File size: 2,334 Bytes
476455e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 | import argparse
import json
import os
import random
import pandas as pd
import glob
import pickle as pkl
import xgboost
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--max_depth", type=int, default=5)
parser.add_argument("--eta", type=float, default=0.05)
parser.add_argument("--gamma", type=int, default=4)
parser.add_argument("--min_child_weight", type=int, default=6)
parser.add_argument("--silent", type=int, default=0)
parser.add_argument("--objective", type=str, default="reg:logistic")
parser.add_argument("--num_round", type=int, default=10)
parser.add_argument("--train", type=str, default=os.environ.get("SM_CHANNEL_TRAIN"))
parser.add_argument("--validation", type=str, default=os.environ.get("SM_CHANNEL_VALIDATION"))
args = parser.parse_args()
return args
def main():
args = parse_args()
train_files_path, validation_files_path = args.train, args.validation
train_features_path = os.path.join(args.train, "train_features.csv")
train_labels_path = os.path.join(args.train, "train_labels.csv")
val_features_path = os.path.join(args.validation, "val_features.csv")
val_labels_path = os.path.join(args.validation, "val_labels.csv")
print("Loading training dataframes...")
df_train_features = pd.read_csv(train_features_path)
df_train_labels = pd.read_csv(train_labels_path)
print("Loading validation dataframes...")
df_val_features = pd.read_csv(val_features_path)
df_val_labels = pd.read_csv(val_labels_path)
X = df_train_features.values
y = df_train_labels.values
val_X = df_val_features.values
val_y = df_val_labels.values
dtrain = xgboost.DMatrix(X, label=y)
dval = xgboost.DMatrix(val_X, label=val_y)
watchlist = [(dtrain, "train"), (dval, "validation")]
params = {
"max_depth": args.max_depth,
"eta": args.eta,
"gamma": args.gamma,
"min_child_weight": args.min_child_weight,
"silent": args.silent,
"objective": args.objective,
}
bst = xgboost.train(
params=params, dtrain=dtrain, evals=watchlist, num_boost_round=args.num_round
)
model_dir = os.environ.get("SM_MODEL_DIR")
pkl.dump(bst, open(model_dir + "/model.bin", "wb"))
if __name__ == "__main__":
main()
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