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merged 5 commits into from
Jul 25, 2022
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Completes UNB-2346 - Update Python API reference #31

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48797f0
Completes UNB-2346 - Update Python API reference
gustavocidornelas Jul 18, 2022
bf7845d
Fix typo
gustavocidornelas Jul 18, 2022
5a1a316
Re-wording on the API reference
gustavocidornelas Jul 25, 2022
4daa5f0
Removed tokenizer from tabular data example
gustavocidornelas Jul 25, 2022
d96ef8e
Small re-wordings
gustavocidornelas Jul 25, 2022
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9 changes: 9 additions & 0 deletions docs/source/reference/upload.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -13,6 +13,15 @@ Client constructor

UnboxClient

Project creation and loading
----------------------------
.. autosummary::
:toctree: api/
:template: class.rst

UnboxClient.create_project
UnboxClient.load_project

Upload functions
----------------
.. autosummary::
Expand Down
168 changes: 141 additions & 27 deletions unboxapi/__init__.py
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Expand Up @@ -62,7 +62,7 @@ class DeploymentType(Enum):
ONPREM = 1
AWS = 2
GCP = 3
AZURE = 4
AZURE = 43


# NOTE: Don't modify this unless you are deploying on-prem.
Expand Down Expand Up @@ -103,6 +103,39 @@ def create_project(
name: str,
description: str,
):
"""Creates a project on the Unbox platform.

Parameters
----------
name : str
Name of your project.

.. important::
The project name must be unique in a user's account. Furthermore, this is the name
that is used to identify the project when uploading models and datasets.
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reword: This name must be unique within a user's collection of projects.

Don't think the second line is needed

description : str
Project description.

Returns
-------
Project
An object that is used to upload models and datasets to the Unbox platform that
also contains information about the project.

Examples
--------
Instantiate the client and create the project:

>>> import unboxapi
>>> client = unboxapi.UnboxClient('YOUR_API_KEY_HERE')
>>>
>>> project = client.create_project(name="Churn prediction",
... description="My first error analysis playground")

With the Project object created, you are able to start uploading models and datasets to the platform. Refer to :obj:`add_model`
and obj:`add_dataset` or obj:`add_dataframe` for detailed examples.
"""
# ----------------------------- Schema validation ---------------------------- #
project_schema = ProjectSchema()
try:
Expand All @@ -123,6 +156,34 @@ def create_project(
return Project(project_data, self.upload, self.subscription_plan, self)

def load_project(self, name: str):
"""Loads an existing project from the Unbox platform.

Parameters
----------
name : str
Name of the project to be loaded. The name of the project is the one displayed on the Unbox platform.

.. note::
If you haven't created the project yet, you should use the :obj:`create_project` method.

Returns
-------
Project
An object that is used to upload models and datasets to the Unbox platform that
also contains information about the project.

Examples
--------
Instantiate the client and load the project:

>>> import unboxapi
>>> client = unboxapi.UnboxClient('YOUR_API_KEY_HERE')
>>>
>>> project = client.load_project(name="Churn prediction")

With the Project object loaded, you are able to upload models and datasets to the platform. Refer to :obj:`add_model`
and obj:`add_dataset` or obj:`add_dataframe` for detailed examples.
"""
endpoint = f"load_project/{name}"
project_data = self.api.get_request(endpoint)
return Project(project_data, self.upload, self.subscription_plan, self)
Expand Down Expand Up @@ -153,6 +214,13 @@ def add_model(
----------
name : str
Name of your model.

.. important::
Versioning models on the Unbox platform happens via the ``name`` argument. If ``add_model`` is called
with a ``name`` that still does not exist inside the project, Unbox treats it as the **first version** of a new model lineage.
On the other hand, if the ``name`` argument value already exists inside the project, Unbox treats it as a **new version** of an existing
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"if the name argument value already exists" --> "if a model with the specified name already exists"
model lineage.

task_type : :obj:`TaskType`
Type of ML task. E.g. :obj:`TaskType.TextClassification`.
function :
Expand All @@ -174,8 +242,8 @@ def add_model(
feature_names : List[str], default []
List of input feature names. Only applicable if your ``task_type`` is
:obj:`TaskType.TabularClassification` or :obj:`TaskType.TabularRegression`.
categorical_features_map : Dict[str], default {}
A dict containing a list of category names for each feature that is categorical. E.g. `{'Weather': ['Hot', 'Cold']}`.
categorical_feature_names : List[str], default []
A list containing the feature names for each feature that is categorical. E.g. `["Gender", "Geography"]`.
Only applicable if your ``task_type`` is :obj:`TaskType.TabularClassification` or :obj:`TaskType.TabularRegression`.
train_sample_df : pd.DataFrame, default None
A random sample of >= 100 rows from your training dataset. This is used to support explainability features.
Expand Down Expand Up @@ -213,11 +281,24 @@ def add_model(
Our `sample notebooks <https://github.com/unboxai/unboxapi-python-client/tree/main/examples>`_ and
`tutorials <https://unbox.readme.io/docs/overview-of-tutorial-tracks>`_.

Instantiate the client:

The models are uploaded directly into a **project** on the Unbox platform, using the ``project.add_model()`` method.
Therefore, you need to get the project object to start uploading your models.

First, instantiate the client:

>>> import unboxapi
>>> client = unboxapi.UnboxClient('YOUR_API_KEY_HERE')

Then, get the project object. If you don't have a project yet, you need to create one using the :obj:`create_project` method:

>>> project = client.create_project(name="Your project name",
... description="Your project description")

Otherwise, if you already have a project created on the platform, you just need to load it using the :obj:`load_project` method:

>>> project = client.load_project(name="Your project name")

**If your task type is tabular classification...**

Let's say your dataset looks like the following:
Expand All @@ -236,7 +317,7 @@ def add_model(
>>> task_type = TaskType.TabularClassification
>>> class_names = ['Retained', 'Churned']
>>> feature_names = ['CreditScore', 'Geography', 'Balance']
>>> categorical_features_map = {'CreditScore': ['France', 'Germany', 'Spain']}
>>> categorical_feature_names = ['Geography']

Now let's say you've trained a simple ``scikit-learn`` model on data that looks like the above.

Expand All @@ -251,8 +332,8 @@ def add_model(
The ``model`` arg must be the actual trained model object, and the ``input_features`` arg must be a 2D numpy array
containing a batch of features that will be passed to the model as inputs.

You can optionally include other kwargs in the function, including tokenizers, variables, encoders etc.
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curious why you removed tokenizers?
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Oh, just because this is inside the example for tabular classification. I kept it for the example for NLP.

I'm trying to think now of uses of a tokenizer on a tabular task type... I think it would be a bit unusual, but I guess it can exist. I'll add it back just in case
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Oh no, you're right. Keep it removed for tabular :)
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Ok!
You simply pass those kwargs to the ``client.add_model`` function call when you upload the model.
You can optionally include other kwargs in the function, including variables, encoders etc.
You simply pass those kwargs to the ``project.add_model`` function call when you upload the model.

Here's an example of the ``predict_proba`` function in action:

Expand Down Expand Up @@ -286,15 +367,16 @@ def add_model(

You can now upload this dataset to Unbox:

>>> model = client.add_model(
... name='Churn Classifier',
>>> model = project.add_model(
... name='Linear classifiers',
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Can you make this name singular? I don't want users to think "Linear classifiers" should be used to compare several different candidates
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Done!
... description='First iteration of vanilla logistic regression',
... task_type=task_type,
... function=predict_proba,
... model=sklearn_model,
... model_type=model_type,
... class_names=class_names,
... feature_names=feature_names,
... categorical_features_map=categorical_features_map,
... categorical_feature_names=categorical_feature_names,
... train_sample_df=train_sample_df,
... train_sample_label_column_name=train_sample_label_column_name,
... )
Expand Down Expand Up @@ -332,7 +414,7 @@ def add_model(
strings.

You can optionally include other kwargs in the function, including tokenizers, variables, encoders etc.
You simply pass those kwargs to the ``client.add_model`` function call when you upload the model.
You simply pass those kwargs to the ``project.add_model`` function call when you upload the model.

Here's an example of the ``predict_proba`` function in action:

Expand Down Expand Up @@ -363,15 +445,23 @@ def add_model(

You can now upload this dataset to Unbox:

>>> model = client.add_model(
... name='Churn Classifier',
>>> model = project.add_model(
... name='Linear classifiers',
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still plural
... task_type=task_type,
... function=predict_proba,
... model=sklearn_model,
... model_type=model_type,
... class_names=class_names,
... )
>>> model.to_dict()

.. note::
If inside the given project the ``add_model`` method is called with ``name='Linear classifiers'`` for the first time,
a new model lineage will be created with ``Linear classifier`` as a name and ``description`` will be the first commit
on that new tree. In the future, if you'd like to commit a new version to that same lineage, you can simply call `add_model`
using ``name='Linear classifier'`` again and use ``description`` with the new commit message. Alternatively, if you'd like
to start a new separate lineage inside that project, you can call the ``add_model`` method with a different ``name``, e.g.,
``name ='Nonlinear classifiers'``.
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here you confuse "Linear classifier" with "Linear classifiers"

Make everything just "Linear Classifier" pls
"""
# ---------------------------- Schema validations ---------------------------- #
if task_type not in [
Expand Down Expand Up @@ -650,8 +740,8 @@ def add_dataset(
text_column_name : str, default None
Column header in the csv containing the input text. Only applicable if your ``task_type`` is
:obj:`TaskType.TextClassification`.
categorical_features_map : Dict[str, List[str]], default {}
A dict containing a list of category names for each feature that is categorical. E.g. `{'Weather': ['Hot', 'Cold']}`.
categorical_feature_names : List[str], default []
A list containing the feature names for each feature that is categorical. E.g. `["Gender", "Geography"]`.
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Can you improve this wording? "The feature names for each feature" is awkward

"A list containing the names of all categorical features used by the model. ..."
Only applicable if your ``task_type`` is :obj:`TaskType.TabularClassification` or :obj:`TaskType.TabularRegression`.
tag_column_name : str, default None
Column header in the csv containing tags you want pre-populated in Unbox.
Expand Down Expand Up @@ -683,11 +773,23 @@ def add_dataset(

Examples
--------
Instantiate the client:
The datasets are uploaded directly into a **project** on the Unbox platform, using the ``project.add_dataset()`` method.
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Datasets are uploaded. No need for "The datasets"
Therefore, you need to get the project object to start uploading your datasets.

First, instantiate the client:

>>> import unboxapi
>>> client = unboxapi.UnboxClient('YOUR_API_KEY_HERE')

Then, get the project object. If you don't have a project yet, you need to create one using the :obj:`create_project` method:

>>> project = client.create_project(name="Your project name",
... description="Your project description")

Otherwise, if you already have a project created on the platform, you just need to load it using the :obj:`load_project` method:

>>> project = client.load_project(name="Your project name")

**If your task type is tabular classification...**

Let's say your dataset looks like the following:
Expand All @@ -711,18 +813,18 @@ def add_dataset(
>>> class_names = ['Retained', 'Churned']
>>> feature_names = ['CreditScore', 'Geography', 'Balance']
>>> label_column_name = 'Churned'
>>> categorical_features_map = {'CreditScore': ['France', 'Germany', 'Spain']}
>>> categorical_feature_names = ['Geography']

You can now upload this dataset to Unbox:

>>> dataset = client.add_dataset(
>>> dataset = project.add_dataset(
... name='Churn Validation',
... task_type=task_type,
... file_path='/path/to/dataset.csv',
... class_names=class_names,
... label_column_name=label_column_name,
... feature_names=feature_names,
... categorical_features_map=categorical_map,
... categorical_feature_names=categorical_feature_names,
... )
>>> dataset.to_dict()

Expand All @@ -748,7 +850,7 @@ def add_dataset(

You can now upload this dataset to Unbox:

>>> dataset = client.add_dataset(
>>> dataset = project.add_dataset(
... name='Churn Validation',
... task_type=task_type,
... file_path='/path/to/dataset.csv',
Expand Down Expand Up @@ -933,8 +1035,8 @@ def add_dataframe(
text_column_name : str, default None
Column header in the csv containing the input text. Only applicable if your ``task_type`` is
:obj:`TaskType.TextClassification`.
categorical_features_map : Dict[str, List[str]], default {}
A dict containing a list of category names for each feature that is categorical. E.g. `{'Weather': ['Hot', 'Cold']}`.
categorical_feature_names : List[str], default []
A list containing the feature names for each feature that is categorical. E.g. `["Gender", "Geography"]`.
Only applicable if your ``task_type`` is :obj:`TaskType.TabularClassification` or :obj:`TaskType.TabularRegression`.
description : str, default None
Commit message for this version.
Expand Down Expand Up @@ -964,11 +1066,23 @@ def add_dataframe(

Examples
--------
Instantiate the client
The dataframes are uploaded directly into a **project** on the Unbox platform, using the ``project.add_dataframe()`` method.
Therefore, you need to get the project object to start uploading your dataframes.
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I think you can get rid of these first two sentences in all the examples sections. Show, don't tell.
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True, I'll remove them

First, instantiate the client:

>>> import unboxapi
>>> client = unboxapi.UnboxClient('YOUR_API_KEY_HERE')

Then, get the project object. If you don't have a project yet, you need to create one using the :obj:`create_project` method:

>>> project = client.create_project(name="Your project name",
... description="Your project description")

Otherwise, if you already have a project created on the platform, you just need to load it using the :obj:`load_project` method:

>>> project = client.load_project(name="Your project name")

**If your task type is tabular classification...**

Let's say your dataframe looks like the following:
Expand All @@ -991,18 +1105,18 @@ def add_dataframe(
>>> class_names = ['Retained', 'Churned']
>>> feature_names = ['CreditScore', 'Geography', 'Balance']
>>> label_column_name = 'Churned'
>>> categorical_features_map = {'CreditScore': ['France', 'Germany', 'Spain']}
>>> categorical_feature_names = ['Geography']

You can now upload this dataset to Unbox:

>>> dataset = client.add_dataset(
>>> dataset = project.add_dataset(
... name='Churn Validation',
... task_type=task_type,
... df=df,
... class_names=class_names,
... feature_names=feature_names,
... label_column_name=label_column_name,
... categorical_features_map=categorical_map,
... categorical_feature_names=categorical_feature_names,
... )
>>> dataset.to_dict()

Expand All @@ -1027,7 +1141,7 @@ def add_dataframe(

You can now upload this dataset to Unbox:

>>> dataset = client.add_dataset(
>>> dataset = project.add_dataset(
... name='Churn Validation',
... task_type=task_type,
... df=df,
Expand Down