| Name | sqapi JSON |
| Version |
0.58
JSON |
| download |
| home_page | https://bitbucket.org/ariell/pysq |
| Summary | A python package that simplifies interactions with the SQUIDLE+ API. It can be used to integrate automated labelling from machine learning algorithms and plenty other cool things. |
| upload_time | 2024-10-01 10:35:36 |
| maintainer | None |
| docs_url | None |
| author | Greybits Engineering |
| requires_python | None |
| license | MIT |
| keywords |
squidle+
api
sq
machine learning
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
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|
# SQAPI
`sqapi` is a python package that simplifies interactions with the
[SQUIDLE+ API](https://squidle.org/api/help?template=api_help_page.html).
It can be used for everything from creating simple queries through to integrating automated
labelling from machine learning algorithms and plenty other cool things.
### Installation
To install the `sqapi` module, you can use `pip`
```shell
pip install sqapi
```
### What is this?
The `sqapi` module helps to build the `HTTP` requests that are sent to the [SQUIDLE+](squidle.org) `API`. These are
`GET`, `POST`, `PATCH` or `DELETE` requests. Setting `verbosity=2` on the `sqapi` module will print the `HTTP`
requests that are being made.
`sqapi` takes care of authentication, and simplifies the creation of API queries.
For example:
```python
from sqapi.api import SQAPI
api = SQAPI(host=<HOST>, api_key=<API_KEY>, verbosity=2) # instantiate the sqapi module
r=api.get(<ENDPOINT>) # define a get request using a specific endpoint
r.filter(<NAME>,<OPERATORE>,<VALUE>) # define a filter to compare a property with a value using an operator
data = r.execute().json() # perform the request & return result as JSON dict (don't set template)
```
For more information about structuring queries, check out the [Making API queries](https://squidle.org/api/help?template=api_help_page.html#api_query)
section of the SQ+ API documentation page.
Instantiating `sqapi` without an API key argument will prompt for a user login, i.e.:
```python
sqapi = SQAPI(host=<HOST>, verbosity=2) # instantiate the sqapi module
```
You can also use it to apply built-in templates to the data that comes out of the API:
```python
r.template(<TEMPLATE>) # format the output of the request using an inbuilt HTML template
html = r.execute().text # perform the request & return result as text (eg: for html)
```
> **IMPORTANT:** in order to proceed, you will need a user account on [SQUIDLE+](https://squidle.org). You will also
> need to activate your API key.
## Examples
### Creating queries
This is by no means an extensive list of possible API queries. The API is extensive and the models are documented
[here](https://squidle.org/api/help?template=api_help_page.html) and the creation of queries is documented
[here](https://squidle.org/api/help?template=api_help_page.html#api_query). `SQAPI` enables a convenient mechanism
for creating these queries inside of Python. For example, a basic API query to list all the annotations that have valid
labels starting with 'ecklonia' within a spatially constrained bounding box would be:
```json
{
"filters": [
{
"name": "label",
"op": "has",
"val": {
"name": "name",
"op": "ilike",
"val": "ecklonia%"
}
},
{
"name": "point",
"op": "has",
"val": {
"name": "media",
"op": "has",
"val": {
"name": "poses",
"op": "any",
"val": {
"name": "geom",
"op": "geo_in_bbox",
"val": [
{
"lat": -32.020013585799155,
"lon": 115.49980113118502
},
{
"lat": -32.01995006531625,
"lon": 115.49987604949759
}
]
}
}
}
}
]
}
```
The result of that query can be accessed dynamically through
[here as pretty JSON](https://squidle.org/api/annotation?template=json.html&q={"filters":[{"name":"point","op":"has","val":{"name":"has_xy","op":"eq","val":true}},{"name":"point","op":"has","val":{"name":"media","op":"has","val":{"name":"poses","op":"any","val":{"name":"geom","op":"geo_in_bbox","val":[{"lat":-32.020013585799155,"lon":115.49980113118502},{"lat":-32.01995006531625,"lon":115.49987604949759}]}}}}]}) or
[here as raw JSON](https://squidle.org/api/annotation?q={"filters":[{"name":"point","op":"has","val":{"name":"has_xy","op":"eq","val":true}},{"name":"point","op":"has","val":{"name":"media","op":"has","val":{"name":"poses","op":"any","val":{"name":"geom","op":"geo_in_bbox","val":[{"lat":-32.020013585799155,"lon":115.49980113118502},{"lat":-32.01995006531625,"lon":115.49987604949759}]}}}}]}) or
[here with a template](https://squidle.org/iframe/api/annotation?template=models/annotation/list_thumbnails.html&q={"filters":[{"name":"point","op":"has","val":{"name":"has_xy","op":"eq","val":true}},{"name":"point","op":"has","val":{"name":"media","op":"has","val":{"name":"poses","op":"any","val":{"name":"geom","op":"geo_in_bbox","val":[{"lat":-32.020013585799155,"lon":115.49980113118502},{"lat":-32.01995006531625,"lon":115.49987604949759}]}}}}]}&include_link=true).
Note with a logged in browser session, that link will extract cropped thumbnails around each annotation, but without logging in,
you'll just see a thumbnail the whole image associated with that annotation.
The Python code required to build that query could be something like:
```python
from sqapi.api import SQAPI, query_filter as qf
api = SQAPI(host="https://squidle.org", ) # optionally pass in api_key to avoid log in prompt
r = api.get("/api/annotation")
r.filter("label", "has", qf("name","ilike","ecklonia%")) # filter for label name, % here is a wildcard matching anything
bbox = [{"lat": -32.020013585799155,"lon": 115.49980113118502},{"lat": -32.01995006531625,"lon": 115.49987604949759}]
r.filter("point", "has", qf("media", "has", qf("poses", "any", qf("geom", "geo_in_bbox", bbox)))) # filter within bounding box
```
### Random annotation BOT
This is an example of an automated labelling bot that selects random class labels to assign to points.
It provides terrible suggestions, however it provides a simple boiler-plate example of how to integrate a
Machine Learning algorithm for label suggestions.
Set up the environment and a mini project. Creating a virtual environment, is optional, but recommended.
```shell
mkdir sqbot_demo && cd sqbot_demo # make a directory
virtualenv -p python3 env # create a virtual environment
source env/bin/activate # activate it
pip install sqapi # install sqapi
```
#### 1. Create a bot class
In this example, we'll create an automated classifier that suggests random label suggestions for an annotation_set.
It uses the `Annotator` class from the `sqapi.annotate` module, which does most of the heavy-lifting. The implementation
of the classifier is relatively straight forward.
In your project directory, create a file named `run_bot.py`:
```python
# run_bot.py
import random
from sqapi.annotate import Annotator
from sqapi.helpers import cli_init
from sqapi.media import SQMediaObject
class RandoBOT(Annotator):
"""
An example of an automated labelling bot that selects random class labels to assign to points.
It provides terrible suggestions, however it provides a simple boiler-plate example of how to integrate a
Machine Learning algorithm for label suggestions.
"""
def classify_point(self, mediaobj: SQMediaObject, x, y, t):
# This method is used to generate a point prediction
# TODO: use the mediaobj, x and y point position to generate a real classifier_code and prob
classifier_code = random.randint(0,3) # get a random code (0-3) to match with label_map_file
prob = round(random.random(), 2) # generate a random probability
return classifier_code, prob
if __name__ == '__main__':
# Instantiate the bot from the default CLI arguments
bot = cli_init(RandoBOT)
# Build the annotation_set query (this can be far more complex, see examples for more)
annotation_set_id = input("Enter the ID of the annotation_set that you want to classify: ")
r = bot.sqapi.get("/api/annotation_set").filter(name="id", op="eq", val=annotation_set_id)
# Run the classifier with the annotation_set query
bot.start(r)
```
The `create_parser` method is a convenience tool that allows you to build a command line parser from the
signature of the `__init__` methods for the RandoBot class and all inherited base classes (ie: `Annotator`).
This allows you to pass arguments from the command line, and helps to show how to use it:
```shell
# from the command line, run:
python run_bot.py --help
```
It will show you all the required parameters from the base class `Annotator` as well as any extra arguments added to the
`__init__` method of the `RandoBot` class. Parameter descriptions come from the comment block.
It is necessary to build an `annotation_set` query that will tell the `RandoBot` instance which datasets to classify.
The example above prompts for an `annotation_set_id`, however, it is possible to define much more complex queries to run
the automated classifier.
```python
from sqapi.api import query_filter as qf
r = bot.sqapi.get("/api/annotation_set") # create the query, as above
# Only return annotation_sets that do not already have suggestions from this user
# This is necessary to avoid repeating suggestions each time the process runs
r.filter_not(qf("children", "any", val=qf("user_id", "eq", bot.sqapi.current_user.get("id"))))
# Filter annotation sets based on ID, as above
r.filter("id", "eq", ANNOTATION_SET_ID)
# Constrain date ranges to annotation_sets ceated after a specific date
r.filter("created_at", "gt", AFTER_DATE)
# Filter annotation_sets based on a user group
r.filter("usergroups", "any", val=qf("id", "eq", USER_GROUP_ID))
# Filter annotation_sets based on the users' affiliation
r.filter("user", "has", val=qf("affiliations_usergroups", "any", val=qf("group_id", "eq", AFFILIATION_GROUP_ID)))
# Filter annotation_sets based on the number of images in the media_collection
r.filter("media_count", "lte", MEDIA_COUNT_MAX)
```
In order to add these additional parameters, you can add arguments to the command-line parser.
For examples on how to do that, checkout the [sqbot](https://bitbucket.org/ariell/sqbot/) repository.
#### 2. Create a Label Mapper File
Before you run it, you also need to create a label map file to pass into the `--label_map_file` argument.
This maps the outputs from your classifier to real class labels in the system.
In your project directory, create a file named `rando_bot_label_map.json`
with the following content:
```json
[
[{"name":"vocab_elements","op":"any","val":{"name":"key","op":"eq","val":"214344"}}],
[{"name":"vocab_elements","op":"any","val":{"name":"key","op":"eq","val":"1839"}}],
[{"name":"vocab_elements","op":"any","val":{"name":"key","op":"eq","val":"82001000"}}],
null
]
```
The example above, shows a label_map file for a classifier that outputs an integer value classifier code, where `0-2`
would be a Label class that uses the vocab_element to lookup a Label across the Label Schemes. `3` would output nothing,
which won't be submitted.
Note: if your classifier outputs a string code instead of an integer, you can define a similar
mapping file using a dict lookup, like:
```json
{
"ECK": [{"name":"vocab_elements","op":"any","val":{"name":"key","op":"eq","val":"214344"}}],
"ASC": [{"name":"vocab_elements","op":"any","val":{"name":"key","op":"eq","val":"1839"}}],
"SUB": [{"name":"vocab_elements","op":"any","val":{"name":"key","op":"eq","val":"82001000"}}]
}
```
That will attempt to map the classifier outputs `ECK`, `ASC` and `SUB` to a Label in SQ+.
#### 3. Run your bot
Now you're ready to run your classifier `RandoBot`, by simply executing this from the command line:
```shell
# bash
# Install sqapi module using pip
pip install sqapi
# See help to show all arguments
python run_bot.py --help
# Run automated labeler algorithm with selected arguments
python run_bot.py --host https://staging.squidle.org --label_map_file rando_bot_label_map.json --prob_thresh 0.5 --email_results
```
This will prompt you for a annotation_set id and attempt to provide automated suggestions on the labels it contains
using random class allocations and probabilities. It will only submit suggestions with a probability > 0.5 and
it will run once (as defined by the `--poll_delay=-1` parameter)
and it will send an email to the owner the annotation_set once complete.
Now all that's left is for you to make the labels and probabilities real, and bob's your uncle,
you've made an automated classifier.
The [sqbot](https://bitbucket.org/ariell/sqbot/) library has some examples that you can use and/or adapt for
your purposes. Specifically with regard to classifiers:
Raw data
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"keywords": "SQUIDLE+, API, SQ, Machine Learning",
"author": "Greybits Engineering",
"author_email": null,
"download_url": "https://files.pythonhosted.org/packages/28/2c/14a1fe81aeef85734466d66c0da41eb33891fbf0d9d1e48b8cc57ca3494e/sqapi-0.58.tar.gz",
"platform": null,
"description": "# SQAPI\n\n`sqapi` is a python package that simplifies interactions with the \n[SQUIDLE+ API](https://squidle.org/api/help?template=api_help_page.html).\nIt can be used for everything from creating simple queries through to integrating automated \nlabelling from machine learning algorithms and plenty other cool things.\n\n### Installation\nTo install the `sqapi` module, you can use `pip`\n```shell\npip install sqapi \n```\n\n### What is this?\nThe `sqapi` module helps to build the `HTTP` requests that are sent to the [SQUIDLE+](squidle.org) `API`. These are \n`GET`, `POST`, `PATCH` or `DELETE` requests. Setting `verbosity=2` on the `sqapi` module will print the `HTTP` \nrequests that are being made.\n\n`sqapi` takes care of authentication, and simplifies the creation of API queries. \nFor example:\n\n```python\nfrom sqapi.api import SQAPI\n\napi = SQAPI(host=<HOST>, api_key=<API_KEY>, verbosity=2) # instantiate the sqapi module\nr=api.get(<ENDPOINT>) # define a get request using a specific endpoint\nr.filter(<NAME>,<OPERATORE>,<VALUE>) # define a filter to compare a property with a value using an operator\ndata = r.execute().json() # perform the request & return result as JSON dict (don't set template)\n```\n\nFor more information about structuring queries, check out the [Making API queries](https://squidle.org/api/help?template=api_help_page.html#api_query)\nsection of the SQ+ API documentation page.\n\nInstantiating `sqapi` without an API key argument will prompt for a user login, i.e.:\n```python\nsqapi = SQAPI(host=<HOST>, verbosity=2) # instantiate the sqapi module\n```\n\nYou can also use it to apply built-in templates to the data that comes out of the API:\n```python\nr.template(<TEMPLATE>) # format the output of the request using an inbuilt HTML template\nhtml = r.execute().text # perform the request & return result as text (eg: for html)\n```\n\n> **IMPORTANT:** in order to proceed, you will need a user account on [SQUIDLE+](https://squidle.org). You will also \n> need to activate your API key.\n\n## Examples\n### Creating queries\nThis is by no means an extensive list of possible API queries. The API is extensive and the models are documented\n[here](https://squidle.org/api/help?template=api_help_page.html) and the creation of queries is documented \n[here](https://squidle.org/api/help?template=api_help_page.html#api_query). `SQAPI` enables a convenient mechanism \nfor creating these queries inside of Python. For example, a basic API query to list all the annotations that have valid \nlabels starting with 'ecklonia' within a spatially constrained bounding box would be:\n```json\n{\n \"filters\": [\n {\n \"name\": \"label\",\n \"op\": \"has\",\n \"val\": {\n \"name\": \"name\",\n \"op\": \"ilike\",\n \"val\": \"ecklonia%\"\n }\n },\n {\n \"name\": \"point\",\n \"op\": \"has\",\n \"val\": {\n \"name\": \"media\",\n \"op\": \"has\",\n \"val\": {\n \"name\": \"poses\",\n \"op\": \"any\",\n \"val\": {\n \"name\": \"geom\",\n \"op\": \"geo_in_bbox\",\n \"val\": [\n {\n \"lat\": -32.020013585799155,\n \"lon\": 115.49980113118502\n },\n {\n \"lat\": -32.01995006531625,\n \"lon\": 115.49987604949759\n }\n ]\n }\n }\n }\n }\n ]\n}\n```\nThe result of that query can be accessed dynamically through \n[here as pretty JSON](https://squidle.org/api/annotation?template=json.html&q={\"filters\":[{\"name\":\"point\",\"op\":\"has\",\"val\":{\"name\":\"has_xy\",\"op\":\"eq\",\"val\":true}},{\"name\":\"point\",\"op\":\"has\",\"val\":{\"name\":\"media\",\"op\":\"has\",\"val\":{\"name\":\"poses\",\"op\":\"any\",\"val\":{\"name\":\"geom\",\"op\":\"geo_in_bbox\",\"val\":[{\"lat\":-32.020013585799155,\"lon\":115.49980113118502},{\"lat\":-32.01995006531625,\"lon\":115.49987604949759}]}}}}]}) or\n[here as raw JSON](https://squidle.org/api/annotation?q={\"filters\":[{\"name\":\"point\",\"op\":\"has\",\"val\":{\"name\":\"has_xy\",\"op\":\"eq\",\"val\":true}},{\"name\":\"point\",\"op\":\"has\",\"val\":{\"name\":\"media\",\"op\":\"has\",\"val\":{\"name\":\"poses\",\"op\":\"any\",\"val\":{\"name\":\"geom\",\"op\":\"geo_in_bbox\",\"val\":[{\"lat\":-32.020013585799155,\"lon\":115.49980113118502},{\"lat\":-32.01995006531625,\"lon\":115.49987604949759}]}}}}]}) or \n[here with a template](https://squidle.org/iframe/api/annotation?template=models/annotation/list_thumbnails.html&q={\"filters\":[{\"name\":\"point\",\"op\":\"has\",\"val\":{\"name\":\"has_xy\",\"op\":\"eq\",\"val\":true}},{\"name\":\"point\",\"op\":\"has\",\"val\":{\"name\":\"media\",\"op\":\"has\",\"val\":{\"name\":\"poses\",\"op\":\"any\",\"val\":{\"name\":\"geom\",\"op\":\"geo_in_bbox\",\"val\":[{\"lat\":-32.020013585799155,\"lon\":115.49980113118502},{\"lat\":-32.01995006531625,\"lon\":115.49987604949759}]}}}}]}&include_link=true).\nNote with a logged in browser session, that link will extract cropped thumbnails around each annotation, but without logging in,\nyou'll just see a thumbnail the whole image associated with that annotation.\nThe Python code required to build that query could be something like:\n```python\nfrom sqapi.api import SQAPI, query_filter as qf\napi = SQAPI(host=\"https://squidle.org\", ) # optionally pass in api_key to avoid log in prompt\nr = api.get(\"/api/annotation\")\nr.filter(\"label\", \"has\", qf(\"name\",\"ilike\",\"ecklonia%\")) # filter for label name, % here is a wildcard matching anything\nbbox = [{\"lat\": -32.020013585799155,\"lon\": 115.49980113118502},{\"lat\": -32.01995006531625,\"lon\": 115.49987604949759}]\nr.filter(\"point\", \"has\", qf(\"media\", \"has\", qf(\"poses\", \"any\", qf(\"geom\", \"geo_in_bbox\", bbox)))) # filter within bounding box\n```\n\n### Random annotation BOT\nThis is an example of an automated labelling bot that selects random class labels to assign to points.\nIt provides terrible suggestions, however it provides a simple boiler-plate example of how to integrate a\nMachine Learning algorithm for label suggestions.\n\nSet up the environment and a mini project. Creating a virtual environment, is optional, but recommended.\n```shell\nmkdir sqbot_demo && cd sqbot_demo # make a directory\nvirtualenv -p python3 env # create a virtual environment\nsource env/bin/activate # activate it\npip install sqapi # install sqapi\n```\n\n#### 1. Create a bot class\nIn this example, we'll create an automated classifier that suggests random label suggestions for an annotation_set. \nIt uses the `Annotator` class from the `sqapi.annotate` module, which does most of the heavy-lifting. The implementation\nof the classifier is relatively straight forward.\nIn your project directory, create a file named `run_bot.py`:\n\n```python\n# run_bot.py\nimport random\nfrom sqapi.annotate import Annotator\nfrom sqapi.helpers import cli_init\nfrom sqapi.media import SQMediaObject\n\n\nclass RandoBOT(Annotator):\n \"\"\"\n An example of an automated labelling bot that selects random class labels to assign to points.\n It provides terrible suggestions, however it provides a simple boiler-plate example of how to integrate a\n Machine Learning algorithm for label suggestions.\n \"\"\" \n def classify_point(self, mediaobj: SQMediaObject, x, y, t):\n # This method is used to generate a point prediction\n # TODO: use the mediaobj, x and y point position to generate a real classifier_code and prob\n classifier_code = random.randint(0,3) # get a random code (0-3) to match with label_map_file\n prob = round(random.random(), 2) # generate a random probability\n return classifier_code, prob\n\n\nif __name__ == '__main__':\n # Instantiate the bot from the default CLI arguments\n bot = cli_init(RandoBOT) \n \n # Build the annotation_set query (this can be far more complex, see examples for more)\n annotation_set_id = input(\"Enter the ID of the annotation_set that you want to classify: \")\n r = bot.sqapi.get(\"/api/annotation_set\").filter(name=\"id\", op=\"eq\", val=annotation_set_id) \n \n # Run the classifier with the annotation_set query\n bot.start(r)\n```\n\nThe `create_parser` method is a convenience tool that allows you to build a command line parser from the \nsignature of the `__init__` methods for the RandoBot class and all inherited base classes (ie: `Annotator`).\n\nThis allows you to pass arguments from the command line, and helps to show how to use it:\n```shell\n# from the command line, run:\npython run_bot.py --help\n```\n\nIt will show you all the required parameters from the base class `Annotator` as well as any extra arguments added to the \n`__init__` method of the `RandoBot` class. Parameter descriptions come from the comment block.\n\nIt is necessary to build an `annotation_set` query that will tell the `RandoBot` instance which datasets to classify.\nThe example above prompts for an `annotation_set_id`, however, it is possible to define much more complex queries to run\nthe automated classifier. \n\n```python\nfrom sqapi.api import query_filter as qf\nr = bot.sqapi.get(\"/api/annotation_set\") # create the query, as above\n\n# Only return annotation_sets that do not already have suggestions from this user\n# This is necessary to avoid repeating suggestions each time the process runs\nr.filter_not(qf(\"children\", \"any\", val=qf(\"user_id\", \"eq\", bot.sqapi.current_user.get(\"id\"))))\n\n# Filter annotation sets based on ID, as above\nr.filter(\"id\", \"eq\", ANNOTATION_SET_ID)\n\n# Constrain date ranges to annotation_sets ceated after a specific date\nr.filter(\"created_at\", \"gt\", AFTER_DATE)\n\n# Filter annotation_sets based on a user group\nr.filter(\"usergroups\", \"any\", val=qf(\"id\", \"eq\", USER_GROUP_ID))\n\n# Filter annotation_sets based on the users' affiliation\nr.filter(\"user\", \"has\", val=qf(\"affiliations_usergroups\", \"any\", val=qf(\"group_id\", \"eq\", AFFILIATION_GROUP_ID)))\n\n# Filter annotation_sets based on the number of images in the media_collection\nr.filter(\"media_count\", \"lte\", MEDIA_COUNT_MAX)\n```\n\nIn order to add these additional parameters, you can add arguments to the command-line parser.\nFor examples on how to do that, checkout the [sqbot](https://bitbucket.org/ariell/sqbot/) repository.\n\n#### 2. Create a Label Mapper File\nBefore you run it, you also need to create a label map file to pass into the `--label_map_file` argument. \nThis maps the outputs from your classifier to real class labels in the system.\n\nIn your project directory, create a file named `rando_bot_label_map.json` \nwith the following content:\n```json\n[\n [{\"name\":\"vocab_elements\",\"op\":\"any\",\"val\":{\"name\":\"key\",\"op\":\"eq\",\"val\":\"214344\"}}],\n [{\"name\":\"vocab_elements\",\"op\":\"any\",\"val\":{\"name\":\"key\",\"op\":\"eq\",\"val\":\"1839\"}}],\n [{\"name\":\"vocab_elements\",\"op\":\"any\",\"val\":{\"name\":\"key\",\"op\":\"eq\",\"val\":\"82001000\"}}],\n null \n]\n```\nThe example above, shows a label_map file for a classifier that outputs an integer value classifier code, where `0-2` \nwould be a Label class that uses the vocab_element to lookup a Label across the Label Schemes. `3` would output nothing, \nwhich won't be submitted.\n\nNote: if your classifier outputs a string code instead of an integer, you can define a similar \nmapping file using a dict lookup, like:\n```json\n{\n \"ECK\": [{\"name\":\"vocab_elements\",\"op\":\"any\",\"val\":{\"name\":\"key\",\"op\":\"eq\",\"val\":\"214344\"}}],\n \"ASC\": [{\"name\":\"vocab_elements\",\"op\":\"any\",\"val\":{\"name\":\"key\",\"op\":\"eq\",\"val\":\"1839\"}}],\n \"SUB\": [{\"name\":\"vocab_elements\",\"op\":\"any\",\"val\":{\"name\":\"key\",\"op\":\"eq\",\"val\":\"82001000\"}}]\n}\n```\nThat will attempt to map the classifier outputs `ECK`, `ASC` and `SUB` to a Label in SQ+.\n\n#### 3. Run your bot\nNow you're ready to run your classifier `RandoBot`, by simply executing this from the command line:\n```shell\n# bash\n# Install sqapi module using pip\npip install sqapi\n# See help to show all arguments\npython run_bot.py --help\n# Run automated labeler algorithm with selected arguments\npython run_bot.py --host https://staging.squidle.org --label_map_file rando_bot_label_map.json --prob_thresh 0.5 --email_results\n```\n\nThis will prompt you for a annotation_set id and attempt to provide automated suggestions on the labels it contains \nusing random class allocations and probabilities. It will only submit suggestions with a probability > 0.5 and \nit will run once (as defined by the `--poll_delay=-1` parameter) \nand it will send an email to the owner the annotation_set once complete.\n\nNow all that's left is for you to make the labels and probabilities real, and bob's your uncle,\nyou've made an automated classifier.\n\nThe [sqbot](https://bitbucket.org/ariell/sqbot/) library has some examples that you can use and/or adapt for \nyour purposes. Specifically with regard to classifiers:\n",
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