def CrossValidate_LR(X, y, config, output_layer_size):
kf = StratifiedShuffleSplit(n_splits=5, test_size=0.2, random_state=42)
kf.get_n_splits(X, y)
config["loss"] = "ce"
config["seed"] = 1234
config.update({
"input_layer_size": X.shape[1],
"output_layer_size": output_layer_size
})
lr = [1, 0.5, 0.1, 0.05, 0.01, 0.005, 0.001]
opt = ["adam", "nag", "momentum", "gd"]
# lr = [0.1]
# opt = ["nag"]
f = open('LR_ModelSelection.txt', 'w')
f.write('lr\tOptimizer\tCV\tAccuracy\tPrecision\tRecall\tF1\n')
for l in lr:
for o in opt:
config["lr"] = l
config["opt"] = o
i = 0
for train_index, val_index in kf.split(X, y):
print('lr {}, CV={}...............\n'.format(l, i))
X_train, X_val = X[train_index], X[val_index]
y_train, y_val = y[train_index], y[val_index]
# Configuring the neural network with the hyperparameters for Logistic Regression
LR = LogisticRegression(config)
i += 1
# Train, validate and test
Accuracy, Precision, Recall, F1 = LR.Train_LR(
X_train, y_train, X_val, y_val)
f.write('{}\t{}\t{}\t{}\t{}\t{}\t{}\n'.format(
l, o, i, Accuracy, Precision, Recall, F1))
f.close()
def Train_LR(X, y, config, output_layer_size):
# Setting config
config["loss"] = "ce"
config["seed"] = 1234
config["lr"] = 0.5
config["opt"] = "adam"
config.update({
"input_layer_size": X.shape[1],
"output_layer_size": output_layer_size
})
np.random.seed(1234)
# Splitting dta into train and val
X_train, X_val, y_train, y_val = train_test_split(X,
y,
test_size=0.2,
stratify=y,
random_state=42)
# Configuring the neural network with the hyperparameters for Logistic Regression
LR = LogisticRegression(config)
# Train, validate and test
Accuracy, Precision, Recall, F1 = LR.Train_LR(X_train, y_train, X_val,
y_val)
print("\n\n\n\n", "=" * 60, sep="")
print("\n\t\tPerformance on Validation data\n")
print("=" * 60)
print(
'\n\t\tAccuracy={}\n\n\t\tPrecision={}\n\n\t\tRecall={}\n\n\t\tF1={}\n\n'
.format(Accuracy, Precision, Recall, F1))
print("-" * 60)
print("\n\n\n")
