import numpy as np
from Utils import pickle
labels = [
"toxic", "severe_toxic", "obscene", "threat", "insult", "identity_hate"
]
#labels = pickle.load("Objects/labels.list")
results = pickle.load("Objects/results.dict")
epochs = range(1, len(results["train_time"]) + 1)
file = open("Out/results.txt", "w")
file.write("Results\n\n")
file.write("Script time: " + str(np.round(results["script_time"], 5)) + " s\n")
file.write("Training time: " + str(np.round(results["train_time"][-1], 5)) +
" s\n\n\n")
file.write("Overall accuracy:\n\n")
file.write("epoch,acc,val_acc\n")
for epoch, acc, val_acc in zip(epochs, results["acc_mean"],
results["val_acc_mean"]):
epoch = str(epoch) + ","
acc = str(np.round(acc, 5)) + ","
val_acc = str(np.round(val_acc, 5)) + "\n"
file.write(epoch + acc + val_acc)
file.write("\n")
file.write("Overall ROC_AUC: " + str(np.round(results["roc_mean"], 5)) + "," +
str(np.round(results["val_roc_mean"], 5)) + "\n\n\n")
for i, label in enumerate(labels):
file.write(label + " accuracy:\n\n")
Y_train = np.load("Objects/Y_train.npy")
Y_test = np.load("Objects/Y_test.npy")
Y_test[Y_test >= 0.5] = 1
Y_test[Y_test < 0.5] = 0
Y_train = (Y_train - np.mean(Y_train, axis=0, keepdims=True)) / np.std(
Y_train, axis=0, keepdims=True)
Y_test = (Y_test - np.mean(Y_test, axis=0, keepdims=True)) / np.std(
Y_test, axis=0, keepdims=True)
R_train = Y_train.T.dot(Y_train) / Y_train.shape[0]
R_test = Y_test.T.dot(Y_test) / Y_test.shape[0]
labels = pickle.load("Objects/labels.list")
file = open("Out/corrs.txt", "w")
file.write(
"Correlations in training set and in test predictions respectively:\n\n")
D = {}
for i in range(R_train.shape[0]):
for j in range(i + 1, R_train.shape[1]):
D.update({labels[i] + ", " + labels[j]: [R_train[i, j], R_test[i, j]]})
file.write(labels[i] + ", " + labels[j] + ": " +
str(np.round(R_train[i, j], 5)) + ", " +
str(np.round(R_test[i, j], 5)) + "\n")
file.close()
import numpy as np
from Utils import pickle
labels = pickle.load("Objects/labels.list")
results = pickle.load("Objects/results.dict")
file = open("Out/results.txt", "w")
file.write("Results\n\n")
file.write("Script time: "+str(np.round(results["script_time"], 5))+" s\n")
file.write("Training time: "+str(np.round(results["train_time"], 5))+" s\n\n")
file.write("Overall accuracy: "+str(np.round(results["acc_mean"], 5))+","+str(np.round(results["val_acc_mean"], 5))+"\n")
file.write("Overall ROC_AUC: "+str(np.round(results["roc_mean"], 5))+","+str(np.round(results["val_roc_mean"], 5))+"\n\n\n")
for i, label in enumerate(labels):
file.write(label+" accuracy: "+str(np.round(results["acc"][i], 5))+","+str(np.round(results["val_acc"][i], 5))+"\n")
file.write(label+" ROC_AUC: "+str(np.round(results["roc"][i], 5))+","+str(np.round(results["val_roc"][i], 5))+"\n\n")
file.close()