In this first part of the tutorial, we will run the simple Iris example that is included in the source distribution of Palladium. The Iris data set consists of a number of entries describing Iris flowers of three different types and is often used as an introductory example for machine learning.
It is assumed that you have already run through the
:ref:`installation`. You can either download the files needed for the
tutorial here: :download:`config.py <../../examples/iris/config.py>`
and :download:`iris.data <../../examples/iris/iris.data>`.
Alternatively, you can find the files in the source tree of Palladium.
It should include the iris example in the examples/iris folder.
Navigate to that folder and list its contents:
cd examples/iris
lsYou will notice that there are two files here. One is iris.data which
is a CSV file with the dataset we want to train with. For each
training example, iris.data defines four features and one of the
three classes to predict.
The other file, config.py is our Palladium configuration file. It has
all the configuration necessary to load the dataset CSV file and to
train it with a random forest classifier.
All the following commands require you to set an environment variable
to point to the config.py file. In general, when using any of
Palladium's scripts, you will want to have that environment variable set and
pointing to your current project's config.py. Using Bash, you
could set the PALLADIUM_CONFIG environment variable so that it is picked
up by subsequent calls to Palladium like so:
export PALLADIUM_CONFIG=config.pyNow we're all set to fit our Iris model:
pld-fitThis command will print a number of lines and hopefully finish with
the message Wrote model with version 1. If you list the contents
of the directory you are in again, you will notice that there is a new
file called iris-model.db. This is the SQLite database that Palladium
created and saved our trained model in. We can now use this trained
model and test it on a held-out test set:
pld-testThis will output an accuracy score, which should be something around 96 percent.
If you run pld-fit again, you'll notice that it outputs Wrote
model with version 2. The next call to pld-test will use that
newer model to run tests. To test the first model that you trained,
run:
pld-test --model-version=1Let us try and use the web service that is included with Palladium to use our trained model to generate predictions. Run this command to bring up the web server:
pld-devserverAnd now type this address into your browser's address bar (assuming that you're running the server locally):
http://localhost:5000/predict?sepal%20length=6.3&sepal%20width=2.5&petal%20length=4.9&petal%20width=1.5
The server should print out something like this:
{
"result": "Iris-virginica",
"metadata": {
"service_name": "iris",
"error_code": 0,
"status": "OK",
"service_version": "0.1"
}
}At this point we've already run through the palladium important scripts that Palladium provides.
In this section, we'll take a closer look at the Iris example's
config.py file and how it wires together the components that we
use to train and predict on the Iris dataset.
Open up the config.py file inside the examples/iris directory
in Palladium's source folder and let us now walk step-by-step through the
entries of this file.
Note
Despite the .py file ending, config.py is not conventional
Python source code. The file ending exists to help your editor to
use Python syntax highlighting. But all that config.py consists
of is a single Python dictionary.
The first configuration entry we'll find inside config.py is
something called dataset_loader_train. This is where we configure
our dataset loader that helps us load the training data from the CSV
file with the data, and define which rows should be used as data and
target values. The first entry inside dataset_loader_train
defines the type of dataset loader we want to use. That is
:class:`palladium.dataset.CSV`:
'dataset_loader_train': {
'!': 'palladium.dataset.CSV',The rest of what is inside the dataset_loader_train are the
keyword arguments that are used to initialize the
:class:`~palladium.dataset.CSV` class. The full definition of
dataset_loader_train looks like this:
'dataset_loader_train': {
'!': 'palladium.dataset.CSV',
'path': 'iris.data',
'names': [
'sepal length',
'sepal width',
'petal length',
'petal width',
'species',
],
'target_column': 'species',
'nrows': 100,
}You can take a look at :class:`~palladium.dataset.CSV`'s API to find
out what parameters the CSV dataset loader accepts and what they mean.
But to summarize: the path is the path to the CSV file. In our
case, this is the relative path to iris.data. Because our CSV
file doesn't have the column names in the first line, we have to
provide the column names using the names parameter. The
target_column defines which of the columns should be used as the
value to be predicted; this is the last column, which we named
species. The nrows parameter tells
:class:`~palladium.dataset.CSV` to return only the first hundred
samples from our CSV file.
If you take a look at the next section in the config file, which is
dataset_loader_test, you will notice that it is very similar to
the first one. In fact, the only difference between
dataset_loader_train and dataset_loader_test is that the
latter uses a different subset of the samples available in the same
CSV file. So instead of using nrows, dataset_loader_test uses
the skiprows parameter and thus skips the first hundred examples
(in order to separate training and testing data):
'skiprows': 100,Note
At this point you may be wondering if there's a way to not repeat
the entire dataset_loader_train section to define the test
dataset loader, just to change the skiprows argument, there is!
Check out the `configuration`_ docs for details on how to use the
__copy__ special keyword.
Under the hood, :class:`~palladium.dataset.CSV` uses
:func:`pandas.io.parsers.read_csv` to do the actual loading. Any
additional named parameters passed to :class:`~palladium.dataset.CSV`
are passed on to :func:`~pandas.io.parsers.read_csv`. In our example,
that is the case for the nrows and skiprows parameters.
Palladium also includes a dataset loader for loading data from an SQL database: :class:`palladium.dataset.SQL`.
But if you find yourself in need to write your own dataset loader, then that is pretty easy to do: Take a look at Palladium's :class:`~palladium.interfaces.DatasetLoader` interface that documents how a :class:`~palladium.interfaces.DatasetLoader` like :class:`~palladium.dataset.CSV` needs to look like.
The next section in our Iris configuration example is model. Here
we define which machine learning algorithm we intend to use. In our
case we'll be using a logistic regression classifier out of
scikit-learn:
'model': {
'!': 'sklearn.linear_model.LogisticRegression',
'C': 0.3,
},Notice how we parametrize :class:`~sklearn.tree.LogisticRegression`
with the regularization parameter C set to 0.3. To find out
which other parameters exist for the
:class:`~sklearn.linear_model.LogisticRegression` classifier, refer to
the scikit-learn docs.
If you've written your own scikit-learn estimator before, you'll already know how to write your own :class:`palladium.interfaces.Model` class. You'll want to implement :meth:`~palladium.interfaces.Model.fit` for model fitting, and :meth:`~palladium.interfaces.Model.predict` for prediction of target values. And possibly :meth:`~palladium.interfaces.Model.predict_proba` if you're dealing with class probabilities.
If you need to do pre-processing of your data, say scaling, value
imputation, feature selection, or the like, before you pass the data
into the ML algorithm (such as the
:class:`~sklearn.linear_model.LogisticRegression` classifier), you'll
want to take a look at scikit-learn pipelines. A Palladium
model is not bound to be a simple estimator class; it can be a
composite of several pre-processing steps or transformations, and the
algorithm combined.
At this point, feel free to change the configuration file to maybe try out different values for C. Can you find a setting for C that produces better accuracy?
Finding the right set of hyper parameters for your model can be tedious. That is where grid search comes in. Using grid search, we can quickly try out a few parameters and use cross-validation to see which of them work best.
Try running pld-grid-search and see what happens:
pld-grid-searchAt the end, you should see something like this output:
mean_fit_time mean_score_time mean_test_score mean_train_score param_C \
2 0.000811 0.000268 0.95 0.954831 1
1 0.001456 0.000426 0.91 0.924974 0.3
0 0.002270 0.001272 0.84 0.835621 0.1
params rank_test_score split0_test_score split0_train_score \
2 {'C': 1.0} 1 1.000000 0.938462
1 {'C': 0.3} 2 0.971429 0.923077
0 {'C': 0.1} 3 0.914286 0.876923
split1_test_score split1_train_score split2_test_score \
2 0.878788 0.970149 0.96875
1 0.848485 0.925373 0.90625
0 0.757576 0.835821 0.84375
split2_train_score std_fit_time std_score_time std_test_score \
2 0.955882 0.000148 0.000048 0.051585
1 0.926471 0.000659 0.000089 0.050734
0 0.794118 0.000016 0.000751 0.064636
std_train_score
2 0.012958
1 0.001414
0 0.033805
What happened? We just tried out three different values for C, and
used a three-fold cross-validation to determine the best setting. The
first line is the winner. It tells us that the mean cross-validation
accuracy of the model with C set to 1.0 (params) is 0.95
(mean_test_score) and that the standard deviation between
accuracies in the cross-validation folds is 0.051585.
You can also ask to save these results by passing a CSV filename to
the --save-results option. If you want to persist the best model
out of the grid search, run pld-grid-search with the
--persist-best flag.
Let us take a look at the configuration of grid_search:
'grid_search': {
'param_grid': {
'C': [0.1, 0.3, 1.0],
},
'return_train_score': True,
'verbose': 4,
}What parameters should be checked can be specified in the entry
param_grid. If more than one parameter with sets of values to
check are provided, all possible combinations are explored by grid
search. verbose allows to set the level for grid search
messages. With return_train_score set to True, the result will
also include scores for the training data for each fold.
It is possible to set other parameters of grid search, e.g., how many jobs to be run in parallel can be specified in n_jobs (if set to -1, all cores are used).
Palladium uses :class:`sklearn.grid_search.GridSearchCV` to do the actual
work. Thus, you'll want to take a look at the scikit-learn docs for
grid search
to understand what these parameters mean and what other parameters
exist for grid_search.
Usually we'll want the pld-fit command to save the trained model
to disk.
The model_persister in the Iris config.py file is set up to
save those models into a SQLite database. Let us take a look at that
part of the configuration:
'model_persister': {
'!': 'palladium.persistence.CachedUpdatePersister',
'update_cache_rrule': {'freq': 'HOURLY'},
'impl': {
'!': 'palladium.persistence.Database',
'url': 'sqlite:///iris-model.db',
},
},The :class:`palladium.persistence.CachedUpdatePersister` wraps the persister actually responsible for reading and writing models. It is possible to provide an update rule which specifies intervals to update the model. In the configuration above, the update_cache_rrule is set to an hourly update (in real applications, the frequency will palladium likely be much lower like daily or weekly). For details how to define these rules we refer to the python-dateutil docs. If no update_cache_rrule is provided, the model will not be updated automatically. The impl entry of this model persister specifies the actual persister to be wrapped.
The :class:`palladium.persistence.Database` persister takes a single
argument url which is the URL of the database to save the fitted
model into. It will automatically create a table called models if
such a table doesn't exist yet. Please refer to the SQLAlchemy docs
for details on which databases are supported, and how to form the
database URL.
Palladium ships with another model persister called :class:`palladium.persistence.File` that writes pickles to the file system. If you want to store your model anywhere else, or if you do not use Python's pickle but something else, you might want to take a look at the :class:`~palladium.interfaces.ModelPersister` interface, which describes the necessary methods. The location for storing the files can be chosen freely. However, the path has to contain a placeholder for adding the model's version:
'model_persister': {
'!': 'palladium.persistence.CachedUpdatePersister',
'impl': {
'!': 'palladium.persistence.File',
'path': 'model-{version}.pickle',
},
},If you prefer to use a REST backend like Artifactory for persisting your models, you can use the RestPersister:
'model_persister': {
'!': 'palladium.persistence.CachedUpdatePersister',
'impl': {
'!': 'palladium.persistence.Rest',
'url': 'http://localhost:8081/artifactory/modelz/{version}',
'auth': ('username', 'passw0rd'),
},
},The next component in the Iris example configuration is called
predict_service. The :class:`palladium.interfaces.PredictService` is
the workhorse behind what us happening in the /predict HTTP
endpoint. Let us take a look at how it is configured:
'predict_service': {
'!': 'palladium.server.PredictService',
'mapping': [
('sepal length', 'float'),
('sepal width', 'float'),
('petal length', 'float'),
('petal width', 'float'),
],
}Again, the specific implementation of the predict_service that we
use is specified through the ! setting.
The mapping defines which request parameters are to be expected.
In this example, we expect a float number for each of sepal
length, sepal width, petal length, petal width. Note
that this is exactly the order in which the data was fed into the
algorithm for model fitting.
An example request might then look like this (assuming that you're running a server locally on port 5000):
http://localhost:5000/predict?sepal%20length=6.3&sepal%20width=2.5&petal%20length=4.9&petal%20width=1.5
The :class:`palladium.server.PredictService` implementation that we use in this example has some more settings.
Its responsibility is also to create an HTTP response. In our example, if the prediction was successful (i.e., no errors whatsoever occurred), then the :class:`~palladium.server.PredictService` will generate a JSON response with an HTTP status code of 200:
{
"result": "Iris-virginica",
"metadata": {
"service_name": "iris",
"error_code": 0,
"status": "OK",
"service_version": "0.1"
}
}In case of a malformed request, you will see a status code of 400 and this response body:
{
"metadata": {
"service_name": "iris",
"error_message": "BadRequest: ...",
"error_code": -1,
"status": "ERROR",
"service_version": "0.1"
}
}If you want the predict service to work differently, then chances are that you get away subclassing from the :class:`~palladium.server.PredictService` class and override one of its methods. E.g. to change the way that API responses to the web look like, you would override the :meth:`~palladium.server.PredictService.response_from_prediction` and :meth:`~palladium.server.PredictService.response_from_exception` methods, which are responsible for creating the JSON responses.
Predict service specifications can be customized by setting the
entry_point and decorator_list_name in the configuration. It
is also possible to specify more than one predict service which can be
reached by different entry points, e.g., if a model should be used in
two different contexts with different parameters or response
formats. This is an example how to specify two predict services with
different entry points:
'predict_service1': {
'!': 'mypackage.server.PredictService',
'mapping': [
('sepal length', 'float'),
('sepal width', 'float'),
('petal length', 'float'),
('petal width', 'float'),
],
'entry_point': '/predict',
'decorator_list_name': 'predict_decorators',
}
'predict_service2': {
'!': 'mypackage.server.PredictServiceID',
'mapping': [
('id', 'int'),
],
'entry_point': '/predict-by-id',
'decorator_list_name': 'predict_decorators_id',
}It is not necessary that the decorator list as specified by
decorator_list_name is defined in the configuration; if it does
not exist, no decorators will be used for this predict service.
Note
If entry_point and decorator_list_name are omitted,
/predict and predict_decorators will be used as default
values, leading to the same behavior as it was the case in earlier
Palladium versions.
As mentioned in the Model section, it is entirely
possible to implement your own machine learning model and use it.
Remember that the only interface our model needed to implement was
:class:`palladium.interfaces.Model`. That means we can also use a
scikit-learn Pipeline to do the
job. Let us extend our Iris example to use a pipeline with two
elements: a :class:`sklearn.preprocessing.PolynomialFeatures`
transform and a :class:`sklearn.linear_model.LogisticRegression`
classifier. To do this, let us create a file called iris.py in the
same folder as we have our config.py with the following contents:
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import PolynomialFeatures
def model(**kwargs):
pipeline = Pipeline([
('poly', PolynomialFeatures()),
('clf', LogisticRegression()),
])
pipeline.set_params(**kwargs)
return pipelineThe special **kwargs argument allows us to pass configuration
options for both the poly and the clf elements of our pipeline
in the configuration file. Let us try this: we change the model
entry in config.py to look like this:
'model': {
'!': 'iris.model',
'clf__C': 0.3,
},Just like in our previous example, we are setting the C hyper
parameter of our :class:`~sklearn.linear_model.LogisticRegression` to
be 0.3. However, this time, we have to prefix the parameter name
by clf__ to tell the pipeline that we want to the set a parameter
of the clf part of the pipeline. If you want to used grid search
with this pipeline, keep in mind that you will also need to adapt the
parameter's name in the grid search section to clf_C.
It is also possible to use nested lists in configurations. With this feature, pipelines can be also defined purely through the configuration file, e.g.:
'model': {
'!': 'sklearn.pipeline.Pipeline',
'steps': [['clf', {'!': 'sklearn.linear_model.LinearRegression'}],
],
},