102 lines
3.2 KiB
Python
102 lines
3.2 KiB
Python
import mlflow
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import mlflow.keras
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from mlflow.models.signature import infer_signature
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from mlflow.models import Model
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import pandas as pd
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from sacred import Experiment
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from sacred.observers import MongoObserver, FileStorageObserver
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import os
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import tensorflow as tf
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from tensorflow.python.framework import tensor_spec
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import numpy as np
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os.environ["SACRED_NO_GIT"] = "1"
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ex = Experiment('s487187-training', interactive=True, save_git_info=False)
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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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mlflow.set_tracking_uri("http://172.17.0.1:5000")
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mlflow.set_experiment("s487187")
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@ex.config
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def my_config():
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data_file = 'data.csv'
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model_file = 'model.h5'
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epochs = 10
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batch_size = 32
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test_size = 0.2
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random_state = 42
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@ex.capture
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def train_model(data_file, model_file, epochs, batch_size, test_size, random_state):
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import pandas as pd
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from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import MinMaxScaler
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import tensorflow as tf
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from imblearn.over_sampling import SMOTE
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smote = SMOTE(random_state=random_state)
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data = pd.read_csv(data_file, sep=';', header=0)
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print('Total rows:', len(data))
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print('Rows with medal:', len(data.dropna(subset=['Medal'])))
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data = pd.get_dummies(data, columns=['Sex', 'Medal'])
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data = data.drop(columns=['Name', 'Team', 'NOC', 'Games', 'Year', 'Season', 'City', 'Sport', 'Event'])
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scaler = MinMaxScaler()
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data = pd.DataFrame(scaler.fit_transform(data), columns=data.columns)
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X = data.filter(regex='Sex|Age')
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y = data.filter(regex='Medal')
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y = pd.get_dummies(y)
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X = X.fillna(0)
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y = y.fillna(0)
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y = y.values
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X_resampled, y_resampled = smote.fit_resample(X, y)
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X_train, X_test, y_train, y_test = train_test_split(X_resampled, y_resampled, test_size=test_size, random_state=random_state)
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model = tf.keras.models.Sequential()
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model.add(tf.keras.layers.Dense(64, input_dim=X_train.shape[1], activation='relu'))
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model.add(tf.keras.layers.Dense(32, activation='relu'))
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model.add(tf.keras.layers.Dense(y.shape[1], activation='softmax'))
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model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
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model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size)
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loss, accuracy = model.evaluate(X_test, y_test)
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print('Test accuracy:', accuracy)
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print('Test loss:', loss)
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model.save("model.h5")
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X_train_numpy = X_train.values
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signature = infer_signature(X_train_numpy, model.predict(X_train_numpy))
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input_example = X_train.head(1).values
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# input_signature = {
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# 'input': tensor_spec.TensorSpec(shape=X_train.iloc[0].shape, dtype=X_train.dtypes[0])
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# }
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mlflow.keras.log_model(model, "model")
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mlflow.log_artifact("model.h5")
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# Use the ndarray form for infer_signature and input_example
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signature = infer_signature(X_train_numpy, model.predict(X_train_numpy))
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input_example = X_train.head(1).values
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mlflow.keras.save_model(model, "model", signature=signature, input_example=input_example)
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return accuracy
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@ex.main
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def run_experiment():
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accuracy = train_model()
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ex.log_scalar('accuracy', accuracy)
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ex.add_artifact('model.h5')
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ex.run()
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