K-fold Train Version3

# config.py
TRAINING_FILE = "../input/mnist_train_folds.csv"
MODEL_OUTPUT = "../models/"
# model_dispatcher.py
from sklearn import tree
from sklearn import ensemble
models = {
    "decision_tree_gini": tree.DecisionTreeClassifier(
    criterion="gini"
    ),
    "decision_tree_entropy": tree.DecisionTreeClassifier(
    criterion="entropy"
    ),
    "rf": ensemble.RandomForestClassifier(),
}
# train.py
import argparse
import os
import joblib
import pandas as pd
from sklearn import metrics
import config
import model_dispatcher

def run(fold, model):
    # read the training data with folds
    df = pd.read_csv(config.TRAINING_FILE)
    # training data is where kfold is not equal to provided fold
    # also, note that we reset the index
    df_train = df[df.kfold != fold].reset_index(drop=True)
    # validation data is where kfold is equal to provided fold
    df_valid = df[df.kfold == fold].reset_index(drop=True)
    # drop the label column from dataframe and convert it to
    # a numpy array by using .values.
    # target is label column in the dataframe
    x_train = df_train.drop("label", axis=1).values
    y_train = df_train.label.values
    # similarly, for validation, we have
    x_valid = df_valid.drop("label", axis=1).values
    y_valid = df_valid.label.values
    # fetch the model from model_dispatcher
    clf = model_dispatcher.models[model]
    # fir the model on training data
    clf.fit(x_train, y_train)
    # create predictions for validation samples
    preds = clf.predict(x_valid)
    # calculate & print accuracy
    accuracy = metrics.accuracy_score(y_valid, preds)
    print(f"Fold={fold}, Accuracy={accuracy}")
    # save the model
    joblib.dump(
        clf,
        os.path.join(config.MODEL_OUTPUT, f"dt_{fold}.bin")
    )


if __name__ == "__main__":
    # initialize ArgumentParser class of argparse
    parser = argparse.ArgumentParser()
    # add the different arguments needed and their type
    # currently, only need fold
    parser.add_argument(
        "--fold",
        type=int
    )
    parser.add_argument(
        "--model",
        type=str
    )
    # read the arguments from the command line
    args = parser.parse_args()
    # run the fold specified by command line arguments
    run(fold=args.fold,
        model=args.model
        )
================================================
#!/bin/sh
# run.sh
python train.py --fold 0 --model rf
python train.py --fold 1 --model rf
python train.py --fold 2 --model rf
python train.py --fold 3 --model rf
python train.py --fold 4 --model rf
================================================
sh run.sh