datfid 0.1.27
SDK to access the DATFID API hosted on Hugging Face Spaces
License: MIT
Requires Python: >=3.7

# DATFID SDK

A Python SDK to access the DATFID API to forecast your data.

## Features

- **Easy model fitting**: Build panel data models with time-dependent and static features.
- **Flexible lag handling**: Specify lags for the dependent variable and selected features.
- **Forecasting**: Generate future predictions with aligned timestamps and IDs.
- **Statistical options**: Filter features by significance and apply mean-variance tests.
- **White box full interpretability**: Get fully interpretable model with equation, estimated parameters, and standard errors.

## Installation

```bash
pip install datfid
```

## Usage

Before using the SDK, please request an access token by emailing **admin@datfid.com** or by visiting our website [datfid.com](https://datfid.com).

```python
from datfid import DATFIDClient

# Initialize the client with your DATFID token
client = DATFIDClient(token="your_DATFID_token")

# Fit a model
fit_result = client.fit_model(
    df=dataframe,
    id_col="name of id column",
    time_col="name of time column",
    y="name of dependent variable",
    lag_y="starting lag : ending lag",
    lagged_features={
        "feature 1": "starting lag : ending lag",
        "feature 2": "starting lag : ending lag"
    },
    current_features=["feature 3", "feature 4"],
    filter_by_significance=True/False,
    meanvar_test=True/False
)

# Generate forecasts
forecast_df = client.forecast_model(
    df_forecast=dataframe
)

# The forecast DataFrame contains the individual IDs and timestamps
# from the original data plus a "forecast" column with predicted values.
```

## Example 1

Sample dataset from GitHub (Food and Beverages demand forecasting):

```python
import pandas as pd
from datfid import DATFIDClient

# Initialize the client with your DATFID token
client = DATFIDClient(token="your_DATFID_token")

# Load dataset for model fitting
url_fit = "https://raw.githubusercontent.com/datfid-valeriidashuk/sample-datasets/main/Food_Beverages.xlsx"
df = pd.read_excel(url_fit)

# Fit the model
result = client.fit_model(df=df,
                          id_col="Product",
                          time_col="Time",
                          y="Revenue",
                          current_features='all',
                          filter_by_significance=True
                          )

# Load dataset for forecasting
url_forecast = "https://raw.githubusercontent.com/datfid-valeriidashuk/sample-datasets/main/Food_Beverages_forecast.xlsx"
df_forecast = pd.read_excel(url_forecast)

# Forecast revenue using the fitted model
forecast = client.forecast_model(df_forecast=df_forecast)
```

## Example 2

Slightly larger sample dataset from GitHub (Banking sector, forecasting loan probability):

```python
import pandas as pd
from datfid import DATFIDClient

# Initialize the client with your DATFID token
client = DATFIDClient(token="your_DATFID_token")

# Load dataset for model fitting
url_fit = "https://raw.githubusercontent.com/datfid-valeriidashuk/sample-datasets/main/Banking_extended.xlsx"
df = pd.read_excel(url_fit)

# Fit the model
result = client.fit_model(df=df,
                          id_col="Individual",
                          time_col="Time",
                          y="Loan Probability",
                          lag_y="1:3",
                          lagged_features={"Income Level": "1:3"},
                          filter_by_significance=True)

# Load dataset for forecasting
url_forecast = "https://raw.githubusercontent.com/datfid-valeriidashuk/sample-datasets/main/Banking_extended_forecast.xlsx"
df_forecast = pd.read_excel(url_forecast)

# Forecast loan probability using the fitted model
forecast = client.forecast_model(df_forecast=df_forecast)
```

## API Reference

### DATFIDClient

#### `client = DATFIDClient(token: str)`
Initialize the client with your DATFID token.

#### `client.fit_model(df: pd.DataFrame, id_col: str, time_col: str, y: str, lag_y: Optional[Union[int, str, list[int]]] = None, lagged_features: Optional[Dict[str, int]] = None, current_features: Optional[list] = None, filter_by_significance: bool = False, meanvar_test: bool = False) -> SimpleNamespace`
Fit a model using the provided dataset.

#### `client.forecast_model(df_forecast: pd.DataFrame) -> pd.DataFrame`
Generate forecasts using the fitted model.