# PyHasura
A library for conveniently working with Hasura, GraphQL, File Formats, and some basic Machine Learning.
## Getting Started
### HasuraClient
```python
# Create Hasura Client
import os
from dotenv import load_dotenv
from pyhasura import gql_client, HasuraClient, ExportFormat
from pprint import pprint
load_dotenv() # Load environment variables from .env
hasura_client = HasuraClient(uri=os.environ.get("HASURA_URI"), admin_secret=os.environ.get("HASURA_ADMIN_SECRET"))
```
### Query for a Result
```python
result = hasura_client.execute("""
query findCarts {
carts {
is_complete
cart_items {
quantity
product {
price
}
}
}
cart_items {
id
}
}
""")
pprint(result)
```
### Convert Results to a Dictionary of Alternate Formats
```python
result = hasura_client.convert_output_format(ExportFormat.ARROW)
pprint(result)
result = hasura_client.convert_output_format(ExportFormat.CSV)
pprint(result)
result = hasura_client.convert_output_format(ExportFormat.PARQUET)
pprint(result)
result = hasura_client.convert_output_format(ExportFormat.DATAFRAME)
pprint(result)
result = hasura_client.convert_output_format(ExportFormat.FLAT)
pprint(result)
```
### Write Results, one file for each root entry in the query
```python
result = hasura_client.write_to_file(output_format=ExportFormat.ARROW)
pprint(result)
result = hasura_client.write_to_file(output_format=ExportFormat.CSV)
pprint(result)
result = hasura_client.write_to_file(output_format=ExportFormat.PARQUET)
pprint(result)
result = hasura_client.write_to_file(output_format=ExportFormat.FLAT)
pprint(result)
result = hasura_client.write_to_file(output_format=ExportFormat.NATURAL)
pprint(result)
```
### Detect Anomalies
Uses DictVectorizer. Assumes text is categorical, or enumerators.
To Do - allow an alternate vectorizer - e.g. Word2Vec. To include more semantic meaning in anomaly detection.
```python
result = hasura_client.anomalies()
pprint(result)
result = hasura_client.anomalies(threshold=.03)
pprint(result)
```
### Train and Serialize then Re-Use for Anomaly Detection
Typically, do this to train on some historical dataset and then
search for anomalies in an alternate (maybe current) dataset.
```python
result = hasura_client.anomalies_training()
pprint(result)
result = hasura_client.anomalies(training_files=result, threshold=0)
pprint(result)
```
### Clustering
Uses KMedoids clustering. You are always working on a dictionary of datasets.
You need to define the number of clusters for each dataset in a corresponding input dictionary.
You can auto-generate the optimal number of clusters and use that as the input.
```python
result = hasura_client.optimal_number_of_clusters(1,8)
pprint(result)
result = hasura_client.clusters(result)
pprint(result)
```
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