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import numpy as np
import importlib
import sys
import time
import os
# fmt:off
sys.path.append('../model')
sys.path.append('../functions/matrix_generator')
sys.path.append('../functions/data_extractor')
import tdce_model as tdce
import material_fc_layer as mfl
import employee_fc_layer as efl
import capital_fc_layer as cfl
import loss
import cost_matrix_class as cmc
import display_input_variation as diva
import viyacrab_augmentation as viya
import adjust_data as ajd
import result_display as rd
importlib.reload(tdce)
importlib.reload(mfl)
importlib.reload(efl)
importlib.reload(cfl)
importlib.reload(loss)
importlib.reload(tdce)
importlib.reload(cmc)
importlib.reload(diva)
importlib.reload(viya)
importlib.reload(ajd)
importlib.reload(rd)
# fmt:on
def inital_layer(
material_cost_matrix,
employee_cost_matrix,
capital_cost_matrix,
):
total_col = 0
# Material FC Layer
row, high, col = material_cost_matrix.shape
material_layer_1 = mfl.MaterialFCLayer(col, 1)
# material_layer_1.annotate(material_cost_matrix, material_amount_matrix)
total_col += col
# Monthy Employee FC Layer
row, high, col = employee_cost_matrix.shape
employee_layer_1 = efl.EmployeeFCLayer(col, 1, 8)
total_col += col
# monthy_employee_layer_1.annotate(monthy_employee_cost_matrix, duration_matrix)
# Capital Cost FC Layer
row, high, col = capital_cost_matrix.shape
capital_cost_layer1 = cfl.CapitalCostFCLayer(col, 1, 21)
total_col += col
# capital_cost_layer1.annotate(
# capital_cost_matrix, life_time_matrix, machine_hour_matrix, duration_matrix)
return (
material_layer_1,
employee_layer_1,
capital_cost_layer1,
)
def create_learning(
epoch,
learning_rate,
inside_learning_rate,
material_cost_matrix,
material_amount_matrix,
employee_cost_matrix,
employee_duration_matrix,
employee_day_amount_matrix,
capital_cost_matrix,
day_amount_matrix,
capital_duration_matrix,
result_matrix,
folder_name,
validation_payload,
breakpoint,
early_stopping=False,
patience_round=10,
use_model_weight=False,
):
# Initial Model
tdce_model = tdce.TDCEModel()
# Initial Layer
(
material_layer_1,
employee_layer_1,
capital_cost_layer1,
) = inital_layer(
capital_cost_matrix=capital_cost_matrix,
employee_cost_matrix=employee_cost_matrix,
material_cost_matrix=material_cost_matrix,
)
tdce_model.inital_inside_element(
material_layer=material_layer_1,
capital_cost_layer=capital_cost_layer1,
employee_layer=employee_layer_1,
)
tdce_model.use(loss=loss.mse, loss_prime=loss.mse_prime,
loss_percent=loss.rmspe)
tdce_model.set_learning_rate(
inside_learning_rate[0],
inside_learning_rate[1],
inside_learning_rate[2],
)
start_time = time.time()
if early_stopping:
tdce_model.activate_early_stopping()
tdce_model.edit_patience_round(patience_round)
if use_model_weight:
tdce_model.activete_model_weight()
tdce_model.fit_with_validation(
epoch=epoch,
learning_rate=learning_rate,
material_amount_matrix=material_amount_matrix,
material_cost_matrix=material_cost_matrix,
employee_cost_matrix=employee_cost_matrix,
employee_duration_matrix=employee_duration_matrix,
employee_day_amount_matrix=employee_day_amount_matrix,
result_matrix=result_matrix,
capital_cost_matrix=capital_cost_matrix,
day_amount_matrix=day_amount_matrix,
validation_payload=validation_payload,
capital_cost_duration_matrix=capital_duration_matrix,
)
end_time = time.time()
time_usage = end_time - start_time
print(f"Learning Rate: {learning_rate} & {inside_learning_rate}")
print(f"Time Using {time_usage} Second")
error_list = tdce_model.get_epoch_error()
sample_error_list = tdce_model.get_sample_error()
error_df = pd.DataFrame(error_list)
sample_error_df = pd.DataFrame(sample_error_list)
before_breakpoint_error = error_df[error_df["epoch"] < breakpoint]
bb_minimum_error = before_breakpoint_error["error"].min()
bb_minimum_percent_error = before_breakpoint_error["error_percent"].min()
bb_minimum_validate_error = before_breakpoint_error["validate_error"].min()
bb_minimum_validate_percent_error = before_breakpoint_error[
"validate_error_percent"
].min()
minimum_error = error_df["error"].min()
minimum_percent_error = error_df["error_percent"].min()
minimum_validate_error = error_df["validate_error"].min()
minimum_validate_percent_error = error_df["validate_error_percent"].min()
try:
os.mkdir(f"{folder_name}")
except FileExistsError:
print("Folder is Exist")
pass
error_df.to_csv(f"{folder_name}/{epoch}-{inside_learning_rate[0]}.csv")
sample_error_df.to_csv(
f"{folder_name}/error-list-{epoch}-{inside_learning_rate[0]}.csv"
)
sample_payload = tdce_model.get_sample_payload()
sample_payload_df = pd.DataFrame(sample_payload)
sample_payload_df.to_csv(
f"{folder_name}/sample-payload-list-{epoch}-{inside_learning_rate[0]}.csv",
index=False,
)
return (
minimum_error,
time_usage,
minimum_percent_error,
minimum_validate_error,
minimum_validate_percent_error,
bb_minimum_error,
bb_minimum_percent_error,
bb_minimum_validate_error,
bb_minimum_validate_percent_error,
)
def run_experiment(
data_directory,
epoch,
folder_name,
round_number,
learning_rate_list,
model_learning_rate,
breakpoint,
early_stopping=False,
patience_round=10,
augmentation=False,
remove_outlier_qtr=False,
outlier_index=1.5,
use_model_weight=False,
):
print("Run Experiment Script is initial !")
try:
os.mkdir(folder_name)
except FileExistsError:
print("Folder is Exist")
pass
cost_generator = cmc.CostMatrixGenerator()
cost_generator.change_data_directory(data_directory)
cost_generator.load_data()
if remove_outlier_qtr:
cost_generator.remove_outlier_iqr(outlier_index)
(
new_process_df,
new_employee_usage,
new_material_usage,
new_capital_cost_usage,
) = cost_generator.get_data()
(new_capital_cost_usage, new_employee_usage, new_material_usage) = (
ajd.adjust_to_match_process(
capital_cost_usage=new_capital_cost_usage,
employee_usage=new_employee_usage,
material_usage=new_material_usage,
new_process_df=new_process_df,
)
)
new_variation = diva.display_input_variation(
new_process_df,
new_material_usage,
new_employee_usage,
new_capital_cost_usage,
)
new_variation.to_csv(f"{folder_name}/data_variation_after_outlier.csv")
new_process_df.to_csv(f"{folder_name}/process_df_after_outlier.csv")
try:
print("Data Variation After Outlier Removed")
display(new_variation)
except Exception as e:
print("This is not Jupyter Notebook", e)
# Save Variation of Overall Data
input_variation = diva.display_input_variation_by_directory(data_directory)
input_variation.to_csv(f"{folder_name}/data_variation.csv")
overall_result = []
# Iteration over the set of Learning Rates
for learn_r in learning_rate_list:
round_err_list = []
round_time_list = []
round_percent_list = []
round_validate_err_list = []
round_validate_err_percent_list = []
round_brakpoint_err_list = []
round_brakpoint_err_percent_list = []
round_brakpoint_val_err_list = []
round_brakpoint_val_err_percent_list = []
round_err_dict = {"learning_rate": str(learn_r)}
for round in range(round_number):
##
(
train_process_df,
train_employee_usage,
train_material_usage,
train_capital_cost,
validate_process_df,
validate_employee_usage,
validate_material_usage,
validate_capital_cost,
) = cost_generator.train_test_split_without_matrix(0.7)
# Generate Cost Matrix for Validation Set
validation_payload = cost_generator.get_validation_payload(
validate_process_df
)
# Display Variation of Train Data
train_variation = diva.display_input_variation(
train_process_df,
train_material_usage,
train_employee_usage,
train_capital_cost,
)
train_variation.to_csv(
f"{folder_name}/train_data_variation_{round}.csv")
train_process_df.to_csv(
f"{folder_name}/train_process_df_{round}.csv")
# Display Variation of Validation Data
validate_variation = diva.display_input_variation(
validate_process_df,
validate_material_usage,
validate_employee_usage,
validate_capital_cost,
)
validate_variation.to_csv(
f"{folder_name}/validate_data_variation_{round}.csv"
)
if augmentation:
# TODO: Increase the Generalization of the Model
# Augmented the Imbalance Class of Training Data
train_process_df.to_csv(
f"{folder_name}/train_process_df_before_augmented_{round}.csv"
)
train_process_df = viya.vy_training_augmentation(
train_process_df)
# Display Variation of Train Data After Augmented
train_variation = diva.display_input_variation(
train_process_df,
train_material_usage,
train_employee_usage,
train_capital_cost,
)
train_variation.to_csv(
f"{folder_name}/train_data_variation_after_augmented_{round}.csv"
)
train_process_df.to_csv(
f"{folder_name}/train_process_df_after_augmented_{round}.csv"
)
# Generate Matrix From Training Set
(
material_cost_matrix,
material_amount_matrix,
employee_cost_matrix,
employee_duration_matrix,
employee_day_amount_matrix,
capital_cost_matrix,
day_amount_matrix,
capital_cost_duration_matrix, # New On Finetune
result_matrix,
) = cost_generator.generate_data_from_input(
train_process_df,
train_material_usage,
train_employee_usage,
train_capital_cost,
)
# Create Training
(
round_err,
round_time,
round_err_percent,
val_err,
val_err_percent,
brakpoint_err,
breakpoint_err_percent,
breakpoint_val_err,
breakpoint_val_err_percent,
) = create_learning(
epoch=epoch,
learning_rate=model_learning_rate,
folder_name=f"{folder_name}/round{round + 1}",
# Material
material_amount_matrix=material_amount_matrix,
material_cost_matrix=material_cost_matrix,
# Employee
employee_cost_matrix=employee_cost_matrix,
employee_duration_matrix=employee_duration_matrix,
employee_day_amount_matrix=employee_day_amount_matrix,
# Capital Cost
capital_cost_matrix=capital_cost_matrix,
day_amount_matrix=day_amount_matrix,
capital_duration_matrix=capital_cost_duration_matrix,
# Result Matrix
result_matrix=result_matrix,
# Validation Payload
validation_payload=validation_payload,
inside_learning_rate=[learn_r, learn_r, learn_r],
# because it start with -1
breakpoint=breakpoint,
early_stopping=early_stopping,
patience_round=patience_round,
use_model_weight=use_model_weight,
)
print(
f"Learning Rate {learn_r} Round {round} / {round_number} Finish with Error {round_err} ({round_err_percent} %), time comsume {round_time} "
)
round_err_list.append(round_err)
round_time_list.append(round_time)
round_percent_list.append(round_err_percent)
round_validate_err_list.append(val_err)
round_validate_err_percent_list.append(val_err_percent)
round_brakpoint_err_list.append(brakpoint_err)
round_brakpoint_err_percent_list.append(breakpoint_err_percent)
round_brakpoint_val_err_list.append(breakpoint_val_err)
round_brakpoint_val_err_percent_list.append(
breakpoint_val_err_percent)
round_err_dict[f"round_{round}_err"] = round_err
round_err_dict[f"round_{round}_duration"] = round_time
round_err_dict[f"round_{round}_err_percent"] = round_err_percent
round_err_dict[f"round_{round}_val_err"] = val_err
round_err_dict[f"round_{round}_val_err_percent"] = val_err_percent
round_err_dict[f"round_{round}_brakpoint_err"] = brakpoint_err
round_err_dict[f"round_{round}_brakpoint_err_percent"] = (
breakpoint_err_percent
)
round_err_dict[f"round_{round}_brakpoint_val_err"] = breakpoint_val_err
round_err_dict[f"round_{round}_brakpoint_val_err_percent"] = (
breakpoint_val_err_percent
)
average_error = np.average(round_err_list)
average_time = np.average(round_time_list)
average_error_percent = np.average(round_percent_list)
average_val_err = np.average(round_validate_err_list)
average_val_err_percent = np.average(round_validate_err_percent_list)
breakpoint_average_error = np.average(round_brakpoint_err_list)
breakpoint_average_error_percent = np.average(
round_brakpoint_err_percent_list)
breakpoint_average_val_error = np.average(round_brakpoint_val_err_list)
breakpoint_average_val_error_percent = np.average(
round_brakpoint_val_err_percent_list
)
print(
f"Success for learning rate {learn_r} : Average error {average_error} ({average_error_percent}%) Average Time {average_time}"
)
round_err_dict["average_error"] = average_error
round_err_dict["average_err_percent"] = average_error_percent
round_err_dict["average_time"] = average_time
round_err_dict["average_validate_error"] = average_val_err
round_err_dict["average_validate_error_percent"] = average_val_err_percent
round_err_dict["average_brakpoint_error"] = breakpoint_average_error
round_err_dict["average_brakpoint_error_percent"] = (
breakpoint_average_error_percent
)
round_err_dict["average_brakpoint_val_error"] = breakpoint_average_val_error
round_err_dict["average_brakpoint_val_error_percent"] = (
breakpoint_average_val_error_percent
)
overall_result.append(round_err_dict)
overall_result_df = pd.DataFrame(overall_result)
overall_result_df.to_csv(f"{folder_name}/overall_result.csv")
rd.creating_error_csv(
primary_directory_name=folder_name,
learning_rate=learning_rate_list,
iteration_number=epoch,
round_number=round_number,
breakpoint_number=breakpoint,
)
return overall_result_df
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