Quick start

The steps to fetch data points of one or more tags, train a model, or save it on the Quartic AI Platform are as follows:

Step 1

Initialize the APIClient with the authentication details. Currently, Quartic SDK supports two kinds of authentication: Basic Authentication and OAuth2.0. In Basic Authentication, the user must pass the parameters of username and password; and in OAuth2.0, the client token.

For our example, if the authentication used is Basic Authentication, the Quartic host is https://test.quartic.ai, and the username and password is username and password, then the APIClient will look like this:

from quartic_sdk import APIClient

client = APIClient("https://test.quartic.ai", username="username", password="password")

Step 2

Fetch primitive objects. These objects do not require any extra parameters and can be fetched directly from the client object. The list returned will contain the class object EntityList, which consists of the methods required for getting instances and depends on the given parameters.

assets = client.assets()
context_frames = client.context_frame_definitions()

Step 3

Fetch a tag of an asset, which will be further used to fetch data points. Pass the start_time and the stop_time of the query in epoch. For example, for a duration of 1 day, from 1 Jan 2021 to 2 Jan 2021, the corresponding time in epoch would be 1609439400000 and 1609525800000.

asset = assets.first()
asset_tags = asset.get_tags()
feature_tags = [tag.id for tag in asset_tags[:5]]
target_tag = asset_tags.last().id
asset_data = asset.data(start_time=1609439400000, stop_time=1609525800000)

Step 4

Save the tag data in the data frame.

import pandas as pd
combined_data_frame = pd.DataFrame(columns=feature_tags)
for data in asset_data:
    combined_data_frame = combined_data_frame.append(data)

Step 5

Once the client is initialized and data fetched, models can now be trained, tested and deployed to Quartic AI Platform using below:

from quartic_sdk.model import BaseQuarticModel
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split

class ExampleModel(BaseQuarticModel):
    def __init__(self, model):
        self.model = model
        super().__init__("My Sample Model", description='This is a simple model to give a quick start for user')

    def predict(self, input_df):
        return self.model.predict(input_df)

model = linear_model.LinearRegression()
X_train, X_test, y_train, y_test = train_test_split(combined_data_frame[feature_tags],
                df[[feature_tags[-1]]].values.ravel(), random_state=42)

model.fit(X_train, y_train)
example_model = ExampleModel(model)

example_model.save(client, output_tag_name='Prediction Tag Name',
                   feature_tags=feature_tags, target_tag=target_tag,