From 134226fa79ba6ab85d70bfb34c705e289eabe769 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jakub=20Zar=C4=99ba?= Date: Wed, 10 May 2023 20:42:51 +0200 Subject: [PATCH] s --- train.py | 100 +++++++++++++++++++++++++++---------------------------- 1 file changed, 50 insertions(+), 50 deletions(-) diff --git a/train.py b/train.py index b67d1ea..0eac8d5 100644 --- a/train.py +++ b/train.py @@ -1,69 +1,69 @@ from sacred import Experiment from sacred.observers import MongoObserver, FileStorageObserver -ex = Experiment('s487187-training') -ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred')) -ex.observers.append(FileStorageObserver('results')) -ex.use_git = False +# ex = Experiment('s487187-training') +# ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred')) +# ex.observers.append(FileStorageObserver('results')) +# ex.use_git = False -@ex.config -def my_config(): - data_file = 'data.csv' - model_file = 'model.h5' - epochs = 10 - batch_size = 32 - test_size = 0.2 - random_state = 42 +# @ex.config +# def my_config(): +data_file = 'data.csv' +model_file = 'model.h5' +epochs = 10 +batch_size = 32 +test_size = 0.2 +random_state = 42 -@ex.capture -def train_model(data_file, model_file, epochs, batch_size, test_size, random_state): - import pandas as pd - from sklearn.model_selection import train_test_split - from sklearn.preprocessing import MinMaxScaler - import tensorflow as tf - from imblearn.over_sampling import SMOTE +# @ex.capture +# def train_model(data_file, model_file, epochs, batch_size, test_size, random_state): +import pandas as pd +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import MinMaxScaler +import tensorflow as tf +from imblearn.over_sampling import SMOTE - smote = SMOTE(random_state=random_state) - data = pd.read_csv(data_file, sep=';') +smote = SMOTE(random_state=random_state) +data = pd.read_csv(data_file, sep=';') - print('Total rows:', len(data)) - print('Rows with medal:', len(data.dropna(subset=['Medal']))) +print('Total rows:', len(data)) +print('Rows with medal:', len(data.dropna(subset=['Medal']))) - data = pd.get_dummies(data, columns=['Sex', 'Medal']) - data = data.drop(columns=['Name', 'Team', 'NOC', 'Games', 'Year', 'Season', 'City', 'Sport', 'Event']) +data = pd.get_dummies(data, columns=['Sex', 'Medal']) +data = data.drop(columns=['Name', 'Team', 'NOC', 'Games', 'Year', 'Season', 'City', 'Sport', 'Event']) - scaler = MinMaxScaler() - data = pd.DataFrame(scaler.fit_transform(data), columns=data.columns) +scaler = MinMaxScaler() +data = pd.DataFrame(scaler.fit_transform(data), columns=data.columns) - X = data.filter(regex='Sex|Age') - y = data.filter(regex='Medal') - y = pd.get_dummies(y) +X = data.filter(regex='Sex|Age') +y = data.filter(regex='Medal') +y = pd.get_dummies(y) - X = X.fillna(0) - y = y.fillna(0) +X = X.fillna(0) +y = y.fillna(0) - y = y.values +y = y.values - X_resampled, y_resampled = smote.fit_resample(X, y) - X_train, X_test, y_train, y_test = train_test_split(X_resampled, y_resampled, test_size=test_size, random_state=random_state) +X_resampled, y_resampled = smote.fit_resample(X, y) +X_train, X_test, y_train, y_test = train_test_split(X_resampled, y_resampled, test_size=test_size, random_state=random_state) - model = tf.keras.models.Sequential() - model.add(tf.keras.layers.Dense(64, input_dim=X_train.shape[1], activation='relu')) - model.add(tf.keras.layers.Dense(32, activation='relu')) - model.add(tf.keras.layers.Dense(y.shape[1], activation='softmax')) +model = tf.keras.models.Sequential() +model.add(tf.keras.layers.Dense(64, input_dim=X_train.shape[1], activation='relu')) +model.add(tf.keras.layers.Dense(32, activation='relu')) +model.add(tf.keras.layers.Dense(y.shape[1], activation='softmax')) - model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) +model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) - model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size) - loss, accuracy = model.evaluate(X_test, y_test) - print('Test accuracy:', accuracy) +model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size) +loss, accuracy = model.evaluate(X_test, y_test) +print('Test accuracy:', accuracy) - model.save(model_file) +model.save(model_file) - return accuracy +# return accuracy -@ex.main -def run_experiment(): - accuracy = train_model() - ex.log_scalar('accuracy', accuracy) - ex.add_artifact('model.h5') +# @ex.main +# def run_experiment(): +# accuracy = train_model() +# ex.log_scalar('accuracy', accuracy) +# ex.add_artifact('model.h5')