ium_434765/neural_network.py

import pandas as pd
import numpy as np
from sklearn.metrics import mean_squared_error

from tensorflow import keras
import sys

def normalize_data(data):
    return (data - np.min(data)) / (np.max(data) - np.min(data))


data = pd.read_csv("data_train", sep=',',  skip_blank_lines=True, nrows=1087, error_bad_lines=False,
                   names=["video_id", "last_trending_date", "publish_date", "publish_hour", "category_id",
                          "channel_title", "views", "likes", "dislikes", "comment_count", "comments_disabled",
                          "ratings_disabled", "tag_appeared_in_title_count", "tag_appeared_in_title", "title",
                          "tags", "description", "trend_day_count", "trend_publish_diff", "trend_tag_highest",
                          "trend_tag_total", "tags_count", "subscriber"]).dropna()

X = []
for datum in data[["views"]].values:
    try:
        X.append(int(datum))
    except:
        print(datum)
X = pd.DataFrame(X)

y = []
for datum in data[["likes"]].values:
    try:
        y.append(int(datum))
    except:
        print(datum)
y = pd.DataFrame(y)

min_val_sub = np.min(X)
max_val_sub = np.max(X)
X = (X - min_val_sub) / (max_val_sub - min_val_sub)
print(min_val_sub)
print(max_val_sub)

min_val_like = np.min(y)
max_val_like = np.max(y)
y = (y - min_val_like) / (max_val_like - min_val_like)

print(min_val_like)
print(max_val_like)


model = keras.Sequential([
    keras.layers.Dense(512,input_dim = X.shape[1], activation='relu'),
    keras.layers.Dense(256, activation='relu'),
    keras.layers.Dense(256, activation='relu'),
    keras.layers.Dense(128, activation='relu'),
    keras.layers.Dense(1,activation='linear'),
])

model.compile(loss='mean_absolute_error', optimizer="Adam", metrics=['mean_absolute_error'])

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')