39 lines
1.4 KiB
Python
39 lines
1.4 KiB
Python
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import pandas as pd
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import numpy as np
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from sklearn.metrics import mean_squared_error
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from tensorflow import keras
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model = keras.models.load_model('model')
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data = pd.read_csv("data_dev", sep=',', error_bad_lines=False,
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skip_blank_lines=True, nrows=527, names=["video_id", "last_trending_date",
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"publish_date", "publish_hour", "category_id",
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"channel_title", "views", "likes", "dislikes",
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"comment_count"]).dropna()
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X_test = data.loc[:,data.columns == "views"].astype(int)
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y_test = data.loc[:,data.columns == "likes"].astype(int)
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min_val_sub = np.min(X_test)
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max_val_sub = np.max(X_test)
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X_test = (X_test - min_val_sub) / (max_val_sub - min_val_sub)
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print(min_val_sub)
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print(max_val_sub)
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min_val_like = np.min(y_test)
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max_val_like = np.max(y_test)
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print(min_val_like)
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print(max_val_like)
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prediction = model.predict(X_test)
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prediction_denormalized = []
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for pred in prediction:
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denorm = pred[0] * (max_val_like[0] - min_val_like[0]) + min_val_like[0]
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prediction_denormalized.append(denorm)
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f = open("predictions.txt", "w")
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for (pred, test) in zip(prediction_denormalized, y_test.values):
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f.write("predicted: %s expected: %s\n" % (str(pred), str(test[0])))
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error = mean_squared_error(y_test, prediction_denormalized)
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print(error)
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