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# mlpack command-line quickstart guide
This page describes how you can quickly get started using mlpack from the
command-line and gives a few examples of usage, and pointers to deeper
documentation.
This quickstart guide is also available for [C++](cpp.md), [Python](python.md),
[R](r.md), [Julia](julia.md), and [Go](go.md).
## Installing mlpack
Installing mlpack is straightforward and can be done with your system's package
manager. For instance, for Ubuntu or Debian the command is simply
```sh
sudo apt-get install mlpack-bin
```
On Fedora or Red Hat:
```sh
sudo dnf install mlpack
```
If you use a different distribution, mlpack may be packaged under a different
name. And if it is not packaged, you can use a Docker image from Dockerhub:
```sh
docker run -it mlpack/mlpack /bin/bash
```
This Docker image has mlpack's command-line bindings already built and
installed.
If you prefer to build the command-line programs from scratch, follow the
instructions in the
[installation guide](../user/install.md#compile-bindings-manually).
## Simple quickstart example
As a really simple example of how to use mlpack from the command-line, let's do
some simple classification on a subset of the standard machine learning
`covertype` dataset. We'll first split the dataset into a training set and a
testing set, then we'll train an mlpack random forest on the training data, and
finally we'll print the accuracy of the random forest on the test dataset.
You can copy-paste this code directly into your shell to run it.
```sh
# Get the dataset and unpack it.
wget https://www.mlpack.org/datasets/covertype-small.data.csv.gz
wget https://www.mlpack.org/datasets/covertype-small.labels.csv.gz
gunzip covertype-small.data.csv.gz covertype-small.labels.csv.gz
# Split the dataset; 70% into a training set and 30% into a test set.
# Each of these options has a shorthand single-character option but here we type
# it all out for clarity.
mlpack_preprocess_split \
--input_file covertype-small.data.csv \
--input_labels_file covertype-small.labels.csv \
--training_file covertype-small.train.csv \
--training_labels_file covertype-small.train.labels.csv \
--test_file covertype-small.test.csv \
--test_labels_file covertype-small.test.labels.csv \
--test_ratio 0.3 \
--verbose
# Train a random forest.
mlpack_random_forest \
--training_file covertype-small.train.csv \
--labels_file covertype-small.train.labels.csv \
--num_trees 10 \
--minimum_leaf_size 3 \
--print_training_accuracy \
--output_model_file rf-model.bin \
--verbose
# Now predict the labels of the test points and print the accuracy.
# Also, save the test set predictions to the file 'predictions.csv'.
mlpack_random_forest \
--input_model_file rf-model.bin \
--test_file covertype-small.test.csv \
--test_labels_file covertype-small.test.labels.csv \
--predictions_file predictions.csv \
--verbose
```
We can see by looking at the output that we achieve reasonably good accuracy on
the test dataset (80%+). The file `predictions.csv` could also be used by
other tools; for instance, we can easily calculate the number of points that
were predicted incorrectly:
```sh
$ diff -U 0 predictions.csv covertype-small.test.labels.csv | grep '^@@' | wc -l
```
It's easy to modify the code above to do more complex things, or to use
different mlpack learners, or to interface with other machine learning toolkits.
## Using mlpack for movie recommendations
In this example, we'll train a collaborative filtering model using mlpack's
`mlpack_cf` program. We'll train this on the
[MovieLens dataset](https://grouplens.org/datasets/movielens/), and then we'll
use the model that we train to give recommendations.
You can copy-paste this code directly into the command line to run it.
```sh
wget https://www.mlpack.org/datasets/ml-20m/ratings-only.csv.gz
wget https://www.mlpack.org/datasets/ml-20m/movies.csv.gz
gunzip ratings-only.csv.gz
gunzip movies.csv.gz
# Hold out 10% of the dataset into a test set so we can evaluate performance.
mlpack_preprocess_split \
--input_file ratings-only.csv \
--training_file ratings-train.csv \
--test_file ratings-test.csv \
--test_ratio 0.1 \
--verbose
# Train the model. Change the rank to increase/decrease the complexity of the
# model.
mlpack_cf \
--training_file ratings-train.csv \
--test_file ratings-test.csv \
--rank 10 \
--algorithm RegSVD \
--output_model_file cf-model.bin \
--verbose
# Now query the 5 top movies for user 1.
echo "1" > query.csv;
mlpack_cf \
--input_model_file cf-model.bin \
--query_file query.csv \
--recommendations 10 \
--output_file recommendations.csv \
--verbose
# Get the names of the movies for user 1.
echo "Recommendations for user 1:"
for i in `seq 1 10`; do
item=`cat recommendations.csv | awk -F',' '{ print $'$i' }'`;
head -n $(($item + 2)) movies.csv | tail -1 | \
sed 's/^[^,]*,[^,]*,//' | \
sed 's/\(.*\),.*$/\1/' | sed 's/"//g';
done
```
Here is some example output, showing that user 1 seems to have good taste in
movies:
```
Recommendations for user 1:
Casablanca (1942)
Pan's Labyrinth (Laberinto del fauno, El) (2006)
Godfather, The (1972)
Answer This! (2010)
Life Is Beautiful (La Vita รจ bella) (1997)
Adventures of Tintin, The (2011)
Dark Knight, The (2008)
Out for Justice (1991)
Dr. Strangelove or: How I Learned to Stop Worrying and Love the Bomb (1964)
Schindler's List (1993)
```
## Next steps with mlpack
Now that you have done some simple work with mlpack, you have seen how it can
easily plug into a data science production workflow for the command line. But
these two examples have only shown a little bit of the functionality of mlpack.
Lots of other commands are available with different functionality. A full list
of commands and full documentation for each can be found on the following page:
- [CLI program documentation](https://www.mlpack.org/doc/user/bindings/cli.html)
Also, mlpack is much more flexible from C++ and allows much greater
functionality. So, more complicated tasks are possible if you are willing to
write C++. To get started learning about mlpack in C++, the
[C++ quickstart](cpp.md) is a good place to start.
|
