.. toctree::
   :hidden:

BigML Bindings: 101 - Using a PCA
=================================

The PCA model is used to find the linear combination of your original
features that best describes your data. In that sense, the goal of the model
is to provide a transformation that allows dimensionality reduction.
Following the schema described in the `prediction workflow <api_sketch.html>`_,
document, this is the code snippet that shows the minimal workflow to
create a PCA model and produce a single projection.


.. code-block:: python

    from bigml.api import BigML
    # step 0: creating a connection to the service (default credentials)
    # check how to set your credentials in the Authentication section
    api = BigML()
    # step 1: creating a source from the data in your local "data/iris.csv" file
    source = api.create_source("data/iris.csv")
    # waiting for the source to be finished. Results will be stored in `source`
    api.ok(source)
    # step 3: creating a dataset from the previously created `source`
    dataset = api.create_dataset(source)
    # waiting for the dataset to be finished
    api.ok(dataset)
    # step 5: creating a PCA model
    pca = api.create_pca(dataset)
    # waiting for the PCA to be finished
    api.ok(pca)
    # the input data to project
    input_data = {"petal length": 4, "sepal length": 2, "petal width": 1,
                  "sepal witdh": 3}
    # getting the transformed components, the projection
    projection = api.create_projection(pca, input_data)

In the previous code, the `api.ok <creating_resources.html>`_
method is used to wait for the resource
to be finished before calling the next create method
or accessing the resource properties.
In the first case, we could skip that `api.ok`call because the next
`create` method would internally do the waiting when needed.

If you want to configure some of the attributes of your PCA,
like selecting a default numeric value, you can use the second argument
in the create call.


.. code-block:: python

    # step 5: creating a PCA and using mean as numeric value when missing
    pca = api.create_pca(dataset, {"default_numeric_value": "mean"})
    # waiting for the PCA to be finished
    api.ok(pca)

You can check all the available creation arguments in the `API documentation
<https://bigml.com/api/pca#pc_pca_arguments>`_.

If you want to add the generated principal components to the original
dataset (or a different dataset), you can do so by creating
a `batch_projection` resource. In the example, we'll be assuming you already
created a `PCA` following the steps 0 to 5 in the previous snippet and
that you want to score the same data you used in the PCA model.

.. code-block:: python

    test_dataset = dataset
    # step 10: creating a batch projection
    batch_projection = api.create_batch_projection(pca, test_dataset)
    # waiting for the batch_projection to be finished
    api.ok(batch_projection)
    # downloading the results to your computer
    api.download_batch_projection(batch_projection,
                                  filename='my_dir/my_projection.csv')

The batch projection output (as well as any of the resources created)
can be configured using additional arguments in the corresponding create calls.
For instance, to include all the information in the original dataset in the
output you would change `step 10` to:

.. code-block:: python

    batch_projection = api.create_batch_projection(pca, test_dataset,
                                                   {"all_fields": True})

Check the `API documentation <https://bigml.com/api/>`_ to learn about the
available configuration options for any BigML resource.

You can also project your data locally using the `PCA`
class in the `pca` module. A simple example of that is:

.. code-block:: python

    from bigml.pca import PCA
    local_pca = PCA("pca/6878ec46983efc21b000001b")
    # Getting the projection of some input data
    local_pca.projection({"petal length": 4, "sepal length": 2,
                          "petal width": 1, "sepal witdh": 3})

Or you could store first your PCA information in a file and use that
file to create the local `PCA` object:

.. code-block:: python

    # downloading the anomaly detector JSON to a local file
    from bigml.api import BigML
    api = BigML()
    api.export("pca/6878ec46983efc21b000001b",
               filename="my_pca.json")
    # creating a PCA  object using the information in the file
    from bigml.pca import PCA
    local_pca = PCA("my_pca.json")
    # getting the projection for some input data
    local_pca.projection({"petal length": 4, "sepal length": 2,
                          "petal width": 1, "sepal witdh": 3})

If you want to get the projection locally for all the rows in a CSV file
(first line should contain the field headers):

.. code-block:: python

    import csv
    from bigml.pca import PCA
    local_pca = PCA("pca/68714c667811dd5057000ab5")
    with open("test_data.csv") as test_handler:
        reader = csv.DictReader(test_handler)
        for input_data in reader:
        # predicting for all rows
            print local_pca.projection(input_data)

Every modeling resource in BigML has its corresponding local class. Check
the `Local resources <index.html#local-resources>`_ section of the
documentation to learn more about them.