removed evaluation in train.py
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parent
837e1aebc0
commit
54bf45f0f6
@ -1,6 +1,6 @@
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pipeline {
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agent {
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docker { image 'adnovac/ium_s434760:1.1' }
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docker { image 'adnovac/ium_s434760:1.2' }
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}
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parameters{
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buildSelector(
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@ -29,7 +29,7 @@ pipeline {
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copyArtifacts(fingerprintArtifacts: true, optional: true, projectName: 's434760-evaluation', selector: buildParameter('WHICH_BUILD_THIS'))
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}
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}
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stage('train')
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stage('evaluate')
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{
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steps
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{
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@ -46,7 +46,7 @@ pipeline {
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stage('send email') {
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steps {
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emailext body: currentBuild.result ?: 'SUCCESS',
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subject: 's434760 - validation',
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subject: 's434760 - evaluation',
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to: 'annnow19@st.amu.edu.pl'
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}
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}
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@ -1,6 +1,6 @@
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pipeline {
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agent {
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docker { image 'adnovac/ium_s434760:1.0' }
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docker { image 'adnovac/ium_s434760:1.2' }
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}
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parameters{
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buildSelector(
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3330
results.csv
3330
results.csv
File diff suppressed because it is too large
Load Diff
58
train.py
58
train.py
@ -12,46 +12,24 @@ X=train_data[input_columns].to_numpy()
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Y=train_data[["Overall"]].to_numpy()
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model = None
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if path.exists(model_name):
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model = keras.models.load_model(model_name)
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else:
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model = keras.Sequential(name="fifa_overall")
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model.add(keras.Input(shape=(len(input_columns),), name="player_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 = keras.Sequential(name="fifa_overall")
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model.add(keras.Input(shape=(len(input_columns),), name="player_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.RMSprop(),
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loss=keras.losses.MeanSquaredError(),
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)
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model.compile(
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optimizer=keras.optimizers.RMSprop(),
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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=int(sys.argv[1]),
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epochs=int(sys.argv[2]),
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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=int(sys.argv[1]),
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epochs=int(sys.argv[2]),
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)
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model.save(model_name)
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test_data=pd.read_csv('test.csv')
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X_test=test_data[input_columns].to_numpy()
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Y_test=test_data[["Overall"]].to_numpy()
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results_train = model.evaluate(X, Y, batch_size=128)
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results_test = model.evaluate(X_test, Y_test, batch_size=128)
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y_pred = model(X_test)
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lines = ["Name;Overall;Predicted overall\n"]
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for i in range(len(X_test)):
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name = test_data["Name"][i]
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lines.append(f"{name};{int(Y_test[i])};{int(y_pred[i])}\n")
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with open('results.csv', 'w+', encoding="UTF-8") as f:
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f.writelines(lines)
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with open('evaluation_result.txt', 'w+', encoding="UTF-8") as f:
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f.write(f"Train: {str(results_train)}\nTest: {str(results_test)}")
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model.save(model_name)
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