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evaluation branch
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Karolina Oparczyk 2021-05-17 22:04:40 +02:00
parent e6ed31bed8
commit 630aba3217
3 changed files with 38 additions and 33 deletions
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0
JenkinsFIleEval Normal file
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38
evaluate_network.py Normal file
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@ -0,0 +1,38 @@
import pandas as pd
import numpy as np
from sklearn.metrics import mean_squared_error
from tensorflow import keras
model = keras.models.load_model('model')
data = pd.read_csv("data_dev", sep=',', error_bad_lines=False,
skip_blank_lines=True, nrows=527, names=["video_id", "last_trending_date",
"publish_date", "publish_hour", "category_id",
"channel_title", "views", "likes", "dislikes",
"comment_count"]).dropna()
X_test = data.loc[:,data.columns == "views"].astype(int)
y_test = data.loc[:,data.columns == "likes"].astype(int)
min_val_sub = np.min(X_test)
max_val_sub = np.max(X_test)
X_test = (X_test - min_val_sub) / (max_val_sub - min_val_sub)
print(min_val_sub)
print(max_val_sub)
min_val_like = np.min(y_test)
max_val_like = np.max(y_test)
print(min_val_like)
print(max_val_like)
prediction = model.predict(X_test)
prediction_denormalized = []
for pred in prediction:
denorm = pred[0] * (max_val_like[0] - min_val_like[0]) + min_val_like[0]
prediction_denormalized.append(denorm)
f = open("predictions.txt", "w")
for (pred, test) in zip(prediction_denormalized, y_test.values):
f.write("predicted: %s expected: %s\n" % (str(pred), str(test[0])))
error = mean_squared_error(y_test, prediction_denormalized)
print(error)

33
neural_network.py
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@ -42,37 +42,4 @@ model.compile(loss='mean_absolute_error', optimizer="Adam", metrics=['mean_absol
model.fit(X, y, epochs=int(sys.argv[1]), validation_split = 0.3) model.fit(X, y, epochs=int(sys.argv[1]), validation_split = 0.3)
data = pd.read_csv("data_dev", sep=',', error_bad_lines=False,
skip_blank_lines=True, nrows=527, names=["video_id", "last_trending_date",
"publish_date", "publish_hour", "category_id",
"channel_title", "views", "likes", "dislikes",
"comment_count"]).dropna()
X_test = data.loc[:,data.columns == "views"].astype(int)
y_test = data.loc[:,data.columns == "likes"].astype(int)
min_val_sub = np.min(X_test)
max_val_sub = np.max(X_test)
X_test = (X_test - min_val_sub) / (max_val_sub - min_val_sub)
print(min_val_sub)
print(max_val_sub)
min_val_like = np.min(y_test)
max_val_like = np.max(y_test)
print(min_val_like)
print(max_val_like)
prediction = model.predict(X_test)
prediction_denormalized = []
for pred in prediction:
denorm = pred[0] * (max_val_like[0] - min_val_like[0]) + min_val_like[0]
prediction_denormalized.append(denorm)
f = open("predictions.txt", "w")
for (pred, test) in zip(prediction_denormalized, y_test.values):
f.write("predicted: %s expected: %s\n" % (str(pred), str(test[0])))
error = mean_squared_error(y_test, prediction_denormalized)
print(error)
model.save('model') model.save('model')