64 lines
2.0 KiB
Python
64 lines
2.0 KiB
Python
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import sys
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import pandas as pd
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import numpy as np
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from tensorflow import keras
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from sklearn.metrics import accuracy_score
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from tensorflow.keras import layers
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import mlflow
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def train(epochs, batch_size):
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model_name = "model"
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train = pd.read_csv('train.csv', header=None, skiprows=1)
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indexNames = train[train[1] == 2].index
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train.drop(indexNames, inplace=True)
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cols = [0, 2, 3]
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X = train[cols].to_numpy()
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y = train[1].to_numpy()
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X = np.asarray(X).astype('float32')
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model = keras.Sequential(name="winner")
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model.add(keras.Input(shape=(3), name="game_info"))
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model.add(layers.Dense(4, activation="relu", name="layer1"))
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model.add(layers.Dense(8, activation="relu", name="layer2"))
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model.add(layers.Dense(8, activation="relu", name="layer3"))
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model.add(layers.Dense(5, activation="relu", name="layer4"))
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model.add(layers.Dense(1, activation="relu", name="output"))
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model.compile(
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optimizer=keras.optimizers.Adam(),
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loss=keras.losses.MeanSquaredError(),
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)
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history = model.fit(
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X,
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y,
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batch_size=batch_size,
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epochs=epochs, )
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model.save(model_name)
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test = pd.read_csv('test.csv', header=None, skiprows=1)
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cols = [0, 2, 3]
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indexNames = test[test[1] == 2].index
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test.drop(indexNames, inplace=True)
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X_test = test[cols].to_numpy()
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y_test = test[1].to_numpy()
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X_test = np.asarray(X_test).astype('float32')
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predictions = model.predict(X_test)
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pd.DataFrame(predictions).to_csv('results.csv', sep='\t', index=False, header=False)
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acc = accuracy_score(y_test, predictions)
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print('Accuracy: ', acc)
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return acc, model
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epochs = int(sys.argv[1]) if len(sys.argv) > 1 else 15
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batch_size = int(sys.argv[2]) if len(sys.argv) > 2 else 16
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with mlflow.start_run():
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acc, model = train(epochs, batch_size)
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mlflow.log_param("epochs", epochs)
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mlflow.log_param("batch_size", batch_size)
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mlflow.log_metric("accuracy", acc)
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mlflow.keras.log_model(model, 'model')
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