High level Python functions for interacting with Techtonique APIs
pip install techtonique_apis-
File examples: https://github.com/Techtonique/datasets/tree/main/time_series
-
Get a token: https://www.techtonique.net/token (store in .env, in current directory as
TECHTONIQUE_TOKEN)There are 100 free API calls in the free tier/months.
from techtonique_apis import TechtoniqueAPI
api = TechtoniqueAPI()
# Example 1: Forecasting
forecast_result = api.forecasting(
file_path="/Users/t/Documents/datasets/time_series/univariate/a10.csv",
base_model="RidgeCV",
n_hidden_features=5,
lags=25,
type_pi="kde",
replications=10,
h=5
)
print("Forecasting result:", forecast_result)
# Example 2: Machine Learning Regression
regression_result = api.mlregression(
file_path="/Users/t/Documents/datasets/tabular/regression/mtcars2.csv",
base_model="ElasticNet",
n_hidden_features=5,
return_pi=True
)
print("Regression result:", regression_result)
# Example 3: GBDT Classification
gbdt_classification_result = api.gbdt_classification(
file_path="/Users/t/Documents/datasets/tabular/classification/iris_dataset2.csv",
model_type="lightgbm"
)
print("GBDT Classification result:", gbdt_classification_result)
# Example 4: Reserving
reserving_result = api.reserving(
file_path="/Users/t/Documents/datasets/tabular/triangle/raa.csv",
method="chainladder"
)
print("Reserving result:", reserving_result)
# Example 5: Survival Analysis
survival_result = api.survival_curve(
file_path="/Users/t/Documents/datasets/tabular/survival/kidney.csv",
method="km",
patient_id=123
)
print("Survival curve result:", survival_result)
# Example 6: Scenarios
scenarios_result = api.simulate_scenario(
model="GBM",
n=10,
frequency="quarterly",
x0=100,
horizon=5,
theta1=0,
theta2=0.5,
theta3=0.5,
)
print("Scenarios result:", scenarios_result)MIT