72 lines
2.0 KiB
Python
72 lines
2.0 KiB
Python
import sys
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import pandas as pd
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import numpy as np
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import tensorflow as tf
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from tensorflow.keras import layers
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from tensorflow.keras.layers.experimental import preprocessing
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from sacred import Experiment
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from sacred.observers import FileStorageObserver
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from sklearn.metrics import mean_squared_error
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from sacred.observers import MongoObserver
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ex = Experiment("434784_sacred_scopes", interactive=False, save_git_info=False)
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ex.observers.append(FileStorageObserver('sacred_runs/runs'))
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ex.observers.append(MongoObserver(
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url='mongodb://mongo_user:mongo_password_IUM_2021@172.17.0.1:27017', db_name='sacred'))
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@ex.config
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def my_config():
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epochs = 10
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batch_size = 10
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@ex.capture
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def prepare(epochs, batch_size, _run):
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sc = pd.read_csv('who_suicide_statistics.csv')
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sc.dropna(inplace=True)
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sc = pd.get_dummies(
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sc, columns=['age', 'sex', 'country'], prefix='', prefix_sep='')
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train, validate, test = np.split(sc.sample(frac=1, random_state=42),
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[int(.6*len(sc)), int(.8*len(sc))])
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X_train = train.loc[:, train.columns != 'suicides_no']
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y_train = train[['suicides_no']]
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X_test = test.loc[:, train.columns != 'suicides_no']
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y_test = test[['suicides_no']]
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normalizer = preprocessing.Normalization()
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normalizer.adapt(np.array(X_train))
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model = tf.keras.Sequential([
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normalizer,
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layers.Dense(units=1)
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])
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model.compile(
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optimizer=tf.optimizers.Adam(learning_rate=0.1),
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loss='mean_absolute_error')
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model.fit(
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X_train, y_train,
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batch_size=batch_size,
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epochs=epochs,
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validation_split=0.2)
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model.save_weights('suicide_model.h5')
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predictions = model.predict(X_test)
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error = mean_squared_error(y_test, predictions)
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_run.info["mean_squared_error"] = str(error)
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_run.log_scalar("mean_squared_error", int(error))
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return error
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@ex.automain
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def my_main(epochs, batch_size):
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print(prepare())
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r = ex.run()
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ex.add_artifact('suicide_model.h5')
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