108 lines
3.6 KiB
Python
108 lines
3.6 KiB
Python
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from datetime import datetime
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import pandas as pd
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import numpy as np
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from sacred.observers import FileStorageObserver, MongoObserver
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from sacred import Experiment
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from sklearn.metrics import mean_squared_error
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from tensorflow import keras
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ex = Experiment("sacred_scopes", interactive=True)
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# ex.observers.append(MongoObserver(url='mongodb://mongo_user:mongo_password_IUM_2021@172.17.0.1:27017',
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# db_name='sacred'))
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ex.observers.append(FileStorageObserver('my_runs'))
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@ex.config
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def my_config():
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epochs_amount = 30
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def normalize_data(data):
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return (data - np.min(data)) / (np.max(data) - np.min(data))
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@ex.capture
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def prepare_model(epochs_amount, _run):
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_run.info["prepare_message_ts"] = str(datetime.now())
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data = pd.read_csv("data_train", sep=',', skip_blank_lines=True, nrows=1087, error_bad_lines=False,
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names=["vipip install sacreddeo_id", "last_trending_date", "publish_date", "publish_hour",
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"category_id",
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"channel_title", "views", "likes", "dislikes", "comment_count"]).dropna()
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X = data.loc[:, data.columns == "views"].astype(int)
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y = data.loc[:, data.columns == "likes"].astype(int)
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min_val_sub = np.min(X)
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max_val_sub = np.max(X)
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X = (X - 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)
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max_val_like = np.max(y)
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y = (y - min_val_like) / (max_val_like - min_val_like)
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print(min_val_like)
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print(max_val_like)
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model = keras.Sequential([
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keras.layers.Dense(512, input_dim=X.shape[1], activation='relu'),
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keras.layers.Dense(256, activation='relu'),
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keras.layers.Dense(256, activation='relu'),
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keras.layers.Dense(128, activation='relu'),
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keras.layers.Dense(1, activation='linear'),
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])
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model.compile(loss='mean_absolute_error', optimizer="Adam", metrics=['mean_absolute_error'])
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model.fit(X, y, epochs=int(epochs_amount), validation_split=0.3)
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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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model.save('model')
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_run.log_scalar("training.metrics", error)
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return error
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@ex.main
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def my_main(epochs_amount):
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print(prepare_model())
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ex.run()
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ex.add_artifact("model.pb")
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