56 lines
2.1 KiB
Python
56 lines
2.1 KiB
Python
import pandas as pd
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from tensorflow import keras
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import mean_squared_error as rmse
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from sacred import Experiment
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from datetime import datetime
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from sacred.observers import FileStorageObserver
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ex = Experiment("file_observer", interactive=False, save_git_info=False)
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ex.observers.append(FileStorageObserver('sacred/my_runs'))
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@ex.config
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def my_config():
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test_size = 0.2
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epochs = 100
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batch_size = 32
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@ex.capture
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def create_model(test_size, epochs, batch_size, _run):
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_run.info["prepare_model_ts"] = str(datetime.now())
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df = pd.read_csv('country_vaccinations.csv').dropna()
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dataset = df.iloc[:, 3:-3]
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dataset = df.groupby(by=["country"], dropna=True).sum()
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X = dataset.loc[:,dataset.columns != "daily_vaccinations"]
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y = dataset.loc[:,dataset.columns == "daily_vaccinations"]
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = test_size, random_state = 6)
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model = keras.Sequential([
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keras.layers.Dense(512,input_dim = X_train.shape[1],kernel_initializer='normal', activation='relu'),
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keras.layers.Dense(512,kernel_initializer='normal', activation='relu'),
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keras.layers.Dense(256,kernel_initializer='normal', activation='relu'),
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keras.layers.Dense(256,kernel_initializer='normal', activation='relu'),
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keras.layers.Dense(128,kernel_initializer='normal', activation='relu'),
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keras.layers.Dense(1,kernel_initializer='normal', 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_train, y_train, epochs = epochs, validation_split = 0.3, batch_size = batch_size)
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prediction = model.predict(X_test)
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rmse_result = rmse(y_test, prediction, squared = False)
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print(prediction)
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_run.info["Results: "] = rmse_result
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model.save('vaccines_model')
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return rmse_result
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@ex.automain
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def my_main(test_size, epochs, batch_size):
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print(create_model())
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r = ex.run()
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ex.add_artifact("vaccines_model/saved_model.pb") |