Model Configuration File

From the configuraton file, below is an example of the contents in german-credit-risk-sgd.py.

"models": {
    "model_configs": [
        {
            "model_name": "German Credit Risk-SGD",
            "model_script": "german-credit-risk-sgd.py",
            "update": True,
            "overwrite": True
        },
    ]
},

Contents of german-credit-risk-sgd.py.

import pandas as pd
from sklearn.compose import ColumnTransformer
from sklearn.linear_model import SGDClassifier
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from cpdflow.model.model import get_input_data_schema

# Train model
df = pd.read_csv("german_credit_data_biased_training.csv")
target = "Risk"
protected_attributes = ["Age"]
y = df[target]
X = df.drop([target] + protected_attributes, axis=1)
ct = ColumnTransformer([("ohe", OneHotEncoder(), X.select_dtypes(include=["object"]).columns.tolist())])
scaler = StandardScaler(with_mean=False)

model = Pipeline([("ct", ct), ("scaler", scaler), ("clf", SGDClassifier(loss="modified_huber"))]).fit(X, y)

input_data_schema = get_input_data_schema(X=X)

custom_metrics = {
    "average_precision": 0.0 # add custom metrics if needed
}

Note

The 3 required variables from the model script are,

1. target - Label name.

2. model - A fitted/trained model that is ready to be inferred.

3. input_data_schema - A list of fields that is used for training.