# import tf for classification
import tensorflow as tf
from load_data import LoadData
# make a data loader obj
loadData = LoadData("./kr-vs-k.csv")
# retrive the data dict
data = loadData.load_processed_data()
# a input function
def input_fn_generator(data):
def input_fn():
# load the features
features = data["features"].values
labels = data["labels"].values
# convert the data into tensors
features = tf.convert_to_tensor(value=features, dtype=tf.float32)
labels = tf.convert_to_tensor(value=labels, dtype=tf.int32)
# make a tensor dataset
dataset = tf.data.Dataset.from_tensor_slices((features, labels))
# make a batch of data
dataset = dataset.shuffle(100).batch(64)
# make a oneshot iterator
iterator = tf.compat.v1.data.make_one_shot_iterator(dataset)
print("Saving model {}".format(learner.__class__.__name__))
save_model(learner, learner.__class__.__name__)
# predict model
print("Predicting Model")
prediction_test = learner.predict(X_test)
results["accu"] = f1_score(y_test, prediction_test, average="micro")
return results
# load data
dataLoader = LoadData("./kr-vs-k.csv") # data loader for kr-vs-k
data = dataLoader.load_processed_data() # load preprocessed data
# split data
X_train, X_test, y_train, y_test = train_test_split(data["features"],
data["labels"],
test_size=0.2,
random_state=42)
# initalize classifiers
clf_DT = DecisionTreeClassifier() # decission tree classifier
clf_KNN = KNeighborsClassifier(n_neighbors=4) # KNN classifier
clf_RF = RandomForestClassifier(
n_estimators=1000) # Random Forest with 100 trees
# list of classifiers to iterate
classifier_list = [clf_DT, clf_KNN, clf_RF]
