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Updated v2 to use f1 score and dont retrain model

master
Bianca Steffes 2 months ago
parent
93211b811e
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a3efef8153
2 changed files with 48 additions and 24 deletions
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  1. 33
      main.py
  2. 39
      pipeline.py

33
main.py
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@ -126,7 +126,7 @@ def load_previous_results(filename):
return results
def main_two_v2(model_type):
seq_length = range(20,31, 10)
seq_length = range(10,31, 5)
for sequence_length in seq_length:
for data_filename in os.listdir(dataset_path):
timespan_id = hour_timespan_str
@ -156,13 +156,13 @@ def main_two_v2(model_type):
user_data_train = prepare_user_data(tr)
user_data_val = prepare_user_data(val)
best_models = train_models_v2(user_data_train, user_data_val,
best_model = train_models_v2(user_data_train, user_data_val,
sequence_length=sequence_length,
model_type=model_type)
results = load_previous_results(result_filename_v2)
results = pd.concat([results,
evaluate_model_on_test_data(model=best_models[sequence_length]['model'],
evaluate_model_on_test_data(model=best_model,
test_df=te,
sequence_length=sequence_length,
time_span_id=timespan_id,
@ -259,8 +259,35 @@ def visualise_results_v1():
# Fazit: keine eindeutig besseren Versionen erkennbar
def visualise_results_v2():
results = pd.DataFrame(json.load(open(result_filename_v2)))
with_threshold = results[results[threshold_str] == with_threshold_str]
without_threshold = results[results[threshold_str] == without_threshold_str]
fig, axes = plt.subplots(2, 3)
ax_col_id = 0
ax_row_id = -1
for timespan in [hour_timespan_str,min_timespan_str]:
ax_row_id +=1
for model in [model_type_lstm, model_type_bilstm, model_type_gru]:
with_sub = with_threshold[(with_threshold[timespan_str] == timespan) & (with_threshold[model_type_str] == model)]
without_sub = without_threshold[(without_threshold[timespan_str] == timespan) & (without_threshold[model_type_str] == model)]
with_sub = with_sub.sort_values(sequence_length_str)
without_sub = without_sub.sort_values(sequence_length_str)
ax = axes[ax_row_id, ax_col_id]
ax.set_title(model+' '+timespan)
ax.plot(with_sub[sequence_length_str], with_sub[f1_string], label=with_threshold_str)
ax.plot(without_sub[sequence_length_str], without_sub[f1_string], label=without_threshold_str)
ax.legend()
ax_col_id +=1
ax_col_id %= 3
fig.tight_layout()
fig.savefig(figure_path+'v2_results.svg')
# Fazit: keine eindeutig besseren Versionen erkennbar
if __name__ == "__main__":
# main_two_v1()
# visualise_results_v1()
main_two_v2(model_type=model_type_gru)
#visualise_results_v2()
print('Done')

39
pipeline.py
View File

@ -2,11 +2,10 @@ import keras_tuner
import numpy as np
import pandas as pd
import shutil
import os
from keras import Input
from keras.src.metrics import F1Score
from pandas import ExcelWriter
import keras_tuner as kt
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, Dropout, Bidirectional,GRU
from tensorflow.keras.optimizers import Adam
@ -14,7 +13,7 @@ from tensorflow.keras.callbacks import ReduceLROnPlateau, EarlyStopping
from keras_tuner import RandomSearch
from sklearn.metrics import accuracy_score
epochs = 30
epochs = 50
model_type_gru = 'GRU'
model_type_lstm = 'LSTM'
model_type_bilstm = 'BiLSTM'
@ -144,10 +143,10 @@ def train_models(user_data, user_data_val, sequence_lengths, tuner_dir="./workin
# === Training & Validation ===
def train_models_v2(user_data, user_data_val, sequence_length, model_type):
tuner_dir = "./working/tuner"
tuner_dir = "./working/tuner/"+model_type
early_stopping = EarlyStopping(monitor='val_loss', patience=3, restore_best_weights=True)
lr_scheduler = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=2)
early_stopping = EarlyStopping(monitor='val_f1', patience=3, restore_best_weights=True)
lr_scheduler = ReduceLROnPlateau(monitor='val_f1', factor=0.5, patience=2)
shutil.rmtree(tuner_dir, ignore_errors=True)
@ -157,41 +156,39 @@ def train_models_v2(user_data, user_data_val, sequence_length, model_type):
n_features = x.shape[2]
users = list(user_data.keys())
y_val = np.array(y_val).reshape(-1, 1)
y = np.array(y).reshape(-1, 1)
def build_model(hp):
units_hp = hp.Int('units', 2, 256, step=2, sampling="log")
model = Sequential()
model.add(Input((sequence_length, n_features)))
if model_type==model_type_bilstm:
model.add(Bidirectional(LSTM(units=hp.Int('units', 32, 256, step=2),
input_shape=(sequence_length, n_features))))
model.add(Bidirectional(LSTM(units=units_hp)))
if model_type==model_type_lstm:
model.add(LSTM(units=hp.Int('units', 32, 256, step=2),
input_shape=(sequence_length, n_features)))
model.add(LSTM(units=units_hp))
if model_type==model_type_gru:
model.add(GRU(units=hp.Int('units', 32, 256, step=2),
input_shape=(sequence_length, n_features)))
model.add(GRU(units=units_hp))
model.add(Dropout(hp.Float('dropout_rate', 0.1, 0.5, step=0.1)))
model.add(Dense(len(users), activation='softmax'))
model.compile(
optimizer=Adam(learning_rate=hp.Choice('learning_rate', [1e-2, 1e-3, 1e-4])),
loss='sparse_categorical_crossentropy',
metrics=['accuracy']
metrics=[F1Score(name='f1', average='weighted')]
)
return model
tuner = RandomSearch(
build_model,
objective='val_loss',
max_trials=100,
objective=keras_tuner.Objective("val_f1", direction="max"),
max_trials=120,
directory=tuner_dir,
)
tuner.search(x, y, epochs=epochs, validation_data=(x_val, y_val),
callbacks=[early_stopping, lr_scheduler])
best_hps = tuner.get_best_hyperparameters(1)[0]
best_model = tuner.hypermodel.build(best_hps)
best_model.fit(x, y, epochs=epochs, validation_data=(x_val, y_val),
callbacks=[early_stopping, lr_scheduler])
return best_model
return tuner.get_best_models(num_models=1)[0]
# === Evaluation ===

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