60 lines
2.1 KiB
Python
60 lines
2.1 KiB
Python
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import tensorflow as tf
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from sacred import Experiment
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from sacred.observers import FileStorageObserver
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import pandas as pd
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import sklearn
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import sklearn.model_selection
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import numpy as np
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ex = Experiment('452662')
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ex.observers.append(FileStorageObserver.create('my_runs'))
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#ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
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def normalize(df,feature_name):
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result = df.copy()
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max_value = df[feature_name].max()
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min_value = df[feature_name].min()
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result[feature_name] = (df[feature_name] - min_value) / (max_value - min_value)
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return result
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@ex.automain
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def run_experiment():
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cars = pd.read_csv('zbior_ium/Car_Prices_Poland_Kaggle.csv')
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cars = cars.drop(73436) #wiersz z błednymi danymi
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cars_normalized = normalize(cars,'vol_engine')
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cars_train, cars_test = sklearn.model_selection.train_test_split(cars_normalized, test_size=23586, random_state=1)
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cars_dev, cars_test = sklearn.model_selection.train_test_split(cars_test, test_size=11793, random_state=1)
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cars_train.rename(columns = {list(cars_train)[0]: 'id'}, inplace = True)
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cars_test.rename(columns = {list(cars_test)[0]: 'id'}, inplace = True)
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cars_train.to_csv('train.csv')
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cars_test.to_csv('test.csv')
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feature_cols = ['year', 'mileage', 'vol_engine']
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inputs = tf.keras.Input(shape=(len(feature_cols),))
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x = tf.keras.layers.Dense(10, activation='relu')(inputs)
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x = tf.keras.layers.Dense(10, activation='relu')(x)
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outputs = tf.keras.layers.Dense(1, activation='linear')(x)
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model = tf.keras.Model(inputs=inputs, outputs=outputs)
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model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
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loss='mse', metrics=['mae'])
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model.fit(cars_train[feature_cols], cars_train['price'], epochs=100)
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ex.add_resource('train_data.csv')
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ex.add_resource('test_data.csv')
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ex.add_artifact(__file__)
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model.save('model.h5')
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ex.add_artifact('model.h5')
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metrics = model.evaluate(cars_train[feature_cols], cars_train['price'])
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ex.log_scalar('mse', metrics[0])
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ex.log_scalar('mae', metrics[1])
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