_prediction), 3)))

Accuracy: 0.885
AUC: 0.942


Again, do the same for the test set.

In [58]:
!vw -i $PATH_TO_ALL_DATA/movie_reviews_model.vw -t \ -d$PATH_TO_ALL_DATA/movie_reviews_test.vw \
-p $PATH_TO_ALL_DATA/movie_test_pred.txt --quiet  In [59]: with open(os.path.join(PATH_TO_ALL_DATA, 'movie_test_pred.txt')) as pred_file: test_prediction = [float(label) for label in pred_file.readlines()] print("Accuracy: {}".format(round(accuracy_score(y_test, [int(pred_prob > 0) for pred_prob in test_prediction]), 3))) print("AUC: {}".format(round(roc_auc_score(y_test, test_prediction), 3)))  Accuracy: 0.88 AUC: 0.94  Let's try to achieve a higher accuracy by incorporating bigrams. In [60]: !vw -d$PATH_TO_ALL_DATA/movie_reviews_train.vw \
--loss_function hinge --ngram 2 -f $PATH_TO_ALL_DATA/movie_reviews_model2.vw --quiet  In [61]: !vw -i$PATH_TO_ALL_DATA/movie_reviews_model2.vw -t -d $PATH_TO_ALL_DATA/movie_reviews_valid.vw \ -p$PATH_TO_ALL_DATA/movie_valid_pred2.txt --quiet

In [62]:
with open(os.path.join(PATH_TO_ALL_DATA, 'movie_valid_pred2.txt')) as pred_file:
valid_prediction = [float(label)
print("Accuracy: {}".format(round(accuracy_score(valid_labels,
[int(pred_prob > 0) for pred_prob in valid_prediction]), 3)))
print("AUC: {}".format(round(roc_auc_score(valid_labels, valid_prediction), 3)))

Accuracy: 0.894
AUC: 0.954

In [63]:
!vw -i $PATH_TO_ALL_DATA/movie_reviews_model2.vw -t -d$PATH_TO_ALL_DATA/movie_reviews_test.vw \
-p \$PATH_TO_ALL_DATA/movie_test_pred2.txt --quiet

In [64]:
with open(os.path.join(PATH_TO_ALL_DATA, 'movie_test_pred2.txt')) as pred_file:
test_prediction2 = [float(label)
print("Accuracy: {}".format(round(accuracy_score(y_test,
[int(pred_prob > 0) for pred_prob in test_prediction2]), 3)))
print("AUC: {}".format(round(roc_auc_score(y_test, test_prediction2), 3)))

Accuracy: 0.888
AUC: 0.952


Adding bigrams really helped to improve our model!

### 3.4. Classifying gigabytes of StackOverflow questions¶

This section has been moved to Kaggle, please explore this Kernel.

## 4. Demo assignment¶

To better understand stochastic learning, you can complete this assignment where you'll be asked to implement a stochastic gradient regressor from scratch. The assignment is just for you to practice, and goes with a solution.