RPPP/src/evaluate.py

import os
from pathlib import Path

import joblib
import numpy as np
import pandas as pd

import src.reddit_utils as r_utils
from src.utilities import dump_yaml, read_yaml

pre_process_params = read_yaml("params.yaml", "pre_process")
model_params = read_yaml("params.yaml", "train")

CHUNK_SIZE = pre_process_params["chunk_size"]
TARGET_LABEL = pre_process_params["target_col"]
COLS_FOR_EVAL = []

if model_params["use_text_cols"]:
    COLS_FOR_EVAL += r_utils.TEXT_COL_NAME

if model_params["use_number_category_cols"]:
    COLS_FOR_EVAL += r_utils.NUM_COL_NAMES + r_utils.CAT_COL_NAMES


def load_transform_and_eval():
    print("loading transformer and model...")
    model = joblib.load(r_utils.MODEL_PATH)

    y_proba = np.array([])
    y_pred = np.array([])
    y = np.array([])
    print("Loading test data and testing model...")
    for i, chunk in enumerate(
        pd.read_csv(
            os.path.join("data/processed", r_utils.TEST_DF_PATH),
            chunksize=CHUNK_SIZE,
        )
    ):
        print(f"Testing on chunk {i+1}...")
        df_X = chunk[COLS_FOR_EVAL]
        y_proba = np.concatenate((y_pred, model.predict_proba(df_X)[:, 1]))
        y_pred = np.concatenate((y_pred, model.predict(df_X)))
        y = np.concatenate((y, chunk[TARGET_LABEL]))

    print("Calculating metrics")
    metrics = r_utils.calculate_metrics(y_pred, y_proba, y)

    print("Logging metrics...")
    metrics_path = Path("models/metrics/test.yaml")
    dump_yaml(metrics, metrics_path)


if __name__ == "__main__":
    load_transform_and_eval()
    print("Model evaluation done!")